Scopes

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 We explore how the most fundamental genetic element - DNA/RNA sequences - impacts the transcriptome from a cross-omics view. 

 We are interested in interpreting the non-coding GWAS in neurological disorders and identifying the causality. 

 Using cutting-edge in silico strategies, we are developing various treatment modes to ameliorate the disease phenotypes in multiple neurodegenerative disorders. 

Amyotrophic lateral sclerosis (ALS) is a lethal neurodegenerative disease characterized by progressive loss of motor neurons, muscle weakness, and paralysis, culminating in death within a few years of diagnosis. Multiple genetic and environmental risk factors, cellular stressors, and dysregulated molecular pathways have been implicated, making it clear that ALS is not driven by a single pathogenetic mechanism but rather emerges from a confluence of biological processes. This heterogeneity complicates both diagnosis and therapy, as patients present with variable phenotypes, disease trajectories, and responses to interventions. Understanding the underlying etiological diversity at a molecular and pathway level is therefore critical. We have developed a highly innovative deep learning approach to screen ALS transcriptomics from a systematic view. The ongoing research is generating multiple novel insights into the disease etiology and prioritizing candidate drug molecules to normalize ALS dysregulation. 

The purpose of project is to study the functional impact of mobile element insertions (MEIs) in neurological disorders (NDs) using new developments in deep learning techniques. MEIs are transposable DNA fragments that are able to insert throughout the human genome. There are at least 124 independent MEIs associated with human diseases. Approximately 20% of these diseases represent a spectrum of NDs, yet the overall contribute of MEIs to the etiology of NDs has not been systematically estimated. To address this, we will (1) characterize functional MEIs in GTEx cohorts in healthy individuals; (2) build a comprehensive functional map of MEIs to determine tissue-specific and brain-specific impact; and (3) impute transcriptional changes on various NDs. 

Recent studies have shown that neurons can control gene expression during critical developmental stages by coupling alternative splicing with nonsense mediated decay (NMD). This mechanism relies on inclusion of cassette exons, known as poison exons, to create in-frame premature termination codons that trigger transcript NMD and reduce protein expression. Importantly, genetic variants promoting constitutive inclusion of poison exons have been associated with neurodevelopmental and, more recently, neurodegenerative diseases. However, the field lacks rigorous methods to identify and annotate poison exons and variants affecting their splicing. This project aims at leveraging tissue-specific RNASeq from large cohorts and comprehensively identify poison exons.

We have previously identified the causal mechanism of X-linked Dystonia-Parkinsonism (XDP), a rare and lethal neurodegenerative disorder, and found a series of mis-splicing events in the disease-driver gene TAF1. Here we are taking advantage of iPSC-derived neuronal culture, CRISPR-based genome engineering, and multiple RNASeq strategies to pursue molecular treatments that restore the aberrant transcriptional signals in XDP lines. These results will serve as a key part of drug development process for XDP patients, which is actively led by the Collaborative Center for XDP (CCXDP) of MGH and its pharmaceutical collaborators.

Familial dysautonomia (FD), a hereditary sensory and autonomic neuropathy, is caused by a mutation in the Elongator complex protein 1 (ELP1) gene that leads to a tissue-specific reduction of ELP1 protein. Our collaboration with Susan Slaugenhaupt Lab at the Center for Genomic Medicine of MGH has revealed potential gene and pathway signatures underlying the disease manifestation. It is also evident that specific neuronal subtypes are sensitive to ELP1 reduction. In this project, we will focus on deconvolving the molecular etiology of FD at single-cell (sc) level using scRNA and spatial transcriptional measurements.