The Alternative Sigma Factors SigE and SigB Are Involved in Tolerance and Persistence to Antitubercular Drugs

Pisu, Davide; Provvedi, Roberta; Espinosa, Dulce Mata; Payan, Jorge Barrios; Boldrin, Francesca; Palù, Giorgio; Hernández-Pando, Rogelio; Manganelli, Riccardo

doi:10.1128/aac.01596-17

Cited by 41 publications

(41 citation statements)

References 31 publications

(37 reference statements)

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“…This reduction suggests that sigE is required for tolerance of certain drugs. Indeed, this recapitulates the growth defects observed in a ∆sigE mutant when exposed to various drugs [47]. sigE is an alternative sigma factor that is thought to play a regulatory role in response to various stresses.…”

Section: Application To Datasets With Multiple Conditionssupporting

confidence: 65%

Statistical Analysis of Variability in TnSeq Data Across Conditions Using Zero-Inflated Negative Binomial Regression

Subramaniyam

DeJesus

Zaveri

et al. 2019

Preprint

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9Deep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality 10 of genomic loci under different environmental conditions. Various analytical methods have been described 11 for identifying conditionally essential genes whose tolerance for insertions varies between two conditions. 12 However, for large-scale experiments involving many conditions, it would be useful to have a method for 13 identifying genes that exhibit significant variability in insertions across multiple conditions. In this paper, 14 we introduce a novel statistical method for identifying genes with significant variability of insertion counts 15 across multiple conditions based on Zero-Inflated Negative Binomial (ZINB) regression. Using likelihood 16 ratio tests, we show that the ZINB fits TnSeq data better than either ANOVA or a Negative Bionomial (as a 17 generalized linear model). We use ZINB regression to identify genes required for infection of M. tuberculosis 18 H37Rv in C57BL/6 mice. We also use ZINB to perform a restrospective analysis of genes conditionally 19 essential in H37Rv cultures exposed to multiple antibiotics. Our results show that, not only does ZINB 20 generally identify most of the genes found by pairwise resampling (and vastly out-performs ANOVA), but it 21 also identifies additional genes where variability is detectable only when the magnitudes of insertion counts 22 are treated separately from local differences in saturation, as in the ZINB model. 23 1 Introduction 24Deep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality 25 of genomic loci under different environmental conditions [1]. In a transposon (Tn) mutant library (such as 26 1 made with the Himar1 transposon), insertions generally occur at random locations throughout the genome 27 (restricted to TA dinucelotides for Himar1 [2]). The absence (or reduction) of insertions in a locus is used 28 to infer conditional essentiality, based on killing (or growth impairment) that depletes those clones from 29 the population. While the abundance of clones with insertions at different sites can be profiled efficiently 30 through deep sequencing, there are a number of sources of noise that induce a high degree of variability 31 in insertion counts at each site, including: variations in mutant abundance during library construction, 32 stochastic differences among samples, biases due to sample preparation protocol and sequencing technology, 33 and other effects. Previous statistical methods have been developed for quantitative assessment of essential 34 genes in single conditions, as well as pairwise comparisons of conditional essentiality. Statistical methods 35 for characterizing essential regions in a genome include those based on tests of sums of insertion counts 36 in genes [3], gaps [4], bimodality of empirical distributions [5], non-parametric tests of counts [6], Poisson 37 distributions [7], and Hidden Markov Models [8]. Statistical methods for evaluating conditional essen...

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Section: Application To Datasets With Multiple Conditionssupporting

confidence: 65%

Statistical Analysis of Variability in TnSeq Data Across Conditions Using Zero-Inflated Negative Binomial Regression

Subramaniyam

DeJesus

Zaveri

et al. 2019

Preprint

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“…One example is the association of stochastic variation in katG expression to INH‐tolerance in single mycobacterial cells (Wakamoto et al ., ). Previous studies have shown that mycobacterial σ B is important for stress responses, including cell envelope stress and hypoxia (Fontan et al ., ) and for tolerance to several drugs (Pisu et al ., ; Yang et al ., ). These studies have been performed at the population level.…”

Section: Discussionmentioning

confidence: 97%

“…Deletion of sigB gene did not change the MIC of mycobacteria to INH (Yang et al ., ), but we showed that it decreased the fraction of survivors to INH treatment both under optimal growth conditions and in the starvation model (Fig. ) (Pisu et al ., ), which suggests that σ B may promote Msm tolerance to INH. By using a fluorescent reporter, we demonstrated that the increased expression of σ B positively correlates with INH tolerance, as measured by CFU recovery during INH treatment.…”

Section: Discussionmentioning

confidence: 97%

“…Mtb encodes a homologue of σ S named σ B . Previous studies have shown that deletion of Mtb sigB dramatically decreased bacterial survival under hypoxic conditions (Fontan et al ., ) as well as in the presence of anti‐tuberculosis drugs (Pisu et al ., ), suggesting that genes controlled by σ B may play roles in generating drug‐tolerant Mtb .…”

Section: Introductionmentioning

confidence: 97%

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RbpA and σ^B association regulates polyphosphate levels to modulate mycobacterial isoniazid‐tolerance

Wang

Cumming

Mao

et al. 2018

Molecular Microbiology

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To facilitate survival under drug stresses, a small population of Mycobacterium tuberculosis can tolerate bactericidal concentrations of drugs without genetic mutations. These drug-tolerant mycobacteria can be induced by environmental stresses and contribute to recalcitrant infections. However, mechanisms underlying the development of drug-tolerant mycobacteria remain obscure. Herein, we characterized a regulatory pathway which is important for the tolerance to isoniazid (INH) in Mycobacterium smegmatis. We found that the RNA polymerase binding protein RbpA associates with the stress response sigma factor σ , to activate the transcription of ppk1, the gene encoding polyphosphate kinase. Subsequently, intracellular levels of inorganic polyphosphate increase to promote INH-tolerant mycobacteria. Interestingly, σ and ppk1 expression varied proportionately in mycobacterial populations and positively correlated with tolerance to INH in individual mycobacteria. Moreover, sigB and ppk1 transcription are both induced upon nutrient depletion, a condition that stimulates the formation of INH-tolerant mycobacteria. Over-expression of ppk1 in rbpA knockdown or sigB deleted strains successfully restored the number of INH-tolerant mycobacteria under both normal growth and nutrient starved conditions. These data suggest that RbpA and σ regulate ppk1 expression to control drug tolerance both during the logarithmic growth phase and under the nutrition starved conditions.

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“…In 2017, we showed that a M. tuberculosis sigE mutant was more susceptible than its parental strain to several drugs, including INH, rifampin, streptomycin, gentamycin, vancomycin, and ethambutol, whereas a sigB mutant was more sensitive only to INH and ethambutol which both target mycolic acids biosynthesis ( Pisu et al, 2017 ). Moreover, we showed that persisters able to escape INH and streptomycin killing occurred less frequently in both mutants compared to the wild type, while those escaping killing by vancomycin appeared less frequently than in the wild type only in the sigE mutant.…”

Section: Role Of Sigma Factors In Tolerance and Persistencementioning

confidence: 99%

Tolerance and Persistence to Drugs: A Main Challenge in the Fight Against Mycobacterium tuberculosis

et al. 2020

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The treatment of tuberculosis is extremely long. One of the reasons why Mycobacterium tuberculosis elimination from the organism takes so long is that in particular environmental conditions it can become tolerant to drugs and/or develop persisters able to survive killing even from very high drug concentrations. Tolerance develops in response to a harsh environment exposure encountered by bacteria during infection, mainly due to the action of the immune system, whereas persistence results from the presence of heterogeneous bacterial populations with different degrees of drug sensitivity, and can be induced by exposure to stress conditions. Here, we review the actual knowledge on the stress response mechanisms enacted by M. tuberculosis during infection, which leads to increased drug tolerance or development of a highly drug-resistant subpopulation.

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The Alternative Sigma Factors SigE and SigB Are Involved in Tolerance and Persistence to Antitubercular Drugs

Cited by 41 publications

References 31 publications

Statistical Analysis of Variability in TnSeq Data Across Conditions Using Zero-Inflated Negative Binomial Regression

Statistical Analysis of Variability in TnSeq Data Across Conditions Using Zero-Inflated Negative Binomial Regression

RbpA and σ^B association regulates polyphosphate levels to modulate mycobacterial isoniazid‐tolerance

Tolerance and Persistence to Drugs: A Main Challenge in the Fight Against Mycobacterium tuberculosis

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The Alternative Sigma Factors SigE and SigB Are Involved in Tolerance and Persistence to Antitubercular Drugs

Cited by 41 publications

References 31 publications

Statistical Analysis of Variability in TnSeq Data Across Conditions Using Zero-Inflated Negative Binomial Regression

Statistical Analysis of Variability in TnSeq Data Across Conditions Using Zero-Inflated Negative Binomial Regression

RbpA and σB association regulates polyphosphate levels to modulate mycobacterial isoniazid‐tolerance

Tolerance and Persistence to Drugs: A Main Challenge in the Fight Against Mycobacterium tuberculosis

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RbpA and σ^B association regulates polyphosphate levels to modulate mycobacterial isoniazid‐tolerance