Background Antibiotic treatment has a well-established detrimental effect on the gut bacterial composition, but effects on the fungal community are less clear. Bacteria in the lumen of the gastrointestinal tract may limit fungal colonization and invasion. Antibiotic drugs targeting bacteria are therefore seen as an important risk factor for fungal infections and induced allergies. However, antibiotic effects on gut bacterial-fungal interactions, including disruption and resilience of fungal community compositions, were not investigated in humans. We analysed stool samples collected from 14 healthy human participants over 3 months following a 6-day antibiotic administration. We integrated data from shotgun metagenomics, metatranscriptomics, metabolomics, and fungal ITS2 sequencing. Results While the bacterial community recovered mostly over 3 months post treatment, the fungal community was shifted from mutualism at baseline to competition. Half of the bacterial-fungal interactions present before drug intervention had disappeared 3 months later. During treatment, fungal abundances were associated with the expression of bacterial genes with functions for cell growth and repair. By extending the metagenomic species approach, we revealed bacterial strains inhibiting the opportunistic fungal pathogen Candida albicans. We demonstrated in vitro how C. albicans pathogenicity and host cell damage might be controlled naturally in the human gut by bacterial metabolites such as propionate or 5-dodecenoate. Conclusions We demonstrated that antibacterial drugs have long-term influence on the human gut mycobiome. While bacterial communities recovered mostly 30-days post antibacterial treatment, the fungal community was shifted from mutualism towards competition.

Background Coastal marine environments are one of the most productive ecosystems on Earth. However, anthropogenic impacts exert significant pressure on coastal marine biodiversity, contributing to functional shifts in microbial communities and human health risk factors. However, relatively little is known about the impact of eutrophication—human-derived nutrient pollution—on the marine microbial biosphere. Results Here, we tested the hypothesis that benthic microbial diversity and function varies along a pollution gradient, with a focus on human pathogens and antibiotic resistance genes. Comprehensive metagenomic analysis including taxonomic investigation, functional detection, and ARG annotation revealed that zinc, lead, total volatile solids, and ammonia nitrogen were correlated with microbial diversity and function. We propose several microbes, including Planctomycetes and sulfate-reducing microbes as candidates to reflect pollution concentration. Annotation of antibiotic resistance genes showed that the highest abundance of efflux pumps was found at the most polluted site, corroborating the relationship between pollution and human health risk factors. This result suggests that sediments at polluted sites harbor microbes with a higher capacity to reduce intracellular levels of antibiotics, heavy metals, or other environmental contaminants. Conclusions Our findings suggest a correlation between pollution and the marine sediment microbiome and provide insight into the role of high-turnover microbial communities as well as potential pathogenic organisms as real-time indicators of water quality, with implications for human health and demonstrate the inner functional shifts contributed by the microcommunities. Electronic supplementary material The online version of this article (10.1186/s40168-019-0714-6) contains supplementary material, which is available to authorized users.

Background Gastrointestinal microbiota play an important role in animal host immunity, nutrient metabolism, and energy acquisition, and have therefore drawn increasing attentions. This study compared the diversity of the gut microbiota of both wild and captive bharals, which is an ungulate herbivore of caprid from the Qinghai-Tibet plateau. Results The sequencing of the V4-V5 region of the 16S rRNA gene via high-throughput sequencing technology showed that the dominant bacterial phyla are Firmicutes and Bacteroides both in wild and captive bharals. However, their abundance differed significantly between groups. Firmicutes were significantly higher in wild bharals, while Bacteroides were significantly higher in captive bharals. Different diets are likely a key influencing factor in the diversity and abundance of gut microbiota in bharals. Conclusions Changes in diets affect the diversity of gut microbiota and the relative abundance of pathogenic bacteria, increasing the risk of diseases outbreak in captive bharals. The results of this study suggest that the structure and function of the gut microbiota should be regulated via dietary intervention, accurate provision of an individualized diet, and optimization of the functional network of gut microbiota and its interaction with the host. This will improve the ex situ protection of wild animals.

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Background: The incidence of triple negative breast cancer (TNBC) is at a relatively high level, and our study aimed to identify differentially expressed genes (DEGs) in TNBC and explore the key pathways and genes of TNBC. Methods: The gene expression profiling (GSE86945, GSE86946 and GSE102088) data were obtained from Gene Expression Omnibus Datasets, DEGs were identified by using R software, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools, and the protein-protein interaction (PPI) network of the DEGs was constructed by the STRING database and visualized by Cytoscape software. Finally, the survival value of hub DEGs in breast cancer patients were performed by the Kaplan–Meier plotter online tool. Results: A total of 2998 DEGs were identified between TNBC and health breast tissue, including 411 up-regulated DEGs and 2587 down-regulated DEGs. GO analysis results showed that down-regulated DEGs were enriched in gene expression (BP), extracellular exosome (CC), and nucleic acid binding, and up-regulated were enriched in chromatin assembly (BP), nucleosome (CC), and DNA binding (MF). KEGG pathway results showed that DEGs were mainly enriched in Pathways in cancer and Systemic lupus erythematosus and so on. Top 10 hub genes were picked out from PPI network by connective degree, and 7 of top 10 hub genes were significantly related with adverse overall survival in breast cancer patients ( P < .05). Further analysis found that only EGFR had a significant association with the prognosis of triple-negative breast cancer ( P < .05). Conclusions: Our study showed that DEGs were enriched in pathways in cancer, top 10 DEGs belong to up-regulated DEGs, and 7 gene connected with poor prognosis in breast cancer, including HSP90AA1 , SRC , HSPA8 , ESR1 , ACTB , PPP2CA , and RPL4 . These can provide some guidance for our research on the diagnosis and prognosis of TNBC, and further research is needed to evaluate their value in the targeted therapy of TNBC.

Background A growing body of evidence suggests that the gut microbiota is strongly linked to general human health. Microbiome-directed interventions, such as diet and exercise, are acknowledged as a viable and achievable strategy for preventing disorders and improving human health. However, due to the significant inter-individual diversity of the gut microbiota between subjects, lifestyle recommendations are expected to have distinct and highly variable impacts to the microbiome structure. Results Here, through a large-scale meta-analysis including 1448 shotgun metagenomics samples obtained longitudinally from 396 individuals during lifestyle studies, we revealed Bacteroides stercoris, Prevotella copri, and Bacteroides vulgatus as biomarkers of microbiota’s resistance to structural changes, and aromatic and non-aromatic amino acid biosynthesis as important regulator of microbiome dynamics. We established criteria for distinguishing between significant compositional changes from normal microbiota fluctuation and classified individuals based on their level of response. We further developed a machine learning model for predicting “responders” and “non-responders” independently of the type of intervention with an area under the curve of up to 0.86 in external validation cohorts of different ethnicities. Conclusions We propose here that microbiome-based stratification is possible for identifying individuals with highly plastic or highly resistant microbial structures. Identifying subjects that will not respond to generalized lifestyle therapeutic interventions targeting the restructuring of gut microbiota is important to ensure that primary end-points of clinical studies are reached.

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