Dysregulation of NRSF/REST via EHMT1 is associated with psychiatric disorders and Kleefstra syndrome, Z scores
Description:
EHMT1 is an epigenetic repressor that is causal for Kleefstra Syndrome (KS), a neurodevelopmental disorder (NDD) leading to intelectual disability, and is associated with schizophrenia. Here, the researchers aim to show we show that reduced EHMT1 activity decreases NRSF/REST protein leading to abnormal neuronal gene expression and progression of neurodevelopment in human iPSC. Five induced pluripotent stem cell samples (from fibroblasts of adult, male, skin) were used. The stem cells were gifted from: Lieber Institute for Brain Development, Johns Hopkins Medical Campus. Total RNA extracted from a control hiPSC line and control cells treated for 72h with various concentrations of UNC0638 i.e 50, 100, 200 or 250nM as a model for Kleefstra syndrome. Polyadenylated adaptors were ligated to the 3′-end, 5′-adaptors were then ligated, and the resulting RNAs were reverse transcribed to generate cDNA that can be amplified by PCR. The amplified product was run on low range ultra agarose in TBE buffer and a size-selection was performed to ensure that the cDNA used for sequencing primarily contains miRNAs rather than other RNA contaminants. Expression values were calculated by the method detailed in 'HBA-DEALS: accurate and simultaneous identification of differential expression and splicing using hierarchical Bayesian analysis' (Genome Biol. 2020, PMID: 32660516), and Z scores calculated. Genes were annotated as Ensembl gene ids. SRA Study id ERP130338.
Data from GEO GSE194368 and analyzed using GEO2R, only top gene shown. Authors identified transcriptional adaptations of GR signaling in the amygdala of humans with OUD. Thus, GRs, their coregulators and downstream systems may represent viable therapeutic targets to treat the “stress side” of OUD.
Authors:
Stephanie A Carmack, Janaina C M Vendruscolo, M Adrienne McGinn, Jorge Miranda-Barrientos, Vez Repunte-Canonigo, Gabriel D Bosse, Daniele Mercatelli, Federico M Giorgi, Yu Fu, Anthony J Hinrich, Francine M Jodelka, Karen Ling, Robert O Messing, Randall T Peterson, Frank Rigo, Scott Edwards, Pietro P Sanna, Marisela Morales, Michelle L Hastings, George F Koob, Leandro F Vendruscolo
Data from GEO GSE194368 and analyzed using GEO2R, only top gene shown. Authors identified transcriptional adaptations of GR signaling in the amygdala of humans with OUD. Thus, GRs, their coregulators and downstream systems may represent viable therapeutic targets to treat the “stress side” of OUD.
Authors:
Stephanie A Carmack, Janaina C M Vendruscolo, M Adrienne McGinn, Jorge Miranda-Barrientos, Vez Repunte-Canonigo, Gabriel D Bosse, Daniele Mercatelli, Federico M Giorgi, Yu Fu, Anthony J Hinrich, Francine M Jodelka, Karen Ling, Robert O Messing, Randall T Peterson, Frank Rigo, Scott Edwards, Pietro P Sanna, Marisela Morales, Michelle L Hastings, George F Koob, Leandro F Vendruscolo
The dataset used in this study (Bulk RNA-Seq) was previously published and can be found at NCBI GEO (GSE182321), this analysis was conducted by GEO2R to compare control and OUD samples, only top differentially expressed genes are reported. To understand mechanisms and identify potential targets for intervention in the current crisis of opioid use disorder (OUD), postmortem brains represent an under-utilized resource. To refine previously reported gene signatures of neurobiological alterations in OUD from the dorsolateral prefrontal cortex (Brodmann Area 9, BA9), we explored the role of microRNAs (miRNA) as powerful epigenetic regulators of gene function.
The dataset used in this study (Bulk RNA-Seq) was previously published and can be found at NCBI GEO (GSE182321), this analysis was conducted by GEO2R to compare control and OUD samples, only top differentially expressed genes are reported. To understand mechanisms and identify potential targets for intervention in the current crisis of opioid use disorder (OUD), postmortem brains represent an under-utilized resource. To refine previously reported gene signatures of neurobiological alterations in OUD from the dorsolateral prefrontal cortex (Brodmann Area 9, BA9), we explored the role of microRNAs (miRNA) as powerful epigenetic regulators of gene function.
Transcriptional alterations in dorsolateral prefrontal cortex and nucleus accumbens implicate neuroinflammation and synaptic remodeling in opioid use disorder. Transcriptomic profile of 20 control subjects and 20 OUD subjects in brain region DLPFC and NAC. Analyzed using GEO2R (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174409) separately for each brain region, comparing OUD and control samples.
Authors:
Xiangning Xue, Wei Zong, Jill R Glausier, Sam-Moon Kim, Micah A Shelton, BaDoi N Phan, Chaitanya Srinivasan, Andreas R Pfenning, George C Tseng, David A Lewis, Marianne L Seney, Ryan W Logan
Transcriptional alterations in dorsolateral prefrontal cortex and nucleus accumbens implicate neuroinflammation and synaptic remodeling in opioid use disorder. Transcriptomic profile of 20 control subjects and 20 OUD subjects in brain region DLPFC and NAC. Analyzed using GEO2R (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174409) separately for each brain region, comparing OUD and control samples.
Authors:
Xiangning Xue, Wei Zong, Jill R Glausier, Sam-Moon Kim, Micah A Shelton, BaDoi N Phan, Chaitanya Srinivasan, Andreas R Pfenning, George C Tseng, David A Lewis, Marianne L Seney, Ryan W Logan
Transcriptional alterations in dorsolateral prefrontal cortex and nucleus accumbens implicate neuroinflammation and synaptic remodeling in opioid use disorder. Transcriptomic profile of 20 control subjects and 20 OUD subjects in brain region DLPFC and NAC. Analyzed using GEO2R (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174409) separately for each brain region, comparing OUD and control samples.
Authors:
Xiangning Xue, Wei Zong, Jill R Glausier, Sam-Moon Kim, Micah A Shelton, BaDoi N Phan, Chaitanya Srinivasan, Andreas R Pfenning, George C Tseng, David A Lewis, Marianne L Seney, Ryan W Logan
Postmortem tissue samples of the dorsolateral prefrontal cortex (DLPFC) from 153 deceased individuals (Mage = 35.4; 62% male; 77% European ancestry). Study groups included 72 brain samples from individuals who died of acute opioid intoxication, 53 psychiatric controls, and 28 normal controls. Whole transcriptome RNA-sequencing was used to generate exon counts, and differential expression was tested using limma-voom. Analyses were adjusted for relevant sociodemographic characteristics, technical covariates, and cryptic relatedness using quality surrogate variables. Weighted correlation network analysis and gene set enrichment analyses also were conducted.
Authors:
David W Sosnowski, Andrew E Jaffe, Ran Tao, Amy Deep-Soboslay, Chang Shu, Sarven Sabunciyan, Joel E Kleinman, Thomas M Hyde, Brion S Maher
Postmortem tissue samples of the dorsolateral prefrontal cortex (DLPFC) from 153 deceased individuals (Mage = 35.4; 62% male; 77% European ancestry). Study groups included 72 brain samples from individuals who died of acute opioid intoxication, 53 psychiatric controls, and 28 normal controls. Whole transcriptome RNA-sequencing was used to generate exon counts, and differential expression was tested using limma-voom. Analyses were adjusted for relevant sociodemographic characteristics, technical covariates, and cryptic relatedness using quality surrogate variables. Weighted correlation network analysis and gene set enrichment analyses also were conducted.
Authors:
David W Sosnowski, Andrew E Jaffe, Ran Tao, Amy Deep-Soboslay, Chang Shu, Sarven Sabunciyan, Joel E Kleinman, Thomas M Hyde, Brion S Maher
Opioid_human_dorsolateral prefrontal cortex_reanalysis of Corradin et al. 2022_log2FC
Description:
doi: https://doi.org/10.1101/2024.01.12.24301153. This study is a re-analysis of publicly available data and a meta-analysis investigating differential gene expression associated with opioid use disorder from Corradin et al. 2022 (PMID: 35301427); Mendez et al. 2021 (PMID: 34385598); Seney et al. 2021 (PMID: 34380600); and Sosnowski et al. 2022 (PMID:36845993 ). All four of these studies used human postmortem dorsolateral prefrontal cortex (DLPFC) brain tissue from donors identified as dying from OOD through toxicology assays administered by forensic scientists and phenotypic evidence of opioid addiction. Each of these independent studies had modest sample sizes (N = 40-153) and compared bulk RNA-seq data from individuals who died from OOD to individuals who died from non–drug use causes.
Opioid_human_dorsolateral prefrontal cortex_reanalysis of Corradin et al. 2022_qvalue
Description:
doi: https://doi.org/10.1101/2024.01.12.24301153. This study is a re-analysis of publicly available data and a meta-analysis investigating differential gene expression associated with opioid use disorder from Corradin et al. 2022 (PMID: 35301427); Mendez et al. 2021 (PMID: 34385598); Seney et al. 2021 (PMID: 34380600); and Sosnowski et al. 2022 (PMID:36845993 ). All four of these studies used human postmortem dorsolateral prefrontal cortex (DLPFC) brain tissue from donors identified as dying from OOD through toxicology assays administered by forensic scientists and phenotypic evidence of opioid addiction. Each of these independent studies had modest sample sizes (N = 40-153) and compared bulk RNA-seq data from individuals who died from OOD to individuals who died from non–drug use causes.
Opioid_human_dorsolateral prefrontal cortex_reanalysis of Mendez et al 2021_log2FC
Description:
doi: https://doi.org/10.1101/2024.01.12.24301153. This study is a re-analysis of publicly available data and a meta-analysis investigating differential gene expression associated with opioid use disorder from Corradin et al. 2022 (PMID: 35301427); Mendez et al. 2021 (PMID: 34385598); Seney et al. 2021 (PMID: 34380600); and Sosnowski et al. 2022 (PMID:36845993 ). All four of these studies used human postmortem dorsolateral prefrontal cortex (DLPFC) brain tissue from donors identified as dying from OOD through toxicology assays administered by forensic scientists and phenotypic evidence of opioid addiction. Each of these independent studies had modest sample sizes (N = 40-153) and compared bulk RNA-seq data from individuals who died from OOD to individuals who died from non–drug use causes.
doi: https://doi.org/10.1101/2024.01.12.24301153. This study is a re-analysis of publicly available data and a meta-analysis investigating differential gene expression associated with opioid use disorder from Corradin et al. 2022 (PMID: 35301427); Mendez et al. 2021 (PMID: 34385598); Seney et al. 2021 (PMID: 34380600); and Sosnowski et al. 2022 (PMID:36845993 ). All four of these studies used human postmortem dorsolateral prefrontal cortex (DLPFC) brain tissue from donors identified as dying from OOD through toxicology assays administered by forensic scientists and phenotypic evidence of opioid addiction. Each of these independent studies had modest sample sizes (N = 40-153) and compared bulk RNA-seq data from individuals who died from OOD to individuals who died from non–drug use causes.
Opioid_human_dorsolateral prefrontal cortex_reanalysis of Seney et al 2021_log2FC
Description:
doi: https://doi.org/10.1101/2024.01.12.24301153. This study is a re-analysis of publicly available data and a meta-analysis investigating differential gene expression associated with opioid use disorder from Corradin et al. 2022 (PMID: 35301427); Mendez et al. 2021 (PMID: 34385598); Seney et al. 2021 (PMID: 34380600); and Sosnowski et al. 2022 (PMID:36845993 ). All four of these studies used human postmortem dorsolateral prefrontal cortex (DLPFC) brain tissue from donors identified as dying from OOD through toxicology assays administered by forensic scientists and phenotypic evidence of opioid addiction. Each of these independent studies had modest sample sizes (N = 40-153) and compared bulk RNA-seq data from individuals who died from OOD to individuals who died from non–drug use causes.
Opioid_human_dorsolateral prefrontal cortex_reanalysis of Seney et al 2021_qvalue
Description:
doi: https://doi.org/10.1101/2024.01.12.24301153. This study is a re-analysis of publicly available data and a meta-analysis investigating differential gene expression associated with opioid use disorder from Corradin et al. 2022 (PMID: 35301427); Mendez et al. 2021 (PMID: 34385598); Seney et al. 2021 (PMID: 34380600); and Sosnowski et al. 2022 (PMID:36845993 ). All four of these studies used human postmortem dorsolateral prefrontal cortex (DLPFC) brain tissue from donors identified as dying from OOD through toxicology assays administered by forensic scientists and phenotypic evidence of opioid addiction. Each of these independent studies had modest sample sizes (N = 40-153) and compared bulk RNA-seq data from individuals who died from OOD to individuals who died from non–drug use causes.
Opioid_human_dorsolateral prefrontal cortex_reanalysis of Sosnowski et al 2022_log2FC
Description:
doi: https://doi.org/10.1101/2024.01.12.24301153. This study is a re-analysis of publicly available data and a meta-analysis investigating differential gene expression associated with opioid use disorder from Corradin et al. 2022 (PMID: 35301427); Mendez et al. 2021 (PMID: 34385598); Seney et al. 2021 (PMID: 34380600); and Sosnowski et al. 2022 (PMID:36845993 ). All four of these studies used human postmortem dorsolateral prefrontal cortex (DLPFC) brain tissue from donors identified as dying from OOD through toxicology assays administered by forensic scientists and phenotypic evidence of opioid addiction. Each of these independent studies had modest sample sizes (N = 40-153) and compared bulk RNA-seq data from individuals who died from OOD to individuals who died from non–drug use causes.
Opioid_human_dorsolateral prefrontal cortex_reanalysis of Sosnowski et al 2022_qvalue
Description:
doi: https://doi.org/10.1101/2024.01.12.24301153. This study is a re-analysis of publicly available data and a meta-analysis investigating differential gene expression associated with opioid use disorder from Corradin et al. 2022 (PMID: 35301427); Mendez et al. 2021 (PMID: 34385598); Seney et al. 2021 (PMID: 34380600); and Sosnowski et al. 2022 (PMID:36845993 ). All four of these studies used human postmortem dorsolateral prefrontal cortex (DLPFC) brain tissue from donors identified as dying from OOD through toxicology assays administered by forensic scientists and phenotypic evidence of opioid addiction. Each of these independent studies had modest sample sizes (N = 40-153) and compared bulk RNA-seq data from individuals who died from OOD to individuals who died from non–drug use causes.
Predicted DEG from gSEM GWAS in nucleus accumbens of OA cases vs. controls_pvalue
Description:
The genomic structural equation modeling (gSEM) analysis includes results on european ancestry (EA) from the Genetics of Opioid Addiction Consortium (GENOA) meta-analysis, Million Veteran Program (MVP), Psychiatric Genetics Consortium Substance Use Disorders Group (PGC-SUD), and the Partners Health cohorts. The MVP results are based on a meta-analysis including MVP parts 1 and 2 (total of 8529 OUD cases and 71,200 opioid-exposed controls), along with Yale-Penn and Study of Addiction: Genetics and Environment (SAGE) cohorts (total of 10,544 OUD cases and 72,163 opioid-exposed controls). The PGC-SUD results include 4,503 opioid dependence cases and 4173 unexposed controls. Unexposed controls are used for the PGC-SUD because the exposed controls results have negative heritability estimates. The partners health cohort includes 1,039 OUD cases and 10,743 exposed controls. Note that the GENOA GWAS, Yale-Penn, and SAGE parts of the MVP, and the PGC-SUD results include overlapping samples. However, accounting for this sample overlap is a feature of the gSEM approach applied in this study. A total of 2,434,903 variants were present in all cohorts and tested for association with the OA latent variable in the gSEM analysis using a total sample size of 403,915 (23,367 cases and 384,619 controls; effective sample size of 88,114). We applied S-PrediXcan using GTEx version 8 eQTL gene models (http://predictdb.org/) with the gSEM GWAS summary statistics as input to estimate genetically driven differential gene expression in human brain tissues associated with opioid addiction (OA). Fourteen gene-tissue combinations surpassed correction for the total number of gene models and brain tissues (156,215 tests) with a false discovery rate (FDR) less than 0.05 (Table 1; all results presented in Supplementary Table 14). Genes and pvalues presented include those with all FDR values.
Authors:
Nathan Gaddis, Ravi Mathur, Jesse Marks, Linran Zhou, Bryan Quach, Alex Waldrop, Orna Levran, Arpana Agrawal, Matthew Randesi, Miriam Adelson, Paul W Jeffries, Nicholas G Martin, Louisa Degenhardt, Grant W Montgomery, Leah Wetherill, Dongbing Lai, Kathleen Bucholz, Tatiana Foroud, Bernice Porjesz, Valgerdur Runarsdottir, Thorarinn Tyrfingsson, Gudmundur Einarsson, Daniel F Gudbjartsson, Bradley Todd Webb, Richard C Crist, Henry R Kranzler, Richard Sherva, Hang Zhou, Gary Hulse, Dieter Wildenauer, Erin Kelty, John Attia, Elizabeth G Holliday, Mark McEvoy, Rodney J Scott, Sibylle G Schwab, Brion S Maher, Richard Gruza, Mary Jeanne Kreek, Elliot C Nelson, Thorgeir Thorgeirsson, Kari Stefansson, Wade H Berrettini, Joel Gelernter, Howard J Edenberg, Laura Bierut, Dana B Hancock, Eric Otto Johnson
QTL for METH responses for home cage activity on Chr5 at D5Ncvs56 (28.53 Mbp , Build 37)
Description:
METH responses for home cage activity spans 3.53 - 53.53 Mbp (NCBI Build 37) on Chr5. This interval was obtained by using an interval width of 25 Mbp around the peak marker (Build 37, MGI, http://informatics.jax.org).
QTL for alcohol preference locus on Chr5 at D5Mit11 (47.40 Mbp , Build 37)
Description:
alcohol preference locus spans 22.40 - 72.40 Mbp (NCBI Build 37) on Chr5. This interval was obtained by using an interval width of 25 Mbp around the peak marker (Build 37, MGI, http://informatics.jax.org).
QTL for alcohol preference locus on Chr5 at D5Mit11 (47.40 Mbp , Build 37)
Description:
alcohol preference locus spans 22.40 - 72.40 Mbp (NCBI Build 37) on Chr5. This interval was obtained by using an interval width of 25 Mbp around the peak marker (Build 37, MGI, http://informatics.jax.org).
GeneWeaver