Tuberculosis treatment failure associated with evolution of antibiotic resilience

Liu, Qingyun; Zhu, Junhao; Dulberger, Charles L.; Stanley, Sydney; Wilson, Sean; Chung, Eun Seon; Wang, Xin; Culviner, Peter H.; Liu, Yue J.; Hicks, Nathan D.; Babunovic, Gregory H.; Giffen, Samantha R.; Aldridge, Bree B.; Garner, Ethan C.; Rubín, Eric J.; Chao, Michael C.; Fortune, Sarah M.

doi:10.1126/science.abq2787

Cited by 41 publications

(67 citation statements)

References 60 publications

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“…Plates were incubated for 2-3 days at 37°C and images were captured with a 12-megapixel camera with a 120° FOV at 0, 6, 8, 13, 21, 25, 30, 36, 48, and 56 h after plating. Our previously devised colony tracing pipeline 44 was augmented with two deep learning models 45 that were trained to automate the identification of the grid lines of each plate and also to perform semantic segmentation of plate photos. The pipeline was applied as previously to track the expansion of individual colony areas over time and was repurposed to generate CFU counts displayed in Fig.…”

Section: Quantitative Imaging Of Colony Growthmentioning

confidence: 99%

Chemically-induced targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy

Won

Zhu

Kandror

et al. 2023

Preprint

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Proteolysis-targeting chimeras (PROTACs) represent a new therapeutic modality involving selectively directing disease-causing proteins for degradation through proteolytic systems. Our ability to exploit this targeted protein degradation (TPD) approach for antibiotic development remains nascent due to our limited understanding of which bacterial proteins will be labile TPD targets. Here, we use a genetic system to model chemically-induced proximity and degradation to screen essential proteins in Mycobacterium smegmatis (Msm), a model for the major human pathogen M. tuberculosis (Mtb). We find that drug-induced proximity to the bacterial ClpC1P1P2 proteolytic complex is sufficient to degrade many, but not all, endogenous Msm proteins, profoundly inhibiting bacterial growth for some targets. We also show that TPD can potentiate the effects of existing antibiotics targeting the same pathways and complexes. Together, our results identify specific endogenous mycobacterial proteins as attractive targets for future Mtb PROTAC development, as both standalone antibiotics and potentiators of existing antibiotic efficacy.

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Section: Quantitative Imaging Of Colony Growthmentioning

confidence: 99%

Chemically-induced targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy

Won

Zhu

Kandror

et al. 2023

Preprint

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“…Another recent study showed that the resR transcriptional regulator is a frequent target of positive selection for mutations and that strains with these mutations exhibit antibiotic “resilience,” which describes the potential for antibiotic tolerance and genetic resistance. The bacilli harboring these mutations exhibited more rapid post-antibiotic recovery than wild-type cells ( Liu et al., 2022 ). This new finding demonstrates that we must better understand the various strategies Mtb uses to recover from drug treatment beyond standard definitions of non-replication and dormancy so that we can design combination therapies that target these subpopulations.…”

Section: Looking Forward: the Future Of Tb Combination Therapy And Mo...mentioning

confidence: 99%

Tools to develop antibiotic combinations that target drug tolerance in Mycobacterium tuberculosis

Greenstein

Aldridge²

2023

Front. Cell. Infect. Microbiol.

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Combination therapy is necessary to treat tuberculosis to decrease the rate of disease relapse and prevent the acquisition of drug resistance, and shorter regimens are urgently needed. The adaptation of Mycobacterium tuberculosis to various lesion microenvironments in infection induces various states of slow replication and non-replication and subsequent antibiotic tolerance. This non-heritable tolerance to treatment necessitates lengthy combination therapy. Therefore, it is critical to develop combination therapies that specifically target the different types of drug-tolerant cells in infection. As new tools to study drug combinations earlier in the drug development pipeline are being actively developed, we must consider how to best model the drug-tolerant cells to use these tools to design the best antibiotic combinations that target those cells and shorten tuberculosis therapy. In this review, we discuss the factors underlying types of drug tolerance, how combination therapy targets these populations of bacteria, and how drug tolerance is currently modeled for the development of tuberculosis multidrug therapy. We highlight areas for future studies to develop new tools that better model drug tolerance in tuberculosis infection specifically for combination therapy testing to bring the best drug regimens forward to the clinic.

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“…Rv1339, which has been implicated in modulating Mtb antibiotic susceptibility and is undergoing positive selection in TB patients globally was the only monogenic determinant associated with treatment failure (OR=5•34, CI=1•21-23•58, P=0•027), but not cavitary disease, suggesting disparate mechanisms may be accounting for these two distinct clinical outcomes associated with the L1.1.1.1 strains. 10,46,49 We performed Mantel-Haenszel analyses to ensure that the associations we identified with L1.1.1.1 and Rv1339 mutations are not explained by patient age or sex (S8 Table ).…”

Section: Mtb L1 Clade Is Associated With Poor Clinical Outcomes and I...mentioning

confidence: 99%

“…5,6 Recent studies have sought to use the wealth of Mtb whole genome sequencing data to find novel genetic associations with drug resistance, several of which have been experimentally shown to cause altered drug responses. [7][8][9][10] A few studies have sought to find bacterial genetic associations with more complex clinical outcomes like treatment failure, transmission, and dissemination. [10][11][12][13][14][15][16] Interpretation of genetic associations is complicated by Mtb's high degree of genetic linkage.…”

Section: Introductionmentioning

confidence: 99%

“…[7][8][9][10] A few studies have sought to find bacterial genetic associations with more complex clinical outcomes like treatment failure, transmission, and dissemination. [10][11][12][13][14][15][16] Interpretation of genetic associations is complicated by Mtb's high degree of genetic linkage. 17 In addition, the biologic basis of most clinical phenotypes and the functions of most Mtb genes in vivo are unclear, and combinations of genomic changes may also work in concert to produce the overall clinical phenotype of interest.…”

Section: Introductionmentioning

confidence: 99%

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High-throughput phenogenotypingof Mycobacteria tuberculosisclinical strains reveals bacterial determinants of treatment outcomes

Stanley

Spaulding

Liu

et al. 2023

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Background: Combatting the tuberculosis (TB) epidemic caused by Mycobacterium tuberculosis (Mtb) necessitates a better understanding of the factors contributing to patient clinical outcomes and transmission. While host and environmental factors have been evaluated, the impact of Mtb genetic background and phenotypic diversity is underexplored. Previous work has made associations between Mtb genetic lineages and some clinical and epidemiological features, but the bacterial traits underlying these connections are largely unknown. Methods: We developed a high-throughput functional genomics platform for defining genotype-phenotype relationships across a panel of Mtb clinical isolates. These phenotypic fitness profiles function as intermediate traits which can be linked to Mtb genetic variants and associated with clinical and epidemiological outcomes. We applied this approach to a collection of 158 Mtb strains from a study of Mtb transmission in Ho Chi Minh City, Vietnam. Mtb strains were genetically tagged in multiplicate, which allowed us to pool the strains and assess in vitro competitive fitness using deep sequencing across a set of 14 host-relevant antibiotic and metabolic conditions. Phylogenetic and monogenic associations with these intermediate traits were identified and then associated with clinical outcomes. Findings: Mtb clinical strains have a broad range of growth and drug response dynamics that can be clustered by their phylogenetic relationships. We identified novel monogenic associations with Mtb fitness in various metabolic and antibiotic conditions. Among these, we find that mutations in Rv1339, a phosphodiesterase, which were identified through their association with slow growth in glycerol, are further associated with treatment failure. We also identify a previously uncharacterized subclade of Lineage 1 strains (L1.1.1.1) that is phenotypically distinguished by slow growth under most antibiotic and metabolic stress conditions in vitro. This clade is associated with cavitary disease, treatment failure, and demonstrates increased transmission potential. Interpretation: High-throughput phenogenotyping of Mtb clinical strains enabled bacterial intermediate trait identification that can provide a mechanistic link between Mtb genetic variation and patient clinical outcomes. Mtb strains associated with cavitary disease, treatment failure, and transmission potential display intermediate phenotypes distinguished by slow growth under various antibiotic and metabolic conditions. These data suggest that Mtb growth regulation is an adaptive advantage for host bacterial success in human populations, in at least some circumstances. These data further suggest markers for the underlying bacterial processes that govern these clinical outcomes.

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Tuberculosis treatment failure associated with evolution of antibiotic resilience

Cited by 41 publications

References 60 publications

Chemically-induced targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy

Chemically-induced targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy

Tools to develop antibiotic combinations that target drug tolerance in Mycobacterium tuberculosis

High-throughput phenogenotypingof Mycobacteria tuberculosisclinical strains reveals bacterial determinants of treatment outcomes

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