Genome content predicts the carbon catabolic preferences of heterotrophic bacteria

Bridging dietary polysaccharides and gut microbiome: How to achieve precision modulation for gut health promotion

Dietary polysaccharides function not only as indispensable nutrients and energy sources for the host organism but also as critical substrates for the gut microbiota. Gut microorganisms possess the ability to selectively degrade and metabolize specific dietary polysaccharides, thus fostering their proliferation and yielding crucial bioactive metabolites that potentially influence host metabolic and immune pathways. Dysbiosis of the gut microbiota has been extensively documented to be closely linked with the onset and progression of various diseases; in this regard, the precision modulation strategy of the gut microbiome via dietary polysaccharides holds substantial potential to enhance human health. Here, we delve into the therapeutic potential of dietary polysaccharides for the precision modulation of specific gut microorganisms via dietary interventions, with particular emphasis on their implications for the prevention and management of metabolic and inflammatory disorders. Given the complexity of the human gut microbiome and the varying degrees to which different bacterial members utilize carbohydrates, we conduct an in-depth analysis of the differential utilization of dietary polysaccharides by key gut microbiome, with particular emphasis on the role of carbohydrate-active enzymes in these processes. Furthermore, we elucidate the pivotal role of carbohydrate utilization within microbial cross-feeding networks and its significance in maintaining gut homeostasis. In summary, this review investigates the precision modulation of gut microbiota through dietary polysaccharides, with the aim of establishing a theoretical foundation for the development of personalized nutritional interventions. These strategies hold substantial potential for enhancing human health and offer valuable opportunities for the prevention and treatment of microbiota-associated diseases.

The architecture of theory and data in microbiome design: towards an S-matrix for microbiomes

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used a range of mathematical models to understand, predict, and control microbiomes, ranging from mechanistic models, putting microbial populations and their interactions as the focus, to purely statistical approaches, searching for patterns in empirical and experimental data. We review the success and limitations of these modeling approaches when designing novel microbiomes, especially when guided by (inevitably) incomplete experimental data. Although successful at predicting generic patterns of community assembly, mechanistic and phenomenological models tend to fall short of the precision needed to design and implement specific functionality in a microbiome. We argue that to effectively design microbiomes with optimal functions in diverse environments, ecologists should combine data-driven techniques with mechanistic models - a middle, third way for using theory to inform design.

Codon bias, nucleotide selection, and genome size predict in situ bacterial growth rate and transcription in rewetted soil

In soils, the first rain after a prolonged dry period represents a major pulse event impacting soil microbial community function, yet we lack a full understanding of the genomic traits associated with the microbial response to rewetting. Genomic traits such as codon usage bias and genome size have been linked to bacterial growth in soils-however, often through measurements in culture. Here, we used metagenome-assembled genomes (MAGs) with 18O-water stable isotope probing and metatranscriptomics to track genomic traits associated with growth and transcription of soil microorganisms over one week following rewetting of a grassland soil. We found that codon bias in ribosomal protein genes was the strongest predictor of growth rate. We also found higher growth rates in bacteria with smaller genomes, suggesting that reduced genome size enables a faster response to pulses in soil bacteria. Faster transcriptional upregulation of ribosomal protein genes was associated with high codon bias and increased nucleotide skew. We found that several of these relationships existed within phyla, indicating that these associations between genomic traits and activity could be generalized characteristics of soil bacteria. Finally, we used publicly available metagenomes to assess the distribution of codon bias across a pH gradient and found that microbial communities in higher pH soils-which are often more water limited and pulse driven-have higher codon usage bias in their ribosomal protein genes. Together, these results provide evidence that genomic characteristics affect soil microbial activity during rewetting and pose a potential fitness advantage for soil bacteria where water and nutrient availability are episodic.

A Global Relationship Between Genome Size and Encoded Carbon Metabolic Strategies of Soil Bacteria

Microbial traits are critical for carbon sequestration and degradation in terrestrial ecosystems. Yet, our understanding of the relationship between carbon metabolic strategies and genomic traits like genome size remains limited. To address this knowledge gap, we conducted a global-scale meta-analysis of 2650 genomes, integrated whole-genome sequencing data, and performed a continental-scale metagenomic field study. We found that genome size was tightly associated with an increase in the ratio between genes encoding for polysaccharide decomposition and biomass synthesis that we defined as the carbon acquisition-to-biomass yield ratio (A/Y). We also show that horizontal gene transfer played a major evolutionary role in the expanded bacterial capacities in carbon acquisition. Our continental-scale field study further revealed a significantly negative relationship between the A/Y ratio and soil organic carbon stocks. Our work demonstrates a global relationship between genome size and the encoded carbon metabolic strategies of soil bacteria across terrestrial microbiomes.

Annotation-free prediction of microbial dioxygen utilization

Aerobes require dioxygen (O2) to grow; anaerobes do not. However, nearly all microbes-aerobes, anaerobes, and facultative organisms alike-express enzymes whose substrates include O2, if only for detoxification. This presents a challenge when trying to assess which organisms are aerobic from genomic data alone. This challenge can be overcome by noting that O2 utilization has wide-ranging effects on microbes: aerobes typically have larger genomes encoding distinctive O2-utilizing enzymes, for example. These effects permit high-quality prediction of O2 utilization from annotated genome sequences, with several models displaying ≈80% accuracy on a ternary classification task for which blind guessing is only 33% accurate. Since genome annotation is compute-intensive and relies on many assumptions, we asked if annotation-free methods also perform well. We discovered that simple and efficient models based entirely on genomic sequence content-e.g., triplets of amino acids-perform as well as intensive annotation-based classifiers, enabling rapid processing of genomes. We further show that amino acid trimers are useful because they encode information about protein composition and phylogeny. To showcase the utility of rapid prediction, we estimated the prevalence of aerobes and anaerobes in diverse natural environments cataloged in the Earth Microbiome Project. Focusing on a well-studied O2 gradient in the Black Sea, we found quantitative correspondence between local chemistry (O2:sulfide concentration ratio) and the composition of microbial communities. We, therefore, suggest that statistical methods like ours might be used to estimate, or "sense," pivotal features of the chemical environment using DNA sequencing data.IMPORTANCEWe now have access to sequence data from a wide variety of natural environments. These data document a bewildering diversity of microbes, many known only from their genomes. Physiology-an organism's capacity to engage metabolically with its environment-may provide a more useful lens than taxonomy for understanding microbial communities. As an example of this broader principle, we developed algorithms that accurately predict microbial dioxygen utilization directly from genome sequences without annotating genes, e.g., by considering only the amino acids in protein sequences. Annotation-free algorithms enable rapid characterization of natural samples, highlighting quantitative correspondence between sequences and local O2 levels in a data set from the Black Sea. This example suggests that DNA sequencing might be repurposed as a multi-pronged chemical sensor, estimating concentrations of O2 and other key facets of complex natural settings.

Evolution in microbial microcosms is highly parallel, regardless of the presence of interacting species

Evolution often follows similar trajectories in replicate populations, suggesting that it may be predictable. However, populations are naturally embedded in multispecies communities, and the extent to which evolution is contingent on the specific species interacting with the focal population is still largely unexplored. Here, we study adaptations in strains of 11 different species, experimentally evolved both in isolation and in various pairwise co-cultures. Although partner-specific effects are detectable, evolution was mostly shared between strains evolved with different partners; similar changes occurred in strains' growth abilities, in community properties, and in about half of the repeatedly mutated genes. This pattern persisted even in species pre-adapted to the abiotic conditions. These findings indicate that evolution may not always depend strongly on the biotic environment, making predictions regarding coevolutionary dynamics less challenging than previously thought. A record of this paper's transparent peer review process is included in the supplemental information.

Metabolic Plasticity Shapes Microbial Communities across a Temperature Gradient

AbstractA central challenge in community ecology is understanding and predicting the effects of abiotic factors on community assembly. In particular, microbial communities play a central role in the ecosystem, but we do not understand how changing factors like temperature are going to affect community composition or function. In this article, we studied the self-assembly of multiple communities in synthetic environments to understand changes in microbial community composition based on metabolic responses of different functional groups along a temperature gradient. In many microbial communities, different microbial functional groups coexist through the partitioning of carbon sources in an emergent trophic structure (cross-feeding). In this system, respirofermentative bacteria display a preference for the sugars supplied as the only carbon source but secrete secondary carbon sources (organic acids) that are more efficiently consumed by obligate respirators. As a consequence of this trophic structure, the metabolic plasticity of the respirofermenters has downstream consequences for the relative abundance of respirators across temperatures. We found that the effects of different temperatures on microbial composition can largely be described by an increase in fermentation by-products with increasing temperatures from the respirofermentative bacteria. This research highlights the importance of metabolic plasticity and metabolic trade-offs in predicting species interactions and community dynamics across abiotic gradients.

Unveiling the deterministic dynamics of microbial meta-metabolism: a multi-omics investigation of anaerobic biodegradation

BACKGROUND

Microbial anaerobic metabolism is a key driver of biogeochemical cycles, influencing ecosystem function and health of both natural and engineered environments. However, the temporal dynamics of the intricate interactions between microorganisms and the organic metabolites are still poorly understood. Leveraging metagenomic and metabolomic approaches, we unveiled the principles governing microbial metabolism during a 96-day anaerobic bioreactor experiment.

RESULTS

During the turnover and assembly of metabolites, homogeneous selection was predominant, peaking at 84.05% on day 12. Consistent dynamic coordination between microbes and metabolites was observed regarding their composition and assembly processes. Our findings suggested that microbes drove deterministic metabolite turnover, leading to consistent molecular conversions across parallel reactors. Moreover, due to the more favorable thermodynamics of N-containing organic biotransformations, microbes preferentially carried out sequential degradations from N-containing to S-containing compounds. Similarly, the metabolic strategy of C18 lipid-like molecules could switch from synthesis to degradation due to nutrient exhaustion and thermodynamical disadvantage. This indicated that community biotransformation thermodynamics emerged as a key regulator of both catabolic and synthetic metabolisms, shaping metabolic strategy shifts at the community level. Furthermore, the co-occurrence network of microbes-metabolites was structured around microbial metabolic functions centered on methanogenesis, with CH4 as a network hub, connecting with 62.15% of total nodes as 1st and 2nd neighbors. Microbes aggregate molecules with different molecular traits and are modularized depending on their metabolic abilities. They established increasingly positive relationships with high-molecular-weight molecules, facilitating resource acquisition and energy utilization. This metabolic complementarity and substance exchange further underscored the cooperative nature of microbial interactions.

CONCLUSIONS

All results revealed three key rules governing microbial anaerobic degradation. These rules indicate that microbes adapt to environmental conditions according to their community-level metabolic trade-offs and synergistic metabolic functions, further driving the deterministic dynamics of molecular composition. This research offers valuable insights for enhancing the prediction and regulation of microbial activities and carbon flow in anaerobic environments. Video Abstract.

Interactive tools for functional annotation of bacterial genomes

Automated annotations of protein functions are error-prone because of our lack of knowledge of protein functions. For example, it is often impossible to predict the correct substrate for an enzyme or a transporter. Furthermore, much of the knowledge that we do have about the functions of proteins is missing from the underlying databases. We discuss how to use interactive tools to quickly find different kinds of information relevant to a protein's function. Many of these tools are available via PaperBLAST (http://papers.genomics.lbl.gov). Combining these tools often allows us to infer a protein's function. Ideally, accurate annotations would allow us to predict a bacterium's capabilities from its genome sequence, but in practice, this remains challenging. We describe interactive tools that infer potential capabilities from a genome sequence or that search a genome to find proteins that might perform a specific function of interest. Database URL: http://papers.genomics.lbl.gov.

Digital Microbe: a genome-informed data integration framework for team science on emerging model organisms

The remarkable pace of genomic data generation is rapidly transforming our understanding of life at the micron scale. Yet this data stream also creates challenges for team science. A single microbe can have multiple versions of genome architecture, functional gene annotations, and gene identifiers; additionally, the lack of mechanisms for collating and preserving advances in this knowledge raises barriers to community coalescence around shared datasets. "Digital Microbes" are frameworks for interoperable and reproducible collaborative science through open source, community-curated data packages built on a (pan)genomic foundation. Housed within an integrative software environment, Digital Microbes ensure real-time alignment of research efforts for collaborative teams and facilitate novel scientific insights as new layers of data are added. Here we describe two Digital Microbes: 1) the heterotrophic marine bacterium Ruegeria pomeroyi DSS-3 with > 100 transcriptomic datasets from lab and field studies, and 2) the pangenome of the cosmopolitan marine heterotroph Alteromonas containing 339 genomes. Examples demonstrate how an integrated framework collating public (pan)genome-informed data can generate novel and reproducible findings.

Evolution and stability of complex microbial communities driven by trade-offs

Microbial communities are ubiquitous in nature and play an important role in ecology and human health. Cross-feeding is thought to be core to microbial communities, though it remains unclear precisely why it emerges. Why have multi-species microbial communities evolved in many contexts and what protects microbial consortia from invasion? Here, we review recent insights into the emergence and stability of coexistence in microbial communities. A particular focus is the long-term evolutionary stability of coexistence, as observed for microbial communities that spontaneously evolved in the E. coli long-term evolution experiment (LTEE). We analyze these findings in the context of recent work on trade-offs between competing microbial objectives, which can constitute a mechanistic basis for the emergence of coexistence. Coexisting communities, rather than monocultures of the 'fittest' single strain, can form stable endpoints of evolutionary trajectories. Hence, the emergence of coexistence might be an obligatory outcome in the evolution of microbial communities. This implies that rather than embodying fragile metastable configurations, some microbial communities can constitute formidable ecosystems that are difficult to disrupt.

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems

Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome's functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute's Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data.

IMPORTANCE

Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome's metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

Simple Genomic Traits Predict Rates of Polysaccharide Biodegradation

Natural and chemically modified polysaccharides are extensively employed across a wide array of industries, leading to their prevalence in the waste streams of industrialized societies. With projected increasing demand, a pressing challenge is to swiftly assess and predict their biodegradability to inform the development of new sustainable materials. In this study, we developed a scalable method to evaluate polysaccharide breakdown by measuring microbial growth and analyzing microbial genomes. Our approach, applied to polysaccharides with various structures, correlates strongly with well-established regulatory methods based on oxygen demand. We show that modifications to the polysaccharide structure decreased degradability and favored the growth of microbes adapted to break down chemically modified sugars. More broadly, we discovered two main types of microbial communities associated with different polysaccharide structures─one dominated by fast-growing microbes and another by specialized degraders. Surprisingly, we were able to predict biodegradation rates based only on two genomic features that define these communities: the abundance of genes related to rRNA (indicating fast growth) and the abundance of glycoside hydrolases (enzymes that break down polysaccharides), which together predict nearly 70% of the variation in polysaccharide breakdown. This suggests a trade-off, whereby microbes are either adapted for fast growth or for degrading complex polysaccharide chains, but not both. Finally, we observe that viral elements (prophages) encoded in the genomes of degrading microbes are induced in easily degradable polysaccharides, leading to complex dynamics in biomass accumulation during degradation. In summary, our work provides a practical approach for efficiently assessing polymer degradability and offers genomic insights into how microbes break down polysaccharides.

Connecting microbial community assembly and function

Microbial ecology is moving away from purely descriptive analyses to experiments that can determine the underlying mechanisms driving changes in community assembly and function. More species-rich microbial communities generally have higher functional capabilities depending on if there is positive selection of certain species or complementarity among different species. When building synthetic communities or laboratory enrichment cultures, there are specific choices that can increase the number of species able to coexist. Higher resource complexity or the addition of physical niches are two of the many factors leading to greater biodiversity and associated increases in functional capabilities. We can use principles from community ecology and knowledge of microbial physiology to generate improved microbiomes for use in medicine, agriculture, or environmental management.

On the evolution of chromosomal regions with high gene strand bias in bacteria

On circular bacterial chromosomes, the majority of genes are coded on the leading strand. This gene strand bias (GSB) ranges from up to 85% in some Bacillota to a little more than 50% in other phyla. The factors determining the extent of the strand bias remain to be found. Here, we report that species in the phylum Gemmatimonadota share a unique chromosome architecture, distinct from neighboring phyla: in a conserved 600-kb region around the terminus of replication, almost all genes were located on the leading strands, while on the remaining part of the chromosome, the strand preference was more balanced. The high strand bias (HSB) region harbors the rRNA clusters, core, and highly expressed genes. Selective pressure for reduction of collisions with DNA replication to minimize detrimental mutations can explain the conservation of essential genes in this region. Repetitive and mobile elements are underrepresented, suggesting reduced recombination frequency by structural isolation from other parts of the chromosome. We propose that the HSB region forms a distinct chromosomal domain. Gemmatimonadota chromosomes evolved mainly by expansion through horizontal gene transfer and duplications outside of the ancient high strand bias region. In support of our hypothesis, we could further identify two Spiroplasma strains on a similar evolutionary path.IMPORTANCEOn bacterial chromosomes, a preferred location of genes on the leading strand has evolved to reduce conflicts between replication and transcription. Despite a vast body of research, the question why bacteria show large differences in their gene strand bias is still not solved. The discovery of "hybrid" chromosomes in different phyla, including Gemmatimonadota, in which a conserved high strand bias is found exclusively in a region at ter, points toward a role of nucleoid structure, additional to replication, in the evolution of strand preferences. A fine-grained structural analysis of the ever-increasing number of available bacterial genomes could help to better understand the forces that shape the sequential and spatial organization of the cell's information content.

Carbonic anhydrase and bacterial metabolism: a chance for antibacterial drug discovery

INTRODUCTION

Carbonic anhydrases (CAs, EC 4.2.1.1) play a pivotal role in the regulation of carbon dioxide , bicarbonate, and hydrogen ions within bacterial cells, ensuring pH homeostasis and facilitating energy production. We conducted a systematic literature search (PubMed, Web of Science, and Google Scholar) to examine the intricate interplay between CAs and bacterial metabolism, revealing the potential of CA inhibitors (CAIs) as innovative therapeutic agents against pathogenic bacteria.

AREA COVERED

Inhibition of bacterial CAs was explored in various pathogens, emphasizing the CA roles in microbial virulence, survival, and adaptability. Escherichia coli, a valid and convenient model microorganism, was recently used to investigate the effects of acetazolamide (AAZ) on the bacterial life cycle. Furthermore, the effectiveness of CAIs against pathogenic bacteria has been further substantiated for Vancomycin-Resistant Enterococci (VRE) and antibiotic-resistant Neisseria gonorrhoeae strains.

EXPERT OPINION

CAIs target bacterial metabolic pathways, offering alternatives to conventional therapies. They hold promise against drug-resistant microorganisms such as VRE and N. gonorrhoeae strains. CAIs offer promising avenues for addressing antibiotic resistance and underscore their potential as novel antibacterial agents. Recognizing the central role of CAs in bacterial growth and pathogenicity will pave the way for innovative infection control and treatment strategies possibly also for other antibiotic resistant species.

Shaping of microbial phenotypes by trade-offs

Growth rate maximization is an important fitness strategy for microbes. However, the wide distribution of slow-growing oligotrophic microbes in ecosystems suggests that rapid growth is often not favored across ecological environments. In many circumstances, there exist trade-offs between growth and other important traits (e.g., adaptability and survival) due to physiological and proteome constraints. Investments on alternative traits could compromise growth rate and microbes need to adopt bet-hedging strategies to improve fitness in fluctuating environments. Here we review the mechanistic role of trade-offs in controlling bacterial growth and further highlight its ecological implications in driving the emergences of many important ecological phenomena such as co-existence, population heterogeneity and oligotrophic/copiotrophic lifestyles.

Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations

The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

Nitrogen removal characteristics of novel bacterium Klebsiella sp. TSH15 by assimilatory/dissimilatory nitrate reduction and ammonia assimilation

A novel strain with heterotrophic nitrification and aerobic denitrification was screened and identified as Klebsiella sp. TSH15 by 16S rRNA. The results demonstrated that the ammonia-N and nitrate-N removal rates were 2.99 mg/L/h and 2.53 mg/L/h under optimal conditions, respectively. The analysis of the whole genome indicated that strain TSH15 contained the key genes involved in assimilatory/dissimilatory nitrate reduction and ammonia assimilation, including nas, nar, nir, nor, glnA, gltB, gdhA, and amt. The relative expression levels of key nitrogen removal genes were further detected by RT-qPCR. The results indicated that the N metabolic pathways of strain TSH15 were the conversion of nitrate or nitrite to ammonia by assimilatory/dissimilatory nitrate reduction (NO3-→NO2-→NH4+) and further conversion of ammonia to glutamate (NH4+-N → Glutamate) by ammonia assimilation. These results indicated that the strain TSH15 had the potential to be applied to practical sewage treatment in the future.

Dynamics of carbon substrate competition among heterotrophic microorganisms

Growing evidence suggests that interactions among heterotrophic microorganisms influence the efficiency and rate of organic matter turnover. These interactions are dynamic and shaped by the composition and availability of resources in their surrounding environment. Heterotrophic microorganisms inhabiting marine environments often encounter fluctuations in the quality and quantity of carbon inputs, ranging from simple sugars to large, complex compounds. Here, we experimentally tested how the chemical complexity of carbon substrates affects competition and growth dynamics between two heterotrophic marine isolates. We tracked cell density using species-specific polymerase chain reaction (PCR) assays and measured rates of microbial CO2 production along with associated isotopic signatures (13C and 14C) to quantify the impact of these interactions on organic matter remineralization. The observed cell densities revealed substrate-driven interactions: one species exhibited a competitive advantage and quickly outgrew the other when incubated with a labile compound whereas both species seemed to coexist harmoniously in the presence of more complex organic matter. Rates of CO2 respiration revealed that coincubation of these isolates enhanced organic matter turnover, sometimes by nearly 2-fold, compared to their incubation as mono-cultures. Isotopic signatures of respired CO2 indicated that coincubation resulted in a greater remineralization of macromolecular organic matter. These results demonstrate that simple substrates promote competition whereas high substrate complexity reduces competitiveness and promotes the partitioning of degradative activities into distinct niches, facilitating coordinated utilization of the carbon pool. Taken together, this study yields new insight into how the quality of organic matter plays a pivotal role in determining microbial interactions within marine environments.

Direct observations of microbial community succession on sinking marine particles

Microbial community dynamics on sinking particles control the amount of carbon that reaches the deep ocean and the length of time that carbon is stored, with potentially profound impacts on Earth's climate. A mechanistic understanding of the controls on sinking particle distributions has been hindered by limited depth- and time-resolved sampling and methods that cannot distinguish individual particles. Here, we analyze microbial communities on nearly 400 individual sinking particles in conjunction with more conventional composite particle samples to determine how particle colonization and community assembly might control carbon sequestration in the deep ocean. We observed community succession with corresponding changes in microbial metabolic potential on the larger sinking particles transporting a significant fraction of carbon to the deep sea. Microbial community richness decreased as particles aged and sank; however, richness increased with particle size and the attenuation of carbon export. This suggests that the theory of island biogeography applies to sinking marine particles. Changes in POC flux attenuation with time and microbial community composition with depth were reproduced in a mechanistic ecosystem model that reflected a range of POC labilities and microbial growth rates. Our results highlight microbial community dynamics and processes on individual sinking particles, the isolation of which is necessary to improve mechanistic models of ocean carbon uptake.

Emergence of metabolic coupling to the heterotroph Alteromonas promotes dark survival in Prochlorococcus

Prochlorococcus is found throughout the euphotic zone in the oligotrophic open ocean. Deep mixing and sinking while attached to particles can, however, transport Prochlorococcus cells below this sunlit zone, depriving them of light for extended periods of time. Previous work has shown that Prochlorococcus by itself cannot survive extended periods of darkness. However, when co-cultured with a heterotrophic microbe and subjected to repeated periods of extended darkness, Prochlorococcus cells develop an epigenetically inherited dark-tolerant phenotype that can survive longer periods of darkness. Here we examine the metabolic and physiological changes underlying this adaptation using co-cultures of dark-tolerant and parental strains of Prochlorococcus, each grown with the heterotroph Alteromonas under diel light:dark conditions. The relative abundance of Alteromonas was higher in dark-tolerant than parental co-cultures, while dark-tolerant Prochlorococcus cells were larger, contained less chlorophyll, and were less synchronized to the light:dark cycle. Meta-transcriptome analysis revealed that dark-tolerant co-cultures undergo a joint change, in which Prochlorococcus undergoes a relative shift from photosynthesis to respiration, while Alteromonas shifts toward using more organic acids instead of sugars. Furthermore, the transcriptome data suggested enhanced biosynthesis of amino acids and purines in dark-tolerant Prochlorococcus and enhanced degradation of these compounds in Alteromonas. Collectively, our results demonstrate that dark adaptation involves a strengthening of the metabolic coupling between Prochlorococcus and Alteromonas, presumably mediated by an enhanced, and compositionally modified, carbon exchange between the two species.