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See detailPersistence of birth mode-dependent effects on gut microbiome composition, immune system stimulation and antimicrobial resistance during the first year of life
Busi, Susheel Bhanu UL; de Nies, Laura UL; Habier, Janine UL et al

in ISME Communications (2021)

Caesarean section delivery (CSD) disrupts mother-to-neonate transmission of specific microbial strains and functional repertoires as well as linked immune system priming. Here we investigate whether ... [more ▼]

Caesarean section delivery (CSD) disrupts mother-to-neonate transmission of specific microbial strains and functional repertoires as well as linked immune system priming. Here we investigate whether differences in microbiome composition and impacts on host physiology persist at 1 year of age. We perform high-resolution, quantitative metagenomic analyses of the gut microbiomes of infants born by vaginal delivery (VD) or by CSD, from immediately after birth through to 1 year of life. Several microbial populations show distinct enrichments in CSD-born infants at 1 year of age including strains of Bacteroides caccae, Bifidobacterium bifidum and Ruminococcus gnavus, whereas others are present at higher levels in the VD group including Faecalibacterium prausnitizii, Bifidobacterium breve and Bifidobacterium kashiwanohense. The stimulation of healthy donor-derived primary human immune cells with LPS isolated from neonatal stool samples results in higher levels of tumour necrosis factor alpha (TNF-α) in the case of CSD extracts over time, compared to extracts from VD infants for which no such changes were observed during the first year of life. Functional analyses of the VD metagenomes at 1 year of age demonstrate a significant increase in the biosynthesis of the natural antibiotics, carbapenem and phenazine. Concurrently, we find antimicrobial resistance (AMR) genes against several classes of antibiotics in both VD and CSD. The abundance of AMR genes against synthetic (including semi-synthetic) agents such as phenicol, pleuromutilin and diaminopyrimidine are increased in CSD children at day 5 after birth. In addition, we find that mobile genetic elements, including phages, encode AMR genes such as glycopeptide, diaminopyrimidine and multidrug resistance genes. Our results demonstrate persistent effects at 1 year of life resulting from birth mode-dependent differences in earliest gut microbiome colonisation. [less ▲]

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See detailPathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data
de Nies, Laura UL; Lopes, Sara; Busi, Susheel Bhanu UL et al

in Microbiome (2021)

Background Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors including bacterial toxins contribute to pathogenicity. Additionally, antimicrobial ... [more ▼]

Background Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors including bacterial toxins contribute to pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is essential to identify virulence factors and antimicrobial resistance genes in situ. At present, there is a clear lack of computational approaches to simultaneously identify these factors in metagenomic datasets. Results Here, we present PathoFact, a tool for the contextualized prediction of virulence factors, bacterial toxins, and antimicrobial resistance genes with high accuracy (0.921, 0.832 and 0.979, respectively) and specificity (0.957, 0.989 and 0.994). We evaluate the performance of PathoFact on simulated metagenomic datasets and perform a comparison to two other general workflows for the analysis of metagenomic data. PathoFact outperforms all existing workflows in predicting virulence factors and toxin genes. It performs comparably to one pipeline regarding the prediction of antimicrobial resistance while outperforming the others. We further demonstrate the performance of PathoFact on three publicly available case-control metagenomic datasets representing an actual infection as well as chronic diseases in which either pathogenic potential or bacterial toxins are hypothesized to play a role. In each case, we identify virulence factors and AMR genes which differentiated between the case and control groups, thereby revealing novel gene associations with the studied diseases. Conclusion PathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, bacterial toxins, and antimicrobial resistance genes in metagenomic data. Additionally, our tool combines the prediction of these pathogenicity factors with the identification of mobile genetic elements. This provides further depth to the analysis by considering the genomic context of the pertinent genes. Furthermore, PathoFact’s modules for virulence factors, toxins, and antimicrobial resistance genes can be applied independently, thereby making it a flexible and versatile tool. PathoFact, its models, and databases are freely available at https://pathofact.lcsb.uni.lu. [less ▲]

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