A generic approach to infer community-level fitness of microbial genes.

The gene content in a metagenomic pool defines the function potential of a microbial community. Natural selection, operating on the level of genomes or genes, shapes the evolution of community functions by enriching some genes while depriving the others. Despite the importance of microbiomes in the environment and health, a general metric to evaluate the community-wide fitness of microbial genes remains lacking. In this work, we adapt the classic neutral model of species and use it to predict how the abundances of different genes will be shaped by selection, regardless of at which level the selection acts. We establish a simple metric that quantitatively infers the average survival capability of each gene in a microbiome. We then experimentally validate the predictions using synthetic communities of barcoded Escherichia coli strains undergoing neutral assembly and competition. We further show that this approach can be applied to publicly available metagenomic datasets to gain insights into the environment-function interplay of natural microbiomes.

GeNLP: a web tool for NLP-based exploration and prediction of microbial gene function.

SUMMARY: GeNLP is a web application that enables exploring microbial gene "semantics" and predictions of uncharacterized gene families based on their genomic context. It utilizes a pre-trained language model to uncover gene relationships and allows users to access and utilize the data as well as make their own predictions through an interactive interface. AVAILABILITY AND IMPLEMENTATION: The web application is accessible from all browsers at: http://gnlp.bursteinlab.org/. All source codes are freely available from GitHub under the MIT license here: https://github.com/burstein-lab/genomic-nlp-server.

Linking microbial genes to plasma and stool metabolites uncovers host-microbial interactions underlying ulcerative colitis disease course.

Understanding the role of the microbiome in inflammatory diseases requires the identification of microbial effector molecules. We established an approach to link disease-associated microbes to microbial metabolites by integrating paired metagenomics, stool and plasma metabolomics, and culturomics. We identified host-microbial interactions correlated with disease activity, inflammation, and the clinical course of ulcerative colitis (UC) in the Predicting Response to Standardized Colitis Therapy (PROTECT) pediatric inception cohort. In severe disease, metabolite changes included increased dipeptides and tauro-conjugated bile acids (BAs) and decreased amino-acid-conjugated BAs in stool, whereas in plasma polyamines (N-acetylputrescine and N1-acetylspermidine) increased. Using patient samples and Veillonella parvula as a model, we uncovered nitrate- and lactate-dependent metabolic pathways, experimentally linking V. parvula expansion to immunomodulatory tryptophan metabolite production. Additionally, V. parvula metabolizes immunosuppressive thiopurine drugs through xdhA xanthine dehydrogenase, potentially impairing the therapeutic response. Our findings demonstrate that the microbiome contributes to disease-associated metabolite changes, underscoring the importance of these interactions in disease pathology and treatment.

Fecal microbial gene transfer contributes to the high-grain diet-induced augmentation of aminoglycoside resistance in dairy cattle.

A high-grain (HG) diet can rapidly lower the rumen pH and thus modify the gastrointestinal microbiome in dairy cattle. Although the prevalence of antibiotic resistance is strongly linked with the gut microbiome, the influences of HG diet on animals' gut resistome remain largely unexplored. Here, we examined the impact and mechanism of an HG diet on the fecal resistome in dairy cattle by metagenomically characterizing the gut microbiome. Eight lactating Holstein cattle were randomly allocated into two groups and fed either a conventional (CON) or HG diet for 3 weeks. The fecal microbiome and resistome were significantly altered in dairy cattle from HG, demonstrating an adaptive response that peaks at day 14 after the dietary transition. Importantly, we determined that feeding an HG diet specifically elevated the prevalence of resistance to aminoglycosides (0.11 vs 0.24 RPKG, P < 0.05). This diet-induced resistance increase is interrelated with the disproportional propagation of microbes in Lachnospiraceae, indicating a potential reservoir of aminoglycosides resistance. We further showed that the prevalence of acquired resistance genes was also modified by introducing a different diet, likely due to the augmented frequency of lateral gene transfer (LGT) in microbes (CON vs HG: 254 vs 287 taxa) such as Lachnospiraceae. Consequently, we present that diet transition is associated with fecal resistome modification in dairy cattle and an HG diet specifically enriched aminoglycosides resistance that is likely by stimulating microbial LGT.IMPORTANCEThe increasing prevalence of antimicrobial resistance is one of the most severe threats to public health, and developing novel mitigation strategies deserves our top priority. High-grain (HG) diet is commonly applied in dairy cattle to enhance animals' performance to produce more high-quality milk. We present that despite such benefits, the application of an HG diet is correlated with an elevated prevalence of resistance to aminoglycosides, and this is a combined effect of the expansion of antibiotic-resistant bacteria and increased frequency of lateral gene transfer in the fecal microbiome of dairy cattle. Our results provided new knowledge in a typically ignored area by showing an unexpected enrichment of antibiotic resistance under an HG diet. Importantly, our findings laid the foundation for designing potential dietary intervention strategies to lower the prevalence of antibiotic resistance in dairy production.

Vegetation type, not the legacy of warming, modifies the response of microbial functional genes and greenhouse gas fluxes to drought in Oro-Arctic and alpine regions.

Climate warming and summer droughts alter soil microbial activity, affecting greenhouse gas (GHG) emissions in Arctic and alpine regions. However, the long-term effects of warming, and implications for future microbial resilience, are poorly understood. Using one alpine and three Arctic soils subjected to in situ long-term experimental warming, we simulated drought in laboratory incubations to test how microbial functional-gene abundance affects fluxes in three GHGs: carbon dioxide, methane, and nitrous oxide. We found that responses of functional gene abundances to drought and warming are strongly associated with vegetation type and soil carbon. Our sites ranged from a wet, forb dominated, soil carbon-rich systems to a drier, soil carbon-poor alpine site. Resilience of functional gene abundances, and in turn methane and carbon dioxide fluxes, was lower in the wetter, carbon-rich systems. However, we did not detect an effect of drought or warming on nitrous oxide fluxes. All gene-GHG relationships were modified by vegetation type, with stronger effects being observed in wetter, forb-rich soils. These results suggest that impacts of warming and drought on GHG emissions are linked to a complex set of microbial gene abundances and may be habitat-specific.

Targeted curation of the gut microbial gene content modulating human cardiovascular disease.

IMPORTANCE: One of the most-cited examples of the gut microbiome modulating human disease is the microbial metabolism of quaternary amines from protein-rich foods. By-products of this microbial processing promote atherosclerotic heart disease, a leading cause of human mortality globally. Our research addresses current knowledge gaps in our understanding of this microbial metabolism by holistically inventorying the microorganisms and expressed genes catalyzing critical atherosclerosis-promoting and -ameliorating reactions in the human gut. This led to the creation of an open-access resource, the Methylated Amine Gene Inventory of Catabolism database, the first systematic inventory of gut methylated amine metabolism. More importantly, using this resource we deliver here, we show for the first time that these gut microbial genes can predict human disease, paving the way for microbiota-inspired diagnostics and interventions.

Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data.

The identification of microbial characteristics associated with diseases is crucial for disease diagnosis and therapy. However, the presence of heterogeneity, high dimensionality, and large amounts of microbial data presents tremendous challenges in discovering key microbial features. In this paper, we present IDAM, a novel computational method for inferring disease-associated gene modules from metagenomic and metatranscriptomic data. This method integrates gene context conservation (uber-operons) and regulatory mechanisms (gene co-expression patterns) within a mathematical graph model to explore gene modules associated with specific diseases. It alleviates reliance on prior meta-data. We applied IDAM to publicly available datasets from inflammatory bowel disease, melanoma, type 1 diabetes mellitus, and irritable bowel syndrome. The results demonstrated the superior performance of IDAM in inferring disease-associated characteristics compared to existing popular tools. Furthermore, we showcased the high reproducibility of the gene modules inferred by IDAM using independent cohorts with inflammatory bowel disease. We believe that IDAM can be a highly advantageous method for exploring disease-associated microbial characteristics. The source code of IDAM is freely available at https://github.com/OSU-BMBL/IDAM, and the web server can be accessed at https://bmblx.bmi.osumc.edu/idam/.

Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases.

The human microbiome plays a crucial role in human health and is associated with a number of human diseases. Determining microbiome functional roles in human diseases remains a biological challenge due to the high dimensionality of metagenome gene features. However, existing models were limited in providing biological interpretability, where the functional role of microbes in human diseases is unexplored. Here we propose to utilize a neural network-based model incorporating Gene Ontology (GO) relationship network to discover the microbe functionality in human diseases. We use four benchmark datasets, including diabetes, liver cirrhosis, inflammatory bowel disease, and colorectal cancer, to explore the microbe functionality in the human diseases. Our model discovered and visualized the novel candidates' important microbiome genes and their functions by calculating the important score of each gene and GO term in the network. Furthermore, we demonstrate that our model achieves a competitive performance in predicting the disease by comparison with other non-Gene Ontology informed models. The discovered candidates' important microbiome genes and their functions provide novel insights into microbe functional contribution.

A catalog of microbial genes and metagenome-assembled genomes from the quail gut microbiome.

The gut microbiome plays an important role in quail feed efficiency, immunity, production, and even behavior. Gut microbial gene catalogs and reference genomes are important for understanding the quail gut microbiome. However, quail gut microbes are lacked sequenced genomes and functional information to date. In this study, we report the first catalog of the microbial genes and metagenome-assembled genomes (MAGs) in fecal and cecum luminal content samples from 3 quail breeds using deep metagenomic sequencing. We identified a total of 2,419,425 nonredundant genes in the quail genome catalog, and a total of 473 MAGs were reconstructed through binning analysis. At 95% average nucleotide identity, the 473 MAGs were clustered into 283 species-level genome bins (SGBs), of which 225 SGBs belonged to species without any available genomes in the current database. Based on the quail gene catalog and MAGs, we identified 142 discriminative bacterial species and 244 discriminative MAGs between Chinese yellow quails and Japanese quails. The discriminative MAGs suggested a strain-level difference in the gut microbial composition. Additionally, a total of 25 Kyoto Encyclopedia of Genes and Genomes functional terms and 88 carbohydrate-active enzymes were distinctly enriched between Chinese yellow quails and Japanese quails. Most of the different species and MAGs were significantly interrelated with the shifts in the functional capacities of the quail gut microbiome. Taken together, we constructed a quail gut microbial gene catalog and enlarged the reference of quail gut microbial genomes. The results of this study provide a powerful and invaluable resource for quail gut microbiome-related research.

Generalizing the Domain-Gene-Species Reconciliation Framework to Microbial Genes and Domains.

Protein domains play an important role in the function and evolution of many gene families. Previous studies have shown that domains are frequently lost or gained during gene family evolution. Yet, most computational approaches for studying gene family evolution do not account for domain-level evolution within genes. To address this limitation, a new three-level reconciliation framework, called the Domain-Gene-Species (DGS) reconciliation model, has been recently developed to simultaneously model the evolution of a domain family inside one or more gene families and the evolution of those gene families inside a species tree. However, the existing model applies only to multi-cellular eukaryotes where horizontal gene transfer is negligible. In this work, we generalize the existing DGS reconciliation model by allowing for the spread of genes and domains across species boundaries through horizontal transfer. We show that the problem of computing optimal generalized DGS reconciliations, though NP-hard, is approximable to within a constant factor, where the specific approximation ratio depends on the "event costs" used. We provide two different approximation algorithms for the problem and demonstrate the impact of the generalized framework using both simulated and real biological data. Our results show that our new algorithms result in highly accurate reconstructions of domain family evolution for microbes.

Comparison of the Strengths and Weaknesses of Machine Learning Algorithms and Feature Selection on KEGG Database Microbial Gene Pathway Annotation and Its Effects on Reconstructed Network Topology.

The development of tools for the annotation of genes from newly sequenced species has not evolved much from homologous alignment to prior annotated species. While the quality of gene annotations continues to decline as we sequence and assemble more evolutionary distant gut microbiome species, machine learning presents a high quality alternative to traditional techniques. In this study, we investigate the relative performance of common classical and nonclassical machine learning algorithms in the problem of gene annotation using human microbiome-associated species genes from the KEGG database. The majority of the ensemble, clustering, and deep learning algorithms that we investigated showed higher prediction accuracy than CD-Hit in predicting partial KEGG function. Motif-based, machine-learning methods of annotation in new species were faster and had higher precision-recall than methods of homologous alignment or orthologous gene clustering. Gradient boosted ensemble methods and neural networks also predicted higher connectivity in reconstructed KEGG pathways, finding twice as many new pathway interactions than blast alignment. The use of motif-based, machine-learning algorithms in annotation software will allow researchers to develop powerful tools to interact with bacterial microbiomes in ways previously unachievable through homologous sequence alignment alone.

Microbial genes outperform species and SNVs as diagnostic markers for Crohn's disease on multicohort fecal metagenomes empowered by artificial intelligence.

Dysbiosis of gut microbial community is associated with the pathogenesis of CD and may serve as a promising noninvasive diagnostic tool. We aimed to compare the performances of the microbial markers of different biological levels by conducting a multidimensional analysis on the microbial metagenomes of CD. We collected fecal metagenomic datasets generated from eight cohorts that altogether include 870 CD patients and 548 healthy controls. Microbial alterations in CD patients were assessed at multidimensional levels including species, gene, and SNV level, and then diagnostic models were constructed using artificial intelligence algorithm. A total of 227 species, 1047 microbial genes, and 21,877 microbial SNVs were identified that differed between CD and controls. The species, gene, and SNV models achieved an average AUC of 0.97, 0.95, and 0.77, respectively. Notably, the gene model exhibited superior diagnostic capability, achieving an average AUC of 0.89 and 0.91 for internal and external validations, respectively. Moreover, the gene model was specific for CD against other microbiome-related diseases. Furthermore, we found that phosphotransferase system (PTS) contributed substantially to the diagnostic capability of the gene model. The outstanding performance of PTS was mainly explained by genes celB and manY, which demonstrated high predictabilities for CD with metagenomic datasets and was validated in an independent cohort by qRT-PCR analysis. Our global metagenomic analysis unravels the multidimensional alterations of the microbial communities in CD and identifies microbial genes as robust diagnostic biomarkers across geographically and culturally distinct cohorts.

Soil microbial gene expression in an agricultural ecosystem varies with time and neonicotinoid seed treatments.

Neonicotinoids, a class of systemic insecticides, have been widely used for decades against various insect pests. Previous studies have reported non-target effects of neonicotinoids on some beneficial macro- and micro-organisms. Considering the crucial role the soil microbiota plays in sustaining soil fertility, it is critical to understand how neonicotinoid exposure affects the microbial taxonomic composition and gene expression. However, most studies to date have evaluated soil microbial taxonomic compositions or assessed microbial functions based on soil biochemical analysis. In this study, we have applied a metatranscriptomic approach to quantify the variability in soil microbial gene expression in a 2 year soybean/corn crop rotation in Quebec, Canada. We identified weak and temporally inconsistent effects of neonicotinoid application on soil microbial gene expression, as well as a strong temporal variation in soil microbial gene expression among months and years. Neonicotinoid seed treatment altered the expression of a small number of microbial genes, including genes associated with heat shock proteins, regulatory functions, metabolic processes and DNA repair. These changes in gene expression varied during the growing season and between years. Overall, the composition of soil microbial expressed genes seems to be more resilient and less affected by neonicotinoid application than soil microbial taxonomic composition. Our study is among the first to document the effects of neonicotinoid seed treatment on microbial gene expression and highlights the strong temporal variability of soil microbial gene expression and its responses to neonicotinoid seed treatments.

Seasonality of biogeochemically relevant microbial genes in a coastal ocean microbiome.

Microbes drive the biogeochemical cycles of marine ecosystems through their vast metabolic diversity. While we have a fairly good understanding of the spatial distribution of these metabolic processes in various ecosystems, less is known about their seasonal dynamics. We investigated the annual patterns of 21 biogeochemical relevant functions in an oligotrophic coastal ocean site by analysing the presence of key genes, analysing high-rank gene taxonomy and the dynamics of nucleotide variants. Most genes presented seasonality: photoheterotrophic processes were enriched during spring, phosphorous-related genes were dominant during summer, coinciding with potential phosphate limitation, and assimilatory nitrate reductases appeared mostly during summer and autumn, correlating negatively with nitrate availability. Additionally, we identified the main taxa driving each function at each season and described the role of underrecognized taxa such as Litoricolaceae in carbon fixation (rbcL), urea degradation (ureC), and CO oxidation (coxL). Finally, the seasonality of single variants of some families presented a decoupling between the taxonomic abundance patterns and the functional gene patterns, implying functional specialization of the different genera. Our study unveils the seasonality of key biogeochemical functions and the main taxonomic groups that harbour these relevant functions in a coastal ocean ecosystem.

Linking microbial biogeochemical cycling genes to the rhizosphere of pioneering plants in a glacier foreland.

Glacier retreat raises global concerns but brings about the moment to study soil and ecosystem development. In nutrient-limited glacier forelands, the adaptability of pioneering plant and microbial species is facilitated by their interactions, including rhizosphere effects, but the details of this adaptability are not yet understood. In the rhizosphere of five pioneering plants, we comprehensively deciphered the microbial taxonomic and functional compositions. Two nitrogen-fixing microbial genera, Bradyrhizobium and Mesorhizobium, were among the most abundant taxa in the rhizomicrobiome. Moreover, several rhizobial genera, including Rhizobium, Pararhizobium, Allohrizobium, and Sinorhizobium, head the list of major modules in microbial co-occurrence networks, highlighting the vital roles of nitrogen-cycling taxa in the rhizomicrobiome of pioneering plants. Microbial genes involved in nitrogen, sulfur, phosphorus, and methane cycles were simultaneously correlated with microbial community dissimilarity, and 12 functional pathways were detected with distinct relative abundances among soils. Zooming in on the nitrogen-cycling genes, nifW, narC, nasA, nasB, and nirA were mainly responsible for the significant differences between soils. Furthermore, soil pH and the carbon/nitrogen ratio were among the topsoil properties interacting with nitrogen and sulfur cycling gene dissimilarity. These results explicitly linked biogeochemical cycling genes to the rhizomicrobiome and soil properties, revealing the roles of these genes as microbial drivers in mediating rhizosphere soil-plant-microbiome interactions.

Soil nitrogen functional transformation microbial genes response to biochar application in different irrigation paddy field in southern China.

Growing evidence points to the controlled irrigation (CI) and biochar application (BA) having agricultural economic value and ecological benefits, but their synergistic effect and microbial mechanism of nitrogen conversion remain unknown in paddy fields. The effects of different BA (0, 20, 40 t/hm2) on the soil nitrogen functional transformation microbial genes (nifH, AOA-amoA, AOB-amoA) in different irrigation (CI, flooding irrigation) were clarified. After one seasonal growth of paddy, the correlation between the abundance of functional genes OUT and soil nitrogen transformation environment factors during the typical growth period was analyzed. High-throughput sequencing results illustrated that the application of CC (40 t/hm2 biochar) increased the nifH genes bacterial community abundance; the abundance of dominant microorganism increased by 79.68~86.19%. Because biochar can potentially control the rates of N cycling in soil systems by adsorbing ammonia and increasing NH4+ storage, it increased soil NH4+-N and NO3--N content by 60.77% and 26.14%, improving microbial nitrogen fixation. Rare species Nitrosopumilus, Nitrosococcus, and Methylocystis appeared in biochar treatments group, which increased the diversity of microbial in paddy. The combined use of CI and BA affected soil inorganic nitrogen content, temperature (T), pH, Eh, etc., which affected urease, urea hydrolysis, and nitrogen functional transformation microorganism genes. Correlation analysis shows that soil NH4+-N, T, and Eh, respectively, are significant factors for the formation of nifH, AOA-amoA, and AOB-amoA soil bacterial communities, respectively. This study suggests that to maintain the biodiversity of soil and realize the sustainable development of rice cultivation, CI is of great importance in combination with BA.

Deciphering microbial gene function using natural language processing.

Revealing the function of uncharacterized genes is a fundamental challenge in an era of ever-increasing volumes of sequencing data. Here, we present a concept for tackling this challenge using deep learning methodologies adopted from natural language processing (NLP). We repurpose NLP algorithms to model "gene semantics" based on a biological corpus of more than 360 million microbial genes within their genomic context. We use the language models to predict functional categories for 56,617 genes and find that out of 1369 genes associated with recently discovered defense systems, 98% are inferred correctly. We then systematically evaluate the "discovery potential" of different functional categories, pinpointing those with the most genes yet to be characterized. Finally, we demonstrate our method's ability to discover systems associated with microbial interaction and defense. Our results highlight that combining microbial genomics and language models is a promising avenue for revealing gene functions in microbes.

Biosynthesis of selenium-containing small molecules in diverse microorganisms.

Selenium is an essential micronutrient in diverse organisms. Two routes are known for its insertion into proteins and nucleic acids, via selenocysteine and 2-selenouridine, respectively1. However, despite its importance, pathways for specific incorporation of selenium into small molecules have remained elusive. Here we use a genome-mining strategy in various microorganisms to uncover a widespread three-gene cluster that encodes a dedicated pathway for producing selenoneine, the selenium analogue of the multifunctional molecule ergothioneine2,3. We elucidate the reactions of all three proteins and uncover two novel selenium-carbon bond-forming enzymes and the biosynthetic pathway for production of a selenosugar, which is an unexpected intermediate en route to the final product. Our findings expand the scope of biological selenium utilization, suggest that the selenometabolome is more diverse than previously thought, and set the stage for the discovery of other selenium-containing natural products.

Deciphering Microbial Gene Family Evolution Using Duplication-Transfer-Loss Reconciliation and RANGER-DTL.

Phylogenetic reconciliation has emerged as a principled, highly effective technique for investigating the origin, spread, and evolutionary history of microbial gene families. Proper application of phylogenetic reconciliation requires a clear understanding of potential pitfalls and sources of error, and knowledge of the most effective reconciliation-based tools and protocols to use to maximize accuracy. In this book chapter, we provide a brief overview of Duplication-Transfer-Loss (DTL) reconciliation, the standard reconciliation model used to study microbial gene families and provide a step-by-step computational protocol to maximize the accuracy of DTL reconciliation and minimize false-positive evolutionary inferences.