Transcriptome profiling of dopaminergic neurons derived from an ADHD induced pluripotent stem cell (iPSC) model
Namipashaki, A.; Arnatkeviciute, A.; Hellyer, S. D.; Nowell, C. J.; Walsh, K. S.; Polo, J. M.; Gregory, K. J.; Bellgrove, M. A.; Hawi, Z.
Show abstract
The most recent ADHD-GWAS meta-analysis highlighted the potential role of 76 genes enriched among genes expressed in early brain development and associated with midbrain dopaminergic neurons. However, the precise functional importance of the GWAS-identified single nucleotide polymorphisms (SNPs) remain unknown. In contrast to GWAS, transcriptome analysis directly investigates gene products by assessing the transcribed RNA. This allows one to gain functional insights into gene expression paving the way for a better understanding of the molecular risk mechanisms of conditions, such as ADHD. In this study, we performed transcriptome profiling of highly homogeneous dopamine neurons developed from induced pluripotent cells (iPSCs) that were derived from an individual with ADHD and a neurotypical comparison individual. Comparative gene expression analysis between the examined lines revealed that the top differentially expressed genes (DEGs) were predominantly associated with nervous system functions related to neuronal development and dopaminergic regulation. Notably, 29 of the DEGs overlapped with those identified by ADHD-GWAS meta- analysis. These genes are overrepresented in biological processes including developmental growth regulation, axonogenesis, and nervous system development. In addition, gene set analysis revealed significant enrichment for meta categories such as ion channel activity, synaptic function and assembly, neuronal development and cell differentiation. Further, we observed significantly reduced projections in the ADHD dopamine neurons at the mid- differentiation stage (day 14 in vitro), providing preliminary support for the delayed neuronal maturation hypotheses of ADHD. This study underscores the potential of using iPSC-derived cell type-specific models that integrate genome and transcriptome analyses for biological discovery in ADHD.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Drinking and smoking polygenic risk is associated with neurodevelopmental outcomes of children and young adults independently of psychopathology and substance use 94%
- Rapid effects of valproic acid on the fetal brain transcriptome: Implications for brain development and autism 94%
- Dopaminergic Changes in the Subgenual Cingulate Cortex in Dementia with Lewy Bodies Associates with Presence of Depression 94%
Similar papers in this journal
- Transcriptome Analysis of Human Induced Excitatory Neurons Supports a Strong Effect of Clozapine on Cholesterol Biosynthesis 95%
- Volumetric alteration of olfactory bulb and immune-related molecular changes in olfactory epithelium in first episode psychosis patients 94%
- Current progress in understanding Schizophrenia using genomics and pluripotent stem cells: A Meta-analytical overview 94%
Similar papers in this journal
Similar papers in this journal
- Preterm birth alters the maturation of the GABAergic system in the human prefrontal cortex 93%
- Molecular Changes in Prader-Willi Syndrome Neurons Reveals Clues About Increased Autism Susceptibility. 92%
- Broad influence of mutant ataxin-3 on the proteome of the adult brain, young neurons, and axons reveals central molecular processes and biomarkers in SCA3/MJD using knock-in mouse model 90%
Similar papers in this journal
- Common and rare variant analyses implicate late-infancy cerebellar development and immune genes in ADHD 95%
- Hippocampal transcriptome analysis following maternal separation implicates altered RNA processing in a mouse model of fetal alcohol spectrum disorder 93%
- A mouse model of ATRX deficiency with cognitive deficits and autistic traits 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.