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Previously, we showed that the germ cell-specific nuclear protein RBMXL2 represses cryptic splicing patterns during meiosis and is required for male fertility (Ehrmann et al., 2019). Here, we show that in somatic cells the similar yet ubiquitously expressed RBMX protein has similar functions. RBMX regulates a distinct class of exons that exceed the median human exon size. RBMX protein-RNA interactions are enriched within ultra-long exons, particularly within genes involved in genome stability, and repress the selection of cryptic splice sites that would compromise gene function. The RBMX gene is silenced during male meiosis due to sex chromosome inactivation. To test whether RBMXL2 might replace the function of RBMX during meiosis we induced expression of RBMXL2 and the more distantly related RBMY protein in somatic cells, finding each could rescue aberrant patterns of RNA processing caused by RBMX depletion. The C-terminal disordered domain of RBMXL2 is sufficient to rescue proper splicing control after RBMX depletion. Our data indicate that RBMX and RBMXL2 have parallel roles in somatic tissues and the germline that must have been conserved for at least 200 million years of mammalian evolution. We propose RBMX family proteins are particularly important for the splicing inclusion of some ultra-long exons with increased intrinsic susceptibility to cryptic splice site selection.

Abnormalities of the arterial valves, including bicuspid aortic valve (BAV) are amongst the most common congenital defects and are a significant cause of morbidity as well as predisposition to disease in later life. Despite this, and compounded by their small size and relative inaccessibility, there is still much to understand about how the arterial valves form and remodel during embryogenesis, both at the morphological and genetic level. Here we set out to address this in human embryos, using Spatial Transcriptomics (ST). We show that ST can be used to investigate the transcriptome of the developing arterial valves, circumventing the problems of accurately dissecting out these tiny structures from the developing embryo. We show that the transcriptome of CS16 and CS19 arterial valves overlap considerably, despite being several days apart in terms of human gestation, and that expression data confirm that the great majority of the most differentially expressed genes are valve-specific. Moreover, we show that the transcriptome of the human arterial valves overlaps with that of mouse atrioventricular valves from a range of gestations, validating our dataset but also highlighting novel genes, including four that are not found in the mouse genome and have not previously been linked to valve development. Importantly, our data suggests that valve transcriptomes are under-represented when using commonly used databases to filter for genes important in cardiac development; this means that causative variants in valve-related genes may be excluded during filtering for genomic data analyses for, for example, BAV. Finally, we highlight "novel" pathways that likely play important roles in arterial valve development, showing that mouse knockouts of RBP1 have arterial valve defects. Thus, this study has confirmed the utility of ST for studies of the developing heart valves and broadens our knowledge of the genes and signalling pathways important in human valve development.

Abstract Background Probabilistic functional integrated networks (PFINs) are designed to aid our understanding of cellular biology and can be used to generate testable hypotheses about protein function. PFINs are generally created by scoring the quality of interaction datasets against a Gold Standard dataset, usually chosen from a separate high-quality data source, prior to their integration. Use of an external Gold Standard has several drawbacks, including data redundancy, data loss and the need for identifier mapping, which can complicate the network build and impact on PFIN performance. Additionally, there typically are no Gold Standard data for non-model organisms. Results We describe the development of an integration technique, ssNet, that scores and integrates both high-throughput and low-throughout data from a single source database in a consistent manner without the need for an external Gold Standard dataset. Using data from Saccharomyces cerevisiae we show that ssNet is easier and faster, overcoming the challenges of data redundancy, Gold Standard bias and ID mapping. In addition ssNet results in less loss of data and produces a more complete network. Conclusions The ssNet method allows PFINs to be built successfully from a single database, while producing comparable network performance to networks scored using an external Gold Standard source and with reduced data loss.

Macrophages are key target cells of Zika virus (ZIKV) infection, implicated as a viral reservoir seeding sanctuary sites such as the central nervous system and testes. This rests on the apparent ability of macrophages to sustain ZIKV replication without experiencing cytopathic effects. ZIKV infection of macrophages triggers an innate immune response involving type I interferons (IFN-I), key antiviral cytokines that play a complex role in ZIKV pathogenesis in animal models. To investigate the functional role of the IFN-I response we generated human induced pluripotent stem cell (iPSC)-derived macrophages from a patient with complete deficiency of IFNAR2, the high affinity IFN-I receptor subunit. Accompanying the profound defect of IFN-I signalling in IFNAR2 deficient iPS-macrophages we observed significantly enhanced ZIKV replication and cell death, revealing the inherent cytopathicity of ZIKV towards macrophages. These observations were recapitulated by genetic and pharmacological ablation of IFN-I signalling in control iPS-macrophages and extended to a model of iPS-microglia. Thus, the capacity of macrophages to support noncytolytic ZIKV replication depends on an equilibrium set by IFN-I, suggesting that innate antiviral responses might counterintuitively promote ZIKV persistence via the maintenance of tissue viral reservoirs relevant to pathogenesis.

Background & Aims: Serum microRNA (miRNA) levels are known to change in non-alcoholic fatty liver disease (NAFLD) and may serve as useful biomarkers. This study aimed to profile miRNAs comprehensively at all NAFLD stages. Methods: We profiled 2,083 serum miRNAs in a discovery cohort (183 cases with NAFLD representing the complete NAFLD spectrum and 10 population controls). miRNA libraries generated by HTG EdgeSeq were sequenced by Illumina NextSeq. Selected serum miRNAs were profiled in 372 additional cases with NAFLD and 15 population controls by quantitative reverse transcriptase PCR. Results: Levels of 275 miRNAs differed between cases and population controls. Fewer differences were seen within individual NAFLD stages, but miR-193a-5p consistently showed increased levels in all comparisons. Relative to NAFL/non-alcoholic steatohepatitis (NASH) with mild fibrosis (stage 0/1), 3 miRNAs (miR-193a-5p, miR-378d, and miR378d) were increased in cases with NASH and clinically significant fibrosis (stages 2–4), 7 (miR193a-5p, miR-378d, miR-378e, miR-320b, miR-320c, miR-320d, and miR-320e) increased in cases with NAFLD activity score (NAS) 5–8 compared with lower NAS, and 3 (miR-193a-5p, miR-378d, and miR-378e) increased but 1 (miR-19b-3p) decreased in steatosis, activity, and fibrosis (SAF) activity score 2–4 compared with lower SAF activity. The significant findings for miR-193a-5p were replicated in the additional cohort with NAFLD. Studies in Hep G2 cells showed that following palmitic acid treatment, miR-193a-5p expression decreased significantly. Gene targets for miR-193a-5p were investigated in liver RNAseq data for a case subgroup (n = 80); liver GPX8 levels correlated positively with serum miR-193a-5p. Conclusions: Serum miR-193a-5p levels correlate strongly with NAFLD activity grade and fibrosis stage. MiR-193a-5p may have a role in the hepatic response to oxidative stress and is a potential clinically tractable circulating biomarker for progressive NAFLD. Lay summary: MicroRNAs (miRNAs) are small pieces of nucleic acid that may turn expression of genes on or off. These molecules can be detected in the blood circulation, and their levels in blood may change in liver disease including non-alcoholic fatty liver disease (NAFLD). To see if we could detect specific miRNA associated with advanced stages of NAFLD, we carried out miRNA sequencing in a group of 183 patients with NAFLD of varying severity together with 10 population controls. We found that a number of miRNAs showed changes, mainly increases, in serum levels but that 1 particular miRNA miR-193a-5p consistently increased. We confirmed this increase in a second group of cases with NAFLD. Measuring this miRNA in a blood sample may be a useful way to determine whether a patient has advanced NAFLD without an invasive liver biopsy.

Ultraviolet B radiation (UVB) exerts pleiotropic effects on human skin. DNA damage response and repair pathways are activated by UVB; if damage cannot be repaired, apoptosis ensues. Although cumulative UVB exposure predisposes to skin cancer, UVB phototherapy is widely used as an effective treatment for psoriasis. Previous studies defined the therapeutic action spectrum of UVB and showed that psoriasis is resistant to apoptosis. This study aimed to investigate early molecular responses within psoriasis plaques following irradiation with single equi-erythemogenic doses of clinically-effective (311 nm, narrow-band) compared to clinically-ineffective (290 nm) UVB. Forty-eight micro-dissected epidermal samples from 20 psoriatic patients were analyzed using microarrays. Our bioinformatic analysis compared gene expression between 311 nm irradiated, 290 nm irradiated and control psoriasis epidermis to specifically identify 311 nm UVB differentially expressed genes (DEGs) and their upstream regulatory pathways. Key DEGs and pathways were validated by immunohistochemical analysis.There was a dynamic induction and repression of 311 nm UVB DEGs between 6 h and 18 h, only a limited number of DEGs maintained their designated expression status between time-points. Key disease and function pathways included apoptosis, cell death, cell migration and leucocyte chemotaxis. DNA damage response pathways, NRF2-mediated oxidative stress response and P53 signalling were key nodes, interconnecting apoptosis and cell cycle arrest. Interferon signalling, dendritic cell maturation, granulocyte adhesion and atherosclerotic pathways were also differentially regulated. Consistent with these findings, top transcriptional regulators of 311 nm UVB DEGs related to: a) apoptosis, DNA damage response and cell cycle control; b) innate/acquired immune regulation and inflammation; c) hypoxia/redox response and angiogenesis; d) circadian rhythmicity; f) EGR/AP1 signalling and keratinocyte differentiation; and g) mitochondrial biogenesis.This research provides important insights into the molecular targets of 311 nm UVB, underscoring key roles for apoptosis and cell death. These and the other key pathways delineated may be central to the therapeutic effects of 311 nm in psoriasis.

Summary Topoisomerase II creates a double-strand break intermediate with topoisomerase covalently coupled to the DNA via a 5′-phosphotyrosyl bond. These intermediate complexes can become cytotoxic protein-DNA adducts and DSB repair at these lesions requires removal of topoisomerase II. To analyse removal of topoisomerase II from genomic DNA we adapted the trapped in agarose DNA immunostaining assay. Recombinant MRE11 from 2 sources removed topoisomerase IIα from genomic DNA in vitro, as did MRE11 immunoprecipitates isolated from A-TLD or K562 cells. Basal topoisomerase II complex levels were very high in A-TLD cells lacking full-length wild type MRE11, suggesting that MRE11 facilitates the processing of topoisomerase complexes that arise as part of normal cellular metabolism. In K562 cells inhibition of MRE11, PARP or replication increased topoisomerase IIα and β complex levels formed in the absence of an anti-topoisomerase II drug.

Drug development is both increasing in cost whilst decreasing in productivity. There is a general acceptance that the current paradigm of R&D needs to change. One alternative approach is drug repositioning. With target-based approaches utilised heavily in the field of drug discovery, it becomes increasingly necessary to have a systematic method to rank gene-disease associations. Although methods already exist to collect, integrate and score these associations, they are often not a reliable reflection of expert knowledge. Furthermore, the amount of data available in all areas covered by bioinformatics is increasing dramatically year on year. It thus makes sense to move away from more generalised hypothesis driven approaches to research to one that allows data to generate their own hypothesis. We introduce an integrated, data driven approach to drug repositioning. We first apply a Bayesian statistics approach to rank 309,885 gene-disease associations using existing knowledge. Ranked associations are then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network, we show how our approach identifies diseases of the central nervous system (CNS) to be an area of interest. CNS disorders are identified due to the low numbers of such disorders that currently have marketed treatments, in comparison to other therapeutic areas. We then systematically mine our network for semantic subgraphs that allow us to infer drug-disease relations that are not captured in the network. We identify and rank 275,934 drug-disease has_indication associations after filtering those that are more likely to be side effects, whilst commenting on the top ranked associations in more detail. The dataset has been created in Neo4j and is available for download at https://bitbucket.org/ncl-intbio/genediseaserepositioning along with a Java implementation of the searching algorithm.

Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.

BackgroundFatigue is a debilitating condition with a significant impact on patients' quality of life. Fatigue is frequently reported by patients suffering from primary Sjögren's Syndrome (pSS), a chronic autoimmune condition characterised by dryness of the eyes and the mouth. However, although fatigue is common in pSS, it does not manifest in all sufferers, providing an excellent model with which to explore the potential underpinning biological mechanisms.MethodsWhole blood samples from 133 fully-phenotyped pSS patients stratified for the presence of fatigue, collected by the UK primary Sjögren's Syndrome Registry, were used for whole genome microarray. The resulting data were analysed both on a gene by gene basis and using pre-defined groups of genes. Finally, gene set enrichment analysis (GSEA) was used as a feature selection technique for input into a support vector machine (SVM) classifier. Classification was assessed using area under curve (AUC) of receiver operator characteristic and standard error of Wilcoxon statistic, SE(W).ResultsAlthough no genes were individually found to be associated with fatigue, 19 metabolic pathways were enriched in the high fatigue patient group using GSEA. Analysis revealed that these enrichments arose from the presence of a subset of 55 genes. A radial kernel SVM classifier with this subset of genes as input displayed significantly improved performance over classifiers using all pathway genes as input. The classifiers had AUCs of 0.866 (SE(W) 0.002) and 0.525 (SE(W) 0.006), respectively.ConclusionsSystematic analysis of gene expression data from pSS patients discordant for fatigue identified 55 genes which are predictive of fatigue level using SVM classification. This list represents the first step in understanding the underlying pathophysiological mechanisms of fatigue in patients with pSS.

There are two major pathways leading to induction of NF-κB subunits. The classical (or canonical) pathway typically leads to the induction of RelA or c-Rel containing complexes, and involves the degradation of IκBα in a manner dependent on IκB kinase (IKK) β and the IKK regulatory subunit NEMO. The alternative (or non-canonical) pathway, involves the inducible processing of p100 to p52, leading to the induction of NF-κB2(p52)/RelB containing complexes, and is dependent on IKKα and NF-κB inducing kinase (NIK). Here we demonstrate that in primary human fibroblasts, the alternative NF-κB pathway subunits NF-κB2 and RelB have multiple, but distinct, effects on the expression of key regulators of the cell cycle, reactive oxygen species (ROS) generation and protein stability. Specifically, following siRNA knockdown, quantitative PCR, western blot analyses and chromatin immunoprecipitation (ChIP) show that NF-κB2 regulates the expression of CDK4 and CDK6, while RelB, through the regulation of genes such as PSMA5 and ANAPC1, regulates the stability of p21WAF1 and the tumour suppressor p53. These combine to regulate the activity of the retinoblastoma protein, Rb, leading to induction of polycomb protein EZH2 expression. Moreover, our ChIP analysis demonstrates that EZH2 is also a direct NF-κB target gene. Microarray analysis revealed that in fibroblasts, EZH2 antagonizes a subset of p53 target genes previously associated with the senescent cell phenotype, including DEK and RacGAP1. We show that this pathway provides the major route of crosstalk between the alternative NF-κB pathway and p53, a consequence of which is to suppress cell senescence. Importantly, we find that activation of NF-κB also induces EZH2 expression in CD40L stimulated cells from Chronic Lymphocytic Leukemia patients. We therefore propose that this pathway provides a mechanism through which microenvironment induced NF-κB can inhibit tumor suppressor function and promote tumorigenesis.