Unlocking Marinisomatota: Metabolic Versatility and Mixotrophic Adaptations in Marine Ecosystems

Abstract

Marinisomatota (formerly Marinimicrobia, SAR406) are ubiquitous and abundant marine bacteria now recognized for remarkable metabolic plasticity, including recently discovered mixotrophic capabilities. This article synthesizes cutting-edge research on their phylogenetic diversity, distinct metabolic strategies (photoautotrophic potential MS0, heterotrophic MS1/MS2), and the genomic basis for their adaptation to oligotrophic oceans. We explore advanced methodologies for cultivating these previously 'unculturable' organisms and analyzing their complex ecophysiology. For researchers and drug development professionals, we evaluate their biotechnological potential, drawing parallels with successful microalgal applications in drug delivery and biohybrid systems, while providing a critical framework for validating their ecological roles and comparative advantages over other marine microbes.

Decoding Marinisomatota: From Microbial Dark Matter to Metabolically Diverse Mixotrophs

The phylum Marinisomatota (formerly recognized as Marinimicrobia, Marine Group A, and SAR406) represents a ubiquitous and abundant group of microorganisms in global marine environments. Traditionally characterized as heterotrophic, recent genomic evidence reveals unexpected metabolic versatility, including photoautotrophic capabilities, challenging previous ecological classifications [1]. This phylum exemplifies the critical role of mixotrophic adaptations—the combination of autotrophic and heterotrophic metabolic strategies—in microbial survival and proliferation. Within the context of a broader thesis on marine mixotrophy, understanding the phylogenetic diversity and distribution of Marinisomatota is paramount, as it provides a model system for deciphering how metabolic plasticity influences ecological success across oceanic gradients, from sunlit surfaces to the dark ocean [1] [2].

Phylogenetic Framework and Genomic Diversity

Advances in metagenomics have enabled the detailed phylogenetic classification of Marinisomatota, a group largely comprised of uncultured lineages. A comprehensive analysis of global ocean metagenomes has enabled the reconstruction of 1,588 Marinisomatota genomes, providing a robust framework for understanding their phylogenetic breadth [1].

This genomic effort has delineated the phylum into a structured taxonomy encompassing one class, two orders, 14 families, 31 genera, and 67 species [1]. The relationship between these taxonomic ranks and their functional genomic traits is statistically significant, following broader patterns observed across the bacterial and archaeal tree of life, where taxonomy explains a substantial portion of the variance in functional potential [3].

Table 1: Taxonomic Summary of the Marinisomatota Phylum Based on Metagenome-Assembled Genomes (MAGs)

Taxonomic Rank	Number of Lineages
Phylum	1
Class	1
Order	2
Family	14
Genus	31
Species	67

The diagram below illustrates the logical workflow for reconstructing the phylogenetic diversity of Marinisomatota from environmental samples to taxonomic classification, highlighting the key bioinformatic steps.

Global Distribution and Ecological Niches

Marinisomatota are a pervasive component of marine ecosystems, with a distribution strongly influenced by latitude and depth. They are predominantly found in low-latitude marine regions, where their relative abundances can range dramatically from 0.18% to 36.21% of the microbial community [1]. This wide abundance range indicates a high degree of niche specialization within the phylum.

Their ecological success is linked to their ability to inhabit both the translucent (photic) zone and the aphotic zone, transitioning between these layers [1]. This vertical distribution is supported by diverse metabolic strategies that allow them to cope with varying light conditions and nutrient availability. Specific families, including S15-B10, TCS55, UBA1611, UBA2128, and UBA8226, are of particular interest as they possess genetic potential for light-dependent processes, a key adaptation for life in the photic zone and at its boundary [1].

Metabolic Strategies and Mixotrophic Adaptations

The genomic analysis of Marinisomatota has revealed three distinct metabolic strategies (MS), which are a central focus for understanding mixotrophic adaptations in marine microorganisms [1]. These strategies represent potential evolutionary responses to nutrient limitations in the ocean.

Table 2: Metabolic Strategies Identified in Marinisomatota

Metabolic Strategy	Trophic Mode	Key Functional Characteristics	Ecological Implication
MS0	Photoautotrophic Potential	Capacity for Crassulacean acid metabolism (M00169); ability to harness light for COâ‚‚ fixation [1].	Adaption to the translucent zone, leveraging light energy.
MS1	Heterotrophic	Pronounced glycolytic pathway for carbon processing [1].	Dominance in organic carbon-rich niches.
MS2	Heterotrophic	Lacks glycolysis; utilizes alternative carbon processing pathways [1].	Specialized role in distinct biogeochemical cycles.

The following diagram maps the logical relationship between environmental constraints, the resulting metabolic adaptations, and their ecological outcomes, framing the core thesis of mixotrophy in Marinisomatota.

Experimental Protocols and Methodologies

Studying uncultured phyla like Marinisomatota requires a suite of culture-independent techniques. The following protocols detail the key methodologies for generating the data discussed in this review.

Metagenomic Assembly and Genome Reconstruction

This protocol is used to reconstruct genomes directly from environmental DNA, bypassing the need for cultivation [1].

• Sample Collection and Filtration: Seawater samples are collected from various depths (e.g., from translucent to aphotic zones). Microbial biomass is concentrated via sequential filtration through filters (e.g., 0.22 µm pore size).
• DNA Extraction and Library Preparation: Environmental DNA is extracted using commercial kits designed for complex environmental samples (e.g., DNeasy PowerSoil Pro Kit, Qiagen). The extracted DNA is sheared, and sequencing libraries are prepared for platforms like Illumina or PacBio.
• Sequencing and Quality Control: Libraries are sequenced to generate high-throughput reads. Raw reads are processed to remove adapters and low-quality sequences using tools like Trimmomatic or FastP.
• Genome Assembly and Binning: Quality-controlled reads are assembled into contigs using assemblers such as MEGAHIT or SPAdes. Contigs are then binned into putative genomes (MAGs) based on sequence composition and abundance coverage, using tools like MetaBAT2, MaxBin2, or CONCOCT.
• Genome Refinement and Quality Assessment: Bins are refined and reassembled using tools like REFINE. The quality of MAGs is assessed using CheckM, with high-quality MAGs typically requiring >70% completeness and <10% contamination.
• Taxonomic Classification: MAGs are classified phylogenetically using the Genome Taxonomy Database (GTDB) toolkit (GTDB-Tk), which places them within a standardized taxonomic framework [3].

Metabolic Pathway Analysis

This protocol identifies and infers the functional potential of the reconstructed Marinisomatota genomes [1].

• Gene Prediction and Annotation: Open Reading Frames (ORFs) are predicted from MAGs using tools like Prodigal. Predicted protein sequences are annotated by searching against databases of orthologous groups (e.g., Clusters of Orthologous Groups - COGs) and metabolic pathways (e.g., KEGG, MetaCyT) using tools like eggNOG-mapper or Prokka.
• Functional Categorization: Annotated genes are sorted into functional categories (COG-FCs) such as "Energy production and conversion" or "Carbohydrate transport and metabolism" to quantify metabolic potential [3].
• Pathway Verification: Specific metabolic pathways (e.g., Crassulacean acid metabolism, glycolysis) are verified by checking for the presence of a complete set of key marker genes (e.g., M00169 for CAM) in the annotated genomes.
• Metatranscriptomic Integration (Optional): To assess active metabolism, RNA is extracted from parallel samples, converted to cDNA, and sequenced. Transcriptomic reads are mapped back to the MAGs to quantify the expression levels of key metabolic genes.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents and Materials for Marinisomatota Research

Item	Function/Application	Example Kits/Tools
Environmental DNA Extraction Kits	Isolate high-quality, inhibitor-free microbial DNA from complex seawater and sediment samples.	DNeasy PowerSoil Pro Kit (Qiagen)
Metagenomic Sequencing Library Prep Kits	Prepare fragmented DNA for high-throughput sequencing on platforms like Illumina.	Illumina DNA Prep Kit
Computational Tools for Genome Binning	Reconstruct genomes from complex metagenomic data based on sequence composition and abundance.	MetaBAT2, MaxBin2
Phylogenomic Classification Software	Assign standardized taxonomy to genomes based on conserved marker genes.	GTDB-Tk (Genome Taxonomy Database Toolkit) [3]
Functional Annotation Pipelines	Predict genes and assign functional categories to protein sequences from MAGs.	eggNOG-mapper, Prokka
Clusters of Orthologous Groups (COGs) Database	A functional scheme for quantitatively comparing genomic content and metabolic traits across diverse microbial lineages [3].	COGs Database
Benzyl-L-serine	(S)-2-(Benzylamino)-3-hydroxypropanoic Acid	Explore (S)-2-(Benzylamino)-3-hydroxypropanoic acid, a chiral building block for pharmaceutical research. This product is for Research Use Only (RUO). Not for human use.
Fmoc-Ser(HPO3Bzl)-OH	Fmoc-Ser(HPO3Bzl)-OH, CAS:158171-14-3, MF:C25H24NO8P, MW:497.4 g/mol	Chemical Reagent

The phylum known historically as Marinimicrobia, Marine Group A, or SAR406 represents one of the most abundant yet uncultivated microbial lineages in the ocean. For decades, this group belonged to the "microbial dark matter" of marine ecosystems—frequently detected through 16S rRNA gene surveys but lacking isolated representatives for detailed physiological characterization. Recent advances in genomic sequencing and cultivation techniques have culminated in a significant taxonomic revision, leading to the formal proposal of the phylum Marinisomatota [4] [5]. This reclassification, formalized with the isolation of Fidelibacter multiformis strain IA91T, marks a pivotal transition from environmental sequence-based classification to a taxonomy grounded in genomic and phenotypic characteristics [5]. The new phylogenetic framework reveals remarkable metabolic versatility within this phylum, particularly in mixotrophic adaptations that enable these organisms to transition between different energy acquisition strategies in response to fluctuating oceanic conditions. This whitepaper examines the taxonomic journey, ecological significance, and experimental approaches that have illuminated the role of Marinisomatota in global marine biogeochemical cycles, with special emphasis on their mixotrophic capabilities that blur traditional boundaries between trophic strategies.

Historical Context and Taxonomic Evolution

The Era of Environmental Sequencing

The initial discovery of Marinisomatota dates back to early molecular surveys of marine microbial diversity using 16S rRNA gene sequencing. These studies revealed a ubiquitous group that appeared frequently in clone libraries from various marine habitats, particularly in the deep ocean. Initially designated by multiple names—Marine Group A, SAR406, and candidate phylum "Marinimicrobia"—this lineage was recognized as one of the most abundant bacterial groups in the ocean yet remained recalcitrant to cultivation [4] [6]. Global ocean sampling initiatives consistently detected this phylum across diverse marine environments, from sunlit surface waters to the deep biosphere, with early studies suggesting their potential involvement in sulfur and nitrogen cycling [6]. Despite their apparent abundance, the lack of cultivated representatives meant that their metabolic capabilities and ecological roles could only be inferred from fragment genomic data obtained through single-cell genomics and metagenome-assembled genomes (MAGs).

Formal Taxonomic Proposal

A breakthrough came with the successful isolation and characterization of Fidelibacter multiformis strain IA91T from deep subsurface aquifers, which provided the necessary reference point for formal taxonomic classification [5]. This Gram-negative, anaerobic, chemoheterotrophic bacterium exhibits a unique dependence on exogenous muropeptides derived from other bacteria for cell wall formation, growth, and even cellular morphology—an energy-saving strategy likely adapted to nutrient-limited conditions [5]. Based on comprehensive phylogenetic analyses of 16S rRNA gene sequences and conserved protein markers, researchers formally proposed the phylum Marinisomatota to encompass this lineage, establishing a structured taxonomy with Fidelibacter as the type genus [4] [5]. The phylum name Fidelibacterota has also been proposed in parallel, reflecting ongoing developments in microbial taxonomy [4]. This formal classification replaced the various historical designations and provided a standardized framework for future studies of this ecologically significant group.

Table 1: Historical Nomenclature of Marinisomatota

Designation	Context of Use	Taxonomic Level	Reference
Marine Group A	Early 16S rRNA surveys	Candidate division	[6]
SAR406	Sargasso Sea project	Clade name	[4]
Candidatus Marinimicrobia	Uncultivated status	Candidate phylum	[5]
Marinisomatota	Genome Taxonomy Database	Validated phylum	[7] [8]
Fidelibacterota	Formal proposal	Validated phylum	[4] [5]

Phylogenetic Analysis and Taxonomic Framework

Genomic Insights into Phylum Diversity

The application of genome-resolved metagenomics has dramatically expanded our understanding of Marinisomatota diversity, revealing a phylum with substantial genetic and functional variation. A recent comprehensive analysis retrieved 1,588 Marinisomatota genomes from global ocean datasets, representing 1 class, 2 orders, 14 families, 31 genera, and 67 species [7]. This extensive genomic catalog has enabled researchers to construct robust phylogenetic frameworks that reflect the evolutionary relationships within this phylum. Phylogenetic trees constructed using conserved marker genes involved in replication, transcription, and translation have confirmed the deep-branching position of Marinisomatota within the bacterial domain, with the isolated representative Fidelibacter multiformis serving as a taxonomic anchor [5]. The diversity within this phylum manifests not only at the sequence level but also in genome size, structure, and functional potential, with some members exhibiting streamlined genomes characteristic of adaptation to nutrient-poor environments, while others maintain larger genomes with expanded metabolic capabilities [6].

Relationship to Other Bacterial Phyla

Comparative genomic analyses place Marinisomatota as a phylogenetically distinct bacterial phylum with evolutionary relationships to other deeply branching microbial lineages. PhyloPhlAn analysis of conserved marker genes has shown that Marinisomatota branches separately from its closest cultured relative, Caldithrix abyssi, a thermophilic bacterium [6]. Within the structured taxonomy of the Genome Taxonomy Database (GTDB), Marinisomatota occupies a distinct position among bacterial phyla, with some classifications placing it as one of the few bacterial phyla not assigned to a specific kingdom [4]. This deep-branching phylogenetic position suggests an ancient origin for this lineage, with subsequent diversification potentially driven by adaptation to different ecological niches along ocean depth gradients and redox gradients. The phylogenetic distinctness of Marinisomatota from other major marine bacterial phyla such as Proteobacteria, Planctomycetota, and Chloroflexota underscores its unique evolutionary trajectory and ecological significance in marine ecosystems [8].

Metabolic Strategies and Mixotrophic Adaptations

Spectrum of Metabolic Capabilities

Marinisomatota exhibit remarkable metabolic plasticity, employing diverse energy acquisition strategies that challenge traditional classification schemes. Recent research has identified three distinct metabolic strategies within the phylum: MS0 (photoautotrophic potential), MS1 (heterotrophic with pronounced glycolytic pathway), and MS2 (heterotrophic without glycolysis) [7]. This metabolic diversification appears to be a strategic response to nutrient limitations in the ocean, allowing different clades to occupy complementary ecological niches. The MS0 strategy, found in five specific families (S15-B10, TCS55, UBA1611, UBA2128, and UBA8226), includes the genetic potential for light-dependent processes associated with Crassulacean acid metabolism (M00169), enabling these organisms to potentially fix carbon dioxide while simultaneously harnessing light energy [7]. This capacity for mixotrophy—combining phototrophy with heterotrophy—represents a significant adaptation to the fluctuating energy conditions of the marine environment, particularly in the transition zones between sunlit surface waters and the dark ocean interior.

Ecological Implications of Metabolic Plasticity

The metabolic flexibility of Marinisomatota has profound implications for their ecological success and biogeochemical function across diverse marine habitats. Members employing the MS0 strategy can potentially act as primary producers in aphotic zones through chemosynthesis while also utilizing any available light energy in transitional zones, giving them a competitive advantage in energy-limited environments [7]. In contrast, MS1 and MS2 strategists play crucial roles in the degradation of complex organic matter, with some populations possessing extensive repertoires of glycoside hydrolases, polysaccharide lyases, and sugar transporters for breaking down a broad spectrum of polysaccharides including chitin, cellulose, pectin, alginate, chondroitin, and carrageenan [9]. This functional diversity enables Marinisomatota to participate in multiple biogeochemical cycles, including carbon, sulfur, and nitrogen cycling, with some clades expressing nitrous oxide reductase (nosZ), potentially acting as a global sink for the greenhouse gas nitrous oxide [6]. The emergence of these specialized metabolic strategies illustrates how niche partitioning within a phylogenetic lineage can drive the evolution of novel nutritional groups with complementary ecosystem functions.

Table 2: Metabolic Strategies in Marinisomatota

Metabolic Strategy	Energy & Carbon Sources	Key Enzymes/Pathways	Ecological Distribution
MS0 (Photoautotrophic potential)	Light, COâ‚‚	Crassulacean acid metabolism, proteorhodopsin	Translucent zone, transitioning to aphotic layer
MS1 (Heterotrophic with glycolysis)	Organic carbon	Complete glycolytic pathway, diverse CAZymes	Various depths, particle-associated
MS2 (Heterotrophic without glycolysis)	Organic carbon	Limited glycolysis, alternative sugar degradation	Deep waters, nutrient-limited zones
Auxotrophic (e.g., F. multiformis)	Muropeptides, yeast extract	Peptidoglycan recycling enzymes	Deep subsurface, syntrophic communities

Genomic Features Underpinning Metabolic Diversity

The metabolic versatility of Marinisomatota is reflected in their genomic characteristics, which show signs of adaptation to different energy regimes across the phylum. Some surface water-inhabiting clades exhibit genome streamlining—reduced genome size, high coding density, and low functional redundancy—comparable to that observed in Candidatus Pelagibacter, suggesting adaptation to nutrient-poor conditions [6]. These streamlined genomes often encode proteorhodopsin, a light-driven proton pump that can supplement energy acquisition in surface waters [6]. In contrast, other Marinisomatota lineages maintain larger genomes with expanded metabolic capabilities, including diverse respiratory complexes, specialized nutrient transporters, and extensive arrays of carbohydrate-active enzymes (CAZymes) [9]. Notably, some Marinisomatota populations possess up to 35 different glycoside hydrolases, often in multiple copies, along with extracellular CAZymes and multiple sugar transporters, enabling them to degrade complex organic matter that is inaccessible to many other marine microorganisms [9]. This genomic diversity underscores how evolutionary processes have shaped the metabolic capabilities of different Marinisomatota lineages to exploit specific ecological niches along marine energy gradients.

Ecological Distribution and Biogeography

Global Distribution Patterns

Marinisomatota demonstrate a ubiquitous distribution across the world's oceans, with distinct biogeographic patterns linked to environmental gradients. Comprehensive analyses of global metagenomic datasets reveal that these organisms are predominantly found in low-latitude marine regions, with relative abundances ranging from 0.18% to 36.21% of microbial communities [7]. Their distribution is strongly influenced by depth, with different clades occupying specific positions along the water column defined by light availability, oxygen concentration, and nutrient profiles. While historically characterized as abundant in deep waters, particularly in oxygen minimum zones (OMZs) [6], more recent studies have revealed a more complex distribution pattern with specific clades adapted to surface, mesopelagic, and bathypelagic habitats. Under the Ross Ice Shelf in Antarctica, Marinisomatota were identified as one of the six dominant microbial phyla in this dark, oligotrophic environment, highlighting their adaptability to extreme conditions [8]. Their successful colonization of such diverse marine habitats reflects the metabolic plasticity encoded in their genomes and their ability to exploit different energy sources across varying environmental conditions.

Niche Specialization Along Environmental Gradients

Different Marinisomatota clades exhibit distinct preferences for specific environmental conditions, resulting in niche partitioning along eco-thermodynamic gradients. Research has shown that evolutionary diversification within this phylum appears to be closely related to energy yields, with increased co-metabolic interactions in more deeply branching clades [6]. In oxygen minimum zones, certain Marinisomatota clades become particularly abundant and appear to participate in sulfur and nitrogen cycling, filling previously unassigned niches in these ecosystems [6]. Their distribution follows sharp redox gradients, with specific clades associated with oxic, dysoxic, suboxic, anoxic, and sulfidic conditions [6]. This niche specialization is facilitated by diverse respiratory capabilities, with different clades possessing genes for aerobic respiration, nitrate reduction, and sulfur oxidation. The pattern of niche partitioning along energy gradients illustrates how thermodynamic principles shape microbial community structure and suggests that Marinisomatota clades have evolved to exploit specific energy disequilibria in the marine environment, effectively dividing the resource spectrum through metabolic specialization.

Research Methodologies and Experimental Approaches

Genomic and Metagenomic Techniques

The study of Marinisomatota has relied heavily on cultivation-independent genomic techniques that have enabled researchers to bypass the challenges associated with growing these fastidious organisms in pure culture. Single-cell amplified genomes (SAGs) have been instrumental in obtaining initial genomic glimpses of these organisms, with assemblies ranging from 0.39 to 2.01 million bases and completeness estimates from <10% to >90% [6]. To overcome the limitations of incomplete SAGs, researchers have employed metagenome-assembled genomes (MAGs) constructed from large metagenomic datasets, improving genome completeness to an average of 87% and enabling more comprehensive metabolic reconstruction [6]. The integration of metatranscriptomics has further allowed researchers to identify actively expressed genes under in situ conditions, providing insights into the functional activity of different Marinisomatota clades across environmental gradients [6]. These approaches have revealed that streamlined Marinisomatota clades in oxic waters express genes for aerobic respiration and proteorhodopsin, while those in oxygen-deficient zones express genes involved in sulfur and nitrogen metabolism [6]. The combination of these techniques has progressively illuminated the functional capabilities and ecological roles of this once-enigmatic phylum.

Cultivation Techniques and Breakthroughs

The successful cultivation of Fidelibacter multiformis represented a watershed moment in Marinisomatota research, achieved through innovative approaches tailored to the fastidious nature of these organisms. The isolation strategy involved using sediment and formation water samples from deep aquifers incubated without additional nutrients under an N₂/CO₂ (80:20) atmosphere at 45°C [5]. Researchers employed the deep agar slant method combined with dilution-to-extinction in saline mineral medium amended with yeast extract and muropeptides obtained from the culture supernatant of a co-isolated Bacillota strain (Acc8) or from enzymatic digestion of Bacillus subtilis peptidoglycan [5]. This cultivation design acknowledged the organism's auxotrophy for peptidoglycan derivatives, an energy-saving strategy that likely contributes to its survival in nutrient-limited environments. Physiological characterization revealed that strain IA91T is a Gram-negative, obligatory anaerobic, chemoheterotrophic bacterium with a limited substrate spectrum, utilizing only yeast extract, muropeptides, and D-lactate [5]. Its growth is stimulated by co-cultivation with hydrogen-scavenging methanogenic archaea, indicating syntrophic interactions in its natural habitat. This cultivation breakthrough has provided an essential reference point for validating genomic predictions and conducting detailed physiological studies of this previously uncultivated phylum.

Research Workflow for Marinisomatota Characterization

Table 3: Essential Research Reagents and Materials for Marinisomatota Studies

Reagent/Material	Specific Example	Application in Research	Function
Growth Medium	Saline mineral medium with yeast extract and muropeptides	Cultivation of Fidelibacter multiformis	Provides nutrients and essential cell wall precursors for auxotrophic growth
Muropeptide Source	Bacillus subtilis peptidoglycan or culture supernatant of strain Acc8	Isolation and cultivation	Supplies peptidoglycan recycling intermediates required for growth
DNA Extraction Kit	Modified Zhou protocol with chemical, physical, and enzymatic steps	Nucleic acid extraction from environmental samples	Recovers high-quality DNA from diverse sample types including sediments
16S rRNA Primers	518F/926R (bacterial), 517F/958R (archaeal)	Amplicon sequencing	Amplifies variable regions for phylogenetic analysis
Sequence Database	SILVA SSU Ref dataset, GTDB	Phylogenetic placement	Provides reference sequences for taxonomic classification
Metagenomic Software	RAxML, MOTHUR, DADA2	Phylogenetic tree construction and community analysis	Enables diversity analysis and phylogenetic placement

The reclassification of the marine SAR406/Marinimicrobia lineage as Marinisomatota represents more than a taxonomic revision—it marks the transition from a poorly understood group of uncultivated microorganisms to a recognized phylum with defined metabolic capabilities and ecological roles. The formal description of Fidelibacter multiformis and the associated taxonomic framework have provided an essential reference point for interpreting the extensive genomic data available for this group. Research has revealed Marinisomatota as metabolically versatile organisms with mixotrophic capabilities that enable them to occupy diverse niches across marine energy gradients. Their involvement in carbon, sulfur, and nitrogen cycling, particularly through processes such as nitrous oxide reduction and complex polysaccharide degradation, highlights their significance in global biogeochemical cycles. Future research directions should focus on expanding cultivation efforts to capture the full phylogenetic diversity within this phylum, elucidating the molecular mechanisms underlying their metabolic flexibility, and quantifying their contributions to elemental cycling in different ocean provinces. The study of Marinisomatota exemplifies how integrated approaches combining cultivation-independent genomics with innovative cultivation techniques can illuminate the biology of microbial dark matter and reveal its importance in ecosystem functioning.

Marinisomatota (a phylum previously recognized as Marinimicrobia, Marine Group A, and SAR406) represents a ubiquitous and abundant group of microorganisms in global marine environments. Traditionally characterized as heterotrophic, recent metagenomic studies have revealed unexpected metabolic versatility within this phylum, including the capacity for light-dependent metabolic processes. This ecological success is largely driven by the emergence of three distinct core metabolic strategies: MS0 (photoautotrophic potential), MS1 (heterotrophic with pronounced glycolytic pathway), and MS2 (heterotrophic without glycolysis). These specialized strategies represent evolutionary adaptations to nutrient limitations and varying energy sources across different oceanic layers, from the translucent zone to the aphotic depths [1] [2].

The identification of these trophic strategies fundamentally changes our understanding of Marinisomatota's role in marine biogeochemical cycles. Rather than occupying a single ecological niche, different Marinisomatota lineages have evolved specialized metabolic configurations that optimize energy acquisition under specific environmental constraints. This metabolic diversification enables the phylum to colonize diverse marine habitats from surface waters to the deep sea, with relative abundances ranging from 0.18% to 36.21% across low-latitude marine regions [1]. The strategic deployment of MS0, MS1, and MS2 strategies across environmental gradients demonstrates a sophisticated evolutionary response to the thermodynamic challenges of marine environments.

Defining the Core Metabolic Strategies

MS0: Photoautotrophic Potential

The MS0 metabolic strategy represents Marinisomatota lineages with demonstrated capacity for light-dependent carbon fixation and organic compound synthesis. These organisms potentially utilize Crassulacean acid metabolism (M00169) to harness light energy for autotrophic processes, enabling them to function as primary producers in specific marine niches [1]. This strategy is particularly advantageous in the translucent ocean zone or during transitions between light-rich and light-depleted layers, where the ability to switch between energy sources provides a competitive advantage.

Genomic analyses reveal that MS0-type Marinisomatota possess genetic machinery for light harvesting and carbon fixation pathways that differentiate them from strictly heterotrophic relatives. Five specific families within Marinisomatota (S15-B10, TCS55, UBA1611, UBA2128, and UBA8226) exhibit the genetic potential for these light-dependent processes [1]. The photoautotrophic capabilities in these lineages likely contribute significantly to primary production in specific oceanic regions, challenging the traditional paradigm that categorizes all Marinisomatota as heterotrophic.

MS1: Glycolytic Heterotrophic

The MS1 strategy characterizes heterotrophic Marinisomatota that utilize a pronounced glycolytic pathway for energy extraction from organic compounds. This metabolic configuration emphasizes the efficient breakdown of complex organic molecules through glycolysis, followed by subsequent energy-yielding processes [1]. The enhanced glycolytic capacity suggests specialization in processing particulate organic matter or high-energy dissolved organic compounds in the water column.

Organisms employing the MS1 strategy likely play crucial roles in the microbial loop, participating in the breakdown and recycling of organic matter derived from phytoplankton and other marine primary producers. The prominence of glycolysis indicates adaptation to environments with periodic inputs of fresh organic substrate, where rapid energy extraction provides competitive advantage. This strategy represents a specialized form of heterotrophy that optimizes carbon and energy flow through glycolytic fluxes, distinguishing MS1 organisms from other heterotrophic Marinisomatota with alternative energy extraction mechanisms.

MS2: Non-Glycolytic Heterotrophic

The MS2 strategy encompasses heterotrophic Marinisomatota that utilize pathways other than glycolysis for organic matter assimilation and energy production [1]. These organisms have evolved alternative enzymatic machinery for processing organic carbon, potentially including specialized transporter systems and extracellular enzymes for initial substrate breakdown [2]. This non-glycolytic heterotrophic strategy may be advantageous in energy-limited environments or for utilizing specific organic compounds not efficiently processed through glycolytic pathways.

The metabolic flexibility afforded by the MS2 strategy likely enables these organisms to access different organic carbon pools than their MS1 counterparts, reducing direct competition for resources. The specific biochemical pathways utilized in MS2 metabolism remain to be fully characterized but may involve specialized degradation pathways for complex or recalcitrant organic compounds that are abundant in deep marine environments. This strategic division within heterotrophic Marinisomatota represents a remarkable example of niche partitioning through metabolic specialization.

Ecological Distribution and Genomic Framework

Comprehensive metagenomic analyses have revealed the taxonomic breadth and distribution patterns of these metabolic strategies across global ocean basins. Through reconstruction of 1,588 Marinisomatota genomes representing one class, two orders, 14 families, 31 genera, and 67 species, researchers have established a robust genomic framework for understanding the ecological distribution of MS0, MS1, and MS2 strategies [1].

Table 1: Genomic Diversity and Ecological Range of Marinisomatota

Taxonomic Level	Diversity	Relative Abundance Range	Primary Distribution
Phylum	Marinisomatota	0.18% - 36.21%	Global oceans
Class	1	Not specified	Low-latitude marine regions
Orders	2	Not specified	Not specified
Families	14	Not specified	Not specified
Genera	31	Not specified	Not specified
Species	67	Not specified	Not specified

The ecological distribution of these metabolic strategies is strongly influenced by environmental gradients, particularly light availability and nutrient concentrations. MS0-type Marinisomatota predominate in the photic zone where light energy can power autotrophic processes, while MS1 and MS2 strategies show distinct vertical partitioning through the water column [1]. This distribution pattern reflects adaptive radiation within the phylum, enabling different lineages to optimize their metabolic machinery for specific environmental conditions.

Table 2: Metabolic Strategy Distribution and Environmental Preferences

Metabolic Strategy	Energy Source	Carbon Acquisition	Characteristic Pathways	Preferred Environment
MS0	Light	COâ‚‚ fixation	Crassulacean acid metabolism (M00169)	Translucent zone, transition layers
MS1	Organic compounds	Organic carbon assimilation	Enhanced glycolysis	Regions with fresh organic inputs
MS2	Organic compounds	Organic carbon assimilation	Alternative non-glycolytic pathways	Energy-limited or specialized niches

The emergence of these three distinct metabolic strategies represents an evolutionary response to nutrient limitations and energy availability constraints in oceanic ecosystems [1]. This metabolic diversification reduces direct competition between lineages and enables more efficient utilization of the varied energy sources available across marine depth gradients. The result is a complex ecological dynamic where closely related organisms employ fundamentally different metabolic strategies to occupy distinct niches within the same ecosystem.

Research Methodologies and Experimental Protocols

Metagenomic Genome Reconstruction and Analysis

The identification and characterization of MS0, MS1, and MS2 metabolic strategies relies on sophisticated metagenomic approaches that reconstruct metabolic potential from environmental DNA sequences. The following protocol outlines the key steps for conducting such analyses:

• Sample Collection and Processing: Seawater samples are collected from multiple depth layers using Niskin bottles (typically 36-48 liters per depth). Samples are sequentially filtered through 3-μm and 0.22-μm polycarbonate membranes to capture different size fractions of microbial communities. Filters are immediately preserved in liquid nitrogen onboard and transferred to -80°C freezers for long-term storage [10].

• DNA Extraction and Sequencing: Microbial community DNA is extracted from filters using commercial kits with modifications to maximize yield from low-biomass samples. Quality control measures include spectrophotometric and fluorometric quantification. Metagenomic libraries are prepared and sequenced using Illumina or similar platforms, generating 150-300 bp paired-end reads [10].

• Metagenomic Assembly and Binning: Quality-filtered reads are assembled into contigs using metaSPAdes or similar assemblers. Contigs ≥2,000 bp are typically retained for downstream analysis. Genome binning is performed using composition-based and abundance-based algorithms to reconstruct metagenome-assembled genomes (MAGs). MAG quality is assessed using completeness and contamination estimates based on conserved single-copy genes [1] [11].

• Metabolic Pathway Reconstruction: Putative protein-coding genes are identified in MAGs and functionally annotated against reference databases. Metabolic pathways are reconstructed using pathway-specific hidden Markov models and enzyme commission number assignments. Carbon fixation pathways are identified through presence of key marker genes and complete pathway modules [1] [11].

• Phylogenomic Analysis: Reference trees are constructed using concatenated sets of conserved marker genes (e.g., 31/bac120/arc122 gene sets). Phylogenetic placement of MAGs enables taxonomic classification and evolutionary inference of metabolic traits [11].

Metabolic Strategy Classification Criteria

Classification of Marinisomatota MAGs into MS0, MS1, and MS2 categories follows specific genomic and metabolic criteria:

• MS0 Designation: Presence of complete or near-complete pathways for light harvesting and carbon fixation, particularly genes associated with Crassulacean acid metabolism (M00169). Key marker genes include those encoding proteorhodopsin-like proteins and carbon fixation enzymes [1].

• MS1 Designation: Presence of complete glycol pathways with enhanced complement of glycolytic enzymes, absence of carbon fixation pathways, and presence of organic carbon transporter systems. Distinctive features include high representation of enzymes in the Embden-Meyerhof-Parnas pathway [1].

• MS2 Designation: Presence of heterotrophic metabolic machinery with absence or reduction of glycolytic pathways, complemented by alternative energy extraction pathways such as the Entner-Doudoroff pathway or specialized degradation enzymes [1].

Metabolic Pathway Visualization and Integration

The three metabolic strategies of Marinisomatota can be understood through their distinct pathway configurations and energy transformation processes. The following diagram illustrates the core metabolic networks and their interconnections:

The coordination between these metabolic strategies becomes evident when examining their responses to environmental gradients. MS0 strategies dominate where light energy is available, while MS1 and MS2 strategies partition heterotrophic niches based on the quality and accessibility of organic carbon sources. This metabolic specialization creates a complex network of energy and carbon flow through Marinisomatota populations across oceanic depth gradients.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents and Materials for Marinisomatota Metabolic Studies

Reagent/Material	Specifications	Primary Function	Application Examples
Polycarbonate Membranes	3-μm and 0.22-μm pore sizes	Size-fractionation of microbial communities	Sequential filtration to separate particle-associated vs. free-living cells [10]
Niskin Bottles	10-48 L capacity, sterile	Seawater sample collection	Depth-stratified sampling from multiple ocean layers [10]
DNA Extraction Kits	Modified for environmental samples	High-yield microbial DNA extraction	Community DNA isolation from low-biomass filters [10] [11]
Metagenomic Sequencing Kits	Illumina-compatible	Library preparation for high-throughput sequencing	Generating short-read data for assembly and binning [1] [11]
Reference Databases	MetaCyc, KEGG, COG	Functional annotation of predicted genes	Metabolic pathway reconstruction and classification [11] [12]
Single-Copy Marker Gene Sets	31/bac120/arc122	Phylogenomic analysis and quality assessment	MAG quality evaluation and taxonomic placement [11]
Metabolic Pathway Databases	Custom HMM profiles	Identification of specific metabolic pathways	Detection of carbon fixation and energy metabolism genes [1] [11]
Fmoc-Gly-OH-2,2-d2	Fmoc-Gly-OH-2,2-d2, CAS:284665-11-8, MF:C17H15NO4, MW:299.32 g/mol	Chemical Reagent	Bench Chemicals
Boc-Leucinol	Boc-Leucinol, CAS:82010-31-9, MF:C11H23NO3, MW:217.31 g/mol	Chemical Reagent	Bench Chemicals

Discussion: Implications for Marine Carbon Cycling and Microbial Ecology

The identification of three distinct metabolic strategies within Marinisomatota has profound implications for understanding marine carbon cycling and microbial ecosystem dynamics. The MS0 strategy contributes to primary production through light-dependent carbon fixation, particularly in transitional zones where light penetration varies. Meanwhile, the partitioning of heterotrophic metabolism into MS1 (glycolytic) and MS2 (non-glycolytic) strategies suggests sophisticated niche specialization in organic matter processing [1].

This metabolic diversification enables Marinisomatota to collectively access multiple energy channels, forming what can be conceptualized as an "internal mixotrophy" at the phylogenetic level rather than the organismal level. While individual strains may specialize in one strategy, the phylum as a whole exhibits metabolic plasticity that ensures its persistence across fluctuating marine conditions [1] [2]. This ecological strategy may explain the remarkable ubiquity and abundance of Marinisomatota across diverse marine ecosystems.

The presence of these three strategies also has implications for understanding microbial responses to environmental change. As ocean temperatures rise due to climate change, the differential resilience of these metabolic strategies will influence community composition and biogeochemical function. Recent research suggests that evolutionary adaptation can enhance thermal resilience in marine mixotrophs over evolutionary timescales, with evolved reductions in respiration rate mitigating the effects of temperature increases [13]. This evolutionary potential may shape the future distribution of MS0, MS1, and MS2 strategies in warming oceans.

Furthermore, the discovery of photoautotrophic potential in Marinisomatota (MS0 strategy) challenges traditional classification of microbial functional groups and necessitates reconsideration of carbon flux models in oceanic ecosystems. The significant relative abundance of Marinisomatota (up to 36.21% in some regions) means that even modest photoautotrophic activity could contribute meaningfully to primary production estimates, particularly in mesopelagic zones where light is limited but not absent [1].

Future research should focus on cultivating representative strains from each metabolic strategy to validate genomic predictions through physiological experiments. Additionally, targeted metatranscriptomic and metaproteomic approaches could reveal how these metabolic strategies are dynamically regulated in response to diel cycles, nutrient pulses, and other environmental fluctuations. Understanding the regulatory networks that control the expression of these metabolic strategies will provide deeper insight into how Marinisomatota navigates the complex energy landscape of the ocean.

Ecological niche theory provides a fundamental framework for understanding the distribution and function of marine microorganisms. In the vast oligotrophic waters of low-latitude oceans, characterized by limited nutrient availability and high temperatures, niche partitioning becomes particularly pronounced. Within this context, the phylum Marinisomatota (formerly known as Marinimicrobia, Marine Group A, and SAR406) represents a widespread and highly abundant group whose ecological roles and adaptive strategies are only beginning to be understood [2]. This whitepaper synthesizes current research on niche prevalence in these environments, with particular emphasis on mixotrophic adaptations that enable survival under nutrient constraints, drawing upon recent genomic, metagenomic, and experimental evidence.

The study of Marinisomatota exemplifies the critical need to move beyond taxonomic inventories toward functional ecology. These organisms dominate large oceanic regions yet have resisted cultivation, requiring innovative approaches to elucidate their metabolic capabilities and ecological niches [2]. Understanding their prevalence and functional adaptations provides crucial insights into biogeochemical cycling and ecosystem resilience in the warming ocean, with implications for predicting global change impacts on marine systems.

Niche Partitioning and Environmental Drivers

In oligotrophic systems, subtle environmental gradients create distinct niches that support specialized microbial communities. Multiple studies have demonstrated clear patterns of niche partitioning among marine picophytoplankton and bacterioplankton along light, temperature, and nutrient axes.

Picophytoplankton Niche Partitioning

Research in the Red Sea, considered a model for the future warm ocean, reveals distinct vertical segregation of picophytoplankton populations [14]. Table 1 summarizes the realized niches of major picophytoplankton groups based on empirical observations.

Table 1: Realized Niches of Picophytoplankton in Warm Oligotrophic Seas

Population	Light Preference (% Surface PAR)	Temperature Optimum (°C)	Contribution to Total Biomass	Dominance Conditions
Synechococcus	~77%	~30.6	47.6%	Warmest surface waters (>30°C)
Picoeukaryotes	~6.4%	~30.0	26.4%	Lower light, warm waters
Prochlorococcus	~3.2%	Not specified	25.9%	Deep, low-light layers

This niche partitioning reflects fundamental physiological adaptations. Synechococcus demonstrates remarkable thermal adaptability, dominating in the warmest surface waters above 30°C, while Prochlorococcus is segregated to deeper, dimmer waters [14]. The dominance of Synechococcus in the Red Sea contrasts with patterns in other subtropical gyres where Prochlorococcus typically prevails, suggesting temperature plays a defining role in community structure.

Seasonal and Interannual Succession Patterns

Long-term time-series data from the Southern California Current reveal oscillatory patterns in microbial community structure driven by seasonal and ENSO (El Niño-Southern Oscillation) cycles [15]. These studies demonstrate a consistent succession between large-genome lineages during cold, nutrient-rich conditions and small-genome lineages (including Prochlorococcus and Pelagibacter) during warm, nutrient-deplete periods.

Key findings from an 11-year metagenomic study include [15]:

• Seasonal oscillations: Clear taxonomic succession with cold-water taxa (Cytophagaceae, Alteromonadaceae) peaking in winter and spring versus warm-water taxa (Pelagibacteraceae, Prochlorococcaceae) dominating in summer and fall
• Genome size trade-offs: Average microbial genome size oscillates seasonally, with larger genomes during spring blooms and smaller genomes during oligotrophic periods
• Climate linkages: Interannual shifts in community composition correlate strongly with ENSO cycles, with El Niño conditions favoring oligotrophic taxa

These taxonomic shifts translate to functional changes, with clear oscillations in metabolic potential related to nutrient stress response, organic carbon degradation, and biomass stoichiometry [15].

Methodological Approaches for Niche Characterization

Nutrient Limitation Bioassays

Determining nutrient limitation patterns is fundamental to understanding niche constraints in oligotrophic systems. The GEOTRACES GP21 expedition conducted eleven nutrient addition bioassay experiments across the South Pacific Ocean along approximately 30°S [16].

Table 2: Experimental Protocol for Nutrient Addition Bioassays

Component	Specification	Purpose
Experimental Sites	11 locations spanning >10,000 km from Chilean coast to western subtropical gyre	Assess spatial variability in nutrient limitation
Nutrient Treatments	Control, +N, +Fe, +P, +Co, +Zn, and combinations (N+Fe, N+P, N+Fe+Zn, etc.)	Identify primary and serial nutrient limitations
Incubation Duration	~48 hours	Measure physiological response times
Response Metrics	Chlorophyll-a accumulation, Fv/Fm (photosystem II efficiency)	Quantify growth response and physiological stress
Trace Metal Protocols	Trace-metal-clean techniques during sampling and processing	Prevent contamination in low-nutrient environments

This experimental approach revealed a cross-Pacific gradient in serial nutrient limitation: primary nitrogen limitation at all sites, with serial iron limitation in the eastern and central basin, transitioning to serial phosphate limitation in the western basin [16]. These patterns were corroborated by Fv/Fm responses, with declines following N addition indicating enhanced iron stress in regions where iron was approaching co-limiting concentrations.

Physiological State Discrimination using Triple Metabarcoding

Traditional DNA-based metabarcoding cannot distinguish active, dormant, and dead microbial fractions, potentially misrepresenting functional diversity. The triple metabarcoding approach (TMA) addresses this limitation by integrating three complementary analyses [17]:

Table 3: Triple Metabarcoding Approach (TMA) Specifications

Analysis Type	Target Molecule	Physiological Information
DNA-seq	Environmental rDNA pool	Total community (active + dormant + dead)
RNA-seq	rRNA transcripts	Active community (detectable rRNA)
PMA-seq	DNA from intact cells (PMA-treated)	Viable community (active + dormant)

Experimental workflow:

• Sample collection: Water column and sediment samples from tropical coastal ecosystems
• PMA treatment: Propidium monoazide cross-links to DNA of dead cells with compromised membranes
• Nucleic acid extraction: Parallel processing for DNA, RNA, and PMA-treated DNA
• Sequencing: High-throughput amplicon sequencing of 16S and 18S rRNA genes/transcripts
• Bioinformatic classification:
  • Active: Phylotypes present in both DNA-seq and RNA-seq
  • Dormant: Phylotypes present in DNA-seq and PMA-seq but absent in RNA-seq
  • Dead: Phylotypes present in DNA-seq but absent in both PMA-seq and RNA-seq

Application of TMA to tropical coastal systems revealed that dead microbial fractions contribute disproportionately to DNA-based diversity metrics (≤5% of sequences but 32-53% of ASV richness in plankton), while dormant fractions represent a significant reservoir of diversity potential, particularly in water columns (20-62% of viable community richness) [17]. Active communities responded to distinct environmental drivers compared to total DNA-based assemblages, highlighting the importance of physiological discrimination for accurate niche characterization.

Marinisomatota Metabolic Diversity and Niche Specialization

Marinisomatota represents a widespread yet poorly characterized bacterial phylum in marine systems. Recent genomic reconstructions have revealed remarkable metabolic versatility that enables niche specialization across diverse marine environments.

Genomic Insights into Metabolic Strategies

A comprehensive analysis of 1,588 Marinisomatota genomes reconstructed from global ocean datasets identified three distinct metabolic modes [2]:

Table 4: Marinisomatota Metabolic Modes and Niche Specialization

Metabolic Mode	Key Characteristics	Ecological Distribution
MS0	Photoautotrophic potential	Not specified
MS1	Heterotrophic with enhanced glycolytic capacity	Not specified
MS2	Heterotrophic without glycolysis	Not specified

The presence of these distinct metabolic modes suggests that Marinisomatota have evolved mixotrophic adaptations allowing them to alternate between autotrophic and heterotrophic strategies depending on environmental conditions [2]. This metabolic plasticity represents a significant advantage in oligotrophic environments where nutrient availability is variable and unpredictable.

Evolutionary Resilience to Thermal Stress

Theoretical models suggest that evolutionary adaptation can enhance thermal resilience in marine mixotrophs over evolutionary timescales. A mechanistic model incorporating both evolution and metabolic plasticity demonstrates that evolved reductions in respiration can compensate for thermal sensitivity at high temperatures [18]. Key findings include:

• Evolutionary compensation: Adapted populations exhibit less metabolic variability in response to temperature fluctuations
• Timescale dependence: Plastic responses dominate over short timescales, while evolutionary adaptations become significant over longer periods
• Carbon budget modifications: Evolved changes to respiratory metabolism enhance fitness under thermal stress

These evolutionary adaptations likely contribute to the prevalence of Marinisomatota and related mixotrophic organisms in warm, low-latitude oceans where temperature stress is increasingly common.

Research Tools and Reagent Solutions

Cutting-edge research on ecological niches in marine systems relies on specialized methodologies and reagents. The following table summarizes key research solutions derived from the cited studies.

Table 5: Research Reagent Solutions for Marine Microbial Niche Studies

Reagent/Method	Application	Function in Research
Propidium Monoazide (PMA)	Viability discrimination	Cross-links DNA from dead cells with compromised membranes, enabling selective analysis of intact cells
Ethoxyzolamide	CCM inhibition	Inhibits biophysical carbon concentration mechanisms in algae
3-Mercaptopicolinic Acid	CCM inhibition	Inhibits biochemical carbon concentration mechanisms (PEPC) in algae
Trace Metal Clean Protocols	Nutrient limitation experiments	Prevents contamination during sampling and processing for low-nutrient environments
Modified Low-Nutrient Media	Cultivation of oligotrophic bacteria	Enriches for previously uncultured taxa by mimicking natural nutrient conditions
Diffusion-Based Cultivation	Microbial isolation	Creates chemical gradients that facilitate growth of challenging microorganisms

Visualization of Metabolic Pathways and Workflows

Marinisomatota Mixotrophic Metabolism

Triple Metabarcoding Workflow

The study of ecological niches in low-latitude oceans and oligotrophic waters reveals complex patterns of microbial adaptation and specialization. Marinisomatota exemplifies the mixotrophic strategies that enable persistence in these nutrient-limited environments, with genomic evidence pointing to distinct metabolic modes that facilitate niche partitioning. Methodological advances, including triple metabarcoding and nutrient bioassays, provide increasingly refined tools for characterizing these niches and their environmental drivers.

Understanding niche prevalence and adaptation mechanisms in these systems is critical for predicting responses to ongoing climate change. The consistent microbial responses observed across seasonal and interannual timescales suggest that warming will drive systematic shifts in community composition and function, with potential consequences for biogeochemical cycles [15]. Future research integrating genomic, experimental, and modeling approaches will further elucidate the evolutionary ecology of marine microbes and their roles in ocean ecosystems.

Mixotrophy, the combination of different metabolic modes such as phototrophy and heterotrophy, represents a key adaptation for survival in dynamic marine environments. Within the context of marine bacterioplankton, the phylum Marinisomatota has been identified as a group containing active, transcriptionally dominant members of deep-sea microbial communities, suggesting significant ecological roles [19]. Although not primarily known as phototrophs, understanding the genetic toolkits of light harvesting and crassulacean acid metabolism (CAM) provides a foundational framework for investigating potential mixotrophic capabilities and carbon concentration mechanisms in these and other marine microbes. CAM, a water-conserving adaptation that has evolved independently over 60 times in vascular plants, utilizes a temporal separation of carbon acquisition and fixation to maximize water-use efficiency [20] [21] [22]. This review synthesizes the core genetic components of these systems and their relevance to microbial adaptations in marine environments, with implications for understanding the physiological ecology of groups like Marinisomatota.

Core Genetic Toolkit for Crassulacean Acid Metabolism

The CAM Biochemical Pathway and Key Enzymes

CAM photosynthesis operates through four distinct phases that separate carbon fixation temporally, concentrating COâ‚‚ around Rubisco to minimize photorespiration and maximize water-use efficiency [22]. The core CAM cycle requires a coordinated set of enzymatic activities that facilitate nocturnal COâ‚‚ uptake, temporary carbon storage, and daytime decarboxylation and refixation.

Table 1: Core Enzymatic Components of the CAM Genetic Toolkit

Enzyme	Gene Symbol	Functional Role in CAM	Subcellular Localization
Phosphoenolpyruvate carboxylase (PEPC)	PPC	Primary nocturnal COâ‚‚ fixation using phosphoenolpyruvate (PEP) as substrate	Cytosol
NAD(P)-malate dehydrogenase	MDH	Reduction of oxaloacetate to malate following PEPC carboxylation	Cytosol
NAD(P)-malic enzyme	ME	Decarboxylation of malate to pyruvate with COâ‚‚ release	Mitochondria/Cytosol
PEP carboxykinase	PCK	Alternative decarboxylase in some CAM species	Cytosol
Pyruvate orthophosphate dikinase	PPDK	Conversion of pyruvate to PEP during daytime phase	Chloroplast
Carbonic anhydrase	CA	Interconversion of CO₂ to HCO₃⁻ for PEPC activity	Cytosol
Vacuolar H⁺-ATPase	VHA	Acidification of vacuole for malate storage	Tonoplast
BOC-D-GLU-OH	BOC-D-GLU-OH, CAS:34404-28-9, MF:C10H17NO6, MW:247.24 g/mol	Chemical Reagent	Bench Chemicals
3,6-Dichlorotrimellitic acid	3,6-Dichlorotrimellitic acid, CAS:137071-78-4, MF:C9H4Cl2O6, MW:279.03 g/mol	Chemical Reagent	Bench Chemicals

The operational workflow of these core components follows a tightly regulated diurnal pattern, as illustrated below:

Figure 1: The core CAM biochemical cycle showing nocturnal carboxylation and diurnal decarboxylation phases

Regulatory and Transport Components

Beyond the core enzymatic toolkit, CAM operation requires specialized regulatory systems and transport mechanisms. The circadian clock regulatory network controls the diurnal expression patterns of key CAM enzymes, ensuring proper temporal coordination of carboxylation and decarboxylation phases [22]. This includes post-translational regulation through PEPC kinase (PPCK), which phosphorylates PEPC to enhance its nocturnal activity while reducing its sensitivity to malate inhibition.

Tonoplast transport systems are equally critical, featuring voltage-gated inward-rectifying malate channels and tonoplast dicarboxylate transporters that facilitate malate accumulation in vacuoles during nighttime and its release during daytime [22]. The vacuolar H⁺-ATPase maintains the proton gradient necessary for malate compartmentalization, while specialized transporters at chloroplast and mitochondrial membranes coordinate metabolite flux between organelles.

Genetic Components for Light Harvesting Systems

Photosynthetic Reaction Centers and Antenna Systems

In marine phototrophic bacteria, light harvesting is accomplished through sophisticated pigment-protein complexes that capture light energy and transfer it to reaction centers. While Marinisomatota are not known as dedicated phototrophs, understanding these systems provides insight into the genetic potential for light utilization in mixotrophic marine bacteria.

The core genetic components for bacterial light harvesting include:

• Reaction center proteins (PufL, PufM): Form the structural and catalytic core of type II photosynthetic reaction centers in anoxygenic phototrophic bacteria
• Light-harvesting complex proteins (PufA, PufB): Create antenna complexes for photon capture and energy transfer
• Bacteriochlorophyll biosynthesis genes (BchG, BchH, BchX, BchY, BchZ): Encode enzymes for bacteriochlorophyll synthesis
• Carotenoid biosynthesis genes (CrtE, CrtI, CrtB, CrtY): Produce accessory pigments for photoprotection and additional light harvesting

Genomic analyses of marine environments have revealed diverse phototrophic populations in deep-sea settings, including members of Chloroflexota, Proteobacteria, and Cyanobacteria [19] [23]. These organisms employ specialized light-harvesting adaptations suited to the light quality and intensity available in their specific marine habitats.

Rhodopsin-Based Phototrophy

An alternative light harvesting strategy found in marine bacterioplankton involves proteorhodopsins and related bacterial rhodopsins, which function as light-driven proton pumps. These systems are genetically compact, requiring only:

• Rhodopsin apoprotein (Rho): The transmembrane protein that binds retinal chromophore
• Retinal biosynthesis genes (Blh, CruF): Enzymes for β-carotene conversion to retinal
• Chromophore lyases: For covalent attachment of retinal to apoprotein

Rhodopsin-based phototrophy has been identified in diverse marine bacterial lineages, including SAR11, SAR86, and Flavobacteriales in oxic marine waters [23]. This simple system allows heterotrophic bacteria to supplement their energy needs with light-derived proton motive force, representing a form of metabolic flexibility with potential relevance to understanding adaptations in marine microbial communities.

Methodologies for Analyzing Metabolic Pathways in Marine Microbes

Genome-Resolved Metagenomics and Metatranscriptomics

Investigating the genetic potential of marine microbes like Marinisomatota requires sophisticated omics approaches that can resolve genetic capabilities within complex environmental communities. The following experimental workflow outlines the key methodological steps:

Figure 2: Genome-resolved metagenomic workflow for analyzing metabolic potential in marine microbiomes

Critical methodological considerations for investigating marine microbial metabolic potential include:

• In situ preservation: Advanced sampling devices like the Multiple In Situ Nucleic Acid Collections (MISNAC) system minimize RNA degradation and community shifts during sample retrieval from depth [19]. This system filters large volumes of seawater (∼80 L) in situ and immediately preserves nucleic acids, providing more accurate representation of in situ microbial activity.

• Metagenome assembly and binning: High-quality metagenome-assembled genomes (MAGs) are reconstructed using assemblers like metaSPAdes followed by binning tools that group contigs into putative genomes based on sequence composition and coverage [24] [25] [23]. Quality assessment using metrics like completeness and contamination is essential.

• Metabolic inference: Functional annotation of MAGs using databases like KEGG, EggNOG, and CAZy enables reconstruction of metabolic pathways and identification of key enzymatic functions [24] [26]. Comparative genomics across seasonal gradients can reveal adaptive metabolic shifts.

Expression Analysis and Activity Validation

Gene expression profiling through metatranscriptomics provides critical insights into actively utilized metabolic pathways. Key methodological aspects include:

• RNA extraction and cDNA synthesis: DNA-free RNA extraction followed by double-stranded cDNA synthesis using systems like the Ovation RNA-Seq System [19]
• Differential expression analysis: Identification of significantly upregulated genes under different environmental conditions (e.g., winter vs. summer)
• Pathway activation inference: Integration of expression data with metabolic reconstructions to determine operational pathways

For example, seasonal metatranscriptomic analyses in the South China Sea revealed that despite stable communities of active prokaryotic taxa (including Marinisomatales) across seasons, their metabolic profiles differed significantly [19]. Winter conditions promoted autotrophic COâ‚‚ fixation via the 3HP/4HB cycle, while summer conditions favored heterotrophic strategies utilizing fatty acids, benzoate, and Hâ‚‚.

Research Reagent Solutions for Marine Microbial Metabolism Studies

Table 2: Essential Research Reagents for Investigating Metabolic Pathways in Marine Microbes

Reagent/Category	Specific Examples	Research Application	Key Features
Nucleic Acid Preservation	RNAlater, MISNAC lysis buffer (GuHCl, DTT, Triton X-100, Proteinase K, lysozyme)	In situ microbial RNA preservation for transcriptomics	Inhibits RNases, stabilizes RNA for transport from field sites
Nucleic Acid Extraction Kits	DNeasy PowerSoil Kit, DNeasy PowerBiofilm Kit	DNA extraction from complex environmental matrices	Effective for difficult-to-lyse microorganisms, removes inhibitors
Library Preparation	Nextera XT Library Prep Kit, VAHTS Universal DNA Library Prep Kit	Metagenomic and metatranscriptomic library construction	Compatible with low-input samples, dual index adapters for multiplexing
Sequencing Platforms	Illumina HiSeq 2500 (rapid run mode)	High-throughput sequencing of microbial communities	250 bp paired-end reads, ~5-6 Gb per sample
Metabolic Pathway Databases	KEGG, SEED, CAZy, GTDB	Functional annotation of metagenomic data	Curated metabolic modules, genome taxonomy standardisation
Sequence Processing Tools	metaSPAdes, Trimmomatic, FastQ Screen	Quality control, assembly, and contamination removal	Optimized for metagenomic data, adapter trimming, contaminant filtering

Ecological Context and Seasonal Dynamics of Marine Microbial Metabolism

Marinisomatota and other active microbial taxa in deep-sea environments exhibit significant seasonal variations in their metabolic activities, responding to fluctuations in surface-derived organic matter inputs. In the South China Sea, Marinisomatales were identified among the dominant transcriptionally active prokaryotic taxa that remained stable across winter and summer seasons, despite shifts in the broader prokaryotic community structure [19].

Table 3: Seasonal Metabolic Shifts in Deep-Sea Microbial Communities

Metabolic Process	Winter Conditions	Summer Conditions	Key Microbial Taxa
Carbon Fixation	Enhanced COâ‚‚ fixation via 3HP/4HB cycle	Reduced COâ‚‚ fixation	Nitrososphaerales
Energy Generation	Ammonia oxidation, CO oxidation	Utilization of fatty acids, benzoate, Hâ‚‚	SAR324, Burkholderiales
Carbon Processing	Autotrophic metabolism dominant	Heterotrophic metabolism dominant	Marinisomatales, UBA11654
Community Composition	Enriched in Pseudomonadales, Bacillales, Rhodobacterales	Dominated by Burkholderiales	Stable active core community
Ecological Drivers	Possibly linked to deep-water convection	Related to anaerobic respiration within organic particles	Particle-associated vs. free-living

These seasonal metabolic shifts demonstrate the metabolic flexibility of marine microbial communities, including potentially mixotrophic adaptations that allow taxa like Marinisomatales to maintain activity across changing environmental conditions. The stability of actively transcribing taxa despite compositional changes in the broader community suggests specialized adaptations that enable persistence in deep-sea environments.

The genetic toolkits for light harvesting and crassulacean acid metabolism represent sophisticated adaptations for optimizing energy acquisition and carbon assimilation. While CAM is primarily characterized in vascular plants, understanding its genetic architecture and regulatory logic provides valuable insights for investigating metabolic flexibility in marine microbial systems. For groups like Marinisomatota, which demonstrate persistent activity in deep-sea environments across seasonal gradients, the potential for novel carbon concentration mechanisms or metabolic flexibility represents an intriguing research frontier.

Future research directions should focus on:

• Functional characterization of Marinisomatota metabolism through single-cell genomics and targeted isolation efforts
• Experimental manipulation of microbial communities to assess metabolic plasticity under different nutrient and light conditions
• Comparative genomics across seasonal gradients to identify genetic elements associated with metabolic shifts
• Integration of omics data with biogeochemical measurements to quantitatively link genetic potential with ecosystem function

Advancing our understanding of these genetic toolkits and their expression in marine environments will enhance our ability to predict microbial responses to environmental change and identify potential biotechnological applications of microbial carbon concentration mechanisms.

Nutrient limitation is a fundamental driver of evolutionary adaptation in oceanic ecosystems, selecting for sophisticated physiological and metabolic strategies that enable microorganisms to thrive in resource-scarce environments. Marine phytoplankton growth is commonly restricted by the availability of essential nutrients, particularly nitrogen (N), iron (Fe), and phosphorus (P), across vast expanses of the global ocean [27]. This limitation exerts profound selective pressure on marine microbial communities, leading to the emergence of specialized adaptations, with mixotrophy representing a key evolutionary innovation that enhances fitness under fluctuating nutrient conditions [1] [28]. The metabolic plasticity afforded by mixotrophy enables organisms to simultaneously utilize inorganic and organic carbon sources, as well as exploit alternative nutrient acquisition pathways, providing a competitive advantage in nutrient-poor waters [29] [30].

The phylum Marinisomatota (formerly recognized as Marinimicrobia, Marine Group A, and SAR406) exemplifies these evolutionary adaptations, with recent genomic evidence revealing previously unrecognized metabolic versatility that challenges traditional functional classifications [1]. This in-depth technical guide examines the evolutionary drivers behind nutrient limitation adaptations in oceanic ecosystems, with a specific focus on mixotrophic strategies within Marinisomatota, providing researchers and drug development professionals with experimental frameworks and analytical tools for investigating these complex adaptive mechanisms.

Global Patterns of Oceanic Nutrient Limitation

Primary Limitation Regimes

Experimental data syntheses reveal three dominant nutrient limitation regimes across the global ocean. Analysis of nutrient amendment experiments demonstrates that phytoplankton net growth is significantly enhanced through increasing the number of different nutrients supplied, regardless of latitude, temperature, or trophic status, indicating that surface seawaters often approach a state of nutrient co-limitation [27].

Table 1: Primary Nutrient Limitation Patterns in the Global Ocean

Limitation Type	Geographic Regions	Prevalence	Key Contributing Factors
Nitrogen (N) Limitation	Stratified subtropical gyres, summertime Arctic Ocean	39% of experiments (n=62)	Strong stratification limiting vertical nutrient supply [27]
Iron (Fe) Limitation	Upwelling regions (e.g., Eastern Tropical Pacific)	32% of experiments (n=50)	Elevated N concentrations with low Fe availability away from aerosol sources [27]
N-Fe Co-limitation	Transitional regions between N and Fe limited systems	9% of experiments (n=14)	Intermediate nutrient supply regimes [27] [31]
Manganese (Mn) Limitation	Southern Ocean	<5% of experiments	Mn-deficient deep waters upwelling in regions with restricted Mn sources [27]

Emerging Shifts in Limitation Regimes

Climate change is driving significant alterations in oceanic nutrient regimes. Analysis of over 30,000 nitrate and phosphate depth profiles observed between 1972 and 2022 reveals that upper ocean phosphate has declined worldwide while nitrate remains stable, suggesting a shift toward phosphorus limitation in many ocean regions [32]. This trend is attributed to weakened vertical nutrient transport due to ocean warming, with nitrogen fixation partially replenishing nitrate but no equivalent biological source existing for phosphate [32]. This intensifying P limitation has potentially severe implications for marine productivity and fisheries, as fish larval growth rates correlate with phosphorus availability in ecosystems [32].

Mixotrophy as an Adaptive Strategy

Conceptual Framework and Classification

Mixotrophy represents a continuum of metabolic strategies that combine phototrophy and heterotrophy, providing flexibility to respond to fluctuating nutrient conditions and light availability [28] [30]. This adaptation is particularly advantageous in environments where resources are variable or limiting, allowing organisms to maintain metabolic activity despite nutrient scarcity.

Table 2: Classification of Mixotrophic Mechanisms in Marine Microorganisms

Mechanism Type	Carbon Acquisition Method	Representative Taxa	Ecological Context
Absorbotrophic (Osmomixotrophy)	Uptake of dissolved organic carbon (DOC) via transport or pinocytosis	Cryptomonas sp., some Marinisomatota	DOC-rich, light-limited environments (e.g., brownified waters) [28] [30]
Phagotrophic (Necrotrophic)	Engulfment of particulate prey (bacteria, small protists)	Mixotrophic ciliates, some dinoflagellates	Nutrient-poor systems with abundant bacterial populations [28] [30]
Biotrophic	Retention of functional endosymbionts or stolen chloroplasts	Paramecium bursaria (zoochlorellae), Mesodinium rubrum	Stable symbiotic relationships in nutrient-poor waters [30]

Evolutionary Advantages in Nutrient-Limited Environments

The persistence of N-limitation as a dominant feature in marine waters, despite the presence of diverse Nâ‚‚-fixing microorganisms, highlights the energetic constraints of nutrient acquisition in oceanic systems [33]. Nâ‚‚ fixation has exceptionally high energy demands (approximately 16 ATP molecules plus 8 electrons per Nâ‚‚ molecule fixed) and is sensitive to oxygen inactivation, making it metabolically costly compared to utilizing combined nitrogen sources when available [33]. Mixotrophy provides an alternative strategy by allowing access to organic nitrogen sources through heterotrophic processes while maintaining photosynthetic capability.

Experimental studies demonstrate that mixotrophic cultivation significantly enhances biomass production in microalgae compared to strict phototrophy. For instance, Nannochloropsis granulata, Phaeodactylum tricornutum, and Chlorella sp. showed markedly improved biomass yields under mixotrophic conditions with glycerol supplementation [29]. This enhanced productivity stems from the synergistic effect of utilizing both inorganic carbon (COâ‚‚) via photosynthesis and organic carbon (e.g., glycerol) through heterotrophic metabolism, effectively bypassing the carbon limitation that often constrains phototrophic growth in nutrient-poor waters [29].

Marinisomatota: A Case Study in Metabolic Adaptation

Ecological Distribution and Genomic Features

Marinisomatota are ubiquitous in global oceans, with relative abundances ranging from 0.18% to 36.21% across low-latitude marine regions [1]. Metagenomic analysis of 1,588 Marinisomatota genomes revealed extensive phylogenetic diversity, encompassing one class, two orders, 14 families, 31 genera, and 67 species, reflecting successful adaptation to diverse marine niches [1].

Table 3: Metabolic Strategies Identified in Marinisomatota

Metabolic Strategy	Energy Generation	Carbon Acquisition	Ecological Preference
MS0 (Photoautotrophic Potential)	Light-dependent processes	COâ‚‚ fixation via Crassulacean acid metabolism (M00169)	Translucent zone, euphotic layer [1]
MS1 (Heterotrophic with Glycolysis)	Organic carbon oxidation	Organic carbon uptake with pronounced glycolytic pathway	Transition zone between translucent and aphotic layers [1]
MS2 (Heterotrophic without Glycolysis)	Organic carbon oxidation	Organic carbon uptake via alternative pathways	Aphotic layer, deep ocean regions [1]

Evolutionary Drivers and Adaptive Significance

The emergence of distinct metabolic strategies within Marinisomatota represents an evolutionary response to nutrient and energy limitations in different ocean layers [1]. The presence of light-dependent metabolic potential in five families (S15-B10, TCS55, UBA1611, UBA2128, and UBA8226) suggests evolutionary adaptation to maximize energy capture in the photic zone, while the heterotrophic strategies (MS1 and MS2) reflect specialization for deeper waters where light energy is unavailable [1].

This metabolic plasticity enables Marinisomatota to occupy ecological niches across depth gradients, with mixotrophic capabilities providing competitive advantage in the transition zone between well-lit surface waters and dark deep waters. The ability to shift between metabolic strategies based on resource availability represents a key evolutionary innovation that enhances fitness in the heterogeneous marine environment [1].

Experimental Approaches and Methodologies

Cultivation-Based Assessment of Mixotrophy

Protocol 1: Comparative Growth Assessment Under Different Trophic Conditions

This protocol evaluates mixotrophic capabilities in marine microorganisms by comparing growth under phototrophic, heterotrophic, and mixotrophic conditions [29].

• Strain Selection and Preculture: Obtain target strains from culture collections (e.g., Gothenburg University Marine Algal Culture Collection). Maintain precultures in 40 mL flasks at 22°C with continuous light (20 μmol photons m⁻² s⁻¹) and shaking at 100 rpm. Use artificial seawater medium (e.g., GoldMedium) with addition of antibiotics (100 μg/L ampicillin) to regulate bacterial growth [29].

• Experimental Conditions Preparation:

  • Phototrophy: Growth in basal medium without external carbon source (pH ~6.7)
  • Mixotrophy: Growth in basal medium supplemented with 4.6 g/L glycerol (pH ~6.9)
  • Phototrophy with bicarbonate: Growth in basal medium with 1.26 g/L bicarbonate (pH ~8.2)
  • Mixotrophy with glycerol and bicarbonate: Growth in basal medium with both 4.6 g/L glycerol and 1.26 g/L bicarbonate (pH ~8.3) [29]

• Growth Monitoring and Biomass Assessment: Monitor growth daily by measuring optical density at 750 nm (OD₇₅₀). After 10 days of cultivation, harvest biomass by filtering 2-3 mL of culture through pre-weighed 0.2 μm filters. Rinse with sterile physiological solution, dry at 100°C for 24 hours, and weigh to determine biomass yield using the formula:

  Dry weight (g/L) = (Weight of dried filter with biomass - Initial weight of filter) / Volume of filtered culture [29]

Protocol 2: Transcriptomic Analysis of Mixotrophic Transitions

This protocol examines molecular responses to changing nutrient conditions, using Cryptomonas sp. as a model [28].

• Experimental Treatments: Cultivate organisms under five DOC concentrations (1.5, 10, 30, 50, and 90 mg C/L) representing a browning gradient, plus control conditions (phototrophic, glucose-supplemented phototrophic, and heterotrophic).

• RNA Extraction and Sequencing: Harvest cells during exponential growth phase. Extract total RNA using standard kits. Prepare cDNA libraries and perform sequencing on an appropriate platform (e.g., Illumina).

• Bioinformatic Analysis: Process raw reads (quality control, adapter trimming, filtering). Map reads to reference genome and perform differential gene expression analysis. Conduct functional enrichment analysis of differentially expressed genes focusing on metabolic pathways [28].

Stable Isotope Tracing for Metabolic Flux Analysis

Protocol 3: Quantifying Carbon Source Utilization

This approach quantifies the relative contributions of different carbon sources to biomass production under mixotrophic conditions [28].

• Isotope Labeling: Supplement cultures with ¹³C-labeled NaHCO₃ for phototrophic carbon tracking or ¹³C-labeled organic substrates (e.g., glucose, glycerol) for heterotrophic carbon tracking.

• Lipid Extraction and Analysis: Harvest cells by filtration. Extract total lipids using chloroform:methanol:water (4:2:1) via sonication. Separate phospholipid fractions via solid-phase extraction using silica cartridges.

• Isotopic Analysis: Analyze ¹³C incorporation into membrane lipids using gas chromatography coupled to isotope ratio mass spectrometry (GC-IRMS) [28].

Figure 1: Experimental workflow for investigating mixotrophic adaptations in marine microorganisms, integrating cultivation-based assessments, molecular analyses, and metabolic flux measurements.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 4: Essential Research Reagents for Investigating Mixotrophic Adaptations

Reagent/Material	Specifications	Application	Key Considerations
Artificial Seawater Media	GoldMedium (Aqualgae) with Solution A (macronutrients) and Solution B (trace elements/vitamins) [29]	Base medium for marine microorganism cultivation	Prepare salt solutions I and II separately; autoclave components individually (121°C, 21 min) [29]
Organic Carbon Sources	Glycerol (4.6 g/L), glucose (5 mg C/L) [29] [28]	Mixotrophic cultivation	Glycerol is cost-effective for large-scale applications; concentration optimization required for different taxa [29]
Inorganic Carbon Sources	Sodium bicarbonate (1.26 g/L) [29]	Phototrophic cultivation with carbon supplementation	Acts as both carbon source and pH buffer; optimizes biomass production in mathematical models [29]
Stable Isotope Tracers	¹³C-NaHCO₃ (5% enriched), ¹³C-glucose [28]	Metabolic flux analysis	Use isotope-enriched substrates to track carbon allocation into different biomass components [28]
Antibiotics	Ampicillin (100 μg/L) [29]	Bacterial growth suppression in non-axenic cultures	Controls bacterial contamination without inhibiting microalgal growth [29]
DOC Supplement	Terrestrial DOC mix (1.5-90 mg C/L) [28]	Simulating browning conditions in experiments	Represents natural organic matter input from terrestrial systems [28]
Lipid Extraction Solvents	Chloroform:methanol:water (4:2:1) [28]	Lipid extraction for biochemical analysis	Enables separation of lipid fractions for compositional and isotopic analysis [28]
VD3-d6	VD3-d6, CAS:118584-54-6, MF:C27H44O, MW:390.7 g/mol	Chemical Reagent	Bench Chemicals
FTI-277 hydrochloride	FTI-277 hydrochloride, MF:C22H30ClN3O3S2, MW:484.1 g/mol	Chemical Reagent	Bench Chemicals

Metabolic Pathways and Regulatory Networks

Figure 2: Regulatory networks and metabolic switching in response to nutrient limitation, showing how environmental cues trigger transcriptional reprogramming and metabolic adaptations in mixotrophic organisms.

Implications for Biotechnology and Drug Discovery

The adaptive strategies of mixotrophic microorganisms, particularly Marinisomatota, offer significant potential for biotechnological applications and drug discovery. Enhanced production of bioactive compounds under mixotrophic conditions has been demonstrated in several microalgal species. Phaeodactylum tricornutum exhibits higher antiproliferative activity against human melanoma cells and antibacterial effects against Staphylococcus aureus when cultivated under mixotrophic conditions [29]. Similarly, Chlorella sp. shows antibacterial activity against S. aureus, with metabolomics analysis identifying compounds responsible for the observed bioactivity [29].

The metabolic plasticity of mixotrophic organisms represents a promising resource for discovering novel pharmaceutical compounds. The ability to produce diverse secondary metabolites under different nutrient conditions expands the chemical diversity available for screening programs. Furthermore, the unique adaptations of Marinisomatota to nutrient-limited environments may yield enzymes and metabolic pathways with novel properties for industrial applications [1].

From a biotechnological perspective, mixotrophic cultivation enhances biomass production and accumulation of valuable biomolecules. Optimization of carbon sources (e.g., glycerol and glucose concentrations) and cultivation parameters can significantly increase yields of lipids for biofuel production, as demonstrated with Scenedesmus obliquus [34]. Similar optimization approaches can be applied to enhance production of specific bioactive compounds in mixotrophic microorganisms [29] [34].

Adaptation to nutrient limitation represents a fundamental evolutionary driver in oceanic ecosystems, selecting for sophisticated metabolic strategies that maximize resource acquisition and utilization efficiency. Mixotrophy emerges as a key adaptation, enabling organisms to maintain metabolic functionality across fluctuating nutrient regimes. The phylum Marinisomatota exemplifies these adaptations, with genomic evidence revealing specialized metabolic strategies that facilitate survival in distinct ocean layers.

Understanding these evolutionary adaptations requires integrated methodological approaches combining cultivation experiments, molecular analyses, and metabolic flux measurements. The experimental protocols and research tools detailed in this technical guide provide a framework for investigating mixotrophic adaptations in marine microorganisms. Furthermore, the enhanced production of bioactive compounds under mixotrophic conditions highlights the biotechnological and pharmaceutical potential of these adaptations, offering promising avenues for drug discovery and bioproduct development.

Cultivating the Uncultured: Advanced Techniques and Biomedical Applications

Within marine ecosystems, the phylum Marinisomatota (formerly recognized as Marinimicrobia, Marine Group A, and SAR406) represents a widespread and highly abundant group of microorganisms whose ecological roles remain partially obscured due to low cultivation yields [2]. Recent genomic reconstructions of 1,588 Marinisomatota genomes from global ocean datasets reveal a remarkable metabolic versatility, identifying three distinct metabolic modes: MS0 (photoautotrophic potential), MS1 (heterotrophic with enhanced glycolytic capacity), and MS2 (heterotrophic without glycolysis), demonstrating a potential for mixotrophic adaptations [2]. These adaptations are evolutionary responses to nutrient limitation in oceanic ecosystems, allowing Marinisomatota to combine autotrophic carbon fixation and heterotrophic metabolism for a competitive survival advantage [2].

Overcoming the cultivation bottleneck is paramount for validating these in silico predictions and for detailed physiological and biochemical studies, including drug discovery research. Traditional cultivation methods, which often rely on high-nutrient media, have proven ineffective for the majority of marine microbes; an estimated >99% of these microorganisms remain uncultured and uncharacterized under laboratory conditions [2]. This article explores the integration of diffusion-based cultivation systems and low-nutrient media as an innovative and targeted approach to finally isolate and study previously uncultured Marinisomatota, thereby advancing our understanding of their mixotrophic lifestyles and biotechnological potential.

Core Methodology: A Technical Guide

The diffusion-based integrative cultivation method represents a paradigm shift from conventional, nutrient-rich cultivation techniques. It is specifically designed to mimic the low-nutrient and spatially structured conditions of marine sediments, thereby facilitating the growth of elusive microorganisms like Marinisomatota [2].

Experimental Protocol for Diffusion-Based Cultivation

The following provides a detailed, step-by-step protocol for establishing a diffusion-based cultivation system, as adapted from recent successful studies [2].

Step 1: Preparation of Low-Nutrient Media

• Base Medium Formulation: Prepare a low-nutrient base medium using filtered, autoclaved natural seawater. Alternatively, artificial seawater can be formulated to match the ionic composition of the target marine environment (e.g., the hadal zone sediment from which samples were collected).
• Nutrient Modification: The medium should be modified to be oligotrophic, containing significantly lower concentrations of organic carbon and complex nutrients than standard media like Marine Agar. This avoids the metabolic shock that can inhibit or kill slow-growing, nutrient-sensitive microbes [2].
• Gelling Agent: A low-concentration gelling agent, such as agar (e.g., 0.5-1.0%) or gellan gum, is added to the base medium to create a semi-solid matrix that will form the inner plug.

Step 2: Inoculum Processing

• Sample Source: The method was successfully applied to marine sediment samples [2].
• Minimal Processing: The sediment inoculum is subjected to minimal processing to preserve the viability of delicate cells. Avoid vigorous homogenization or centrifugation that may cause cellular damage.

Step 3: Assembly of the Diffusion Chamber

• Chamber Structure: The system typically consists of a central chamber or well containing the semi-solid, low-nutrient medium inoculated with the processed environmental sample.
• Diffusion Membrane: This central chamber is sealed on both sides with semi-permeable membranes (e.g., polycarbonate or cellulose esters with a defined pore size, such as 0.03 µm or 0.1 µm). These membranes allow for the passive diffusion of signaling molecules, nutrients, and waste products between the inner medium and the outer environment, while physically retaining the bacterial cells inside the chamber.
• Outer Reservoir: The sealed central chamber is then placed into a larger container (such as a Petri dish) filled with a reservoir of the same, uninoculated, low-nutrient medium. This outer reservoir acts as a nutrient source and sink for waste, creating a continuous, gentle nutrient flux.

Step 4: Incubation and Monitoring

• Incubation Conditions: Chambers are incubated for extended periods (weeks to months) under conditions that mimic the in situ environment of the target microbes. For Marinisomatota, this would likely involve dark or low-light conditions at low temperatures (e.g., 4-10°C).
• Monitoring Growth: Growth is monitored periodically by visual inspection for colony formation within the inner chamber or by molecular methods (e.g., PCR amplification of 16S rRNA genes from the chamber content).

Step 5: Isolation and Purification

• Once microbial growth is observed, the inner chamber is aseptically opened.
• Colonies or turbid areas are sub-cultured onto fresh low-nutrient media, repeating the process iteratively to obtain pure isolates.

Key Research Reagent Solutions

The following table details the essential materials and reagents required for the successful implementation of this cultivation method.

Table 1: Essential Research Reagents and Materials for Diffusion-Based Cultivation

Item	Function/Description	Key Consideration
Semi-Permeable Membranes	Physical barrier allowing chemical diffusion while retaining cells [2].	Pore size (e.g., 0.03 µm) is critical for effective cell containment and molecule passage [2].
Low-Nutrient Media	Oligotrophic growth medium mimicking natural seawater conditions [2].	Uses filtered natural or artificial seawater; avoids rich nutrient sources to prevent overgrowth of fastidious organisms [2].
Gelling Agent	Creates a semi-solid matrix for the inner chamber and outer reservoir [2].	Agar or gellan gum at low concentrations (e.g., 0.5-1.0%) to facilitate diffusion.
Artificial Sea Salts	Formulates base medium with ionic composition matching the target environment.	Enables replication of specific physicochemical conditions (e.g., pressure, pH) for extremophiles.
Environmental Inoculum	Source of uncultured microbial diversity, such as marine sediments [2].	Minimal processing is required to maintain microbial viability and community interactions.

Efficacy and Outcomes: Quantitative Data

The application of this diffusion-based cultivation method has demonstrated a significant improvement over traditional techniques, particularly for accessing the "microbial dark matter" that includes groups like Marinisomatota.

Table 2: Quantitative Performance of Diffusion-Based Cultivation vs. Traditional Methods

Performance Metric	Diffusion-Based Method	Traditional Cultivation Methods
Total Isolates Obtained	196 isolates from a single study [2]	Not explicitly stated, but historically low for such taxa.
Novelty Ratio (Previously Uncultured Taxa)	58% (115 of 196 isolates) [2]	Typically very low.
Cultivation of Rare Phyla	Successful isolation of species from rarely cultured phyla like Verrucomicrobiota and Balneolota [2].	Rarely achieved.
Theoretical Basis	Mimics natural substrate diffusion and chemical gradients; reduces stress from high nutrients [2].	Often uses high-nutrient concentrations that inhibit oligotrophic specialists.

The workflow diagram below illustrates the logical sequence and key decision points in the diffusion-based cultivation process, from sample collection to the final isolation of novel strains.

Application in Marine Marinisomatota Research

The successful cultivation of Marinisomatota is critical for moving beyond genomic predictions to experimental validation of their physiological capabilities. Genomic analyses suggest that different Marinisomatota lineages have evolved distinct metabolic strategies to thrive in nutrient-poor oceanic zones [2]. The proposed mixotrophic adaptations, allowing them to switch between autotrophic and heterotrophic metabolism, are a key hypothesis requiring empirical testing [2].

The diffusion-based cultivation system is uniquely suited to test these hypotheses for several reasons:

• Mimicking Natural Environment: The method replicates the low-nutrient conditions and subtle chemical gradients of the deep ocean, preventing the metabolic shock that can occur when introducing these organisms to standard, nutrient-rich laboratory media [2].
• Studying Metabolic Versatility: Isolates obtained through this method can be used in controlled experiments to verify their ability to utilize both inorganic carbon (via dissolved COâ‚‚) and a variety of organic carbon sources simultaneously, confirming mixotrophy.
• Supporting Drug Discovery: Pure cultures enable the detailed study of secondary metabolite production, which is of immense interest for discovering novel bioactive compounds for pharmaceutical development. Access to previously uncultivable species dramatically expands the pool of potential drug leads.

The diagram below maps the proposed metabolic versatility of Marinisomatota, integrating genomic predictions with the potential for experimental validation through cultivation.

Metagenomic and Metatranscriptomic Workflows for Functional Analysis

Functional metagenomics and metatranscriptomics have revolutionized our understanding of microbial communities in marine environments, enabling researchers to decipher the metabolic potential and in situ activities of uncultured microorganisms. Within marine ecosystems, Marinisomatota (formerly MAR407 and SAR406) represents a ubiquitous, yet poorly understood bacterial phylum frequently detected in deep oceanic waters, including under ice shelves and in oxygen minimum zones. These organisms are hypothesized to play significant roles in carbon and sulfur cycling in aphotic, oligotrophic marine environments. Research indicates Marinisomatota members are often highly abundant in dark, oligotrophic ecosystems such as the waters beneath the Ross Ice Shelf, where they contribute to chemosynthetically-driven systems [8].

Framing metagenomic and metatranscriptomic workflows within the context of mixotrophic adaptations is particularly relevant for Marinisomatota research. While not photosynthetic, their metabolic strategies in energy-limited environments may involve versatile metabolic pathways that allow them to alternate between different energy sources and carbon acquisition strategies, similar to the metabolic flexibility observed in other marine microbes [35]. Studying these adaptations requires specialized workflows that can capture their functional potential and gene expression patterns in response to fluctuating environmental conditions, such as those found along physicochemical gradients in deep-sea brine interfaces or stratified water columns [35] [8].

Sample Collection and Preservation from Marine Environments

Proper sample collection and preservation are critical for obtaining high-quality nucleic acids for downstream metagenomic and metatranscriptomic analyses. For marine microbial communities, especially those inhabiting extreme or stratified environments, precision sampling is essential to resolve community structure and function across subtle environmental gradients.

• Water Sampling: For deep-sea or stratified water column studies, conduct sampling using Niskin bottles mounted on a CTD rosette system. Measure environmental parameters (salinity, temperature, depth, oxygen) in real-time with CTD sensors to target specific physicochemical gradients [35]. For interface zones like brine-seawater boundaries, sub-sample Niskin bottles into smaller fractions (e.g., corresponding to ~10 cm layers) to achieve high vertical resolution [35].
• Filtration: Immediately filter water samples through 0.2 μm polyethersulfone (PES) membranes using a peristaltic or vacuum pump system to capture microbial biomass. For metatranscriptomic studies, complete filtration rapidly (within minutes) to preserve RNA integrity and minimize changes in gene expression [35].
• Preservation: Preserve filters for DNA extraction in lysis buffer (e.g., containing EDTA) or place in cryovials and flash-freeze in liquid nitrogen. For RNA studies, preserve filters in RNAlater or similar RNA stabilization reagent. Store all samples at -80°C until nucleic acid extraction [35].

Nucleic Acid Extraction, Library Preparation, and Sequencing

The quality of nucleic acid extraction directly impacts the representativeness of metagenomic and metatranscriptomic libraries, particularly for diverse microbial communities.

Table 1: Nucleic Acid Extraction and Sequencing Methods for Marine Microbiome Studies

Step	Method/Kit	Key Specifications	Considerations for Marinisomatota
DNA Extraction	Qiagen DNeasy PowerSoil Pro Kit [36]	Effective for diverse environmental samples; includes mechanical and chemical lysis	Ensures lysis of diverse bacterial cell walls
RNA Extraction	Combined mechanical and chemical lysis	Followed by DNase treatment; quality check via electrophoresis/fluorometry [37]	Removes DNA contamination prior to cDNA synthesis
RNA/DNA Quality Control	Agarose gel electrophoresis; Nanodrop; Qubit Fluorometer [36]	Assess integrity, concentration, and purity	A260/A280 ~1.8-2.0 indicates pure nucleic acids
Library Preparation	Illumina NovaSeq kits (e.g., NEBNext Ultra DNA Library Prep) [36] [37]	Compatible with Illumina platforms; size selection steps	Optimize for desired insert size (~350-550 bp)
Sequencing Platform	Illumina NovaSeq [36] [37]	2 × 150 bp paired-end reads common	Provides sufficient coverage for assembly

Computational Analysis and Functional Annotation

The computational workflow for processing metagenomic and metatranscriptomic data involves multiple steps from quality control to functional annotation, with specific considerations for uncovering mixotrophic adaptations in Marinisomatota.

The following workflow illustrates the key computational steps for analyzing marine metagenomic and metatranscriptomic data, with particular emphasis on functional characterization of Marinisomatota:

• Quality Control and Filtering: Process raw sequencing reads with tools like fastp [36] or Trimmomatic to remove adapter sequences, low-quality bases, and short reads. This step is crucial for reducing artifacts in downstream assemblies.

• Assembly and Gene Prediction: Perform de novo assembly of quality-filtered reads using MEGAHIT [36] or metaSPAdes with the --min-contig-len 500 parameter to generate contigs. Predict open reading frames (ORFs) from assembled contigs using METAProdigal [36] or Prodigal in metagenomic mode.

• Gene Clustering and Dereplication: Cluster predicted protein sequences to reduce redundancy using CD-HIT [36] with parameters such as -c 0.95 (95% identity cutoff) and -aS 0.9 (90% alignment coverage). This generates a non-redundant gene catalog for downstream analyses.

• Taxonomic Annotation: Classify genes and contigs taxonomically using Kraken2 with a customized database containing bacterial, archaeal, and eukaryotic sequences [36], or use MMseqs2 against specialized databases like MetaEuk [38]. For Marinisomatota, confirm phylogenetic placement using conserved single-copy marker genes.

• Functional Annotation: Annotate protein sequences by searching against functional databases:

  • KEGG using kofamscan or GhostKOALA to map genes to metabolic pathways [36] [37]
  • eggNOG using eggNOG-mapper for orthology assignments [36]
  • CAZy for carbohydrate-active enzymes relevant to carbon cycling [36]
  • Custom databases (e.g., NCycDB, SCycDB, FeGenie) for specific biogeochemical cycles [36]

• Quantification and Differential Expression: For metatranscriptomics, map quality-filtered reads back to the gene catalog using Salmon [36] or Bowtie2 to quantify transcript abundances. Perform differential expression analysis between conditions using DESeq2 [37] to identify significantly upregulated metabolic pathways under different environmental conditions.

• Metabolic Reconstruction: Integrate taxonomic, functional, and expression data to reconstruct metabolic pathways and infer ecosystem roles. For Marinisomatota, focus on pathways for sulfur oxidation, carbon fixation, and organic carbon degradation to elucidate their potential mixotrophic capabilities [35].

Specialized Analytical Approaches for Mixotrophic Adaptations

Identifying Metabolic Versatility and Redundancy

Marinisomatota in deep-sea environments demonstrate remarkable metabolic versatility that enables survival in energy-limited conditions. Analytical approaches should specifically target pathways indicative of mixotrophic potential:

• Carbon Fixation Pathways: Screen for key enzymes in autotrophic pathways, particularly the reductive TCA cycle (e.g., ATP-citrate lyase, 2-oxoglutarate:ferredoxin oxidoreductase) commonly used by chemosynthetic bacteria in aphotic environments [35]. The presence of these genes alongside heterotrophic capabilities suggests metabolic flexibility.

• Sulfur Oxidation Capacity: Identify genes for sulfur compound oxidation, including sulfide:quinone oxidoreductase (SQR) and the thiosulfate oxidation (SOX) system [35]. The composition and completeness of these pathways can indicate specialization for different sulfur substrates prevalent in stratified environments.

• Organic Carbon Utilization: Annotate genes for transporters and enzymes involved in the degradation of complex organic compounds, such as extracellular peptidases, carbohydrate-active enzymes (CAZymes), and transporters for amino acids and carbohydrates [8]. Co-occurrence of these genes with carbon fixation pathways suggests mixotrophic potential.

• Nitrogen Metabolism: Screen for genes involved in nitrogen acquisition, including those for ammonium uptake, nitrate reduction, and dissimilatory nitrate reduction [36], as nitrogen availability often limits microbial growth in marine systems.

Table 2: Key Metabolic Genes for Detecting Mixotrophic Adaptations in Marinisomatota

Metabolic Function	Key Marker Genes/Enzymes	Detection Methods	Relevance to Marinisomatota
Carbon Fixation	ATP-citrate lyase (ACL), 2-oxoglutarate:ferredoxin oxidoreductase (OGOR)	KEGG, custom HMM profiles	Reductive TCA cycle common in deep-sea chemolithoautotrophs
Sulfur Oxidation	Sulfide:quinone oxidoreductase (SQR), SoxXYZAB system	SCycDB, FeGenie databases [36]	Energy generation from reduced sulfur compounds
Organic Carbon Degradation	Peptidases, CAZymes (glycoside hydrolases)	MEROPS, CAZy database [36]	Heterotrophic capability alongside autotrophy
Nitrogen Metabolism	Ammonium transporters (Amt), nitrate reductases (Nar)	NCycDB, KEGG [36]	Nitrogen acquisition in oligotrophic conditions
Hydrogen Oxidation	Group 1h and 1l [NiFe]-hydrogenases	Custom HMM profiles, KEGG	Alternative energy source in sulfidic environments

Sequence Similarity Networks for Functional Analysis

Sequence Similarity Network (SSN) analysis provides a powerful approach for exploring functional diversity and metabolic specificity without complete reliance on reference databases, making it particularly valuable for studying undercharacterized phyla like Marinisomatota.

• Network Construction: Build SSNs by calculating pairwise similarities between protein sequences using tools like DIAMOND or MMseqs2, then visualize networks with Cytoscape. Cluster sequences into protein families by applying similarity thresholds (e.g., 30-50% identity) and alignment coverage parameters [38].

• Metabolic Specificity Analysis: Identify protein families associated with specific trophic modes by overlaying taxonomic and metabolic information on network clusters. This approach can reveal metabolic redundancy (similar functions across taxa) and specialization (unique adaptations) within Marinisomatota lineages [38].

• Dark Matter Exploration: SSNs enable the inclusion of the "microbial dark matter" - sequences without matches in reference databases - in functional analyses, allowing discovery of novel enzymes and metabolic pathways in Marinisomatota that may contribute to their adaptation to specific marine niches [38].

Metabolic Reconstruction of Marinisomatota: A Case Study

Genomic Insights from Metagenome-Assembled Genomes (MAGs)

Metabolic reconstruction from high-quality MAGs provides the most direct approach for elucidating the functional capabilities of uncultured Marinisomatota. The following protocol outlines this process:

• MAG Binning: Group contigs from metagenomic assemblies into MAGs using composition and coverage-based binning algorithms such as MetaBAT2, MaxBin2, or CONCOCT. Refine bins using DAS Tool and check completion and contamination with CheckM [35].

• Metabolic Pathway Analysis: Annotate MAGs with PROKKA or DRAM and manually curate key metabolic pathways. Pay particular attention to:

  • Energy Conservation: Electron transport chain complexes, quinone pools, and Rnf-type ion translocating complexes
  • Carbon Metabolism: Complete pathways for CO2 fixation, organic carbon transport, and central carbon metabolism
  • Sulfur Oxidation: Presence of sqr, sox, and dsr genes and their genomic context
  • Nitrogen and Phosphorus Acquisition: Transport and assimilation systems for limiting nutrients [35]

• Comparative Genomics: Compare Marinisomatota MAGs with those from related lineages and from different environments to identify habitat-specific adaptations. Analyze genomic islands and horizontally acquired genes that may contribute to niche specialization [35].

Integrating Metatranscriptomic Data to Infer In Situ Activity

Metatranscriptomic data provides crucial insights into the actual expression of metabolic pathways identified in MAGs, helping distinguish between genetic potential and realized function:

• Pathway Expression Profiling: Quantify expression levels of key metabolic pathways under different environmental conditions. For Marinisomatota, focus on expression patterns of carbon fixation, sulfur oxidation, and organic carbon degradation genes across diel cycles or physicochemical gradients [37].

• Differential Expression Analysis: Use DESeq2 to identify significantly upregulated pathways in response to environmental changes. For instance, increased expression of sulfur oxidation genes in sulfidic conditions would confirm their utilization of reduced sulfur compounds as energy sources [37].

• Co-expression Network Analysis: Construct gene co-expression networks using WGCNA to identify coordinately regulated gene modules. This can reveal how Marinisomatota integrate different metabolic processes and adapt their transcriptional programs to environmental fluctuations [37].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents and Materials for Marine Metagenomic Studies

Category	Specific Product/Kit	Application	Key Features
DNA Extraction	Qiagen DNeasy PowerSoil Pro Kit [36]	Nucleic acid extraction from marine filters	Effective for diverse environmental samples; includes inhibitors removal
RNA Stabilization	RNAlater RNA Stabilization Solution	Field preservation of RNA	Stabilizes RNA immediately after sampling for transcriptomic studies
Library Prep	NEBNext Ultra DNA Library Prep Kit [36]	Sequencing library construction	Compatible with Illumina platforms; includes size selection
Sequence Databases	KEGG, eggNOG, CAZy, MetaCyc [36]	Functional annotation	Curated metabolic pathways and orthology groups
Analysis Tools	MEGAHIT, metaSPAdes [36]	Metagenomic assembly	Optimized for complex microbial communities
Analysis Tools	Prodigal/METAProdigal [36]	Gene prediction	Specific parameters for metagenomic data
Analysis Tools	DIAMOND, MMseqs2 [36] [38]	Sequence similarity search	Fast, sensitive alignment for large datasets
Analysis Tools	Kraken2, Bracken [36]	Taxonomic classification	k-mer based classification with abundance estimation
Analysis Tools	CheckM, DAS Tool [35]	MAG assessment	Evaluation of MAG quality and refinement
SAG dihydrochloride	SAG dihydrochloride, CAS:364590-63-6, MF:C28H32Cl3N3O2S, MW:581.0 g/mol	Chemical Reagent	Bench Chemicals
(S,R,S)-AHPC-PEG2-C4-Cl	(S,R,S)-AHPC-PEG2-C4-Cl, MF:C32H47ClN4O6S, MW:651.3 g/mol	Chemical Reagent	Bench Chemicals

Integrated metagenomic and metatranscriptomic workflows provide powerful approaches for elucidating the functional adaptations of understudied marine bacterial phyla like Marinisomatota. The protocols and analytical frameworks outlined here enable researchers to reconstruct metabolic networks and identify potential mixotrophic strategies that facilitate survival in energy-limited marine environments. As these methods continue to evolve, they will further illuminate the ecological roles of Marinisomatota in global biogeochemical cycles, particularly in the context of changing ocean conditions. The combination of precise sampling along environmental gradients, high-quality MAG reconstruction, and expression profiling represents a particularly promising path forward for uncovering the metabolic versatility of these enigmatic bacteria.

Genome-resolved metagenomics has revolutionized microbial ecology by enabling researchers to reconstruct metabolic blueprints of microorganisms directly from environmental samples, without the need for laboratory cultivation. This approach involves sequencing the collective genetic material from an environmental sample (metagenomics), followed by computational binning of sequences into Metagenome-Assembled Genomes (MAGs). The power of this methodology is particularly evident in studying marine ecosystems, where an estimated >70% of microorganisms resist cultivation using standard techniques [39]. When applied to 1,588 genomes, this approach provides unprecedented statistical power to identify metabolic pathways and infer ecological interactions across diverse microbial lineages.

The reconstruction of metabolic pathways from MAGs relies on comparing predicted protein sequences to established databases of enzymatic functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups (COGs). Advanced algorithms then map these enzymes to known biochemical reactions, assembling them into potential metabolic networks based on genomic proximity and phylogenetic conservation. This process has revealed remarkable insights into the functional capacities of microbial dark matter - the vast fraction of uncultured microorganisms that dominate global ecosystems [39]. For marine bacteria like Marinisomatota, genome-resolved metagenomics has been instrumental in identifying their potential for mixotrophic adaptations, allowing them to utilize both organic and inorganic energy sources in fluctuating marine environments [40].

Metabolic Modeling of Microbial Communities

Reconstruction Tools and Databases

The accuracy of metabolic pathway reconstruction depends significantly on the computational tools and biochemical databases employed. Comparative analyses have revealed that different automated reconstruction tools yield models with varying numbers of genes, reactions, and metabolic functionalities, even when based on the same genomic input [41]. Three widely-used tools include:

• CarveMe: Employs a top-down approach, using a universal template model and carving out reactions based on annotated genomic sequences. It generates models quickly but may miss some lineage-specific pathways [41].
• gapseq: Utilizes a bottom-up approach, constructing models by mapping reactions based on annotated genomic sequences using comprehensive biochemical information from various data sources. It typically produces models with more reactions and metabolites but may include more dead-end metabolites [41].
• KBase: Another bottom-up approach that leverages the ModelSEED database for consistent biochemical namespace and reaction definitions, facilitating community modeling [41].

Table 1: Comparison of Automated Reconstruction Tools

Tool	Approach	Key Features	Database	Considerations
CarveMe	Top-down	Fast model generation; ready-to-use networks	Custom universal model	May miss lineage-specific pathways
gapseq	Bottom-up	Comprehensive biochemical information; multiple data sources	Integrated multi-database	Higher number of dead-end metabolites
KBase	Bottom-up	User-friendly platform; immediately functional models	ModelSEED	Database-dependent reaction set

A promising strategy to mitigate tool-specific biases is the construction of consensus models that integrate reconstructions from multiple tools. Studies have demonstrated that consensus models encompass larger numbers of reactions and metabolites while reducing dead-end metabolites, providing more comprehensive and unbiased assessments of community metabolic potential [41].

Constraint-Based Modeling Approaches

Once metabolic models are reconstructed, constraint-based modeling techniques simulate metabolic fluxes under different environmental conditions. For microbial communities, three primary approaches are used:

• Mixed-bag approach: Integrates all metabolic pathways and transport reactions into a single model with one cytosolic and one extracellular compartment. Suitable for analyzing interactions between communities [41].
• Compartmentalization: Combines multiple GEMs into a single stoichiometric matrix with each species assigned to a distinct compartment. Appropriate for understanding organism interactions within a community [41].
• Costless secretion: Simulates models using a dynamically and iteratively updated medium based on exchange reactions and metabolites within the community [41].

These approaches have been applied to diverse environments, including marine systems, where they help identify cross-feeding relationships and nutrient cycling dynamics [42].

Marinisomatota: A Model for Mixotrophic Adaptations

Ecological Distribution and Genomic Features

Marinisomatota (formerly known as MARINOSOMATIA) represents a bacterial lineage frequently detected in marine environments through 16S rRNA gene surveys. Genome-resolved metagenomics has enabled the reconstruction of Marinisomatota MAGs from diverse marine habitats, including hypersaline microbial mats [39], bathypelagic zones [40], and hadal sediments [43]. Genomic analysis reveals these organisms typically possess medium-sized genomes (2-4 Mbp) with GC content ranging from 40-50%, reflecting their adaptation to marine environments.

A hallmark of Marinisomatota genomes is their genetic potential for polysaccharide degradation [39]. Analyses of MAGs from hypersaline microbial mats have identified numerous genes encoding carbohydrate-active enzymes (CAZymes), including glycoside hydrolases, polysaccharide lyases, and carbohydrate esterases. This enzymatic arsenal suggests Marinisomatota play important roles in carbon cycling through the breakdown of complex organic matter in marine ecosystems.

Table 2: Metabolic Capabilities of Marinisomatota Inferred from MAGs

Metabolic Process	Genetic Potential	Key Genes Identified	Ecological Role
Carbon Metabolism	Polysaccharide degradation	Glycoside Hydrolases (GHs), Polysaccharide Lyases (PLs)	Organic matter decomposition
Energy Production	Mixed acid fermentation	Lactate dehydrogenase, alcohol dehydrogenase	Energy generation under low oxygen
Nitrogen Metabolism	Denitrification	Nitrate reductase (narG), nitrite reductase (nirK)	Nitrogen cycling in anaerobic microniches
Stress Response	Osmoprotection	Compatible solute transporters	Adaptation to changing salinity

Mixotrophic Capabilities

Mixotrophy - the ability to combine autotrophic and heterotrophic metabolic strategies - provides a significant advantage in marine environments where nutrient availability fluctuates. Genome-resolved metagenomics has revealed that Marinisomatota MAGs encode pathways for both organic carbon assimilation and potential energy generation from inorganic sources [40].

While complete carbon fixation pathways (such as the Calvin Benson Bassham cycle) are typically absent in Marinisomatota, some MAGs contain genes for partial lithotrophy, including sulfur oxidation pathways (sox genes) [39]. This metabolic flexibility allows Marinisomatota to utilize organic carbon while potentially supplementing energy needs through inorganic compound oxidation when organic substrates are scarce.

The genetic basis for mixotrophy in Marinisomatota includes:

• Transporter diversity: Genes encoding transporters for various organic compounds (sugars, amino acids, organic acids)
• Electron transport chain components: Genes for respiratory complexes that can utilize alternative electron donors/acceptors
• Regulatory systems: Two-component signal transduction systems that likely sense nutrient availability and modulate metabolic priorities

Methodological Workflow for Genome-Resolved Metagenomics

Sample Collection and DNA Sequencing

The initial phase of genome-resolved metagenomics involves careful sample collection and preservation to maintain DNA integrity. For marine samples, this typically involves filtration systems that separate microbial cells by size fractionation - commonly distinguishing between free-living (0.2-0.8 μm) and particle-attached (0.8-20 μm) communities [40]. Immediate preservation of samples is critical, often using RNAlater or flash-freezing at -80°C to prevent nucleic acid degradation.

DNA extraction should be performed using kits designed for diverse microbial lineages, such as the DNeasy PowerSoil Kit (Qiagen) [39]. For metagenomic sequencing, both Illumina short-read (e.g., NovaSeq 6000) and PacBio long-read platforms are employed, with the latter becoming increasingly valuable for resolving complex genomic regions and improving assembly continuity [44]. Recent protocols often combine both technologies - using Illumina for high accuracy and PacBio for scaffolding - to generate optimal assemblies.

Genome Assembly and Binning

Quality control of raw sequencing reads is performed using tools like Fastp (version 0.23.2) with parameters such as "-q 20 -u 20 -g -c -W 5 -3 -l 90" to trim low-quality bases and adapter sequences [43]. Metagenomic assembly is typically conducted using MEGAHIT (version 1.2.9) with the "--presets meta-sensitive" option, which is optimized for diverse microbial communities [43]. Contigs shorter than 1000 bp are generally discarded to reduce fragmentation.

The core genome resolution process involves binning, where contigs are grouped into putative genomes based on sequence composition (k-mer frequencies) and abundance patterns across multiple samples. This is typically performed using automated pipelines like MetaWRAP (version 1.3) [43], which integrates multiple binning algorithms (MetaBAT2, MaxBin2, and CONCOCT) to produce higher-quality bins. The refinement module of MetaWRAP then integrates these primary MAG sets to generate consensus bins.

Quality Assessment and Dereplication

Quality assessment of MAGs is critical for downstream analyses. CheckM (version 1.2.2) is commonly used to estimate completeness and contamination based on the presence of single-copy marker genes [43]. Medium-quality MAGs are typically defined as ≥50% complete with <10% contamination, while high-quality MAGs meet ≥70% completeness with <5% contamination [39]. Additional quality metrics include N50 (a measure of contig length), total assembly size, and the presence of rRNA and tRNA genes.

To avoid redundancy in collections of 1,588 genomes, dereplication is performed using tools like dRep (version 3.2.2) with a 95% average nucleotide identity (ANI) threshold, which approximates the species boundary [43]. This process ensures that the genomic collection represents distinct microbial populations rather than multiple versions of the same genome.

Functional Annotation and Metabolic Reconstruction

Gene Prediction and Annotation

Gene prediction on MAGs is performed using Prodigal (version 2.6.3), which identifies protein-coding sequences in microbial genomes [43]. Open reading frames (ORFs) shorter than 33 amino acids are typically filtered out to reduce false positives. The resulting protein sequences are then annotated using multiple databases:

• KEGG: Annotated using KofamKOALA (version 1.3.0) to assign KO identifiers and map to metabolic pathways [43]
• EggNOG: Annotated using emapper (version 2.1.7) for functional categories and orthologous groups [43]
• UniProt: Searched using DIAMOND (version 2.0.14) with thresholds of >30% identity and >50% query coverage [43]
• CAZy: Identified using dbCAN for carbohydrate-active enzymes [43]

For metabolic reconstruction, special attention is paid to key metabolic genes, including those involved in carbon fixation (e.g., aclB, rbcL), nitrogen cycling (e.g., nxrA, nirK), and sulfur metabolism (e.g., soxB, dsrA) [43].

Metabolic Pathway Gap Filling

A critical step in metabolic reconstruction is pathway gap analysis, which identifies missing reactions in otherwise complete pathways. The COMMIT pipeline implements an iterative gap-filling approach that starts with a minimal medium and dynamically updates it with metabolites predicted to be secreted by community members [41]. This method has been shown to produce more physiologically realistic models compared to single-genome gap-filling.

Studies have demonstrated that the order of gap-filling has minimal impact on the final solution, with correlation coefficients between MAG abundance and added reactions ranging from 0-0.3 [41]. This robustness makes the approach suitable for diverse communities, including those dominated by Marinisomatota and other marine bacteria.

The Scientist's Toolkit: Essential Research Reagents and Computational Tools

Table 3: Essential Resources for Genome-Resolved Metagenomics

Category	Item	Specifications	Application
Wet Lab	DNeasy PowerSoil Kit	Qiagen Cat. No. 12888	DNA extraction from diverse environmental samples
	RNAlater Stabilization Solution	Thermo Fisher Cat. No. AM7020	Sample preservation for RNA/DNA integrity
	Illumina NovaSeq 6000	PE150 configuration	High-throughput metagenomic sequencing
	PacBio Sequel II System	HiFi long-read sequencing	Improved genome assembly continuity
Computational	Fastp	Version 0.23.4	Quality control and adapter trimming
	MEGAHIT	Version 1.2.9	Metagenomic assembly with meta-sensitive preset
	MetaWRAP	Version 1.3	Binning pipeline integrating multiple algorithms
	CheckM	Version 1.2.2	MAG quality assessment (completeness/contamination)
	KofamKOALA	Version 1.3.0	KEGG orthology annotation for metabolic reconstruction
Databases	GTDB	Release 226	Taxonomic classification of MAGs
	KEGG	Version 20231230	Metabolic pathway annotation
	EggNOG	Version 5.0.2	Functional classification of gene products
(S,R,S)-AHPC-PEG3-NH2 hydrochloride	(S,R,S)-AHPC-PEG3-NH2 hydrochloride, MF:C30H46ClN5O7S, MW:656.2 g/mol	Chemical Reagent	Bench Chemicals
SUN 1334H	SUN 1334H, CAS:607736-84-5, MF:C23H28Cl2F2N2O3, MW:489.4 g/mol	Chemical Reagent	Bench Chemicals

Genome-resolved metagenomics applied to 1,588 genomes provides unprecedented resolution for understanding metabolic potential in complex microbial communities. The reconstruction of metabolic pathways from MAGs has revealed the remarkable functional diversity of previously uncultured lineages like Marinisomatota, highlighting their adaptations to marine environments through mixotrophic strategies. As databases expand and algorithms improve, the integration of multi-omic data (metatranscriptomics, metaproteomics) with metabolic models will further enhance our ability to predict community responses to environmental changes and engineer microbial consortia for biotechnological applications.

The exploration of innovative drug delivery systems represents a paramount objective in modern therapeutic science. Recent breakthroughs have positioned microalgae, a diverse group of photosynthetic microorganisms, as remarkably promising platforms for targeted therapeutic applications. These organisms exhibit unique biological properties—including diverse motility mechanisms, exceptional biocompatibility, and the ability to produce high-value bioactive compounds—that can be harnessed for advanced medical purposes [45]. Simultaneously, in the distinct field of marine microbial ecology, investigations into Marinisomatota (formerly known as Marinimicrobia) have revealed astonishing metabolic plasticity, including mixotrophic capabilities that allow these ubiquitous marine bacteria to transition between different nutritional strategies [1] [46]. This whitepaper elucidates the striking biomedical parallels between engineered microalgae delivery systems and the inherent adaptive strategies of Marinisomatota, arguing that these convergent evolutionary solutions can inform the development of next-generation, environmentally-responsive therapeutic platforms.

The fundamental connection between these seemingly disparate biological systems lies in their sophisticated adaptation to nutrient acquisition and energy conversion in challenging environments. Microalgae have evolved complex sensory and motility mechanisms to optimize their position in light and nutrient gradients, properties that biomedical engineers are now exploiting for targeted drug delivery [45]. Similarly, Marinisomatota have developed flexible metabolic strategies to thrive across the translucent and aphotic zones of the ocean, switching between photoautotrophic, heterotrophic, and mixotrophic lifestyles based on environmental conditions [1]. This inherent biological intelligence offers invaluable design principles for creating responsive drug delivery systems that can adapt to the dynamic physiological conditions within the human body.

Microalgae as Innovative Drug Delivery Platforms: Mechanisms and Applications

Unique Biophysical Properties of Microalgae

Microalgae possess an exceptional combination of biological attributes that make them ideally suited for drug delivery applications. Their natural biocompatibility and classification as Generally Recognized as Safe (GRAS) organisms provide a significant regulatory advantage for biomedical applications [45]. The diverse morphological characteristics of different microalgae species—ranging from the uniform spherical structure of Chlorella (3-5 μm in diameter, smaller than human red blood cells) to the distinctive helical morphology of Spirulina—enable their development as specialized drug carriers for various therapeutic contexts [45].

A critical advantage of microalgae lies in their motility mechanisms, which can be harnessed for targeted drug delivery. Their inherent phototactic capabilities allow precise navigation toward light stimuli, enabling external control over their movement within the body [45]. Furthermore, microalgae can be functionally engineered to exhibit magnetotactic properties through the attachment of magnetic nanoparticles, facilitating guidance via external magnetic fields—a particularly valuable approach for deep tissue targeting where light penetration is limited [45]. These intrinsic motility systems, combined with their diverse surface chemistry rich in carboxyl, hydroxyl, and amine functional groups, make microalgae exceptionally versatile platforms for drug conjugation and functionalization [45].

Biomedical Applications and Functionalization Strategies

The application spectrum of microalgae-based drug delivery systems spans multiple therapeutic domains, with particularly promising results in oncology, wound healing, and immunotherapy. In cancer therapeutics, microalgae offer the dual advantage of targeted drug delivery and ability to mitigate tumor hypoxia through continuous oxygen production via photosynthesis [45]. Their functionalization employs sophisticated bioconjugation techniques, including both non-covalent binding and covalent interactions, as well as advanced surface coatings, cell permeation, and encapsulation methodologies [45].

Table 1: Microalgae Species and Their Biomedical Applications

Microalgae Species	Unique Properties	Biomedical Applications	Functionalization Methods
Chlorella pyrenoidosa	Uniform spherical shape (3-5 μm), excellent biocompatibility	Targeted cancer therapy, drug delivery platform	Covalent bonding, surface coating, encapsulation
Spirulina spp.	3D spiral structure, high surface area	Gastrointestinal drug delivery, magnetic-guided therapy	Magnetic nanoparticle attachment, drug loading
Diatoms (Phaeodactylum tricornutum)	Porous silica cell walls, natural biosilica	Bone tissue repair, drug carrier	Genetic engineering, surface functionalization
Chlamydomonas reinhardtii	Model organism, well-characterized genetics	Biohybrid robots, drug screening	Cell wall modification, genetic engineering

Engineering innovations have yielded remarkable precision in controlling microalgae-based systems. For instance, automated algal robots capable of precise adhesion and light-controlled movement have been developed, demonstrating the potential for targeted therapeutic delivery [45]. Genetic engineering tools, particularly CRISPR/Cas9 systems, have been successfully adapted for microalgae, opening possibilities for more sophisticated genetic modifications to enhance their therapeutic capabilities [45]. These advances collectively position microalgae as versatile, controllable, and highly adaptable platforms for addressing fundamental challenges in targeted drug delivery.

Experimental Protocols for Microalgae Bioengineering and Analysis

Cultivation and Growth Optimization

Establishing robust cultivation protocols is fundamental to microalgae-based drug delivery research. The selected strains—typically Phaeodactylum tricornutum, Chlorella sp., and Nannochloropsis granulata—are maintained in controlled environments at 22°C with continuous light exposure (approximately 20 μmol photons m⁻² s⁻¹) and constant shaking at 100 rpm to ensure proper mixing and aeration [47]. The culture medium composition is critical for optimizing biomass production and bioactive compound synthesis. The standard GoldMedium (GM) protocol involves two autoclaved stock solutions: Solution A (macronutrients: sodium nitrate and sodium dihydrogen phosphate) and Solution B (trace elements and vitamins premix), which are combined with artificial seawater solutions under sterile conditions [47].

To enhance biomass yield and metabolic diversity, mixotrophic cultivation strategies have been developed that combine light energy with both inorganic and organic carbon sources [47]. The experimental conditions for comparative growth studies include:

• Phototrophy control: GM without an external carbon source (pH ~6.7)
• Mixotrophy: GM supplemented with 4.6 g/L of glycerol (pH ~6.9)
• Phototrophy with bicarbonate: GM supplemented with 1.26 g/L of bicarbonate (pH ~8.2)
• Mixotrophy with glycerol and bicarbonate: GM supplemented with both 4.6 g/L of glycerol and 1.26 g/L of bicarbonate (pH ~8.3) [47]

Growth monitoring is performed through daily optical density measurements at 750 nm (OD₇₅₀), while biomass yield is quantified after 10 days of cultivation by filtering 2-3 mL of culture through pre-weighed 0.2 μm filters, followed by drying at 100°C for 24 hours and gravimetric analysis [47].

Bioactivity Assessment and Metabolite Analysis

Comprehensive bioactivity profiling is essential for validating the therapeutic potential of microalgae extracts. Antibacterial efficacy is evaluated against model organisms like Staphylococcus aureus, while antiproliferative activity is assessed using human cancer cell lines such as melanoma cells [47]. Cytotoxicity assays follow standardized protocols, with cells maintained at 37°C in a 5% CO₂ atmosphere and harvested at <70% confluence [47].

Metabolomic analysis to identify bioactive compounds employs advanced MS-HPLC techniques, enabling the characterization of diverse metabolite classes including phenolic compounds, peptides, terpenes, polysaccharides, polyacetylenes, sterols, alkaloids, aromatic organic acids, shikimic acid, polyketides, hydroquinones, and fatty acids [47]. These compounds have demonstrated remarkable biological activities, with Phaeodactylum tricornutum exhibiting significant antiproliferative effects against human melanoma cells and antibacterial activity against Staphylococcus aureus [47].

Table 2: Key Research Reagent Solutions for Microalgae Drug Delivery Research

Reagent/Category	Specification/Function	Application Examples
Culture Media	GoldMedium (GM) with Solutions A (macronutrients) & B (trace elements/vitamins)	Axenic culture maintenance, biomass production
Carbon Sources	Glycerol (4.6 g/L), Bicarbonate (1.26 g/L)	Mixotrophic cultivation, enhanced biomass yield
Functionalization Agents	Magnetic nanoparticles, Drug conjugates	Magnetotactic engineering, therapeutic loading
Analytical Tools	MS-HPLC, Spectrofluorometry, Scintillation counting	Metabolite identification, bioactivity assessment
Cell Culture	Human melanoma cells, Staphylococcus aureus	Cytotoxicity screening, antibacterial assays

Marinisomatota Metabolic Strategies: Ecological Parallels to Engineering Design

Diversity and Metabolic Flexibility in Marine Environments

Marinisomatota represent a ubiquitous and abundant group of marine microorganisms traditionally characterized as heterotrophic, but recent metagenomic and metatranscriptomic analyses have revealed astonishing metabolic complexity [1]. Comprehensive analysis of 1,588 Marinisomatota genomes has identified one class, two orders, 14 families, 31 genera, and 67 species, with relative abundances ranging from 0.18% to 36.21% across low-latitude marine regions [1]. This phylogenetic diversity underpins a remarkable spectrum of metabolic strategies that enable Marinisomatota to thrive across diverse marine environments, from oxygenated surface waters to oxygen-minimum zones and even the dark, oligotrophic waters beneath the Ross Ice Shelf [1] [8].

The metabolic classification of Marinisomatota reveals three distinct strategic categories: MS0 (photoautotrophic potential), MS1 (heterotrophic with pronounced glycolytic pathway), and MS2 (heterotrophic without glycolysis) [1]. This metabolic specialization represents an adaptive response to nutrient limitations in oceanic environments, with different subgroups optimizing their energy acquisition strategies for specific ecological niches. Of particular relevance to drug delivery applications is the discovery that several Marinisomatota families (S15-B10, TCS55, UBA1611, UBA2128, and UBA8226) exhibit potential for light-dependent processes associated with Crassulacean acid metabolism (M00169), demonstrating capacity for mixotrophic metabolism that combines phototrophy and heterotrophy [1].

Environmental Sensing and Adaptive Response Mechanisms

The ecological success of Marinisomatota stems from their sophisticated ability to sense and respond to environmental gradients, a capability with direct parallels to engineered drug delivery systems. In the dark, oligotrophic waters beneath the Ross Ice Shelf—an environment characterized by sparse microbial populations (0.9-1.2×10⁵ cells mL⁻¹) and extremely low heterotrophic production (0.3-0.6 μmol C m⁻³ d⁻¹)—Marinisomatota persist as dominant community members [8]. Their survival in this extreme environment depends on flexible metabolic strategies that allow them to transition between different energy acquisition modes based on resource availability.

This metabolic plasticity enables Marinisomatota to maintain functionality across steep chemical gradients, such as the vertical ammonium gradient observed beneath the Ross Ice Shelf, where concentrations decrease tenfold from 440 nM at the basal layer to 40-50 nM in deeper waters [8]. The genetic basis for this adaptability includes diverse sensor-regulator systems that modulate metabolic pathways in response to fluctuating nutrient conditions. From an engineering perspective, these natural microbial systems offer valuable design principles for creating drug delivery platforms that can dynamically respond to changing physiological conditions, such as variations in pH, oxygen tension, or metabolite concentrations in diseased versus healthy tissues.

Integrated Workflows: From Microalgae Engineering to Therapeutic Application

The development of microalgae-based drug delivery systems requires the integration of multidisciplinary approaches spanning microbiology, materials science, and biomedical engineering. The following workflow diagram illustrates the key stages in this process, highlighting the critical decision points and methodological considerations:

Diagram 1: Microalgae-based drug delivery system development workflow illustrating key stages from strain selection to therapeutic validation.

This integrated development pipeline begins with careful strain selection based on morphological and physiological characteristics suited to the specific therapeutic application. The cultivation phase employs mixotrophic strategies to enhance biomass production and bioactive compound synthesis, drawing inspiration from the metabolic flexibility observed in Marinisomatota [47] [1]. Biofunctionalization leverages both genetic engineering and surface modification approaches to introduce or enhance targeting capabilities, while therapeutic loading utilizes conjugation chemistry or encapsulation techniques to ensure optimal drug payload and release kinetics.

Convergence of Biological Principles: Mixotrophy as a Unifying Concept

Metabolic Parallels Between Engineered Systems and Natural Adaptations

The conceptual bridge between microalgae-driven drug delivery systems and Marinisomatota ecology centers on the principle of mixotrophy—the ability to combine multiple metabolic strategies for enhanced environmental adaptability. In microalgae engineering, mixotrophic cultivation significantly improves biomass production and metabolic diversity across tested species including Nannochloropsis granulata, Phaeodactylum tricornutum, and Chlorella sp. [47]. This cultivated metabolic flexibility directly parallels the innate mixotrophic capabilities discovered in Marinisomatota, where specific lineages can simultaneously utilize light energy through Crassulacean acid metabolism while maintaining heterotrophic capacities [1].

This metabolic convergence has profound implications for drug delivery system design. Just as Marinisomatota transition between metabolic states to optimize energy acquisition in response to oceanic gradients, engineered microalgae systems can be designed to switch between different propulsion and targeting modalities based on physiological cues. For instance, a microalgae-based delivery system could utilize phototactic navigation in superficial tissues while employing chemotactic or magnetotactic guidance in deeper anatomical regions, mirroring the context-dependent metabolic strategy shifts observed in Marinisomatota [1] [45].

Ecological Success Principles as Engineering Design Rules

The evolutionary success of Marinisomatota in oligotrophic marine environments offers valuable design principles for creating robust, environmentally-responsive therapeutic systems. Their persistence in the nutrient-depleted waters beneath the Ross Ice Shelf—where they form a taxonomically distinct community adapted to extreme oligotrophy—demonstrates remarkable metabolic efficiency and resource optimization [8]. These ecological adaptations can inform the engineering of drug delivery systems with enhanced persistence and functionality in the challenging physiological environments of diseased tissues, which often exhibit nutrient deprivation, hypoxia, and metabolic waste accumulation.

Table 3: Metabolic Strategies and Their Biomedical Applications

Metabolic Strategy	Marinisomatota Expression	Microalgae Engineering Application	Therapeutic Advantage
Mixotrophy	Combines light-dependent processes with organic carbon uptake	Enhanced biomass and bioactive compound production	Improved therapeutic payload and efficacy
Metabolic Plasticity	Shifts between MS0, MS1, MS2 strategies based on environment	Adaptive energy management in physiological environments	Extended functional persistence in target tissues
Nutrient Scavenging	Efficient uptake in oligotrophic conditions	Enhanced targeting of pathological metabolite gradients	Improved accumulation in diseased microenvironments
Sensor-Regulator Systems	Response to chemical and energy gradients	Engineering of environmentally-responsive promoters	Context-dependent drug release at target sites

The genomic features underlying Marinisomatota's ecological success—including streamlined genomes in epipelagic lineages and specialized nutrient uptake systems—provide a blueprint for optimizing microbial therapeutic chassis [1] [46]. By incorporating these biological design principles, next-generation microalgae delivery systems can achieve enhanced targeting precision, greater payload capacity, and improved adaptability to the dynamic pathophysiological conditions encountered in human disease.

The biomedical parallels between microalgae-driven drug delivery systems and Marinisomatota metabolic strategies reveal a compelling convergence of biological principles across evolutionary domains. Engineered microalgae platforms leverage precisely controlled mixotrophic cultivation to enhance therapeutic compound production [47], while Marinisomatota employ naturally evolved mixotrophic adaptations to optimize energy acquisition in marine environments [1]. This intersection of biotechnology and microbial ecology offers a rich conceptual framework for advancing targeted therapeutic systems.

Future research directions should prioritize the development of increasingly sophisticated environmentally-responsive control systems inspired by the sensor-regulator networks that enable Marinisomatota to thrive across diverse marine gradients [1] [8]. Additionally, the integration of synthetic biology approaches—including CRISPR-based genome editing [45]—will enable more precise engineering of microalgae chassis with enhanced targeting capabilities and therapeutic payloads. The continued investigation of marine microbial ecosystems will undoubtedly yield additional design principles for next-generation biomedical technologies, further bridging the conceptual divide between ecological adaptation and therapeutic innovation.

The field of medical robotics faces fundamental challenges in navigation, energy autonomy, and biocompatibility when operating within the complex environments of the human body. Conventional rigid robots often struggle with adaptive locomotion and safe interaction with delicate biological tissues. Meanwhile, in marine microbial ecology, Marinisomatota demonstrate remarkable mixotrophic adaptations, seamlessly transitioning between heterotrophic and photoautotrophic metabolic strategies to thrive in nutrient-poor pelagic zones [1]. This metabolic flexibility, governed by three distinct metabolic strategies (MS0: photoautotrophic potential, MS1: heterotrophic with pronounced glycolysis, and MS2: heterotrophic without glycolysis), represents an evolutionary optimization for energy acquisition in constrained environments [1].

This whitepaper explores a transformative approach: harnessing design principles from natural morphologies and metabolic strategies to engineer next-generation biohybrid medical robots. By treating the adaptive mechanisms of marine microorganisms as a blueprint for engineering design, we can develop biohybrid systems that emulate the energy efficiency, environmental responsiveness, and morphological intelligence of biological organisms. This convergence of biology and engineering enables the creation of medical robots capable of autonomous navigation through complex physiological environments, targeted therapeutic delivery, and minimally invasive interventions with unprecedented precision and safety profiles.

Core Principles: Biological Design Paradigms for Engineering Applications

Marinisomatota Metabolic Strategies as Engineering Analogues

The metabolic versatility observed in Marinisomatota provides a conceptual framework for addressing the critical challenge of energy autonomy in biohybrid medical robotics. These marine microorganisms employ light-dependent processes associated with Crassulacean acid metabolism (M00169) to fix carbon dioxide and synthesize organic compounds, particularly when transitioning between the translucent and aphotic ocean layers [1]. This metabolic flexibility enables survival in resource-limited environments through dynamic energy harvesting strategies.

In biohybrid robotics, this principle translates to systems capable of multi-modal energy harvesting, utilizing biochemical gradients, external fields, and endogenous physiological energy sources. The Marinisomatota's three metabolic strategies (MS0, MS1, MS2) represent a biological precedent for designing robots that can dynamically switch between power sources based on environmental availability, much like their microbial counterparts [1].

Muscle-Based Actuation for Medical Robotics

Biohybrid robots integrate living muscle tissues with synthetic components to create systems with unique capabilities. Skeletal muscle tissues enable precise control for complex movements like walking and gripping, while cardiac muscles offer autonomous, rhythmic contractions ideal for pumping and sustained locomotion [48]. These natural actuators demonstrate superior energy efficiency compared to conventional synthetic actuators, with the added advantages of inherent adaptability, self-repair capabilities, and responsive behavior to environmental stimuli [48].

Table 1: Muscle Tissue Applications in Biohybrid Medical Robotics

Muscle Type	Control Mechanism	Medical Applications	Performance Characteristics
Skeletal Muscle	Precise external control (optical, electrical stimulation)	Targeted drug delivery, micro-manipulation, walking robots	Force: 2.92 ± 0.07 mN [48]; Responsiveness: Up to 10 Hz [48]
Cardiac Muscle	Autonomous rhythmic contraction	Pumps, swimming robots, continuous flow systems	Continuous actuation; Speeds up to 800 μm/s in swimmers [48]
Smooth Muscle	Involuntary control	Peristaltic systems, fluid regulation	Fine-scale fluid transport; less commonly utilized currently [48]

The functional advantages of muscle-based biohybrid systems are further enhanced through co-culture strategies with supportive cell types. Incorporating cardiac fibroblasts, endothelial cells, fibroadipogenic progenitors (FAPs), and motor neurons into engineered constructs creates more physiologically relevant microenvironments that enhance tissue maturation, contractile function, and functional integration [48]. Particularly promising is the incorporation of macrophages, which through their distinct polarization states (pro-inflammatory M1 and pro-regenerative M2), can orchestrate tissue development and remodeling in engineered muscle constructs [48].

Experimental Methodologies for Biohybrid System Development

Fabrication and Maturation of Muscle-Based Actuators

Protocol 1: Engineering Skeletal Muscle Tissues for Precision Actuation

• Cell Sourcing: Isolate primary myoblasts from neonatal rat hindlimb muscles or utilize immortalized cell lines (C2C12 mouse myoblasts). For clinical translation, employ human skeletal muscle cells (hSkMCs) derived from biopsies or stem cell differentiation protocols [48].
• Scaffold Preparation: Prepare fibrin-thrombin hydrogel solution (10-20 mg/mL fibrinogen) in culture medium. Alternatively, use Matrigel or gelatin methacryloyl (GelMA) for specific application requirements [48].
• Tissue Formation: Mix cell suspension with hydrogel solution at density of 5-20 × 10^6 cells/mL. Seed into custom molds with attachment points. Culture in growth medium (DMEM with 10% FBS, 4 mM L-glutamine) for 3-5 days [48].
• Differentiation: Switch to differentiation medium (DMEM with 2% horse serum) for 7-14 days to promote myotube formation [48].
• Functional Enhancement: Apply electromechanical stimulation regimens using custom bioreactors. Implement cyclic mechanical loading (0.5-2 Hz) with simultaneous electrical pulse stimulation (1-5 V, 1-10 ms pulses) to enhance myogenic differentiation and contractile force [48].

Protocol 2: Development of Cardiac Muscle Tissues for Autonomous Actuation

• Cell Isolation: Derive cardiomyocytes from neonatal rat ventricular myocytes or human-induced pluripotent stem cells (hiPSCs) using established differentiation protocols [48].
• Tissue Engineering: Suspend cells in fibrin-based hydrogel at density of 10-30 × 10^6 cells/mL. Pattern using 3D bioprinting or microfluidic devices to create aligned tissue architectures [48].
• Maturation: Co-culture with cardiac fibroblasts and endothelial cells (ratio 3:1:1) in advanced culture systems that permit mechanical conditioning and electrical pacing to promote structural and electrophysiological maturation [48].

Evolutionary Morphology Generation for Biohybrid Devices

The design of optimal morphologies for biohybrid medical robots presents significant challenges due to the complex interaction between biological and synthetic components. Evolutionary algorithms, particularly those utilizing Compositional Pattern Producing Networks (CPPNs), have demonstrated the capacity to outperform human designers in generating efficient morphological designs [49].

Table 2: Evolutionary Morphology Generation Workflow

Step	Process	Parameters	Output
1. Problem Definition	Define environmental constraints and functional objectives	Target environment (e.g., vascular system), required movements, material constraints	Specification of fitness functions
2. Representation	Encode potential morphologies using CPPNs	Node types with mathematical functions (Gaussian, sine, sigmoid), network topology	Genotype representation of morphologies
3. Simulation	Evaluate performance in physics engine (Voxelyze)	Material properties (Young's modulus: ~5 Mpa), Poisson's ratio, actuation patterns	Quantitative performance metrics
4. Optimization	Apply Age-Fitness-Pareto-Optimization (AFPO)	Population size, generation count, mutation rates	Evolved morphologies with improved fitness

Protocol 3: Evolutionary Morphology Generation for Biohybrid Catheters

• Environment Setup: Configure Voxelyze simulation environment with physiological parameters (fluid viscosity, flow rates, vessel constraints) relevant to the target anatomy [49].
• Material Definition: Specify biocompatible materials with appropriate mechanical properties (Young's modulus: ~5 Mpa, Poisson's ratio constrained to biocompatible silicones or thermoplastic polymers) [49].
• CPPN Configuration: Initialize CPPN networks with randomized topologies and mathematical functions (sine, Gaussian, sigmoid, linear) to generate morphological patterns [49].
• Fitness Evaluation: Simulate each morphology and quantify performance metrics (navigation efficiency, stability, actuation effectiveness) within the target environment [49].
• Evolutionary Optimization: Implement AFPO algorithm to select, mutate, and recombine best-performing morphologies across generations, progressively increasing complexity and functionality [49].

The following diagram illustrates the evolutionary morphology generation workflow for biohybrid catheter design:

Bio-Hybrid Magnetic Functionalization for Targeted Delivery

Protocol 4: Synthesis of Bio-Hybrid Magnetic Robotics for Therapeutic Delivery

• Cell Selection: Choose appropriate biological components based on application: red blood cells (prolonged circulation), neutrophils (inflammatory targeting), or macrophages (tissue penetration) [50].
• Magnetic Functionalization:
  • MNP Loading: Incubate cells with synthetic superparamagnetic iron oxide nanoparticles (SPIONs) (10-50 nm diameter) at concentrations of 50-200 μg/mL for 2-12 hours [50].
  • Surface Modification: Conjugate MNPs to cell surfaces via amide bond formation, electrostatic attraction, or biotin-streptavidin binding for enhanced magnetic responsiveness [50].
  • Membrane Coating: Fabricate RBC membrane-encased magnetic nanowires by combining gold nanowire motors with RBC-derived membrane vesicles [50].
• Therapeutic Loading: Encapsulate drug payloads (e.g., doxorubicin, immunotherapeutics) via electroporation, sonication, or co-incubation depending on cell type and cargo properties [50].
• Navigation Control: Apply external magnetic fields (rotating magnetic fields for propulsion, static gradients for directional guidance) for precise control through biological environments [50].

Implementation Framework: Integrated Workflow for Biohybrid Medical Robot Development

The development of functional biohybrid systems requires a multidisciplinary approach integrating concepts from marine microbiology, tissue engineering, and robotics. The following diagram illustrates the complete implementation framework from biological inspiration to functional medical robot:

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents for Biohybrid System Development

Category	Specific Reagents/Materials	Function and Application
Cell Sources	Primary myoblasts (rat neonatal), C2C12 mouse myoblasts, human skeletal muscle cells (hSkMCs), human-induced pluripotent stem cells (hiPSCs)	Engineered muscle tissue formation; species-specific models [48]
Hydrogel Scaffolds	Fibrin-thrombin, Matrigel, gelatin methacryloyl (GelMA), collagen-based hydrogels	Mimics native extracellular matrix; provides 3D support structure for cell growth and differentiation [48]
Magnetic Components	Superparamagnetic iron oxide nanoparticles (SPIONs, 10-50 nm), functionalized MNP surfaces (amine, carboxyl)	Enables external magnetic control and navigation; facilitates tracking via imaging [50]
Supportive Cells	Cardiac fibroblasts, endothelial cells, fibroadipogenic progenitors (FAPs), motor neurons, macrophages	Enhances tissue maturation, vascularization, and functional integration; improves contractile performance [48]
Culture Supplements	Differentiation factors (horse serum, growth factor cocktails), electromechanical stimulation bioreactors	Promotes myogenic differentiation and tissue maturation; enhances contractile force generation [48]
Simulation Tools	Voxelyze physics engine, CPPN-NEAT/AFPO algorithms, custom fitness evaluation functions	Digital design and optimization of biohybrid morphologies prior to fabrication [49]
Lactose-3'-sulfate	Lactose-3'-sulfate, CAS:159358-51-7, MF:C12H22O14S, MW:422.36 g/mol	Chemical Reagent
N-Desmethyl Sildenafil-d8	N-Desmethyl Sildenafil-d8, CAS:1185168-06-2, MF:C21H28N6O4S, MW:468.6 g/mol	Chemical Reagent

The integration of biological design principles with advanced engineering frameworks presents a transformative pathway for medical robotics. By harnessing the metabolic strategies of Marinisomatota as inspiration for energy management and the contractile capabilities of engineered muscle tissues for actuation, we can develop biohybrid systems with unprecedented adaptability, efficiency, and biocompatibility. The implementation of evolutionary morphology generation enables the discovery of optimal designs that would be difficult to conceptualize through conventional engineering approaches.

These biohybrid systems promise to revolutionize medical interventions through enhanced navigation capabilities in complex physiological environments, reduced immune responses, and improved therapeutic precision. As research advances in tissue engineering, control systems, and manufacturing scalability, biohybrid medical robots derived from natural morphologies and metabolic principles will transition from laboratory demonstrations to clinical applications, ultimately enabling new paradigms in minimally invasive medicine and targeted therapeutic delivery.

Mixotrophic metabolism, which combines both autotrophic and heterotrophic nutritional strategies, represents a highly efficient mechanism for organic matter degradation and nutrient cycling in marine environments. Within the context of Marinisomatota research, mixotrophic adaptations enable these organisms to utilize diverse carbon and energy sources, providing exceptional metabolic flexibility for bioremediation applications. This guide examines the core principles, experimental data, and methodological protocols essential for investigating mixotrophic bioremediation systems, with particular emphasis on their capacity to address complex environmental pollutants under varying nutrient conditions.

The fundamental advantage of mixotrophy in bioremediation lies in its ability to overcome substrate limitations that often constrain purely autotrophic or heterotrophic processes. In marine recirculating aquaculture systems (RAS), for instance, mixotrophic denitrification strategies utilizing both organic carbon and reduced sulfur compounds have demonstrated significant potential for treating wastewater with characteristic low C/N ratios [51]. These systems achieve simultaneous heterotrophic and autotrophic denitrification, offering a cost-effective solution to carbon source limitation while mitigating risks of secondary pollution.

Quantitative Performance of Mixotrophic Bioremediation Systems

Nitrogen Removal Efficiency Under Antibiotic Stress

Table 1: Performance of Mixotrophic Denitrification Under Florfenicol Stress

Florfenicol Concentration (mg/L)	NO₃⁻-N Removal Efficiency (%)	Electron Donor Utilization	Key Enzyme Activities Affected
0.0 (Control)	>99.5%	Efficient	Baseline levels
0.1	>99.5%	Efficient	Minimal inhibition
0.5	>99.5%	Efficient	Slight decrease
1.0	>99.5%	Moderately efficient	Noticeable decrease
2.0	85.5% (initial)	Significantly inhibited	Complex I, Quinone pool, Cytochrome c, NAR, NIR

Data derived from sequencing batch reactors treating recirculating aquaculture wastewater demonstrates that high florfenicol concentrations (2.0 mg/L) significantly inhibit mixotrophic denitrification performance, with deterioration in nitrogen removal from 100% (control) to 85.5% during the initial exposure phase [51]. The utilization of both organic and inorganic electron donors was impaired, reflected in decreased activities of key enzymes associated with electron transfer (complex I, quinone pool, and cytochrome c) and electron consumption (nitrate reductase and nitrite reductase).

Heavy Metal Removal Through Scale-Up Cultivation

Table 2: Bioreactor Scale-Up Parameters for Cadmium Removal Using Mixotrophic Acidophiles

Bioreactor Scale	Working Volume	Cd Removal Efficiency	Dominant Microbial Communities	Key Process Parameters
Lab-scale (Flask)	0.1 L	Baseline	Acidithiobacillaceae, Debaryomycetaceae	pH drop to <2.5, cell density >1×10⁹ cells/mL
Lab-scale (Beaker)	2 L	Comparable to 0.1L	Similar community structure	Consistent temperature (32°C)
Pilot-scale	10 L	Slightly decreased	Bacterial community stable	Aeration and mixing optimized
Pilot-scale	100 L	Slightly decreased	Bacterial community stable	Scaling parameters maintained
Commercial-scale	1 m³	Moderately decreased	Fungal community simplified	Oxygen transfer limitations
Commercial-scale	10 m³	Significantly decreased	Distinct community structure	Significant process delays

In the remediation of cadmium-contaminated soils, a six-stage scale-up cultivation process of mixotrophic acidophiles demonstrated that bioreactor expansion led to delayed sulfur and glucose oxidations, resulting in reduced decline in solution pH and cell density [52]. Although minimal differences occurred in bacterial alpha-diversity and community structure until the largest (10 m³) scale, fungal alpha-diversity decreased progressively with increasing bioreactor volume. Throughout the scale-up, Acidithiobacillaceae and Debaryomycetaceae dominated the bacterial and fungal communities, respectively [52]. Correlation analysis confirmed that bacterial community shifts, driven by bioreactor volume changes, decreased pH through sulfur oxidation, thereby indirectly enhancing Cd removal efficiency.

Experimental Protocols for Mixotrophic Bioremediation Research

Mixotrophic Denitrification System Setup

Protocol 1: Establishing Mixotrophic Denitrification Systems for Aquaculture Wastewater Treatment

• Bioreactor Configuration: Set up sequencing batch reactors (SBRs) with effective volume of 1.2 L each. Maintain hydraulic retention time (HRT) at 16 h with 50% water volume exchange per cycle [51].

• Operational Cycle Parameters:

  • Feeding: 3 minutes
  • Agitation: 420 minutes
  • Sedimentation: 54 minutes
  • Effluent withdrawal: 3 minutes
  • Temperature control: 25 ± 1°C

• Inoculum and Medium Preparation:

  • Seed sludge: Source from secondary sedimentation tank of municipal wastewater treatment plants
  • Synthetic mariculture wastewater: Salinity of 3.0%
  • Carbon source: Mariculture solid waste fermentation liquid containing volatile fatty acids and sulfide
  • Electron donors: Provide both organic (volatile fatty acids) and inorganic (sulfide) sources

• System Acclimation: Operate systems for 30 days to achieve stable nitrogen removal rate of approximately 100% before introducing experimental variables such as antibiotic stress.

• Stress Exposure: Establish concentration gradient of target stressor (e.g., florfenicol at 0, 0.1, 0.5, 1.0, and 2.0 mg/L) to evaluate system performance under inhibitory conditions.

Scale-Up Cultivation of Mixotrophic Acidophiles

Protocol 2: Six-Stage Scale-Up for Heavy Metal Removal Applications

• Initial Inoculum Development:

  • Combine leachate from bioleaching heap of sulfide minerals (10 mL) with Cd-contaminated soil (10 g)
  • Add to 250 mL conical flask containing 100 mL of basal medium [52]
  • Basal medium composition: 3 g/L (NHâ‚„)â‚‚SOâ‚„, 0.1 g/L KCl, 0.5 g/L Kâ‚‚HPOâ‚„, 0.5 g/L MgSO₄·7Hâ‚‚O, 0.01 g/L Ca(NO₃)â‚‚, 1 g/L elemental sulfur, 0.7 g/L glucose, and 0.3 g/L yeast extract
  • Adjust pH to 3.5 using sulfuric acid
  • Incubate at 32°C with agitation at 175 rpm

• Culture Transfers:

  • Monitor pH regularly; when pH drops below 2.5, transfer supernatant microbial fluid to fresh basal medium (10% vol/vol)
  • Repeat subculturing process 10 times to establish robust, adapted consortium

• Scale-Up Progression:

  • Implement six-stage scale-up: 0.1 L → 2 L → 10 L → 100 L → 1 m³ → 10 m³
  • Use appropriately sized vessels for each stage: glass conical flasks for lab-scale, polyethylene reactors for larger scales
  • Maintain seed liquid dose at 10% (vol/vol) for each transfer
  • Use identical cultivation media across all scales with constant temperature (32°C) and initial pH (3.5)
  • Transfer cultures when solution pH drops below 2.5 or cell density exceeds 1×10⁹ cells/mL

• Soil Bioremediation Application:

  • Combine air-dried, sieved Cd-contaminated soil (10 g) with microbial solution (100 mL) from each scale-up stage
  • Conduct in 250 mL conical flasks with appropriate controls
  • Monitor Cd removal efficiency through periodic sampling and analysis

Analysis Methods for System Performance

Protocol 3: Analytical Approaches for Assessing Mixotrophic Bioremediation Efficiency

• Nitrogen Species Quantification:

  • Measure NO₃⁻-N, NO₂⁻-N, and NH₄⁺-N concentrations following standard methods
  • Monitor sulfate concentrations to track sulfur oxidation

• Enzyme Activity Assays:

  • Evaluate electron transport system activity via dehydrogenase activity measurements
  • Assess key enzyme activities including nitrate reductase (NAR) and nitrite reductase (NIR)
  • Measure complex I, quinone pool, and cytochrome c activities

• Microbial Community Analysis:

  • Extract DNA from samples using standardized kits
  • Perform 16S rRNA gene amplification and sequencing for bacterial community analysis
  • Conduct ITS region sequencing for fungal community characterization
  • Employ bioinformatic tools (QIIME2, PICRUSt2) for data processing and functional prediction

• Extracellular Polymeric Substances (EPS) Characterization:

  • Extract EPS using thermal extraction method
  • Quantify protein content with Folin phenol reagent [51]
  • Determine polysaccharide content using anthrone-sulfuric acid method
  • Analyze three-dimensional fluorescence excitation-emission matrix (EEM) spectroscopy with regional integration analysis

Metabolic Pathways in Mixotrophic Bioremediation

Figure 1: Mixotrophic Metabolic Pathways in Marinisomatota. The diagram illustrates the integration of autotrophic and heterotrophic metabolic processes that enable efficient organic matter degradation and nutrient cycling.

The metabolic flexibility of mixotrophic systems allows for simultaneous operation of autotrophic and heterotrophic pathways, creating robust bioremediation capabilities. In marine Marinisomatota and related mixotrophic consortia, these integrated pathways facilitate the degradation of diverse organic contaminants while maintaining efficient nutrient cycling, particularly for nitrogen and sulfur [51]. The electron transport system serves as a critical junction point where electrons derived from both inorganic and organic sources are shuttled to support denitrification processes.

Research Reagent Solutions for Mixotrophic Bioremediation

Table 3: Essential Research Reagents for Mixotrophic Bioremediation Studies

Reagent Category	Specific Examples	Function in Research	Application Context
Electron Donors	Volatile fatty acids (acetate, propionate), Sulfide (S²⁻), Elemental sulfur (S⁰), Glycerol	Provide reduction equivalents for metabolic processes, support both autotrophic and heterotrophic pathways	Mixotrophic denitrification systems [51], Microalgae cultivation [47]
Nutrient Media Components	(NH₄)₂SO₄, K₂HPO₄, MgSO₄·7H₂O, Ca(NO₃)₂, Yeast extract	Supply essential macro/micronutrients for microbial growth, maintain osmotic balance	Basal medium for acidophile cultivation [52], Artificial seawater medium [53]
Carbon Sources	Sodium bicarbonate, Glycerol, Glucose, Carbon dioxide	Support autotrophic carbon fixation and heterotrophic carbon assimilation, pH buffering	Microalgae mixotrophic cultivation [47], Enrichment cultures [53]
Stressor Compounds	Florfenicol, Cadmium, Other heavy metals, Emerging pollutants	Evaluate system resilience, study inhibitory effects on microbial functions	Antibiotic stress tests [51], Heavy metal bioremediation [52] [54]
Analytical Reagents	Folin phenol reagent, Anthrone-sulfuric acid, DNA extraction kits, PCR reagents	Quantify biomolecules, extract and analyze nucleic acids, characterize microbial communities	EPS quantification [51], Molecular community analysis [55]
pH Control Agents	Sulfuric acid, Sodium hydroxide, Bicarbonate buffer systems	Maintain optimal pH ranges for specific microbial groups, simulate environmental conditions	Acidophile cultures (pH 3.5) [52], Bicarbonate-buffered systems [47]

Mixotrophic bioremediation systems represent a technologically promising approach for addressing complex environmental challenges involving organic matter degradation and nutrient cycling. The integrated metabolic capabilities of these systems, particularly within marine Marinisomatota and associated consortia, enable efficient processing of diverse contaminants while maintaining system stability under various stress conditions. The experimental protocols and analytical frameworks presented in this technical guide provide researchers with standardized methodologies for advancing this field, with particular relevance for applications in marine aquaculture, heavy metal contamination, and emerging pollutant mitigation. Future research directions should focus on optimizing scale-up parameters, enhancing stress resilience through community engineering, and developing real-time monitoring approaches for industrial implementation.

Overcoming Research Barriers: From Cultivation Challenges to Metabolic Optimization

Marine environments host an immense diversity of microorganisms, with an estimated abundance of 10⁴–10⁷ cells/ml in seawater and 10³–10¹⁰ cells/cm³ in sediments [56]. Despite their crucial roles in global biogeochemical cycles, the vast majority of these microbial residents—over 99%—have not been cultivated under standard laboratory conditions [56]. This extensive "microbial dark matter" represents a critical gap in our understanding of marine ecosystems and their metabolic potential, particularly for lineages like Marinisomatota (formerly known as Marinimicrobia, Marine Group A, and SAR406), which are widespread and highly abundant in marine environments yet remain poorly characterized [57].

The challenges in microbial cultivation stem from multiple factors, including unsuitable growth conditions, low growth rates, requirements for metabolites produced by other microbes, and the presence of dormant cells that resist conventional cultivation attempts [56]. Furthermore, many marine microorganisms exist in a viable but nonculturable (VBNC) state—a survival strategy where bacteria remain metabolically active but cannot form colonies on conventional media [56]. Addressing these challenges requires innovative approaches that better mimic natural habitats and address the specific metabolic needs of target organisms.

Advanced Cultivation Strategies for Unculturable Microbes

Environment-Mimicking Techniques

Table 1: Advanced Cultivation Methods for Unculturable Marine Microbes

Method	Key Principle	Target Microbes	Success Examples
In Situ Cultivation	Uses diffusion chambers to allow chemical exchange with natural environment	Diverse uncultured marine bacteria	Isolation of a bacterium producing new diketopiperazines [56]
Droplet Microfluidics	High-throughput single-cell encapsulation in microscale droplets	Various uncultured microorganisms	Enables single-cell cultivation and co-cultivation [56]
Diffusion-Based Method	Modified low-nutrient media in diffusion-based devices	Verrucomicrobiota and Balneolota	196 isolates, 115 representing novel taxa [57]
Resuscitation Stimuli	Application of signaling molecules to reverse VBNC state	Dormant marine bacteria	Use of Rpf, YeaZ, quorum sensing molecules [56]
Enrichment Culture	Increases abundance of rare biosphere members	Rare active bacteria	Isolation of novel Gemmatimonadota strains [58]

In situ cultivation methods represent a paradigm shift in microbial isolation by maintaining a connection to the natural environment during the initial cultivation phase. The diffusion chamber technique allows continuous chemical exchange between the natural environment and the enclosed microorganisms, providing essential growth factors and signals that would be absent in artificial media [56]. Advanced iterations like the iChip (isolation chip) and microbial trap have further improved success rates by creating semi-permeable barriers between target cells and their native habitat while permitting nutrient exchange [56]. These approaches have successfully cultivated previously uncultivable marine bacteria, including one strain that produces three new diketopiperazines with potential bioactivity [56].

For high-throughput applications, microfluidic droplet-based techniques offer significant advantages, including single-cell resolution, automation potential, and reduced cost [56]. This method involves encapsulating individual cells in microscopic droplets that function as independent bioreactors, allowing for the screening of thousands of cultivation conditions in parallel. The technique has proven valuable for microbial co-cultivation studies, enzyme screening, and facilitating single-cell whole-genome sequencing [56].

The resuscitation of VBNC cells represents another promising strategy, employing compounds that stimulate a return to replicative states. Effective resuscitation stimuli include resuscitation-promoting factors (Rpfs), sodium pyruvate, quorum sensing molecules, catalase, and siderophores [56]. These approaches address the physiological barriers to cultivation rather than merely optimizing nutrient composition.

Targeted Cultivation Based on Ecological Function

Understanding the ecological role and metabolic capabilities of target organisms enables more precise cultivation strategies. For example, research on nitrite-oxidizing bacteria (NOB) in the Mariana Trench revealed clear niche partitioning between slope and bottom sediments, with distinct adaptations to pressure and nutrient availability [43]. Such ecological insights can guide the development of specialized media and conditions that address the specific requirements of target microorganisms.

The reverse-genomics approach leverages genomic information from uncultured organisms to design tailored cultivation media. By identifying potential metabolic capabilities and nutrient requirements through genome analysis, researchers can create defined media that support the growth of previously unculturable taxa [56]. This strategy has been successfully applied to isolate novel members of the Gemmatimonadota phylum from marine sediments [58].

Mixotrophic Adaptations in Marine Marinisomatota: Implications for Cultivation

The phylum Marinisomatota exemplifies the metabolic versatility that complicates traditional cultivation approaches. Recent genomic analyses of 1,588 Marinisomatota genomes from global ocean datasets revealed three distinct metabolic modes: MS0 (with photoautotrophic potential), MS1 (heterotrophic with enhanced glycolytic capacity), and MS2 (heterotrophic without glycolysis) [57]. This metabolic diversity suggests potential for mixotrophic adaptations within this phylum, allowing these organisms to alternate between autotrophic and heterotrophic strategies based on environmental conditions.

Mixotrophy represents a widespread survival strategy in marine environments, particularly in nutrient-limited systems. Organisms employing this strategy can combine photosynthetic carbon fixation with the ingestion of living prey, providing flexibility to balance energy and nutrient demands [59]. In the oligotrophic gyres, for example, mixotrophic plankton make up >80% of the pigmented biomass and are responsible for 40-95% of bacterial grazing [59]. This trophic flexibility has profound implications for cultivation, as it necessitates media and conditions that support multiple metabolic modes simultaneously.

For Marinisomatota, the potential for mixotrophy suggests that successful cultivation may require providing both organic carbon sources and appropriate conditions for light-independent autotrophy (such as the rTCA cycle or other carbon fixation pathways). The ability to shift between metabolic strategies likely reflects an evolutionary adaptation to the fluctuating nutrient conditions in oceanic ecosystems [57]. Cultivation approaches should therefore incorporate dynamic condition regimes that allow for these metabolic transitions, rather than static media formulations.

Table 2: Metabolic Strategies Identified in Marinisomatota Lineages

Metabolic Mode	Key Characteristics	Ecological Distribution	Cultivation Implications
MS0	Potential for photoautotrophy	Varied oceanic regions	May require light or alternative energy sources
MS1	Heterotrophic with enhanced glycolytic capacity	Widespread distribution	Organic carbon sources essential
MS2	Heterotrophic without glycolysis	Specific niches	Limited carbohydrate utilization capability
Mixotrophic Potential	Combination of metabolic strategies	Adaptation to nutrient limitation	Multiple nutrient and energy sources needed

Experimental Protocols for Targeted Isolation

Diffusion-Based Isolation from Marine Sediments

A recent innovative protocol demonstrated exceptional success in cultivating previously uncultured bacteria from marine sediments [57]. This diffusion-based integrative approach employs modified low-nutrient media and involves the following steps:

• Sample Preparation: Collect sediment cores and suspend 1 g of sediment in 10 mL of sterile artificial seawater. Homogenize gently without vigorous shaking to preserve delicate cells.

• Media Preparation: Create low-nutrient media mimicking the chemical composition of the native porewater, with particular attention to ionic composition and micronutrient profiles. Avoid rich nutrient sources that may inhibit oligotrophic specialists.

• Diffusion Chamber Setup: Use specialized devices with semi-permeable membranes (0.1 - 0.4 µm pore size) that allow chemical exchange while containing bacterial cells. Alternatively, use diluted agar (0.3-0.5%) in Petri dishes with permeable membranes.

• Inoculation and Incubation: Apply sediment suspensions to the diffusion systems and incubate at in situ temperatures for extended periods (4-12 weeks). Regularly monitor for slow-growing microcolonies.

• Recovery and Purification: Transfer developing colonies to conventional media with gradual nutrient increase to acclimatize cells to laboratory conditions.

This method yielded 196 bacterial isolates from marine sediments, with 115 representing previously uncultured taxa—a remarkable 58% novelty ratio [57]. The approach was particularly effective for rarely cultured phyla like Verrucomicrobiota and Balneolota.

Enrichment Strategy for Marine Gemmatimonadota

The isolation of the first marine members of the Gemmatimonadota phylum illustrates the power of targeted enrichment strategies [58]:

• Aerobic Enrichment: Establish sediment slurries in marine broth with continuous gentle agitation to maintain suspension while avoiding shear stress.

• Subculturing: Transfer 10% of the enrichment to fresh media every 4-6 weeks, gradually reducing nutrient complexity.

• Isolation on Solid Media: Spread enrichment cultures on marine agar 2216 supplemented with specific nutrient sources identified through genomic analysis.

• Characterization of Fastidious Strains: Implement physiological tests under conditions identified through ecological distribution analysis, noting specific requirements for NaCl (1-8% optimum at 3%) and temperature ranges (35-37°C optimum).

This approach successfully isolated four novel strains representing a new species within the Gemmatimonadota, leading to the proposal of new taxa: Gaopeijia maritima gen. nov., sp. nov. within the Gaopeijiales ord. nov. [58].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents for Cultivating Uncultured Marine Microbes

Reagent/Category	Specific Examples	Function/Application
Specialized Media	Marine Agar 2216, Modified low-nutrient media	Provides base nutrients while mimicking natural conditions
Signaling Molecules	Resuscitation-promoting factors (Rpfs), YeaZ, N-acyl homoserine lactones	Reverses VBNC state and stimulates growth initiation
Membrane Filters	Semi-permeable membranes (0.1-0.4 µm)	Creates diffusion barriers for in situ cultivation techniques
Antioxidants	Sodium pyruvate, Catalase, Superoxide dismutase	Countacts oxidative stress during isolation
Salt Formulations	Artificial seawater mixtures, Specific ion supplements	Maintains osmotic balance and provides essential ions
Gelling Agents	Washed agar, Gellan gum	Creates solid surfaces with reduced inhibitor content
Nutrient Sources	Specific amino acids, Carbon sources identified through genomics	Targets metabolic capabilities of desired organisms
DL-Sulforaphane glutathione	DL-Sulforaphane glutathione, CAS:289711-21-3, MF:C16H28N4O7S3, MW:484.6 g/mol	Chemical Reagent
TPU-0037A	TPU-0037A, MF:C46H72N4O10, MW:841.1 g/mol	Chemical Reagent

Cultivating the unculturable marine microbiome requires moving beyond traditional methods to embrace integrated strategies that address the physiological and ecological complexity of marine microorganisms. The remarkable success of diffusion-based methods in isolating hundreds of novel taxa demonstrates the power of approaches that better mimic natural environmental conditions [57]. Similarly, the isolation of previously uncultured Gemmatimonadota through targeted enrichment highlights the value of combining genomic insights with cultivation methodology [58].

For challenging groups like Marinisomatota with their potential for mixotrophic adaptations, successful cultivation will likely require dynamic condition regimes that support metabolic flexibility. Future advances will depend on continued integration of genomic, metatranscriptomic, and environmental data to design increasingly sophisticated cultivation platforms that address the specific requirements of microbial dark matter.

As these innovative cultivation methods continue to evolve, they promise to unlock the tremendous biotechnological and ecological potential of the previously uncultured marine microbiome, from novel antimicrobial compounds and biocatalysts to fundamental insights into marine ecosystem functioning [60]. The systematic application of the strategies outlined here represents our best path forward in addressing the persistent challenge of microbial uncultivability.

Within the intricate framework of research on mixotrophic adaptations in marine Marinisomatota, optimizing growth conditions is not merely a methodological step but a fundamental investigative focus. Marinisomatota, ubiquitous in marine environments, exhibit remarkable metabolic versatility, a key to their ecological success [2]. Their ability to utilize a mixotrophic strategy—combining autotrophic carbon fixation with heterotrophic metabolism—represents a significant evolutionary adaptation to nutrient limitation in oceanic ecosystems [2]. Understanding and manipulating the environmental triggers and nutrient balances that govern their growth is therefore paramount. This guide provides an in-depth technical examination of the critical factors, including nitrogen and phosphorus stoichiometry, light intensity, and temperature, that must be precisely controlled to unlock the full physiological and biotechnological potential of these and similar marine microorganisms.

Nutrient Optimization for Growth and Metabolism

Nutrient availability serves as the primary regulator of microbial growth and metabolic direction. The precise balancing of macronutrients, particularly nitrogen and phosphorus, can be leveraged to shift cellular processes from biomass proliferation to the accumulation of valuable intracellular compounds, such as lipids.

The Role of Nitrogen in Metabolic Regulation

Nitrogen is a fundamental component of proteins, nucleic acids, and chlorophyll, constituting 1% to 14% of the dry weight of microbial cells [61]. Its concentration in the culture medium exerts a profound influence on growth rates and biochemical composition.

• Growth vs. Lipid Production: A critical trade-off exists between biomass production and lipid accumulation. Nitrogen-replete conditions (typically >0.4 g L⁻¹) promote rapid growth and high biomass yields [61]. In contrast, nitrogen limitation or starvation triggers a metabolic shift that inhibits protein synthesis and redirects carbon flux toward the accumulation of storage lipids, particularly triacylglycerides (TAGs) [62] [61]. For instance, in Chlorella zofingiensis, rapid growth occurs under nitrogen-sufficient conditions, while nitrogen starvation induces significant lipid accumulation, desirable for biodiesel production [62]. This stress response can lead to a two-fold or greater increase in lipid content compared to nutrient-replete conditions [62].
• Forms of Nitrogen and Thresholds: Microalgae can assimilate nitrogen as nitrate (NO₃⁻), nitrite (NO₂⁻), ammonium (NH₄⁺), or urea [62]. Nitrate is widely used due to its stability, whereas ammonium can be toxic at concentrations above 25 μM and may cause undesirable pH shifts [62]. A general classification for nitrogen supply is as follows [61]:
  • Replete -N: >0.4 g L⁻¹
  • Moderate -N: 0.4–0.2 g L⁻¹
  • Moderate N-limitation: 0.19–0.1 g L⁻¹
  • Strong N-limitation: <0.1 g L⁻¹
  • Without nitrogen: 0 g L⁻¹

The Role of Phosphorus in Cellular Energetics

Phosphorus is essential for energy transfer (ATP), nucleic acid synthesis, and forming phospholipid membranes. Its limitation similarly impacts growth and metabolism.

• Lipid Induction: Like nitrogen stress, phosphorus limitation can enhance lipid production. Studies have shown that phosphorus starvation can triple the total lipid content in species like Chloroidium ellipsoideum [61].
• Application Thresholds: The degree of phosphorus limitation is categorized as [61]:
  • Replete -P: >0.2 g L⁻¹
  • Moderate -P: 0.2–0.02 g L⁻¹
  • Moderate P-limitation: 0.019–0.01 g L⁻¹
  • Strong P-limitation: <0.01 g L⁻¹
  • Without phosphorus: 0 g L⁻¹

The following table summarizes the response of various microalgae to nitrogen and phosphorus manipulation, illustrating the consistent trend of stress-induced lipid accumulation.

Table 1: Microalgal Growth and Lipid Response to Nitrogen and Phosphorus Variation

Microalgae Species	Nutrient Condition	Effect on Growth	Effect on Lipids & Biochemical Composition	Reference
Chlorella zofingiensis	Nitrogen starvation (0 g/L) vs. repletion (1.1 g/L)	Growth inhibition under starvation	Greatly increased lipid accumulation	[62]
Isochrysis galbana	Nitrogen deficiency (below 288 mg/L)	Decreased growth	Highest carbohydrates (47%) and fatty acids (75%)	[62]
Chlamydomonas reinhardtii	Nitrogen deficiency	Biomass inhibition up to 31.7%	Total fatty acid yield increased up to 93%	[62]
Nannochloropsis sp.	Shift from nitrogen-sufficient to -deficient	Not specified	Lipid content increased from 32% to 60%	[62]
Phaeodactylum tricornutum	Nitrogen-deficient medium	Not specified	High lipid content (53.04 ± 3.26%)	[62]
Chloroidium ellipsoideum	Phosphorus starvation	Not specified	Total lipids increased threefold	[61]

Interactive Effects of Environmental Triggers

While nutrients are crucial, growth optimization requires a holistic understanding of how multiple environmental factors interact synergistically or antagonistically. The physiological and metabolic processes of microalgae are influenced by multiple factors rather than a single factor in isolation [63].

Temperature and Light Interactions

Temperature significantly affects enzyme activities and thereby impacts cellular metabolic rates. Generally, rising temperatures promote growth by increasing the photosynthetic rate within a species-specific tolerance range [63]. For example, the diatom Thalassiosira weissflogii exhibits an exponential increase in specific growth rate from 0.34 d⁻¹ at 10°C to 1.24 d⁻¹ at 25°C, with an optimum temperature of approximately 28.2°C [63].

Light intensity is a key driver of photosynthesis. Microalgae adjust their pigment content (e.g., chlorophyll, fucoxanthin) in response to light changes and synthesize photoprotective pigments under high light conditions [63]. Furthermore, lipid accumulation is often enhanced under high light conditions, as the limited energy supply under low light is alleviated [63].

Critically, these factors do not operate independently. Light intensity significantly affects the temperature sensitivity of microalgae, as demonstrated by a 4–5°C decrease in the optimum growth temperature for 57 phytoplankton species under light limitation [63]. This interaction underscores the necessity of considering the full environmental context.

Nitrate and Multi-Factor Interactions

The effect of nitrate concentration is also modulated by other factors. A study on T. weissflogii and Pyramimonas sp. found that their growth saturated at nitrate concentrations around 100 μmol L⁻¹, with a threshold for growth limitation near 4 μmol L⁻¹ [63]. However, the interactive effects of temperature and nutrient concentration significantly impact lipid accumulation, with increased lipid productivity observed in Monoraphidium contortum and Chlorella vulgaris under conditions of combined nitrogen and temperature limitation [63]. The following table synthesizes key findings from a multi-factorial study.

Table 2: Interactive Effects of Temperature, Light, and Nitrate on Microalgae

Parameter Investigated	Key Finding on Interactive Effects	Example Species / Context	Reference
Growth Rate	Light intensity affects temperature sensitivity; optimum growth temperature decreases under light limitation.	57 phytoplankton species (decrease of 4–5°C)	[63]
Lipid Accumulation	Interactive effects of temperature and nutrient concentration significantly impact lipid accumulation.	Monoraphidium contortum, Chlorella vulgaris under N & temperature limitation	[63]
Nitrate Utilization	Nitrate threshold for growth limitation is consistent across species, but overall yield is modified by temperature and light.	Thalassiosira weissflogii, Pyramimonas sp. (threshold ~4 μmol L⁻¹)	[63]
Photosynthetic Physiology	High light and temperature can lead to the synthesis of photoprotective pigments and impact fatty acid saturation.	General microalgae response	[63]

Metabolic Pathways and Niche Adaptation

Marine microorganisms, including Marinisomatota, have evolved sophisticated metabolic strategies to thrive in diverse and often oligotrophic environments.

Mixotrophy in Marinisomatota

Research on Marinisomatota has revealed distinct metabolic modes that underscore their ecological flexibility. Genomic analyses have identified three primary strategies [2]:

• MS0: Exhibiting photoautotrophic potential.
• MS1: Heterotrophic with enhanced glycolytic capacity.
• MS2: Heterotrophic without glycolysis. These metabolic strategies, particularly the potential for mixotrophy in some lineages, are evolutionary responses to nutrient limitation, allowing them to switch between carbon sources for competitive survival [2].

Overcoming Thermodynamic Constraints

In anaerobic ecosystems like methanogenic environments, a "hydrogen conflict" can arise where Hâ‚‚-sensitive metabolisms are inhibited by Hâ‚‚ produced from co-occurring, Hâ‚‚-tolerant metabolisms [64]. Uncultured organisms have developed innovative strategies to circumvent this limitation, including [64]:

• Metabolic coupling of favorable and unfavorable catabolic processes.
• Shifting from Hâ‚‚ transfer to interspecies transfer of formate and electrons (via cytochrome- or pili-mediated direct interspecies electron transfer, DIET).
• Integrating low-concentration Oâ‚‚ metabolism as an ancillary thermodynamics-enhancing electron sink.

Niche Partitioning in Extreme Environments

Studies in the Mariana Trench provide a striking example of microbial adaptation and niche partitioning. Nitrite-oxidizing bacteria (NOB) show clear genomic and functional differentiation between the trench slope (6000–10,000 m) and the bottom (>10,000 m) [65]. Slope-dominant NOB possess expanded genetic arsenals for antioxidation and osmoprotection and show higher expression of nitrite oxidation (nxrA) and carbon fixation (aclA) genes [65]. This fine-scale adaptation to microniches, driven by environmental gradients like pressure and organic matter flux, is a critical mechanism for optimizing growth and function in extreme biospheres [65] [66].

Experimental Protocols and Methodologies

Robust and reproducible experimental protocols are essential for investigating the complex interactions governing microbial growth.

Semi-Continuous Cultivation for Steady-State Growth

For precise physiological studies, semi-continuous cultivation is often employed. In this method [63]:

• Inoculation: Algal cells are inoculated into a medium (e.g., artificial seawater enriched with Aquil medium recipe) within photobioreactors.
• Condition Maintenance: Cultures are maintained in incubators with controlled light-dark cycles (e.g., 12:12 h), temperature, and light intensity provided by cool white fluorescent tubes.
• Dilution: A fixed percentage of the culture is periodically replaced with fresh medium to maintain cells in exponential growth phase under relatively stable conditions. This approach avoids the complicating factors of nutrient depletion and light attenuation found in high-density batch cultures.

Multi-Factorial Experimental Design

To effectively deconvolute interactive effects, a structured experimental design is necessary:

• Single-Factor Experiments: Initially, a range of levels for each factor (e.g., five levels of temperature, light, nitrate) are tested independently to capture the basic response curve of the target microorganisms [63].
• Full Factorial Experiment: A full factorial design is then implemented to clarify the interactive effects of multiple factors. This involves cultivating organisms under all possible combinations of the selected factor levels, allowing for the statistical determination of main effects and interactions [63].

Omics-Driven Eco-Thermodynamic Analysis

For uncultured or complex environmental communities, cultivation-independent methods are critical:

• Sampling: Environmental samples (e.g., sediment push cores) are preserved in situ (e.g., with RNAlater) and stored at -80°C until processing [65].
• Sequencing and Assembly: Metagenomic DNA is sequenced, and reads are assembled into contigs. Metagenome-assembled genomes (MAGs) are binned and refined, retaining only high-quality drafts (e.g., >70% completeness, <5% contamination) [64] [65].
• Metabolic Reconstruction and Activity Assessment: Genes in MAGs are predicted and annotated against functional databases (KEGG, EggNOG, etc.). Metatranscriptomic reads are mapped to these genes to quantify expression levels (e.g., in TPM - Transcripts Per Million) [64] [65].
• Integration with Thermodynamics: The predicted metabolic capabilities and expressed pathways are evaluated in the context of environmental conditions and thermodynamic feasibility to infer in situ behavior and interactions [64].

Signaling Pathways and Metabolic Logic

The molecular response to nutrient stress involves coordinated signaling and re-routing of central carbon metabolism. A generalized pathway for lipid accumulation under nitrogen stress is outlined below.

Nitrogen Stress-Induced Lipid Accumulation

This diagram illustrates the key metabolic shifts following nitrogen starvation. The signal inhibits TOR signaling, leading to the repression of protein synthesis and the activation of membrane lipid recycling. Concurrently, fixed carbon from photosynthesis is redirected from general growth towards the acetyl-CoA pool. This pool is funneled into de novo fatty acid biosynthesis, culminating in the assembly and storage of neutral lipids, primarily triacylglycerols (TAGs), in lipid droplets [62] [61].

The experimental workflow for a multi-omics investigation of microbial community function, as applied in extreme environments, can be visualized as follows.

Multi-Omic Community Analysis Workflow

This workflow details the process from sample collection to integrated data analysis. Environmental samples undergo parallel DNA and RNA extraction, followed by high-throughput sequencing. Metagenomic reads are assembled, and genomes are binned to reconstruct Metagenome-Assembled Genomes (MAGs). Genes are predicted and functionally annotated. Meanwhile, metatranscriptomic reads are mapped to the gene catalog to quantify expression. Finally, genomic potential and expression data are integrated with environmental parameters and thermodynamic models to infer in situ activity and interactions [64] [65].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents and Materials for Growth Optimization Studies

Reagent/Material	Function & Application	Example & Notes
Artificial Seawater Medium	Base for preparing culture media; provides essential ions and trace metals without the variability of natural seawater.	Aquil medium recipe is commonly used and can be modified with specific nutrient concentrations [63].
Nitrogen Sources	To investigate the effect of nitrogen form and concentration on growth and metabolism.	Nitrate (e.g., NaNO₃) is stable and widely used. Ammonium salts require careful pH monitoring due to toxicity [62].
Phosphorus Sources	To manipulate phosphorus availability and study phosphorus stress-induced metabolic changes.	Typically added as phosphate (e.g., Kâ‚‚HPOâ‚„) [61].
Polycarbonate Bottles	Used as culture vessels; resistant to corrosion by seawater and can be sterilized.	500 mL bottles are typical for small-scale experiments [63].
RNAlater Stabilization Solution	For immediate stabilization and preservation of RNA in environmental and culture samples prior to RNA extraction.	Critical for obtaining accurate metatranscriptomic data from field samples [65] [66].
PowerSoil DNA Isolation Kit	Efficient extraction of high-quality genomic DNA from complex environmental samples, including microbial mats and filters.	Standard for metagenomic studies from diverse sample types [66].
Functional Annotation Databases	For assigning putative functions to predicted genes from MAGs.	KEGG, EggNOG, and UniProt are standard databases used for comprehensive annotation [64] [65].
N-Acetyl sulfapyridine-d4	Sulfapyridine-d4|Deuterated Internal Standard	Sulfapyridine-d4 is a deuterated internal standard for precise LC-MS/MS quantification in pharmacokinetic and placental research. For Research Use Only. Not for human or veterinary use.
ALLO-1	ALLO-1 Autophagy Adaptor Protein	ALLO-1 (allophagy-1) is a key autophagy adaptor for studying mitochondrial inheritance. This product is for research use only (RUO). Not for human use.

Mixotrophy, the ability of organisms to combine autotrophic and heterotrophic nutrition, represents a critical ecological strategy across diverse biological kingdoms [67]. In marine environments, the phylum Marinisomatota (formerly recognized as Marinimicrobia) comprises ubiquitous and abundant microorganisms that exemplify this metabolic plasticity [1]. Understanding the regulatory mechanisms that govern the switch between trophic modes in these organisms is fundamental to elucidating their ecological success and biogeochemical function.

Research reveals that Marinisomatota dominate low-latitude marine regions with relative abundances reaching up to 36.21% in some areas [1]. Their trophic flexibility enables survival in nutrient-poor environments where neither pure autotrophy nor heterotrophy would be sustainable alone. This in-depth technical guide synthesizes current knowledge on the metabolic pathway regulation controlling trophic mode switching in mixotrophic organisms, with specific focus on Marinisomatota as model systems, providing both theoretical frameworks and practical methodologies for researchers investigating these complex metabolic networks.

Metabolic Strategies in Marinisomatota

Comprehensive metagenomic and metatranscriptomic analyses of global ocean samples have identified three distinct metabolic strategies within Marinisomatota populations [1]:

Table 1: Metabolic Strategies in Marinisomatota

Metabolic Strategy	Trophic Mode	Key Characteristics	Genetic Markers
MS0	Photoautotrophic potential	Capacity for light-dependent COâ‚‚ fixation and organic compound synthesis	Genes for Crassulacean acid metabolism (M00169)
MS1	Heterotrophic with pronounced glycolysis	Organic carbon assimilation with high glycolytic flux	Enhanced expression of glycolytic pathway genes
MS2	Heterotrophic without glycolysis	Organic carbon utilization via alternative pathways	Limited glycolytic enzyme expression

The emergence of these specialized strategies represents an adaptive response to nutrient limitations within ocean ecosystems [1]. The MS0 strategy, observed in families S15-B10, TCS55, UBA1611, UBA2128, and UBA8226, demonstrates potential for light-dependent processes associated with Crassulacean acid metabolism, enabling these organisms to thrive in the translucent zone or during transitions between light and dark layers of the water column [1].

Fundamental Regulatory Mechanisms

Genetic Determinants of Trophic Transitions

Genomic analyses of Marinisomatota have revealed a complex genetic architecture underlying metabolic plasticity. From 1,588 reconstructed genomes representing one class, two orders, 14 families, 31 genera, and 67 species, researchers have identified key regulatory elements controlling trophic transitions [1]. The expression of genes involved in glycerol utilization, including GPDH1, TIM1, and GAPDH1, shows significant light dependence, decreasing dramatically when organisms transition from light to dark environments [68].

This light-mediated genetic regulation creates a molecular switch that controls carbon processing pathways. Despite reduced glycerol uptake in darkness, mixotrophic organisms upregulate genes associated with pyrimidine metabolism and DNA replication, indicating integrated metabolic reprogramming that coordinates energy acquisition with biosynthetic processes [68].

Ecological Stoichiometry as a Regulatory Framework

Ecological stoichiometry provides a theoretical foundation for understanding trophic mode switching, focusing on the balance of elements (C:N:P) and their interplay with biological processes [69]. Mixotrophic organisms maintain stoichiometric homeostasis - the ability to maintain stable internal nutrient composition despite fluctuating environmental conditions - by switching between nutritional modes [69].

When inorganic nutrients (N, P) are limited relative to carbon, mixotrophs increase heterotrophic activity to acquire these limiting nutrients from prey. Conversely, when light or carbon fixation is limiting, they enhance photosynthetic activity. This metabolic flexibility allows mixotrophs to outcompete strict autotrophs and heterotrophs under variable resource conditions, particularly in nutrient-poor environments [69].

Table 2: Stoichiometric Regulation of Trophic Modes

Environmental Condition	Preferred Trophic Mode	Stoichiometric Imbalance	Physiological Response
Low N:P ratio	Enhanced heterotrophy	Carbon limitation relative to nutrients	Prey capture to acquire carbon
High N:P ratio	Enhanced autotrophy	Nutrient limitation relative to carbon	Photosynthesis to acquire energy
Low light availability	Increased phagotrophy	Energy limitation	Grazing to supplement energy needs
Low prey availability	Increased photosynthesis	Nutrient acquisition limitation	Carbon fixation to maintain growth

Experimental Methodologies for Investigating Trophic Switching

Transcriptomic and Metabolomic Approaches

Time-series transcriptomic analysis under controlled light-dark transitions has proven effective for elucidating light regulation of organic carbon utilization. The following protocol outlines a comprehensive approach for investigating trophic mode switching:

Experimental Protocol: Time-Series Transcriptomic Analysis

• Culture Conditions: Establish replicate cultures in medium containing organic carbon source (e.g., 2 g L⁻¹ glycerol) under controlled light conditions (12:12 light:dark cycle recommended) [68]

• Sampling Strategy:

  • Collect samples at multiple timepoints during light-to-dark transition (e.g., 0, 2, 4, 8, 12 hours post-transition)
  • Include parallel autotrophic controls without organic carbon supplementation

• RNA Extraction and Sequencing:

  • Preserve samples immediately in RNA stabilization reagent
  • Extract total RNA using commercial kits with DNase treatment
  • Prepare libraries for Illumina sequencing (minimum 20 million reads per sample)
  • Perform quality control (RIN > 8.0 recommended)

• Bioinformatic Analysis:

  • Map reads to reference genome (if available) or perform de novo assembly
  • Identify differentially expressed genes (DEGs) across timepoints
  • Conduct pathway enrichment analysis (KEGG, GO databases)
  • Validate key findings with RT-qPCR

• Integrated Metabolomic Profiling:

  • Extract metabolites using methanol:water:chloroform system
  • Analyze via LC-MS/MS for polar metabolites and GC-MS for non-polar metabolites
  • Integrate transcriptomic and metabolomic datasets using multivariate statistics

This approach revealed that in mixotrophic Cylindrotheca sp., amino acid and aminoacyl-tRNA metabolisms were enhanced at different timepoints of diurnal cycles compared to autotrophic controls, providing insights into the coordination of metabolic pathways during trophic transitions [68].

Stable Isotope and Fatty Acid Tracer Techniques

Quantifying heterotrophic contribution in mixotrophic organisms presents methodological challenges. Conventional stable isotope approaches using carbon (Δ¹³C) may underestimate heterotrophy due to selective nutrient incorporation [67]. The following protocol combines multiple biomarkers for more accurate assessment:

Experimental Protocol: Multi-Biomarker Trophic Assessment

• Experimental Design:

  • Establish feeding gradient from pure autotrophy to pure heterotrophy
  • Use ¹³C and ¹⁵N-labeled prey or organic substrates
  • Maintain controlled light conditions relevant to natural habitat

• Sample Processing:

  • Separate host and symbiont fractions if applicable
  • Preserve samples for bulk tissue isotope analysis
  • Extract lipids for fatty acid profiling
  • Preserve additional material for compound-specific isotope analysis

• Analytical Measurements:

  • Analyze bulk tissue δ¹³C and δ¹⁵N values via IRMS
  • Profile fatty acid composition via GC-MS
  • Perform compound-specific isotope analysis of amino acids (if equipment available)

• Data Interpretation:

  • Calculate heterotrophic contribution using isotope mixing models
  • Identify fatty acid biomarkers of heterotrophic feeding
  • Correlate fatty acid patterns with bulk isotope values

This integrated approach demonstrated that fatty acids and nitrogen were effectively incorporated into both coral host and symbiont tissues, while carbon showed more complex patterns, highlighting the value of multiple biomarker systems for quantifying trophic dynamics [67].

Visualization of Metabolic Regulation Networks

Figure 1. Regulatory network controlling trophic mode switching in mixotrophic Marinisomatota. Environmental cues including light availability, nutrient concentrations, and carbon resources are sensed through specialized systems that integrate signals to modulate genetic regulation of metabolic pathways. Key light-dependent enzymes (GPDH1, TIM1, GAPDH1) control heterotrophic processing of organic carbon, while nutrient sensors regulate glycolytic capacity and photosynthetic apparatus genes coordinate autotrophic function. The output is a dynamic balance between trophic modes optimized for current environmental conditions.

Figure 2. Comprehensive experimental workflow for investigating trophic mode regulation. The integrated approach begins with controlled cultivation under mixotrophic conditions followed by systematic perturbation (e.g., light-dark transitions). Multi-omics data generation captures molecular responses at transcript, metabolite, and isotopic levels, with subsequent computational integration revealing regulatory networks and metabolic fluxes. Validation through targeted molecular and physiological measurements confirms mechanistic insights into trophic switching behavior.

Research Reagent Solutions

Table 3: Essential Research Reagents for Investigating Trophic Switching

Reagent/Category	Specific Examples	Function/Application	Technical Notes
Organic Carbon Sources	Glycerol, glucose, acetate	Induce heterotrophic metabolism; concentration optimization required	Glycerol most effective for many marine mixotrophs at 2 g L⁻¹ [68]
Isotopic Tracers	¹³C-labeled bicarbonate, ¹⁵N-labeled ammonium, ¹³C/¹⁵N-labeled prey	Quantify nutrient fluxes and trophic contributions	Essential for distinguishing autotrophic vs. heterotrophic carbon incorporation [67]
Molecular Biology Kits	RNA stabilization reagents, DNA/RNA extraction kits, library prep kits	Preserve and process samples for transcriptomic analysis	Critical for capturing rapid transcriptional changes during trophic transitions [68]
Analytical Standards	Fatty acid methyl esters (FAMEs), stable isotope standards	Calibrate instruments for metabolomic and isotopic analysis	Required for accurate quantification of metabolic and trophic markers [67]
Culture Media Components	Artificial seawater base, vitamin and trace metal mixes, buffering systems	Maintain physiological relevance while controlling experimental conditions	f/2 media modifications commonly used for marine microbial cultures [68]
Photobiology Equipment	Programmable LED light systems, PAR sensors, neutral density filters	Control light quality, quantity, and photoperiod	Precise light control essential for investigating light-dependent regulation [1] [68]

Discussion and Research Implications

The regulation of trophic mode switching in mixotrophic Marinisomatota represents a sophisticated adaptive strategy that enhances fitness in fluctuating marine environments. The identified metabolic strategies (MS0, MS1, MS2) reflect specialized responses to nutrient limitations, with light serving as a key regulatory signal for carbon processing pathways [1]. The light-dependent expression of glycerol utilization genes (GPDH1, TIM1, GAPDH1) provides a molecular mechanism for coordinating autotrophic and heterotrophic metabolism according to energy availability [68].

From an ecological perspective, the stoichiometric regulation of mixotrophy creates feedback mechanisms that influence nutrient cycling across ecosystem scales. Under nutrient-limited conditions, mixotrophs increase in abundance relative to strict autotrophs and heterotrophs, potentially stabilizing community structure and elemental fluxes [69]. Furthermore, their ability to maintain homeostatic elemental composition makes them high-quality food for zooplankton grazers, influencing energy transfer efficiency through marine food webs [69].

Methodologically, the integration of multiple analytical approaches - transcriptomics, metabolomics, stable isotope analysis, and fatty acid profiling - provides complementary insights that address limitations of individual techniques. Notably, conventional carbon isotope approaches may substantially underestimate heterotrophic contributions due to selective nutrient incorporation patterns [67]. Future research should prioritize multi-optic integration and development of model systems for genetic manipulation to test hypothesized regulatory mechanisms.

The switch between trophic modes in mixotrophic Marinisomatota is governed by a complex regulatory network that integrates environmental cues (light, nutrient availability) with genetic and metabolic programming. Three distinct metabolic strategies have evolved to optimize energy acquisition under different resource regimes, with light serving as a master regulator of carbon processing pathways. The experimental frameworks and methodologies presented here provide researchers with comprehensive tools for investigating these regulatory mechanisms across biological scales - from genetic determinants to ecosystem consequences.

Understanding trophic mode regulation has significant implications for predicting microbial responses to environmental change, modeling marine carbon cycling, and potentially harnessing mixotrophic metabolism for biotechnology applications. As climate change alters ocean conditions, the ecological advantages of mixotrophy may lead to increased dominance of these versatile organisms in marine ecosystems, highlighting the importance of continued research into the mechanisms controlling their metabolic flexibility.

In the vast nutrient-scarce expanses of the ocean, the ability to access phosphorus from unconventional sources represents a critical ecological adaptation. Phosphonates, characterized by a highly stable carbon-phosphorus (C–P) bond, constitute a significant component of the marine dissolved organic phosphorus pool [70] [71]. The phn operon encodes the complex molecular machinery required for bacterial phosphonate catabolism, enabling microorganisms to cleave this recalcitrant bond and utilize phosphonates as a source of phosphorus. Recent genomic and metagenomic surveys reveal that this operon is not a monolithic entity but exhibits remarkable genetic heterogeneity across bacterial taxa. This diversity encompasses variations in gene content, operon structure, and regulatory elements, which in turn dictate substrate specificity and metabolic efficiency [72] [71]. Understanding this heterogeneity is paramount, particularly when framed within the study of mixotrophic adaptations in marine bacteria like Marinisomatota, where metabolic flexibility governs survival in oligotrophic environments. This guide provides an in-depth technical analysis of phn operon diversity and presents the experimental methodologies essential for probing its functional consequences in marine microbial systems.

Architectural Diversity of the phn Operon

The canonical phn operon, first characterized in Escherichia coli, consists of up to 14 genes (phnCDEFGHIJKLMNOP) [70]. These genes encode proteins with functions ranging from substrate transport to the core C–P bond cleavage reaction. However, comparative genomics reveals that this structure is far from universal, especially in marine bacteria.

Variations in Gene Content and Organization

Studies on marine Vibrio species have uncovered significant divergence from the E. coli model. For instance, the operon in V. nigripulchritudo ATCC 27043 contains 13 genes, lacking phnO [72]. Genomic analyses of Vibrio isolates have identified at least nine distinct types of phn operons, classified primarily based on the phylogeny of key genes like phnJ and phnL and the specific arrangement of their flanking genes [72]. This structural variation is not random; specific clusters (e.g., Cluster I and II) are frequently identified in Vibrio isolates and are common in metagenomic data from both coastal and open ocean environments [72].

Functional Implications of Structural Variation

The genetic heterogeneity of the phn operon has direct functional consequences. The specific composition of the operon influences the substrate range of phosphonates that a bacterium can utilize. The core catalytic unit, particularly the phnJ gene, is strictly conserved as it encodes a protein that directly catalyzes the radical SAM-dependent C–P bond cleavage, leading to methane release from methylphosphonate (MPn) [72] [70]. Variations in other components, such as those involved in transport or regulation, may fine-tune the system for efficiency under different environmental conditions, such as the extreme phosphorus depletion of the Mediterranean Sea, where C–P lyase genes are notably enriched [71].

Table 1: Core Genes of the phn Operon and Their Functional Roles

Gene	Function	Conservation Notes
phnC, D, E	Encodes an ABC transporter for phosphonate uptake [70].	Common but not universal; subject to variation.
phnG, H, I, J, K, L, M	Forms the core catalytic unit for C–P bond cleavage [70].	phnJ is strictly conserved and essential for methane production [72].
phnF	Putative repressor protein [70].	Regulatory function can vary.
phnN, O, P	Accessory/regulatory proteins of uncertain function [70].	Frequently absent or divergent (e.g., phnO missing in some Vibrio [72]).

The phn Operon in the Context of Marinisomatota Mixotrophy

The phylum Marinisomatota (formerly Marinimicrobia, SAR406) is ubiquitous in the global ocean, particularly in the deep chlorophyll maximum and mesopelagic zones where nutrients are scarce [2] [1]. Traditionally considered heterotrophic, recent metagenomic studies have revealed extraordinary metabolic versatility within this group, including the potential for mixotrophic adaptations [2] [1].

Metabolic Strategies and Niche Partitioning

Genomic reconstructions of 1,588 Marinisomatota genomes led to the identification of three distinct metabolic modes: MS0 (possessing photoautotrophic potential), MS1 (heterotrophic with enhanced glycolytic capacity), and MS2 (heterotrophic without glycolysis) [1]. The presence of the phn operon across these metabolic types would enable Marinisomatota to access phosphorus from the substantial marine phosphonate pool, a crucial advantage in P-limited environments. This phosphonate utilization capability likely co-evolved with other energy-generating strategies (e.g., light harvesting or organic carbon consumption) as part of a suite of adaptations to optimize energy and nutrient acquisition in the oligotrophic ocean [2] [1].

Table 2: Metabolic Strategies and Potential Phosphonate Use in Marinisomatota

Metabolic Strategy	Defining Characteristics	Ecological Niche & Potential Role of Phosphonate Utilization
MS0	Photoautotrophic potential [1].	Likely dominant in deeper euphotic zone; phosphonate use supplements P for photosynthesis.
MS1	Heterotrophic with pronounced glycolytic pathway [1].	Versatile strategy; phosphonate use supports carbon and energy metabolism via glycolysis.
MS2	Heterotrophic without glycolysis [1].	Specialized heterotrophy; phosphonate use is critical for obtaining P from organic matter.

Experimental Protocols for Analyzing phn Operon Diversity and Function

A multi-pronged approach is required to fully decipher the diversity and functional implications of the phn operon in environmental samples and isolated strains.

Isolation and Screening of Phosphonate-Utilizing Bacteria

Protocol: Enrichment and Isolation of MPn-demethylating Bacteria [72]

• Sample Collection: Collect coastal seawater or marine sediments.
• Enrichment Culture: Inoculate 60 mL of seawater into 100-mL sterile vials. Amend the culture with:
  • Glucose (C source) to a final concentration of 1000 μmol L⁻¹.
  • Nitrate (N source) to a final concentration of 160 μmol L⁻¹.
  • Methylphosphonate (MPn) as the P source to a final concentration of 10 μmol L⁻¹.
• Incubation: Incubate at 28°C in a shaker for 5 days.
• Isolation: Spread diluted samples from the enrichment culture onto marine agar (e.g., 2216E) or TCBS plates. Purify individual colonies through repeated streaking.
• Methane Measurement: Confirm MPn-demethylating capability by measuring methane production in pure cultures amended with MPn using gas chromatography.

Genomic and Metagenomic Analysis

Protocol: Identifying and Characterizing phn Operons in Genomes [71]

• DNA Extraction: Perform high-quality DNA extraction from pure cultures or environmental biomass.
• Sequencing: Utilize both long-read (PacBio, Nanopore) and short-read (Illumina) technologies for complete, closed genomes or metagenome-assembled genomes (MAGs).
• Gene Calling & Annotation: Use automated pipelines (e.g., PROKKA, DRAM) followed by manual curation. Specifically search for phn genes using curated HMM profiles or BLAST against a custom database of verified phn sequences.
• Operon Structure Mapping: Examine the genomic context and synteny of identified phn genes to define the structure of the operon. Compare clusters across strains to identify conserved and variable regions.
• Metagenomic Screening: Map metagenomic reads from projects like TARA Oceans or Munida Time-Series to your curated phn gene database to assess the distribution and abundance of different operon types across oceanic provinces.

Transcriptomic Analysis of Gene Expression

Protocol: Assessing in situ Expression of the* phn* Operon [72] [71]

• Incubation with MPn: Grow isolates or environmental samples with MPn as the sole P source. Use phosphate-replete and no-P controls for comparison.
• RNA Extraction: Extract total RNA from cells harvested at mid-log growth phase.
• Library Preparation and Sequencing: Deplete ribosomal RNA and prepare strand-specific RNA-seq libraries for sequencing on a platform like Illumina.
• Differential Expression Analysis: Map sequence reads to the reference genome or metagenome. Normalize read counts and perform statistical testing (e.g., with DESeq2) to identify significantly upregulated genes, including those in the phn operon, in response to MPn amendment.

Visualization of phn Operon Research Workflows

The following diagram illustrates the integrated multi-omics workflow for characterizing phn operon diversity and function, from sample to insight.

Diagram 1: Integrated Workflow for phn Operon Analysis.

The Scientist's Toolkit: Key Research Reagents and Materials

Table 3: Essential Reagents for Investigating the phn Operon and Phosphonate Metabolism

Reagent / Material	Function / Application	Example Use Case
Methylphosphonate (MPn)	Model phosphonate substrate for functional assays [72].	Used in enrichment cultures and methane production experiments to screen for and confirm C–P lyase activity.
Low-Nutrient Marine Media	Cultivation of oligotrophic marine bacteria [2].	Isolation of fastidious phosphonate-utilizing bacteria (e.g., using diffusion chambers).
Custom phn Gene Databases	Curated sequence databases for gene annotation and screening [71].	Essential for accurate identification and classification of diverse phn operons in metagenomic and genomic datasets.
Stable Isotope-Labeled Phosphonates	Tracer studies to quantify phosphonate assimilation and mineralization rates [70].	Allows for tracking the fate of phosphonate-derived P, C, and N into biomass and metabolites.
phnJ-Specific Primers/Probes	Targeting the core C–P lyase gene for qPCR or FISH [72].	Quantifying the abundance and distribution of bacteria with C–P lyase potential in environmental samples.

The genetic heterogeneity of the phn operon is a fundamental aspect of microbial adaptation to the marine phosphorus cycle. Its diverse architectural forms underpin variations in substrate utilization and ecological function. For groups like Marinisomatota, the integration of phosphonate catabolism via the phn operon with other metabolic strategies (e.g., photoautotrophy and heterotrophy) is a hallmark of their mixotrophic lifestyle, allowing them to thrive in the nutrient-poor vastness of the global ocean. The continued application of the integrated genomic, transcriptomic, and cultivation-based protocols outlined in this guide will be essential to fully elucidate the complex relationship between genetic diversity, metabolic function, and ecological success in the marine microbiome.

Biomass and Product Yield Challenges in Industrial Scaling

The pursuit of industrial-scale biomass utilization represents a critical frontier in renewable energy and bioproduct development. However, scaling processes from laboratory benchtops to industrial facilities presents a unique set of technical and logistical challenges that can significantly impact product yield and economic viability. Within the specific context of marine microbiomes, these challenges are further complicated by the biological constraints of marine microorganisms like Marinisomatota and the harsh operational environments of marine systems. The fundamental obstacle lies in competing with the established scale of the petroleum industry; while a large ethanol plant might produce the energy equivalent of 12,000 barrels of oil per day, the smallest economically efficient petroleum refinery processes 200,000 barrels daily [73]. This scale disparity underscores the immense effort required for biomass to meaningfully contribute to the energy and chemical sectors. This guide examines the core challenges, quantitative yield limitations, and advanced methodologies relevant to researchers aiming to overcome these barriers, with particular emphasis on implications for mixotrophic Marinisomatota research.

Core Technical and Logistical Challenges

Scaling biomass processes involves navigating a complex landscape of operational and economic constraints that are often interconnected.

• 2.1 Feedstock Supply Chain and Logistics: A primary challenge is the "tyranny of distance." Unlike petroleum, which is concentrated in specific locations, biomass is geographically dispersed and has low energy density, making collection and transportation costly [73] [74]. For example, transporting wood with 30% moisture content means moving 300 kg of water for every ton of wood [74]. This is particularly relevant for marine biomass, where harvesting from open waters presents additional difficulties. Seasonal variability further disrupts a consistent supply, necessitating large-scale storage solutions for year-round operation [75].

• 2.2 Conversion Technology and Process Scale-Up: Many advanced biomass conversion technologies, such as pyrolysis and gasification, are not yet mature enough for widespread commercial deployment [73] [75]. A significant technical hurdle is purifying intermediate products from inorganic contaminants that can poison catalysts in subsequent processing steps [73]. Furthermore, moving from batch processing, common in lab settings, to the continuous flow processes that characterize petroleum refining is a major challenge requiring new separation and purification technologies [73].

• 2.3 Economic and Environmental Viability: Biomass projects struggle with economic competitiveness against fossil fuels. High capital investment, fluctuating feedstock costs, and operational expenses impact profitability [75]. From an environmental perspective, while biomass is often considered carbon-neutral, its lifecycle greenhouse gas balance depends heavily on feedstock type, land-use changes, and conversion efficiency [75]. Unsustainable biomass farming can lead to deforestation, soil degradation, and biodiversity loss, creating a complex trade-off between energy production and ecological preservation [75].

Table 1: Key Challenges in Scaling Biomass Processes for Industrial Applications

Challenge Category	Specific Obstacle	Impact on Scaling & Yield
Feedstock Supply [73] [74]	Geographic dispersion & low energy density	Increases transportation costs and energy input, reducing net yield and economic viability.
	Seasonal variability & inconsistent supply	Complicates consistent reactor operation, potentially lowering annual product yield.
Conversion Process [73]	Contamination of intermediates (inorganics)	Poisons catalysts, reduces conversion efficiency, and lowers final product yield.
	Immaturity of thermochemical pathways (e.g., pyrolysis)	Leads to unpredictable process performance and higher risk for industrial investors.
	Batch vs. Continuous Processing	Limits throughput and increases downtime, hindering ability to match petroleum industry scale.
Economic & Environmental [75]	High capital and operational costs	Challenges financial competitiveness with established fossil fuel industries.
	Land-use competition and sustainability concerns	Can limit feedstock availability and trigger regulatory hurdles for project approval.

Quantitative Analysis of Biomass Yield Efficiency

Understanding the yield limitations of biological systems is fundamental to scaling. Quantitative data provides a baseline for measuring improvements via metabolic engineering or process optimization.

In marine nitrogen cycle processes, anaerobic ammonium oxidation (anammox) bacteria, such as "Candidatus Scalindua," exhibit notoriously low growth rates and biomass yield efficiencies. One study on the marine anammox bacterium "Ca. Scalindua sp." determined its biomass yield efficiency was 0.030 at 28°C and 3.5% salinity [76]. This low yield is coupled with a long doubling time of 14.4 days, which is markedly lower than those of freshwater anammox genera [76]. The stoichiometry of the anammox process highlights this low carbon fixation yield, as shown in the equation below from Strous et al. (1998), which was later recalculated by Lotti et al. (2015) but still reflects low biomass conversion [76].

Anammox Stoichiometry (Strous et al.): 1NH₄⁺ + 1.32NO₂⁻ + 0.066HCO₃⁻ + 0.13H⁺ → 1.02N₂ + 0.26NO₃⁻ + 0.066CH₂O₀.₅N₀.₁₅ + 2.03H₂O

This equation demonstrates that a large amount of inorganic carbon (HCO₃⁻) is processed to produce a very small quantity of biomass (CH₂O₀.₅N₀.₁₅). Environmental factors critically influence these yields; for instance, the biomass yield efficiency of "Ca. Scalindua sp." is significantly affected by salinity, with carbon uptake being higher at 1.5%–3.5% salinity than at extremes [76]. Such precise quantitative relationships are essential for modeling the productivity of marine systems at scale.

Table 2: Biomass Yield and Growth Parameters of Anammox Bacteria

Bacterial Genus / Type	Doubling Time (Days)	Biomass Yield Efficiency	Environmental Conditions	Source
'Ca. Scalindua sp.' (Marine)	14.4	0.030	28°C, 3.5% Salinity	[76]
'Ca. Brocadia sinica' (Freshwater)	7	Not Specified	Laboratory Enrichment	[76]
'Ca. Kuenenia stuttgartiensis' (Freshwater)	8.3 - 11	Not Specified	Laboratory Enrichment	[76]
Anammox from Activated Sludge	3.6 - 5.4	Not Specified	Quantitative PCR Estimate	[76]

Methodologies for Yield Optimization and Process Enhancement

Overcoming yield challenges requires a multi-faceted approach, combining advanced microbial engineering, sophisticated process design, and rigorous analytical techniques.

• 4.1 Marine Microbiome Engineering: For marine systems, a promising strategy is the engineering of macroalgal and microbial microbiomes to enhance host health and resilience. A proposed holistic framework integrates multi-omics and metabolic modeling to identify key functional traits in the microbiome [77]. This is followed by selecting microbes for their functional and environmental compatibility, using high-throughput rapid isolation techniques, and finally validating the engineered consortia in vivo [77]. This approach aims to unlock the potential of microbiome engineering to improve biomass yield and carbon sequestration in macroalgal farming.

• 4.2 Integrated Analytical and Data Management Protocols: Robust data management is the foundation of reliable yield analysis. This involves careful data checking for errors, defining and coding variables, and employing appropriate statistical methods [78]. Quantitative data analysis uses descriptive statistics (mean, median, mode, standard deviation) to summarize samples and inferential statistics (p-values) accompanied by measures of magnitude (effect sizes) to test hypotheses and interpret the true scale of observed effects [78]. This rigor is essential for evaluating the success of scaling trials.

• 4.3 Process Integration and Optimization: At a systems level, optimizing the entire value chain is crucial. This includes exploring methods to combine chemical technologies of different scales to maximize impact [73]. For thermochemical conversion, such as pyrolysis, research is needed to improve the quality of the produced bio-oil and to develop efficient catalytic upgrading processes to transform it into stable, high-yield fuels and chemicals [73]. Pre-treatment and densification of biomass into formats like pellets are also crucial methodologies for improving energy density and reducing transport costs [74].

Research Toolkit for Biomass Scaling Studies

Table 3: Essential Research Reagent Solutions for Marine Biomass and Metagenomic Studies

Reagent / Material	Function in Research Context
Artificial Sea Salt (e.g., SEALIFE)	To create synthetic marine nutrient media for controlled laboratory cultivation and enrichment of marine microorganisms like Marinisomatota or anammox bacteria [76].
Trace Element Solutions I & II	To provide essential micronutrients (e.g., metals) required for the growth and metabolic activity of specialized microbial communities in defined media [76].
DNA Extraction Kit (e.g., for soil/sediment)	To isolate high-quality genomic DNA from complex environmental samples like marine sediment or biomass enrichment cultures for subsequent metagenomic analysis [76].
16S rRNA PCR Primers (e.g., Pla46f, 1390r)	To amplify specific phylogenetic marker genes from DNA extracts, enabling the identification and phylogenetic analysis of microbial community members, such as Planctomycetes [76].
Cloning Kit (e.g., pCR-XL-TOPO)	To insert PCR-amplified DNA fragments into vectors for sequencing, facilitating the construction of gene libraries to assess microbial diversity [76].
Fluorescence In Situ Hybridization (FISH) Probes	To visually identify and quantify specific microbial taxa (e.g., using probe Amx368 for anammox) within a mixed culture or environmental sample via microscopy [76].
Volatile Fatty Acids (VFAs) Standard Solutions	To use as substrates in metabolic activity assays (e.g., to test their impact on anammox activity and carbon uptake) or for calibrating analytical equipment like HPLC [76].

Visualizing Metabolic Strategies and Experimental Workflows

The mixotrophic adaptations of Marinisomatota can be conceptualized as a series of metabolic decisions based on environmental conditions. The following diagram illustrates the proposed strategies and their triggers.

Metabolic Strategy Triggers

A generalized experimental workflow for investigating and optimizing biomass yield, from initial cultivation to data analysis, is outlined below. This protocol is adaptable for studying various microbial systems, including Marinisomatota.

Yield Optimization Workflow

Scaling biomass processes for industrial yield is a multidisciplinary challenge spanning logistics, microbiology, chemical engineering, and data science. The path forward requires integrated approaches that address the entire value chain, from developing resilient, high-yield microbial strains through advanced microbiome engineering to designing efficient and cost-effective large-scale conversion processes. The lessons learned from overcoming yield barriers in terrestrial and waste-based biomass systems provide a crucial framework for tackling the unique challenges presented by promising marine organisms like the mixotrophic Marinisomatota.

Integration with Existing Bioprocesses for Sustainable Biorefineries

The pursuit of industrial sustainability is driving the transformation of conventional bioprocesses into integrated biorefineries that maximize resource efficiency and minimize waste. Within this context, mixotrophic metabolism—the ability of organisms to simultaneously utilize inorganic carbon (CO₂) through photosynthesis and organic carbon through heterotrophic assimilation—represents a revolutionary biological strategy for enhancing bioprocess efficiency. Recent research on marine microorganisms, particularly the phylum Marinisomatota (formerly known as Marinimicrobia, Marine Group A, or SAR406), has revealed unexpected metabolic versatility with profound implications for sustainable biorefinery design [7] [1]. These ubiquitous marine bacteria, traditionally classified as heterotrophic, have demonstrated the capacity for light-dependent carbon fixation while transitioning between ocean layers, employing three distinct metabolic strategies: MS0 (photoautotrophic potential), MS1 (heterotrophic with pronounced glycolysis), and MS2 (heterotrophic without glycolysis) [7] [1].

The integration of mixotrophic systems into existing bioprocess frameworks enables unprecedented synergies by combining the carbon-capture benefits of phototrophy with the high biomass yields of heterotrophy. This technical guide examines the mechanistic basis, operational parameters, and integration strategies for implementing mixotrophic processes within sustainable biorefineries, with emphasis on recently discovered metabolic capabilities in marine microorganisms and their translational potential.

Marine Marinisomatota: A Model for Metabolic Flexibility

Ecological Distribution and Metabolic Classification

Marinisomatota represent a globally distributed phylum predominantly found in low-latitude marine regions, with relative abundances ranging from 0.18% to 36.21% across oceanic provinces [7]. Through metagenomic and metatranscriptomic analysis of 1,588 Marinisomatota genomes, researchers have identified one class, two orders, 14 families, 31 genera, and 67 species, revealing previously unrecognized metabolic complexity [7] [1]. Among the 14 families, five (S15-B10, TCS55, UBA1611, UBA2128, and UBA8226) exhibit genetic potential for light-dependent processes associated with Crassulacean acid metabolism (M00169) [7].

The three distinct metabolic strategies identified within this phylum represent adaptive responses to nutrient limitations in marine environments:

• MS0 (Photoautotrophic Potential): Capable of light-mediated carbon fixation while potentially maintaining organic carbon assimilation capabilities.
• MS1 (Heterotrophic with Pronounced Glycolysis): Specialized in organic carbon utilization with enhanced glycolytic pathway activity.
• MS2 (Heterotrophic without Glycolysis): Employs alternative carbon assimilation pathways independent of classical glycolysis [7] [1].

This metabolic plasticity enables Marinisomatota to thrive across the translucent and aphotic zones of the ocean, transitioning between trophic modes in response to environmental gradients [1]. The genetic basis for these adaptations provides a blueprint for engineering flexible metabolic networks in industrial bioprocesses.

Implications for Biorefinery Integration Strategies

The metabolic continuum observed in Marinisomatota offers valuable insights for biorefinery design, particularly in developing systems that can dynamically respond to fluctuating resource availability. The emergence of mixotrophic strategies in response to nutrient limitations demonstrates how microbial communities optimize energy investment under constraints [7]. In biorefinery contexts, this principle can be applied to:

• Dynamic Media Formulation: Strategically adjusting organic/inorganic carbon ratios to steer metabolic flux.
• Tiered Cultivation Systems: Creating spatial or temporal gradients that select for desired metabolic phenotypes.
• Co-culture Optimization: Leveraging complementary metabolic strategies for substrate utilization.

Table 1: Metabolic Strategies in Marinisomatota and Their Bioprocess Applications

Metabolic Strategy	Genetic Features	Environmental Niche	Bioprocess Application
MS0 (Photoautotrophic Potential)	Light-dependent metabolism genes; Crassulacean acid metabolism	Translucent zone	Photobioreactors with organic carbon supplementation
MS1 (Heterotrophic with Glycolysis)	Enhanced glycolytic pathway; Organic carbon transporters	Transition zone between translucent and aphotic layers	Mixed-substrate fermentation systems
MS2 (Heterotrophic without Glycolysis)	Alternative carbon assimilation; Bypass glycolysis	Aphotic zone	Utilization of complex organic waste streams

Quantitative Analysis of Mixotrophic Performance

Enhanced Biomass and Metabolite Production

Substantial experimental evidence demonstrates the superiority of mixotrophic cultivation across diverse microalgae and cyanobacteria, with significant implications for biorefinery output metrics. In marine diatoms including Phaeodactylum tricornutum and Cylindrotheca sp., mixotrophic cultivation with glycerol has been shown to increase biomass production by 52-62% compared to phototrophic controls [47] [68]. This biomass enhancement directly translates to increased yields of valuable metabolites, with fucoxanthin productivity increasing by 29-41% in Cylindrotheca sp. under mixotrophic conditions [68].

Similar trends have been documented in green microalgae, with Chlorella vulgaris achieving maximum cell density of 3.52×10⁷ cells/mL, specific growth rate of 0.75 d⁻¹, and cell dry weight of 3.48 g/L under mixotrophic conditions—significantly exceeding values from photoautotrophic or heterotrophic modes [79]. The carbon source assimilation rates further illustrate the synergistic effect of mixotrophy, with sodium bicarbonate consumption reaching 635 mg/L/d (compared to 505 mg/L/d in photoautotrophy) and sodium acetate consumption reaching 614 mg/L/d (compared to 645 mg/L/d in heterotrophy) [79].

Table 2: Comparative Performance Metrics Across Trophic Modes

Organism	Cultivation Mode	Biomass Yield	Specific Growth Rate	Target Metabolite Yield	Carbon Utilization Efficiency
Phaeodactylum tricornutum	Phototrophic	Baseline	Baseline	Baseline antiproliferative activity	505 mg/L/d NaHCO₃
	Mixotrophic	+62%	+35%	Enhanced antiproliferative & antibacterial	635 mg/L/d NaHCO₃
Cylindrotheca sp.	Phototrophic	Baseline	Baseline	3.316 mg/L/d fucoxanthin	COâ‚‚ only
	Mixotrophic (2g/L glycerol)	+52%	+30%	3.630 mg/L/d fucoxanthin	COâ‚‚ + glycerol
Chlorella vulgaris	Phototrophic	2.11 g/L	0.42 d⁻¹	Not specified	505 mg/L/d NaHCO₃
	Heterotrophic	2.89 g/L	0.58 d⁻¹	Not specified	645 mg/L/d acetate
	Mixotrophic	3.48 g/L	0.75 d⁻¹	Not specified	635 mg/L/d NaHCO₃ + 614 mg/L/d acetate

Bioactivity Enhancement Under Mixotrophy

Beyond quantitative biomass metrics, mixotrophic cultivation qualitatively enhances the bioactivity profile of microbial products. In Phaeodactylum tricornutum, mixotrophically cultivated cells exhibited significantly higher antiproliferative activity against human melanoma cells and stronger antibacterial effects against Staphylococcus aureus compared to phototrophic counterparts [47]. Metabolomics analysis identified an expanded spectrum of bioactive compounds in mixotrophic Chlorella sp. and Phaeodactylum tricornutum, suggesting that the metabolic interplay between photosynthetic and heterotrophic pathways generates a more diverse secondary metabolome [47]. This bioactivity enhancement has substantial implications for pharmaceutical applications within integrated biorefineries.

Experimental Protocols for Mixotrophic Process Development

Marine Microalgal Cultivation with Glycerol Supplementation

Objective: To establish mixotrophic cultivation conditions for enhanced biomass and bioactive compound production in marine microalgae.

Materials and Reagents:

• Strains: Phaeodactylum tricornutum, Chlorella sp., Nannochloropsis granulata [47]
• Basal Medium: GoldMedium (GM) prepared with artificial seawater [47]
• Organic Carbon Source: Glycerol (technical grade, 4.6 g/L) [47] [80]
• Inorganic Carbon Source: Sodium bicarbonate (1.26 g/L) [47]
• Antibiotic: Ampicillin (100 μg/L) for bacterial control [47]
• Culture Vessels: 40 mL flasks with vented filter caps [47]

Experimental Conditions:

• Phototrophy Control: GM without external carbon source (pH ~6.7)
• Mixotrophy: GM supplemented with 4.6 g/L glycerol (pH ~6.9)
• Phototrophy with Bicarbonate: GM with 1.26 g/L bicarbonate (pH ~8.2)
• Mixotrophy with Bicarbonate and Glycerol: GM with both carbon sources (pH ~8.3) [47]

Cultivation Parameters:

• Temperature: 22°C
• Light Intensity: 20 μmol photons m⁻² s⁻¹ (constant)
• Shaking: 100 rpm for flask cultures [47]
• Aeration: Filtered air at 1.5 L/min for multicultivator systems [47]

Analytical Methods:

• Growth Monitoring: Daily optical density at 750 nm (OD₇₅₀)
• Biomass Quantification: Dry weight determination after filtration through pre-weighed 0.2 μm filters, rinsed with physiological solution, and dried at 100°C for 24 hours [47]
• Bioactivity Assessment:
  • Antiproliferative activity against human melanoma cells
  • Antibacterial activity against Staphylococcus aureus [47]
• Metabolite Profiling: MS-HPLC for compound identification [47]

Metabolic Flux Analysis in Mixotrophic Chlorella vulgaris

Objective: To quantify carbon source utilization efficiencies and key enzyme activities under mixotrophic conditions.

Materials and Reagents:

• Strain: Chlorella vulgaris [79]
• Media: f/2 medium with variations [79]
• Carbon Sources: Sodium bicarbonate (NaHCO₃) and sodium acetate [79]
• Culture Conditions: 25±1°C, 5,000±100 lux, 12:12 light:dark photoperiod [79]

Experimental Setup:

• Photoautotrophic Control: NaHCO₃ as sole carbon source
• Heterotrophic Control: Sodium acetate as sole carbon source in darkness
• Mixotrophic: NaHCO₃ + sodium acetate with illumination [79]

Analytical Procedures:

• Cell Counting: Hemocytometer with Lugol's iodine staining [79]
• Carbon Consumption:
  • Bicarbonate: Neutralization titration [79]
  • Acetate: Gas chromatography [79]
• Enzyme Activity Assays (ELISA method):
  • Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco)
  • Citrate synthase (CS)
  • Fructose-1,6-bisphosphate aldolase (FBA)
  • Glyceraldehyde-3-phosphate dehydrogenase (GAPDH)
  • Glucose-6-phosphate dehydrogenase (G6PDH) [79]
• Chlorophyll Fluorescence: WATER-PAM fluorometer for FV/FM, Y(NO), Y(NPQ), Y(II) [79]
• Product Analysis:
  • Starch: Plant Starch Content Determination Kit
  • Lipids: Chloroform-methanol extraction [79]

Process Integration Strategies and Engineering Solutions

Coastal Integrated Marine Biorefineries (CIMB)

The CIMB concept represents a paradigm shift in biorefinery design by fully leveraging marine resources—seawater as medium, marine biomass as feedstock, and marine microorganisms as biocatalysts [81]. This approach eliminates competition with freshwater resources and arable land while enabling novel process integrations:

• Marine Fermentation: Utilizing halotolerant yeast strains (e.g., Saccharomyces cerevisiae AZ65) that demonstrate superior inhibitor tolerance and ethanol productivity in seawater-based media [81].
• Waste Stream Integration: Incorporating crude glycerol from biodiesel production as mixotrophic substrate, simultaneously reducing production costs and enhancing resource circularity [80].
• Carbon Capture Integration: Direct utilization of flue gas COâ‚‚ for phototrophic growth phase with organic carbon supplementation during high-density cultivation [81].

The CIMB configuration enables the production of multiple output streams: bioethanol from fermented carbohydrates, high-value chemicals (HVCs) such as fucoxanthin and EPA, biofertilizers from spent biomass, and distilled water as a byproduct of concentration processes [81].

Multi-Operation Integrated Strategy for Enhanced Productivity

Research with Chlorella sorokiniana FZU60 demonstrates the efficacy of integrated operational strategies combining mixotrophy with light regime optimization. Through pulse feeding of acetate (1 g/L every 48 hours) to alternate between mixotrophic growth and photoinduction phases, researchers achieved lutein content of 9.57 mg/g and productivity of 11.57 mg/L/d—exceeding most reported values in literature [82]. This strategy leverages the biomass accumulation advantages of mixotrophy while maintaining light-induced stimulation of carotenoid biosynthesis.

Bioprocess Monitoring and Control Systems

Advanced monitoring technologies enable real-time optimization of mixotrophic processes:

• In-line Photobioreactor Systems: Multicultivator MC 1000-OD with individual tube lighting and aeration control for parallel condition testing [47].
• Chlorophyll Fluorescence Monitoring: Provides non-destructive assessment of photosynthetic performance under mixotrophic conditions [79].
• Automated Metabolite Tracking: MS-HPLC systems for continuous metabolomic profiling during trophic transitions [47].

Molecular Mechanisms and Metabolic Networks

Carbon and Energy Metabolism in Mixotrophy

The synergistic effect of mixotrophy originates from fundamental rearrangements in central carbon metabolism. In Chlorella vulgaris, mixotrophic cultivation enhances Rubisco activity to 9.36 U/mL—3.09 and 4.85 times higher than photoautotrophic and heterotrophic modes, respectively [79]. This indicates simultaneous activation of photosynthetic carbon fixation and organic carbon assimilation pathways rather than simple additive effects.

The metabolic network underlying mixotrophic growth involves:

• Parallel Carbon Processing: Simultaneous operation of Calvin-Benson-Bassham (CBB) cycle and organic carbon assimilation pathways.
• Energy Redistribution: Reduced light dependency for ATP generation, enabling more efficient carbon partitioning to biosynthesis.
• Redox Balancing: Organic carbon oxidation provides reducing equivalents that supplement photosynthetic electron transport.

Genetic Regulation of Mixotrophic Transitions

Transcriptomic analyses of mixotrophic Cylindrotheca sp. reveal light-dependent regulation of glycerol utilization genes, with GPDH1, TIM1, and GAPDH1 showing highest light dependence [68]. Despite reduced glycerol uptake in darkness, genes associated with pyrimidine metabolism and DNA replication remain upregulated, suggesting integrated regulation of carbon metabolism and cell cycle progression [68]. Additionally, amino acid and aminoacyl-tRNA metabolisms are enhanced at different diurnal cycle timepoints, indicating temporal coordination of biosynthetic processes with carbon availability [68].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for Mixotrophic Bioprocess Development

Reagent/Category	Specification/Function	Application Example
Organic Carbon Sources
Glycerol (technical grade)	4.6 g/L; Byproduct valorization	Phaeodactylum tricornutum mixotrophy [47]
Sodium acetate	614-645 mg/L/d consumption rate	Chlorella vulgaris carbon metabolism [79]
Inorganic Carbon Sources
Sodium bicarbonate	1.26 g/L; pH buffering & carbon source	Bicarbonate-supplemented mixotrophy [47]
COâ‚‚-enriched air	2.5% (v/v); Enhanced carbon availability	Elliptochloris sp. cultivation [80]
Culture Media
GoldMedium (GM)	Artificial seawater base; Macronutrient & vitamin premix	Marine microalgae cultivation [47]
f/2 medium	Standard marine medium; Nitrate & phosphate sources	Chlorella vulgaris experiments [79]
K9 modified medium	pH 2.5; Acidophilic microalga cultivation	Elliptochloris sp. from acidic habitat [80]
Analytical Tools
MS-HPLC	Metabolite identification & quantification	Bioactive compound profiling [47]
WATER-PAM fluorometer	Chlorophyll fluorescence parameters	Photosynthetic performance [79]
ELISA kits	Enzyme activity assays (Rubisco, CS, FBA, etc.)	Metabolic flux analysis [79]

The integration of mixotrophic systems based on marine microorganisms represents a transformative approach to sustainable biorefining. The recently discovered metabolic versatility of Marinisomatota provides natural models for engineering flexible production platforms that dynamically respond to resource availability. Quantitative data unequivocally demonstrates the superiority of mixotrophic cultivation for enhancing both biomass yields and valuable metabolite production across diverse microbial species.

Future development should focus on several critical areas:

• Genetic Tool Development: Expanding CRISPR-Cas systems [83] for precise metabolic engineering of mixotrophic traits in industrial strains.
• Process Integration: Implementing multi-operation strategies that strategically alternate between trophic modes to maximize productivity [82].
• Marine Resource Utilization: Advancing Coastal Integrated Marine Biorefineries that eliminate freshwater dependence while enabling novel product streams [81].
• Waste Stream Valorization: Optimizing technical glycerol and other industrial byproducts as mixotrophic substrates within circular economy frameworks [80].

The experimental protocols and integration strategies outlined in this technical guide provide a foundation for implementing mixotrophic processes within existing bioprocess infrastructure. As understanding of the molecular mechanisms governing mixotrophic metabolism deepens, further optimization of these integrated systems will accelerate the development of truly sustainable biorefineries that simultaneously address energy, environmental, and resource challenges.

Validating Ecological Roles and Comparative Analysis with Other Marine Microbes

Marinisomatota (formerly known as Marinimicrobia, Marine Group A, and SAR406) are ubiquitous and abundant microorganisms in global ocean ecosystems. Traditionally characterized as heterotrophic, emerging research reveals significant metabolic versatility within this phylum, particularly mixotrophic adaptations that allow them to bridge trophic pathways. This ecological role validation examines how Marinisomatota contribute to carbon and nutrient cycling through flexible metabolic strategies, highlighting their function as pivotal regulators of marine biogeochemical processes. Their capacity to shift between phototrophic and heterotrophic metabolisms represents a significant adaptation to nutrient limitation in oceanic environments, positioning them as key players in ocean carbon sequestration and nutrient regeneration [1].

The integration of mixotrophic strategies enables Marinisomatota to optimize energy acquisition under varying environmental conditions, thereby influencing the efficiency of carbon transfer in marine food webs. This review synthesizes recent genomic and metatranscriptomic evidence to validate their ecological roles, providing methodological frameworks for studying their contributions to global biogeochemical cycles within the context of increasing climate perturbations [1] [84].

Metabolic Diversity and Ecological Distribution

Genomic Insights into Metabolic Versatility

Comprehensive analysis of 1,588 reconstructed Marinisomatota genomes from global ocean datasets has revealed extensive metabolic diversity spanning one class, two orders, 14 families, 31 genera, and 67 species [1]. These organisms demonstrate a remarkable 360-fold variation in relative abundance across low-latitude marine regions (0.18-36.21%), indicating specialized niche adaptation [1]. The genomic data reveals three distinct metabolic strategies designated MS0, MS1, and MS2:

Table 1: Metabolic Strategies in Marinisomatota

Metabolic Strategy	Trophic Mode	Key Characteristics	Ecological Distribution
MS0	Photoautotrophic potential	Crassulacean acid metabolism (M00169); light-dependent carbon fixation	Translucent zone, transitioning to aphotic layers
MS1	Heterotrophic with enhanced glycolytic capacity	Pronounced glycolytic pathway; organic carbon consumption	Widespread across water columns
MS2	Heterotrophic without glycolysis	Organic carbon consumption without glycolytic specialization	Deep water masses and nutrient-limited regions

Among the 14 identified families, five (S15-B10, TCS55, UBA1611, UBA2128, and UBA8226) exhibit genetic potential for light-dependent processes associated with Crassulacean acid metabolism (M00169) [1]. This metabolic flexibility likely represents an evolutionary response to nutrient limitations in oceanic ecosystems, allowing Marinisomatota to occupy distinct ecological niches along depth gradients and across water masses.

Quantitative Distribution Patterns

Marinisomatota distribution follows predictable patterns correlated with oceanographic features. The quantitative data derived from global metagenomic analyses reveals several key distribution trends:

Table 2: Quantitative Distribution of Marinisomatota in Global Oceans

Parameter	Value/Range	Significance
Relative Abundance	0.18% - 36.21%	Indicates highly variable but potentially dominant presence in microbial communities
Genomes Reconstructed	1,588	Representative genomic diversity across global sampling sites
Taxonomic Diversity	1 class, 2 orders, 14 families, 31 genera, 67 species	Substantial phylogenetic breadth within the phylum
Families with Photoautotrophic Potential	5 of 14 families (S15-B10, TCS55, UBA1611, UBA2128, UBA8226)	Significant proportion capable of light-dependent metabolism

These distribution patterns align with physical oceanographic features, particularly the global overturning circulation that acts as a "microbial conveyor belt" transporting microorganisms across vast distances and depths [85]. Recent research has identified six distinct microbial "cohorts" in the South Pacific, three corresponding to depth zones and three aligning with major water masses (Antarctic Bottom Water, Upper Circumpolar Deep Water, and ancient Pacific Deep Water) [85]. Each cohort harbors unique microbial species and functional genes shaped by temperature, pressure, nutrient levels, and water mass age.

Methodological Framework for Ecological Role Validation

Genomic and Metatranscriptomic Approaches

Validating the ecological roles of Marinisomatota requires integrated omics approaches that link metabolic potential with in situ activity:

Metagenomic Assembly and Binning:

• Sample collection across depth profiles and water masses using CTD rosettes with Niskin bottles [85]
• Sequential filtration through 0.22-μm pore-size membranes to capture microbial biomass
• DNA extraction using commercial kits with modifications for low-biomass samples
• Shotgun sequencing on Illumina platforms with minimum 10 Gb sequence data per sample
• Genome reconstruction using assembly (Megahit, SPAdes) followed by binning (MetaBAT2, MaxBin2)
• Genome quality assessment using CheckM with minimum completeness >70% and contamination <10%

Metatranscriptomic Analysis:

• RNA extraction preserving expression profiles
• mRNA enrichment via rRNA depletion (RiboZero kits)
• cDNA synthesis and library preparation for Illumina sequencing
• Read mapping to reconstructed genomes using Salmon or Bowtie2
• Normalization using TPM (Transcripts Per Million) counts
• Differential expression analysis (DESeq2) to identify active metabolic pathways

Experimental Validation of Metabolic Functions

Stable Isotope Probing (SIP):

• Incubation experiments with (^{13})C-labeled bicarbonate (photoautotrophy assessment)
• Incubation with (^{13})C-labeled organic substrates (heterotrophy assessment)
• Density gradient centrifugation to separate (^{13})C-labeled DNA
• Sequencing of heavy-fraction DNA to link substrate utilization to specific Marinisomatota taxa

Rate Measurements:

• Carbon fixation rates using (^{14})C-bicarbonate incorporation experiments
• Nutrient uptake measurements with (^{15})N-labeled ammonium/nitrate
• Respiration rates via oxygen microsensors in conjunction with specific metabolic inhibitors

Figure 1: Integrated Workflow for Validating Marinisomatota Ecological Roles

Carbon Cycling Contributions

Direct Carbon Processing Mechanisms

Marinisomatota influence carbon cycling through multiple direct mechanisms:

Mixotrophic Carbon Flow:

• MS0-type Marinisomatota contribute to carbon fixation through light-dependent processes while simultaneously consuming organic matter
• This dual capability enhances carbon use efficiency compared to obligate autotrophs or heterotrophs
• Carbon fixation occurs via Crassulacean acid metabolism (M00169) in specific families [1]

Organic Matter Transformation:

• Heterotrophic Marinisomatota (MS1 and MS2 types) initiate the breakdown of large organic molecules through extracellular enzymes
• Subsequent substrate uptake occurs via specialized transporters, particularly TonB-dependent transporters [2]
• Genomic evidence reveals distinct substrate processing and assimilation strategies among different Marinisomatota taxa

Carbon Export and Sequestration

The unique behaviors of mixotrophic protists, including certain Marinisomatota, directly impact carbon export through the biological pump:

Mucosphere-Mediated Export:

• Some mixotrophic protists craft three-dimensional mucilage feeding structures that trap nutrient-rich plankton prey [84]
• These mucospheres are jettisoned after feeding and are negatively buoyant, contributing to particulate carbon sinking from the surface ocean [84]
• Estimated carbon flux ranges from 0.04–0.29 mg C m⁻² d⁻¹, increasing by approximately 4-fold when attached microorganisms are included [84]

Trophic Transfer Efficiency:

• Mixotrophy enables more efficient trophic transfer of carbon up the food chain by offsetting respired carbon losses with photosynthesis [84]
• Modeling suggests this allows organisms in the ecosystem to be larger and increases sinking of organic material compared to food webs dominated by strict phototrophy and heterotrophy [84]

Figure 2: Marinisomatota Carbon Cycling Pathways

Nutrient Cycling Contributions

Nitrogen Transformation Processes

Marinisomatota participate in critical nitrogen cycling processes, particularly in deep ocean and sediment environments:

Anaerobic Ammonium Oxidation (anammox):

• While not directly performing anammox, Marinisomatota coexist with anammox bacteria in coastal sediments [2]
• They likely contribute to the community stability necessary for maintaining anammox processes
• Rare taxa within these communities are particularly sensitive to dispersal limitations and environmental selection [2]

Organic Nitrogen Processing:

• Genomic evidence reveals capabilities for processing dissolved organic nitrogen compounds
• Extracellular enzyme systems target nitrogen-containing organic molecules
• TonB-dependent transporters facilitate uptake of nitrogenous compounds [2]

Phosphorus and Sulfur Cycling

Phosphonate Metabolism:

• Related microbial processes involve degradation of methylphosphonate (MPn), contributing to the oceanic methane paradox [2]
• While not directly documented in Marinisomatota, similar metabolic capabilities may exist given their genomic versatility
• Phosphonate metabolism genes (phn operons) show remarkable genetic diversity in marine bacteria [2]

Sulfur Transformation:

• Indirect evidence suggests involvement in sulfur cycling through association with sulfur-oxidizing and reducing microorganisms
• Metabolic flexibility may allow adaptation to sulfidic environments, particularly in deep-sea and sediment habitats

Research Reagent Solutions Toolkit

Table 3: Essential Research Reagents for Marinisomatota Studies

Reagent/Category	Specific Examples	Function/Application
DNA Extraction Kits	DNeasy PowerWater Kit, MetaPolyzyme-enhanced extraction	Maximize yield from low-biomass water samples for metagenomics
RNA Preservation	RNAlater, Zymo RNA Shield	Preserve in situ gene expression profiles for metatranscriptomics
Stable Isotopes	(^{13})C-bicarbonate, (^{15})N-ammonium, (^{13})C-acetate	Track carbon and nutrient fluxes via SIP experiments
Metabolic Inhibitors	Ethoxyzolamide, 3-mercaptopicolinic acid	Inhibit specific CCMs to elucidate metabolic pathways [2]
Enzyme Assays	APF (aminopeptidase fluorogenic substrate), MUF-substrate analogs	Measure extracellular enzyme activities for organic matter degradation
Cultivation Media	Modified low-nutrient media with diffusion chambers	Isolate previously uncultured Marinisomatota taxa [2]
Sequence Capture	SAR406-specific probes, Phylum-targeted baits	Enrich Marinisomatota sequences from complex metagenomes

Research Implications and Future Directions

The validated ecological roles of Marinisomatota in carbon and nutrient cycling have significant implications for understanding ocean biogeochemistry and predicting ecosystem responses to environmental change. Their mixotrophic adaptations represent a crucial mechanism for maintaining metabolic activity under nutrient limitation, potentially enhancing ecosystem resilience [1]. Future research priorities should include:

• Development of Cultivation Techniques: Advanced diffusion-based integrative cultivation methods using modified low-nutrient media have shown promise in isolating previously uncultured marine bacteria, achieving a novelty ratio of 58% [2]. Similar approaches should be applied specifically to Marinisomatota.

• Single-Cell Genomics: Application of single-cell sequencing technologies to resolve individual metabolic capabilities and reduce genomic binning ambiguities.

• Environmental Manipulation Experiments: Controlled studies examining Marinisomatota responses to warming, acidification, and deoxygenation to project climate change impacts.

• Global Biogeochemical Modeling: Incorporation of mixotrophic strategies into next-generation ocean models to improve predictions of carbon export and nutrient cycling.

The integration of genomic, transcriptomic, and experimental approaches provides a powerful framework for validating the ecological contributions of Marinisomatota to marine biogeochemical cycles. Their metabolic flexibility positions them as key mediators of carbon and nutrient fluxes in the global ocean, with implications for understanding ecosystem responses to ongoing environmental change.

Marine ecosystems are characterized by complex microbial networks that govern global biogeochemical cycles. Within these networks, the phylum Marinisomatota (formerly known as Marinimicrobia, SAR406, or Marine Group A) represents a widespread yet enigmatic group of microorganisms particularly abundant in the deep ocean and other marine environments [57] [8]. Despite their recognized prevalence, Marinisomatota remain poorly characterized due to challenges in cultivation, with the majority of representatives uncultured under laboratory conditions [57]. The application of comparative genomics to metagenome-assembled genomes (MAGs) has revolutionized our understanding of this phylum, revealing extraordinary metabolic versatility that blurs traditional boundaries between heterotrophic and phototrophic lifestyles.

This technical guide examines the genomic features of Marinisomatota in comparison with other marine heterotrophs and phototrophs, framing the analysis within the emerging paradigm of mixotrophic adaptations in marine systems. Mixotrophy—the combination of autotrophic and heterotrophic metabolic strategies—provides a competitive advantage in nutrient-limited oceanic ecosystems [57]. Recent genomic evidence suggests Marinisomatota have evolved diverse metabolic strategies that reflect evolutionary responses to nutrient limitation, positioning them as key players in marine carbon cycling [57] [86].

Ecological Distribution and Genomic Characteristics

Habitat Range and Prevalence

Marinisomatota demonstrate a remarkably broad distribution across marine environments, with distinct genomic adaptations corresponding to specific ecological niches:

• Dark Ocean Ecosystems: Marinisomatota are particularly abundant in the mesopelagic and bathypelagic zones, where they can represent significant components of microbial communities [8]. Genomic analyses from the Ross Ice Shelf cavity revealed Marinisomatota as one of the six dominant phyla in these dark, oligotrophic waters, alongside Proteobacteria, SAR324, Crenarchaeota, Chloroflexota, and Planctomycetota [8].

• Stratified Water Columns: In the Black Sea, Marinisomatota dominate the redoxcline (150 m depth), comprising approximately 30% of the microbial community based on 16S rRNA gene fragment abundance [23]. This distribution highlights their adaptation to low-oxygen conditions and ability to utilize alternative electron acceptors.

• Hypersaline Environments: Genome-resolved studies of Guerrero Negro hypersaline microbial mats have recovered Marinisomatota MAGs, indicating their participation in complex stratified communities where they interact with phototrophic and sulfur-cycling microorganisms [25].

• Global Ocean Transects: Large-scale genomic reconstructions from global ocean datasets have enabled the classification of Marinisomatota into distinct metabolic subgroups with specific distribution patterns correlated with nutrient availability and depth [57].

Comparative Genomic Features

Genomic analysis of Marinisomatota reveals distinctive characteristics that differentiate them from other marine bacteria:

Table 1: Comparative Genomic Features of Marine Microorganisms

Phylum/Group	Genome Size (Mbp)	GC Content	Metabolic Strategy	Carbon Acquisition	Characteristic Genes
Marinisomatota	Variable (2.5-4.5)	Moderate	Mixotrophic	rTCA cycle, organic carbon uptake	[FeFe]-hydrogenases, sulfur oxidation genes
SAR11	Small (1.3-1.5)	Low	Heterotrophic	Organic carbon uptake	ABC transporters, proteorhodopsin
Cyanobacteria	Variable (1.7-10)	Moderate-High	Phototrophic	Calvin cycle	Photosystem I/II, phycobilisomes
Chloroflexota	Large (3.5-6.5)	High	Phototrophic/Mixotrophic	3-Hydroxypropionate cycle	Chlorosomes, bacteriochlorophyll synthesis
Nitrospinae	Moderate (2.5-3.5)	Moderate	Chemolithoautotrophic	rTCA cycle	Nitrite oxidoreductase (Nxr)

Marinisomatota genomes typically encode complete or partial pathways for both heterotrophic and autotrophic carbon assimilation, supporting their classification as potential mixotrophs [57]. Their genomic features reflect adaptation to energy-limited environments, with streamlined genomes in some subgroups and expanded metabolic capabilities in others.

Metabolic Versatility and Mixotrophic Adaptations

Carbon Metabolism Strategies

Comparative genomic analyses reveal three distinct metabolic modes within the Marinisomatota phylum, designated MS0, MS1, and MS2 [57]:

• MS0 (Photoautotrophic Potential): This subgroup possesses genes suggestive of potential light-driven energy metabolism, though unlike canonical phototrophs, they lack complete photosystem complexes. The MS0 group may utilize novel photoproteins or employ rhodopsin-like systems for energy capture.

• MS1 (Heterotrophic with Enhanced Glycolytic Capacity): These genomes encode complete glycolytic pathways and specialized transporters for organic carbon substrates, similar to heterotrophic groups like SAR11 and Bacteroidota, but with additional capabilities for inorganic energy metabolism.

• MS2 (Heterotrophic without Glycolysis): This subgroup lacks complete Embden-Meyerhof glycolysis but possesses alternate carbon degradation pathways, including the Entner-Doudoroff pathway and pentose phosphate pathway, enabling utilization of complex organic matter.

The reconstruction of 1,588 Marinisomatota genomes from global ocean datasets demonstrated that these metabolic strategies represent evolutionary responses to nutrient limitation in oceanic ecosystems [57]. This metabolic diversity enables niche partitioning within the phylum, with different subgroups dominating specific marine environments.

Comparative Analysis with Other Marine Microbes

When compared with dedicated heterotrophs and phototrophs, Marinisomatota exhibit unique metabolic features:

Table 2: Metabolic Capabilities Across Marine Microbial Groups

Metabolic Pathway/Function	Marinisomatota	Marine Heterotrophs	Marine Phototrophs
rTCA Cycle	Partial/Complete	Generally absent	Present in some (Chlorobi)
Calvin Cycle	Generally absent	Absent	Present (Cyanobacteria)
Glycolysis	Variable (group-specific)	Present	Present
Organic Carbon Transport	Extensive	Extensive	Limited
Sulfur Oxidation	Present in some lineages	Generally absent	Present in some (PSB)
Hydrogen Oxidation	Present in some lineages	Rare	Rare
Nitrogen Metabolism	Variable	Variable	Specialized

Unlike obligate heterotrophs such as SAR11 that primarily rely on imported organic carbon, Marinisomatota can potentially supplement their energy needs through lithotrophic processes. Similarly, while they share some autotrophic capabilities with phototrophs like Cyanobacteria, Marinisomatota lack complete photosynthetic apparatus and instead utilize alternative energy-capturing systems.

The functional linkage between extracellular enzymes and TonB-dependent transporters in Marinisomatota suggests sophisticated strategies for organic matter acquisition similar to those observed in Bacteroidota and Gammaproteobacteria [57]. However, their additional capacity for inorganic energy generation distinguishes them from these dedicated heterotrophs.

Figure 1: Metabolic Integration in Marinisomatota. The diagram illustrates how Marinisomatota integrate heterotrophic and autotrophic metabolic processes, with regulatory mechanisms for resource allocation based on energy status and substrate availability.

Research Methodologies and Experimental Approaches

Genome-Resolved Metagenomics

The study of uncultivated lineages like Marinisomatota relies heavily on genome-resolved metagenomics, which involves several key steps:

Sample Collection and Processing:

• Environmental Sampling: Collect microbial biomass from various marine niches (water column, sediments, microbial mats) using filtration systems or sediment corers [25] [43].
• Preservation: Immediately preserve samples in RNAlater or at -80°C to maintain DNA integrity [25] [43].
• DNA Extraction: Use commercial kits (e.g., DNeasy Power Biofilm Kit) with modifications for efficient lysis of diverse microbial cells [25].

Library Preparation and Sequencing:

• Fragment DNA to appropriate size (300-800 bp)
• Prepare Illumina-compatible libraries with dual indexing
• Sequence using HiSeq or NovaSeq platforms (2×150 bp configuration recommended) [25]

Bioinformatic Processing:

• Quality Control: Trim adapters and filter low-quality reads using Trimmomatic or fastp [25] [43].
• Contaminant Removal: Screen against host genomes (human, PhiX) using Fastq-Screen [25].
• Assembly: Perform de novo assembly with MEGAHIT or metaSPAdes using meta-sensitive parameters [43].
• Binning: Reconstruct MAGs using multiple algorithms (MetaBAT2, MaxBin2, CONCOCT) followed by consolidation with MetaWRAP or DAS Tool [43].
• Quality Assessment: Evaluate MAG completeness and contamination with CheckM; retain MAGs with >70% completeness and <5% contamination [43].
• Taxonomic Classification: Assign taxonomy using GTDB-Tk against the Genome Taxonomy Database [43].

Metabolic Reconstruction:

• Gene Prediction: Identify protein-coding genes with Prodigal [43].
• Functional Annotation: Annotate against KEGG, EggNOG, and specialized databases using KofamKOALA, emapper, or DIAMOND [43].
• Pathway Analysis: Reconstruct metabolic pathways from annotated genes and identify gaps or variations from canonical pathways.

Figure 2: Genome-Resolved Metagenomics Workflow. The comprehensive pipeline from sample collection to comparative genomic analysis enables reconstruction of metabolic potential from uncultivated microorganisms like Marinisomatota.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents and Materials for Studying Marinisomatota

Category	Specific Products/Kits	Application	Key Features
DNA Preservation	RNAlater, DNA/RNA Shield	Sample stabilization	Maintains nucleic acid integrity during transport and storage
DNA Extraction	DNeasy PowerBiofilm Kit, Phenol-Chloroform protocols	Microbial DNA isolation	Efficient lysis of diverse microbial cells; removes inhibitors
Library Preparation	Illumina DNA Prep, Nextera XT	Sequencing library construction	Compatible with low-input DNA; dual indexing for multiplexing
Sequence Quality Control	Trimmomatic, fastp, FastQC	Raw read processing	Adapter trimming; quality filtering; comprehensive reports
Metagenome Assembly	MEGAHIT, metaSPAdes	Contig reconstruction	Meta-sensitive modes; handles diverse community complexity
Genome Binning	MetaBAT2, MaxBin2, CONCOCT	MAG reconstruction	Complementarity algorithms; tetranucleotide frequency analysis
Quality Assessment	CheckM, CheckM2	MAG evaluation	Lineage-specific marker genes; completeness/contamination estimates
Taxonomic Classification	GTDB-Tk, CAT/BAT	Phylogenetic placement	Genome-based taxonomy; consistent nomenclature
Functional Annotation	KofamKOALA, eggNOG-mapper, PROKKA	Gene function prediction	Hierarchical orthology; metabolic pathway mapping
Metabolic Pathway Analysis	MetaCyc, KEGG Mapper	Pathway reconstruction	Gap filling; variant pathway detection

Ecological Implications and Biogeochemical Significance

The metabolic versatility of Marinisomatota positions them as crucial mediators of carbon and energy flow in marine ecosystems, particularly in environments where nutrients are limited. Their potential mixotrophic capabilities allow them to bridge traditional metabolic divisions, potentially contributing to both primary production and organic matter remineralization.

In the context of climate change, the expansion of oligotrophic ocean regions due to global warming may favor microbial groups with metabolic flexibility [87] [86]. Mixotrophic strategies, like those observed in Marinisomatota, provide competitive advantages in such conditions by enabling utilization of diverse energy and carbon sources. Projections suggest that oligotrophic subtropical ocean biomes will expand in a warming ocean, potentially increasing the ecological relevance of mixotrophic microorganisms [86].

Network analyses from various marine environments have revealed that mixotrophic taxa often occupy crucial positions in microbial interaction networks, serving as connectors between different trophic levels [43] [86]. This supports the hypothesis that Marinisomatota play important roles in maintaining the stability and functioning of marine microbial food webs, particularly in stratified water columns where they can form linkages between surface-derived organic matter and deep ocean processes.

Future Research Directions

While significant progress has been made in understanding Marinisomatota through genomic approaches, several research frontiers remain:

• Cultivation Efforts: Developing novel cultivation strategies to isolate representative Marinisomatota strains using diffusion-based methods [57] or targeted enrichment based on genomic predictions.

• Multi-Omics Integration: Combining metagenomics with metatranscriptomics, metaproteomics, and metabolomics to validate predicted metabolic functions and quantify their contributions to biogeochemical cycling.

• Single-Cell Genomics: Applying single-cell amplified genome approaches to access diversity within Marinisomatota subgroups that may be underrepresented in MAGs due to low abundance.

• Experimental Manipulations: Using substrate-amendment experiments [25] to test metabolic predictions and quantify process rates under controlled conditions.

• Global Biogeography: Expanding comparative genomic analyses across oceanic provinces to identify core and flexible genomic elements and their relationship to environmental gradients.

Addressing these research directions will deepen our understanding of how Marinisomatota and other mixotrophic microorganisms influence ocean ecosystem functioning and biogeochemical cycles, particularly in the context of ongoing climate change and ocean biogeochemical alterations.

Mixotrophy, the combination of autotrophic and heterotrophic metabolic strategies, provides a critical competitive advantage for microorganisms in nutrient-limited marine ecosystems [57]. The phylum Marinisomatota (formerly recognized as Marinimicrobia, Marine Group A, and SAR406) represents a widespread and abundant group in the ocean whose ecological success is increasingly linked to metabolic versatility [57]. Understanding the functional activity of extracellular enzymes and transporters in these bacteria is essential for elucidating their role in marine carbon cycling and ecosystem dynamics [57] [88]. This technical guide provides a comprehensive framework for assessing these functional activities through integrated multi-omics and biochemical approaches, contextualized within broader research on mixotrophic adaptations in Marinisomatota.

Genomic investigations have revealed that Marinisomatota populations exhibit distinct metabolic strategies, classified into three primary modes: MS0 (photoautotrophic potential), MS1 (heterotrophic with enhanced glycolytic capacity), and MS2 (heterotrophic without glycolysis) [57]. This metabolic plasticity necessitates sophisticated systems for substrate acquisition and processing. This whitepaper details methodologies for quantifying the expression and activity of these systems, enabling researchers to decode the functional ecology of this ecologically significant bacterial phylum.

Marinisomatota Ecology and Enzymatic Frameworks

Marinisomatota are ubiquitous in global oceans, and their genomic features suggest a potential for mixotrophic adaptations that allow them to thrive in oligotrophic environments [57]. Their distribution and metabolic activity are influenced by factors such as nutrient availability, depth, and organic matter composition [43]. A recent genome-resolved metagenomic study of marine sediments further highlighted metabolic plasticity and a broad biosynthetic potential as key survival strategies for bacteria in fluctuating environments [88], traits that are highly relevant to Marinisomatota.

The functional assessment of these bacteria focuses on their repertoire of carbohydrate-active enzymes (CAZymes), sulfatases, and substrate-specific transporters. These systems work in concert to initiate the breakdown of high molecular weight organic matter—such as complex algal polysaccharides—and transport the resulting oligomers and monomers for cellular metabolism [89]. The TonB-dependent transporter (TBDT) system is particularly important, as it facilitates the energy-dependent uptake of hydrolyzed substrates across the outer membrane in Gram-negative bacteria [89]. The functional linkage between extracellular enzymes and transporters is a critical metric for evaluating an organism's capacity to participate in marine carbon cycling [57].

Table 1: Key Enzyme Classes Involved in Degradation of Algal Polysaccharides

Enzyme Class	EC Number	Target Substrate	Relevant Polysaccharide	Significance in Marinisomatota Ecology
α-L-fucosidases	EC 3.2.1.51, EC 3.2.1.111	L-fucose linkages	Fucoidan	Key enzyme for degrading brown algal cell walls; targets a major fucoidan monomer [89].
Laminarinase	EC 3.2.1.39	β-1,3-glucan linkages	Laminarin	Hydrolyzes storage carbohydrate of brown algae; central to carbon acquisition [90].
Glycosyl Hydrolases (GH)	EC 3.2.1.-	Various glycosidic bonds	Multiple (e.g., cellulose, xylan)	Large family of enzymes; specific GH families (e.g., GH29, GH95) indicate substrate specialization [89].
Sulfatases	EC 3.1.6.-	Sulfate esters	Fucoidan, Chondroitin Sulfate	Removes sulfate groups from sulfated polysaccharides; essential for complete degradation [89].

Methodologies for Functional Potential Assessment

Metagenomic and Metatranscriptomic Sequencing

Protocol 1: Shotgun Metagenomic Sequencing for Functional Gene Profiling

• Sample Collection and DNA Extraction: Collect microbial biomass from water or sediment samples via filtration (e.g., onto 0.22 µm filters) or centrifugation. For sediment samples like those in Venice Lagoon studies, preserve subsamples in RNAlater at -80°C for concurrent DNA/RNA work [88]. Extract high-molecular-weight DNA using a commercial kit (e.g., DNeasy PowerSoil Pro Kit, Qiagen).
• Library Preparation and Sequencing: Fragment the purified DNA to an average size of 400-500 bp. Prepare sequencing libraries using a standard Illumina protocol (e.g., Nextera DNA Flex Library Prep). Sequence on an Illumina platform (e.g., NovaSeq 6000) to generate a minimum of 20 million paired-end (2x150 bp) reads per sample.
• Bioinformatic Analysis:
  • Quality Control: Trim adapters and low-quality bases from raw reads using fastp (v0.23.4) with parameters -q 20 -u 20 -g -c -W 5 -l 90 [43].
  • Co-assembly and Binning: Perform de novo assembly per sample or as a co-assembly using MEGAHIT (v1.2.9) with --presets meta-sensitive [43]. Bin contigs (>1000 bp) into Metagenome-Assembled Genomes (MAGs) using an integrated pipeline like MetaWRAP (v1.3) with binner tools MetaBAT2, MaxBin2, and CONCOCT [43].
  • MAG Refinement and Taxonomy: Use the MetaWRAP refine module to consolidate bins. Assess MAG quality (completeness >70%, contamination <5%) with CheckM (v1.2.2) and assign taxonomy with GTDB-Tk (v2.4.1) against the GTDB database [43].
  • Functional Annotation: Predict genes from MAGs and unbinned contigs with Prodigal (v2.6.3). Annotate against functional databases (KEGG via KofamKOALA, UniProt via DIAMOND, EggNOG via emapper) and the CAZy database via dbCAN2 to identify glycosyl hydrolases, sulfatases, and transporters [89] [43].

Protocol 2: Metatranscriptomic Analysis for Gene Expression

• RNA Extraction and Sequencing: Extract total RNA from samples preserved in RNAlater using a kit designed for microbial RNA (e.g., RNeasy PowerMicrobiome Kit, Qiagen). Deplete ribosomal RNA (rRNA) using a kit like the QIAseq FastSelect –rRNA HMR Kit. Construct cDNA libraries (e.g., with the Illumina Stranded Total RNA Prep Kit) and sequence on an Illumina platform.
• Transcriptomic Quantification: Trim raw RNA-seq reads with fastp. Map quality-filtered reads to the gene catalog generated from metagenomic assembly using BBmap or Salmon. Calculate normalized expression values (e.g., Transcripts Per Million, TPM) for key genes of interest, such as those encoding glycosyl hydrolases and TonB-dependent transporters [43].

Data Interpretation and Quantitative Profiling

Metagenomic data reveals the potential of a community, while metatranscriptomics indicates active metabolic processes. For Marinisomatota, analysis should focus on the abundance and expression of genes related to the degradation of phytoplankton-derived organic matter. A study on coastal waters demonstrated a functional linkage between extracellular enzymes and TonB-dependent transporters in heterotrophic prokaryotic communities, with Gammaproteobacteria, Alphaproteobacteria, and Bacteroidota playing critical roles [57].

Table 2: Representative Quantitative Data from Marine Microbiome Studies

Study Focus / Location	Target Functional Group / Gene	Quantitative Metric	Value	Interpretation
Coastal Water Organic Matter Cycling [57]	Extracellular Enzymes & Transporters	Metagenomic Relative Abundance	Varies by taxa	Gammaproteobacteria, Alphaproteobacteria, Bacteroidota dominant in degradation.
	TonB-dependent Transporters	Functional linkage to enzymes	Positive correlation	Key mechanistic link between hydrolysis and assimilation.
Nitrite-Oxidizing Bacteria (NOB), Mariana Trench [43]	nxrA (nitrite oxidation)	Metatranscriptomic Expression (TPM)	1.48x higher on slope	Slope NOB are more transcriptionally active in nitrite oxidation.
	aclA (carbon fixation)	Metatranscriptomic Expression (TPM)	1.28x higher on slope	Slope NOB show enhanced carbon fixation activity.
Fucus vesiculosus Degradation [89]	Reducing Sugars from hydrolysis	Concentration in Enrichment Culture	Peaked at 4.09 ± 0.47 mol (as L-fucose eq.)	Indicates high enzymatic activity on algal polysaccharides.
	Algal Biomass	Weight Loss over 20 days	21.52 ± 0.14%	Direct measure of microbial degradation efficiency.

Functional Activity Assessment Workflow

Methodologies for Measuring Actual Enzyme Activity

Fluorescently Labeled Substrate Hydrolysis Assays

Protocol 3: Measuring Hydrolysis Rates of High Molecular Weight Substrates

This protocol, adapted from Arnosti et al. [90], measures the actual hydrolysis of polysaccharides relevant to algal biomass (e.g., laminarin, fucoidan, xylan).

• Substrate Preparation: Purchase or prepare polysaccharides (e.g., laminarin from Sigma-Aldrich). Label them with fluorescent tags (e.g., Fluoresceinamine) as described in Arnosti (2003) to create Fluorescently Labeled Polysaccharides (FLA-PS).
• Sample Incubation: Concentrate the particle-associated microbial fraction by filtering seawater through a 10 µm cartridge filter [90]. For sediments, create a slurry with sterile artificial seawater. Set up triplicate incubations containing a buffer, the FLA-PS substrate (final concentration ~5-10 µM monomer-equivalent), and the microbial inoculum. Include killed controls (e.g., with sodium azide).
• Reaction and Termination: Incubate in the dark at in situ temperature with shaking. At regular time points (e.g., 0, 2, 5, 12, 24 hours), subsample the reaction mix and stop the enzymatic reaction by heating to 100°C for 10 minutes or adding a stop solution.
• Analysis and Quantification: Separate the hydrolysis products from the high molecular weight substrate using gel permeation chromatography (e.g., with a Sephadex G-50 column) or by capillary electrophoresis. Detect and quantify the fluorescent-labeled fragments. Calculate hydrolysis rates from the increase in low molecular weight fluorescence over time [90].

Enzyme Activity Staining and Verification

Protocol 4: In Situ Zymography for Spatial Localization

Note: While traditionally used on tissue sections, this principle can be adapted for microbial biofilms on particles or agar plates.

• Gel Preparation: Incorporate a specific substrate (e.g., 0.1% laminarin or fucoidan) into a low-gelling-temperature agarose gel (e.g., 1% in a suitable buffer).
• Sample Overlay: Gently overlay the substrate-containing gel onto a filter containing the microbial community of interest (e.g., the retentate from Protocol 3) or onto a bacterial colony.
• Incubation and Staining: Incubate the assembly in a humid chamber at room temperature for several hours. After incubation, visualize zones of hydrolysis by staining the gel with a 0.5% (w/v) Congo Red solution for 15 minutes, followed by destaining with 1 M NaCl. Clear zones against a red background indicate enzymatic activity [91].

Protocol 5: Cloning, Expression, and Characterization of Target Enzymes

This protocol follows the approach of community dynamics and metagenomic analyses [89] to verify the function of putative enzymes identified bioinformatically.

• Gene Synthesis and Cloning: Identify and synthesize genes encoding putative enzymes (e.g., GH29 α-L-fucosidases) from MAGs. Clone the genes into a protein expression vector (e.g., pET series) and transform into an expression host like E. coli BL21(DE3).
• Protein Expression and Purification: Grow transformed cells to mid-log phase and induce protein expression with IPTG. Purify the recombinant protein using affinity chromatography (e.g., His-tag purification via Ni-NTA resin).
• Biochemical Characterization:
  • Activity Assay: Measure enzyme activity by incubating the purified enzyme with its substrate (e.g., p-nitrophenyl-α-L-fucopyranoside for fucosidases) and quantifying the release of the chromophore/fluorophore over time spectrophotometrically or fluorometrically.
  • Optimal Conditions: Characterize the enzyme's optimal pH, temperature, and salinity. For instance, the characterized α-L-fucosidases FUJM18 and FUJM20 were remarkably active at elevated temperatures [89].
  • Substrate Specificity: Test the enzyme against a panel of related and unrelated polysaccharides to determine its specificity.

Enzyme-Transporter Linkage in Substrate Acquisition

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents and Kits for Functional Activity Assessment

Item Name	Supplier Examples	Function in Protocol
DNeasy PowerSoil Pro Kit	Qiagen	High-quality DNA extraction from complex environmental samples like sediment.
RNeasy PowerMicrobiome Kit / RNApower Soil RNA Isolation Kit	Qiagen / Thermo Fisher	Simultaneous co-isolation of DNA and RNA, or RNA only, from environmental samples.
Illumina DNA Prep Kit & Illumina Stranded Total RNA Prep Kit	Illumina	Library preparation for metagenomic (DNA) and metatranscriptomic (RNA) sequencing.
Fluoresceinamine Isomer I	Sigma-Aldrich, Thermo Fisher	Fluorescent dye for labeling polysaccharides to create FLA-PS for hydrolysis assays.
Laminarin from Laminaria digitata, Fucoidan from Fucus vesiculosus	Sigma-Aldrich, Megazyme	Native polysaccharide substrates for enzyme activity assays and zymography.
p-Nitrophenyl-α-L-fucopyranoside	Carbosynth, Sigma-Aldrich	Chromogenic synthetic substrate for kinetic assays of α-L-fucosidase activity.
pET Series Vectors (e.g., pET-28a, pET-30a)	Novagen / MilliporeSigma	Protein expression plasmids for cloning and expressing target enzymes in E. coli.
HisTrap HP Ni-NTA Columns	Cytiva	Affinity chromatography columns for purifying recombinant His-tagged enzymes.
Sephadex G-50 Fine	Cytiva	Gel filtration medium for separating hydrolyzed fragments from intact FLA-PS.

The integrated application of the genomic, transcriptomic, and biochemical methods detailed in this guide provides a powerful framework for assessing the functional activity of extracellular enzymes and transporters in Marinisomatota and other marine bacteria. By linking genetic potential with measured activity, researchers can move beyond cataloging microbial diversity to truly understanding their functional roles in mixotrophy and biogeochemical cycling. The observed metabolic plasticity and high biosynthetic potential in related marine bacteria [88] underscore the importance of these functional assessments. As methodologies continue to advance, particularly in single-cell 'omics' and high-throughput activity screening, our ability to decipher the complex strategies that enable microbial success in the ocean's vast ecosystems will be greatly enhanced.

Marine microorganisms exhibit distinct distribution patterns directly correlated with specific oceanographic parameters. Understanding these relationships is critical for predicting ecological responses to environmental change. This technical guide synthesizes current research on the environmental drivers of microbial abundance, with a specific focus on the phylum Marinisomatota (formerly Marinimicrobia, Marine Group A, SAR406), a ubiquitous and abundant group in global oceans. Framed within a broader thesis on mixotrophic adaptations, this review details how parameters such as temperature, nutrient concentration, and water mass properties correlate with microbial community structure and metabolic function. We provide a structured analysis of quantitative data, standardized methodologies for field observation, and visualization tools to aid researchers in exploring these complex ecological relationships.

The spatial and temporal distribution of marine microorganisms is not random; it is a quantifiable function of environmental conditions. Complex oceanographic parameters act as selective filters, shaping microbial community composition, diversity, and functional potential [15] [92]. The phylum Marinisomatota serves as an exemplary model for studying these correlations due to its global distribution and recently revealed metabolic versatility, including potential for mixotrophic adaptations [1].

Long-term time-series studies, such as the 11-year metagenomic survey in the southern California Current, have demonstrated clear successional patterns in microbial lineages linked to seasonal and interannual climate cycles like the El Niño-Southern Oscillation (ENSO) [15]. Similarly, research in dynamic current systems like the Kuroshio-Oyashio Extension (KOE) has shown that microbial community structure is shaped by the interplay of water masses with different properties [92]. This guide synthesizes findings from these and other studies to provide a technical framework for linking microbial abundance to the physical, chemical, and biological parameters of the ocean.

Quantitative Data: Correlating Marinisomatota and Environmental Factors

The table below synthesizes key oceanographic parameters and their documented correlations with microbial community properties, with a specific focus on Marinisomatota.

Table 1: Correlation of oceanographic parameters with microbial community properties and Marinisomatota characteristics.

Oceanographic Parameter	Correlation with Microbial Community	Specific Correlation with Marinisomatota
Temperature	Strong negative correlation with average genome size; warmer conditions select for small-genome oligotrophic lineages (e.g., Pelagibacteraceae, Prochlorococcaceae) [15].	Prevalent in low-latitude (warmer) marine regions, with relative abundances ranging from 0.18% to 36.21% [1].
Nutrient Concentration (e.g., Nitrate, Phosphate)	Inverse correlation with temperature; high nutrients in cold, upwelled waters support large-genome lineages and phytoplankton blooms [15] [92].	Emergence of distinct metabolic strategies (MS0, MS1, MS2) is a hypothesized response to nutrient limitations in the ocean [1].
Water Mass / Current System	Microbial community composition clusters by distinct water masses (e.g., Cold Water Area vs. Warm Water Area in the KOE region) [92].	Spatial distribution and abundance are influenced by the complex hydrographic conditions of major current systems [1] [92].
Seasonal & Climate Cycles (e.g., ENSO)	Taxonomic composition and functional potential show interannual succession tied to the ENSO cycle, separating communities during El Niño vs. La Niña conditions [15].	The interplay of life history traits and metabolic strategies in Marinisomatota underscores adaptations to periodic climatic variations [1].
Salinity	A key factor shaping microbial diversity and distribution, often acting in concert with temperature and water mass properties [92].	Ecological distribution is shaped by a combination of environmental factors, including salinity gradients [1].

Metabolic Strategies of Marinisomatota

Genomic analyses have revealed distinct metabolic strategies within the Marinisomatota phylum, which are linked to their ecological distribution [1]. These strategies represent adaptations to varying energy and nutrient regimes.

Table 2: Identified metabolic strategies within the Marinisomatota phylum.

Metabolic Strategy	Trophic Mode	Key Genetic and Functional Features
MS0	Photoautotrophic Potential	Potential for light-dependent processes associated with Crassulacean acid metabolism (CAM) [1].
MS1	Heterotrophic	Pronounced glycolytic pathway for carbon processing [1].
MS2	Heterotrophic	Lacks glycolysis, utilizing alternative pathways for carbon processing [1].

Experimental Protocols for Field Observation and Analysis

Establishing correlations between microbial abundance and oceanographic parameters requires a standardized approach to field sampling, data generation, and bioinformatic analysis. The following protocol outlines the key steps, as employed by recent studies.

Field Sampling and Environmental Data Collection

Objective: To collect representative seawater samples and concurrent environmental metadata.

• Sample Collection: Seawater samples are typically collected from the surface layer (e.g., using Niskin bottles mounted on a CTD rosette). For time-series studies, samples are collected repeatedly from the same location over months or years [15] [92].
• Environmental Parameter Measurement:
  • In-situ sensors: Record temperature, salinity, turbidity, density, conductivity, and chlorophyll a (a proxy for phytoplankton biomass) in real-time [15] [92].
  • Discrete water samples: Analyze for nutrient concentrations (nitrate, phosphate, silicate) and particulate organic matter (POM) in the laboratory [15].

Metagenomic Sequencing and Data Processing

Objective: To characterize the taxonomic and functional potential of the microbial community.

• Biomass Filtration: Filter a known volume of seawater through a series of filters (e.g., 0.22µm pore size) to capture microbial cells.
• DNA Extraction: Use commercial kits or standardized protocols to extract high-molecular-weight genomic DNA from the filters.
• Library Preparation and Sequencing: Prepare metagenomic sequencing libraries and sequence on an appropriate high-throughput platform (e.g., Illumina). The 11-year Southern California Bight study, for example, generated 3.47 Tbp of data from 267 metagenomes [15].
• Bioinformatic Processing:
  • Assembly & Binning: Assemble sequencing reads into contigs and bin them into Metagenome-Assembled Genomes (MAGs). The Marinisomatota study reconstructed 1,588 genomes using this approach [1].
  • Taxonomic Assignment: Assign taxonomy using a consensus phylogenetic placement based on genes within the assemblies [15].
  • Functional Annotation: Annotate genes using functional classification databases such as COG, KEGG, Pfam, and TIGRfam to determine metabolic potential [15] [1].

Statistical and Ecological Analysis

Objective: To quantify links between environmental data, microbial diversity, and functional potential.

• Community Composition Analysis: Use multivariate statistical methods (e.g., PERMANOVA) to test the influence of environmental factors on community structure [15] [92].
• Time-Series Analysis: Fit linear models to normalized data to calculate seasonal and interannual anomalies for specific lineages [15].
• Multi-Table Integration: Employ analyses like Multi-table Co-inertia Analysis (MCOA) to quantify the shared variance across taxonomic and multiple functional classification schemes [15].

The following diagram illustrates the integrated workflow from sampling to data analysis.

Diagram 1: Experimental workflow from field sampling to data analysis.

Visualization of Marinisomatota Metabolic Strategies

The metabolic flexibility of Marinisomatota can be conceptualized as a set of distinct strategies triggered by environmental conditions. The following diagram illustrates the hypothesized relationship between environmental parameters and the activation of these metabolic pathways.

Diagram 2: Environmental triggers of Marinisomatota metabolic strategies.

The Scientist's Toolkit: Research Reagent Solutions

The following table details key reagents, materials, and tools essential for conducting research in marine microbial ecology and environmental genomics.

Table 3: Essential reagents, materials, and tools for marine microbial ecology research.

Item / Solution	Function / Application
CTD Rosette with Niskin Bottles	Standard oceanographic equipment for collecting seawater samples at precise depths and simultaneously measuring conductivity (salinity), temperature, and depth.
DNA Extraction Kits	Commercial kits (e.g., DNeasy PowerWater Kit from QIAGEN) are optimized for efficient lysis of diverse microbial cells and purification of high-quality DNA from environmental water filters.
Metagenomic Sequencing Kits	Library preparation kits for high-throughput sequencing platforms (e.g., Illumina Nextera) to prepare metagenomic libraries from environmental DNA.
Functional Databases (KEGG, COG, Pfam)	Curated databases used for the functional annotation of predicted genes from metagenomic assemblies, enabling reconstruction of metabolic pathways [15].
Bioinformatic Suites & Platforms	Integrated platforms (e.g., Majorbio Cloud Platform) provide computational resources and standardized pipelines for processing and analyzing large-scale metagenomic data [92].
Darwin Core Standard	A standardized glossary of terms for sharing and integrating biological diversity data, crucial for making data FAIR and contributing to global repositories like OBIS and GBIF [93].

The correlation between microbial abundance and oceanographic parameters is a foundational concept in marine microbial ecology. The phylum Marinisomatota, with its recently uncovered metabolic diversity and mixotrophic potential, exemplifies how genomic insights can deepen our understanding of these environmental relationships. The structured data, standardized protocols, and visualization tools provided in this guide offer a technical framework for researchers to continue investigating these critical links. As climate change alters ocean temperature, stratification, and nutrient cycles, applying these methodologies will be essential for forecasting the responses of marine ecosystems and the biogeochemical cycles they govern.

The discovery of widespread mixotrophy—the simultaneous combination of phototrophy and heterotrophy—within the bacterial phylum Marinisomatota (formerly SAR406 or Marinimicrobia) challenges long-held taxonomic boundaries in microbial metabolism [1]. Traditionally, mixotrophy was primarily associated with eukaryotic protists, but emerging research reveals that this metabolic strategy represents a convergent evolutionary adaptation to nutrient-poor environments across biological kingdoms. This whitepaper delineates the fundamental distinctions between the metabolic architectures of marine Marinisomatota and eukaryotic mixotrophs, providing a technical framework for researchers investigating these organisms for biotechnological and pharmaceutical applications.

Within marine ecosystems, Marinisomatota have been identified as ubiquitous and abundant organisms, with relative abundances ranging from 0.18% to 36.21% across low-latitude marine regions [1]. Their recently uncovered metabolic versatility positions them as significant contributors to oceanic carbon cycling, while simultaneously offering novel pathways for bioengineering. By contrasting their genetic and metabolic frameworks with those of eukaryotic mixotrophs, this guide aims to equip scientists with the methodological and conceptual tools necessary to exploit these organisms' unique capabilities.

Metabolic Architecture of Marine Marinisomatota

Genomic Foundations and Ecological Distribution

Large-scale metagenomic analyses have revealed the extensive phylogenetic diversity of Marinisomatota, with 1,588 reconstructed genomes representing one class, two orders, 14 families, 31 genera, and 67 species [1]. This taxonomic breadth underlies a remarkable spectrum of metabolic strategies, which can be categorized into three distinct functional groups:

• MS0 (Photoautotrophic Potential): These lineages possess genetic machinery for light-dependent processes associated with Crassulacean acid metabolism (M00169), enabling carbon dioxide fixation and organic compound synthesis [1].
• MS1 (Heterotrophic with Glycolysis): Organisms utilizing this strategy maintain a pronounced glycolytic pathway for organic carbon assimilation while potentially retaining limited phototrophic capabilities.
• MS2 (Heterotrophic without Glycolysis): These heterotrophic specialists have largely abandoned photosynthetic pathways while adapting alternative energy acquisition mechanisms.

The emergence of these specialized metabolic strategies appears to be an evolutionary response to nutrient limitations within oceanic environments, particularly in the transition zone between translucent and aphotic layers where light and organic carbon availability fluctuate [1]. This niche partitioning demonstrates how metabolic flexibility underpins ecological success in variable environments.

Key Metabolic Pathways and Energy Conservation

Marinisomatota's metabolic plasticity is encoded within specialized gene clusters that enable rapid adaptation to shifting resource availability. Among 14 identified families, S15-B10, TCS55, UBA1611, UBA2128, and UBA8226 exhibit particular promise for light-dependent processes [1]. Their capacity to harness light for carbon fixation provides a critical survival advantage in oligotrophic waters, where organic carbon sources are scarce.

Unlike eukaryotic mixotrophs that often rely on phagocytosis for organic carbon acquisition, Marinisomatota utilize sophisticated transporter systems for organic substrate uptake, coupled with potentially novel phototrophic mechanisms that differ fundamentally from chloroplast-based photosynthesis. This transporter-based strategy represents a more energy-efficient approach to mixotrophy that avoids the substantial overhead of maintaining phagocytic machinery.

Eukaryotic Mixotrophs: Diverse Nutritional Strategies

Classification Frameworks for Eukaryotic Mixotrophy

Eukaryotic mixotrophs employ a far more diverse array of nutritional strategies, which researchers have categorized into several functional frameworks. The Jones classification system organizes these organisms based on the relative importance of feeding versus photosynthesis [94]:

• Group A: Primarily heterotrophic (phagotrophic), only using light energy when prey concentration is limited
• Group B: Primarily phototrophic, using feeding as an auxiliary strategy when light is limited
• Group C: Require both light and feeding for growth, utilizing feeding when light is limited
• Group D: predominantly phototrophic, only using phagotrophy during extended dark periods

An alternative classification by Mitra et al. specifically developed for marine planktonic mixotrophs distinguishes between [94]:

• Constitutive Mixotrophs (CMs): Innate ability to perform photosynthesis while also being phagotrophic
• Non-Constitutive Mixotrophs (NCMs): Must consume prey to acquire photosynthetic capacity, further divided into generalists (GNCM) and specialists (SNCM)

Genetic and Functional Signatures

Metatranscriptomic analyses of freshwater microbial eukaryotes have revealed significant metabolic versatility among mixotrophic species [38]. Sequence similarity network (SSN)-based approaches analyzing 2,165,106 proteins have demonstrated substantial sharing of protein families between mixotrophic and both strictly phototrophic and heterotrophic microorganisms [38]. This genetic promiscuity underscores the functional redundancy that enables metabolic flexibility in response to environmental conditions.

Unlike the transporter-based organic carbon acquisition in Marinisomatota, many eukaryotic mixotrophs utilize phagotrophy, with key genetic markers including peptidases, proton pumps, and lysosomal enzymes (e.g., cathepsin and rhodopsin) [38]. These genes facilitate the capture, ingestion, and digestion of bacterial prey, representing a more energetically costly but nutritionally diverse strategy compared to bacterial approaches.

Table 1: Comparative Features of Eukaryotic and Bacterial (Marinisomatota) Mixotrophs

Feature	Eukaryotic Mixotrophs	Marinisomatota
Carbon Acquisition	Phagocytosis, osmotrophy, photosynthesis	Transporter-based uptake, potential photoautotrophy
Genetic Flexibility	Protein family sharing between trophic modes	Three distinct metabolic strategies (MS0, MS1, MS2)
Ecological Distribution	Freshwater and marine environments; ~50% of tiny plankton [94]	Global oceans; 0.18-36.21% relative abundance [1]
Key Markers	PsbO (phototrophy), cathepsin, rhodopsin (phagotrophy) [38]	Crassulacean acid metabolism genes (M00169) [1]
Energy Conservation	Mitochondrial respiration, photophosphorylation	Varied ATPases, potential proton gradients

Methodological Approaches for Studying Mixotrophic Metabolism

Cultivation-Based Techniques

For eukaryotic microalgae, controlled cultivation systems enable precise manipulation of trophic conditions. The following protocol for assessing mixotrophic potential in marine microalgae exemplifies this approach [29]:

• Strain Selection and Preculturing: Select target species (e.g., Phaeodactylum tricornutum, Chlorella sp., Nannochloropsis granulata) and maintain precultures in 40 mL flasks at 22°C with continuous light (20 μmol photons m⁻² s⁻¹) using enriched marine media such as GoldMedium.

• Experimental Conditions Setup:

  • Phototrophy: Growth in basal medium without external organic carbon
  • Mixotrophy: Supplement medium with 4.6 g/L glycerol as organic carbon source
  • Phototrophy with Bicarbonate: Supplement with 1.26 g/L bicarbonate
  • Mixotrophy with Glycerol and Bicarbonate: Combine both supplements

• Growth Monitoring: Measure optical density at 750 nm (OD₇₅₀) daily using spectrophotometry with 1 mL cuvettes (10 mm light path).

• Biomass Quantification: After 10 days, filter 2-3 mL of culture through pre-weighed 0.2 μm filters, rinse with physiological solution, dry at 100°C for 24 hours, and calculate dry weight using the formula: Dry weight (g/L) = (Weight of dried filter with biomass - Initial weight of filter) / Volume of filtered culture [29].

This method has demonstrated significant biomass yield improvements under mixotrophic conditions, with Phaeodactylum tricornutum emerging as a particularly promising candidate for bioactive compound production [29].

Sequence Similarity Network (SSN) Analysis

For uncultivable organisms like many Marinisomatota, metatranscriptomic approaches coupled with SSN analysis provide powerful alternatives [38]:

• Sample Collection and RNA Extraction: Collect environmental samples from target habitats (e.g., water column depth profiles, sediment layers). For lake ecosystems, sample both oxic and anoxic zones at multiple depths and times.

• Metatranscriptomic Library Construction:

  • Extract total RNA and prepare sequencing libraries
  • Sequence using Illumina NovaSeq 6000 (2×150 bp configuration)
  • Assemble paired-end reads using Velvet/Oases with k-mer size optimization
  • Remove contigs <150 bp and reduce redundancy using CD-HIT-EST (95% identity, 90% coverage)

• Protein Prediction and Annotation:

  • Predict proteins using Transdecoder.LongOrfs and TransDecoder.Predict
  • Screen against AntiFam database to remove spurious ORFs
  • Assign KEGG Orthology identifiers using KoFamScan
  • Perform taxonomic affiliation with MMseqs2 against MetaEuk database

• SSN Construction and Analysis:

  • Build network with vertices representing sequences and edges representing similarity/coverage
  • Cluster sequences into connected components based on similarity thresholds
  • Identify trophic mode-specific protein clusters through topological analysis

This approach enables researchers to include the substantial "microbial dark matter" (40-60% of sequences without database matches) in functional analyses, revealing novel metabolic pathways [38].

Figure 1: Metatranscriptomic workflow for identifying mixotrophic metabolic pathways in environmental samples using Sequence Similarity Networks (SSN).

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 2: Essential Research Reagents for Investigating Mixotrophic Metabolism

Reagent/Material	Function	Application Examples
GoldMedium (GM) [29]	Cultivation medium for marine microalgae	Supporting growth of Phaeodactylum tricornutum, Chlorella sp., and Nannochloropsis granulata under different trophic conditions
Glycerol [29]	Organic carbon source for mixotrophic cultivation	Added at 4.6 g/L to induce mixotrophic growth in eukaryotic microalgae
Bicarbonate [29]	Inorganic carbon source and pH buffer	Supplemented at 1.26 g/L to enhance phototrophic growth and stabilize pH
Formaldehyde [95]	Fixative for microbial samples	Preservation of samples for bacterial enumeration (2% final concentration)
DAPI Stain [95]	Fluorescent DNA dye	Cell counting and visualization using epifluorescence microscopy
Lugol's Solution [95]	Protist preservative and stain	Fixation and identification of protist species in grazing experiments
AntiFam Database [38]	HMM database for spurious ORF detection	Quality control in metatranscriptomic analyses to remove false protein predictions
KO HMM Profiles [38]	Protein family hidden Markov models	Functional annotation of predicted proteins in omics studies

Biotechnological and Pharmaceutical Applications

Enhancing Bioactive Compound Production

Mixotrophic cultivation strategies significantly enhance the production of valuable bioactive compounds in eukaryotic microalgae. Research demonstrates that Phaeodactylum tricornutum cultivated under mixotrophic conditions exhibits substantially higher antiproliferative activity against human melanoma cells and increased antibacterial effects against Staphylococcus aureus compared to phototrophic cultures [29]. Similarly, Chlorella sp. shows enhanced antibacterial activity under mixotrophic conditions, broadening its potential pharmaceutical applications.

Metabolomic analyses have identified numerous compounds responsible for this enhanced bioactivity, including polyunsaturated fatty acids (e.g., eicosapentaenoic acid), pigments (e.g., fucoxanthin), and antimicrobial peptides [29]. The metabolic flexibility of mixotrophic organisms enables more diverse secondary metabolite profiles, representing a promising resource for drug discovery pipelines.

Engineering Transporter Systems for Biotechnology

In eukaryotic microalgae, transporter proteins represent critical gatekeepers for organic carbon acquisition and present promising targets for metabolic engineering [96]. Recent advances in characterizing these transporters—including those for sugars, organic acids, and other carbon substrates—enable genetic manipulation to enhance mixotrophic capabilities for industrial applications.

Engineering these transporter systems holds particular promise for increasing biomass yields and target compound production in cultivated microalgae. Unlike the complex genetic manipulation required to introduce entirely new metabolic pathways, transporter engineering offers a more straightforward approach to enhancing organic carbon utilization efficiency, potentially revolutionizing commercial microalgal biotechnology.

Figure 2: Metabolic integration in mixotrophic organisms showing inputs, processing modules, and potential biotechnological outputs.

Research Gaps and Future Directions

Despite significant advances in understanding mixotrophic metabolism across kingdoms, critical knowledge gaps remain. For Marinisomatota, the specific molecular mechanisms underlying their photoautotrophic capabilities require further characterization, particularly the functional expression of Crassulacean acid metabolism-like pathways [1]. Additionally, most eukaryotic mixotroph research has focused on readily cultivable species, leaving substantial diversity unexplored.

Future research priorities should include:

• Development of targeted cultivation methods for uncultivated mixotrophic bacteria
• Single-cell genomic and transcriptomic approaches to elucidate metabolic plasticity at fine taxonomic scales
• High-throughput screening of mixotroph-derived compound libraries for pharmaceutical applications
• Engineering synthetic mixotrophy in industrial microorganisms for improved carbon utilization

The continued investigation of mixotrophic metabolism across biological kingdoms promises not only to advance fundamental understanding of microbial ecology but also to unlock novel biotechnological platforms for sustainable chemical production and therapeutic discovery.

Mixotrophy, the combination of autotrophic and heterotrophic nutrition in a single organism, is a widespread trophic strategy in marine ecosystems. This guide explores its profound impact on trophic transfer efficiency and carbon export, with a specific focus on emerging research concerning the phylum Marinisomatota (formerly recognized as Marinimicrobia, Marine Group A, and SAR406). Traditionally viewed as heterotrophic, certain members of Marinisomatota have demonstrated the capacity for light-dependent metabolic processes, revealing a complex mixotrophic potential that influences global biogeochemical cycles [1] [7]. Understanding the mechanisms through which mixotrophy alters ecosystem function is critical for accurate predictive modeling of carbon sequestration in the ocean.

Core Concepts and Definitions

Defining Mixotrophic Strategies

Mixotrophs are not a uniform group; they exist on a spectrum between pure phototrophy and pure phagotrophy. A functional classification system identifies three primary physiological types [97]:

• Type I (Ideal Mixotrophs): Capable of using phototrophy and phagotrophy equally well, though this type is considered rare in nature.
• Type II (Primarily Phototrophic): Phagocytic "algae" that primarily engage in photosynthesis but use phagotrophy to acquire inorganic nutrients (e.g., nitrogen, phosphorus) when dissolved nutrients are limiting.
• Type III (Primarily Heterotrophic): Photosynthetic "protozoa" that are predominantly heterotrophic but supplement their carbon budget using stolen chloroplasts (kleptoplastidy) or by harboring algal endosymbionts.

Key Ecosystem Metrics

• Trophic Transfer Efficiency: The efficiency with which energy and biomass are passed from one trophic level to the next. Mixotrophy enhances this by allowing consumers to use photosynthesis to offset respiratory losses [98].
• Carbon Export (The Biological Pump): The process of transporting organic carbon from the surface ocean to the deep sea, leading to long-term carbon storage. The particle transfer efficiency (Teff) is a key metric, defined as the fraction of sinking particulate organic carbon (POC) that reaches the deep ocean before remineralization [99].

Impact of Mixotrophy on Ecosystem Function

Enhancement of Trophic Transfer Efficiency

Global model simulations demonstrate that incorporating mixotrophy fundamentally changes the flow of energy. In a traditional "two-guild" model (strict phytoplankton-zooplankton dichotomy), energy transfer is inefficient due to respiratory losses at each trophic level. In a mixotrophic model, consumers can use photosynthesis to compensate for these losses, leading to a much more efficient transfer of biomass to higher trophic levels [98]. This enhanced efficiency supports greater biomass in larger size classes.

Boosting Carbon Export and Sequestration

The shift in community structure toward larger organisms, driven by mixotrophy, directly enhances the biological carbon pump. Larger organisms produce larger, faster-sinking particles, such as fecal pellets and aggregates, which are more likely to reach the deep ocean before being remineralized. Model results indicate that mixotrophy can lead to an approximately threefold increase in global mean organism size and an ~35% increase in sinking carbon flux [98]. This represents a significant enhancement of the ocean's capacity for carbon sequestration.

Metabolic Strategies in Marinisomatota

Recent metagenomic studies of the ubiquitous phylum Marinisomatota have identified distinct metabolic strategies that underscore their ecological flexibility [1] [7]:

• MS0: Exhibits photoautotrophic potential.
• MS1: Heterotrophic with a pronounced glycolytic pathway.
• MS2: Heterotrophic without glycolysis. The emergence of these strategies, particularly the potential for mixotrophy in families like S15-B10 and TCS55, allows Marinisomatota to thrive across different oceanic regions and contributes to their role in biogeochemical cycling [1].

Table 1: Quantitative Impacts of Mixotrophy on Global Ocean Biogeochemistry

Ecosystem Metric	Impact of Mixotrophy	Source of Evidence
Global Mean Plankton Size	~3-fold increase (from 17 µm to 46 µm diameter)	Global Ecosystem Model [98]
Sinking Carbon Flux	~35% increase	Global Ecosystem Model [98]
Biomass in Microplankton	Significant increase in 20-200 µm size range	Global Ecosystem Model [98]
Marinisomatota Abundance	Ranges from 0.18% to 36.21% in low-latitude regions	Metagenomic Survey [1]

Methodologies for Studying Mixotrophy

Genome-Scale Metabolic Modeling (GEM)

Application: Used to predict the metabolic capabilities of diatoms like Cylindrotheca closterium and to infer trophic modes from environmental transcriptomic data [100].

Protocol:

• Reconstruction: Develop a draft metabolic network from an annotated genome using toolboxes like RAVEN and COBRA.
• Curation & Gap-Filling: Manually verify gene-protein-reaction (GPR) associations and add necessary reactions to enable the production of all biomass precursors.
• Compartmentalization: Localize reactions within subcellular compartments (e.g., cytosol, chloroplast, mitochondria).
• Contextualization: Integrate transcriptomic data (e.g., from Tara Oceans) with the GEM to predict in situ metabolic fluxes and trophic strategies (photoautotrophic, mixotrophic, or heterotrophic) under different environmental conditions [100].

Global Ecosystem Modeling

Application: To assess the large-scale biogeochemical impacts of mixotrophy by comparing simulations with and without strict trophic guilds [98].

Protocol:

• Model Formulation: Develop a size-structured plankton food web model. The "two-guild" model has separate phytoplankton and zooplankton in each size class. The "mixotrophy" model has a single population per size class capable of both resource uptake and predation.
• Parameterization: Define model parameters based on empirical data for processes such as nutrient uptake, grazing, and mortality.
• Simulation & Validation: Run global simulations and validate output against observed distributions of chlorophyll a, primary production, and nutrient concentrations.
• Analysis: Quantify emergent properties like trophic transfer efficiency, community size structure, and vertical carbon flux.

Metagenomic and Metatranscriptomic Analysis

Application: To investigate the ecological distribution and in situ metabolic activity of specific groups like Marinisomatota [1].

Protocol:

• Sampling: Collect microbial biomass from various oceanic regions and depths via filtration.
• Sequencing: Extract and sequence total environmental DNA (metagenomics) and RNA (metatranscriptomics).
• Genome Reconstruction: Assemble sequences and bin them into Metagenome-Assembled Genomes (MAGs).
• Annotation & Analysis: Annotate metabolic pathways and analyze gene expression profiles to identify active metabolic strategies and their distribution relative to environmental gradients.

Diagram 1: Ecosystem impact of mixotrophy logical flow.

The Scientist's Toolkit

Table 2: Essential Research Reagents and Computational Tools

Item / Tool	Function / Application	Specific Example
RAVEN & COBRA Toolboxes	Software for reconstructing, curating, and simulating Genome-Scale Metabolic Models (GEMs).	Predicting diatom trophic mode from transcriptomic data [100].
Metagenomic Assemblers	Software for assembling sequencing reads into longer contigs and genomes from complex environmental samples.	Reconstructing 1,588 Marinisomatota genomes from global ocean data [1].
BiGG Models Database	A knowledgebase of curated metabolic models and reactions used for standardizing and validating GEMs.	Incorporating reactions during GEM reconstruction [100].
Neutrally Buoyant Sediment Traps	Instruments that collect sinking particulate matter at depth without the flow disruption of traditional traps.	Directly measuring vertical flux of Particulate Organic Carbon (POC) [99].
Underwater Visual Profilers (UVP)	Instruments that capture in-situ images of marine particles and plankton to resolve size spectra and composition.	Establishing the spatial pattern of particle size in the mesopelagic zone [99].

The integration of mixotrophy, as exemplified by the metabolic flexibility of groups like Marinisomatota and diatoms, is essential for a modern understanding of marine ecosystems. Moving beyond the obsolete phytoplankton-zooplankton dichotomy reveals a more efficient and interconnected food web. The evidence is clear: mixotrophy significantly enhances the transfer of biomass to higher trophic levels and amplifies the ocean's biological pump, leading to greater long-term carbon storage. Future research, particularly the integration of genomic and modeling approaches, will be crucial for refining our predictions of how these vital ecological functions will respond to a changing climate.

Conclusion

Marinisomatota represent a paradigm shift in understanding marine microbial ecology, with their recently elucidated mixotrophic adaptations revealing unexpected metabolic sophistication in ocean ecosystems. The discovery of three distinct metabolic strategies underscores their evolutionary innovation in responding to nutrient limitation, while advanced cultivation and omics techniques are finally making these 'microbial dark matter' organisms accessible for detailed study. For biomedical researchers, Marinisomatota offer tantalizing potential as novel platforms for biotechnological innovation, drawing inspiration from established microalgal applications in drug delivery and biohybrid systems. Future research should prioritize functional characterization of their diverse metabolic pathways, development of robust genetic tools, and exploration of their specific bioactive compound potential. As climate change alters ocean ecosystems, understanding the role of versatile microbes like Marinisomatota becomes increasingly crucial for predicting biogeochemical cycles and developing sustainable biomedical and environmental solutions.

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