Integrative genetic and network approaches to identify key regulators of cardiac fibrosis

Excessive fibrogenic response is a pathological hallmark of chronic complex diseases, including cardiovascular disease. To date, very few gene targets for cardiac fibrosis that led to effective treatments have been identified in humans. In this thesis I study and dissect the genetic component underlying cardiac fibrosis. This study integrates histomorphometric measurements of fibrosis in the rat left ventricle (LV) with gene expression (RNA-Seq from LV) and genetic data in a panel of recombinant inbred (RI) rat strains (n=30). In addition, I integrated RNA-seq LV and genetic data in humans (n=187, healthy and dilated cardiomyopathy (DCM) patients), as well as DCM genome-wide association studies (GWAS) data. I started by carrying out an unbiased co-expression network analysis in the rat heart. The reconstructed cardiac transcriptional modules were associated with quantitative levels of fibrosis. Co-expression networks were also independently built in the heart of DCM patients and by using the rat data, co-expression networks associated with fibrosis, conserved across rats and humans and not present in control human heart were prioritised. In the prioritised networks, I also analysed their cardiac cell type specificity, differential expression after TGFβ induction, potential driving transcription factors and conservation in other fibrotic diseases by analysing human data collected from other organs. Furthermore, I aimed to identify common genetic regulators of the networks (also called master genetic regulators) by using Bayesian multivariate regression approaches. Finally, I integrated GWAS data in DCM (n=2,287) to dissect the genetic basis of DCM. This systems genetics study evidences that there are transcriptional processes involved in the human cardiac fibrogenic response that are conserved across rats and humans, some of them also underlying DCM aetiology. In an attempt to suggest new gene targets for cardiac fibrosis, I also identified the WWP2 gene as a novel trans-acting genetic regulator of cardiac fibrosis.Open Acces

MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues.

MOTIVATION: Analysing the joint association between a large set of responses and predictors is a fundamental statistical task in integrative genomics, exemplified by numerous expression Quantitative Trait Loci (eQTL) studies. Of particular interest are the so-called ': hotspots ': , important genetic variants that regulate the expression of many genes. Recently, attention has focussed on whether eQTLs are common to several tissues, cell-types or, more generally, conditions or whether they are specific to a particular condition. RESULTS: We have implemented MT-HESS, a Bayesian hierarchical model that analyses the association between a large set of predictors, e.g. SNPs, and many responses, e.g. gene expression, in multiple tissues, cells or conditions. Our Bayesian sparse regression algorithm goes beyond ': one-at-a-time ': association tests between SNPs and responses and uses a fully multivariate model search across all linear combinations of SNPs, coupled with a model of the correlation between condition/tissue-specific responses. In addition, we use a hierarchical structure to leverage shared information across different genes, thus improving the detection of hotspots. We show the increase of power resulting from our new approach in an extensive simulation study. Our analysis of two case studies highlights new hotspots that would remain undetected by standard approaches and shows how greater prediction power can be achieved when several tissues are jointly considered. AVAILABILITY AND IMPLEMENTATION: C[Formula: see text] source code and documentation including compilation instructions are available under GNU licence at http://www.mrc-bsu.cam.ac.uk/software/

Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery

Background The relationship between monogenic and polygenic forms of epilepsy is poorly understood, and the extent to which the genetic and acquired epilepsies share common pathways is unclear. Here, we use an integrated systems-level analysis of brain gene expression data to identify molecular networks disrupted in epilepsy. Results We identify a co-expression network of 320 genes (M30), which is significantly enriched for non-synonymous de novo mutations ascertained from patients with monogenic epilepsy, and for common variants associated with polygenic epilepsy. The genes in M30 network are expressed widely in the human brain under tight developmental control, and encode physically interacting proteins involved in synaptic processes. The most highly connected proteins within M30 network are preferentially disrupted by deleterious de novo mutations for monogenic epilepsy, in line with the centrality-lethality hypothesis. Analysis of M30 expression revealed consistent down-regulation in the epileptic brain in heterogeneous forms of epilepsy including human temporal lobe epilepsy, a mouse model of acquired temporal lobe epilepsy, and a mouse model of monogenic Dravet (SCN1A) disease. These results suggest functional disruption of M30 via gene mutation or altered expression as a convergent mechanism regulating susceptibility to epilepsy broadly. Using the large collection of drug-induced gene expression data from Connectivity Map, several drugs were predicted to preferentially restore the down-regulation of M30 in epilepsy toward health, most notably valproic acid, whose effect on M30 expression was replicated in neurons. Conclusions Taken together, our results suggest targeting the expression of M30 as a potential new therapeutic strategy in epilepsy

Author Correction: WWP2 regulates pathological cardiac fibrosis by modulating SMAD2 signaling.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease–associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease

Famílies botàniques de plantes medicinals

Facultat de Farmàcia, Universitat de Barcelona. Ensenyament: Grau de Farmàcia, Assignatura: Botànica Farmacèutica, Curs: 2013-2014, Coordinadors: Joan Simon, Cèsar Blanché i Maria Bosch.Els materials que aquí es presenten són els recull de 175 treballs d’una família botànica d’interès medicinal realitzats de manera individual. Els treballs han estat realitzat per la totalitat dels estudiants dels grups M-2 i M-3 de l’assignatura Botànica Farmacèutica durant els mesos d’abril i maig del curs 2013-14. Tots els treballs s’han dut a terme a través de la plataforma de GoogleDocs i han estat tutoritzats pel professor de l’assignatura i revisats i finalment co-avaluats entre els propis estudiants. L’objectiu principal de l’activitat ha estat fomentar l’aprenentatge autònom i col·laboratiu en Botànica farmacèutica

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Transcriptional Network Analysis for the Regulation of Left Ventricular Hypertrophy and Microvascular Remodeling

Hypertension and cardiomyopathies share maladaptive changes of cardiac morphology, eventually leading to heart failure. These include left ventricular hypertrophy (LVH), myocardial fibrosis, and structural remodeling of coronary microcirculation, which is the morphologic hallmark of coronary microvascular dysfunction. To pinpoint the complex molecular mechanisms and pathways underlying LVH-associated cardiac remodeling independent of blood pressure effects, we employed gene network approaches to the rat heart. We used the Spontaneously Hypertensive Rat model showing many features of human hypertensive cardiomyopathy, for which we collected histological and histomorphometric data of the heart and coronary vasculature, and genome-wide cardiac gene expression. Here, we provide a large catalogue of gene co-expression networks in the heart that are significantly associated with quantitative variation in LVH, microvascular remodeling, and fibrosis-related traits. Many of these networks were significantly conserved to human idiopathic and/or ischemic cardiomyopathy patients, suggesting a potential role for these co-expressed genes in human heart disease

Multi-tissue Analysis of Co-expression Networks by Higher-Order Generalized Singular Value Decomposition Identifies Functionally Coherent Transcriptional Modules

<div><p>Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing <i>ad-hoc</i> input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed heat shock protein (Hsp) and cardiomyopathy genes (<i>Bag3</i>, <i>Cryab</i>, <i>Kras</i>, <i>Emd</i>, <i>Plec</i>), which was significantly replicated using separate failing heart and liver gene expression datasets in humans, thus revealing a conserved functional role for Hsp genes in cardiovascular disease.</p></div