pubmed.ncbi.nlm.nih.gov

Transcriptional signatures of participant-derived neural progenitor cells and neurons implicate altered Wnt signaling in Phelan-McDermid syndrome and autism - PubMed

  • ️Wed Jan 01 2020

Transcriptional signatures of participant-derived neural progenitor cells and neurons implicate altered Wnt signaling in Phelan-McDermid syndrome and autism

Michael S Breen et al. Mol Autism. 2020.

Abstract

Background: Phelan-McDermid syndrome (PMS) is a rare genetic disorder with high risk of autism spectrum disorder (ASD), intellectual disability, and language delay, and is caused by 22q13.3 deletions or mutations in the SHANK3 gene. To date, the molecular and pathway changes resulting from SHANK3 haploinsufficiency in PMS remain poorly understood. Uncovering these mechanisms is critical for understanding pathobiology of PMS and, ultimately, for the development of new therapeutic interventions.

Methods: We developed human-induced pluripotent stem cell (hiPSC)-based models of PMS by reprogramming peripheral blood samples from individuals with PMS (n = 7) and their unaffected siblings (n = 6). For each participant, up to three hiPSC clones were generated and differentiated into induced neural progenitor cells (hiPSC-NPCs; n = 39) and induced forebrain neurons (hiPSC-neurons; n = 41). Genome-wide RNA-sequencing was applied to explore transcriptional differences between PMS probands and unaffected siblings.

Results: Transcriptome analyses identified 391 differentially expressed genes (DEGs) in hiPSC-NPCs and 82 DEGs in hiPSC-neurons, when comparing cells from PMS probands and unaffected siblings (FDR < 5%). Genes under-expressed in PMS were implicated in Wnt signaling, embryonic development, and protein translation, while over-expressed genes were enriched for pre- and postsynaptic density genes, regulation of synaptic plasticity, and G-protein-gated potassium channel activity. Gene co-expression network analysis identified two modules in hiPSC-neurons that were over-expressed in PMS, implicating postsynaptic signaling and GDP binding, and both modules harbored a significant enrichment of genetic risk loci for developmental delay and intellectual disability. Finally, PMS-associated genes were integrated with other ASD hiPSC transcriptome findings and several points of convergence were identified, indicating altered Wnt signaling and extracellular matrix.

Limitations: Given the rarity of the condition, we could not carry out experimental validation in independent biological samples. In addition, functional and morphological phenotypes caused by loss of SHANK3 were not characterized here.

Conclusions: This is the largest human neural sample analyzed in PMS. Genome-wide RNA-sequencing in hiPSC-derived neural cells from individuals with PMS revealed both shared and distinct transcriptional signatures across hiPSC-NPCs and hiPSC-neurons, including many genes implicated in risk for ASD, as well as specific neurobiological pathways, including the Wnt pathway.

Keywords: Autism spectrum disorder; Neural progenitor cells; Neurons; RNA-sequencing; Stem cells.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1

Data quality control metrics. a Representative images of hiPSC-NPCs (left) and 6-week-old forebrain neurons (right) from control (top) and PMS probands (bottom). hiPSC-NPCs stained with PAX6 (red), NESTIN (green); hiPSC-neurons stained with MAP2 (green), DAPI-stained nuclei (blue). Pairwise correlations compared (b) hiPSC-NPC and (c) hiPSC-neuron transcriptomes from the same clone and same induction (n = 12, n = 31, respectively), same clone but different induction (n = 46, n = 55, respectively), all related family members (n = 68, n = 57, respectively) and all unrelated family members (n = 505, n = 677, respectively). Analysis of variance for multiple comparisons was used to test for differences between the means of correlation coefficients. d Linear mixed modelling was used to compute the percentage of gene expression variance explained according to six factors, which represent potential biological sources of variability. Differences in cell types and donor as a repeated measure, followed by excitatory neuron cell composition (estimated using CiberSort in grey) explains the largest amount of variability in the transcriptome data. e Principal components analysis of gene expression data from hiPSC-NPCs (red) and hiPSC-neurons (blue), each unique shape denotes one specific donor. Note, there was no distinct stratification by PMS case status based on global expression profiles. (f) Genes that vary most across donors are enriched for brain cis-eQTLs. Fold enrichment (log2) for the 2000 top cis-eQTLs discovered in post mortem dorsolateral prefrontal cortex data generated by the CommonMind Consortium shown for six sources of variation, plus residuals. Each line indicates the fold enrichment for genes with the fraction of variance explained exceeding the cutoff indicated on the x-axis. Enrichments are shown on the x-axis until less than 100 genes pass the cutoff

Fig. 2
Fig. 2

Genes and pathways associated with PMS. Differential gene expression analyses adjusted for sequencing batch, biological sex, RIN, and individual donor as a repeated measure using the dupCorrelation function in the limma R package. Volcano plots compare the extent of PMS-associated log2 fold-changes to -log10 multiple test corrected p value in a hiPSC-NPCs and b hiPSC-neurons. Black dotted line indicates genes passing an adjusted p < 0.05. c Genome-wide concordance of PMS-associated log2 fold-changes was examined between hiPSC-NPCs and hiPSC-neurons. Inset Venn diagram displays the overlap of significant differentially expressed genes between the two cell types. Functional enrichment analysis of PMS dysregulated genes that show d under-expression in hiPSC-NPCs, e under-expression in hiPSC-neurons, and f over-expression in hiPSC-neurons. All enrichment terms displayed pass a multiple test corrected p value. g Log2 fold-change plot of significantly under-expressed genes in PMS and their respective gene ontology term. Abbreviations: Reg of Wnt, regulation of Wnt signaling; ECM, extracellular matrix

Fig. 3
Fig. 3

Genes co-expression analysis and enrichment. a A total of 19 co-expression modules were identified in hiPSC-NPCs and 22 modules were identified in hiPSC-neurons, and each module was tested for enrichment of genetic risk loci for ASD, ID, and DD using findings from other large-scale studies. Modules were also examined for enrichment of target genes of FMRP, an RNA binding protein that is associated with ASD risk, as well as differentially expressed genes identified in the current study (see Fig. 2). Enrichment was assessed using a Fisher’s exact test to assess the statistical significance and p values were adjusted for multiple testing using the Bonferroni procedure. We required an adjusted p value < 0.05 (*) to claim that a gene set is enriched within a user-defined list of genes. b Module eigengene (ME) values were associated with PMS for hiPSC-NPCs (triangles) and hiPSC-neurons (circles). Next, genes in hiPSC-NPCs were then forced to construct modules using the gene-module assignments identified in hiPSC-neurons, and vice versa, and these ME values were also associated with PMS. c Functional enrichment was performed on four PMS-associated modules and the top eight enrichment terms (removing redundant annotations) are displayed

Fig. 4
Fig. 4

Replication of hiPSC-neuron RNA-seq. A replication set of hiPSC-neurons collected at 6 weeks in culture were subjected to RNA-seq. a Correlation coefficients between samples from the same donor and same clone (technical replicates), same clone but different induction (biological replicates), and correlations between all other samples. A Wilcoxon rank-sum test was used to test for differences between the means of correlation coefficients. b The second replication batch of hiPSC-neurons were used to derive differential gene expression signatures between PMS probands and unaffected siblings. The PMS-associated log2 fold-changes from this replication set (x-axis) were compared to PMS-associated log2 fold-changes from the discovery set of samples, which were derived using combinations technical replicates and biological replicates at different weeks in culture (y-axis)

Similar articles

Cited by

References

    1. Betancur C, Buxbaum JD. SHANK3 haploinsufficiency: a "common" but underdiagnosed highly penetrant monogenic cause of autism spectrum disorders. Mol Autism. 2013;4(1):17. - PMC - PubMed
    1. Leblond CS, Nava C, Polge A, Gauthier J, Huguet G, Lumbroso S, Giuliano F, Stordeur C, Depienne C, Mouzat K, et al. Meta-analysis of SHANK mutations in autism spectrum disorders: a gradient of severity in cognitive impairments. PLoS Genet. 2014;10(9):e1004580. - PMC - PubMed
    1. Boccuto L, Lauri M, Sarasua SM, Skinner CD, Buccella D, Dwivedi A, Orteschi D, Collins JS, Zollino M, Visconti P, et al. Prevalence of SHANK3 variants in patients with different subtypes of autism spectrum disorders. Eur J Hum Genet. 2013;21(3):310–316. - PMC - PubMed
    1. De Rubeis S, Siper PM, Durkin A, Weissman J, Muratet F, Halpern D, Trelles MDP, Frank Y, Lozano R, Wang AT, et al. Delineation of the genetic and clinical spectrum of Phelan-McDermid syndrome caused by SHANK3 point mutations. Mol Autism. 2018;9:31. - PMC - PubMed
    1. Mitz AR, Philyaw TJ, Boccuto L, Shcheglovitov A, Sarasua SM, Kaufmann WE, Thurm A. Identification of 22q13 genes most likely to contribute to Phelan McDermid syndrome. Eur J Hum Genet. 2018;26(3):293–302. - PMC - PubMed

Publication types

MeSH terms

Supplementary concepts