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Arjun Raj

Publications and source records attributed to Arjun Raj.

2 recordsLinked to original sources

A hyperactive transcriptional state marks genome reactivation at the mitosis-G1 transition

During mitosis, RNA polymerase II (Pol II) and many transcription factors dissociate from chromatin, and transcription ceases globally. Transcription is known to restart in bulk by telophase, but whether de novo transcription at the mitosis-G1 transition is in any way distinct from later in interphase remains unknown. We tracked Pol II occupancy genome-wide in mammalian cells progressing from mitosis through late G1. Unexpectedly, during the earliest rounds of transcription at the mitosis-G1 transition, ~50% of active genes and distal enhancers exhibit a spike in transcription, exceeding levels observed later in G1 phase. Enhancer-promoter chromatin contacts are depleted during mitosis and restored rapidly upon G1 entry, but do not spike. Of the chromatin-associated features examined, histone H3 lysine 27 acetylation levels at individual loci in mitosis best predict the mitosis-G1 transcriptional spike. Single-molecule RNA imaging supports that the mitosis-G1 transcriptional spike can constitute the maximum transcriptional activity per DNA copy throughout the cell division cycle. The transcriptional spike occurs heterogeneously and propagates to cell-to-cell differences in mature mRNA expression. Our results raise the possibility that passage through the mitosis-G1 transition might predispose cells to diverge in gene expression states.

Molecular Biology

Transcriptional bursting explains the noise-versus-mean relationship in mRNA and protein levels

Recent analysis (Dey et al, 2015), demonstrates that the HIV-1 Long Terminal Repeat (HIV LTR) promoter exhibits a range of possible transcriptional burst sizes and frequencies for any mean-expression level. However, these results have also been interpreted as demonstrating that cell-to-cell expression variability (noise) and mean are uncorrelated, a significant deviation from previous results. Here, we re-examine the available mRNA and protein abundance data for the HIV LTR and find that noise in mRNA and protein expression scales inversely with the mean along analytically predicted transcriptional burst-size manifolds. We then experimentally perturb transcriptional activity to test a prediction of the multiple burst-size model: that increasing burst frequency will cause mRNA noise to decrease along given burst-size lines as mRNA levels increase. The data show that mRNA and protein noise decrease as mean expression increases, supporting the canonical inverse correlation between noise and mean.\n\nConflict of InterestThe authors declare that they have no conflict of interest.

Systems Biology