Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics

Protein interaction networks in the vasculature prioritize genes and pathways underlying coronary artery disease

Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes to map their protein-protein interactions (PPIs) in human vascular cells and elucidate their roles in CAD. The resulting PPI networks contain interactions that are outside of known biology in the vasculature and are enriched for genes involved in immunity-related and arterial-wall-specific mechanisms. Several PPI networks derived from smooth muscle cells are significantly enriched for genetic variants associated with CAD and related vascular phenotypes. Furthermore, the networks identify 61 genes that are found in genetic loci associated with risk of CAD, prioritizing them as the causal candidates within these loci. These findings indicate that the PPI networks we have generated are a rich resource for guiding future research into the molecular pathogenesis of CAD.

Proteomics of Plasma and Plasma-Treated Podocytes: Application to Focal and Segmental Glomerulosclerosis

Focal and segmental glomerulosclerosis (FSGS) is a severe form of idiopathic nephrotic syndrome (INS), a glomerulopathy of presumably immune origin that is attributed to extrarenal pathogenic circulating factors. The recurrence of FSGS (rFSGS) after transplant occurs in 30% to 50% of cases. The direct analysis of patient plasma proteome has scarcely been addressed to date, mainly due to the methodological difficulties associated with plasma complexity and dynamic range. In this study, first, we compared different methods of plasma preparation, second, we compared the plasma proteomes of rFSGS and controls using two preparation methods, and third, we analyzed the early proximal signaling events in podocytes subjected to patient plasma, through a combination of phosphoproteomics and lipid-raft proteomics (raftomics). By combining immunodepletion and high pH fractionation, we performed a differential proteomic analysis of soluble plasma proteins and of extracellular vesicles (EV) obtained from healthy controls, non-INS patient controls, and rFSGS patients (n = 4). In both the soluble- and the EV-protein sets from the rFSGS patients, we found a statistically significant increase in a cluster of proteins involved in neutrophil degranulation. A group of lipid-binding proteins, generally associated with lipoproteins, was found to be decreased in the soluble set from the rFSGS patients. In addition, three amino acid transporters involved in mTORC1 activation were found to be significantly increased in the EV from the rFSGS. Next, we incubated human podocytes for 30 min with 10% plasma from both groups of patients. The phosphoproteomics and raftomics of the podocytes revealed profound differences in the proteins involved in the mTOR pathway, in autophagy, and in cytoskeleton organization. We analyzed the correlation between the abundance of plasma and plasma-regulated podocyte proteins. The observed changes highlight some of the mechanisms involved in FSGS recurrence and could be used as specific early markers of circulating-factor activity in podocytes.

Outlining cardiac ion channel protein interactors and their signature in the human electrocardiogram

Protein-protein interactions are essential for normal cellular processes and signaling events. Defining these interaction networks is therefore crucial for understanding complex cellular functions and interpretation of disease-associated gene variants. We need to build a comprehensive picture of the interactions, their affinities and interdependencies in the specific organ to decipher hitherto poorly understood signaling mechanisms through ion channels. Here we report the experimental identification of the ensemble of protein interactors for 13 types of ion channels in murine cardiac tissue. Of these, we validated the functional importance of ten interactors on cardiac electrophysiology through genetic knockouts in zebrafish, gene silencing in mice, super-resolution microscopy and patch clamp experiments. Furthermore, we establish a computational framework to reconstruct human cardiomyocyte ion channel networks from deep proteome mapping of human heart tissue and human heart single-cell gene expression data. Finally, we integrate the ion channel interactome with human population genetics data to identify proteins that influence the electrocardiogram (ECG). We demonstrate that the combined channel network is enriched for proteins influencing the ECG, with 44% of the network proteins significantly associated with an ECG phenotype. Altogether, we define interactomes of 13 major cardiac ion channels, contextualize their relevance to human electrophysiology and validate functional roles of ten interactors, including two regulators of the sodium current (epsin-2 and gelsolin). Overall, our data provide a roadmap for our understanding of the molecular machinery that regulates cardiac electrophysiology.

Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia

Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.

ANKRD1 activates the Wnt signaling pathway by modulating CAV3 expression and thus promotes BMSC osteogenic differentiation and bone formation in ovariectomized mice

Bone marrow-derived mesenchymal stem cells (BMSCs) are considered promising materials for treating bone diseases such as osteoporosis (OP). This research explored the functions and molecular mechanism of ankyrin repeat domain 1 (ANKRD1) in BMSC osteogenesis. An OP model in mice was established by bilateral ovariectomy. Manipulation of ANKRD1 expression in BMSCs or femurs was achieved by lentivirus infection. Increased ANKRD1 expression was observed in BMSCs during osteogenic induction. Silencing of ANKRD1 impaired the osteogenesis of BMSCs, as shown by the decreased alkaline phosphatase (ALP) activity, osteogenic gene (Runx2, Col1a1, Bglap, and Spp1) expression, and mineralized formation. ANKRD1-mediated promotion of osteogenesis was also reproduced in mouse MC3T3-E1 preosteoblastic cells. Activation of Wnt/β-catenin signaling, a well-known osteogenic stimulus, was also impaired in ANKRD1-silenced BMSCs. Overexpression of ANKRD1 resulted in the opposite effects on osteogenesis and Wnt/β-catenin signaling. Mechanistic studies revealed that ANKRD1 modulated caveolin-3 (CAV3) expression by reducing CAV3 ubiquitination, and the knockdown of CAV3 impaired the functions of ANKRD1. Additionally, a very low level of ANKRD1 was observed in the BMSCs from OP mice. Rescue of ANKRD1 significantly restored osteogenic differentiation and Wnt signaling activation in BMSCs from ovariectomized mice. The results of micro-CT, H&E staining, and IHC staining showed that ANKRD1 also promoted bone formation and Wnt activation and ameliorated pathological alterations in the femurs of OP mice. Collectively, this study demonstrated that ANKRD1 plays an important role in regulating the osteogenic differentiation of BMSCs and is a promising target for the treatment of OP and other bone diseases.

Protein interaction studies in human induced neurons indicate convergent biology underlying autism spectrum disorders

Autism spectrum disorders (ASDs) have been linked to genes with enriched expression in the brain, but it is unclear how these genes converge into cell-type-specific networks. We built a protein-protein interaction network for 13 ASD-associated genes in human excitatory neurons derived from induced pluripotent stem cells (iPSCs). The network contains newly reported interactions and is enriched for genetic and transcriptional perturbations observed in individuals with ASDs. We leveraged the network data to show that the ASD-linked brain-specific isoform of ANK2 is important for its interactions with synaptic proteins and to characterize a PTEN-AKAP8L interaction that influences neuronal growth. The IGF2BP1-3 complex emerged as a convergent point in the network that may regulate a transcriptional circuit of ASD-associated genes. Our findings showcase cell-type-specific interactomes as a framework to complement genetic and transcriptomic data and illustrate how both individual and convergent interactions can lead to biological insights into ASDs.

Transcription factor network analysis identifies REST/NRSF as an intrinsic regulator of CNS regeneration in mice

The inability of neurons to regenerate long axons within the CNS is a major impediment to improving outcome after spinal cord injury, stroke, and other CNS insults. Recent advances have uncovered an intrinsic program that involves coordinate regulation by multiple transcription factors that can be manipulated to enhance growth in the peripheral nervous system. Here, we use a systems genomics approach to characterize regulatory relationships of regeneration-associated transcription factors, identifying RE1-Silencing Transcription Factor (REST; Neuron-Restrictive Silencer Factor, NRSF) as a predicted upstream suppressor of a pro-regenerative gene program associated with axon regeneration in the CNS. We validate our predictions using multiple paradigms, showing that mature mice bearing cell type-specific deletions of REST or expressing dominant-negative mutant REST show improved regeneration of the corticospinal tract and optic nerve after spinal cord injury and optic nerve crush, which is accompanied by upregulation of regeneration-associated genes in cortical motor neurons and retinal ganglion cells, respectively. These analyses identify a role for REST as an upstream suppressor of the intrinsic regenerative program in the CNS and demonstrate the utility of a systems biology approach involving integrative genomics and bio-informatics to prioritize hypotheses relevant to CNS repair.

Genoppi is an open-source software for robust and standardized integration of proteomic and genetic data

Combining genetic and cell-type-specific proteomic datasets can generate biological insights and therapeutic hypotheses, but a technical and statistical framework for such analyses is lacking. Here, we present an open-source computational tool called Genoppi (lagelab.org/genoppi) that enables robust, standardized, and intuitive integration of quantitative proteomic results with genetic data. We use Genoppi to analyze 16 cell-type-specific protein interaction datasets of four proteins (BCL2, TDP-43, MDM2, PTEN) involved in cancer and neurological disease. Through systematic quality control of the data and integration with published protein interactions, we show a general pattern of both cell-type-independent and cell-type-specific interactions across three cancer cell types and one human iPSC-derived neuronal cell type. Furthermore, through the integration of proteomic and genetic datasets in Genoppi, our results suggest that the neuron-specific interactions of these proteins are mediating their genetic involvement in neurodegenerative diseases. Importantly, our analyses suggest that human iPSC-derived neurons are a relevant model system for studying the involvement of BCL2 and TDP-43 in amyotrophic lateral sclerosis.

Quantitative proteome comparison of human hearts with those of model organisms

Delineating human cardiac pathologies and their basic molecular mechanisms relies on research conducted in model organisms. Yet translating findings from preclinical models to humans present a significant challenge, in part due to differences in cardiac protein expression between humans and model organisms. Proteins immediately determine cellular function, yet their large-scale investigation in hearts has lagged behind those of genes and transcripts. Here, we set out to bridge this knowledge gap: By analyzing protein profiles in humans and commonly used model organisms across cardiac chambers, we determine their commonalities and regional differences. We analyzed cardiac tissue from each chamber of human, pig, horse, rat, mouse, and zebrafish in biological replicates. Using mass spectrometry–based proteomics workflows, we measured and evaluated the abundance of approximately 7,000 proteins in each species. The resulting knowledgebase of cardiac protein signatures is accessible through an online database: atlas.cardiacproteomics.com. Our combined analysis allows for quantitative evaluation of protein abundances across cardiac chambers, as well as comparisons of cardiac protein profiles across model organisms. Up to a quarter of proteins with differential abundances between atria and ventricles showed opposite chamber-specific enrichment between species; these included numerous proteins implicated in cardiac disease. The generated proteomics resource facilitates translational prospects of cardiac studies from model organisms to humans by comparisons of disease-linked protein networks across species.

This study provides protein abundance profiles for thousands of proteins across cardiac chambers for humans and five commonly used model organisms. This quantitative proteomics dataset represents the most comprehensive such resource to date, and can be queried via a web browser to identify the most appropriate model organism for future studies.

Ibrutinib-Mediated Atrial Fibrillation Attributable to Inhibition of C-Terminal Src Kinase

BACKGROUND

Ibrutinib is a Bruton tyrosine kinase inhibitor with remarkable efficacy against B-cell cancers. Ibrutinib also increases the risk of atrial fibrillation (AF), which remains poorly understood.

METHODS

We performed electrophysiology studies on mice treated with ibrutinib to assess inducibility of AF. Chemoproteomic analysis of cardiac lysates identified candidate ibrutinib targets, which were further evaluated in genetic mouse models and additional pharmacological experiments. The pharmacovigilance database, VigiBase, was queried to determine whether drug inhibition of an identified candidate kinase was associated with increased reporting of AF.

RESULTS

We demonstrate that treatment of mice with ibrutinib for 4 weeks results in inducible AF, left atrial enlargement, myocardial fibrosis, and inflammation. This effect was reproduced in mice lacking Bruton tyrosine kinase, but not in mice treated with 4 weeks of acalabrutinib, a more specific Bruton tyrosine kinase inhibitor, demonstrating that AF is an off-target side effect. Chemoproteomic profiling identified a short list of candidate kinases that was narrowed by additional experimentation leaving CSK (C-terminal Src kinase) as the strongest candidate for ibrutinib-induced AF. Cardiac-specific Csk knockout in mice led to increased AF, left atrial enlargement, fibrosis, and inflammation, phenocopying ibrutinib treatment. Disproportionality analyses in VigiBase confirmed increased reporting of AF associated with kinase inhibitors blocking Csk versus non-Csk inhibitors, with a reporting odds ratio of 8.0 (95% CI, 7.3-8.7; P<0.0001).

CONCLUSIONS

These data identify Csk inhibition as the mechanism through which ibrutinib leads to AF. Registration: URL: https://ww.clinicaltrials.gov; Unique identifier: NCT03530215.

Co-expression of calcium and hERG potassium channels reduces the incidence of proarrhythmic events

AIMS

Cardiac electrical activity is extraordinarily robust. However, when it goes wrong it can have fatal consequences. Electrical activity in the heart is controlled by the carefully orchestrated activity of more than a dozen different ion conductances. While there is considerable variability in cardiac ion channel expression levels between individuals, studies in rodents have indicated that there are modules of ion channels whose expression co-vary. The aim of this study was to investigate whether meta-analytic co-expression analysis of large-scale gene expression datasets could identify modules of co-expressed cardiac ion channel genes in human hearts that are of functional importance.

METHODS AND RESULTS

Meta-analysis of 3653 public human RNA-seq datasets identified a strong correlation between expression of CACNA1C (L-type calcium current, ICaL) and KCNH2 (rapid delayed rectifier K+ current, IKr), which was also observed in human adult heart tissue samples. In silico modelling suggested that co-expression of CACNA1C and KCNH2 would limit the variability in action potential duration seen with variations in expression of ion channel genes and reduce susceptibility to early afterdepolarizations, a surrogate marker for proarrhythmia. We also found that levels of KCNH2 and CACNA1C expression are correlated in human-induced pluripotent stem cell-derived cardiac myocytes and the levels of CACNA1C and KCNH2 expression were inversely correlated with the magnitude of changes in repolarization duration following inhibition of IKr.

CONCLUSION

Meta-analytic approaches of multiple independent human gene expression datasets can be used to identify gene modules that are important for regulating heart function. Specifically, we have verified that there is co-expression of CACNA1C and KCNH2 ion channel genes in human heart tissue, and in silico analyses suggest that CACNA1C-KCNH2 co-expression increases the robustness of cardiac electrical activity.

Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named ‘core genes’, while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer’s Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets—consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer–where we identify 109 candidate core genes.

A recent theory suggests that only a small number of genes underpin the biology of a disease, these genes are called ‘core genes’, and for most diseases, these core genes remain unknown. The suggested methods for finding them requires complex and expensive experiments. We reasoned that if we merge currently available datasets in smart ways, we may be able to uncover these ‘core genes’. Our method finds “hub” proteins by merging lists of genes previously linked with disease to information on how proteins interact with each other. We found that many of these hub proteins have central roles in disease, such as insulin for both A1C measurement and Type 2 Diabetes, BRCA1 in Breast cancer, and Amyloid Precursor Protein in Alzheimer’s Disease. We think these ‘hub’ proteins are candidate ‘core genes’, and offer our method as a way to find ‘core genes’ by utilizing publicly available reference datasets.

Quantitative Proteomics of Human Heart Samples Collected In Vivo Reveal the Remodeled Protein Landscape of Dilated Left Atrium Without Atrial Fibrillation

Genetic and genomic research has greatly advanced our understanding of heart disease. Yet, comprehensive, in-depth, quantitative maps of protein expression in hearts of living humans are still lacking. Using samples obtained during valve replacement surgery in patients with mitral valve prolapse (MVP), we set out to define inter-chamber differences, the intersect of proteomic data with genetic or genomic datasets, and the impact of left atrial dilation on the proteome of patients with no history of atrial fibrillation (AF).We collected biopsies from right atria (RA), left atria (LA) and left ventricle (LV) of seven male patients with mitral valve regurgitation with dilated LA but no history of AF. Biopsy samples were analyzed by high-resolution mass spectrometry (MS), where peptides were pre-fractionated by reverse phase high-pressure liquid chromatography prior to MS measurement on a Q-Exactive-HF Orbitrap instrument. We identified 7,314 proteins based on 130,728 peptides. Results were confirmed in an independent set of biopsies collected from three additional individuals. Comparative analysis against data from post-mortem samples showed enhanced quantitative power and confidence level in samples collected from living hearts. Our analysis, combined with data from genome wide association studies suggested candidate gene associations to MVP, identified higher abundance in ventricle for proteins associated with cardiomyopathies and revealed the dilated LA proteome, demonstrating differential representation of molecules previously associated with AF, in non-AF hearts.This is the largest dataset of cardiac protein expression from human samples collected in vivo It provides a comprehensive resource that allows insight into molecular fingerprints of MVP and facilitates novel inferences between genomic data and disease mechanisms. We propose that over-representation of proteins in ventricle is consequent not to redundancy but to functional need, and conclude that changes in abundance of proteins known to associate with AF are not sufficient for arrhythmogenesis.

uKIN Combines New and Prior Information with Guided Network Propagation to Accurately Identify Disease Genes

Protein interaction networks provide a powerful framework for identifying genes causal for complex genetic diseases. Here, we introduce a general framework, uKIN, that uses prior knowledge of disease-associated genes to guide, within known protein-protein interaction networks, random walks that are initiated from newly identified candidate genes. In large-scale testing across 24 cancer types, we demonstrate that our network propagation approach for integrating both prior and new information not only better identifies cancer driver genes than using either source of information alone but also readily outperforms other state-of-the-art network-based approaches. We also apply our approach to genome-wide association data to identify genes functionally relevant for several complex diseases. Overall, our work suggests that guided network propagation approaches that utilize both prior and new data are a powerful means to identify disease genes. uKIN is freely available for download at: https://github.com/Singh-Lab/uKIN.

Quantitative proteomics characterization of acutely isolated primary adult rat cardiomyocytes and fibroblasts

Our heart is comprised of many different cell types that all contribute to cardiac function. An important step in deciphering the molecular complexity of our heart is to decipher the molecular composition of the various cardiac cell types. Here we set out to delineate a comprehensive protein expression profile of the two most prevalent cell types in the heart: cardiomyocytes and cardiac fibroblasts. To this end, we isolated cardiomyocytes and fibroblasts from rat hearts and combined state-of-the-art flow cytometry with high-resolution mass spectrometry to investigate their proteome profiles right after isolation. We measured and quantified 5240 proteins in cardiomyocytes and 6328 proteins in cardiac fibroblasts. In addition to providing a global protein profile for these cardiac cell types, we also present specific findings, such as unique expression of ion channels and transcription factors for each cell type. For instance, we show that the sodium channel Scn7a and the cation channel Trpm7 are expressed in fibroblasts but not in cardiomyocytes, which underscores the importance of investigating the endogenous cell host prior to functional studies. Our dataset represents a valuable resource on protein expression profiles in these two primary cardiac cells types.

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

Reevaluation of genetic variants previously associated with arrhythmogenic right ventricular cardiomyopathy integrating population-based cohorts and proteomics data

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is one of the most common causes of sudden cardiac death in young people. Patients diagnosed with ARVC may experience increased likelihood of development of anxiety and depression, emphasizing the need for accurate diagnosis. To assist future genetic diagnosis and avoidance of misdiagnosis, we evaluated the reported monogenic disease-causing variants in ARVD/C Genetic Variants Database, Human Gene Mutation Database, and ClinVar. Within the aforementioned databases, 630 monogenic disease-causing variants from 18 genes were identified. In the genome Aggregation Database, 226 of these were identified; 68 of which were found at greater than expected prevalence. Furthermore, 37/226 genetic variants were identified amongst the 409 000 UK biobank participants, 23 were not associated with ARVC. Among the 14 remaining variants, 13 were previously found with greater than expected prevalence for a monogenic variant. Nevertheless, they were associated with serious cardiac phenotypes, suggesting that these 13 variants may be disease-modifiers of ARVC, rather than monogenic disease-causing. In summary, more than 10% of variants previously reported to cause ARVC were found unlikely to be associated with highly penetrant monogenic forms of ARVC. Notably, all variants in OBSCN and MYBPC3 were found, making these unlikely to be monogenic causes of ARVC.

Disruption of Ca2+i Homeostasis and Connexin 43 Hemichannel Function in the Right Ventricle Precedes Overt Arrhythmogenic Cardiomyopathy in Plakophilin-2-Deficient Mice

BACKGROUND

Plakophilin-2 (PKP2) is classically defined as a desmosomal protein. Mutations in PKP2 associate with most cases of gene-positive arrhythmogenic right ventricular cardiomyopathy. A better understanding of PKP2 cardiac biology can help elucidate the mechanisms underlying arrhythmic and cardiomyopathic events consequent to PKP2 deficiency. Here, we sought to capture early molecular/cellular events that can act as nascent arrhythmic/cardiomyopathic substrates.

METHODS

We used multiple imaging, biochemical and high-resolution mass spectrometry methods to study functional/structural properties of cells/tissues derived from cardiomyocyte-specific, tamoxifen-activated, PKP2 knockout mice (PKP2cKO) 14 days post-tamoxifen injection, a time point preceding overt electrical or structural phenotypes. Myocytes from right or left ventricular free wall were studied separately.

RESULTS

Most properties of PKP2cKO left ventricular myocytes were not different from control; in contrast, PKP2cKO right ventricular (RV) myocytes showed increased amplitude and duration of Ca²⁺ transients, increased Ca²⁺ in the cytoplasm and sarcoplasmic reticulum, increased frequency of spontaneous Ca²⁺ release events (sparks) even at comparable sarcoplasmic reticulum load, and dynamic Ca²⁺ accumulation in mitochondria. We also observed early- and delayed-after transients in RV myocytes and heightened susceptibility to arrhythmias in Langendorff-perfused hearts. In addition, ryanodine receptor 2 in PKP2cKO-RV cells presented enhanced Ca²⁺ sensitivity and preferential phosphorylation in a domain known to modulate Ca²⁺ gating. RNAseq at 14 days post-tamoxifen showed no relevant difference in transcript abundance between RV and left ventricle, neither in control nor in PKP2cKO cells. Instead, we found an RV-predominant increase in membrane permeability that can permit Ca²⁺ entry into the cell. Connexin 43 ablation mitigated the membrane permeability increase, accumulation of cytoplasmic Ca²⁺, increased frequency of sparks and early stages of RV dysfunction. Connexin 43 hemichannel block with GAP19 normalized [Ca²⁺]_i homeostasis. Similarly, protein kinase C inhibition normalized spark frequency at comparable sarcoplasmic reticulum load levels.

CONCLUSIONS

Loss of PKP2 creates an RV-predominant arrhythmogenic substrate (Ca²⁺ dysregulation) that precedes the cardiomyopathy; this is, at least in part, mediated by a Connexin 43-dependent membrane conduit and repressed by protein kinase C inhibitors. Given that asymmetric Ca²⁺ dysregulation precedes the cardiomyopathic stage, we speculate that abnormal Ca²⁺ handling in RV myocytes can be a trigger for gross structural changes observed at a later stage.

Quantitative proteomics and single-nucleus transcriptomics of the sinus node elucidates the foundation of cardiac pacemaking

The sinus node is a collection of highly specialised cells constituting the heart’s pacemaker. The molecular underpinnings of its pacemaking abilities are debated. Using high-resolution mass spectrometry, we here quantify >7,000 proteins from sinus node and neighbouring atrial muscle. Abundances of 575 proteins differ between the two tissues. By performing single-nucleus RNA sequencing of sinus node biopsies, we attribute measured protein abundances to specific cell types. The data reveal significant differences in ion channels responsible for the membrane clock, but not in Ca²⁺ clock proteins, suggesting that the membrane clock underpins pacemaking. Consistently, incorporation of ion channel expression differences into a biophysically-detailed atrial action potential model result in pacemaking and a sinus node-like action potential. Combining our quantitative proteomics data with computational modeling, we estimate ion channel copy numbers for sinus node myocytes. Our findings provide detailed insights into the unique molecular make-up of the cardiac pacemaker.

The sinus node generates rhythmic heartbeat but the molecular basis of pacemaking is still under debate. Here, the authors combine quantitative proteomics and single-nucleus transcriptomics to characterize the molecular composition of the sinus node and provide insights into the underpinnings of pacemaking.

Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data

BACKGROUND

Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D). One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes. However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.

METHODS

Here, we describe implementation of an analytical pipeline to address this question. First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals. Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks. Third, we use GWAS data to test the T2D association enrichment of the "non-seed" proteins introduced into the network, as a measure of the overall functional connectivity of the network.

RESULTS

We find (a) non-seed proteins in the T2D protein-interaction network so generated (comprising 705 nodes) are enriched for association to T2D (p = 0.0014) but not control traits, (b) stronger T2D-enrichment for islets than other tissues when we use RNA expression data to generate tissue-specific PPI networks and (c) enhanced enrichment (p = 3.9 × 10^- 5) when we combine the analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion.

CONCLUSIONS

These analyses reveal a pattern of non-random functional connectivity between candidate causal genes at T2D GWAS loci and highlight the products of genes including YWHAG, SMAD4 or CDK2 as potential contributors to T2D-relevant islet dysfunction. The approach we describe can be applied to other complex genetic and genomic datasets, facilitating integration of diverse data types into disease-associated networks.

Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value <1 × 10-5 were investigated in replication cohorts that included 18,545 European, 16,453 Asian, and 2,710 Hispanic subjects. After correction for multiple testing, the C allele of rs142293996 in an intron of nuclear VCP-like (NVL) was associated with DR in European discovery cohorts (P = 2.1 × 10-9), but did not reach genome-wide significance after meta-analysis with replication cohorts. We applied the Disease Association Protein-Protein Link Evaluator (DAPPLE) to our discovery results to test for evidence of risk being spread across underlying molecular pathways. One protein-protein interaction network built from genes in regions associated with proliferative DR was found to have significant connectivity (P = 0.0009) and corroborated with gene set enrichment analyses. These findings suggest that genetic variation in NVL, as well as variation within a protein-protein interaction network that includes genes implicated in inflammation, may influence risk for DR.

Rare truncating variants in the sarcomeric protein titin associate with familial and early-onset atrial fibrillation

A family history of atrial fibrillation constitutes a substantial risk of developing the disease, however, the pathogenesis of this complex disease is poorly understood. We perform whole-exome sequencing on 24 families with at least three family members diagnosed with atrial fibrillation (AF) and find that titin-truncating variants (TTNtv) are significantly enriched in these patients (P = 1.76 × 10⁻⁶). This finding is replicated in an independent cohort of early-onset lone AF patients (n = 399; odds ratio = 36.8; P = 4.13 × 10⁻⁶). A CRISPR/Cas9 modified zebrafish carrying a truncating variant of titin is used to investigate TTNtv effect in atrial development. We observe compromised assembly of the sarcomere in both atria and ventricle, longer PR interval, and heterozygous adult zebrafish have a higher degree of fibrosis in the atria, indicating that TTNtv are important risk factors for AF. This aligns with the early onset of the disease and adds an important dimension to the understanding of the molecular predisposition for AF.

Common genetic variants in structural proteins contribute to risk of atrial fibrillation (AF). Here, using whole-exome sequencing, the authors identify rare truncating variants in TTN that associate with familial and early-onset AF and show defects in cardiac sarcomere assembly in ttn.2-mutant zebrafish.

GeNets: a unified web platform for network-based genomic analyses

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a particular genetic dataset. Via GeNets users can train a machine-learning model (Quack) to make such comparisons; and they can execute, store, and share analyses of genetic and RNA sequencing datasets.

Gene co-opening network deciphers gene functional relationships

Genome sequencing technology has generated a vast amount of genomic and epigenomic data, and has provided us a great opportunity to study gene functions on a global scale from an epigenomic view. In the last decade, network-based studies, such as those based on PPI networks and co-expression networks, have shown good performance in capturing functional relationships between genes. However, the functions of a gene and the mechanism of interaction of genes with each other to elucidate their functions are still not entirely clear. Here, we construct a gene co-opening network based on chromatin accessibility of genes. We show that genes related to a specific biological process or the same disease tend to be clustered in the co-opening network. This understanding allows us to detect functional clusters from the network and to predict new functions for genes. We further apply the network to prioritize disease genes for Psoriasis, and demonstrate the power of the joint analysis of the co-opening network and GWAS data in identifying disease genes. Taken together, the co-opening network provides a new viewpoint for the elucidation of gene associations and the interpretation of disease mechanisms.

Genetics of Cardiovascular Disease: Fishing for Causality

Cardiovascular disease (CVD) is still the leading cause of death in all western world countries and genetic predisposition in combination with traditional risk factors frequently mediates their manifestation. Genome-wide association (GWA) studies revealed numerous potentially disease modifying genetic loci often including several SNPs and associated genes. However, pure genetic association does not prove direct or indirect relevance of the modifier region on pathogenesis, nor does it define within the associated region the exact genetic driver of the disease. Therefore, the relevance of the identified genetic disease associations needs to be confirmed either in monogenic traits or in experimental in vivo model system by functional genomic studies. In this review, we focus on the use of functional genomic approaches such as gene knock-down or CRISPR/Cas9-mediated genome editing in the zebrafish model to validate disease-associated genomic loci and to identify novel cardiovascular disease genes. We summarize the benefits of the zebrafish for cardiovascular research and highlight examples demonstrating the successful combination of GWA studies and functional genomics in zebrafish to broaden our knowledge on the genetic and molecular underpinnings of cardiovascular diseases.

Systematic characterization of pan-cancer mutation clusters

Cancer genome sequencing has shown that driver genes can often be distinguished not only by the elevated mutation frequency but also by specific nucleotide positions that accumulate changes at a high rate. However, properties associated with a residue's potential to drive tumorigenesis when mutated have not yet been systematically investigated. Here, using a novel methodological approach, we identify and characterize a compendium of 180 hotspot residues within 160 human proteins which occur with a significant frequency and are likely to have functionally relevant impact. We find that such mutations (i) are more prominent in proteins that can exist in the on and off state, (ii) reflect the identity of a tumor of origin, and (iii) often localize within interfaces which mediate interactions with other proteins or ligands. Following, we further examine structural data for human protein complexes and identify a number of additional protein interfaces that accumulate cancer mutations at a high rate. Jointly, these analyses suggest that disruption and dysregulation of protein interactions can be instrumental in switching functions of cancer proteins and activating downstream changes.

The Notch Interactome: Complexity in Signaling Circuitry

The Notch pathway controls a very broad spectrum of cell fates in metazoans during development, influencing proliferation, differentiation and cell death. Given its central role in normal development and homeostasis, misregulation of Notch signals can lead to various disorders including cancer. How the Notch pathway mediates such pleiotropic and differential effects is of fundamental importance. It is becoming increasingly clear through a number of large-scale genetic and proteomic studies that Notch interacts with a staggeringly large number of other genes and pathways in a context-dependent, complex, and highly regulated network, which determines the ultimate biological outcome. How best to interpret and analyze the continuously increasing wealth of data on Notch interactors remains a challenge. Here we review the current state of genetic and proteomic data related to the Notch interactome.

Control of endothelial cell tube formation by Notch ligand intracellular domain interactions with activator protein 1 (AP-1)

Notch signaling is a ubiquitous signal transduction pathway found in most if not all metazoan cell types characterized to date. It is indispensable for cell differentiation as well as tissue growth, tissue remodeling, and apoptosis. Although the canonical Notch signaling pathway is well characterized, accumulating evidence points to the existence of multiple, less well-defined layers of regulation. In this study, we investigated the function of the intracellular domain (ICD) of the Notch ligand Delta-like 4 (DLL4). We provide evidence that the DLL4 ICD is required for normal DLL4 subcellular localization. We further show that it is cleaved and interacts with the JUN proto-oncogene, which forms part of the activator protein 1 (AP-1) transcription factor complex. Mechanistically, the DLL4 ICD inhibited JUN binding to DNA and thereby controlled the expression of JUN target genes, including DLL4 Our work further demonstrated that JUN strongly stimulates endothelial cell tube formation and that DLL4 constrains this process. These results raise the possibility that Notch/DLL4 signaling is bidirectional and suggest that the DLL4 ICD could represent a point of cross-talk between Notch and receptor tyrosine kinase (RTK) signaling.

LITTLE FISH, BIG DATA: ZEBRAFISH AS A MODEL FOR CARDIOVASCULAR AND METABOLIC DISEASE

The burden of cardiovascular and metabolic diseases worldwide is staggering. The emergence of systems approaches in biology promises new therapies, faster and cheaper diagnostics, and personalized medicine. However, a profound understanding of pathogenic mechanisms at the cellular and molecular levels remains a fundamental requirement for discovery and therapeutics. Animal models of human disease are cornerstones of drug discovery as they allow identification of novel pharmacological targets by linking gene function with pathogenesis. The zebrafish model has been used for decades to study development and pathophysiology. More than ever, the specific strengths of the zebrafish model make it a prime partner in an age of discovery transformed by big-data approaches to genomics and disease. Zebrafish share a largely conserved physiology and anatomy with mammals. They allow a wide range of genetic manipulations, including the latest genome engineering approaches. They can be bred and studied with remarkable speed, enabling a range of large-scale phenotypic screens. Finally, zebrafish demonstrate an impressive regenerative capacity scientists hope to unlock in humans. Here, we provide a comprehensive guide on applications of zebrafish to investigate cardiovascular and metabolic diseases. We delineate advantages and limitations of zebrafish models of human disease and summarize their most significant contributions to understanding disease progression to date.

Multi-omics approaches to disease

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of “integrative genetics”. Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.

A scored human protein-protein interaction network to catalyze genomic interpretation

Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.

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

BACKGROUND

The relationship between monogenic and polygenic forms of epilepsy is poorly understood and the extent to which the genetic and acquired epilepsies share common pathways is unclear. Here, we use an integrated systems-level analysis of brain gene expression data to identify molecular networks disrupted in epilepsy.

RESULTS

We identified a co-expression network of 320 genes (M30), which is significantly enriched for non-synonymous de novo mutations ascertained from patients with monogenic epilepsy and for common variants associated with polygenic epilepsy. The genes in the M30 network are expressed widely in the human brain under tight developmental control and encode physically interacting proteins involved in synaptic processes. The most highly connected proteins within the M30 network were preferentially disrupted by deleterious de novo mutations for monogenic epilepsy, in line with the centrality-lethality hypothesis. Analysis of M30 expression revealed consistent downregulation in the epileptic brain in heterogeneous forms of epilepsy including human temporal lobe epilepsy, a mouse model of acquired temporal lobe epilepsy, and a mouse model of monogenic Dravet (SCN1A) disease. These results suggest functional disruption of M30 via gene mutation or altered expression as a convergent mechanism regulating susceptibility to epilepsy broadly. Using the large collection of drug-induced gene expression data from Connectivity Map, several drugs were predicted to preferentially restore the downregulation of M30 in epilepsy toward health, most notably valproic acid, whose effect on M30 expression was replicated in neurons.

CONCLUSIONS

Taken together, our results suggest targeting the expression of M30 as a potential new therapeutic strategy in epilepsy.

A Matter of the Heart: The African Clawed Frog Xenopus as a Model for Studying Vertebrate Cardiogenesis and Congenital Heart Defects

The African clawed frog, Xenopus, is a valuable non-mammalian model organism to investigate vertebrate heart development and to explore the underlying molecular mechanisms of human congenital heart defects (CHDs). In this review, we outline the similarities between Xenopus and mammalian cardiogenesis, and provide an overview of well-studied cardiac genes in Xenopus, which have been associated with congenital heart conditions. Additionally, we highlight advantages of modeling candidate genes derived from genome wide association studies (GWAS) in Xenopus and discuss commonly used techniques.

In Vivo Interaction Proteomics in Caenorhabditis elegans Embryos Provides New Insights into P Granule Dynamics

Studying protein interactions in whole organisms is fundamental to understanding development. Here, we combine in vivo expressed GFP-tagged proteins with quantitative proteomics to identify protein-protein interactions of selected key proteins involved in early C. elegans embryogenesis. Co-affinity purification of interaction partners for eight bait proteins resulted in a pilot in vivo interaction map of proteins with a focus on early development. Our network reflects known biology and is highly enriched in functionally relevant interactions. To demonstrate the utility of the map, we looked for new regulators of P granule dynamics and found that GEI-12, a novel binding partner of the DYRK family kinase MBK-2, is a key regulator of P granule formation and germline maintenance. Our data corroborate a recently proposed model in which the phosphorylation state of GEI-12 controls P granule dynamics. In addition, we find that GEI-12 also induces granule formation in mammalian cells, suggesting a common regulatory mechanism in worms and humans. Our results show that in vivo interaction proteomics provides unique insights into animal development.

Identification of shared and unique susceptibility pathways among cancers of the lung, breast, and prostate from genome-wide association studies and tissue-specific protein interactions

Results from genome-wide association studies (GWAS) have indicated that strong single-gene effects are the exception, not the rule, for most diseases. We assessed the joint effects of germline genetic variations through a pathway-based approach that considers the tissue-specific contexts of GWAS findings. From GWAS meta-analyses of lung cancer (12 160 cases/16 838 controls), breast cancer (15 748 cases/18 084 controls) and prostate cancer (14 160 cases/12 724 controls) in individuals of European ancestry, we determined the tissue-specific interaction networks of proteins expressed from genes that are likely to be affected by disease-associated variants. Reactome pathways exhibiting enrichment of proteins from each network were compared across the cancers. Our results show that pathways associated with all three cancers tend to be broad cellular processes required for growth and survival. Significant examples include the nerve growth factor (P = 7.86 × 10(-33)), epidermal growth factor (P = 1.18 × 10(-31)) and fibroblast growth factor (P = 2.47 × 10(-31)) signaling pathways. However, within these shared pathways, the genes that influence risk largely differ by cancer. Pathways found to be unique for a single cancer focus on more specific cellular functions, such as interleukin signaling in lung cancer (P = 1.69 × 10(-15)), apoptosis initiation by Bad in breast cancer (P = 3.14 × 10(-9)) and cellular responses to hypoxia in prostate cancer (P = 2.14 × 10(-9)). We present the largest comparative cross-cancer pathway analysis of GWAS to date. Our approach can also be applied to the study of inherited mechanisms underlying risk across multiple diseases in general.

Reconciling proteomics with next generation sequencing

Both genomics and proteomics technologies have matured in the last decade to a level where they are able to deliver system-wide data on the qualitative and quantitative abundance of their respective molecular entities, that is DNA/RNA and proteins. A next logical step is the collective use of these technologies, ideally gathering data on matching samples. The first large scale so-called proteogenomics studies are emerging, and display the benefits each of these layers of analysis has on the other layers to together generate more meaningful insight into the connection between the phenotype/physiology and genotype of the system under study. Here we review a selected number of these studies, highlighting what they can uniquely deliver. We also discuss the future potential and remaining challenges, from a somewhat proteome biased perspective.

Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported.

The Rotterdam Study: 2016 objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over 1200 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration, but mechanistic understanding continues to lag behind the pace of gene discovery. Understanding the role of specific genetic variants in the brain involves dissecting a functional hierarchy that encompasses molecular pathways, diverse cell types, neural circuits and, ultimately, cognition and behaviour. With a focus on transcriptomics, this Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context. We provide a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology.

Quantitative affinity purification mass spectrometry: a versatile technology to study protein-protein interactions

While the genomic revolution has dramatically accelerated the discovery of disease-associated genes, the functional characterization of the corresponding proteins lags behind. Most proteins fulfill their tasks in complexes with other proteins, and analysis of protein–protein interactions (PPIs) can therefore provide insights into protein function. Several methods can be used to generate large-scale protein interaction networks. However, most of these approaches are not quantitative and therefore cannot reveal how perturbations affect the network. Here, we illustrate how a clever combination of quantitative mass spectrometry with different biochemical methods provides a rich toolkit to study different aspects of PPIs including topology, subunit stoichiometry, and dynamic behavior.

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 Alzheimer’s disease (AD), Huntingtin (HTT) for Huntington’s disease, Parkin (PARK2) for Parkinson’s 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.

Hosp et al. show that quantitative interaction proteomics of neurodegenerative disease proteins captures interactions relevant to pathogenesis. Differential interactome mapping reveals preferential binding of the mitochondrial protein LRPPRC with an early-onset Alzheimer’s disease (AD) variant of APP, potentially contributing to mitochondrial dysfunction observed in AD.

Parsing interindividual drug variability: an emerging role for systems pharmacology

There is notable interindividual heterogeneity in drug response, affecting both drug efficacy and toxicity, resulting in patient harm and the inefficient utilization of limited healthcare resources. Pharmacogenomics is at the forefront of research to understand interindividual drug response variability, but although many genotype-drug response associations have been identified, translation of pharmacogenomic associations into clinical practice has been hampered by inconsistent findings and inadequate predictive values. These limitations are in part due to the complex interplay between drug-specific, human body and environmental factors influencing drug response and therefore pharmacogenomics, whilst intrinsically necessary, is by itself unlikely to adequately parse drug variability. The emergent, interdisciplinary and rapidly developing field of systems pharmacology, which incorporates but goes beyond pharmacogenomics, holds significant potential to further parse interindividual drug variability. Systems pharmacology broadly encompasses two distinct research efforts, pharmacologically-orientated systems biology and pharmacometrics. Pharmacologically-orientated systems biology utilizes high throughput omics technologies, including next-generation sequencing, transcriptomics and proteomics, to identify factors associated with differential drug response within the different levels of biological organization in the hierarchical human body. Increasingly complex pharmacometric models are being developed that quantitatively integrate factors associated with drug response. Although distinct, these research areas complement one another and continual development can be facilitated by iterating between dynamic experimental and computational findings. Ultimately, quantitative data-derived models of sufficient detail will be required to help realize the goal of precision medicine.

The BioGRID interaction database: 2015 update

The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein interactions curated from the primary biomedical literature for all major model organism species and humans. As of September 2014, the BioGRID contains 749 912 interactions as drawn from 43 149 publications that represent 30 model organisms. This interaction count represents a 50% increase compared to our previous 2013 BioGRID update. BioGRID data are freely distributed through partner model organism databases and meta-databases and are directly downloadable in a variety of formats. In addition to general curation of the published literature for the major model species, BioGRID undertakes themed curation projects in areas of particular relevance for biomedical sciences, such as the ubiquitin-proteasome system and various human disease-associated interaction networks. BioGRID curation is coordinated through an Interaction Management System (IMS) that facilitates the compilation interaction records through structured evidence codes, phenotype ontologies, and gene annotation. The BioGRID architecture has been improved in order to support a broader range of interaction and post-translational modification types, to allow the representation of more complex multi-gene/protein interactions, to account for cellular phenotypes through structured ontologies, to expedite curation through semi-automated text-mining approaches, and to enhance curation quality control.