29 June 2010
Mammalian gene expression patterns, and their variability across populations of cells, are regulated by factors specific to each gene in concert with its surrounding cellular and genomic environment. Gene expression is an intrinsically stochastic process due to the random nature of the biochemical reaction involved, and this intrinsic "noise" can contribute significantly to cell-cell expression variability, even within clonal populations of cells, with important phenotypic consequences. HIV infection is an ideal system for studying a number of fundamental aspects of mammalian gene expression, including control of expression heterogeneities, and regulation by surrounding genomic regions. Upon infection of human immune cells, the HIV genome becomes integrated semi-randomly into the host-cell genome, effectively sampling host-cell genomic regulatory environments. Subsequent stochastically-generated heterogeneities in the dynamics of viral gene expression, modulated by the host-cellular environment at the site of viral integration, have been hypothesized as contributing importantly to specify viral-production phenotypes — from non-productive infections that produce no virus, to productive infections that spread the virus and kill the host cell on a time-scale of days, to latent infections that might become reactivated after a delay period of many years and represent the greatest current obstacle to complete eradication the virus in patients.
I will briefly introduce the concept of intrinsic noise in cellular gene expression, the types of experiments that one might use to quantify it, computational models that have been developed to infer the underlying dynamics that account for the expression heterogeneities quantified in such measurements, and several studies that have investigated the contributions of specific regulatory elements on expression heterogeneities, such as promoter architecture and concentrations of cellular factors. Then I will tell you about measurements in our lab that quantify the modulation of expression variability with genomic integration from a diverse collection of clonal cell populations, each generated by a different single integration of an HIV-promoter/GFP-reporter cassette in (cultured) Jurkat T-cells. Systematically fitting a stochastic model of cellular gene expression to our cytometry measurements reveals an underlying transcriptional dynamic with multiple transcripts produced during short, infrequent bursts, that quantitatively accounts for the wide, highly skewed, protein expression distributions that we observe. We find that the size of transcriptional bursts is the primary systematic covariate over the sampled genomic integrations, rather than their frequency. Though we have yet to identify the molecular/genetic features that account for this puzzling result, I will share with you some speculation and a few preliminary results from ongoing studies in our lab that aim to uncover these features, as well as a model for how the integration-modulated heterogeneities in basal HIV expression patterns, that we have quantified in this study, might act as a determinant of infected-cell fate and contribute to clinical latency.
virtual cell events