Kartiek Kanduri

Network properties of human disease genes with pleiotropic effects.

BackgroundThe ability of a gene to cause a disease is known to be associated with the topological position of its protein product in the molecular interaction network. Pleiotropy, in human genetic diseases, refers to the ability of different mutations within the same gene to cause different pathological effects. Here, we hypothesized that the ability of human disease genes to cause pleiotropic effects would be associated with their network properties.ResultsShared genes, with pleiotropic effects, were more central than specific genes that were associated with one disease, in the protein interaction network. Furthermore, shared genes associated with phenotypically divergent diseases (phenodiv genes) were more central than those associated with phenotypically similar diseases. Shared genes had a higher number of disease gene interactors compared to specific genes, implying higher likelihood of finding a novel disease gene in their network neighborhood. Shared genes had a relatively restricted tissue co-expression with interactors, contrary to specific genes. This could be a function of shared genes leading to pleiotropy. Essential and phenodiv genes had comparable connectivities and hence we investigated for differences in network attributes conferring lethality and pleiotropy, respectively. Essential and phenodiv genes were found to be intra-modular and inter-modular hubs with the former being highly co-expressed with their interactors contrary to the latter. Essential genes were predominantly nuclear proteins with transcriptional regulation activities while phenodiv genes were cytoplasmic proteins involved in signal transduction.ConclusionThe properties of a disease gene in molecular interaction network determine its role in manifesting different and divergent diseases.

Quantitative proteomics analysis of signalosome dynamics in primary T cells identifies the surface receptor CD6 as a Lat adaptor-independent TCR signaling hub.

T cell antigen receptor (TCR)-mediated activation of T cells requires the interaction of dozens of proteins. Here we used quantitative mass spectrometry and activated primary CD4+ T cells from mice in which a tag for affinity purification was knocked into several genes to determine the composition and dynamics of multiprotein complexes that formed around the kinase Zap70 and the adaptors Lat and SLP-76. Most of the 112 high-confidence time-resolved protein interactions we observed were previously unknown. The surface receptor CD6 was able to initiate its own signaling pathway by recruiting SLP-76 and the guanine nucleotide–exchange factor Vav1 regardless of the presence of Lat. Our findings provide a more complete model of TCR signaling in which CD6 constitutes a signaling hub that contributes to the diversification of TCR signaling.

Expression profiles of long non-coding RNAs located in autoimmune disease-associated regions reveal immune cell-type specificity

BackgroundAlthough genome-wide association studies (GWAS) have identified hundreds of variants associated with a risk for autoimmune and immune-related disorders (AID), our understanding of the disease mechanisms is still limited. In particular, more than 90% of the risk variants lie in non-coding regions, and almost 10% of these map to long non-coding RNA transcripts (lncRNAs). lncRNAs are known to show more cell-type specificity than protein-coding genes.MethodsWe aimed to characterize lncRNAs and protein-coding genes located in loci associated with nine AIDs which have been well-defined by Immunochip analysis and by transcriptome analysis across seven populations of peripheral blood leukocytes (granulocytes, monocytes, natural killer (NK) cells, B cells, memory T cells, naive CD4+ and naive CD8+ T cells) and four populations of cord blood-derived T-helper cells (precursor, primary, and polarized (Th1, Th2) T-helper cells).ResultsWe show that lncRNAs mapping to loci shared between AID are significantly enriched in immune cell types compared to lncRNAs from the whole genome (α <0.005). We were not able to prioritize single cell types relevant for specific diseases, but we observed five different cell types enriched (α <0.005) in five AID (NK cells for inflammatory bowel disease, juvenile idiopathic arthritis, primary biliary cirrhosis, and psoriasis; memory T and CD8+ T cells in juvenile idiopathic arthritis, primary biliary cirrhosis, psoriasis, and rheumatoid arthritis; Th0 and Th2 cells for inflammatory bowel disease, juvenile idiopathic arthritis, primary biliary cirrhosis, psoriasis, and rheumatoid arthritis). Furthermore, we show that co-expression analyses of lncRNAs and protein-coding genes can predict the signaling pathways in which these AID-associated lncRNAs are involved.ConclusionsThe observed enrichment of lncRNA transcripts in AID loci implies lncRNAs play an important role in AID etiology and suggests that lncRNA genes should be studied in more detail to interpret GWAS findings correctly. The co-expression results strongly support a model in which the lncRNA and protein-coding genes function together in the same pathways.

Systematic annotation of celiac disease loci refines pathological pathways and suggests a genetic explanation for increased interferon-gamma levels

Although genome-wide association studies and fine mapping have identified 39 non-HLA loci associated with celiac disease (CD), it is difficult to pinpoint the functional variants and susceptibility genes in these loci. We applied integrative approaches to annotate and prioritize functional single nucleotide polymorphisms (SNPs), genes and pathways affected in CD. CD-associated SNPs were intersected with regulatory elements categorized by the ENCODE project to prioritize functional variants, while results from cis-expression quantitative trait loci (eQTL) mapping in 1469 blood samples were combined with co-expression analyses to prioritize causative genes. To identify the key cell types involved in CD, we performed pathway analysis on RNA-sequencing data from different immune cell populations and on publicly available expression data on non-immune tissues. We discovered that CD SNPs are significantly enriched in B-cell-specific enhancer regions, suggesting that, besides T-cell processes, B-cell responses play a major role in CD. By combining eQTL and co-expression analyses, we prioritized 43 susceptibility genes in 36 loci. Pathway and tissue-specific expression analyses on these genes suggested enrichment of CD genes in the Th1, Th2 and Th17 pathways, but also predicted a role for four genes in the intestinal barrier function. We also discovered an intricate transcriptional connectivity between CD susceptibility genes and interferon-γ, a key effector in CD, despite the absence of CD-associated SNPs in the IFNG locus. Using systems biology, we prioritized the CD-associated functional SNPs and genes. By highlighting a role for B cells in CD, which classically has been described as a T-cell-driven disease, we offer new insights into the mechanisms and pathways underlying CD.

MicroRNAs act complementarily to regulate disease-related mRNA modules in human diseases

MicroRNAs (miRNAs) play a key role in regulating mRNA expression, and individual miRNAs have been proposed as diagnostic and therapeutic candidates. The identification of such candidates is complicated by the involvement of multiple miRNAs and mRNAs as well as unknown disease topology of the miRNAs. Here, we investigated if disease-associated miRNAs regulate modules of disease-associated mRNAs, if those miRNAs act complementarily or synergistically, and if single or combinations of miRNAs can be targeted to alter module functions. We first analyzed publicly available miRNA and mRNA expression data for five different diseases. Integrated target prediction and network-based analysis showed that the miRNAs regulated modules of disease-relevant genes. Most of the miRNAs acted complementarily to regulate multiple mRNAs. To functionally test these findings, we repeated the analysis using our own miRNA and mRNA expression data from CD4+ T cells from patients with seasonal allergic rhinitis. This is a good model of complex diseases because of its well-defined phenotype and pathogenesis. Combined computational and functional studies confirmed that miRNAs mainly acted complementarily and that a combination of two complementary miRNAs, miR-223 and miR-139-3p, could be targeted to alter disease-relevant module functions, namely, the release of type 2 helper T-cell (Th2) cytokines. Taken together, our findings indicate that miRNAs act complementarily to regulate modules of disease-related mRNAs and can be targeted to alter disease-relevant functions.

Combining network modeling and gene expression microarray analysis to explore the dynamics of Th1 and Th2 cell regulation

Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease.

Proviral integration site for Moloney murine leukemia virus (PIM) kinases promote human T helper 1 cell differentiation

Background: T helper (Th) cell differentiation is a complex process regulated by multiple factors. Results: PIM kinases promote Th1 differentiation through regulating the expression of genes important for this process. Conclusion: PIM kinases were identified as new regulators of Th1 cell differentiation. Significance: This study provides new insights into the mechanisms controlling Th cell differentiation. The differentiation of human primary T helper 1 (Th1) cells from naïve precursor cells is regulated by a complex, interrelated signaling network. The identification of factors regulating the early steps of Th1 cell polarization can provide important insight in the development of therapeutics for many inflammatory and autoimmune diseases. The serine/threonine-specific proviral integration site for Moloney murine leukemia virus (PIM) kinases PIM1 and PIM2 have been implicated in the cytokine-dependent proliferation and survival of lymphocytes. We have established that the third member of this family, PIM3, is also expressed in human primary Th cells and identified a new function for the entire PIM kinase family in T lymphocytes. Although PIM kinases are expressed more in Th1 than Th2 cells, we demonstrate here that these kinases positively influence Th1 cell differentiation. Our RNA interference results from human primary Th cells also suggest that PIM kinases promote the production of IFNγ, the hallmark cytokine produced by Th1 cells. Consistent with this, they also seem to be important for the up-regulation of the critical Th1-driving factor, T box expressed in T cells (T-BET), and the IL-12/STAT4 signaling pathway during the early Th1 differentiation process. In summary, we have identified PIM kinases as new regulators of human primary Th1 cell differentiation, thus providing new insights into the mechanisms controlling the selective development of human Th cell subsets.

Tubulin- and actin-associating GIMAP4 is required for IFN-γ secretion during Th cell differentiation

Although GTPase of the immunity‐associated protein (GIMAP) family are known to be most highly expressed in the cells of the immune system, their function and role remain still poorly characterized. Small GTPases in general are known to be involved in many cellular processes in a cell type‐specific manner and to contribute to specific differentiation processes. Among GIMAP family, GIMAP4 is the only member reported to have true GTPase activity, and its transcription is found to be differentially regulated during early human CD4+ T helper (Th) lymphocyte differentiation. GIMAP4 has been previously connected mainly with T‐ and B‐cell development and survival and T‐cell apoptosis. Here we show GIMAP4 to be localized into cytoskeletal elements and with the component of the trans golgi network, which suggests it to have a function in cellular transport processes. We demonstrate that depletion of GIMAP4 with RNAi results in downregulation of endoplasmic reticulum localizing chaperone VMA21. Most importantly, we discovered that GIMAP4 regulates secretion of cytokines in early differentiating human CD4+ Th lymphocytes and in particular the secretion of interferon‐γ also affecting its downstream targets.

Transcriptional Repressor HIC1 Contributes to Suppressive Function of Human Induced Regulatory T Cells

Summary Regulatory T (Treg) cells are critical in regulating the immune response. In vitro induced Treg (iTreg) cells have significant potential in clinical medicine. However, applying iTreg cells as therapeutics is complicated by the poor stability of human iTreg cells and their variable suppressive activity. Therefore, it is important to understand the molecular mechanisms of human iTreg cell specification. We identified hypermethylated in cancer 1 (HIC1) as a transcription factor upregulated early during the differentiation of human iTreg cells. Although FOXP3 expression was unaffected, HIC1 deficiency led to a considerable loss of suppression by iTreg cells with a concomitant increase in the expression of effector T cell associated genes. SNPs linked to several immune-mediated disorders were enriched around HIC1 binding sites, and in vitro binding assays indicated that these SNPs may alter the binding of HIC1. Our results suggest that HIC1 is an important contributor to iTreg cell development and function.

snpEnrichR: Analyzing co-localization of SNPs and their proxies in genomic regions

Abstract Motivation Co-localization of trait associated SNPs for specific transcription-factor binding sites or regulatory regions in the genome can yield profound insight into underlying causal mechanisms. Analysis is complicated because the truly causal SNPs are generally unknown and can be either SNPs reported in GWAS studies or other proxy SNPs in their linkage disequilibrium. Hence, a comprehensive pipeline for SNP co-localization analysis that utilizes all relevant information about both the genotyped SNPs and their proxies is needed. Results We developed an R package snpEnrichR for SNP co-localization analysis. The software integrates different tools for random SNP generation and genome co-localization analysis to automatize and help users to create custom SNP co-localization analysis. We show via an example that including proxy SNPs in SNP co-localization analysis enhances the sensitivity of co-localization detection. Availability and implementation The software is available at https://github.com/kartiek/snpEnrichR.