ARG-ANNOT, a New Bioinformatic Tool To Discover Antibiotic Resistance Genes in Bacterial Genomes
Sushim K. Gupta,Babu Roshan Padmanabhan,Seydina M. Diene,Rafael López-Rojas,Marie Kempf,Luce Landraud,Jean-Marc Rolain +6 more
TLDR
A concise database for BLAST using a Bio-Edit interface that can detect AR genetic determinants in bacterial genomes and can rapidly and easily discover putative new AR geneticeterminants is created.Abstract:
ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation) is a new bioinformatic tool that was created to detect existing and putative new antibiotic resistance (AR) genes in bacterial genomes. ARG-ANNOT uses a local BLAST program in Bio-Edit software that allows the user to analyze sequences without a Web interface. All AR genetic determinants were collected from published works and online resources; nucleotide and protein sequences were retrieved from the NCBI GenBank database. After building a database that includes 1,689 antibiotic resistance genes, the software was tested in a blind manner using 100 random sequences selected from the database to verify that the sensitivity and specificity were at 100% even when partial sequences were queried. Notably, BLAST analysis results obtained using the rmtF gene sequence (a new aminoglycoside-modifying enzyme gene sequence that is not included in the database) as a query revealed that the tool was able to link this sequence to short sequences (17 to 40 bp) found in other genes of the rmt family with significant E values. Finally, the analysis of 178 Acinetobacter baumannii and 20 Staphylococcus aureus genomes allowed the detection of a significantly higher number of AR genes than the Resfinder gene analyzer and 11 point mutations in target genes known to be associated with AR. The average time for the analysis of a genome was 3.35 ± 0.13 min. We have created a concise database for BLAST using a Bio-Edit interface that can detect AR genetic determinants in bacterial genomes and can rapidly and easily discover putative new AR genetic determinants.read more
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The emergence of successful Streptococcus pyogenes lineages through convergent pathways of capsule loss and recombination directing high toxin expression
Claire E. Turner,Claire E. Turner,Matthew T. G. Holden,Matthew T. G. Holden,Beth Blane,Carolyne Horner,Sharon J. Peacock,Shiranee Sriskandan +7 more
TL;DR: It is found that recombination of this locus and promoter has occurred in other diverse genotypes, events that may allow these genotypes to expand in the population, and an association between the loss of hyaluronic acid capsule synthesis and high toxin expression is identified, which is proposed may be associated with an adaptive advantage.
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Molecular Identification and Selection of Probiotic Strains able to Reduce the Serum TMAO Level in Mice Challenged with Choline
Latha Ramireddy,Hau-Yang Tsen,Yu-Chen Chiang,Chen-Ying Hung,Shih-Rong Wu,San-Land Young,Jin-Seng Lin,Chien-Hsun Huang,Shih-Hau Chiu,Chien-Chi Chen,Chih-Chieh Chen +10 more
TL;DR: In this article, the authors suggest a method to select potential probiotic strains to reduce the serum concentration of TMAO in mice fed with choline, which may be due to the additive and synergetic effect and the contribution of strain LP1145 and Lamm1345.
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Phage-plasmids spread antibiotic resistance genes through infection and lysogenic conversion
Eugen Pfeifer,Robert Hamilton +1 more
TL;DR: In this article , a group of temperate bacterial viruses, termed phage-plasmids, commonly encode different and multiple types of resistance genes of high clinical importance, often in integrons.
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Small and equipped: the rich repertoire of antibiotic resistance genes in Candidate Phyla Radiation genomes
TL;DR: In this article, a thorough in-silico analysis on 4,062 Candidate Phyla Radiation (CPR) genomes was conducted to test whether these ultrasmall microorganisms might encode for antibiotic resistance (AR)-like enzymes.
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Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes
TL;DR: In this article , a machine learning framework is presented to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction, which leads to a performance improvement and more findings.
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