S-glutathionylation, a redox switch disrupting angiogenic balance to promote the preeclampsia phenotype

Background Elevation of circulating anti-angiogenic factors is pivotal in the development of the preeclampsia (PE) phenotype of incomplete vascular remodelling, hypertension and kidney dysfunction during pregnancy. Oxidative stress is explicitly linked to PE with high levels measurable in the placenta. Yet antioxidant therapy has failed, in some cases worsening pregnancy outcomes. The modulation of protein activity by reversible oxidative post-translational modifications (oxPTM) under low levels of reactive oxygen species is emerging as an important “redox-switch” mechanism in cardiovascular diseases, although oxPTM have not been investigated in the context of PE. Of significance, S-glutathionylation is a common oxPTM which reversal by glutaredoxin (Grx) is predominant in preeclamptic placenta and was associated with attenuated revascularisation and sFlt-1 elevation in mice. Purpose We aimed to identify the molecular basis for how S-glutathionylation reversal by Grx may contribute to pregnancy-induced vascular complications by modulating angiogenic signalling at the maternofoetal interface. Methods We combined physiological in vivo assessment with bioinformatics proteomic analysis and exon-level microarray to investigate the role of S-glutathionylation in the development of PE phenotype. In vitro studies using primary endothelial cells (EC) and iPS-derived trophoblasts investigated the effects of oxPTM reversal on angiogenic signalling in individual placental cell types and the functional consequences were assessed in 3D models replicating early-pregnancy events. Results Overexpressing Grx transgenic mice (TG) developed gestational hypertension, kidney dysfunction and elevated plasma levels of the anti-angiogenic factor sFlt-1 compared to their littermate controls (WT) during timed pregnancy. Grx-mediated oxPTM reversal in EC disrupted angiogenic sprouting and promoted anti-angiogenic signals by increasing sFlt-1:PlGF ratio and decreasing endoglin levels. The rise in sFlt-1 was associated with an isoform switch promoting sFlt-e15a over sFlt-i13. In trophoblasts, Grx overexpression inhibited migration and syncytialisation and modulated angiogenic balance in a cell type-specific manner. The sFlt1-e15a:PlGF ratio was increased in syncytiotrophoblasts and decreased in extra-villous trophoblasts, while endoglin expression was decreased in both cell types. A genome-wide exon-level profiling of TG vs WT mice placenta revealed a global alteration of alternative splicing events. Conclusion Grx-mediated removal of oxPTM directly disrupts placental angiogenic balance via dysregulation of sFlt-1 isoforms, which may promote the PE phenotype of impaired vascular remodelling, hypertension and kidney dysfunction during pregnancy. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Horizon 2020 - Marie Skłodowska-Curie grant agreement (iPLACENTA)

Developmental dynamics of voltage-gated sodium channel isoform expression in the human and mouse brain

Background Genetic variants in the voltage-gated sodium channels SCN1A, SCN2A, SCN3A, and SCN8A are leading causes of epilepsy, developmental delay, and autism spectrum disorder. The mRNA splicing patterns of all four genes vary across development in the rodent brain, including mutually exclusive copies of the fifth protein-coding exon detected in the neonate (5N) and adult (5A). A second pair of mutually exclusive exons is reported in SCN8A only (18N and 18A). We aimed to quantify the expression of individual exons in the developing human brain. Methods RNA-seq data from 783 human brain samples across development were analyzed to estimate exon-level expression. Developmental changes in exon utilization were validated by assessing intron splicing. Exon expression was also estimated in RNA-seq data from 58 developing mouse neocortical samples. Results In the mature human neocortex, exon 5A is consistently expressed at least 4-fold higher than exon 5N in all four genes. For SCN2A, SCN3A, and SCN8A, a brain-wide synchronized 5N to 5A transition occurs between 24 post-conceptual weeks (2nd trimester) and 6 years of age. In mice, the equivalent 5N to 5A transition begins at or before embryonic day 15.5. In SCN8A, over 90% of transcripts in the mature human cortex include exon 18A. Early in fetal development, most transcripts include 18N or skip both 18N and 18A, with a transition to 18A inclusion occurring from 13 post-conceptual weeks to 6 months of age. No other protein-coding exons showed comparably dynamic developmental trajectories. Conclusions Exon usage in SCN1A, SCN2A, SCN3A, and SCN8A changes dramatically during human brain development. These splice isoforms, which alter the biophysical properties of the encoded channels, may account for some of the observed phenotypic differences across development and between specific variants. Manipulation of the proportion of splicing isoforms at appropriate stages of development may act as a therapeutic strategy for specific mutations or even epilepsy in general.

Panel Informativity Optimizer (PIO): an R package to improve cancer NGS panel informativity

Mutation detection by next generation sequencing (NGS) is routinely used for cancer diagnosis. Selecting an optimal set of genes for a given cancer is not trivial as it has to optimize informativity (i.e. the number of patients with at least one mutation in the panel), while minimizing panel length in order to reduce sequencing costs and increase sensitivity. We propose herein Panel Informativity Optimizer (PIO), an open-source software developed as an R package with a user-friendly graphical interface to help optimize cancer NGS panel informativity. Using patient-level mutational data from either private datasets or preloaded dataset of 91 independent cohort from 31 different cancer type, PIO selects an optimal set of genomic intervals to maximize informativity and panel size in a given cancer type. Different options are offered such as the definition of genomic intervals at the gene or exon level, and the use of optimization strategy at the patient or patient per kilobase level. PIO can also propose an optimal set of genomic intervals to increase informativity of custom panels. A panel tester function is also available for panel benchmarking. Using public databases, as well as data from real-life settings, we demonstrate that PIO allows panel size reduction of up to 1000kb, and accurately predicts the performance of custom or commercial panels. PIO is available online at https://vincentalcazer.shinyapps.io/Panel_informativity_optimizer/ or can be set on a locale machine from https://github.com/VincentAlcazer/PIO.

Deregulation of epigenetic marks is correlated to differential exon usage of developmental genes

ABSTRACTAlternative exon usage is known to affect a large portion of genes in mammalian genomes. Importantly, different splice forms sometimes lead to distinctly different protein functions. We analyzed data from the Human Epigenome Atlas (version 9) whereby we connected the differential usage of exons in various developmental stages of human cells/tissues to differential epigenetic modifications at the exon level. In total, we analyzed 19 human tissues, adult cells, and cultured cells that mimic early developmental stages. We found that the differential occurrence of protein isoforms across developmental stages was often associated with changes in histone marks at exon boundary regions. Many of the genes that are differentially regulated at the exon level were found to be functionally associated with development and metabolism.

Proteomic and Transcriptomic Analysis Identify Spliceosome as a Significant Component of the Molecular Machinery in the Pituitary Tumors Derived from POU1F1- and NR5A1-Cell Lineages

Background: Pituitary adenomas (PA) are the second most common tumor in the central nervous system and have low counts of mutated genes. Splicing occurs in 95% of the coding RNA. There is scarce information about the spliceosome and mRNA-isoforms in PA, and therefore we carried out proteomic and transcriptomic analysis to identify spliceosome components and mRNA isoforms in PA. Methods: Proteomic profile analysis was carried out by nano-HPLC and mass spectrometry with a quadrupole time-of-flight mass spectrometer. The mRNA isoforms and transcriptomic profiles were carried out by microarray technology. With proteins and mRNA information we carried out Gene Ontology and exon level analysis to identify splicing-related events. Results: Approximately 2000 proteins were identified in pituitary tumors. Spliceosome proteins such as SRSF1, U2AF1 and RBM42 among others were found in PA. These results were validated at mRNA level, which showed up-regulation of spliceosome genes in PA. Spliceosome-related genes segregate and categorize PA tumor subtypes. The PA showed alterations in CDK18 and THY1 mRNA isoforms which could be tumor specific. Conclusions: Spliceosome components are significant constituents of the PA molecular machinery and could be used as molecular markers and therapeutic targets. Splicing-related genes and mRNA-isoforms profiles characterize tumor subtypes.

Abstract 12675: Comprehensive Analysis of Cardiomyopathy and Arrhythmia Genes Yields Unanticipated Molecular Diagnoses

Introduction: Professional societies recommend genetic testing to improve diagnosis and inform management of inherited cardiovascular disease, yet genetic testing is not widely utilized in cardiovascular practice. To reduce barriers to genetic testing and facilitate following of existing guidelines, we initiated a program of sponsored genetic testing with genetic counseling at no cost to patients suspected of having a genetic arrhythmia or cardiomyopathy. Here, we describe unanticipated molecular diagnoses provided by a comprehensive analysis of cardiomyopathy and arrhythmia genes. Methods: With IRB approval, de-identified genetic and clinical data provided by ordering clinicians were reviewed from 1,606 individuals referred for testing through the sponsored, no-charge Detect Cardiomyopathy and Arrhythmia genetic testing program between July 2019 and January 2020. Testing consisted of a cardiomyopathy and arrhythmia panel of up to 150 genes detecting single nucleotide, small indel, and exon-level deletion and duplication variants. Results: Overall, 20.5% (329/1606) of patients had a pathogenic or likely pathogenic (P/LP) variant identified. The most common reasons for referral were hypertrophic cardiomyopathy (40%), dilated cardiomyopathy (24%), and long QT syndrome (13%). The diagnostic yield was 25% (130/527) among patients whose healthcare provider reported a high or moderate index of clinical suspicion for a genetic cardiomyopathy, of whom 2% (2/130) had P/LP variants only in the arrhythmia gene KCNQ1 . Conversely, among patients with a high or moderate index of clinical suspicion for a genetic arrhythmia, the diagnostic yield was 20% (28/137), of which 18% (5/28) had P/LP variants only in the cardiomyopathy-associated genes MYBPC3 (2) and TTR (3). Conclusions: These data demonstrate that comprehensive genetic testing, without cost as a barrier, identifies clinically-relevant variants in 1 in 5 suspected cardiomyopathy or arrhythmia patients. Notably, genetic testing with a multi-condition panel yielded unanticipated molecular findings likely to change clinical management in up to 18% of genetically-positive patients. These unanticipated findings would have likely been missed by targeted, disease-specific panels.

A tail-based test to detect differential expression in RNA-sequencing data

RNA sequencing data have been abundantly generated in biomedical research for biomarker discovery and other studies. Such data at the exon level are usually heavily tailed and correlated. Conventional statistical tests based on the mean or median difference for differential expression likely suffer from low power when the between-group difference occurs mostly in the upper or lower tail of the distribution of gene expression. We propose a tail-based test to make comparisons between groups in terms of a specific distribution area rather than a single location. The proposed test, which is derived from quantile regression, adjusts for covariates and accounts for within-sample dependence among the exons through a specified correlation structure. Through Monte Carlo simulation studies, we show that the proposed test is generally more powerful and robust in detecting differential expression than commonly used tests based on the mean or a single quantile. An application to TCGA lung adenocarcinoma data demonstrates the promise of the proposed method in terms of biomarker discovery.