3 ′-5 ′ crosstalk contributes to transcriptional bursting

Abstract

Background Transcription in mammalian cells is a complex stochastic process involving shuttling of polymerase between genes and phase-separated liquid condensates. It occurs in bursts, which results in vastly different numbers of an mRNA species in isogenic cell populations. Several factors contributing to transcriptional bursting have been identified, usually classified as intrinsic, in other words local to single genes, or extrinsic, relating to the macroscopic state of the cell. However, some possible contributors have not been explored yet. Here, we focus on processes at the 3 ′ and 5 ′ ends of a gene that enable reinitiation of transcription upon termination. Results Using Bayesian methodology, we measure the transcriptional bursting in inducible transgenes, showing that perturbation of polymerase shuttling typically reduces burst size, increases burst frequency, and thus limits transcriptional noise. Analysis based on paired-end tag sequencing (PolII ChIA-PET) suggests that this effect is genome wide. The observed noise patterns are also reproduced by a generative model that captures major characteristics of the polymerase flux between the ends of a gene and a phase-separated compartment. Conclusions Interactions between the 3 ′ and 5 ′ ends of a gene, which facilitate polymerase recycling, are major contributors to transcriptional noise.

A simple guide to de novo transcriptome assembly and annotation

“…RNA-seq reads contain a mixture of fragments corresponding to different parts of different transcripts. Transcriptional noise [ 51 , 52 ], sequencing artifacts [ 53 ] and transcript isoforms originating from alternative splicing [ 54 , 55 ] are also represented in these data. The objective of assembly is to accurately disambiguate the origin of the reads and reconstruct an accurate representation of the parent sequences.…”

Bayesian inference of PolII dynamics over the exclusion process

“…Further, the transcription of mRNA is a very complex process and it may be interesting to include features not encoded in the model used for this study. Further TASEP variants, such as those incorporating non-Markovian jump dynamics or Langmuir kinetics [48][49][50], are relevant for the modelling of PolII recycling and its early detachment from DNA [51,52]. Potential extensions also include estimation of the parameters that encode the system's size and asymmetry (a and b, respectively) and the boundary values.…”

Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision

“…Thus, differences in transcript numbers between cells could be due to a cell size difference (an interesting phenotype in its own right) with no changes in rates of reactions. Ideally this would be accounted for, and whilst it is possible on the scale of a few genes with RNA FISH, through microscopy [ 57 ] or flow cytometry [ 58 ] and even transcriptome-wide with barcoding [ 59 ] – this is laborious and expensive. Recently, scRNA-sequencing combined with cell imaging measurements using microfluidic devices has been demonstrated [ 60 , 61 ].…”

“…A further issue with scRNA-seq datasets is illustrated well by the differing conclusions obtained from these compared to alternatives, such as single molecule RNA FISH (smFISH) [ 57 ] or live cell imaging [ 80 ]; whereas scRNA-seq data suggest that the vast majority of genes feature relatively little variability, consistent with a Poisson distribution of transcript numbers [ 11 ], imaging data usually shows variability higher than a Poisson’s (e.g. [ 58 , 80 , 113 ]).…”