Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
[Preprint]. 2023 Sep 1:2023.08.30.555019.
doi: 10.1101/2023.08.30.555019.

Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis

Nil Aygün  1   2 , Oleh Krupa  1   2 , Jessica Mory  1   2 , Brandon Le  1   2 , Jordan Valone  1   2 , Dan Liang  1   2 , Michael I Love  1   3 , Jason L Stein  1   2   4
Affiliations

Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis

Nil Aygün et al. bioRxiv. .

Update in

Abstract

The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk post-mortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA-editing and alternative polyadenylation (APA), within a cell-type-specific population of human neural progenitors and neurons. More RNA-editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting genetically mediated post-transcriptional regulation during brain development lead to differences in brain function.

Keywords: RNA-editing; alternative polyadenylation; genome-wide association studies; missing regulation; neurogenesis; quantitative trait loci.

PubMed Disclaimer

Conflict of interest statement

Declarations of Interest The authors declare no competing interests.

Figures

Figure 1.
Figure 1.. Study design and features of RNA-editing sites
(A) Study design to identify cell-type-specific edQTLs and apaQTLs. (B) Proportions of RNA-editing events discovered in each cell type. (C) Pie chart showing proportions of RNA-editing events within Alu repeats, other repeats or non-repeat regions in the genome per cell type. (D) Proportions of RNA-editing events across genomic regions. CDS: Coding sequence, 3’UTR: Three prime untranslated region and 5’UTR: Five prime untranslated region (E) Overlap of RNA-edit sites with GTEx Cortex or BrainVar datasets. The edit sites overlapped are defined as annotated and the sites which do not overlap are defined as novel. (F) Local motif enrichment for annotated and novel edit sites per cell-type. Number of edit sites (n) in each category is reported.
Figure 2.
Figure 2.. Cell-type-specific RNA-editing during human neurogenesis
(A) Comparison of Alu editing index (AEI) between progenitors and neurons. T-test p-value was reported. (B) Differential expression of ADAR1, ADAR2 and ADAR3 genes between progenitors and neurons. Adjusted p-value (adj.pval) and log fold change (logFC) from limma were reported. (C) Overlap of RNA-editing sites discovered in progenitors, neurons and fetal bulk data. (D) Enrichment of cell-type-specific RNA-editing sites within edit sites dysregulated in brain-relevant diseases. The number of edit sites overlap is reported, and the proportion of disease-specific edit sites overlapped with cell-type-specific edit sites is shown on the y-axis. Asterisks indicate significant enrichment (FDR < 0.05). Autism (Autism Spectrum Disorder), Fragile X1 (Fragile X syndrome from UC Davis database), Fragile X2 (Fragile X syndrome from NIH biobank dataset), GBM (Glioblastoma), SCZ ACC (Schizophrenia from anterior cingulate cortex), and SCZ DLPFC (Schizophrenia from dorsolateral prefrontal cortex). (E) Editing rate of the edit site (chr1:3813767:A>G) at the 3’UTR of CEP104 gene was positively correlated with the proportion of TUJ1+ neurons. Gene model for CEP104 and the genomic position of the edit site are given at the left. Scatter plot illustrating the correlation between edit rate and the relative abundance of TUJ1+ neurons, and correlation coefficient (r) and p-values are shown. Representative immunocytochemistry images for TUJ1 (in green) and DAPI (in blue) staining of Donors 18 and 321 (D18 and D321) are shown, scale bar is 100 μm.
Figure 3.
Figure 3.. Cell-type-specific edQTLs
(A) Overlap of edSites detected in progenitors, neurons, and fetal bulk data. (B) Primary edQTLs were more significantly associated with editing as they were closer to the edit sites. Association p-values at −log10 scale are shown on the y-axis and distance from edit sites are shown on the x-axis for progenitors (left) and neurons (right), density of the data points are indicated by color density per cell-type. (C) Gene ontology results for edGenes found in neurons. (D) Enrichment of significant edQTLs within different RNA secondary structures illustrated at left. Enrichment p-values at −log10 scale are shown on the y-axis across structures and data are colored by cell-types. The number of variants (n) significantly associated with edit sites and located within each structure are shown.
Figure 4.
Figure 4.. Cell-type-specific alternative polyadenylation and apaQTLs
(A) Principal component analysis for alternative polyadenylation (APA) site usage colored by cell-type. (B) Differentially expressed genes between progenitors and neurons that encode proteins playing a role in alternative polyadenylation. Fold change (logFC) is given on the x-axis and data points are colored by adjusted p-value at −log10 scale. logFC > 0 indicates the genes upregulated in neurons and logFC < 0 indicates the genes upregulated in progenitors, and logFC = 0 is shown with dashed vertical line line. (C) Two APA sites of CALM1 which were differentially expressed between progenitors and neurons are shown. Gene model for CALM1 is provided above, and relative read count per APA sites for each cell-type are shown where relative read coverage calculated as ratio of number of count supporting each APA site to total number of reads including both APA sites are shown on the y-axis; and genomic position of the reads are shown on the x-axis. Differential expression of APA2 (longer 3’UTR isoform) is shown between cell-types. (D) Overlap of APA sites regulated by primary aSNPs across progenitors, neurons and fetal bulk brain data. (E) Primary apaQTLs were more significantly associated with APA as they were closer to APA sites. Association p-values at −log10 scale are shown on the y-axis and distance from APA sites are shown on the x-axis for progenitors (left) and neurons (right), density of the data points are indicated by color density per cell-type. (F) apaQTL overlapping with canonical polyadenylation signal motif AAUAAA for gene RPL221 is shown for each cell type. Read coverage per genotype is shown.
Figure 5.
Figure 5.. Comparison of cell-type-specific molecular QTLs
(A) Distribution of primary eQTLs, sQTLs, edQTLs and apaQTLs from transcription start site (TSS) per cell-type, the distance from TSS is shown on the x-axis. (B) Distribution of primary eQTLs, sQTLs, edQTLs and apaQTLs from transcription termination site (TTS) per cell-type, the distance from TTS is shown on the x-axis. (C) Distribution of primary eQTLs, sQTLs, edQTLs and apaQTLs from splice sites per cell-type, the distance from splice is shown on the x-axis. Two distances were calculated relative to intron start and end sites, and the shortest distance was used for comparison for each QTL data. (D) Overlap of primary e/s/ed/apaQTLs via π1 statistics (progenitors in purple and neurons in green). Matrices are colored based on the proportion of progenitor and neuron primary edSNP-edGene, aSNP-aGene, sSNP-sGene, eSNP-eGene pairs that were non-null associations (π1) in each of QTL datasets. (E) pLOUEF values for aGenes, edGenes, eGenes and sGenes per cell type are shown. P-values from t-test are reported.
Figure 6.
Figure 6.. Colocalization of cell-type-specific edQTLs and apaQTLs with brain-relevant trait GWAS loci
(A) Genomics tracks illustrating that an edQTL within CCDC88A gene locus in neurons was colocalized with an index variant for education attainment GWAS; whereas there was not any significant eQTL for CCDC88A. Data points were colored based on the pairwise LD r2 with rs563320407; p-values for each association are shown on the y-axis and genomic positions of genetic variants are shown on the x-axis. (B) Boxplot illustrating distribution of editing rate across rs56320407. (C) The predicted secondary structure of the IRAlu hairpin for T and C alleles of rs56320407. Edit sites and genetic variants are indicated by red and blue colors, respectively. (D) Genomics tracks illustrating that an apaQTL within EP300 gene locus in progenitor was colocalized with an index variant for schizophrenia GWAS; whereas there was not any significant eQTL for EP300 gene. Data points were colored based on the pairwise LD r2 with rs35508493; p-values for each association are shown on the y-axis and genomic positions of genetic variants are shown on the x-axis. (E) Boxplot illustrating distribution of editing rate across rs56320407. (F) microRNA binding sites are shown for the genomic region which differs between two potential 3’UTR isoforms of EP300 gene (genomic coordinates for APA site 1: chr22:41,178,956-41,180,079 and APA site 2: chr22:41,178,956-41,179,495).
See this image and copyright information in PMC

References

    1. Stahl E.A., Breen G., Forstner A.J., McQuillin A., Ripke S., Trubetskoy V., Mattheisen M., Wang Y., Coleman J.R.I., Gaspar H.A., et al. (2019). Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803. - PMC - PubMed
    1. Howard D.M., Adams M.J., Clarke T.-K., Hafferty J.D., Gibson J., Shirali M., Coleman J.R.I., Hagenaars S.P., Ward J., Wigmore E.M., et al. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352. - PMC - PubMed
    1. Pardiñas A.F., Holmans P., Pocklington A.J., Escott-Price V., Ripke S., Carrera N., Legge S.E., Bishop S., Cameron D., Hamshere M.L., et al. (2018). Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389. - PMC - PubMed
    1. Savage J.E., Jansen P.R., Stringer S., Watanabe K., Bryois J., de Leeuw C.A., Nagel M., Awasthi S., Barr P.B., Coleman J.R.I., et al. (2018). Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919. - PMC - PubMed
    1. Grasby K.L., Jahanshad N., Painter J.N., Colodro-Conde L., Bralten J., Hibar D.P., Lind P.A., Pizzagalli F., Ching C.R.K., McMahon M.A.B., et al. (2020). The genetic architecture of the human cerebral cortex. Science 367. 10.1126/science.aay6690. - DOI - PMC - PubMed

Publication types