scholarly journals Global distribution of mcr gene variants in 214,095 metagenomic samples

2022 ◽  
Author(s):  
Hannah-Marie Martiny ◽  
Patrick Munk ◽  
Christian Brinch ◽  
Judit Szarvas ◽  
Frank Aarestrup ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Large Scale ◽  
Metagenomic Data ◽  
Gene Variants ◽  
Genomic Context ◽  
Antimicrobial Resistance Genes ◽  
History Of ◽  
Metagenomic Study ◽  
Initial Discovery ◽  
Plasmid Replicons

Abstract Since the initial discovery of a mobilized colistin resistance gene (mcr-1), several other variants have been reported, some of which might have circulated a while before being discovered. Metagenomic data provides an opportunity to re-analyze available older data to understand the evolutionary history of recently discovered antimicrobial resistance genes (ARGs). Here, we present a large-scale metagenomic study of 442 Tbp of sequencing reads from 214,095 samples to identify the host and geographical distribution and genomic context of nine mcr gene variants (mcr-1 to mcr-9). Our results show that the dissemination of each variant is not uniform. Instead, the source and location play a role in the spread. Despite the very diverse distribution, the genomic background of the mcr genes remains unchanged as the same mobile genetic elements and plasmid replicons occur. This work emphasizes the importance of sharing genomic data for surveillance of ARGs in our fight against antimicrobial resistance.

scholarly journals Global distribution of mcr gene variants in 214,095 metagenomic samples

2021 ◽  
Author(s):  
Hannah-Marie Martiny ◽  
Patrick Munk ◽  
Christian Brinch ◽  
Judit Szarvas ◽  
Frank Aarestrup ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Large Scale ◽  
Metagenomic Data ◽  
Gene Variants ◽  
Genomic Context ◽  
Antimicrobial Resistance Genes ◽  
History Of ◽  
Metagenomic Study ◽  
Initial Discovery ◽  
Plasmid Replicons

Abstract Since the initial discovery of a mobilized colistin resistance gene (mcr-1), several other variants have been reported, some of which might have circulated a while before being discovered. Metagenomic data provides an opportunity to re-analyze available older data to understand the evolutionary history of recently discovered antimicrobial resistance genes (ARGs). Here, we present a large-scale metagenomic study of 442 Tbp of sequencing reads from 214,095 samples to identify the host and geographical distribution and genomic context of nine mcr gene variants (mcr-1 to mcr-9). Our results show that the dissemination of each variant is not uniform. Instead, the source and location play a role in the spread. Despite the very diverse distribution, the genomic background of the mcr genes remains unchanged as the same mobile genetic elements and plasmid replicons occur. This work emphasizes the importance of sharing genomic data for surveillance of ARGs in our fight against antimicrobial resistance.

scholarly journals PathoFact: A pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data

2020 ◽  
Author(s):  
Laura de Nies ◽  
Sara Lopes ◽  
Anna Heintz-Buschart ◽  
Cedric Christian Laczny ◽  
Patrick May ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Virulence Factors ◽  
Resistance Genes ◽  
Bacterial Toxins ◽  
Metagenomic Data ◽  
Genomic Context ◽  
Pathogenic Potential ◽  
Signature Genes ◽  
Antimicrobial Resistance Genes ◽  
Versatile Tool

AbstractBackgroundPathogenic microorganisms cause disease by invading, colonizing and damaging their host. Virulence factors including bacterial toxins contribute to their pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is therefore essential to identify virulence factors and antimicrobial resistance genes in metagenomic datasets. At present, there is a clear lack of computational approaches to simultaneously identifying these factors. Here we present PathoFact, a tool for the contextualized prediction of virulence factors and antimicrobial resistance genes in metagenomic data.ResultsPathoFact predicts virulence factors, bacterial toxins and antimicrobial resistance genes with high accuracy (0.92, 0.83 and 0.99) and specificity (0.96, 0.99 and 0.98), respectively. The performance of PathoFact was furthermore demonstrated 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 were predicted to play a role. With PathoFact, we identified virulence factors (including toxins) and antimicrobial resistance genes, and identified signature genes which differentiated between the disease and control groups.ConclusionPathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, toxins and antimicrobial resistance genes in metagenomic data. Additionally, PathoFact 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, each module (virulence factors, toxin and antimicrobial resistance genes) of PathoFact is also a standalone component, making it a flexible and versatile tool. PathoFact is freely available online at https://git-r3lab.uni.lu/laura.denies/PathoFact.

scholarly journals PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Laura de Nies ◽  
Sara Lopes ◽  
Susheel Bhanu Busi ◽  
Valentina Galata ◽  
Anna Heintz-Buschart ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Virulence Factors ◽  
Resistance Genes ◽  
Bacterial Toxins ◽  
Metagenomic Data ◽  
Genomic Context ◽  
Pathogenic Potential ◽  
Antimicrobial Resistance Genes ◽  
Versatile Tool ◽  
And Control

Abstract 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.

Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Akshatha Prasanna ◽  
Vidya Niranjan
Keyword(s):  
Antimicrobial Resistance ◽  
High Performance ◽  
Pathogen Detection ◽  
Bacterial Species ◽  
Workflow Management ◽  
Metagenomic Data ◽  
Antimicrobial Resistance Genes ◽  
Culture Independent ◽  
Automated Pipeline ◽  
User Friendly

Background: Since bacteria are the earliest known organisms, there has been significant interest in their variety and biology, most certainly concerning human health. Recent advances in Metagenomics sequencing (mNGS), a culture-independent sequencing technology have facilitated an accelerated development in clinical microbiology and our understanding of pathogens. Objective: For the implementation of mNGS in routine clinical practice to become feasible, a practical and scalable strategy for the study of mNGS data is essential. This study presents a robust automated pipeline to analyze clinical metagenomic data for pathogen identification and classification. Method: The proposed Clin-mNGS pipeline is an integrated, open-source, scalable, reproducible, and user-friendly framework scripted using the Snakemake workflow management software. The implementation avoids the hassle of manual installation and configuration of the multiple command-line tools and dependencies. The approach directly screens pathogens from clinical raw reads and generates consolidated reports for each sample. Results: The pipeline is demonstrated using publicly available data and is tested on a desktop Linux system and a High-performance cluster. The study compares variability in results from different tools and versions. The versions of the tools are made user modifiable. The pipeline results in quality check, filtered reads, host subtraction, assembled contigs, assembly metrics, relative abundances of bacterial species, antimicrobial resistance genes, plasmid finding, and virulence factors identification. The results obtained from the pipeline are evaluated based on sensitivity and positive predictive value. Conclusion: Clin-mNGS is an automated Snakemake pipeline validated for the analysis of microbial clinical metagenomics reads to perform taxonomic classification and antimicrobial resistance prediction.

scholarly journals Genomic analysis reveals fifty years of antimicrobial resistance evolution in husbandry Escherichia coli from China

2021 ◽  
Author(s):  
Lu Yang ◽  
Yingbo Shen ◽  
Junyao Jiang ◽  
Xueyang Wang ◽  
Dongyan Shao ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Antimicrobial Resistance ◽  
Large Scale ◽  
Antimicrobial Agents ◽  
Bacterial Genome ◽  
Careful Consideration ◽  
Meat Production ◽  
Antimicrobial Use ◽  
Sequencing Analysis ◽  
Antimicrobial Resistance Genes

Abstract Antimicrobial agents have been used in meat production for decades and its consumption is considered an key driver for the emergence and dissemination of antimicrobial resistance (AMR). However, large-scale studies on AMR changes in animal isolates since the introduction of antimicrobial usage remain scarce. We applied whole genome sequencing analysis to 982 animal-derived Escherichia coli collected in China from 1970s to 2019 and found increasing trends for the presence of numerous antimicrobial resistance genes (ARGs), including those conferring resistance to critically important agents for veterinary (florfenicol and norfloxacin) and human medicine (colistin, cephalosporins, and meropenem). Extensive diversity and increasing complexity of ARGs and their associated mobile genetic elements (MGEs) such as plasmids were also observed. The plasmids, IncC, IncHI2, IncK, IncI, IncX and IncF played a key role as highly effective vehicles for disseminating ARGs. Correlation analysis also revealed an association between antimicrobial production and emergence of ARGs at a spatial and temporal level. Prohibiting or strictly curtailing antimicrobial use in animals will potentially negate the current trends of AMR as the bacterial genome is highly changeable and using different drugs of the same class, or even unrelated classes, may co-select for MGEs carrying a plethora of co-existing ARGs. Therefore, limiting or ceasing antimicrobial use in animals to control AMR requires careful consideration.

Formula-fed and Clostridium difficile infected neonatal piglets carry a highly diverse and abundant set of antimicrobial resistance genes

2020 ◽  
Author(s):  
Robert Pieper ◽  
Temesgen Dadi ◽  
Lukasz Grzeskowiak ◽  
Laura Pieper ◽  
Britta Siegmund ◽  
...  
Keyword(s):  
Clostridium Difficile ◽  
Antimicrobial Resistance ◽  
Resistance Genes ◽  
Microbial Colonization ◽  
Metagenomic Data ◽  
Neonatal Piglets ◽  
Gut Colonization ◽  
Antimicrobial Resistance Genes ◽  
Antimicrobial Resistance Gene ◽  
Genes Encoding

Abstract Background: Clostridium difficile infection (CDI) is an increasing zoonotic health threat and has also been documented as a cause of enteritis outbreaks in neonatal pigs. Furthermore, CDI in neonatal piglets cause changes in microbial gut colonization. We hypothesized that an imbalanced microbial colonization in piglets with CDI could be associated with an altered abundance of antimicrobial resistance genes. Results: We analyzed fecal metagenomic data of lactating sows (S), their piglets during suckling (SP), the same piglets two weeks after weaning (WP), 5-day old artificially reared and formula-fed siblings (FP) and FP infected with C. difficile (FP-CD) for microbiota composition and antimicrobial resistance gene abundance. FP and FP-CD piglets had an immature-type microbiota and increased abundance of antimicrobial resistance genes. A co-occurrence of genes encoding for resistance against aminoglycosides (e.g. aph(3”)-lb, aph(6)-ld, ant(2”)-la), β-lactams (blaCTX-M, blaTEM), fluoroquinolones (pat(A) macrolides (mph(A)), sulfonamides (sul1, sul2), polypeptides (e.g. pmrB, pmrC, arnA, bac(A)) and tetracyclines (e.g. tet(A-D),) was observed. Conclusion: Increased abundance of antimicrobial resistance genes in formula feeding and concomitant CDI may be associated with therapeutic resistance later in life and warrant further studies.

scholarly journals Evolution of ColE1-like plasmids across γ-Proteobacteria: From bacteriocin production to antimicrobial resistance

PLoS Genetics ◽  
2021 ◽  
Vol 17 (11) ◽  
pp. e1009919
Author(s):  
Manuel Ares-Arroyo ◽  
Eduardo P. C. Rocha ◽  
Bruno Gonzalez-Zorn
Keyword(s):  
Public Health ◽  
Antimicrobial Resistance ◽  
Resistance Genes ◽  
Evolutionary History ◽  
Hidden Markov ◽  
Bacteriocin Production ◽  
Antimicrobial Resistance Genes ◽  
History Of ◽  
Functional Shift ◽  
First Time

Antimicrobial resistance is one of the major threats to Public Health worldwide. Understanding the transfer and maintenance of antimicrobial resistance genes mediated by mobile genetic elements is thus urgent. In this work, we focus on the ColE1-like plasmid family, whose distinctive replication and multicopy nature has given rise to key discoveries and tools in molecular biology. Despite being massively used, the hosts, functions, and evolutionary history of these plasmids remain poorly known. Here, we built specific Hidden Markov Model(HMM) profiles to search ColE1 replicons within genomes. We identified 1,035 ColE1 plasmids in five Orders of γ-Proteobacteria, several of which are described here for the first time. The phylogenetic analysis of these replicons and their characteristic MOBP5/HEN relaxases suggest that ColE1 plasmids have diverged apart, with little transfer across orders, but frequent transfer across families. Additionally, ColE1 plasmids show a functional shift over the last decades, losing their characteristic bacteriocin production while gaining several antimicrobial resistance genes, mainly enzymatic determinants and including several extended-spectrum betalactamases and carbapenemases. Furthermore, ColE1 plasmids facilitate the intragenomic mobilization of these determinants, as various replicons were identified co-integrated with large non-ColE1 plasmids, mostly via transposases. These results illustrate how families of plasmids evolve and adapt their gene repertoires to bacterial adaptive requirements.

scholarly journals Large-Scale Identification of AbaR-Type Genomic Islands in Acinetobacter baumannii Reveals Diverse Insertion Sites and Clonal Lineage-Specific Antimicrobial Resistance Gene Profiles

2019 ◽  
Vol 63 (4) ◽  
Author(s):  
Dexi Bi ◽  
Ruting Xie ◽  
Jiayi Zheng ◽  
Huiqiong Yang ◽  
Xingchen Zhu ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Resistance Gene ◽  
Large Scale ◽  
Genomic Islands ◽  
Clonal Lineage ◽  
Content Type ◽  
Gene Profiles ◽  
Antimicrobial Resistance Genes ◽  
Antimicrobial Resistance Gene ◽  
Insertion Sites

ABSTRACT AbaR-type genomic islands (AbaRs) are important elements responsible for antimicrobial resistance in Acinetobacter baumannii. This study performed a large-scale identification of AbaRs to understand their distribution and compositions of antimicrobial resistance genes. We identified 2.89-kb left-end and 1.87-kb right-end conserved sequences (CSs) and developed a bioinformatics approach to identify AbaRs, using the CSs as signatures, in 3,148 publicly available genomes. AbaRs were prevalent in A. baumannii, being found in 2,091 genomes. They were sparse in other Acinetobacter species and confined only to this genus. Results from 111 complete genomes showed that over 85% of AbaRs resided on chromosomes. The external flanks adjacent to the inverted repeats available in all identified CSs were mapped to an AbaR-free chromosome or searched in the NCBI database for empty loci to define insertion sites. Surprisingly, 84 insertion sites with diverse origins were revealed, including 51 scattered on the chromosome, 20 plasmid borne, 12 located on prophages, transposons, ISAba1, complex AbaRs, and genomic islands of other types, and one uncharacterized, and some were strongly associated with clonal lineages. Finally, we found 994 antimicrobial resistance genes covering 28 unique genes from 70.9% (299/422) of intact AbaRs currently available. The resistance gene profiles displayed an apparent clonal lineage-specific pattern, highlighting the distinct features of AbaRs in global clone 1 (GC1) and GC2. The tet(B) gene was highly specific to the AbaRs in GC2. In conclusion, AbaRs have diverse insertion sites on the chromosome and mobile genetic elements (MGEs) and display distinct antimicrobial resistance gene profiles in different clonal lineages.

scholarly journals Spatiotemporal persistence of multiple, diverse clades and toxins of Corynebacterium diphtheriae

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Robert C. Will ◽  
Thandavarayan Ramamurthy ◽  
Naresh Chand Sharma ◽  
Balaji Veeraraghavan ◽  
Lucky Sangal ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Antimicrobial Resistance ◽  
Resistance Genes ◽  
Corynebacterium Diphtheriae ◽  
Core Gene ◽  
Genomic Variation ◽  
Gene Variants ◽  
Antimicrobial Resistance Genes ◽  
Structural Impact ◽  
High Level

AbstractDiphtheria is a respiratory disease caused by the bacterium Corynebacterium diphtheriae. Although the development of a toxin-based vaccine in the 1930s has allowed a high level of control over the disease, cases have increased in recent years. Here, we describe the genomic variation of 502 C. diphtheriae isolates across 16 countries and territories over 122 years. We generate a core gene phylogeny and determine the presence of antimicrobial resistance genes and variation within the tox gene of 291 tox+ isolates. Numerous, highly diverse clusters of C. diphtheriae are observed across the phylogeny, each containing isolates from multiple countries, regions and time of isolation. The number of antimicrobial resistance genes, as well as the breadth of antibiotic resistance, is substantially greater in the last decade than ever before. We identified and analysed 18 tox gene variants, with mutations estimated to be of medium to high structural impact.

scholarly journals Unravelling native plant resistomes – The Sphagnum microbiome harbours versatile and novel antimicrobial resistance genes

2019 ◽  
Author(s):  
Melanie-Maria Obermeier ◽  
Julian Taffner ◽  
Alessandro Bergna ◽  
Anja Poehlein ◽  
Tomislav Cernava ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Antimicrobial Resistance ◽  
Resistance Genes ◽  
Resistance Mechanisms ◽  
Culture Collection ◽  
Metagenomic Data ◽  
Native Plant ◽  
Resistance Determinants ◽  
Target Spectrum ◽  
Antimicrobial Resistance Genes

The expanding antibiotic resistance crisis calls for a more in depth understanding of the importance of antimicrobial resistance genes (ARGs) in pristine environments. We, therefore, studied the microbiota associated with Sphagnum forming the main vegetation in undomesticated, evolutionary old bog ecosystems. In our complementary analysis of a culture collection, metagenomic data and a fosmid library, we identified a low abundant but highly diverse pool of resistance determinants, which targets an unexpected broad range of antibiotics including natural and synthetic compounds. This derives both, from the extraordinarily high abundance of efflux pumps (80%), and the unexpectedly versatile set of ARGs underlying all major resistance mechanisms. The overall target spectrum of detected resistance determinants spans 21 antibiotic classes, whereby β-lactamases and vancomycin resistance appeared as the predominant resistances in all screenings. Multi-resistance was frequently observed among bacterial isolates, e.g. in Serratia, Pandorea, Paraburkhotderia and Rouxiella. In a search for novel ARGs we identified the new class A β-lactamase Mm3. The native Sphagnum resistome comprising a highly diversified and partially novel set of ARGs contributes to the bog ecosystem’s plasticity. Our results shed light onto the antibiotic resistance background of non-agricultural plants and highlight the ecological link between natural and clinically relevant resistomes.

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