3 '-5 ' crosstalk contributes to transcriptional bursting

The HIV-1 Transcriptional Program: From Initiation to Elongation Control

A large body of work in the last four decades has revealed the key pillars of HIV-1 transcription control at the initiation and elongation steps. Here, I provide a recount of this collective knowledge starting with the genomic elements (DNA and nascent TAR RNA stem-loop) and transcription factors (cellular and the viral transactivator Tat), and later transitioning to the assembly and regulation of transcription initiation and elongation complexes, and the role of chromatin structure. Compelling evidence support a core HIV-1 transcriptional program regulated by the sequential and concerted action of cellular transcription factors and Tat to promote initiation and sustain elongation, highlighting the efficiency of a small virus to take over its host to produce the high levels of transcription required for viral replication. I summarize new advances including the use of CRISPR-Cas9, genetic tools for acute factor depletion, and imaging to study transcriptional dynamics, bursting and the progression through the multiple phases of the transcriptional cycle. Finally, I describe current challenges to future major advances and discuss areas that deserve more attention to both bolster our basic knowledge of the core HIV-1 transcriptional program and open up new therapeutic opportunities.

N-terminal tagging of RNA Polymerase II shapes transcriptomes more than C-terminal alterations

RNA polymerase II (Pol II) has a C-terminal domain (CTD) that is unstructured, consisting of a large number of heptad repeats, and whose precise function remains unclear. Here, we investigate how altering the CTD's length and fusing it with protein tags affects transcriptional output on a genome-wide scale in mammalian cells at single-cell resolution. While transcription generally appears to occur in burst-like fashion, where RNA is predominantly made during short bursts of activity that are interspersed with periods of transcriptional silence, the CTD's role in shaping these dynamics seems gene-dependent; global patterns of bursting appear mostly robust to CTD alterations. Introducing protein tags with defined structures to the N terminus cause transcriptome-wide effects, however. We find the type of tag to dominate characteristics of the resulting transcriptomes. This is possibly due to Pol II-interacting factors, including non-coding RNAs, whose expression correlates with the tags. Proteins involved in liquid-liquid phase separation appear prominently.

Co-Transcriptional Regulation of HBV Replication: RNA Quality Also Matters

Chronic hepatitis B (CHB) virus infection is a major public health burden and the leading cause of hepatocellular carcinoma. Despite the efficacy of current treatments, hepatitis B virus (HBV) cannot be fully eradicated due to the persistence of its minichromosome, or covalently closed circular DNA (cccDNA). The HBV community is investing large human and financial resources to develop new therapeutic strategies that either silence or ideally degrade cccDNA, to cure HBV completely or functionally. cccDNA transcription is considered to be the key step for HBV replication. Transcription not only influences the levels of viral RNA produced, but also directly impacts their quality, generating multiple variants. Growing evidence advocates for the role of the co-transcriptional regulation of HBV RNAs during CHB and viral replication, paving the way for the development of novel therapies targeting these processes. This review focuses on the mechanisms controlling the different co-transcriptional processes that HBV RNAs undergo, and their contribution to both viral replication and HBV-induced liver pathogenesis.

Mathematical Models of HIV-1 Dynamics, Transcription, and Latency

HIV-1 latency is a major barrier to curing infections with antiretroviral therapy and, consequently, to eliminating the disease globally. The establishment, maintenance, and potential clearance of latent infection are complex dynamic processes and can be best described with the help of mathematical models followed by experimental validation. Here, we review the use of viral dynamics models for HIV-1, with a focus on applications to the latent reservoir. Such models have been used to explain the multi-phasic decay of viral load during antiretroviral therapy, the early seeding of the latent reservoir during acute infection and the limited inflow during treatment, the dynamics of viral blips, and the phenomenon of post-treatment control. Finally, we discuss that mathematical models have been used to predict the efficacy of potential HIV-1 cure strategies, such as latency-reversing agents, early treatment initiation, or gene therapies, and to provide guidance for designing trials of these novel interventions.

Bayesian inference of polymerase dynamics over the exclusion process

Transcription is a complex phenomenon that permits the conversion of genetic information into phenotype by means of an enzyme called RNA polymerase, which erratically moves along and scans the DNA template. We perform Bayesian inference over a paradigmatic mechanistic model of non-equilibrium statistical physics, i.e. the asymmetric exclusion processes in the hydrodynamic limit, assuming a Gaussian process prior for the polymerase progression rate as a latent variable. Our framework allows us to infer the speed of polymerases during transcription given their spatial distribution, while avoiding the explicit inversion of the system's dynamics. The results, which show processing rates strongly varying with genomic position and minor role of traffic-like congestion, may have strong implications for the understanding of gene expression.

Synergising single-cell resolution and 4sU labelling boosts inference of transcriptional bursting

Despite the recent rise of RNA-seq datasets combining single-cell (sc) resolution with 4-thiouridine (4sU) labelling, analytical methods exploiting their power to dissect transcriptional bursting are lacking. Here, we present a mathematical model and Bayesian inference implementation to facilitate genome-wide joint parameter estimation and confidence quantification (R package: burstMCMC). We demonstrate that, unlike conventional scRNA-seq, 4sU scRNA-seq resolves temporal parameters and furthermore boosts inference of dimensionless parameters via a synergy between single-cell resolution and 4sU labelling. We apply our method to published 4sU scRNA-seq data and linked with ChIP-seq data, we uncover previously obscured associations between different parameters and histone modifications.

From structure to molecular condensates: emerging mechanisms for Mediator function

Mediator is a large modular protein assembly whose function as a coactivator of transcription is conserved in all eukaryotes. The Mediator complex can integrate and relay signals from gene-specific activators bound at enhancers to activate the general transcription machinery located at promoters. It has thus been described as a bridge between these elements during initiation of transcription. Here, we review recent studies on Mediator relating to its structure, gene specificity and general requirement, roles in chromatin architecture as well as novel concepts involving phase separation and transcriptional bursting. We revisit the mechanism of action of Mediator and ultimately put forward models for its mode of action in gene activation.

A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription

Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the process by which transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility impacts transcriptional bursting by regulating the assembly of transcription factor and polymerase complexes on promoters, suggesting that the effect of an activating signal on transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-κB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-κB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway.

Many genes are transcribed in infrequent bursts of mRNA production through a process called transcriptional bursting, which contributes to variability in responses between cells. Heterogeneity in cell responses can have important biological impacts, such as whether a cell supports viral replication or responds to a drug, and thus there is an effort to describe this process with mathematical models to predict biological outcomes. Previous models described bursting as a transition between an “OFF” state or an “ON” state, an elegant and simple mathematical representation of complex molecular mechanisms, but one which failed to capture how upstream activation signals affected bursting. To address this, we added an additional promoter state to better reflect biological mechanisms underlying bursting. By fitting this model to variable activation of quiescent HIV infections in T cells, we showed that our model more accurately described viral expression variability across cells in response to an upstream stimulus. Our work highlights how mathematical models can be further developed to understand complex biological mechanisms and suggests ways to connect transcriptional bursting to upstream activation pathways.

CFIm25 regulates human stem cell function independently of its role in mRNA alternative polyadenylation

It has recently been shown that CFIm25, a canonical mRNA 3’ processing factor, could play a variety of physiological roles through its molecular function in the regulation of mRNA alternative polyadenylation (APA). Here, we used CRISPR/Cas9-mediated gene editing approach in human embryonic stem cells (hESCs) for CFIm25, and obtained three gene knockdown/mutant cell lines. CFIm25 gene editing resulted in higher proliferation rate and impaired differentiation potential for hESCs, with these effects likely to be directly regulated by the target genes, including the pluripotency factor rex1. Mechanistically, we unexpected found that perturbation in CFIm25 gene expression did not significantly affect cellular mRNA 3’ processing efficiency and APA profile. Rather, we provided evidences that CFIm25 may impact RNA polymerase II (RNAPII) occupancy at the body of transcribed genes, and promote the expression level of a group of transcripts associated with cellular proliferation and/or differentiation. Taken together, these results reveal novel mechanisms underlying CFIm25ʹs modulation in determination of cell fate, and provide evidence that the process of mammalian gene transcription may be regulated by an mRNA 3’ processing factor.

A simple guide to de novo transcriptome assembly and annotation

A transcriptome constructed from short-read RNA sequencing (RNA-seq) is an easily attainable proxy catalog of protein-coding genes when genome assembly is unnecessary, expensive or difficult. In the absence of a sequenced genome to guide the reconstruction process, the transcriptome must be assembled de novo using only the information available in the RNA-seq reads. Subsequently, the sequences must be annotated in order to identify sequence-intrinsic and evolutionary features in them (for example, protein-coding regions). Although straightforward at first glance, de novo transcriptome assembly and annotation can quickly prove to be challenging undertakings. In addition to familiarizing themselves with the conceptual and technical intricacies of the tasks at hand and the numerous pre- and post-processing steps involved, those interested must also grapple with an overwhelmingly large choice of tools. The lack of standardized workflows, fast pace of development of new tools and techniques and paucity of authoritative literature have served to exacerbate the difficulty of the task even further. Here, we present a comprehensive overview of de novo transcriptome assembly and annotation. We discuss the procedures involved, including pre- and post-processing steps, and present a compendium of corresponding tools.

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

RNA-seq, including single cell RNA-seq (scRNA-seq), is plagued by insufficient sensitivity and lack of precision. As a result, the full potential of (sc)RNA-seq is limited. Major factors in this respect are the presence of global bias in most datasets, which affects detection and quantitation of RNA in a length-dependent fashion. In particular, scRNA-seq is affected by technical noise and a high rate of dropouts, where the vast majority of original transcripts is not converted into sequencing reads. We discuss these biases origins and implications, bioinformatics approaches to correct for them, and how biases can be exploited to infer characteristics of the sample preparation process, which in turn can be used to improve library preparation.