In mammals, the carboxy-terminal domain (CTD) of RNA polymerase (Pol) II consists of 52 conserved heptapeptide repeats containing the consensus sequence Tyr1-Ser2-Pro3-Thr4-Ser5-Pro6-Ser7. Post-translational modifications of the CTD coordinate the transcription cycle and various steps of mRNA maturation. Here we describe Tyr1 phosphorylation (Tyr1P) as a hallmark of promoter (5′ associated) Pol II in mammalian cells, in contrast to what was described in yeast. Tyr1P is predominantly found in antisense orientation at promoters but is also specifically enriched at active enhancers. Mutation of Tyr1 to phenylalanine (Y1F) prevents the formation of the hyper-phosphorylated Pol IIO form, induces degradation of Pol II to the truncated Pol IIB form, and results in a lethal phenotype. Our results suggest that Tyr1P has evolved specialized and essential functions in higher eukaryotes associated with antisense promoter and enhancer transcription, and Pol II stability.

Moonlighting proteins are a subclass of multifunctional proteins whose functions are unrelated. Although they may play important roles in cells, there has been no large-scale method to identify them, nor any effort to characterize them as a group. Here, we propose the first method for the identification of ‘extreme multifunctional’ proteins from an interactome as a first step to characterize moonlighting proteins. By combining network topological information with protein annotations, we identify 430 extreme multifunctional proteins (3% of the human interactome). We show that the candidates form a distinct sub-group of proteins, characterized by specific features, which form a signature of extreme multifunctionality. Overall, extreme multifunctional proteins are enriched in linear motifs and less intrinsically disordered than network hubs. We also provide MoonDB, a database containing information on all the candidates identified in the analysis and a set of manually curated human moonlighting proteins.

Recent advances in the analysis of high-throughput expression data have led to the development of tools that scaled-up their focus from single-gene to gene set level. For example, the popular Gene Set Enrichment Analysis (GSEA) algorithm can detect moderate but coordinated expression changes of groups of presumably related genes between pairs of experimental conditions. This considerably improves extraction of information from high-throughput gene expression data. However, although many gene sets covering a large panel of biological fields are available in public databases, the ability to generate home-made gene sets relevant to one’s biological question is crucial but remains a substantial challenge to most biologists lacking statistic or bioinformatic expertise. This is all the more the case when attempting to define a gene set specific of one condition compared to many other ones. Thus, there is a crucial need for an easy-to-use software for generation of relevant home-made gene sets from complex datasets, their use in GSEA, and the correction of the results when applied to multiple comparisons of many experimental conditions. We developed BubbleGUM (GSEA Unlimited Map), a tool that allows to automatically extract molecular signatures from transcriptomic data and perform exhaustive GSEA with multiple testing correction. One original feature of BubbleGUM notably resides in its capacity to integrate and compare numerous GSEA results into an easy-to-grasp graphical representation. We applied our method to generate transcriptomic fingerprints for murine cell types and to assess their enrichments in human cell types. This analysis allowed us to confirm homologies between mouse and human immunocytes. BubbleGUM is an open-source software that allows to automatically generate molecular signatures out of complex expression datasets and to assess directly their enrichment by GSEA on independent datasets. Enrichments are displayed in a graphical output that helps interpreting the results. This innovative methodology has recently been used to answer important questions in functional genomics, such as the degree of similarities between microarray datasets from different laboratories or with different experimental models or clinical cohorts. BubbleGUM is executable through an intuitive interface so that both bioinformaticians and biologists can use it. It is available at http://www.ciml.univ-mrs.fr/applications/BubbleGUM/index.html .

The human transcriptome contains thousands of long non-coding RNAs (lncRNAs). Characterizing their function is a current challenge. An emerging concept is that lncRNAs serve as protein scaffolds, forming ribonucleoproteins and bringing proteins in proximity. However, only few scaffolding lncRNAs have been characterized and the prevalence of this function is unknown. Here, we propose the first computational approach aimed at predicting scaffolding lncRNAs at large scale. We predicted the largest human lncRNA–protein interaction network to date using the catRAPID omics algorithm. In combination with tissue expression and statistical approaches, we identified 847 lncRNAs (∼5% of the long non-coding transcriptome) predicted to scaffold half of the known protein complexes and network modules. Lastly, we show that the association of certain lncRNAs to disease may involve their scaffolding ability. Overall, our results suggest for the first time that RNA-mediated scaffolding of protein complexes and modules may be a common mechanism in human cells.

In innate immune responses, induction of type‐I interferons (IFNs) prevents virus spreading while viral replication is delayed by protein synthesis inhibition. We asked how cells perform these apparently contradictory activities. Using single fibroblast monitoring by flow cytometry and mathematical modeling, we demonstrate that type‐I IFN production is linked to cell9s ability to enter dsRNA‐activated PKR‐dependent translational arrest and then overcome this inhibition by decreasing eIF2α phosphorylation through phosphatase 1c cofactor GADD34 (Ppp1r15a) expression. GADD34 expression, shown here to be dependent on the IRF3 transcription factor, is responsible for a biochemical cycle permitting pulse of IFN synthesis to occur in cells undergoing protein synthesis inhibition. Translation arrest is further demonstrated to be key for anti‐viral response by acting synergistically with MAVS activation to amplify TBK1 signaling and IFN‐β mRNA transcription, while GADD34‐dependent protein synthesis recovery contributes to the heterogeneous expression of IFN observed in dsRNA‐activated cells. Synopsis Double‐stranded ribonucleic acid (dsRNA) recognition triggers type‐I interferon (IFN) expression, while causing PKR‐dependent translation initiation factor‐2 phosphorylation (eIF2) and protein synthesis inhibition. Expression of the phosphatase 1 cofactor GADD34 is required to overcome translation inhibition and enable infected cells to secrete IFNs by displaying an undulating protein synthesis dynamics that also modulates the magnitude of the anti‐viral response. Translation recovery upon PKR activation is dependent on GADD34 and is essential to produce IFN‐β. Upon dsRNA recognition, GADD34 transcription is IRF3 dependent. Translation arrest strongly enhances the activation of the IRFs and NF‐κB pathways upon viral sensor activation. The intensity of translational arrest, IRF signaling, and GADD34‐dependent translation recovery is coordinated to minimize viral translation and maximize interferon secretion by adopting an oscillatory dynamic at the single‐cell level, which can account for apparent IFN‐β stochastic expression.