An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography

Divergence of gut bacteria through the selection of genomic variants implicated in the metabolism of sugars, amino acids, and purines by small extracellular vesicles in milk

Here, we report that small extracellular vesicles (sEVs) in milk mediate the communication between bacteria and animal kingdoms, increase the divergence of bacteria in the intestine, and alter metabolite production by bacteria. We show that bovine milk sEVs select approximately 55,000 genomic variants in 19 species of bacteria from the murine cecum ex vivo. The genomic variants are transcribed into mRNA. The selection of genomic variants by milk sEVs alters bacterial metabolism, leading to an up to 12-fold difference in the abundance of more than 1000 metabolites in bacteria cultured in milk sEV-free media compared to sEV-containing media. Evidence is particularly strong that selection of genomic variants by milk sEV changes the metabolism of sugars, amino acids, and purines which might contribute to the development of spatial learning and memory deficiencies and seizure phenotypes reported for milk sEV-depleted infants and mice. Human milk is a rich source of sEVs, whereas formula contains only trace amounts of milk sEVs. This report implicates nutritional sEVs in altered microbial metabolism beyond the mere selection of bacterial communities.

Intraspecies dynamics underlie the apparent stability of two important skin microbiome species

Adult human facial skin microbiomes are remarkably similar at the species level, dominated by Cutibacterium acnes and Staphylococcus epidermidis, yet each person harbors a unique community of strains. Understanding how person-specific communities assemble is critical for designing microbiome-based therapies. Here, using 4,055 isolate genomes and 356 metagenomes, we reconstruct on-person evolutionary history to reveal on- and between-person strain dynamics. We find that multiple cells are typically involved in transmission, indicating ample opportunity for migration. Despite this accessibility, family members share only some of their strains. S. epidermidis communities are dynamic, with each strain persisting for an average of only 2 years. C. acnes strains are more stable and have a higher colonization rate during the transition to an adult facial skin microbiome, suggesting this window could facilitate engraftment of therapeutic strains. These previously undetectable dynamics may influence the design of microbiome therapeutics and motivate the study of their effects on hosts.

SourceApp: A Novel Metagenomic Source Tracking Tool that can Distinguish between Fecal Microbiomes Using Genome-To-Source Associations Benchmarked Against Mixed Input Spike-In Mesocosms

Methodologies utilizing metagenomics are attractive to fecal source tracking (FST) aims for assessing the presence and proportions of various fecal inputs simultaneously. Yet, compared to established culture- or PCR-based techniques, metagenomic approaches for these purposes are rarely benchmarked or contextualized for practice. We performed shotgun sequencing experiments (n = 35) of mesocosms constructed from the water of a well-studied recreational and drinking water reservoir spiked with various fecal (n = 6 animal sources, 3 wastewater sources, and 1 septage source) and synthetic microbiome spike-ins (n = 1) introduced at predetermined cell concentrations to simulate fecal pollution events of known composition. We built source-associated genome databases using publicly available reference genomes and metagenome assembled genomes (MAGs) recovered from short- and long-read sequencing of the fecal spike-ins, and then created an associated bioinformatic tool, called SourceApp, for inferring source attribution and apportionment by mapping the metagenomic data to these genome databases. SourceApp's performance varied substantially by source, with cows being underestimated due to under sampling of cow fecal microbiomes. Parameter tuning revealed sensitivity and specificity near 0.90 overall, which exceeded all alternative tools. SourceApp can assist researchers with analyzing and interpreting shotgun sequencing data and developing standard operating procedures on the frontiers of metagenomic FST.

Accurate estimation of intraspecific microbial gene content variation in metagenomic data with MIDAS v3 and StrainPGC

Metagenomics has greatly expanded our understanding of the human gut microbiome by revealing a vast diversity of bacterial species within and across individuals. Even within a single species, different strains can have highly divergent gene content, affecting traits such as antibiotic resistance, metabolism, and virulence. Methods that harness metagenomic data to resolve strain-level differences in functional potential are crucial for understanding the causes and consequences of this intraspecific diversity. The enormous size of pangenome references, strain mixing within samples, and inconsistent sequencing depth present challenges for existing tools that analyze samples one at a time. To address this gap, we updated the MIDAS pangenome profiler, now released as version 3, and developed StrainPGC, an approach to strain-specific gene content estimation that combines strain tracking and correlations across multiple samples. We validate our integrated analysis using a complex synthetic community of strains from the human gut and find that StrainPGC outperforms existing approaches. Analyzing a large, publicly available metagenome collection from inflammatory bowel disease patients and healthy controls, we catalog the functional repertoires of thousands of strains across hundreds of species, capturing extensive diversity missing from reference databases. Finally, we apply StrainPGC to metagenomes from a clinical trial of fecal microbiota transplantation for the treatment of ulcerative colitis. We identify two Escherichia coli strains, from two different donors, that are both frequently transmitted to patients but have notable differences in functional potential. StrainPGC and MIDAS v3 together enable precise, intraspecific pangenomic investigations using large collections of metagenomic data without microbial isolation or de novo assembly.

Strain tracking in complex microbiomes using synteny analysis reveals per-species modes of evolution

Microbial species diversify into strains through single-nucleotide mutations and structural changes, such as recombination, insertions and deletions. Most strain-comparison methods quantify differences in single-nucleotide polymorphisms (SNPs) and are insensitive to structural changes. However, recombination is an important driver of phenotypic diversification in many species, including human pathogens. We introduce SynTracker, a tool that compares microbial strains using genome synteny-the order of sequence blocks in homologous genomic regions-in pairs of metagenomic assemblies or genomes. Genome synteny is a rich source of genomic information untapped by current strain-comparison tools. SynTracker has low sensitivity to SNPs, has no database requirement and is robust to sequencing errors. It outperforms existing tools when tracking strains in metagenomic data and is particularly suited for phages, plasmids and other low-data contexts. Applied to single-species datasets and human gut metagenomes, SynTracker, combined with an SNP-based tool, detects strains enriched in either point mutations or structural changes, providing insights into microbial evolution in situ.

Gut microbiota and their influence in brain cancer milieu

Microbial communities are not simply remnants of the past but dynamic entities that continuously evolve under the selective pressures of nature, reflecting the intricate and adaptive processes of evolution. The microbiota residing in the various regions of the human body has numerous roles in different physiological processes such as nutrition, metabolism, immune regulation, etc. In the zeal of achieving empirical insights into the ambit of the gut microbiome, the research over the years led to the revelation of reciprocal interaction between the gut microbiome and the cognitive functioning of the human body. Dysbiosis in the gut microbial composition disturbs the homeostatic cognitive functioning of the human body. This dysbiosis has been associated with various chronic diseases, including brain cancer, such as glioma, glioblastoma, etc. This review explores the mechanistic role of dysbiosis-mediated progression of brain cancers and their subtypes. Moreover, it demonstrates the regulatory role of microbial metabolites produced by the gut microbiota, such as short-chain fatty acids, amino acids, lipids, etc., in the tumour progression. Further, we also provide valuable insights into the microbiota mediating the efficiency of therapeutic regimens, thereby leveraging gut microbiota as potential biomarkers and targets for improved treatment outcomes.

Small amounts of misassembly can have disproportionate effects on pangenome-based metagenomic analyses

Individual genes from microbiomes can drive host-level phenotypes. To help identify such candidate genes, several recent tools estimate microbial gene copy numbers directly from metagenomes. These tools rely on alignments to pangenomes, which, in turn, are derived from the set of all individual genomes from one species. While large-scale metagenomic assembly efforts have made pangenome estimates more complete, mixed communities can also introduce contamination into assemblies, and it is unknown how robust pangenome-based metagenomic analyses are to these errors. To gain insight into this problem, we re-analyzed a case-control study of the gut microbiome in cirrhosis, focusing on commensal Clostridia previously implicated in this disease. We tested for differentially prevalent genes in the Lachnospiraceae and then investigated which were likely to be contaminants using sequence similarity searches. Out of 86 differentially prevalent genes, we found that 33 (38%) were probably contaminants originating in taxa such as Veillonella and Haemophilus, unrelated genera that were independently correlated with disease status. Our results demonstrate that even small amounts of contamination in metagenome assemblies, below typical quality thresholds, can threaten to overwhelm gene-level metagenomic analyses. However, we also show that such contaminants can be accurately identified using a method based on gene-to-species correlation. After removing these contaminants, we observe that several flagellar motility gene clusters in the Lachnospira eligens pangenome are associated with cirrhosis status. We have integrated our analyses into an analysis and visualization pipeline, PanSweep, that can automatically identify cases where pangenome contamination may bias the results of gene-resolved analyses.IMPORTANCEMetagenome-assembled genomes, or MAGs, can be constructed without pure cultures of microbes. Large-scale efforts to build MAGs have yielded more complete pangenomes (i.e., sets of all genes found in one species). Pangenomes allow us to measure strain variation in gene content, which can strongly affect phenotype. However, because MAGs come from mixed communities, they can contaminate pangenomes with unrelated DNA; how much this impacts downstream analyses has not been studied. Using a metagenomic study of gut microbes in cirrhosis as our test case, we investigate how contamination affects analyses of microbial gene content. Surprisingly, even small, typical amounts of MAG contamination (<5%) result in disproportionately high levels of false positive associations (38%). Fortunately, we show that most contaminants can be automatically flagged and provide a simple method for doing so. Furthermore, applying this method reveals a new association between cirrhosis and gut microbial motility.

Multimodal metagenomic analysis reveals microbial InDels as superior biomarkers for pediatric Crohn's disease

BACKGROUND AND AIMS

The gut microbiome is closely associated with pediatric Crohn's disease (CD), while the multidimensional microbial signature and their capabilities for distinguishing pediatric CD are underexplored. This study aims to characterize the microbial alterations in pediatric CD and develop a robust classification model.

METHODS

A total of 1175 fecal metagenomic sequencing samples, predominantly from 3 cohorts of pediatric CD patients, were re-analyzed from raw sequencing data using uniform process pipelines to obtain multidimensional microbial alterations in pediatric CD, including taxonomic profiles, functional profiles, and multi-type genetic variants. Random forest algorithms were used to construct classification models after comparing multiple machine learning algorithms.

RESULTS

We found pediatric CD samples exhibited reduced microbial diversity and unique microbial characteristics. Pronounced abundance differences in 45 species and 1357 KEGG orthology genes. Particularly, Enterocloster bolteae emerged as a pivotal pediatric CD-associated species. Additionally, we identified a vast amount of microbial genetic variants linked to pediatric CD, including 192 structural variants, 1256 insertions/deletions (InDels), and 3567 single nucleotide variants, with a considerable portion of these variants located in non-genic regions. The InDel-based model outperformed other predictive models against multidimensional microbial signatures, achieving an area under the ROC curve (AUC) of 0.982. The robustness and disease specificity were further confirmed in an independent CD cohort (AUC = 0.996) and 5 other microbiome-associated pediatric cohorts.

CONCLUSIONS

Our study provided a comprehensive landscape of microbial alterations in pediatric CD and introduced a highly effective diagnostic model rooted in microbial InDels, which contributes to the development of noninvasive diagnostic tools and targeted therapies.

Human microbiome acquisition and transmission

As humans, we host personal microbiomes intricately connected to our biology and health. Far from being isolated entities, our microbiomes are dynamically shaped by microbial exchange with the surroundings, in lifelong microbiome acquisition and transmission processes. In this Review, we explore recent studies on how our microbiomes are transmitted, beginning at birth and during interactions with other humans and the environment. We also describe the key methodological aspects of transmission inference, based on the uniqueness of the building blocks of the microbiome - single microbial strains. A better understanding of human microbiome transmission will have implications for studies of microbial host regulation, of microbiome-associated diseases, and for effective microbiome-targeting strategies. Besides exchanging strains with other humans, there is also preliminary evidence we acquire microorganisms from animals and food, and thus a complete understanding of microbiome acquisition and transmission can only be attained by adopting a One Health perspective.

Quantifying compositional variability in microbial communities with FAVA

Microbial communities vary across space, time, and individual hosts, generating a need for statistical methods capable of quantifying variability across multiple microbiome samples at once. To understand heterogeneity across microbiome samples from different host individuals, sampling times, spatial locations, or experimental replicates, we present FAVA (FST-based Assessment of Variability across vectors of relative Abundances), a framework for characterizing compositional variability across two or more microbiome samples. FAVA quantifies variability across many samples of taxonomic or functional relative abundances in a single index ranging between 0 and 1, equaling 0 when all samples are identical and 1 when each sample is entirely composed of a single taxon (and at least two distinct taxa are present across samples). Its definition relies on the population-genetic statistic FST, with samples playing the role of "populations" and taxa playing the role of "alleles." Its mathematical properties allow users to compare datasets with different numbers of samples and taxonomic categories. We introduce extensions that incorporate phylogenetic similarity among taxa and spatial or temporal distances between samples. We demonstrate FAVA in two examples. First, we use FAVA to measure how the taxonomic and functional variability of gastrointestinal microbiomes across individuals from seven ruminant species changes along the gastrointestinal tract. Second, we use FAVA to quantify the increase in temporal variability of gut microbiomes in healthy humans following an antibiotic course and to measure the duration of the antibiotic's influence on temporal microbiome variability. We have implemented this tool in an R package, FAVA, for use in pipelines for the analysis of microbial relative abundances.

Genetic Diversity and Anatoxin Profiles of Freshwater Benthic Cyanobacteria From Nova Scotia (Canada)

Some mat-forming cyanobacteria produce harmful cyanotoxins, yet benthic species remain understudied compared to planktonic counterparts. This study assesses the diversity, distribution and toxin production of mat-forming cyanobacteria across lentic and lotic systems in Nova Scotia, Canada. We documented greater cyanobacterial species richness in lentic environments, with six dominant species distributed into two major Microcoleus clades, five of which represent putative novel taxa. Two Microcoleus species with the genetic repertoire to produce anatoxins were prevalent. One has been previously reported in Canada, while the second represents a novel species found exclusively in an environment impacted by discharge from a water treatment plant. We observed variability in the gene clusters responsible for the biosynthesis of anatoxin-a and associated analogues (ATXs), including the discovery of a novel anaG variant with a ~ 1.7 kb insertion in a Microcoleus strain dominating homoanatoxin-producing mats. This extended anaG, encoding a polyketide synthase with an additional methyltransferase domain, coexists with shorter variants, leading to the production of a mixture of ATXs. These findings highlight the genetic diversity of benthic cyanobacteria in freshwater environments, with Microcoleus as the primary contributor to the production of ATXs in both lentic and lotic systems, underscoring their potential to produce harmful toxins.

stana: an R package for metagenotyping analysis and interactive application based on clinical data

Metagenotyping of metagenomic data has recently attracted increasing attention as it resolves intraspecies diversity by identifying single nucleotide variants. Furthermore, gene copy number analysis within species provides a deeper understanding of metabolic functions in microbial communities. However, a platform for examining metagenotyping results based on relevant grouping data is lacking. Here, we have developed the R package, stana, for the processing and analysis of metagenotyping results. The package consists of modules for preprocessing, statistical analysis, functional analysis and visualization. An interactive analysis environment for exploring the metagenotyping results was also developed and publicly released with over 1000 publicly available metagenome samples related to human diseases. Three examples exploring the relationship between the metagenotypes of the gut microbiome and human diseases are presented-end-stage renal disease, Crohn's disease and Parkinson's disease. The results suggest that stana facilitated the confirmation of the original study's findings and the generation of a new hypothesis. The GitHub repository for the package is available at https://github.com/noriakis/stana.

Shared environments complicate the use of strain-resolved metagenomics to infer microbiome transmission

BACKGROUND

In humans and other social animals, social partners have more similar microbiomes than expected by chance, suggesting that social contact transfers microorganisms. Yet, social microbiome transmission can be difficult to identify based on compositional data alone. To overcome this challenge, recent studies have used information about microbial strain sharing (i.e., the shared presence of highly similar microbial sequences) to infer transmission. However, the degree to which strain sharing is influenced by shared traits and environments among social partners, rather than transmission per se, is not well understood.

RESULTS

Here, we first use a fecal microbiota transplant dataset to show that strain sharing can recapitulate true transmission networks under ideal settings when donor-recipient pairs are unambiguous and recipients are sampled shortly after transmission. In contrast, in gut metagenomes from a wild baboon population, we find that demographic and environmental factors can override signals of strain sharing among social partners.

CONCLUSIONS

We conclude that strain-level analyses provide useful information about microbiome similarity, but other facets of study design, especially longitudinal sampling and careful consideration of host characteristics, are essential for inferring the underlying mechanisms of strain sharing and resolving true social transmission network. Video Abstract.

Intraspecies associations from strain-rich metagenome samples

Genetically distinct strains of a species can vary widely in phenotype, reducing the utility of species-resolved microbiome measurements for detecting associations with health or disease. While metagenomics theoretically provides information on all strains in a sample, current strain-resolved analysis methods face a tradeoff: de novo genotyping approaches can detect novel strains but struggle when applied to strain-rich or low-coverage samples, while reference database methods work robustly across sample types but are insensitive to novel diversity. We present PHLAME, a method that bridges this divide by combining the advantages of reference-based approaches with novelty awareness. PHLAME explicitly defines clades at multiple phylogenetic levels and introduces a probabilistic, mutation-based, framework to accurately quantify novelty from the nearest reference. By applying PHLAME to publicly available human skin and vaginal metagenomes, we uncover previously undetected clade associations with coexisting species, geography, and host age. The ability to characterize intraspecies associations and dynamics in previously inaccessible environments will propel new mechanistic insights from accumulating metagenomic data.

Depletion of key gut bacteria predicts disrupted bile acid metabolism in inflammatory bowel disease

The gut microbiome plays a key role in bile acid (BA) metabolism, where a diversity of metabolic products contribute to human health and disease. In particular, Inflammatory Bowel Disease (IBD) is characterized by a low concentration of secondary bile acids (SBAs), whose transformation from primary bile acids (PBAs) is an essential function performed solely by gut bacteria. BA-transformation activity mediated by the bile acid inducible (bai) operon has been functionally characterized in the genus Clostridium, and homologous bai gene sequences have been found in metagenome-assembled genomes (MAGs) belonging to other taxa in the human gut, but it is unclear which species of bai-carrying bacteria perform physiologically significant amounts of bile acid transformation in healthy and sick individuals. Here, we analyzed hundreds of stool samples with paired metagenomic and metabolomic data from IBD patients and controls and found that the abundance of the bai operon in metagenomic samples was highly predictive of that sample's high- or low-SBA metabolic state. We further found that bai genes from the Clostridium species best characterized as BA transformers were more prevalent in IBD patients than in non-IBD controls, while bai genes from uncharacterized taxa known only from MAGs were much more physiologically relevant in non-IBD samples. These un-isolated clades of BA-transforming bacteria merit further research; as beyond their prevalence in the human population, we found some cases in which they engrafted in IBD patients who had undergone fecal microbiota transplantation and experienced a clinical response.IMPORTANCEIn this paper, we identify specific bacteria that perform an important metabolic function in the human gut and demonstrate that in the guts of a large subset of patients with IBD, these bacteria are missing and the function is defective. This is a rare example where the correlation between the absence of specific bacteria and the dysfunction of metabolism is directly observed, not in mice nor in the lab, but in physiologic microbial communities in the human gut. Our results point to a path for studying how a small but important set of bacteria is affected by conditions in the IBD gut and perhaps to the development of interventions to mitigate the loss of these bacteria in IBD.

Inference of the Demographic Histories and Selective Effects of Human Gut Commensal Microbiota Over the Course of Human History

Despite the importance of gut commensal microbiota to human health, there is little knowledge about their evolutionary histories, including their demographic histories and distributions of fitness effects (DFEs) of mutations. Here, we infer the demographic histories and DFEs for amino acid-changing mutations of 39 of the most prevalent and abundant commensal gut microbial species found in Westernized individuals over timescales exceeding human generations. Some species display contractions in population size and others expansions, with several of these events coinciding with several key historical moments in human history. DFEs across species vary from highly to mildly deleterious, with differences between accessory and core gene DFEs largely driven by genetic drift. Within genera, DFEs tend to be more congruent, reflective of underlying phylogenetic relationships. Together, these findings suggest that gut microbes have distinct demographic and selective histories.

A review on recent taxonomic updates of gut bacteria associated with social bees, with a curated genomic reference database

Honeybees and bumblebees play a crucial role as essential pollinators. The special gut microbiome of social bees is a key factor in determining the overall fitness and health of the host. Although bees harbor relatively simple microbial communities at the genus level, recent studies have unveiled significant genetic divergence and variations in gene content within each bacterial genus. However, a comprehensive and refined genomics-based taxonomic database specific to social bee gut microbiomes remains lacking. Here, we first provided an overview of the current knowledge on the distribution and function of social bee gut bacteria, as well as the factors that influence the gut population dynamics. We then consolidated all available genomes of the gut bacteria of social bees and refined the species-level taxonomy, by constructing a maximum-likelihood core genome phylogeny and calculating genome-wide pairwise average nucleotide identity. On the basis of the refined species taxonomy, we constructed a curated genomic reference database, named the bee gut microbe genome sequence database (BGM-GDb). To evaluate the species-profiling performance of the curated BGM-GDb, we retrieved a series of bee gut metagenomic data and inferred the species-level composition using metagenomic intra-species diversity analysis system (MIDAS), and then compared the results with those obtained from a prebuilt MIDAS database. We found that compared with the default database, the BGM-GDb excelled in aligned read counts and bacterial richness. Overall, this high-resolution and precise genomic reference database will facilitate research in understanding the gut community structure of social bees.

Previously hidden intraspecies dynamics underlie the apparent stability of two important skin microbiome species

Adult human facial skin microbiomes are remarkably similar at the species-level, dominated by Cutibacterium acnes and Staphylococcus epidermidis, yet each person harbors a unique community of strains. Understanding how person-specific communities assemble is critical for designing microbiome-based therapies. Here, using 4,055 isolate genomes and 360 metagenomes, we reconstruct on-person evolutionary history to reveal on and between-person strain dynamics. We find that multiple cells are typically involved in transmission, indicating ample opportunity for migration. Despite this accessibility, family members share only some of their strains. S. epidermidis communities are dynamic, with each strain persisting for an average of only 2 years. C. acnes strains are more stable and have a higher colonization rate during the transition to an adult facial skin microbiome, suggesting this window could facilitate engraftment of therapeutic strains. These previously undetectable dynamics may influence the design of microbiome therapeutics and motivate the study of their effects on hosts.

Uniform bacterial genetic diversity along the guts of mice inoculated with human stool

Environmental gradients exist throughout the digestive tract, driving spatial variation in the membership and abundance of bacterial species along the gut. However, less is known about the distribution of genetic diversity within bacterial species along the gut. Understanding this distribution is important because bacterial genetic variants confer traits important for the functioning of the microbiome and are also known to impart phenotypes to the hosts, including local inflammation along the gut and the ability to digest food. Thus, to be able to understand how the microbiome functions at a mechanistic level, it is essential to understand how genetic diversity is organized along the gut and the ecological and evolutionary processes that give rise to this organization. In this study, we analyzed bacterial genetic diversity of approximately 30 common gut commensals in five regions along the gut lumen in germ-free mice colonized with the same healthy human stool sample. While species membership and abundances varied considerably along the gut, genetic diversity within species was substantially more uniform. Driving this uniformity were similar strain frequencies along the gut, implying that multiple, genetically divergent strains of the same species can coexist within a host without spatially segregating. Additionally, the approximately 60 unique evolutionary adaptations arising within mice tended to sweep throughout the gut, showing little specificity for particular gut regions. Together, our findings show that genetic diversity may be more uniform along the gut than species diversity, which implies that species presence-absence may play a larger role than genetic variation in responding to varied environments along the gut.

Ribosomal protein phylogeography offers quantitative insights into the efficacy of genome-resolved surveys of microbial communities

The increasing availability of microbial genomes is essential to gain insights into microbial ecology and evolution that can propel biotechnological and biomedical advances. Recent advances in genome recovery have significantly expanded the catalogue of microbial genomes from diverse habitats. However, the ability to explain how well a set of genomes account for the diversity in a given environment remains challenging for individual studies or biome-specific databases. Here we present EcoPhylo, a computational workflow to characterize the phylogeography of any gene family through integrated analyses of genomes and metagenomes, and our application of this approach to ribosomal proteins to quantify phylogeny-aware genome recovery rates across three biomes. Our findings show that genome recovery rates vary widely across taxa and biomes, and that single amplified genomes, metagenome-assembled genomes, and isolate genomes have non-uniform yet quantifiable representation of environmental microbes. EcoPhylo reveals highly resolved, reference-free, multi-domain phylogenies in conjunction with distribution patterns of individual clades across environments, providing a means to assess genome recovery in individual studies and benchmark biome-level genome collections.

Life history strategies complement niche partitioning to support the coexistence of closely related Gilliamella species in the bee gut

The maintenance of bacterial diversity at both species and strain levels is crucial for the sustainability of honey bee gut microbiota and host health. Periodic or random fluctuation in diet typically alters the metabolic niches available to gut microbes, thereby continuously reshaping bacterial diversity and interspecific interactions. It remains unclear how closely related bacteria adapt to these fluctuations and maintain coexistence within the bee gut. Here, we demonstrate that the five predominant Gilliamella species associated with Apis cerana, a widely distributed Asiatic honey bee, have diverged in carbohydrate metabolism to adapt to distinct nutrient niches driven by dietary fluctuation. Specifically, the glycan-specialists gain improved growth on a pollen-rich diet, but are overall inferior in competition to non-glycan-specialist on either a simple sugar or sugar-pollen diet, when co-inoculated in the bee host and transmitted across generations. Strikingly, despite of their disadvantage in a high-sugar condition, the glycan-specialists are found prevalent in natural A. cerana guts. We further reveal that these bacteria have adopted a life history strategy characterized by high biomass yield on a low-concentration sugar diet, allowing them to thrive under poor nutritional conditions, such as when the bee hosts undergo periodical starvation. Transcriptome analyses indicate that the divergence in life history strategies is attributed to gene expression programming rather than genetic variation. This study highlights the importance of integrative metabolic strategies in carbohydrate utilization, which facilitate the coexistence of closely related Gilliamella species in a changing bee gut environment.

A conserved bacterial genetic basis for commensal-host specificity

Animals selectively acquire specific symbiotic gut bacteria from their environments that aid host fitness. To colonize, a symbiont must locate its niche and sustain growth within the gut. Adhesins are bacterial cell surface proteins that facilitate attachment to host tissues and are often virulence factors for opportunistic pathogens. However, the attachments are often transient and nonspecific, and additional mechanisms are required to sustain infection. In this work, we use live imaging of individual symbiotic bacterial cells colonizing the gut of living Drosophila melanogaster to show that Lactiplantibacillus plantarum specifically recognizes the fruit fly foregut as a distinct physical niche. L. plantarum establishes stably within its niche through host-specific adhesins encoded by genes carried on a colonization island. The adhesin binding domains are conserved throughout the Lactobacillales, and the island also encodes a secretion system widely conserved among commensal and pathogenic bacteria.

The oral-gut microbiome axis in health and disease

The human body hosts trillions of microorganisms throughout many diverse habitats with different physico-chemical characteristics. Among them, the oral cavity and the gut harbour some of the most dense and diverse microbial communities. Although these two sites are physiologically distinct, they are directly connected and can influence each other in several ways. For example, oral microorganisms can reach and colonize the gastrointestinal tract, particularly in the context of gut dysbiosis. However, the mechanisms of colonization and the role that the oral microbiome plays in causing or exacerbating diseases in other organs have not yet been fully elucidated. Here, we describe recent advances in our understanding of how the oral and intestinal microbiota interplay in relation to their impact on human health and disease.

Sequencing-based analysis of microbiomes

Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.

kMetaShot: a fast and reliable taxonomy classifier for metagenome-assembled genomes

The advent of high-throughput sequencing (HTS) technologies unlocked the complexity of the microbial world through the development of metagenomics, which now provides an unprecedented and comprehensive overview of its taxonomic and functional contribution in a huge variety of macro- and micro-ecosystems. In particular, shotgun metagenomics allows the reconstruction of microbial genomes, through the assembly of reads into MAGs (metagenome-assembled genomes). In fact, MAGs represent an information-rich proxy for inferring the taxonomic composition and the functional contribution of microbiomes, even if the relevant analytical approaches are not trivial and still improvable. In this regard, tools like CAMITAX and GTDBtk have implemented complex approaches, relying on marker gene identification and sequence alignments, requiring a large processing time. With the aim of deploying an effective tool for fast and reliable MAG taxonomic classification, we present here kMetaShot, a taxonomy classifier based on k-mer/minimizer counting. We benchmarked kMetaShot against CAMITAX and GTDBtk by using both in silico and real mock communities and demonstrated how, while implementing a fast and concise algorithm, it outperforms the other tools in terms of classification accuracy. Additionally, kMetaShot is an easy-to-install and easy-to-use bioinformatic tool that is also suitable for researchers with few command-line skills. It is available and documented at https://github.com/gdefazio/kMetaShot.

Pervasive selective sweeps across human gut microbiomes

The human gut microbiome is composed of a highly diverse consortia of species which are continually evolving within and across hosts. The ability to identify adaptations common to many human gut microbiomes would not only reveal shared selection pressures across hosts, but also key drivers of functional differentiation of the microbiome that may affect community structure and host traits. However, to date there has not been a systematic scan for adaptations that have spread across human gut microbiomes. Here, we develop a novel selection scan statistic named the integrated Linkage Disequilibrium Score (iLDS) that can detect the spread of adaptive haplotypes across host microbiomes via migration and horizontal gene transfer. Specifically, iLDS leverages signals of hitchhiking of deleterious variants with the beneficial variant. Application of the statistic to ~30 of the most prevalent commensal gut species from 24 populations around the world revealed more than 300 selective sweeps across species. We find an enrichment for selective sweeps at loci involved in carbohydrate metabolism-potentially indicative of adaptation to features of host diet-and we find that the targets of selection significantly differ between Westernized and non-Westernized populations. Underscoring the potential role of diet in driving selection, we find a selective sweep absent from non-Westernized populations but ubiquitous in Westernized populations at a locus known to be involved in the metabolism of maltodextrin, a synthetic starch that has recently become a widespread component of Western diets. In summary, we demonstrate that selective sweeps across host microbiomes are a common feature of the evolution of the human gut microbiome, and that targets of selection may be strongly impacted by host diet.

Shared environments complicate the use of strain-resolved metagenomics to infer microbiome transmission

In humans and other social animals, social partners have more similar microbiomes than expected by chance, suggesting that social contact transfers microorganisms. Yet, social microbiome transmission can be difficult to identify based on compositional data alone. To overcome this challenge, recent studies have used information about microbial strain sharing (i.e., the shared presence of highly similar microbial sequences) to infer transmission. However, the degree to which strain sharing is influenced by shared traits and environments among social partners, rather than transmission per se, is not well understood. Here, we first use a fecal microbiota transplant dataset to show that strain sharing can recapitulate true transmission networks under ideal settings when donor-recipient pairs are unambiguous and recipients are sampled shortly after transmission. In contrast, in gut metagenomes from a wild baboon population, we find that demographic and environmental factors can override signals of strain sharing among social partners. We conclude that strain-level analyses provide useful information about microbiome similarity, but other facets of study design, especially longitudinal sampling and careful consideration of host characteristics, are essential for inferring the underlying mechanisms.

Small amounts of misassembly can have disproportionate effects on pangenome-based metagenomic analyses

Individual genes from microbiomes can drive host-level phenotypes. To help identify such candidate genes, several recent tools estimate microbial gene copy numbers directly from metagenomes. These tools rely on alignments to pangenomes, which in turn are derived from the set of all individual genomes from one species. While large-scale metagenomic assembly efforts have made pangenome estimates more complete, mixed communities can also introduce contamination into assemblies, and it is unknown how robust pangenome-based metagenomic analyses are to these errors. To gain insight into this problem, we re-analyzed a case-control study of the gut microbiome in cirrhosis, focusing on commensal Clostridia previously implicated in this disease. We tested for differentially prevalent genes in the Lachnospiraceae, then investigated which were likely to be contaminants using sequence similarity searches. Out of 86 differentially prevalent genes, we found that 33 (38%) were probably contaminants originating in taxa such as Veillonella and Haemophilus, unrelated genera that were independently correlated with disease status. Our results demonstrate that even small amounts of contamination in metagenome assemblies, below typical quality thresholds, can threaten to overwhelm gene-level metagenomic analyses. However, we also show that such contaminants can be accurately identified using a method based on gene-to-species correlation. After removing these contaminants, we observe that several flagellar motility gene clusters in the Lachnospira eligens pangenome are associated with cirrhosis status. We have integrated our analyses into an analysis and visualization pipeline, PanSweep, that can automatically identify cases where pangenome contamination may bias the results of gene-resolved analyses.

Importance

Metagenome-assembled genomes, or MAGs, can be constructed without pure cultures of microbes. Large scale efforts to build MAGs have yielded more complete pangenomes (i.e., sets of all genes found in one species). Pangenomes allow us to measure strain variation in gene content, which can strongly affect phenotype. However, because MAGs come from mixed communities, they can contaminate pangenomes with unrelated DNA, and how much this impacts downstream analyses has not been studied. Using a metagenomic study of gut microbes in cirrhosis as our test case, we investigate how contamination affects analyses of microbial gene content. Surprisingly, even small, typical amounts of MAG contamination (<5%) result in disproportionately high levels of false positive associations (38%). Fortunately, we show that most contaminants can be automatically flagged, and provide a simple method for doing so. Furthermore, applying this method reveals a new association between cirrhosis and gut microbial motility.

Microsynteny is a powerful front for microbial strain tracking

Genomic diversity within species can be driven by both point mutations and larger structural variations, but so far, strain-tracking approaches have focused mostly on the former. In a recent issue of Nature Biotechnology, Ley and colleagues1 introduce SynTracker, a tool designed for scalable strain tracking with microsynteny in low-coverage metagenomic settings.

From vacant to vivid: The nutritional landscape drives infant gut microbiota establishment

From the moment of birth, the newborn gastrointestinal tract is infiltrated by various bacteria originating from both maternal and environmental sources. These colonizing bacteria form a complex microbiota community that undergoes continuous changes until adulthood and plays an important role in infant health. The maturation of the infant gut microbiota is driven by many factors and follows a distinct patterned trajectory, with specific bacterial taxa establish in the intestine in accordance with developmental milestones as the infant grows. In this review, we highlight how elements such as diet and host physiology select for specific microbial functions and shape the composition of the bacterial community in the large intestine.

Marine bacteria Alteromonas spp. require UDP-glucose-4-epimerase for aggregation and production of sticky exopolymer

The physiology and ecology of particle-associated marine bacteria are of growing interest, but our knowledge of their aggregation behavior and mechanisms controlling their association with particles remains limited. We have found that a particle-associated isolate, Alteromonas sp. ALT199 strain 4B03, and the related type-strain A. macleodii 27126 both form large (>500 μm) aggregates while growing in rich medium. A non-clumping variant (NCV) of 4B03 spontaneously arose in the lab, and whole-genome sequencing revealed a partial deletion in the gene encoding UDP-glucose-4-epimerase (galEΔ308-324). In 27126, a knock-out of galE (ΔgalE::kmr) resulted in a loss of aggregation, mimicking the NCV. Microscopic analysis shows that both 4B03 and 27126 rapidly form large aggregates, whereas their respective galE mutants remain primarily as single planktonic cells or clusters of a few cells. Strains 4B03 and 27126 also form aggregates with chitin particles, but their galE mutants do not. Alcian Blue staining shows that 4B03 and 27126 produce large transparent exopolymer particles (TEP), but their galE mutants are deficient in this regard. This study demonstrates the capabilities of cell-cell aggregation, aggregation of chitin particles, and production of TEP in strains of Alteromonas, a widespread particle-associated genus of heterotrophic marine bacteria. A genetic requirement for galE is evident for each of the above capabilities, expanding the known breadth of requirement for this gene in biofilm-related processes.

IMPORTANCE

Heterotrophic marine bacteria have a central role in the global carbon cycle. Well-known for releasing CO2 by decomposition and respiration, they may also contribute to particulate organic matter (POM) aggregation, which can promote CO2 sequestration via the formation of marine snow. We find that two members of the prevalent particle-associated genus Alteromonas can form aggregates comprising cells alone or cells and chitin particles, indicating their ability to drive POM aggregation. In line with their multivalent aggregation capability, both strains produce TEP, an excreted polysaccharide central to POM aggregation in the ocean. We demonstrate a genetic requirement for galE in aggregation and large TEP formation, building our mechanistic understanding of these aggregative capabilities. These findings point toward a role for heterotrophic bacteria in POM aggregation in the ocean and support broader efforts to understand bacterial controls on the global carbon cycle based on microbial activities, community structure, and meta-omic profiling.

Synergistic pectin deconstruction is a prerequisite for mutualistic interactions between honeybee gut bacteria

The honeybee gut microbiome is crucial for degrading diverse pollen glycans. Yet it is unclear how this process shapes the interactions among bacteria. Here, we demonstrate a conditional mutualistic interaction between strains of two honeybee gut bacteria Bifidobacterium asteroides and Gilliamella apicola. When co-occurring in vitro and in vivo, Bifidobacterium provides complementary demethylation service to promote Gilliamella growth on methylated homogalacturonan, an enriched polysaccharide of pectin. In exchange, Gilliamella shares digestive products with Bifidobacterium, through which a positive interaction is established. This positive interaction vanishes when Bifidobacterium is not required on a non-methylated diet. Results from biochemical and gene expression analyses combined with model simulation further suggest that the ratio change of the two major homogalacturonan breakdown products, galacturonic acid (GalA) and di-GalA, determines the bacterial interaction. This study unravels how glycan metabolism may shape the interactions between honeybee gut bacteria.

The gut-airway microbiome axis in health and respiratory diseases

Communication between the gut and remote organs, such as the brain or the cardiovascular system, has been well established and recent studies provide evidence for a potential bidirectional gut-airway axis. Observations from animal and human studies indicate that respiratory insults influence the activity of the gut microbiome and that microbial ligands and metabolic products generated by the gut microbiome shape respiratory immunity. Information exchange between these two large mucosal surface areas regulates microorganism-immune interactions, with significant implications for the clinical and treatment outcomes of a range of respiratory conditions, including asthma, chronic obstructive pulmonary disease and lung cancer. In this Review, we summarize the most recent data in this field, offering insights into mechanisms of gut-airway crosstalk across spatial and temporal gradients and their relevance for respiratory health.

Discovery of disease-adapted bacterial lineages in inflammatory bowel diseases

Gut bacteria are implicated in inflammatory bowel disease (IBD), but the strains driving these associations are unknown. Large-scale studies of microbiome evolution could reveal the imprint of disease on gut bacteria, thus pinpointing the strains and genes that may underlie inflammation. Here, we use stool metagenomes of thousands of IBD patients and healthy controls to reconstruct 140,000 strain genotypes, revealing hundreds of lineages enriched in IBD. We demonstrate that these strains are ancient, taxonomically diverse, and ubiquitous in humans. Moreover, disease-associated strains outcompete their healthy counterparts during inflammation, implying long-term adaptation to disease. Strain genetic differences map onto known axes of inflammation, including oxidative stress, nutrient biosynthesis, and immune evasion. Lastly, the loss of health-associated strains of Eggerthella lenta was predictive of fecal calprotectin, a biomarker of disease severity. Our work identifies reservoirs of strain diversity that may impact inflammatory disease and can be extended to other microbiome-associated diseases.

Quantifying compositional variability in microbial communities with FAVA

Microbial communities vary across space, time, and individual hosts, presenting new challenges for the development of statistics measuring the variability of community composition. To understand differences across microbiome samples from different host individuals, sampling times, spatial locations, or experimental replicates, we present FAVA, a new normalized measure for characterizing compositional variability across multiple microbiome samples. FAVA quantifies variability across many samples of taxonomic or functional relative abundances in a single index ranging between 0 and 1, equaling 0 when all samples are identical and equaling 1 when each sample is entirely comprised of a single taxon. Its definition relies on the population-genetic statisticF S T , with samples playing the role of "populations" and taxa playing the role of "alleles." Its convenient mathematical properties allow users to compare disparate data sets. For example, FAVA values are commensurable across different numbers of taxonomic categories and different numbers of samples considered. We introduce extensions that incorporate phylogenetic similarity among taxa and spatial or temporal distances between samples. We illustrate how FAVA can be used to describe across-individual taxonomic variability in ruminant microbiomes at different regions along the gastrointestinal tract. In a second example, a longitudinal analysis of gut microbiomes of healthy human adults taking an antibiotic, we use FAVA to quantify the increase in temporal variability of microbiomes following the antibiotic course and to measure the duration of the antibiotic's influence on microbial variability. We have implemented this tool in an R package, FAVA, which can fit easily into existing pipelines for the analysis of microbial relative abundances.

Genome-resolved metagenomics: a game changer for microbiome medicine

Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.

In Vivo Expression of Chicken Gut Anaerobes Identifies Carbohydrate- or Amino Acid-Utilising, Motile or Type VI Secretion System-Expressing Bacteria

Complex gut microbiota increases chickens' resistance to enteric pathogens. However, the principles of this phenomenon are not understood in detail. One of the possibilities for how to decipher the role of gut microbiota in chickens' resistance to enteric pathogens is to systematically characterise the gene expression of individual gut microbiota members colonising the chicken caecum. To reach this aim, newly hatched chicks were inoculated with bacterial species whose whole genomic sequence was known. Total protein purified from the chicken caecum was analysed by mass spectrometry, and the obtained spectra were searched against strain-specific protein databases generated from known genomic sequences. Campylobacter jejuni, Phascolarctobacterium sp. and Sutterella massiliensis did not utilise carbohydrates when colonising the chicken caecum. On the other hand, Bacteroides, Mediterranea, Marseilla, Megamonas, Megasphaera, Bifidobacterium, Blautia, Escherichia coli and Succinatimonas fermented carbohydrates. C. jejuni was the only motile bacterium, and Bacteroides mediterraneensis expressed the type VI secretion system. Classification of in vivo expression is key for understanding the role of individual species in complex microbial populations colonising the intestinal tract. Knowledge of the expression of motility, the type VI secretion system, and preference for carbohydrate or amino acid fermentation is important for the selection of bacteria for defined competitive exclusion products.

Birthmode and environment-dependent microbiota transmission dynamics are complemented by breastfeeding during the first year

The composition and maturation of the early-life microbiota are modulated by a number of perinatal factors, whose interplay in relation to microbial vertical transmission remains inadequately elucidated. Using recent strain-tracking methodologies, we analyzed mother-to-infant microbiota transmission in two different birth environments: hospital-born (vaginal/cesarean) and home-born (vaginal) infants and their mothers. While delivery mode primarily explains initial compositional differences, place of birth impacts transmission timing-being early in homebirths and delayed in cesarean deliveries. Transmission patterns vary greatly across species and birth groups, yet certain species, like Bifidobacterium longum, are consistently vertically transmitted regardless of delivery setting. Strain-level analysis of B. longum highlights relevant and consistent subspecies replacement patterns mainly explained by breastfeeding practices, which drive changes in human milk oligosaccharide (HMO) degrading capabilities. Our findings highlight how delivery setting, breastfeeding duration, and other lifestyle preferences collectively shape vertical transmission, impacting infant gut colonization during early life.

Identification of the mutual gliding locus as a factor for gut colonization in non-native bee hosts using the ARTP mutagenesis

BACKGROUND

The gut microbiota and their hosts profoundly affect each other's physiology and evolution. Identifying host-selected traits is crucial to understanding the processes that govern the evolving interactions between animals and symbiotic microbes. Current experimental approaches mainly focus on the model bacteria, like hypermutating Escherichia coli or the evolutionary changes of wild stains by host transmissions. A method called atmospheric and room temperature plasma (ARTP) may overcome the bottleneck of low spontaneous mutation rates while maintaining mild conditions for the gut bacteria.

RESULTS

We established an experimental symbiotic system with gnotobiotic bee models to unravel the molecular mechanisms promoting host colonization. By in vivo serial passage, we tracked the genetic changes of ARTP-treated Snodgrassella strains from Bombus terrestris in the non-native honeybee host. We observed that passaged isolates showing genetic changes in the mutual gliding locus have a competitive advantage in the non-native host. Specifically, alleles in the orphan mglB, the GTPase activating protein, promoted colonization potentially by altering the type IV pili-dependent motility of the cells. Finally, competition assays confirmed that the mutations out-competed the ancestral strain in the non-native honeybee gut but not in the native host.

CONCLUSIONS

Using the ARTP mutagenesis to generate a mutation library of gut symbionts, we explored the potential genetic mechanisms for improved gut colonization in non-native hosts. Our findings demonstrate the implication of the cell mutual-gliding motility in host association and provide an experimental system for future study on host-microbe interactions. Video Abstract.

Decoding populations in the ocean microbiome

Understanding the characteristics and structure of populations is fundamental to comprehending ecosystem processes and evolutionary adaptations. While the study of animal and plant populations has spanned a few centuries, microbial populations have been under scientific scrutiny for a considerably shorter period. In the ocean, analyzing the genetic composition of microbial populations and their adaptations to multiple niches can yield important insights into ecosystem function and the microbiome's response to global change. However, microbial populations have remained elusive to the scientific community due to the challenges associated with isolating microorganisms in the laboratory. Today, advancements in large-scale metagenomics and metatranscriptomics facilitate the investigation of populations from many uncultured microbial species directly from their habitats. The knowledge acquired thus far reveals substantial genetic diversity among various microbial species, showcasing distinct patterns of population differentiation and adaptations, and highlighting the significant role of selection in structuring populations. In the coming years, population genomics is expected to significantly increase our understanding of the architecture and functioning of the ocean microbiome, providing insights into its vulnerability or resilience in the face of ongoing global change. Video Abstract.

BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis

Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.

A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and "transduce" signals to adjust internal processes. We hypothesized that an ecosystem's unique stimuli leave a sensor "fingerprint," able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection's nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient's disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.IMPORTANCEMicrobes infect, colonize, and proliferate due to their ability to sense and respond quickly to their surroundings. In this research, we extract the sensory proteins from a diverse range of environmental, engineered, and host-associated metagenomes. We trained machine learning classifiers using sensors as features such that it is possible to predict the ecosystem for a metagenome from its sensor profile. We use the optimized model's feature importance to identify the most impactful and predictive sensors in different environments. We next use the sensor profile from human gut metagenomes to classify their disease states and explore which sensors can explain differences between diseases. The sensors most predictive of environmental labels here, most of which correspond to uncharacterized proteins, are a useful starting point for the discovery of important environment signals and the development of possible diagnostic interventions.

Commensal antimicrobial resistance mediates microbiome resilience to antibiotic disruption

Despite their therapeutic benefits, antibiotics exert collateral damage on the microbiome and promote antimicrobial resistance. However, the mechanisms governing microbiome recovery from antibiotics are poorly understood. Treatment of Mycobacterium tuberculosis, the world's most common infection, represents the longest antimicrobial exposure in humans. Here, we investigate gut microbiome dynamics over 20 months of multidrug-resistant tuberculosis (TB) and 6 months of drug-sensitive TB treatment in humans. We find that gut microbiome dynamics and TB clearance are shared predictive cofactors of the resolution of TB-driven inflammation. The initial severe taxonomic and functional microbiome disruption, pathobiont domination, and enhancement of antibiotic resistance that initially accompanied long-term antibiotics were countered by later recovery of commensals. This resilience was driven by the competing evolution of antimicrobial resistance mutations in pathobionts and commensals, with commensal strains with resistance mutations reestablishing dominance. Fecal-microbiota transplantation of the antibiotic-resistant commensal microbiome in mice recapitulated resistance to further antibiotic disruption. These findings demonstrate that antimicrobial resistance mutations in commensals can have paradoxically beneficial effects by promoting microbiome resilience to antimicrobials and identify microbiome dynamics as a predictor of disease resolution in antibiotic therapy of a chronic infection.

Global biogeography and ecological implications of cobamide-producing prokaryotes

Cobamides, a class of essential coenzymes synthesized only by a subset of prokaryotes, are model nutrients in microbial interaction studies and play significant roles in global ecosystems. Yet, their spatial patterns and functional roles remain poorly understood. Herein, we present an in-depth examination of cobamide-producing microorganisms, drawn from a comprehensive analysis of 2862 marine and 2979 soil metagenomic samples. A total of 1934 nonredundant metagenome-assembled genomes (MAGs) potentially capable of producing cobamides de novo were identified. The cobamide-producing MAGs are taxonomically diverse but habitat specific. They constituted only a fraction of all the recovered MAGs, with the majority of MAGs being potential cobamide users. By mapping the distribution of cobamide producers in marine and soil environments, distinct latitudinal gradients were observed: the marine environment showed peak abundance at the equator, whereas soil environments peaked at mid-latitudes. Importantly, significant and positive links between the abundance of cobamide producers and the diversity and functions of microbial communities were observed, as well as their promotional roles in essential biogeochemical cycles. These associations were more pronounced in marine samples than in soil samples, which suggests a heightened propensity for microorganisms to engage in cobamide sharing in fluid environments relative to the more spatially restricted soil environment. These findings shed light on the global patterns and potential ecological roles of cobamide-producing microorganisms in marine and soil ecosystems, enhancing our understanding of large-scale microbial interactions.

Leveraging genomic information to predict environmental preferences of bacteria

Genomic information is now available for a broad diversity of bacteria, including uncultivated taxa. However, we have corresponding knowledge on environmental preferences (i.e. bacterial growth responses across gradients in oxygen, pH, temperature, salinity, and other environmental conditions) for a relatively narrow swath of bacterial diversity. These limits to our understanding of bacterial ecologies constrain our ability to predict how assemblages will shift in response to global change factors, design effective probiotics, or guide cultivation efforts. We need innovative approaches that take advantage of expanding genome databases to accurately infer the environmental preferences of bacteria and validate the accuracy of these inferences. By doing so, we can broaden our quantitative understanding of the environmental preferences of the majority of bacterial taxa that remain uncharacterized. With this perspective, we highlight why it is important to infer environmental preferences from genomic information and discuss the range of potential strategies for doing so. In particular, we highlight concrete examples of how both cultivation-independent and cultivation-dependent approaches can be integrated with genomic data to develop predictive models. We also emphasize the limitations and pitfalls of these approaches and the specific knowledge gaps that need to be addressed to successfully expand our understanding of the environmental preferences of bacteria.

Functional host-specific adaptation of the intestinal microbiome in hominids

Fine-scale knowledge of the changes in composition and function of the human gut microbiome compared that of our closest relatives is critical for understanding the evolutionary processes underlying its developmental trajectory. To infer taxonomic and functional changes in the gut microbiome across hominids at different timescales, we perform high-resolution metagenomic-based analyzes of the fecal microbiome from over two hundred samples including diverse human populations, as well as wild-living chimpanzees, bonobos, and gorillas. We find human-associated taxa depleted within non-human apes and patterns of host-specific gut microbiota, suggesting the widespread acquisition of novel microbial clades along the evolutionary divergence of hosts. In contrast, we reveal multiple lines of evidence for a pervasive loss of diversity in human populations in correlation with a high Human Development Index, including evolutionarily conserved clades. Similarly, patterns of co-phylogeny between microbes and hosts are found to be disrupted in humans. Together with identifying individual microbial taxa and functional adaptations that correlate to host phylogeny, these findings offer insights into specific candidates playing a role in the diverging trajectories of the gut microbiome of hominids. We find that repeated horizontal gene transfer and gene loss, as well as the adaptation to transient microaerobic conditions appear to have played a role in the evolution of the human gut microbiome.

Strain-resolved metagenomics approaches applied to biogas upgrading

Genetic heterogeneity is a common trait in microbial populations, caused by de novo mutations and changes in variant frequencies over time. Microbes can thus differ genetically within the same species and acquire different phenotypes. For instance, performance and stability of anaerobic reactors are linked to the composition of the microbiome involved in the digestion process and to the environmental parameters imposing selective pressure on the metagenome, shaping its evolution. Changes at the strain level have the potential to determine variations in microbial functions, and their characterization could provide new insight into ecological and evolutionary processes driving anaerobic digestion. In this work, single nucleotide variant dynamics were studied in two time-course biogas upgrading experiments, testing alternative carbon sources and the response to exogenous hydrogen addition. A cumulative total of 76,229 and 64,289 high-confidence single nucleotide variants were discerned in the experiments related to carbon substrate availability and hydrogen addition, respectively. By combining complementary bioinformatic approaches, the study reconstructed the precise strain count-two for both hydrogenotrophic archaea-and tracked their abundance over time, while also characterizing tens of genes under strong selection. Results in the dominant archaea revealed the presence of nearly 100 variants within genes encoding enzymes involved in hydrogenotrophic methanogenesis. In the bacterial counterparts, 119 mutations were identified across 23 genes associated with the Wood-Ljungdahl pathway, suggesting a possible impact on the syntrophic acetate-oxidation process. Strain replacement events took place in both experiments, confirming the trends suggested by the variants trajectories and providing a comprehensive understanding of the biogas upgrading microbiome at the strain level. Overall, this resolution level allowed us to reveal fine-scale evolutionary mechanisms, functional dynamics, and strain-level metabolic variation that could contribute to the selection of key species actively involved in the carbon dioxide fixation process.

The effect of host admixture on wild house mouse gut microbiota is weak when accounting for spatial autocorrelation

The question of how interactions between the gut microbiome and vertebrate hosts contribute to host adaptation and speciation is one of the major problems in current evolutionary research. Using bacteriome and mycobiome metabarcoding, we examined how these two components of the gut microbiota vary with the degree of host admixture in secondary contact between two house mouse subspecies (Mus musculus musculus and M. m. domesticus). We used a large data set collected at two replicates of the hybrid zone and model-based statistical analyses to ensure the robustness of our results. Assuming that the microbiota of wild hosts suffers from spatial autocorrelation, we directly compared the results of statistical models that were spatially naive with those that accounted for spatial autocorrelation. We showed that neglecting spatial autocorrelation can strongly affect the results and lead to misleading conclusions. The spatial analyses showed little difference between subspecies, both in microbiome composition and in individual bacterial lineages. Similarly, the degree of admixture had minimal effects on the gut bacteriome and mycobiome and was caused by changes in a few microbial lineages that correspond to the common symbionts of free-living house mice. In contrast to previous studies, these data do not support the hypothesis that the microbiota plays an important role in host reproductive isolation in this particular model system.

Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer

Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.