Biology subjects

Immanuel, S. R. C.

Publications and source records attributed to Immanuel, S. R. C..

4 recordsLinked to original sources

bioRxiv2021

Transcriptome signature of cell viability predicts drug response and drug interaction for Tuberculosis

The treatment of tuberculosis (TB), which kills 1.8 million each year, remains difficult, especially with the emergence of multidrug resistant strains of Mycobacterium tuberculosis (Mtb). While there is an urgent need for new drug regimens to treat TB, the process of drug evaluation is slow and inefficient owing to the slow growth rate of the pathogen, the complexity of performing bacteriologic assays in a high-containment facility, and the context-dependent variability in drug sensitivity of the pathogen. Here, we report the development of "DRonA" and "MLSynergy", algorithms to perform rapid drug response assays and predict response of Mtb to novel drug combinations. Using a novel transcriptome signature for cell viability, DRonA accurately detects bacterial 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 novel synergistic and antagonistic multi-drug 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.

microbiology↗

bioRxiv2021

Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks of Mycobacterium tuberculosis

The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states, underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME (Phenotype of Regulatory influences Integrated with Metabolism and Environment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs.

systems biology↗

bioRxiv2020

Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile

Though Clostridioides difficile is among the most studied anaerobes, the interplay of metabolism and regulation that underlies its ability to colonize the human gut is unknown. We have compiled public resources into three models and a portal to support comprehensive systems analysis of C. difficile. First, by leveraging 151 transcriptomes from 11 studies we generated a regulatory model (EGRIN) that organizes 90% of C. difficile genes into 297 high quality conditional co-regulation modules. EGRIN predictions, validated with independent datasets, recapitulated and extended regulons of key transcription factors, implicating new genes for sporulation, carbohydrate transport and metabolism. Second, by advancing a metabolic model, we discovered that 15 amino acids, diverse carbohydrates, and 10 metabolic genes are essential for C. difficile growth within an intestinal environment. Finally, by integrating EGRIN with the metabolic model, we developed a PRIME model that revealed unprecedented insights into combinatorial control of essential processes for in vivo colonization of C. difficile and its interactions with commensals. We have developed an interactive web portal (http://networks.systemsbiology.net/cdiff-portal/) to disseminate all data, algorithms, and models to support collaborative systems analyses of C. difficile.

systems biology↗

bioRxiv2020

Disrupting the ArcA regulatory network increases tetracycline susceptibility of TetR Escherichia coli

There is an urgent need for strategies to discover secondary drugs to prevent or disrupt antimicrobial resistance (AMR), which is causing >700,000 deaths annually. Here, we demonstrate that tetracycline resistant (TetR) Escherichia coli undergoes global transcriptional and metabolic remodeling, including down-regulation of tricarboxylic acid cycle and disruption of redox homeostasis, to support consumption of the proton motive force for tetracycline efflux. Targeted knockout of ArcA, identified by network analysis as a master regulator among 25 transcription factors of this new compensatory physiological state, significantly increased the susceptibility of TetR E. coli to tetracycline treatment. A drug, sertraline, which generated a similar metabolome profile as the arcA knockout strain also synergistically re-sensitized TetR E. coli to tetracycline. The potentiating effect of sertraline was eliminated upon knocking out arcA, demonstrating that the mechanism of synergy was through action of sertraline on the tetracycline-induced ArcA network in the TetR strain. Our findings demonstrate that targeting mechanistic drivers of compensatory physiological states could be a generalizable strategy to re-sensitize AMR pathogens to lost antibiotics.

systems biology↗