Bacteriocins to Thwart Bacterial Resistance in Gram Negative Bacteria

Telhig, Soufiane; Said, Laila Ben; Zirah, Séverine; Fliss, Ismaı̈l; Rebuffat, Sylvie

doi:10.3389/fmicb.2020.586433

Cited by 57 publications

(71 citation statements)

References 227 publications

(398 reference statements)

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“…It has been reported that the gene fliI encodes a virulence factor, and some studies focused on the correlation between antimicrobial resistance and bacterial virulence ( Beceiro et al , 2013 ; Deng et al , 2019 ). The gene pepB encodes the peptidase B, which is related to the production of bacteriocins, narrow-spectrum antimicrobial peptides produced by bacteria ( Suzuki et al , 2001 ; Telhig et al , 2020 ). MurB is the key biosynthetic enzyme involved in the synthesis of peptidoglycan, the key component of the cell wall ( Nasiri et al , 2017 ; Walsh and Wencewicz, 2014 ).…”

Section: Resultsmentioning

confidence: 99%

Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning

et al. 2021

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Motivation Antimicrobial resistance (AMR) is one of the biggest global problems threatening human and animal health. Rapid and accurate AMR diagnostic methods are thus very urgently needed. However, traditional antimicrobial susceptibility testing (AST) is time-consuming, low throughput, and viable only for cultivable bacteria. Machine learning methods may pave the way for automated AMR prediction based on genomic data of the bacteria. However, comparing different machine learning methods for the prediction of AMR based on different encodings and whole-genome sequencing data without previously known knowledge remains to be done. Results In the current study, we evaluated logistic regression (LR), support vector machine (SVM), random forest (RF), and convolutional neural network (CNN) for the prediction of AMR for the antibiotics ciprofloxacin (CIP), cefotaxime (CTX), ceftazidime (CTZ), and gentamicin (GEN). We could demonstrate that these models can effectively predict AMR with label encoding, one-hot encoding, and frequency matrix chaos game representation (FCGR encoding) on whole-genome sequencing data. We trained these models on a large AMR dataset and evaluated them on an independent public data set. Generally, RFs and CNNs perform better than LR and SVM with AUCs up to 0.96. Furthermore, we were able to identify mutations that are associated with AMR for each antibiotic. Availability Source code in data preparation and model training are provided at GitHub website (https://github.com/YunxiaoRen/ML-iAMR). Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning

et al. 2021

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“… 450 , 451 When applicable to Gram-negative bacteria these antimicrobial peptides are designated “microcins” . 447 , 452 A large class of these are the so-called ribosomally synthesized and post-translationally modified peptides (RiPPs), 453 , 454 and they are primarily secreted by dedicated ABC transporter systems, bypassing the Sec-pathway. Microcins are gene-encoded antibacterial peptides, with molecular weight below 10 kDa, 455 and could be both modified and nonmodified, with the latter group encompassing a great variety of lasso peptides, siderophore peptides, nucleotide peptides, and linear azole(in)e-containing peptides.…”

Section: Classes Of Transporter Proteinsmentioning

confidence: 99%

Structure, Assembly, and Function of Tripartite Efflux and Type 1 Secretion Systems in Gram-Negative Bacteria

et al. 2021

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Tripartite efflux pumps and the related type 1 secretion systems (T1SSs) in Gram-negative organisms are diverse in function, energization, and structural organization. They form continuous conduits spanning both the inner and the outer membrane and are composed of three principal components—the energized inner membrane transporters (belonging to ABC, RND, and MFS families), the outer membrane factor channel-like proteins, and linking the two, the periplasmic adaptor proteins (PAPs), also known as the membrane fusion proteins (MFPs). In this review we summarize the recent advances in understanding of structural biology, function, and regulation of these systems, highlighting the previously undescribed role of PAPs in providing a common architectural scaffold across diverse families of transporters. Despite being built from a limited number of basic structural domains, these complexes present a staggering variety of architectures. While key insights have been derived from the RND transporter systems, a closer inspection of the operation and structural organization of different tripartite systems reveals unexpected analogies between them, including those formed around MFS- and ATP-driven transporters, suggesting that they operate around basic common principles. Based on that we are proposing a new integrated model of PAP-mediated communication within the conformational cycling of tripartite systems, which could be expanded to other types of assemblies.

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“…RiPPs such as nisin have been first investigated because of their antibiotic properties ( Mattick and Hirsch, 1947 ); however, with the growing recognition that in the environment, bacteria live in complex communities rather than as free planktonic cells ( Hall-Stoodley et al, 2004 ; Bjarnsholt et al, 2013 ), the functions of RiPPs are likely much more diverse. Indeed, bacteria can adopt complex lifestyles ( Hibbing et al, 2010 ), with RiPPs providing not only a competitive advantage against competing species ( Telhig et al, 2020 ), but also playing a major role in communication ( Ibrahim et al, 2007 ; Liu et al, 2010 ), biofilm formation and the acquisition of metals ( Hider and Kong, 2010 ; Dassama et al, 2017 ). More recently, RiPPs have also been proposed to play a pivotal role in the homeostasis of the human microbiota ( Benjdia and Berteau, 2016 ; Balskus, 2018 ; Chittim et al, 2018 ; Balty et al, 2020 ), with the recent discovery of several novel antibiotics from this complex ecosystem ( Rothschild et al, 2018 ) including colicin V ( Cohen et al, 2018 ), humimycin ( Chu et al, 2018 ) and ruminococcin C ( Balty et al, 2019 ; Chiumento et al, 2019 ; Balty et al, 2020 ).…”

mentioning

confidence: 99%

Radical SAM Enzymes and Ribosomally‐Synthesized and Post‐translationally Modified Peptides: A Growing Importance in the Microbiomes

Benjdia

Berteau

2021

Front. Chem.

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To face the current antibiotic resistance crisis, novel strategies are urgently required. Indeed, in the last 30 years, despite considerable efforts involving notably high-throughput screening and combinatorial libraries, only few antibiotics have been launched to the market. Natural products have markedly contributed to the discovery of novel antibiotics, chemistry and drug leads, with more than half anti-infective and anticancer drugs approved by the FDA being of natural origin or inspired by natural products. Among them, thanks to their modular structure and simple biosynthetic logic, ribosomally synthesized and posttranslationally modified peptides (RiPPs) are promising scaffolds. In addition, recent studies have highlighted the pivotal role of RiPPs in the human microbiota which remains an untapped source of natural products. In this review, we report on recent developments in radical SAM enzymology and how these unique biocatalysts have been shown to install complex and sometimes unprecedented posttranslational modifications in RiPPs with a special focus on microbiome derived enzymes.

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Bacteriocins to Thwart Bacterial Resistance in Gram Negative Bacteria

Cited by 57 publications

References 227 publications

Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning

Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning

Structure, Assembly, and Function of Tripartite Efflux and Type 1 Secretion Systems in Gram-Negative Bacteria

Radical SAM Enzymes and Ribosomally‐Synthesized and Post‐translationally Modified Peptides: A Growing Importance in the Microbiomes

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