Ujjwal Neogi

The rapid increase in dengue fever incidence poses a formidable challenge to global public health, with an estimated 100 million symptomatic infections annually and a geographical spread predicted to widen due to climate change and urbanization. The COMBAT initiative, a pioneering European Research Constellation, aims to address this by leveraging advanced technologies and knowledge from endemic regions to develop innovative solutions for dengue prevention and treatment. This project advances super-resolution microscopy to observe rare events in host-virus interactions and introduces novel brain-on-chip technology for in-depth dengue pathogenesis modeling. It focuses on developing innovative antiviral strategies to target dengue virus (DENV) entry and mitigate cytokine storms through host-directed therapy. Furthermore, it leverages multi-omics strategies to identify early predictive biomarkers of severe dengue and develop artificial intelligence tools with the aim of crafting comprehensive policies for the EU’s pandemic preparedness. COMBAT employs a unique experimental strategy that integrates cutting-edge technologies such as super-resolution optical microscopy, AI, and machine learning for advanced modeling of the dengue virus-host interactions and early prediction of severe outcomes. By combining efforts in drug repurposing, development of a brain-on-chip model for neuropathogenesis studies, and employing nanoscale imaging for in-depth analysis of the virus's impact, COMBAT represents a significant leap forward in the fight against dengue. The project not only aims to enhance our understanding of the disease but also to create scalable and affordable tools for pandemic preparedness, setting a new standard in interdisciplinary research to COMBAT viral epidemics.

Biomass burning for cooking and heating produces a toxic cocktail of particulate matter (PM) and gaseous pollutants, resulting in chronic lung issues. Around 0.5-0.6 million deaths occur annually in India, mainly in the Indo-Gangetic Plains, from exposure to indoor air pollutants, indicating the multifaceted healthcare, economic and environmental burden in households, where biomass burning prevails. And yet, ground-based indoor exposure data are sparse, there is poor awareness about air pollution, and clean energy initiatives are lagging. We hypothesize that biomass combustion and PM exposure is related to specific pollutant classes (triggers) and their synergistic interactions, which trace early metabolic changes in plasma to diagnose asthma, lung aging, and chronic pulmonary obstructions. We aim to 1) conduct socioeconomic and health surveys and develop real-time indoor pollutant monitoring using automated sensor networks, 2) analyze organic compounds and oxidative metals that cause pulmonary stress, and 3) identify novel biomarkers in plasma and combine this with integrative transcriptomics, epidemiological and environmental chemical data. The proposed synergistic and integrated work packages on PM characteristics and metabolomics will establish causality with exposure posing a breakthrough for early clinical diagnosis. This strategy will help healthcare measures and mitigation plans advance the UN Sustainable Development Goals 3 and 7 for better air quality and health.

Human Immunodeficiency Virus type 1 (HIV-1) persists in long-lived infected cells that are not affected by antiretroviral treatment (ART) and the barrier to the functional HIV-cure. Our recent studies, preliminary data, and others indicated that targeting the immune-metabolic pathways towards central carbon metabolism (CCM) is a potential therapeutic tool to modulate immune response with the hope to clear HIV from reservoirs. We aim to apply computer-driven genome-scale metabolic models (GSMM) to the transcriptomics data from long-term successfully treated cohorts to characterize the metabolic and signaling rearrangement of host cells upon HIV-1 persistence (Aim#1). Further we will perform single-cell metabolic analysis, immune phenotyping and RNAflow based reservoir quantification on the patient materials to understand the diverse metabolic state associated with the latent viral reservoir (Aim#2). Finally we will modulate CCM in an ex vivo primary latent cell model and in vivo latency mouse models to understand the metabolic modulation of HIV-persistence (Aim#3). Our study thus uses a novel approach to delineate the possibility of utilizing immunometabolism as a new target towards HIV-1 management and cure that allow a fruitful route to the computational guiding of experimental HIV-reservoir eradication strategies. Detailed metabolic mapping of the well-treated individuals and its association with the HIV-reservoir can provide novel direction towards HIV-cure strategies.