Dataset Information

Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance.

ABSTRACT: Diagnosing antimicrobial resistance (AMR) in the clinic is based on empirical evidence and current gold standard laboratory phenotypic methods. Genotypic methods have the potential advantages of being faster and cheaper, and having improved mechanistic resolution over phenotypic methods. We generated and applied rule-based and logistic regression models to predict the AMR phenotype from Escherichia coli and Pseudomonas aeruginosa multidrug-resistant clinical isolate genomes. By inspecting and evaluating these models, we identified previously unknown β-lactamase substrate activities. In total, 22 unknown β-lactamase substrate activities were experimentally validated using targeted gene expression studies. Our results demonstrate that generating and analysing predictive models can help guide researchers to the mechanisms driving resistance and improve annotation of AMR genes and phenotypic prediction, and suggest that we cannot solely rely on curated knowledge to predict resistance phenotypes.

SUBMITTER: Tsang KK

PROVIDER: S-EPMC8115898 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance.

Tsang Kara K KK Maguire Finlay F Zubyk Haley L HL Chou Sommer S Edalatmand Arman A Wright Gerard D GD Beiko Robert G RG McArthur Andrew G AG

Microbial genomics 20210101 1

Diagnosing antimicrobial resistance (AMR) in the clinic is based on empirical evidence and current gold standard laboratory phenotypic methods. Genotypic methods have the potential advantages of being faster and cheaper, and having improved mechanistic resolution over phenotypic methods. We generated and applied rule-based and logistic regression models to predict the AMR phenotype from Escherichia coli and Pseudomonas aeruginosa multidrug-resistant clinical isolate genomes. By ins ...[more]

PMID: 33416461

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Project description:Penicillin (PEN) is a low-cost option for anthrax treatment, but naturally occurring resistance has been reported. β-Lactamase expression (bla1, bla2) in Bacillus anthracis is regulated by a sigma factor (SigP) and its cognate anti-sigma factor (RsiP). Mutations leading to truncation of RsiP were previously described as a basis for PEN resistance. Here, we analyze whole-genome sequencing (WGS) data and compare the chromosomal sigP-bla1 regions from 374 B. anthracis strains to determine the frequency of mutations, identify mutations associated with PEN resistance, and evaluate the usefulness of WGS for predicting PEN resistance. Few (3.5%) strains contained at least 1 of 11 different mutations in sigP, rsiP, or bla1. Nine of these mutations have not been previously associated with PEN resistance. Four strains showed PEN resistance (PEN-R) by conventional broth microdilution, including 1 strain with a novel frameshift in rsiP. One strain that carries the same rsiP frameshift mutation as that found previously in a PEN-R strain showed a PEN-susceptible (PEN-S) phenotype and exhibited decreased bla1 and bla2 transcription. An unexpectedly small colony size, a reduced growth rate, and undetectable β-lactamase activity levels (culture supernatant and cell lysate) were observed in this PEN-S strain. Sequence analysis revealed mutations in genes associated with growth defects that may contribute to this phenotype. While B. anthracis rsiP mutations cannot be exclusively used to predict resistance, four of the five strains with rsiP mutations were PEN-R. Therefore, the B. anthracis sigP-bla1 region is a useful locus for WGS-based PEN resistance prediction, but phenotypic testing remains essential. IMPORTANCE Determination of antimicrobial susceptibility of B. anthracis is essential for the appropriate distribution of antimicrobial agents for postexposure prophylaxis (PEP) and treatment of anthrax. Analysis of WGS data allows for the rapid detection of mutations in antimicrobial resistance (AMR) genes in an isolate, but the presence of a mutation in an AMR gene does not always accurately predict resistance. As mutations in the anti-sigma factor RsiP have been previously associated with high-level penicillin resistance in a limited number of strains, we investigated WGS assemblies from 374 strains to determine the frequency of mutations and performed functional antimicrobial susceptibility testing. Of the five strains that contained mutations in rsiP, only four were PEN-R by functional antimicrobial susceptibility testing. We conclude that while sequence analysis of this region is useful for AMR prediction in B. anthracis, genetic analysis should not be used exclusively and phenotypic susceptibility testing remains essential.

Project description:AmpC is a group I, class C -lactamase present in most Enterobacteriaceae and in Pseudomonas aeruginosa and other nonfermenting gram-negative bacilli. The β-lactam class of antibiotics is one of the most important structural classes of antibacterial compounds and act by inhibiting the bacterial D ,D - transpeptidases that are responsible for the final step of peptidoglycan cross-linking. Our main aim in the study is to screen possible inhibitors against AmpC / β - lactamase (an enzyme responsible for antimicrobial activity in Pseudomonas aeruginosa), through virtual screening of 1364 NCI (National Cancer Institute) diversity set II compounds. Homology Model of AmpC / β - lactamase was constructed using MODELLER and the Model was validated using PROCHECK and Verify 3D programs to obtain a stable structure, which was further used for virtual screening of NCI (National Cancer Institute) diversity set II compounds through molecular Docking studies using Autodock. The amino acid sequence of the β - lactamase was also subjected to ScanProsite web server to find any pattern present in the sequence. After the prediction of 3-dimensional model of AmpC/ β-lactamase, the possible Active sites ofβ - lactamase were determined using LIGSITE(csc) and CastP web servers simultaneously. The Docked complexes were validated and Enumerated based on the Autodock Scoring function to pick out the best inhibitor based on Autodock energy score. Thus from the entire 1364 NCI diversity set II compounds which were Docked, the best four docking solutions were selected (ZINC12670903, ZINC17465965, ZINC11681166 and ZINC13099024). Further the Complexes were analyzed through LIGPLOT for their interaction for the 4 best docked NCI diversity set II compounds. Thus from the Complex scoring and binding ability it is deciphered that these NCI diversity set II compounds could be promising inhibitors for Pseudomonas aeruginosa using AmpC /β - lactamase as Drug target yet pharmacological studies have to confirm it.

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Identifying novel β-lactamase substrate activity through in silico prediction of antimicrobial resistance.

Publications

Identifying novel β-lactamase substrate activity through <i>in silico</i> prediction of antimicrobial resistance.

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