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Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.

ABSTRACT: Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses.

SUBMITTER: Palmer AC

PROVIDER: S-EPMC6231494 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.

Palmer Adam C AC Chait Remy R Kishony Roy R

Molecular biology and evolution 20181101 11

Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression ...[more]

PMID: 30169679

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Project description:The activation of unrecognized antibiotic resistance genes in the bacterial cell can give rise to antibiotic resistance without the need for major mutations or horizontal gene transfer. We hypothesize that bacteria harbor an extensive array of diverse cryptic genes that can be activated in response to antibiotics via adaptive resistance. To test this hypothesis, we developed a plasmid assay to randomly manipulate gene copy numbers in Escherichia coli cells and identify genes that conferred resistance when amplified. We then tested for cryptic resistance to 18 antibiotics and identified genes conferring resistance. E. coli could become resistant to 50% of the antibiotics tested, including chloramphenicol, d-cycloserine, polymyxin B, and 6 beta-lactam antibiotics, following this manipulation. Known antibiotic resistance genes comprised 13% of the total identified genes, where 87% were unclassified (cryptic) antibiotic resistance genes. These unclassified genes encoded cell membrane proteins, stress response/DNA repair proteins, transporters, and miscellaneous or hypothetical proteins. Stress response/DNA repair genes have a broad antibiotic resistance potential, as this gene class, in aggregate, conferred cryptic resistance to nearly all resistance-positive antibiotics. We found that antibiotics that are hydrophilic, those that are amphipathic, and those that inhibit the cytoplasmic membrane or cell wall biosynthesis were more likely to induce cryptic resistance in E. coli. This study reveals a diversity of cryptic genes that confer an antibiotic resistance phenotype when present in high copy number. Thus, our assay can identify potential novel resistance genes while also describing which antibiotics are prone to induce cryptic antibiotic resistance in E. coli. IMPORTANCE Predicting where new antibiotic resistance genes will rise is a challenge and is especially important when new antibiotics are developed. Adaptive resistance allows sensitive bacterial cells to become transiently resistant to antibiotics. This provides an opportune time for cells to develop more efficient resistance mechanisms, such as tolerance and permanent resistance to higher antibiotic concentrations. The biochemical diversity harbored within bacterial genomes may lead to the presence of genes that could confer resistance when timely activated. Therefore, it is crucial to understand adaptive resistance to identify potential resistance genes and prolong antibiotics. Here, we investigate cryptic resistance, an adaptive resistance mechanism, and identify unknown (cryptic) antibiotic resistance genes that confer resistance when amplified in a laboratory strain of E. coli. We also pinpoint antibiotic characteristics that are likely to induce cryptic resistance. This study may help detect novel antibiotic resistance genes and provide the foundation to help develop more effective antibiotics.

Project description:Riemerella anatipestifer is an important pathogen in waterfowl, and is generally multidrug resistant. This study assessed the current status of Riemerella anatipestifer antibiotic resistance and antibiotic-resistance genes (ARGs), compared the results of different detection methods, and evaluated a new method of studying the association between antibiotic resistance and ARGs in Riemerella anatipestifer. In this study, 51 strains of Riemerella anatipestifer were isolated from ducks on several farms, their resistance to 28 antibiotics was assessed, and the isolates were subjected to whole-genome sequencing. The number of ARGs carried by Riemerella anatipestifer was predicted, compared, and analyzed, and the consistency between ARGs and antibiotic-resistance phenotypes was assessed. The potential for loss of resistance genes during the sequencing and assembly of genome-wide framework map was assessed, and a new ARG detection method was pilot tested. The 51 strains of Riemerella anatipestifer were multidrug resistant (MDR) and had high level of resistance to aminoglycosides, trimethoprim, lincosamides, polypeptides, and macrolides. Based on the genome-wide framework map of the 51 strains, 3 local databases of ABRicate software and 1 online database of CARD website were used to detect ARGs, and a mean of 4 to 5 ARGs were identified per isolate. Although the detection results differed according to the database used, the general performance was consistent. The online website detected more types of ARGs than the ABRicate software. The association between ARGs and antibiotic-resistance phenotypes was assessed, and the ermF gene was identified as a possible key ARGs regulating macrolide resistance of Riemerella anatipestifer. The method used to investigate and detect Riemerella anatipestifer ARGs was convenient and rapid, and had strong accuracy and pertinence. The ARGs detection method reported here combined the advantages of PCR and genome detection, and could greatly reduce workload and detect ARGs more precisely.

Project description:BackgroundBacterial communities in humans, animals, and the external environment maintain a large collection of antibiotic resistance genes (ARGs). However, few of these ARGs are well-characterized and thus established in existing resistance gene databases. In contrast, the remaining latent ARGs are typically unknown and overlooked in most sequencing-based studies. Our view of the resistome and its diversity is therefore incomplete, which hampers our ability to assess risk for promotion and spread of yet undiscovered resistance determinants.ResultsA reference database consisting of both established and latent ARGs (ARGs not present in current resistance gene repositories) was created. By analyzing more than 10,000 metagenomic samples, we showed that latent ARGs were more abundant and diverse than established ARGs in all studied environments, including the human- and animal-associated microbiomes. The pan-resistomes, i.e., all ARGs present in an environment, were heavily dominated by latent ARGs. In comparison, the core-resistome, i.e., ARGs that were commonly encountered, comprised both latent and established ARGs. We identified several latent ARGs shared between environments and/or present in human pathogens. Context analysis of these genes showed that they were located on mobile genetic elements, including conjugative elements. We, furthermore, identified that wastewater microbiomes had a surprisingly large pan- and core-resistome, which makes it a potentially high-risk environment for the mobilization and promotion of latent ARGs.ConclusionsOur results show that latent ARGs are ubiquitously present in all environments and constitute a diverse reservoir from which new resistance determinants can be recruited to pathogens. Several latent ARGs already had high mobile potential and were present in human pathogens, suggesting that they may constitute emerging threats to human health. We conclude that the full resistome-including both latent and established ARGs-needs to be considered to properly assess the risks associated with antibiotic selection pressures. Video Abstract.

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Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.

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Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.

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