Duel of the fates: the role of transcriptional circuits and noise in CD4+ cells

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

The BulkECexplorer compiles endothelial bulk transcriptomes to predict functional versus leaky transcription

Transcriptomic data can be mined to understand the molecular activity of cell types. Yet, functional genes may remain undetected in RNA sequencing (RNA-seq) experiments for technical reasons, such as insufficient read depth or gene dropout. Conversely, RNA-seq experiments may detect lowly expressed mRNAs thought to be biologically irrelevant products of leaky transcription. To represent a cell type's functional transcriptome more accurately, we propose compiling many bulk RNA-seq datasets into a compendium and applying established classification models to predict whether detected transcripts are likely products of active or leaky transcription. Here, we present the BulkECexplorer (bulk RNA-seq endothelial cell explorer) compendium of 240 bulk RNA-seq datasets from five vascular endothelial cell subtypes. This resource reports transcript counts for genes of interest and predicts whether detected transcripts are likely the products of active or leaky gene expression. Beyond its usefulness for vascular biology research, this resource provides a blueprint for developing analogous tools for other cell types.

Signaling pathways have an inherent need for noise to acquire information

BACKGROUND

All living systems acquire information about their environment. At the cellular level, they do so through signaling pathways. Such pathways rely on reversible binding interactions between molecules that detect and transmit the presence of an extracellular cue or signal to the cell's interior. These interactions are inherently stochastic and thus noisy. On the one hand, noise can cause a signaling pathway to produce the same response for different stimuli, which reduces the amount of information a pathway acquires. On the other hand, in processes such as stochastic resonance, noise can improve the detection of weak stimuli and thus the acquisition of information. It is not clear whether the kinetic parameters that determine a pathway's operation cause noise to reduce or increase the acquisition of information.

RESULTS

We analyze how the kinetic properties of the reversible binding interactions used by signaling pathways affect the relationship between noise, the response to a signal, and information acquisition. Our results show that, under a wide range of biologically sensible parameter values, a noisy dynamic of reversible binding interactions is necessary to produce distinct responses to different stimuli. As a consequence, noise is indispensable for the acquisition of information in signaling pathways.

CONCLUSIONS

Our observations go beyond previous work by showing that noise plays a positive role in signaling pathways, demonstrating that noise is essential when such pathways acquire information.

Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression

Polycomb repressive complexes (PRCs) are important histone modifiers, which silence gene expression; yet, there exists a subset of PRC-bound genes actively transcribed by RNA polymerase II (RNAPII). It is likely that the role of Polycomb repressive complex is to dampen expression of these PRC-active genes. However, it is unclear how this flipping between chromatin states alters the kinetics of transcription. Here, we integrate histone modifications and RNAPII states derived from bulk ChIP-seq data with single-cell RNA-sequencing data. We find that Polycomb repressive complex-active genes have greater cell-to-cell variation in expression than active genes, and these results are validated by knockout experiments. We also show that PRC-active genes are clustered on chromosomes in both two and three dimensions, and interactions with active enhancers promote a stabilization of gene expression noise. These findings provide new insights into how chromatin regulation modulates stochastic gene expression and transcriptional bursting, with implications for regulation of pluripotency and development.Polycomb repressive complexes modify histones but it is unclear how changes in chromatin states alter kinetics of transcription. Here, the authors use single-cell RNAseq and ChIPseq to find that actively transcribed genes with Polycomb marks have greater cell-to-cell variation in expression.

The Shine-Dalgarno sequence of riboswitch-regulated single mRNAs shows ligand-dependent accessibility bursts

In response to intracellular signals in Gram-negative bacteria, translational riboswitches—commonly embedded in messenger RNAs (mRNAs)—regulate gene expression through inhibition of translation initiation. It is generally thought that this regulation originates from occlusion of the Shine-Dalgarno (SD) sequence upon ligand binding; however, little direct evidence exists. Here we develop Single Molecule Kinetic Analysis of RNA Transient Structure (SiM-KARTS) to investigate the ligand-dependent accessibility of the SD sequence of an mRNA hosting the 7-aminomethyl-7-deazaguanine (preQ₁)-sensing riboswitch. Spike train analysis reveals that individual mRNA molecules alternate between two conformational states, distinguished by ‘bursts' of probe binding associated with increased SD sequence accessibility. Addition of preQ₁ decreases the lifetime of the SD's high-accessibility (bursting) state and prolongs the time between bursts. In addition, ligand-jump experiments reveal imperfect riboswitching of single mRNA molecules. Such complex ligand sensing by individual mRNA molecules rationalizes the nuanced ligand response observed during bulk mRNA translation.

In response to intracellular signals, bacterial translational riboswitches embedded in mRNAs can regulate gene expression through inhibition of translation initiation. Here, the authors describe SiM-KARTS, a novel approach for detecting changes in the structure of single RNA molecules in response to a ligand.

Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis

T helper 2 (Th2) cells regulate helminth infections, allergic disorders, tumor immunity, and pregnancy by secreting various cytokines. It is likely that there are undiscovered Th2 signaling molecules. Although steroids are known to be immunoregulators, de novo steroid production from immune cells has not been previously characterized. Here, we demonstrate production of the steroid pregnenolone by Th2 cells in vitro and in vivo in a helminth infection model. Single-cell RNA sequencing and quantitative PCR analysis suggest that pregnenolone synthesis in Th2 cells is related to immunosuppression. In support of this, we show that pregnenolone inhibits Th cell proliferation and B cell immunoglobulin class switching. We also show that steroidogenic Th2 cells inhibit Th cell proliferation in a Cyp11a1 enzyme-dependent manner. We propose pregnenolone as a “lymphosteroid,” a steroid produced by lymphocytes. We speculate that this de novo steroid production may be an intrinsic phenomenon of Th2-mediated immune responses to actively restore immune homeostasis.

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Differential upregulation of the steroid biosynthetic pathway during Th2 differentiation

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T helper cells produce the steroid pregnenolone in vitro and in vivo

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Steroidogenic Th2 cells suppress Th cell proliferation in a Cyp11a1-dependent manner

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Pregnenolone inhibits B cell immunoglobulin class switching in vitro

System wide analyses have underestimated protein abundances and the importance of transcription in mammals

Large scale surveys in mammalian tissue culture cells suggest that the protein expressed at the median abundance is present at 8,000–16,000 molecules per cell and that differences in mRNA expression between genes explain only 10–40% of the differences in protein levels. We find, however, that these surveys have significantly underestimated protein abundances and the relative importance of transcription. Using individual measurements for 61 housekeeping proteins to rescale whole proteome data from Schwanhausser et al. (2011), we find that the median protein detected is expressed at 170,000 molecules per cell and that our corrected protein abundance estimates show a higher correlation with mRNA abundances than do the uncorrected protein data. In addition, we estimated the impact of further errors in mRNA and protein abundances using direct experimental measurements of these errors. The resulting analysis suggests that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwanhausser et al. (2011), though because one major source of error could not be estimated the true percent contribution should be higher. We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression. We show that the variance in translation rates directly measured by ribosome profiling is only 12% of that inferred by Schwanhausser et al. (2011), and that the measured and inferred translation rates correlate poorly (R² = 0.13). Based on this, our second strategy suggests that mRNA levels explain ∼81% of the variance in protein levels. We also determined the percent contributions of transcription, RNA degradation, translation and protein degradation to the variance in protein abundances using both of our strategies. While the magnitudes of the two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role. Finally, the above estimates only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the absence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the ∼40% of genes that are non-expressed.

Depletion of stromal cells expressing fibroblast activation protein-α from skeletal muscle and bone marrow results in cachexia and anemia

Fibroblast activation protein-α (FAP) identifies stromal cells of mesenchymal origin in human cancers and chronic inflammatory lesions. In mouse models of cancer, they have been shown to be immune suppressive, but studies of their occurrence and function in normal tissues have been limited. With a transgenic mouse line permitting the bioluminescent imaging of FAP(+) cells, we find that they reside in most tissues of the adult mouse. FAP(+) cells from three sites, skeletal muscle, adipose tissue, and pancreas, have highly similar transcriptomes, suggesting a shared lineage. FAP(+) cells of skeletal muscle are the major local source of follistatin, and in bone marrow they express Cxcl12 and KitL. Experimental ablation of these cells causes loss of muscle mass and a reduction of B-lymphopoiesis and erythropoiesis, revealing their essential functions in maintaining normal muscle mass and hematopoiesis, respectively. Remarkably, these cells are altered at these sites in transplantable and spontaneous mouse models of cancer-induced cachexia and anemia. Thus, the FAP(+) stromal cell may have roles in two adverse consequences of cancer: their acquisition by tumors may cause failure of immunosurveillance, and their alteration in normal tissues contributes to the paraneoplastic syndromes of cachexia and anemia.

Are gene loops the cause of transcriptional noise?

Expression levels of the same mRNA or protein vary significantly among the cells of an otherwise identical population. Such biological noise has great functional implications and is largely due to transcriptional bursting, the episodic production of mRNAs in short, intense bursts, interspersed by periods of transcriptional inactivity. Bursting has been demonstrated in a wide range of pro- and eukaryotic species, attesting to its universal importance. However, the mechanistic origins of bursting remain elusive. A different type of phenomenon, which has also been suggested to be widespread, is the physical interaction between the promoter and 3' end of a gene. Several functional roles have been proposed for such gene loops, including the facilitation of transcriptional reinitiation. Here, I discuss the most recent findings related to these subjects and argue that gene loops are a likely cause of transcriptional bursting and, thus, biological noise.

Interplay between gene expression noise and regulatory network architecture

Complex regulatory networks orchestrate most cellular processes in biological systems. Genes in such networks are subject to expression noise, resulting in isogenic cell populations exhibiting cell-to-cell variation in protein levels. Increasing evidence suggests that cells have evolved regulatory strategies to limit, tolerate or amplify expression noise. In this context, fundamental questions arise: how can the architecture of gene regulatory networks generate, make use of or be constrained by expression noise? Here, we discuss the interplay between expression noise and gene regulatory network at different levels of organization, ranging from a single regulatory interaction to entire regulatory networks. We then consider how this interplay impacts a variety of phenomena, such as pathogenicity, disease, adaptation to changing environments, differential cell-fate outcome and incomplete or partial penetrance effects. Finally, we highlight recent technological developments that permit measurements at the single-cell level, and discuss directions for future research.