Planned Pharmacologic Target Entrez Gene Identifier

MADGene: retrieval and processing of gene identifier lists for the analysis of heterogeneous microarray datasets

Bioinformatics ◽

10.1093/bioinformatics/btq710 ◽

2011 ◽

Vol 27 (5) ◽

pp. 725-726 ◽

Cited By ~ 32

Author(s):

Daniel Baron ◽

Audrey Bihouée ◽

Raluca Teusan ◽

Emeric Dubois ◽

Frédérique Savagner ◽

...

Keyword(s):

Gene Identifier ◽

Microarray Datasets

Gene Identifier

10.32388/pwfj2c ◽

2020 ◽

Author(s):

Keyword(s):

Gene Identifier

Exploration of the Neisseria resistome reveals resistance mechanisms in commensals that may be acquired by N. gonorrhoeae through horizontal gene transfer

10.1101/2020.07.30.228593 ◽

2020 ◽

Author(s):

Michael A Fiore ◽

Jordan C Raisman ◽

Narayan H Wong ◽

André O Hudson ◽

Crista B Wadsworth

Keyword(s):

Antibiotic Resistance ◽

Antibiotic Resistance Genes ◽

Tetracycline Resistance ◽

Dna Gyrase ◽

Resistance Mechanisms ◽

Gene Identifier ◽

Prospective Surveillance ◽

Beta Lactamases ◽

Cephalosporin Resistance ◽

Resistance Protein

Non-pathogenic Neisseria have repeatedly been demonstrated to transfer antibiotic resistance genes to their pathogenic relative, Neisseria gonorrhoeae. However, the resistance genotypes and subsequent phenotypes of non-pathogens within the genus have been studied and described less frequently. Here, we use Etests to characterize the minimum inhibitory concentrations (MICs) of a panel of Neisseria (n=26) - including several commensal species - acquired from the CDC & FDA's Antibiotic Resistance (AR) Isolate Bank to a suite of diverse antibiotics. We furthermore use whole genome sequencing and the Comprehensive Antibiotic Resistance Database (CARD) Resistance Gene Identifier (RGI) platform to predict possible causal resistance-encoding mutations. Within this panel, resistant isolates to all tested antimicrobials including penicillin (n=5/26), ceftriaxone (n=2/26), cefixime (n=3/26), tetracycline (n=10/26), azithromycin (n=11/26), and ciprofloxacin (n=4/26) were found. In total we identify 63 distinct mutations predicted by RGI to be involved in resistance. The presence of several of these mutations had clear associations with increases in MIC such as: DNA gyrase subunit A (gyrA) (S91F) and ciprofloxacin, tetracycline resistance protein (tetM) and 30S ribosomal protein S10 (rpsJ) (V57M) and tetracycline, and TEM-type beta-lactamases and penicillin. However, mutations with strong associations to macrolide and cephalosporin resistance were not conclusive. This work serves as an initial exploration into the resistance-encoding mutations harbored by non-pathogenic Neisseria, which will ultimately aid in prospective surveillance for novel resistance mechanisms that may be rapidly acquired by N. gonorrhoeae.

In Silico Prediction and Comparison of Resistomes in Model Pseudomonas Strains by Resistance Gene Identifier (RGI)

10.1101/2021.11.15.468576 ◽

2021 ◽

Author(s):

Bharat BK Kwatra

Keyword(s):

Resistance Gene ◽

In Silico ◽

Plant Pathogens ◽

Antibiotic Resistance Gene ◽

In Silico Analysis ◽

Multi Drug Resistance ◽

Research Database ◽

Gene Identifier ◽

Pseudomonas Spp ◽

Plant Growth Promoting

Pseudomonas is a genus of bacteria including strains of human and plant pathogens, plant-growth promoting and biological control agents. While most Pseudomonas strains are known resistant to several antibiotics, their genetic elements conferring antimicrobial resistance (AMR) are largely unexplored systematically. The current study exploits a robust AMR gene predicting tool Resistance Gene Identifier of most recently updated version 5.2.0 based on newly curated database (the Comprehensive Antibiotic Research Database version 3.1.3) to detect AMR genes from thirteen genomes of Pseudomonas strains affiliated with seven species, including twelve pseudomonads as popularly studied model strains plus a well-known Pseudomonas protegens CHA0. A list of 281 AMR genes have been detected in five genomes of Pseudomonas aeruginosa, while 32 in the rest Pseudomonas spp. strains. Among the species, P. aeruginosa, P. fluorescens, P. protegens and P. stutzeri have the resistome of multi-drug resistance, while the rest is resistant to narrower spectrum of drugs. All Pseudomonas spp. investigated here have resistance genes to antibiotics classes of fluoroquinolone and tetracycline, which is consistent with an antibiotic resistance gene hit of adeF (ARO No. 3000777, resistant to fluoroquinolone, tetracycline) has found in high redundancy in almost all Pseudomonas species except P. aeruginosa and P. stutzeri, implying the limit of these classes of drugs for treating pseudomonads. While inter-species data were focused here, further analysis will be conducted to reveal the features of inter-strain level features of pseudomonads. The in silico analysis will complement wet-lab research for designing treating strategies of these bacteria.

Exploration of the Neisseria Resistome Reveals Resistance Mechanisms in Commensals That May Be Acquired by N. gonorrhoeae through Horizontal Gene Transfer

Antibiotics ◽

10.3390/antibiotics9100656 ◽

2020 ◽

Vol 9 (10) ◽

pp. 656

Author(s):

Michael A. Fiore ◽

Jordan C. Raisman ◽

Narayan H. Wong ◽

André O. Hudson ◽

Crista B. Wadsworth

Keyword(s):

Antibiotic Resistance ◽

Tetracycline Resistance ◽

Dna Gyrase ◽

Resistance Mechanisms ◽

Gene Identifier ◽

Prospective Surveillance ◽

Commensal Species ◽

Cephalosporin Resistance ◽

Resistance Protein ◽

Initial Exploration

Nonpathogenic Neisseria transfer mutations encoding antibiotic resistance to their pathogenic relative Neisseria gonorrhoeae. However, the resistance genotypes and subsequent phenotypes of nonpathogens within the genus have been described infrequently. Here, we characterize the minimum inhibitory concentrations (MICs) of a panel of Neisseria (n = 26)—including several commensal species—to a suite of diverse antibiotics. We furthermore use whole genome sequencing and the Comprehensive Antibiotic Resistance Database Resistance Gene Identifier (RGI) platform to predict putative resistance-encoding mutations. Resistant isolates to all tested antimicrobials including penicillin (n = 5/26), ceftriaxone (n = 2/26), cefixime (n = 3/26), tetracycline (n = 10/26), azithromycin (n = 11/26), and ciprofloxacin (n = 4/26) were found. In total, 63 distinct mutations were predicted by RGI to be involved in resistance. The presence of several mutations had clear associations with increased MIC such as DNA gyrase subunit A (gyrA) (S91F) and ciprofloxacin, tetracycline resistance protein (tetM) and 30S ribosomal protein S10 (rpsJ) (V57M) and tetracycline, and TEM-type β-lactamases and penicillin. However, mutations with strong associations to macrolide and cephalosporin resistance were not conclusive. This work serves as an initial exploration into the resistance-encoding mutations harbored by nonpathogenic Neisseria, which will ultimately aid in prospective surveillance for novel resistance mechanisms that may be rapidly acquired by N. gonorrhoeae.

In Silico Prediction and Comparison of Resistomes in Model Pseudomonas Strains by Resistance Gene Identifier (RGI)

ACTA SCIENTIFIC MICROBIOLOGY ◽

10.31080/asmi.2021.04.0951 ◽

2021 ◽

pp. 49-55

Author(s):

Huayu Qin ◽

Ming Liu ◽

Shiqi Xu

Keyword(s):

Resistance Gene ◽

In Silico ◽

In Silico Prediction ◽

Gene Identifier

CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database

Nucleic Acids Research ◽

10.1093/nar/gkz935 ◽

2019 ◽

Cited By ~ 94

Author(s):

Brian P Alcock ◽

Amogelang R Raphenya ◽

Tammy T Y Lau ◽

Kara K Tsang ◽

Mégane Bouchard ◽

...

Keyword(s):

Antibiotic Resistance ◽

Molecular Basis ◽

Reference Data ◽

Controlled Vocabulary ◽

Gene Identifier ◽

New Classification ◽

High Quality ◽

Reference Sequences ◽

Analytical Tools ◽

Statistical Summary

Abstract The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD’s Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.