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Ocean metabolism constitutes a complex, multiscale ensemble of biochemical reaction networks harbored within and between the boundaries of a myriad of organisms. Gaining a quantitative understanding of how these networks operate requires mathematical tools capable of solving in silico the resource allocation problem each cell faces in real life. Toward this goal, stoichiometric modeling of metabolism, such as flux balance analysis, has emerged as a powerful computational tool for unraveling the intricacies of metabolic processes in microbes, microbial communities, and multicellular organisms. Here, we provide an overview of this approach and its applications, future prospects, and practical considerations in the context of marine sciences. We explore how flux balance analysis has been employed to study marine organisms, help elucidate nutrient cycling, and predict metabolic capabilities within diverse marine environments, and highlight future prospects for this field in advancing our knowledge of marine ecosystems and their sustainability. 

Abstract 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. 

Metabolism is the complex network of chemical reactions occurring within every cell and organism, maintaining life, mediating ecosystem processes and affecting Earth’s climate. Experiments and models of microbial metabolism often focus on one specific scale, overlooking the connectivity between molecules, cells and ecosystems. Here we highlight quantitative metabolic principles that exhibit commonalities across scales, which we argue could help to achieve an integrated perspective on microbial life. Mass, electron and energy balance provide quantitative constraints on their flow within metabolic networks, organisms and ecosystems, shaping how each responds to its environment. The mechanisms underlying these flows, such as enzyme–substrate interactions, often involve encounter and handling stages that are represented by equations similar to those for cells and resources, or predators and prey. We propose that these formal similarities reflect shared principles and discuss how their investigation through experiments and models may contribute to a common language for studying microbial metabolism across scales. 

ABSTRACT Heterotrophic marine bacteria utilize and recycle dissolved organic matter (DOM), impacting biogeochemical cycles. It is currently unclear to what extent distinct DOM components can be used by different heterotrophic clades. Here, we ask how a natural microbial community from the Eastern Mediterranean Sea (EMS) responds to different molecular classes of DOM (peptides, amino acids, amino sugars, disaccharides, monosaccharides, and organic acids) comprising much of the biomass of living organisms. Bulk bacterial activity increased after 24 h for all treatments relative to the control, while glucose and ATP uptake decreased or remained unchanged. Moreover, while the per-cell uptake rate of glucose and ATP decreased, that of Leucin significantly increased for amino acids, reflecting their importance as common metabolic currencies in the marine environment.Pseudoalteromonadaceaedominated the peptides treatment, while differentVibrionaceaestrains became dominant in response to amino acids and amino sugars.Marinomonadaceaegrew well on organic acids, andAlteromonadaseaeon disaccharides. A comparison with a recent laboratory-based study reveals similar peptide preferences forPseudoalteromonadaceae, whileAlteromonadaceae, for example, grew well in the lab on many substrates but dominated in seawater samples only when disaccharides were added. We further demonstrate a potential correlation between the genetic capacity for degrading amino sugars and the dominance of specific clades in these treatments. These results highlight the diversity in DOM utilization among heterotrophic bacteria and complexities in the response of natural communities. IMPORTANCEA major goal of microbial ecology is to predict the dynamics of natural communities based on the identity of the organisms, their physiological traits, and their genomes. Our results show that several clades of heterotrophic bacteria each grow in response to one or more specific classes of organic matter. For some clades, but not others, growth in a complex community is similar to that of isolated strains in laboratory monoculture. Additionally, by measuring how the entire community responds to various classes of organic matter, we show that these results are ecologically relevant, and propose that some of these resources are utilized through common uptake pathways. Tracing the path between different resources to the specific microbes that utilize them, and identifying commonalities and differences between different natural communities and between them and lab cultures, is an important step toward understanding microbial community dynamics and predicting how communities will respond to perturbations. 

Abstract Environmental microbiome engineering is emerging as a potential avenue for climate change mitigation. In this process, microbial inocula are introduced to natural microbial communities to tune activities that regulate the long‐term stabilization of carbon in ecosystems. In this review, we outline the process of environmental engineering and synthesize key considerations about ecosystem functions to target, means of sourcing microorganisms, strategies for designing microbial inocula, methods to deliver inocula, and the factors that enable inocula to establish within a resident community and modify an ecosystem function target. Recent work, enabled by high‐throughput technologies and modeling approaches, indicate that microbial inocula designed from the top‐down, particularly through directed evolution, may generally have a higher chance of establishing within existing microbial communities than other historical approaches to microbiome engineering. We address outstanding questions about the determinants of inocula establishment and provide suggestions for further research about the possibilities and challenges of environmental microbiome engineering as a tool to combat climate change. 

ABSTRACT Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available athttps://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCEMapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.