ABSTRACT
BACKGROUND: Dilated cardiomyopathy (DCM) is a life-threatening heart disease and a common cause of heart failure due to systolic dysfunction and subsequent left or biventricular dilatation. A significant number of cases have a genetic etiology; however, as a complex disease, the exact genetic risk factors are largely unknown, and many patients remain without a molecular diagnosis. METHODS: We performed GWAS followed by whole-genome, transcriptome, and immunohistochemical analyses in a spontaneously occurring canine model of DCM. Canine gene discovery was followed up in three human DCM cohorts. RESULTS: Our results revealed two independent additive loci associated with the typical DCM phenotype comprising left ventricular systolic dysfunction and dilatation. We highlight two novel candidate genes, RNF207 and PRKAA2, known for their involvement in cardiac action potentials, energy homeostasis, and morphology. We further illustrate the distinct genetic etiologies underlying the typical DCM phenotype and ventricular premature contractions. Finally, we followed up on the canine discoveries in human DCM patients and discovered candidate variants in our two novel genes. CONCLUSIONS: Collectively, our study yields insight into the molecular pathophysiology of DCM and provides a large animal model for preclinical studies.
Subject(s)
Cardiomyopathy, Dilated , Humans , Animals , Dogs , Cardiomyopathy, Dilated/genetics , Cardiomyopathy, Dilated/veterinary , Homeostasis , Models, Animal , Phenotype , Risk FactorsABSTRACT
Zebrafish, a popular organism for studying embryonic development and for modeling human diseases, has so far lacked a systematic functional annotation program akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created a central repository to store and process zebrafish developmental functional genomic data. Our data coordination center ( https://danio-code.zfin.org ) combines a total of 1,802 sets of unpublished and re-analyzed published genomic data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements throughout development, including classes with distinct features dependent on their activity in time and space. We delineated the distinct distance topology and chromatin features between regulatory elements active during zygotic genome activation and those active during organogenesis. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predicted functional relationships between them beyond sequence similarity, thus extending the utility of zebrafish developmental genomics to mammals.
Subject(s)
Databases, Genetic , Gene Expression Regulation, Developmental , Genome , Genomics , Regulatory Sequences, Nucleic Acid , Zebrafish Proteins , Zebrafish , Animals , Chromatin/genetics , Genome/genetics , Humans , Mice , Molecular Sequence Annotation , Organogenesis/genetics , Regulatory Sequences, Nucleic Acid/genetics , Zebrafish/embryology , Zebrafish/genetics , Zebrafish Proteins/geneticsSubject(s)
Genomics , Molecular Sequence Annotation , Sequence Analysis, DNA , Computational Biology , Gene Ontology , Genomics/methods , Genomics/standards , Metadata , Molecular Sequence Annotation/methods , Molecular Sequence Annotation/standards , Sequence Analysis, DNA/methods , Sequence Analysis, DNA/standardsABSTRACT
BACKGROUND: Over the past few years the variety of experimental designs and protocols for sequencing experiments increased greatly. To ensure the wide usability of the produced data beyond an individual project, rich and systematic annotation of the underlying experiments is crucial. FINDINGS: We first developed an annotation structure that captures the overall experimental design as well as the relevant details of the steps from the biological sample to the library preparation, the sequencing procedure, and the sequencing and processed files. Through various design features, such as controlled vocabularies and different field requirements, we ensured a high annotation quality, comparability, and ease of annotation. The structure can be easily adapted to a large variety of species. We then implemented the annotation strategy in a user-hosted web platform with data import, query, and export functionality. CONCLUSIONS: We present here an annotation structure and user-hosted platform for sequencing experiment data, suitable for lab-internal documentation, collaborations, and large-scale annotation efforts.
Subject(s)
Molecular Sequence Annotation/methods , Sequence Analysis/methods , Software , Molecular Sequence Annotation/standards , Sequence Analysis/standardsABSTRACT
MOTIVATION: Signal transduction via calcium ions (Ca2+) represents a fundamental signaling pathway in all eukaryotic cells. A large portion of the human genome encodes proteins used to assemble signaling systems that can transduce signals with diverse spatial and temporal dynamics. RESULTS: Here, we provide a map of all of the genes involved in Ca2+ signaling and link these genes to human genetic disorders. Using Gene Ontology terms and genome databases, 1805 genes were identified as regulators or targets of intracellular Ca2+ signals. Associating these 1805 genes with human genetic disorders uncovered 1470 diseases with mutated 'Ca2+ genes'. A network with scale-free properties appeared when the Ca2+ genes were mapped to their associated genetic disorders. AVAILABILITY AND IMPLEMENTATION: The Ca2+ genome database is freely available at http://cagedb.uhlenlab.org and will foster studies of gene functions and genetic disorders associated with Ca2+ signaling. CONTACT: per.uhlen@ki.se. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Subject(s)
Calcium Signaling/genetics , Chromosome Mapping/methods , Databases, Genetic , Genome, Human , Genomics/methods , Human Genetics/methods , HumansABSTRACT
BACKGROUND: Heart rate variability is the variation of the time interval between consecutive heartbeats. Entropy is a commonly used tool to describe the regularity of data sets. Entropy functions are defined using multiple parameters, the selection of which is controversial and depends on the intended purpose. This study describes the results of tests conducted to support parameter selection, towards the goal of enabling further biomarker discovery. METHODS: This study deals with approximate, sample, fuzzy, and fuzzy measure entropies. All data were obtained from PhysioNet, a free-access, on-line archive of physiological signals, and represent various medical conditions. Five tests were defined and conducted to examine the influence of: varying the threshold value r (as multiples of the sample standard deviation σ, or the entropy-maximizing rChon), the data length N, the weighting factors n for fuzzy and fuzzy measure entropies, and the thresholds rF and rL for fuzzy measure entropy. The results were tested for normality using Lilliefors' composite goodness-of-fit test. Consequently, the p-value was calculated with either a two sample t-test or a Wilcoxon rank sum test. RESULTS: The first test shows a cross-over of entropy values with regard to a change of r. Thus, a clear statement that a higher entropy corresponds to a high irregularity is not possible, but is rather an indicator of differences in regularity. N should be at least 200 data points for r = 0.2 σ and should even exceed a length of 1000 for r = rChon. The results for the weighting parameters n for the fuzzy membership function show different behavior when coupled with different r values, therefore the weighting parameters have been chosen independently for the different threshold values. The tests concerning rF and rL showed that there is no optimal choice, but r = rF = rL is reasonable with r = rChon or r = 0.2σ. CONCLUSIONS: Some of the tests showed a dependency of the test significance on the data at hand. Nevertheless, as the medical conditions are unknown beforehand, compromises had to be made. Optimal parameter combinations are suggested for the methods considered. Yet, due to the high number of potential parameter combinations, further investigations of entropy for heart rate variability data will be necessary.