Inhibition of leukotriene-B4 signalling-mediated host response to tuberculosis is a potential mode of adjunctive host-directed therapy.

Treatment of tuberculosis (TB) is faced with several challenges including the long treatment duration, drug toxicity and tissue pathology. Host-directed therapy provides promising avenues to find compounds for adjunctively assisting antimycobacterials in the TB treatment regimen, by promoting pathogen eradication or limiting tissue destruction. Eicosanoids are a class of lipid molecules that are potent mediators of inflammation and have been implicated in aspects of the host response against TB. Here, we have explored the blood transcriptome of pulmonary TB patients to understand the activity of leukotriene B4, a pro-inflammatory eicosanoid. Our study shows a significant upregulation in the leukotriene B4 signalling pathway in active TB patients, which is reversed with TB treatment. We have further utilized our in-house network analysis algorithm, ResponseNet, to identify potential downstream signal effectors of leukotriene B4 in TB patients including STAT1/2 and NADPH oxidase at a systemic as well as local level, followed by experimental validation of the same. Finally, we show the potential of inhibiting leukotriene B4 signalling as a mode of adjunctive host-directed therapy against TB. This study provides a new mode of TB treatment along with mechanistic insights which can be further explored in pre-clinical trials.

Systems-level profiling of early peripheral host-response landscape variations across COVID-19 severity states in an Indian cohort.

Host immune response to COVID-19 plays a significant role in regulating disease severity. Although big data analysis has provided significant insights into the host biology of COVID-19 across the world, very few such studies have been performed in the Indian population. This study utilizes a transcriptome-integrated network analysis approach to compare the immune responses between asymptomatic or mild and moderate-severe COVID-19 patients in an Indian cohort. An immune suppression phenotype is observed in the early stages of moderate-severe COVID-19 manifestation. A number of pathways are identified that play crucial roles in the host control of the disease such as the type I interferon response and classical complement pathway which show different activity levels across the severity spectrum. This study also identifies two transcription factors, IRF7 and ESR1, to be important in regulating the severity of COVID-19. Overall this study provides a deep understanding of the peripheral immune landscape in the COVID-19 severity spectrum in the Indian genetic background and opens up future research avenues to compare immune responses across global populations.

PathTracer Comprehensively Identifies Hypoxia-Induced Dormancy Adaptations in Mycobacterium tuberculosis.

Mining large-scale data to discover biologically relevant information remains a challenge despite the rapid development of bioinformatics tools. Here, we have developed a new tool, PathTracer, to identify biologically relevant information flows by mining genome-wide protein-protein interaction networks following integration of gene expression data. PathTracer successfully mines interactions between genes and traces the most perturbed paths of perceived activities under the conditions of the study. We further demonstrated the utility of this tool by identifying adaptation mechanisms of hypoxia-induced dormancy in Mycobacterium tuberculosis (Mtb).

The MYCN 5' UTR as a therapeutic target in neuroblastoma.

Tumor MYCN amplification is seen in high-risk neuroblastoma, yet direct targeting of this oncogenic transcription factor has been challenging. Here, we take advantage of the dependence of MYCN-amplified neuroblastoma cells on increased protein synthesis to inhibit the activity of eukaryotic translation initiation factor 4A1 (eIF4A1) using an amidino-rocaglate, CMLD012824. Consistent with the role of this RNA helicase in resolving structural barriers in 5' untranslated regions (UTRs), CMLD012824 increased eIF4A1 affinity for polypurine-rich 5' UTRs, including that of the MYCN and associated transcripts with critical roles in cell proliferation. CMLD012824-mediated clamping of eIF4A1 spanned the full lengths of mRNAs, while translational inhibition was mediated through 5' UTR binding in a cap-dependent and -independent manner. Finally, CMLD012824 led to growth inhibition in MYCN-amplified neuroblastoma models without generalized toxicity. Our studies highlight the key role of eIF4A1 in MYCN-amplified neuroblastoma and demonstrate the therapeutic potential of disrupting its function.

Pressured cytotoxic T cell epitope strength among SARS-CoV-2 variants correlates with COVID-19 severity.

Heterogeneity in susceptibility among individuals to COVID-19 has been evident through the pandemic worldwide. Cytotoxic T lymphocyte (CTL) responses generated against pathogens in certain individuals are known to impose selection pressure on the pathogen, thus driving emergence of new variants. In this study, we probe the role played by host genetic heterogeneity in terms of HLA-genotypes in determining differential COVID-19 severity in patients. We use bioinformatic tools for CTL epitope prediction to identify epitopes under immune pressure. Using HLA-genotype data of COVID-19 patients from a local cohort, we observe that the recognition of pressured epitopes from the parent strain Wuhan-Hu-1 correlates with COVID-19 severity. We also identify and rank list HLA-alleles and epitopes that offer protectivity against severe disease in infected individuals. Finally, we shortlist a set of 6 pressured and protective epitopes that represent regions in the viral proteome that are under high immune pressure across SARS-CoV-2 variants. Identification of such epitopes, defined by the distribution of HLA-genotypes among members of a population, could potentially aid in prediction of indigenous variants of SARS-CoV-2 and other pathogens.

Cysteine desulfurase (IscS)-mediated fine-tuning of bioenergetics and SUF expression prevents Mycobacterium tuberculosis hypervirulence.

Iron-sulfur (Fe-S) biogenesis requires multiprotein assembly systems, SUF and ISC, in most prokaryotes. M. tuberculosis (Mtb) encodes a complete SUF system, the depletion of which was bactericidal. The ISC operon is truncated to a single gene iscS (cysteine desulfurase), whose function remains uncertain. Here, we show that MtbΔiscS is bioenergetically deficient and hypersensitive to oxidative stress, antibiotics, and hypoxia. MtbΔiscS resisted killing by nitric oxide (NO). RNA sequencing indicates that IscS is important for expressing regulons of DosR and Fe-S-containing transcription factors, WhiB3 and SufR. Unlike wild-type Mtb, MtbΔiscS could not enter a stable persistent state, continued replicating in mice, and showed hypervirulence. The suf operon was overexpressed in MtbΔiscS during infection in a NO-dependent manner. Suppressing suf expression in MtbΔiscS either by CRISPR interference or upon infection in inducible NO-deficient mice arrests hypervirulence. Together, Mtb redesigned the ISC system to "fine-tune" the expression of SUF machinery for establishing persistence without causing detrimental disease in the host.

A 9-gene biomarker panel identifies bacterial coinfections in culture-negative COVID-19 cases.

Confirmatory diagnosis of bacterial coinfections with COVID-19 is challenging due to the limited specificity of the widely used gold-standard culture sensitivity test despite clinical presentations. A misdiagnosis can either lead to increased health complications or overuse of antibiotics in COVID-19 patients. With a multi-step systems biology pipeline, we have identified a 9-gene biomarker panel from host blood that can identify bacterial coinfection in COVID-19 patients, even in culture-negative cases. We have also formulated a qPCR-based score that diagnoses bacterial coinfection with COVID-19 with the accuracy, specificity, and sensitivity of 0.93, 0.96, and 0.89, respectively. This gene signature and score can assist in the clinical decision-making process of necessary and timely prescription of antibiotics in suspected bacterial coinfection cases with COVID-19 and thereby help to reduce the associated morbidity and mortality.

Immune Subtyping in Latent Tuberculosis.

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.

VB10, a new blood biomarker for differential diagnosis and recovery monitoring of acute viral and bacterial infections.

BACKGROUND: Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics. METHODS: We combine blood transcriptomes from six independent datasets (n = 756) with a knowledge-based human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort. FINDING: We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB10) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB10 in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB10 is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB10 score on publicly available COVID-19 data and find that our score detected viral infection in patient samples. INTERPRETATION: Our results point to the promise of VB10 as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts. FUNDING: Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.

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

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.