Kousik Kundu

The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease

Summary Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.

Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells

Summary Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

Semi-Supervised Prediction of SH2-Peptide Interactions from Imbalanced High-Throughput Data

Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2-domains, freely available to the scientific community under the following URLs: http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/SH2PepInt.tar.gz and http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/Genome-wide-predictions.tar.gz, respectively.

MoDPepInt: an interactive web server for prediction of modular domain-peptide interactions

Summary: MoDPepInt (Modular Domain Peptide Interaction) is a new easy-to-use web server for the prediction of binding partners for modular protein domains. Currently, we offer models for SH2, SH3 and PDZ domains via the tools SH2PepInt, SH3PepInt and PDZPepInt, respectively. More specifically, our server offers predictions for 51 SH2 human domains and 69 SH3 human domains via single domain models, and predictions for 226 PDZ domains across several species, via 43 multidomain models. All models are based on support vector machines with different kernel functions ranging from polynomial, to Gaussian, to advanced graph kernels. In this way, we model non-linear interactions between amino acid residues. Results were validated on manually curated datasets achieving competitive performance against various state-of-the-art approaches. Availability and implementation: The MoDPepInt server is available under the URL http://modpepint.informatik.uni-freiburg.de/ Contact: backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.

A graph kernel approach for alignment-free domain–peptide interaction prediction with an application to human SH3 domains

Motivation: State-of-the-art experimental data for determining binding specificities of peptide recognition modules (PRMs) is obtained by high-throughput approaches like peptide arrays. Most prediction tools applicable to this kind of data are based on an initial multiple alignment of the peptide ligands. Building an initial alignment can be error-prone, especially in the case of the proline-rich peptides bound by the SH3 domains. Results: Here, we present a machine-learning approach based on an efficient graph-kernel technique to predict the specificity of a large set of 70 human SH3 domains, which are an important class of PRMs. The graph-kernel strategy allows us to (i) integrate several types of physico-chemical information for each amino acid, (ii) consider high-order correlations between these features and (iii) eliminate the need for an initial peptide alignment. We build specialized models for each human SH3 domain and achieve competitive predictive performance of 0.73 area under precision-recall curve, compared with 0.27 area under precision-recall curve for state-of-the-art methods based on position weight matrices. We show that better models can be obtained when we use information on the noninteracting peptides (negative examples), which is currently not used by the state-of-the art approaches based on position weight matrices. To this end, we analyze two strategies to identify subsets of high confidence negative data. The techniques introduced here are more general and hence can also be used for any other protein domains, which interact with short peptides (i.e. other PRMs). Availability: The program with the predictive models can be found at http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/SH3PepInt.tar.gz. We also provide a genome-wide prediction for all 70 human SH3 domains, which can be found under http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/Genome-Wide-Predictions.tar.gz. Contact: backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Cluster based prediction of PDZ-peptide interactions

BackgroundPDZ domains are one of the most promiscuous protein recognition modules that bind with short linear peptides and play an important role in cellular signaling. Recently, few high-throughput techniques (e.g. protein microarray screen, phage display) have been applied to determine in-vitro binding specificity of PDZ domains. Currently, many computational methods are available to predict PDZ-peptide interactions but they often provide domain specific models and/or have a limited domain coverage.ResultsHere, we composed the largest set of PDZ domains derived from human, mouse, fly and worm proteomes and defined binding models for PDZ domain families to improve the domain coverage and prediction specificity. For that purpose, we first identified a novel set of 138 PDZ families, comprising of 548 PDZ domains from aforementioned organisms, based on efficient clustering according to their sequence identity. For 43 PDZ families, covering 226 PDZ domains with available interaction data, we built specialized models using a support vector machine approach. The advantage of family-wise models is that they can also be used to determine the binding specificity of a newly characterized PDZ domain with sufficient sequence identity to the known families. Since most current experimental approaches provide only positive data, we have to cope with the class imbalance problem. Thus, to enrich the negative class, we introduced a powerful semi-supervised technique to generate high confidence non-interaction data. We report competitive predictive performance with respect to state-of-the-art approaches.ConclusionsOur approach has several contributions. First, we show that domain coverage can be increased by applying accurate clustering technique. Second, we developed an approach based on a semi-supervised strategy to get high confidence negative data. Third, we allowed high order correlations between the amino acid positions in the binding peptides. Fourth, our method is general enough and will easily be applicable to other peptide recognition modules such as SH2 domains and finally, we performed a genome-wide prediction for 101 human and 102 mouse PDZ domains and uncovered novel interactions with biological relevance. We make all the predictive models and genome-wide predictions freely available to the scientific community.

Shared genetic effects on chromatin and gene expression reveal widespread enhancer priming in immune response

Noncoding regulatory variants play an important role in the genetics of complex traits. Although quantitative trait locus (QTL) mapping is a powerful approach to identify these variants, many genetic effects may remain unobserved when cells are sampled in only one of a large number of possible environments. Using a novel induced pluripotent stem cell-derived system, we mapped QTLs regulating chromatin accessibility and gene expression in macrophages in four conditions mimicking the interplay between interferon-gamma response and Salmonella infection. We found that approximately 50% of condition-specific effects on gene expression altered chromatin accessibility prior to stimulation. Furthermore, 6% of the chromatin accessibility QTLs regulated multiple neighbouring regions and these interactions were modulated by stimulation, occasionally producing condition-specific changes in gene expression. Profiling additional states also doubled the number of expression QTLs that could be confidently colocalised with disease associations. Thus, a substantial fraction of disease-associated variants may affect ‘primed’ regulatory elements in naive cells.Noncoding regulatory variants are often highly context-specific, modulating gene expression in a small subset of possible cellular states. Although these genetic effects are likely to play important roles in disease, the molecular mechanisms underlying context-specificity are not well understood. Here, we identify shared quantitative trait loci (QTLs) for chromatin accessibility and gene expression (eQTLs) and show that a large fraction (~60%) of eQTLs that appear following macrophage immune stimulation alter chromatin accessibility in unstimulated cells, suggesting they perturb enhancer priming. We show that such variants are likely to influence the binding of cell type specific transcription factors (TFs), such as PU.1, which then indirectly alter the binding of stimulus-specific TFs, such as NF-κB or STAT2. Our results imply that, although chromatin accessibility assays are powerful for fine mapping causal noncoding variants, detecting their downstream impact on gene expression will be challenging, requiring profiling of large numbers of stimulated cellular states and timepoints.

Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response

Regulatory variants are often context specific, modulating gene expression in a subset of possible cellular states. Although these genetic effects can play important roles in disease, the molecular mechanisms underlying context specificity are poorly understood. Here, we identified shared quantitative trait loci (QTLs) for chromatin accessibility and gene expression in human macrophages exposed to IFNγ, Salmonella and IFNγ plus Salmonella. We observed that ~60% of stimulus-specific expression QTLs with a detectable effect on chromatin altered the chromatin accessibility in naive cells, thus suggesting that they perturb enhancer priming. Such variants probably influence binding of cell-type-specific transcription factors, such as PU.1, which can then indirectly alter the binding of stimulus-specific transcription factors, such as NF-κB or STAT2. Thus, although chromatin accessibility assays are powerful for fine-mapping causal regulatory variants, detecting their downstream effects on gene expression will be challenging, requiring profiling of large numbers of stimulated cellular states and time points.Analysis of chromatin accessibility and expression quantitative trait loci in stimulated or naïve macrophages identifies loci that constitutively alter chromatin but affect expression only after stimulation, thus indicating an effect on enhancer priming.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching

Abstract The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

GRIN3B missense mutation as an inherited risk factor for schizophrenia: whole-exome sequencing in a family with a familiar history of psychotic disorders

Summary Glutamate is the most important excitatory neurotransmitter in the brain. The N-methyl-D-aspartate (NMDA) receptor is a glutamate-gated ionotropic cation channel that is composed of several subunits and modulated by a glycine binding site. Many forms of synaptic plasticity depend on the influx of calcium ions through NMDA receptors, and NMDA receptor dysfunction has been linked to a number of neuropsychiatric disorders, including schizophrenia. Whole-exome sequencing was performed in a family with a strong history of psychotic disorders over three generations. We used an iterative strategy to obtain condense and meaningful variants. In this highly affected family, we found a frameshift mutation (rs10666583) in the GRIN3B gene, which codes for the GluN3B subunit of the NMDA receptor in all family members with a psychotic disorder, but not in the healthy relatives. Matsuno et al., also reported this null variant as a risk factor for schizophrenia in 2015. In a broader sample of 22 patients with psychosis, the allele frequency of the rs10666583 mutation variant was increased compared to those of healthy population samples and unaffected relatives. Compared to the 1000 Genomes Project population, we found a significant increase of this variant with a large effect size among patients. The amino acid shift degrades the S1/S2 glycine binding domain of the dominant modulatory GluN3B subunit of the NMDA receptor, which subsequently affects the permeability of the channel pore to calcium ions. A decreased glycine affinity for the GluN3B subunit might cause impaired functional capability of the NMDA receptor and could be an important risk factor for the pathogenesis of psychotic disorders.