Abundance and diversity of resistomes differ between healthy human oral cavities and gut

AbstractThe global threat of the “antimicrobial resistance apocalypse” that has arisen in recent years has driven the use of high-throughput sequencing techniques to monitor the profile of resistance genes, known as the “resistome”, in microbial populations. The human oral cavity contains a poorly explored reservoir of these genes, and little is known about their abundance and diversity, or how their profile compares with antimicrobial resistance genes in the gut. Here we analyse the resistome profiles of 788 oral cavities worldwide and compare these profiles with paired stool samples from shotgun metagenomic data. We find country and body site-specific differences in the prevalence of antimicrobial resistance genes, classes and mechanisms in oral and stool samples. However, the abundance of these antimicrobial resistance classes do not correlate with antibiotic prescription rates. A greater similarity was found in interpersonal resistomes between the same body sites than intrapersonal resistomes across different body sites. Between individuals, the oral cavity contains the highest and lowest abundances of specific antimicrobial resistance genes, but a lower diversity of resistance genes compared to the gut, which is likely influenced by differences in microbial composition and exposure to antimicrobial selection pressures. Co-occurrence analysis shows contrasting ARG-species associations between saliva and stool samples. This is the first study to date that characterises the oral cavity resistome worldwide, identifying its distinctive signatures compared to the gut.Maintenance and persistence of antimicrobial resistance is likely to vary across different body sites. Thus, we highlight the importance of characterising the resistome across body sites to uncover the antimicrobial resistance potential in the human body.

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

10.21203/rs.3.rs-17413/v1 ◽

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.

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Expression of virulence and antimicrobial resistance genes among Escherichia coli clinical isolates from blood and stool samples

Reviews in Medical Microbiology ◽

10.1097/mrm.0000000000000167 ◽

2019 ◽

Vol 30 (3) ◽

pp. 137-141

Author(s):

Ali Rajabi ◽

Hossein Rajabi-vardanjani ◽

Kobra Rastiyani ◽

Mais Emad Ahmed ◽

Seyede Amene Mirforughi ◽

...

Keyword(s):

Escherichia Coli ◽

Antimicrobial Resistance ◽

Resistance Genes ◽

Clinical Isolates ◽

Antimicrobial Resistance Genes ◽

Stool Samples

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Research and Technological Advances Regarding the Study of the Spread of Antimicrobial Resistance Genes and Antimicrobial-Resistant Bacteria Related to Animal Husbandry

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16244896 ◽

2019 ◽

Vol 16 (24) ◽

pp. 4896

Author(s):

Na Li ◽

Chong Liu ◽

Zhiguo Zhang ◽

Hongna Li ◽

Tingting Song ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Resistance Genes ◽

High Throughput Sequencing ◽

Animal Husbandry ◽

Control Measures ◽

Research Progress ◽

Future Research ◽

Resistant Bacteria ◽

Technological Advances ◽

Antimicrobial Resistance Genes

The extensive use of antimicrobials in animal farms poses serious safety hazards to both the environment and public health, and this trend is likely to continue. Antimicrobial resistance genes (ARGs) are a class of emerging pollutants that are difficult to remove once introduced. Understanding the environmental transfer of antimicrobial-resistant bacteria (ARB) and ARGs is pivotal for creating control measures. In this review, we summarize the research progress on the spread and detection of ARB and ARG pollution related to animal husbandry. Molecular methods such as high-throughput sequencing have greatly enriched the information about ARB communities. However, it remains challenging to delineate mechanisms regarding ARG induction, transmission, and tempo-spatial changes in the whole process, from animal husbandry to multiple ecosystems. As a result, future research should be more focused on the mechanisms of ARG induction, transmission, and control. We also expect that future research will rely more heavily on metagenomic -analysis, metatranscriptomic sequencing, and multi-omics technologies

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

10.1101/2020.03.24.006148 ◽

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.

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

10.1101/695973 ◽

2019 ◽

Cited By ~ 1

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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Metagenomic Approaches to Analyze Antimicrobial Resistance: An Overview

Frontiers in Genetics ◽

10.3389/fgene.2020.575592 ◽

2021 ◽

Vol 11 ◽

Author(s):

Vinicius A. C. de Abreu ◽

José Perdigão ◽

Sintia Almeida

Keyword(s):

Antimicrobial Resistance ◽

High Throughput Sequencing ◽

Genetic Recombination ◽

Public Health Problem ◽

Metagenomic Data ◽

Global Public Health ◽

Community Based ◽

Sequencing Technologies ◽

Antimicrobial Resistance Genes ◽

Functional Gene Analysis

Antimicrobial resistance is a major global public health problem, which develops when pathogens acquire antimicrobial resistance genes (ARGs), primarily through genetic recombination between commensal and pathogenic microbes. The resistome is a collection of all ARGs. In microorganisms, the primary method of ARG acquisition is horizontal gene transfer (HGT). Thus, understanding and identifying HGTs, can provide insight into the mechanisms of antimicrobial resistance transmission and dissemination. The use of high-throughput sequencing technologies has made the analysis of ARG sequences feasible and accessible. In particular, the metagenomic approach has facilitated the identification of community-based antimicrobial resistance. This approach is useful, as it allows access to the genomic data in an environmental sample without the need to isolate and culture microorganisms prior to analysis. Here, we aimed to reflect on the challenges of analyzing metagenomic data in the three main approaches for studying antimicrobial resistance: (i) analysis of microbial diversity, (ii) functional gene analysis, and (iii) searching the most complete and pertinent resistome databases.

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

Microbiome ◽

10.1186/s40168-020-00993-9 ◽

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.

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

10.21203/rs.3.rs-57299/v1 ◽

2020 ◽

Author(s):

Robert Pieper ◽

Temesgen Dadi ◽

Lukasz Grzeskowiak ◽

Laura Pieper ◽

Britta Siegmund ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Resistance Genes ◽

Strong Association ◽

Microbial Colonization ◽

Metagenomic Data ◽

Neonatal Piglets ◽

Clostridioides Difficile ◽

Gut Colonization ◽

Antimicrobial Resistance Genes ◽

Antimicrobial Resistance Gene

Abstract Background Clostridioides 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. A strong association between Escherichia, Streptococcus, Shigella, Enterococcus and Fusobacterium taxa with the expansion of the resistome in FP and FP-CD piglets were found.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.

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Detection of antimicrobial resistance genes in urban air

10.22541/au.163552322.23797169/v1 ◽

2021 ◽

Author(s):

Ágnes Becsei ◽

Norbert Solymosi ◽

István Csabai ◽

Donát Magyar

Keyword(s):

Antimicrobial Resistance ◽

Resistance Genes ◽

Pathogenic Bacteria ◽

Metagenomic Analysis ◽

Ion Torrent ◽

Outdoor Air ◽

Microbial Composition ◽

The Core ◽

Antimicrobial Resistance Genes ◽

Environmental Microbes

To understand antibiotic resistance in pathogenic bacteria, we need to monitor environmental microbes as reservoirs of antimicrobial resistance genes (ARGs). These bacteria are present in the air and can be investigated with the whole metagenome shotgun sequencing approach. This study aimed to investigate the feasibility of a method for metagenomic analysis of microbial composition and ARGs in the outdoor air. Air samples were collected with a Harvard impactor in the PM10 range at 50 m from a hospital in Budapest. From the DNA yielded from samples of PM10 fraction single-end reads were generated with an Ion Torrent sequencer. During the metagenomic analysis, reads were classified taxonomically. The core bacteriome was defined. Reads were assembled to contigs and the ARG content was analyzed. The dominant genera in the core bacteriome were Bacillus, Acinetobacter, Leclercia and Paenibacillus. Among the identified ARGs best hits were vanRA, Bla1, mphL, Escherichia coli EF-Tu mutants conferring resistance to Pulvomycin, BcI, FosB, and mphM. Despite the low DNA content of the samples of PM10 fraction, the number of detected airborne ARGs was surprisingly high.

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