scholarly journals * Virulence factors, antimicrobial resistance genes and protein structure of crohn's disease-derived

Gut ◽  
2011 ◽  
Vol 60 (Suppl 1) ◽  
pp. A202-A203
Author(s):  
T. Elliott ◽  
B. Hudspith ◽  
N. Rayment ◽  
L. Randall ◽  
G. Wu ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Protein Structure ◽  
Antimicrobial Resistance ◽  
Crohn's Disease ◽  
Virulence Factors ◽  
Resistance Genes ◽  
Antimicrobial Resistance Genes

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 Genomic Investigation of Antimicrobial-Resistant Salmonella enterica Isolates From Dead Chick Embryos in China

2021 ◽  
Vol 12 ◽  
Author(s):  
Mohammed Elbediwi ◽  
Yanting Tang ◽  
Dawei Shi ◽  
Hazem Ramadan ◽  
Yaohui Xu ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Chick Embryo ◽  
Virulence Factors ◽  
Resistance Genes ◽  
Salmonella Enterica ◽  
Multidrug Resistant ◽  
Henan Province ◽  
Health Concern ◽  
Antimicrobial Resistance Genes ◽  
Genomic Characterization

Salmonella spp. is recognized as an important zoonotic pathogen. The emergence of antimicrobial resistance in Salmonella enterica poses a great public health concern worldwide. While the knowledge on the incidence and the characterization of different S. enterica serovars causing chick embryo death remains obscure in China. In this study, we obtained 45 S. enterica isolates from 2,139 dead chick embryo samples collected from 28 breeding chicken hatcheries in Henan province. The antimicrobial susceptibility assay was performed by the broth microdilution method and the results showed that 31/45 (68.8%) isolates were multidrug-resistant (≥3 antimicrobial classes). Besides the highest resistance rate was observed in the aminoglycoside class, all the isolates were susceptible to chloramphenicol, azithromycin, and imipenem. Furthermore, genomic characterization revealed that S. Enteritidis (33.33%; 15/45) was a frequent serovar that harbored a higher number of virulence factors compared to other serovars. Importantly, genes encoding β-lactamases were identified in three serovars (Thompson, Enteritidis, and Kottbus), whereas plasmid-mediated quinolone resistance genes (qnrB4) were detected in certain isolates of S. Thompson and the two S. Kottbus isolates. All the examined isolates harbored the typical virulence factors from Salmonella pathogenicity islands 1 and 2 (SPI-1 and SPI-2). Additionally, a correlation analysis between the antimicrobial resistance genes, phenotype, and plasmids was conducted among Salmonella isolates. It showed strong positive correlations (r < 0.6) between the different antimicrobial-resistant genes belonging to certain antimicrobial classes. Besides, IncF plasmid showed a strong negative correlation (r > −0.6) with IncHI2 and IncHI2A plasmids. Together, our study demonstrated antimicrobial-resistant S. enterica circulating in breeding chicken hatcheries in Henan province, highlighting the advanced approach, by using genomic characterization and statistical analysis, in conducting the routine monitoring of the emerging antimicrobial-resistant pathogens. Our findings also proposed that the day-old breeder chicks trading could be one of the potential pathways for the dissemination of multidrug-resistant S. enterica serovars.

scholarly journals Screening for Antimicrobial Resistance Genes and Virulence Factors via Genome Sequencing

2011 ◽  
Vol 77 (8) ◽  
pp. 2785-2787 ◽  
Author(s):  
Mads Bennedsen ◽  
Birgitte Stuer-Lauridsen ◽  
Morten Danielsen ◽  
Eric Johansen
Keyword(s):  
Antimicrobial Resistance ◽  
Genome Sequencing ◽  
Virulence Factors ◽  
Virulence Factor ◽  
Resistance Genes ◽  
In Silico ◽  
Human Nutrition ◽  
Content Type ◽  
Antimicrobial Resistance Genes ◽  
Virulence Factor Genes

ABSTRACTSecond-generation genome sequencing and alignment of the resulting reads toin silicogenomes containing antimicrobial resistance and virulence factor genes were used to screen for undesirable genes in 28 strains which could be used in human nutrition. No virulence factor genes were detected, while several isolates contained antimicrobial resistance genes.

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.

scholarly journals Distribution and Clonal Diversity of Staphylococcus aureus and Other Staphylococci in Surface Waters: Detection of ST425-t742 and ST130-t843 mecC-Positive MRSA Strains

Antibiotics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1416
Author(s):  
Vanessa Silva ◽  
Eugénia Ferreira ◽  
Vera Manageiro ◽  
Lígia Reis ◽  
María Teresa Tejedor-Junco ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Multidrug Resistance ◽  
Antimicrobial Resistance ◽  
Virulence Factors ◽  
Resistance Genes ◽  
Surface Waters ◽  
Water Samples ◽  
Coagulase Negative Staphylococci ◽  
Spa Typing ◽  
Antimicrobial Resistance Genes

Natural aquatic environments represent one of the most important vehicles of bacterial dissemination. Therefore, we aimed to isolate staphylococci from surface waters and to investigate the presence of antimicrobial resistance genes and virulence factors as well as the genetic lineages of all Staphylococcus aureus isolates. Staphylococci were recovered from water samples collected from 78 surface waters, including rivers, streams, irrigation ditches, dams, lakes, and fountains. The presence of antimicrobial resistance genes and virulence factors was investigated by PCR. Multilocus sequence typing and spa-typing were performed in all S. aureus isolates. From the 78 water samples, 33 S. aureus, one S. pseudintermedius, and 51 coagulase-negative staphylococci (CoNS) were identified. Among the S. aureus isolates, four MRSA were identified, and all harbored the mecC gene. Fourteen S. aureus were susceptible to all antimicrobials tested and the remaining showed resistance to penicillin, erythromycin and/or tetracycline encoded by the blaZ, ermT, msr(A/B), tetL, and vgaA genes. Regarding the clonal lineages, one mecC-MRSA isolate belonged to spa-type t843 and sequence type (ST) 130 and the other three to t742 and ST425. The remaining S. aureus were ascribed 14 spa-types and 17 sequence types. Eleven species of CoNS were isolated: S. sciuri, S. lentus, S. xylosus, S. epidermidis, S. cohnii spp. urealyticus, S. vitulinus, S. caprae, S. carnosus spp. Carnosus, S. equorum, S. simulans, and S. succinus. Thirteen CoNS isolates had a multidrug resistance profile and carried the following genes: mecA, msr(A/B), mph(C), aph(3′)-IIIa, aac(6′)-Ie–aph(2′’)-Ia, dfrA, fusB, catpC221, and tetK. A high diversity of staphylococci was isolated from surface waters including mecCMRSA strains and isolates presenting multidrug-resistance profiles. Studies on the prevalence of antibiotic-resistant staphylococci in surface waters are still very scarce but extremely important to estimate the contribution of the aquatic environment in the spread of these bacteria.

scholarly journals Culture-Independent Genotyping, Virulence and Antimicrobial Resistance Gene Identification of Staphylococcus aureus from Orthopaedic Implant-Associated Infections

2021 ◽  
Vol 9 (4) ◽  
pp. 707
Author(s):  
J. Christopher Noone ◽  
Fabienne Antunes Ferreira ◽  
Hege Vangstein Aamot
Keyword(s):  
Staphylococcus Aureus ◽  
Antimicrobial Resistance ◽  
Resistance Genes ◽  
Virulence Gene ◽  
Orthopaedic Implant ◽  
Metagenomic Sequencing ◽  
Gene Detection ◽  
Shotgun Metagenomic Sequencing ◽  
Antimicrobial Resistance Genes ◽  
Culture Independent

Our culture-independent nanopore shotgun metagenomic sequencing protocol on biopsies has the potential for same-day diagnostics of orthopaedic implant-associated infections (OIAI). As OIAI are frequently caused by Staphylococcus aureus, we included S. aureus genotyping and virulence gene detection to exploit the protocol to its fullest. The aim was to evaluate S. aureus genotyping, virulence and antimicrobial resistance genes detection using the shotgun metagenomic sequencing protocol. This proof of concept study included six patients with S. aureus-associated OIAI at Akershus University Hospital, Norway. Five tissue biopsies from each patient were divided in two: (1) conventional microbiological diagnostics and genotyping, and whole genome sequencing (WGS) of S. aureus isolates; (2) shotgun metagenomic sequencing of DNA from the biopsies. Consensus sequences were analysed using spaTyper, MLST, VirulenceFinder, and ResFinder from the Center for Genomic Epidemiology (CGE). MLST was also compared using krocus. All spa-types, one CGE and four krocus MLST results matched Sanger sequencing results. Virulence gene detection matched between WGS and shotgun metagenomic sequencing. ResFinder results corresponded to resistance phenotype. S. aureus spa-typing, and identification of virulence and antimicrobial resistance genes are possible using our shotgun metagenomics protocol. MLST requires further optimization. The protocol has potential application to other species and infection types.

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