Feedback from nuclear RNA on transcription promotes robust RNA concentration homeostasis in human cells

Gene-specific RNA homeostasis revealed by perturbation of coactivator complexes

Transcript buffering entails the reciprocal modulation of mRNA synthesis and degradation rates to maintain stable RNA levels under varying cellular conditions. Current research supports a global, non-sequence-specific connection between mRNA synthesis and degradation, but the underlying mechanisms are still unclear. In this study, we investigated changes in RNA metabolism following acute depletion of TIP60/KAT5, the acetyltransferase subunit of the NuA4 transcriptional coactivator complex, in mouse embryonic stem cells. By combining RNA sequencing of nuclear, cytoplasmic, and newly synthesised transcript fractions with biophysical modelling, we demonstrate that TIP60 predominantly enhances transcription of numerous genes, while a smaller set of genes undergoes TIP60-dependent transcriptional repression. Surprisingly, transcription changes caused by TIP60 depletion were offset by corresponding changes in RNA nuclear export and cytoplasmic stability, indicating gene-specific buffering mechanisms. Similarly, disruption of the unrelated ATAC coactivator complex also resulted in gene-specific transcript buffering. These findings reveal that transcript buffering functions at a gene-specific level and suggest that cells dynamically adjust RNA splicing, export, and degradation in response to individual RNA synthesis alterations, thereby sustaining cellular homeostasis.

Nuclear RNA homeostasis promotes systems-level coordination of cell fate and senescence

Understanding cellular coordination remains a challenge despite knowledge of individual pathways. The RNA exosome, targeting a wide range of RNA substrates, is often downregulated in cellular senescence. Utilizing an auxin-inducible system, we observed that RNA exosome depletion in embryonic stem cells significantly affects the transcriptome and proteome, causing pluripotency loss and pre-senescence onset. Mechanistically, exosome depletion triggers acute nuclear RNA aggregation, disrupting nuclear RNA-protein equilibrium. This disturbance limits nuclear protein availability and hinders polymerase initiation and engagement, reducing gene transcription. Concurrently, it promptly disrupts nucleolar transcription, ribosomal processes, and nuclear exporting, resulting in a translational shutdown. Prolonged exosome depletion induces nuclear structural changes resembling senescent cells, including aberrant chromatin compaction, chromocenter disassembly, and intensified heterochromatic foci. These effects suggest that the dynamic turnover of nuclear RNA orchestrates crosstalk between essential processes to optimize cellular function. Disruptions in nuclear RNA homeostasis result in systemic functional decline, altering the cell state and promoting senescence.

Multiple direct and indirect roles of Paf1C in elongation, splicing, and histone post-translational modifications

Paf1C is a highly conserved protein complex with critical functions during eukaryotic transcription. Previous studies have shown that Paf1C is multi-functional, controlling specific aspects of transcription, ranging from RNAPII processivity to histone modifications. However, it is unclear how specific Paf1C subunits directly impact transcription and coupled processes. We have compared conditional depletion to steady-state deletion for each Paf1C subunit to determine the direct and indirect contributions to gene expression in Saccharomyces cerevisiae. Using nascent transcript sequencing, RNAPII profiling, and modeling of transcription elongation dynamics, we have demonstrated direct effects of Paf1C subunits on RNAPII processivity and elongation rate and indirect effects on transcript splicing and repression of antisense transcripts. Further, our results suggest that the direct transcriptional effects of Paf1C cannot be readily assigned to any particular histone modification. This work comprehensively analyzes both the immediate and extended roles of each Paf1C subunit in transcription elongation and transcript regulation.

Somatic polyploidy supports biosynthesis and tissue function by increasing transcriptional output

Cell size and biosynthetic capacity generally increase with increased DNA content. Polyploidy has therefore been proposed to be an adaptive strategy to increase cell size in specialized tissues with high biosynthetic demands. However, if and how DNA concentration limits cellular biosynthesis in vivo is not well understood, and the impacts of polyploidy in non-disease states is not well studied. Here, we show that polyploidy in the C. elegans intestine is critical for cell growth and yolk biosynthesis, a central role of this organ. Artificially lowering the DNA/cytoplasm ratio by reducing polyploidization in the intestine gave rise to smaller cells with more dilute mRNA. Highly-expressed transcripts were more sensitive to this mRNA dilution, whereas lowly-expressed genes were partially compensated - in part by loading more RNA Polymerase II on the remaining genomes. DNA-dilute cells had normal total protein concentration, which we propose is achieved by increasing production of translational machinery at the expense of specialized, cell-type specific proteins.

Mitochondrial metabolism and the continuing search for ultimate regulators of developmental rate

The rate of embryonic development is a species-specific trait that depends on the properties of the intracellular environment, namely, the rate at which gene products flow through the central dogma of molecular biology. Although any given step in the production and degradation of gene products could theoretically be co-opted by evolution to modulate developmental speed, species are observed to accelerate or slow down all steps simultaneously. This suggests the rate of these molecular processes is jointly regulated by an upstream, ultimate factor. Mitochondrial metabolism was recently proposed to act as an ultimate regulator by controlling the pace of protein synthesis upstream of developmental tempo. Alternative candidates for ultimate regulators include species-specific gene expression levels of factors involved in the central dogma, as well as species-specific cell size. Overall, much work remains to be done before we can confidently identify the ultimate causes of species-specific developmental rates.

PAPAS promotes differentiation of mammary epithelial cells and suppresses breast carcinogenesis

Extensive remodeling of the female mammary epithelium during development and pregnancy has been linked to cancer susceptibility. The faithful response of mammary epithelial cells (MECs) to hormone signaling is key to avoiding breast cancer development. Here, we show that lactogenic differentiation of murine MECs requires silencing of genes encoding ribosomal RNA (rRNA) by the antisense transcript PAPAS. Accordingly, knockdown of PAPAS derepresses rRNA genes, attenuates the response to lactogenic hormones, and induces malignant transformation. Restoring PAPAS levels in breast cancer cells reduces tumorigenicity and lung invasion and activates many interferon-regulated genes previously linked to metastasis suppression. Mechanistically, PAPAS transcription depends on R-loop formation at the 3' end of rRNA genes, which is repressed by RNase H1 and replication protein A (RPA) overexpression in breast cancer cells. Depletion of PAPAS and upregulation of RNase H1 and RPA in human breast cancer underpin the clinical relevance of our findings.

RNA polymerase II dynamics and mRNA stability feedback scale mRNA amounts with cell size

A fundamental feature of cellular growth is that total protein and RNA amounts increase with cell size to keep concentrations approximately constant. A key component of this is that global transcription rates increase in larger cells. Here, we identify RNA polymerase II (RNAPII) as the limiting factor scaling mRNA transcription with cell size in budding yeast, as transcription is highly sensitive to the dosage of RNAPII but not to other components of the transcriptional machinery. Our experiments support a dynamic equilibrium model where global RNAPII transcription at a given size is set by the mass action recruitment kinetics of unengaged nucleoplasmic RNAPII to the genome. However, this only drives a sub-linear increase in transcription with size, which is then partially compensated for by a decrease in mRNA decay rates as cells enlarge. Thus, limiting RNAPII and feedback on mRNA stability work in concert to scale mRNA amounts with cell size.

Cellular state landscape and herpes simplex virus type 1 infection progression are connected

Prediction, prevention and treatment of virus infections require understanding of cell-to-cell variability that leads to heterogenous disease outcomes, but the source of this heterogeneity has yet to be clarified. To study the multimodal response of single human cells to herpes simplex virus type 1 (HSV-1) infection, we mapped high-dimensional viral and cellular state spaces throughout the infection using multiplexed imaging and quantitative single-cell measurements of viral and cellular mRNAs and proteins. Here we show that the high-dimensional cellular state scape can predict heterogenous infections, and cells move through the cellular state landscape according to infection progression. Spatial information reveals that infection changes the cellular state of both infected cells and of their neighbors. The multiplexed imaging of HSV-1-induced cellular modifications links infection progression to changes in signaling responses, transcriptional activity, and processing bodies. Our data show that multiplexed quantification of responses at the single-cell level, across thousands of cells helps predict infections and identify new targets for antivirals.

bakR: uncovering differential RNA synthesis and degradation kinetics transcriptome-wide with Bayesian hierarchical modeling

Differential expression analysis of RNA sequencing (RNA-seq) data can identify changes in cellular RNA levels, but provides limited information about the kinetic mechanisms underlying such changes. Nucleotide recoding RNA-seq methods (NR-seq; e.g., TimeLapse-seq, SLAM-seq, etc.) address this shortcoming and are widely used approaches to identify changes in RNA synthesis and degradation kinetics. While advanced statistical models implemented in user-friendly software (e.g., DESeq2) have ensured the statistical rigor of differential expression analyses, no such tools that facilitate differential kinetic analysis with NR-seq exist. Here, we report the development of Bayesian analysis of the kinetics of RNA (bakR; https:// github.com/simonlabcode/bakR), an R package to address this need. bakR relies on Bayesian hierarchical modeling of NR-seq data to increase statistical power by sharing information across transcripts. Analyses of simulated data confirmed that bakR implementations of the hierarchical model outperform attempts to analyze differential kinetics with existing models. bakR also uncovers biological signals in real NR-seq data sets and provides improved analyses of existing data sets. This work establishes bakR as an important tool for identifying differential RNA synthesis and degradation kinetics.

Apicobasal RNA asymmetries regulate cell fate in the early mouse embryo

The spatial sorting of RNA transcripts is fundamental for the refinement of gene expression to distinct subcellular regions. Although, in non-mammalian early embryogenesis, differential RNA localisation presages cell fate determination, in mammals it remains unclear. Here, we uncover apical-to-basal RNA asymmetries in outer blastomeres of 16-cell stage mouse preimplantation embryos. Basally directed RNA transport is facilitated in a microtubule- and lysosome-mediated manner. Yet, despite an increased accumulation of RNA transcripts in basal regions, higher translation activity occurs at the more dispersed apical RNA foci, demonstrated by spatial heterogeneities in RNA subtypes, RNA-organelle interactions and translation events. During the transition to the 32-cell stage, the biased inheritance of RNA transcripts, coupled with differential translation capacity, regulates cell fate allocation of trophectoderm and cells destined to form the pluripotent inner cell mass. Our study identifies a paradigm for the spatiotemporal regulation of post-transcriptional gene expression governing mammalian preimplantation embryogenesis and cell fate.

Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps

Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a conditional variational autoencoder to learn representations of molecular pixel profiles that are consistent across heterogeneous cell populations and experimental perturbations. Clustering these pixel-level representations identifies consistent subcellular landmarks, which can be quantitatively compared in terms of their size, shape, molecular composition and relative spatial organization. Using high-resolution multiplexed immunofluorescence, this reveals how subcellular organization changes upon perturbation of RNA synthesis, RNA processing or cell size, and uncovers links between the molecular composition of membraneless organelles and cell-to-cell variability in bulk RNA synthesis rates. By capturing interpretable cellular phenotypes, we anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organization to identify the rules by which context shapes physiology and disease.

Do not panic: An intron-centric guide to alternative splicing

This review is an attempt to establish concepts of splicing and alternative splicing giving proper relevance to introns, the key actors in this mechanism. It might also work as a guide for those who found their favorite gene undergoes alternative splicing and could benefit from gaining a theoretical framework to understand the possible impacts of this process. This is not a thorough review of all the work in the field, but rather a critical review of some of the most relevant work done to understand the underlying mechanisms of splicing and the key questions that remain unanswered such as: What is the physiological relevance of alternative splicing? What are the functions of the different outcomes? To what extent do different alternative splicing types contribute to the proteome? Intron retention is the most frequent alternative splicing event in plants and, although scientifically neglected, it is also common in animals. This is a heterogeneous type of alternative splicing that includes different sub-types with features that have distinctive consequences in the resulting transcripts. Remarkably, intron retention can be a dead end for a transcript, but it could also be a stable intermediate whose processing is resumed upon a particular signal or change in the cell status. New sequencing technologies combined with the study of intron lariats in different conditions might help to answer key questions and could help us to understand the actual relevance of introns in gene expression regulation.

Prediction of transcript structure and concentration using RNA-Seq data

Alternative splicing (AS) is a key post-transcriptional modification that helps in increasing protein diversity. Almost 90% of the protein-coding genes in humans are known to undergo AS and code for different transcripts. Some transcripts are associated with diseases such as breast cancer, lung cancer and glioblastoma. Hence, these transcripts can serve as novel therapeutic and prognostic targets for drug discovery. Herein, we have developed a pipeline, Finding Alternative Splicing Events (FASE), as the R package that includes modules to determine the structure and concentration of transcripts using differential AS. To predict the correct structure of expressed transcripts in given conditions, FASE combines the AS events with the information of exons, introns and junctions using graph theory. The estimated concentration of predicted transcripts is reported as the relative expression in terms of log2CPM. Using FASE, we were able to identify several unique transcripts of EMILIN1 and SLK genes in the TCGA-BRCA data, which were validated using RT-PCR. The experimental study demonstrated consistent results, which signify the high accuracy and precision of the developed methods. In conclusion, the developed pipeline, FASE, can efficiently predict novel transcripts that are missed in general transcript-level differential expression analysis. It can be applied selectively from a single gene to simple or complex genome even in multiple experimental conditions for the identification of differential AS-based biomarkers, prognostic targets and novel therapeutics.

Coupling gene expression dynamics to cell size dynamics and cell cycle events: Exact and approximate solutions of the extended telegraph model

The standard model describing the fluctuations of mRNA numbers in single cells is the telegraph model which includes synthesis and degradation of mRNA, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by the cell cycle phase, cellular growth and division, and other crucial aspects of cellular biology. Here, we derive the analytical time-dependent solution of an extended telegraph model that explicitly considers the doubling of gene copy numbers upon DNA replication, dependence of the mRNA synthesis rate on cellular volume, gene dosage compensation, partitioning of molecules during cell division, cell-cycle duration variability, and cell-size control strategies. Based on the time-dependent solution, we obtain the analytical distributions of transcript numbers for lineage and population measurements in steady-state growth and also find a linear relation between the Fano factor of mRNA fluctuations and cell volume fluctuations. We show that generally the lineage and population distributions in steady-state growth cannot be accurately approximated by the steady-state solution of extrinsic noise models, i.e. a telegraph model with parameters drawn from probability distributions. This is because the mRNA lifetime is often not small enough compared to the cell cycle duration to erase the memory of division and replication. Accurate approximations are possible when this memory is weak, e.g. for genes with bursty expression and for which there is sufficient gene dosage compensation when replication occurs.

Increasing cell size remodels the proteome and promotes senescence

Cell size is tightly controlled in healthy tissues, but it is unclear how deviations in cell size affect cell physiology. To address this, we measured how the cell's proteome changes with increasing cell size. Size-dependent protein concentration changes are widespread and predicted by subcellular localization, size-dependent mRNA concentrations, and protein turnover. As proliferating cells grow larger, concentration changes typically associated with cellular senescence are increasingly pronounced, suggesting that large size may be a cause rather than just a consequence of cell senescence. Consistent with this hypothesis, larger cells are prone to replicative, DNA-damage-induced, and CDK4/6i-induced senescence. Size-dependent changes to the proteome, including those associated with senescence, are not observed when an increase in cell size is accompanied by an increase in ploidy. Together, our findings show how cell size could impact many aspects of cell physiology by remodeling the proteome and provide a rationale for cell size control and polyploidization.

Mechanisms of cellular mRNA transcript homeostasis

For most genes, mRNA transcript abundance scales with cell size to ensure a constant concentration. Scaling of mRNA synthesis rates with cell size plays an important role, with regulation of the activity and abundance of RNA polymerase II (Pol II) now emerging as a key point of control. However, there is also considerable evidence for feedback mechanisms that kinetically couple the rates of mRNA synthesis, nuclear export, and degradation to allow cells to compensate for changes in one by adjusting the others. Researchers are beginning to integrate results from these different fields to reveal the mechanisms underlying transcript homeostasis. This will be crucial for moving beyond our current understanding of relative gene expression towards an appreciation of how absolute transcript levels are linked to other aspects of the cellular phenotype.