Multi-Omics Lab

Multi-Omics Lab - Unlocking liver Biology through High-Resolution Mass Spectrometry

Welcome to our state-of-the-art Multi-Omics Lab, where cutting-edge research meets advanced technology to unravel the complexities of liver diseases. Our lab specializes in proteomics, metabolomics, lipidomics, and meta-proteomics, enabling a comprehensive understanding of liver diseases at molecular and functional levels.We are equipped with 2 high-resolution mass spectrometry (HRMS) Q exactive plus and an array of advanced analytical tools, we deliver precise and in-depth profiling of proteins, metabolites, lipids, and microbial protein communities. This multi-disciplinary approach empowers us to explore diverse biological questions, identify biomarkers, and understand disease mechanisms with unmatched accuracy and high impact publications.

Key Focus:

• Proteomics: Quantitative and qualitative analysis of proteins to decode cellular functions and pathways.
• Metabolomics: Profiling small molecules and metabolites to uncover metabolic alterations and biochemical pathways.
• Lipidomics: In-depth study of lipid structures and dynamics to explore their roles in health and disease (Liver diseases).
• Meta-Proteomics: Investigation of complex microbial communities to reveal their functional interplay with hosts and environments.
• Mycobiome : Investigation of complex microbial communities to reveal their functional interplay with hosts and environments

Our lab caters to academic researchers, healthcare professionals at ILBS we also work for industries seeking innovative solutions for fundamental and translational research.
Whether it's biomarker discovery, drug development, or precision medicine, our multi-omics capabilities provide transformative insights.

At the Multi-Omics Lab, we are committed to excellence, collaboration, and driving breakthroughs in biological and medical research.

Key areas

• Acute liver failure : Acute liver failure (ALF) is associated with high mortality due to immune dysfunction and increased vulnerability to infections. In our study, plasma lipidomics and mycobiome analysis in ALF patients revealed dysregulated lipid species and fungal communities, strongly correlating with disease severity and early mortality. Specific lipids and fungal peptides, such as Cryptococcus amylolentus and Penicillium oxalicum, were linked to lipid metabolism and inflammation, predicting early mortality.

  Neha Sharma et.al 2024 Clinical and Molecular Hepatology impact factor 14

• Albuminome: Additionally, albumin-bound biomarkers reflecting mitochondrial damage and hyperinflammation were identified as indicators of poor outcomes in ALF. These findings highlight the potential of lipidomic and albuminome analysis for predicting ALF severity and mortality.

  Neha Sharma et.al 2023 Journal of Hepatology impact factor 27

• Severe alcoholic hepatitis: We demonstrate herein how urobilinogen (bilirubin byproduct metabolized by bacteria) levels > 0.07 mg/ml could segregate SAH patients predisposed to corticosteroid non-response or poor outcomes. Our observations provide direct evidence that increase in circulating urobilinogen level is associated with neutrophil activation, increase in gut permeability and corticosteroid response in SAH patients. This finding is significant for clinicians and researchers, as it suggests a new target for therapeutic intervention. Managing urobilinogen levels through fecal microbiota transplantation (FMT) or affinity-based removal may offer promising strategies to improve outcomes in SAH patients

  Manisha Yadav et.al 2024 Clinical and Molecular Hepatology impact factor 14

• Sepsis biology: This study identifies a novel metabolite signature panel for predicting early mortality in pediatric cirrhosis-sepsis (PC-S) patients. By analyzing plasma metaproteomics-metabolomics in 160 patients, we found increased levels of tryptophan metabolites, specific bacterial peptides (Salmonella, Escherichia coli), and metabolites like deoxyribose-1-phosphate and N5-citryl-D-ornithine that segregated PC-S patients. Machine learning algorithms, using these biomarkers, predicted mortality with high accuracy. This panel can be applied to a single drop of blood for early mortality prediction in PC-S.

  Mathew Babu et.al 2023 Hepatology communication impact factor 5.5

• Gall bladder carcinoma : This study investigates bile molecular signatures for early detection of carcinoma of the gall bladder (CAGB), which has a poor prognosis. Proteomic and metabolomic analysis identified signatures which correlated significantly with clinical parameters and showed over 90% accuracy in detecting CAGB. Validation of four key metabolites (toluene, 5,6-DHET, creatine, phenylacetaldehyde) in separate cohorts demonstrated 99% accuracy and high sensitivity/specificity. These findings provide crucial insights and potential metabolomic panels for early CAGB detection.

  Nupur Sharma et.al 2023 Molecular therapy oncology impact factor 6.5

• Machine Learning : Our lab is also trained for Machine Learning. At the forefront of technological innovation, the Machine Learning facility in our Lab is dedicated to advancing the field of artificial intelligence and data-driven solutions. we specialize in areas such as deep learning, natural language processing, computer vision, reinforcement learning, and predictive analytics.

Team Leaders:

• Dr Jaswinder Singh Maras

Team Members:

• Ms. Neha Sharam, PhD Fellow
• Ms. Vasundhara Yadav, PhD Fellow
• Ms. Sanju Yadav, JRF
• Mr. Vipul Sharma, JRF