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Henry, C. S.

Publications and source records attributed to Henry, C. S..

At least 19 recordsLinked to original sources
bioRxiv2026

The ModelSEED Biochemistry Database, 2026 update: grading multi-source thermodynamics

The ModelSEED Biochemistry Database (https://modelseed.org) supplies foundational mass- and charge-balanced reaction networks for metabolic reconstructions. Here we present an update on the biochemistry where the database has been expanded to [~] 46,000 compounds, and [~] 56,000 reactions, featuring [~] 37,000 metabolic structures. We have expanded our approach for handling thermodynamic data, enabling multiple sources of data to be derived, integrated, and presented to the wider research community. We now publish predictions of pKa, reaction energy (and respective uncertainties), and estimates of reaction direction from multiple independent sources. Each reaction is graded gold, silver or bronze according to the strength of the evidence behind it, so that users can weigh its reliability directly. We also release reaction directions predicted by an ensemble of large language models. This multi-source approach exposes agreements and discrepancies between sources for [~] 33,000 reactions. Finally, to ensure data integrity, a new conflict-resolution pipeline reconciles structures across sources, documenting input from curators. Our work is publicly available at https://github.com/ModelSEED/ModelSEEDDatabase.

biochemistry↗
bioRxiv2026

Data coherence over data volume drives generalisable genome-based prediction of microbial carbon utilisation

Microbial carbon utilisation is a foundational ecological phenotype that remains difficult to predict from genomes despite well-characterised pathways. Machine-learning models generalise poorly across datasets, a failure usually attributed to training-set size and taxonomic bias. To test this, we integrated binary growth phenotypes for 819 strains across 240 carbon sources from four datasets. Balanced accuracy fell from 0.86 within datasets to 0.62 across them, and testing on close relatives recovered only 0.03 of that drop, so mechanistically inconsistent genotype-phenotype relationships drove models to dataset-correlated shortcuts. Restricting training to concordant samples (measured growth matched their annotated pathway) doubled the carbon sources recovering known pathway genes across datasets (6 to 12 of 15), whereas matched random subsets did not. Adding over 8000 literature-curated BacDive genomes to the training set did not improve cross-dataset performance more than the smaller concordant set, suggesting coherence matters more than volume. Because such filtering requires a mechanistic predictor, we tested a mechanism-free alternative combining phylogenetic agreement and experimental labels, which recovered part of the gain but not the recall advantage. Concordance-trained models were bounded specialists, rescuing mechanistic false negatives twice as often as false positives (44\% versus 19\%), mostly metabolic generalists. To locate those bounds, model confidence defined an applicability domain, and prioritising low-confidence genomes for training improved cross-dataset accuracy more than random or diversity-based sampling, especially for the weaker phenotypes. This recasts generalisation in biological machine learning as a problem of label-mechanism agreement and applicability-domain definition, alongside data volume and algorithm choice.

bioinformatics↗
bioRxiv2026

Integrative Modeling and Analysis of Fungal Central Carbon Metabolism

Over a thousand fungal genomes have been sequenced, yet manually curated genome-scale metabolic models (GEMs) are available for only a limited number of species. Moreover, these models have often been developed independently, leading to inconsistencies in namespaces, compartment definitions, and pathway representations that hinder comparative analysis, the systematic reuse of prior curation efforts, and the integration of consolidated metabolic knowledge. Here, we present the Consolidated Fungal Core Metabolism Model (CFCMM), constructed by integrating thirteen published fungal models spanning Ascomycota, Mucoromycota, and both Crabtree-positive and Crabtree-negative yeasts. We harmonized metabolites and reactions into a non-redundant shared ModelSEED ontological space, standardized compartmentalization, and refined gene-protein-reaction (GPR) rules. Using pathway-level visualization and systematic gap detection, we further improved the integrated network through literature-guided curation to correct stoichiometry, stereospecificity, and pathway architecture. Orthologous protein family reconstruction and functional annotation workflows were used to validate and inform GPR associations, with particular emphasis on ambiguous enzyme superfamilies and membrane-associated components. Using the resulting CFCMM, we built high-quality central carbon core models for each fungus and performed flux balance analysis to quantify ATP-yield variation under aerobic and anaerobic conditions, explicitly evaluating scenarios driven by differences in electron transport chain (ETC) composition. Simulations reproduced the expected fermentative yield of approximately 2 mmol ATP per mmol glucose under anaerobic conditions and separated the thirteen fungi into two bioenergetic groups under aerobic respiration based on Complex I status, with predicted yields of approximately 30 versus 22 mmol ATP per mmol glucose. Forcing flux through the alternative oxidase bypass further reduced ATP yields to approximately 12 and 4 mmol ATP per mmol glucose in Complex I-containing and Complex I-lacking fungi, respectively. Collectively, this work provides a manually curated, ModelSEED-consistent, and extensible fungal core metabolic template, deployed in DOE KBase as a resource for automated reconstruction of central carbon core models from any sequenced fungal genome. In addition, the CFCMM provides modular components for developing GEMs with more accurate energy predictions and enables robust comparative analyses of fungal bioenergetics and core metabolic diversity.

systems biology↗
bioRxiv2026

Functional Network Analysis of Fungal Pathogen Colletotrichum sublineola Effectors in Sorghum Anthracnose

Colletotrichum sublineola (Cs) is a hemibiotrophic fungal pathogen that causes anthracnose in Sorghum bicolor, leading to significant yield losses. To enable infection, Cs secretes effectors - proteins, small RNAs, and metabolites - that damage the plant cell wall or enter the plant cell to suppress immune responses and manipulate host metabolism. Effectors can detoxify host antimicrobials, alter nutrient processing, and evade host immunity. Paradoxically, some effectors can also trigger pattern-triggered immunity (PTI), especially in biotrophic and necrotrophic fungi. More than half of fungal protein effectors lack conserved domains and functional network annotations. In this study, we identified prospective Cs effectors, separating those with non-conserved domains and classifying those with conserved domains by protein families. Comparative genomics is employed to predict effector functions and analyze their roles. Using their predicted locations and domains, we mapped the effectors into functional subsystems related to PTI. These include interactions in the apoplast, oxidative stress response, protein modification and degradation systems, and Cysteine-rich Fungus-specific Epidermal Growth Factor-like Module (CFEM) domain proteins involved in immune regulation. Our functional network analysis advances the understanding of Cs pathogenicity and offers insights into effector infection mechanisms.

pathology↗
bioRxiv2025

EC-Bench: A Benchmark for Enzyme Commission NumberPrediction

Enzymes are proteins that catalyze specific biochemical reactions in cells. Enzyme Commission (EC) numbers are used to annotate enzymes in a four-level hierarchy that classifies enzymes based on the specific chemical reactions they catalyze. Accurate EC number prediction is essential for understanding enzyme functions. Despite the availability of numerous methods for predicting EC numbers from protein sequences, there is no unified framework for evaluating and studying such methods systematically. This gap limits the ability of the community to identify the most effective approaches for enzyme annotation. We introduce EC-Bench, a benchmark for EC number prediction, consisting of 1) an initial representative set of existing methods (including homology-based, deep learning, contrastive learning, and language model methods), 2) existing and novel accuracy and efficiency performance metrics, and 3) selected datasets to allow for comprehensive comparative study. EC-Bench is open-source and provides a framework for researchers to not only compare among existing methods objectively under uniform conditions, but also to introduce and effectively evaluate performance of new methods in a comparative frame-work. To demonstrate the utility of EC-Bench, we perform extensive experimentation to compare the existing EC number prediction methods and establish their advantages and disadvantages in a variety of prediction tasks, namely "exact EC number prediction", "EC number completion" and (partial or additional) "EC number recommendation". We find wide variation in the performance of different methods, but also subtle but potentially useful differences in the performance of different methods across tasks and for different parts of the EC hierarchy.

bioinformatics↗
bioRxiv2025

Simulating Iron Deficiency in Plant Plastids With a Flexible Physics-Informed Neural Network Approach

Flux balance analysis has proven to be a successful approach in metabolic engineering and systems biology to predict intracellular fluxes of large genome-scale networks and the essentiality of genes encoding enzymes and regulatory factors. Flux balance analysis (FBA) relies on a key assumption of a metabolic state being persistent ("steady") over a given time frame. This assumption works well for microbial growth because of the ease with which microbial media can be fixed, biomass can be decomposed, and growth rates can be measured. However, the assumption is far less tenable for the cells and tissues of complex multicellular organisms, particularly if any integrated data is sampled from a heterogeneous collection of developing cells continually interacting between and across tissues. These will likely exhibit transient metabolic states equilibrating over varying timescales, and many FBA studies in complex organisms typically either ignore time as a parameter, or integrate data taken over long timescales (days/weeks). In this work, we adopt and modify a previously published machine learning approach originally developed to hybridize several aspects of a constraint-based approach with machine-learning in order to predict growth. This approach accommodates transient state dynamics, at the cost of tolerating a controlled amount of slack in the steady-state assumption, in order to enable transcript-constrained flux estimation in plant tissues without optimizing a growth objective. For our case study, we reconstruct the metabolism of the plastid of Poplar and Sorghum, integrating data sampled from leaf tissue under varying levels of iron bioavailability. The two species diverge sharply: Sorghum suppresses its photosynthetic electron transport and Calvin cycle abruptly at day 7 and its carbon delivery to biomass collapses to a third of control by day 21, whereas Poplar declines gradually and retains roughly 70%. Normalizing each reaction score to the plastid proteome pool makes the two species comparable and exposes reallocation as the pool contracts, including a split within Sorghums sulfate assimilation that tracks which enzymes carry iron cofactors.

bioinformatics↗
bioRxiv2024

Emergence and disruption of cooperativity in a denitrifying microbial community

Anthropogenic perturbations to the nitrogen cycle, primarily through use of synthetic fertilizers, is driving an unprecedented increase in the emission of nitrous oxide (N2O), a potent greenhouse gas and an ozone depleting substance, causing urgency in identifying the sources and sinks of N2O. Microbial denitrification is a primary contributor to biotic production of N2O in anoxic regions of soil, marine systems, and wastewater treatment facilities. Here, through comprehensive genome analysis, we show that pathway partitioning is a ubiquitous mechanism of complete denitrification within microbial communities. We have investigated mechanisms and consequences of process partitioning of denitrification through detailed physiological characterization and kinetic modeling of a synthetic community of Rhodanobacter R12 and Acidovorax 3H11. We have discovered that these two bacterial isolates, from a heavily nitrate (NO3-) contaminated superfund site, complete denitrification through the exchange of nitrite (NO2-) and nitric oxide (NO). The process partitioning of denitrification and other processes, including amino acid metabolism, contribute to increased cooperativity within this denitrifying community. We demonstrate that certain contexts, such as high NO3-, cause unbalanced growth of community members, due to differences in their substrate utilization kinetics. The altered growth characteristics of community members drives accumulation of toxic NO2-, which disrupts denitrification causing N2O off gassing.

microbiology↗
bioRxiv2024

Diet outperforms microbial transplant to drive microbiome recovery post-antibiotics

High-fat, low-fiber Western-style diets (WD) induce microbiome dysbiosis characterized by reduced taxonomic diversity and metabolic breadth1,2, which in turn increases risk for a wide array of metabolic3-5, immune6 and systemic pathologies. Recent work has established that WD can impair microbiome resilience to acute perturbations like antibiotic treatment7,8, although we know little about the mechanism of impairment and the specific host consequences of prolonged post-antibiotic dysbiosis. Here, we characterize the trajectory by which the gut microbiome recovers its taxonomic and functional profile after antibiotic treatment in mice on regular chow (RC) and WD, and find that only mice on RC undergo a rapid successional process of recovery. Metabolic modeling indicates that RC diet promotes the development of syntrophic cross- feeding interactions, while on WD, a dominant taxon monopolizes readily available resources without releasing syntrophic byproducts. Intervention experiments reveal that an appropriate dietary resource environment is both necessary and sufficient for rapid and robust microbiome recovery, whereas microbial transplant is neither. Furthermore, prolonged post-antibiotic dysbiosis in mice on WD renders them susceptible to infection by the intestinal pathogen Salmonella enterica serovar Typhimurium. Our data challenge widespread enthusiasm for fecal microbiota transplant (FMT) as a strategy to address dysbiosis and demonstrate that specific dietary interventions are, at minimum, an essential prerequisite for effective FMT, and may afford a safer, more natural, and less invasive alternative to FMT.

microbiology↗
bioRxiv2024

Application of chimeric antigens to paper-based diagnostics for detection of West Nile virus infections of Crocodylus porosus: a novel animal test case

Laboratory-based diagnostics like plaque reduction neutralization tests (PRNT) and ELISA are commonly used to detect seroconversion to flavivirus infections. However, faster, qualitative screening methods are needed for quicker diagnosis and better patient outcomes. Lateral flow assays (LFAs) can provide rapid results (5-15 mins) at the point-of-care, yet few commercial flavivirus antibody detection LFAs are available. We developed an LFA using novel chimeric viral antigens produced by genetically modifying the mosquito restricted Binjari virus (BinJV) to display the outer virion proteins of pathogenic viruses such as West Nile virus (WNV). The BinJV chimeric platform offers various advantages for diagnostic assay development, including rapid construction of new chimeras in response to emerging viral variants, safe, scalable antigen manufacturing, and structural indistinguishability to the wild-type pathogenic virion. As a demonstration of feasibility, we applied chimeric WNV (BinJV/WNV) antigen to LFA as the capture/test line reagent for detection of seroconversion of crocodilians to WNV - a virus affecting crocodilians on multiple continents. We verified the antigenic conservation of the chimera when applied to the LFA detection surface using monoclonal antibodies. Using well-characterised sera (n=60) from WNV seropositive or flavivirus naive Australian saltwater crocodiles (Crocodylus porosus), we illustrated 100% sensitivity and specificity, with results achieved in less than 15 minutes. The LFA further accurately detected seroconversion in animals experimentally infected with WNV. This qualitative screening method can be performed both inside and outside of a laboratory, and the assay design will guide the optimization of similar tests for vector borne virus infection detection in both humans and other animals.

biochemistry↗
bioRxiv2023

ModelSEED v2: High-throughput genome-scale metabolic model reconstruction with enhanced energy biosynthesis pathway prediction

Since the release of ModelSEED in 2010, the systems biology research community has used the ModelSEED genome-scale metabolic model reconstruction pipeline to build over 200,000 draft metabolic reconstructions that support hundreds of publications. Here we describe the first comprehensive update to this reconstruction tool, with new features such as (i) a dramatically improved representation of energy metabolism, which ensures that models produce accurate amounts of ATP per mol of nutrient consumed; (ii) a new template for Archaea model reconstruction; and (iii) a significantly improved curation of all metabolic pathways with mappings to RAST subsystems annotations. We applied the improved pipeline to build and analyze core and genome-scale models for Archaea and Bacteria genomes in KEGG. The new ModelSEED pipeline generates larger models that possess more reactions and genes and require fewer gap-filled reactions. In addition, we see conserved patterns in the ATP biosynthesis mechanism across phylogeny, and identify clades where our understanding of energy biosynthesis is still poor. ModelSEED v2 is available through KBase and the ModelSEED website (https://modelseed.org/), which supports reconstruction, gap-filling, flux balance analysis and model export through a modern web interface.

systems biology↗
bioRxiv2023

Information- and Communication-Centric Approach in Cell Metabolism for Analyzing Behavior of Microbial Communities

Microorganisms naturally form community ecosystems to improve fitness in diverse environments and conduct otherwise intractable processes. Microbial communities are therefore central to biogeochemical cycling, human health, agricultural productivity, and technologies as nuanced as nanotechnology-enabled devices; however, the combinatorial scaling of exchanges with the environment that predicate community functions are experimentally untenable. Several computational tools have been presented to capture these exchanges, yet, no attempt has been made to understand the total information flow to a community from its environment. We therefore adapted a recently developed model for singular organisms, which blends molecular communication and the Shannon Information theory to quantify information flow, to communities and exemplify this expanded model on idealized communities: one of Escherichia coli (E. coli) and Pseudomonas fluorescens to emulate an ecological community and the other of Bacteroides thetaiotaomicron (B. theta) and Kleb Ciella to emulate a human microbiome interaction. Each of these sample communities exhibit critical syntrophy in certain environmental conditions, which should be evident through our community mutual information model. We further explored alternative frameworks for constructing community genome-scale metabolic models (GEMs) - mixed-bag and compartmentalized. Our study revealed that information flow is greater through communities than isolated models, and that the mixed-bag framework conducts greater information flow than the compartmentalized framework for community GEMs, presumably because the latter is encumbered with transport reactions that are absent in the former. This community Mutual Information model is furthermore wrapped as a KBase Application (Run Flux Mutual Information Analysis, RFMIA) for optimum accessibility to biological investigators. We anticipate that this unique quantitative approach to consider information flow through metabolic systems will accelerate both basic and applied discovery in diverse biological fields. Author SummaryMicroorganisms frequently communicate information via information-bearing molecules, which must be fundamentally understood to engineer biological cells that properly engage with their environments, such as the envisioned Internet of Bio-NanoThings. The study of these molecular communications has employed information and communication theory to analyze the exchanged information via chemical reactions and molecular transport. We introduce an information- and communication-centric computational approach to estimate the information flow in biological cells and its impacts on the behavior of single organisms and communities. This study complements our previous work of cell metabolism by developing an end-to-end perspective of molecular communication based on enzyme-regulated reactions. We explore the mutual information using Shannon information theory, measured in bits, between influential nutrients and cellular growth rate. The developed RFMIA computational tool is deployed in the U.S. Department of Energys Systems Biology Knowledgebase, where it quantitatively estimates information flow in both organism and community metabolic networks and extends recent developments in computer communications to explore and explain a new biology for the open-source community.

bioinformatics↗
bioRxiv2023

Machine Learning-Driven Phenotype Predictions based on Genome Annotations

Over the past two decades, there has been a remarkable and exponential expansion in the availability of genome sequences, encompassing a vast number of isolate genomes, amounting to hundreds of thousands, and now extending to millions of metagenome-assembled genomes. The rapid and accurate interpretation of this data, along with the profiling of diverse phenotypes such as respiration type, antimicrobial resistance, or carbon utilization, is essential for a wide range of medical and research applications. Here, we leverage sequenced-based functional annotations obtained from the RAST annotation algorithm as predictors and employ six machine learning algorithms (K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Neural Networks, Logistic Regression, and Decision Trees) to generate classifiers that can accurately predict phenotypes of unclassified bacterial organisms. We apply this approach in two case studies focused on respiration types (aerobic, anaerobic, and facultative anaerobic) and Gram-stain types (Gram negative and Gram positive). We demonstrate that all six classifiers accurately classify the phenotypes of Gram stain and respiration type, and discuss the biological significance of the predicted outcomes. We also present four new applications that have been deployed in The Department of Energy Systems Biology Knowledgebase (KBase) that enable users to: (i) Upload high-quality data to train classifiers; (ii) Annotate genomes in the training set with the RAST annotation algorithm; (iii) Build six different genome classifiers; and (iv) Predict the phenotype of unclassified genomes. (https://narrative.kbase.us/#catalog/modules/kb_genomeclassification)

systems biology↗
bioRxiv2023

A functional microbiome catalog crowdsourced from North American rivers

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial processes governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling, sequencing, and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). This resource profiled the identity, distribution, function, and expression of thousands of microbial genomes across rivers covering 90% of United States watersheds. Specifically, GROWdb encompasses 1,469 microbial species from 27 phyla, including novel lineages from 10 families and 128 genera, and defines the core river microbiome for the first time at genome level. GROWdb analyses coupled to extensive geospatial information revealed local and regional drivers of microbial community structuring, while also presenting a myriad of foundational hypotheses about ecosystem function. Building upon the previously conceived River Continuum Concept1, we layer on microbial functional trait expression, which suggests the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures2, 3 so that it can be widely accessed across disciplines for watershed predictive modeling and microbiome-based management practices.

microbiology↗
bioRxiv2023

Microfluidic organotypic device to test intestinal mucosal barrier permeability ex vivo

To protect the body from external pathogens, the intestines have sophisticated epithelial and mucosal barriers. Disruptions to barrier integrity are associated with a variety of disorders such as irritable bowel disease, Crohns Disease, and celiac disease. One critical component of all barriers are collagens in the extracellular matrix. While the importance of the intestinal barrier is established, current models lack the ability to represent the complex biology that occurs at these barriers. For the current study a microfluidic device model was modified to determine the effectiveness of collagen breakdown to cause barrier disruption. Bacterial collagenase was added for 48 h to the luminal channel of a dual flow microfluidic device to examine changes in intestinal barrier integrity. Tissues exhibited dose-dependent alterations in immunoreactive collagen-1 and claudin-1, and coincident disruption of the epithelial monolayer barrier as indicated by goblet cell morphologies. This ex vivo model system offers promise for further studies exploring factors that affect gut barrier integrity and potential downstream consequences that cannot be studied in current models.

bioengineering↗
bioRxiv2022

A microbial community growth model for dynamic phenotype predictions

1Microbial communities are increasingly recognized as key drivers in animal health, agricultural productivity, industrial operations, and ecological systems. The abundance of chemical interactions in these complex communities, however, can complicate or evade experimental studies, which hinders basic understanding and limits efforts to rationally design communities for applications in the aforementioned fields. Numerous computational approaches have been proposed to deduce these metabolic interactions - notably including flux balance analysis (FBA) and systems of ordinary differential equations (ODEs) - yet, these methods either fail to capture the dynamic phenotype expression of community members or lack the abstractions required to fit or explain the diverse experimental omics data that can be acquired today. We therefore developed a dynamic model (CommPhitting) that deduces phenotype abundances and growth kinetics for each community member, concurrent with metabolic concentrations, by coupling flux profiles for each phenotype with experimental growth and -omics data of the community. These data are captured as variables and coefficients within a mixed integer linear optimization problem (MILP) designed to represent the associated biological processes. This problem finds the globally optimized fit to all experimental data of a trial, thereby most accurately computing aspects of the community: (1) species and phenotype abundances over time; (2) a linearized growth kinetic constant for each phenotype; and (3) metabolite concentrations over time. We exemplify CommPhitting by applying it to study batch growth of an idealized two-member community of the model organisms (Escherichia coli and Pseudomonas flourescens) that exhibits cross-feeding in maltose media. Measurements of this community from our accompanying experimental studies - including total biomass, species biomass, and metabolite abundances over time - were parameterized into a CommPhitting simulation. The resultant kinetics constants and biomass proportions for each member phenotype would be difficult to ascertain experimentally, yet are important for understanding community responses to environmental perturbations and therefore engineering applications: e.g. for bioproduction. We believe that CommPhitting - which is generalized for a diversity of data types and formats, and is further available and amply documented as a Python API - will augment basic understanding of microbial communities and will accelerate the engineering of synthetic communities for diverse applications in medicine, agriculture, industry, and ecology.

systems biology↗
bioRxiv2022

kb_DRAM: Annotating and functional profiling of genomes with DRAM in KBase

SummaryAnnotation is predicting the location of and assigning function to genes in a genome. DRAM is a tool developed to annotate bacterial, archaeal and viral genomes derived from pure cultures or metagenomes. DRAM distills multiple gene annotations to summaries of functional potential. Despite these benefits, a downside of DRAM is processing requires large computation resources, which limits its accessibility, and it did not integrate with downstream metabolic modelling tools. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated in the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point and click interface, as well as generate genome scale metabolic models from these DRAM annotations. Availability and ImplementationThe kb_DRAM software is available at https://github.com/shafferm/kb_DRAM. The kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. A narrative with examples of running all KBase apps is available at https://kbase.us/n/88325/84/. ContactMichael Shaffer, michael.t.shaffer@colostate.edu; Kelly Wrighton, kelly.wrighton@colostate.edu Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗
bioRxiv2022

Dynamic genetic adaptation of Bacteroides thetaiotaomicron murine gut colonization

To understand how a bacterium ultimately succeeds or fails in adapting to a new environment, it is essential to assess the temporal dynamics of its fitness over the course of colonization. The mammalian gut, into which exogenous microorganisms are regularly introduced, represents a biologically and clinically relevant system to explore microbial adaptational processes. In this study, we introduce a human-derived commensal organism, Bacteroides thetaiotaomicron, into the guts of germ-free mice to 1) determine whether the genetic requirements for colonization shift over time and, if so, 2) characterize the biological functions required for microbial survival at different points of colonization. The results of a high-throughput functional genetics assay (BarSeq), transcriptomics, and metabolomics converge on several conclusions. First, adaptation to the host gut occurs in distinct stages. We observed drastic changes in gene usage during the first week, shifting from high expression of amino acid biosynthesis to polysaccharide utilization genes. These changes were sustained thereafter, except for the continued upregulation of a single polysaccharide utilization locus responsible for the degradation of raffinose-family oligosaccharides rich in the standard chow diet fed to our mice. Spontaneous mutations in wildtype Bt also evolve around this locus, highlighting the importance of efficient carbohydrate metabolism in long-term persistence within a monoassociated gut. To improve microbiome-based therapies, it will be important to appreciate and meet the distinct needs of the organism during each stage of colonization. ImportanceMicrobes regularly disperse across and adapt to new environments and ecological niches. A clinically significant microbial niche home to trillions of microbes is the mammalian gut. Temporal processes of microbial adaptation over the course of gut colonization are poorly understood on a genetic, transcriptional, and metabolite level. In this study, we leverage a three-pronged approach to characterize gut colonization as a dynamic process with shifting genetic determinants of microbial fitness. This study sheds light on host colonization by Bacteroides thetaiotaomicron, an organism that is prevalent and dominant across healthy human microbiomes, and not only identifies key pathways involved in colonization, but determines the timing of when these pathways are most vital to colonization success. By demonstrating that the key determinants of colonization success in the gut change over time, the results of this study highlight the importance of considering ecological dynamics in developing more effective microbiome-based therapies.

microbiology↗
bioRxiv2021

Metabolite Damage and Damage-Control in a Minimal Genome

Analysis of the genes retained in the minimized Mycoplasma JCVI-Syn3A genome established that systems that repair or preempt metabolite damage are essential to life. Several genes with known metabolite damage repair or preemption functions were identified and experimentally validated, including 5-formyltetrahydrofolate cyclo-ligase, CoA disulfide reductase, and certain hydrolases. Furthermore, we discovered that an enigmatic YqeK hydrolase domain fused to NadD has a novel proofreading function in NAD synthesis and could double as a MutT-like sanitizing enzyme for the nucleotide pool. Finally, we combined metabolomics and cheminformatics approaches to extend the core metabolic map of JCVI-Syn3A to include promiscuous enzymatic reactions and spontaneous side reactions. This extension revealed that several key metabolite damage-control systems remain to be identified in JCVI-Syn3A, such as that for methylglyoxal.

microbiology↗