mSystems

Variation in bacterial composition inside a host is a result of complex dynamics of microbial community assembly, but little is known about these dynamics. To deconstruct the factors that contribute to this variation, we used a combination of experimental and modeling approaches. We found that demographic stochasticity and stationary heterogeneity in the host carrying capacity or bacterial growth rate are insufficient to explain quantitatively the variation observed in our empirical data. Instead, we found that the data can be understood if the host-bacteria system can be viewed as stochastically switching between high and low growth rates phenotypes. This suggests the dynamics significantly more complex than logistic growth used in canonical models of microbiome assembly. We develop mathematical models of this process that can explain various aspects of our data. We highlight the limitations of snapshot data in describing variation in host-associated communities and the importance of using time-series data along with mathematical models to understand microbial dynamics within a host.

Understanding pathogen metabolism is critical for identifying key functions for drug targeting, establishing effective in vitro experimental systems, etc., particularly for metabolically unique organisms such as Leptospira. Pathogenic Leptospira are thought to infect humans from environmental sources; however, direct isolation from environmental samples remains technically challenging and is not yet well established. Here, we report that a ubiquitous environmental bacterium, Massilia sp., produces metabolites that promote the growth of Leptospira interrogans, encountered through an incidental contamination event, and identified in this study. Gas chromatography-tandem mass spectrometry (GC-MS/MS) analysis showed demonstrated that cultivating of Massilia sp. in R2A medium resulted in the accumulation of metabolites, including branched-chain amino acid (BCAA) intermediates, compared to fresh medium. By combining genome-scale metabolic modeling with experimental validation using cell-free culture supernatant supplementation assays, we demonstrate that BCAA intermediates, particularly 2-ketoisocaproic acid (4-methyl-2-oxopentanoate; 4MOP), a leucine biosynthetic intermediate produced by Massilia sp., enhance Leptospira growth. To investigate the metabolic role of 4MOP, we incorporated transcriptomic data into a genome-scale metabolic network model to generate condition-specific models. Resulted flux distributions indicated that Leptospira catabolized imported 4MOP to produce acetyl-CoA. Our results reveal a previously unrecognized metabolic interaction where metabolites produced by environmental bacteria support the growth of pathogenic Leptospira, offering mechanistic insight into its metabolic requirement. These findings have implications to understand the environmental persistence of Leptospira through its metabolic dependencies on coexisting microbes, and they also help develop better strategies for this pathogen. ImportancePathogenic Leptospira persist in environmental reservoirs, yet the mechanisms supporting their growth remain poorly defined. Here, we find that metabolites produced by common environmental bacteria, Massilia sp., can promote Leptospira growth, suggesting a previously unrecognized metabolic dependency on coexisting microbes. Importantly, this study indicates that combining genome-scale metabolic modeling with experimental validation provides a useful framework for identifying metabolic interactions that are otherwise difficult to resolve using conventional culture-based approaches. Current strategy may facilitate the systematic identification of growth-supporting metabolites and provide a basis for improving selective cultivation for uncultured or difficult to culture organisms. Determination of growth promoting metabolites advances our understanding of pathogen persistence in natural environments and offers a generalized framework to study metabolically dependent microorganisms.

Honeypot ants engage in a convergently-evolved phenotype called repletism, where specialized workers expand their crops and gasters to store vast amounts of food internally. They then store that food for months to support colonies during times of food scarcity. This fascinating phenotype is not well-understood and very little is known about the microbial interactions happening within the fructose-rich replete crop. Previous research using amplicon sequencing showed that Fructilactobacillus makes up nearly 100% of the crop microbiomes of Myrmecocystus mexicanus repletes. This striking result and successful isolation of those strains led to the present investigation into the phylogenetic diversity of these strains and any clues to the nature of the symbiotic relationship between them and the ant host. We find that the isolates from these repletes represented two evolutionary lineages, both most closely related to F. fructivorans. One of those lineages was also found to be phylogenetically and metabolically distinct from all other Fructilactobacillus reference genomes used in this study. This discovery in a genus of bacteria that are highly relevant for fermented human foods and will also lay the groundwork for future understanding of the convergent evolutionary mechanisms of repletism in ants. 3. Impact statementThese analyses add to the literature by identifying a new microbe within a genus that is relevant to food systems. In addition, the host phenotype is convergently evolved and likely microbe-mediated (or at least highly exposed). Understanding this system allows for the testing of ideas of coevolutionary hypotheses with natural replicates. We expect interest to come from food safety and probiotics researchers, evolutionary biologists that think about the impacts of microbes, microbial ecologists interested in novel systems, and those interested in bacteria that may display unique metabolic possibilities. This output allows for the clear future examination of this system with many clear hypotheses. Our analysis allows for the creation of a new and unique model system of host-microbe symbiosis. 4. Data summaryNew genomes assembled in this work can be found under BioProject ID PRJNA1449409. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1449409. Reference bacterial genomes were obtained from NCBI at the following accession numbers: SAMN20557570, SAMN28081009, SAMN28081010, SAMN02597458, SAMN43111088, SAMN28081011, SAMN28535231, SAMN04505734, SAMN28081013, SAMN33452149, SAMN37926504, SAMN02849426, SAMN02470196, SAMN02369432, SAMN02797779, SAMN02797782, SAMN02797768, SAMN09762388, SAMN12785275, SAMEA117660288. The honeypot ant genome was obtained from SAMN37666067. Raw proteomics files will be uploaded to ProteomeXchange with a unique identifier upon manuscript acceptance.

Commensal and pathogenic E. coli strains are increasingly found to be resistant to {beta}-lactams, one of the most widely prescribed classes of antibiotics. Understanding escape to such treatments is complex since {beta}-lactams have several cellular targets and since several mechanisms might be involved in treatment escape in a combined manner. Surprisingly, the accumulated knowledge has not yet proven effective enough to predict the bacterial response to antibiotic treatments at both cellular and population levels with quantitative accuracy for -producing bacteria. Here, we propose a mathematical model that captures in a comprehensive way key phenomena happening at the molecular, cellular, and population levels, as well as their interactions. Our growth-fragmentation model gives a central role to cellular heterogeneity and filamentation as a way for cells to gain time until the degradation of the antibiotic by the {beta}-lactamases released by the dead cell population. Importantly, the model can account for the observed temporal evolution of the total (live and dead) biomass and of the live cell numbers for various antibiotic concentrations. To our knowledge, this is the first model able to quantitatively reconciliate these two classical views on cell death (OD and CFUs) for clinical isolates expressing extended-spectrum beta-lactamases (ESBL). Moreover, our model has a strong predictive power. When calibrated using a slight extension of OD-based data that we propose here, it can predict the CFU profiles in initial and delayed treatments despite inoculum effects, and suggest non-trivial optimal treatments. Generating quality data in quantity has been essential for model development and validation on non-model E. coli strains. We developed protocols to increase the reproducibility of growth kinetics assays and to increase the throughput of CFU assays.

Host-microbe systems are evolutionary niches that produce coevolved biological interactions and are a key component of global health. However, these systems have historically been a difficult field of biological research due to their experimental intractability. Impactful advances in global health will be obtained by leveraging in silico screens to identify genes involved in mediating interspecific interactions. These predictions will progress our understanding of these systems and lay the groundwork for future in vitro and in vivo experiments and bioengineering projects. A driver of host-manipulation and intracellular survival utilized by host-associated microbes is molecular mimicry, a critical mechanism that can occur at any level from DNA to protein structures. We applied protein structure prediction and alignment tools to explore host-associated bacterial structural proteomes for examples of protein structure mimicry. By leveraging the Legionella pneumophila proteome and its many known structural mimics, we developed and validated a screen that can be applied to virtually any host-microbe system to uncover signals of protein mimicry. These mimics represent candidate proteins that mediate host interactions in microbial proteomes. We successfully applied this screen to other microbes with demonstrated effects on global health, Helicobacter pylori and Wolbachia, identifying protein mimic candidates in each proteome. We discuss the roles these candidates may play in important Wolbachia-induced phenotypes and show that Wobachia infection can partially rescue the loss of one of these factors. This work demonstrates how a genome-wide screen for candidates of host-manipulation and intracellular survival offers an opportunity to identify functionally important genes in host-microbe systems.

Bacterial growth often alters the environment, which in turn can impact interspecies interactions among bacteria. Here, we used an in vitro batch system containing mucin beads to emulate the dynamic host environment and to study its impact on the interactions between two abundant and prevalent human gut bacteria, the primary fermenter Bacteroides thetaiotaomicron and the butyrate producer Roseburia intestinalis. By combining machine learning and flow cytometry, we found that the number of viable B. thetaiotaomicron cells decreases with glucose consumption due to acid production, while R. intestinalis survives post-glucose depletion by entering a slow growth mode. Both species attach to mucin beads, but only viable cell counts of B. thetaiotaomicron increase significantly. The number of viable co-culture cells varies significantly over time compared to those of monocultures. A combination of targeted metabolomics and RNA-seq showed that the slow growth mode of R. intestinalis represents a diauxic shift towards acetate and lactate consumption, whereas B. thetaiotaomicron survives glucose depletion and low pH by foraging on mucin sugars. In addition, most of the mucin monosaccharides we tested inhibited the growth of R. intestinalis but not B. thetaiotaomicron. We encoded these causal relationships in a kinetic model, which reproduced the observed dynamics. In summary, we explored how R. intestinalis and B. thetaiotaomicron respond to nutrient scarcity and how this affects their dynamics. We highlight the importance of understanding bacterial metabolic strategies to effectively modulate microbial dynamics in changing conditions.

Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is a complex ecosystem of interdependent taxa with an exponentially complex web of interactions involving these taxa, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and composition, and have proposed several biomarkers. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture. We aim to help complete this picture by venturing beyond taxa abundances and into taxa relationships (i.e. cooperation and competition), using a publicly available gut microbiome dataset from 30 Control (15 female, 15 male) and 28 ADHD (15 female, 13 male) undergraduate students. We conduct our study in two parts. We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature (diversity, differential, biomarker, and composition) to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p < 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We conclude by summarizing our results, and provide conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field.

The bacterial DNA damage response is a critical, coordinated response to endogenous and exogenous sources of DNA damage. Response dynamics are dependent on coordinated synthesis and loss of relevant proteins. While much is known about its global transcriptional control, changes in protein abundance that occur upon DNA damage are less well characterized at the system level. Here, we perform a proteome-wide survey of the DNA damage response in Caulobacter crescentus. We find that while most protein abundance changes upon DNA damage are readily explained by changes in transcription, there are exceptions. The survey also allowed us to identify the novel DNA damage response factor, YaaA, which has been overlooked by previously published, transcription- focused studies. A similar survey in a {Delta}lon strain was performed to explore lons role in DNA damage survival. The {Delta}lon strain had a smaller dynamic range of protein abundance changes in general upon DNA damage compared to the wild type strain. This system-wide change to the dynamics of the response may explain this strains sensitivity to DNA damage. Our proteome survey of the DNA damage response provides additional insight into the complex regulation of stress response and nominates a novel response factor that was overlooked in prior studies. IMPORTANCEThe DNA damage response helps bacteria to react to and potentially survive DNA damage. The mutagenesis induced during this stress response contributes to the development of antibiotic resistance. Understanding how bacteria coordinate their response to DNA damage could help us to combat this growing threat to human health. While the transcriptional regulation of the bacterial DNA damage response has been characterized, this study is the first to our knowledge to assess the proteomic response to DNA damage in Caulobacter.

Sow colostrum and milk provide essential nutrients, immune protection, and one of the earliest microbial exposures for piglets. However, the microbial composition, functional potential, and host interactions of these mammary secretions remain poorly characterized. Here, we combined culturomics, metagenomics, and proteomics to comprehensively characterize the microbiome and proteome of sow colostrum and milk collected at farrowing and at 7 and 21 days postpartum. We recovered 132 bacterial isolates representing at least 42 species, including 15 putative novel taxa. These isolates included both potentially pathogenic species such as Sarcina perfringens and Streptococcus suis and potentially beneficial bacterial species like Lactobacillus amylovorus and Lactiplantibacillus plantarum. The microbial composition and functional potential shifted significantly as the milk matured, with L. amylovorus, Limosilactobacillus reuteri, and Rothia spp. among the most relatively abundant taxa. Several antimicrobial resistance genes, including erm(C), tet(K), tet(M), lnu(A), poxtA, and fexB, were identified on contigs encoding plasmid replicons in the isolates, indicating potential for horizontal gene transfer. Functional annotation of isolate genomes indicated broad carbohydrate-active enzyme (CAZyme) repertoires, including those conferring {beta}-galactosidase activity and the capacity to metabolize milk oligosaccharides. The colostrum and milk proteome also shifted during lactation, reflecting declining immune-related proteins and increasing metabolic and structural proteins. Correlations between specific microbial taxa and host proteins, including Rothia spp. and immune proteins or glycoproteins, suggested potential host-microbe interactions during lactation. Together, these findings provide a multi-omic perspective on how mammary microbiome dynamics and host responses during lactation may influence neonatal microbial colonization and health.

To address the emergence of antimicrobial-resistant pathogens in livestock, microbiome-based strategies are increasingly being sought to reduce antimicrobial use. Here, we describe the intranasal application of bacterial therapeutics (BTs) for mitigating bovine respiratory disease (BRD) and used structural equation modeling to investigate the causal networks after BT application. Beef cattle received i) an intranasal cocktail of previously characterized BT strains, ii) an injection of metaphylactic antimicrobial (tulathromycin), or iii) intranasal saline. Despite being transient colonizers, inoculated BT strains induced longitudinal modulation of the nasopharyngeal bacterial microbiota while showing no adverse effect on animal health. The BT-mediated changes in bacteria included reduced diversity and richness and strengthened cooperative and competitive interactions. In contrast, tulathromycin increased bacterial diversity and antibiotic resistance, and disrupted bacterial interactions. Overall, a single intranasal dose of BTs can modulate the bovine respiratory microbiota, highlighting that microbiome-based strategies have the potential in being utilized to mitigate BRD in feedlot cattle.

Laboratory models provide tractable, reproducible systems that have long served as foundational tools in microbiology. However, the extent to which these models accurately mimic the biological environments they represent remains poorly understood. A quantitative framework was recently introduced to assess how well laboratory models capture microbial physiology in situ. However, applications of this framework have been limited to characterizing the physiology of a single species in human infections, leaving a gap in our understanding of overall microbial community physiology in polymicrobial contexts. Here, we extended this framework to evaluate the accuracy of laboratory model systems in capturing community-level functions in polymicrobial infections. As a proof of concept, we applied the extended framework to a polymicrobial model of human chronic wounds (CW) infection. CWs harbor metabolically diverse bacterial species that engage in a range of microbe-microbe interactions, ultimately impacting community dynamics and disease progression. However, studies on the mechanistic drivers of chronic wound infection have relied on single species or pairwise approaches. Here, we demonstrate that our adapted framework can be used to develop accurate polymicrobial models. Further, we demonstrate that this extended framework can be used to evaluate the occurrence of known microbe-microbe interactions. Building on our prior work in large-scale metagenomic and metatranscriptomic analysis, we propose a highly accurate 6-member synthetic bacterial community model that is representative of the taxonomic and functional complexity of human CW infections. This approach will support the development of ecologically relevant polymicrobial models and the development of better treatment strategies.

Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut, essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we present MetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents, each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach--encompassing environmental conditions, agent information, and metabolic pathways--we simulated different communities to showcase the potential of the model. Using our in-silico platform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases. MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology as it offers new insights into predicting and analyzing microbial communities. ImportanceOur study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include: O_LIPrediction of metabolic cross-feeding and spatial organization in multi-species communities C_LIO_LIInsights into how oxygen gradients and nutrient availability shape community composition in different gut regions C_LIO_LIIdentification of spatially-regulated metabolic pathways and enzymes in E. coli C_LI We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

BackgroundAntimicrobial resistance (AMR) is a significant global public health concern associated with millions of deaths annually. Agriculture has been attributed as a leading factor in AMR and multidrug resistance (MDR) associated with swine production estimated as one of the largest agricultural consumers of antibiotics. Therefore, studying and understanding AMR in swine has global relevance. AMR research has received increased attention in recent years. However, we are still building our understanding of genetic variation within a complex gut microbiome system that impacts AMR and MDR. In order to evaluate the gut resistome, we evaluated genetic variation before, during, and after antibiotic treatments. We studied three treatment groups: non-antibiotic controls (C), chlortetracycline (CTC) treated, and tiamulin (TMU) treated. We collected fecal samples from each group and performed metagenomic sequencing for a longitudinal analysis of genetic variation and functions. ResultsWe generated 772,688,506 reads and 81 metagenome assembled genomes (MAGs). Interestingly, we identified a subset of 11 MAGs with sustained detection and high sustained entropy (SDHSE). Entropy described genetic variation throughout the MAG. Our SDHSE MAGs were considered MDR as they were identified prior to, throughout, and after CTC and TMU treatments as well as in the C piglets. SDHSE MAGs were especially concerning as they harbored relatively high variation. Consistently high variation indicated that these microbial populations may contain hypermutable elements which has been associated with increased chance of AMR and MDR acquisition. Our SDHSE MAGs demonstrated that MDR organisms (MDRO) are present in swine, and likely additional hosts contributing to global AMR. Altogether, our study provides comprehensive genetic support of MDR populations within the gut microbiome of swine.

Cheeses are fermented dairy products consumed worldwide. Their global diversity results from various local variables, including technological practices, as well as the metabolic activity of diverse microorganisms. In Europe, this typicity is exemplified by Protected Designation of Origin (PDO) cheeses, for which genetic diversity remains largely unexplored. Combining culturomics (n = 373 bacterial genomes) and metagenomic (n = 146 metagenomes), we performed a national-scale survey of the microbial diversity encompassing 44 French PDO cheeses. Taxonomic (bacteria, fungi and viruses) and functional profiling reveal a high diversity in the cheese rind, mainly driven by the cheese technology. We also reconstructed 1,119 bacterial metagenome-assembled genomes (MAGs) encompassing seven phyla, including Actinomycetota, Bacillota, Pseudomonadota and Bacteroidota. Using GTDB as a reference, we identified 221 MAGs encompassing 46 genera, as well as 44 bacterial isolate genomes encompassing eight genera, which represent potentially 81 new species (based on <95% ANI). These species were particularly numerous among the genera Halomonas, Psychrobacter and Brachybacterium. Similar results were observed when compared with the cFMD database. We combined our genomic and metagenomic datasets into a catalog of 26.2 million protein clusters, with 50% of these clusters remaining unassigned to a known function and taxonomy. We illustrated the potential of this resource by searching for methionine gamma-lyase (MGL), an enzyme playing a significant role in cheese flavor. This protein was predominantly found in Pseudoalteromonas, a potentially new MGL-producing genus, Serratia, Pseudomonas, Proteus and Hafnia, and its prevalence varied with cheese technology. Our study provides a substantial genomic resource for food microbiologists and cheesemakers to further explore the biotechnological potential of PDO cheese biodiversity.

Metagenomic sequencing is presumed to provide unbiased sampling of all the genetic material in a sample. Downstream analysis methods, such as binning, gene copy number analysis, structural variations, or single nucleotide polymorphism analysis, commonly assume an even distribution across the genome after accounting for known artefacts such as GC content. We discovered coverage bias across gut microbiome species, manifesting as a difference in coverage before and after bacterial transcription start sites. Using matched metatranscriptomic and metagenomic sequencing data, we demonstrate that this bias correlates with gene expression. Potential artefacts such as the sequencing technology, reference genome used for alignment, and mappability bias were investigated across multiple datasets and shown to not be factors for association. While GC bias was found correlated with coverage bias, the association of coverage bias with gene expression remains significant after adjusting for GC bias. Paired-end read mapping demonstrated an enrichment in 5 read ends immediately downstream of the TSS which was partly a byproduct of unmapped reads upstream of the TSS. Our observations suggest the existence of strain-level variation where sequence variation in the promoter site region is preventing proper read alignment to the reference genome. The correlation of this phenomenon with gene expression may also reflect evolutionary footprints for fine-tuning the regulation of gene expression. Understanding the source of this sequence variation and the biological implications of this artefact will be useful not only to better characterise microbial functions but also to improve interpretations of strain level dynamics. ImportanceSequencing coverage calculated from metagenomic sequencing data is extensively used in the microbiome field, providing valuable information about microbial abundances, gene (functional) abundances, growth rates, and genomic variations. Understanding factors that impact the distribution of coverage along genomes is therefore important for multiple applications. In this study, we report on uneven read coverage across the transcription start sites of bacterial genomes that is correlated with gene expression levels. We determine that this bias is independent of multiple factors including GC bias, and arises due to higher strain divergence from reference genomes upstream of the transcript start site. We propose that evolutionary finetuning of gene expression in competitive microbial ecosystems can drive genetic mutations at the promoter site. Our findings suggest the potential to glean gene regulatory information from metagenomic data, and better understand how ecological factors shape genomes in the microbiome and their sequencing coverage.

IntroductionCholera, caused by Vibrio cholerae, is a severe diarrheal disease threatening global health. The flagellum of V. cholerae functions not only for motility but also as an environmental sensor regulating virulence. The trade-off mechanism between motility loss and enhanced host adaptation remains unclear. ObjectiveThis study aimed to elucidate how flagellar mutations, which lead to a loss of motility, affect host adaptability in V. cholerae and to uncover the underlying molecular basis. MethodsWe analyzed 3,135 cholera-related samples to identify mutation hotspots in flagellar genes. A relevant flagellar mutant library was constructed and assessed for host adaptability. Multiple approaches, including molecular, genetic, transcriptomic, and proteomic analyses, were applied to dissect the FlgM-mediated pathway, with key findings revalidated in an adult mouse model using a specifically constructed 6N-labeled mutant pool. ResultsBig data analysis reveals those flagellar mutations in V. cholerae cluster in key structural and regulatory genes, including flhB, fliA, fliF, fliD, and fliM. Flagellar mutations led to a scenario where the secretion level of the regulator FlgM was negatively correlated with host adaptability. Intracellular FlgM inactivates either the {sigma}28 factor FliA directly or acts through the VarS/VarA-CsrA/BCD system, ultimately leading to the derepression of the quorum-sensing master regulator named hapR. HapR can directly bind to the promoters of genes involving in methionine transportation to regulate adaptability of V. cholerae. The 6N-labeled mutants pool experiment reconfirmed that motility loss promotes host adaptability via the FlgM-FliA-HapR-methionine axis. ConclusionOur findings demonstrate that secretion levels of the flagellar regulator FlgM drive an adaptive shift in V. cholerae from motility to host adaptability, mediated through quorum sensing and methionine metabolic reprogramming. This reveals a novel mechanism underlying bacterial evolution and pathogenicity.

In mycobacteria, regulation of transcript degradation is known to occur in response to environmental stress and to facilitate adaptation. However, the mechanisms underlying this regulation are unknown. Here we sought to gain an understanding of the mechanisms controlling mRNA stability by investigating the transcript properties associated with variance in transcript stability and stress-induced transcript stabilization. We performed transcriptome-wide mRNA degradation profiling of Mycolicibacterium smegmatis in both log phase growth and hypoxia-induced growth arrest. The transcriptome was globally stabilized in response to hypoxia, with all transcripts having longer half-lives, but some having greater degrees of stabilization than others. The transcripts of essential genes were generally stabilized more than those of non-essential genes. We then developed machine learning models that utilized a compendium of transcript properties and enabled us to identify the non-linear collective effect of diverse properties on transcript stability and stabilization. The comparisons of these properties confirmed the association of 5 UTRs with transcript stability, along with other differences between leadered and leaderless transcripts. Our analysis highlighted the protective effect of translation in log phase but not in hypoxia-induced growth arrest. Steady-state transcript abundance had a weak negative association with transcript half-life that was stronger in hypoxia, while coding sequence length showed an unexpected correlation with half-life in hypoxia only. In summary, we found that transcript properties are differentially associated with transcript stability depending on both the transcript type and the growth condition. Our results reveal the complex interplay between transcript features and microenvironment that shapes transcript stability in mycobacteria.

The growth rate of single bacterial cells is continuously disturbed by random fluctuations in biosynthesis rates and by deterministic cell-cycle events, such as division, genome duplication, and septum formation. It is not understood whether, and how, bacteria reject these disturbances. Here we quantified growth and constitutive protein expression dynamics of single Bacillus subtilis cells, as a function of cell-cycle-progression. Variation in birth size and growth rate, resulting from unequal cell division, is largely compensated for when cells divide again. We analysed the cell-cycle-dynamics of these compensations and found that both growth and protein expression exhibited biphasic behaviour. During a first phase of variable duration, the absolute rates were approximately constant and cells behaved as sizers. In the second phase, rates increased and growth behaviour exhibited characteristics of a timer-strategy. This work shows how cell-cycle-dependent rate adjustments of biosynthesis and growth are integrated to compensate for physio-logical disturbances caused by cell division.\n\nIMPORTANCEUnder constant conditions, bacterial populations can maintain a fixed average cell size and constant exponential growth rate. At the single cell-level, however, cell-division can cause significant physiological perturbations, requiring compensatory mechanisms to restore the growth-related characteristics of individual cells toward that of the average cell. Currently, there is still a major gap in our understanding of the dynamics of these mechanisms, i.e. how adjustments in growth, metabolism and biosynthesis are integrated during the bacterial cell-cycle to compensate the disturbances caused by cell division. Here we quantify growth and constitutive protein expression in individual bacterial cells at sub-cell-cycle resolution. Significantly, both growth and protein production rates display structured and coordinated cell-cycle-dependent dynamics. These patterns reveal the dynamics of growth rate and size compensations during cell-cycle progression. Our findings provide a dynamic cell-cycle perspective that offers novel avenues for the interpretation of physiological processes that underlie cellular homeostasis in bacteria.

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

Understanding how pangenomes originate and evolve is crucial for predicting evolutionary trajectories and uncovering ecological interactions of bacterial pathogens. Pangenome fluidity has been attributed to adaptive evolution, yet the underlying ecological drivers for bacterial pathogens persisting in natural reservoirs remain poorly understood. Listeria monocytogenes (Lm), a foodborne pathogen causing fatal listeriosis, serves as an ideal model for investigating the ecological mechanisms underlying pangenome fluidity in bacterial pathogens due to its high evolutionary divergence, broad ecological versatility, and significant public health concern. Through pangenome analysis of 177 Lm isolates representing three evolutionary lineages (I, II, and III) that we isolated from soils across the United States, we found that substantial genome variation was strongly associated with climatic factors (e.g. precipitation and temperature), soil properties (e.g. aluminum, pH, and molybdenum), and bacterial community composition, particularly Nitrospirae, Planctomycetes, Acidobacteria, and Cyanobacteria. These factors exerted selective pressure across many gene functions, with pronounced effects on genes involved in cell envelope synthesis, defense mechanisms, and replication, recombination, and repair. Among Lm lineages occupying varied habitats, distinct pangenome properties were observed. Lineage III exhibited a highly fluid pangenome, which was attributed to local adaptation to nutrient-limited conditions and strong dispersal limitation. In contrast, lineage I maintained a conserved pangenome, likely due to frequent homogenizing dispersal. Consistent with these dispersal patterns, we identified an elevated risk of soil-to-human transmission in lineage I, evidenced by epidemiological links between three soil-derived and 17 clinical isolates. Collectively, this study reveals the pivotal role of environmental selection imposed by both abiotic factors and bacterial communities in governing the adaptive pangenome evolution in bacterial pathogens. It also highlights significant differences in pangenome flexibility, ecological niches, and transmission dynamics across lineages of the same pathogen species, underscoring the need for tailored source tracking strategies. AUTHOR SUMMARYStudying the full set of genes found in different strains of a bacterium (i.e. pangenome) helps us understand how bacterial pathogens develop and adapt to changes in the environment. Here, we focused on Listeria monocytogenes (Lm), a pathogen capable of spreading through food and surviving in diverse environments, to understand how environmental factors and the way that bacteria move across locations can influence the pangenome content in this important bacterium. By analyzing the genomes of 177 Lm strains representing three evolutionary lineages (I, II, and III) collected from soils across the United States, we found that variation in climate, soil chemistry, and surrounding bacteria (e.g., Nitrospirae) was closely linked to genetic differences among strains. These environmental conditions seemed to affect genes that help build the cell envelop, protect the bacteria from harm, and fix damaged DNA. We also observed different levels of genome flexibility across Lm lineages which were found to be related to how they move across different locations. Lineage III showed evidence of barriers to spreading, which may enhance genetic differentiation across populations, leading to a more flexible pangenome. In contrast, lineage I appeared to spread more readily and was epidemiologically linked to human clinical cases, which may facilitate genetic exchange that reduce pangenome diversity. This study shows that both non-living environmental conditions--like precipitation and pH--and nearby groups of bacteria play a big role in shaping how bacterial pathogens change their genes to survive. It also highlights that different subtypes of the same pathogen can have different gene flexibility and spread in different ways, calling for specific biocontrol measures.