Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion.

paper Cited Public

Authors: Lin, Mingyan; Pedrosa, Erika; Hrabovsky, Anastasia; Chen, Jian; Puliafito, Benjamin R; Gilbert, Stephanie R; Zheng, Deyou; Lachman, Herbert M
Year: 2016
Journal: BMC systems biology
PMID: 27846841
DOI: 10.1186/s12918-016-0366-0
PMCID: PMC5111260

BACKGROUND: Individuals with 22q11.2 Deletion Syndrome (22q11.2 DS) are a specific high-risk group for developing schizophrenia (SZ), schizoaffective disorder (SAD) and autism spectrum disorders (ASD). Several genes in the deleted region have been implicated in the development of SZ, e.g., PRODH and DGCR8. However, the mechanistic connection between these genes and the neuropsychiatric phenotype remains unclear. To elucidate the molecular consequences of 22q11.2 deletion in early neural development, we carried out RNA-seq analysis to investigate gene expression in early differentiating human neurons derived from induced pluripotent stem cells (iPSCs) of 22q11.2 DS SZ and SAD patients. METHODS: Eight cases (ten iPSC-neuron samples in total including duplicate clones) and seven controls (nine in total including duplicate clones) were subjected to RNA sequencing. Using a systems level analysis, differentially expressed genes/gene-modules and pathway of interests were identified. Lastly, we related our findings from in vitro neuronal cultures to brain development by mapping differentially expressed genes to BrainSpan transcriptomes. RESULTS: We observed ~2-fold reduction in expression of almost all genes in the 22q11.2 region in SZ (37 genes reached p-value < 0.05, 36 of which reached a false discovery rate < 0.05). Outside of the deleted region, 745 genes showed significant differences in expression between SZ and control neurons (p < 0.05). Function enrichment and network analysis of the differentially expressed genes uncovered converging evidence on abnormal expression in key functional pathways, such as apoptosis, cell cycle and survival, and MAPK signaling in the SZ and SAD samples. By leveraging transcriptome profiles of normal human brain tissues across human development into adulthood, we showed that the differentially expressed genes converge on a sub-network mediated by CDC45 and the cell cycle, which would be disrupted by the 22q11.2 deletion during embryonic brain development, and another sub-network modulated by PRODH, which could contribute to disruption of brain function during adolescence. CONCLUSIONS: This study has provided evidence for disruption of potential molecular events in SZ patient with 22q11.2 deletion and related our findings from in vitro neuronal cultures to functional perturbations that can occur during brain development in SZ.

Fig. 1

Differentially expressed genes in 22q11.2 SZ neurons and their enriched functions. a Heat map showing relative expression of 782 genes that exhibited significant change between control and SZ at p-value < 0.05 (503 increased in SZ; 279 decreased). b Bar plot presenting batch-corrected expression values of 22q11.2 genes in control and SZ samples. Three genes flanking the deleted region at either side were also included. Asterisks (*) indicate significant differential expression at the genome-wide level (FDR < 0.05). c Enriched GO terms of the DEGs as determined by the software David. d Enriched pathways for the DEGs as determined by the software Toppgene

Fig. 2

qPCR validation and NPC proliferation assay. a qPCR analysis of six DEGs using neuronal RNAs from 4–5 controls and 4–5 patients. Relative expression values were determined and pooled by phenotype. Error bars show standard error of the mean; Two-tailed t-test p-value <0.01 (**) and <0.05 (*) are indicated. b Proliferation rates of NPCs derived from control (n = 4) and 22q.11.2 (n = 4) iPSCs. An equal number of cells were plated for each sample (10,000) on day 0. Fold changes were calculated by normalizing the day 1 controls to 1.00, and the control and patient samples were pooled. Statistical significance was determined using a two-tailed Student’s t-test. The asterisk denotes statistically significant difference in proliferation at p < 0.05

Fig. 3

WGCNA modules and function enrichment. a Module preservation Z-summary statistics of 15 modules identified by WGCNA. b Barplot showing the –log10 (p-value) (y-axis) of expression differences between SZ samples and controls for 13 modules whose module structures were well preserved between SZ and control samples (Z > 10, A). c Network of top pathways in the differentially expressed pink module. Each node represents a term and an edge between two nodes indicates that the two terms share genes. d The top pathways in the pink module as determined by the software Enrichr

Fig. 4

Enrichment of DEGs in a region of chromosome 6 predicted to interact with 22q11.2. Scatterplot presenting the –log10 (p-values) (y-axis) of changes in gene expression for each gene along chromosome 6. The heat map below shows the reported Hi-C correlation between each section of chromosome 6 and the 22q11.2 region. Note that the 6p21 region, which is highlighted by a yellow box in the heat map, was enriched for differentially expressed genes, and it displayed the strongest physical interaction with 22q11.2

Fig. 5

Highest co-expression of DEGs in specific brain regions. a Interconnectedness of transcriptional coexpression networks at various developmental stages and in different brain regions based on DEGs or randomly selected genes were evaluated using RNA-seq data from the BrainSpan Atlas. A pair of genes was defined as coexpressed if their expression correlation coefficient |R| was ≥ 0.9, across all tissues from a given brain region and a given developmental stage (Table 1). Dotted lines indicate numbers of connections (i.e., edges) in networks actually observed for DEGs, while the histograms represent distributions of the numbers of edges in 10,000 simulated networks derived from randomly picked genes. b Multiple testing adjusted –log10(P-value) for the significance of difference between the numbers of observed and simulated network edges

Fig. 6

ClueGO network for top correlating DEGs and co-expressed non-DEGs in frontal cortex during the embryonic stage. The size of the nodes reflects the statistical significance of the terms. Only terms with p-values < 0.05 are shown. For grouping terms, the initial group size was set to 3 and the percentage for merging groups was set to 50%. A term can be included in more than one group. Different groups were colored differently. The group leading term (in bold) is the most significant term of the group

Fig. 7

ClueGO network for top correlating DEGs and co-expressed non-DEGs in frontal cortex during the adolescence stage. See annotation in Fig. 6

#	Section	Preview
60	Discussion	Intracellular signaling pathways in the developing cortex also likely contribute to the progenitor…
61	Discussion	Noticeably, similar changes were not seen in PRODH mutant mice [134]. In our analysis, this gene,…
62	Discussion	be re-established only in the adolescent stage of the BrainSpan data (WGCNA module preservation…
63	Discussion	Note that PRODH and DGCR6¸which map very closely to each other (25 kb), were the top two connecting…
64	Discussion	Finally, COMT (catechol-O-methyltransferase), which modulates cortical dopamine levels, was the…
65	Discussion	Despite these interesting findings and observations, there are a few caveats and limitations in our…
66	Conclusions	Gene expression profiling using neurons derived from iPSCs, and our bioinformatics analysis, have…

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In this knowledge base

Title	Year	PMID
Genetics of Alcohol Use Disorder: A Role for Induced Pluripotent Stem Cells?	2018	29897633
Evaluating Synthetic Activation and Repression of Neuropsychiatric-Related Genes in hiPSC-Derived NPCs, Neurons, and Astrocytes.	2017	28757163

External

Title	Authors	Journal	Year	Link
Control of retrotransposon-driven activation of the interferon response by the double-stranded RNA binding protein DGCR8.	Gázquez-Gutiérrez A et al.	—	2026	→
Developmental convergence and divergence in human stem cell models of autism.	Gordon A et al.	—	2026	→
Aberrant pace of cortical neuron development in brain organoids from patients with 22q11.2 deletion syndrome-associated schizophrenia.	Rao SB et al.	—	2025	→
An antisense oligonucleotide-based strategy to ameliorate cognitive dysfunction in the 22q11.2 Deletion Syndrome.	Thakur P et al.	—	2025	→
A systematic review of miRNA expression in schizophrenia spectrum disorders across the blood and the brain.	Kotas M et al.	—	2025	→
Five differentially expressed proteins identified to serve as potential blood biomarkers for schizophrenia screening based on proteomics.	Li R et al.	—	2025	→
Peering into the mind: unraveling schizophrenia's secrets using models.	Nani JV et al.	—	2025	→
Rare Copy Number Variants Reveal Critical Cell Types and Periods of Brain Development in Neurodevelopmental Disorders.	Malwade S et al.	—	2025	→
Advances in the understanding of the pathophysiology of schizophrenia and bipolar disorder through induced pluripotent stem cell models.	Perrottelli A et al.	—	2024	→
Developmental convergence and divergence in human stem cell models of autism spectrum disorder	Gordon A et al.	—	2024	—
Lymphoblast transcriptome analysis in 22q11.2 deletion syndrome individuals with schizophrenia-spectrum disorder.	Michaelovsky E et al.	—	2024	→
NACC2, a molecular effector of miR-132 regulation at the interface between adult neurogenesis and Alzheimer's disease.	Penning A et al.	—	2024	→
Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes.	Karunakaran KB et al.	—	2024	→
Thalamocortical organoids enable in vitro modeling of 22q11.2 microdeletion associated with neuropsychiatric disorders.	Shin D et al.	—	2024	→
A CRISPR-engineered isogenic model of the 22q11.2 A-B syndromic deletion.	Paranjape N et al.	—	2023	→
A genetics-first approach to understanding autism and schizophrenia spectrum disorders: the 22q11.2 deletion syndrome.	Fiksinski AM et al.	—	2023	→
Coexisting Conditions Modifying Phenotypes of Patients with 22q11.2 Deletion Syndrome.	Smyk M et al.	—	2023	→
Deciphering the Molecular Mechanisms of Autonomic Nervous System Neuron Induction through Integrative Bioinformatics Analysis.	Takayama Y et al.	—	2023	→
Deep psychophysiological phenotyping of adolescents and adults with 22q11.2 deletion syndrome: a multilevel approach to defining core disease processes.	Parker DA et al.	—	2023	→
Inhibition of Abl Kinase by Imatinib Can Rescue the Compromised Barrier Function of 22q11.2DS Patient-iPSC-Derived Blood-Brain Barriers.	Li Y et al.	—	2023	→
The molecular pathology of schizophrenia: an overview of existing knowledge and new directions for future research.	Nakamura T et al.	—	2023	→
Unlocking Neural Function with 3D In Vitro Models: A Technical Review of Self-Assembled, Guided, and Bioprinted Brain Organoids and Their Applications in the Study of Neurodevelopmental and Neurodegenerative Disorders.	D'Antoni C et al.	—	2023	→
Current advancements of modelling schizophrenia using patient-derived induced pluripotent stem cells.	Dubonyte U et al.	—	2022	→
Electrophysiological measures from human iPSC-derived neurons are associated with schizophrenia clinical status and predict individual cognitive performance.	Page SC et al.	—	2022	→
Establishment of fishing cat cell biobanking for sustainable conservation.	Sukparangsi W et al.	—	2022	→
High abundance of CDC45 inhibits cell proliferation through elevation of HSPA6.	Fu Y et al.	—	2022	→
Impact of COMT, PRODH and DISC1 Genetic Variants on Cognitive Performance of Patients with Schizophrenia.	Fricke-Galindo I et al.	—	2022	→
Modelling the Human Blood-Brain Barrier in Huntington Disease.	Vignone D et al.	—	2022	→
T Cell Transcriptome in Chromosome 22q11.2 Deletion Syndrome.	Raje NR et al.	—	2022	→
The 22q11.2 region regulates presynaptic gene-products linked to schizophrenia.	Nehme R et al.	—	2022	→
What genes are differentially expressed in individuals with schizophrenia? A systematic review.	Merikangas AK et al.	—	2022	→
A Novel Balanced Chromosomal Translocation in an Azoospermic Male: A Case Report.	Chakraborty A et al.	—	2021	→
Cellular Models in Schizophrenia Research.	Abashkin DA et al.	—	2021	→
Convergent and distributed effects of the 3q29 deletion on the human neural transcriptome.	Sefik E et al.	—	2021	→
Directional Persistence of Cell Migration in Schizophrenia Patient-Derived Olfactory Cells.	Tee JY et al.	—	2021	→
ERK/MAPK signalling in the developing brain: Perturbations and consequences.	Iroegbu JD et al.	—	2021	→
Investigation of Neurodevelopmental Deficits of 22 q11.2 Deletion Syndrome with a Patient-iPSC-Derived Blood-Brain Barrier Model.	Li Y et al.	—	2021	→
Mind the translational gap: using iPS cell models to bridge from genetic discoveries to perturbed pathways and therapeutic targets.	Pintacuda G et al.	—	2021	→
Network Effects of the 15q13.3 Microdeletion on the Transcriptome and Epigenome in Human-Induced Neurons.	Zhang S et al.	—	2021	→
Prioritizing Genetic Contributors to Cortical Alterations in 22q11.2 Deletion Syndrome Using Imaging Transcriptomics.	Forsyth JK et al.	—	2021	→
Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example.	Imami AS et al.	—	2021	→
The Perspectives of Early Diagnosis of Schizophrenia Through the Detection of Epigenomics-Based Biomarkers in iPSC-Derived Neurons.	Lee D et al.	—	2021	→
Transcriptomic networks implicate neuronal energetic abnormalities in three mouse models harboring autism and schizophrenia-associated mutations.	Gordon A et al.	—	2021	→
Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models.	Zuo Y et al.	—	2021	→
Comparative Transcriptomic Analysis of Cerebral Organoids and Cortical Neuron Cultures Derived from Human Induced Pluripotent Stem Cells.	Kathuria A et al.	—	2020	→
Dissecting transcriptomic signatures of neuronal differentiation and maturation using iPSCs.	Burke EE et al.	—	2020	→
Human in vitro models for understanding mechanisms of autism spectrum disorder.	Gordon A et al.	—	2020	→
Investigation of Schizophrenia with Human Induced Pluripotent Stem Cells.	Powell SK et al.	—	2020	→
Mental health dished up-the use of iPSC models in neuropsychiatric research.	McNeill RV et al.	—	2020	→
Negative Symptoms of Schizophrenia and Dopaminergic Transmission: Translational Models and Perspectives Opened by iPSC Techniques.	Collo G et al.	—	2020	→
Neuronal defects in a human cellular model of 22q11.2 deletion syndrome.	Khan TA et al.	—	2020	→
Studying Abnormal Chromosomal Diseases Using Patient-Derived Induced Pluripotent Stem Cells.	Hayashi Y et al.	—	2020	→
Transcriptomic Landscape and Functional Characterization of Induced Pluripotent Stem Cell-Derived Cerebral Organoids in Schizophrenia.	Kathuria A et al.	—	2020	→
Can Animal Models of Copy Number Variants That Predispose to Schizophrenia Elucidate Underlying Biology?	Forsingdal A et al.	—	2019	→
Contribution of induced pluripotent stem cell technologies to the understanding of cellular phenotypes in schizophrenia.	Balan S et al.	—	2019	→
Downregulation of genes outside the deleted region in individuals with 22q11.2 deletion syndrome.	Dantas AG et al.	—	2019	→
Epigenome-wide DNA methylation in placentas from preterm infants: association with maternal socioeconomic status.	Santos HP et al.	—	2019	→
Mitochondrial deficits in human iPSC-derived neurons from patients with 22q11.2 deletion syndrome and schizophrenia.	Li J et al.	—	2019	→
New considerations for hiPSC-based models of neuropsychiatric disorders.	Hoffman GE et al.	—	2019	→
Childhood-Onset Schizophrenia: Insights from Induced Pluripotent Stem Cells.	Hoffmann A et al.	—	2018	→
Genetic and clinical features of social cognition in 22q11.2 deletion syndrome.	Lattanzi GM et al.	—	2018	→
Genetics of Alcohol Use Disorder: A Role for Induced Pluripotent Stem Cells?	Prytkova I et al.	—	2018	→
Modeling Neuropsychiatric and Neurodegenerative Diseases With Induced Pluripotent Stem Cells.	LaMarca EA et al.	—	2018	→
Newfound sex differences in axonal structure underlie differential outcomes from in vitro traumatic axonal injury.	Dollé JP et al.	—	2018	→
CRISPR/Cas9-mediated heterozygous knockout of the autism gene CHD8 and characterization of its transcriptional networks in cerebral organoids derived from iPS cells.	Wang P et al.	—	2017	→
Evaluating Synthetic Activation and Repression of Neuropsychiatric-Related Genes in hiPSC-Derived NPCs, Neurons, and Astrocytes.	Ho SM et al.	—	2017	→
Modeling schizophrenia pathogenesis using patient-derived induced pluripotent stem cells (iPSCs).	Noh H et al.	—	2017	→
Prospects for Modeling Abnormal Neuronal Function in Schizophrenia Using Human Induced Pluripotent Stem Cells.	Prytkova I et al.	—	2017	→
Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with post-mortem adult brains.	Hoffman GE et al.	—	2017	→
Transcriptome analysis of microglia in a mouse model of Rett syndrome: differential expression of genes associated with microglia/macrophage activation and cellular stress.	Zhao D et al.	—	2017	→
Update on the 22q11.2 deletion syndrome and its relevance to schizophrenia.	Van L et al.	—	2017	→