GR-regulated enhancers play a central role in the gene regulatory networks underlying drug addiction

Substance abuse and addiction represent a major public health problem that impacts multiple dimensions of society, including healthcare, economy, and workforce. In 2021, over 100,000 drug overdose deaths have been reported in the US with an alarming increase in fatalities related to opioids and psychostimulants. Understanding of the fundamental gene regulatory mechanisms underlying addiction and related behaviors could facilitate more effective treatments. To explore how repeated drug exposure alters gene regulatory networks in the brain, we combined capped small (cs)RNA-seq, which accurately captures nascent-like initiating transcripts from total RNA, with Hi-C and single nuclei (sn)ATAC-seq. We profiled initiating transcripts in two addiction-related brain regions, the prefrontal cortex (PFC) and the nucleus accumbens (NAc), from rats that were never exposed to drugs or were subjected to prolonged abstinence after oxycodone or cocaine intravenous self-administration (IVSA). Interrogating over 100,000 active transcription start regions (TSRs) revealed that most TSRs had hallmarks of bona-fide enhancers and highlighted the KLF/SP1, RFX and AP1 transcription factors families as central to establish brain-specific gene regulatory programs. Analysis of rats with addiction-like behaviors versus controls identified addiction-associated repression of transcription at regulatory enhancers recognized by nuclear receptor subfamily 3 group C (NR3C) factors, which include glucocorticoid receptors. Cell-type deconvolution analysis using snATAC-seq uncovered a potential role of glial cells in driving the gene regulatory programs associated with addiction-related phenotypes. These findings highlight the power of advanced transcriptomics methods to provide insight into how addiction perturbs gene regulatory programs in the brain.

Role of synovial fibroblast subsets across synovial pathotypes in rheumatoid arthritis: a deconvolution analysis

ObjectivesTo integrate published single-cell RNA sequencing (scRNA-seq) data and assess the contribution of synovial fibroblast (SF) subsets to synovial pathotypes and respective clinical characteristics in treatment-naïve early arthritis.MethodsIn this in silico study, we integrated scRNA-seq data from published studies with additional unpublished in-house data. Standard Seurat, Harmony and Liger workflow was performed for integration and differential gene expression analysis. We estimated single cell type proportions in bulk RNA-seq data (deconvolution) from synovial tissue from 87 treatment-naïve early arthritis patients in the Pathobiology of Early Arthritis Cohort using MuSiC. SF proportions across synovial pathotypes (fibroid, lymphoid and myeloid) and relationship of disease activity measurements across different synovial pathotypes were assessed.ResultsWe identified four SF clusters with respective marker genes: PRG4+ SF (CD55, MMP3, PRG4, THY1neg); CXCL12+ SF (CXCL12, CCL2, ADAMTS1, THY1low); POSTN+ SF (POSTN, collagen genes, THY1); CXCL14+ SF (CXCL14, C3, CD34, ASPN, THY1) that correspond to lining (PRG4+ SF) and sublining (CXCL12+ SF, POSTN+ + and CXCL14+ SF) SF subsets. CXCL12+ SF and POSTN+ + were most prominent in the fibroid while PRG4+ SF appeared highest in the myeloid pathotype. Corresponding, lining assessed by histology (assessed by Krenn-Score) was thicker in the myeloid, but also in the lymphoid pathotype + the fibroid pathotype. PRG4+ SF correlated positively with disease severity parameters in the fibroid, POSTN+ SF in the lymphoid pathotype whereas CXCL14+ SF showed negative association with disease severity in all pathotypes.ConclusionThis study shows a so far unexplored association between distinct synovial pathologies and SF subtypes defined by scRNA-seq. The knowledge of the diverse interplay of SF with immune cells will advance opportunities for tailored targeted treatments.

Development of a Machine Learning Classifier for Brain Tumors Diagnosis Based on DNA Methylation Profile

Background: More than 150 types of brain tumors have been documented. Accurate diagnosis is important for making appropriate therapeutic decisions in treating the diseases. The goal of this study is to develop a DNA methylation profile-based classifier to accurately identify various kinds of brain tumors.Methods: Thirteen datasets of DNA methylation profiles were downloaded from the Gene Expression Omnibus (GEO) database, of which GSE90496 and GSE109379 were used as the training set and the validation set, respectively, and the remaining 11 sets were used as the independent test set. The random forest algorithm was used to select the CpG sites based on the importance of the features and a multilayer perceptron (MLP) model was trained to classify the samples. Deconvolution with the debCAM package was used to explore the cellular composition difference among tumors.Results: From training datasets with 2,801 samples, 396,568 CpG sites were retained after preprocessing, of which 767 were selected as the modeling features. A three-layer MLP model was developed, which consists of 1,320 nodes in the hidden layer, to predict the histological types of brain tumors. The prediction accuracy is 99.2, 87.0, and 96.58%, respectively, on the training, validation and test sets. The results of deconvolution analysis showed that the cell proportions of different tumor subtypes were different, and it is approximately enough to distinguish different tumor entities.Conclusion: We developed a classifier that is robust for the classification of central nervous system tumors, and tried to analyze the reasons for the classification performance.

HIF-Mediated and Non-HIF-Mediated Differential Gene Expressions in Sickle Cell Reticulocyte and Their Impact on Clinical Manifestations

Reticulocytosis in sickle cell disease (SCD) is driven by tissue hypoxia from hemolytic anemia and vascular occlusion. Gene expression changes caused by hypoxia and other factors during reticulocytosis may impact SCD outcomes. We detected 1226 differentially expressed genes in SCD reticulocyte transcriptome compared to normal Black controls. To assess the role of hypoxia-mediating HIFs from other regulation of changes of the SCD reticulocyte transcriptome, we compared differential expression in SCD to that in Chuvash erythrocytosis (CE), a disorder characterized by constitutive upregulation of HIFs in normoxia. Of the SCD differentially expressed genes, 28% were shared between CE and SCD and thus classified as HIF-mediated. The HIF-mediated changes were generally in genes promoting erythroid maturation. We found that genes encoding the response to endoplasmic reticulum stress generally lacked HIF mediation. We then investigated the clinical correlation of erythroid gene expression for the 1226 differentially expressed genes detected in SCD reticulocytes, using clinical measures and gene expression data previously profiled in peripheral blood mononuclear cells (PBMCs) of 157 SCD patients at the University of Illinois at Chicago (UIC). Normal PBMCs contain only a small number of erythroid progenitors, but in SCD or CE PBMCs the erythroid transcriptome is enriched due to elevated circulating erythroid progenitors from heightened erythropoiesis (PMID: 32399971). We applied deconvolution analysis to assess the clinical correlation of erythroid gene expression, using a 16-gene expression signature of erythroid progenitors previously identified in SCD PBMCs. Deconvolution analysis uses the proportion of cell/tissue or specific marker genes (here the erythroid specific 16-gene signature) to dissect gene expression variation in biological samples with cell/tissue type heterogeneity. We correlated, in the 157 UIC patients, erythroid gene expression with i) degree of anemia as indicated by hemoglobin concentration, ii) vaso-occlusive severe pain episodes per year, and iii) degree of hemolysis measured by a hemolysis index. The analysis identified 231 genes associated with at least one of the complications. Increased expression of 40 erythroid specific genes, including 15 HIF-mediated genes, was associated with all three complications. These 40 genes are all upregulated in SCD reticulocytes and correlated with low hemoglobin concentration, frequent severe pain episodes, and high hemolysis index, suggesting that these manifestations may share a relationship to stress erythropoiesis-driven transcriptional activity. Expression quantitative trait loci (eQTL) contain genetic polymorphisms that associate with gene expression level, which can be viewed as a natural experiment to investigate the causal relations between gene expression change and phenotypic outcomes. To assess the causal effect of erythroid gene expression, we tested association between erythroid eQTL and the clinical manifestations in 906 SCD patients from the Walk-PHaSST and PUSH cohorts. We first mapped erythroid eQTL in the 157 UIC patients, who were previously genotyped by array, applying deconvolution algorithm on the same PBMC data for the 1226 differential genes in SCD reticulocytes, and detected 54 distinct eQTL for 30 genes at 5% false discovery rate. After adjusting for multiple comparisons, we found that the C allele of rs16911905, located in the β-globin cluster and associated with increased erythroid expression of HBD (encodes δ-globin of hemoglobin A 2), significantly correlated with lower hemoglobin concentration (β=-0.064, 95% CI -0.092 - -0.036, P=6.7×10 -6). The C allele was also associated with higher hemolytic rate (P=0.031), less frequent pain episodes (P=0.045), and increased erythroid expression of HBB here encoding sickle β-globin (P=5.1x10 -5). The association of the C allele with lower hemoglobin concentration was then validated in 242 patients from the UIC cohort (β=-0.071, 95% CI -0.13 - -0.011, P=0.023), as was the trend of association with higher hemolytic rate (P=0.0031) and less pain episodes (P=0.034). Our findings reveal HIF- and non-HIF-mediated genes in SCD stress erythropoiesis, and identify novel clinical associations for a HBD eQTL. Our study highlights the correlation of altered erythroid gene expression with SCD hemolytic and vaso-occlusive manifestations. Disclosures Saraf: Global Blood Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding. Gordeuk: Modus Therapeutics: Consultancy; Novartis: Research Funding; Incyte: Research Funding; Emmaus: Consultancy, Research Funding; Global Blood Therapeutics: Consultancy, Research Funding; CSL Behring: Consultancy.

Molecular programs of fibrotic change in aging human lung

Lung fibrosis is increasingly detected with aging and has been associated with poor outcomes in acute lung injury or infection. However, the molecular programs driving this pro-fibrotic evolution are unclear. Here we profile distal lung samples from healthy human donors across the lifespan. Gene expression profiling by bulk RNAseq reveals both increasing cellular senescence and pro-fibrotic pathway activation with age. Quantitation of telomere length shows progressive shortening with age, which is associated with DNA damage foci and cellular senescence. Cell type deconvolution analysis of the RNAseq data indicates a progressive loss of lung epithelial cells and an increasing proportion of fibroblasts with age. Consistent with this pro-fibrotic profile, second harmonic imaging of aged lungs demonstrates increased density of interstitial collagen as well as decreased alveolar expansion and surfactant secretion. In this work, we reveal the transcriptional and structural features of fibrosis and associated functional impairment in normal lung aging.

TAMI-12. CANCER-IMMUNE CELL INTERACTIONS DRIVE TRANSITIONS TO MESENCHYMAL-LIKE STATES IN GLIOBLASTOMA

Communication between cancer cells and immune cells is a key determinant of the glioblastoma ecosystem and its response to therapies, but remains poorly understood. Here we leveraged single-cell RNA-sequencing (scRNA-seq) of human samples and mouse models, deconvolution analysis of bulk specimens from The Cancer Genome Atlas (TCGA) and functional approaches to dissect cellular states and cross-talk in glioblastoma. We demonstrate that macrophages induce a transition of glioblastoma cells into mesenchymal-like (MES-like) states. This effect is mediated, both in vitro and in vivo, by macrophage-derived Oncostatin M (OSM) and its cognate receptor OSMR on glioblastoma cells. We show that MES-like glioblastoma states are associated with increased T cells cytotoxicity and potentially with better clinical response to immunotherapies. Overall, our work dissects the cellular interactions within the glioblastoma microenvironment, with potential implications for therapies.

Amide I Band Analysis Applied to Vibrational Micro-Spectroscopies of Gingival Crevicular Fluid Samples for Orthodontic Treatment Monitoring

Vibrational micro-spectroscopies were applied to investigate the gingival crevicular fluid (GCF) for monitoring orthodontic treatment with fixed appliances. The GCF samples were investigated using Fourier transform infrared, Raman, and surface-enhanced Raman micro-spectroscopies. The GCF spectra collected at different times of orthodontic tooth movement were used to characterize the biochemical changes occurring during the treatment. We examined the amide I band region by means of deconvolution analysis using Gaussian–Lorentzian curves for infrared spectra and Lorentzian curves for Raman spectra. This analysis allowed us to evidence the contribution of the different subcomponents of the amide I band and the changes occurring during orthodontic treatment. These changes can be ascribed to modifications in the secondary structure of protein content and could contribute to make vibrational spectroscopies a useful tool for monitoring the individual patient’s response to orthodontic force application.