Antibiotic-resistant bacteria show widespread collateral sensitivity to antimicrobial peptides

Lázár, Viktória; Martins, Ana; Spohn, Réka; Daruka, Lejla; Grézal, Gábor; Fekete, G.; Számel, Mónika; Jangir, Pramod Kumar; Kintses, Bálint; Csörgő, Bálint; Nyerges, Ákos; Györkei, Ádám; Kincses, András; Dér, András; Walter, Fruzsina R.; Deli, Mária A.; Urbán, Edit; Hegedűs, Zsófia; Olajos, Gábor; Méhi, Orsolya; Bálint, Balázs; Nagy, István; Martinek, Tamás A.; Papp, Balázs; Pál, Csaba

doi:10.1038/s41564-018-0164-0

Cited by 323 publications

(263 citation statements)

References 66 publications

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“…The i-modulon structure is conserved across transcriptomic datasets Each of the five transcriptomic datasets were created using different technologies or generated by different research groups, spanning fifteen years (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019). In addition, each 150 dataset contained vastly different experimental conditions and genotypes, such as overexpressed cellular division proteins in the MA-1 dataset 32 , diverse nutritional supplements in the RNAseq-1 dataset, or strains evolved to resist antibiotics in the RNAseq-2 dataset 31 . Although the presence of different conditions in each dataset complicated the identification of consistent i-modulons, many of the i-modulons generated from the five datasets unexpectedly shared similar genes and 155 annotations.…”

Section: Resultsmentioning

confidence: 99%

Matrix factorization recovers consistent regulatory signals from disparate datasets

Sastry¹,

Hu²,

Heckmann³

et al. 2020

Preprint

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15The availability of gene expression data has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent 20 datasets, including both RNA-seq and microarray datasets. We also show that echoes of this structure remain in the proteome, accelerating biological discovery through multi-omics analysis.We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. ICA thus enables deep analysis of disparate data 25 to uncover new insights that were not visible in the individual datasets.Publicly available datasets, such as the NCBI Gene Expression Omnibus (GEO) 1 and Array Express 2 , contain thousands of transcriptomics datasets that are often designed and 30 analyzed for a specific study. Historically, microarrays were the platform of choice for transcriptomic interrogation. Over the past decade, usage of RNA sequencing (RNA-seq) has surpassed microarrays due to its higher sensitivity and ability to detect new transcripts 3 . Public repositories for proteomics datasets have introduced additional reusable expression datasets 4,5 . 35Multiple consortia have performed extensive comparisons of expression levels across different microarray and RNA-seq platforms 6-8 . These studies showed that absolute gene expression levels cannot be accurately measured by either expression profiling technique, whereas relative abundances are consistent across a wide range of transcriptomics platforms, with appropriate quality controls. However, transcript levels alone cannot predict protein 40 expression levels 9 To further complicate matters, batch effects and technical heterogeneity continue to present significant challenges to successful integration of omics datasets 10 . Differential expression analysis is the most common analytical method applied to transcriptomics datasets. However, differential expression analysis is limited in dimensionality, 45 interpretability, and reproducibility; it can only be applied to pairs of experimental conditions, requires additional analysis to interpret large swaths of differentially expressed genes 11,12 , and is highly dependent on the quantification pipeline 13,14 . Alternatively, machine learning methods, especially matrix factorization 15,16 , have provided new tools for extracting low-dimensional biological information from large omics data. 50In particular, independent component analysis (ICA) has been shown to extract biologically significant gene sets from many transcriptomics datasets 17-22 and two proteomics datasets 23 . ICA outperformed 42 module detect...

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Section: Resultsmentioning

confidence: 99%

Matrix factorization recovers consistent regulatory signals from disparate datasets

Sastry¹,

Hu²,

Heckmann³

et al. 2020

Preprint

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“…It also informs on the ‘latent resistome’, that is, the collection of genes where a change from native expression level enhances resistance to a particular drug 26 . We applied a sensitive competition assay by monitoring growth of a pooled plasmid library overexpressing all the E. coli ORFs (Figure 1a), as we reported earlier 27 . Specifically, E. coli cells carrying the pooled plasmid collection were grown in the presence or absence of one of the 15 AMPs tested, at a sub-inhibitory concentration that increased the doubling time of the whole population by 2-fold.…”

Section: Resultsmentioning

confidence: 99%

Chemical-genetic profiling reveals cross-resistance and collateral sensitivity between antimicrobial peptides

Jangir

2019

Preprint

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27Antimicrobial peptides (AMPs) are key effectors of the innate immune system and promising ther-28 apeutic agents. Yet, knowledge on how to design AMPs with minimal cross-resistance to human 29 host-defense peptides remains limited. Here, with a chemical-genetic approach, we systemati-30 cally assessed the resistance determinants of Escherichia coli against 15 different AMPs. Alt-31 hough generalizations about AMP resistance are common in the literature, we found that AMPs 32 with different physicochemical properties and cellular targets vary considerably in their resistance 33 determinants. As a consequence, collateral sensitivity effects were common: numerous genes 34 decreased susceptibility to one AMP while simultaneously sensitized to others. Finally, the chem- 35ical-genetic map predicted the cross-resistance spectrum of laboratory-evolved human-B-defen-36 sin-3 resistant lineages. Our work substantially broadens the scope of known resistance-modu-37 lating genes and explores the pleiotropic effects of AMP resistance. In the future, the chemical-38 genetic map could inform efforts to minimize cross-resistance between therapeutic and human 39 host AMPs. 40 41 Introduction 42 Antimicrobial peptides (AMPs) play a crucial role in general defense mechanisms against micro-43 bial pathogens in all classes of life. Although there is a considerable diversity in their amino acid 44 content, length, and structure, AMPs are typically positively charged and amphipathic mole-45 cules 1,2 . These properties allow them to adsorb onto the bacterial cell surface and penetrate 46 through the membrane to exert their diverse antibacterial actions 3 . As AMPs have a broad spec-47 trum of activity, considerable efforts have been allocated to the research and development of 48 novel anti-infective compounds originating from AMPs 4,5 . However, the clinical development of 49AMP therapies, has also raised concerns that these approaches may drive bacterial evolution of 50 resistance to human host-defense peptides 6,7 . As well, therapeutic AMPs are required to be active 51 against pathogenic bacteria, many of which have already evolved resistance against human host 52 AMPs 8 . Therefore, ideally, resistance mechanisms against therapeutic and host AMPs should not 53 overlap. 54Accumulating evidence suggest that AMPs differ considerably in their mode of actions, 55 which may influence the specific microbial resistance mechanisms against them 1,9 . First, there 56 are substantial differences in the electrostatic interactions and transport processes that lead to 57 the cellular uptake of AMPs 3,10 . Second, the cellular targets of AMPs are also diverse in nature. 58For instance, apart from their membrane-disruptive activities, AMPs inhibit intracellular processes 59 3 such as bacterial DNA and RNA synthesis, translation, cell wall synthesis, and diverse metabolic 60 pathways 1,11 . However, the extent to which the genetic determinants of resistance differ across 61 AMPs remains unclear, because most of our knowledge comes from case stud...

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“…Collateral sensitivity was first described as early as 1950s [47] and was recently verified in a series of laboratory evolved resistant strains of E. coli and P. aeruginosa [48–50]. It was proposed that collateral sensitivity is a common phenomenon in AMR strains and can be exploited to eradicate clinical resistant strains or reduce resistance development by programming the drug pairs that produce reciprocal collateral sensitivity [39, 51, 52]. However, a recent study reported the lack of collateral sensitivity in clinical P. aeruginosa isolates from the cystic fibrosis (CF) patients [53].…”

Section: Resultsmentioning

confidence: 99%

Native CRISPR-Cas mediated in situ genome editing reveals the exquisite interplay of resistance mutations in clinical multidrug resistant Pseudomonas aeruginosa

et al. 2018

Preprint

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23Antimicrobial resistance (AMR) is imposing a global public health threat. Despite its 24 importance, resistance characterization in the native background of clinically isolated resistant 25 pathogens is frequently hindered by the lack of genome editing tools in these "non-model" 26 strains. Pseudomonas aeruginosa is both a prototypical multidrug resistant (MDR) pathogen 27 and a model species for understanding CRISPR-Cas functions. In this study, we report the 28 successful development of the first native type I-F CRISPR-Cas mediated, one-step genome 29 editing technique in a paradigmatic MDR strain PA154197. The technique is readily applicable 30 in additional type I-F CRISPR-containing, clinical/environmental P. aeruginosa isolates. A 31 two-step In-Del strategy is further developed to edit genomic locus lacking an effective PAM 32 (protospacer adjacent motif) or within an essential gene, which together principally allows any 33 type of non-lethal genomic manipulations in these strains. Exploiting these powerful 34 techniques, a series of reverse mutations are constructed and the key resistant determinants of 35 the MDR PA154197 are elucidated which include over-production of two multidrug efflux 36 pumps MexAB-OprM and MexEF-OprN, and a typical fluoroquinolone (FQ) resistance 37 mutation T83I in the drug target gene gyrA. Characterizing antimicrobial susceptibilities in 38 isogenic strains containing various combinations of single, double, or all three key resistance 39 determinants reveal that i) extensive synergy exists between the target mutation and over-40 production of efflux pumps, and between the two over-produced tripartite efflux pumps to 41 confer clinically significant FQ resistance; ii) while basal level MexAB-OprM confers 42 resistance only to penicillins, its over-production leads to substantial resistance to all 43 antipseudonmonal β-lactams and additional resistance to FQs; iii) despite the acquisition and 44 over-production of multiple resistant mutations, no obvious evolutionary trade-off of collateral 45 sensitivity is developed in PA154197. Together, these results provide new insights into 46 3 resistance development in clinical MDR P. aeruginosa strains and demonstrate the great 47 potentials of native CRISPR systems in AMR research. 48 49 Author Summary 50 Genome editing and manipulation can revolutionize the understanding, exploitation, and 51 control of microbial species. Despite the presence of well-established genetic manipulation 52 tools in various model strains, their applicability in the medically, environmentally, and 53 industrially important, "non-model" strains is often hampered owing to the vast diversity of 54 DNA homeostasis in these strains and the cytotoxicity of the heterologous CRISPR-Cas9/Cpf1 55 systems. Harnessing the native CRISPR-Cas systems broadly distributed in prokaryotes with 56 built-in genome targeting activity presents a promising and effective approach to resolve these 57 obstacles. We explored and exploited this methodology in the prototypical multi...

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Antibiotic-resistant bacteria show widespread collateral sensitivity to antimicrobial peptides

Cited by 323 publications

References 66 publications

Matrix factorization recovers consistent regulatory signals from disparate datasets

Matrix factorization recovers consistent regulatory signals from disparate datasets

Chemical-genetic profiling reveals cross-resistance and collateral sensitivity between antimicrobial peptides

Native CRISPR-Cas mediated in situ genome editing reveals the exquisite interplay of resistance mutations in clinical multidrug resistant Pseudomonas aeruginosa

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