Biology subjects

Reames, C. J.

Publications and source records attributed to Reames, C. J..

4 recordsLinked to original sources

bioRxiv2024

Chemical genetic interactions elucidate pathways controlling tuberculosis antibiotic efficacy during infection

Successful tuberculosis therapy requires treatment with an unwieldy multidrug combination for several months. Thus, there is a growing need to identify novel genetic vulnerabilities that can be leveraged to develop new, more effective antitubercular drugs. Consequently, recent efforts to optimize TB therapy have exploited Mtb chemical genetics to identify pathways influencing antibiotic efficacy, novel mechanisms of antibiotic action, and new targets for TB drug discovery. However, the influence of the complex host environment on these interactions remains largely unknown, leaving the therapeutic potential of the identified targets unclear. In this study, we leveraged a library of conditional mutants targeting 467 essential Mtb genes to characterize the chemical-genetic interactions (CGIs) with TB drugs directly in the mouse infection model. We found that these in vivo CGIs differ significantly from those identified in vitro. Both drug-specific and drug-agnostic effects were identified, and many were preserved during treatment with a multidrug combination, suggesting numerous strategies for enhancing therapy. This work also elucidated the complex effects of pyrazinamide (PZA), a drug that relies on aspects of the infection environment for efficacy. Specifically, our work supports the importance of coenzyme A synthesis inhibition during infection, as well as the antagonistic effect of iron limitation on PZA activity. In addition, we found that inhibition of thiamine and purine synthesis increases PZA efficacy, suggesting novel therapeutically exploitable metabolic dependencies. Our findings present a map of the unique in vivo CGIs, characterizing the mechanism of PZA activity in vivo and identifying novel targets for TB drug development. SignificanceThe inevitable rise of multi-drug-resistant tuberculosis underscores the urgent need for new TB drugs and novel drug targets while prioritizing synergistic drug combinations. Chemical-genetic interaction (CGI) studies have delineated bacterial pathways influencing antibiotic efficacy and uncovered druggable pathways that synergize with TB drugs. However, most studies are conducted in vitro, limiting our understanding of how the host environment influences drug-mutant interactions. Using an inducible mutant library targeting essential Mtb genes to characterize CGIs during infection, this study reveals that CGIs are both drug-specific and drug-agnostic and differ significantly from those observed in vitro. Synergistic CGIs comprised distinct metabolic pathways mediating antibiotic efficacy, revealing novel drug mechanisms of action, and defining potential drug targets that would synergize with frontline antitubercular drugs.

microbiology↗

bioRxiv2024

Cross species modeling reveals a role for the unfolded protein response in shaping the transcriptional reaction to Mycobacterium tuberculosis infection

Numerous blood mRNA signatures have been developed to diagnose tuberculosis (TB) disease. The utility of these signatures in diverse populations depends on the inclusion of ubiquitously expressed features, such as type 1 interferon (IFN) production and innate immune cell activities. As a result, these signatures are generally insensitive to heterogeneous responses between individuals. Designing more effective therapies will require understanding the diverse mechanisms underlying pathogenesis by associating them with appropriate preclinical animal models. To address this critical animal-to-human gap, we applied a modeling framework, Translatable Components Regression, which is designed to account for biological heterogeneity by identifying multiple orthogonal axes of variation that are common to humans and animal models. Our framework was capable of distinguishing human active TB from latent TB infection using a model derived from murine data. This discrimination was based on differential expression of numerous biological pathways in addition to the common IFN and neutrophil signatures. Prominent among these predictive pathways was protein translation, which we show is a feature of the Mtb infection-induced Unfolded Protein Response (UPR) in macrophages. We show that this cellular stress pathway controls a variety of immune-related functions in Mtb-infected mouse macrophages, suggesting a possible causative role during the development of TB disease. Significance StatementDespite tuberculosis being one of the top causes of global mortality, the mechanisms that control the progression of disease are still not fully understand. Here we leverage a systems-level modeling approach that incorporates transcriptomics data across thousands of genes from both a traditional tuberculosis mouse model and human clinical samples to implicate a previously unappreciated mechanism in pathogenesis, the unfolded protein response. We validate these findings in a mouse macrophage model and pinpoint which branch of the unfolded protein response might be activated during tuberculosis infection. These insights, originally derived from our cross-species model, may allow us to better understand human tuberculosis pathogenesis and potentially identify therapeutic targets to prevent active tuberculosis.

systems biology↗

bioRxiv2021

Chemical-genetic interaction mapping links carbon metabolism and cell wall structure to tuberculosis drug efficacy

Current chemotherapy against Mycobacterium tuberculosis (Mtb), an important human pathogen, requires a multidrug regimen lasting several months. While efforts have been made to optimize therapy by exploiting drug-drug synergies, testing new drug combinations in relevant host environments remains arduous. In particular, host environments profoundly affect the bacterial metabolic state and drug efficacy, limiting the accuracy of predictions based on in vitro assays alone. In this study, we utilize conditional Mtb knockdown mutants of essential genes as an experimentally-tractable surrogate for drug treatment, and probe the relationship between Mtb carbon metabolism and chemical-genetic interactions (CGI). We examined the anti-tubercular drugs isoniazid, rifampicin and moxifloxacin, and found that CGI are differentially responsive to the metabolic state, defining both environment-independent and -dependent interactions. Specifically, growth on the in vivo-relevant carbon source, cholesterol, reduced rifampicin efficacy by altering mycobacterial cell surface lipid composition. We report that a variety of perturbations in cell wall synthesis pathways restore rifampicin efficacy during growth on cholesterol, and that both environment-independent and cholesterol-dependent in vitro CGI could be leveraged to enhance bacterial clearance in the mouse infection model. Our findings present an atlas of novel chemical-genetic-environmental interactions that can be used to optimize drug-drug interactions as well as provide a framework for understanding in vitro correlates of in vivo efficacy. SignificanceEfforts to improve tuberculosis therapy include optimizing multi-drug regimens to take advantage of drug-drug synergies. However, the complex host environment has a profound effect on bacterial metabolic state and drug activity, making predictions of optimal drug combinations difficult. In this study, we leverage a newly developed library of conditional knockdown Mycobacterium tuberculosis mutants in which genetic depletion of essential genes mimics the effect of drug therapy. This tractable system allowed us to assess the effect of growth condition on predicted drug-drug interactions. We found that these interactions can be differentially sensitive to the metabolic state and select in vitro-defined interactions can be leveraged to accelerate bacterial killing during infection. These findings suggest new strategies for optimizing tuberculosis therapy.

microbiology↗

bioRxiv2020

Host-pathogen genetic interactions underlie tuberculosis susceptibility in genetically diverse mice

The outcome of an encounter with Mycobacterium tuberculosis depends on the pathogens ability to adapt to the variable immune pressures exerted by the host. Understanding this interplay has proven difficult, largely because experimentally tractable animal models do not recapitulate the heterogeneity of tuberculosis disease. We leveraged the genetically diverse Collaborative Cross (CC) mouse panel in conjunction with a library of Mtb mutants to associate bacterial genetic requirements with host genetics and immunity. We report that CC strains vary dramatically in their susceptibility to infection and produce qualitatively distinct immune states. Global analysis of Mtb mutant fitness across the CC panel revealed that many virulence pathways are only in specific host microenvironments, identifying the large fraction of the pathogens genome that has been maintained to ensure fitness in a diverse population. Both immunological and bacterial traits were associated with genetic variants distributed across the mouse genome, identifying the specific host-pathogen genetic interactions that influence pathogenesis.

genetics↗