Cell Type-specific Expression Research Articles

Cohesin rad21 mutation dysregulates erythropoiesis and granulopoiesis output within the whole kidney marrow of adult zebrafish.

Cohesin complex is essential for cell division and for regulating cell type-specific gene expression programs. Mutations in genes encoding the cohesin subunits are associated with hematological malignancies, pre-leukemia and clonal hematopoiesis of indeterminate potential. In this study, we examined how cohesin mutation impacts hematopoiesis using adult zebrafish that carry heterozygous germline nonsense mutation in the cohesin subunit, rad21 (rad21+/-) that is orthologous to human RAD21. Single cell RNA sequencing analyses showed that adult zebrafish harboring rad21+/- mutation exhibit significant transcriptional dysregulation within the whole kidney marrow and have altered erythroid and granulocyte output. Erythroid progenitors were expanded in rad21+/- and erythroid differentiation was altered. The expression profile of several erythroid genes, including gata1a, was dysregulated in rad21+/- erythroid cells. Mature granulocyte population declined in rad21+/-, and the transcriptional program of granulocytes was impaired but granulocytic maturation was maintained. Granulocytes from rad21+/- showed upregulation of stress hematopoiesis factor, cebpb. These findings show that normal rad21 is required to maintain steady erythropoiesis and granulopoiesis in the adult zebrafish marrow.

Abstract 4142799: Central role of LIM domain binding 3 (LDB3) protein in Tetralogy of Fallot assessed by single-nuclei multiome profiling of paediatric right ventricular samples

Background and aims: Tetralogy of Fallot (TOF; EC: 1565/2019) is the most common cyanotic congenital heart defect with a world-wide incidence of 0.02–0.04%. The pathophysiological mechanisms driven by variable genetic background are poorly understood. We applied the single-nuclei multiome profiling, composed of ATAC and RNA-seq from the same nuclei, of right ventricular samples of children with TOF. The final aim was to define cell types and characterize gene regulation states in different cell clusters including activation and maturation. Methods: Right ventricular samples were prospectively gathered during routinely performed cardiac surgery for TOF correction (EC: 1565/2019). In total, six paediatric patient samples were processed, and on average ~1,500 nuclei were sequenced per sample. Data was analysed using Scanpy to cluster and annotate cells, pycisTopic to identify cell stats and cis-regulatory topics, and SCENIC+ for enhancer-driven gene Regulatory network (eRegulon) construction. Results: Through manual annotation, using published markers, we identified cell types including cardiomyocyte, fibroblast, endothelial, pericyte, immune and neuronal cells. From this, we observed cell type specific expression, with overexpression of decorin in fibroblasts, PTPRC in immune cells, and neurexin genes in neuronal cells found. Differential expression analysis highlighted patient specific patterns of expression, of which gene set enrichment showed enrichment for genes associated with actomyosin structure organisation, dilated cardiomyopathy and cardiac muscle contraction. Integration of the gene expression data with the ATAC-seq data, across all patients, allowed for the construction of eRegulons networks. In detail, in the cardiomyocyte-annotated clusters, we identified enrichment of MEF2, MEIS2 and EPAS1 family enhancers with a central role of LIM domain binding 3 protein (LDB3, associated with cytoskeletal actin and muscle alpha-actin binding) (Fig. 1). Conclusion: Single nuclei multiome profiling of paediatric right ventricular TOF samples identified several patient specific patterns of expression and regulatory networks, related to the development of cyanotic congenital heart disease, and sheds light to the potential drivers of TOF.

Brain single-cell transcriptomics highlights comorbidity-related cell type-specific changes of Parkinson’s disease with major depressive disorder after paraquat exposure

Paraquat (PQ), a commonly used herbicide, is a potent environmental neurotoxin associated with Parkinson’s disease (PD) and major depressive disorder (MDD). While the involvement of various brain cell types in the etiology of each disorder is well recognized, the specific cell subtypes implicated in the comorbidity of PD and MDD, especially under PQ neurotoxicity, remain poorly understood. In this study, we used single-cell RNA sequencing (scRNA-seq) to analyze brain tissues from mice with PQ-induced PD with MDD. By integrating genomic data with scRNA-seq profiles, we identified differences in cellular heterogeneity related to the pathogenesis of PD and MDD under PQ exposure. Our analysis of risk enrichment in genes with cell type-specific expression patterns revealed that astrocytes are predominantly linked to the comorbidity of PQ-induced PD and MDD. Furthermore, we identified a specific astrocyte subtype that plays a major role in the comorbidity-related changes observed in PQ-induced PD and MDD. This subtype appears to interact with and potentially transform into MDD-specific and PD-specific subtypes. Additionally, pathways related to chemical synaptic function and neuro-projection development were involved in all key stages of PD and MDD co-occurrence. We also identified RNF7 and MTCH2 as shared diagnostic hub genes for PD and MDD, which changed significantly in astrocytes following PQ exposure. These genes may serve as potential markers for astrocyte-specific prognostic diagnosis of PQ-induced PD with MDD. In summary, this study provides the first scRNA-seq profile of comorbidity in a PQ-exposed model. It highlights the heterogeneity of astrocytes in comorbidity and elucidates potential mechanisms underlying the co-occurrence of PD and MDD. These findings emphasize the need for further research into the pathogenesis of PD comorbid with MDD and offer novel insights into PQ neurotoxicity.

Functional genomics implicates natural killer cells in the pathogenesis of ankylosing spondylitis

Multiple lines of evidence indicate that ankylosing spondylitis (AS) is a lymphocyte-driven disease. However, which lymphocyte populations are critical in AS pathogenesis is not known. In this study, we aimed to identify the key cell types mediating the genetic risk in AS using an unbiased functional genomics approach. We integrated genome-wide association study (GWAS) data with epigenomic and transcriptomic datasets of human immune cells. To quantify enrichment of cell type-specific open chromatin or gene expression in AS risk loci, we used three published methods - LDSC-SEG, SNPsea, scDRS - that have successfully identified relevant cell types in other diseases. Natural killer (NK) cell-specific open chromatin regions are significantly enriched in heritability for AS, compared to other immune cell types such as T cells, B cells, and monocytes. This finding was consistent between two AS GWAS. Using RNA-seq data, we validated that genes in AS risk loci are enriched in NK cell-specific gene expression. Using the human Space-Time Gut Cell Atlas, we also found significant upregulation of AS-associated genes predominantly in NK cells. We performed co-localization analyses between GWAS risk loci and genetic variants associated with gene expression (eQTL) to find putative target genes. This revealed four AS risk loci affecting regulation of candidate target genes in NK cells: two known loci, ERAP1 and TNFRSF1A, and two under-studied loci, ENTR1 (aka SDCCAG3) and B3GNT2. Our findings suggest that NK cells may play a crucial role in AS development and highlight four putative target genes for functional follow-up in NK cells.

Binding profiles for 961 Drosophila and C. elegans transcription factors reveal tissue-specific regulatory relationships.

A catalog of transcription factor (TF) binding sites in the genome is critical for deciphering regulatory relationships. Here we present the culmination of the efforts of the Model Organism ENCyclopedia Of DNA Elements (modENCODE) and the model organism Encyclopedia of Regulatory Networks (modERN) consortia to systematically assay TF binding events in vivo in two major model organisms, Drosophila melanogaster (fly) and Caenorhabditis elegans (worm). These datasets comprise 605 TFs identifying 3.6M sites in the fly and 356 TFs identifying 0.9 M sites in the worm and represent the majority of the regulatory space in each genome. We demonstrate that TFs associate with chromatin in clusters termed "metapeaks", that larger metapeaks have characteristics of high occupancy target (HOT) regions, and that the importance of consensus sequence motifs bound by TFs depends on metapeak size and complexity. Combining ChIP-seq data with single cell RNA-seq data in a machine learning model identifies TFs with a prominent role in promoting target gene expression in specific cell types, even differentiating between parent-daughter cells during embryogenesis. These data are a rich resource for the community that should fuel and guide future investigations into TF function. To facilitate data accessibility and utility, all strains expressing GFP-tagged TFs are available at the stock centers for each organism. The chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center, GEO, and through a direct interface that provides rapid access to processed data sets and summary analyses, as well as widgets to probe the cell type-specific TF-target relationships.

Stem and progenitor cell proliferation are independently regulated by cell type-specific cyclinD genes.

Regeneration and homeostatic turnover of solid tissues depend on the proliferation of symmetrically dividing adult stem cells, which either remain stem cells or differentiate based on their niche position. Here we demonstrate that in zebrafish lateral line sensory organs, stem and progenitor cell proliferation are independently regulated by two cyclinD genes. Loss of ccnd2a impairs stem cell proliferation during development, while loss of ccndx disrupts hair cell progenitor proliferation but allows normal differentiation. Notably, ccnd2a can functionally replace ccndx , indicating that the respective effects of these Cyclins on proliferation are due to cell type-specific expression. However, even though hair cell progenitors differentiate normally in ccndx mutants, they are mispolarized due to hes2 and Emx2 downregulation. Thus, regulated proliferation ensures that equal numbers of hair cells are polarized in opposite directions. Our study reveals cell type-specific roles for cyclinD genes in regulating the different populations of symmetrically dividing cells governing organ development and regeneration, with implications for regenerative medicine and disease.

Spatial mRNA expression patterns of orexin receptors in the dorsal hippocampus.

Orexins are wake-promoting neuropeptides that originate from hypothalamic neurons projecting to widespread brain areas throughout the central nervous system. They modulate various physiological functions via their orexin 1 (OXR1) and 2 (OXR2) receptors, including sleep-wake rhythm but also cognitive functions such as memory formation. Here, we provide a detailed analysis of OXR1 and OXR2 mRNA expression profiles in the dorsal hippocampus as a key region for memory formation, using RNAscope multiplex in situ hybridization. Interconnected subareas relevant for cognition and memory such as the medial prefrontal cortex and the nucleus reuniens of the thalamus were assessed as well. Both receptor types display distinct profiles, with the highest percentage of OXR1 mRNA-positive cells in the hilus of the dentate gyrus. Here, the content of OXR1 mRNA per cell was slightly modulated at selected time points over a 12h light/ 12 dark light phase. Using RNAScope and quantitative polymerase chain reaction approaches, we began to address a cell-type specific expression of OXR1 in hilar GABAergic interneurons. The distinct expression profiles of both receptor subtypes within hippocampal subareas and circuits provide an interesting basis for future interventional studies on orexin receptor function in spatial and contextual memory.

Deep Phenotyping of a Mouse Model for Hearing Instability Disorders.

Hearing instability in Slc26a4-insufficiency mice may be due to differential expression of genes related to ion homeostasis and activated macrophages. Hearing instability (HI) disorders, defined by either hearing fluctuation or sudden loss, remain incompletely understood. Recent studies have described a Slc26a4 (pendrin)-insufficiency mouse model (DE17.5) that offers a genetically driven model for HI, although deep audiometric and immunohistologic phenotyping of this model remains poorly characterized. Homozygous DE17.5 mice with (F) and without (NF) HI were delineated by serial auditory brainstem responses (ABR) between postnatal days 30 and 60 and compared with adult phenotypically wild-type Slc26a4-heterozygous controls without evidence of HI (Het). HI was defined as a change in threshold of at least 15 dB in at least two frequencies or at least 20 dB in at least one frequency from the previous week. Stria vascularis (SV) cell type-specific gene expression, endolymphatic hydrops (EH), endocochlear potential (EP), and macrophage activation were analyzed and compared between the cohorts. F mice demonstrated significant reductions in the expression of cell type-specific genes related to ion homeostasis and increased macrophage activation within the SV compared with NF and Het cohorts. Both F and NF DE17.5 homozygous mice demonstrated reductions in EP and increased EH compared with the Het cohort. Deep phenotyping of DE17.5 mice demonstrates changes in EP and EH compared with control; however, the HI phenotype was associated with differential ion homeostasis gene expression and increased macrophage activation in the SV. This provides potential further insights into the underlying pathogenesis and possible immunologic contributions of HI in humans.

Farm-dust mediated protection of childhood asthma: Mass cytometry reveals novel cellular regulation.

Farm-dust mediated asthma protection in childhood was replicated in numerous epidemiological studies. Central immune mechanisms are not fully understood. This exploratory study aimed to disentangle underlying immunological regulation of farm-dust mediated protection in peripheral blood on a single-cell level. Single-cell protein expression of invitro farm-dust stimulated and unstimulated cells from allergic asthmatics and healthy controls were measured using mass cytometry. Analysis of innate and adaptive cellular proportions (linear regression) and T-cell proliferation was performed. Functional marker intensity was investigated using Earth Mover's Distance and the Monte Carlo permutation test. Farm-dust stimulation induced cell type-specific regulation: Key-features of farm-dust stimulation comprised opposing regulation of immune-cell frequencies (downregulated innate cell populations (monocytes/DCs (p < .001), NK-cells (p < .05)) and upregulated adaptive populations (B-cells, CD4+ T-cells (both p < .05)), reduced CD4+ CD25- T-cell proliferation, and differential cell type-specific functional marker expression. Following stimulation, functional marker analysis revealed induced activation (CD25) in T-cells and NK-T-cells in both phenotypes even after correction for multiple testing. Cytotoxicity (GZMB) and inflammation (pERK1/2, pp38) related markers were reduced in T-cells exclusively in asthmatic children. Asthma-associated markers (Gata3, RORγ, and HLA-DR) were reduced in T- and innate- cell populations of asthmatics following stimulation. B-cells displayed a phenotypically independent increase of diverse functional markers upon farm-dust stimulation. This study mimicking invivo environmental exposure identified a novel profile of immune-regulatory markers using mass cytometry demonstrating decreased asthma-associated markers following farm-dust stimulation. These findings may be key for further studies on asthma prevention in childhood.

Genetic Heterogeneity Across Dimensions of Alcohol Use Behaviors.

Increasingly large samples in genome-wide association studies (GWASs) for alcohol use behaviors (AUBs) have led to an influx of implicated genes, yet the clinical and functional understanding of these associations remains low, in part because most GWASs do not account for the complex and varied manifestations of AUBs. This study applied a multidimensional framework to investigate the latent genetic structure underlying heterogeneous dimensions of AUBs. Multimodal assessments (self-report, interview, electronic health records) were obtained from approximately 400,000 UK Biobank participants. GWAS was conducted for 18 distinct AUBs, including consumption, drinking patterns, alcohol problems, and clinical sequelae. Latent genetic factors were identified and carried forward to GWAS using genomic structural equation modeling, followed by functional annotation, genetic correlation, and enrichment analyses to interpret the genetic associations. Four latent factors were identified: Problems, Consumption, BeerPref (declining alcohol consumption with a preference for drinking beer), and AtypicalPref (drinking fortified wine and spirits). The latent factors were moderately correlated (rg values, 0.12-0.57) and had distinct patterns of associations, with BeerPref in particular implicating many novel genomic regions. Patterns of regional and cell type-specific gene expression in the brain also differed between the latent factors. Deep phenotyping is an important next step to improve understanding of the genetic etiology of AUBs, in addition to increasing sample size. Further effort is required to uncover the genetic heterogeneity underlying AUBs using methods that account for their complex, multidimensional nature.

A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets.

Despite major successes with inhibitory receptor blockade in cancer, the identification of novel inhibitory receptors as putative drug targets is needed due to lack of durable responses, therapy resistance, and side effects. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies estimated that our genome contains over 1600 ITIM-bearing transmembrane proteins. However, testing and development of these candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. Therefore, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify putative inhibitory receptors. Using transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories. Known and putative inhibitory receptors were expressed across different immune cell subsets with cell type-specific expression patterns. Furthermore, putative immune inhibitory receptors were differentially expressed in subsets of tumour infiltrating T cells. In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel human inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.

A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets

Despite major successes with inhibitory receptor blockade in cancer, the identification of novel inhibitory receptors as putative drug targets is needed due to lack of durable responses, therapy resistance, and side effects. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies estimated that our genome contains over 1600 ITIM-bearing transmembrane proteins. However, testing and development of these candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. Therefore, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify putative inhibitory receptors. Using transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories. Known and putative inhibitory receptors were expressed across different immune cell subsets with cell type-specific expression patterns. Furthermore, putative immune inhibitory receptors were differentially expressed in subsets of tumour infiltrating T cells. In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel human inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.

Low level exposure to BDE-47 facilitates the development of prostate cancer through TOP2A/LDHA/lactylation positive feedback circuit

2,2′,4,4′-tetra brominated diphenyl ether (BDE-47) is one of the most widely distributed congeners of polybrominated diphenyl ethers. While the relationships between BDE-47 exposure and other hormone-dependent cancers (such as breast cancer) are well established, no previous study has examined whether BDE-47 exposure is related to the development of prostate cancer (PCa). Through bulk and single-cell RNA sequencing (scRNA-seq) analyses, as well as in vitro and in vivo experiments, this study aims to investigate the effect of BDE-47 exposure on PCa progression. Herein, we found that low dose BDE-47 promoted the growth of PCa cells (PC3 and LNCaP) in a dose-dependent manner in vitro and in vivo. Based on Comparative Toxicogenomics Database (CTD) and The Cancer Genome Atlas (TCGA), we obtained 34 BDE-47-related and PCa-related genes through screening and overlapping. These genes were significantly enriched in fatty acid metabolism-related gene ontology (GO) terms, which were also enriched for genes targeting BDE-47 obtained from the UniProt. Through scRNA-seq data, certain cell type-specific expression was observed for CYP2E1, PIK3R1, FGF2, and TOP2A in PCa tissues from men. Molecular docking simulation showed that BDE-47 was tightly bound to the protein residues of AOX1, PIK3R1, FGF2, CAV2, CYP2E1 and TOP2A. Further screening in terms of patient overall survival, receiver operating characteristics curve (ROC) curve and Gleason score grading system narrowed the candidate genes down to TOP2A. Mechanistically, the growth-promoting effect of BDE-47 on PCa cells could be reversed by TOP2A inhibitor. RNA-seq followed by experimental verification demonstrated that TOP2A promoted PCa progression through upregulating LDHA and glycolysis. Furthermore, lactate upregulated TOP2A transcription through lactylation of H3K18la in PCa cells, which could be rescued by LDHA knockdown. Taken together, low dose BDE-47 triggered PCa progression through TOP2A/LDHA/lactylation positive feedback circuit, thus revealing epigenetic shifting and metabolic reprogramming underpinning BDE-47-induced carcinogenesis of the prostate.

641P Modeling cell type specific and sporadic DUX4 gene expression in FSHD

641P Modeling cell type specific and sporadic DUX4 gene expression in FSHD

ScRNAbox: empowering single-cell RNA sequencing on high performance computing systems

BackgroundSingle-cell RNA sequencing (scRNAseq) offers powerful insights, but the surge in sample sizes demands more computational power than local workstations can provide. Consequently, high-performance computing (HPC) systems have become imperative. Existing web apps designed to analyze scRNAseq data lack scalability and integration capabilities, while analysis packages demand coding expertise, hindering accessibility.ResultsIn response, we introduce scRNAbox, an innovative scRNAseq analysis pipeline meticulously crafted for HPC systems. This end-to-end solution, executed via the SLURM workload manager, efficiently processes raw data from standard and Hashtag samples. It incorporates quality control filtering, sample integration, clustering, cluster annotation tools, and facilitates cell type-specific differential gene expression analysis between two groups. We demonstrate the application of scRNAbox by analyzing two publicly available datasets.ConclusionScRNAbox is a comprehensive end-to-end pipeline designed to streamline the processing and analysis of scRNAseq data. By responding to the pressing demand for a user-friendly, HPC solution, scRNAbox bridges the gap between the growing computational demands of scRNAseq analysis and the coding expertise required to meet them.

Like sisters but not twins - vasopressin and oxytocin excite BNST neurons via cell type-specific expression of oxytocin receptor to reduce anxious arousal.

Interoceptive signals dynamically interact with the environment to shape appropriate defensive behaviors. Hypothalamic hormones arginine-vasopressin (AVP) and oxytocin (OT) regulate physiological states, including water and electrolyte balance, circadian rhythmicity, and defensive behaviors. Both AVP and OT neurons project to dorsolateral bed nucleus of stria terminalis (BNSTDL), which expresses oxytocin receptors (OTR) and vasopressin receptors and mediates fear responses. However, understanding the integrated role of neurohypophysial hormones is complicated by the cross-reactivity of AVP and OT and their mutual receptor promiscuity. Here, we provide evidence that the effects of neurohypophysial hormones on BNST excitability are driven by input specificity and cell type-specific receptor selectivity. We show that OTR-expressing BNSTDL neurons, excited by hypothalamic OT and AVP inputs via OTR, play a major role in regulating BNSTDL excitability, overcoming threat avoidance, and reducing threat-elicited anxious arousal. Therefore, OTR-BNSTDL neurons are perfectly suited to drive the dynamic interactions balancing external threat risk and physiological needs.

A germline PAF1 paralog complex ensures cell type-specific gene expression.

Animal germline development and fertility rely on paralogs of general transcription factors that recruit RNA polymerase II to ensure cell type-specific gene expression. It remains unclear whether gene expression processes downstream from such paralog-based transcription is distinct from that of canonical RNA polymerase II genes. In Drosophila, the testis-specific TBP-associated factors (tTAFs) activate over a thousand spermatocyte-specific gene promoters to enable meiosis and germ cell differentiation. Here, we show that efficient termination of tTAF-activated transcription relies on testis-specific paralogs of canonical polymerase-associated factor 1 complex (PAF1C) proteins, which form a testis-specific PAF1C (tPAF). Consequently, tPAF mutants show aberrant expression of hundreds of downstream genes due to read-in transcription. Furthermore, tPAF facilitates expression of Y-linked male fertility factor genes and thus serves to maintain spermatocyte-specific gene expression. Consistently, tPAF is required for the segregation of meiotic chromosomes and male fertility. Supported by comparative in vivo protein interaction assays, we provide a mechanistic model for the functional divergence of tPAF and the PAF1C and identify transcription termination as a developmentally regulated process required for germline-specific gene expression.

Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-seq data

BackgroundThe widely adopted bulk RNA-seq measures the gene expression average of cells, masking cell type heterogeneity, which confounds downstream analyses. Therefore, identifying the cellular composition and cell type-specific gene expression profiles (GEPs) facilitates the study of the underlying mechanisms of various biological processes. Although single-cell RNA-seq focuses on cell type heterogeneity in gene expression, it requires specialized and expensive resources and currently is not practical for a large number of samples or a routine clinical setting. Recently, computational deconvolution methodologies have been developed, while many of them only estimate cell type composition or cell type-specific GEPs by requiring the other as input. The development of more accurate deconvolution methods to infer cell type abundance and cell type-specific GEPs is still essential.ResultsWe propose a new deconvolution algorithm, DSSC, which infers cell type-specific gene expression and cell type proportions of heterogeneous samples simultaneously by leveraging gene-gene and sample-sample similarities in bulk expression and single-cell RNA-seq data. Through comparisons with the other existing methods, we demonstrate that DSSC is effective in inferring both cell type proportions and cell type-specific GEPs across simulated pseudo-bulk data (including intra-dataset and inter-dataset simulations) and experimental bulk data (including mixture data and real experimental data). DSSC shows robustness to the change of marker gene number and sample size and also has cost and time efficiencies.ConclusionsDSSC provides a practical and promising alternative to the experimental techniques to characterize cellular composition and heterogeneity in the gene expression of heterogeneous samples.

Abstract C058: A novel approach to understanding pancreas cancer risk through enhancer- promoter interactions

Abstract Genetic or epigenetic variations in regulatory enhancer elements increase susceptibility to a range of pathologies, including pancreas cancer. Pinpointing genes affecting pancreas cancer risk holds promise for early detection, prevention, and effective therapies. Despite recent advances, linking enhancer elements to target genes and predicting the transcriptional outcome of enhancer dysfunction remain significant challenges. Using 3D chromatin assays, we generated an extensive enhancer interaction dataset for the human pancreas, spanning more than 20 donors and five major cell types, including both the exocrine and endocrine compartments. We employed a network approach to parse chromatin interactions into enhancer-promoter tree models, facilitating quantitative, genome-wide analysis of enhancer connectivity. Using the tree models, we developed a machine learning algorithm capable of estimating the impact of enhancer perturbations on cell-type specific gene expression in the human pancreas. Complementing this computational approach, we streamlined an experimental platform using CRISPR-interference and RNA-FISH coupled with high-throughput imaging to quantitatively measure the effect size of enhancer perturbations in primary human pancreas cells. Our enhancer tree models enabled functional annotation of genetic variants associated with pancreas diseases, revealing novel target genes in specific cell types. For pancreas cancer (PDAC), our data suggests a stronger association of disease susceptibility variants with acinar cells, even though ductal cells historically have been the focus of PDAC. This discovery linked three potentially functional variants associated with PDAC risk to acinar cell enhancers, revealing novel target genes to further investigation. By integrating genomics with mechanistic findings into enhancer function, our work offers a framework for understanding genetic basis of pancreas cancer disease risk. Citation Format: Li Wang, Songjoon Baek, Gauri Prasad, Thucnhi Truongvo, Jason Hoskins, Laufey Amundadottir, Efsun Arda. A novel approach to understanding pancreas cancer risk through enhancer- promoter interactions [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C058.

Prefrontal cortex astrocytes in major depressive disorder: exploring pathogenic mechanisms and potential therapeutic targets.

Major depressive disorder (MDD) is a prevalent mental health condition characterized by persistent feelings of sadness and hopelessness, affecting millions globally. The precise molecular mechanisms underlying MDD remain elusive, necessitating comprehensive investigations. Our study integrates transcriptomic analysis, functional assays, and computational modeling to explore the molecular landscape of MDD, focusing on the DLPFC. We identify key genomic alterations and co-expression modules associated with MDD, highlighting potential therapeutic targets. Functional enrichment and protein-protein interaction analyses emphasize the role of astrocytes in MDD progression. Machine learning is employed to develop a predictive model for MDD risk assessment. Single-cell and spatial transcriptomic analyses provide insights into cell type-specific expression patterns, particularly regarding astrocytes. We have identified significant genomic alterations and co-expression modules associated with MDD in the DLPFC. Key genes involved in neuroactive ligand-receptor interaction pathways, notably in astrocytes, have been highlighted. Additionally, we developed a predictive model for MDD risk assessment based on selected key genes. Single-cell and spatial transcriptomic analyses underscored the role of astrocytes in MDD. Virtual screening of compounds targeting GPR37L1, KCNJ10, and PPP1R3C proteins has identified potential therapeutic candidates. In summary, our comprehensive approach enhances the understanding of MDD's molecular underpinnings and offers promising opportunities for advancing therapeutic interventions, ultimately aiming to alleviate the burden of this debilitating mental health condition. KEY MESSAGES: Our investigation furnishes insightful revelations concerning the dysregulation of astrocyte-associated processes in MDD. We have pinpointed specific genes, namely KCNJ10, PPP1R3C, and GPR37L1, as potential candidates warranting further exploration and therapeutic intervention. We incorporate a virtual screening of small molecule compounds targeting KCNJ10, PPP1R3C, and GPR37L1, presenting a promising trajectory for drug discovery in MDD.