From genomics to
therapeutics:
Single-cell dissection and manipulation of disease circuitry

Manolis Kellis, Ph.D.
Professor, MIT Computer
Science and Artificial Intelligence Lab
Member, Broad Institute of MIT and Harvard

Date:
Monday, October 4th, 2021, 19:00GR

zoom: https://zoom.us/j/97078540320    

Abstract:
Disease-associated variants lie primarily in non-coding regions, increasing the
urgency of understanding how gene-regulatory circuitry impacts human disease.
To address this challenge, we generate comparative genomics, epigenomic, and
transcriptional maps, spanning 823 human tissues, 1500 individuals, and 20
million single cells. We link variants to target genes, upstream regulators,
cell types of action, and perturbed pathways, and predict causal genes and
regions to provide unbiased views of disease mechanisms, sometimes re-shaping
our understanding. We find that Alzheimer’s variants act primarily through
immune processes, rather than neuronal processes, and the strongest genetic
association with obesity acts via energy storage/dissipation rather than
appetite/exercise decisions. We combine single-cell profiles, tissue-level
variation, and genetic variation across healthy and diseased individuals to map
genetic effects into epigenomic, transcriptional, and function changes at
single-cell resolution, to recognize cell-type-specific disease-associated
somatic mutations indicative of mosaicism, and to recognize multi-tissue
single-cell effects of exercise and obesity. We expand these methods to
electronic health records to recognize multi-phenotype effects of genetics,
environment, and disease, combining clinical notes, lab tests, and diverse data
modalities despite missing data. We integrate large cohorts to factorize
phenotype-genotype correlations to reveal distinct biological contributors of complex
diseases and traits, to partition disease complexity, and to stratify patients
for pathway-matched treatments. Lastly, we develop massively-parallel,
programmable and modular technologies for manipulating these pathways by
high-throughput reporter assays, genome editing, and gene targeting in human
cells and mice, to propose new therapeutic hypotheses in Alzheimer’s, obesity,
and cancer. These results provide a roadmap for translating genetic findings
into mechanistic insights and ultimately new therapeutic avenues for complex
disease and cancer.

Bio:
Manolis Kellis is a professor of computer science at MIT, a member of the
Broad Institute of MIT and Harvard, a principal investigator of the Computer
Science and Artificial Intelligence Lab at MIT, and head of the MIT
Computational Biology Group (compbio.mit.edu). His research includes disease
circuitry, genetics, genomics, epigenomics, coding genes, non-coding RNAs,
regulatory genomics, and comparative genomics, applied to Alzheimer’s Disease,
Obesity, Schizophrenia, Cardiac Disorders, Cancer, and Immune Disorders, and
multiple other disorders. He has led several large-scale genomics projects,
including the Roadmap Epigenomics project, the ENCODE project, the Genotype
Tissue-Expression (GTEx) project, and comparative genomics projects in mammals,
flies, and yeasts. He received the US Presidential Early Career Award in
Science and Engineering (PECASE) by US President Barack Obama, the Mendel Medal
for Outstanding Achievements in Science, the NSF CAREER award, the Alfred P.
Sloan Fellowship, the Technology Review TR35 recognition, the AIT Niki Award,
and the Sprowls award for the best Ph.D. thesis in computer science at MIT. He
has authored over 240 journal publications cited more than 120,000 times. He has
obtained more than 20 multi-year grants from the NIH, and his trainees hold
faculty positions at Stanford, Harvard, CMU, McGill, Johns Hopkins, UCLA, and
other top universities. He lived in Greece and France before moving to the US,
and he studied and conducted research at MIT, the Xerox Palo Alto Research
Center, and the Cold Spring Harbor Lab. For more info, see: compbio.mit.edu