Differentially expressed genes from RPE compared to Normal Retina
Description:
Transcriptome profiling from macular retina and RPE/choroid samples from 27 unrelated eye tissue donors, was performed using RNA-sequencing. Human donor eye collection were obtained from Utah Lions Eye Bank within a 6-hour post-mortem interval and donors aged 60-90 years. Sample types were Normal Retina, Intermediate AMD Retina, Neovascular AMD Retina, Normal macular retina pigment epithelium (RPE), Intermediate AMD RPE, and Neovascular AMD RPE. Age Related Macular Degeneration (AMD) phenotyping was determined using the Age-Related Eye Disease Study (AREDS) severity grading scale, where AREDS category 0/1 was considered normal, AREDS category 3 intermediate AMD, and AREDS category 4b neovascular AMD. Samples from Normal RPE were compared to Normal Retina, and are presented with fold change > 1.5 and and P < 0.05. This gene set was annotated from the Supplementry Table of BioRxiv pre-print paper ‘Patterns of gene expression and allele-specific expression vary among macular tissues and clinical stages of Age-related Macular Degeneration’ by Zhang et.al (2022) doi: https://doi.org/10.1101/2022.12.19.521092
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
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.
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.
List of positional candidate genes after correcting for multiple testing and controlling the false discovery rate from genome wide association studies (GWAS) retrieved from the NHGRI-EBI Catalog of published genome-wide association studies (http://www.ebi.ac.uk/gwas/). The disease/trait examined in this study, as reported by the authors, was Stearic acid (18:0) plasma levels. The EFO term phospholipid measurement was annotated to this set after curation by NHGRI-EBI. Intergenic SNPS were mapped to both the upstream and downstream gene. P-value uploaded. This gene set was generated using gwas2gs v. 0.1.8 and the GWAS Catalog v. 1.0.1.
Authors:
JH Wu, RN Lemaitre, A Manichaikul, W Guan, T Tanaka, M Foy, EK Kabagambe, L Djousse, D Siscovick, AM Fretts, C Johnson, IB King, BM Psaty, B McKnight, SS Rich, YD Chen, JA Nettleton, W Tang, S Bandinelli, DR Jacobs, BL Browning, CC Laurie, X Gu, MY Tsai, LM Steffen, L Ferrucci, M Fornage, D Mozaffarian
Postmortem human brain tissue from the caudate nucleus region of a total of 48 individuals with Alcohol Use Disorder (AUD) and 51 control individuals were taken and RNA extracted from frozen tissue. Sequencing was carried out using the NovaSeq 6000 (Illumina) platform, and gene expression analysis was carried out with respect to AUD and control samples. Gene symbols from Entrez ids are used and Logbase2 FC as provided by the authors are annotated.
Authors:
Lea Zillich, Eric Poisel, Josef Frank, Jerome C Foo, Marion M Friske, Fabian Streit, Lea Sirignano, Stefanie Heilmann-Heimbach, André Heimbach, Per Hoffmann, Franziska Degenhardt, Anita C Hansson, Georgy Bakalkin, Markus M Nöthen, Marcella Rietschel, Rainer Spanagel, Stephanie H Witt
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.
Human induced pluripotent stem cell (iPSC) lines, A and B, derived from two healthy adult male individuals, were used to generate hCOs for RNA-sequencing. Methodone treatment began on Day 9 of organoid culture, the first day of the neural proliferation stage, and concluded at Day 60. Nuclease-free water was used as a vehicular control. Cortical organoids were collected 2 months (60 days) after initiating organoid culture. Each well of hCOs (15–20 organoids) was a separate biological replicate for a given treatment condition (i.e., treated or untreated). RNA was extracted from frozen organoid pellets using the Direct-Zol Miniprep Plus Kit (Zymo, Irvine, CA) according to the manufacturer’s instructions. Samples were multiplexed and sequenced on the Illumina NovaSeq 6000 S4 to produce approximately 100 million, 100 base pair, paired end reads per sample. 3 control and 3 methadone-treated samples were sequenced from cell line A, and 4 control and 4 treated samples from cell line B. Raw fastq file quality assessment and read alignment to the hg19 genome (GRCh37, RefSeq GCF_000001405.13) were performed. Significantly differentially expressed genes (DEGs) were selected based on the confident effect size of their log2(Fold Change) values at FDR<0.05. Genes presented are without cutoffs and were obtained using the GEO2R tool by GW curators (GEO accession: GSE210682).
Authors:
Ila Dwivedi, Andrew B Caldwell, Dan Zhou, Wei Wu, Shankar Subramaniam, Gabriel G Haddad
MAGMA genes associated with non-SU EXT factor_pvalue
Description:
GWAS summary statistics were selected from publicly-available GWAS of individuals of European ancestry on a variety of internalizing, externalizing, and substance use traits. Selection of externalizing psychopathology and substance use traits was partly based on a recent genomic SEM investigation of externalizing and included three measures of substance use: lifetime cannabis use (N=162,082), lifetime smoking initiation (N=632,802), and the number of alcoholic drinks per week (N=537,349). Additionally, we included four measures of non-substance use externalizing problems, including attention-deficit/hyperactivity disorder (ADHD; N=55,290), general risk tolerance and speeding behavior (N=939,908), reverse-coded age at first sexual intercourse (N=317,694) and number of sexual partners (N=370,711) obtained from the UK Biobank (http://www.nealelab.is/uk-biobank/), and antisocial behavior. Target data were drawn from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative sample of 20,745 youth starting in grades 7-12 in the United States. Specifically, substance use and demographic data were drawn from the Wave IV survey, which occurred from 2008-2009, on a subset of participants (N=15,071). Participants ranged in age from 24-34 (mean age=28.98 years; SD=1.75). The three resulting higher-order factors represented: 1) substance use (SU)-related psychopathology (including variance shared across substance use, internalizing, and externalizing psychopathology), 2) variance in internalizing traits not related to substance use (non-SU internalizing), and 3) variance in externalizing traits not related to substance use (non-SU externalizing). We calculated the effective n’s for each factor consistent with the approach in Demange, Malanchini (23); these sample sizes are as follows: 1,734,340 (SU psychopathology-related), 1,164,731 (Non-SU internalizing), and 730,198 (Non-SU externalizing). After performing Q-SNP analysis, 1,720 Q-SNPs were removed, leaving 1,557,030 SNPs for analysis. No gene-set associations passed Bonferroni corrections in the MAGMA gene-set analysis. In the MAGMA gene-property tissue expression analyses, pituitary, cortex and cerebellum brain tissues were implicated, as well as early-mid-prenatal developmental stage.
Authors:
Leslie A Brick, Chelsie E Benca-Bachman, Emma C Johnson, Daniel E Gustavson, Matthew Carper, Rohan Hc Palmer
GWAS summary statistics were selected from publicly-available GWAS of individuals of European ancestry on a variety of internalizing, externalizing, and substance use traits. Selection of externalizing psychopathology and substance use traits was partly based on a recent genomic SEM investigation of externalizing and included three measures of substance use: lifetime cannabis use (N=162,082), lifetime smoking initiation (N=632,802), and the number of alcoholic drinks per week (N=537,349). Additionally, we included four measures of non-substance use externalizing problems, including attention-deficit/hyperactivity disorder (ADHD; N=55,290), general risk tolerance and speeding behavior (N=939,908), reverse-coded age at first sexual intercourse (N=317,694) and number of sexual partners (N=370,711) obtained from the UK Biobank (http://www.nealelab.is/uk-biobank/), and antisocial behavior. Target data were drawn from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative sample of 20,745 youth starting in grades 7-12 in the United States. Specifically, substance use and demographic data were drawn from the Wave IV survey, which occurred from 2008-2009, on a subset of participants (N=15,071). Participants ranged in age from 24-34 (mean age=28.98 years; SD=1.75). The three resulting higher-order factors represented: 1) substance use (SU)-related psychopathology (including variance shared across substance use, internalizing, and externalizing psychopathology), 2) variance in internalizing traits not related to substance use (non-SU internalizing), and 3) variance in externalizing traits not related to substance use (non-SU externalizing). We calculated the effective n’s for each factor consistent with the approach in Demange, Malanchini (23); these sample sizes are as follows: 1,734,340 (SU psychopathology-related), 1,164,731 (Non-SU internalizing), and 730,198 (Non-SU externalizing). After performing Q-SNP analysis, 1,720 Q-SNPs were removed, leaving 1,557,030 SNPs for analysis. No gene sets passed Bonferroni correction in the MAGMA gene-set analysis. MAGMA gene-property tissue expression analyses implicated the pituitary and several brain tissues (cortex, cerebellum, and nucleus accumbens), as well as early-prenatal and early-mid-prenatal developmental stages of brain tissue.
Authors:
Leslie A Brick, Chelsie E Benca-Bachman, Emma C Johnson, Daniel E Gustavson, Matthew Carper, Rohan Hc Palmer
Hippocampus Gene Expression Correlates for NOVEL_RATIO measured in BXD RI Males obtained using GeneNetwork Hippocampus Consortium M430v2 (Jun06) RMA. The NOVEL_RATIO measures Novel Open Field - locomotion in the periphery as a function of total locomotion. under the domain Cocaine. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
Cerebellum Gene Expression Correlates for OF_DIST_10_15 measured in BXD RI Males obtained using SJUT Cerebellum mRNA M430 (Mar05) RMA. The OF_DIST_10_15 measures Open Field - Total distance traveled 10-15 minutes under the domain Basal Behavior. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
Cerebellum Gene Expression Correlates for OF_DIST_15_20 measured in BXD RI Males obtained using SJUT Cerebellum mRNA M430 (Mar05) RMA. The OF_DIST_15_20 measures Open Field - Total distance traveled 15-20 minutes under the domain Basal Behavior. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
Cerebellum Gene Expression Correlates for OF_DIST_15_20 measured in BXD RI Females & Males obtained using SJUT Cerebellum mRNA M430 (Mar05) RMA. The OF_DIST_15_20 measures Open Field - Total distance traveled 15-20 minutes under the domain Basal Behavior. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
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