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, 159 (7), 1511-23

A Highly Conserved Program of Neuronal Microexons Is Misregulated in Autistic Brains


A Highly Conserved Program of Neuronal Microexons Is Misregulated in Autistic Brains

Manuel Irimia et al. Cell.


Alternative splicing (AS) generates vast transcriptomic and proteomic complexity. However, which of the myriad of detected AS events provide important biological functions is not well understood. Here, we define the largest program of functionally coordinated, neural-regulated AS described to date in mammals. Relative to all other types of AS within this program, 3-15 nucleotide "microexons" display the most striking evolutionary conservation and switch-like regulation. These microexons modulate the function of interaction domains of proteins involved in neurogenesis. Most neural microexons are regulated by the neuronal-specific splicing factor nSR100/SRRM4, through its binding to adjacent intronic enhancer motifs. Neural microexons are frequently misregulated in the brains of individuals with autism spectrum disorder, and this misregulation is associated with reduced levels of nSR100. The results thus reveal a highly conserved program of dynamic microexon regulation associated with the remodeling of protein-interaction networks during neurogenesis, the misregulation of which is linked to autism.


Figure 1
Figure 1. An extensive program of neural-regulated AS
A) Distribution by type of human AS events with increased/decreased neural inclusion of the alternative sequence. Alt3/5, alternative splice site acceptor/donor selection; IR, intron retention; Microexons, 3-27 nt exons; Single/Multi AltEx, single/multiple cassette exons. B) Predicted impact of non-neural and neural-regulated AS events on proteomes. Neural-regulated events are more often predicted to generate isoforms preserving open reading frame (ORF) when the alternative sequence is included and excluded (“ORF-preserving isoforms”, black), than to disrupt ORFs (i.e. the exon leads to a frame shift and/or introduces a premature termination codon) specifically in neural samples (“ORF disruption in brain”, dark grey) or in non-neural samples (“ORF preservation in brain”, light grey). See Extended Experimental Procedures for details. C) Enrichment map for GO and KEGG categories in genes with neural-regulated AS that are predicted to generate alternative protein isoforms (top), and representative GO terms and their associated enrichment p-value for each subnetwork (bottom). The node size is proportional to the number of genes associated with the GO category, and the width of the edges to the number of genes shared between GO categories.
Figure 2
Figure 2. A landscape of highly conserved neural microexons
A) Difference in exon inclusion level (ΔPSI) between the average PSIs for neural samples and non-neural samples (Y-axis) for bins of increasing exon lengths (X-axis). Microexons are defined as exons with lengths of 3-27 nt. Restricting the analysis to alternative exons with a PSI range across samples of >50 showed a similar pattern (data not shown). B) Number of exons by length whose inclusion level is higher (blue), lower (red) or not different (grey) in neural compared to non-neural samples. Short exons tend to be multiple of 3 nts and have higher inclusion in neural samples. C) Percent of neural-regulated microexons (of lengths of 3-15 and 16-27 nt) and longer exons that are predicted to generate alternative ORF-preserving isoforms (black), disrupt the ORF in/outside neural tissues (dark/light grey), or overlap non-coding sequences (white). D) Higher evolutionary conservation of alternative microexons compared to longer alternative exons at the genomic, transcriptomic (i.e. whether the exon is alternatively spliced in both species), and neural-regulatory level. Y-axis shows the percent of conservation at each specific level between human and mouse. p-values correspond to two-sided proportion tests. E) Percent of alternative microexons and longer exons that are detected as neural-regulated (average absolute ΔPSI>25) in each vertebrate species. F) Alternative 3-15 and 16-27 nt microexons show higher average phastCons scores at their intronic boundaries than longer alternative and constitutive exons. See also Figure S2.
Figure 3
Figure 3. Switch-like regulation of microexons during neuronal differentiation
A) Heatmap of PSI changes (ΔPSIs) between time points during differentiation of ESCs to glutamatergic neurons in vitro (Hubbard et al., 2013). Yellow/pink indicate increased/decreased PSI at a given transition (T1 to T5). Unsupervised clustering detects eight clusters of exons based on their dynamic PSI regulation (clusters I-VIII, legend). Right, top: scheme of the neuronal differentiation assay time points of sample collection, and analyzed transitions. Right, bottom: PSIs for each microexons (grey lines) in five selected clusters; red lines show the median for the cluster at each time point. B) Representative RT-PCR assays monitoring AS patterns of microexons during neuronal differentiation in Ap1s2 (9 nt), Mef2d (21 nt), Apbb1 (6 nt), Ap1b1 (21nt), Enah (12 nt) and Shank2 (9 and 21 nt). See also Figure S3.
Figure 4
Figure 4. nSR100 is a positive, direct regulator of most microexons
A) Percent of neural-regulated exons within each length class that is affected by nSR100 expression in human 293T kidney cells (absolute ΔPSI > 15 [orange] or absolute ΔPSI > 25 [red]). p-values correspond to two-sided proportion tests of affected vs. non-affected events. B) Average normalized density of nSR100 cross-linked sites in 200 nt windows encompassing neural-regulated exons of different length classes. FPB, Fragments Per Billion. C) Cumulative distribution plots indicating the position of the first UGC motif within 200 nts upstream of neural-regulated microexons and longer exons, as well as non-neural and constitutive exons. p<0.0001 for all comparisons against microexons, Wilcoxon Sum Rank test. See also Figure S4.
Figure 5
Figure 5. Microexons possess distinct protein-coding features
For each analysis, values are shown for neural-regulated, 3-15 nt microexons and longer (>27 nt) exons, as well as non-neural AS exons (see Figure S5 for other types of exons). A) Percent of exons with a high average (>0.67), mid-range (0.33 to 0.67) and low disorder rate (<0.33). B) Fraction of amino acids (AA) that overlap a PFAM protein domain. C) Percent of AA within PFAM domains predicted to be on the protein surface. D) Percent of AA types based on their properties; p-values correspond to the comparison of charged (acid and basic) versus uncharged (polar and apolar) AAs. E) Percent of exons that are adjacent to a domain (within 0-5 (black) or 6-10 AAs (grey)); p-values correspond to the comparison of exons within 0-5 AAs. F) Percent of residues overlapping PFAM domains involved in linear motif or lipid binding. G) Percent of residues overlapping binding motifs predicted by ANCHOR. H) Percent of exons with proteins identified as belonging to one or more protein complexes (data from (Havugimana et al., 2012)). All p-values correspond to proportion tests except for A (3-way Fisher test) and C (Wilcoxon Sum Ranks test). See also Figure S5.
Figure 6
Figure 6. Microexons regulate protein-protein interactions
A) Structural alignment of APBB1-PTB1 (pink) and APBB1-PTB2 (cyan) domains. Residues located at the protein-binding interface of APBB1-PTB2 are shown in blue. Inset shows the microexon residues in APBB1-PTB1 (E462-R463). B) Upon superimposition of APBB1-PTB1 (pink) and APBB1-PTB2 (cyan) domains, the microexon (magenta) is located close to the APBB1-PTB2 binding partner (APP protein fragment, blue), suggesting the microexon in PTB1 may affect protein binding. C) Quantification of LUMIER-normalized luciferase intensity ratio (NLIR) values for RL-tagged Apbb1, with or without the microexon, or with a mutated version consisting of two Alanine substitutions (ALA-mic.), co-immunoprecipitated with 3Flag-tagged Kat5. D, E) 293T cells were transfected HA-tagged Apbb1 (D) or AP1S2 (E) constructs, with or without the respective microexon, together with 3Flag-tagged Kat5 (D) or AP1B1 (E), as indicated. Immunoprecipitation was performed with anti-Flag (D) or anti-HA (E) antibody, and the immunoprecipitates were blotted with anti-HA or anti-Flag antibody, as indicated. Results shown in (E) were confirmed in a biological replicate experiment (Figure S6D). p-values in C and D correspond to t-tests for four and three replicates, respectively; error bars indicate standard error. Asterisk in panel E indicates a band corresponding to the light chain of the HA antibody.
Figure 7
Figure 7. Microexons are often misregulated in ASD
A) Percent of alternative exons of each length class that are misregulated in ASD (absolute ΔPSI>10 between PSI-averaged ASD and control groups. in ba41/42/22 brain regions. Dark shading, lower inclusion in ASD; light shading, higher inclusion in ASD; p-values correspond to proportion tests. B) Expression of nSR100 across the 12 control and 12 ASD individuals, Adjusted Fragments Per Kilobase Of Exon Per Million Fragments Mapped (FPKMs) were calculated using a regression analysis that accounts for variation derived from differences in RNA integrity, brain sample batch, sequencing depth, and 5' - 3' bias in measurements of gene-level FPKM values. C) Percent of exons within each length class misregulated in autistic compared to control brains (average absolute ΔPSI>10) for nSR100-regulated (ΔPSI>25 in the nSR100-overexpressing compared to control 293T cells) and non-nSR100-regulated (absolute ΔPSI<5) exons. D) Distribution of correlation coefficients between PSIs and nSR100 expression values across stratified ASD and control samples for microexons that are (n=59) or are not (n=69) regulated by nSR100. Only microexons with sufficient read coverage to derive accurate PSI quantifications in at least 9 ASD and 9 control ba41/42/22 samples were included. p-value correspond to Wilcoxon Sum Rank test. E) GO categories significantly enriched in genes with microexons that are misregulated in ASD. F) A protein-protein interaction network involving genes with ASD misregulated microexons (ΔPSI > 10) in ba41/42/22 brain regions. Genes with major effect mutations, and smaller effect risk genes, are indicated in red and shaded ovals, respectively. Genes grouped by functional category are indicated. See also Figure S7.

Comment in

  • Microexons--tiny but Mighty
    C Scheckel et al. EMBO J 34 (3), 273-4. PMID 25535247.
    The landscape of alternative splicing is only beginning to unravel, and the functional consequences are often unclear. Two articles in Cell and Genome Research

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