Mechanisms of tissue and cell-type specificity in heritable traits and diseases

Expanding the spectrum of phenotypes for MPDZ: Report of four unrelated families and review of the literature

MPDZ, a gene with diverse functions mediating cell-cell junction interactions, receptor signaling, and binding multivalent scaffold proteins, is associated with a spectrum of clinically heterogeneous phenotypes with biallelic perturbation. Despite its clinical relevance, the mechanistic underpinnings of these variants remain elusive, underscoring the need for extensive case series and functional investigations. In this study, we conducted a systematic review of cases in the literature through two electronic databases following the PRISMA guidelines. We selected nine studies, including 18 patients, with homozygous or compound heterozygous variants in MPDZ and added five patients from four unrelated families with novel MPDZ variants. To evaluate the role of Mpdz on hearing, we analyzed available auditory electrophysiology data from a knockout murine model (Mpdzem1(IMPC)J/em1(IMPC)J) generated by the International Mouse Phenotyping Consortium. Using exome and genome sequencing, we identified three families with compound heterozygous variants, and one family with a homozygous frameshift variant. MPDZ-related disease is clinically heterogenous with hydrocephaly, vision impairment, hearing impairment and cardiovascular disease occurring most frequently. Additionally, we describe two unrelated patients with spasticity, expanding the phenotypic spectrum. Our murine analysis of the Mpdzem1(IMPC)J/em1(IMPC)J allele showed severe hearing impairment. Overall, we expand understanding of MPDZ-related phenotypes and highlight hearing impairment and spasticity among the heterogeneous phenotypes.

Deciphering the impact of genomic variation on function

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas, PINNACLE learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PINNACLE's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. PINNACLE outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models. PINNACLE's ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology.

Immune disease dialogue of chemokine-based cell communications as revealed by single-cell RNA sequencing meta-analysis

Immune-mediated diseases are characterized by aberrant immune responses, posing significant challenges to global health. In both inflammatory and autoimmune diseases, dysregulated immune reactions mediated by tissue-residing immune and non-immune cells precipitate chronic inflammation and tissue damage that is amplified by peripheral immune cell extravasation into the tissue. Chemokine receptors are pivotal in orchestrating immune cell migration, yet deciphering the signaling code across cell types, diseases and tissues remains an open challenge. To delineate disease-specific cell-cell communications involved in immune cell migration, we conducted a meta-analysis of publicly available single-cell RNA sequencing (scRNA-seq) data across diverse immune diseases and tissues. Our comprehensive analysis spanned multiple immune disorders affecting major organs: atopic dermatitis and psoriasis (skin), chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis (lung), ulcerative colitis (colon), IgA nephropathy and lupus nephritis (kidney). By interrogating ligand-receptor (L-R) interactions, alterations in cell proportions, and differential gene expression, we unveiled intricate disease-specific and common immune cell chemoattraction and extravasation patterns. Our findings delineate disease-specific L-R networks and shed light on shared immune responses across tissues and diseases. Insights gleaned from this analysis hold promise for the development of targeted therapeutics aimed at modulating immune cell migration to mitigate inflammation and tissue damage. This nuanced understanding of immune cell dynamics at the single-cell resolution opens avenues for precision medicine in immune disease management.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, Pinnacle learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. Pinnacle's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. Pinnacle outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. Pinnacle's ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data

Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types.

A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease

BACKGROUND

Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types such as macrophages.

METHODS

We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo.

RESULTS

We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus.

CONCLUSIONS

Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.

Circadian-driven tissue specificity is constrained under caloric restricted feeding conditions

Tissue specificity is a fundamental property of an organ that affects numerous biological processes, including aging and longevity, and is regulated by the circadian clock. However, the distinction between circadian-affected tissue specificity and other tissue specificities remains poorly understood. Here, using multi-omics data on circadian rhythms in mice, we discovered that approximately 35% of tissue-specific genes are directly affected by circadian regulation. These circadian-affected tissue-specific genes have higher expression levels and are associated with metabolism in hepatocytes. They also exhibit specific features in long-reads sequencing data. Notably, these genes are associated with aging and longevity at both the gene level and at the network module level. The expression of these genes oscillates in response to caloric restricted feeding regimens, which have been demonstrated to promote longevity. In addition, aging and longevity genes are disrupted in various circadian disorders. Our study indicates that the modulation of circadian-affected tissue specificity is essential for understanding the circadian mechanisms that regulate aging and longevity at the genomic level.

Fine-mapping causal tissues and genes at disease-associated loci

Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

Genetic Contribution to Medium-Term Disease Activity in Multiple Sclerosis

Multiple sclerosis (MS) is a complex disorder characterized by high heterogeneity in terms of phenotypic expression, prognosis and treatment response. In the present study, we aimed to explore the genetic contribution to MS disease activity at different levels: genes, pathways and tissue-specific networks. Two cohorts of relapsing-remitting MS patients who started a first-line treatment (n = 1294) were enrolled to evaluate the genetic association with disease activity after 4 years of follow-up. The analyses were performed at whole-genome SNP and gene level, followed by the construction of gene-gene interaction networks specific for brain and lymphocytes. The resulting gene modules were evaluated to highlight key players from a topological and functional perspective. We identified 23 variants and 223 genes with suggestive association to 4-years disease activity, highlighting genes like PON2 involved in oxidative stress and in mitochondria functions and other genes, like ILRUN, involved in the modulation of the immune system. Network analyses led to the identification of a brain module composed of 228 genes and a lymphocytes module composed of 287 genes. The network analysis allowed us to prioritize genes relevant for their topological properties; among them, there are MPHOSPH9 (connector hub in both brain and lymphocyte module) and OPA1 (in brain module), two genes already implicated in MS. Modules showed the enrichment of both shared and tissue-specific pathways, mainly implicated in inflammation. In conclusion, our results suggest that the processes underlying disease activity act on shared mechanisms across brain and lymphocyte tissues.

Prioritization of Kidney Cell Types Highlights Myofibroblast Cells in Regulating Human Blood Pressure

Introduction

Blood pressure (BP) is a highly heritable trait with over 2000 underlying genomic loci identified to date. Although the kidney plays a key role, little is known about specific cell types involved in the genetic regulation of BP.

Methods

Here, we applied stratified linkage disequilibrium score (LDSC) regression to connect BP genome-wide association studies (GWAS) results to specific cell types of the mature human kidney. We used the largest single-stage BP genome-wide analysis to date, including up to 1,028,980 adults of European ancestry, and single-cell transcriptomic data from 14 mature human kidneys, with mean age of 41 years.

Results

Our analyses prioritized myofibroblasts and endothelial cells, among the total of 33 annotated cell type, as specifically involved in BP regulation (P < 0.05/33, i.e., 0.001515). Enrichment of heritability for systolic BP (SBP) was observed in myofibroblast cells in mature human kidney cortex, and enrichment of heritability for diastolic BP (DBP) was observed in descending vasa recta and peritubular capillary endothelial cells as well as stromal myofibroblast cells. The new finding of myofibroblast, the significant cell type for both BP traits, was consistent in 8 replication efforts using 7 sets of independent data, including in human fetal kidney, in East-Asian (EAS) ancestry, using mouse single-cell RNA sequencing (scRNA-seq) data, and when using another prioritization method.

Conclusion

Our findings provide a solid basis for follow-up studies to further identify genes and mechanisms in myofibroblast cells that underlie the regulation of BP.

Targeted delivery strategies: The interactions and applications of nanoparticles in liver diseases

In recent years, nanoparticles have been broadly utilized in various drugs delivery formulations. Nanodelivery systems have shown promise in solving problems associated with the distribution of hydrophobic drugs and have promoted the accumulation of nanomedicines in the circulation or in organs. However, the injection dose of nanoparticles (NPs) is much greater than that needed by diseased tissues or organs. In other words, most of the NPs are localized off-target and do not reach the desired tissue or organs. With the rapid development of biodegradable and biosafety nanomaterials, the nanovectors represent assurance of safety. However, the off-target effects also induce concerns about the application of NPs, especially in the delivery of gene editing tools. Therefore, a complete understanding of the biological responses to NPs in the body will clearly guide the design of targeted delivery of NPs. The different properties of various nanodelivery systems may induce diverse interactions between carriers and organs. In this review, we describe the relationship between the liver, the most influenced organ of systemic administration of NPs, and targeted delivery nanoplatforms. Various transport vehicles have adopted multiple delivery strategies for the targeted delivery to the cells in the homeostasis liver and in diseased liver. Additionally, nanodelivery systems provide a novel strategy for treating incurable diseases. The appearance of a targeted delivery has profoundly improved the application of NPs to liver diseases.

Inferring causal cell types of human diseases and risk variants from candidate regulatory elements

The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (~2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.

The broken Alzheimer's disease genome

The complex pathobiology of late-onset Alzheimer's disease (AD) poses significant challenges to therapeutic and preventative interventions. Despite these difficulties, genomics and related disciplines are allowing fundamental mechanistic insights to emerge with clarity, particularly with the introduction of high-resolution sequencing technologies. After all, the disrupted processes at the interface between DNA and gene expression, which we call the broken AD genome, offer detailed quantitative evidence unrestrained by preconceived notions about the disease. In addition to highlighting biological pathways beyond the classical pathology hallmarks, these advances have revitalized drug discovery efforts and are driving improvements in clinical tools. We review genetic, epigenomic, and gene expression findings related to AD pathogenesis and explore how their integration enables a better understanding of the multicellular imbalances contributing to this heterogeneous condition. The frontiers opening on the back of these research milestones promise a future of AD care that is both more personalized and predictive.

Proteomic Profiling of Messenger Ribonucleoproteins in Mouse Tissues Based on Formaldehyde Cross-Linking

Messenger ribonucleoprotein particles (mRNPs) are vital for tissue-specific gene expression via mediating posttranscriptional regulations. However, proteomic profiling of proteins in mRNPs, i.e., mRNA-associated proteins (mRAPs), has been challenging at the tissue level. Herein, we report the development of formaldehyde cross-linking-based mRNA-associated protein profiling (FAXRAP), a chemical strategy that enables the identification of mRAPs in both cultured cells and intact mouse organs. Applying FAXRAP, tissue-specific mRAPs were systematically profiled in the mouse liver, kidney, heart, and brain. Furthermore, brain mRAPs in Parkinson's disease (PD) mouse model were investigated, which revealed a global decrease of mRNP assembly in the brain of mice with PD. We envision that FAXRAP will facilitate uncovering the posttranscriptional regulation networks in various biological systems.

An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context/tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease-relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers new insights into disease mechanisms.

Advancing equity in human genomics through tissue-specific multi-ancestry molecular data

There is a pressing need to generate molecular data from diverse tissues across global populations. These currently missing data are necessary to resolve genome-wide association study loci, identify effector genes, and move the translational genomics needle beyond European-ancestry individuals and the minority of diseases for which blood is the relevant tissue.

Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.

The landscape of SETBP1 gene expression and transcription factor activity across human tissues

The SET binding protein 1 (SETBP1) gene encodes a transcription factor (TF) involved in various cellular processes. Variants in SETBP1 can result in three different diseases determined by the introduction (germline vs. somatic) and location of the variant. Germline variants cause the ultra-rare pediatric Schinzel Giedion Syndrome (SGS) and SETBP1 haploinsufficiency disorder (SETBP1-HD), characterized by severe multisystemic abnormalities with neurodegeneration or a less severe brain phenotype accompanied by hypotonia and strabismus, respectively. Somatic variants in SETBP1 are associated with hematological malignancies and cancer development in other tissues in adults. To better understand the tissue-specific mechanisms involving SETBP1, we analyzed publicly available RNA-sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) project. We found SETBP1 and its known target genes were widely expressed across 31 adult human tissues. K-means clustering identified three distinct expression patterns of SETBP1 targets across tissues. Functional enrichment analysis (FEA) of each cluster revealed gene sets related to transcriptional regulation, DNA binding, and mitochondrial function. TF activity analysis of SETBP1 and its target TFs revealed tissue-specific TF activity, underscoring the role of tissue context-driven regulation and suggesting its impact in SETBP1-associated disease. In addition to uncovering tissue-specific molecular signatures of SETBP1 expression and TF activity, we provide a Shiny web application to facilitate exploring TF activity across human tissues for 758 TFs. This study provides insight into the landscape of SETBP1 expression and TF activity across 31 non-diseased human tissues and reveals tissue-specific expression and activity of SETBP1 and its targets. In conjunction with the web application we constructed, our framework enables researchers to generate hypotheses related to the role tissue backgrounds play with respect to gene expression and TF activity in different disease contexts.

Differential Proteomic Analysis of Human Sperm: A Systematic Review to Identify Candidate Targets to Monitor Sperm Quality

PURPOSE

The advent of proteomics provides new opportunities to investigate the molecular mechanisms underlying male infertility. The selection of relevant targets based on a single analysis is not always feasible, due to the growing number of proteomic studies with conflicting results. Thus, this study aimed to systematically review investigations comparing the sperm proteome of normozoospermic and infertile men to define a panel of proteins with the potential to be used to evaluate sperm quality.

MATERIALS AND METHODS

A literature search was conducted on PubMed, Web of Science, and Scopus databases following the PRISMA guidelines. To identify proteins systematically reported, first the studies were divided by condition into four groups (asthenozoospermia, low motility, unexplained infertility, and infertility related to risk factors) and then, all studies were analysed simultaneously (poor sperm quality). To gain molecular insights regarding identified proteins, additional searches were performed within the Human Protein Atlas, Mouse Genome Informatics, UniProt, and PubMed databases.

RESULTS

Thirty-two studies were included and divided into 4 sub-analysis groups. A total of 2752 proteins were collected, of which 38, 1, 3 and 2 were indicated as potential markers for asthenozoospermia, low motility, unexplained infertility and infertility related to risk factors, respectively, and 58 for poor sperm quality. Among the identified proteins, ACR, ACRBP, ACRV1, ACTL9, AKAP4, ATG3, CCT2, CFAP276, CFAP52, FAM209A, GGH, HPRT1, LYZL4, PRDX6, PRSS37, REEP6, ROPN1B, SPACA3, SOD1, SPEM1, SPESP1, SPINK2, TEKT5, and ZPBP were highlighted due to their roles in male reproductive tissues, association with infertility phenotypes or participation in specific biological functions in spermatozoa.

CONCLUSIONS

Sperm proteomics allows the identification of protein markers with the potential to overcome limitations in male infertility diagnosis and to understand changes in sperm function at the molecular level. This study provides a reliable list of systematically reported proteins that could be potential targets for further basic and clinical studies.

A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders

Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generated single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. Seventy-five percent of elements (44 of 59) validated in an in vivo transgenic reporter assay, demonstrating that single cell accessibility is a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieved significant reduction in our variant search space and nominated candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as new candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work provides novel non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.

Role of Genetic Variation in Transcriptional Regulatory Elements in Heart Rhythm

Genetic predisposition to cardiac arrhythmias has been a field of intense investigation. Research initially focused on rare hereditary arrhythmias, but over the last two decades, the role of genetic variation (single nucleotide polymorphisms) in heart rate, rhythm, and arrhythmias has been taken into consideration as well. In particular, genome-wide association studies have identified hundreds of genomic loci associated with quantitative electrocardiographic traits, atrial fibrillation, and less common arrhythmias such as Brugada syndrome. A significant number of associated variants have been found to systematically localize in non-coding regulatory elements that control the tissue-specific and temporal transcription of genes encoding transcription factors, ion channels, and other proteins. However, the identification of causal variants and the mechanism underlying their impact on phenotype has proven difficult due to the complex tissue-specific, time-resolved, condition-dependent, and combinatorial function of regulatory elements, as well as their modest conservation across different model species. In this review, we discuss research efforts aimed at identifying and characterizing-trait-associated variant regulatory elements and the molecular mechanisms underlying their impact on heart rate or rhythm.

Evaluation of noninvasive biospecimens for transcriptome studies

Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.

Cell-type-specific gene expression and regulation in the cerebral cortex and kidney of atypical Setbp1S858R Schinzel Giedion Syndrome mice

Schinzel Giedion Syndrome (SGS) is an ultra-rare autosomal dominant Mendelian disease presenting with abnormalities spanning multiple organ systems. The most notable phenotypes involve severe developmental delay, progressive brain atrophy, and drug-resistant seizures. SGS is caused by spontaneous variants in SETBP1, which encodes for the epigenetic hub SETBP1 transcription factor (TF). SETBP1 variants causing classical SGS cluster at the degron, disrupting SETBP1 protein degradation and resulting in toxic accumulation, while those located outside cause milder atypical SGS. Due to the multisystem phenotype, we evaluated gene expression and regulatory programs altered in atypical SGS by snRNA-seq of the cerebral cortex and kidney of Setbp1S858R heterozygous mice (corresponds to the human likely pathogenic SETBP1S867R variant) compared to matched wild-type mice by constructing cell-type-specific regulatory networks. Setbp1 was differentially expressed in excitatory neurons, but known SETBP1 targets were differentially expressed and regulated in many cell types. Our findings suggest molecular drivers underlying neurodevelopmental phenotypes in classical SGS also drive atypical SGS, persist after birth, and are present in the kidney. Our results indicate SETBP1's role as an epigenetic hub leads to cell-type-specific differences in TF activity, gene targeting, and regulatory rewiring. This research provides a framework for investigating cell-type-specific variant impact on gene expression and regulation.

Genomic insights into the comorbidity between type 2 diabetes and schizophrenia

Multimorbidity represents an increasingly important public health challenge with far-reaching implications for health management and policy. Mental health and metabolic diseases have a well-established epidemiological association. In this study, we investigate the genetic intersection between type 2 diabetes and schizophrenia. We use Mendelian randomization to examine potential causal relationships between the two conditions and related endophenotypes. We report no compelling evidence that type 2 diabetes genetic liability potentially causally influences schizophrenia risk and vice versa. Our findings show that increased body mass index (BMI) has a protective effect against schizophrenia, in contrast to the well-known risk-increasing effect of BMI on type 2 diabetes risk. We identify evidence of colocalization of association signals for these two conditions at 11 genomic loci, six of which have opposing directions of effect for type 2 diabetes and schizophrenia. To elucidate these colocalizing signals, we integrate multi-omics data from bulk and single-cell gene expression studies, along with functional information. We identify high-confidence effector genes and find that they are enriched for homeostasis and lipid-related pathways. We also highlight drug repurposing opportunities including N-methyl-D-aspartate (NMDA) receptor antagonists. Our findings provide insights into shared biological mechanisms for type 2 diabetes and schizophrenia, highlighting common factors that influence the risk of the two conditions in opposite directions and shedding light on the complex nature of this comorbidity.

The landscape of SETBP1 gene expression and transcription factor activity across human tissues

Background

The SET binding protein 1 (SETBP1) gene encodes a transcription factor (TF) involved in various cellular processes. Distinct SETBP1 variants have been linked to three different diseases. Germline variants cause the ultra-rare pediatric Schinzel Giedion Syndrome (SGS) and SETBP1 haploinsufficiency disorder (SETBP1-HD), characterized by severe multisystemic abnormalities with neurodegeneration or a less severe brain phenotype accompanied by hypotonia and strabismus, respectively. Somatic variants in SETBP1 are associated with hematological malignancies and cancer development in other tissues in adults.

Results

To better understand the tissue-specific mechanisms involving SETBP1, we analyzed publicly available RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project. We found SETBP1, and its known target genes were widely expressed across 31 adult human tissues. K-means clustering identified three distinct expression patterns of SETBP1 targets across tissues. Functional enrichment analysis (FEA) of each cluster revealed gene sets related to transcription regulation, DNA binding, and mitochondrial function. TF activity analysis of SETBP1 and its target TFs revealed tissue-specific TF activity, underscoring the role of tissue context-driven regulation and suggesting its impact in SETBP1-associated disease. In addition to uncovering tissue-specific molecular signatures of SETBP1 expression and TF activity, we provide a Shiny web application to facilitate exploring TF activity across human tissues for 758 TFs.

Conclusions

This study provides insight into the landscape of SETBP1 expression and TF activity across 31 non-diseased human tissues and reveals tissue-specific expression and activity of SETBP1 and its targets. In conjunction with the web application we constructed, our framework enables researchers to generate hypotheses related to the role tissue backgrounds play with respect to gene expression and TF activity in different disease contexts.

Modeling of horizontal pleiotropy identifies possible causal gene expression in systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is a chronic autoimmune condition with complex causes involving genetic and environmental factors. While genome-wide association studies (GWASs) have identified genetic loci associated with SLE, the functional genomic elements responsible for disease development remain largely unknown. Mendelian Randomization (MR) is an instrumental variable approach to causal inference based on data from observational studies, where genetic variants are employed as instrumental variables (IVs).

Methods

This study utilized a two-step strategy to identify causal genes for SLE. In the first step, the classical MR method was employed, assuming the absence of horizontal pleiotropy, to estimate the causal effect of gene expression on SLE. In the second step, advanced probabilistic MR methods (PMR-Egger, MRAID, and MR-MtRobin) were applied to the genes identified in the first step, considering horizontal pleiotropy, to filter out false positives. PMR-Egger and MRAID analyses utilized whole blood expression quantitative trait loci (eQTL) and SLE GWAS summary data, while MR-MtRobin analysis used an independent eQTL dataset from multiple immune cell types along with the same SLE GWAS data.

Results

The initial MR analysis identified 142 genes, including 43 outside of chromosome 6. Subsequently, applying the advanced MR methods reduced the number of genes with significant causal effects on SLE to 66. PMR-Egger, MRAID, and MR-MtRobin, respectively, identified 13, 7, and 16 non-chromosome 6 genes with significant causal effects. All methods identified expression of PHRF1 gene as causal for SLE. A comprehensive literature review was conducted to enhance understanding of the functional roles and mechanisms of the identified genes in SLE development.

Conclusions

The findings from the three MR methods exhibited overlapping genes with causal effects on SLE, demonstrating consistent results. However, each method also uncovered unique genes due to different modelling assumptions and technical factors, highlighting the complementary nature of the approaches. Importantly, MRAID demonstrated a reduced percentage of causal genes from the Major Histocompatibility complex (MHC) region on chromosome 6, indicating its potential in minimizing false positive findings. This study contributes to unraveling the mechanisms underlying SLE by employing advanced probabilistic MR methods to identify causal genes, thereby enhancing our understanding of SLE pathogenesis.

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data

Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.

Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.

Modeling tissue co-regulation estimates tissue-specific contributions to disease

Integrative analyses of genome-wide association studies and gene expression data have implicated many disease-critical tissues. However, co-regulation of genetic effects on gene expression across tissues impedes distinguishing biologically causal tissues from tagging tissues. In the present study, we introduce tissue co-regulation score regression (TCSC), which disentangles causal tissues from tagging tissues by regressing gene-disease association statistics (from transcriptome-wide association studies) on tissue co-regulation scores, reflecting correlations of predicted gene expression across genes and tissues. We applied TCSC to 78 diseases/traits (average n = 302,000) and gene expression prediction models for 48 GTEx tissues. TCSC identified 21 causal tissue-trait pairs at a 5% false discovery rate (FDR), including well-established findings, biologically plausible new findings (for example, aorta artery and glaucoma) and increased specificity of known tissue-trait associations (for example, subcutaneous adipose, but not visceral adipose, and high-density lipoprotein). TCSC also identified 17 causal tissue-trait covariance pairs at 5% FDR. In conclusion, TCSC is a precise method for distinguishing causal tissues from tagging tissues.

Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation

How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.

Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation

Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based 'omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.

Predicting gene knockout effects from expression data

BACKGROUND

The study of gene essentiality, which measures the importance of a gene for cell division and survival, is used for the identification of cancer drug targets and understanding of tissue-specific manifestation of genetic conditions. In this work, we analyze essentiality and gene expression data from over 900 cancer lines from the DepMap project to create predictive models of gene essentiality.

METHODS

We developed machine learning algorithms to identify those genes whose essentiality levels are explained by the expression of a small set of "modifier genes". To identify these gene sets, we developed an ensemble of statistical tests capturing linear and non-linear dependencies. We trained several regression models predicting the essentiality of each target gene, and used an automated model selection procedure to identify the optimal model and hyperparameters. Overall, we examined linear models, gradient boosted trees, Gaussian process regression models, and deep learning networks.

RESULTS

We identified nearly 3000 genes for which we accurately predict essentiality using gene expression data of a small set of modifier genes. We show that both in the number of genes we successfully make predictions for, as well as in the prediction accuracy, our model outperforms current state-of-the-art works.

CONCLUSIONS

Our modeling framework avoids overfitting by identifying the small set of modifier genes, which are of clinical and genetic importance, and ignores the expression of noisy and irrelevant genes. Doing so improves the accuracy of essentiality prediction in various conditions and provides interpretable models. Overall, we present an accurate computational approach, as well as interpretable modeling of essentiality in a wide range of cellular conditions, thus contributing to a better understanding of the molecular mechanisms that govern tissue-specific effects of genetic disease and cancer.

CAPRIN1 haploinsufficiency causes a neurodevelopmental disorder with language impairment, ADHD and ASD

We describe an autosomal dominant disorder associated with loss-of-function variants in the Cell cycle associated protein 1 (CAPRIN1; MIM*601178). CAPRIN1 encodes a ubiquitous protein that regulates the transport and translation of neuronal mRNAs critical for synaptic plasticity, as well as mRNAs encoding proteins important for cell proliferation and migration in multiple cell types. We identified 12 cases with loss-of-function CAPRIN1 variants, and a neurodevelopmental phenotype characterized by language impairment/speech delay (100%), intellectual disability (83%), attention deficit hyperactivity disorder (82%) and autism spectrum disorder (67%). Affected individuals also had respiratory problems (50%), limb/skeletal anomalies (50%), developmental delay (42%) feeding difficulties (33%), seizures (33%) and ophthalmologic problems (33%). In patient-derived lymphoblasts and fibroblasts, we showed a monoallelic expression of the wild-type allele, and a reduction of the transcript and protein compatible with a half dose. To further study pathogenic mechanisms, we generated sCAPRIN1+/- human induced pluripotent stem cells via CRISPR-Cas9 mutagenesis and differentiated them into neuronal progenitor cells and cortical neurons. CAPRIN1 loss caused reduced neuronal processes, overall disruption of the neuronal organization and an increased neuronal degeneration. We also observed an alteration of mRNA translation in CAPRIN1+/- neurons, compatible with its suggested function as translational inhibitor. CAPRIN1+/- neurons also showed an impaired calcium signalling and increased oxidative stress, two mechanisms that may directly affect neuronal networks development, maintenance and function. According to what was previously observed in the mouse model, measurements of activity in CAPRIN1+/- neurons via micro-electrode arrays indicated lower spike rates and bursts, with an overall reduced activity. In conclusion, we demonstrate that CAPRIN1 haploinsufficiency causes a novel autosomal dominant neurodevelopmental disorder and identify morphological and functional alterations associated with this disorder in human neuronal models.

Targeting stressor-induced dysfunctions in protein-protein interaction networks via epichaperomes

Diseases are manifestations of complex changes in protein-protein interaction (PPI) networks whereby stressors, genetic, environmental, and combinations thereof, alter molecular interactions and perturb the individual from the level of cells and tissues to the entire organism. Targeting stressor-induced dysfunctions in PPI networks has therefore become a promising but technically challenging frontier in therapeutics discovery. This opinion provides a new framework based upon disrupting epichaperomes - pathological entities that enable dysfunctional rewiring of PPI networks - as a mechanism to revert context-specific PPI network dysfunction to a normative state. We speculate on the implications of recent research in this area for a precision medicine approach to detecting and treating complex diseases, including cancer and neurodegenerative disorders.

Identification of genetic mechanisms for tissue-specific genetic effects based on CRISPR screens

A major challenge in genetic studies of complex diseases is to determine how the action of risk genes is restricted to a tissue or cell type. Here, we investigate tissue specificity of gene action using CRISPR screens from 786 cancer cell lines originating from 24 tissues. We find that the expression pattern of the gene across tissues explains only a minority of cases of tissue-specificity (9%), while gene amplification and the expression levels of paralogs account for 39.5% and 15.5%, respectively. In addition, the transfer of small molecules to mutant cells explains tissue-specific gene action in blood. The tissue-specific genes we found are not specific just for human cancer cell lines: we found that the tissue-specific genes are intolerant to functional mutations in the human population and are associated with human diseases more than genes that are essential across all cell types. Our findings offer important insights into genetic mechanisms for tissue specificity of human diseases.

scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies

BACKGROUND

The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes.

RESULTS

scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants.

CONCLUSIONS

We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.

Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics

Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted notable cell-disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease-dependent M-cell program in ulcerative colitis and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell-type programs were associated, whereas only disease-dependent epithelial cell programs were prominent, suggesting a role in disease response rather than initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (scDRS), an approach that links scRNA-seq with polygenic disease risk at single-cell resolution, independent of annotated cell types. scDRS identifies cells exhibiting excess expression across disease-associated genes implicated by genome-wide association studies (GWASs). We applied scDRS to 74 diseases/traits and 1.3 million single-cell gene-expression profiles across 31 tissues/organs. Cell-type-level results broadly recapitulated known cell-type-disease associations. Individual-cell-level results identified subpopulations of disease-associated cells not captured by existing cell-type labels, including T cell subpopulations associated with inflammatory bowel disease, partially characterized by their effector-like states; neuron subpopulations associated with schizophrenia, partially characterized by their spatial locations; and hepatocyte subpopulations associated with triglyceride levels, partially characterized by their higher ploidy levels. Genes whose expression was correlated with the scDRS score across cells (reflecting coexpression with GWAS disease-associated genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease

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Biological processes are exerted by groups of physically interacting proteins.

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Proteins display variable biological roles depending on tissue-interactomic context.

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Tissue-specific protein-protein interaction networks reveal functional diversification.

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Most disease associated genes/proteins display tissue-specific phenotypes.

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Protein interaction network analysis is a valuable resource to identify disease genes.

Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods.

Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease

More than 800 million people suffer from kidney disease, yet the mechanism of kidney dysfunction is poorly understood. In the present study, we define the genetic association with kidney function in 1.5 million individuals and identify 878 (126 new) loci. We map the genotype effect on the methylome in 443 kidneys, transcriptome in 686 samples and single-cell open chromatin in 57,229 kidney cells. Heritability analysis reveals that methylation variation explains a larger fraction of heritability than gene expression. We present a multi-stage prioritization strategy and prioritize target genes for 87% of kidney function loci. We highlight key roles of proximal tubules and metabolism in kidney function regulation. Furthermore, the causal role of SLC47A1 in kidney disease is defined in mice with genetic loss of Slc47a1 and in human individuals carrying loss-of-function variants. Our findings emphasize the key role of bulk and single-cell epigenomic information in translating genome-wide association studies into identifying causal genes, cellular origins and mechanisms of complex traits.

Genes and Diseases: Insights from Transcriptomics Studies

Results of expression studies can be useful to clarify the genotype-phenotype relationship. However, according to data from recent literature, there is a large group of genes that are revealed as differentially expressed (DE) in many studies, regardless of the biological context. Additional analyses could shed more light on the relationships between genes, their differential expression, and diseases. We generated a set of 9972 disease genes from five gene-phenotype databases (OMIM, ORPHANET, DDG2P, DisGeNet and MalaCards) and a report of the International Union of Immunological Societies. To study transcriptomics of disease and non-disease genes in healthy tissues, we obtained data from the Human Protein Atlas (HPA) website. We analyzed the dependency between expression in healthy tissues and gene occurrence in Gene Expression Omnibus series using tools within the Enrichr libraries. The results of expression studies were annotated with Gene Ontology (GO) and Human Phenotype Ontology (HPO) terms. Using transcriptomics analysis of healthy tissues, we validated the previous findings of higher expression levels of disease genes in pathologically linked tissues compared to other tissues. Preferentially DE genes were generally highly expressed in one or multiple tissues and were enriched for disease genes. According to the results of GO enrichment analyses, both down- and up-regulated DE genes most often took part in immune response, translation and tissue-specific processes. A connection between DE-related pathology and the diversity of HPO terms was found. Investigating a link between expression and phenotype contributes to understanding the mode of development and progression of human diseases.

PCGA: a comprehensive web server for phenotype-cell-gene association analysis

Most complex disease-associated loci mapped by genome-wide association studies (GWAS) are located in non-coding regions. It remains elusive which genes the associated loci regulate and in which tissues/cell types the regulation occurs. Here, we present PCGA (https://pmglab.top/pcga), a comprehensive web server for jointly estimating both associated tissues/cell types and susceptibility genes for complex phenotypes by GWAS summary statistics. The web server is built on our published method, DESE, which represents an effective method to mutually estimate driver tissues and genes by integrating GWAS summary statistics and transcriptome data. By collecting and processing extensive bulk and single-cell RNA sequencing datasets, PCGA has included expression profiles of 54 human tissues, 2,214 human cell types and 4,384 mouse cell types, which provide the basis for estimating associated tissues/cell types and genes for complex phenotypes. We develop a framework to sequentially estimate associated tissues and cell types of a complex phenotype according to their hierarchical relationships we curated. Meanwhile, we construct a phenotype-cell-gene association landscape by estimating the associated tissues/cell types and genes of 1,871 public GWASs. The association landscape is generally consistent with biological knowledge and can be searched and browsed at the PCGA website.

Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity

The spatial and temporal domain of a gene's expression can range from ubiquitous to highly specific. Quantifying the degree to which this expression is unique to a specific tissue or developmental timepoint can provide insight into the etiology of genetic diseases. However, quantifying specificity remains challenging as measures of specificity are sensitive to similarity between samples in the sample set. For example, in the Gene-Tissue Expression project (GTEx), brain subregions are overrepresented at 13 of 54 (24%) unique tissues sampled. In this dataset, existing specificity measures have a decreased ability to identify genes specific to the brain relative to other organs. To solve this problem, we leverage sample similarity information to weight samples such that overrepresented tissues do not have an outsized effect on specificity estimates. We test this reweighting procedure on 4 measures of specificity, Z-score, Tau, Tsi and Gini, in the GTEx data and in single cell datasets for zebrafish and mouse. For all of these measures, incorporating sample similarity information to weight samples results in greater stability of sets of genes called as specific and decreases the overall variance in the change of specificity estimates as sample sets become more unbalanced. Furthermore, the genes with the largest improvement in their specificity estimate's stability are those with functions related to the overrepresented sample types. Our results demonstrate that incorporating similarity information improves specificity estimates' stability to the choice of the sample set used to define the transcriptome, providing more robust and reproducible measures of specificity for downstream analyses.

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.