When the Same Genotype Leads to Multiple Phenotypes: Measuring and Manipulating Partial Penetrance Underlying Disease

Abstract:
When a genotype is partially penetrant, not all individuals in an isogenic population display the same phenotype.  This effect is seen widely across biology: genetics are only a modest predictor of disease for monozygotic twins; only a fraction of cells during cellular reprogramming become pluripotent; mutations lead to cancer in only a fraction of cases.  Partial penetrance is often difficult to measure, hard to target therapeutically, and its underlying mechanisms remain unclear.  Here I demonstrate that retroviruses, such as HIV, provide an excellent system to study penetrance.  HIV integrates semi-randomly across the genome and, as we’ve recently shown (Cell 2015), initiates a program of probabilistic fate-choice between active replication or transcriptionally-silent ‘latency’.  The latent virus can stochastically switch to active replication, obligating patients to take antiretroviral drugs for life.  This partially penetrant behavior of latent-or-active switching is the primary barrier to curing HIV-AIDS.  In my research, I take advantage of the fact that viral fate is sensitive to its genomic location and repurpose it as a genome-wide probe.  I will describe methods to measure viral fate switching across genomic sites through next-generation sequencing and time-lapse microscopy.  From a therapeutic perspective, I will discuss how these measurements enable computational identification of latency-reversing drugs.  From a cellular perspective, the measurements provide a probabilistic map of transcription across the genome.  This quantifies transcriptional traits of sites such as leakiness, inducibility, or constitutive behavior – information that is immediately relevant in picking safe harbors for genetic engineering.  Broadly, measuring how specific genetic features enhance or suppress viral fate switching reveals how these features function natively and how they may be targeted when associated with disease.

Event Details

Date/Time:

  • Thursday, 2017, May 4 - 3:00pm to 4:00pm

Location:
Georgia Tech, EBB 1005