Pioneering Genomic Discoveries

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

GENDULF is a novel algorithm designed to identify genetic modifiers for monogenetic diseases using both healthy and disease gene expression data. It works by detecting patterns of co-expression uniquely observed in healthy tissues.  We have applied this algorithm to characterize the functional aspects of modifier genes that may be clinically significant for rare diseases such as Cystic Fibrosis and Spinal Muscular Atrophy. 

We have developed an end-to-end workflow for processing and analyzing Hi-C data in human cell lines. Our research demonstrates that the spatial (3D) proximity of genes within a pathway highly correlates with that 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 that tend to be in spatial proximity in a context-specific manner. By applying this workflow, we can characterize how modulator genes contribute to developmental disorders, including various neurological conditions.  This provides the first evidence that the pathway-centric organization of the 3D nucleome involves functionally related interacting driver genes that tend to be in spatial proximity in a context-specific manner. By applying this workflow, we can characterize how modulator genes contribute to developmental disorders, including various neurological conditions. 

We have developed an end-to-end in-silico workflow to quantify mRNA alternative splicing (AS). Through this approach, we demonstrated crucial features of intron retention (IR) in chronic lymphocytic leukemia (CLL) and confirmed in vivo how these transcriptome alterations potentially impact the pathophysiology of the disease.  Our findings suggest that this approach may be used to test the broader implications of IR and AS in general carcinogenesis.

We have developed a workflow for integrating eukaryotic proteomes by utilizing complete genome sequences and protein-coding gene annotations. By performing a comprehensive gene-set-centric analysis of proteomic diversity between humans and 54 eukaryotic organisms, we have established a catalog of organisms most similar to humans across specific pathways, processes, expression patterns, and diseases.  We corroborated our findings using species-specific mass spectrometry data. This algorithmic implementation is designed to facilitate the in-vivo validation of human pathophysiology within the most relevant model organisms for a given disease.

We have developed an end-to-end workflow for processing and analyzing the gut microbiome of hospitalized children with Celiac Disease (CD). Our research involved rigorous multivariate association, cross-sectional, and longitudinal analyses using metagenomic and metabolomic data collected at birth, three months, and six months of age.  The study explores how genetic predisposition and environmental risk factors impact gut microbiota composition, function, and the metabolome. Our findings provide unprecedented insights into the taxonomic and functional shifts in the developing gut microbiota of infants at risk for CD, specifically linking these risk factors to detrimental immunomodulatory and inflammatory effects.