MATRIN3 deficiency triggers autoinflammation via cGAS-STING activation.

Interferon-stimulated genes (ISGs) comprise a program of immune effectors important for host immune defense. When uncontrolled, ISGs play a central role in interferonopathies and other inflammatory diseases. The mechanisms responsible for turning on ISGs are not completely known. By investigating MATRIN3 (MATR3), a nuclear RNA-binding protein mutated in familial ALS, we found that perturbing MATR3 results in elevated expression of ISGs. Using an integrative approach, we elucidate a pathway that leads to activation of cGAS-STING. This outlines a plausible mechanism for pathogenesis in a subset of ALS, and suggests new diagnostic and therapeutic approaches for this fatal disease.

Mature microRNA-binding protein QKI promotes microRNA-mediated gene silencing.

Although Argonaute (AGO) proteins have been the focus of microRNA (miRNA) studies, we observed AGO-free mature miRNAs directly interacting with RNA-binding proteins, implying the sophisticated nature of fine-tuning gene regulation by miRNAs. To investigate microRNA-binding proteins (miRBPs) globally, we analyzed PAR-CLIP data sets to identify RBP quaking (QKI) as a novel miRBP for let-7b. Potential existence of AGO-free miRNAs were further verified by measuring miRNA levels in genetically engineered AGO-depleted human and mouse cells. We have shown that QKI regulates miRNA-mediated gene silencing at multiple steps, and collectively serves as an auxiliary factor empowering AGO2/let-7b-mediated gene silencing. Depletion of QKI decreases interaction of AGO2 with let-7b and target mRNA, consequently controlling target mRNA decay. This finding indicates that QKI is a complementary factor in miRNA-mediated mRNA decay. QKI, however, also suppresses the dissociation of let-7b from AGO2, and slows the assembly of AGO2/miRNA/target mRNA complexes at the single-molecule level. We also revealed that QKI overexpression suppresses cMYC expression at post-transcriptional level, and decreases proliferation and migration of HeLa cells, demonstrating that QKI is a tumour suppressor gene by in part augmenting let-7b activity. Our data show that QKI is a new type of RBP implicated in the versatile regulation of miRNA-mediated gene silencing.

Matrin3 regulates mitotic spindle dynamics by controlling alternative splicing of CDC14B.

Matrin3 is an RNA-binding protein that regulates diverse RNA-related processes, including mRNA splicing. Although Matrin3 has been intensively studied in neurodegenerative diseases, its function in cancer remains unclear. Here, we report Matrin3-mediated regulation of mitotic spindle dynamics in colorectal cancer (CRC) cells. We comprehensively identified RNAs bound and regulated by Matrin3 in CRC cells and focused on CDC14B, one of the top Matrin3 targets. Matrin3 knockdown results in increased inclusion of an exon containing a premature termination codon in the CDC14B transcript and simultaneous down-regulation of the standard CDC14B transcript. Knockdown of CDC14B phenocopies the defects in mitotic spindle dynamics upon Matrin3 knockdown, and the elongated and misoriented mitotic spindle observed upon Matrin3 knockdown are rescued upon overexpression of CDC14B, suggesting that CDC14B is a key downstream effector of Matrin3. Collectively, these data reveal a role for the Matrin3/CDC14B axis in control of mitotic spindle dynamics.

The nucleolus is the site for inflammatory RNA decay during infection.

Inflammatory cytokines are key signaling molecules that can promote an immune response, thus their RNA turnover must be tightly controlled during infection. Most studies investigate the RNA decay pathways in the cytosol or nucleoplasm but never focused on the nucleolus. Although this organelle has well-studied roles in ribosome biogenesis and cellular stress sensing, the mechanism of RNA decay within the nucleolus is not completely understood. Here, we report that the nucleolus is an essential site of inflammatory pre-mRNA instability during infection. RNA-sequencing analysis reveals that not only do inflammatory genes have higher intronic read densities compared with non-inflammatory genes, but their pre-mRNAs are highly enriched in nucleoli during infection. Notably, nucleolin (NCL) acts as a guide factor for recruiting cytosine or uracil (C/U)-rich sequence-containing inflammatory pre-mRNAs and the Rrp6-exosome complex to the nucleolus through a physical interaction, thereby enabling targeted RNA delivery to Rrp6-exosomes and subsequent degradation. Consequently, Ncl depletion causes aberrant hyperinflammation, resulting in a severe lethality in response to LPS. Importantly, the dynamics of NCL post-translational modifications determine its functional activity in phases of LPS. This process represents a nucleolus-dependent pathway for maintaining inflammatory gene expression integrity and immunological homeostasis during infection.

Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification.

Efficient identification of chemical probes for the manipulation and understanding of biological systems demands specificity for target proteins. Computational means to optimize candidate compound selection for experimental selectivity evaluation are being sought. The active learning virtual screening method has demonstrated the ability to efficiently converge on predictive models with reduced datasets, though its applicability domain to probe identification has yet to be determined. In this article, we challenge active learning's ability to predict inhibitory bioactivity profiles of selective compounds when learning from chemogenomic features found in non-selective ligand-target pairs. Comparison of controls versus multiple molecule representations de-convolutes factors contributing to predictive capability. Experiments using the matrix metalloproteinase family demonstrate maximum probe bioactivity prediction achieved from only approximately 20% of non-probe bioactivity; this data volume is consistent with prior chemogenomic active learning studies despite the increased difficulty from chemical biology experimental settings used here. Feature weight analyses are combined with a custom visualization to unambiguously detail how active learning arrives at classification decisions, yielding clarified expectations for chemogenomic modeling. The results influence tactical decisions for computational probe design and discovery.

A Survey of Web-Based Chemogenomic Data Resources.

Chemogenomics is a comparatively nascent branch dealing with the effects of drugs and chemicals on molecular level systems. With the emergence of this new epoch, the quantity of data sources is also unprecedentedly increasing. Despite having a plethora of a databases, the variation in bioactivity measurement as well as bias toward specific protein studies, varied computational procedures and redundant information make data mining tedious, especially for newcomers in the field. In this chapter, we give an overview of hands-on data collection and domains of applicability from some useful Web-based chemogenomic resources that are accessible with nothing more than a Web browser. This overview can help assist users in acquiring chemogenomic datasets for their project at hand.

Chemogenomic Active Learning's Domain of Applicability on Small, Sparse qHTS Matrices: A Study Using Cytochrome P450 and Nuclear Hormone Receptor Families.

Computational models for predicting the activity of small molecules against targets are now routinely developed and used in academia and industry, partially due to public bioactivity databases. While models based on bigger datasets are the trend, recent studies such as chemogenomic active learning have shown that only a fraction of data is needed for effective models in many cases. In this article, the chemogenomic active learning method is discussed and used to newly analyze public databases containing nuclear hormone receptor and cytochrome P450 enzyme family bioactivity. In addition to existing results on kinases and G-protein coupled receptors, results here demonstrate the active learning methodology's effectiveness on extracting informative ligand-target pairs in sparse data scenarios. Experiments to assess the domain of the applicability demonstrate the influence of ligand profiles of similar targets within the family.

In silico prediction of structure and functions for some proteins of male-specific region of the human Y chromosome.

Male-specific region of the human Y chromosome (MSY) comprises 95% of its length that is functionally active. This portion inherits in block from father to male offspring. Most of the genes in the MSY region are involved in male-specific function, such as sex determination and spermatogenesis; also contains genes probably involved in other cellular functions. However, a detailed characterization of numerous MSY-encoded proteins still remains to be done. In this study, 12 uncharacterized proteins of MSY were analyzed through bioinformatics tools for structural and functional characterization. Within these 12 proteins, a total of 55 domains were found, with DnaJ domain signature corresponding to be the highest (11%) followed by both FAD-dependent pyridine nucleotide reductase signature and fumarate lyase superfamily signature (9%). The 3D structures of our selected proteins were built up using homology modeling and the protein threading approaches. These predicted structures confirmed in detail the stereochemistry; indicating reasonably good quality model. Furthermore the predicted functions and the proteins with whom they interact established their biological role and their mechanism of action at molecular level. The results of these structure-functional annotations provide a comprehensive view of the proteins encoded by MSY, which sheds light on their biological functions and molecular mechanisms. The data presented in this study may assist in future prognosis of several human diseases such as Turner syndrome, gonadal sex reversal, spermatogenic failure, and gonadoblastoma.