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In silico serine ?-lactamases analysis reveals a huge potential resistome in environmental and pathogenic species.

ABSTRACT: The secretion of antimicrobial compounds is an ancient mechanism with clear survival benefits for microbes competing with other microorganisms. Consequently, mechanisms that confer resistance are also ancient and may represent an underestimated reservoir in environmental bacteria. In this context, ?-lactamases (BLs) are of great interest due to their long-term presence and diversification in the hospital environment, leading to the emergence of Gram-negative pathogens that are resistant to cephalosporins (extended spectrum BLs?=?ESBLs) and carbapenems (carbapenemases). In the current study, protein sequence databases were used to analyze BLs, and the results revealed a substantial number of unknown and functionally uncharacterized BLs in a multitude of environmental and pathogenic species. Together, these BLs represent an uncharacterized reservoir of potentially transferable resistance genes. Considering all available data, in silico approaches appear to more adequately reflect a given resistome than analyses of limited datasets. This approach leads to a more precise definition of BL clades and conserved motifs. Moreover, it may support the prediction of new resistance determinants and improve the tailored development of robust molecular diagnostics.

SUBMITTER: Brandt C

PROVIDER: S-EPMC5324141 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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In silico serine β-lactamases analysis reveals a huge potential resistome in environmental and pathogenic species.

Brandt Christian C Braun Sascha D SD Stein Claudia C Slickers Peter P Ehricht Ralf R Pletz Mathias W MW Makarewicz Oliwia O

Scientific reports 20170224

The secretion of antimicrobial compounds is an ancient mechanism with clear survival benefits for microbes competing with other microorganisms. Consequently, mechanisms that confer resistance are also ancient and may represent an underestimated reservoir in environmental bacteria. In this context, β-lactamases (BLs) are of great interest due to their long-term presence and diversification in the hospital environment, leading to the emergence of Gram-negative pathogens that are resistant to cepha ...[more]

PMID: 28233789

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Project description:β-lactam is the most used antibiotic class in the clinical area and it acts on blocking the bacteria cell wall synthesis, causing cell death. However, some bacteria have evolved resistance to these antibiotics mainly due the production of enzymes known as β-lactamases. Hospital sewage is an important source of dispersion of multidrug-resistant bacteria in rivers and oceans. In this work, we used next-generation DNA sequencing to explore the diversity and dissemination of serine β-lactamases in two hospital sewage from Rio de Janeiro, Brazil (South Zone, SZ and North Zone, NZ), presenting different profiles, and to compare them with public environmental data available. Also, we propose a Hidden-Markov-Model approach to screen potential serine β-lactamases genes (in public environments samples and generated hospital sewage data), exploring its evolutionary relationships. Due to the high variability in β-lactamases, we used a position-specific scoring matrix search method (RPS-BLAST) against conserved domain database profiles (CDD, Pfam, and COG) followed by visual inspection to detect conserved motifs, to increase the reliability of the results and remove possible false positives. We were able to identify novel β-lactamases from Brazilian hospital sewage and to estimate relative abundance of its types. The highest relative abundance found in SZ was the Class A (50%), while Class D is predominant in NZ (55%). CfxA (65%) and ACC (47%) types were the most abundant genes detected in SZ, while in NZ the most frequent were OXA-10 (32%), CfxA (28%), ACC (21%), CEPA (20%), and FOX (19%). Phylogenetic analysis revealed β-lactamases from Brazilian hospital sewage grouped in the same clade and close to sequences belonging to Firmicutes and Bacteroidetes groups, but distant from potential β-lactamases screened from public environmental data, that grouped closer to β-lactamases of Proteobacteria. Our results demonstrated that HMM-based approach identified homologs of serine β-lactamases, indicating the specificity and high sensitivity of this approach in large datasets, contributing for the identification and classification of a large number of homologous genes, comprising possible new ones. Phylogenetic analysis revealed the potential reservoir of β-lactam resistance genes in the environment, contributing to understanding the evolution and dissemination of these genes.

Project description:Beta-lactamases represent the main bacterial mechanism of resistance to beta-lactam antibiotics and are a significant challenge to modern medicine. We have developed an automated classification and analysis protocol that exploits structure- and sequence-based approaches and which allows us to propose a grouping of serine beta-lactamases that more consistently captures and rationalizes the existing three classification schemes: Classes, (A, C and D, which vary in their implementation of the mechanism of action); Types (that largely reflect evolutionary distance measured by sequence similarity); and Variant groups (which largely correspond with the Bush-Jacoby clinical groups). Our analysis platform exploits a suite of in-house and public tools to identify Functional Determinants (FDs), i.e. residue sites, responsible for conferring different phenotypes between different classes, different types and different variants. We focused on Class A beta-lactamases, the most highly populated and clinically relevant class, to identify FDs implicated in the distinct phenotypes associated with different Class A Types and Variants. We show that our FunFHMMer method can separate the known beta-lactamase classes and identify those positions likely to be responsible for the different implementations of the mechanism of action in these enzymes. Two novel algorithms, ASSP and SSPA, allow detection of FD sites likely to contribute to the broadening of the substrate profiles. Using our approaches, we recognise 151 Class A types in UniProt. Finally, we used our beta-lactamase FunFams and ASSP profiles to detect 4 novel Class A types in microbiome samples. Our platforms have been validated by literature studies, in silico analysis and some targeted experimental verification. Although developed for the serine beta-lactamases they could be used to classify and analyse any diverse protein superfamily where sub-families have diverged over both long and short evolutionary timescales.

Dataset Information

In silico serine ?-lactamases analysis reveals a huge potential resistome in environmental and pathogenic species.

Publications

In silico serine β-lactamases analysis reveals a huge potential resistome in environmental and pathogenic species.

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