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Mammalian tissues defective in nonsense-mediated mRNA decay display highly aberrant splicing patterns - PubMed

  • ️Sun Jan 01 2012

Mammalian tissues defective in nonsense-mediated mRNA decay display highly aberrant splicing patterns

Joachim Weischenfeldt et al. Genome Biol. 2012.

Abstract

Background: Nonsense-mediated mRNA decay (NMD) affects the outcome of alternative splicing by degrading mRNA isoforms with premature termination codons. Splicing regulators constitute important NMD targets; however, the extent to which loss of NMD causes extensive deregulation of alternative splicing has not previously been assayed in a global, unbiased manner. Here, we combine mouse genetics and RNA-seq to provide the first in vivo analysis of the global impact of NMD on splicing patterns in two primary mouse tissues ablated for the NMD factor UPF2.

Results: We developed a bioinformatic pipeline that maps RNA-seq data to a combinatorial exon database, predicts NMD-susceptibility for mRNA isoforms and calculates the distribution of major splice isoform classes. We present a catalog of NMD-regulated alternative splicing events, showing that isoforms of 30% of all expressed genes are upregulated in NMD-deficient cells and that NMD targets all major splicing classes. Importantly, NMD-dependent effects are not restricted to premature termination codon+ isoforms but also involve an abundance of splicing events that do not generate premature termination codons. Supporting their functional importance, the latter events are associated with high intronic conservation.

Conclusions: Our data demonstrate that NMD regulates alternative splicing outcomes through an intricate web of splicing regulators and that its loss leads to the deregulation of a panoply of splicing events, providing novel insights into its role in core- and tissue-specific regulation of gene expression. Thus, our study extends the importance of NMD from an mRNA quality pathway to a regulator of several layers of gene expression.

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Figures

Figure 1
Figure 1

Mapping and downstream analysis of RNA-seq data. (a) Total number of RNA-seq reads mapped to exonic, junction, intronic and intergenic regions as well as the fraction of mapped reads for all four samples. Pie charts below visualize the distribution of all mapped reads. (b) An example of an output from the pipeline, visualized in the UCSC genome browser, showing the Upf2 gene locus, with junctions supported by reads (horizontal bars in each sub-window), and exon coverage (vertical bars). Refer to Figure S1 in Additional file 2 for another example of a genome browser output. No minimum read cutoff was used for junction visualization. The very low exon coverage present for knocked out exons 2 and 3 in KO samples represents a miniscule amount of non-recombined tissue/cells.

Figure 2
Figure 2

Number of regulated splicing and PTC occurrences in Upf2 KO tissues. (a) Splice junctions with a minimum of three reads in WT or KO samples were grouped into upregulated (≥2-fold up in KO), downregulated (≥2-fold down in KO) or unregulated. Genes corresponding to the junctions are also shown. Left: number of junctions and genes and percentage in parentheses for the relative fraction in each tissue. The top half of the table lists results for all junctions, the bottom half for PTC+ junctions only. Right: pie charts visualize the relative distribution of regulated junctions and corresponding genes. (b) Venn diagram of major Gene Ontology terms associated with liver-unique genes (top), BMM-unique genes (bottom) and common genes (overlap).

Figure 3
Figure 3

Loss of UPF2 leads to increased AS-NMD. (a) Proportions of regulated canonical junctions versus junctions supporting AS events (red, upregulated; yellow, unregulated; green, downregulated), with additional distribution pie charts underneath displaying the predicted percentage of PTC+ junctions versus PTC- junctions (junctions that do not elicit a PTC) of upregulated junctions (black, PTC+; grey, PTC-). (b) The number of AS junctions and the number of PTC+ junctions are shown as a function of number of junctions per gene in the top and bottom panels, respectively. The slopes of the curves were calculated by linear interpolation. In total, our pipeline detected 10,061 unique AS junctions in BMMs (8,107 in WT and 9,447 in KO) compared to 33,164 in the liver (25,390 in WT and 31,041 in KO), and calculations in the table (top) show the number of unique AS junctions per million mapped reads for each sample. No minimum read cutoff was used in (b).

Figure 4
Figure 4

Splice isoform classes are differentially affected by loss of UPF2. (a) Schematic drawing of the main isoform classes detected in our pipeline. (b) RT-PCR validation of 25 splicing events predicted from our pipeline (see Table S9 in Additional file 12 for a list of the 49 validated events out of 50 tested). Top: normal AS events. Middle: PTC upon inclusion events. Bottom: PTC upon exclusion by ES. (c) Western blotting from two different liver pairs, showing UPF2 (rabbit α-UPF2), SRp55, SRp40 and SRp30 (mouse α-mAb104) and β-actin (rabbit α-actin). The asterisk denotes the truncated UPF2 isoform found in cells ablated for NMD. (d) MXE splicing of Pkm2 and the inclusion of both mutually exclusive exons in the KO sample.

Figure 5
Figure 5

Introns flanking regulated exons are highly conserved. (a, b) Mean per position phastCon conservation scores around SES events are shown for exclusion events upregulated in the KO sample (a), and for inclusion events upregulated in the KO sample (b). Shown are PTC+ exclusion/inclusion events (red line), PTC- exclusion/inclusion events (green line) and unregulated skipping events (grey line). Yellow lines are scores for all mm9 RefSeq exons and 75 bp into surrounding introns. Data shown are for liver. Exclusion events: PTC+, 439; PTC-, 251. Inclusion events: PTC+, 64; PTC-, 162. Unregulated events: 3,494. Mm9 RefSeq exons: 274,281. See Figure S4 in Additional file 10 for a graph with BMM data. Numbers on the x-axis indicate nucleotide intervals - 25 and 75 nucleotides for exons and flanking introns, respectively. Curves represent a cubic smoothing spline fitted to data.

Figure 6
Figure 6

Low-expressed junctions are enriched in Upf2 KO samples. PTC-content (y-axis) in junctions binned by expression (RPKM; x-axis). All junctions for samples were binned in increments of 1 RPKM and the PTC+/PTC- ratio for each bin was calculated. No minimum read cutoff was applied.

Figure 7
Figure 7

Conservation of regulated PTCs and surrounding exons. (a) Mean per-position phastCon scores are shown, centered on the PTC, for upregulated junctions in liver and BMM. To visualize conservation around PTCs in comparison to normal exonic areas, phastCon scores from a random sample (n = 4,000) of BMM and liver-expressed exons were subtracted from either sample. For normal STOPs, phastCon-scores from random RefSeq exons (n = 4,000) were subtracted. Normal STOPs are based on all RefSeq transcript models. Ranges of scores do not extend into introns, and may be shorter than 100 bp for individual PTCs. For PTC+ junctions, a KO/WT fold change of 2 was required. BMM PTC+ positions: 884. Liver PTC+ positions: 3,091. Normal RefSeq STOP positions: 23,231. (b) Distribution of stop codons: for intronic, intergenic and exonic bins, all mm9 trinucleotides in all three reading frames were sampled. RefSeq STOPs represent normal stop codons for all 21,470 RefSeq transcript models (for genes with multiple models, the longest was used). For BMM (n = 497), PTC-inducing junctions were required to have a log2(KO/WT) fold change > 2, and a minimum of 5 reads for both genotypes summed. For liver (n = 670), PTC-inducing junctions were required to have a log2(KO/WT) fold change > 2, and a minimum of 10 reads for both genotypes summed. Fisher's exact test was used to test for significance.

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