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Transcriptome signature of cell viability predicts drug response and drug interaction in Mycobacterium tuberculosis.

ABSTRACT: There is an urgent need for new drug regimens to rapidly cure tuberculosis. Here, we report the development of drug response assayer (DRonA) and "MLSynergy," algorithms to perform rapid drug response assays and predict response of Mycobacterium tuberculosis (Mtb) to drug combinations. Using a transcriptome signature for cell viability, DRonA detects Mtb killing by diverse mechanisms in broth culture, macrophage infection, and patient sputum, providing an efficient and more sensitive alternative to time- and resource-intensive bacteriologic assays. Further, MLSynergy builds on DRonA to predict synergistic and antagonistic multidrug combinations using transcriptomes of Mtb treated with single drugs. Together, DRonA and MLSynergy represent a generalizable framework for rapid monitoring of drug effects in host-relevant contexts and accelerate the discovery of efficacious high-order drug combinations.

SUBMITTER: Srinivas V

PROVIDER: S-EPMC8688151 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Transcriptome signature of cell viability predicts drug response and drug interaction in <i>Mycobacterium tuberculosis</i>.

Srinivas Vivek V Ruiz Rene A RA Pan Min M Immanuel Selva Rupa Christinal SRC Peterson Eliza J R EJR Baliga Nitin S NS

Cell reports methods 20211220 8

There is an urgent need for new drug regimens to rapidly cure tuberculosis. Here, we report the development of drug response assayer (DRonA) and "MLSynergy," algorithms to perform rapid drug response assays and predict response of <i>Mycobacterium tuberculosis</i> (Mtb) to drug combinations. Using a transcriptome signature for cell viability, DRonA detects Mtb killing by diverse mechanisms in broth culture, macrophage infection, and patient sputum, providing an efficient and more sensitive alter ...[more]

PMID: 34977849

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Project description:Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradient-boosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without "co-occurrent resistance" markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for third-line drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other large-scale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation.

Project description:BackgroundThe DevR(DosR) regulon is implicated in hypoxic adaptation and virulence of Mycobacterium tuberculosis. The present study was designed to decipher the impact of perturbation in DevR-mediated signaling on these properties.Methodology/principal findingsM. tb complemented (Comp) strains expressing different levels of DevR were constructed in Mut1* background (expressing DevR N-terminal domain in fusion with AphI (DevR(N)-Kan) and in Mut2ΔdevR background (deletion mutant). They were compared for their hypoxia adaptation and virulence properties. Diverse phenotypes were noted; basal level expression (∼5.3±2.3 µM) when induced to levels equivalent to WT levels (∼25.8±9.3 µM) was associated with robust DevR regulon induction and hypoxic adaptation (Comp 9* and 10*), whereas low-level expression (detectable at transcript level) as in Comp 11* and Comp15 was associated with an adaptation defect. Intermediate-level expression (∼3.3±1.2 µM) partially restored hypoxic adaptation functions in Comp2, but not in Comp1* bacteria that co-expressed DevR(N)-Kan. Comp* strains in Mut1* background also exhibited diverse virulence phenotypes; high/very low-level DevR expression was associated with virulence whereas intermediate-level expression was associated with low virulence. Transcription profiling and gene expression analysis revealed up-regulation of the phosphate starvation response (PSR) in Mut1* and Comp11* bacteria, but not in WT/Mut2ΔdevR/other Comp strains, indicating a plasticity in expression pathways that is determined by the magnitude of signaling perturbation through DevR(N)-Kan.Conclusions/significanceA minimum DevR concentration of ∼3.3±1.2 µM (as in Comp2 bacteria) is required to support HspX expression in the standing culture hypoxia model. The relative intracellular concentrations of DevR and DevR(N)-Kan appear to be critical for determining dormancy regulon induction, hypoxic adaptation and virulence. Dysregulated DevR(N)-Kan-mediated signaling selectively triggers the PSR in bacteria expressing no/very low level of DevR. Our findings illustrate the important role of appropriate two-component-mediated signaling in pathogen physiology and the resilience of bacteria when such signaling is perturbed.

Dataset Information

Transcriptome signature of cell viability predicts drug response and drug interaction in Mycobacterium tuberculosis.

Publications

Transcriptome signature of cell viability predicts drug response and drug interaction in <i>Mycobacterium tuberculosis</i>.

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