Transformative Science and Technology Solutions

Our state of thart integration of genomic mutations at the DNA, RNA, and protein levels demonstrate that metabolic disruption in tumors profoundly affects carcinogenesis, mutagenesis, and immunotherapy response. 

GENDULF is a novel algorithm that identifies genetic modifiers for monogenetic diseases from healthy and disease gene expression data, by detecting patterns of co-expression that are uniquely observed in healthy tissues. We applied the algorithm to characterize functional aspects of modifier genes that may be clinically important for rare diseases like Cystic Fibrosis, Spinal Muscular Atrophy etc.

 We developed an end-to-end workflow for processing and analyzing Hi-C data in human cell lines. We demonstrated that the spatial (3D) proximity of genes in a pathway highly correlates with the pathway’s context-specific expression and functional activities. This provides the first evidence that the pathway-centric organization of the 3D nucleome involves functionally related interacting driver genes tending to be in spatial-proximity in a context-specific manner. Applying this workflow, we characterize how modulator genes contribute to developmental disorders, such as neurological disorders.   

We developed end-to-end in-silico workflow to quantify Alternative Splicing (AS) of mRNA. We demonstrated crucial features of Intron Retention (IR) in Chronic Lymphocytic Leukemia (CLL), and in-vivo confirmed how these transcriptome alterations potentially impact leukemia’s pathophysiology. Our approach may be used to test broader implications of the IR/AS in carcinogenesis generally.

We developed a workflow for integrating proteomes of eukaryotes by exploiting the complete genome sequences and protein-coding gene annotations. We performed a comprehensive gene-set-centric analysis of proteomic diversity between humans and 54 eukaryotic organisms, resulting in a catalog of organisms most similar to humans in terms of specific pathways, processes, expression patterns, and diseases. We corroborated our findings using species-specific mass spectrometry data. This algorithmic implementation can empower in vivo validation of human pathophysiology for a given disease in a relevant model organism.  

 We developed end-to-end workflow for processing and analyzing the gut Microbiome from hospitalized children who suffered from Celiac Disease (CD). Here we performed rigorous multivariate association, cross-sectional, and longitudinal analyses using metagenomic and metabolomic data collected at birth, 3-months and 6-months of age to explore the impact of genetic predisposition and environmental risk factors on the gut microbiota composition, function, and metabolome. Our study provides unprecedented insights into major taxonomic and functional shifts in the developing gut microbiota of infants at risk of CD linking genetic and environmental risk factors to detrimental immunomodulatory and inflammatory effects.