Matthias Heinig

A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk

Combined analyses of gene networks and DNA sequence variation can provide new insights into the aetiology of common diseases that may not be apparent from genome-wide association studies alone. Recent advances in rat genomics are facilitating systems-genetics approaches. Here we report the use of integrated genome-wide approaches across seven rat tissues to identify gene networks and the loci underlying their regulation. We defined an interferon regulatory factor 7 (IRF7)-driven inflammatory network (IDIN) enriched for viral response genes, which represents a molecular biomarker for macrophages and which was regulated in multiple tissues by a locus on rat chromosome 15q25. We show that Epstein–Barr virus induced gene 2 (Ebi2, also known as Gpr183), which lies at this locus and controls B lymphocyte migration, is expressed in macrophages and regulates the IDIN. The human orthologous locus on chromosome 13q32 controlled the human equivalent of the IDIN, which was conserved in monocytes. IDIN genes were more likely to associate with susceptibility to type 1 diabetes (T1D)—a macrophage-associated autoimmune disease—than randomly selected immune response genes (P = 8.85 × 10−6). The human locus controlling the IDIN was associated with the risk of T1D at single nucleotide polymorphism rs9585056 (P = 7.0 × 10−10; odds ratio, 1.15), which was one of five single nucleotide polymorphisms in this region associated with EBI2 (GPR183) expression. These data implicate IRF7 network genes and their regulatory locus in the pathogenesis of T1D.

Soluble epoxide hydrolase is a susceptibility factor for heart failure in a rat model of human disease

We aimed to identify genetic variants associated with heart failure by using a rat model of the human disease. We performed invasive cardiac hemodynamic measurements in F2 crosses between spontaneously hypertensive heart failure (SHHF) rats and reference strains. We combined linkage analyses with genome-wide expression profiling and identified Ephx2 as a heart failure susceptibility gene in SHHF rats. Specifically, we found that cis variation at Ephx2 segregated with heart failure and with increased transcript expression, protein expression and enzyme activity, leading to a more rapid hydrolysis of cardioprotective epoxyeicosatrienoic acids. To confirm our results, we tested the role of Ephx2 in heart failure using knockout mice. Ephx2 gene ablation protected from pressure overload–induced heart failure and cardiac arrhythmias. We further demonstrated differential regulation of EPHX2 in human heart failure, suggesting a cross-species role for Ephx2 in this complex disease.

Cutaneous Na+ Storage Strengthens the Antimicrobial Barrier Function of the Skin and Boosts Macrophage-Driven Host Defense

Immune cells regulate a hypertonic microenvironment in the skin; however, the biological advantage of increased skin Na(+) concentrations is unknown. We found that Na(+) accumulated at the site of bacterial skin infections in humans and in mice. We used the protozoan parasite Leishmania major as a model of skin-prone macrophage infection to test the hypothesis that skin-Na(+) storage facilitates antimicrobial host defense. Activation of macrophages in the presence of high NaCl concentrations modified epigenetic markers and enhanced p38 mitogen-activated protein kinase (p38/MAPK)-dependent nuclear factor of activated T cells 5 (NFAT5) activation. This high-salt response resulted in elevated type-2 nitric oxide synthase (Nos2)-dependent NO production and improved Leishmania major control. Finally, we found that increasing Na(+) content in the skin by a high-salt diet boosted activation of macrophages in a Nfat5-dependent manner and promoted cutaneous antimicrobial defense. We suggest that the hypertonic microenvironment could serve as a barrier to infection.

Combined sequence-based and genetic mapping analysis of complex traits in outbred rats

Genetic mapping on fully sequenced individuals is transforming understanding of the relationship between molecular variation and variation in complex traits. Here we report a combined sequence and genetic mapping analysis in outbred rats that maps 355 quantitative trait loci for 122 phenotypes. We identify 35 causal genes involved in 31 phenotypes, implicating new genes in models of anxiety, heart disease and multiple sclerosis. The relationship between sequence and genetic variation is unexpectedly complex: at approximately 40% of quantitative trait loci, a single sequence variant cannot account for the phenotypic effect. Using comparable sequence and mapping data from mice, we show that the extent and spatial pattern of variation in inbred rats differ substantially from those of inbred mice and that the genetic variants in orthologous genes rarely contribute to the same phenotype in both species.

Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs

The transcription factor affinity prediction (TRAP) method calculates the affinity of transcription factors for DNA sequences on the basis of a biophysical model. This method has proven to be useful for several applications, including for determining the putative target genes of a given factor. This protocol covers two other applications: (i) determining which transcription factors have the highest affinity in a set of sequences (illustrated with chromatin immunoprecipitation–sequencing (ChIP-seq) peaks), and (ii) finding which factor is the most affected by a regulatory single-nucleotide polymorphism. The protocol describes how to use the TRAP web tools to address these questions, and it also presents a way to run TRAP on random control sequences to better estimate the significance of the results. All of the tools are fully available online and do not need any additional installation. The complete protocol takes about 45 min, but each individual tool runs in a few minutes.

Fine Mapping of the 1p36 Deletion Syndrome Identifies Mutation of PRDM16 as a Cause of Cardiomyopathy

Deletion 1p36 syndrome is recognized as the most common terminal deletion syndrome. Here, we describe the loss of a gene within the deletion that is responsible for the cardiomyopathy associated with monosomy 1p36, and we confirm its role in nonsyndromic left ventricular noncompaction cardiomyopathy (LVNC) and dilated cardiomyopathy (DCM). With our own data and publically available data from array comparative genomic hybridization (aCGH), we identified a minimal deletion for the cardiomyopathy associated with 1p36del syndrome that included only the terminal 14 exons of the transcription factor PRDM16 (PR domain containing 16), a gene that had previously been shown to direct brown fat determination and differentiation. Resequencing of PRDM16 in a cohort of 75 nonsyndromic individuals with LVNC detected three mutations, including one truncation mutant, one frameshift null mutation, and a single missense mutant. In addition, in a series of cardiac biopsies from 131 individuals with DCM, we found 5 individuals with 4 previously unreported nonsynonymous variants in the coding region of PRDM16. None of the PRDM16 mutations identified were observed in more than 6,400 controls. PRDM16 has not previously been associated with cardiac disease but is localized in the nuclei of cardiomyocytes throughout murine and human development and in the adult heart. Modeling of PRDM16 haploinsufficiency and a human truncation mutant in zebrafish resulted in both contractile dysfunction and partial uncoupling of cardiomyocytes and also revealed evidence of impaired cardiomyocyte proliferative capacity. In conclusion, mutation of PRDM16 causes the cardiomyopathy in 1p36 deletion syndrome as well as a proportion of nonsyndromic LVNC and DCM.

Quantifying the effect of sequence variation on regulatory interactions

The increasing amount of sequence data provides new opportunities and challenges to derive mechanistic models that can link sequence variations to phenotypic diversity. Here we introduce a new computational framework to suggest possible consequences of sequence variations on regulatory networks. Our method, called sTRAP (strap.molgen.mpg.de), analyses variations in the DNA sequence and predicts quantitative changes to the binding strength of any transcription factor for which there is a binding model. We have tested the method against a set of known associations between SNPs and their regulatory consequences. Our predictions are robust with respect to different parameters and model assumptions. Importantly we set an objective and quantifiable benchmark against which future improvements can be compared. Given the good performance of our method, we developed a publicly available tool that can serve as an important starting point for routine analysis of disease‐associated sequence regions. Hum Mutat 30:1–7, 2010.

Quantitative Interaction Proteomics of Neurodegenerative Disease Proteins

Several proteins have been linked to neurodegenerative disorders (NDDs), but their molecular function is not completely understood. Here, we used quantitative interaction proteomics to identify binding partners of Amyloid beta precursor protein (APP) and Presenilin-1 (PSEN1) for Alzheimers disease (AD), Huntingtin (HTT) for Huntingtons disease, Parkin (PARK2) for Parkinsons disease, and Ataxin-1 (ATXN1) for spinocerebellar ataxia type 1. Our network reveals common signatures of protein degradation and misfolding and recapitulates known biology. Toxicity modifier screens and comparison to genome-wide association studies show that interaction partners are significantly linked to disease phenotypes in vivo. Direct comparison of wild-type proteins and disease-associated variants identified binders involved in pathogenesis, highlighting the value of differential interactome mapping. Finally, we show that the mitochondrial protein LRPPRC interacts preferentially with an early-onset AD variant of APP. This interaction appears to induce mitochondrial dysfunction, which is an early phenotype of AD.

Translational regulation shapes the molecular landscape of complex disease phenotypes

The extent of translational control of gene expression in mammalian tissues remains largely unknown. Here we perform genome-wide RNA sequencing and ribosome profiling in heart and liver tissues to investigate strain-specific translational regulation in the spontaneously hypertensive rat (SHR/Ola). For the most part, transcriptional variation is equally apparent at the translational level and there is limited evidence of translational buffering. Remarkably, we observe hundreds of strain-specific differences in translation, almost doubling the number of differentially expressed genes. The integration of genetic, transcriptional and translational data sets reveals distinct signatures in 3′UTR variation, RNA-binding protein motifs and miRNA expression associated with translational regulation of gene expression. We show that a large number of genes associated with heart and liver traits in human genome-wide association studies are primarily translationally regulated. Capturing interindividual differences in the translated genome will lead to new insights into the genes and regulatory pathways underlying disease phenotypes.

Natural variation of histone modification and its impact on gene expression in the rat genome

Histone modifications are epigenetic marks that play fundamental roles in many biological processes including the control of chromatin-mediated regulation of gene expression. Little is known about interindividual variability of histone modification levels across the genome and to what extent they are influenced by genetic variation. We annotated the rat genome with histone modification maps, identified differences in histone trimethyl-lysine levels among strains, and described their underlying genetic basis at the genome-wide scale using ChIP-seq in heart and liver tissues in a panel of rat recombinant inbred and their progenitor strains. We identified extensive variation of histone methylation levels among individuals and mapped hundreds of underlying cis- and trans-acting loci throughout the genome that regulate histone methylation levels in an allele-specific manner. Interestingly, most histone methylation level variation was trans-linked and the most prominent QTL identified influenced H3K4me3 levels at 899 putative promoters throughout the genome in the heart. Cis- acting variation was enriched in binding sites of distinct transcription factors in heart and liver. The integrated analysis of DNA variation together with histone methylation and gene expression levels showed that histoneQTLs are an important predictor of gene expression and that a joint analysis significantly enhanced the prediction of gene expression traits (eQTLs). Our data suggest that genetic variation has a widespread impact on histone trimethylation marks that may help to uncover novel genotype-phenotype relationships.