Transcriptome Diversity Research Articles

Transcriptomic profiling of chronic hand eczema skin reveals shared immune pathways and molecular drivers across subtypes.

Chronic hand eczema (CHE) is a common skin disease with different subtypes, but knowledge of the molecular patterns associated with each subtype is limited. To characterize the CHE transcriptome across subtypes. Using RNA-sequencing, we studied the transcriptome of 220 full-thickness skin biopsies collected from palms, dorsa, and arms from 96 patients with CHE and/or atopic dermatitis (AD) and 32 healthy controls. The primary analysis focused on 16 healthy and 54 lesional CHE palm samples that were further stratified by AD status and unique etiology. Differentially expressed genes (DEGs) were identified across the cohort and Ingenuity pathway analysis (IPA) was used for pathway analysis and upstream regulator prediction. We identified anatomical site-specific transcriptomic variations, showing unique characteristics in both healthy and CHE-affected palm skin. In CHE palms, we identified 2333 DEGs versus healthy palms. Upregulated genes predominantly involved keratinocyte host inflammation and immune signaling, while downregulated genes were linked to lipid metabolism and epidermal barrier function. IPA revealed numerous activated pro-inflammatory pathways, dominated by Th1 and Th2. Key upstream regulators included type 1 (IFNG, TNF, STAT1, IL-2) and type 2 (IL-4) associated molecules, and IL-1β. Lesional palm signatures were broadly shared across CHE subtypes. No DEGs were found between allergic- and irritant contact dermatitis CHE. Subtype-specific pathway and upstream regulator activity variations were noted. The lesional CHE transcriptome is primarily shared among subtypes and is characterized by activation of several immune pathways, dominated by Th1 and Th2. Key shared upstream regulators were identified, highlighting potential universal therapeutic targets.

Identification of genetic loci and candidate genes underlying Fusarium crown rot resistance in wheat.

A major locus Qfcr.cau-1B conferring resistance to Fusarium crown rot was identified and validated. The putative gene underlying this locus was pinpointed via virus-induced gene silencing. Fusarium crown rot (FCR), caused by various Fusarium pathogens such as Fusarium pseudograminearum and F. culmorum, is a severe soil-borne disease which significantly affected wheat (Triticum aestivum) production in many arid and semi-arid cropping regions of the world. In this study, a total of 5 QTLs associated with FCR resistance were detected on chromosomes 1B, 2B, 3A, 5A, and 7D using a population of 120 F8 recombinant inbred lines (RIL) derived from a cross between two Chinese germplasm 20828 and SY95-71. A major locus Qfcr.cau-1B, which accounted for up to 28.33% of the phenotypic variation with a LOD value of 10.99, was consistently detected across all three trials conducted. The effect of Qfcr.cau-1B on FCR resistance was further validated using a F5 RIL population between 20828 and BLS2. Integrated transcriptome and sequence variation analysis showed that three genes including TraesCS1B02G017700, TraesCS1B02G016400, and TraesCS1B02G022300 were potential candidate genes for Qfcr.cau-1B. Of these three genes, the virus-induced silencing of TraesCS1B02G022300 significantly promoted FCR severity, indicating its positive role in FCR resistance. Taken together, results from this study expand our understanding on genetic basis of FCR resistance in wheat and will be indicative for cloning genes conferring FCR resistance.

Human enteric glia diversity in health and disease: new avenues for the treatment of Hirschsprung disease.

The enteric nervous system (ENS), comprised of neurons and glia, regulates intestinal motility. Hirschsprung disease (HSCR) results from defects in ENS formation, yet while neuronal aspects have been extensively studied, enteric glia remain disregarded. This study aimed to explore enteric glia diversity in health and disease. Full-thickness intestinal resection material from pediatric controls and HSCR patients was collected, dissociated and enriched for the ENS population through fluorescence-activated cell sorting. Single-cell RNA sequencing was performed to uncover the transcriptomic diversity of the ENS in HSCR patients and controls, as well as in wildtype and ret mutant zebrafish. Immunofluorescence and fluorescence in situ hybridization confirmed the presence of distinct subtypes. Two major enteric glial classes emerged in the pediatric intestine: Schwann-like enteric glia, reminiscent of Schwann cells, and Enteric glia, expressing classical glial markers. Comparative analysis with previously published datasets confirmed our classification and revealed that whilst classical enteric glia are predominant prenatally, Schwann-like enteric glia become more abundant postnatally. In HSCR, ganglionic segments mirrored controls, while aganglionic segments, only featured Schwann-like enteric glia. Leveraging the regenerative potential of Schwann cells, we explored therapeutic options using a ret mutant zebrafish. Prucalopride, a serotonin-receptor (5-HT) agonist, induced neurogenesis partially rescuing the HSCR phenotype in ret+/- mutants. Two major enteric glial classes were identified in the pediatric intestine, highlighting the significant postnatal contribution of Schwann-like enteric glia to glial heterogeneity. Crucially, these glial subtypes persist in aganglionic segments of HSCR patients, offering a new target for their treatment using 5-HT agonists.

Post-transcriptional regulation of the transcriptional apparatus in neuronal development

Post-transcriptional mechanisms, such as alternative splicing and polyadenylation, are recognized as critical regulatory processes that increase transcriptomic and proteomic diversity. The advent of next-generation sequencing and whole-genome analyses has revealed that numerous transcription and epigenetic regulators, including transcription factors and histone-modifying enzymes, undergo alternative splicing, most notably in the nervous system. Given the complexity of regulatory processes in the brain, it is conceivable that many of these splice variants control different aspects of neuronal development. Mutations or dysregulation of splicing and transcription regulatory proteins are frequently linked to various neurodevelopmental disorders, highlighting the importance of understanding the role of neuron-specific alternative splicing in maintaining proper transcriptional regulation in the brain. This review consolidates current insights into the role of alternative splicing in influencing transcriptional and chromatin regulatory programs in neuronal development.

Differential quantification of alternative splicing events on spliced pangenome graphs.

Pangenomes are becoming a powerful framework to perform many bioinformatics analyses taking into account the genetic variability of a population, thus reducing the bias introduced by a single reference genome. With the wider diffusion of pangenomes, integrating genetic variability with transcriptome diversity is becoming a natural extension that demands specific methods for its exploration. In this work, we extend the notion of spliced pangenomes to that of annotated spliced pangenomes; this allows us to introduce a formal definition of Alternative Splicing (AS) events on a graph structure. To investigate the usage of graph pangenomes for the quantification of AS events across conditions, we developed pantas, the first pangenomic method for the detection and differential analysis of AS events from short RNA-Seq reads. A comparison with state-of-the-art linear reference-based approaches proves that pantas achieves competitive accuracy, making spliced pangenomes effective for conducting AS events quantification and opening future directions for the analysis of population-based transcriptomes.

The landscape of fusion transcripts in plants: a new insight into genome complexity

BackgroundFusion transcripts (FTs), generated by the fusion of genes at the DNA level or RNA-level splicing events significantly contribute to transcriptome diversity. FTs are usually considered unique features of neoplasia and serve as biomarkers and therapeutic targets for multiple cancers. The latest findings show the presence of FTs in normal human physiology. Several discrete reports mentioned the presence of fusion transcripts in planta, has important roles in stress responses, morphological alterations, or traits (e.g. seed size, etc.).ResultsIn this study, we identified 169,197 fusion transcripts in 2795 transcriptome datasets of Arabidopsis thaliana, Cicer arietinum, and Oryza sativa by using a combination of tools, and confirmed the translational activity of 150 fusion transcripts through proteomic datasets. Analysis of the FT junction sequences and their association with epigenetic factors, as revealed by ChIP-Seq datasets, demonstrated an organised process of fusion formation at the DNA level. We investigated the possible impact of three-dimensional chromatin conformation on intra-chromosomal fusion events by leveraging the Hi-C datasets with the incidence of fusion transcripts. We further utilised the long-read RNA-Seq datasets to validate the most reoccurring fusion transcripts in each plant species followed by further authentication through RT-PCR and Sanger sequencing.ConclusionsOur findings suggest that a significant portion of fusion events may be attributed to alternative splicing during transcription, accounting for numerous fusion events without a proportional increase in the number of RNA pairs. Even non-nuclear DNA transcripts from mitochondria and chloroplasts can participate in intra- and inter-chromosomal fusion formation. Genes in close spatial proximity are more prone to undergoing fusion formation, especially in intra-chromosomal FTs. Most of the fusion transcripts may not undergo translation and serve as long non-coding RNAs. The low validation rate of FTs in plants indicated that the fusion transcripts are expressed at very low levels, like in the case of humans. FTs often originate from parental genes involved in essential biological processes, suggesting their relevance across diverse tissues and stress conditions. This study presents a comprehensive repository of fusion transcripts, offering valuable insights into their roles in vital physiological processes and stress responses.

Integrative mapping of human CD8+ T cells in inflammation and cancer.

CD8+ T cells exhibit remarkable phenotypic diversity in inflammation and cancer. However, a comprehensive understanding of their clonal landscape and dynamics remains elusive. Here we introduce scAtlasVAE, a deep-learning-based model for the integration of large-scale single-cell RNA sequencing data and cross-atlas comparisons. scAtlasVAE has enabled us to construct an extensive human CD8+ T cell atlas, comprising 1,151,678 cells from 961 samples across 68 studies and 42 disease conditions, with paired T cell receptor information. Through incorporating information in T cell receptor clonal expansion and sharing, we have successfully established connections between distinct cell subtypes and shed light on their phenotypic and functional transitions. Notably, our approach characterizes three distinct exhausted T cell subtypes and reveals diverse transcriptome and clonal sharing patterns in autoimmune and immune-related adverse event inflammation. Furthermore, scAtlasVAE facilitates the automatic annotation of CD8+ T cell subtypes in query single-cell RNA sequencing datasets, enabling unbiased and scalable analyses. In conclusion, our work presents a comprehensive single-cell reference and computational framework for CD8+ T cell research.

Supervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA.

Alternative polyadenylation (APA) is an important driver of transcriptome diversity that generates messenger RNA isoforms with distinct 3' ends. The rapid development of single-cell and spatial transcriptomic technologies opened up new opportunities for exploring APA data to discover hidden cell subpopulations invisible in conventional gene expression analysis. However, conventional gene-level analysis tools are not fully applicable to APA data, and commonly used unsupervised dimensionality reduction methods often disregard experimentally derived annotations such as cell type identities. Here, we proposed a supervised analytical framework termed spvAPA, specifically used for APA analysis from both single-cell and spatial transcriptomics data. First, an iterative imputation method based on weighted nearest neighbor was designed to recover missing APA signatures, by integrating both gene expression and APA modalities. Second, a supervised feature selection method based on sparse partial least squares discriminant analysis was devised to identify APA features distinguishing cell types or spatial morphologies. Additionally, spvAPA improves the visualization of high-dimensional data for discovering novel cell subtypes, which considers APA features and dual modalities of gene expression and APA. Evaluations across nine single-cell and spatial transcriptomics datasets demonstrate the effectiveness and applicability of spvAPA. spvAPA is available at https://github.com/BMILAB/spvAPA.

Global transcriptome modulation by xenobiotics: the role of alternative splicing in adaptive responses to chemical exposures

BackgroundXenobiotic exposures can extensively influence the expression and alternative splicing of drug-metabolizing enzymes, including cytochromes P450 (CYPs), though their transcriptome-wide impact on splicing remains underexplored. This study used a well-characterized splicing event in the Cyp2b2 gene to validate a sandwich-cultured primary rat hepatocyte model for studying global splicing in vitro. Using endpoint PCR, RNA sequencing, and bioinformatics tools (rSeqDiff, rMATs, IGV), we analyzed differential gene expression and splicing in CYP and nuclear receptor genes, as well as the entire transcriptome, to understand how xenobiotic exposures shape alternative splicing and activate xenosensors.MethodsPrimary rat hepatocytes in sandwich culture were exposed to two methylenedioxybenzene (MDB) congeners and carbamazepine, with gene expression and splicing assessed. A 3D-clustergram integrating KEGG pathway analysis with differential gene expression provided distinct splicing landscapes for each xenobiotic, showing that splicing diversity does not always align with gene expression changes.ResultsEndpoint PCR revealed a Cyp2b2v to wild-type Cyp2b2 splicing ratio near 1:1 (100%) under most conditions, while RNA-seq showed a stable baseline closer to 40%. C6-MDB reduced this ratio to ~ 50% by PCR and ~ 39% by RNA-seq, showing slight method-dependent variations yet consistent trends. In contrast, exon 6 skipping in Cyp1a1 occurred only with MDB exposure, implicating AHR activation. Xenobiotic treatments also induced alternative splicing in defensome and stress-responsive genes, including the phase II enzyme Gstm3, Albumin, Orm1, and Fxyd1, highlighting their roles in xenobiotic response modulation. Significant splicing changes in factors such as SRSF1, SRSF7, and METTL3 suggest a coordinated feedback loop involving epitranscriptomic modulation and cross-talk within SR protein networks, refining splice site selection, transcript stability, and cellular fate.ConclusionsThis study demonstrates how xenobiotic structural features influence gene expression and splicing, revealing splicing patterns that expand our understanding of transcriptome diversity and function. By identifying regulatory mechanisms, including AHR activation, epitranscriptomic modulation, and crosstalk within SR protein networks, that shape adaptive responses to xenobiotic stress, this work offers insights into the splicing and transcriptional networks that maintain cellular homeostasis. These findings provide predictive biomarkers for toxic exposures and highlight the potential of splicing profiles as diagnostic tools for assessing the health impacts of chemical exposure.

A transcriptome data set for comparing skin, muscle and dorsal root ganglion between acute and chronic postsurgical pain rats

Chronic postsurgical pain (CPSP), with a high prevalence and rising epidemic of opioids crisis, is typically derived from acute postoperative pain. Our knowledge on the forming of chronic pain mostly derives from mechanistic studies of pain processing in the brain and spinal cord circuits, yet most pharmacological interventions targeting CNS came to be unhelpful in preventing CPSP. Revealing the peripheral mechanisms behind the transition from acute to chronic pain after surgery could shine a light on the novel analgesic regimens. Based on two recognized animal models in simulation of acute and chronic postsurgical pain, we provide a next-generation RNA sequencing (RNA-seq) data set to evaluate the time-course transcriptomic variation in the tissue of skin, muscle and dorsal root ganglion (DRG) in these two pain models. The aim of this study is to identify the potential origin and mechanism of the persistent postoperative pain, and further to explore effective and safer analgesic regimens for surgical patients.

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data.

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variants calling, LongSom re-annotates marker gene based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variants detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.

Circular RNA Profiling Using High-Accuracy Long-Read Nanopore Sequencing of Multiple Myeloma Cell Lines Reveals the Wide Diversity of the Circular Transcriptome

Circular RNA Profiling Using High-Accuracy Long-Read Nanopore Sequencing of Multiple Myeloma Cell Lines Reveals the Wide Diversity of the Circular Transcriptome

MoPepGen: Rapid and Comprehensive Identification of Non-canonical Peptides.

Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteomic diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient detection and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.

Shared and unique patterns of autonomous human endogenous retrovirus loci transcriptomes in CD14 + monocytes from individuals with physical trauma or infection with COVID-19

Since previous studies have suggested that the RNAs of human endogenous retrovirus (HERV) might be involved in regulating innate immunity, it is important to investigate the HERV transcriptome patterns in innate immune cell types such as CD14 + monocytes. Using single cell RNA-seq datasets from resting or stimulated PBMCs mapped to 3,220 known discrete autonomous proviral HERV loci, we found individual-specific variation in HERV transcriptomes between HERV loci in CD14 + monocytes. Analysis of paired datasets from the same individual that were cultured in vitro with LPS or without (i.e. control) revealed 36 HERV loci in CD14 + monocytes that were detected only after activation. To extend our analysis to in vivo activated CD14 + monocytes, we used two scRNA-seq datasets from studies that had demonstrated activation of circulating CD14 + monocytes in patients with physical trauma or patients hospitalized with COVID-19 infections. For direct comparison between the trauma and COVID-19 datasets, we first analyzed 1.625 billion sequence reads from a composite pangenome control of 21 normal individuals. Comparison of the sequence read depth of HERV loci in the trauma or COVID-19 samples to the pangenome control revealed that 39 loci in the COVID-19 and 11 HERV loci in the trauma samples were significantly different (Mann-Whitney U test), with 9 HERV loci shared between the COVID-19 and trauma datasets. The capacity to compare HERV loci transcriptome patterns in innate immune cells, like CD14 + monocytes, across different pathological conditions will lead to greater understanding of the physiological role of HERV expression in health and disease.

Real-time and programmable transcriptome sequencing with PROFIT-seq

The high diversity and complexity of the eukaryotic transcriptome make it difficult to effectively detect specific transcripts of interest. Current targeted RNA sequencing methods often require complex pre-sequencing enrichment steps, which can compromise the comprehensive characterization of the entire transcriptome. Here we describe programmable full-length isoform transcriptome sequencing (PROFIT-seq), a method that enriches target transcripts while maintaining unbiased quantification of the whole transcriptome. PROFIT-seq employs combinatorial reverse transcription to capture polyadenylated, non-polyadenylated and circular RNAs, coupled with a programmable control system that selectively enriches target transcripts during sequencing. This approach achieves over 3-fold increase in effective data yield and reduces the time required for detecting specific pathogens or key mutations by 75%. We applied PROFIT-seq to study colorectal polyp development, revealing the intricate relationship between host immune responses and bacterial infection. PROFIT-seq offers a powerful tool for accurate and efficient sequencing of target transcripts while preserving overall transcriptome quantification, with broad applications in clinical diagnostics and targeted enrichment scenarios.

Transcriptome Analysis Deciphers the Underlying Molecular Mechanism of Peanut Lateral Branch Angle Formation Using Erect Branching Mutant.

Background The growth habit (GH), also named the branching habit, is an important agronomic trait of peanut and mainly determined by the lateral branch angle (LBA). The branching habit is closely related to peanut mechanized farming, pegging, yield, and disease management. Objectives However, the molecular basis underlying peanut LBA needs to be uncovered. Methods In the present study, an erect branching peanut mutant, eg06g, was obtained via 60Co γ-ray-radiating mutagenesis of a spreading-type peanut cultivar, Georgia-06G (G06G). RNA-seq was performed to compare the transcriptome variation of the upper sides and lower sides of the lateral branch of eg06g and G06G. Results In total, 4908 differentially expressed genes (DEGs) and 5833 DEGs were identified between eg06g and G06G from the lower sides and upper sides of the lateral branch, respectively. GO, KEGG, and clustering enrichment analysis indicated that the carbohydrate metabolic process, cell wall organization or biogenesis, and plant hormone signal transduction were mainly enriched in eg06g. Conclusions Further analysis showed that the genes involved in starch biosynthesis were upregulated in eg06g, which contributed to amyloplast sedimentation and gravity perception. Auxin homeostasis and transport-related genes were found to be upregulated in eg06g, which altered the redistribution of auxin in eg06g and in turn triggered apoplastic acidification and activated cell wall modification-related enzymes, leading to tiller angle establishment through the promotion of cell elongation at the lower side of the lateral branch. In addition, cytokinin and GA also demonstrated synergistic action to finely regulate the formation of peanut lateral branch angles. Collectively, our findings provide new insights into the molecular regulation of peanut LBA and present genetic materials for breeding peanut cultivars with ideotypes.

Transcriptomic diversity of amygdalar subdivisions across humans and nonhuman primates.

The amygdaloid complex mediates learning, memory, and emotions. Understanding the cellular and anatomical features that are specialized in the amygdala of primates versus other vertebrates requires a systematic, anatomically-resolved molecular analysis of constituent cell populations. We analyzed five nuclear subdivisions of the primate amygdala with single-nucleus RNA sequencing in macaques, baboons, and humans to examine gene expression profiles for excitatory and inhibitory neurons and confirmed our results with single-molecule FISH analysis. We identified distinct subtypes of FOXP2 + interneurons in the intercalated cell masses and protein-kinase C-δ interneurons in the central nucleus. We also establish that glutamatergic, pyramidal-like neurons are transcriptionally specialized within the basal, lateral, or accessory basal nuclei. Understanding the molecular heterogeneity of anatomically-resolved amygdalar neuron types provides a cellular framework for improving existing models of how amygdalar neural circuits contribute to cognition and mental health in humans by using nonhuman primates as a translational bridge.

Cell and transcriptomic diversity of infrapatellar fat pad during knee osteoarthritis

ObjectivesIn this study, we employ a multiomic approach to identify major cell types and subsets, and their transcriptomic profiles within the infrapatellar fat pad (IFP), and to determine differences in...

12353 Differential Gene Expression And Pathway Analysis In Growth Hormone Secreting Pituitary Tumors According To Granulation Pattern

Abstract Disclosure: H. Shin: None. Differential gene expression and pathway analysis in growth hormone-secreting pituitary tumors according to granulation pattern. Hye Ju Shin [1], Kyung Won Kim[2], Chan Woo Kang[2], Eun Jig Lee[2], Cheol Ryong Ku[2]. [1]Department of Clinical Drug Discovery & Development, Yonsei University College of Medicine, Seoul, Republic of Korea, [2]Endocrinology, Institute of Endocrine Research, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea Objective: The purpose of this research is to explain clinical variations in patients with acromegaly by exploring gene expression differences according to granulation patterns in growth hormone (GH)-secreting pituitary tumors. Materials & methods: Transcriptome analysis was conducted on 6 normal pituitary tissues and 15 GH-secreting pituitary tumors, including 9 densely granulated somatotroph tumors (DGSTs) and 6 sparsely granulated somatotroph tumors (SGSTs). Results: The median age at diagnosis was 50 years, with a predominance of female patients (60%). We identified 3111 differentially expressed genes (DEGs) in tumors compared to normal pituitaries, with 1117 DEGs unique to a specific granulation within tumors. SGST showed enriched the pathways involved in neuronal development (axon development, axonogenesis, axon guidance, synapse and presynapse organization, gliogenesis, glial cell differentiation, nerve development, synaptic membrane adhesion, semaphorin-plexin signaling pathway involved in neuron projection guidance) and acute inflammatory response. No pathways were significantly downregulated in SGST compared with those in DGST. Three signaling pathways (JAK-STAT, phosphatidylinositol 3-kinase, and MAP cascade) and three neuronal development processes (generation of neurons, nerve development, and neurogenesis) were significantly enriched in both GO and GSEA analysis. The PPI networks constructed with the up- and downregulated DEGs consisted of 128 and 132 PPI pairs, respectively. There were 10 hub proteins, top highly connected DEGs: ANLN, TLE3, TLE2, DMD, ADRA1A, RBFOX1, MMP2, LMO1, RUNX1T1, and NPPA. Conclusion: The results indicate that granulation-specific gene expression may underpin diverse clinical characteristics in acromegaly. Overall, this study emphasizes the potential for further investigation into these transcriptomic variations and their roles in disease pathology, particularly the involvement of genes linked to neuronal development, inflammatory response, and JAK-STAT signaling in SGST. Presentation: 6/3/2024

Multi-omics analysis of the molecular response to glucocorticoids - insights into shared genetic risk from psychiatric to medical disorders

BackgroundAlterations in the effects of glucocorticoids have been implicated in mediating some of the negative health effects associated with chronic stress, including increased risk for psychiatric disorders as well as cardiovascular and metabolic diseases. This study investigates how genetic variants influence gene expression and DNA methylation (DNAm) in response to glucocorticoid receptor (GR)-activation, and their association with disease risk. MethodsWe measured DNAm (n=199) and gene expression (n=297) in peripheral blood before and after GR-activation with dexamethasone, with matched genotype data available for all samples. A comprehensive molecular quantitative trait locus (QTL) analysis was conducted, mapping GR-response methylation (me)QTLs, GR-response expression (e)QTLs, and GR-response expression quantitative trait methylation (eQTM). A multi-level network analysis was employed to map the complex relationships between the transcriptome, epigenome, and genetic variation. ResultsWe identified 3,772 GR-response meCpGs corresponding to 104,828 local GR-response meQTLs that did not strongly overlap with baseline meQTLs. eQTM and eQTL analyses revealed distinct genetic influences on gene expression and DNAm. Multi-level network analysis uncovered GR-response network trio QTLs, characterized by SNP-CpG-transcript combinations where meQTLs act as both eQTLs and eQTMs. GR-response trio variants were enriched in GWAS for psychiatric, respiratory, autoimmune and cardiovascular diseases and conferred a higher relative heritability per SNP than GR-response meQTL and baseline QTL SNP. ConclusionsGenetic variants modulating the molecular effects of glucocorticoids are associated with psychiatric as well as medical diseases and not uncovered in baseline QTL analyses.