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1.
J Am Heart Assoc. 2018 Nov 6;7(21):e009243. doi: 10.1161/JAHA.118.009243.

Systems Genetics Approaches in Rat Identify Novel Genes and Gene Networks Associated With Cardiac Conduction.

Author information

1
1 Department of Experimental Cardiology Heart Centre Academic Medical Center Amsterdam Amsterdam The Netherlands.
2
2 Maastricht Centre for Systems Biology Maastricht University Maastricht The Netherlands.
3
4 Duke-NUS Medical School Singapore.
4
6 Institute of Physiology Academy of Sciences of the Czech Republic Prague Czech Republic.
5
5 Institute of Computational Biology Helmholtz Zentrum München München Germany.
6
3 The MRC London Institute of Medical Sciences Imperial College London London United Kingdom.

Abstract

Background Electrocardiographic ( ECG ) parameters are regarded as intermediate phenotypes of cardiac arrhythmias. Insight into the genetic underpinnings of these parameters is expected to contribute to the understanding of cardiac arrhythmia mechanisms. Here we used HXB / BXH recombinant inbred rat strains to uncover genetic loci and candidate genes modulating ECG parameters. Methods and Results RR interval, PR interval, QRS duration, and QT c interval were measured from ECG s obtained in 6 male rats from each of the 29 available HXB / BXH recombinant inbred strains. Genes at loci displaying significant quantitative trait loci (QTL) effects were prioritized by assessing the presence of protein-altering variants, and by assessment of cis expression QTL ( eQTL ) effects and correlation of transcript abundance to the respective trait in the heart. Cardiac RNA -seq data were additionally used to generate gene co-expression networks. QTL analysis of ECG parameters identified 2 QTL for PR interval, respectively, on chromosomes 10 and 17. At the chromosome 10 QTL , cis- eQTL effects were identified for Acbd4, Cd300lg, Fam171a2, and Arhgap27; the transcript abundance in the heart of these 4 genes was correlated with PR interval. At the chromosome 17 QTL , a cis- eQTL was uncovered for Nhlrc1 candidate gene; the transcript abundance of this gene was also correlated with PR interval. Co-expression analysis furthermore identified 50 gene networks, 6 of which were correlated with PR interval or QRS duration, both parameters of cardiac conduction. Conclusions These newly identified genetic loci and gene networks associated with the ECG parameters of cardiac conduction provide a starting point for future studies with the potential of identifying novel mechanisms underlying cardiac electrical function.

KEYWORDS:

bioinformatics; electrophysiology; rats

PMID:
30608189
DOI:
10.1161/JAHA.118.009243
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2.
Cell Syst. 2018 Dec 26;7(6):567-579.e6. doi: 10.1016/j.cels.2018.10.013. Epub 2018 Nov 28.

Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model.

Author information

1
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany; Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching 85748, Germany.
2
Alacris Theranostics GmbH, Berlin 12489, Germany; Max Planck Institute for Molecular Genetics, Berlin 14195, Germany.
3
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.
4
KG Jebsen Centre for Psychosis Research, NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo 0450, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0450, Norway.
5
Alacris Theranostics GmbH, Berlin 12489, Germany.
6
Max Planck Institute for Molecular Genetics, Berlin 14195, Germany; Dahlem Centre for Genome Research and Medical Systems Biology, Berlin 12489, Germany.
7
Alacris Theranostics GmbH, Berlin 12489, Germany; Max Planck Institute for Molecular Genetics, Berlin 14195, Germany. Electronic address: b.lange@alacris.de.
8
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany; Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching 85748, Germany. Electronic address: jan.hasenauer@helmholtz-muenchen.de.

Abstract

Mechanistic models are essential to deepen the understanding of complex diseases at the molecular level. Nowadays, high-throughput molecular and phenotypic characterizations are possible, but the integration of such data with prior knowledge on signaling pathways is limited by the availability of scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. This framework is over 104 times faster than state-of-the-art methods, which enables modeling at previously infeasible scales. By applying the framework to a model describing major cancer-associated pathways (>1,200 species and >2,600 reactions), we could predict the effect of drug combinations from single drug data. This is the first integration of high-throughput datasets using large-scale mechanistic models. We anticipate this to be the starting point for development of more comprehensive models allowing a deeper mechanistic insight.

KEYWORDS:

biomarker; cancer signaling; drug response; drug synergy; mechanistic modeling; parameter estimation; sequencing data; systems biology

PMID:
30503647
DOI:
10.1016/j.cels.2018.10.013
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3.
Epilepsia. 2018 Aug;59(8):1577-1582. doi: 10.1111/epi.14514. Epub 2018 Jul 15.

Unilateral temporal interictal epileptiform discharges correctly predict the epileptogenic zone in lesional temporal lobe epilepsy.

Author information

1
Epilepsy Center, Department of Neurology, University Hospital, Ludwig-Maximilians-University, Munich, Germany.
2
Institute of Computational Biology, Helmholtz Center for Environmental Health, Munich, Germany.

Abstract

OBJECTIVE:

To evaluate the necessity of recording ictal electroencephalography (EEG) in patients with temporal lobe epilepsy (TLE) considered for resective surgery who have unilateral temporal interictal epileptiform discharges (IEDs) and concordant ipsitemporal magnetic resonance imaging (MRI) pathology. To calculate the necessary number of recorded EEG seizure patterns (ESPs) to achieve adequate lateralization probability.

METHODS:

In a retrospective analysis, the localization and lateralization of interictal and ictal EEG of 304 patients with lesional TLE were analyzed. The probability of further contralateral ESPs was calculated based on a total of 1967 recorded ESPs, using Bayes' theorem.

RESULTS:

Two hundred seventy-one patients had unilateral TLE, and in 98% of them (265 of 271), IEDs were recorded during video-EEG monitoring. Purely unilateral temporal IEDs were present in 61% (166 of 271 patients). Ipsilateral temporal MRI pathology was found in 83% (138 of 166). Ictal EEG was concordant with the clinical side of TLE in 99% (136 of 138) of these patients. Two patients had discordant ictal EEG with both ipsilateral and contralateral ESPs. Epilepsy surgery with resection in the lesioned temporal lobe was still performed, and both patients remain seizure-free. Probability calculations demonstrate that at least 6 recorded unilateral ESPs result in a >95% probability for a concordance of >0.9 of any further ESPs.

SIGNIFICANCE:

The combination of purely unilateral temporal IED with ipsitemporal MRI pathology is sufficient to identify the epileptogenic zone, and the recording of ictal ESP did not add any surgically relevant information in these 138 patients. Rarely, discordant ESPs might be recorded, but the surgical outcome remains excellent after surgery on the lesioned side.

KEYWORDS:

MRI; epilepsy surgery; ictal EEG; interictal EEG; predictive value; seizure pattern

PMID:
30009572
DOI:
10.1111/epi.14514
[Indexed for MEDLINE]
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4.
Front Cardiovasc Med. 2018 Jun 5;5:59. doi: 10.3389/fcvm.2018.00059. eCollection 2018.

Using Gene Expression to Annotate Cardiovascular GWAS Loci.

Author information

1
Institute of Computational Biology, Helmholtz Zentrum München German Research Center for Environmental Health, Neuherberg, Germany.
2
Department of Informatics, Technical University of Munich, Munich, Germany.

Abstract

Genetic variants at hundreds of loci associated with cardiovascular phenotypes have been identified by genome wide association studies. Most of these variants are located in intronic or intergenic regions rendering the functional and mechanistic follow up difficult. These non-protein-coding regions harbor regulatory sequences. Thus the study of genetic variants associated with transcription-so called expression quantitative trait loci-has emerged as a promising approach to identify regulatory sequence variants. The genes and pathways they control constitute candidate causal drivers at cardiovascular risk loci. This review provides an overview of the expression quantitative trait loci resources available for cardiovascular genetics research and the most commonly used approaches for candidate gene identification.

KEYWORDS:

GWAS; cardiovascular disease; eQTL; expression quantitative trait loci; genome wide association study

Publication type

Publication type

5.
Mol Metab. 2018 Feb;8:180-188. doi: 10.1016/j.molmet.2017.11.010. Epub 2017 Nov 22.

Cadm2 regulates body weight and energy homeostasis in mice.

Author information

1
Max Delbrück Center for Molecular Medicine, Robert Rössle Strasse 10, 13125 Berlin, Germany.
2
Helmholtz Zentrum München, Institute of Computational Biology, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany.
3
Max Delbrück Center for Molecular Medicine, Robert Rössle Strasse 10, 13125 Berlin, Germany. Electronic address: matthew.poy@mdc-berlin.de.

Abstract

OBJECTIVE:

Obesity is strongly linked to genes regulating neuronal signaling and function, implicating the central nervous system in the maintenance of body weight and energy metabolism. Genome-wide association studies identified significant associations between body mass index (BMI) and multiple loci near Cell adhesion molecule2 (CADM2), which encodes a mediator of synaptic signaling enriched in the brain. Here we sought to further understand the role of Cadm2 in the pathogenesis of hyperglycemia and weight gain.

METHODS:

We first analyzed Cadm2 expression in the brain of both human subjects and mouse models and subsequently characterized a loss-of-function mouse model of Cadm2 for alterations in glucose and energy homeostasis.

RESULTS:

We show that the risk variant rs13078960 associates with increased CADM2 expression in the hypothalamus of human subjects. Increased Cadm2 expression in several brain regions of Lepob/ob mice was ameliorated after leptin treatment. Deletion of Cadm2 in obese mice (Cadm2/ob) resulted in reduced adiposity, systemic glucose levels, and improved insulin sensitivity. Cadm2-deficient mice exhibited increased locomotor activity, energy expenditure rate, and core body temperature identifying Cadm2 as a potent regulator of systemic energy homeostasis.

CONCLUSIONS:

Together these data illustrate that reducing Cadm2 expression can reverse several traits associated with the metabolic syndrome including obesity, insulin resistance, and impaired glucose homeostasis.

KEYWORDS:

Cadm2/SynCAM2; Energy homeostasis; Genome-wide association studies; Insulin sensitivity; Leptin signaling

PMID:
29217450
PMCID:
PMC5985021
DOI:
10.1016/j.molmet.2017.11.010
[Indexed for MEDLINE]
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6.
Genome Biol. 2017 Sep 14;18(1):170. doi: 10.1186/s13059-017-1286-z.

Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy.

Author information

1
Institute of Computational Biology, Helmholtz Zentrum München, München, Germany.
2
Department of Informatics, Technical University of Munich, Munich, Germany.
3
Department of Clinical and Experimental Cardiology, Heart Center, Academic Medical Center, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105AZ, The Netherlands.
4
Maastricht Centre for Systems Biology, Maastricht University, Maastricht, The Netherlands.
5
National Heart Research Institute Singapore, National Heart Centre Singapore, 168752, Singapore, Singapore.
6
Division of Cardiovascular & Metabolic Disorders, Duke-National University of Singapore, 169857, Singapore, Singapore.
7
National Heart and Lung Institute, Imperial College London, London, UK.
8
NIHR Cardiovascular Biomedical Research Unit at Royal Brompton and Harefield Hospitals and Imperial College London, London, UK.
9
Medical Research Council (MRC) London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, London, UK.
10
Cardiovascular and Metabolic Sciences, Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, Robert-Rössle-Str. 10, 13125, Berlin, Germany.
11
Institute for Cardiomyopathies Heidelberg & Department of Cardiology, Angiology and Pneumology, University Heidelberg, Heidelberg, Germany.
12
Deutsches Zentrum für Herz-Kreislauf-Forschung, Heidelberg/Mannheim, Germany.
13
Institute of Human Genetics, Genetic Epidemiology, University of Münster, Münster, Germany.
14
Department of Biochemistry, Genetic Epidemiology and Statistical Genetics, CARIM School for Cardiovascular Diseases, Maastricht Center for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
15
Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS 1166, Team Genomics & Pathophysiology of Cardiovascular Diseases, F-75013, Paris, France.
16
ICAN Institute for Cardiometabolism and Nutrition, F-75013, Paris, France.
17
Université Versailles Saint Quentin, AP-HP, CESP, INSERM U1018, Hôpital Ambroise Paré, Boulogne-Billancourt, France.
18
Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary.
19
Department of Medicine, University of Miami School of Medicine, Miami, FL, USA.
20
Department of Molecular and Cellular Pharmacology, University of Miami School of Medicine, Miami, FL, USA.
21
Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA.
22
Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
23
Sydney Heart Bank, Department of Anatomy, Bosch Institute, The University of Sydney, Sydney, Australia.
24
Program in Cardiovascular and Metabolic Disorders, Center for Computational Biology, DUKE-NUS Medical School, Singapore, 169857, Singapore.
25
Department of Clinical and Experimental Cardiology, Heart Center, Academic Medical Center, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105AZ, The Netherlands. c.r.bezzina@amc.uva.nl.
26
Cardiovascular and Metabolic Sciences, Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, Robert-Rössle-Str. 10, 13125, Berlin, Germany. nhuebner@mdc-berlin.de.
27
Deutsches Zentrum für Herz-Kreislauf-Forschung, Heidelberg/Mannheim, Germany. nhuebner@mdc-berlin.de.
28
Charité-Universitätsmedizin, Berlin, Germany. nhuebner@mdc-berlin.de.
29
Deutsches Zentrum für Herz-Kreislauf-Forschung, Berlin, Germany. nhuebner@mdc-berlin.de.

Abstract

BACKGROUND:

Genetic variation is an important determinant of RNA transcription and splicing, which in turn contributes to variation in human traits, including cardiovascular diseases.

RESULTS:

Here we report the first in-depth survey of heart transcriptome variation using RNA-sequencing in 97 patients with dilated cardiomyopathy and 108 non-diseased controls. We reveal extensive differences of gene expression and splicing between dilated cardiomyopathy patients and controls, affecting known as well as novel dilated cardiomyopathy genes. Moreover, we show a widespread effect of genetic variation on the regulation of transcription, isoform usage, and allele-specific expression. Systematic annotation of genome-wide association SNPs identifies 60 functional candidate genes for heart phenotypes, representing 20% of all published heart genome-wide association loci. Focusing on the dilated cardiomyopathy phenotype we found that eQTL variants are also enriched for dilated cardiomyopathy genome-wide association signals in two independent cohorts.

CONCLUSIONS:

RNA transcription, splicing, and allele-specific expression are each important determinants of the dilated cardiomyopathy phenotype and are controlled by genetic factors. Our results represent a powerful resource for the field of cardiovascular genetics.

KEYWORDS:

Dilated cardiomyopathy; Gene expression; Genetics; Heart; eQTL

PMID:
28903782
PMCID:
PMC5598015
DOI:
10.1186/s13059-017-1286-z
[Indexed for MEDLINE]
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7.
Nat Neurosci. 2017 Aug;20(8):1096-1103. doi: 10.1038/nn.4590. Epub 2017 Jun 19.

Regulation of body weight and energy homeostasis by neuronal cell adhesion molecule 1.

Author information

1
Max Delbrück Center for Molecular Medicine, Berlin, Germany.
2
Leibniz Institute for Molecular Pharmacology, Berlin, Germany.
3
CECAD Research Center, University of Cologne, Cologne, Germany.
4
Cluster of Excellence NeuroCure, Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany.
5
Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine, Berlin, Germany.
6
University of Geneva, Medical Faculty, Department of Cell Physiology and Metabolism, Centre Médical Universitaire (CMU), Geneva, Switzerland.
7
Charité - Universitätsmedizin Berlin, Department of Endocrinology, Diabetes and Nutrition, Center for Cardiovascular Research, Berlin, Germany.
8
Program in Integrative Cell Signaling and Neurobiology of Metabolism, Department of Comparative Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
9
Institute for Diabetes and Obesity, Helmholtz Centre for Health and Environment and Division of Metabolic Diseases, Technical University Munich, Munich, Germany.
10
Department of Pharmacology, University of Heidelberg, Heidelberg, Germany.
11
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
12
Section of Metabolic Vascular Medicine and Paul Langerhans Institute Dresden of the Helmholtz Center Munich at University Hospital and Faculty of Medicine, TU Dresden, Medical Clinic III, University Clinic Dresden, Dresden, Germany.
13
Division of Diabetes and Nutritional Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
14
Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
15
Department of Anatomy and Histology, University of Veterinary Sciences, Budapest, Hungary.
16
Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany.

Abstract

Susceptibility to obesity is linked to genes regulating neurotransmission, pancreatic beta-cell function and energy homeostasis. Genome-wide association studies have identified associations between body mass index and two loci near cell adhesion molecule 1 (CADM1) and cell adhesion molecule 2 (CADM2), which encode membrane proteins that mediate synaptic assembly. We found that these respective risk variants associate with increased CADM1 and CADM2 expression in the hypothalamus of human subjects. Expression of both genes was elevated in obese mice, and induction of Cadm1 in excitatory neurons facilitated weight gain while exacerbating energy expenditure. Loss of Cadm1 protected mice from obesity, and tract-tracing analysis revealed Cadm1-positive innervation of POMC neurons via afferent projections originating from beyond the arcuate nucleus. Reducing Cadm1 expression in the hypothalamus and hippocampus promoted a negative energy balance and weight loss. These data identify essential roles for Cadm1-mediated neuronal input in weight regulation and provide insight into the central pathways contributing to human obesity.

PMID:
28628102
PMCID:
PMC5533218
DOI:
10.1038/nn.4590
[Indexed for MEDLINE]
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8.
Cell Rep. 2017 Apr 18;19(3):643-654. doi: 10.1016/j.celrep.2017.03.069.

Rapid Genome-wide Recruitment of RNA Polymerase II Drives Transcription, Splicing, and Translation Events during T Cell Responses.

Author information

1
Institute for Diabetes and Obesity (IDO), German Center for Environmental Health GmbH, Munich 85748, Germany; German Center for Diabetes Research (DZD), German Center for Environmental Health GmbH, Munich 85764, Germany.
2
Institute for Diabetes and Obesity (IDO), German Center for Environmental Health GmbH, Munich 85748, Germany.
3
Institute for Computational Biology (ICB), German Center for Environmental Health GmbH, Munich 85764, Germany.
4
Institute for Informatics, Ludwig-Maximilians-Universität München, Munich 80333, Germany. Electronic address: caroline.friedel@bio.ifi.lmu.de.
5
Institute for Diabetes and Obesity (IDO), German Center for Environmental Health GmbH, Munich 85748, Germany; German Center for Diabetes Research (DZD), German Center for Environmental Health GmbH, Munich 85764, Germany. Electronic address: elke.glasmacher@helmholtz-muenchen.de.

Abstract

Activation of immune cells results in rapid functional changes, but how such fast changes are accomplished remains enigmatic. By combining time courses of 4sU-seq, RNA-seq, ribosome profiling (RP), and RNA polymerase II (RNA Pol II) ChIP-seq during T cell activation, we illustrate genome-wide temporal dynamics for ∼10,000 genes. This approach reveals not only immediate-early and posttranscriptionally regulated genes but also coupled changes in transcription and translation for >90% of genes. Recruitment, rather than release of paused RNA Pol II, primarily mediates transcriptional changes. This coincides with a genome-wide temporary slowdown in cotranscriptional splicing, even for polyadenylated mRNAs that are localized at the chromatin. Subsequent splicing optimization correlates with increasing Ser-2 phosphorylation of the RNA Pol II carboxy-terminal domain (CTD) and activation of the positive transcription elongation factor (pTEFb). Thus, rapid de novo recruitment of RNA Pol II dictates the course of events during T cell activation, particularly transcription, splicing, and consequently translation.

KEYWORDS:

4sU; H3K36; RNA Pol II; Ser-2 RNA Pol II; Ser-5 RNA Pol II; T cell activation; cotranscriptional splicing; immediate-early genes; immune response; ribosome profiling

PMID:
28423325
DOI:
10.1016/j.celrep.2017.03.069
[Indexed for MEDLINE]
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9.
PLoS One. 2017 Feb 3;12(2):e0170458. doi: 10.1371/journal.pone.0170458. eCollection 2017.

Transcriptome-wide co-expression analysis identifies LRRC2 as a novel mediator of mitochondrial and cardiac function.

Author information

1
Cardiovascular Center, Medical College of Wisconsin, Milwaukee, WI, United States of America.
2
School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
3
Division of Cardiovascular Disease, University of Alabama at Birmingham, Birmingham, AL, United States of America.
4
Center for Heart Failure Research, Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
5
National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore.
6
Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
7
Experimental Cardiology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
8
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
9
Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
10
MRC Clinical Sciences Centre, Imperial College London, London, UK, Duke-NUS Graduate Medical School, Singapore, Singapore.

Abstract

Mitochondrial dysfunction contributes to myriad monogenic and complex pathologies. To understand the underlying mechanisms, it is essential to define the full complement of proteins that modulate mitochondrial function. To identify such proteins, we performed a meta-analysis of publicly available gene expression data. Gene co-expression analysis of a large and heterogeneous compendium of microarray data nominated a sub-population of transcripts that whilst highly correlated with known mitochondrial protein-encoding transcripts (MPETs), are not themselves recognized as generating proteins either localized to the mitochondrion or pertinent to functions therein. To focus the analysis on a medically-important condition with a strong yet incompletely understood mitochondrial component, candidates were cross-referenced with an MPET-enriched module independently generated via genome-wide co-expression network analysis of a human heart failure gene expression dataset. The strongest uncharacterized candidate in the analysis was Leucine Rich Repeat Containing 2 (LRRC2). LRRC2 was found to be localized to the mitochondria in human cells and transcriptionally-regulated by the mitochondrial master regulator Pgc-1α. We report that Lrrc2 transcript abundance correlates with that of β-MHC, a canonical marker of cardiac hypertrophy in humans and experimentally demonstrated an elevation in Lrrc2 transcript in in vitro and in vivo rodent models of cardiac hypertrophy as well as in patients with dilated cardiomyopathy. RNAi-mediated Lrrc2 knockdown in a rat-derived cardiomyocyte cell line resulted in enhanced expression of canonical hypertrophic biomarkers as well as increased mitochondrial mass in the context of increased Pgc-1α expression. In conclusion, our meta-analysis represents a simple yet powerful springboard for the nomination of putative mitochondrially-pertinent proteins relevant to cardiac function and enabled the identification of LRRC2 as a novel mitochondrially-relevant protein and regulator of the hypertrophic response.

PMID:
28158196
PMCID:
PMC5291451
DOI:
10.1371/journal.pone.0170458
[Indexed for MEDLINE]
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10.
Methods Mol Biol. 2017;1488:217-237.

Epigenetics and Control of RNAs.

Author information

1
Max-Delbrück-Center for Molecular Medicine (MDC), 13125, Berlin, Germany.
2
Max-Delbrück-Center for Molecular Medicine (MDC), 13125, Berlin, Germany. nhuebner@mdc-berlin.de.
3
DZHK (German Centre for Cardiovascular Research), Partner Site, 13347, Berlin, Germany. nhuebner@mdc-berlin.de.
4
Charité-Universitätsmedizin, 10117, Berlin, Germany. nhuebner@mdc-berlin.de.
5
Helmholtz Zentrum München, Institute of Computational Biology (ICB), Neuherberg, 85764, Germany. matthias.heinig@helmholtz-muenchen.de.

Abstract

Histone modifications are epigenetic marks that fundamentally impact the regulation of gene expression. Integrating histone modification information in the analysis of gene expression traits (eQTL mapping) has been shown to significantly enhance the prediction of eQTLs. In this chapter, we describe (1) how to perform quantitative trait locus (QTL) analysis using histone modification levels as traits and (2) how to integrate these data with information on RNA expression for the elucidation of the epigenetic control of transcript levels. We will provide a comprehensive introduction into the topic, describe in detail how ChIP-seq data are analyzed and elaborate on how to integrate ChIP-seq and RNA-seq data from a segregating disease animal model for the identification of the epigenetic control of RNA expression.

KEYWORDS:

ChIP-seq; Histone modifications; Integrative analysis; QTL mapping; RNA expression; Recombinant inbred panel

PMID:
27933526
DOI:
10.1007/978-1-4939-6427-7_9
[Indexed for MEDLINE]
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11.
J Am Coll Cardiol. 2016 Sep 27;68(13):1435-1448. doi: 10.1016/j.jacc.2016.07.729.

52 Genetic Loci Influencing Myocardial Mass.

van der Harst P1, van Setten J2, Verweij N3, Vogler G4, Franke L5, Maurano MT6, Wang X7, Mateo Leach I3, Eijgelsheim M8, Sotoodehnia N9, Hayward C10, Sorice R11, Meirelles O12, Lyytikäinen LP13, Polašek O14, Tanaka T15, Arking DE16, Ulivi S17, Trompet S18, Müller-Nurasyid M19, Smith AV20, Dörr M21, Kerr KF22, Magnani JW23, Del Greco M F24, Zhang W25, Nolte IM26, Silva CT27, Padmanabhan S28, Tragante V2, Esko T29, Abecasis GR30, Adriaens ME31, Andersen K32, Barnett P33, Bis JC34, Bodmer R4, Buckley BM35, Campbell H36, Cannon MV3, Chakravarti A16, Chen LY37, Delitala A38, Devereux RB39, Doevendans PA40, Dominiczak AF28, Ferrucci L15, Ford I41, Gieger C42, Harris TB43, Haugen E44, Heinig M45, Hernandez DG46, Hillege HL3, Hirschhorn JN47, Hofman A8, Hubner N48, Hwang SJ49, Iorio A50, Kähönen M51, Kellis M52, Kolcic I53, Kooner IK54, Kooner JS55, Kors JA56, Lakatta EG57, Lage K58, Launer LJ43, Levy D59, Lundby A60, Macfarlane PW61, May D62, Meitinger T63, Metspalu A64, Nappo S11, Naitza S38, Neph S44, Nord AS65, Nutile T11, Okin PM39, Olsen JV66, Oostra BA67, Penninger JM68, Pennacchio LA69, Pers TH70, Perz S71, Peters A72, Pinto YM73, Pfeufer A74, Pilia MG38, Pramstaller PP75, Prins BP76, Raitakari OT77, Raychaudhuri S78, Rice KM22, Rossin EJ79, Rotter JI80, Schafer S81, Schlessinger D12, Schmidt CO82, Sehmi J55, Silljé HHW3, Sinagra G50, Sinner MF83, Slowikowski K84, Soliman EZ85, Spector TD86, Spiering W87, Stamatoyannopoulos JA44, Stolk RP26, Strauch K88, Tan ST55, Tarasov KV57, Trinh B4, Uitterlinden AG8, van den Boogaard M33, van Duijn CM67, van Gilst WH3, Viikari JS89, Visscher PM90, Vitart V10, Völker U91, Waldenberger M92, Weichenberger CX24, Westra HJ93, Wijmenga C5, Wolffenbuttel BH94, Yang J95, Bezzina CR73, Munroe PB96, Snieder H26, Wright AF10, Rudan I36, Boyer LA7, Asselbergs FW97, van Veldhuisen DJ3, Stricker BH8, Psaty BM98, Ciullo M99, Sanna S38, Lehtimäki T13, Wilson JF100, Bandinelli S101, Alonso A102, Gasparini P103, Jukema JW104, Kääb S105, Gudnason V20, Felix SB21, Heckbert SR106, de Boer RA3, Newton-Cheh C107, Hicks AA24, Chambers JC25, Jamshidi Y76, Visel A108, Christoffels VM33, Isaacs A109, Samani NJ110, de Bakker PIW111.

Author information

1
Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, the Netherlands. Electronic address: p.van.der.harst@umcg.nl.
2
Department of Medical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands.
3
Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
4
Development, Aging and Regeneration, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California.
5
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
6
Department of Genome Sciences, University of Washington, Seattle, Washington; Department of Medicine, Division of Oncology, University of Washington, Seattle, Washington; Department of Pathology, New York University Langone Medical Center, New York, New York; Institute for Systems Genetics, New York University Langone Medical Center, New York, New York.
7
Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts.
8
Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands; Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands.
9
Division of Cardiology, Cardiovascular Health Research Unit, University of Washington, Seattle, Washington.
10
MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland.
11
Institute of Genetics and Biophysics A. Buzzati-Traverso, Naples, Italy.
12
Laboratory of Genetics, National Institute on Aging, Baltimore, Maryland.
13
Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland; Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland.
14
Centre for Global Health Research, The Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland; Department of Public Health, Faculty of Medicine, University of Split, Split, Croatia.
15
Translational Gerontology Branch, National Institute on Aging, Baltimore, Maryland.
16
Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
17
Institute for Maternal and Child Health, IRCCS "Burlo Garofolo," Trieste, Italy.
18
Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands; Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands.
19
Department of Medicine I, University Hospital Munich, Campus Grosshadern, Ludwig-Maximilians-University, Munich, Germany; Institute of Medical Informatics, Biometry and Epidemiology, Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany; Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.
20
Icelandic Heart Association, Kópavogur, Iceland; University of Iceland, Reykjavik, Iceland.
21
Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany; DZHK partner site, Greifswald, Germany.
22
Department of Biostatistics, University of Washington, Seattle, Washington.
23
Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.
24
Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), Bolzano, Italy (Affiliated Institute of the University of Lübeck, Lübeck, Germany).
25
Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom; Ealing Hospital NHS Trust, Middlesex, United Kingdom.
26
Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
27
Genetic Epidemiology Unit, Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Doctoral Program in Biomedical Sciences, Universidad del Rosario, Bogotá, Colombia; Department of Genetics (GENIUROS), Escuela de Medicina y Ciencias de la salud, Universidad del Rosario, Bogotá, Colombia.
28
Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom.
29
Estonian Genome Center, University of Tartu, Tartu, Estonia; Division of Endocrinology and Center for Basic and Translational Obesity Research, Children's Hospital Boston, Boston, Massachusetts.
30
Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.
31
Department of Experimental Cardiology, University of Amsterdam, Academic Medical Center, Amsterdam, the Netherlands; Maastricht Centre for Systems Biology, Maastricht University, Maastricht, the Netherlands.
32
University of Iceland, Reykjavik, Iceland; Landspitali University Hospital, Reykjavik, Iceland.
33
Department of Anatomy, Embryology and Physiology, University of Amsterdam, Academic Medical Center, Amsterdam, the Netherlands.
34
Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington.
35
Department of Pharmacology and Therapeutics, University College Cork, Cork, Ireland.
36
Centre for Global Health Research, The Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland.
37
Department of Medicine, Cardiovascular Division, University of Minnesota, Minneapolis, Minnesota.
38
Istituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Cagliari, Italy.
39
Department of Medicine, Division of Cardiology, Weill Cornell Medicine, New York, New York.
40
Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands.
41
Robertson Center for Biostatistics, University of Glasgow, Glasgow, United Kingdom.
42
Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
43
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, Maryland.
44
Department of Genome Sciences, University of Washington, Seattle, Washington.
45
Cardiovascular and Metabolic Diseases, Max-Delbrück-Center for Molecular Medicine (MDC), Berlin, Germany; Department of Computational Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany; Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
46
Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland.
47
Division of Endocrinology and Center for Basic and Translational Obesity Research, Children's Hospital Boston, Boston, Massachusetts; Medical and Population Genetics Program, Broad Institute, Cambridge, Massachusetts; Department of Genetics, Harvard Medical School, Boston, Massachusetts.
48
Cardiovascular and Metabolic Diseases, Max-Delbrück-Center for Molecular Medicine (MDC), Berlin, Germany; DZHK partner site, Berlin, Germany.
49
Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
50
Cardiovascular Department and Postgraduate School of Cardiovascular Disease, University of Trieste, Trieste, Italy.
51
Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland; Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland.
52
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts; Broad Institute, Cambridge, Massachusetts.
53
Department of Public Health, Faculty of Medicine, University of Split, Split, Croatia.
54
Ealing Hospital NHS Trust, Middlesex, United Kingdom.
55
Ealing Hospital NHS Trust, Middlesex, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom.
56
Department of Medical Informatics, Erasmus Medical Center, Rotterdam, the Netherlands.
57
Laboratory of Cardiovascular Science, National Institute on Aging, Baltimore, Maryland.
58
Broad Institute, Cambridge, Massachusetts; Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Harvard University, Boston, Massachusetts.
59
Center for Population Studies, National Heart, Lung, and Blood Institute, National Institute of Health, Bethesda, Maryland.
60
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
61
Electrocardiology Section, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom.
62
Department of Family Medicine, Clalit Health Services, and The Hebrew University-Hadassah Medical School, Jerusalem, Israel.
63
DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany; Institute of Human Genetics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Human Genetics, Technische Universität München, Munich, Germany.
64
Estonian Genome Center, University of Tartu, Tartu, Estonia.
65
Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California; Center for Neuroscience, Departments of Neurobiology, Physiology, and Behavior and Psychiatry and Behavioral Sciences, University of California, Davis, California.
66
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
67
Genetic Epidemiology Unit, Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.
68
Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria.
69
Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California; DOE Joint Genome Institute, Walnut Creek, California.
70
Division of Endocrinology and Center for Basic and Translational Obesity Research, Children's Hospital Boston, Boston, Massachusetts; Medical and Population Genetics Program, Broad Institute, Cambridge, Massachusetts; Novo Nordisk Foundation Centre for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark; Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark.
71
Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute for Biological and Medical Imaging, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
72
DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany; Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
73
Department of Experimental Cardiology, University of Amsterdam, Academic Medical Center, Amsterdam, the Netherlands.
74
Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), Bolzano, Italy (Affiliated Institute of the University of Lübeck, Lübeck, Germany); Department of Bioinformatics and Systems Biology IBIS, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
75
Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), Bolzano, Italy (Affiliated Institute of the University of Lübeck, Lübeck, Germany); Department of Neurology, General Central Hospital, Bolzano, Italy; Department of Neurology, University of Lübeck, Lübeck, Germany.
76
Cardiogenetics Lab, Human Genetics Research Centre, St. George's University of London, London, United Kingdom.
77
Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland; Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.
78
Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
79
Harvard Medical School, Harvard University, Boston, Massachusetts; Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts.
80
Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics and Medicine, Harbor-UCLA Medical Center, Torrance, California.
81
Cardiovascular and Metabolic Diseases, Max-Delbrück-Center for Molecular Medicine (MDC), Berlin, Germany; DZHK partner site, Berlin, Germany; National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore.
82
Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
83
Department of Medicine I, University Hospital Munich, Campus Grosshadern, Ludwig-Maximilians-University, Munich, Germany.
84
Bioinformatics and Integrative Genomics, Harvard University, Cambridge, Massachusetts.
85
Epidemiological Cardiology Research Center, Wake Forest School of Medicine, Winston Salem, North Carolina.
86
Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom.
87
Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.
88
Institute of Medical Informatics, Biometry and Epidemiology, Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany; Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
89
Division of Medicine, Turku University Hospital, Turku, Finland; Department of Medicine, University of Turku, Turku, Finland.
90
Queensland Brain Institute, University of Queensland, St. Lucia, Australia; University of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, Australia.
91
DZHK partner site, Greifswald, Germany; Department of Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.
92
Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
93
Medical and Population Genetics Program, Broad Institute, Cambridge, Massachusetts; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts; Partners Center for Personalized Genetic Medicine, Boston, Massachusetts.
94
Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
95
Queensland Brain Institute, University of Queensland, St. Lucia, Australia.
96
Clinical Pharmacology and Barts and The London Genome Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom; National Institute for Health Research Biomedical Research Unit, Barts and the London School of Medicine, Queen Mary University of London, London, United Kingdom.
97
Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, the Netherlands; Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom.
98
Departments of Medicine, Epidemiology, and Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, Washington; Group Health Research Institute, Group Health Cooperative, Seattle, Washington.
99
Institute of Genetics and Biophysics A. Buzzati-Traverso, Naples, Italy; IRCCS Neuromed, Isernia, Italy.
100
MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Scotland; Centre for Global Health Research, The Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland.
101
Geriatric Unit, Azienda Sanitaria Firenze, Florence, Italy.
102
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
103
Institute for Maternal and Child Health, IRCCS "Burlo Garofolo," Trieste, Italy; University of Trieste, Trieste, Italy; Sidra Medical and Research Center, Doha, Qatar.
104
Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands; Netherlands Heart Institute, Utrecht, the Netherlands.
105
Department of Medicine I, University Hospital Munich, Campus Grosshadern, Ludwig-Maximilians-University, Munich, Germany; DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.
106
Group Health Research Institute, Group Health Cooperative, Seattle, Washington; Department of Epidemiology, University of Washington, Seattle, Washington.
107
Medical and Population Genetics Program, Broad Institute, Cambridge, Massachusetts; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts; Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts.
108
Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California; DOE Joint Genome Institute, Walnut Creek, California; School of Natural Sciences, University of California, Merced, California.
109
Genetic Epidemiology Unit, Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology, Department of Biochemistry, Maastricht University, Maastricht, the Netherlands.
110
Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Leicester, United Kingdom; National Institute for Health Research, Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom.
111
Department of Medical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Epidemiology, University Medical Center Utrecht, Utrecht, the Netherlands.

Abstract

BACKGROUND:

Myocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death.

OBJECTIVES:

This meta-analysis sought to gain insights into the genetic determinants of myocardial mass.

METHODS:

We carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment.

RESULTS:

We identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p < 1 × 10(-8). These loci are enriched in regions of open chromatin, histone modifications, and transcription factor binding, suggesting that they represent regions of the genome that are actively transcribed in the human heart. Pathway analyses provided evidence that these loci play a role in cardiac hypertrophy. We further highlighted 67 candidate genes at the identified loci that are preferentially expressed in cardiac tissue and associated with cardiac abnormalities in Drosophila melanogaster and Mus musculus. We validated the regulatory function of a novel variant in the SCN5A/SCN10A locus in vitro and in vivo.

CONCLUSIONS:

Taken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets.

KEYWORDS:

QRS; electrocardiogram; genetic association study; heart failure; left ventricular hypertrophy

PMID:
27659466
PMCID:
PMC5478167
DOI:
10.1016/j.jacc.2016.07.729
[Indexed for MEDLINE]
Free PMC Article
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MeSH terms, Grant support

MeSH terms

Grant support

12.
Genetics. 2016 Aug;203(4):1629-40. doi: 10.1534/genetics.116.187153. Epub 2016 Jun 3.

Principles of microRNA Regulation Revealed Through Modeling microRNA Expression Quantitative Trait Loci.

Author information

1
RNA Bioinformatics, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany budach@molgen.mpg.de.
2
Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
3
RNA Bioinformatics, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany High Throughput Genomics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany.

Abstract

Extensive work has been dedicated to study mechanisms of microRNA-mediated gene regulation. However, the transcriptional regulation of microRNAs themselves is far less well understood, due to difficulties determining the transcription start sites of transient primary transcripts. This challenge can be addressed using expression quantitative trait loci (eQTLs) whose regulatory effects represent a natural source of perturbation of cis-regulatory elements. Here we used previously published cis-microRNA-eQTL data for the human GM12878 cell line, promoter predictions, and other functional annotations to determine the relationship between functional elements and microRNA regulation. We built a logistic regression model that classifies microRNA/SNP pairs into eQTLs or non-eQTLs with 85% accuracy; shows microRNA-eQTL enrichment for microRNA precursors, promoters, enhancers, and transcription factor binding sites; and depletion for repressed chromatin. Interestingly, although there is a large overlap between microRNA eQTLs and messenger RNA eQTLs of host genes, 74% of these shared eQTLs affect microRNA and host expression independently. Considering microRNA-only eQTLs we find a significant enrichment for intronic promoters, validating the existence of alternative promoters for intragenic microRNAs. Finally, in line with the GM12878 cell line derived from B cells, we find genome-wide association (GWA) variants associated to blood-related traits more likely to be microRNA eQTLs than random GWA and non-GWA variants, aiding the interpretation of GWA results.

KEYWORDS:

eQTL; microRNA; promoter; regulation; variation

PMID:
27260304
PMCID:
PMC4981266
DOI:
10.1534/genetics.116.187153
[Indexed for MEDLINE]
Free PMC Article
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13.
Genomics Proteomics Bioinformatics. 2016 Aug;14(4):235-43. doi: 10.1016/j.gpb.2016.03.006. Epub 2016 May 17.

Comparison of Cox Model Methods in A Low-dimensional Setting with Few Events.

Author information

1
Department of General and Interventional Cardiology, University Heart Center Hamburg-Eppendorf, 20246 Hamburg, Germany. Electronic address: f.ojeda-echevarria@uke.de.
2
Department of General and Interventional Cardiology, University Heart Center Hamburg-Eppendorf, 20246 Hamburg, Germany; German Center for Cardiovascular Research (DZHK), Hamburg/Kiel/Luebeck, Germany.
3
Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Institut National pour la Santé et la Recherche Médicale (INSERM), Unité Mixte de Recherche en Santé (UMR_S) 1166, F-75013 Paris, France; Institute for Cardiometabolism and Nutrition (ICAN), F-75013 Paris, France.
4
Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany; German Center for Cardiovascular Research (DZHK), Hamburg/Kiel/Luebeck, Germany.
5
Institute of Computational Biology, German Research Center for Environmental Health, Helmholtz Zentrum München, 85764 Neuherberg, Germany.

Abstract

Prognostic models based on survival data frequently make use of the Cox proportional hazards model. Developing reliable Cox models with few events relative to the number of predictors can be challenging, even in low-dimensional datasets, with a much larger number of observations than variables. In such a setting we examined the performance of methods used to estimate a Cox model, including (i) full model using all available predictors and estimated by standard techniques, (ii) backward elimination (BE), (iii) ridge regression, (iv) least absolute shrinkage and selection operator (lasso), and (v) elastic net. Based on a prospective cohort of patients with manifest coronary artery disease (CAD), we performed a simulation study to compare the predictive accuracy, calibration, and discrimination of these approaches. Candidate predictors for incident cardiovascular events we used included clinical variables, biomarkers, and a selection of genetic variants associated with CAD. The penalized methods, i.e., ridge, lasso, and elastic net, showed a comparable performance, in terms of predictive accuracy, calibration, and discrimination, and outperformed BE and the full model. Excessive shrinkage was observed in some cases for the penalized methods, mostly on the simulation scenarios having the lowest ratio of a number of events to the number of variables. We conclude that in similar settings, these three penalized methods can be used interchangeably. The full model and backward elimination are not recommended in rare event scenarios.

KEYWORDS:

Coronary artery disease; Events per variable; Penalized regression; Proportional hazards regression

PMID:
27224515
PMCID:
PMC4996851
DOI:
10.1016/j.gpb.2016.03.006
[Indexed for MEDLINE]
Free PMC Article
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14.
Genome Med. 2016 Mar 17;8(1):28. doi: 10.1186/s13073-016-0280-5.

A roadmap of constitutive NF-κB activity in Hodgkin lymphoma: Dominant roles of p50 and p52 revealed by genome-wide analyses.

Author information

1
Signal Transduction in Tumor Cells, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125, Berlin, Germany.
2
Department of Computational Biology, Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany.
3
Genetics and Genomics of Cardiovascular Diseases, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125, Berlin, Germany.
4
Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstr.1, 85764, Neuherberg, Germany.
5
Computational Biology and Data Mining, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125, Berlin, Germany.
6
Present address: Johannes Gutenberg University, 55128, Mainz, Germany.
7
Max-Delbrück-Center for Molecular Medicine, 13125, Berlin, Germany.
8
Hematology, Oncology and Tumor Immunology, Charité-Universitätsmedizin Berlin, 13353, Berlin, Germany.
9
Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany.
10
Signal Transduction in Tumor Cells, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125, Berlin, Germany. Scheidereit@mdc-berlin.de.

Abstract

BACKGROUND:

NF-κB is widely involved in lymphoid malignancies; however, the functional roles and specific transcriptomes of NF-κB dimers with distinct subunit compositions have been unclear.

METHODS:

Using combined ChIP-sequencing and microarray analyses, we determined the cistromes and target gene signatures of canonical and non-canonical NF-κB species in Hodgkin lymphoma (HL) cells.

RESULTS:

We found that the various NF-κB subunits are recruited to regions with redundant κB motifs in a large number of genes. Yet canonical and non-canonical NF-κB dimers up- and downregulate gene sets that are both distinct and overlapping, and are associated with diverse biological functions. p50 and p52 are formed through NIK-dependent p105 and p100 precursor processing in HL cells and are the predominant DNA binding subunits. Logistic regression analyses of combinations of the p50, p52, RelA, and RelB subunits in binding regions that have been assigned to genes they regulate reveal a cross-contribution of p52 and p50 to canonical and non-canonical transcriptomes. These analyses also indicate that the subunit occupancy pattern of NF-κB binding regions and their distance from the genes they regulate are determinants of gene activation versus repression. The pathway-specific signatures of activated and repressed genes distinguish HL from other NF-κB-associated lymphoid malignancies and inversely correlate with gene expression patterns in normal germinal center B cells, which are presumed to be the precursors of HL cells.

CONCLUSIONS:

We provide insights that are relevant for lymphomas with constitutive NF-κB activation and generally for the decoding of the mechanisms of differential gene regulation through canonical and non-canonical NF-κB signaling.

KEYWORDS:

B lymphocytes; ChIP sequencing; Transcription factor; cell death; consensus sequence; enhancer; gene expression; inflammation; lymphoma; promoter

PMID:
26988706
PMCID:
PMC4794921
DOI:
10.1186/s13073-016-0280-5
[Indexed for MEDLINE]
Free PMC Article
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15.
Nat Commun. 2015 Nov 6;6:8804. doi: 10.1038/ncomms9804.

Meta-analysis identifies seven susceptibility loci involved in the atopic march.

Author information

1
Max-Delbrück-Center (MDC) for Molecular Medicine, Berlin, Germany.
2
Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité University Medical Center, Berlin, Germany.
3
Population Health Research Institute, St George's, University of London, London, UK.
4
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
5
Department of Dermatology, Allergology, and Venerology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
6
Inserm, UMR-946, F-75010 Paris, France.
7
Université Paris Diderot, Sorbonne Paris Cité, Institut Universitaire d'Hématologie, F-75007 Paris, France.
8
Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
9
University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands.
10
Dr von Hauner Children's Hospital, Ludwig Maximilians University Munich, Member of the German Center for Lung Research (DZL), Munich, Germany.
11
Lung Institute of Western Australia, University of Western Australia, Perth, Western Australia, Australia.
12
Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Melbourne, Victoria, Australia.
13
Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
14
Department of Pediatric Pneumology and Allergy, University Children`s Hospital Regensburg (KUNO), Regensburg, Germany.
15
School of Women and Infant's Health, University of Western Australia, Perth, Western Australia, Australia.
16
School of Social and Community Medicine, University of Bristol, Bristol, UK.
17
Clinic and Polyclinic of Dermatology, University Medicine Greifswald, Greifswald, Germany.
18
Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany.
19
Department of Biosciences and Nutrition, and Center for Innovative Medicine (CIMED), Karolinska Institutet, Stockholm, Sweden.
20
Department of Pediatrics, Pediatric Chest Center, Erasmus University Medical Center, Rotterdam, The Netherlands.
21
Department of Pulmonology and GRIAC Research Institute, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
22
Swiss Tropical and Public Health Institute and the University of Basel, Basel, Switzerland.
23
Division of Pediatric Cardiology and Pulmonology, Department of Pediatrics, Innsbruck Medical University, Innsbruck, Austria.
24
Siberian State Medical University, Tomsk, Russia.
25
Research Institute of Medical Genetics, Tomsk, Russia.
26
Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
27
Centre de santé et de services sociaux de Chicoutimi, Saguenay, Québec, Canada.
28
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
29
Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia.
30
Department of Respiratory Medicine, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
31
Busselton Population Medical Research Foundation, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia.
32
Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
33
Department of Otolaryngology-Head and Neck Surgery, Temple University, School of Medicine, Philadelphia, Pennsylvania, USA.
34
Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
35
Institute of Epidemiology, Christian-Albrechts-University of Kiel, Kiel, Germany.
36
Research Unit of Molecular Epidemiology and Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
37
University Children's Hospital, Albert Ludwigs University, Freiburg, Germany.
38
University Children's Hospital, University of Cologne, Cologne, Germany.
39
Institute of Social Medicine, Epidemiology and Health Economics, Charité University Medical Center, Berlin, Germany.
40
Institute for Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.
41
Institute of Human Genetics, University of Bonn, Bonn, Germany.
42
Department of Genomics, Life &Brain Center, University of Bonn, Bonn, Germany.
43
Division of Medical Genetics, University Hospital Basel and Department of Biomedicine, University of Basel, Basel, Switzerland.
44
Institute of Neuroscience and Medicine (INM-1), Structural and Functional Organisation of the Brain, Genomic Imaging, Research Centre Jülich, Jülich, Germany.
45
Queensland Children's Medical Research Institute, University of Queensland, Brisbane, Queensland, Australia.
46
Institute for Community Medicine, Study of Health in Pomerania/KEF, University Medicine Greifswald, Greifswald, Germany.
47
Max Planck Institute for Molecular Genetics, Berlin, Germany.
48
Telethon Kids Institute, University of Western Australia, Perth, Western Australia, Australia.
49
Department of Pediatric Pneumology and Immunology, Charité University Medical Center, Berlin, Germany.
50
Université du Québec à Chicoutimi, Saguenay, Québec, Canada.
51
Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany.
52
Department of Pediatric Pulmonology and Pediatric Allergology, University of Groningen, University Medical Center Groningen, Beatrix Children's Hospital and GRIAC Research Institute, Groningen, The Netherlands.
53
Sachs' Children's Hospital, Stockholm, Sweden.

Abstract

Eczema often precedes the development of asthma in a disease course called the 'atopic march'. To unravel the genes underlying this characteristic pattern of allergic disease, we conduct a multi-stage genome-wide association study on infantile eczema followed by childhood asthma in 12 populations including 2,428 cases and 17,034 controls. Here we report two novel loci specific for the combined eczema plus asthma phenotype, which are associated with allergic disease for the first time; rs9357733 located in EFHC1 on chromosome 6p12.3 (OR 1.27; P=2.1 × 10(-8)) and rs993226 between TMTC2 and SLC6A15 on chromosome 12q21.3 (OR 1.58; P=5.3 × 10(-9)). Additional susceptibility loci identified at genome-wide significance are FLG (1q21.3), IL4/KIF3A (5q31.1), AP5B1/OVOL1 (11q13.1), C11orf30/LRRC32 (11q13.5) and IKZF3 (17q21). We show that predominantly eczema loci increase the risk for the atopic march. Our findings suggest that eczema may play an important role in the development of asthma after eczema.

PMID:
26542096
PMCID:
PMC4667629
DOI:
10.1038/ncomms9804
[Indexed for MEDLINE]
Free PMC Article
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Publication type, MeSH terms, Substances, Grant support

16.
J Neuroinflammation. 2015 Oct 26;12:192. doi: 10.1186/s12974-015-0413-6.

Complement receptor 2 is up regulated in the spinal cord following nerve root injury and modulates the spinal cord response.

Author information

1
Department of Clinical Neuroscience, Neuroimmunology Unit, Karolinska Institutet, Stockholm, Sweden. rickard.lindblom@ki.se.
2
Department of Cardiothoracic Surgery and Anaesthesia, Uppsala University Hospital, Uppsala, Sweden. rickard.lindblom@ki.se.
3
Neuroimmunology Unit L8:04 CMM, Karolinska University Hospital, 171 76, Stockholm, Sweden. rickard.lindblom@ki.se.
4
Department of Neuroscience, Division of Neuronal Regeneration, Karolinska Institutet, Stockholm, Sweden.
5
Department of Clinical Neuroscience, Neuroimmunology Unit, Karolinska Institutet, Stockholm, Sweden.
6
Experimental Genetics of Cardiovascular Diseases, Max-Delbrück Center for Molecular Medicine, Berlin, Germany.
7
Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.

Abstract

BACKGROUND:

Activation of the complement system has been implicated in both acute and chronic states of neurodegeneration. However, a detailed understanding of this complex network of interacting components is still lacking.

METHODS:

Large-scale global expression profiling in a rat F2(DAxPVG) intercross identified a strong cis-regulatory influence on the local expression of complement receptor 2 (Cr2) in the spinal cord after ventral root avulsion (VRA). Expression of Cr2 in the spinal cord was studied in a separate cohort of DA and PVG rats at different time-points after VRA, and also following sciatic nerve transection (SNT) in the same strains. Consequently, Cr2 (-/-) mice and Wt controls were used to further explore the role of Cr2 in the spinal cord following SNT. The in vivo experiments were complemented by astrocyte and microglia cell cultures.

RESULTS:

Expression of Cr2 in naïve spinal cord was low but strongly up regulated at 5-7 days after both VRA and SNT. Levels of Cr2 expression, as well as astrocyte activation, was higher in PVG rats than DA rats following both VRA and SNT. Subsequent in vitro studies proposed astrocytes as the main source of Cr2 expression. A functional role for Cr2 is suggested by the finding that transgenic mice lacking Cr2 displayed increased loss of synaptic nerve terminals following nerve injury. We also detected increased levels of soluble CR2 (sCR2) in the cerebrospinal fluid of rats following VRA.

CONCLUSIONS:

These results demonstrate that local expression of Cr2 in the central nervous system is part of the axotomy reaction and is suggested to modulate subsequent complement mediated effects.

PMID:
26502875
PMCID:
PMC4624364
DOI:
10.1186/s12974-015-0413-6
[Indexed for MEDLINE]
Free PMC Article
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17.
J Clin Invest. 2015 Nov 2;125(11):4223-38. doi: 10.1172/JCI80919. Epub 2015 Oct 20.

High salt reduces the activation of IL-4- and IL-13-stimulated macrophages.

Abstract

A high intake of dietary salt (NaCl) has been implicated in the development of hypertension, chronic inflammation, and autoimmune diseases. We have recently shown that salt has a proinflammatory effect and boosts the activation of Th17 cells and the activation of classical, LPS-induced macrophages (M1). Here, we examined how the activation of alternative (M2) macrophages is affected by salt. In stark contrast to Th17 cells and M1 macrophages, high salt blunted the alternative activation of BM-derived mouse macrophages stimulated with IL-4 and IL-13, M(IL-4+IL-13) macrophages. Salt-induced reduction of M(IL-4+IL-13) activation was not associated with increased polarization toward a proinflammatory M1 phenotype. In vitro, high salt decreased the ability of M(IL-4+IL-13) macrophages to suppress effector T cell proliferation. Moreover, mice fed a high salt diet exhibited reduced M2 activation following chitin injection and delayed wound healing compared with control animals. We further identified a high salt-induced reduction in glycolysis and mitochondrial metabolic output, coupled with blunted AKT and mTOR signaling, which indicates a mechanism by which NaCl inhibits full M2 macrophage activation. Collectively, this study provides evidence that high salt reduces noninflammatory innate immune cell activation and may thus lead to an overall imbalance in immune homeostasis.

PMID:
26485286
PMCID:
PMC4639967
DOI:
10.1172/JCI80919
[Indexed for MEDLINE]
Free PMC Article
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18.
Curr Protoc Hum Genet. 2015 Oct 6;87:11.16.1-14. doi: 10.1002/0471142905.hg1116s87.

Alternative Splicing Signatures in RNA-seq Data: Percent Spliced in (PSI).

Author information

1
Cardiovascular and Metabolic Sciences, Max-Delbrück-Center for Molecular Medicine, Berlin, Germany.
2
National Heart Center Singapore, Singapore.
3
Duke-National University of Singapore, Singapore.
4
Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
5
Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.
6
Present address: Institute of Computational Biology, Helmholtz Zentrum München, Neuerberg, Germany.
7
National Heart and Lung Institute, Imperial College London, London, United Kingdom.
8
German Center for Cardiovascular Research (partner site), Berlin, Germany.
9
Charité-Universitätsmedizin, Berlin, Germany.

Abstract

Thousands of alternative exons are spliced out of messenger RNA to increase protein diversity. High-throughput sequencing of short cDNA fragments (RNA-seq) generates a genome-wide snapshot of these post-transcriptional processes. RNA-seq reads yield insights into the regulation of alternative splicing by revealing the usage of known or unknown splice sites as well as the expression level of exons. Constitutive exons are never covered by split alignments, whereas alternative exonic parts are located within highly expressed splicing junctions. The ratio between reads including or excluding exons, also known as percent spliced in index (PSI), indicates how efficiently sequences of interest are spliced into transcripts. This protocol describes a method to calculate the PSI without prior knowledge of splicing patterns. It provides a quantitative, global assessment of exon usage that can be integrated with other tools that identify differential isoform processing. Novel, complex splicing events along a genetic locus can be visualized in an exon-centric manner and compared across conditions.

KEYWORDS:

PSI; RNA-seq; alternative splicing; isoform expression; percent spliced in; transcript processing

PMID:
26439713
DOI:
10.1002/0471142905.hg1116s87
[Indexed for MEDLINE]
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19.
Nat Biotechnol. 2015 Sep;33(9):933-40. doi: 10.1038/nbt.3299. Epub 2015 Aug 10.

Prediction of human population responses to toxic compounds by a collaborative competition.

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK.
2
Sage Bionetworks, Seattle, Washington, USA.
3
Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
4
The Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
5
Division of Preclinical Innovation, National Institutes of Health Chemical Genomics Center, National Center for Advancing Translational Sciences, Rockville, Maryland, USA.
6
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
7
Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, North Carolina, USA.
8
Department of Public Health, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.

Abstract

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

PMID:
26258538
PMCID:
PMC4568441
DOI:
10.1038/nbt.3299
[Indexed for MEDLINE]
Free PMC Article
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20.
Nat Commun. 2015 May 26;6:7200. doi: 10.1038/ncomms8200.

Translational regulation shapes the molecular landscape of complex disease phenotypes.

Author information

1
1] Cardiovascular and Metabolic Sciences, Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, Robert-Rossle-Strasse 10, 13125 Berlin, Germany [2] National Heart Research Institute Singapore (NHRIS), National Heart Centre Singapore, Singapore 169609, Singapore.
2
Cardiovascular and Metabolic Sciences, Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, Robert-Rossle-Strasse 10, 13125 Berlin, Germany.
3
1] Cardiovascular and Metabolic Sciences, Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, Robert-Rossle-Strasse 10, 13125 Berlin, Germany [2] Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.
4
Institute of Physiology, Academy of Sciences of the Czech Republic, Vídenska 1083, 142 20 Prague 4, Czech Republic.
5
1] Systems Biology of Gene Regulatory Elements, Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, Robert-Rossle-Strasse 10, 13125 Berlin, Germany [2] DZHK (German Centre for Cardiovascular Research), Partner Site, 13347 Berlin, Germany.
6
Hubrecht Institute-KNAW &University Medical Center Utrecht, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands.
7
Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.
8
1] National Heart Research Institute Singapore (NHRIS), National Heart Centre Singapore, Singapore 169609, Singapore [2] National Heart and Lung Institute, Imperial College London, London SW3 6NP, UK [3] Duke-National University of Singapore, Singapore 169857, Singapore.
9
1] Cardiovascular and Metabolic Sciences, Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, Robert-Rossle-Strasse 10, 13125 Berlin, Germany [2] DZHK (German Centre for Cardiovascular Research), Partner Site, 13347 Berlin, Germany [3] Charité-Universitätsmedizin, 10117 Berlin, Germany.

Abstract

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.

PMID:
26007203
PMCID:
PMC4455061
DOI:
10.1038/ncomms8200
[Indexed for MEDLINE]
Free PMC Article
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