A Strategic Target Rescues Trimethoprim Sensitivity in Escherichia coli

Bhosle, Amrisha; Datey, Akshay; Chandrasekharan, Giridhar; Singh, Deepshikha; Chakravortty, Dipshikha; Chandra, Nagasuma

doi:10.1016/j.isci.2020.100986

Cited by 14 publications

(15 citation statements)

References 45 publications

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“…To mine the weighted LTBI networks, we use our previously developed computational method ( 10 , 17 , 21 , 35 ), which involved identification of ‘most active’ and ‘most repressed’ paths in each network based on the path cost. Path cost was computed as the sum of weights of edges present in the path normalized by the number of nodes in the path (Equation 4).…”

Section: Methodsmentioning

confidence: 99%

Immune Subtyping in Latent Tuberculosis

et al. 2021

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Latent tuberculosis infection (LTBI) poses a major roadblock in the global effort to eradicate tuberculosis (TB). A deep understanding of the host responses involved in establishment and maintenance of TB latency is required to propel the development of sensitive methods to detect and treat LTBI. Given that LTBI individuals are typically asymptomatic, it is challenging to differentiate latently infected from uninfected individuals. A major contributor to this problem is that no clear pattern of host response is linked with LTBI, as molecular correlates of latent infection have been hard to identify. In this study, we have analyzed the global perturbations in host response in LTBI individuals as compared to uninfected individuals and particularly the heterogeneity in such response, across LTBI cohorts. For this, we constructed individualized genome-wide host response networks informed by blood transcriptomes for 136 LTBI cases and have used a sensitive network mining algorithm to identify top-ranked host response subnetworks in each case. Our analysis indicates that despite the high heterogeneity in the gene expression profiles among LTBI samples, clear patterns of perturbation are found in the immune response pathways, leading to grouping LTBI samples into 4 different immune-subtypes. Our results suggest that different subnetworks of molecular perturbations are associated with latent tuberculosis.

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Section: Methodsmentioning

confidence: 99%

Immune Subtyping in Latent Tuberculosis

et al. 2021

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“…Response network analysis is an in-house computational pipeline that integrates transcriptome data in protein-protein interaction networks to capture the important perturbations in a cell or organism in a certain condition (Sambarey et al, 2013 , 2017 ; Padiadpu et al, 2016 ; Bhosle et al, 2020 ). We generated response networks for each dormancy stage of each condition by weighing the nodes and edges with transcriptome information using the following Equations (1), (2), and (3).…”

Section: Methodsmentioning

confidence: 99%

“…Response network analysis has been proven to be an elegant tool in strategic identification of drug targets in pathogens due to its capacity of delineating the most perturbed cellular processes and cross-talk in a condition of interest Padiadpu et al (2016), Bhosle et al (2020). Here, we have constructed a knowledgebased functional interactome MtbPPIN and generated dormancy response networks (Dormancy TPN ) for each of the in vitro showing the overlap between DEGs (fold change ≥ ±1.5, FDR ≤ 0.05) between different dormancy models w.r.t.…”

Section: Response Network Analysis Identifies Top-ranked Hubs Associamentioning

confidence: 99%

A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

et al. 2020

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Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

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“…Unfortunately, resistance to sulfa drugs arose rapidly ( 7 ). However, recent studies have shown that OCM inhibitors could serve as effective antibiotics when additional enzymes in the pathway are inhibited ( 8 , 9 ), leading to a renewed interest in these drugs. Further, perturbation of OCM in Salmonella typhimurium attenuates the strain to render it a potential vaccine candidate ( 10 ).…”

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confidence: 99%

Regulation of translation by one-carbon metabolism in bacteria and eukaryotic organelles

Shetty

Varshney

2021

Journal of Biological Chemistry

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Protein synthesis is an energetically costly cellular activity. It is therefore important that the process of mRNA translation remains in excellent synchrony with cellular metabolism and its energy reserves. Unregulated translation could lead to the production of incomplete, mistranslated, or misfolded proteins, squandering the energy needed for cellular sustenance, and causing cytotoxicity. One-carbon metabolism (OCM), an integral part of cellular intermediary metabolism, produces a number of one-carbon unit intermediates (formyl, methylene, methenyl, methyl). These OCM intermediates are required for the production of amino acids like methionine, and biomolecules such as purines, thymidylate, and redox regulators. In this review, we discuss how OCM impacts the translation apparatus (composed of ribosome, tRNA, mRNA, and translation factors) and regulates crucial steps in protein synthesis. More specifically, we address how the OCM metabolites regulate the fidelity and rate of translation initiation in bacteria and eukaryotic organelles such as mitochondria. Modulation of the fidelity of translation initiation by OCM opens new avenues to understand alternative translation mechanisms involved in stress tolerance and drug resistance.

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A Strategic Target Rescues Trimethoprim Sensitivity in Escherichia coli

Cited by 14 publications

References 45 publications

Immune Subtyping in Latent Tuberculosis

Immune Subtyping in Latent Tuberculosis

A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

Regulation of translation by one-carbon metabolism in bacteria and eukaryotic organelles

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