The Mediator complex and the role of protein-protein interactions in the gene regulation machinery

Neurodevelopmental disorder-associated CYFIP2 regulates membraneless organelles and eIF2α phosphorylation via protein interactors and actin cytoskeleton

De novo variants in the Cytoplasmic FMR1-interacting protein 2 (CYFIP2) have been repeatedly associated with neurodevelopmental disorders and epilepsy, underscoring its critical role in brain development and function. While CYFIP2's role in regulating actin polymerization as part of the WAVE regulatory complex (WRC) is well-established, its additional molecular functions remain relatively unexplored. In this study, we performed unbiased quantitative proteomic analysis, revealing 278 differentially expressed proteins (DEPs) in the forebrain of Cyfip2 knock-out embryonic mice compared to wild-type mice. Unexpectedly, these DEPs, in conjunction with previously identified CYFIP2 brain interactors, included not only other WRC components but also numerous proteins associated with membraneless organelles (MLOs) involved in mRNA processing and translation within cells, including the nucleolus, stress granules, and processing bodies. Additionally, single-cell transcriptomic analysis of the Cyfip2 knock-out forebrain revealed gene expression changes linked to cellular stress responses and MLOs. We also observed morphological changes in MLOs in Cyfip2 knock-out brains and CYFIP2 knock-down cells under basal and stress conditions. Lastly, we demonstrated that CYFIP2 knock-down in cells, potentially through WRC-dependent actin regulation, suppressed the phosphorylation levels of the alpha subunit of eukaryotic translation initiation factor 2 (eIF2α), thereby enhancing protein synthesis. These results suggest a physical and functional connection between CYFIP2 and various MLO proteins and also extend CYFIP2's role within the WRC from actin regulation to influencing eIF2α phosphorylation and protein synthesis. With these dual functions, CYFIP2 may fine-tune the balance between MLO formation/dynamics and protein synthesis, a crucial aspect of proper mRNA processing and translation.

MediatorWeb: a protein-protein interaction network database for the RNA polymerase II Mediator complex

The protein-protein interaction (PPI) network of the Mediator complex is very tightly regulated and depends on different developmental and environmental cues. Here, we present an interactive platform for comparative analysis of the Mediator subunits from humans, baker's yeast Saccharomyces cerevisiae, and model plant Arabidopsis thaliana in a user-friendly web-interface database called MediatorWeb. MediatorWeb provides an interface to visualize and analyze the PPI network of Mediator subunits. The database facilitates downloading the untargeted and unweighted network of Mediator complex, its submodules, and individual Mediator subunits to better visualize the importance of individual Mediator subunits or their submodules. Further, MediatorWeb offers network visualization of the Mediator complex and interacting proteins that are functionally annotated. This feature provides clues to understand functions of Mediator subunits in different processes. In an additional tab, MediatorWeb provides quick access to secondary and tertiary structures, as well as residue-level contact information for Mediator subunits in each of the three model organisms. Another useful feature of MediatorWeb is detection of interologs based on orthologous analyses, which can provide clues to understand the functions of Mediator complex in less explored kingdoms. Thus, MediatorWeb and its features can help the user to understand the role of Mediator complex and its subunits in the transcription regulation of gene expression.

Role of the Mediator Complex and MicroRNAs in Breast Cancer Etiology

Transcriptional coactivators play a key role in RNA polymerase II transcription and gene regulation. One of the most important transcriptional coactivators is the Mediator (MED) complex, which is an evolutionary conserved large multiprotein complex. MED transduces the signal between DNA-bound transcriptional activators (gene-specific transcription factors) to the RNA polymerase II transcription machinery to activate transcription. It is known that MED plays an essential role in ER-mediated gene expression mainly through the MED1 subunit, since estrogen receptor (ER) can interact with MED1 by specific protein–protein interactions; therefore, MED1 plays a fundamental role in ER-positive breast cancer (BC) etiology. Additionally, other MED subunits also play a role in BC etiology. On the other hand, microRNAs (miRNAs) are a family of small non-coding RNAs, which can regulate gene expression at the post-transcriptional level by binding in a sequence-specific fashion at the 3′ UTR of the messenger RNA. The miRNAs are also important factors that influence oncogenic signaling in BC by acting as both tumor suppressors and oncogenes. Moreover, miRNAs are involved in endocrine therapy resistance of BC, specifically to tamoxifen, a drug that is used to target ER signaling. In metazoans, very little is known about the transcriptional regulation of miRNA by the MED complex and less about the transcriptional regulation of miRNAs involved in BC initiation and progression. Recently, it has been shown that MED1 is able to regulate the transcription of the ER-dependent miR-191/425 cluster promoting BC cell proliferation and migration. In this review, we will discuss the role of MED1 transcriptional coactivator in the etiology of BC and in endocrine therapy-resistance of BC and also the contribution of other MED subunits to BC development, progression and metastasis. Lastly, we identified miRNAs that potentially can regulate the expression of MED subunits.

Somatic and de novo Germline Variants of MEDs in Human Neural Tube Defects

BACKGROUND

Neural tube defects (NTDs) are among the most common and severe congenital defects in humans. Their genetic etiology is complex and remains poorly understood. The Mediator complex (MED) plays a vital role in neural tube development in animal models. However, no studies have yet examined the role of its human homolog in the etiology of NTDs.

METHODS

In this study, 48 pairs of neural lesion site and umbilical cord tissues from NTD and 21 case-parent trios were involved in screening for NTD-related somatic and germline de novo variants. A series of functional cell assays were performed. We generated a Med12 p.Arg1784Cys knock-in mouse using CRISPR/Cas9 technology to validate the human findings.

RESULTS

One somatic variant, MED12 p.Arg1782Cys, was identified in the lesion site tissue from an NTD fetus. This variant was absent in any other normal tissue from different germ layers of the same case. In 21 case-parent trios, one de novo stop-gain variant, MED13L p.Arg1760∗, was identified. Cellular functional studies showed that MED12 p.Arg1782Cys decreased MED12 protein level and affected the regulation of MED12 on the canonical-WNT signaling pathway. The Med12 p.Arg1784Cys knock-in mouse exhibited exencephaly and spina bifida.

CONCLUSION

These findings provide strong evidence that functional variants of MED genes are associated with the etiology of some NTDs. We demonstrated a potentially important role for somatic variants in the occurrence of NTDs. Our study is the first study in which an NTD-related variant identified in humans was validated in mice using CRISPR/Cas9 technology.

Mechanisms of Enhancer-Promoter Interactions in Higher Eukaryotes

In higher eukaryotes, enhancers determine the activation of developmental gene transcription in specific cell types and stages of embryogenesis. Enhancers transform the signals produced by various transcription factors within a given cell, activating the transcription of the targeted genes. Often, developmental genes can be associated with dozens of enhancers, some of which are located at large distances from the promoters that they regulate. Currently, the mechanisms underlying specific distance interactions between enhancers and promoters remain poorly understood. This review briefly describes the properties of enhancers and discusses the mechanisms of distance interactions and potential proteins involved in this process.

Mediator Roles Going Beyond Transcription

Dysfunctions of nuclear processes including transcription and DNA repair lead to severe human diseases. Gaining an understanding of how these processes operate in the crowded context of chromatin can be particularly challenging. Mediator is a large multiprotein complex conserved in eukaryotes with a key coactivator role in the regulation of RNA polymerase (Pol) II transcription. Despite intensive studies, the molecular mechanisms underlying Mediator function remain to be fully understood. Novel findings have provided insights into the relationship between Mediator and chromatin architecture, revealed its role in connecting transcription with DNA repair and proposed an emerging mechanism of phase separation involving Mediator condensates. Recent developments in the field suggest multiple functions of Mediator going beyond transcriptional processes per se that would explain its involvement in various human pathologies.

Deep Learning in the Study of Protein-Related Interactions

Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein- drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.