Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective.

Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target-disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target-disease associations (https://platform.opentargets.org). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.

Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation.

During activation, T cells undergo extensive gene expression changes that shape the properties of cells to exert their effector function. Understanding the regulation of this process could help explain how genetic variants predispose to immune diseases. Here, we mapped genetic effects on gene expression (expression quantitative trait loci (eQTLs)) using single-cell transcriptomics. We profiled 655,349 CD4+ T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. This identified 38 cell clusters, including transient clusters that were only present at individual time points of activation. We found 6,407 genes whose expression was correlated with genetic variation, of which 2,265 (35%) were dynamically regulated during activation. Furthermore, 127 genes were regulated by variants associated with immune-mediated diseases, with significant enrichment for dynamic effects. Our results emphasize the importance of studying context-specific gene expression regulation and provide insights into the mechanisms underlying genetic susceptibility to immune-mediated diseases.

Network and pathway expansion of genetic disease associations identifies successful drug targets.

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand-receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein-protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence can be used for drug target identification.

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines.

Naïve CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. Here we use quantitative proteomics, bulk RNA-seq, and single-cell RNA-seq of over 40,000 human naïve and memory CD4+ T cells to show that responses to cytokines differ substantially between these cell types. Memory T cells are unable to differentiate into the Th2 phenotype, and acquire a Th17-like phenotype in response to iTreg polarization. Single-cell analyses show that T cells constitute a transcriptional continuum that progresses from naïve to central and effector memory T cells, forming an effectorness gradient accompanied by an increase in the expression of chemokines and cytokines. Finally, we show that T cell activation and cytokine responses are influenced by the effectorness gradient. Our results illustrate the heterogeneity of T cell responses, furthering our understanding of inflammation.

Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases.

Immune-disease-associated variants are enriched in active chromatin regions of T cells and macrophages. However, whether these variants function in specific cell states is unknown. Here we stimulated T cells and macrophages in the presence of 13 cytokines and profiled active and open chromatin regions. T cell activation induced major chromatin remodeling, while the presence of cytokines fine-tuned the magnitude of changes. We developed a statistical method that accounts for subtle changes in the chromatin landscape to identify SNP enrichment across cell states. Our results point towards the role of immune-disease-associated variants in early rather than late activation of memory CD4+ T cells, with modest differences across cytokines. Furthermore, variants associated with inflammatory bowel disease are enriched in type 1 T helper (TH1) cells, whereas variants associated with Alzheimer's disease are enriched in different macrophage cell states. Our results represent an in-depth analysis of immune-disease-associated variants across a comprehensive panel of activation states of T cells and macrophages.

Human Pancreatic ß Cell lncRNAs Control Cell-Specific Regulatory Networks.

Recent studies have uncovered thousands of long non-coding RNAs (lncRNAs) in human pancreatic ß cells. ß cell lncRNAs are often cell type specific and exhibit dynamic regulation during differentiation or upon changing glucose concentrations. Although these features hint at a role of lncRNAs in ß cell gene regulation and diabetes, the function of ß cell lncRNAs remains largely unknown. In this study, we investigated the function of ß cell-specific lncRNAs and transcription factors using transcript knockdowns and co-expression network analysis. This revealed lncRNAs that function in concert with transcription factors to regulate ß cell-specific transcriptional networks. We further demonstrate that the lncRNA PLUTO affects local 3D chromatin structure and transcription of PDX1, encoding a key ß cell transcription factor, and that both PLUTO and PDX1 are downregulated in islets from donors with type 2 diabetes or impaired glucose tolerance. These results implicate lncRNAs in the regulation of ß cell-specific transcription factor networks.

Human ß cell transcriptome analysis uncovers lncRNAs that are tissue-specific, dynamically regulated, and abnormally expressed in type 2 diabetes.

A significant portion of the genome is transcribed as long noncoding RNAs (lncRNAs), several of which are known to control gene expression. The repertoire and regulation of lncRNAs in disease-relevant tissues, however, has not been systematically explored. We report a comprehensive strand-specific transcriptome map of human pancreatic islets and ß cells, and uncover >1100 intergenic and antisense islet-cell lncRNA genes. We find islet lncRNAs that are dynamically regulated and show that they are an integral component of the ß cell differentiation and maturation program. We sequenced the mouse islet transcriptome and identify lncRNA orthologs that are regulated like their human counterparts. Depletion of HI-LNC25, a ß cell-specific lncRNA, downregulated GLIS3 mRNA, thus exemplifying a gene regulatory function of islet lncRNAs. Finally, selected islet lncRNAs were dysregulated in type 2 diabetes or mapped to genetic loci underlying diabetes susceptibility. These findings reveal a new class of islet-cell genes relevant to ß cell programming and diabetes pathophysiology.

S-adenosylhomocysteine hydrolase (AHCY) deficiency: two novel mutations with lethal outcome.

This paper reports studies of two novel, allelic missense mutations found in the S-adenosylhomocysteine hydrolase (AHCY) gene from a new case of AHCY deficiency in an infant girl who died at age four months. The mutations lead to replacement of arginine with cysteine (p.Arg49Cys) and aspartic acid with glycine (p.Asp86Gly). Functional analysis of recombinant proteins containing the mutations detected showed that both dramatically reduce AHCY activity. The p.Arg49Cys mutant protein forms intermolecular disulphide bonds, leading to macromolecular structures that can be prevented by reducing agent DTT. The p.Asp86Gly protein tends to form enzymatically inactive aggregates and the loss of a single negative charge as a result of the mutation is involved in enzyme inactivation. We show that replacing Gly86 with negatively charged Glu86 in mutant protein restores enzymatic activity to 70% of wild-type, whereas changing Gly86 to positively charged Lys86 or uncharged Leu86 does not improve enzyme activity, indicating that the negative charge is important for maintenance of such activity. These studies significantly extend knowledge about the importance of residue 86 for AHCY activity. Residue 86 has not been implicated before in this way and the results suggest that the present model of S- adenosylhomocysteine (AdoHcy) hydrolysis may need refinement. Our functional studies provide novel insight into the molecular defect underlying AHCY deficiency and reveal that both low enzyme activity and protein stability of AHCY contribute to the clinical phenotype.