GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison

Desulfovibrio vulgaris flagellin exacerbates colorectal cancer through activating LRRC19/TRAF6/TAK1 pathway

The initiation and progression of colorectal cancer (CRC) are intimately associated with genetic, environmental and biological factors. Desulfovibrio vulgaris (DSV), a sulfate-reducing bacterium, has been found excessive growth in CRC patients, suggesting a potential role in carcinogenesis. However, the precise mechanisms underlying this association remain incompletely understood. We have found Desulfovibrio was abundant in high-fat diet-induced Apcmin/+ mice, and DSV, a member of Desulfovibrio, triggered colonocyte proliferation of germ-free mice. Furthermore, the level of DSV progressively rose from healthy individuals to CRC patients. Flagella are important accessory structures of bacteria, which can help them colonize and enhance their invasive ability. We found that D. vulgaris flagellin (DVF) drove the proliferation, migration, and invasion of CRC cells and fostered the growth of CRC xenografts. DVF enriched the epithelial-mesenchymal transition (EMT)-associated genes and characterized the facilitation of DVF on EMT. Mechanistically, DVF induced EMT through a functional transmembrane receptor called leucine-rich repeat containing 19 (LRRC19). DVF interacted with LRRC19 to modulate the ubiquitination of tumor necrosis factor receptor-associated factor (TRAF)6, rather than TRAF2. This interaction drove the ubiquitination of pivotal molecule TAK1, further enhancing its autophosphorylation and ultimately contributing to EMT. Collectively, DVF interacts with LRRC19 to activate the TRAF6/TAK1 signaling pathway, thereby promoting the EMT of CRC. These data shed new light on the role of gut microbiota in CRC and establish a potential clinical therapeutic target.

Best practices for developing microbiome-based disease diagnostic classifiers through machine learning

The human gut microbiome, crucial in various diseases, can be utilized to develop diagnostic models through machine learning (ML). The specific tools and parameters used in model construction such as data preprocessing, batch effect removal and modeling algorithms can impact model performance and generalizability. To establish an generally applicable workflow, we divided the ML process into three above-mentioned steps and optimized each sequentially using 83 gut microbiome cohorts across 20 diseases. We tested a total of 156 tool-parameter-algorithm combinations and benchmarked them according to internal- and external- AUCs. At the data preprocessing step, we identified four data preprocessing methods that performed well for regression-type algorithms and one method that excelled for non-regression-type algorithms. At the batch effect removal step, we identified the "ComBat" function from the sva R package as an effective batch effect removal method and compared the performance of various algorithms. Finally, at the ML algorithm selection step, we found that Ridge and Random Forest ranked the best. Our optimized work flow performed similarly comparing with previous exhaustive methods for disease-specific optimizations, thus is generally applicable and can provide a comprehensive guideline for constructing diagnostic models for a range of diseases, potentially serving as a powerful tool for future medical diagnostics.

Discovery of an ene-reductase initiating resveratrol catabolism in gut microbiota and its application in disease treatment

Resveratrol (RSV) is a plant-derived natural compound with multiple biological activities. Upon entering the intestine, RSV undergoes rapid metabolism and transformation by the gut microbiota. In this study, we isolated a bacterium capable of efficiently metabolizing RSV, Eggerthella lenta J01. Through induced enrichment transcriptomics and bioinformatic analyses, we identified an RSV reductase (RER) from E. lenta J01. Using RER structure simulation, site-directed mutagenesis, and biochemical assays, we further determined the key amino acids in RER associated with RSV catalytic activity. Studies in animal models demonstrated that RER enhances RSV's ability to alleviate inflammatory bowel disease. Additionally, bioinformatics analysis revealed that the abundance of the rer gene in the gut microbiota of healthy individuals was higher than in patients with enteritis. Collectively, these findings suggest that the activity of natural products may be modulated by the gut microbiota through metabolic transformations.

Comprehensive analysis of transcriptome and microbiome in colorectal cancer with synchronous polyp patients

Background

Colorectal cancer (CRC) is a prevalent and lethal malignancy, with the role of gut microbiota in its development still unclear. This study examines differences in gut microbiota between CRC patients and healthy controls and explores their association with host gene expression to identify potential diagnostic and therapeutic targets.

Methods

Fecal samples from 10 CRC patients and 13 healthy controls were subjected to 16S rRNA sequencing. Transcriptome sequencing of tumor tissues, normal mucosa, and colorectal polyps from same 10 CRC patients was performed to identify differentially expressed genes (DEGs). Pearson correlation analysis was employed to associate operational taxonomic units (OTUs) with host gene expression.

Results

β-diversity analysis showed significant differences in microbiota between CRC patients and controls (P < 0.01). LEfSe identified 38 distinct bacterial taxa, with genera such as Bacteroides, Peptostreptococcus, and Parabacteroides being enriched in CRC patients. Transcriptome analysis uncovered 1,026 DEGs. Notably, TIMP1 and BCAT1 were positively correlated (r > 0.76, P < 0.01) with pathogenic bacteria like Fusobacterium nucleatum and Peptostreptococcus stomatis. Tumor-related genes TRPM4, MYBL2, and CDKN2A were significantly upregulated and correlated with specific bacterial taxa.

Conclusion

This study underscores the significant alterations in gut microbiota associated with CRC and reveals novel correlations between specific microbes and host gene expression, offering potential diagnostic markers and therapeutic targets for CRC.

Refining microbiome diversity analysis by concatenating and integrating dual 16S rRNA amplicon reads

Understanding the role of human gut microbiota in health and disease requires insights into its taxonomic composition and functional capabilities. This study evaluates whether concatenating paired-end reads enhances data output for gut microbiome analysis compared to the merging approach across various regions of the 16S rRNA gene. We assessed this approach in both mock communities and Korean cohorts with or without ulcerative colitis. Our results indicate that using the direct joining method for the V1-V3 or V6-V8 regions improves taxonomic resolution compared to merging paired-end reads (ME) in post-sequencing data. While predicting microbial function based on 16S rRNA sequencing has inherent limitations, integrating sequencing reads from both the V1-V3 and V6-V8 regions enhanced functional predictions. This was confirmed by whole metagenome sequencing (WMS) of Korean cohorts, where our approach improved taxa detection that was lost using the ME method. Thus, we propose that the integrated dual 16S rRNA sequencing technique serves as a valuable tool for microbiome research by bridging the gap between amplicon sequencing and WMS.

HMicroDB: A Comprehensive Database of Herpetofaunal Microbiota With a Focus on Host Phylogeny, Physiological Traits, and Environment Factors

Symbiotic microbiota strongly impact host physiology. Amphibians and reptiles occupy a pivotal role in the evolutionary history of Animalia, and they are of significant ecological, economic, and scientific value. Many prior studies have found that symbiotic microbiota in herpetofaunal species are closely associated with host phylogeny, physiological traits, and environmental factors; however, insufficient integrated databases hinder researchers from querying, accessing, and reanalyzing these resources. To rectify this, we built the first herpetofaunal microbiota database (HMicroDB; https://herpdb.com/) that integrates 11,697 microbiological samples from 337 host species (covering 23 body sites and associated with 23 host phenotypic or environmental factors), and we identified 11,084 microbial taxa by consistent annotation. The standardised analysis process, cross-dataset integration, user-friendly interface, and interactive visualisation make the HMicroDB a powerful resource for researchers to search, browse, and explore the relationships between symbiotic microbiota, hosts, and environment. This facilitates research in host-microbiota coevolution, biological conservation, and resource utilisation.

Fecal microbiota transplantation: transitioning from chaos and controversial realm to scientific precision era

With the popularization of modern lifestyles, the spectrum of intestinal diseases has become increasingly diverse, presenting significant challenges in its management. Traditional pharmaceutical interventions have struggled to keep pace with these changes, leaving many patients refractory to conventional pharmaceutical treatments. Fecal microbiota transplantation (FMT) has emerged as a promising therapeutic approach for enterogenic diseases. Still, controversies persist regarding its active constituents, mechanism of action, scheme of treatment evaluation, indications, and contraindications. In this review, we investigated the efficacy of FMT in addressing gastrointestinal and extraintestinal conditions, drawing from follow-up data on over 8000 patients. We systematically addressed the controversies surrounding FMT's clinical application. We delved into key issues such as its technical nature, evaluation methods, microbial restoration mechanisms, and impact on the host-microbiota interactions. Additionally, we explored the potential colonization patterns of FMT-engrafted new microbiota throughout the entire intestine and elucidated the specific pathways through which the new microbiota modulates host immunity, metabolism, and genome.

FACdb: a comprehensive resource for genes, gut microbiota, and metabolites in farm animals

Farm animals, including livestock and poultry, play essential economic, social, and cultural roles and are indispensable in human welfare. Farm Animal Connectome database (FACdb) is a comprehensive resource that includes the association networks among gene expression, gut microbiota, and metabolites in farm animals. Although some databases present the relationship between gut microbes, metabolites, and gene expression, these databases are limited to human and mouse species, with limited data for farm animals. In this database, we calculate the associations and summarize the connections among gene expression, gut microbiota, and metabolites in farm animals using six correlation or distance calculation (including Pearson, Spearman, Cosine, Euclidean, Bray-Curtis, and Mahalanobis). FACdb contains over 55 million potential interactions of 73,571 genes, 11,046 gut microbiota, and 4,540 metabolites. It provides an easy-to-use interface for browsing and searching the association information. Additionally, FACdb offers interactive visualization tools to effectively investigate the relationship among the genes, gut microbiota, and metabolites in farm animals. Overall, FACdb is a valuable resource for understanding interactions among gut microbiota, metabolites, and gene expression. It contributes to the further utilization of microbes in animal products and welfare promotion. Compared to mice, pigs or other farm animals share more similarities with humans in molecular, cellular, and organ-level responses, indicating that our database may offer new insights into the relationship among gut microbiota, metabolites, and gene expression in humans.

Global analysis of the metaplasmidome: ecological drivers and spread of antibiotic resistance genes across ecosystems

BACKGROUND

Plasmids act as vehicles for the rapid spread of antibiotic resistance genes (ARGs). However, few studies of the resistome at the community level distinguish between ARGs carried by mobile genetic elements and those carried by chromosomes, and these studies have been limited to a few ecosystems. This is the first study to focus on ARGs carried by the metaplasmidome on a global scale.

RESULTS

This study shows that only a small fraction of the plasmids reconstructed from 27 ecosystems representing 9 biomes are catalogued in public databases. The abundance of ARGs harboured by the metaplasmidome was significantly explained by bacterial richness. Few plasmids with or without ARGs were shared between ecosystems or biomes, suggesting that plasmid distribution on a global scale is mainly driven by ecology rather than geography. The network linking plasmids to their hosts shows that these mobile elements have thus been shared between bacteria across geographically distant environmental niches. However, certain plasmids carrying ARGs involved in human health were identified as being shared between multiple ecosystems and hosted by a wide variety of hosts. Some of these mobile elements, identified as keystone plasmids, were characterised by an enrichment in antibiotic resistance genes (ARGs) and CAS-CRISPR components which may explain their ecological success. The ARGs accounted for 9.2% of the recent horizontal transfers between bacteria and plasmids.

CONCLUSIONS

By comprehensively analysing the plasmidome content of ecosystems, some key habitats have emerged as particularly important for monitoring the spread of ARGs in relation to human health. Of particular note is the potential for air to act as a vector for long-distance transport of ARGs and accessory genes across ecosystems and continents. Video Abstract.

Butyrate-producing Faecalibacterium prausnitzii suppresses natural killer/T-cell lymphoma by dampening the JAK-STAT pathway

BACKGROUND

Natural killer/T-cell lymphoma (NKTCL) is a highly aggressive malignancy with a dismal prognosis, and gaps remain in understanding the determinants influencing disease outcomes.

OBJECTIVE

To characterise the gut microbiota feature and identify potential probiotics that could ameliorate the development of NKTCL.

DESIGN

This cross-sectional study employed shotgun metagenomic sequencing to profile the gut microbiota in two Chinese NKTCL cohorts, with validation conducted in an independent Korean cohort. Univariable and multivariable Cox proportional hazards analyses were applied to assess associations between identified marker species and patient outcomes. Tumour-suppressing effects were investigated using comprehensive in vivo and in vitro models. In addition, metabolomics, RNA sequencing, chromatin immunoprecipitation sequencing, Western blot analysis, immunohistochemistry and lentiviral-mediated gene knockdown system were used to elucidate the underlying mechanisms.

RESULTS

We first unveiled significant gut microbiota dysbiosis in NKTCL patients, prominently marked by a notable reduction in Faecalibacterium prausnitzii which correlated strongly with shorter survival among patients. Subsequently, we substantiated the antitumour properties of F. prausnitzii in NKTCL mouse models. Furthermore, F. prausnitzii culture supernatant demonstrated significant efficacy in inhibiting NKTCL cell growth. Metabolomics analysis revealed butyrate as a critical metabolite underlying these tumour-suppressing effects, validated in three human NKTCL cell lines and multiple tumour-bearing mouse models. Mechanistically, butyrate suppressed the activation of Janus kinase-signal transducer and activator of transcription pathway through enhancing histone acetylation, promoting the expression of suppressor of cytokine signalling 1.

CONCLUSION

These findings uncover a distinctive gut microbiota profile in NKTCL and provide a novel perspective on leveraging the therapeutic potential of F. prausnitzii to ameliorate this malignancy.

TaxSEA: rapid interpretation of microbiome alterations using taxon set enrichment analysis and public databases

Microbial communities are essential regulators of ecosystem function, with their composition commonly assessed through DNA sequencing. Most current tools focus on detecting changes among individual taxa (e.g. species or genera), however in other omics fields, such as transcriptomics, enrichment analyses like gene set enrichment analysis are commonly used to uncover patterns not seen with individual features. Here, we introduce TaxSEA, a taxon set enrichment analysis tool available as an R package, a web portal (https://shiny.taxsea.app), and a Python package. TaxSEA integrates taxon sets from five public microbiota databases (BugSigDB, MiMeDB, GutMGene, mBodyMap, and GMRepoV2) while also allowing users to incorporate custom sets such as taxonomic groupings. In silico assessments show TaxSEA is accurate across a range of set sizes. When applied to differential abundance analysis output from inflammatory bowel disease and type 2 diabetes metagenomic data, TaxSEA can rapidly identify changes in functional groups corresponding to known associations. We also show that TaxSEA is robust to the choice of differential abundance analysis package. In summary, TaxSEA enables researchers to efficiently contextualize their findings within the broader microbiome literature, facilitating rapid interpretation, and advancing understanding of microbiome-host and environmental interactions.

Interspecies interactions mediated by arginine metabolism enhance the stress tolerance of Fusobacterium nucleatum against Bifidobacterium animalis

Colorectal cancer (CRC) is a common cancer accompanied by microbiome dysbiosis. Exploration of probiotics against oncogenic microorganisms is promising for CRC treatment. Here, differential microorganisms between CRC and healthy control were analyzed. Antibacterial experiments, whole-genome sequencing, and metabolic network reconstruction were combined to reveal the anti-Fusobacterium nucleatum mechanism, which was verified by co-culture assay and mendelian randomization analysis. Sequencing results showed that F. nucleatum was enriched in CRC, yet Bifidobacterium animalis decreased gradually from healthy to CRC. Additionally, F. nucleatum could be inhibited by B. animalis. Whole-genome sequencing of B. animalis showed high phylogenetic similarity with known probiotic strains and highlighted its functions for amino acid and carbohydrate metabolism. Metabolic network reconstruction demonstrated that cross-feeding and specific metabolites (acidic molecules, arginine) had a great influence on the coexistence relationship. Finally, the arginine supplement enhanced the competitive ability of F. nucleatum against B. animalis, and the mendelian randomization and metagenomic sequencing analysis confirmed the positive relationship among F. nucleatum, arginine metabolism, and CRC. Thus, whole-genome sequencing and metabolic network reconstruction are valuable for probiotic mining and patient dietary guidance.IMPORTANCEUsing probiotics to inhibit oncogenic microorganisms (Fusobacterium nucleatum) is promising for colorectal cancer (CRC) treatment. In this study, whole-genome sequencing and metabolic network reconstruction were combined to reveal the anti-F. nucleatum mechanism of Bifidobacterium animalis, which was verified by co-culture assay and mendelian randomization analysis. The result indicated that the arginine supplement enhanced the competitive ability of F. nucleatum, which may be harmful to F. nucleatum-infected CRC patients. B. animalis is a potential probiotic to relieve this dilemma. Thus, using in silico simulation methods based on flux balance analysis, such as genome-scale metabolic reconstruction, provides valuable insights for probiotic mining and dietary guidance for cancer patients.

Understanding dysbiosis and resilience in the human gut microbiome: biomarkers, interventions, and challenges

The healthy gut microbiome is important in maintaining health and preventing various chronic and metabolic diseases through interactions with the host via different gut-organ axes, such as the gut-brain, gut-liver, gut-immune, and gut-lung axes. The human gut microbiome is relatively stable, yet can be influenced by numerous factors, such as diet, infections, chronic diseases, and medications which may disrupt its composition and function. Therefore, microbial resilience is suggested as one of the key characteristics of a healthy gut microbiome in humans. However, our understanding of its definition and indicators remains unclear due to insufficient experimental data. Here, we review the impact of key drivers including intrinsic and extrinsic factors such as diet and antibiotics on the human gut microbiome. Additionally, we discuss the concept of a resilient gut microbiome and highlight potential biomarkers including diversity indices and some bacterial taxa as recovery-associated bacteria, resistance genes, antimicrobial peptides, and functional flexibility. These biomarkers can facilitate the identification and prediction of healthy and resilient microbiomes, particularly in precision medicine, through diagnostic tools or machine learning approaches especially after antimicrobial medications that may cause stable dysbiosis. Furthermore, we review current nutrition intervention strategies to maximize microbial resilience, the challenges in investigating microbiome resilience, and future directions in this field of research.

Association Between Altered Microbiota Composition and Immune System-Related Genes in COVID-19 Infection

Microbiota and immunity affect the host's susceptibility to SARS-CoV-2 infection and the severity of COVID-19. This study aimed to identify significant alterations in the microbiota composition, immune signaling pathways, their potential association, and candidate microRNA in COVID-19 patients using an in silico study model. Enrichment online databases and Python programming were utilized to analyze GSE164805, GSE180594, and GSE182279, as well as NGS data of microbiota composition (PRJNA650244 and PRJNA660302) associated with COVID-19, employing amplicon-based/marker gene sequencing methods. C1, TNF, C2, IL1, and CFH genes were found to have a significant impact on immune signaling pathways. Additionally, we observed a notable decrease in Bacteroides spp. and Faecalibacterium sp., while Escherichia coli, Streptococcus spp., and Akkermansia muciniphila showed increased abundance in COVID-19. Notably, A. muciniphila demonstrated an association with immunity through C1 and TNF, while Faecalibacterium sp. was linked to C2 and IL1. The correlation between E. coli and CFH, as well as IL1 and Streptococcus spp. with C2, was identified. hsa-let-7b-5p was identified as a potential candidate that may be involved in the interaction between the microbiota composition, immune response, and COVID-19. In conclusion, integrative in silico analysis shows that these microbiota members are potentially crucial in the immune responses against COVID-19.

Integration of 168,000 samples reveals global patterns of the human gut microbiome

The factors shaping human microbiome variation are a major focus of biomedical research. While other fields have used large sequencing compendia to extract insights requiring otherwise impractical sample sizes, the microbiome field has lacked a comparably sized resource for the 16S rRNA gene amplicon sequencing commonly used to quantify microbiome composition. To address this gap, we processed 168,464 publicly available human gut microbiome samples with a uniform pipeline. We use this compendium to evaluate geographic and technical effects on microbiome variation. We find that regions such as Central and Southern Asia differ significantly from the more thoroughly characterized microbiomes of Europe and Northern America and that composition alone can be used to predict a sample's region of origin. We also find strong associations between microbiome variation and technical factors such as primers and DNA extraction. We anticipate this growing work, the Human Microbiome Compendium, will enable advanced applied and methodological research.

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.

Omilayers: a Python package for efficient data management to support multi-omic analysis

Multi-omic integration involves the management of diverse omic datasets. Conducting an effective analysis of these datasets necessitates a data management system that meets a specific set of requirements, such as rapid storage and retrieval of data with varying numbers of features and mixed data-types, ensurance of reliable and secure database transactions, extension of stored data row and column-wise and facilitation of data distribution. SQLite and DuckDB are embedded databases that fulfil these requirements. However, they utilize the structured query language (SQL) that hinders their implementation by the uninitiated user, and complicates their use in repetitive tasks due to the necessity of writing SQL queries. This study offers Omilayers, a Python package that encapsulates these two databases and exposes a subset of their functionality that is geared towards frequent and repetitive analytical procedures. Synthetic data were used to demonstrate the use of Omilayers and compare the performance of SQLite and DuckDB.

OncoSexome: the landscape of sex-based differences in oncologic diseases

The NIH policy on sex as biological variable (SABV) emphasized the importance of sex-based differences in precision oncology. Over 50% of clinically actionable oncology genes are sex-biased, indicating differences in drug efficacy. Research has identified sex differences in non-reproductive cancers, highlighting the need for comprehensive sex-based cancer data. We therefore developed OncoSexome, a multidimensional knowledge base describing sex-based differences in cancer (https://idrblab.org/OncoSexome/) across four key topics: antineoplastic drugs and responses (SDR), oncology-related biomarkers (SBM), risk factors (SRF) and microbial landscape (SML). SDR covers sex-based differences in 2051 anticancer drugs; SBM describes 12 551 sex-differential biomarkers; SRF illustrates 350 sex-dependent risk factors; SML demonstrates 1386 microbes with sex-differential abundances associated with cancer development. OncoSexome is unique in illuminating multifaceted influences of biological sex on cancer, providing both external and endogenous contributors to cancer development and describing sex-based differences for the broadest oncological classes. Given the increasing global research interest in sex-based differences, OncoSexome is expected to impact future precision oncology practices significantly.

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21 567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles and FR profiles, and identified 14 636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

Normal Gut Microbiomes in Diverse Populations: Clinical Implications

The human microbiome is a sensor and modulator of physiology and homeostasis. Remarkable tractability underpins the promise of therapeutic manipulation of the microbiome. However, the definition of a normal or healthy microbiome has been elusive. This is in part due to the underrepresentation of minority groups and major global regions in microbiome studies to date. We review studies of the microbiome in different populations and highlight a commonality among health-associated microbiome signatures along with major drivers of variation. We also provide an overview of microbiome-associated therapeutic interventions for some widespread, widely studied diseases. We discuss sources of bias and the challenges associated with defining population-specific microbiome reference bases. We propose a roadmap for defining normal microbiome references that can be used for population-customized microbiome therapeutics and diagnostics.

Linking gut microbiota dysbiosis to molecular pathways in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by cognitive decline and synaptic dysfunction. Emerging evidence suggests a significant relationship between gut microbiota and brain health, mediated through the gut-brain axis. Alterations in gut microbiota composition may influence AD progression by affecting molecular pathways and miRNA interactions.

METHODS

We retrieved and analyzed microarray data from 34 tissue samples of AD patients and controls (GEO accession number GSE110298). Differentially expressed genes (DEGs) with the GCS score package in R, considering a p-value < 0.05 and logFC<-1 and logFC>1 to isolate significant gene clusters. Enrichment analysis of signaling pathways and gene ontology was conducted using Enrichr, KEGG, Panther, DAVID, and shiny GO databases. Protein-protein interactions were visualized with Networkanalyst and CytoScape. Gut microbiota in 200 CE patients was analyzed using next-generation sequencing (NGS) data from gutMDisorder and GMrepo databases. miRNA interactions were evaluated using miEAA, Targetscan, MienTurnet, and miRnet databases.

RESULTS

Significant reductions in microbial taxa, including Clostridia (LDA score -4.878208), Firmicutes (LDA score -4.817032), and Faecalibacterium (LDA score -4.40714), were observed in AD patients. Pathway analysis highlighted the involvement of Axon guidance, ErbB, and MAPK signaling pathways in AD. Venn diagram analysis identified 619 intersecting genes in brain and gut tissues, emphasizing pathways such as Axon Guidance and Cell Cycle. miRNA analysis revealed important regulatory miRNAs, including hsa-let-7c, hsa-mir-125b-2, and hsa-mir-145, which target key transcription factors involved in AD pathology.

CONCLUSION

The study demonstrates significant dysbiosis in the gut microbiota of AD patients and underscores the potential role of gut microbiota in AD progression through altered signaling pathways and miRNA interactions. These findings highlight the need for further research into microbiota-based interventions as potential therapeutic strategies for AD.

PTTM: dissecting the profile of tumor tissue microbiome to reveal microbiota features and associations with host transcriptome

Microbiota is present in the human tissue microenvironment and closely related to tumorigenesis and treatment. However, the landscape of tissue microbiome and its relationship with tumors remain less understood. In this study, we re-analyzed the omics data from the 7104 samples (94 projects for 15 cancers) in the NCBI database to obtain microbial profiles. After normalization and decontamination processing, we established classification models to distinguish between different tumors and tumor with adjacent normal tissues. The models had excellent performances, indicating that tissue microbiome had significant tumor specificity. Moreover, a series of key bacteria and bacteria-gene association pairs were screened out based on bioinformatic analysis, such as the tumor-promoting bacteria Fusobacterium, the tumor-suppressing bacteria Actinomyces, and the significant Rhodopseudomonas-COL1A1 association pair. In addition, we created a visual website, PTTM (http://198.46.152.196:7080/), for users to query and download the results. The identified key bacteria and association pairs provide candidate targets for further exploration of the molecular mechanisms of microbial action on tumorigenesis and the development of cancer therapy.

Ellagic acid ameliorates alcohol-induced cognitive and social dysfunction through the gut microbiota-mediated CCL21-CCR7 axis

Chronic alcohol consumption disrupts the balance of the gut microbiome, resulting in alcohol-induced cognitive and social dysfunction (AICSD), and serves as a primary etiological factor for early-onset dementia. Ellagic acid (EA) is a polyphenolic compound belonging to the ellagitannin family, showing potential as a dietary intervention for alleviating cognitive impairments. Nonetheless, the protective effects and underlying mechanisms of EA on AICSD remain unclear. In our study, we employed a multi-omics approach to elucidate the microbiome-mediated mechanism underlying the beneficial effects of EA on AICSD. Firstly, our findings demonstrate that EA significantly ameliorated cognitive and social behavioral deficits as well as neuroinflammation induced by alcohol. Moreover, RNA-seq analysis of hippocampi indicates that EA regulated the KEGG pathway of cytokine-cytokine receptor interaction signaling by downregulating the CCL21-CCR7 axis. Furthermore, we observed that EA effectively restored the dysbiosis of gut microbiota and their derived metabolites induced by chronic alcohol consumption. Strong connections were observed between EA-regulated genes, microbiota and metabolites. Finally, the causal relationship between the microbiome and behavioral changes was further confirmed through antibiotic treatment and fecal microbiota transplantation experiments. Overall, our study provides innovative evidence supporting the role of EA in improving AICSD via regulation of the cytokine-cytokine receptor interaction signaling pathway through the microbiota-mediated CCl21-CCR7 axis. These findings offer valuable insights into both EA-based interventions as well as microbial interventions against AICSD.

Structural elucidation of a capsular polysaccharide from Bacteroides uniformis and its ameliorative impact on DSS-induced colitis in mice

Capsular polysaccharides derived from Bacteroides species have emerged as potential mitigators of intestinal inflammation in murine models. However, research on capsular polysaccharides from B. uniformis, a Bacteroides species with reduced abundance in colons of patients with ulcerative colitis, remains scarce. In this study, we extracted a neutral polysaccharide component from B. uniformis ATCC8492, termed BUCPS1B, using ultrasonic disruption, ethanol precipitation, and anion exchange chromatography. Structural characterization revealed BUCPS1B as a water-soluble polysaccharide with an α-1,4-glucan main chain adorned with minor substituent sugar residues. BUCPS1B alleviated intestinal inflammation in a mouse model of colitis and induced polarization of macrophages into M2-type. Furthermore, BUCPS1B modulated the gut microbiota composition, increased the abundance of the probiotic Akkermansia muciniphila and altered the gut metabolic profile to promote phenylalanine and short chain fatty acids metabolism. BUCPS1B is therefore a promising candidate to prevent inflammation and augment intestinal health.

Insights into the gut microbiome of vitiligo patients from India

BACKGROUND

Vitiligo is an autoimmune disease characterized by loss of pigmentation in the skin. It affects 0.4 to 2% of the global population, but the factors that trigger autoimmunity remain elusive. Previous work on several immune-mediated dermatological disorders has illuminated the substantial roles of the gut microbiome in disease pathogenesis. Here, we examined the gut microbiome composition in a cohort of vitiligo patients and healthy controls from India, including patients with a family history of the disease.

RESULTS

Our results show significant alterations in the gut microbiome of vitiligo patients compared to healthy controls, affecting taxonomic and functional profiles as well as community structure. We observed a reduction in the abundance of several bacterial taxa commonly associated with a healthy gut microbiome and noted a decrease in the abundance of SCFA (Short Chain Fatty Acids) producing taxa in the vitiligo group. Observation of a higher abundance of genes linked to bacteria-mediated degradation of intestinal mucus suggested a potential compromise of the gut mucus barrier in vitiligo. Functional analysis also revealed a higher abundance of fatty acid and lipid metabolism-related genes in the vitiligo group. Combined analysis with data from a French cohort of vitiligo also led to the identification of common genera differentiating healthy and gut microbiome across populations.

CONCLUSION

Our observations, together with available data, strengthen the role of gut microbiome dysbiosis in symptom exacerbation and possibly pathogenesis in vitiligo. The reported microbiome changes also showed similarities with other autoimmune disorders, suggesting common gut microbiome-mediated mechanisms in autoimmune diseases. Further investigation can lead to the exploration of dietary interventions and probiotics for the management of these conditions.

Rapid species-level metagenome profiling and containment estimation with sylph

Profiling metagenomes against databases allows for the detection and quantification of microorganisms, even at low abundances where assembly is not possible. We introduce sylph, a species-level metagenome profiler that estimates genome-to-metagenome containment average nucleotide identity (ANI) through zero-inflated Poisson k-mer statistics, enabling ANI-based taxa detection. On the Critical Assessment of Metagenome Interpretation II (CAMI2) Marine dataset, sylph was the most accurate profiling method of seven tested. For multisample profiling, sylph took >10-fold less central processing unit time compared to Kraken2 and used 30-fold less memory. Sylph's ANI estimates provided an orthogonal signal to abundance, allowing for an ANI-based metagenome-wide association study for Parkinson disease (PD) against 289,232 genomes while confirming known butyrate-PD associations at the strain level. Sylph took <1 min and 16 GB of random-access memory to profile metagenomes against 85,205 prokaryotic and 2,917,516 viral genomes, detecting 30-fold more viral sequences in the human gut compared to RefSeq. Sylph offers precise, efficient profiling with accurate containment ANI estimation even for low-coverage genomes.

Comprehensive data optimization and risk prediction framework: machine learning methods for inflammatory bowel disease prediction based on the human gut microbiome data

Over the past decade, the prevalence of inflammatory bowel disease (IBD) has significantly increased, making early detection crucial for improving patient survival rates. Medical research suggests that changes in the human gut microbiome are closely linked to IBD onset, playing a critical role in its prediction. However, the current gut microbiome data often exhibit missing values and high dimensionality, posing challenges to the accuracy of predictive algorithms. To address these issues, we proposed the comprehensive data optimization and risk prediction framework (CDORPF), an ensemble learning framework designed to predict IBD risk based on the human gut microbiome, aiding early diagnosis. The framework comprised two main components: data optimization and risk prediction. The data optimization module first employed triple optimization imputation (TOI) to impute missing data while preserving the biological characteristics of the microbiome. It then utilized importance-weighted variational autoencoder (IWVAE) to reduce redundant information from the high-dimensional microbiome data. This process resulted in a complete, low-dimensional representation of the data, laying the foundation for improved algorithm efficiency and accuracy. In the risk prediction module, the optimized data was classified using a random forest (RF) model, and hyperparameters were globally optimized using improved aquila optimizer (IAO), which incorporated multiple strategies. Experimental results on IBD-related gut microbiome datasets showed that the proposed framework achieved classification accuracy, recall, and F1 scores exceeding 0.9, outperforming comparison models and serving as a valuable tool for predicting IBD onset risk.

MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework

The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the accuracy is impacted by a lot of factors when predicting host phenotypes with the metagenomic data, e.g. small sample size, class imbalance, high-dimensional features, etc. To address these challenges, we propose MicroHDF, an interpretable deep learning framework to predict host phenotypes, where a cascade layers of deep forest units is designed for handling sample class imbalance and high dimensional features. The experimental results show that the performance of MicroHDF is competitive with that of existing state-of-the-art methods on 13 publicly available datasets of six different diseases. In particular, it performs best with the area under the receiver operating characteristic curve of 0.9182 ± 0.0098 and 0.9469 ± 0.0076 for inflammatory bowel disease (IBD) and liver cirrhosis, respectively. Our MicroHDF also shows better performance and robustness in cross-study validation. Furthermore, MicroHDF is applied to two high-risk diseases, IBD and autism spectrum disorder, as case studies to identify potential biomarkers. In conclusion, our method provides an effective and reliable prediction of the host phenotype and discovers informative features with biological insights.

An expanded database and analytical toolkit for identifying bacterial virulence factors and their associations with chronic diseases

Virulence factor genes (VFGs) play pivotal roles in bacterial infections and have been identified within the human gut microbiota. However, their involvement in chronic diseases remains poorly understood. Here, we establish an expanded VFG database (VFDB 2.0) consisting of 62,332 nonredundant orthologues and alleles of VFGs using species-specific average nucleotide identity ( https://github.com/Wanting-Dong/MetaVF_toolkit/tree/main/databases ). We further develop the MetaVF toolkit, facilitating the precise identification of pathobiont-carried VFGs at the species level. A thorough characterization of VFGs for 5452 commensal isolates from healthy individuals reveals that only 11 of 301 species harbour these factors. Further analyses of VFGs within the gut microbiomes of nine chronic diseases reveal both common and disease-specific VFG features. Notably, in type 2 diabetes patients, long HiFi sequencing confirms that shared VF features are carried by pathobiont strains of Escherichia coli and Klebsiella pneumoniae. These findings underscore the critical importance of identifying and understanding VFGs in microbiome-associated diseases.

Engineered probiotic ameliorates ulcerative colitis by restoring gut microbiota and redox homeostasis

Probiotics are potential treatments for ulcerative colitis (UC), but their efficacy is frequently compromised by gastrointestinal conditions that limit adhesion and activity. Here, we use machine learning and bioinformatics to confirm that patients with UC have decreased prevalence of Lactobacillus genus and increased oxidative stress, which correlate with inflammation severity. Accordingly, we developed a probiotic-based therapeutic that synergistically restores intestinal redox and microbiota homeostasis. Lactobacillus casei (Lac) were induced to form a pericellular film, providing a polysaccharide network for spatially confined crystallization of ultrasmall but highly active selenium dots (Se-Lac). Upon oral administration, the selenium dot-embedded pericellular film efficiently enhanced gastric acid resistance and intestinal mucoadhesion of Lac cells. At the lesion site, the selenium dots scavenged reactive oxygen species, while Lac modulated the gut microbiota. In multiple mouse models and non-human primates, this therapeutic effectively relieved inflammation and reduced colonic damage, thus showing promise as a UC treatment.

Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder

Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder's adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ).

Construction and characterization of stable multi-species biofilms formed by nine core gut bacteria on wheat fiber

Microbial aggregation mainly occurs on the intestinal epithelium, mucosal layer and undigested food particles in the gastrointestinal tract (GIT). Undigested food particles are usually insoluble dietary fiber (IDF), which can be easily obtained through daily diet, but there are few studies investigating whether the gut bacteria adhering to undigested food particles can form multi-species biofilms. In this study, we prepared mono- and multi-species biofilms using 18 core gut bacteria via a dynamic fermentation method, and it was found that multi-species composed of nine core gut bacteria (M9) showed the best biofilm formation ability. Cell counts of the nine bacteria in multi-species biofilms were 9.36, 11.85, 10.17, 9.93, 12.88, 11.39, 10.089, 9.06, and 13.21 Log10 CFU mL-1. M9 was tightly connected and regularly stacked on wheat fiber and had larger particle sizes than mono-species biofilms. M9 retained biofilm formation ability under pH and bile salt stresses. A human feces invasion experiment demonstrated that M9 can stably adhere to wheat fiber under the interference of complex gut bacteria, and the M9 multi-species biofilm had positions that can be filled by various gut bacteria. Metabolome results indicated that the M9 multi-species biofilm had more metabolic productions and more complex interspecies interactions than mono-species biofilms. This study provides a dynamic fermentation method to prepare multi-species biofilms on wheat fiber in vitro. It will also offer a research basis for clarifying whether gut bacteria can utilize IDF to form biofilm structures in vivo and the possible interspecific interactions and physiological functions of bacteria in biofilms.

The Ambiguous Correlation of Blautia with Obesity: A Systematic Review

Obesity is a complex and multifactorial disease with global epidemic proportions, posing significant health and economic challenges. Whilst diet and lifestyle are well-established contributors to the pathogenesis, the gut microbiota's role in obesity development is increasingly recognized. Blautia, as one of the major intestinal bacteria of the Firmicutes phylum, is reported with both potential probiotic properties and causal factors for obesity in different studies, making its role controversial. To summarize the current understanding of the Blautia-obesity correlation and to evaluate the evidence from animal and clinical studies, we used "Blautia" AND "obesity" as keywords searching through PubMed and SpringerLink databases for research articles. After removing duplicates and inadequate articles using the exclusion criteria, we observed different results between studies supporting and opposing the beneficial role of Blautia in obesity at the genus level. Additionally, several studies showed probiotic effectiveness at the species level for Blautia coccoides, B. wexlerae, B. hansenii, B. producta, and B. luti. Therefore, the current evidence does not demonstrate Blautia's direct involvement as a pathogenic microbe in obesity development or progression, which informs future research and therapeutic strategies targeting the gut Blautia in obesity management.

A universal oral microbiome-based signature for periodontitis

We analyzed eight oral microbiota shotgun metagenomic sequencing cohorts from five countries and three continents, identifying 54 species biomarkers and 26 metabolic biomarkers consistently altered in health and disease states across three or more cohorts. Additionally, machine learning models based on taxonomic profiles achieved high accuracy in distinguishing periodontitis patients from controls (internal and external areas under the receiver operating characteristic curves of 0.86 and 0.85, respectively). These results support metagenome-based diagnosis of periodontitis and provide a foundation for further research and effective treatment strategies.

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.

Compared to histamine-2 receptor antagonist, proton pump inhibitor induces stronger oral-to-gut microbial transmission and gut microbiome alterations: a randomised controlled trial

OBJECTIVE

We aim to compare the effects of proton pump inhibitors (PPIs) and histamine-2 receptor antagonists (H2RAs) on the gut microbiota through longitudinal analysis.

DESIGN

Healthy volunteers were randomly assigned to receive either PPI (n=23) or H2RA (n=26) daily for seven consecutive days. We collected oral (saliva) and faecal samples before and after the intervention for metagenomic next-generation sequencing. We analysed intervention-induced alterations in the oral and gut microbiome including microbial abundance and growth rates, oral-to-gut transmissions, and compared differences between the PPI and H2RA groups.

RESULTS

Both interventions disrupted the gut microbiota, with PPIs demonstrating more pronounced effects. PPI usage led to a significantly higher extent of oral-to-gut transmission and promoted the growth of specific oral microbes in the gut. This led to a significant increase in both the number and total abundance of oral species present in the gut, including the identification of known disease-associated species like Fusobacterium nucleatum and Streptococcus anginosus. Overall, gut microbiome-based machine learning classifiers could accurately distinguish PPI from non-PPI users, achieving an area under the receiver operating characteristic curve (AUROC) of 0.924, in contrast to an AUROC of 0.509 for H2RA versus non-H2RA users.

CONCLUSION

Our study provides evidence that PPIs have a greater impact on the gut microbiome and oral-to-gut transmission than H2RAs, shedding light on the mechanism underlying the higher risk of certain diseases associated with prolonged PPI use.

TRIAL REGISTRATION NUMBER

ChiCTR2300072310.

Robust reactive oxygen species modulator hitchhiking yeast microcapsules for colitis alleviation by trilogically intestinal microenvironment renovation

Ulcerative colitis (UC) is characterized by chronic inflammatory processes of the intestinal tract of unknown origin. Current treatments lack understanding on how to effectively alleviate oxidative stress, relieve inflammation, as well as modulate gut microbiota for maintaining intestinal homeostasis synchronously. In this study, a novel drug delivery system based on a metal polyphenol network (MPN) was constructed via metal coordination between epigallocatechin gallate (EGCG) and Fe3+. Curcumin (Cur), an active polyphenolic compound, with distinguished anti-inflammatory activity was assembled and encapsulated into MPN to generate Cur-MPN. The obtained Cur-MPN could serve as a robust reactive oxygen species modulator by efficiently scavenging superoxide radical (O2•-) as well as hydroxyl radical (·OH). By hitchhiking yeast microcapsule (YM), Cur-MPN was then encapsulated into YM to obtain CM@YM. Our findings demonstrated that CM@YM was able to protect Cur-MPN to withstand the harsh gastrointestinal environment and enhance the targeting and retention abilities of the inflamed colon. When administered orally, CM@YM could alleviate DSS-induced colitis with protective and therapeutic effects by scavenging ROS, reducing pro-inflammatory cytokines, and regulating the polarization of macrophages to M1, thus restoring barrier function and maintaining intestinal homeostasis. Importantly, CM@YM also modulated the gut microbiome to a favorable state by improving bacterial diversity and transforming the compositional structure to an anti-inflammatory phenotype as well as increasing the content of short-chain fatty acids (SCFA) (such as acetic acid, propionic acid, and butyric acid). Collectively, with excellent biocompatibility, our findings indicate that synergistically regulating intestinal microenvironment will be a promising approach for UC.

The MTIST platform: a microbiome time series inference standardized test

The human gut microbiome is a promising therapeutic target, but interventions are hampered by our limited understanding of microbial ecosystems. Here, we present a platform to develop, evaluate, and score approaches to learn ecological interactions from microbiome time series data. The microbiome time series inference standardized test (MTIST) comprises: a simulation framework for the in silico generation of microbiome study data akin to what is obtained with quantitative next-generation sequencing approaches, a compilation of a large curated data set generated by the simulation framework representing 648 simulated microbiome studies containing 18,360 time series, with a total of 2,182,800 species abundance measurements, and a scoring method to rank ecological inference algorithms. We use the MTIST platform to rank five implementations of microbiome inference approaches, revealing that while all algorithms performed well on ecosystems with few species (3 and 10), all algorithms failed to infer most interaction in a large ecosystem with 100 member species. However, we do find that the strongest interactions within a large ecosystem are inferred with higher success by all algorithms. Finally, we use the MTIST platform to compare different microbiome study designs, characterizing tradeoffs between samples per subject and number of subjects. Interestingly, we find that when only few samples can be collected per subject, ecological inference is most successful when these samples are collected with highest feasible temporal frequency. Taken together, we provide a computational tool to aid the development of better microbiome ecosystem inference approaches, which will be crucial towards the development of reliable and predictable therapeutic approaches that target the microbiome ecosystem.

Eubacterium album sp. nov., a butyrate-producing bacterium isolated from faeces of a healthy human

An oval to rod-shaped, Gram-stain-positive, strictly anaerobic bacterium, designated LFL-14T, was isolated from the faeces of a healthy Chinese woman. Cells of the strain were non-spore-forming, grew optimally at 37 °C (growth range 30-45 °C) and pH 7.0 (growth range 6.0-9.0) under anaerobic conditions in the liquid modified Gifu anaerobic medium (mGAM). The result of 16S rRNA gene-based analysis indicated that LFL-14T shared an identity of 94.7 0% with Eubacterium ventriosum ATCC 27560T, indicating LFL-14T represented a novel taxon. The results of genome-based analysis revealed that the average nucleotide identity (ANI), the digital DNA-DNA hybridisation (dDDH) and average amino acid identity (AAI) between LFL-14T and its phylogenetically closest neighbour, Eubacterium ventriosum ATCC 27560T, were 77.0 %, 24.6 and 70.9 %, respectively, indicating that LFL-14T represents a novel species of the genus Eubacterium. The genome size of LFL-14T was 2.92 Mbp and the DNA G+C content was 33.14 mol%. We analysed the distribution of the genome of LFL-14T in cohorts of healthy individuals, type 2 diabetes patients (T2D) and patients with non-alcoholic fatty liver disease (NAFLD). We found that its abundance was higher in the T2D cohort, but it had a low average abundance of less than 0.2 % in all three cohorts. The percentages of frequency of occurrence in the T2D, healthy and NAFLD cohorts were 48.87 %, 16.72 % and 13.10 % respectively. The major cellular fatty acids of LFL-14T were C16 : 0 (34.4 %), C17 : 0 2-OH (21.4 %) and C14 : 0 (11.7 %). Additionally, the strain contained diphosphatidylglycerol (DPG) and phosphatidylethanolamine (PE), as well as unidentified phospholipids and unidentified glycolipids. The glucose fermentation products of LFL-14T were acetate and butyrate. In summary, On the basis of its chemotaxonomic, phenotypic, phylogenetic and phylogenomic properties, strain LFL-14T (= CGMCC 1.18005T = KCTC 25580T) is identified as representing a novel species of the genus Eubacterium, for which the name Eubacterium album sp. nov. is proposed.

Roseburia intestinalis Supplementation Could Reverse the Learning and Memory Impairment and m6A Methylation Modification Decrease Caused by 27-Hydroxycholesterol in Mice

The abnormality in N6-methyladenosine (m6A) methylation is involved in the course of Alzheimer's disease (AD), while the intervention of 27-Hydroxycholesterol (27-OHC) can affect the m6A methylation modification in the brain cortex. Disordered gut microbiota is a key link in 27-OHC leading to cognitive impairment, and further studies have found that the abundance of Roseburia intestinalis in the gut is significantly reduced under the intervention of 27-OHC. This study aims to investigate the association of 27-OHC, Roseburia intestinalis in the gut, and brain m6A modification in the learning and memory ability injury. In this study, 9-month-old male C57BL/6J mice were treated with antibiotic cocktails for 6 weeks to sweep the intestinal flora, followed by 27-OHC or normal saline subcutaneous injection, and then Roseburia intestinalis or normal saline gavage were applied to the mouse. The 27-OHC level in the brain, the gut barrier function, the m6A modification in the brain, and the memory ability were measured. From the results, we observed that 27-OHC impairs the gut barrier function, causing a disturbance in the expression of m6A methylation-related enzymes and reducing the m6A methylation modification level in the brain cortex, and finally leads to learning and memory impairment. However, Roseburia intestinalis supplementation could reverse the negative effects mentioned above. This study suggests that 27-OHC-induced learning and memory impairment might be linked to brain m6A methylation modification disturbance, while Roseburia intestinalis, as a probiotic with great potential, could reverse the damage caused by 27-OHC. This research could help reveal the mechanism of 27-OHC-induced neural damage and provide important scientific evidence for the future use of Roseburia intestinalis in neuroprotection.

Meta-analysis of the Microbial Diversity Cultured in Bioreactors Simulating the Gut Microbiome

Understanding the intricate ecological interactions within the gut microbiome and unravelling its impact on human health is a challenging task. Bioreactors are valuable tools that have contributed to our understanding of gut microbial ecology. However, there is a lack of studies describing and comparing the microbial diversity cultivated in these models. This knowledge is crucial for refining current models to reflect the gastrointestinal microbiome accurately. In this study, we analysed the microbial diversity of 1512 samples from 18 studies available in public repositories that employed cultures performed in batches and various bioreactor models to cultivate faecal microbiota. Community structure comparison between samples using t-distributed stochastic neighbour embedding and the Hellinger distance revealed a high variation between projects. The main driver of these differences was the inter-individual variation between the donor faecal inocula. Moreover, there was no overlap in the structure of the microbial communities between studies using the same bioreactor platform. In addition, α-diversity analysis using Hill numbers showed that highly complex bioreactors did not exhibit higher diversities than simpler designs. However, analyses of five projects in which the samples from the faecal inoculum were also provided revealed an amplicon sequence variants enrichment in bioreactors compared to the inoculum. Finally, a comparative analysis of the taxonomy of the families detected in the projects and the GMRepo database revealed bacterial families exclusively found in the bioreactor models. These findings highlight the potential of bioreactors to enrich low-abundance microorganisms from faecal samples, contributing to uncovering the gut microbial "dark matter".

Identification of enterotype and its predictive value for patients with colorectal cancer

BACKGROUND

Gut microbiota dysbiosis involved in the pathogenesis of colorectal cancer (CRC). The characteristics of enterotypes in CRC development have not been determined.

OBJECTIVE

To characterize the gut microbiota of healthy, adenoma, and CRC subjects based on enterotype.

METHODS

The 16 S rRNA sequencing data from 315 newly sequenced individuals and three previously published datasets were collected, providing total data for 367 healthy, 320 adenomas, and 415 CRC subjects. Enterotypes were analyzed for all samples, and differences in microbiota composition across subjects with different disease states in each enterotype were determined. The predictive values of a random forest classifier based on enterotype in distinguishing healthy, adenoma, and CRC subjects were evaluated and validated.

RESULTS

Subjects were classified into one of three enterotypes, namely, Bacteroide- (BA_E), Blautia- (BL_E), and Streptococcus- (S_E) dominated clusters. The taxonomic profiles of these three enterotypes differed among the healthy, adenoma, and CRC cohorts. BA_E group was enriched with Bacteroides and Blautia; BL_E group was enriched by Blautia and Coprococcus; S_E was enriched by Streptococcus and Ruminococcus. Relative abundances of these genera varying among the three human cohorts. In training and validation sets, the S_E cluster showed better performance in distinguishing among CRC patients, adenoma patients, and healthy controls, as well as between CRC and non-CRC individuals, than the other two clusters.

CONCLUSION

This study provides the first evidence to indicate that changes in the microbial composition of enterotypes are associated with disease status, thereby highlighting the diagnostic potential of enterotypes in the treatment of adenoma and CRC.

Application of next-generation sequencing to identify different pathogens

Early and precise detection and identification of various pathogens are essential for epidemiological monitoring, disease management, and reducing the prevalence of clinical infectious diseases. Traditional pathogen detection techniques, which include mass spectrometry, biochemical tests, molecular testing, and culture-based methods, are limited in application and are time-consuming. Next generation sequencing (NGS) has emerged as an essential technology for identifying pathogens. NGS is a cutting-edge sequencing method with high throughput that can create massive volumes of sequences with a broad application prospects in the field of pathogen identification and diagnosis. In this review, we introduce NGS technology in detail, summarizes the application of NGS in that identification of different pathogens, including bacteria, fungi, and viruses, and analyze the challenges and outlook for using NGS to identify clinical pathogens. Thus, this work provides a theoretical basis for NGS studies and provides evidence to support the application of NGS in distinguishing various clinical pathogens.

Comprehensive analysis revealed the immunoinflammatory targets of rheumatoid arthritis based on intestinal flora, miRNA, transcription factors, and RNA-binding proteins databases, GSEA and GSVA pathway observations, and immunoinfiltration typing

OBJECTIVE

Rheumatoid arthritis (RA) is a chronic inflammatory arthritis. This study aimed to identify potential biomarkers and possible pathogenesis of RA using various bioinformatics analysis tools.

METHODS

The GMrepo database provided a visual representation of the analysis of intestinal flora. We selected the GSE55235 and GSE55457 datasets from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) separately. With the intersection of these DEGs with the target genes associated with RA found in the GeneCards database, we obtained the DEGs targeted by RA (DERATGs). Subsequently, Disease Ontology, Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes were used to analyze DERATGs functionally. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on the data from the gene expression matrix. Additionally, the protein-protein interaction network, transcription factor (TF)-targets, target-drug, microRNA (miRNA)-mRNA networks, and RNA-binding proteins (RBPs)-DERATGs correlation analyses were built. The CIBERSORT was used to evaluate the inflammatory immune state. The single-sample GSEA (ssGSEA) algorithm and differential analysis of DERATGs were used among the infiltration degree subtypes.

RESULTS

There were some correlations between the abundance of gut flora and the prevalence of RA. A total of 54 DERATGs were identified, mainly related to immune and inflammatory responses and immunodeficiency diseases. Through GSEA and GSVA analysis, we found pathway alterations related to metabolic regulations, autoimmune diseases, and immunodeficiency-related disorders. We obtained 20 hub genes and 2 subnetworks. Additionally, we found that 39 TFs, 174 drugs, 2310 miRNAs, and several RBPs were related to DERATGs. Mast, plasma, and naive B cells differed during immune infiltration. We discovered DERATGs' differences among subtypes using the ssGSEA algorithm and subtype grouping.

CONCLUSIONS

The findings of this study could help with RA diagnosis, prognosis, and targeted molecular treatment.

The African Human Microbiome Portal: a public web portal of curated metagenomic metadata

There is growing evidence that comprehensive and harmonized metadata are fundamental for effective public data reusability. However, it is often challenging to extract accurate metadata from public repositories. Of particular concern is the metagenomic data related to African individuals, which often omit important information about the particular features of these populations. As part of a collaborative consortium, H3ABioNet, we created a web portal, namely the African Human Microbiome Portal (AHMP), exclusively dedicated to metadata related to African human microbiome samples. Metadata were collected from various public repositories prior to cleaning, curation and harmonization according to a pre-established guideline and using ontology terms. These metadata sets can be accessed at https://microbiome.h3abionet.org/. This web portal is open access and offers an interactive visualization of 14 889 records from 70 bioprojects associated with 72 peer reviewed research articles. It also offers the ability to download harmonized metadata according to the user's applied filters. The AHMP thereby supports metadata search and retrieve operations, facilitating, thus, access to relevant studies linked to the African Human microbiome. Database URL:  https://microbiome.h3abionet.org/.

SPIRE: a Searchable, Planetary-scale mIcrobiome REsource

Meta'omic data on microbial diversity and function accrue exponentially in public repositories, but derived information is often siloed according to data type, study or sampled microbial environment. Here we present SPIRE, a Searchable Planetary-scale mIcrobiome REsource that integrates various consistently processed metagenome-derived microbial data modalities across habitats, geography and phylogeny. SPIRE encompasses 99 146 metagenomic samples from 739 studies covering a wide array of microbial environments and augmented with manually-curated contextual data. Across a total metagenomic assembly of 16 Tbp, SPIRE comprises 35 billion predicted protein sequences and 1.16 million newly constructed metagenome-assembled genomes (MAGs) of medium or high quality. Beyond mapping to the high-quality genome reference provided by proGenomes3 (http://progenomes.embl.de), these novel MAGs form 92 134 novel species-level clusters, the majority of which are unclassified at species level using current tools. SPIRE enables taxonomic profiling of these species clusters via an updated, custom mOTUs database (https://motu-tool.org/) and includes several layers of functional annotation, as well as crosslinks to several (micro-)biological databases. The resource is accessible, searchable and browsable via http://spire.embl.de.

Gut microbiota determines the fate of dietary fiber-targeted interventions in host health

Epidemiological investigation confirmed that the intake of dietary fiber (DF) is closely related to human health, and the most important factor affecting the physiological function of DF, besides its physicochemical properties, is the gut microbiota. This paper mainly summarizes the interaction between DF and gut microbiota, including the influence of DF on the colonization of gut microbiota based on its different physicochemical properties, and the physiological role of gut microbiota in destroying the complex molecular structure of DF by encoding carbohydrate-active enzymes, thus producing small molecular products that affect the metabolism of the host. Taking cardiovascular disease (Atherosclerosis and hypertension), liver disease, and immune diseases as examples, it is confirmed that some DF, such as fructo-oligosaccharide, galactooligosaccharide, xylo-oligosaccharide, and inulin, have prebiotic-like physiological effects. These effects are dependent on the metabolites produced by the gut microbiota. Therefore, this paper further explores how DF affects the gut microbiota's production of substances such as short-chain fatty acids, bile acids, and tryptophan metabolites, and provides a preliminary explanation of the mechanisms associated with their impact on host health. Finally, based on the structural properties of DF and the large heterogeneity in the composition of the population gut microbiota, it may be a future trend to utilize DF and the gut microbiota to correlate host health for precision nutrition by combining the information from population disease databases.

A universe of human gut-derived bacterial prophages: unveiling the hidden viral players in intestinal microecology

Prophages, which are an existing form of temperate phages that integrate into host genomes, have been found extensively present in diverse bacterial species. The human gut microbiome, characterized by its complexity, dynamism, and interconnectivity among multiple species, remains inadequately understood in terms of the global landscape of bacterial prophages. Here, we analyzed 43,942 human gut-derived bacterial genomes (439 species of 12 phyla) and identified 105,613 prophage regions in ~ 92% of them. 16254 complete prophages were distributed in ~ 24% of bacteria, indicating an extremely uneven prophage distribution across various species within the human gut. Among all identified prophages, ~4% possessed cross-genera (2-20 genera) integration capacity, while ~ 17% displayed broad infection host ranges (targeting 2-35 genera). Functional gene annotation revealed that antibiotic-resistance genes and toxin-related genes were detected in ~ 2.5% and ~ 5.8% of all prophages, respectively. Furthermore, through sequence alignments between our obtained prophages and publicly available human gut phageome contigs, we have observed that ~ 72% of non-redundant prophages are previously unreported genomes, and they illuminate ~ 6.5-9.5% of the individual intestinal "viral dark matter". Our study represents the first comprehensive depiction of human gut-derived prophages, provides a substantial collection of reference sequences for forthcoming human gut phageome-related investigations, and potentially enables better risk assessment of prophage dissemination.

Christensenella strain resources, genomic/metabolomic profiling, and association with host at species level

The gut commensal bacteria Christensenellaceae species are negatively associated with many metabolic diseases, and have been seen as promising next-generation probiotics. However, the cultured Christensenellaceae strain resources were limited, and their beneficial mechanisms for improving metabolic diseases have yet to be explored. In this study, we developed a method that enabled the enrichment and cultivation of Christensenellaceae strains from fecal samples. Using this method, a collection of Christensenellaceae Gut Microbial Biobank (ChrisGMB) was established, composed of 87 strains and genomes that represent 14 species of 8 genera. Seven species were first described and the cultured Christensenellaceae resources have been significantly expanded at species and strain levels. Christensenella strains exerted different abilities in utilization of various complex polysaccharides and other carbon sources, exhibited host-adaptation capabilities such as acid tolerance and bile tolerance, produced a wide range of volatile probiotic metabolites and secondary bile acids. Cohort analyses demonstrated that Christensenellaceae and Christensenella were prevalent in various cohorts and the abundances were significantly reduced in T2D and OB cohorts. At species level, Christensenellaceae showed different changes among healthy and disease cohorts. C. faecalis, F. tenuis, L. tenuis, and Guo. tenuis significantly reduced in all the metabolic disease cohorts. The relative abundances of C. minuta, C. hongkongensis and C. massiliensis showed no significant change in NAFLD and ACVD. and C. tenuis and C. acetigenes showed no significant change in ACVD, and Q. tenuis and Geh. tenuis showed no significant change in NAFLD, when compared with the HC cohort. So far as we know, this is the largest collection of cultured resource and first exploration of Christensenellaceae prevalences and abundances at species level.