Plasmids in the human gut reveal neutral dispersal and recombination that is overpowered by inflammatory diseases

Plasmids are pivotal in driving bacterial evolution through horizontal gene transfer. Here, we investigated 3467 human gut microbiome samples across continents and disease states, analyzing 11,086 plasmids. Our analyses reveal that plasmid dispersal is predominantly stochastic, indicating neutral processes as the primary driver of their wide distribution. We find that only 20-25% of plasmid DNA is being selected in various disease states, constraining its distribution across hosts. Selective pressures shape specific plasmid segments with distinct ecological functions, influenced by plasmid mobilization lifestyle, antibiotic usage, and inflammatory gut diseases. Notably, these elements are more commonly shared within groups of individuals with similar health conditions, such as Inflammatory Bowel Disease (IBD), regardless of geographic location across continents. These segments contain essential genes such as iron transport mechanisms- a distinctive gut signature of IBD that impacts the severity of inflammation. Our findings shed light on mechanisms driving plasmid dispersal and selection in the human gut, highlighting their role as carriers of vital gene pools impacting bacterial hosts and ecosystem dynamics.

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Itzik Mizrahi Mar 3, 3, 2024
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Paired-end reads were trimmed and cleaned using Trim Galore v2.6 and assembled into contigs by by Megahit v1.0.3.Plasmids were assembled by by SCAPP v0.1.4.These plasmids were annotated or or assembled using additional programs: MOB-suite, Blastn, PlasForest, and PlasClass.The overlap between different plasmid assemblers was plotted using the "UpSetR" R package.Plasmids were deduplicated using BLASTn v2.10.1+, and reads were mapped to to them using BBmap v38.86.Their abundance per sample was determined using Metabat2 v2.12.1.The read coverage of of plasmids in each sample was computed by by SAMtools mpileup v1.10.We We utilized MOB-suite v3.0.3 to to classify plasmids as as mobilizable or or non-mobilizable.Open Reading Frames were predicted by by Prokka v1.12.Annotations were achieved using anvi'o v7.1.These steps were all run in in parallel using the NeatSeq-Flow workflow platform.AMR genes were predicted using the Resistance Gene Identifier (RGI) v6.0.1.Read taxonomies were determined by by MetaPhlAn v4.0.3.Segments were determined by by BLASTn results and clustered using cd-hit-est v4.8.1.Statistical analyses were carried out using R 3.5.1.Data manipulation was achieved using "tidyverse" and "dplyr" R packages.Graphs were created using R packages "ggplot2", statistics were plotted with "ggpubr" and the graphics were modified using "gghx4" and "ggtext".All enrichment tests were achieved using "clusterProfiler".Jaccard distances were calculated using the "vegan" R package.The network was created using "igraph", permuted with "BiRewire", and visualized using Cytoscape.Chi-square, Fisher's exact, and Wilcoxon rank-sum tests, as as well as as linear models and correlations, were calculated using the "stats" R package.The scripts to to execute the main analyses conducted in in this study are available on on GitHub.

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All studies must disclose on these points even when the disclosure is negative.Samples with read depths below 2 million were discarded from the analyses, as shallow depths may not provide sufficient coverage for accurate assembly of contigs and plasmids, compromising the reliability of downstream analyses.This exclusion resulted in 3,467 samples.
not applicable to this study, as the datasets were obtained based on predetermined disease states and participant numbers, precluding the need for these methodologies.
not applicable to this study, as the datasets were obtained based on predetermined disease states and participant numbers, precluding the need for these methodologies.
not applicable to this study, as the datasets were obtained based on predetermined disease states and participant numbers, precluding the need for these methodologies.
,588 samples were downloaded from the National Center for Biotechnology Information's (NCBI) Sequence Read Archive (SRA) from a total of 26 Bioprojects, spanning different continents and diseases associated with dysbiosis.This breadth of data ensured a comprehensive representation of various microbiome compositions and disease states, making it suitable for the analysis.