Arginine methylation of SM-LIKE PROTEIN 4 antagonistically affects alternative splicing during Arabidopsis stress responses

Abstract

Arabidopsis (Arabidopsis thaliana) PROTEIN ARGININE METHYLTRANSFERASE5 (PRMT5) post-translationally modifies RNA-binding proteins by arginine (R) methylation. However, the impact of this modification on the regulation of RNA processing is largely unknown. We used the spliceosome component, SM-LIKE PROTEIN 4 (LSM4), as a paradigm to study the role of R-methylation in RNA processing. We found that LSM4 regulates alternative splicing (AS) of a suite of its in vivo targets identified here. The lsm4 and prmt5 mutants show a considerable overlap of genes with altered AS raising the possibility that splicing of those genes could be regulated by PRMT5-dependent LSM4 methylation. Indeed, LSM4 methylation impacts AS, particularly of genes linked with stress response. Wild-type LSM4 and an unmethylable version complement the lsm4-1 mutant, suggesting that methylation is not critical for growth in normal environments. However, LSM4 methylation increases with abscisic acid and is necessary for plants to grow under abiotic stress. Conversely, bacterial infection reduces LSM4 methylation, and plants that express unmethylable-LSM4 are more resistant to Pseudomonas than those expressing wild-type LSM4. This tolerance correlates with decreased intron retention of immune-response genes upon infection. Taken together, this provides direct evidence that R-methylation adjusts LSM4 function on pre-mRNA splicing in an antagonistic manner in response to biotic and abiotic stress.

Figure 1.

LSM4 and PRMT5 control an overlapping set of pre-mRNA splicing events. A) Representative photographs showing the phenotypes of plants used for RIP-seq. Plants expressing 35S:YFP-LSM4 and lsm4-1 and WT genotypes, illustrating rescue of developmental defects observed in the lsm4-1 mutant. B) Volcano plot illustrating log₂ fold-change (x-axis) and statistical significance as −log₁₀P-value (y-axis) of the RIP-seq dataset. The dashed line indicates the threshold above which transcripts are significantly enriched (FC > 3 and P-value < 0.05). C) GO-term enrichment analysis of LSM4 direct targets represented as a bubble plot. The size of the bubble is the RF. D) Relative frequencies of different AS types for all detected AS events affected in lsm4-1: Alt 3′ and Alt 5′, alternative acceptor and donor splice sites, respectively; ES, exon skipping; IR, intron retention; Multiple, for cases where more than one type of event was observed. E) Upset plot showing single, pairwise, and triple combinations of genes for the different list of genes found in each high-throughput experiment (DEG: differentially expressed gene; DU: differential usage of splicing sites from RNA-seq experiment; and targets: candidate genes bound to LSM4 found by RIP-seq). The significance of the overlap in the upset graphs was determined by calculation of the hypergeometric probability. **P-value < 0.001. F) IGV view of mapped reads for selected LSM4-bound target transcripts in 35S-GFP and 35S:YFP-LSM4 for the RIP-seq experiment and its expression in WT and lsm4-1 from RNA-seq. The gray line denotes the splicing defect as quantified by RNA-seq analysis. G, H) Venn diagram showing the extent of overlap for pre-mRNA splicing events (G) or genes with splicing events (H) affected in the lsm4-1 mutant and those altered in prmt5-5. The significance of the overlap was determined by calculation of the hypergeometric probability. I) Upset plot of several single, pairwise and triple comparisons of gene lists from diverse analyses. Statistical testing of multiset intersections was calculated using the R package SuperExactTest. ***P-value < 0.0001.

Figure 2.

The RGG domains of LSM4 are not required for lsm4-1 phenotype rescue. A) Scheme showing wild-type (WT) (LSM4^R) and mutant (LSM4^RxK) LSM4 proteins. RGG domains are shown as boxes. B) Representative pictures of WT, lsm4-1, and two independent lines for each type of transgenic plant, LSM4^R and LSM4^RxK. Twelve-days-old seedlings (top panel) and 4-wk-old plants (bottom panel). C) Leaf size of seedlings from 12, 15, and 20 d grown in long-days at 22 °C. At least 25 plants were measured per genotype. Mean and Sd are plotted. D) Methylation of LSM4 protein is impaired by R to K changes. Immunoblot of LSM4^R, LSM4^RxK, and prmt5-5 samples with the SYM10 antibody. The prmt5-5 mutant was used as a negative control for impaired methylation. Ponceau red staining shows equal loading across samples. The arrow represents the expected size for LSM4. E) Immunoblot of WT, YFP-LSM4^R, and YFP-LSM4^RxK samples developed with SYM10 (top) or anti-GFP antibody (bottom). The arrow represents the expected size for the YFP-LSM4 fusion protein. Ponceau red staining shows equal loading across samples. F) Circadian rhythm of leaf movement. Plants’ vertical leaf motion (RLM) was obtained for the first pair of leaves of seedlings entrained under long-day conditions (16 h light/8 h dark) and then transferred to continuous light (LL). WT, wild-type, LSM4^R, three independent lines from lsm4-1 mutant plants transformed with LSM4^R and LSM4^RxK, three independent lines from lsm4-1 mutant plants transformed with LSM4^RxK. (Left) Period length of leaf movement rhythms is estimated by Fast Fourier transform–nonlinear least-square test (FFT–NLLS) (Right). Error bars represent SEM.

Figure 3.

LSM4 R methylation affects a subset of transcripts involved in stress responses. A) Summary of the transcriptome analysis is shown as a Circos plot. The total number of DEGs (left) or affected splicing events (right) in each mutant is shown below the mutant name. Numbers of upregulated or downregulated genes are indicated with arrows. Connecting lines are scaled and represent shared affected events. Blue lines shared across all genotypes; green lines shared between LSM4^RxK and lsm4-1; light violet, shared between LSM4^RxK and LSM4^R, and dark violet shared between LSM4^R and lsm4-1. Numbers above the lines represent the number of genes (left) or events (right). B) Scheme showing the rationale behind analysis to define splicing events dependent on LSM4 methylation. C) The effect of lsm4-1 mutation is larger than the effect of its methylation. ΔPSI/PIR values for the splicing events affected by LSM4 methylation in lsm4-1 vs WT or LSM4^R vs LSM^RxK. The number of events is shown in parentheses. D) IGV view of mapped reads for selected transcripts affected by R methylation in WT, lsm4-1, LSM4^R, and LSM4^RxK. The vertical line denotes the differential splicing event as quantified by RNA-seq analysis. The PIR value is shown for each genotype. E) GO-term enrichment analysis of genes affected by R methylation represented as a bubble plot. The size of the bubble is the RF.

Figure 4.

LSM4 R methylation is required for abiotic stress responses. Time-course of germination (A) and greening (B) percentage for wild type (WT), prmt5-5, transgenic lines of 35S-LSM4^R, and transgenic lines of 35S-LSM4^RxK. Seeds were sown on MS plates (control) or MS plates supplemented with 1 µm ABA. Percentages are relative to control for each genotype at each point. C) Representative seedlings grown on 1 µm ABA for 7 d. D) Four-days-old seedlings were transferred to new MS plates (as control) or to MS supplemented with 1 µm ABA. FW was quantified 7 d post-treatment, and expressed relative to the control for each genotype. E) Percentage of germination and greening after 2 or 8 d in 250 mm sorbitol, respectively. Percentages are relative to control for each genotype at each time-point. F) Representative seedlings grown on sorbitol treatment for 14 d. G) Anthocyanin accumulation of control or 250 mm sorbitol treated seedlings. H and I) Four-days-old seedlings were transferred to 50 mm NaCl and FW (H) and LR) (I) were analyzed 7 d post-treatment. J) Chlorophyll content of plants treated with 75 mm NaCl for 7 d. NaCl data is expressed relative to the control for each genotype. For all experiments depicted in A to J). Data are means ± Sd of nine biological replicates. Different letters indicate significant differences among genotypes, P < 0.05 (one-way ANOVA followed by Tukey's multi-comparison test). K) Methylation of LSM4 protein is changed upon ABA treatment. Immunoblot of LSM4^R samples after 0, 1, or 4 h of 10 µm ABA treatment developed with the SYM10 antibody. The LSM4^RxK mutant was used as a negative control for impaired methylation. Ponceau red staining shows equal loading across samples. The arrow represents the expected size for LSM4.). Data are means ± Sd of three independent experiments. L) IGV view of mapped reads for selected transcripts involved in ABA responses affected by R methylation in WT, lsm4-1, LSM4^R, and LSM4^RxK. The vertical line denotes the splicing defect as quantified by RNA-Seq analysis. The PIR value is shown for each genotype.

Figure 5.

LSM4 methylation modulates plant immunity. A) Venn diagram showing the extent of overlap for pre-mRNA splicing events affected by LSM4 methylation and those altered upon bacterial infection from Golisz et al. (2021). B) Heatmap showing ΔPSI/PIR values calculated for the 113 genes from (A) in LSM4^RxK vs LSM4^R. C) IGV view of mapped reads for selected transcripts coding for TIR-NBS-LRR proteins in WT, lsm4-1, LSM4^R, and LSM4^RxK. The vertical line denotes the splicing defect as quantified by RNA-seq analysis. The PIR value is shown for each genotype. D) IGV view of mapped reads for ZAR1 in WT, lsm4-1, LSM4^R, and LSM4^RxK. The vertical line denotes the splicing defect as quantified by RNA-seq analysis. E) (Left) Methylation of LSM4 protein upon mock treatment (M) or 4 and 24 h of bacterial infection (I) with P. syringae DC3000. Immunoblot of LSM4^R samples with the SYM10 antibody. The LSM4^RxK mutant was used as a negative control for impaired methylation. Ponceau red staining shows equal loading across samples. Arrow represents expected size for LSM4. (Right) Quantitation of three independent experiments. Error bars represent Sd (**P < 0.001; t-test to mock). F, G) RT-PCR to detect splicing defects for AT1G56520 and AT1G72900in LSM4^R and LSM4^RxK upon bacterial infection (I) or mock treatment (M). The WT and lsm4-1 genotypes were measured under controlled conditions to show IR is increased in lsm4-1. Alternative regions are highlighted in red in the diagrams next to the gels and position of the primers used are depicted. The ratio of the splicing is shown below is lane. H) Four-weeks-old plants grown in LD conditions were infected by infiltration with P. syringae DC3000. Bacterial growth was assessed 2 dpi. (CFU: colony-forming units) in WT, two independent lines of LSM4^R, two independent lines of LSM4^RxK plants. Data represent the average of log-transformed bacterial growth (n = 8 independent biological replicates). This experiment was repeated twice with similar results. Error bars indicate SEM. P < 0.05 (one-way ANOVA followed by Tukey's multicomparison test). I and J) Levels of PR1 (I) and PR2 (J) proteins upon bacterial infection (I) or mock treatment (M) determined by western blots. PR1 levels were determined in LSM4^R and LSM4^RxK plants after 24 or 48 h upon bacterial infection. Levels of PR2 were measured after 24 h of infection. Anti-PR1 and anti-PR2 antibodies were used, respectively. Ponceau red staining shows equal loading across samples.

Figure 6.

Proposed model for the role of LSM4 methylation in modulation of stress responses. A) In the absence of lsm4 a wide range of AS defects leads to aberrant phenotypes and altered stress responses. Splicing defects are enriched in IR events. B) Upon exposure to bacterial infection, PRMT5 decreases (Hu et al. 2019) and methylation of LSM4 is reduced (this work). This reduction affects splicing of genes involved in plant immunity to increase bacterial resistance. C) Treatments associated with abiotic stress increase LSM4 methylation (this work and Hu et al. 2017). A decrease in IR of genes involved in abiotic stress response leads to an increase of the functional isoforms correlating with improved adaptation to abiotic stress. Created with BioRender.com (Agreement Number QR26I9QC8X).