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Novel microbial diversity adherent to plant biomass in the herbivore gastrointestinal tract, as revealed by ribosomal intergenic spacer analysis and rrs gene sequencing.

It is well recognized that a dynamic biofilm develops upon plant biomass in the herbivore gastrointestinal tract, but this component of the microbiome has not previously been specifically sampled, or directly compared with the biodiversity present in the planktonic fraction of digesta. In this study, the digesta collected from four sheep fed two different diets was separated into three fractions: the planktonic phase, and the microbial populations either weakly or tightly adherent to plant biomass. The community DNA prepared from each fraction was then subjected to both ribosomal intergenic spacer analysis (RISA) and denaturing gradient gel electrophoresis (DGGE). Both types of analysis showed that dietary factors influence community structure, and that the adherent fractions produced more complex profiles. The RIS-clone libraries prepared from the planktonic and adherent populations were then subjected to restriction fragment length polymorphism (RFLP) and DNA sequence analyses, which resulted in a far greater degree of discrimination among the fractions. Although many of the sequenced clones from the adherent populations were assigned to various clusters within the low G+C Gram-positive bacteria, the clone libraries from animals consuming an all-grass diet were largely comprised of novel lineages of Clostridium, while in animals consuming the starch-containing diet, Selenomonas and Ruminococcus spp. were the dominant low G+C Gram-positive bacteria. Additionally, the libraries from hay-fed animals also contained clones most similar to asaccharolytic Clostridia, and other Gram-positive bacteria that specialize in the transformation of plant phenolic compounds and the formation of cinnamic, phenylacetic and phenylpropionic acids. These results reveal, for the first time, the phylogeny of adherent subpopulations that specialize in the transformation of plant lignins and other secondary compounds, which potentiate polysaccharide hydrolysis by other members of the biofilm.

Animals↗

Investigation of associations between the neonatal gut microbiota and severe viral lower respiratory tract infections in the first 2 years of life: a birth cohort study with metagenomics.

BACKGROUND: Early-life gut microbiota affects immune system development, including the lung immune response (gut-lung axis). We aimed to investigate whether gut microbiota composition in neonates in the first week of life is associated with hospital admissions for viral lower respiratory tract infections (vLRTIs). METHODS: The Baby Biome Study (BBS) is a prospective birth cohort, which enrolled mother-baby pairs between Jan 1, 2016, and Dec 31, 2017, at three UK hospitals. In the present study, we only included BBS babies with a sequenced first-week stool sample and successful data linkage. Stool was collected in the first week of life for shotgun-metagenomic sequencing. We examined the following microbiota features: alpha diversity (Chao1, Shannon, and Simpson indices) and community structures (cluster-partitioning against medoids method). The participants were followed up through linkage to the Hospital Episode Statistics-Admitted Patient Care (HES-APC) database to determine vLRTI hospital admission incidence in the first 2 years of life. We used Poisson mixed-effects models for univariable and multivariable analyses to evaluate the association between microbiota features and vLRTI hospital admission incidence, adjusting for confounders identified through direct acyclic graphs. FINDINGS: 3305 (95%) of the 3476 BBS-enrolled babies for whom consent to data linkage was obtained were included in the present study. 1111 (34%) babies had a first-week sequenced stool sample, of whom 1082 (97%; 564 born vaginally and 518 born by caesarean section) were successfully linked to HES-APC, and had median follow-up of 2·0 years (IQR 1·4-2·9). Most babies were born at term (996 [92%] ≥37 weeks gestational age and 1070 [99%] >35 weeks gestational age) and healthy (1050 [97%] had no comorbidities), and 520 (48%) were female and 562 (52%) were male. Higher first-week gut microbiota alpha diversity was associated with reduced rates of vLRTI hospital admission (Chao1 Index adjusted hazard ratio [HR] 0·92 [95% CI 0·85-0·99]; Shannon Index adjusted HR 0·57 [0·33-0·98]; and Simpson Index adjusted HR 0·36 [0·11-1·20]). Three microbiota clusters were identified. Cluster 1 had a mixed composition and cluster 2 was dominated by Bifidobacterium breve, with both clusters observed in babies born vaginally and by caesarean section. Cluster 3 was found only in vaginally born babies and was dominated by Bifidobacterium longum. Having cluster 1 (mixed) or cluster 2 (B breve dominated) was independently associated with increased rates of vLRTI hospital admission compared with cluster 3 (B longum dominated; cluster 1 [mixed] 3·05 [1·25-7·41] and cluster 2 [B breve dominated] 2·80 [1·06-7·44]). INTERPRETATION: We report observational evidence that first-week gut microbiota differences are associated with clinically severe vLRTI in young children. This study identified bacterial species that could be of interest for vLRTI prevention. This finding has important implications for the design of future research and intervention strategies. FUNDING: The Wellcome Trust and Wellcome Sanger Institute core funding.

Humans↗

Gut microbiota-derived metabolites target C5AR1/KDM2A/HCAR3 axis in inflammatory bowel disease: a multi-machine learning algorithms and molecular docking study.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic recurrent disorder. Gut microbiota-derived metabolites regulate intestinal homeostasis, but their molecular mechanisms in IBD remain unclear. Current studies lack systematic "microbiota-metabolite-target" network mining with multi-method validation. This study integrates network pharmacology, three machine learning algorithms, and molecular docking to construct this regulatory network in IBD. METHODS: Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma (p < 0.05, |log2FC| > 0.5). Weighted gene co-expression network analysis (WGCNA) with an optimal soft threshold of &#x3b2; = 7 was performed to identify key module genes. Candidate genes were obtained by intersecting DEGs, gut microbiota-associated genes from the gutMGene database, and WGCNA module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of candidate genes. Core genes were identified using three machine learning algorithms (LASSO, Boruta, and SVM-RFE), followed by protein-protein interaction (PPI) network analysis. Molecular docking was performed to assess the binding affinities between hub proteins and gut microbiota-derived metabolites. RESULTS: A total of 885 DEGs were identified between the IBD and control groups, including 463 upregulated and 422 downregulated genes. WGCNA identified 280 key module genes from the purple and yellow modules. The intersection of DEGs, gut microbiota-associated genes, and WGCNA module genes yielded 19 core candidate genes. PPI network analysis combined with three machine learning algorithms jointly identified C5AR1, KDM2A, and HCAR3 as core hub genes. ROC curve analysis demonstrated that all three hub genes achieved AUC values greater than 0.7 in both the training and validation sets, indicating excellent diagnostic performance for IBD. Enrichment analysis revealed significant associations with the TNF, NF-&#x3ba;B, and IL-17 signaling pathways. Molecular docking confirmed stable binding of C5AR1 with 1,3-Diphenylpropan-2-Ol (-7.87 &#xb1; 0.83 kcal&#xb7;mol-&#xb9;) and HCAR3 with 3-Indolepropionic Acid (-6.35 &#xb1; 0.70 kcal&#xb7;mol-&#xb9;), both below -5.0 kcal&#xb7;mol-&#xb9;. CONCLUSION: This study first constructs a "gut microbiota-metabolite-hub gene" axis in IBD, providing a computational framework for microbiota-targeted precision therapy, and identifying C5AR1/KDM2A/HCAR3 as computationally predicted diagnostic biomarkers and 1,3-Diphenylpropan-2-Ol/3-Indolepropionic Acid as candidate intervention molecules that warrant further experimental validation.

Molecular Docking Simulation↗