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Quantitative principles of microbial metabolism shared across scales
Abstract
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.
... This could help make unbiased analysis more accessible and scalable, e.g., by combining elements of enumeration methods for different pathway definitions with each other as well as with flux sampling. We hope that this will eventually allow for detailed analysis of metabolic networks across scales, from organisms to communities or even ecosystems [40]. ...
The metabolic network of an organism can be analyzed as a constraint-based model. This analysis can be biased, optimizing an objective such as growth rate, or unbiased, aiming to describe the full feasible space of metabolic fluxes through pathway analysis or random flux sampling. In particular, pathway analysis can decompose the flux space into fundamental and formally defined metabolic pathways. Unbiased methods scale poorly with network size due to combinatorial explosion, but a promising approach to improve scalability is to focus on metabolic subnetworks, e.g., cells’ metabolite exchanges with each other and the environment, rather than the full metabolic networks. Here, we applied pathway enumeration and flux sampling to metabolite exchanges in microbial species and a microbial community, using models ranging from central carbon metabolism to genome-scale and focusing on pathway definitions that allow direct targeting of subnetworks such as metabolite exchanges (elementary conversion modes, elementary flux patterns, and minimal pathways). Enumerating growth-supporting metabolite exchanges, we found that metabolite exchanges from different pathway definitions were related through a hierarchy, and we show that this hierarchical relationship between pathways holds for metabolic networks and subnetworks more generally. Metabolite exchange frequencies, defined as the fraction of pathways in which each metabolite was exchanged, were similar across pathway definitions, with a few specific exchanges explaining large differences in pathway counts. This indicates that biological interpretation of predicted metabolite exchanges is robust to the choice of pathway definition, and it suggests strategies for more scalable pathway analysis. Our results also signal wider biological implications, facilitating detailed and interpretable analysis of metabolite exchanges and other subnetworks in fields such as metabolic engineering and synthetic biology.
... Such behavior has often been attributed to a specific molecular biological process. Since the Monod equation (1) has the same functional form as the Michaelis-Menten equation, a single biochemical process is usually expected to limit cell growth [6][7][8] (see also Fig. 1a). In previous hypotheses, this single "black box" could be the substrate transport into cells [6,9], the net flux of respiration [10], or the (coarsegrained) reaction that couples catabolism and anabolism [11]. ...
Monod's law is a widely accepted phenomenology for bacterial growth. Since it has the same functional form as the Michaelis--Menten equation for enzyme kinetics, cell growth is often considered to be locally constrained by a single reaction. In contrast, this paper shows that a global constraint model of resource allocation to metabolic processes can well describe the nature of cell growth. This concept is a generalization of Liebig's law, a growth law for higher organisms, and explains the dependence of microbial growth on the availability of multiple nutrients, in contrast to Monod's law.
Earth System Models (ESMs) highly simplify their representation of biological processes, leading to major uncertainty in climate change impacts. Despite a growing understanding of molecular networks from genomic data, describing how changing phytoplankton physiology affects the production of key metabolites remains elusive. Here we embed a genome-scale model within a state-of-the-art ESM to deliver an integrated understanding of how gradients of nutritional constraints modulate metabolic reactions and molecular physiology. Applied to the prevalent marine cyanobacteria Prochlorococcus , we find that glycogen and lipid storage can be understood as a consequence of acclimation to environmental gradients. Given the pressing need to assess how biological diversity influences biogeochemical functions, genome-enabled ESMs allow the quantification of the contribution of modeled organisms to the production of dissolved organic carbon and its molecular composition.
More than 70 years after its introduction, the framework of resource density-dependent consumption rates, also known as predator-prey functional responses, remains a core concept in population and food web ecology. Initially, three types of responses were defined: linear (type I), hyperbolic (type II), and sigmoid (type III). Due to its potential to stabilize consumer-resource population dynamics, the sigmoid type III functional response immediately became a “holy grail” in population ecology. However, experimentally proving that type III functional responses exist, whether in controlled laboratory systems or in nature, was challenging. While theoretical and practical advances make identifying type III responses easier today, decades of research have brought only a limited number of studies that provide empirical evidence for type III response curves. Here, we review this evidence from laboratory- and field-based studies published during the last two decades. We found 107 studies that reported type III responses, but these studies ranged across various taxa, interaction types, and ecosystems. To put these studies into context, we also discuss the various biological mechanisms that may lead to the emergence of type III responses. We summarize how three different and mutually independent intricacies bedevil the empirical documentation of type III responses: (1) challenges in statistical modeling of functional responses, (2) inadequate resource density ranges and spacing, and (3) biologically meaningful and realistic design of experimental arenas. Finally, we provide guidelines on how the field should move forward based on these considerations.
Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes. Genome-scale metabolic models have been widely used for quantitative exploration of the relation between genotype and phenotype. Here the authors present GECKO 2, an automated framework for continuous and version controlled update of enzyme-constrained models of metabolism, producing an interesting catalogue of high-quality models for diverse yeasts, bacteria and human metabolism, aiming to facilitate their use in basic science, metabolic engineering and synthetic biology purposes.
Standard niche modelling is based on probabilistic inference from organismal occurrence data but does not benefit yet from genome‐scale descriptions of these organisms. This study overcomes this shortcoming by proposing a new conceptual niche that resumes the whole metabolic capabilities of an organism. The so‐called metabolic niche resumes well‐known traits such as nutrient needs and their dependencies for survival. Despite the computational challenge, its implementation allows the detection of traits and the formal comparison of niches of different organisms, emphasising that the presence–absence of functional genes is not enough to approximate the phenotype. Further statistical exploration of an organism's niche sheds light on genes essential for the metabolic niche and their role in understanding various biological experiments, such as transcriptomics, paving the way for incorporating better genome‐scale description in ecological studies.
Microbial interactions shape the structure and function of microbial communities with profound consequences for biogeochemical cycles and ecosystem health. Yet, most interaction mechanisms are studied only in model systems and their prevalence is unknown. To systematically explore the functional and interaction potential of sequenced marine bacteria, we developed a trait-based approach, and applied it to 473 complete genomes (248 genera), representing a substantial fraction of marine microbial communities. We identified genome functional clusters (GFCs) which group bacterial taxa with common ecology and life history. Most GFCs revealed unique combinations of interaction traits, including the production of siderophores (10% of genomes), phytohormones (3–8%) and different B vitamins (57–70%). Specific GFCs, comprising Alpha- and Gammaproteobacteria, displayed more interaction traits than expected by chance, and are thus predicted to preferentially interact synergistically and/or antagonistically with bacteria and phytoplankton. Linked trait clusters (LTCs) identify traits that may have evolved to act together (e.g., secretion systems, nitrogen metabolism regulation and B vitamin transporters), providing testable hypotheses for complex mechanisms of microbial interactions. Our approach translates multidimensional genomic information into an atlas of marine bacteria and their putative functions, relevant for understanding the fundamental rules that govern community assembly and dynamics. Luca Zoccarato, Daniel Sher et al. leverage publicly available bacterial genomes from marine and other environments to examine traits underlying microbial interactions. Their results provide a valuable resource to investigate clusters of functional and linked traits to better understand marine bacteria community assembly and dynamics.
Marine diazotrophs fix dinitrogen gas into bioavailable nitrogen that drives the ocean nitrogen cycle; yet, efforts to infer global diazotroph distributions have been limited by a sparsity of observations. In situ measurements of nifH gene abundance (essential for nitrogen fixation) are increasingly being used to inform the biogeography of diazotrophs. However, comparing such gene abundances spatially, temporally and between diazotroph species remains difficult. We synthesize existing data on gene‐to‐cell and cell‐to‐biomass conversions for four major diazotroph groups to convert nifH gene counts to abundance‐ and biomass‐based biogeographic “currencies.” Results suggest up to two orders of magnitude uncertainty converting from nifH gene abundance to cell abundance, and up to four orders of magnitude uncertainty from nifH gene abundance to biomass. Uncertainty arises due to large taxonomic variation in cell size and presumed polyploidy, that is, variability in the number of genomes per cell. Such uncertainties hinder comparing biogeographies of different species. Additionally, numerical models need biogeographies for validation, typically in the currency of carbon biomass. Here, we show that conversion uncertainty from nifH gene abundance to biomass overwhelms biomass variability simulated in such models. These results demonstrate a basic currency problem in converting gene abundance observations to biogeographically meaningful quantities for synthesizing studies and modeling approaches. Such issues may also have relevance to other genes and organisms beyond diazotrophs. To avoid biases in interpreting gene counts as a measure of abundance, we suggest converting gene counts to a binary presence/non‐detect metric to map broad biogeographical distributions more robustly.
Genome-scale metabolic models (GEMs) are mathematical representations of metabolism that allow for in silico simulation of metabolic phenotypes and capabilities. A prerequisite for these predictions is an accurate representation of the biomolecular composition of the cell necessary for replication and growth, implemented in GEMs as the so-called biomass objective function (BOF). The BOF contains the metabolic precursors required for synthesis of the cellular macro- and micromolecular constituents (e.g. protein, RNA, DNA), and its composition is highly dependent on the particular organism, strain, and growth condition. Despite its critical role, the BOF is rarely constructed using specific measurements of the modeled organism, drawing the validity of this approach into question. Thus, there is a need to establish robust and reliable protocols for experimental condition-specific biomass determination. Here, we address this challenge by presenting a general pipeline for biomass quantification, evaluating its performance on Escherichia coli K-12 MG1655 sampled during balanced exponential growth under controlled conditions in a batch-fermentor set-up. We significantly improve both the coverage and molecular resolution compared to previously published workflows, quantifying 91.6% of the biomass. Our measurements display great correspondence with previously reported measurements, and we were also able to detect subtle characteristics specific to the particular E. coli strain. Using the modified E. coli GEM iML1515a, we compare the feasible flux ranges of our experimentally determined BOF with the original BOF, finding that the changes in BOF coefficients considerably affect the attainable fluxes at the genome-scale.
Extensive microdiversity within Prochlorococcus , the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes.
Bacteria are the most abundant cells on Earth. They are generally regarded as ancient, but due to striking diversity in their metabolic capacities and widespread lateral gene transfer, the physiology of the first bacteria is unknown. From 1089 reference genomes of bacterial anaerobes, we identified 146 protein families that trace to the last bacterial common ancestor, LBCA, and form the conserved predicted core of its metabolic network, which requires only nine genes to encompass all universal metabolites. Our results indicate that LBCA performed gluconeogenesis towards cell wall synthesis, and had numerous RNA modifications and multifunctional enzymes that permitted life with low gene content. In accordance with recent findings for LUCA and LACA, analyses of thousands of individual gene trees indicate that LBCA was rod-shaped and the first lineage to diverge from the ancestral bacterial stem was most similar to modern Clostridia, followed by other autotrophs that harbor the acetyl-CoA pathway.
Marine phytoplankton contributes nearly half of the total primary production on Earth through photosynthesis. Parameterizations of algal photosynthesis commonly employed in global biogeochemical simulations generally fail to capture the observed vertical structure of primary production. Here we examined the consequences of decoupling photosynthesis (carbon fixation) and biosynthesis (biomass building) with accumulation or exudation of excess photosynthate under energy rich conditions in both regional and global models. The results show that the decoupling of these two processes improved the simulated vertical profile of primary production, increased modeled primary production over 30% globally and over 40% in subtropical oceans, improved simulated meridional patterns of particulate C:N:P and increased modeled surface pool of labile/semi‐labile dissolved organic carbon. More generally, these results highlight the importance of exudation, which results from the decoupling of photosynthesis and biosynthesis, as a major physiological process affecting ocean biogeochemistry.
The debate on the temperature of the environment where life originated is still inconclusive. Metabolic reactions constitute the basis of life, and may be a window to the world where early life was born. Temperature is an important parameter of reaction thermodynamics, which determines whether metabolic reactions can proceed. In this study, the scale of the prebiotic metabolic network at different temperatures was examined by a thermodynamically constrained network expansion simulation. It was found that temperature has limited influence on the scale of the simulated metabolic networks, implying that early life may have occurred in a relatively wide temperature range.
The Earth's oceans are one of the largest sinks in the Earth system for anthropogenic CO2 emissions, acting as a negative feedback on climate change. Earth system models project that climate change will lead to a weakening ocean carbon uptake rate as warm water holds less dissolved CO2 and as biological productivity declines. However, most Earth system models do not incorporate the impact of warming on bacterial remineralisation and rely on simplified representations of plankton ecology that do not resolve the potential impact of climate change on ecosystem structure or elemental stoichiometry. Here, we use a recently developed extension of the cGEnIE (carbon-centric Grid Enabled Integrated Earth system model), ecoGEnIE, featuring a trait-based scheme for plankton ecology (ECOGEM), and also incorporate cGEnIE's temperature-dependent remineralisation (TDR) scheme. This enables evaluation of the impact of both ecological dynamics and temperature-dependent remineralisation on particulate organic carbon (POC) export in response to climate change. We find that including TDR increases cumulative POC export relative to default runs due to increased nutrient recycling (+∼1.3 %), whereas ECOGEM decreases cumulative POC export by enabling a shift to smaller plankton classes (-∼0.9 %). However, interactions with carbonate chemistry cause opposite sign responses for the carbon sink in both cases: TDR leads to a smaller sink relative to default runs (-∼1.0 %), whereas ECOGEM leads to a larger sink (+∼0.2 %). Combining TDR and ECOGEM results in a net strengthening of POC export (+∼0.1 %) and a net reduction in carbon sink (-∼0.7 %) relative to default. These results illustrate the degree to which ecological dynamics and biodiversity modulate the strength of the biological pump, and demonstrate that Earth system models need to incorporate ecological complexity in order to resolve non-linear climate–biosphere feedbacks.
Marine nitrogen-fixing microorganisms are an important source of fixed nitrogen in oceanic ecosystems. The colonial cyanobacterium Trichodesmium and diatom symbionts were thought to be the primary contributors to oceanic N2 fixation until the discovery of the unusual uncultivated symbiotic cyanobacterium UCYN-A (Candidatus Atelocyanobacterium thalassa). UCYN-A has atypical metabolic characteristics lacking the oxygen-evolving photosystem II, the tricarboxylic acid cycle, the carbon-fixation enzyme RuBisCo and de novo biosynthetic pathways for a number of amino acids and nucleotides. Therefore, it is obligately symbiotic with its single-celled haptophyte algal host. UCYN-A receives fixed carbon from its host and returns fixed nitrogen, but further insights into this symbiosis are precluded by both UCYN-A and its host being uncultured. In order to investigate how this syntrophy is coordinated, we reconstructed bottom-up genome-scale metabolic models of UCYN-A and its algal partner to explore possible trophic scenarios, focusing on nitrogen fixation and biomass synthesis. Since both partners are uncultivated and only the genome sequence of UCYN-A is available, we used the phylogenetically related Chrysochromulina tobin as a proxy for the host. Through the use of flux balance analysis (FBA), we determined the minimal set of metabolites and biochemical functions that must be shared between the two organisms to ensure viability and growth. We quantitatively investigated the metabolic characteristics that facilitate daytime N2 fixation in UCYN-A and possible oxygen-scavenging mechanisms needed to create an anaerobic environment to allow nitrogenase to function. This is the first application of an FBA framework to examine the tight metabolic coupling between uncultivated microbes in marine symbiotic communities and provides a roadmap for future efforts focusing on such specialized systems.
Bacteria are the most abundant cells on Earth. They are generally regarded as ancient, but due to striking diversity in their metabolic capacities and widespread lateral gene transfer, the physiology of the first bacteria is unknown. From 1089 reference genomes of bacterial anaerobes, we identified 146 protein families that trace to the last bacterial common ancestor, LBCA, and form the conserved predicted core of its metabolic network, which requires only nine genes to encompass all universal metabolites. Our results indicate that LBCA performed gluconeogenesis towards cell wall synthesis, and had numerous RNA modifications and multifunctional enzymes that permitted life with low gene content. In accordance with recent findings for LUCA and LACA, analyses of thousands of individual gene trees indicate that LBCA was rod-shaped and the first lineage to diverge from the ancestral bacterial stem was most similar to modern Clostridia, followed by other autotrophs that harbor the acetyl-CoA pathway.
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.
Anthropogenic climate change is projected to lead to ocean warming, acidification, deoxygenation, reductions in near-surface nutrients, and changes to primary production, all of which are expected to affect marine ecosystems. Here we assess projections of these drivers of environmental change over the twenty-first century from Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) that were forced under the CMIP6 Shared Socioeconomic Pathways (SSPs). Projections are compared to those from the previous generation (CMIP5) forced under the Representative Concentration Pathways (RCPs). A total of 10 CMIP5 and 13 CMIP6 models are used in the two multi-model ensembles. Under the high-emission scenario SSP5-8.5, the multi-model global mean change (2080–2099 mean values relative to 1870–1899) ± the inter-model SD in sea surface temperature, surface pH, subsurface (100–600 m) oxygen concentration, euphotic (0–100 m) nitrate concentration, and depth-integrated primary production is +3.47±0.78 ∘C, -0.44±0.005, -13.27±5.28, -1.06±0.45 mmolm-3 and -2.99±9.11 %, respectively. Under the low-emission, high-mitigation scenario SSP1-2.6, the corresponding global changes are +1.42±0.32 ∘C, -0.16±0.002, -6.36±2.92, -0.52±0.23 mmolm-3, and -0.56±4.12 %. Projected exposure of the marine ecosystem to these drivers of ocean change depends largely on the extent of future emissions, consistent with previous studies. The ESMs in CMIP6 generally project greater warming, acidification, deoxygenation, and nitrate reductions but lesser primary production declines than those from CMIP5 under comparable radiative forcing. The increased projected ocean warming results from a general increase in the climate sensitivity of CMIP6 models relative to those of CMIP5. This enhanced warming increases upper-ocean stratification in CMIP6 projections, which contributes to greater reductions in upper-ocean nitrate and subsurface oxygen ventilation. The greater surface acidification in CMIP6 is primarily a consequence of the SSPs having higher associated atmospheric CO2 concentrations than their RCP analogues for the same radiative forcing. We find no consistent reduction in inter-model uncertainties, and even an increase in net primary production inter-model uncertainties in CMIP6, as compared to CMIP5.
Freshwater and marine algae can balance nutrient demand and availability by regulating uptake, accumulation and exudation. To obtain insight into these processes under nitrogen (N) and phosphorus (P) limitation, we reanalyze published data from continuous cultures of the chlorophyte Selenastrum minutum. Based on mass budgets, we argue that much of the non‐limiting N and P had passed through the organisms and was present as dissolved organic phosphorus or nitrogen (DOP or DON). We construct a model that describes the production of biomass and dissolved organic matter (DOM) as a function of the growth rate. A fit of this model against the chemostat data suggests a high turnover of the non‐limiting N and P: at the highest growth rates, N and P atoms spent on average only about 3 hours inside an organism, before they were exuded as DON and DOP, respectively. This DOM exudation can explain the observed trends in the algal stoichiometric ratios as a function of the dilution rate. We discuss independent evidence from isotope experiments for this apparently wasteful behavior and we suggest experiments to quantify and characterize DON and DOP exudation further.
Background
It is well established that the functioning of terrestrial ecosystems depends on biophysical and biogeochemical feedbacks occurring at the soil-plant-atmosphere (SPA) interface. However, dynamic biophysical and biogeochemical processes that operate at local scales are seldom studied in conjunction with structural ecosystem properties that arise from broad environmental constraints. As a result, the effect of SPA interactions on how ecosystems respond to, and exert influence on, the global environment remains difficult to predict.ScopeWe review recent findings that link structural and functional SPA interactions and evaluate their potential for predicting ecosystem responses to chronic environmental pressures. Specifically, we propose a quantitative framework for the integrated analysis of three major plant functional groups (evergreen conifers, broadleaf deciduous, and understory shrubs) and their distinct mycorrhizal symbionts under rising levels of carbon dioxide, changing climate, and disturbance regime. First, we explain how symbiotic and competitive strategies involving plants and soil microorganisms influence scale-free patterns of carbon, nutrient, and water use from individual organisms to landscapes. We then focus on the relationship between those patterns and structural traits such as specific leaf area, leaf area index, and soil physical and chemical properties that constrain root connectivity and canopy gas exchange. Finally, we use those relationships to predict how changes in ecosystem structure may affect processes that are important for climate stability.Conclusions
On the basis of emerging ecological theory and empirical biophysical and biogeochemical knowledge, we propose ten interpretive hypotheses that serve as a primary set of hierarchical relationships (or scaling rules), by which local SPA interactions can be spatially and temporally aggregated to inform broad climate change mitigation efforts. To this end, we provide a series of numerical formulations that simplify the net outcome of complex SPA interactions as a first step towards anticipating shifts in terrestrial carbon, water, and nutrient cycles.
Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host–pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.
It has been suggested that a deep memory of early life is hidden in the architecture of metabolic networks, whose reactions could have been catalyzed by small molecules or minerals before genetically encoded enzymes. A major challenge in unravelling these early steps is assessing the plausibility of a connected, thermodynamically consistent proto-metabolism under different geochemical conditions, which are still surrounded by high uncertainty. Here we combine network-based algorithms with physico-chemical constraints on chemical reaction networks to systematically show how different combinations of parameters (temperature, pH, redox potential and availability of molecular precursors) could have affected the evolution of a proto-metabolism. Our analysis of possible trajectories indicates that a subset of boundary conditions converges to an organo-sulfur-based proto-metabolic network fuelled by a thioester- and redox-driven variant of the reductive tricarboxylic acid cycle that is capable of producing lipids and keto acids. Surprisingly, environmental sources of fixed nitrogen and low-potential electron donors are not necessary for the earliest phases of biochemical evolution. We use one of these networks to build a steady-state dynamical metabolic model of a protocell, and find that different combinations of carbon sources and electron donors can support the continuous production of a minimal ancient ‘biomass’ composed of putative early biopolymers and fatty acids.
In the Anthropocene, in which we now live, climate change is impacting most life on Earth. Microorganisms support the existence of all higher trophic life forms. To understand how humans and other life forms on Earth (including those we are yet to discover) can withstand anthropogenic climate change, it is vital to incorporate knowledge of the microbial ‘unseen majority’. We must learn not just how microorganisms affect climate change (including production and consumption of greenhouse gases) but also how they will be affected by climate change and other human activities. This Consensus Statement documents the central role and global importance of microorganisms in climate change biology. It also puts humanity on notice that the impact of climate change will depend heavily on responses of microorganisms, which are essential for achieving an environmentally sustainable future.
Biogeochemical cycles in the ocean are strongly affected by the elemental stoichiometry (C:N:P) of phytoplankton, which largely reflects their macromolecular content. A greater understanding of how this macromolecular content varies among phytoplankton taxa and with resource limitation may strengthen physiological and biogeochemical modeling efforts. We determined the macromolecular basis (protein, carbohydrate, lipid, nucleic acids, pigments) of C:N:P in diatoms and prasinophytes, two globally important phytoplankton taxa, in response to N starvation. Despite their differing cell sizes and evolutionary histories, the relative decline in protein during N starvation was similar in all four species studied and largely determined variations in N content. The accumulation of carbohydrate and lipid dominated the increase in C content and C:N in all species during N starvation, but these processes differed greatly between diatoms and prasinophytes. Diatoms displayed far greater accumulation of carbohydrate with N starvation, possibly due to their greater cell size and storage capacity, resulting in larger increases in C content and C:N. In contrast, the prasinophytes had smaller increases in C and C:N that were largely driven by lipid accumulation. Variation in C:P and N:P was species-specific and mainly determined by residual P pools, which likely represent intracellular storage of inorganic P and accounted for the majority of cellular P in all species throughout N starvation. Our findings indicate that carbohydrate and lipid accumulation may play a key role in determining the environmental and taxonomic variability in phytoplankton C:N. This quantitative assessment of macromolecular and elemental content spanning several marine phytoplankton species can be used to develop physiological models for ecological and biogeochemical applications.
Phototrophic microorganisms are promising resources for green biotechnology. Compared to heterotrophic microorganisms, however, the cellular economy of phototrophic growth is still insufficiently understood. We provide a quantitative analysis of light-limited, light-saturated, and light-inhibited growth of the cyanobacterium Synechocystis sp. PCC 6803 using a reproducible cultivation setup. We report key physiological parameters, including growth rate, cell size, and photosynthetic activity over a wide range of light intensities. Intracellular proteins were quantified to monitor proteome allocation as a function of growth rate. Among other physiological adaptations, we identify an upregulation of the translational machinery and downregulation of light harvesting components with increasing light intensity and growth rate. The resulting growth laws are discussed in the context of a coarse-grained model of phototrophic growth and available data obtained by a comprehensive literature search. Our insights into quantitative aspects of cyanobacterial adaptations to different growth rates have implications to understand and optimize photosynthetic productivity.
The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.
Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism, for model-guided re-engineering, and unraveling cross-feeding in microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual curation required to obtain good quality models. Here, we present an automated tool, CarveMe, for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We show that CarveMe models perform closely to manually curated models in reproducing experimental phenotypes (substrate utilization and gene essentiality). Additionally, we build a collection of 74 models for human gut bacteria and test their ability to reproduce growth on a set of experimentally defined media. Finally, we create a database of 5587 bacterial models and demonstrate its potential for fast generation of microbial community models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.
Significance
The composition of the biosphere is a fundamental question in biology, yet a global quantitative account of the biomass of each taxon is still lacking. We assemble a census of the biomass of all kingdoms of life. This analysis provides a holistic view of the composition of the biosphere and allows us to observe broad patterns over taxonomic categories, geographic locations, and trophic modes.
Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.
Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g., allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical limitations have hindered the production of a genome-scale atlas of small molecule-enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN) of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and we combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis.
Two contemporary effects of humans on aquatic ecosystems are increasing temperatures and increasing nutrient concentrations from fertilizers. The response of organisms to these perturbations has important implications for ecosystem processes. We examined the effects of phosphorus (P) supply and temperature on organismal carbon, nitrogen and phosphorus (C, N, and P) content, cell size and allocation into internal P pools in three strains of recently isolated bacteria (Agrobacterium sp., Flavobacterium sp., and Arthrobacter sp.). We manipulated resource C:P in chemostats and also manipulated temperatures from 10 to 30°C. Dilution rates were maintained for all the strains at ~25% of their temperature-specific maximum growth rate to simulate low growth rates in natural systems. Under these conditions, there were large effects of resource stoichiometry and temperature on biomass stoichiometry, element quotas, and cell size. Each strain was smaller when C-limited and larger when P-limited. Temperature had weak effects on morphology, little effect on C quotas, no effect on N quotas and biomass C:N, but had strong effects on P quotas, biomass N:P and C:P, and RNA. RNA content per cell increased with increasing temperature at most C:P supply ratios, but was more strongly affected by resource stoichiometry than temperature. Because we used a uniform relative growth rate across temperatures, these findings mean that there are important nutrient and temperature affects on biomass composition and stoichiometry that are independent of growth rate. Changes in biomass stoichiometry with temperature were greatest at low P availability, suggesting tighter coupling between temperature and biomass stoichiometry in oligotrophic ecosystems than in eutrophic systems. Because the C:P stoichiometry of biomass affects how bacteria assimilate and remineralize C, increased P availability could disrupt a negative feedback between biomass stoichiometry and C availability.
Although aquatic ecologists and biogeochemists are well aware of the crucial importance of ecosystem functions, i.e., how biota drive biogeochemical processes and vice-versa, linking these fields in conceptual models is still uncommon. Attempts to explain the variability in elemental cycling consequently miss an important biological component and thereby impede a comprehensive understanding of the underlying processes governing energy and matter flow and transformation. The fate of multiple chemical elements in ecosystems is strongly linked by biotic demand and uptake; thus, considering elemental stoichiometry is important for both biogeochemical and ecological research. Nonetheless, assessments of ecological stoichiometry (ES) often focus on the elemental content of biota rather than taking a more holistic view by examining both elemental pools and fluxes (e.g., organismal stoichiometry and ecosystem process rates). ES theory holds the promise to be a unifying concept to link across hierarchical scales of patterns and processes in ecology, but this has not been fully achieved. Therefore, we propose connecting the expertise of aquatic ecologists and biogeochemists with ES theory as a common currency to connect food webs, ecosystem metabolism, and biogeochemistry, as they are inherently concatenated by the transfer of carbon, nitrogen, and phosphorous through biotic and abiotic nutrient transformation and fluxes. Several new studies exist that demonstrate the connections between food web ecology, biogeochemistry, and ecosystem metabolism. In addition to a general introduction into the topic, this paper presents examples of how these fields can be combined with a focus on ES. In this review, a series of concepts have guided the discussion: (1) changing biogeochemistry affects trophic interactions and ecosystem processes by altering the elemental ratios of key species and assemblages; (2) changing trophic dynamics influences the transformation and fluxes of matter across environmental boundaries; (3) changing ecosystem metabolism will alter the chemical diversity of the non-living environment. Finally, we propose that using ES to link nutrient cycling, trophic dynamics, and ecosystem metabolism would allow for a more holistic understanding of ecosystem functions in a changing environment.
Plant and soil microbiomes are integral to the health and productivity of plants and ecosystems, yet researchers struggle to identify microbiome characteristics important for providing beneficial outcomes. Network analysis offers a shift in analytical framework beyond “who is present” to the organization or patterns of coexistence between microbes within the microbiome. Because microbial phenotypes are often significantly impacted by coexisting populations, patterns of coexistence within microbiomes are likely to be especially important in predicting functional outcomes. Here, we provide an overview of the how and why of network analysis in microbiome research, highlighting the ways in which network analyses have provided novel insights into microbiome organization and functional capacities, the diverse network roles of different microbial populations, and the eco-evolutionary dynamics of plant and soil microbiomes.
Expected final online publication date for the Annual Review of Phytopathology, Volume 61 is September 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Microbial life represents the majority of Earth’s biodiversity. Across disparate disciplines from medicine to forestry, scientists continue to discover how the microbiome drives essential, macro-scale processes in plants, animals and entire ecosystems. Yet, there is an emerging realization that Earth’s microbial biodiversity is under threat. Here we advocate for the conservation and restoration of soil microbial life, as well as active incorporation of microbial biodiversity into managed food and forest landscapes, with an emphasis on soil fungi. We analyse 80 experiments to show that native soil microbiome restoration can accelerate plant biomass production by 64% on average, across ecosystems. Enormous potential also exists within managed landscapes, as agriculture and forestry are the dominant uses of land on Earth. Along with improving and stabilizing yields, enhancing microbial biodiversity in managed landscapes is a critical and underappreciated opportunity to build reservoirs, rather than deserts, of microbial life across our planet. As markets emerge to engineer the ecosystem microbiome, we can avert the mistakes of aboveground ecosystem management and avoid microbial monocultures of single high-performing microbial strains, which can exacerbate ecosystem vulnerability to pathogens and extreme events. Harnessing the planet’s breadth of microbial life has the potential to transform ecosystem management, but it requires that we understand how to monitor and conserve the Earth’s microbiome. Efforts to futureproof global microbial biodiversity are proposed, in particular in managed landscapes, to monitor, manage and restore the soil fungal microbiome.
Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. This protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.
The human microbiome plays an important role in human health and disease. Meta-omics analyses provide indispensable data for linking changes in microbiome composition and function to disease etiology. Yet, the lack of a mechanistic understanding of, e.g., microbiome-metabolome links hampers the translation of these findings into effective, novel therapeutics. Here, we propose metabolic modeling of microbial communities through constraint-based reconstruction and analysis (COBRA) as a complementary approach to meta-omics analyses. First, we highlight the importance of microbial metabolism in cardiometabolic diseases, inflammatory bowel disease, colorectal cancer, Alzheimer disease, and Parkinson disease. Next, we demonstrate that microbial community modeling can stratify patients and controls, mechanistically link microbes with fecal metabolites altered in disease, and identify host pathways affected by the microbiome. Finally, we outline our vision for COBRA modeling combined with meta-omics analyses and multivariate statistical analyses to inform and guide clinical trials, yield testable hypotheses, and ultimately propose novel dietary and therapeutic interventions.
Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis, transforming the biology and chemistry of our planet. Genomic and evolutionary studies have revolutionized our understanding of early oxygenic phototrophs, complementing and dramatically extending inferences from the geologic record. Molecular clock estimates point to a Paleoarchean origin (3.6-3.2 billion years ago, bya) of the core proteins of Photosystem II (PSII) involved in oxygenic photosynthesis and a Mesoarchean origin (3.2-2.8 bya) for the last common ancestor of modern cyanobacteria. Nonetheless, most extant cyanobacteria diversified after the Great Oxidation Event (GOE), an environmental watershed ca. 2.45 bya made possible by oxygenic photosynthesis. Throughout their evolutionary history, cyanobacteria have played a key role in the global carbon cycle.
Motivation:
This paper introduces Vivarium-software born of the idea that it should be as easy as possible for computational biologists to define any imaginable mechanistic model, combine it with existing models, and execute them together as an integrated multiscale model. Integrative multiscale modeling confronts the complexity of biology by combining heterogeneous datasets and diverse modeling strategies into unified representations. These integrated models are then run to simulate how the hypothesized mechanisms operate as a whole. But building such models has been a labor-intensive process that requires many contributors, and they are still primarily developed on a case-by-case basis with each project starting anew. New software tools that streamline the integrative modeling effort and facilitate collaboration are therefore essential for future computational biologists.
Results:
Vivarium is a software tool for building integrative multiscale models. It provides an interface that makes individual models into modules that can be wired together in large composite models, parallelized across multiple CPUs, and run with Vivarium's discrete-event simulation engine. Vivarium's utility is demonstrated by building composite models that combine several modeling frameworks: agent based models, ordinary differential equations, stochastic reaction systems, constraint-based models, solid-body physics, and spatial diffusion. This demonstrates just the beginning of what is possible-Vivarium will be able to support future efforts that integrate many more types of models and at many more biological scales.
Availability:
The specific models, simulation pipelines, and notebooks developed for this paper are all available at the vivarium-notebooks repository: https://github.com/vivarium-collective/vivarium-notebooks. Vivarium-core is available at https://github.com/vivarium-collective/vivarium-core, and has been released on PyPI. The Vivarium Collective (https://vivarium-collective.github.io) is a repository of freely-available Vivarium processes and composites, including the processes used in Section 3. Supplementary materials provide with an extensive methodology section, with several code listings that demonstrate the basic interfaces.
Protein quantification is a routine procedure in ecological studies despite the inherent limitations of well‐acknowledged protein determination methods which have been largely overlooked by ecologists. Thus, we want to bridge this knowledge gap, in hopes of improving the way ecologists quantify proteins and interpret findings.
We surveyed the ecological literature to determine how and why ecologists quantify proteins. To determine whether different quantification methods produce comparable results across taxa, and between populations of a single species, we estimated the protein content of eight phylogenetically diverse taxa, and of desert isopods fed different diets, using various derived protocols of the 'crude protein', Bradford and bicinchoninic acid approach (BCA) methods.
We found that ecologists use many protein quantification procedures, often without reporting the crucial information needed to evaluate and repeat their methods. Our empirical work demonstrated that the three quantification methods examined, and their derived protocols, resulted in highly divergent protein estimations that were inconsistent in rank across taxa, preventing conversion between methods. We also found that different quantification methods yielded different answers to whether isopod protein content is affected by diet.
We conclude that commonly used quantification techniques yield distinct protein estimations with varying precision, and no single method is likely to be more accurate than another across taxa which may lead to inconsistent results across taxa and between conspecifics. Inaccurate protein quantification may explain the observed mismatch between organismal N and protein that has plagued some recent studies and that contradicts the principles of ecological stoichiometry. We recommend using a single BCA protocol to reduce inconsistencies across studies, until the promising amino acid analysis becomes more affordable, accurate and accessible to ecologists. Until then, ecologists should consider the abovementioned drawbacks of protein quantification methods and interpret their results accordingly.
Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA–protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.
Changing views and a changing ocean
As a component of many biomolecules, nitrogen is a crucial element for life, especially in nutrient-poor environs such as the open ocean. Atmospheric dinitrogen gas (N 2 ) is abundant but must be fixed by reduction to ammonia, a process limited to certain organisms and environments. Zehr and Capone review changes in our understanding of what marine microorganisms are fixing N 2 , where they live, and what environmental features influence their activity. N 2 fixation is more widely distributed than previously thought, and we still have much to learn about the physiology and regulation involved. We now have better estimates of global- and basin-scale inputs and outputs, but questions remain as to whether the oceanic N cycle is balanced. New tools are enabling better understanding of ocean N 2 fixation despite disruptive consequences from human activities, including ocean acidification and warming.
Science , this issue p. eaay9514
The immune system is inordinately complex with many interacting components determining overall outcomes. Mathematical and computational modelling provides a useful way in which the various contributions of different immunological components can be probed in an integrated manner. Here, we provide an introductory overview and review of mechanistic simulation models. We start out by briefly defining these types of models and contrasting them to other model types that are relevant to the field of immunology. We follow with a few specific examples and then review the different ways one can use such models to answer immunological questions. While our examples focus on immune responses to infection, the overall ideas and descriptions of model uses can be applied to any area of immunology. This Review by Handel and colleagues focuses on how simulation modelling can be used to interrogate the immune system. The authors provide an overview of different model types and encourage immunologists to build their own models.
Advances in metagenome sequencing of the human microbiome have provided a plethora of new insights and revealed a close association of this complex ecosystem with a range of human diseases. However, there is little knowledge about how the different members of the microbial community interact with each other and with the host, and we lack basic mechanistic understanding of these interactions related to health and disease. Mathematical modelling has been demonstrated to be highly advantageous for gaining insights into the dynamics and interactions of complex systems and in recent years, several modelling approaches have been proposed to enhance our understanding of the microbiome. Here, we review the latest developments and current approaches, and highlight how different modelling strategies have been applied to unravel the highly dynamic nature of the human microbiome. Furthermore, we discuss present limitations of different modelling strategies and provide a perspective of how modelling can advance understanding and offer new treatment routes to impact human health. This Review summarizes mathematical modelling approaches that can be applied to microbiome datasets, providing insights into microbial dynamics and interactions in this complex system.
