Genotype-Tissue Expression Project Research Articles

Gene regulatory networks reveal sex difference in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively.MethodsSample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data.ResultsWe found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database.ConclusionsThese findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.Graphical

Integration of multi-modal datasets to estimate human aging

AbstractAging involves complex biological processes leading to the decline of living organisms. As population lifespan increases worldwide, the importance of identifying factors underlying healthy aging has become critical. Integration of multi-modal datasets is a powerful approach for the analysis of complex biological systems, with the potential to uncover novel aging biomarkers. In this study, we leveraged publicly available epigenomic, transcriptomic and telomere length data along with histological images from the Genotype-Tissue Expression project to build tissue-specific regression models for age prediction. Using data from two tissues, lung and ovary, we aimed to compare model performance across data modalities, as well as to assess the improvement resulting from integrating multiple data types. Our results demostrate that methylation outperformed the other data modalities, with a mean absolute error of 3.36 and 4.36 in the test sets for lung and ovary, respectively. These models achieved lower error rates when compared with established state-of-the-art tissue-agnostic methylation models, emphasizing the importance of a tissue-specific approach. Additionally, this work has shown how the application of Hierarchical Image Pyramid Transformers for feature extraction significantly enhances age modeling using histological images. Finally, we evaluated the benefits of integrating multiple data modalities into a single model. Combining methylation data with other data modalities only marginally improved performance likely due to the limited number of available samples. Combining gene expression with histological features yielded more accurate age predictions compared with the individual performance of these data types. Given these results, this study shows how machine learning applications can be extended to/in multi-modal aging research. Code used is available at https://github.com/zroger49/multi_modal_age_prediction.

Long-read proteogenomics to connect disease-associated sQTLs to the protein isoform effectors of disease.

A major fraction of loci identified by genome-wide association studies (GWASs) mediate alternative splicing, but mechanistic interpretation is hindered by the technical limitations of short-read RNA sequencing (RNA-seq), which cannot directly link splicing events to full-length protein isoforms. Long-read RNA-seq represents a powerful tool to characterize transcript isoforms, and recently, infer protein isoform existence. Here, we present an approach that integrates information from GWASs, splicing quantitative trait loci (sQTLs), and PacBio long-read RNA-seq in a disease-relevant model to infer the effects of sQTLs on the ultimate protein isoform products they encode. We demonstrate the utility of our approach using bone mineral density (BMD) GWAS data. We identified 1,863 sQTLs from the Genotype-Tissue Expression (GTEx) project in 732 protein-coding genes that colocalized with BMD associations (H4PP ≥ 0.75). We generated PacBio Iso-Seq data (N= ∼22 million full-length reads) on human osteoblasts, identifying 68,326 protein-coding isoforms, of which 17,375 (25%) were unannotated. By casting the sQTLs onto protein isoforms, we connected 809 sQTLs to 2,029 protein isoforms from 441 genes expressed in osteoblasts. Overall, we found that 74 sQTLs influenced isoforms likely impacted by nonsense-mediated decay and 190 that potentially resulted in the expression of unannotated protein isoforms. Finally, we functionally validated colocalizing sQTLs in TPM2, in which siRNA-mediated knockdown in osteoblasts showed two TPM2 isoforms with opposing effects on mineralization but exhibited no effect upon knockdown of the entire gene. Our approach should be to generalize across diverse clinical traits and to provide insights into protein isoform activities modulated by GWAS loci.

Targeted co-expression networks for the study of traits

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer’s disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN.

Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses.

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these models to large GWAS datasets for breast, prostate, and lung cancers. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene prediction models and identifying disease-associated genes, as evidenced by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.

A prognostic model for anoikis-related genes in pancreatic cancer

Anoikis, a distinct form of programmed cell death, is crucial for both organismal development and maintaining tissue equilibrium. Its role extends to the proliferation and progression of cancer cells. This study aimed to establish an anoikis-related prognostic model to predict the prognosis of pancreatic cancer (PC) patients. Gene expression data and patient clinical profiles were sourced from The Cancer Genome Atlas (TCGA-PAAD: Pancreatic Adenocarcinoma) and the International Cancer Genome Consortium (ICGC-PACA: Pancreatic Ductal Adenocarcinoma). Non-cancerous pancreatic tissue gene expression data were obtained from the Genotype-Tissue Expression (GTEx) project. The R package was used to construct anoikis-related PC prognostic models, which were later validated with the ICGC-PACA database. Survival analyses demonstrated a poorer prognosis for patients in the high-risk group, consistent across both TCGA-PAAD and ICGC-PACA datasets. A nomogram was designed as a predictive tool to estimate patient mortality. The study also analyzed tumor mutations and immune infiltration across various risk groups, uncovering notable differences in tumor mutation patterns and immune landscapes between high- and low-risk groups. In conclusion, this research successfully developed a prognostic model centered on anoikis-related genes, offering a novel tool for predicting the clinical trajectory of PC patients.

A cross-tissue transcriptome-wide association study reveals novel susceptibility genes for migraine

BackgroundMigraine is a common neurological disorder with a strong genetic component. Despite the identification of over 100 loci associated with migraine susceptibility through genome-wide association studies (GWAS), the underlying causative genes and biological mechanisms remain predominantly elusive.MethodsThe FinnGen R10 dataset, consisting of 333,711 subjects (20,908 cases and 312,803 controls), was utilized in conjunction with the Genotype-Tissue Expression Project (GTEx) v8 EQTls files to conduct cross-tissue transcriptome association studies (TWAS). Functional Summary-based Imputation (FUSION) was employed to validate these findings in single tissues. Additionally, candidate susceptibility genes were screened using Gene Analysis combined with Multi-marker Analysis of Genomic Annotation (MAGMA). Subsequent Mendelian randomization (MR) and colocalization analyses were conducted. Furthermore, GeneMANIA analysis was employed to enhance our understanding of the functional implications of these susceptibility genes.ResultsWe identified a total of 19 susceptibility genes associated with migraine in the cross-tissue TWAS analysis. Two novel susceptibility genes, REV1 and SREBF2, were validated through both single tissue TWAS and MAGMA analysis. Mendelian randomization and colocalization analyses further confirmed these findings. REV1 may reduce the migraine risk by regulating DNA damage repair, while SREBF2 may increase the risk of migraine by regulating cholesterol metabolism.ConclusionOur study identified two novel genes whose predicted expression was associated with the risk of migraine, providing new insights into the genetic framework of migraine.

BSG Isoform 2 (ENST00000353555) Is a Prognostic Biomarker in Predicting Unfavorable Overall Survival of Patients With Liver Hepatocellular Carcinoma.

CD147, encoded by the BSGgene, has complex transcripts that encode proteins of different lengths. Total BSG transcription is aprognostic biomarker for patients with liver cancer. This study tried to analyze the expression profile and prognostic significance of BSG transcripts in liver cancer. RNAsequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project, survival data from TCGA, and protein expression data from the Human Protein Atlas were systematically analyzed. Among the four protein-coding transcripts of BSG, ENST00000353555 encoding basigin-2 is the dominant transcript isoform. It might be an independent prognostic biomarker for unfavorable overall survival in patients with liver cancer (HR: 1.404, 95% CI: 1.1224-1.754, p = 0.003). ENST00000353555 might be a prognostic biomarker linking unfavorable overall survival in liver cancer patients.

Nectin-4 expression in primary breast cancer and associated clinical outcomes.

570 Background: Antibody-drug conjugates (ADC) selectively deliver chemotherapy to tumor cells expressing a target antigen. Currently approved ADCs in breast cancer target HER2 or Trop-2. Enfortumab vedotin, an ADC targeting Nectin-4, a cell adhesion molecule, is approved in advanced/metastatic urothelial cancer. This study evaluated the expression landscape of Nectin-4 in breast cancer and its association with clinical outcomes. Methods: Processed mRNAseq data was assessed for expression of the gene encoding Nectin-4 in primary breast cancer tissue and adjacent normal breast tissue from The Cancer Genome Atlas (TCGA), and normal tissue from the Genotype-Tissue Expression (GTEx) Project. Low gene expression was determined as <100 transcripts per million (TPM). TCGA samples had been annotated for putative copy-number alterations using GISTIC 2.0; genes with a score of 2 were marked as amplified. Multivariable Cox regression analysis was used to analyze the association of low vs medium/high gene expression with disease-free survival (DFS) and overall survival (OS), adjusting for age, stage, and subtype. Results: RNA-Seq data was available for 1086 patients with primary breast cancer, including known hormone receptor positive (HR+)/HER2- (n=530), HER2+ (n=112), and triple negative breast cancer (TNBC) (n=120). NECTIN4was amplified in 15% of cases (n=165): 15% (n=78) in HR+, 15% (n=17) in HER2+ and 21% (n=25) in TNBC. NECTIN4 expression was medium/high in 356 (33%) patients: 149 (28%) HR+/HER2- (median 77.8 TPM, IQR 50.1-116.5), 32 (29%) HER2+ (median 73.7 TPM, IQR 51.9-107.8), and 59 (49%) TNBC (median 99.7 TPM, IQR 57.8-148.3). Median expression in normal breast tissue was 47 TPM (TCGA) & 16.74 TPM (GTEx), and highest in normal skin at 205 TPM. In multivariable analysis, medium/high NECTIN4 expression was associated with worse OS (HR 1.96, 95% CI 1.32-2.99, p<0.001). As a continuous variable, increasing NECTIN4 expression levels were associated with worse OS. After correcting for age, stage and subtype, for each 10-unit increase in NECTIN4TPM, the risk of death increased by 5% (HR 1.0050, 95% CI 1.0028 - 1.0072, p < 0.001). Conclusions: NECTIN4 expression was higher in TNBC and associated with worse overall survival. The high expression level in normal skin possibly contributes to the cutaneous toxicity associated with enfortumab vedotin approval and requires careful consideration as ADCs targeting Nectin-4 are developed to target breast cancer.

Molecular mechanism of RBM14-mediated promotion of proliferation, migration, and invasion in osteosarcoma.

Osteosarcoma (OS) is an exceptionally aggressive bone neoplasm that predominantly impacts the paediatric and adolescent population, exhibiting unfavourable prognosis. The importance of RNA binding motif protein 14 (RBM14) in the aetiology of OS is not well understood, despite its established involvement in several other types of cancer. In this study, we conducted an analysis of the expression profiles of RBM14 in cancer tissues and cell lines. To achieve this, we will utilised data obtained from various databases including The Cancer Genome Atlas Program (TCGA) project, The Genotype-Tissue Expression (GTEx) Project, Gene Expression Omnibus (GEO) database, and cancer cell line encyclopedia (CCLE) data. Furthermore, this study also aims to examine the effects of RBM14 on the proliferation, migration, and invasive properties of OS cells using cell functional gain and loss studies. In this study, we carried out an in-depth investigation to explore possible molecular pathways that underlie the regulation of the malignant phenotype found in OS by RBM14. This investigation involved integrating data from RBM14 overexpression, RBM14 knockdown RNA-seq experiments, and an array comprising 6,096 perturbed genes obtained from the Genetic Perturbation Similarity Analysis Database (GPSAdb). This research offers an opportunity to build a robust conceptual framework for the potential advancement of novel therapeutic approaches that are especially aimed at attacking OS. RBM14 plays an active role in OS by significantly contributing to the enhancement of cellular proliferation, migration, and invasion. At the molecular level, it is probable that RBM14 exerts control over the malignant characteristics of OS through its modulation of the Hippo signalling system. The above-mentioned findings underscore the significant importance of RBM14 as an intriguing target for therapy for the mitigation and management of OS. This particular protein holds an excellent opportunity for the development of novel and efficacious therapeutic approaches that possess the potential to yield favorable results for patients affected with OS.

Comprehensive analysis to identify PUS7 as a prognostic biomarker from pan-cancer analysis to osteosarcoma validation.

Pseudouridylation has demonstrated the potential to control the development of numerous malignancies. PUS7(Pseudouridine Synthase 7) is one of the pseudouridine synthases, but the literature on this enzyme is limited to several cancer types. Currently, no investigation has been performed on the systematic pan-cancer analysis concerning PUS7 role in cancer diagnosis and prognosis. Employing public databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), Human Protein Atlas (HPA), UALCAN and Tumor Immune Single-cell Hub (TISCH), this work investigated the PUS7 carcinogenesis in pan-cancer. Differential expression analysis, prognostic survival analysis and biological function were systematically performed. Furthermore, PUS7 potential as an osteosarcoma biomarker for diagnosis and prognosis was assessed in this study. The findings indicated that PUS7 was overexpressed in the majority of malignancies. High PUS7 expression contributed to the poor prognosis among 11 cancer types, including Adrenocortical Cancer (ACC), Bladder Cancer (BLCA), Liver Cancer (LIHC), Kidney Papillary Cell Carcinoma (KIRP), Mesothelioma (MESO), Lower Grade Glioma (LGG), Kidney Chromophobe (KICH), Sarcoma (SARC), osteosarcoma (OS), Pancreatic Cancer (PAAD), and Thyroid Cancer (THCA). In addition, elevated PUS7 expression was linked to advanced TNM across multiple malignancies, including ACC, BLCA, KIRP, LIHC and PAAD. The function enrichment analysis revealed that PUS7 participates in E2F targets, G2M checkpoint, ribosome biogenesis, and rRNA metabolic process. Moreover, PUS7 is also a reliable biomarker and a potential therapeutic target for osteosarcoma. In summary, PUS7 is a putative pan-cancer biomarker that reliably forecasts cancer patients' prognosis. In addition, this enzyme regulates the cell cycle, ribosome biogenesis, and rRNA metabolism. Most importantly, PUS7 possibly regulates osteosarcoma initiation and progression.

The diabetic myocardial transcriptome reveals Erbb3 as a novel biomarker of incident heart failure

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): British Heart Foundation Background/Introduction People with diabetes mellitus (DM) are at high risk of developing heart failure, but the molecular basis of this is not well understood. Elucidating the mechanistic pathways may help to improve patient stratification and develop targeted therapies. Purpose We set out to define differentially expressed genes (DEGs) in diabetic myocardium and validate arising hits in their circulating protein form, with a view to informing biomarker development and improving understanding of diabetic heart disease. Methods Transcriptomic (RNA-seq) data from the Genotype-Tissue Expression (GTEx) project was used to compare gene expression in participants with (n=>100) versus without (n>300) DM (pooled type 1 and 2 diabetes mellitus), in both the right atrial (RA) and left ventricular (LV) myocardium; analyses were adjusted for technical, demographic and disease covariates. Where available, plasma proteomic data from the UK Biobank (UKB) cohort was used to validate DEGs from GTEx in their circulating protein form. Hits with directionally concordant differential expression in GTEx and UKB were further characterised as plasma proteins in UKB, including over 50,000 participants irrespective of DM status; this included analysis of incident heart failure, cardiovascular mortality and left ventricular function on cardiac magnetic resonance imaging. Results Pooled across the RA and LV, there were 64 DEGs associated with DM, with no overlap between the RA and LV hits. UKB plasma protein data was available for 6 of these. Only ERBB3 showed directional concordance with transcriptomic data, being lower in the myocardium and blood of people with DM. People in the lowest quartile of ERBB3 plasma protein had increased adjusted risk of incident heart failure and cardiovascular death than participants in all other quartiles. Lower concentrations of plasma ERBB3 protein were also associated with increased LV mass and impaired LV contractility. Conclusion Lower plasma ERBB3 concentration is more common in people with DM and is associated with LV dysfunction, incident heart failure and cardiovascular mortality. This highlights the potential of ERBB3 as a novel biomarker for individuals at risk of heart failure. Importantly, cancer therapies targeting ERBB3 and related family members can induce heart failure, and recombinant forms of the ERBB3 ligand neuregulin-1 appear to improve LV contractility, further suggesting a role of ERBB3 in the development of diabetic heart disease.

Histologic and Quality Assessment of Genotype-Tissue Expression (GTEx) Research Samples: A Large Postmortem Tissue Collection.

The National Institutes of Health Genotype-Tissue Expression (GTEx) project was developed to elucidate how genetic variation influences gene expression in multiple normal tissues procured from postmortem donors. To provide critical insight into a biospecimen's suitability for subsequent analysis, each biospecimen underwent quality assessment measures that included evaluation for underlying disease and potential effects introduced by preanalytic factors. Electronic images of each tissue collected from nearly 1000 postmortem donors were evaluated by board-certified pathologists for the extent of autolysis, tissue purity, and the type and abundance of any extraneous tissue. Tissue-specific differences in the severity of autolysis and RNA integrity were evaluated, as were potential relationships between these markers and the duration of postmortem interval and rapidity of death. Tissue-specific challenges in the procurement and preservation of the nearly 30 000 tissue specimens collected during the GTEx project are summarized. Differences in the degree of autolysis and RNA integrity number were observed among the 40 tissue types evaluated, and tissue-specific susceptibilities to the duration of postmortem interval and rapidity of death were observed. Ninety-five percent of tissues were of sufficient quality to support RNA sequencing analysis. Biospecimens, annotated whole slide images, de-identified clinical data, and genomic data generated for GTEx represent a high-quality and comprehensive resource for the scientific community that has contributed to its use in approximately 1695 articles. Biospecimens and data collected under the GTEx project are available via the GTEx portal and authorized access to the Database of Genotypes and Phenotypes; procedures and whole slide images are available from the National Cancer Institute.

CARTAR: a comprehensive web tool for identifying potential targets in chimeric antigen receptor therapies using TCGA and GTEx data.

Chimeric antigen receptor (CAR) therapy has emerged as a ground-breaking advancement in cancer treatment, harnessing the power of engineered human immune cells to target and eliminate cancer cells. The escalating interest and investment in CAR therapy in recent years emphasize its profound significance in clinical research, positioning it as a rapidly expanding frontier in the field of personalized cancer therapies. A crucial step in CAR therapy design is choosing the right target as it determines the therapy's effectiveness, safety and specificity against cancer cells, while sparing healthy tissues. Herein, we propose a suite of tools for the identification and analysis of potential CAR targets leveraging expression data from The Cancer Genome Atlas and Genotype-Tissue Expression Project, which are implemented in CARTAR website. These tools focus on pinpointing tumor-associated antigens, ensuring target selectivity and assessing specificity to avoid off-tumor toxicities and can be used to rationally designing dual CARs. In addition, candidate target expression can be explored in cancer cell lines using the expression data for the Cancer Cell Line Encyclopedia. To our best knowledge, CARTAR is the first website dedicated to the systematic search of suitable candidate targets for CAR therapy. CARTAR is publicly accessible at https://gmxenomica.github.io/CARTAR/.

Sex- and species-specific contribution of CD99 to T cell costimulation during multiple sclerosis

BackgroundDifferences in immune responses between women and men are leading to a strong sex bias in the incidence of autoimmune diseases that predominantly affect women, such as multiple sclerosis (MS). MS manifests in more than twice as many women, making sex one of the most important risk factor. However, it is incompletely understood which genes contribute to sex differences in autoimmune incidence. To address that, we conducted a gene expression analysis in female and male human spleen and identified the transmembrane protein CD99 as one of the most significantly differentially expressed genes with marked increase in men. CD99 has been reported to participate in immune cell transmigration and T cell regulation, but sex-specific implications have not been comprehensively investigated.MethodsIn this study, we conducted a gene expression analysis in female and male human spleen using the Genotype-Tissue Expression (GTEx) project dataset to identify differentially expressed genes between women and men. After successful validation on protein level of human immune cell subsets, we assessed hormonal regulation of CD99 as well as its implication on T cell regulation in primary human T cells and Jurkat T cells. In addition, we performed in vivo assays in wildtype mice and in Cd99-deficient mice to further analyze functional consequences of differential CD99 expression.ResultsHere, we found higher CD99 gene expression in male human spleens compared to females and confirmed this expression difference on protein level on the surface of T cells and pDCs. Androgens are likely dispensable as the cause shown by in vitro assays and ex vivo analysis of trans men samples. In cerebrospinal fluid, CD99 was higher on T cells compared to blood. Of note, male MS patients had lower CD99 levels on CD4+ T cells in the CSF, unlike controls. By contrast, both sexes had similar CD99 expression in mice and Cd99-deficient mice showed equal susceptibility to experimental autoimmune encephalomyelitis compared to wildtypes. Functionally, CD99 increased upon human T cell activation and inhibited T cell proliferation after blockade. Accordingly, CD99-deficient Jurkat T cells showed decreased cell proliferation and cluster formation, rescued by CD99 reintroduction.ConclusionsOur results demonstrate that CD99 is sex-specifically regulated in healthy individuals and MS patients and that it is involved in T cell costimulation in humans but not in mice. CD99 could potentially contribute to MS incidence and susceptibility in a sex-specific manner.

In-silico functional analyses identify TMPRSS15-mediated intestinal absorption of lithium as a modulator of lithium response in bipolar disorder

BackgroundThe therapeutic response to lithium in patients with bipolar disorder is highly variable and has a polygenic basis. Genome-wide association studies investigating lithium response have identified several relevant loci, though the precise mechanisms driving these associations are poorly understood. We aimed to prioritise the most likely effector gene and determine the mechanisms underlying an intergenic lithium response locus on chromosome 21 identified by the International Consortium on Lithium Genetics (ConLi+Gen). MethodsWe conducted in-silico functional analyses by integrating and synthesising information from several publicly available functional genetic datasets and databases including the Genotype-Tissue Expression (GTEx) project and HaploReg. ResultsThe findings from this study highlighted TMPRSS15 as the most likely effector gene at the ConLi+Gen lithium response locus. TMPRSS15 encodes enterokinase, a gastrointestinal enzyme responsible for converting trypsinogen into trypsin and thus aiding digestion. Convergent findings from gene-based lookups in human and mouse databases as well as co-expression network analyses of small intestinal RNA-seq data (GTEx) implicated TMPRSS15 in the regulation of intestinal nutrient absorption, including ions like sodium and potassium, which may extend to lithium. LimitationsAlthough the findings from this study indicated that TMPRSS15 was the most likely effector gene at the ConLi+Gen lithium response locus, the evidence was circumstantial. Thus, the conclusions from this study need to be validated in appropriately designed wet-lab studies. ConclusionsThe findings from this study are consistent with a model whereby TMPRSS15 impacts the efficacy of lithium treatment in patients with bipolar disorder by modulating intestinal lithium absorption.

Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia

Transcriptome-wide association studies (TWASs) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have focused on the regulatory effects of risk-associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWASs of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole-genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents an in-depth look into the role of trans eQTLs in the complex molecular mechanisms underlying these diseases.

Matrisome Transcriptome Dynamics during Tissue Aging.

The extracellular matrix (ECM) is a complex three-dimensional network of macromolecules that provides structural support for the cells and plays a significant role in tissue homeostasis and repair. Growing evidence indicates that dysregulation of ECM remodeling contributes to various pathological conditions in the body, including age-associated diseases. In this work, gene expression data of normal human tissues obtained from the Genotype-Tissue Expression project, as well as data from MatrisomeDB 2.0, the ECM-protein knowledge database, are used to estimate the age-dependent matrisome transcriptome dynamics in the blood, heart, brain, liver, kidneys, lungs, and muscle. Differential gene expression (DE) analysis revealed dozens of matrisome genes encoding both structural elements of the ECM and ECM-associated proteins, which had a tissue-specific expression profile with age. Among common DE genes that changed expression with age in at least three tissues, COL18A1, MFAP1, IGFBP7, AEBP1, LTBP2, LTBP4, LG14, EFEMP1, PRELP, BGN, FAM20B, CTSC, CTSS, and CLEC2B were observed. The findings of the study also reveal that there are sex-specific alterations during aging in the matrisome gene expression. Taken together, the results obtained in this work may help in understanding the role of the ECM in tissue aging and might prove valuable for the future development of the field of ECM research in general.

Abstract PO5-09-03: Multi-tissue transcriptome-wide association studies identified genes for intrinsic subtypes of breast cancer

Abstract Background: Though several genome-wide association studies (GWAS) of breast cancer (BC) have identified common variants which differ between intrinsic subtypes, the genes through which these variants act through to impact BC risk have not been fully established. Furthermore, transcriptome-wide association studies (TWAS) have thus far been primarily employed to identify genes associated with overall BC risk, while overlooking how the influence of common variation on gene expression may contribute to subtype-specific differences. Methods: In this study, we performed two complementary multi-tissue TWASs for each of the following BC intrinsic subtypes: Luminal A-like, Luminal B-like, Luminal B/HER2-negative-like, HER2-enriched-like, Triple Negative BC. These two approaches included 1) an expression-based approach that collated TWAS signals from expression quantitative trait loci (eQTLs) across multiple tissues using the Aggregated Cauchy Association Test (ACAT) and 2) a splicing-based approach that utilized two applications of ACAT to collate splicing QTLs (sQTLs) for a given gene in a tissue and then across tissues. To perform these two TWASs, we utilized e/sQTL models trained in 11 tissues from the Genotype-Tissue Expression Project including breast, ovary, uterus, vagina, EBV-transformed lymphocytes, whole blood, spleen, liver, subcutaneous adipose, visceral adipose, and cell-cultured fibroblasts. GWAS summary statistics were previously generated from 133,384 BC cases and 113,789 controls who were participants in the Breast Cancer Association Consortium (BCAC). We further performed our TWAS while conditioning e/sQTL effect sizes on nearby GWAS index SNPs. Additionally, we utilized gene-based fine-mapping of eQTLs and sQTL to identify candidate causal genes for each intrinsic subtype. Results: Overall, we identified 164 genes in 69 loci that were associated with Luminal A-like, 19 genes in 9 loci with Luminal B-like, 18 genes in 11 loci with Luminal B/HER2-negative-like, 10 genes in 7 loci with HER2-enriched-like, and 29 genes in 12 loci with TNBC. Among these genes, 17 genes had not been reported in previous TWAS of BC, and 140 genes, 1 gene, 2 genes, 2 genes, and 16 genes were uniquely associated with each of the intrinsic subtypes, respectively. Additionally, we identified one gene associated with Luminal A-like and one gene with Luminal B-like BC that were each in a locus located at least 1.4 Mb from published GWAS hits. Furthermore, we identified 106, 11, 10, 5, and 21 candidate causal genes for each of the intrinsic subtypes, respectively, that had a posterior inclusion probability of 0.9 in at least one e/sQTL. Conclusion: In summary, our multi-tissue TWAS corroborated previous GWAS loci for overall BC risk and intrinsic subtypes, while underscoring how common variation impacts BC etiology by modulating the expression and splicing of genes in multiple tissue types. Citation Format: James Li, Julian McClellan, Haoyu Zhang, Guimin Gao, Dezheng Huo. Multi-tissue transcriptome-wide association studies identified genes for intrinsic subtypes of breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-09-03.

Pathway analysis for functional characterization of microRNAs

DIANA-miRPath webserver enables the exploration of combined miRNA effects using predicted or experimentally supported miRNA interactions. Its latest version (DIANA-miRPath v4.0, http://www.microrna.gr/miRPathv4 ), introduces the capacity to tailor its target-based miRNA functional analysis engine towards specific biological/experimental contexts. Via a modular interface with rich interaction, annotation and parameterization options, users can perform enrichment analysis on Gene Ontology (GO) terms, KEGG and REACTOME pathways, gene sets from Molecular Signatures Database (MSigDB) and PFAM. Included miRNA interaction sets are derived from state-of-the-art resources of experimentally supported (DIANA-TarBase v8.0, miRTarBase and microCLIP cell-type-specific interactions) or from in silico miRNA-target interactions (DIANA-microT-2023 and TargetScan predictions). Bulk and single-cell expression datasets from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), as well as single-cell expression atlases can be used to assess expression change of targeted genes within terms, across a wide range of states. A discrete module to perform miRNA-tailored CRISPR knock-out screen analyses deems possible the investigation of selected miRNAs within conditions under study. Hellenic Foundation for Research and Innovation (HFRI) under the 1 st Call for HFRI Research Projects to Support Faculty Members & Researchers and Procure High-Value Research Equipment grant (Project Number 2563). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.