Biology subjects

Madan, U.

Publications and source records attributed to Madan, U..

3 recordsLinked to original sources

bioRxiv2022

Prophylactic treatment of Glycyrrhiza glabra mitigates COVID-19 pathology through inhibition of pro-inflammatory cytokines in the hamster model and NETosis.

Severe coronavirus disease (COVID-19) is accompanied with acute respiratory distress syndrome & pulmonary pathology, and is presented mostly with inflammatory cytokine release, dysregulated immune response, skewed neutrophil/ lymphocyte ratio, and hypercoagulable state. Though vaccinations have proved effective in reducing the COVID-19 related mortality, the limitation of use of vaccine against immunocompromised, comorbidity, and emerging variants remains a concern. In the current study we investigate for the first-time the efficacy of Glycyrrhiza glabra (GG) extract, a potent immunomodulator, against SARS-CoV-2 infection in hamsters. Prophylactic treatment with GG showed protection against loss in body weight and 35-40% decrease in lung viral load along with reduced lung pathology in the hamster model. Remarkably, GG reduced the mRNA expression of pro-inflammatory cytokines and Plasminogen activator inhibito-1 (PAI-1). In-vitro, GG acted as potent immunomodulator by reducing Th2 and Th17 differentiation and IL-4 and IL-17A cytokine production. In addition, GG also showed robust potential to suppress ROS, mtROS and NETs generation in a concentration dependent manner in both human polymorphonuclear neutrophils (PMNs) and murine bone marrow derived neutrophils (BMDNs). Taken together, we provide evidence for the protective efficacy of GG against COVID-19 and its putative mechanistic insight, which might be developed as a future immunomodulatory approach against various pathologies with high cytokine production, aberrant neutrophil activation including coronavirus infection.

immunology↗

bioRxiv2022

Innate immune response and distinct metabolomic signatures together drive and shape the SARS-CoV-2-specific T cell response during COVID-19

The underlying factors contributing to the evolution of SARS-CoV-2-specific T cell responses during COVID-19 infection remain unidentified. To address this, we characterized innate and adaptive immune responses with metabolomic profiling longitudinally at three different time points (0-3, 7-9, and 14-16 days post-COVID-19 positivity) from young mildly symptomatic active COVID-19 patients infected during the first wave in mid-2020. We observed that anti-RBD IgG and viral neutralization are significantly reduced against the Delta variant compared to the ancestral strain. In contrast, compared to the ancestral strain, T cell responses remain preserved against the delta and omicron variants. We determined innate immune responses during the early stage of active infection in response to TLR 3/7/8 mediated activation in PBMCs and serum metabolomic profiling. Correlation analysis indicated PBMCs-derived proinflammatory cytokines, IL-18, IL-1{beta}, and IL-23, and the abundance of plasma metabolites involved in arginine biosynthesis were predictive of a robust SARS-CoV-2-specific Th1 response at a later stage (two weeks after PCR positivity). These observations may contribute to designing effective vaccines and adjuvants that promote innate immune responses and metabolites to induce long-lasting anti-SARS-CoV-2 specific T cells response.

immunology↗

bioRxiv2021

Toward More General Embeddings for Protein Design: Harnessing Joint Representations of Sequence and Structure

AO_SCPLOWBSTRACTC_SCPLOWProtein embeddings learned from aligned sequences have been leveraged in a wide array of tasks in protein understanding and engineering. The sequence embeddings are generated through semi-supervised training on millions of sequences with deep neural models defined with hundreds of millions of parameters, and they continue to increase in performance on target tasks with increasing complexity. We report a more data-efficient approach to encode protein information through joint training on protein sequence and structure in a semi-supervised manner. We show that the method is able to encode both types of information to form a rich embedding space which can be used for downstream prediction tasks. We show that the incorporation of rich structural information into the context under consideration boosts the performance of the model by predicting the effects of single-mutations. We attribute increases in accuracy to the value of leveraging proximity within the enriched representation to identify sequentially and spatially close residues that would be affected by the mutation, using experimentally validated or predicted structures.

biochemistry↗