Perturbations in gut microbiota composition in schizophrenia.

Schizophrenia is a severe, complex and long-term psychiatric disorder with unclear etiology. Gut microbes influence the central nervous system via the gut-brain axis. Consequently, investigations of the relationship between gut microbes and schizophrenia are warranted. This study involved 29 patients with schizophrenia and 30 age-matched normal controls. After 16S rRNA gene sequencing and whole-genome shotgun metagenomic sequencing, we analyzed microbial diversity, composition, and function. According to 16S rRNA and metagenomic gene sequencing results, patients with schizophrenia had higher abundances of Clostridium and Megasphaera. Functional analysis showed that sphingolipid, phosphonates and phosphinates, as well as glutamine metabolism were associated with the occurrence and development of schizophrenia. Our data suggest that the gut microbiota exerts an effect on patients with schizophrenia, providing valuable insights into the potential regulation of in the context of this disorder.

VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes.

Identifying viruses from metagenomes is a common step to explore the virus composition in the human gut. Here, we introduce VirRep, a hybrid language representation learning framework, for identifying viruses from human gut metagenomes. VirRep combines a context-aware encoder and an evolution-aware encoder to improve sequence representation by incorporating k-mer patterns and sequence homologies. Benchmarking on both simulated and real datasets with varying viral proportions demonstrates that VirRep outperforms state-of-the-art methods. When applied to fecal metagenomes from a colorectal cancer cohort, VirRep identifies 39 high-quality viral species associated with the disease, many of which cannot be detected by existing methods.

Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data.

The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile modalities, data preprocessing techniques, and classification algorithms, each impacting the model accuracy and generalizability. Despite whole metagenome shotgun sequencing (WMS) gaining popularity for human gut microbiome profiling, a consensus on the optimal methods for ML pipelines in disease diagnosis using WMS data remains elusive. Addressing this gap, we comprehensively evaluated ML methods for diagnosing Crohn's disease and colorectal cancer, using 2,553 fecal WMS samples from 21 case-control studies. Our study uncovered crucial insights: gut-specific, species-level taxonomic features proved to be the most effective for profiling; batch correction was not consistently beneficial for model performance; compositional data transformations markedly improved the models; and while nonlinear ensemble classification algorithms typically offered superior performance, linear models with proper regularization were found to be more effective for diseases that are linearly separable based on microbiome data. An optimal ML pipeline, integrating the most effective methods, was validated for generalizability using holdout data. This research offers practical guidelines for constructing reliable disease diagnostic ML models with fecal WMS data.

No evidence on infectious DNA-based agents in pediatric acute lymphoblastic leukemia using whole metagenome shotgun sequencing.

The etiology of pediatric acute lymphatic leukemia (ALL) is still unclear. Whole-metagenome shotgun sequencing of bone marrow samples in patients with treatment-naïve ALL (n=6) was performed for untargeted investigation of bacterial and viral DNA. The control group consisted of healthy children (n=4) and children with non-oncologic diseases (n=2) undergoing bone marrow sampling. Peripheral blood of all participants was investigated at the same time. After bioinformatical elimination of potential contaminants by comparison with the employed controls, no significant amounts of microbial or viral DNA were identified.

Altitude-dependent agro-ecologies impact the microbiome diversity of scavenging indigenous chicken in Ethiopia.

BACKGROUND: Scavenging indigenous village chickens play a vital role in sub-Saharan Africa, sustaining the livelihood of millions of farmers. These chickens are exposed to vastly different environments and feeds compared to commercial chickens. In this study, we analysed the caecal microbiota of 243 Ethiopian village chickens living in different altitude-dependent agro-ecologies. RESULTS: Differences in bacterial diversity were significantly correlated with differences in specific climate factors, topsoil characteristics, and supplemental diets provided by farmers. Microbiota clustered into three enterotypes, with one particularly enriched at high altitudes. We assembled 9977 taxonomically and functionally diverse metagenome-assembled genomes. The vast majority of these were not found in a dataset of previously published chicken microbes or in the Genome Taxonomy Database. CONCLUSIONS: The wide functional and taxonomic diversity of these microbes highlights their importance in the local adaptation of indigenous poultry, and the significant impacts of environmental factors on the microbiota argue for further discoveries in other agro-ecologies. Video Abstract.

Global compositional and functional states of the human gut microbiome in health and disease.

The human gut microbiota is of increasing interest, with metagenomics a key tool for analyzing bacterial diversity and functionality in health and disease. Despite increasing efforts to expand microbial gene catalogs and an increasing number of metagenome-assembled genomes, there have been few pan-metagenomic association studies and in-depth functional analyses across different geographies and diseases. Here, we explored 6014 human gut metagenome samples across 19 countries and 23 diseases by performing compositional, functional cluster, and integrative analyses. Using interpreted machine learning classification models and statistical methods, we identified Fusobacterium nucleatum and Anaerostipes hadrus with the highest frequencies, enriched and depleted, respectively, across different disease cohorts. Distinct functional distributions were observed in the gut microbiomes of both westernized and nonwesternized populations. These compositional and functional analyses are presented in the open-access Human Gut Microbiome Atlas, allowing for the exploration of the richness, disease, and regional signatures of the gut microbiota across different cohorts.

Methanogenic patterns in the gut microbiome are associated with survival in a population of feral horses.

Gut microbiomes are widely hypothesised to influence host fitness and have been experimentally shown to affect host health and phenotypes under laboratory conditions. However, the extent to which they do so in free-living animal populations and the proximate mechanisms involved remain open questions. In this study, using long-term, individual-based life history and shallow shotgun metagenomic sequencing data (2394 fecal samples from 794 individuals collected between 2013-2019), we quantify relationships between gut microbiome variation and survival in a feral population of horses under natural food limitation (Sable Island, Canada), and test metagenome-derived predictions using short-chain fatty acid data. We report detailed evidence that variation in the gut microbiome is associated with a host fitness proxy in nature and outline hypotheses of pathogenesis and methanogenesis as key causal mechanisms which may underlie such patterns in feral horses, and perhaps, wild herbivores more generally.

Unraveling the habitat preferences, ecological drivers, potential hosts, and auxiliary metabolism of soil giant viruses across China.

BACKGROUND: Soil giant viruses are increasingly believed to have profound effects on ecological functioning by infecting diverse eukaryotes. However, their biogeography and ecology remain poorly understood. RESULTS: In this study, we analyzed 333 soil metagenomes from 5 habitat types (farmland, forest, grassland, Gobi desert, and mine wasteland) across China and identified 533 distinct giant virus phylotypes affiliated with nine families, thereby greatly expanding the diversity of soil giant viruses. Among the nine families, Pithoviridae were the most diverse. The majority of phylotypes exhibited a heterogeneous distribution among habitat types, with a remarkably high proportion of unique phylotypes in mine wasteland. The abundances of phylotypes were negatively correlated with their environmental ranges. A total of 76 phylotypes recovered in this study were detectable in a published global topsoil metagenome dataset. Among climatic, geographical, edaphic, and biotic characteristics, soil eukaryotes were identified as the most important driver of beta-diversity of giant viral communities across habitat types. Moreover, co-occurrence network analysis revealed some pairings between giant viral phylotypes and eukaryotes (protozoa, fungi, and algae). Analysis of 44 medium- to high-quality giant virus genomes recovered from our metagenomes uncovered not only their highly shared functions but also their novel auxiliary metabolic genes related to carbon, sulfur, and phosphorus cycling. CONCLUSIONS: These findings extend our knowledge of diversity, habitat preferences, ecological drivers, potential hosts, and auxiliary metabolism of soil giant viruses. Video Abstract.

An insight into the vaginal microbiome of infertile women in Bangladesh using metagenomic approach.

Introduction: The dysbiosis of vaginal microbiota is recognized as a potential underlying factor contributing to infertility in women. This study aimed to compare the vaginal microbiomes of infertile and fertile women to investigate their relationship with infertility. Methods: Metagenomic analysis was conducted on samples from 5 infertile and 5 fertile individuals using both amplicon 16S and metagenomics shotgun sequencing methods. Results and discussion: In the infertile group, the bacterial community was primarily represented by three major bacterial genera: Lactobacillus (79.42%), Gardnerella (12.56%) and Prevotella (3.33%), whereas, the fertile group exhibited a more diverse composition with over 8 major bacterial genera, accompanied by significantly reduced abundance of Lactobacillus (48.79%) and Gardnerella (6.98%). At the species level, higher abundances of L. iners, L. gasseri and G. vaginalis were observed in the infertile group. Regarding the microbiome composition, only one fertile and two infertile subjects exhibited the healthiest Community State Types, CST-1, while CST-3 was observed among two infertile and one fertile subject, and CST-4 in three other fertile and one infertile subject. Overall, alpha diversity metrics indicated greater diversity and lower species richness in the control (fertile) group, while the infertile group displayed the opposite trend. However, beta-diversity analysis did not show distinct clustering of samples associated with any specific group; instead, it demonstrated CST-type specific clustering. Shotgun metagenomics further confirmed the dominance of Firmicutes, with a greater abundance of Lactobacillus species in the infertile group. Specifically, L. iners and G. vaginalis were identified as the most dominant and highly abundant in the infertile group. Fungi were only identified in the control group, dominated by Penicillium citrinum (62.5%). Metagenome-assembled genomes (MAGs) corroborated read-based taxonomic profiling, with the taxon L. johnsonii identified exclusively in disease samples. MAG identities shared by both groups include Shamonda orthobunyavirus, L. crispatus, Human endogenous retrovirus K113, L. iners, and G. vaginalis. Interestingly, the healthy microbiomes sequenced in this study contained two clusters, Penicillium and Staphylococcus haemolyticus, not found in the public dataset. In conclusion, this study suggests that lower species diversity with a higher abundance of L. iners, L. gasseri and G. vaginalis, may contribute to female infertility in our study datasets. However, larger sample sizes are necessary to further evaluate such association.

Species-level understanding of the bacterial community in Daqu based on full-length 16S rRNA gene sequences.

Daqu is used as the fermentation starter of Baijiu and contributes diversified functional microbes for saccharifying grains and converting sugars into ethanol and aroma components in Baijiu products. Daqu is mainly classified into three types, namely low (LTD), medium (MTD) and high (HTD) temperature Daqu, according to the highest temperatures reached in their fermentation processes. In this study, we used the PacBio small-molecule real-time (SMRT) sequencing technology to determine the full-length 16 S rRNA gene sequences from the metagenomes of 296 samples of different types of Daqu collected from ten provinces in China, and revealed the bacterial diversity at the species level in the Daqu samples. We totally identified 310 bacteria species, including 78 highly abundant species (with a relative abundance >0.1% each) which accounted for 91.90% of the reads from all the Daqu samples. We also recognized the differentially enriched bacterial species in different types of Daqu, and in the Daqu samples with the same type but from different provinces. Specifically, Lactobacillales, Enterobacterales and Bacillaceae were significantly enriched in the LTD, MTD and HTD groups, respectively. The potential co-existence and exclusion relationships among the bacteria species involved in all the Daqu samples and in the LTD, MTD and HTD samples from a specific region were also identified. These results provide a better understanding of the bacterial diversity in different types of Daqu at the species level.

gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes.

BACKGROUND: In recent years, omics technologies have offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user-friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to provide a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline. RESULTS: Here, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from 2 to 4 distinct omics data types, including 16S ribosomal RNA (rRNA) gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration, and visualization approaches, enhancing the toolkit for a more insightful analysis of microbiomes. The functionality of these new features is showcased through the use of 4 microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives. CONCLUSIONS: gNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, offering novel insights in both host-associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.

Time-series metagenomics reveals changing protistan ecology of a temperate dimictic lake.

BACKGROUND: Protists, single-celled eukaryotic organisms, are critical to food web ecology, contributing to primary productivity and connecting small bacteria and archaea to higher trophic levels. Lake Mendota is a large, eutrophic natural lake that is a Long-Term Ecological Research site and among the world's best-studied freshwater systems. Metagenomic samples have been collected and shotgun sequenced from Lake Mendota for the last 20 years. Here, we analyze this comprehensive time series to infer changes to the structure and function of the protistan community and to hypothesize about their interactions with bacteria. RESULTS: Based on small subunit rRNA genes extracted from the metagenomes and metagenome-assembled genomes of microeukaryotes, we identify shifts in the eukaryotic phytoplankton community over time, which we predict to be a consequence of reduced zooplankton grazing pressures after the invasion of a invasive predator (the spiny water flea) to the lake. The metagenomic data also reveal the presence of the spiny water flea and the zebra mussel, a second invasive species to Lake Mendota, prior to their visual identification during routine monitoring. Furthermore, we use species co-occurrence and co-abundance analysis to connect the protistan community with bacterial taxa. Correlation analysis suggests that protists and bacteria may interact or respond similarly to environmental conditions. Cryptophytes declined in the second decade of the timeseries, while many alveolate groups (e.g., ciliates and dinoflagellates) and diatoms increased in abundance, changes that have implications for food web efficiency in Lake Mendota. CONCLUSIONS: We demonstrate that metagenomic sequence-based community analysis can complement existing efforts to monitor protists in Lake Mendota based on microscopy-based count surveys. We observed patterns of seasonal abundance in microeukaryotes in Lake Mendota that corroborated expectations from other systems, including high abundance of cryptophytes in winter and diatoms in fall and spring, but with much higher resolution than previous surveys. Our study identified long-term changes in the abundance of eukaryotic microbes and provided context for the known establishment of an invasive species that catalyzes a trophic cascade involving protists. Our findings are important for decoding potential long-term consequences of human interventions, including invasive species introduction. Video Abstract.

Metagenomic next-generation sequencing of cerebrospinal fluid reveals etiological and microbiological features in patients with various central nervous system infections.

The application of metagenomic next-generation sequencing (mNGS) in pathogens detection of cerebrospinal fluid (CSF) is limited because clinical, microbiological, and biological information are not well connected. We analyzed the 428 enrolled patients' clinical features, pathogens diagnostic efficiency of mNGS in CSF, microbial community structure and composition in CSF, and correlation of microbial and clinical biomarkers in CSF. General characteristics were unspecific but helpful in formulating a differential diagnosis. CSF mNGS has a higher detection rate (34.6%) compared to traditional methods (5.4%). mNGS detection rate was higher when the time from onset to CSF collection was ≤20 days, the CSF leukocytes count was >200 × 106/L, the CSF protein concentration was >1.3 g/L, or CSF glucose concentration was ≤2.5 mmol/L in non-postoperative bacterial CNS infections (CNSi). CSF was not strictly a sterile environment, and the potential pathogens may contribute to the dysbiosis of CSF microbiome. Furthermore, clinical biomarkers were significantly relevant to CNS pathogens. Clinical data are helpful in choosing a proper opportunity to obtain an accurate result of mNGS, and can speculate whether the mNGS results are correct or not. Our study is a pioneering study exploring the CSF microbiome in different CNSIs.

MAGinator enables accurate profiling of de novo MAGs with strain-level phylogenies.

Metagenomic sequencing has provided great advantages in the characterisation of microbiomes, but currently available analysis tools lack the ability to combine subspecies-level taxonomic resolution and accurate abundance estimation with functional profiling of assembled genomes. To define the microbiome and its associations with human health, improved tools are needed to enable comprehensive understanding of the microbial composition and elucidation of the phylogenetic and functional relationships between the microbes. Here, we present MAGinator, a freely available tool, tailored for profiling of shotgun metagenomics datasets. MAGinator provides de novo identification of subspecies-level microbes and accurate abundance estimates of metagenome-assembled genomes (MAGs). MAGinator utilises the information from both gene- and contig-based methods yielding insight into both taxonomic profiles and the origin of genes and genetic content, used for inference of functional content of each sample by host organism. Additionally, MAGinator facilitates the reconstruction of phylogenetic relationships between the MAGs, providing a framework to identify clade-level differences.

Metagenomic comparison of intestinal microbiota between normal and liver fibrotic rhesus macaques (Macaca mulatta).

Liver fibrosis is an important pathological process in chronic liver disease and cirrhosis. Recent studies have found a close association between intestinal microbiota and the development of liver fibrosis. To determine whether there are differences in the intestinal microbiota between rhesus macaques with liver fibrosis (MG) and normal rhesus macaques (MN), fecal samples were collected from 8 male MG and 12 male MN. The biological composition of the intestinal microbiota was then detected using 16S rRNA gene sequencing. The results revealed statistically significant differences in ASVs and Chao1 in the alpha-diversity and the beta-diversity of intestinal microbiota between MG and MN. Both groups shared Prevotella and Lactobacillus as common dominant microbiota. However, beneficial bacteria such as Lactobacillus were significantly less abundant in MG (P = 0.02). Predictive functional analysis using PICRUSt2 gene prediction revealed that MG exhibited a higher relative abundance of functions related to substance transport and metabolic pathways. This study may provide insight into further exploration of the mechanisms by which intestinal microbiota affect liver fibrosis and its potential future use in treating liver fibrosis.

Characterization of BrGH3A, a bovine rumen-derived glycoside hydrolase family 3 ß-glucosidase with a permuted domain arrangement.

The bovine rumen contains a large consortium of residential microbes that release a variety of digestive enzymes for feed degradation. However, the utilization of these microbial enzymes is still limited because these rumen microorganisms are mostly anaerobes and are thus unculturable. Therefore, we applied a sequence-based metagenomic approach to identify a novel 2,445-bp glycoside hydrolase family 3 ß-glucosidase gene known as BrGH3A from the metagenome of bovine ruminal fluid. BrGH3A ß-glucosidase is a 92-kDa polypeptide composed of 814 amino acid residues. Unlike most glycoside hydrolases in the same family, BrGH3A exhibited a permuted domain arrangement consisting of an (α/ß)6 sandwich domain, a fibronectin type III domain and a (ß/α)8 barrel domain. BrGH3A exhibited greater catalytic efficiency toward laminaribiose than cellobiose. We proposed that BrGH3A is an exo-acting ß-glucosidase from Spirochaetales bacteria that is possibly involved in the intracellular degradation of ß-1,3-/1,4-mixed linkage glucans that are present in grass cell walls. BrGH3A exhibits rich diversity in rumen hydrolytic enzymes and may represent a member of a new clan with a permuted domain topology within the large family.

Metatranscriptomics-guided genome-scale metabolic reconstruction reveals the carbon flux and trophic interaction in methanogenic communities.

BACKGROUND: Despite rapid advances in genomic-resolved metagenomics and remarkable explosion of metagenome-assembled genomes (MAGs), the function of uncultivated anaerobic lineages and their interactions in carbon mineralization remain largely uncertain, which has profound implications in biotechnology and biogeochemistry. RESULTS: In this study, we combined long-read sequencing and metatranscriptomics-guided metabolic reconstruction to provide a genome-wide perspective of carbon mineralization flow from polymers to methane in an anaerobic bioreactor. Our results showed that incorporating long reads resulted in a substantial improvement in the quality of metagenomic assemblies, enabling the effective recovery of 132 high-quality genomes meeting stringent criteria of minimum information about a metagenome-assembled genome (MIMAG). In addition, hybrid assembly obtained 51% more prokaryotic genes in comparison to the short-read-only assembly. Metatranscriptomics-guided metabolic reconstruction unveiled the remarkable metabolic flexibility of several novel Bacteroidales-affiliated bacteria and populations from Mesotoga sp. in scavenging amino acids and sugars. In addition to recovering two circular genomes of previously known but fragmented syntrophic bacteria, two newly identified bacteria within Syntrophales were found to be highly engaged in fatty acid oxidation through syntrophic relationships with dominant methanogens Methanoregulaceae bin.74 and Methanothrix sp. bin.206. The activity of bin.206 preferring acetate as substrate exceeded that of bin.74 with increasing loading, reinforcing the substrate determinantal role. CONCLUSION: Overall, our study uncovered some key active anaerobic lineages and their metabolic functions in this complex anaerobic ecosystem, offering a framework for understanding carbon transformations in anaerobic digestion. These findings advance the understanding of metabolic activities and trophic interactions between anaerobic guilds, providing foundational insights into carbon flux within both engineered and natural ecosystems. Video Abstract.

Comparative metagenomic analysis from Sundarbans ecosystems advances our understanding of microbial communities and their functional roles.

The Sundarbans mangrove, located at the mouth of the Ganges and Brahmaputra Rivers, is the world's largest tidal mangrove forest. These mangroves are also one of the most striking sources of microbial diversity, essential in productivity, conservation, nutrient cycling, and rehabilitation. Hence, the main objective of this study was to use metagenome analysis and provide detailed insight into microbial communities and their functional roles in the Sundarbans mangrove ecosystem. A comparative analysis was also done with a non-mangrove region of the Sundarbans ecosystem to assess the capability of the environmental parameters to explain the variation in microbial community composition. The study found several dominant bacteria, viz., Alphaproteobacteria, Actinomycetota, Bacilli, Clostridia, Desulfobacterota, Gammaproteobacteria, and Nitrospira, from the mangrove region. The mangrove sampling site reports several salt-tolerant bacteria like Alkalibacillus haloalkaliphilus, Halomonas anticariensis, and Salinivibrio socompensis. We found some probiotic species, viz., Bacillus clausii, Lactobacillus curvatus, Vibrio mediterranei and Vibrio fluvialis, from the Sundarbans mangrove. Nitrifying bacteria in Sundarbans soils were Nitrococcus mobilis, Nitrosococcus oceani, Nitrosomonas halophila, Nitrospirade fluvii, and others. Methanogenic archaea, viz., Methanoculleus marisnigri, Methanobrevibacter gottschalkii, and Methanolacinia petrolearia, were highly abundant in the mangroves as compared to the non-mangrove soils. The identified methanotrophic bacterial species, viz., Methylobacter tundripaludum, Methylococcus capsulatus, Methylophaga thiooxydans, and Methylosarcina lacus are expected to play a significant role in the degradation of methane in mangrove soil. Among the bioremediation bacterial species identified, Pseudomonas alcaligenes, Pseudomonas mendocina, Paracoccus denitrificans, and Shewanella putrefaciens play a significant role in the remediation of environmental pollution. Overall, our study shows for the first time that the Sundarbans, the largest mangrove ecosystem in the world, has a wide range of methanogenic archaea, methanotrophs, pathogenic, salt-tolerant, probiotic, nitrifying, and bioremediation bacteria.

Metagenome-derived SusD-homologs affiliated with Bacteroidota bind to synthetic polymers.

Starch utilization system (Sus)D-homologs are well known for their carbohydrate-binding capabilities and are part of the sus operon in microorganisms affiliated with the phylum Bacteroidota. Until now, SusD-like proteins have been characterized regarding their affinity toward natural polymers. In this study, three metagenomic SusD homologs (designated SusD1, SusD38489, and SusD70111) were identified and tested with respect to binding to natural and non-natural polymers. SusD1 and SusD38489 are cellulose-binding modules, while SusD70111 preferentially binds chitin. Employing translational fusion proteins with superfolder GFP (sfGFP), pull-down assays, and surface plasmon resonance (SPR) has provided evidence for binding to polyethylene terephthalate (PET) and other synthetic polymers. Structural analysis suggested that a Trp triad might be involved in protein adsorption. Mutation of these residues to Ala resulted in an impaired adsorption to microcrystalline cellulose (MC), but not so to PET and other synthetic polymers. We believe that the characterized SusDs, alongside the methods and considerations presented in this work, will aid further research regarding bioremediation of plastics. IMPORTANCE: SusD1 and SusD38489 can be considered for further applications regarding their putative adsorption toward fossil-fuel based polymers. This is the first time that SusD homologs from the polysaccharide utilization loci (PUL), largely described for the phylum Bacteroidota, are characterized as synthetic polymer-binding proteins.

A cross-sectional comparison of gut metagenomes between dairy workers and community controls.

BACKGROUND: As a nexus of routine antibiotic use and zoonotic pathogen presence, the livestock farming environment is a potential hotspot for the emergence of zoonotic diseases and antibiotic resistant bacteria. Livestock can further facilitate disease transmission by serving as intermediary hosts for pathogens before a spillover event. In light of this, we aimed to characterize the microbiomes and resistomes of dairy workers, whose exposure to the livestock farming environment places them at risk for facilitating community transmission of antibiotic resistant genes and emerging zoonotic diseases. RESULTS: Using shotgun sequencing, we investigated differences in the taxonomy, diversity and gene presence of 10 dairy farm workers and 6 community controls' gut metagenomes, contextualizing these samples with additional publicly available gut metagenomes. We found no significant differences in the prevalence of resistance genes, virulence factors, or taxonomic composition between the two groups. The lack of statistical significance may be attributed, in part, to the limited sample size of our study or the potential similarities in exposures between the dairy workers and community controls. We did, however, observe patterns warranting further investigation including greater abundance of tetracycline resistance genes and prevalence of cephamycin resistance genes as well as lower average gene diversity (even after accounting for differential sequencing depth) in dairy workers' metagenomes. We also found evidence of commensal organism association with tetracycline resistance genes in both groups (including Faecalibacterium prausnitzii, Ligilactobacillus animalis, and Simiaoa sunii). CONCLUSIONS: This study highlights the utility of shotgun metagenomics in examining the microbiomes and resistomes of livestock workers, focusing on a cohort of dairy workers in the United States. While our study revealed no statistically significant differences between groups in taxonomy, diversity and gene presence, we observed patterns in antibiotic resistance gene abundance and prevalence that align with findings from previous studies of livestock workers in China and Europe. Our results lay the groundwork for future research involving larger cohorts of dairy and non-dairy workers to better understand the impact of occupational exposure to livestock farming on the microbiomes and resistomes of workers.