Research Article

m⁶A modification of U6 snRNA modulates usage of two major classes of pre-mRNA 5’ splice site

School of Life Sciences, University of Dundee, United Kingdom
Centre for Plant Sciences, School of Biology, Faculty of Biological Sciences, University of Leeds, United Kingdom
RNA Biology and Molecular Physiology, Faculty of Biology, Bielefeld University, Germany
Department of Biochemistry, University of Oxford, United Kingdom
Cell & Molecular Sciences, James Hutton Institute, United Kingdom

Nov 21, 2022

https://doi.org/10.7554/eLife.78808

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Version of Record published: December 30, 2022 (This version)
Accepted Manuscript published: November 21, 2022 (Go to version)
Accepted: November 20, 2022
Preprint posted: April 5, 2022 (Go to version)
Received: March 21, 2022

1. Of interest
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Research Article Updated Jun 2, 2023
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Abstract

Alternative splicing of messenger RNAs is associated with the evolution of developmentally complex eukaryotes. Splicing is mediated by the spliceosome, and docking of the pre-mRNA 5’ splice site into the spliceosome active site depends upon pairing with the conserved ACAGA sequence of U6 snRNA. In some species, including humans, the central adenosine of the ACAGA box is modified by N⁶ methylation, but the role of this m⁶A modification is poorly understood. Here, we show that m⁶A modified U6 snRNA determines the accuracy and efficiency of splicing. We reveal that the conserved methyltransferase, FIONA1, is required for Arabidopsis U6 snRNA m⁶A modification. Arabidopsis fio1 mutants show disrupted patterns of splicing that can be explained by the sequence composition of 5’ splice sites and cooperative roles for U5 and U6 snRNA in splice site selection. U6 snRNA m⁶A influences 3’ splice site usage. We generalise these findings to reveal two major classes of 5’ splice site in diverse eukaryotes, which display anti-correlated interaction potential with U5 snRNA loop 1 and the U6 snRNA ACAGA box. We conclude that U6 snRNA m⁶A modification contributes to the selection of degenerate 5’ splice sites crucial to alternative splicing.

Editor's evaluation

This is an important paper reporting that an adenosine methyltransferase in the model plant Arabidopsis functions to target a key RNA component of the spliceosome, as in fission yeast, and thereby contributes to intron recognition as well as flowering timing. By contrast, the authors report no major role for the methyltransferase in targeting mRNAs, as reported in several previous studies in Arabidopsis. Overall, the approaches are convincing, although a correlative analysis identifying an intronic sequence feature characteristic of methyltransferase-sensitive introns is not followed up with tests to establish causality of this feature.

https://doi.org/10.7554/eLife.78808.sa0

eLife digest

All the information necessary to build the proteins that perform the biological processes required for life is encoded in the DNA of an organism. Making these proteins requires the DNA sequence of a gene to be transcribed into a ‘messenger RNA’ (mRNA), which is then processed into a final, mature form. This blueprint is then translated to assemble the corresponding protein.

When an mRNA is processed, segments of the sequence that do not code for protein are removed and the remaining coding sequences are joined together in the right order. An intricate molecular machine known as the spliceosome controls this mechanism by recognising the ‘splice sites’ where coding and non-coding sequences meet. Depending on external conditions, the spliceosome can ‘pick-and-mix’ the coding sequences to create different processed mRNAs (and therefore proteins) from a single gene. This alternative splicing mechanism is often used to regulate when certain biological processes take place based on environmental cues; for example, the splicing of genes which control the timing of plant flowering is sensitive to ambient temperatures.

To investigate this mechanism, Parker et al. focused on Arabidopsis thaliana, a plant that blooms later when temperatures are low. This precise timing partly relies on a gene whose mRNA is efficiently spliced in the cold, resulting in an active form of its protein that blocks blooming. Parker et al. grew and screened many A. thaliana plants to find individuals that could flower early in the cold, in which splicing of this gene was disrupted.

A mutant fitting these criteria was identified and subjected to further investigation, which revealed that it could not produce FIONA1. In non-mutant plants, this enzyme chemically modifies one of the components of the spliceosome, a small nuclear RNA known as U6. Parker et al found that there are two types of splice site – one more likely to interact with U6 and another that preferentially interacts with another small nuclear RNA, U5. When FIONA1 is inactive (such as in the mutant identified by Parker et al.), splice sites that tend to strongly interact with U5 are selected. However, when the enzyme is active, splice sites that tend to bind with the chemically modified U6 are used instead.

Further work by Parker et al. showed that these two types of splice sites (‘preferring’ either U5 or U6) are found in equal proportions in the genomes of many species, including humans. This suggests that Parker et al. have uncovered an essential feature of how genomes are organised and splicing is controlled.

Introduction

Split genes are a defining characteristic of eukaryotic genomes (Plaschka et al., 2019). During pre-mRNA transcription, intervening sequences (introns) are excised, and the flanking sequences (exons) are spliced together. In developmentally complex eukaryotes, such as humans or Arabidopsis, most protein-coding genes have introns, and the cis-elements controlling splicing are degenerate. Such sequence variation facilitates alternative splice site selection that, in turn, permits the regulation of mRNA expression and the production of functionally different protein isoforms (Lee and Rio, 2015; Nilsen and Graveley, 2010). Consistent with this, alternative splicing is the foremost genomic predictor of developmental complexity (Chen et al., 2014).

Pre-mRNA splicing is carried out by the spliceosome (Plaschka et al., 2019; Wilkinson et al., 2020). This large, dynamic molecular machine comprises more than 100 proteins and 5 uridylate-rich snRNAs (UsnRNAs). Splicing requires two sequential transesterification reactions. The first reaction, called branching, occurs when the 2’ hydroxyl of the conserved intron branchpoint adenosine performs a nucleophilic attack on the 5’ splice site (5’SS), resulting in a cleaved 5’ exon and a branched lariat-intron intermediate. The second reaction, exon ligation, occurs via nucleophilic attack of the 3’ hydroxyl of the 5’ exon on the 3’ splice site (3’SS). Typically, the 5’SS is first recognised by U1 snRNP, whilst U2 snRNP recognises the branchpoint sequence of pre-mRNA. The preassembled U4/U6.U5 tri-snRNP subsequently joins the spliceosome. The conserved U6 snRNA ACAGA sequence replaces U1 snRNP at the 5’SS and loop 1 of U5 snRNA binds 5’ exon sequences adjacent to the 5’SS. In humans, selection of the 5’SS occurs during this hand-off to U6 and U5 snRNAs, and this step is decoupled from formation of the active site, thus potentially allowing for plasticity of 5’SS selection (Charenton et al., 2019; Fica, 2020). Subsequently, U6 pairs with U2 snRNA and intramolecular U6 snRNA interactions facilitate the positioning of the catalytic metal ions to effect branching and exon ligation (Wilkinson et al., 2020).

U6 snRNA is the most highly conserved UsnRNA, reflecting its crucial roles at the active site of the spliceosome (Wilkinson et al., 2020). Different base modifications are found in each of the UsnRNAs, including U6 snRNA, but the role of these modifications is poorly understood (Morais et al., 2021). The essential ACAGA sequence of Saccharomyces cerevisiae U6 snRNA is unmodified. However, in other species including Schizosaccharomyces pombe (Gu et al., 1996), the plant Vicia faba (Kiss et al., 1987), and human (Shimba et al., 1995), the central adenosine in the corresponding sequence is modified by methylation at the N6 position: AC^m6AGA. In S. cerevisiae, U6 snRNA ACAGA recognises the stringently conserved GUAUGU sequence at the 5’ end of introns, with the central adenosine making a Watson-Crick base pair with the almost invariant U at the +4 position of the 5’SS (5’SS U₊₄) (Neuvéglise et al., 2011; Wan et al., 2019). Conversely, in species with m⁶A-modified U6 snRNA, the 5’SS can be degenerate and the identity of the base at the +4 position varies but is usually enriched for A. Therefore, the pairing between the 5’SS and the U6 snRNA ACAGA box is unlikely to be driven primarily by canonical Watson-Crick base pairs in these species. The precise function of the U6 snRNA m⁶A modification is unknown.

In humans, the U6 snRNA ACAGA sequence is methylated by the conserved methyltransferase METTL16 (Aoyama et al., 2020; Pendleton et al., 2017; Warda et al., 2017). This modification depends upon a specific sequence and distinct structural features of U6 snRNA that are recognised by METTL16. Hairpin sequences that mimic these features of U6 snRNA are found within the 3’UTR of S-adenosylmethionine (SAM) synthetase MAT2A mRNA and are also methylated by METTL16 (Pendleton et al., 2017; Shima et al., 2017). SAM synthetase is the enzyme responsible for production of the methyl donor SAM, which is required for methylation reactions in the cell. Binding of METTL16 to MAT2A mRNA influences splicing and/or stability, regulating MAT2A expression and SAM levels (Pendleton et al., 2017; Shima et al., 2017). In Caenorhabditis elegans, the METTL16 orthologue, METT-10, methylates U6 snRNA and also influences SAM levels by targeting SAM synthetase (sams) genes (Mendel et al., 2021). METT-10 methylates the 3’SS of sams-3/4 intron 2, although in a different sequence and structural context to U6 snRNA (Mendel et al., 2021). In S. pombe, the METTL16 orthologue, MTL16, appears to target only U6 snRNA (Ishigami et al., 2021). S. pombe mutants defective in MTL16 function show less efficient splicing of some introns, resulting in increased levels of intron retention. The S. pombe introns sensitive to loss of MTL16 function are distinguished by having adenosine at the +4 position of the intron (5’SS A₊₄). However, the effect of 5’SS A₊₄ is partially suppressed in introns that have stronger predicted base pairing between U5 snRNA loop 1 and the 5’ exon (Ishigami et al., 2021).

Alternative splicing plays crucial roles in gene regulation, affecting diverse biological processes including the control of flowering in response to ambient temperature (Airoldi et al., 2015; Capovilla et al., 2017). Slight changes in ambient temperature can profoundly influence flowering time (Andrés and Coupland, 2012). Indeed, documented changes in the flowering times of many plant species have provided some of the best biological evidence of recent climate change (Fitter and Fitter, 2002). At cooler ambient temperatures, pre-mRNAs encoding repressors of Arabidopsis flowering, FLM (FLOWERING LOCUS M) and MAF2 (MADS AFFECTING FLOWERING 2), are efficiently spliced (Airoldi et al., 2015; Balasubramanian et al., 2006; Lee et al., 2013; Lutz et al., 2015; Posé et al., 2013; Scortecci et al., 2001). As a result, FLM and MAF2 proteins are produced at cooler temperatures and form higher-order protein complexes with other MADS box factors, such as MAF3 (MADS AFFECTING FLOWERING 3), SVP (SHORT VEGETATIVE PHASE), and FLC (FLOWERING LOCUS C), to directly repress the expression of the genes FT (FLOWERING LOCUS T) and SOC1 (SUPPRESSOR OF OVEREXPRESSION OF CO 1), which promote flowering (Gu et al., 2013). At elevated ambient temperatures, the splicing of MAF2 and FLM introns is adjusted, in different ways, effecting increased levels of non-productive transcripts (Airoldi et al., 2015; Balasubramanian et al., 2006; Lee et al., 2013; Lutz et al., 2015; Posé et al., 2013; Scortecci et al., 2001). Consequently, the abundance of active MAF2 and FLM proteins is compromised at elevated temperatures; repression of genes promoting flowering is relieved, and flowering is enabled (Airoldi et al., 2015; Capovilla et al., 2017; Posé et al., 2013; Rosloski et al., 2013). In the case of MAF2, this temperature-responsive splicing is characterised by a progressive increase in retention of intron 3 as ambient temperature increases (Airoldi et al., 2015). The thermosensory mechanism that underpins the temperature-responsive alternative splicing of MAF2 and FLM is unknown.

We conducted a two-step mutant screen for factors required for the efficient splicing of MAF2 pre-mRNA intron 3 at cooler temperatures. This screen identified an early flowering mutant disrupting the gene encoding the Arabidopsis METTL16 orthologue, FIONA1 (FIO1; Kim et al., 2008). We show that FIO1 is required for m⁶A modification of U6 snRNA. We detect widespread changes in pre-mRNA splicing in fio1 mutants that can be explained by the identity of the base at the 5’SS +4 position of affected introns and by cooperative roles for U5 and U6 snRNA in 5’SS selection. We also reveal that U6 snRNA interactions at 5’SSs affect 3’SS choice. Analysis of other annotated genomes reveal the existence of two major classes of 5’SS. Our findings suggest cooperative and compensatory roles of U5 and U6 snRNA may contribute to 5’SS selection in a range of eukaryotes.

Results

Identification of FIO1 in a two-step screen for early flowering mutants with increased retention of MAF2 intron 3

To identify factors responsible for promoting efficient splicing of MAF2 intron 3 at low ambient temperature, we performed a two-step genetic screen using ethyl methanesulfonate (EMS) as a mutagen (Figure 1A). In the first step, we screened 15,000 EMS lines for early flowering Arabidopsis mutants at 16°C. In the second step, the earliest flowering 100 individuals were re-screened for enhanced MAF2 intron 3 retention at 16°C, using RT-PCR. Two independent mutants (EMS-129 and EMS-213) showed early flowering and increased levels of MAF2 intron 3 retention at 16°C. We focus here on EMS-129 (Figure 1—figure supplement 1A). The causal mutation for the early flowering phenotype of EMS-129 was mapped by back-crossing to the M0 parental line to generate a segregating population (Figure 1—figure supplement 1B). Early flowering plants and aphenotypic sisters were grouped into separate pools for genomic DNA sequencing. EMS-induced G to A transitions were identified, and allele fractions were compared between the two pools (Javorka et al., 2019). A total of 19 homozygous G to A transitions in a 2.6 Mb region on chromosome 2 were associated with the early flowering phenotype (Figure 1B). Of these, only the SNP at position 9,041,454 of chromosome 2, which disrupts the 5’SS of AT2G21070 intron 2, was predicted to have a major impact on gene expression. An EMS mutation in AT2G21070 has previously been described and named fio1-1 (Kim et al., 2008). Crosses between EMS-129 and fio1-1 confirmed allelism because 100% of F1 progeny from EMS-129 lines crossed to fio1-1 flowered early (35 plants) compared to 0% of F1 progeny from EMS-129 lines crossed to Col-0 (15 plants). We isolated an independent transfer-DNA insertion in AT2G21070 (Alonso et al., 2003), which we refer to as fio1-3. Henceforth, we refer to EMS-129 as fio1-4. We summarise the fio1-1, fio1-3, and fio1-4 alleles in Figure 1C. fio1-1 and fio1-3 show increased levels of MAF2 intron 3 retention at low temperature (Figure 1D) and like maf2 mutants, flower early (Figure 1E–F). fio1 alleles share additional visible phenotypes, including reduced apical dominance and stature (Figure 1F). FIO1 encodes the Arabidopsis orthologue of the human methyltransferase, METTL16 (Kim et al., 2008; Pendleton et al., 2017).

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Loss of *FIO1* causes early flowering and reduced splicing of pre-mRNA encoding the floral repressor MAF2.

(A) Schematic showing the format of the two-step mutant screen. (B) Scatter plot showing allele fractions of ethyl methanesulfonate (EMS)-induced G to A transitions in pooled phenotypically normal plants (blue) and early flowering sisters (orange). Dark orange diamonds show SNPs predicted to have a significant impact on the functional expression of a protein-coding gene (nonsense mutations and splice site mutations). The green line shows the test statistic for the G-test between the allele fractions found in early flowering and normal plants, which has been smoothed using a tri-cube kernel with a window size of two megabases (Mb). The 2.6 Mb mapping interval containing *FIO1* is highlighted in grey. (C) Gene track showing the three *fio1* mutant alleles used in this study. *fio1-1* is an EMS mutant with a G→A transition at the –1 position of the 3’ splice site (3’SS) in intron 2 of *FIO1* (Kim et al., 2008). This causes activation of a cryptic 3’SS 15 nt downstream, and the loss of 5 aa of sequence from the *FIO1* open reading frame (shown in orange). *fio1-3* is a T-DNA insertion mutant (SALK_084201) in the first exon of the *FIO1*, disrupting the gene (region downstream of insertion shown in light blue). *fio1-4* is an EMS mutant with a G→A transition at the +1 position of the 5’ splice site (5’SS) in intron 2 of *FIO1*. This causes activation of a cryptic 5’SS 69 nt upstream, and the loss of 23 aa of sequence from the *FIO1* open reading frame (shown in orange). (D) Changes in splicing efficiency determined by Reverse transcription-quantitative PCR (RT-qPCR) visualised as a regression scatterplot showing the change in spliced to retained ratio of *MAF2* intron 3 in *fio1-1* and *fio1-3* at a range of temperatures. Shaded regions show bootstrapped 95% CI for regression lines. (E) Boxplot showing the change in flowering time (days to flowering) observed in the *fio1-1*, *fio1-3*, and *maf2* mutants at a range of temperatures. (F) Photographs showing the early flowering phenotypes of *fio1-1*, *fio1-3*, and *fio1-4* mutants.

Figure 1—source data 1 Ethyl methanesulfonate (EMS) mutations identified in early flowering and MAF2 splicing screen.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig1-data1-v2.xlsx
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Figure 1—source data 2 Sanger sequencing products for FIO1 cDNAs in fio1-1 and fio1-4 mutant.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig1-data2-v2.txt
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Figure 1—source data 3 Sanger sequencing product alignments for FIO1 cDNAs in fio1-1 and fio1-4 mutant.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig1-data3-v2.txt
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Figure 1—source data 4 MAF2 intron 3 splicing quantitative PCR (qPCR) data for Col-0, fio1-1I and fio1-3 mutants.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig1-data4-v2.xlsx
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Figure 1—source data 5 Flowering time data for fio1-1, fio1-3, and maf2 mutants.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig1-data5-v2.xlsx
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Figure 1—source data 6 RT-PCR screening of MAF2 intron 3 splicing in early flowering mutants.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig1-data6-v2.zip
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FIO1-dependent methylation of poly(A)+ RNA is rare or absent

To reveal which RNAs were methylated by FIO1, we first used two orthogonal approaches to determine the impact of FIO1 on poly(A)+mRNA m⁶A modification: liquid chromatography tandem mass spectrometry (LC-MS/MS) and nanopore direct RNA sequencing (DRS). As a positive control for these experiments, we included a hypomorphic fip37-4 allele defective in the Arabidopsis orthologue of the METTL3 m⁶A writer complex component WTAP (Zhong et al., 2008).

LC-MS/MS analysis (Figure 2A) revealed an 81.8% decrease in m⁶A levels in fip37-4 compared to Col-0 (t test p=2.2 × 10⁻⁶). In contrast, a 6.7% decrease in m⁶A levels was observed in poly(A)+RNA purified from fio1-1 (t test p=0.40). A similarly modest decrease in mRNA m⁶A levels has recently been reported for other fio1 alleles (Wang et al., 2022).

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FIO1-dependent m⁶A modification of poly(A)+mRNA is rare or absent, but FIO1 is required for U6 snRNA methylation.

(A) Liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis. Boxplots showing that the ratio of m⁶A to A in poly(A)+mRNA is only modestly reduced in *fio1-1* mutants compared to Col-0. In contrast, m⁶A levels are significantly reduced in the *fip37-4* mutant. (B) The intersection of m⁶A modification sites detected by nanopore direct RNA sequencing of poly(A)+RNA purified from Col-0, *fip37-4*, and *fio1-1*, visualised using an upset plot. All sites shown have significant differences in modification level in a three-way comparison between *fip37-4*, *fio1-1*, and Col-0. Bars show size of intersections between sites which are significant in each two-way comparison. Total intersection sizes displayed in black above each bar. A comparison to previously identified m⁶A sites using the orthogonal technique, miCLIP is included: orange and blue bar fractions show the number of sites within each set intersection that have or do not have an miCLIP peak (Parker et al., 2020) within 5 nt, respectively. Percentage of intersections with miCLIP support is displayed in orange above each bar. A small number of sites are significant in the three-way comparison, but in neither two-way comparison (far left bar). (**C–D**) Detection of RNAs immunoprecipitated from Col-0 and *fio1* mutant alleles with anti-m⁶A antibodies using RT-qPCR analysis. The data are presented as strip-plots with mean and 95% CIs, showing the enrichment of U6 and U2 snRNAs over input using (C) Synaptic systems #202 003 anti-m⁶A antibody and RNA purified from Col-0 and *fio1-1*, and (D) Millipore ABE572 anti-m⁶A antibody and RNA purified from Col-0, *fio1-1*, *fio1-3*, and *fio1-4*. Y axes show −ΔC_t (m⁶A-IP — input) corrected for input dilution factor. Strip-plots show mean values for three or four independent RT-qPCR amplifications on each biological replicate immunopurification experiment.

Figure 2—source data 1 LC:MS/MS data for Col-0, fio1-1, and fip37-4 poly(A)+RNA.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig2-data1-v2.xlsx
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Figure 2—source data 2 Differential modification sites detected from a three-way omparison of Col-0, fip37-4, and fio1-1 mutants using nanopore direct RNA sequencing (DRS) data.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig2-data2-v2.xlsx
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Figure 2—source data 3 m⁶A-IP quatitative PCR (qPCR) data for U6 and U2 snRNAs in Col-0, fio1-1, fio1-3, and fio1-4 mutants.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig2-data3-v2.xlsx
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Figure 2—source data 4 Differential poly(A) site usage results for fip37-4 and fio1-1 identified using nanopore direct RNA sequencing (DRS) data.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig2-data4-v2.xlsx
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We previously used nanopore DRS to map m⁶A sites dependent on the Arabidopsis METTL3-like writer complex component VIRILIZER (VIR), revealing m⁶A enriched in a DR^m6ACH consensus in the 3’ terminal exon of protein coding mRNAs (Parker et al., 2020). We used a similar nanopore DRS experiment to map FIO1-dependent m⁶A. We sequenced poly(A)+RNA purified from four biological replicates each of wild-type (Col-0), fio1-1 and fip37-4, resulting in a total of 22.7 million mapped reads. The corresponding sequencing statistics are detailed in Supplementary file 1. We used the software tool Yanocomp to map differences in mRNA modifications (Parker et al., 2021a). Applying this approach, we identified 37,861 positions that had significantly different modification rates in a three-way comparison between Col-0, fip37-4, and fio1-1 (Figure 2B). Of these, 97.9% had significant changes in modification rate in a pairwise comparison between fip37-4 and Col-0. In contrast, only 7.6% had altered modification rates in fio1-1. Of these, 85.8% also had altered modification rates in fip37-4 (Figure 2B), with larger effect sizes (Figure 2—figure supplement 1A), indicating that they are METTL3-like writer complex-dependent m⁶A sites whose modification rate is indirectly affected by loss of FIO1. The FIP37-dependent modification sites, including those with small effect-size changes in fio1-1, were found in a DRACH consensus in 3’UTR regions (Figure 2—figure supplement 1B–C), consistent with established features of m⁶A modifications deposited by the Arabidopsis METTL3-like writer complex (Parker et al., 2020).

We used the antibody-based technique miCLIP to perform orthogonal validation of predicted modification sites (Parker et al., 2020): FIP37-dependent m⁶A sites were well supported, with 52.8% less than 5 nt from an miCLIP peak (Figure 2B). In contrast, of the 408 sites discovered only in fio1-1, just 21.3% (87) were less than 5 nt from an miCLIP peak, indicating that the majority are likely to be false positives. The 87 positions with miCLIP support were found in a DRACH consensus in 3’UTRs, suggesting that they are false negative FIP37-dependent m⁶A sites rather than genuine FIO1-dependent sites (Figure 2—figure supplement 1D–E).

We did not find FIO1-dependent methylation changes in transcripts of the four Arabidopsis homologues of SAM-synthetase (Figure 2—figure supplement 2A–D). Consequently, the METTL16-dependent regulation of SAM homeostasis identified in metazoans (Mendel et al., 2021; Pendleton et al., 2017; Shima et al., 2017) appears not to be conserved in Arabidopsis. Consistent with previous evidence that m⁶A in 3’UTRs affects cleavage and polyadenylation (Parker et al., 2020), we identified 1104 genes with altered poly(A) site choice in fip37-4, compared to only 49 in fio1-1 (Figure 2—figure supplement 1F). Overall, these findings indicate that FIO1-dependent m⁶A sites in poly(A)+mRNA are rare or absent.

m⁶A modification of U6 snRNA depends upon FIO1

Since FIO1 orthologues methylate U6 snRNA, we asked if FIO1 was required to modify Arabidopsis U6 snRNA. We performed two experiments using two independent anti-m⁶A antibodies to immunopurify methylated RNAs from Col-0 and fio1 alleles. For control, we used U2 snRNA, which is m⁶A methylated in humans, but not by METTL16 (Chen et al., 2020). Using RT-qPCR, we could detect equivalent levels of U6 snRNA in the input RNA purified from different genotypes, suggesting that the abundance of U6 snRNA is unaffected by loss of FIO1 function (Figure 2—figure supplement 3A–B). We detected enrichment of U6 and U2 snRNAs in RNA immunopurified with anti-m⁶A antibodies from Col-0 (Figure 2C–D). The enrichment of U2 snRNA in these experiments was unaffected by loss of FIO1 function. In contrast, we identified significant depletion of U6 snRNA in the anti-m⁶A immunopurified RNA from fio1-1, fio1-3, and fio1-4 alleles (Figure 2C–D). These data are consistent with recent reports (Wang et al., 2022; Xu et al., 2022) indicating that Arabidopsis U6 snRNA is m⁶A-modified at the conserved position of the AC^m6AGA box and that this modification requires active FIO1.

Widespread disruption of pre-mRNA splicing in fio1 mutants

Having found that FIO1 is required for U6 snRNA m⁶A modification, we examined changes in gene expression and pre-mRNA splicing in fio1 mutants. Nanopore DRS is insightful for mapping the complexity of RNA processing and modification, but throughput currently limits the statistical power to detect splicing changes, and basecalling errors complicate the alignment of exon/intron boundaries (Parker et al., 2021b). Consequently, we used Illumina RNA-seq of poly(A)+RNA to analyse gene expression and splicing. Since FIO1 affects the splicing of MAF2, which is sensitive to changes in temperature, we included plants grown at 20°C and shifted to either 4, 12, or 28°C for 4 hr prior to harvesting and poly(A)+RNA purification. We sequenced six biological replicates each of wild-type Col-0 and fio1-3, generating a minimum of 46 million paired end reads, 150 bp in length, per replicate. On average, 97.9% of read pairs were mappable per replicate, resulting in a total of 3.6 billion mapped read pairs. A summary of the sequencing read statistics is given in Supplementary file 1. The RNA-seq data clearly reveal disruption of FIO1 gene expression in fio1-3 (Figure 3—figure supplement 1A).

To detect cryptic splice sites that might be activated in fio1 mutants, we built a condition-specific reference transcriptome derived from the Col-0/fio1-3 Illumina RNA-seq and Col-0/fio1-1 nanopore DRS data using the software tool Stringtie2 (Kovaka et al., 2019). We quantified the expression of these transcripts using Salmon (Patro et al., 2017), then used SUPPA2 to calculate event-level percentage changes in splicing, which also classifies different types of splicing event (Trincado et al., 2018). These percent spliced indices (PSIs) were used to fit linear models for each splicing event to identify changes in splicing dependent on temperature, genotype, and temperature×genotype interactions. Analysis of the Col-0 temperature-shift data served as a control. Consistent with previous studies (Calixto et al., 2018), we found that intron retention is the predominant class of alternative splicing event detected when Arabidopsis is subjected to different temperatures (Figure 3A). In contrast, when we repeated this analysis to classify alternative splicing events that differ between fio1-3 and Col-0, we found that a larger proportion of alternative splicing events were classified as alternative 5’SS usage – 34.4%, compared to only 18.6% of temperature-dependent events (Figure 3B, Figure 3—figure supplement 1B). In addition, we detected changes in the PSI of retained introns, exon skipping, and alternative 3’SS selection (Figure 3B, Figure 3—figure supplement 1B). There was a significant overlap between the alternative splicing events that were sensitive to loss of FIO1, and those that were sensitive to temperature (hypergeometric-test p<1 × 10⁻¹⁶). However, in 64.2% of fio1-sensitive events, loss of FIO1 did not alter splicing responses to temperature (Figure 3B). Of the remaining 2505 splicing events that did have altered temperature sensitivity in the absence of FIO1, 38.4% were alternative 5’SSs, and of these, 69.9% had greater sensitivity to loss of FIO1 at 28°C than at 4°C. This suggests that canonical 5’SS selection at elevated temperature requires FIO1 (Figure 3—figure supplement 1C).

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Splicing events sensitive to temperature and loss of *FIO1.*

(**A–B**) Analysis of Illumina RNA-seq data presented as bar plots showing the proportion of splicing events of each class, as labelled by SUPPA, which have significantly different usage (false discovery rate [FDR] <0.05) at either (A) varying temperatures or (B) in *fio1-3*. In (B), events for which the response to temperature changes in *fio1-3* are shown in orange. (C) Gene track of Illumina RNA-seq reads showing the change in retention of *MAF2* intron 3 in *fio1-3*, at 20°C. Expression is normalised by the read coverage at the −1 position of the 5’ splice site (5’SS). (D) Boxplot of Illumina RNA-seq analysis showing the change in retention of *MAF2* intron 3 at varying temperatures and in *fio1-3*.

Figure 3—source data 1 Differential gene expression analysis results from Illumina RNA-seq experiment on Col-0 and fio1-3 mutants at four temperatures.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig3-data1-v2.xlsx
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Figure 3—source data 2 Differential splicing analysis results from Illumina RNA-seq experiment on Col-0 and fio1-3 mutants at four temperatures.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig3-data2-v2.xlsx
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Analysis of the RNA-seq data confirmed the increased retention of MAF2 intron 3 in fio1-3, consistent with the results of our two-step mutant screen (Figure 3C). Loss of FIO1 decreased the splicing efficiency of MAF2 intron 3 by approximately equivalent amounts at all temperatures (Figure 3D), implying that FIO1 is not required to generate MAF2 intron 3 temperature sensitivity.

We detected changes in gene expression consistent with the early flowering phenotype of fio1 mutant alleles (Figure 3—figure supplement 2A–G). For example, FT and SOC1 mRNA levels were increased in fio1-3 (Figure 3—figure supplement 2A–B). In contrast, the mRNA levels of the floral repressors FLM, MAF2, MAF3, MAF4, MAF5, and FLC were reduced (Figure 3—figure supplement 2C–H). In the case of FLC, both sense and antisense transcript levels were reduced in fio1-3 (Figure 3—figure supplement 2H–I), but there were no detectable changes in splicing patterns or in the alternative polyadenylation of the antisense transcripts (Figure 3—figure supplement 2J). In these experimental conditions, loss of FIO1 did not affect RNA-level expression or splicing of either CO or SVP (Figure 3—figure supplement 2K–L). In contrast, fio1-3 mutants exhibited defects in the splicing of pre-mRNA encoding MAF2, MAF3, FLM, and other genes that influence flowering time, as detailed further below.

Overall, we conclude that the predominant molecular phenotype resulting from loss of FIO1 function is a major disruption to patterns of splicing in a manner that is mostly independent of temperature. To understand the basis of these splicing differences, we analysed each of these splicing classes separately.

Alternative selection of 5’SSs in fio1-3 can be explained by U6 and U5 snRNA target sequences

We identified 2369 changes in 5’SS choice caused by loss of FIO1 function. Alternative 5’SSs were almost equally likely to be selected upstream or downstream, with many examples of 5’SS shifts of exactly −4 nt,+4 nt, and +5 nt (Figure 4—figure supplement 1A). We asked if differences in cis-element features could account for FIO1-dependent 5’SS selection. Comparing 5’SSs that exhibited reduced selection in fio1-3 with their corresponding alternative 5’SSs, which were more frequently selected in fio1-3, we found a significant difference in base composition at the −2 to +5 positions (G-test p<1 × 10⁻¹⁶). Specifically, we identified a //GURAG motif (R=A or G) at 5’SSs sensitive to the loss of FIO1 function (Figure 4A). This motif appeared more commonly in fio1-sensitive 5’SSs than in the full set of 5’SSs observed in the RNA-seq dataset (Figure 4—figure supplement 1B) and is consistent with recognition by the U6 snRNA ACAGA box (Kandels-Lewis and Séraphin, 1993; Lesser and Guthrie, 1993; Sawa and Abelson, 1992). In contrast, the alternative 5’SSs selected more frequently in fio1-3 were characterised by an AG//GU motif (Figure 4B). This motif is consistent with U5 snRNA loop 1 recognition of the upstream exon sequences (Galej et al., 2016; Newman and Norman, 1992). There was no significant difference between the positional base frequencies for 5’SSs that were preferred at lower and elevated temperatures in Col-0 (G-test p=0.28; Figure 4—figure supplement 1C–D), demonstrating that altered 5’SS sequence preference is specific to the fio1-3 mutant and not a general feature of alternative splicing.

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Effect of *fio1-3* on alternative 5’ splice sites.

(**A–B**) Sequence logos and heatmap showing the distribution of U5 snRNA and U6 snRNA interacting sequence classes for 5’ splice sites (5’SSs) which (A) are sensitive to loss of *FIO1* function or (B) have increased usage in *fio1-3* based on the analysis of Illumina RNA-seq data. Motifs are shown for the −3 to +5 positions of the 5’SS. U5 classes are based upon the distance of the −2 to −1 positions of the 5’SS from the consensus motif AG. U6 classes are based upon the distance of the +3 to +5 positions of the 5’SS from the consensus motif RAG. (C) Gene track of Illumina RNA-seq reads showing alternative 5’SS usage at *AtSAR1* intron 21 in *fio1-3*, at 20°C. Expression is normalised by the read coverage at the −1 position of the 5’SS. (D) Illumina RNA-seq analysis visualised with a boxplot showing the change in usage of the cryptic alternative 5’SS (Alt 5’SS 2) in *AtSAR1* intron 21 at varying temperatures in Col-0 and *fio1-3*. (E) Gene track showing alternative 5’SS usage at *AtSAR1* intron 21 in *fio1-1*, identified using nanopore direct RNA sequencing (DRS) read alignments. Alignments have been subsampled to a maximum of 50 per condition. (F) Contingency table showing the relationship between the nucleotide at the +4 position, and the direction of change in 5’SS usage in *fio1-3*, for pairs of alternative 5’SSs with significantly altered usage in *fio1-3* analysed in Illumina RNA-seq data. (G) Boxplot showing effect sizes of pairs of alternative 5’SSs with significantly altered usage in *fio1-3*, separated by +4 position bases (A→U indicates that 5’SS with reduced usage has A₊₄, 5’SS with increased usage has U₊₄). (H) Boxplot showing effect sizes of pairs of alternative 5’SSs with significantly altered usage in *fio1-3*, separated by U5 classification.

Figure 4—source data 1 Differential productive transcription/Nonsense mediated RNA decay (NMD) analysis results from Illumina RNA-seq experiment on Col-0 and fio1-3 mutants at four temperatures.: https://cdn.elifesciences.org/articles/78808/elife-78808-fig4-data1-v2.xlsx
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The widespread changes in 5’SS selection are evident at individual loci. For example, at AtSAR1 (AT1G33410), loss of FIO1 function, at 20°C, results in a 52.2% reduction in the use of the normally near constitutive 5’SS in intron 21, UC//GUGAG, with a reciprocal increase in the use of a cryptic 5’SS UG//GUAUU 26 nt downstream (Figure 4C and D). At elevated temperatures, the selection of this cryptic 5’SS becomes increasingly preferred (Figure 4D). The change in usage of this 5’SS is confirmed in the fio1-1 allele using orthogonal nanopore DRS (Figure 4E). We could also identify cryptic alternative 5’SSs in the second intron of MAF2 (Figure 4—figure supplement 2A–B), and the third exon of another floral repressor, FLM, used in fio1-3 (Figure 4—figure supplement 2C–D). The overall effect of these (and other) splicing changes is to reduce the fraction of productive AtSAR1, MAF2, and FLM transcripts in fio1-3 (Figure 4—figure supplement 2E–G). Finally, at the gene encoding the METTL3-like writer complex component MTB (the orthologue of human METTL14), loss of FIO1 function resulted in a 40.4% increase in the use of a cryptic 5’SS in intron 4 that introduces a premature termination codon into the MTB open reading frame (Figure 4—figure supplement 2H–I). Consequently, loss of FIO1 may have indirect effects on m⁶A deposition by the METTL3-like writer complex.

Remarkably, the presence or absence of an A at the +4 position (A₊₄) correctly separates 78.6% of the 5’SSs exhibiting decreased and increased usage in fio1-3 (Figure 4F). In humans, where pairing of the 5’SS to U6 snRNA occurs before activation of the spliceosome, the 5’SS A₊₄ faces the m⁶A of the U6 snRNA AC^m6AGA box in the B complex before docking of the 5’SS in the active site (Bertram et al., 2017). Of the 5’SSs with increased usage in fio1-3, 48.2% have U₊₄, which could make a Watson–Crick base pair with the corresponding unmethylated residue of U6 snRNA. In total, 61.8% of 5’SS changes in fio1-3 is associated with a switch from A₊₄ to B₊₄ (B=C, G or U), of which 58.8% are A→U₊₄ (Figure 4—figure supplement 3A). In comparison, only 3.8% of alternative 5’SS pairs are reciprocal B→A₊₄ switches, indicating that this shift is strongly directional. A further 8.0% of alternative 5’SS pairs are S→U₊₄ (S=C or G), suggesting that a Watson–Crick A–U base-pair is favoured when U6 snRNA is not m^6-modified. Surprisingly, 22.3% of alternative 5’SS pairs have the same base at the +4 position (Figure 4—figure supplement 3A). However, of these 5’SS pairs, the majority are associated with a G→H₊₅ (H=A, C or U) and/or H→G_–1 switch that weakens interactions with the U6 snRNA ACAGA box and strengthens U5 snRNA loop 1 interactions (Figure 4—figure supplement 3B).

We next examined how the effect size (absolute ΔPSI) of splicing changes correlated with the base at the +4 position of the 5’SS. The largest effect sizes were associated with A→U₊₄ (Figure 4G). In contrast, alternative 5’SS pairs with an A→A₊₄ shift had smaller effect sizes. We found that fio1-3-sensitive 5’SSs with 5’SS G₊₅ had larger effect sizes but that G₊₅ at the alternative 5’SS had less effect, suggesting that G₊₅ is only deleterious in fio1-3 when in combination with A₊₄ (Figure 4—figure supplement 3C). Finally, we found that fio1-3-sensitive 5’SSs with AG//GU motifs had smaller effect sizes, indicating that favourable interactions with U5 snRNA loop 1 can suppress the effect of unfavourable U6 snRNA interactions in fio1-3 (Figure 4H).

In summary, these analyses demonstrate widespread change in 5’SS selection in fio1 mutants. The 5’SSs with a strong match to the U6 snRNA ACAGA box and/or an A₊₄ are most sensitive to loss of FIO1. This sensitivity can be suppressed by a strong match in the 5’ exon to U5 snRNA loop 1. Alternative 5’SSs that are used more in fio1 have U₊₄, as well as 5’ exon sequence features that favour recognition by U5 snRNA loop 1.

FIO1-dependent changes in intron retention and exon skipping can be explained by U6 and U5 snRNA target sequences

In addition to altered 5’SS selection, we detected many instances of intron retention and exon skipping in fio1-3. We therefore asked if these FIO1-sensitive splicing phenotypes were associated with specific sequence motifs.

We identified 2576 introns with altered levels of retention in fio1-3, of which 55.8% had increased retention, and 44.2% had decreased retention (i.e. more splicing). Analysis of 5’SS sequences at introns with increased retention indicates that 76.9% had A₊₄, as well as a weaker interaction potential with U5 snRNA loop 1 (Figure 5A). For example, at WNK1 (AT3G04910), we detected increased retention of intron 5 (CG//GUGAG) in fio1-3 (Figure 5B–C) and fio1-1 (Figure 5D) using Illumina RNA-seq and nanopore DRS analysis, respectively. This indicates that these introns are normally recognised by strong U6 snRNA interactions and are less efficiently spliced in the absence of FIO1-dependent U6 snRNA m⁶A modification. The remaining introns with significantly altered retention are more efficiently spliced in fio1-3. These introns tend to have stronger matches to U5 snRNA loop 1 in the 5’ exon, and only 34.7% have A₊₄, whilst 44.3% have U₊₄ (Figure 5A). This suggests that loss of m⁶A from U6 snRNA actually increases the splicing efficiency of introns with U at the +4 position of the 5’SS.

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Effect of *fio1-3* on retained introns and exon skipping events.

(A) Sequence logos and heatmaps showing the distribution of U5 snRNA and U6 snRNA interacting sequence classes for 5’ splice sites (5’SSs) which have increased (left) and decreased (right) retention in *fio1-3* based on Illumina RNA-seq data analysis. Motifs are shown for the −3 to +5 positions of the 5’SS. U5 classes are based upon the distance of the −2 to −1 positions of the 5’SS from the consensus motif AG. U6 classes are based upon the distance of the +3 to +5 positions of the 5’SS from the consensus motif RAG. (B) Gene track of Illumina RNA-seq reads showing intron retention at *WNK1* intron 5 in *fio1-3*, at 20°C. Expression is normalised by the read coverage at the +1 position of the 3’ splice site (3’SS). (C) Boxplot of Illumina RNA-seq data analysis showing the change in intron retention of *WNK1* intron 5 at varying temperatures, in Col-0 and *fio1-3*. (D) Gene track showing intron retention at *WNK1* intron 5 in *fio1-1*, identified using nanopore direct RNA sequencing (DRS) read alignments. Alignments have been subsampled to a maximum of 50 per condition. (**E–F**) Sequence logos for 5’SSs at introns upstream (left) and downstream (right) of exons with (E) increased skipping or (F) increased retention in *fio1-3* based on Illumina RNA-seq data analysis.

A common form of alternative splicing involves the excision of an exon and flanking introns in a splicing event called exon skipping. These events involve two 5’SSs: those at the upstream intron and those at the downstream intron (i.e. at the 3’ end of the skipped exon; Figure 5E–F). We identified 442 exons with significantly different levels of skipping in fio1-3. Of these, 53.6% have increased levels of inclusion and 46.4% have increased levels of skipping.

In cases where exon skipping increased in fio1-3, more of the 5’SSs at downstream introns had A₊₄ compared to the 5’SSs at upstream introns. Conversely, more of the 5’SSs at upstream introns had strong U5 snRNA loop 1 interacting sequences (Figure 5E, Figure 5—figure supplement 1A). This is consistent with a relative weakening of the recognition of the 5’SS at the downstream intron in fio1-3. For example, at the gene encoding the floral repressor MAF3 (AT5G65060), there is an increase in skipping of exon 2 in fio1-3 (Figure 5—figure supplement 1B–C). The 5’SS at the 3’ end of this exon is UA//GUAAG, whereas the upstream 5’SS (AA//GUAAG) is a stronger match to U5 snRNA loop 1.

In cases where exon inclusion increased in fio1-3, we found that the majority of 5’SSs at upstream introns had A₊₄, whereas a plurality of 5’SSs at downstream introns had U₊₄ (Figure 5F, Figure 5—figure supplement 1A). For example, at PTB1 (AT3G01150), there is an increase in inclusion of exon 3 in fio1-3 (Figure 5—figure supplement 1D–E). The 5’SS at the 3’ end of this exon is AG//GUGUC, whereas the upstream 5’SS is UG//GUGAG. Although both the upstream and downstream 5’SSs are used when the exon is retained, it is possible that the relative strengthening of recognition of the downstream 5’SS in fio1-3 improves exon definition.

In summary, we can identify and characterise changes in intron retention and exon skipping sensitive to loss of FIO1 function. Although the outcome of these splicing events is different from alternative 5’SS selection, they are associated with the same changes in 5’SS sequence as those associated with alternative 5’SS selection.

Alternative 3’SS usage in fio1-3 can be explained by U6 snRNA-dependent interactions between 5’ and 3’SSs

We observed 1484 instances where 3’SS selection was altered in fio1-3. In some cases, these alternative 3’SSs were linked with altered 5’SS choice or intron retention, but this accounted for a relatively small proportion of events (Figure 6—figure supplement 1A). Alternative 3’SS selection was equally likely to switch in an upstream or downstream direction in fio1-3 (Figure 6A). There was a strong enrichment for very local switching of 3’SSs, with 37.9% of alternative 3’SSs occurring within 6 nt of the fio1-3-sensitive 3’SS, and 18.9% of examples occurring at exactly 3 nt upstream or downstream. These examples correspond to NAGNAG-like acceptors, which have previously been characterised in multiple species including human and Arabidopsis (Bradley et al., 2012; Hiller et al., 2004; Schindler et al., 2008).

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Effect of *fio1-3* on alternative 3’ splice site (3’SS) usage.

(A) Histogram showing the distance between alternative 3’SS pairs with significantly different usage in *fio1-3* revealed by Illumina RNA-seq data analysis. Negative distances represent shifts toward greater usage of upstream 3’SSs, whilst positive distances represent shifts toward greater usage of downstream 3’SSs. (**B–C**) Sequence logos for 5’ splice sites (5’SSs; left), upstream 3’SSs (middle), and downstream 3’SSs (right) at pairs of alternative 3’SSs with increased (B) upstream or (C) downstream usage in *fio1-3* revealed by Illumina RNA-seq data analysis. 5’SS logos are for −3 to +5 positions, and 3’SS logos are for −5 to +2 positions. (D) Gene track of Illumina RNA-seq reads showing alternative 3’SS usage at *LHY* intron 5 in *fio1-3*, at 20°C. Expression is normalised by the read coverage at the −1 position of the 5’SS. (E) Boxplot of Illumina RNA-seq data analysis showing the change in usage of the upstream alternative 5’SS (Alt 5’SS 2) in *LHY* intron 5 at varying temperatures in Col-0 and in *fio1-3*. (F) Gene track showing intron retention at *LHY* intron 5 in *fio1-1*, identified using nanopore direct RNA sequencing (DRS) read alignments. Alignments have been subsampled to a maximum of 50 per condition. (G) Boxplot of Illumina RNA-seq data analysis showing the change in usage of downstream 3’SSs in alternative 3’SS pairs with different 5’SS +4 bases, separated by whether the base at the −3 position of the two alternative 3’SSs is the same (e.g. CAG\\CAG\\) or different (e.g. UAG\\CAG\\).

To examine the 5’SSs associated with alternative 3’SSs used in fio1-3, we separated examples with increased upstream and downstream 3’SS usage and performed motif analysis. At introns with a relative increase in upstream 3’SS usage in fio1-3, we found that 80.5% of the corresponding 5’SSs were characterised by A₊₄ (Figure 6B). For example, at LHY (AT1G01060), we identified an AA//GUAAG 5’SS and a UAG\\CAG\\ alternative 3’SS pair in intron 7 (Figure 6D). In fio1-3, at 20°C, there is a shift in favour of the upstream UAG\\ 3’SS (Figure 6E). This 3’SS switch is supported by orthogonal nanopore DRS analysis of the fio1-1 allele (Figure 6F). Conversely, when we analysed the features of introns where a relative increase in downstream 3’SS usage was detected in fio1-3, we found that only 37.0% of these 5’SSs had A₊₄, whereas 48.8% had U₊₄ (Figure 6C). For example, at MAF2 intron 4, we identified an AG//GUAUU 5’SS and a UAG\\ACAG\\ alternative 3’SS pair (Figure 6—figure supplement 1B). In fio1-3, at 20°C, there is a shift in favour of the downstream CAG\\ 3’SS (Figure 6—figure supplement 1C).

3’SS choice involves scanning downstream from the branchpoint to the first available 3’SS motif (Smith et al., 1993). However, competition with downstream 3’SSs can occur within a short range, such as in NAGNAG acceptors. Downstream 3’SSs in fio1-sensitive alternative 3’SS pairs were more likely than upstream 3’SSs to have a cytosine as the −3 position (Figure 6B–C). However, this appears to reflect differences in the background rate of C₋₃ upstream and downstream (Figure 6—figure supplement 1D), rather than a change in 3’SSs motif competitiveness in fio1-3 (Bradley et al., 2012; Smith et al., 1993). To analyse whether the −3 position contributes to changes in alternative 3’SS usage in fio1, we separated fio1-sensitive 3’SS pairs where the base at the −3 position was the same at both the upstream and downstream 3’SS, from examples which had different bases at the −3 position. We found that among both sets of 3’SS pairs, 5’SS A₊₄ was still associated with increased upstream usage in fio1-3, whereas U₊₄ was associated with increased downstream usage (Figure 6G). This demonstrates that changes in 3’SS usage in fio1-3 are caused by a change in the competitiveness of distal 3’SSs, irrespective of 3’SS motif.

These findings link selection of the 3’SS and 5’SS. When U6 snRNA recognition of the 5’SS is favoured by either an m⁶A:A₊₄ interaction in WT Col-0 or A:U₊₄ interaction in fio1-3, this increases the usage of distal 3’SSs. When the U6 snRNA/5’SS interaction is less favoured, then upstream 3’SSs are more likely to be used. We conclude that the interactions of U6 snRNA with the 5’SS can influence usage of competing 3’SSs.

Reanalysis of RNA-seq data from independent fio1 alleles confirms disruption to splicing

In contrast to our findings, three recently published studies of fio1 mutants failed to identify an impact on splicing (Wang et al., 2022; Xu et al., 2022; Sun et al., 2022). We reanalysed these published data to understand the basis for this difference. Two studies performed Illumina RNA-seq analysis (Wang et al., 2022; Sun et al., 2022) but were not well powered with only two replicates per condition. The Illumina RNA-seq data from Wang et al. were of poor quality, with low mapping rates and one unusable replicate due to PCR-bias (Figure 6—figure supplement 2A). Nevertheless, we were able to detect 384 alternative 5’SS events by using our condition-specific transcriptome assembly and performing differential splicing analysis with SUPPA2. These events were associated with a shift from 5’SSs with A₊₄ to alternatives with U₊₄ (Figure 6—figure supplement 2B). We reanalysed the Illumina RNA-Seq data from Sun et al. (two replicates per condition and 20–26 million reads per replicate) to identify 923 alternative 5’SS events in fio1-1 and 947 alternative 5’SS events in a CRISPR-induced mutation (fio1-5; Figure 6—figure supplement 3A–B). There were 675 alternative 5’SS events common to both alleles, and 556 (82.4%) of these were identified in our own analysis of fio1-3 (Figure 6—figure supplement 3C). A third study analysed gene expression changes using nanopore DRS (Xu et al., 2022). Using these data, we could confirm splicing changes observed in our own data, for example at AtSAR1 intron 21, WNK1 intron 5, and MTB intron 4 (Figure 6—figure supplement 4A–C). These changes were also visible in nanopore DRS data generated by Sun et al. (Figure 6—figure supplement 4D–F). We conclude that a combination of under-powered datasets and methodological choices in data analysis explain previous failures to link the loss of U6 snRNA methylation in fio1 mutants to detectable changes in splicing.

Sequences targeted by U5 and U6 snRNAs are anticorrelated in the 5’SSs of developmentally complex eukaryotes

Our results indicate that a strong match to U5 snRNA loop 1 in the 3’ end of the upstream exon offsets the effects of 5’SS A₊₄ unfavourability in fio1-3. We, therefore, reasoned that if U5 and U6 cooperate in 5’SS selection, then strong U5 snRNA loop 1 interactions may globally compensate weaker U6 snRNA ACAGA box interactions and vice versa. To test this hypothesis, we used all annotated Arabidopsis 5’SS sequences to generate position-specific scoring matrices (PSSMs) for the −2 to −1 positions corresponding to the U5 snRNA loop 1 interacting region, and the +3 to +5 positions corresponding to the U6 snRNA ACAGA box interacting region. We then used these PSSMs to score the U5 and U6 snRNA interaction log-likelihood of each individual 5’SS. 5’SSs with AG//GU motifs have high-scoring U5 PSSM scores, and those with //GURAG motifs have high-scoring U6 PSSM scores. This analysis revealed that U5 and U6 snRNA PSSM scores are indeed negatively correlated in Arabidopsis (Spearman’s ρ = −0.36, p<1 × 10⁻¹⁶). We found that 5’SSs lacking a //GURAG motif had significantly higher U5 PSSM scores than //GURAG 5’SSs (Figure 7A and B), whereas BH//GU 5’SSs had significantly higher U6 PSSM scores than AG//GU 5’SSs (Figure 7C and D). We found similar anticorrelations in U5 and U6 PSSM scores in other species, including C. elegans (ρ = −0.38, p<1 × 10⁻¹⁶; Figure 7—figure supplement 1A), Drosophila melanogaster (ρ = −0.30, p<1 × 10⁻¹⁶; Figure 7—figure supplement 1B), Danio rerio (ρ = −0.36, p<1 × 10⁻¹⁶; Figure 7—figure supplement 1C), and Homo sapiens (ρ = −0.17, p<1 × 10⁻¹⁶; Figure 7—figure supplement 1D). These findings indicate that there are two major classes of 5’SS in distinct eukaryotes: //GURAG and AG//GU. These two classes occur with approximately equal frequency in the metazoan genomes that we analysed (Figure 7—figure supplement 1A–D).

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Global analysis of U5 and U6 interaction strengths reveals anticorrelation.

(A) Sequence logos of annotated *Arabidopsis* splice sites showing base frequency probabilities at −3 to +5 positions for (above) 5’ splice sites (5’SSs) with //GURAG sequence and (below) all other 5’SSs. (B) Empirical cumulative distribution function of *Arabidopsis* U5 position-specific scoring matrix (PSSM) log-likelihood scores for 5’SSs with either //GURAG sequence or all other 5’SSs. U5 PSSM scores are calculated using a PSSM derived from all 5’SSs in the Araport11 reference annotation, at the −2 to −1 positions of the 5’SS, inclusive. (C) *Arabidopsis* sequence logos showing base frequency probabilities at the −3 to +5 positions for (above) 5’SSs with AG//GU sequence and (below) all other 5’SSs. (D) Empirical cumulative distribution function of *Arabidopsis* U6 PSSM log-likelihood scores for 5’SSs with different U5 classes. U6 PSSM scores are calculated using a PSSM derived from all 5’SSs in the Araport11 reference annotation, at the +3 to +5 positions of the 5’SS, inclusive. (**E–F**) Scatterplot showing the ratio of PSSM log-likelihoods (log-odds ratio) for U5 and U6 snRNA interacting sequences, at pairs of upstream and downstream alternative 5’SSs in (E) the *Arabidopsis* Araport11 reference annotation or (F) the *H. sapiens* GRCh38 reference annotation. A positive log-odds ratio indicates that the PSSM score of the upstream 5’SS is greater than that of the downstream 5’SS.

Opposing U5 and U6 snRNA interaction potential is a feature of alternative 5’SS pairs in some eukaryote genomes

Given that many 5’SSs appear to have either strong U5 or U6 snRNA interacting sequences, we speculated that opposing U5 and U6 snRNA interaction strengths at pairs of alternative 5’SSs could contribute to alternative splicing. To test this hypothesis, we calculated the log odds ratio of U5 and U6 PSSM scores for all pairs of annotated alternative 5’SSs in Arabidopsis. We found that these ratios were negatively correlated (Spearman’s ρ = −0.25, p<1 × 10⁻¹⁶), indicating that alternative 5’SS pairs tend to have complementary strengths with respect to U5 and U6 snRNA recognition (Figure 7E). The strength of this complementary relationship varies in different organisms: in humans, we found a stronger negative correlation between relative U5 and U6 PSSM scores (ρ = −0.40, p<1 × 10⁻¹⁶; Figure 7F), whereas in C. elegans, the correlation was weaker (ρ = −0.15, p=8.61 × 10⁻⁷; Figure 7—figure supplement 2A), and in D. rerio, there was no correlation (ρ = −0.01, p=0.58; Figure 7—figure supplement 2B). We conclude that changing the relative favourability of 5’SSs with stronger U5 or U6 snRNA interacting sequences, as occurs in fio1-3, could be a mechanism contributing to alternative 5’SS choice.

Discussion

The splicing of mRNAs encoding regulators of flowering time is disrupted in fio1

We identified fio1 through a two-step mutant screen designed to reveal factors that control the splicing of MAF2 intron 3. Importantly, temperature-sensitive splicing of MAF2 intron 3 links ambient temperature to flowering time control. fio1 mutants have increased retention of MAF2 intron 3 compared to WT Col-0. However, splicing of MAF2 intron 3 remains responsive to temperature in fio1 mutants, suggesting that FIO1 is not the thermosensor in this process.

We found that disruption of FIO1 alters the splicing of mRNA encoding not only MAF2 but other floral repressors too, including FLM and MAF3. Detectable levels of sense and antisense RNAs at the locus encoding the floral repressor FLC were reduced in fio1, but no splicing changes were detected. FLM, MAF2, MAF3, and FLC function together with SVP in higher-order protein complexes to repress the expression of FT and SOC1 (Gu et al., 2013). We detected elevated transcript levels of FT and SOC1 in fio1 mutants, consistent with the idea that floral repressor activity had been compromised. Splicing changes were present in transcripts encoding circadian modulators such as LHY and WNK1 (Kumar et al., 2011; Mizoguchi et al., 2002), which may contribute to the lengthening of the circadian period observed in fio1 mutants (Kim et al., 2008). Finally, we found that aberrant splicing limits the functional expression of AtSAR1 in fio1 mutants. AtSAR1 encodes a nucleoporin that controls CO protein abundance (Dong et al., 2006; Li et al., 2020; Parry et al., 2006) and FLC expression (Jung et al., 2013). As a result, sar1 mutants are early flowering due to increased CO protein levels (Li et al., 2020) and reduced FLC expression (Jung et al., 2013; Li et al., 2020). Overall, these findings suggest that the early flowering phenotype of fio1 results from splicing changes that reduce the activity of floral repressors and increase the activity of factors that promote flowering, such as CO.

FIO1-dependent m⁶A modification of U6 snRNA determines splicing accuracy and efficiency

Our data indicate that the major effect of FIO1 occurs through m⁶A modification of U6 snRNA and the subsequent interaction of m⁶A-modified U6 snRNA with target 5’SSs. Recent suggestions that FIO1 does not impact splicing (Wang et al., 2022; Xu et al., 2022) can be explained by underpowered experiments and unsuitable methodological approaches. Our reanalysis of recently published RNA-seq and nanopore DRS data in independent fio1 mutant alleles confirms that splicing is indeed disrupted, consistent with the conserved role of FIO1 in methylating U6 snRNA (Wang et al., 2022; Xu et al., 2022; Sun et al., 2022).

We cannot rule out the possibility that FIO1 directly targets other RNA species not detected by our approaches. However, our nanopore DRS analyses did not reveal widespread FIO1-dependent m⁶A sites in poly(A)+mRNA. We found that 8.7% of FIP37-dependent m⁶A sites had slightly altered modification rates in fio1-1, suggesting that loss of FIO1 might indirectly affect m⁶A sites written by the METTL3-like complex. Consistent with this, splicing of MTB (METTL14) RNA is also defective in fio1 mutants, potentially compromising MTB function. Notably, although data from three recent studies (Wang et al., 2022; Xu et al., 2022; Sun et al., 2022) show almost no overlap in respect of RNAs putatively modified by FIO1-dependent addition of m⁶A (Sun et al., 2022), we find that all these datasets confirm the impact of FIO1 on splice site selection.

We can explain the vast majority of fio1-sensitive splicing events using simple rules governed by sequence features of 5’SSs. When U6 snRNA is m⁶Amodified, 5’SSs with A₊₄ are favoured. In the absence of U6 snRNA m⁶A, 5’SSs with U₊₄ and/or stronger interactions with U5 snRNA loop 1 are favoured. Cryo-electron microscopy (cryo-EM) structures of the human spliceosome suggest U6 snRNA m⁶A faces 5’SS₊₄ (Bertram et al., 2017). Remarkably, we can separate almost 80% of FIO1-dependent 5’SS choices by the identity of the base at the +4 position of 5’SSs alone, suggesting that many of the splicing changes we detect are direct. In contrast, no obvious difference in splice site sequences was associated with temperature-dependent alternative splicing detected in our study.

Our findings are similar to those recently reported for S. pombe, where disruption of the FIO1 orthologue MTL16 results in widespread intron retention (Ishigami et al., 2021). A more diverse set of splicing events is disrupted in Arabidopsis fio1 mutants. Nevertheless, in both species, most changes in splicing can be explained by the impact of m⁶A modification on U6 snRNA recognition of the 5’SS₊₄ position or the relative strength of U5 and U6 snRNA interactions at 5’SSs. Defective splicing of specific introns in S. pombe mtl16Δ strains can be experimentally rescued by expression of mutated U5 snRNAs designed to strengthen U5 loop 1 interactions with the upstream exon (Ishigami et al., 2021). Together, these findings reveal the impact of U6 snRNA m⁶A modification and indicate that U5 and U6 snRNAs have cooperative and compensatory roles in 5’SS selection dependent on 5’SS sequence (Figure 8A).

Figure 8 with 1 supplement see all

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U6 m⁶A:5’SSA _A+4 interactions during splicing.

(A) Model depicting U5 and U6 snRNA interactions with two major classes of 5’SS: //GURAG and AG//GU 5’SSs. //GURAG 5’SSs form strong interactions with U6 snRNA ACAGA (darkly shaded) and weaker interactions with U5 snRNA loop 1 (lightly shaded). AG//GU 5’SSs form strong interactions with U5 snRNA loop 1 and weaker interactions with U6 snRNA ACAGA. (B) Cryo-electron microscopy (cryo-EM) analysis of human pre-B and B complexes with RNA interactions detailed in the expanded section (PDB 6AHD; Bertram et al., 2017) and Prp8 shown in the background as a common scaling reference. The U6 snRNA ACAGA and U5 snRNA loop 1 sequences are missing from cryo-EM structures at this stage, probably because they present as flexible loops. In B complex, C42 and 5’SS G₊₅ form a canonical Watson–Crick pair. m⁶A43 and 5’SS A₊₄ form a trans Hoogsteen sugar edge interaction (Leontis et al., 2002) that caps and stabilises the U6/5’SS helix by stacking because U6 snRNA G44 and 5’SS A₊₃ have not yet formed a stable interaction. The 5’SS is kinked, and U5 snRNA loop 1 is docked on the upstream exon. The methyl group of U6 snRNA m⁶A43 is not modelled in the structure due to lack of resolution. (C) Model depicting U6 m⁶A interactions at different stages of splicing. In B complex, U6 m⁶A43 stabilises the U6/5’SS helix by stacking. As the active site forms in B_act, this role becomes less important because U6 snRNA G44 interacts more stably with 5’SS₊₃ and U6 A45 stacks on the helix stabilised by R554 of SF3B2. In C complex, the U6/5’SS helix is stabilised by N57 of hYJU2. In C* complex, the U6m⁶A43:5’SS₊₄ interaction becomes more important again because the 5’SS₊₃ pivots to a new position. The m⁶A43 and 5’SS₊₄ pair forms part of a continuous helical stack with the docked 3’ splice site (3’SS), which is capped by the interaction between 3’SS₋₃ and Q1522 of Prp8. For more detail, see Figure 8—figure supplement 1.

Two major classes of 5’SS linking cooperative and compensatory roles for U5 and U6 snRNA in 5’SS selection

Our analyses of annotated Arabidopsis 5’SSs identified anti-correlated biases in sequence composition at U5 and U6 snRNA interacting positions. We found the same to be true for 5’SSs in other eukaryotes, including humans. Compensatory patterns of base composition at human 5’SSs have been described (Burge and Karlin, 1997; Carmel et al., 2004; Sibley et al., 2016; Wong et al., 2018; Artemyeva-Isman and Porter, 2021). For example, a ‘seesaw linkage’ pattern was observed where −1 G permits any nucleotide at position +5, and conversely,+5 G permits any nucleotide at position −1. Our findings suggest that such compensatory base composition at 5’SSs can be accounted for by two major classes of 5’SS recognised mainly by interactions with either U5 snRNA loop 1 or the U6 snRNA ACAGA box. Furthermore, the cooperativity of U5 and U6 snRNA function in 5’SS selection may explain why having Gs at both −1 and +5 positions is highly preferential for efficient splicing (Wong et al., 2018). Compensatory interactions are likely to facilitate degeneracy in sequences that can be recognised as 5’SSs. Degeneracy may act as a buffer against deleterious mutations in splice site sequences, as well as lowering barriers to the evolution of new splicing structures. This is important because alternative splicing is clearly associated with developmental complexity (Bush et al., 2017; Nilsen and Graveley, 2010).

What is the role of U6 snRNA m⁶A modification in 5’ and 3’SS selection?

Our analysis of available cryo-EM structures of human spliceosomes (Bertram et al., 2017; Bertram et al., 2020; Fica et al., 2019; Zhang et al., 2018) does not provide clear evidence of a direct interaction between U6 snRNA m⁶A and a spliceosomal protein (Figure 8B; Figure 8—figure supplement 1). Instead, during the transfer of the 5’SS from U1 snRNA to U6 and U5 snRNAs in spliceosomal B complexes, U6 AC^m6AGA m⁶A faces 5’SS A₊₄ in a trans Hoogsteen sugar edge interaction that could stabilise the 5’SS/U6 helix by capping (Figure 8B). This is important because 5’SS A₊₃ is not aligned to stack on A₊₄ at this stage. However, later in spliceosomal B^act and C complexes, 5’SS A₊₃ engages in a more stable interaction with the U6 snRNA G44 adjacent to m⁶A (AC^m6AGA), stacking on top of the 5’SS/U6 helix and stabilising it (Figure 8C; Figure 8—figure supplement 1). Such stabilisation is important because degenerate 5’SS sequences mean that the helix formed with U6 snRNA ACAGA is relatively weak, short, and comprised mostly of non-canonical RNA-RNA interactions (Figure 8B).

Our global RNA-seq analyses reveal that m⁶A-modified U6 snRNA pairs less readily with 5’SSs containing U₊₄ than those containing A₊₄. Biophysical data from model RNAs provide a possible explanation for this finding: an m⁶A-U base pair can form in a duplex, but the methylamino group rotates from a syn geometry on the Watson–Crick face to a higher-energy anti-conformation, positioning the methyl group in the major groove (Roost et al., 2015). As a result, m⁶A has a destabilising effect on A−U basepairs in short RNA helices (Kierzek and Kierzek, 2003; Roost et al., 2015). Therefore, 5’SS U₊₄ may be selected less frequently by m⁶A-modified U6 snRNA because of destabilisation of an already weak helix. In contrast, the thermal stability of m⁶A:A is increased compared to A:A (Roost et al., 2015). Biophysical measurements also indicate m⁶A in unpaired positions base stacks more strongly than the unmodified base, adding substantial stabilisation to adjacent duplexes (Roost et al., 2015). In the absence of U6 snRNA m⁶A modification, our data reveal that 5’SS U₊₄ sites are preferred. This is consistent with a relatively strong interaction being important at the terminal position of the U6 snRNA ACAGA helix in the B complex to stabilise its formation.

The potential role of U6 snRNA m⁶A in 3’SS selection may be explained by U6 ACAGA interactions during the remodelling of the spliceosome for the second splicing reaction (Figure 8C; Figure 8—figure supplement 1). In humans, RNA rearrangements during the C to C* transition include stacking of the 3’SS G₋₁ onto U6 A45 (ACAGA), which remains paired to U₊₂ of the 5’SS (Fica, 2020; Fica et al., 2019; Wilkinson et al., 2017). This results in a continuous helix stack, involving the interaction between U6 A43 and 5’SS A₊₄, which forms the receptor onto which the 3’SS docks. Our data are consistent with a model in which a strong interaction between U6 and the 5’SS +4 position stabilises this receptor, enabling 3’SSs distal to the branchpoint to compete more efficiently with proximal 3’SSs that are favoured by scanning (Smith et al., 1993). A more stable receptor could allow the ATPase Prp22, which proofreads exon ligation (Mayas et al., 2006), to promote more efficient sampling and usage of distal 3’SSs. This has previously been observed in S. cerevisiae (Semlow et al., 2016), where the U6/5’SS helix is intrinsically stronger than in Arabidopsis and humans (Plaschka et al., 2019; Wilkinson et al., 2020).

A regulatory or adaptive role for U6 snRNA m⁶A modification?

Alternative splicing is the result of competition between multiple splicing choices. Our analysis of annotated alternative 5’SS pairs in Arabidopsis (and humans) revealed that when one of the alternative 5’SS pair exhibited strong U5 snRNA loop 1 recognition, the other was more likely to have stronger complementarity to U6 snRNA ACAGA box. This suggests that alternative splicing could be regulated by changing the relative favourability of U5 and U6 snRNA interactions.

It is an open question as to whether m⁶A modification of U6 snRNA is regulated. Either the activity of FIO1 might be controlled or demethylases might act directly upon U6 snRNA. There is a precedent for the control of splicing by regulation of U6 snRNA modifications because pseudouridylation of S. cerevisiae U6 snRNA, which controls entry into filamentous growth, can increase the splicing efficiency of suboptimal introns (Basak and Query, 2014). Human UsnRNAs are targeted by demethylases. For example, FTO targets cap adjacent methylation (m⁶Am) in UsnRNAs (except U6), and this activity may account for splicing differences detected in FTO knockout backgrounds (Mauer et al., 2019). Notably, Arabidopsis mutants defective in the function of the RNA demethylase ALKBH10b flower late (Duan et al., 2017).

Two genome-wide association studies have identified sequence differences at or near the FIO1 locus associated with natural variation in Arabidopsis ecotypes’ flowering time (Price et al., 2020; Sasaki et al., 2015). It will be important to test whether genetic variation alters the efficiency of U6 snRNA m⁶A modification by FIO1 in different ecotypes and what impact this has upon global splicing patterns, including for the regulators of flowering time that we characterise here. There are 13 genes encoding U6 snRNA in the Arabidopsis Col-0 genome (Wang and Brendel, 2004). Currently, we know little about the relative patterns of U6 snRNA gene expression or m⁶A modification status and how this may vary in different ecotypes. Indeed, this paucity of knowledge on U6 snRNA variants’ modification and expression applies to humans too. Our study on U6 snRNA m⁶A modification has important implications for understanding the mechanism of splicing and the evolution of alternative splicing. It will now be interesting to investigate the possibility that modulation of U6 snRNA m⁶A may be regulatory or adaptive.

Materials and methods

Plant material

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The wild-type Col-0 accession, fio1-3 (SALK_084201), and maf2 (SALK_045623) were obtained from Nottingham Arabidopsis Stock Centre. The fio1-1 mutant was a gift from Prof Hong Gil Nam (Daegu Gyeongbuk Institute of Science and Technology), Republic of Korea. The fip37-4 mutant was a gift from Prof Rupert Fray (University of Nottingham), UK.

Plant growth conditions

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Seeds of wild-type Col-0, fip37-4, and fio1-1 used for nanopore DRS and m⁶A immunopurification were surface sterilised and sown on MS10 media plates supplemented with 2% agar, stratified at 4°C for 2 days, germinated in a controlled environment at 20°C under 16 hr light/8 hr dark conditions, and harvested 14 days after transfer to 20°C. Seeds of wild-type Col-0 and fio1-3 used for Illumina RNA-seq were surface sterilised and sown on 0.5 MS media plates supplemented with 2% agar, stratified at 4°C for two days, germinated in a controlled environment at 20°C under 16 hr light/8 hr dark conditions for 8 days. Seedlings were then transferred to either 28, 20, 12, or 4 °C for 4 hr under dark conditions before harvesting. Seeds used for MAF2 splicing analysis were sown on 0.5 MS media plates supplemented with 2% agar, stratified at 4°C for two days, germinated in a controlled environment at 20°C under 16 hr light/8 hr dark conditions, and harvested 14 days after transfer to 20°C.

Mutant screen

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The two-step EMS mutant screen was conducted in a Col-0 line carrying a homozygous transgene in which the genomic MAF2 coding region was translationally fused to luciferase (gMAF2:LUC). A gateway cloning approach was used to introduce gMAF2:LUC into the Alligator vector pFP101, which features a recombination site downstream of a CaMV 35 S promoter and selection by Green Fluorescent Protein (GFP) fluorescence (Bensmihen et al., 2004). Agrobacterium tumefaciens strain GV3101 was used to transform Col-0 via the floral dip method (Clough and Bent, 1998). A homozygous line with strong LUC expression and a clear GFP fluorescence in the seed coat (Alligator selective marker) was identified and used for EMS mutagenesis.

Approximately, 20,000 M0 seeds of the gMAF2:LUC line were soaked overnight in 100 mL of phosphate buffer at 4°C. The buffer was replaced, with EMS added to a final concentration of 25 mM, and the seeds were incubated at room temperature with gentle agitation for 16 hr. The EMS was neutralised with 1 M NaOH, and the seeds were gently washed twice with 100 mM sodium thiosulphate for 15 min, followed by three 15 min washes in distilled water. The M1 seeds were air-dried on filter paper overnight, before planting on soil. The resulting plants were allowed to self-pollinate and grow to maturity. M2 seeds were collected from all plants. Approximately, 15,000 seeds were screened in two steps. In step 1, stratified seeds were planted on soil and grown in controlled environment chambers at 16°C, 16 hr light/8 hr dark, with light intensity of 200 µmol/m²/s. The first 100 plants to flower were selected for further screening. Splicing of MAF2 intron 3 was assessed by RT-qPCR to identify early flowering lines showing enhanced intron retention at 16°C. Although the parental line had the gMAF2:LUC reporter, luciferase activity was not monitored at any stage. Leaf material was frozen and homogenized using QIAGEN TissueLyser LT. RNA was extracted using a Nucleospin II RNA extraction kit (Machery–Nagel). Total RNA (1.5 µg per sample) was reverse-transcribed using the High Capacity cDNA Reverse Transcription Kit with Rnase Inhibitor (Applied Biosystems). Amplification of MAF2 was performed according to manufacturer’s guidelines for 35 PCR cycles using primers 3 and 4 from a previous study (Rosloski et al., 2013). These primers, which span MAF2 intron 3, generate two products, which correspond to MAF2 var2 (MAF2 intron 3 retained) and MAF2 var1 (MAF2 intron 3 excised), respectively. PCR amplified products were separated on a 2% w/v agarose gel to resolve the splice variants. M2 plants showing a significantly enhanced var2:var1 ratio compared to the parental line control were selected for further testing.

Bulk segregant analysis for mutant mapping

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The EMS-129 line was backcrossed to the parental gMAF2:LUC reporter line to generate an F2 segregating population. Phenotypically early flowering and wild-type flowering time plants were identified from this F2 population and separated for bulk DNA sequencing. Genomic DNA was extracted using a Qiagen Plant DNA Maxi kit. Sequencing was carried out by the University of Leeds Next Generation Sequencing (NGS) facility using a HiSeq3000 sequencer with a 150 bp paired end library.

Sequenced reads were mapped to the TAIR10 reference sequence using bwa-mem2 (Vasimuddin et al., 2019). SNPs were called using bcftools and filtered for EMS-induced G>A transitions only (Danecek et al., 2021). Functional consequences of each SNP were predicted using SnpEff (Cingolani et al., 2012). Allele fraction plots were generated from vcf files using matplotlib. Significance testing was performed for each SNP using G-tests and smoothed using a tri-cube kernel with a window size of 2 megabases.

Nanopore DRS

Total RNA isolation

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Total RNA was isolated using Rneasy Plant Mini kit (Qiagen) and treated with TURBO Dnase (ThermoFisher Scientific). The total RNA concentration was measured using a Qubit 1.0 Fluorometer and Qubit RNA BR Assay Kit (ThermoFisher Scientific). RNA quality and integrity were assessed using the NanoDrop 2000 spectrophotometer (ThermoFisher Scientific) and Agilent 2200 TapeStation System (Agilent).

Preparation of libraries for direct RNA sequencing of poly(A)+ mRNA using nanopores

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Total RNA was isolated from the Col-0, fip37-4, and fio1-1 seedlings as detailed above. mRNA isolation and preparation of nanopore DRS libraries using the SQK-RNA002 nanopore DRS Kit (Oxford Nanopore Technologies) were performed as previously described (Parker et al., 2020). Libraries were loaded onto R9.4 SpotON Flow Cells (Oxford Nanopore Technologies) and sequenced using a minION device for a 48 hr runtime. Four biological replicates were performed for each genotype.

Nanopore DRS mapping

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Nanopore DRS reads were basecalled using Guppy version 3.6.0 high accuracy RNA model. For mRNA modification analysis, reads were mapped to the Araport11 reference transcriptome (Cheng et al., 2017) using minimap2 version 2.17 (Li, 2018), with parameters -a -L –cs =short k14 –for-only –secondary =no. For other analyses, reads were mapped to the Arabidopsis TAIR10 genome (Arabidopsis Genome Initiative, 2000) using two pass alignment with minimap2 version 2.17 and 2passtools version 0.3 (Parker et al., 2021b). First pass minimap2 alignment was performed using the parameters -a -L –cs =short -x splice -G20000 –end-seed-pen 12 -uf. 2passtools score was then run with default parameters on each replicate to generate junctions, followed by 2passtools merge to combine them into a final set of guide junctions. Reads were remapped with minimap2 using the same parameters but with the addition of the guide junctions using –junc-bed and –juncbonus =10. Pipelines for processing of nanopore DRS data were built and executed using Snakemake version 6.15.3 (Köster and Rahmann, 2012). Public nanopore DRS data downloaded from ENA accessions PRJNA749003 (Xu et al., 2022) and PRJNA877932 (Sun et al., 2022) were processed in the same way as newly generated data.

Nanopore poly(A)+ mRNA modification analysis

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Differential modification analysis was performed on Col-0, fip37-4, and fio1-1 data using the ‘n-sample’ GitHub branch of Yanocomp (Parker et al., 2021a). Kmer-level signal data were generated using f5c eventalign version 0.13.2 (Gamaarachchi et al., 2020; Loman et al., 2015) and Yanocomp prep. A three-way comparison between the genotypes was performed using Yanocomp gmmtest, with a minimum KS statistic of 0.25. A 5% false discovery rate threshold was used to identify transcriptomic sites with significant changes in modification rate. Motif analysis was performed using meme version 5.1.1 with the parameters -cons NNANN -minw 5 -maxw 5 -mod oops (Bailey et al., 2015). Differential poly(A) site usage was performed using d3pendr version 0.1 with default parameters and thresholded using a 5% false discovery rate and an effect size (measured using earth mover distance) of 25 (Parker et al., 2021c).

Illumina RNA sequencing

Preparation of libraries for Illumina RNA sequencing

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Total RNA was isolated from Col-0 and fio1-3 seedlings using Nucleospin RNA kit (Macherey–Nagel, 740955) and treated with rDNase (Macherey–Nagel, CAS 9003-98-9) on columns according to manufacturers’ instructions. RNA concentration, quality, and integrity were assessed using the NanoDrop 1000 spectrophotometer (Labtech) and agarose gel electrophoresis. Poly(A)+RNA purification and Illumina RNA-seq library preparation were performed by Genewiz UK Ltd. Poly(A)+RNA was selected with NEBNext Poly(A) mRNA Magnetic Isolation Module. Preparation of the sequencing libraries was performed using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs). 150 bp paired-end sequencing was carried out using Illumina Novaseq 6000. Six biological replicates were performed for each genotype.

Illumina RNA sequencing data processing

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Illumina RNA-seq data were assessed for quality using FastQC version 0.11.9 and MultiQC version 1.8 (Andrews, 2017; Ewels et al., 2016). Reads were mapped to the TAIR10 genome using STAR version 2.7.3 a (Dobin et al., 2013) with a splice junction database generated from the Araport11 reference annotation (Cheng et al., 2017). We used Stringtie version 2.1.7 in mix mode (Kovaka et al., 2019) to generate condition-specific transcriptome assemblies from Illumina RNA-seq replicates and pooled Nanopore DRS data. All assemblies were merged with the Araport11 reference annotation using Stringtie merge to create a unified set of transcripts for quantification. Transcript open reading frames were annotated using Transuite version 0.2.2 (Entizne et al., 2020), which was also used to predict nonsense-mediated decay sensitivity. Pipelines were written and executed using Snakemake version 6.15.3 (Köster and Rahmann, 2012).

Transcripts were quantified using Salmon version 1.1.0 (Patro et al., 2017) with the TAIR10 genome assembly as decoys. SUPPA version 2.3 was used to generate event level PSIs from transcript level quantifications (Trincado et al., 2018). PSIs were loaded into Python 3.6.7 using pandas version 1.0, and generalised linear models (GLMs) were fitted per event using statsmodels version 0.11 (Harris et al., 2020; McKinney, 2010; Oliphant, 2007; Seabold and Perktold, 2010). GLMs were used to test the relationship of PSI with genotype, temperature, and genotype×temperature interaction. Calculated p values were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate method. A false discovery rate of 5% was chosen to threshold events with significant changes in PSI which correlated with genotype, temperature, or genotype×temperature. Changes in motif composition were tested using G-tests of base frequencies at the −2 to +5 position of the splice site. To generate sequence logos, 5’SS or 3’SS from sets of alternative splicing events was filtered to remove duplicated positions, and probability logos were plotted using matplotlib version 3.3 and matplotlib_logo (Hunter, 2007; Parker, 2022b). Contingency tables of splice site classes at U5 and U6 interacting positions were generated using the difference of the −2 to −1 positions of the 5’SS from the consensus motif AG, and the difference of the +3 to +5 positions of the 5’SS from the consensus motif RAG, respectively. Classes were ordered by their log-likelihoods using PSSMs generated from all annotated 5’SSs (described below). Heatmaps of contingency tables were generated with seaborn version 0.11 (Waskom, 2021). Gene tracks using reads aligned to the TAIR10 reference genome were generated using pyBigWig version 0.3.17, pysam version 0.18, and matplotlib version 3.3 (Heger et al., 2014; Hunter, 2007; Ramírez et al., 2014).

Public Illumina RNA-seq datasets were downloaded from GSA accession CRA004052 (Wang et al., 2022) and ENA accession PRJNA877932 (Sun et al., 2022). Quality control was performed using FastQC and MultiQC as described above (Andrews, 2017; Ewels et al., 2016). Col-0 replicate 1 of dataset CRA004052 was discarded due to extreme GC-bias, likely caused by PCR overamplification and/or low RNA input. Transcript-level quantification and PSI estimation were performed as described above. PSIs were used to estimate splicing changes in fio1 mutant alleles using SUPPA2 diffSplice (Trincado et al., 2018).

Global splice site analyses

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To measure the predicted strengths of U5 snRNA loop 1 and U6 snRNA ACAGA box interactions for individual sequences, all 5’SSs annotated in the Araport11 reference transcriptome (Cheng et al., 2017) were used to generate log transformed PSSMs. The U5 and U6 log-likelihood scores for individual sequences were then calculated using the −2 to −1 and +3 to +5 positions, respectively. Correlation of U5 and U6 PSSM scores was calculated using Spearman rank correlation coefficient. This analysis was repeated using 5’SSs from the H. sapiens GRCh38 (International Human Genome Sequencing Consortium, 2004), C. elegans Wbcel235 (The C. elegans Sequencing Consortium, 1998), D. melanogaster BDGP6 (dos Santos et al., 2015), and D. rerio GRCz11/danRer11 (Howe et al., 2013) genome assemblies and annotations, downloaded from Ensembl Genomes release 104 (Howe et al., 2021). To measure relative U5 and U6 interactions for pairs of alternative 5’SSs, SUPPA was used to identify pairs of alternative 5’SSs in each genome annotation (Trincado et al., 2018). These alternative 5’SS pairs were ordered by their genomic positions relative to the strand of the parent gene (i.e. upstream and downstream), and log-odds ratios were calculated using downstream 5’SS log-likelihoods as the denominator. Correlation of U5 and U6 log-odds ratios was calculated using Spearman rank correlation coefficient. Scatter plots with linear regression lines were plotted using seaborn (Waskom, 2021), and 95% CIs for regression lines were calculated using bootstrap sampling with replacement.

Immunopurification and detection of m⁶A modified RNA

Synaptic systems anti-m⁶A

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Total RNA was purified from ~300 mg of frozen plant tissue using the miRVana miRNA isolation kit (Ambion) and treated with DNase I (New England Biolabs) according to the manufacturer’s instructions. The quantity and integrity of RNA were checked using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) and Agilent 2200 TapeStation System. Approximately 5 μg of RNA was suspended in 500 μL low-salt buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, and 0.5% v/v NP-40), with 80 U RNAsin Plus RNase inhibitor, (Promega) and 10 μL (1 mg/mL) m⁶A-rabbit polyclonal purified antibody (#202 003 Synaptic Systems). The samples were mixed by rotation for 2 hr at 4°C. Protein A/G magnetic beads (Pierce, ThermoFisher) were washed twice with low-salt buffer, then added to each RNA/antibody sample and mixed with rotation at 4°C for 16 hr. The beads were washed twice with high-salt buffer (50 mM Tris-HCl pH 7.4, 0.5 M NaCl, and 1% v/v NP-40), twice with low-salt buffer, and twice with Polynucleotide kinase (PNK) wash buffer (20 mM Tris-HCl pH 7.4, 10 mM MgCl₂, and 0.2% v/v Tween-20). Immunopurified RNA was eluted by digestion with proteinase K in 200 μL of proteinase K (PK) buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM EDTA, and 0.1% w/v SDS) at 37°C for 20 min with shaking at 300 rpm, followed by phenol-chloroform extraction and sodium acetate/ethanol precipitation.

Immunopurifed RNA (45 ng) was reverse transcribed (RT) with U6 and U2 snRNA reverse primers using SuperScript III Reverse Transcriptase (ThermoFisher), according to the manufacturer’s instructions. RT-qPCR was carried out using the SYBR Green I (Qiagen) mix with primers targeted to U2 and U6 snRNA (Supplementary file 2). The specificity of RT-qPCR was confirmed by sequencing of amplified products.

Millipore anti-m⁶A

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Total RNA was purified from ~600 mg of frozen plant tissue using the miRVana miRNA isolation kit (Ambion) and treated with DNase I (New England Biolabs), according to the manufacturer’s instructions. The quantity and integrity of RNA were checked using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific). After sodium acetate/ethanol precipitation, approximately 10 μg of RNA was resuspended in 20 μL nuclease free water, with 5% of the RNA being kept for the input sample.

Protein A/G magnetic beads (Millipore, 16–663) were washed twice with m⁶A wash buffer (10 mM TrisHCl pH7.4, 150 mM NaCl, and 0.1% v/v NP-40) and then resuspended in 50 μL m⁶A wash buffer. 5 μL (1 mg/mL) of Anti-N6-methyladenosine (m⁶A) antibody (Millipore, ABE572) or Rabbit Anti-IgG (Thermofisher, A16104) was coupled to the washed beads on a roller for 40 min at room temperature. After incubation, beads were washed three times in m⁶A wash buffer. m⁶A immunoprecipitation buffer was then added to the beads (10 mM TrisHcl pH7.4, 150 mM NaCl, 0.1% v/v NP-40, and 17.5 mM EDTA pH 8) with 80 U SUPERase•In RNase Inhibitor, and 10 μL of each RNA sample was added to both the anti-m⁶A and anti-IgG antibody/beads mixture. Samples were mixed by rotation at 4°C for 16 hr. The beads were washed five times with m⁶A wash buffer. Immunopurified and 5% input RNA was eluted by digestion with proteinase K in 150 μL of PK buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 0.1% v/v NP-40, and 0.1% w/v SDS) at 55°C for 30 min, followed by extraction with TRIzol LS (10296028, Thermofisher) and sodium acetate/ethanol precipitation.

Immunopurifed RNA (500 ng) was RT with random hexamers using ThermoFisher MultiscribeTM II Reverse Transcriptase (4311235, Thermo Fisher Scientific) according to the manufacturer’s instructions. RT-qPCR was carried out using the SYBR Green I (Qiagen) mix with primers targeted to U2 and U6 snRNA (Supplementary file 2). The specificity of RT-qPCR was confirmed by sequencing of amplified products.

m⁶A liquid chromatography with tandem mass spectrometry

Request a detailed protocol

Total RNA was isolated as described above. Poly(A)+RNA was purified using the Dynabeads mRNA purification kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. The quality and quantity of mRNA were assessed using a NanoDrop 2000 spectrophotometer and Agilent 2200 TapeStation System. Samples for m⁶A LC-MS/MS were prepared as previously described (Huang et al., 2018) with modifications according to Parker et al., 2021c. LC-MS/MS was carried out by the FingerPrints Proteomics facility at the University of Dundee. m⁶A/A ratio quantification was performed in comparison with the curves obtained from pure adenosine (endogenous P1 receptor agonist, Abcam) and m⁶A (modified adenosine analog, Abcam) nucleoside standards. Statistical analysis was performed using a two-way t-test.

Code availability

Request a detailed protocol

All pipelines, scripts, and notebooks used to generate figures are available from GitHub at https://github.com/bartongroup/Simpson_Davies_Barton_U6_methylation and Zenodo at https://zenodo.org/record/6372644 (Parker, 2022a).

Data availability

Illumina sequencing data from the genetic screen that identified fio1-4 is available from ENA accession PRJEB51468. Col-0, fip37-4 and fio1-1 nanopore DRS data is available from ENA accession PRJEB51364. Col-0 and fio1-3 Illumina RNA-Seq data is available from ENA accession PRJEB51363.

The following data sets were generated

1. Parker MT
2. Soanes BK
3. Kusakina J
4. Larrieu A
5. Knop K
6. Joy N
7. Briedenbach F
8. Sherwood A
9. Barton GJ
10. Fica SM
11. Davies B
12. Simpson GG
(2022) European Nucleotide Archive (EMBL-EBI)
ID PRJEB51468. Mutant screen of early flowering Arabidopsis EMS mutants with increased MAF2 intron retention.

https://www.ebi.ac.uk/ena/browser/view/PRJEB51468
1. Parker MT
2. Soanes BK
3. Kusakina J
4. Larrieu A
5. Knop K
6. Joy N
7. Briedenbach F
8. Sherwood A
9. Barton GJ
10. Fica SM
11. Davies B
12. Simpson GG
(2022) European Nucleotide Archive (EMBL-EBI)
ID PRJEB51364. Nanopore direct RNA sequencing of Col-0 and fio1-3 mutant Arabidopsis.

https://www.ebi.ac.uk/ena/browser/view/PRJEB51364
1. Parker MT
2. Soanes BK
3. Kusakina J
4. Larrieu A
5. Knop K
6. Joy N
7. Briedenbach F
8. Sherwood A
9. Barton GJ
10. Fica SM
11. Davies B
12. Simpson GG
(2022) European Nucleotide Archive (EMBL-EBI)
ID PRJEB51363. Illumina RNA sequencing of Col-0 and fio1-3 mutant Arabidopsis.

https://www.ebi.ac.uk/ena/browser/view/PRJEB51363

The following previously published data sets were used

1. Xu T
2. Wu X
3. Wong CE
4. Fan S
5. Zhang Y
6. Zhang S
7. Liang Z
8. Yu H
(2022) European Nucleotide Archive (EMBL-EBI)
ID PRJNA749003. N6-Adenosine (m6A) Methylation profiling in Arabidopsis thaliana wild-type and fio1-2 by nanopore DRS sequencing.

https://www.ebi.ac.uk/ena/browser/view/PRJNA749003
1. Sun B
2. Bhati KK
3. Song P
4. Edwards A
5. Petri L
6. Kruusvee V
7. Blaakmeer A
8. Dolde U
9. Rodrigues V
10. Straub D
11. Yang J
12. Jia G
13. Wenkel S
(2022) NCBI Gene Expression Omnibus
ID GSE171928. Next Generation Sequencing of Wild Type (Col-0), fio1-1 and fio1-2 methylated RNA immunoprecipitation and transcriptome in Arabidopsis thaliana.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171928
1. Wang C
2. Yang J
3. Song P
4. Zhang W
5. Lu Q
6. Yu Q
7. Jia G
(2022) Genome Sequence Archive (CNCB)
ID CRA004052. Identification and Function Research of Methyltransferase of N6 Methyladenine in Plants.

https://ngdc.cncb.ac.cn/gsa/browse/CRA004052

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Decision letter

Jonathan P Staley

Reviewing Editor; University of Chicago, United States
James L Manley

Senior Editor; Columbia University, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "m⁶A modification of U6 snRNA modulates usage of two major classes of pre-mRNA 5' splice site" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and James Manley as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. A previous report suggested limited splicing changes in the fio1 mutant. The authors should add a comparison and discussion. In addition, FLC mRNA can be m6A methylated. The authors appear to suggest the effect is secondary. More analysis and explanation are required. For instance, the authors could measure m6A level on FLC in fio1 mutants and mta mutants, and compare them with that of wild tpye.

2. The authors used Nanopore m6A sequencing to map m6A in mRNA from wt and fio1 mutant strains. We would suggest either RIP-seq or mass spectrometry measurement to confirm the loss of fio1 leads to limited mRNA m6A changes.

3. p 3 and Figure 1: "Figure 1. Schematic outline of sequential steps of pre-mRNA splicing." I don't see why this is a main figure. It's the type of schematic that could be found in any splicing review. This seems like a Supplemental figure, at most. Instead, a schematic of their screen, described in the first paragraph of the Results, including some pictures of WT and early flowering Arabidopsis mutants obtained from the screen, would be more appropriate (and is needed). The authors should also show the RT-PCR data supporting enhanced MAF2 intron 3 retention at 16{degree sign}C. Overall, the authors provide few details about this screen, including how many EMS mutants were screened. How did the authors identify mutants that alter MAF1 intron 3 splicing, when the difference in intron 3 retention (as shown in Figure 4C) is so subtle?

4. p 6. "Crosses between EMS 129 and either fio1-1, or a Transfer-DNA insertion line disrupting AT2G21070 (SALK_084201; fio1-3) confirmed allelism." No data for this are shown; data supporting these conclusions should be included.

5. Many of the figures are difficult to interpret. For example, Figure 3 legend gives some details of the bioinformatics, but does not say anything about what the actual experiments were. The text is overly-focused on bioinformatics without an adequate description of the experiments that were performed. Please provide information detailing the actual experiments; without this information, these parts of the manuscript are both uninformative and impossible to evaluate.

6. Figure 4C. The change in MAF2 intron 3 retention is seemingly tiny. What exactly is the deltaPsi for this? Can this be confirmed by RT-PCR or RT-qPCR? This highlights an issue raised above: how did the authors identify mutants in the screen by RT-PCR that altered MAF2 intron 3 retention if the change is this subtle?

7. I found the description of the SS sequence enrichment incredibly long (7 pages of text) and overwhelmingly descriptive without a view of what the conclusions would be. Sequence motifs in the context of otherwise disparate intron and exon sequences can be suggestive of a mechanism; but, in the end, confirmation of these sequence effects requires experimental testing within a constant gene sequence. This is necessary to make any solid conclusion based on rigorous science. These are critical experiments that are missing in this manuscript.

8. The model seems to be that U5 and U6 RNAs contribute to a platform for recognition of 5'SSs: a 5'SS must interact sufficiently with this platform in order to make it through splicing; stronger interaction as some positions can compensate for weaker interactions at other positions. I don't think that there's anything new here in terms of mechanism. m6A in U6 contributes to stability; this was already postulated in the S. pombe system (Ishigami et al. 2021). Is this biologically important? Does FIO1 expression change developmentally? or, is it a constitutive feature of U6-5'SS interaction? These questions are not addressed or even raised in this manuscript.

9. p 18. The authors state that "FIO1 buffers spicing fidelity" and "that FIO1 function calibrates the temperature range over which MAF2 alternative slicing occurs", but I don't see any data that directly support these assertions. These seem like extrapolations of the current data that require experimental tests to support them.

10. The authors refer to the "cooperative roles for U5 and U6 snRNA in splice site selection" in the abstract. I don't see any evidence for interactions with U5 in this manuscript, and the proposed role of U6 is not tested experimentally. These two deficits are the most disappointing aspects of this work.

Additional comments:

1. The main text goes on for 31 pages, >12,000 words (not including Figure Legends or References). While there are no strict limits on the length of Research Articles, the eLife Author Guide suggests to "try not to exceed 5,000 words in the main text". The text could be shortened significantly; conciseness would greatly benefit the readability of the manuscript.

The Discussion, at >7 pages, is excessively long and does not critically evaluate the data or conclusion presented in this manuscript.

2. p 7 and Figure 2B. What is the experimental basis of Figure 2B? The authors do not state the experimental data: is it RNA-seq or RT-PCR? In either case, some examples of the primary data for this should be shown.

3. p 6, second paragraph of the Results: "We used bulked segregant analysis and the software tool artMap (Javorka et al., 2019) to identify the causative mutation…". The authors should explain this more explicitly, for non-specialist readers. There are a number of ways in which this could have been done, but the authors do not specify what exactly was the data that led to the determination of the mutation.

4. Figure 4B. This is a comparison of fio1-3 with Col-0. I would like to see volcano plots for all the five splicing events with log2FC on the x-axis and enrichment/significance on the y axis. These bar charts with percentages are too confusing to depict what the authors are actually trying to convey and do not depict the spread of the primary data.

5. Figure 4E shows "absence of change" in MAF2 intron 5, but this intron is clearly efficiently spliced, so the comparison to inefficiently-spliced intron 3 is not particularly meaningful. A more meaningful comparison would be to a different intron that is also inefficiently spliced.

6. Figure 5. Seq logos are shown for 5'SSs that are sensitive to or that are used better in fio1-1 mutants. These can only be meaningful if they are compared to the seq logo of the overall 5'SS in A.t., which is not shown or discussed.

7. Figure 5. Why only show out to pos -2 in the exon? More positions than that can pair with U5.

8. Some of the fonts in the figures are so small as to be unreadable or nearly unreadable, e.g. Figure 4-Figure sup 1 and Figure 5-Figure sup 1.

9. ALKBH10B is a demethylase that affects early flower phenotype. Does this protein also mediate the demethylation of U6 m6A?

https://doi.org/10.7554/eLife.78808.sa1

Author response

Essential revisions:

1. A previous report suggested limited splicing changes in the fio1 mutant. The authors should add a comparison and discussion.

We are grateful for this suggestion. We have completed a re-analysis of recently published data that confirms the effect on splicing that we report here. We have added a new sub-section in the Results that incorporates this comparison and added new content to the Discussion that addresses this issue. We have summarised the analysis we performed below, as well as attempting to provide some additional insight into why other research groups overlooked splicing changes in their RNA-seq data.

A major finding from our study is that FIO1-dependent modification of U6 snRNA plays a significant role in defining splice site usage. This directly contradicts two recently published studies, referred to by the reviewer (Wang et al., 2022; Xu et al., 2022). Wang et al., 2022 claims that “disruption of FIONA1 does not affect global alternative splicing” and Xu et al., 2022 states “FIO1-mediated m⁶A methylation … has a limited effect on alternative splicing”. We show that these conclusions are wrong and that FIO1-induced methylation of U6 snRNA influences global splice site selection.

The Wang et al. study used two replicates per condition of Illumina RNA-seq, with ~35-45 million reads per replicate. Xu et al. used three replicates per condition of nanopore direct RNA sequencing, with ~0.5-1.2 million reads per replicate. Our study used 6 replicates per condition with four temperature treatments per genotype, for a total of 24 replicates per genotype, and a minimum of 50M pairs of 150bp Illumina RNA-seq reads per replicate (3.6 billion mapped reads in total). Consequently, our study has much greater statistical power to detect splicing changes in fio1 mutants, compared to the previous two reports. For the purpose of this response, we have now performed a re-analysis of the previously published data, to explain the basis of the different conclusions reached in these studies.

Wang et al. performed splicing analysis using two replicates per genotype of 150bp paired-end Illumina RNA-seq, with ~35-45 million reads per replicate and used the software tool rMATs (Shen et al., 2014) to perform alternative splicing analysis. rMATs requires a transcriptome annotation file to guide splice site quantification. In contrast to our approach, Wang et al., did not generate a condition-specific transcriptome assembly for alternative splicing quantification, and presumably used the TAIR10 or Araport11 reference annotation (this is not specified in the methods section of their paper). This is important because these annotations would not include the majority of the novel alternative 5’ splice sites and intron retention events that we identified in the fio1 mutant, and so these would not be considered in the analysis. For example, we discover ~70% fewer significant alternative splicing events when we perform splicing analysis on our own data using the reference annotation, rather than the condition-specific assembly that we generated (which includes novel splice isoforms used in fio1 mutants) (Author response image 1 and Figure 3B). Consequently, the impact of FIO1 on splicing is likely to be underreported in the Wang et al. study.

Author response image 1

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Barplots showing the proportion of splicing events of each class, as labelled by SUPPA, which have significantly different usage (FDR < 0.05) in the *fio1-3* mutant, that are discovered when performing differential splicing analysis using the Araport11 reference annotation.

We downloaded the data from Wang et al. and performed splicing analysis using our condition-specific transcriptome annotation. We found that the Wang et al. data were of poor quality because only ~38% of reads were mappable to the Arabidopsis transcriptome. In particular, the first WT Col-0 replicate was unusable and had to be discarded because of PCR amplification bias, which can be seen in the extreme GC-content bias of sequenced reads (Figure 6—figure supplement 2A). However, we were still able to identify 384 alternative 5’SS usage events from the dataset using the software tool SUPPA2 (Trincado et al., 2018), from which we found 58% of fio1-sensitive 5’SSs had an A at the +4 position, compared to only 36% of 5’SSs with increased usage in the fio1 mutant (Figure 6—figure supplement 2B, Chi2 p =3×10^-9). We conclude that the reported limited splicing changes in the Wang et al. publication result from an underpowered dataset compounded by methodological choices in analysis.

Xu et al. performed splicing analysis using three replicates per genotype of nanopore direct RNA sequencing and used the software tool FLAIR (Tang et al., 2020) to identify differentially spliced transcripts. In our manuscript, we chose not to use nanopore direct RNA sequencing to analyse splicing because the low number of reads mean it is underpowered for splicing analysis and because the relatively high sequence error rates make the identification of splice junctions more complex (Parker et al., 2021; Tang et al., 2020). This is particularly problematic in the fio1 mutant where cryptic alternative 5’SSs are mostly only a very short distance from the canonical 5’SS (see Page 8 and Figure 4 —figure supplement 1A in the revised manuscript). Tools like FLAIR perform post-alignment “correction” of unannotated splice junctions to nearby annotated splice junctions. This would incorrectly assign mRNAs originating from the cryptic splice sites used in the fio1 mutant to the reference splice junction and may partly explain why alternative splicing was overlooked in this study.

We were previously able to validate splicing examples detected in our Illumina RNA-seq dataset with our own nanopore direct RNA sequencing data (Figure 4E, 5D, 6F in the revised manuscript). To confirm whether these splicing changes are also detectable in the data from Xu et al., we downloaded their basecalled data and realigned it using minimap2. We found that we could indeed identify the splicing defects we previously reported in our submitted manuscript, such as alternative 5’SS selection in AtSAR1 intron 21 (Author response image 2A), and intron retention in WNK1 intron 5 (Author response image 2B). We conclude that, like the Wang et al. study, the reported limited splicing changes in the Xu et al. publication results from an underpowered dataset compounded by methodological choices.

Author response image 2

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(A) Gene track showing alternative 5’SS selection at *AtSAR1* intron 21 in *fio1-2*, identified using nanopore DRS read alignments reanalysed from Xu et al.2022. (B) Gene track showing intron retention at *WNK1* intron 5 in *fio1-2*, identified using nanopore DRS read alignments reanalysed from Xu et al. 2022.

Finally, another recent study, by Sun et al., sequenced two replicates per condition of Illumina RNAseq derived from fio1-1 and fio1-5 mutants, with ~20-26 million reads per replicate (Sun et al., 2022). Sun et al. did not perform any splicing analysis on their data. We downloaded and reanalysed the data from Sun et al. using the same approach described above for the Illumina RNA-seq data from Wang et al. 2022. Although there were only two replicates, with a low sequencing depth per replicate, we found that these data were of a higher quality than the Wang et al. dataset, with a much higher mappability rate (~95%). We were able to use these data to identify 923 alternative 5’SS events caused by the fio1-1 mutation, and 947 alternative 5’SS events caused by the fio1-5 mutation. There was a clear preference for A₊₄ in fio1-sensitive 5’SSs, and for U₊₄ in 5’SSs with increased use in the fio1 mutants (Figure 6—figure supplement 3A-B). There were 669 events that were common to both genotypes tested by Sun et al., and of these, 556 were also identified in our own RNA-seq analysis as alternatively spliced in fio1-3 mutants (Figure 6—figure supplement 3C).

Sun et al., also performed one replicate each of nanopore DRS for Col-0, fio1-1 and fio1-5. We could identify splicing changes in AtSAR1 and WNK1 using these data as well (Figure 6—figure supplement 4), confirming the findings we reported in our submitted manuscript.

Overall, we find that data from recently published studies support the impact of FIO1 on splicing. We have revised our manuscript to address this issue in a new Results sub-section entitled “Reanalysis of RNA-seq data from independent fio1 alleles confirms disruption to splicing” and in the Discussion subsection entitled “FIO1-dependent m⁶A modification of U6 snRNA determines splicing accuracy and efficiency”.

In addition, FLC mRNA can be m6A methylated. The authors appear to suggest the effect is secondary. More analysis and explanation are required.

There seems to be a slight misunderstanding here. We suggest in our manuscript that because there is no change in splicing of FLC in the fio1 mutant, the impact of loss of FIO1 on FLC expression levels occurs indirect of the methylation of U6 snRNA by FIO1 (see the context on page 19 of our submitted manuscript). We do not make any statements about the methylation of FLC directly, either by FIO1 or the MTA-writer complex. To avoid potential confusion to the reader, we have removed this phrase from the revised manuscript.

For instance, the authors could measure m6A level on FLC in fio1 mutants and mta mutants, and compare them with that of wild tpye.

It is true that two recent studies (Sun et al., 2022; Wang et al., 2022) reported FIO1-dependent methylation of FLC mRNA, but neither study examined whether the reported FLC modification depended upon the MTA-writer complex and neither tested the direct functional impact of the modification by mutation of the modified base(s). We have previously published in eLife that there is a major reduction in detectable expression of sense and antisense RNAs at the FLC locus in mutants defective in the MTA-writer complex component, VIRILIZER (Parker et al., 2020). In the original submitted version of this manuscript, we report a similar finding in fio1-3 (Figure 3—figure supplement 2H-J). Consequently, we are unable to confirm the presence of m⁶A on FLC mRNA because the lack of FLC read coverage in mutants defective in either FIO1 or MTA-writer complex components means that we do not have the statistical power to do so using a comparative approach with nanopore DRS. In our view, the low levels of FLC transcripts also pose a technical barrier to the meaningful measurement of FLC m⁶A using m⁶A-IP (MeRIP) in fio1 and mta mutant backgrounds. Both Wang et al., 2022 and Sun et al., 2022 (who also reported reduced FLC expression in fio1 mutants) used m⁶A-IP-qPCR to address this question, but while Wang et al. reported that they used an m⁶A modified oligo spike-in to control for m⁶A levels, neither study reported how they controlled for the major difference in FLC input levels between wild type and fio1 mutants. Sun et al. were not able to confirm the methylation of FLC using nanopore DRS. Previous studies do not agree on which methylase complex targets FLC, with Wang et al. & Sun et al. suggesting that FIO1 is responsible (Sun et al., 2022; Wang et al., 2022), and a third study suggesting that the MTA-complex is responsible (Xu et al., 2021). Overall, our analysis of mRNA methylation in fip37-4 and fio1 backgrounds indicates that FIO1-dependent mRNA methylation is rare or absent; we do not have evidence to indicate that FLC is an exception.

2. The authors used Nanopore m6A sequencing to map m6A in mRNA from wt and fio1 mutant strains. We would suggest either RIP-seq or mass spectrometry measurement to confirm the loss of fio1 leads to limited mRNA m6A changes.

We are grateful for this suggestion. We have added Liquid Chromatography Tandem Mass Spectrometry (LC:MS/MS) data to the revised manuscript that shows fio1-1 mutants have 6.7% less m⁶A in poly(A)+ purified RNA than WT Col-0. This relatively modest reduction in m⁶A levels is consistent with recently published LC:MS/MS data of Arabidopsis fio1 mutants from Wang et al. 2022. These data are presented as Figure 2A in the revised manuscript, and we have revised the Methods, Results and Discussion sections accordingly.

3. p 3 and Figure 1: "Figure 1. Schematic outline of sequential steps of pre-mRNA splicing." I don't see why this is a main figure. It's the type of schematic that could be found in any splicing review. This seems like a Supplemental figure, at most.

We are grateful for this suggestion and in response we have now removed this figure from the revised manuscript.

Instead, a schematic of their screen, described in the first paragraph of the Results, including some pictures of WT and early flowering Arabidopsis mutants obtained from the screen, would be more appropriate (and is needed).

We are grateful for this suggestion. We have included a new schematic of our mutant screen as revised Figure 1A.

We include as Figure 1 —figure supplement 1B a photograph of the actual F2 segregating population used to map fio1-4, including phenotypic early flowering plants and aphenotypic sisters. For additional clarity, we have incorporated schematic images of the early flowering individuals in Figure 1A, which outlines how the mutant screen was performed. A photograph of the early flowering phenotype of the fio1 alleles used in this study compared to WT is presented as Figure 1F in the revised manuscript.

The authors should also show the RT-PCR data supporting enhanced MAF2 intron 3 retention at 16{degree sign}C.

We provide here Figure 1—figure supplement 1A , the raw RT-PCR data from the second step of the two-step mutant screen that includes the EMS129 line (later referred to as fio1-4) where plants were cultivated at 16°C. In addition, we have included this schematically in Figure 1A outlining how the mutant screen was performed.

Overall, the authors provide few details about this screen, including how many EMS mutants were screened.

We agree that our description of the screen that identified the fio1-4 allele required more detail. We have now prepared a re-analysis of the data from our bulk segregant sequencing and updated the Results, Methods, and figures accordingly. 15,000 EMS M2 seeds were screened using a two-step procedure. The earliest flowering 100 EMS lines at 16°C were selected for splicing analysis by RT-PCR. This approach identified two lines that had increased retention of MAF2 intron 3. We focussed this study on EMS129. The causal mutation for the early flowering phenotype of EMS129 was mapped by backcrossing to the M0 parental line to generate a segregating population. Early flowering plants and aphenotypic sisters were grouped into separate pools for genomic DNA sequencing. We identified G to A transitions caused by EMS mutations using a SNP-calling pipeline, which is now described in greater detail in the revised Methods section. 19 SNPs in a 2.6Mb region on Chromosome 2 were identified as significantly associated with the early flowering phenotype (G test FDR < 0.05, allele fraction 100%). These SNPs and the associated allele fractions are shown in revised Figure 1B in the revised manuscript. No other associations were found on other chromosomes. Of these SNPs, only one was predicted to have a major effect on gene expression: a G to A transition at the +1 position of the 5’SS in the second intron of FIONA1.

How did the authors identify mutants that alter MAF1 intron 3 splicing, when the difference in intron 3 retention (as shown in Figure 4C) is so subtle?

This is explained by the fact that we carried out a two-step mutant screen. MAF2 represses flowering at low ambient temperature. We wanted to identify factors required for the efficient splicing of MAF2 intron 3 at low ambient temperature. We reasoned that if MAF2 intron 3 was spliced less efficiently, the level of MAF2 flowering repressor activity would be reduced and hence plants would flower earlier. In step 1, we screened 15,000 EMS mutants for early flowering individuals grown at 16°C. In step 2, we re-screened the earliest flowering 100 mutant individuals for changes in MAF2 intron 3 splicing by RT-PCR. Although the difference in MAF2 intron 3 processing is indeed relatively modest, we report multiple changes in the splicing and expression of other genes controlling flowering time in fio1 mutants in our manuscript. Consistent with this, fio1 mutants have a very clear early flowering phenotype, and indeed, it was through an early flowering mutant screen that Hong Gil Nam’s lab first isolated the fio1-1 mutant allele (Kim et al., 2008). In the second step of the screen, we used RT-PCR to analyse MAF2 intron 3 splicing in RNA purified from these 100 early flowering mutants. Two of these 100 early flowering individuals showed increased levels of MAF2 intron 3 retention, one of which is the fio1-4 allele that we describe here. Although the difference in MAF2 intron 3 splicing is relatively small, it is clearly identifiable by RT-PCR (see Figure 1—figure supplement 1A ) and confirmed by our RT-qPCR analysis of other alleles (Figure 1D) and Illumina RNA-seq of another allele (see Figure 3 C and D in the revised manuscript).

We believe that the Reviewer’s suggestion to include a schematic on the mutant screen helps to clarify how this two-step screen could identify mutants with relatively small differences in the splicing of MAF2 intron 3 and clarifies this point for the reader. We have also incorporated the phrase “two-step mutant screen" in different sections of the revised manuscript to reinforce this point.

4. p 6. "Crosses between EMS 129 and either fio1-1, or a Transfer-DNA insertion line disrupting AT2G21070 (SALK_084201; fio1-3) confirmed allelism." No data for this are shown; data supporting these conclusions should be included.

We apologise for this oversight and the manuscript has now been revised to provide these data. 100% of EMS129 lines crossed to the fio1-1 mutant flowered early (35 plants), compared to 0% of EMS129 lines crossed to Col-0 (15 plants), confirming allelism. These phenotypes were scored visually at the time of allelism testing and leaf count data were not recorded. Our U6 snRNA m⁶A analysis reveals that all the alleles studied here (fio1-1, fio1-3 and fio1-4) are defective in U6 snRNA m⁶A, independently confirming the allelism test of flowering phenotype, and these data are reported later in the manuscript text (Figure 2D of our revised manuscript). Our global RNA analyses are based on sequencing of both fio1-1 and fio1-3 alleles, which independently validate our conclusions on the impact of FIO1 on splicing. Several other fio1 alleles disrupting AT2G21070 that flower early and lack U6 snRNA m⁶A modification have been reported this year (Sun et al., 2022; Wang et al., 2022; Xu et al., 2022). As we show in response to comment 1 in this document, the splicing phenotypes are shared by these different alleles as revealed by our reanalysis of the data published in these studies. Consequently, our analysis of fio1-1, fio1-2, fio1-3, fio1-4 and fio1-5 support the conclusion that all of these alleles exhibit specific, overlapping splicing defects. The text in the Results section has been revised accordingly.

5. Many of the figures are difficult to interpret. For example, Figure 3 legend gives some details of the bioinformatics, but does not say anything about what the actual experiments were. The text is overly-focused on bioinformatics without an adequate description of the experiments that were performed. Please provide information detailing the actual experiments; without this information, these parts of the manuscript are both uninformative and impossible to evaluate.

We agree that our Figure Legends (and the Results text referring to them) were inadequately described. We have systematically reviewed and revised all figure legends and how they are referred to in the text to clarify their content and explain the analyses of large datasets in a more accessible way.

6. Figure 4C. The change in MAF2 intron 3 retention is seemingly tiny. What exactly is the deltaPsi for this? Can this be confirmed by RT-PCR or RT-qPCR? This highlights an issue raised above: how did the authors identify mutants in the screen by RT-PCR that altered MAF2 intron 3 retention if the change is this subtle?

The ΔPSI measured in our Illumina RNA-seq data is 0.066 representing a 6.6% increase in intron retention in the fio1-3 mutant across all temperature conditions (FDR = 2.7×10^–3). The increase in MAF2 intron 3 retention in a fio1-4 mutant was initially identified using RT-PCR (during the mutant screen, Figure 1—figure supplement 1A ) and splicing differences were confirmed by RT-qPCR in fio1-1 and fio1-3 alleles (Figure 1D of the revised manuscript). Our detailed explanation as to how the mutant was identified is given in response to the same question raised in point 3.

7. I found the description of the SS sequence enrichment incredibly long (7 pages of text) and overwhelmingly descriptive without a view of what the conclusions would be.

We agree that this section of the Results is long and detailed, but we are keen to retain some of this format because it is central to the advances that we make and it provides insight that other studies on mutants defective in FIO1/METTL16 have missed. We have edited each Results section and removed some of the numeric terms to improve readability in the revised manuscript. We have cut 347 words from the SS enrichment section.

We deliberately chose to submit this work to eLife because of its objectives around transparency, open access and transforming academic publishing. It is difficult to interpret “overwhelmingly descriptive” in the context of the series of novel findings that we report in this section. Eve Marder, who was a Deputy Editor of eLife 2015-2019, published an essay in eLife remarking how in manuscript reviews, terms like “mechanism” and “descriptive” are often used as pejoratives without meaning (Marder, 2020). Consequently, it is disappointing to see such expressions reach through to this stage of an eLife review.

Sequence motifs in the context of otherwise disparate intron and exon sequences can be suggestive of a mechanism; but, in the end, confirmation of these sequence effects requires experimental testing within a constant gene sequence. This is necessary to make any solid conclusion based on rigorous science. These are critical experiments that are missing in this manuscript.

Our manuscript does report an experimental test within a constant gene sequence. Our fio1 mutant alleles lack m⁶A modification of U6 snRNA but the nucleotide sequence of splice sites that are used in WT or fio1 mutant alleles are unchanged between these genetic backgrounds genome-wide. We measured splicing that occurred in vivo in otherwise controlled conditions, in multiple cell types of whole organisms exposed to different environmental temperature regimes. High throughput in vivo analyses like ours show that effects which are identifiable in individual introns, like MAF2 intron 3 or AtSAR1 intron 21, are explainable by simple changes in nucleotide preference which generalise across thousands of different contexts. Similar perturbations of the U5 snRNA have previously been reported: as we described on p20 of our submitted manuscript “…defective splicing of specific introns in Schizosaccharomyces pombe mtl16 mutant strains can be experimentally rescued by expression of mutated U5 snRNAs designed to strengthen U5 loop 1 interactions with the upstream exon (Ishigami et al., 2021)”. Importantly, these “genetic complementation” experiments are consistent with the statements that we make with respect to the global distinction of two 5’SS classes. Therefore, there is already orthogonal experimental support for our model.

An alternative interpretation of the reviewer’s comment (“experimental testing within a constant gene sequence”) could be the use of single transgene reporters with base substitutions e.g. at the +4 position of the 5’SS. While we recognise the value of different experimental approaches, our view is that our unbiased global approach is more “rigorous”, because in reality it is not so simple to derive broad conclusions from testing individual reporter genes. This is because splicing outcomes are the result of complex combinatorial inputs, and so the impact of changes in an example reporter gene may not be generalisable to other sequence contexts. Several recent publications illustrate this point:

First, we cited the high throughput study from Adrian Krainer’s lab (Wong et al., 2018), which used a massively parallel splicing assay with minigene constructs to determine the efficiency of all possible 9 nt 5’SS sequences (32,678 sequences) in three different intron contexts. A major conclusion of this comprehensive analysis is that 5’SS preferences were different depending on the broader context of the minigene.

Second, a recent study from Stirling Churchman’s lab (Choquet et al., 2022) used nanopore direct RNA sequencing analysis of human chromatin-associated poly (A)+ RNA to reveal that post-transcriptional splicing of multiple introns within the same transcript follows a defined or preferred order. This illustrates that testing sequences in one intron can be confounded by the combinatorial impact that other introns in the same transcript have on the splicing of the intron under experimental examination (this was also a conclusion of the study from the Krainer lab).

Third, a recent study from Jean Beggs’ lab (Aslanzadeh and Beggs, 2020) shows that the efficiency of splicing is influenced by the rate of RNA Polymerase II (PolII) elongation. Jean Beggs’ study is particularly pertinent here because she looked at Saccharomyces cerevisiae ribosomal protein genes which possess a C, rather than the consensus U at the 5’SS+4 position. Compared to WT, in lines with a slow elongating PolII mutant, non-consensus 5’SS introns were spliced more efficiently, but in lines with a fast-elongating PolII mutant, such introns were spliced less efficiently. Consequently, interpreting the impact of individual positions in 5’SSs could be confounded not only by cis-elements in the test RNA but by the promoter used to drive the expression of otherwise constant gene sequences.

An expectation from the eLife review process is that “extra experiments, analyses, or data collection are only requested if they are essential and can be reasonably completed within about two months”. Because the critical experiments are not specified, it is unclear whether they could be completed in Arabidopsis in such a time frame. For example, the production of stable transgenic Arabidopsis reporter lines takes several months.

8. The model seems to be that U5 and U6 RNAs contribute to a platform for recognition of 5'SSs: a 5'SS must interact sufficiently with this platform in order to make it through splicing;

A key tenet of our study is indeed that U5 and U6 snRNAs act together as a platform in which stronger interactions with either U5 or U6 can compensate for weaker interactions with the other. We demonstrate this through global RNA-Seq analyses using fio1 mutants that alter the sequence preference of U6 snRNA. We show that the use of 5’SSs that have weaker U6 snRNA interactions in fio1 mutants is less affected when these 5’SSs have stronger U5 interacting sequences.

Stronger interaction as some positions can compensate for weaker interactions at other positions.

This is not completely correct. Rather than finding that changes in any 5’SS position can compensate for weaker interactions resulting from loss of U6 snRNA m⁶A, we find specific changes (namely, a change in nucleotide preference at the +4 position and a stronger U5 interacting potential at the –2 and –1 positions) define the altered 5’SS motif for sites used in the fio1 mutant.

I don't think that there's anything new here in terms of mechanism.

Interpretation of this statement is difficult because it is unclear what level of understanding constitutes new “mechanistic” insight (Marder, 2020). Our study expands upon previous work by extending the model of U5/U6 compensation from intron retention events to more complex forms of alternative splicing with two or more competing 5’SSs. This includes not only alternative 5’SSs, but exons with increased and decreased skipping. In each case we show that the model of compensatory U5 and U6 interactions can be used to explain splicing changes. Furthermore, we show that increases in upstream and downstream alternative 3’SS preference can also be explained by a weakening or strengthening of the 5’SS:U6 interaction, respectively. Finally, we provide a detailed analysis of available cryo-EM data to contextualise these findings. We have restructured the Discussion section of the revised manuscript to make these points more clearly.

m6A in U6 contributes to stability; this was already postulated in the S. pombe system (Ishigami et al. 2021).

It is true that Ishigami et al. postulated that m⁶A contributes to the stability of U6 – 5’SS interactions. However, our cryo-EM analysis provides a more detailed interpretation of how this might occur and goes further to incorporate our novel finding on U6 snRNA affecting 3’SS usage. We report that U6_A43 faces 5’SS A₊₄ in a trans Hoogsteen sugar edge interaction that could stabilise the U6/5’SS helix in B complex by capping. We show that this stabilisation may be particularly important in B but not B^act complex because in B^act the U6/5’SShelix is now capped by 5’SSA₊₃ interacting with U6 snRNA G44. We then report that this helix capping property of A43 becomes important again in C* complex consistent with our RNA-Seq data that reveal an impact of U6 snRNA m⁶A modification on 3’SS usage. None of these insights are included in the S. pombe study reported by Ishigami et al., or any other study to date.

The agreement between our manuscript and the S. pombe study by Ishigami et al. is an important strength of our manuscript because together we establish that the conserved role of METTL16/FIONA1 is to methylate U6 snRNA and that this has demonstrable and consistent impacts on pre-mRNA splicing. This is important because recent studies have suggested an alternative role for FIO1 in methylating mRNA that is not supported by our data.

Is this biologically important? Does FIO1 expression change developmentally? or, is it a constitutive feature of U6-5'SS interaction? These questions are not addressed or even raised in this manuscript.

Questions of whether the regulation of FIONA1 and/or U5/U6 compensation could be used to control alternative splicing were addressed in the submitted manuscript. The final Results section on page 18 of the submitted manuscript addresses this: we show that pairs of alternative 5’SSs in Arabidopsis, humans, and other species tend to have opposing U5 and U6 interaction potential, which could be used to modulate alternative splicing. In addition, we dedicated the entire final section of the Discussion to the question of regulation – see page 24 headed “A regulatory or adaptive role for U6 snRNA modification?”

Analysis of publicly available Arabidopsis community resources (eg.: bar.utoronto.ca) suggests that FIO1 mRNA is constitutively expressed (Waese et al., 2017). This does not preclude a condition-specific impact of FIO1 on splicing. Furthermore, constitutive expression of FIO1 mRNA does not preclude regulation of the protein’s activity or targeting. Consequently, it remains an open question as to whether m⁶A levels of U6 snRNAs vary.

U6 snRNA methylation may be a constitutive feature of the U6:5’SS interaction. Nevertheless, its loss has very specific phenotypic impacts, for example on flowering time. Consequently, it is biologically important, regardless of whether it is constitutive or not. Likewise, an example from the splicing field is that mutation of the Brr2 constitutive splicing factor leads to a very specific human disease, Retinitis Pigmentosa (Zhang et al., 2009). Furthermore, whilst loss of m⁶A from U6 aided in the identification of opposing interaction potential of alternative 5’SS pairs, it does not mean that this is the only mechanism by which U5 and U6 interactions could be regulated.

As we discussed in our submitted manuscript, Arabidopsis flowering time GWAS analyses point to a functional impact of natural variation in the sequence of FIO1 genes (Price et al., 2020; Sasaki et al., 2015). We have started a new research programme to understand the impact of this sequence variation, but it is outside the scope of the present study.

We have restructured the Discussion to make these points clearer in our revised manuscript.

9. p 18. The authors state that "FIO1 buffers spicing fidelity" and "that FIO1 function calibrates the temperature range over which MAF2 alternative slicing occurs", but I don't see any data that directly support these assertions. These seem like extrapolations of the current data that require experimental tests to support them.

We agree that these statements need clarifying and we have removed these phrases to make the reporting of our data clearer:

In the course of re-writing the manuscript we have removed the Discussion sub-section entitled “FIO1 buffers splicing fidelity”. A more straightforward way to describe this finding would be “Loss of FIO1 introduces temperature sensitivity to some splicing events”. The data that supported this statement was described in the Results section on page 10 and Figure 3 —figure supplement 1C of the original submitted manuscript (now Figure 3 —figure supplement 1C in the revised manuscript). The data shown in Figure 5D of the submitted manuscript (now Figure 4D in the revised manuscript) is also consistent with this statement. These data are retained in the revised manuscript.

Another way to state “that FIO1 function calibrates the temperature range over which MAF2 alternative splicing occurs” could be the more straightforward “In the absence of FIO1, the temperature range over which variation in MAF2 intron 3 retention occurs is shifted”. The data that supports this statement is shown in Figure 3D of the revised manuscript. Nevertheless, for clarity on this issue, we have removed this phrase from the revised manuscript.

10. The authors refer to the "cooperative roles for U5 and U6 snRNA in splice site selection" in the abstract. I don't see any evidence for interactions with U5 in this manuscript,

Our global analyses document 1000s of splicing events sensitive to FIO1 mutation that can be distinguished by the base identities of 5’SS-U5 snRNA interacting positions. The recognition of –1 and –2 exon positions at 5’SSs by U5 snRNA has been established over a period of approximately 40 years, using techniques ranging from genetic analysis with yeast to cross-linking studies, high-throughput sequencing experiments and direct visualisation using cryo-EM (Wilkinson et al., 2020). It is well established that the –2 and –1 positions interact with the U5 snRNA, and that an AG motif at the –2 to –1 positions of the 5’SS leads to more efficient splicing (Wong et al., 2018). It is similarly uncontroversial that the +3 to +5 positions are important for the interaction with U6 snRNA, and that an RAG motif at the +3 to +5 positions leads to more efficient splicing (Wong et al., 2018). Our analyses of all genomic 5’SS sequences (in Arabidopsis and a range of metazoans) show that when 5’SSs have an AG motif at the –2 to –1 positions of the 5’SS, the requirement for an RAG motif at the +3 to +5 positions is relaxed. This finding is presented in Figure 7C of our manuscript. Furthermore, our RNAseq data shows that the presence of an AG motif at the –2 to –1 positions is able to compensate for a weakened interaction with the U6 snRNA caused by loss of U6 snRNA methylation in fio1 mutants. We showed this using the effect size of alternative splicing changes classified by motif at the –2 to –1 positions, which we presented in Figure 4H of the manuscript. The –2 to –1 positions interact with the U1 and U5 snRNAs, and the interaction with U1 snRNA occurs prior to handover to U5/U6 and hence should be unaffected by loss of U6 snRNA methylation. Therefore, our findings can be explained by stronger U5 snRNA interactions compensating for weakened U6 interactions, supporting a model of cooperative and compensatory roles for U5 and U6 snRNAs in splice site selection. A similar result was also recently reported in S. pombe by Ishigami et al. using RNA-seq data and validated using genetic suppression experiments with modified U5 loop 1 sequences (Ishigami et al., 2021).

and the proposed role of U6 is not tested experimentally. These two deficits are the most disappointing aspects of this work.

This phrasing is somewhat ambiguous because our study is focused on the role of m⁶A modification of U6 snRNA. The role of the U6 snRNA in 5’SS recognition is well established (Wilkinson et al., 2020). The methylation of the central adenosine of the ACAGA box by METTL16 is well established in other eukaryotes (Epstein et al., 1980; Kiss et al., 1987), and we have used m6A-IP qPCR experiments to demonstrate that this methylation is conserved in the Arabidopsis U6 snRNA and directed by the METTL16 homolog FIONA1. We have performed RNA-seq experiments with extremely high levels of depth and replication to dissect the consequences of the loss of U6 snRNA methylation on splicing, and provide multiple in-depth analyses to show that in all cases these consequences can be explained by changes in sequence preference at the +4 position of the 5’SS. We provide multiple lines of evidence from our RNA-seq and 5’SS sequence analyses to show that there are compensatory interactions between U5 and U6 snRNAs in their role in 5’SS recognition. Consequently, it is unclear which aspect of the “proposed role of U6” we have failed to test.

We have used a detailed analysis of the arrangement of the U5 and U6 snRNAs in multiple cryo-EM datasets to interpret the findings of our RNA-seq analyses. A potential follow-up experiment is cryoEM analysis of human spliceosomes prepared with U6 snRNA lacking m⁶A modification (in vitro splicing assays and cryo-EM using Arabidopsis spliceosomes is currently not possible). This would be a major undertaking, however, and these experiments are not feasible within the timeframe for paper revisions recommended by eLife.

An expectation from the eLife review process is that “extra experiments, analyses, or data collection are only requested if they are essential and can be reasonably completed within about two months”. Because the experiments raised here are not specified, it is unclear whether they could be completed in Arabidopsis in such a timeframe.

Additional comments:

1. The main text goes on for 31 pages, >12,000 words (not including Figure Legends or References). While there are no strict limits on the length of Research Articles, the eLife Author Guide suggests to "try not to exceed 5,000 words in the main text". The text could be shortened significantly; conciseness would greatly benefit the readability of the manuscript.

The Discussion, at >7 pages, is excessively long and does not critically evaluate the data or conclusion presented in this manuscript.

The eLife guidelines state: “There is no maximum length for Research Articles, but we suggest that authors try not to exceed 5,000 words in the main text, excluding the Materials and methods, References, and Figure legends.” The word count of the main text that we submitted was 8,952. We have edited each section in the revised manuscript to reduce the word count and improve the readability. In particular, we have reduced the length of the discussion from 2809 words to 2050 words and shortened the Results sections reporting analysis of our RNA-seq experiment from 3227 words to 2880 words. Due to the additional information requested by the reviewers in other areas of the Results section, the overall length of the results has not materially changed: going from 4795 words to 4906 words. The overall length of the main text is now 8,208.

Our manuscript is relatively long because of the range of subjects addressed:

Temperature-sensitive control of flowering
FIO1-dependent modification of U6 snRNA and poly(A)+ mRNA
Explanation of the early flowering phenotype of fio1 mutants
Impacts of U6 m⁶A on splicing
Definition of two classes of 5’ SS in diverse eukaryotes
Interpretation of cryo-EM to explain the role of U6 m⁶A in 5’SS and 3’SS usage
Clarification of recently published studies that mistakenly report no effect of loss of U6snRNA m⁶A modification on splicing

And the different experimental approaches used:

Arabidopsis mutant screening, mapping and phenotyping
Molecular analysis of RNA (RT-PCR, Immunoprecipitation of RNP complexes)
Illumina RNA-seq, nanopore direct RNA-sequencing with splicing and RNA modification analysis
Cryo-EM

This is a rare combination of subjects and approaches that requires attention in the Introduction and Discussion sections. We see the diversity and orthogonal nature of the approaches that we have used as a strength of the manuscript. As a result, we highlight larger implications of our study in the Discussion. We do not agree that the Discussion fails to “critically evaluate the data or conclusion”.

2. p 7 and Figure 2B. What is the experimental basis of Figure 2B? The authors do not state the experimental data: is it RNA-seq or RT-PCR? In either case, some examples of the primary data for this should be shown.

We apologise for this oversight. The data derive from RT-qPCR analysis. We have revised the labelling of the Figure and associated Figure Legend to make this clear. The raw source data for this figure was shared at the time of submission and is available associated with the bioRxiv pre-print. In the current version of the manuscript, it is Figure 1 source data 3.

3. p 6, second paragraph of the Results: "We used bulked segregant analysis and the software tool artMap (Javorka et al., 2019) to identify the causative mutation…". The authors should explain this more explicitly, for non-specialist readers. There are a number of ways in which this could have been done, but the authors do not specify what exactly was the data that led to the determination of the mutation.

As described above, we have updated the Methods and Results sections to explain the screen and the processing of the bulk segregant analysis data more clearly.

4. Figure 4B. This is a comparison of fio1-3 with Col-0. I would like to see volcano plots for all the five splicing events with log2FC on the x-axis and enrichment/significance on the y axis. These bar charts with percentages are too confusing to depict what the authors are actually trying to convey and do not depict the spread of the primary data.

We agree that the bar chart is a high-level visualisation of the data which does not allow readers to interpret effect sizes or significance levels. We have taken the advice of the reviewer and created volcano plots for the four main alternative splicing event types that are focused on in the paper. These volcano plots also show that the most significant effects of loss of FIO1 are changes in alternative 5’SS usage and increases in intron retention. We have included these volcano plots as Figure 3—figure supplement 1B in the revised manuscript.

5. Figure 4E shows "absence of change" in MAF2 intron 5, but this intron is clearly efficiently spliced, so the comparison to inefficiently-spliced intron 3 is not particularly meaningful. A more meaningful comparison would be to a different intron that is also inefficiently spliced.

This comparison was intended to exemplify that FIO1 is not required for the splicing of all introns, by comparison to another intron in the same gene (MAF2). However, we agree that this is not a particularly useful control since efficiently spliced introns may not be as sensitive to splicing perturbations. Consequently, we have removed this figure.

6. Figure 5. Seq logos are shown for 5'SSs that are sensitive to or that are used better in fio1-1 mutants. These can only be meaningful if they are compared to the seq logo of the overall 5'SS in A.t., which is not shown or discussed.

We have now added the sequence logo of all annotated 5’SSs in the condition-specific assembly as Figure 4 —figure supplement 1B in the revised manuscript. Sequence logos for 5’SSs can now be compared to this overall logo. However, we also intend for comparisons to be drawn directly between 5’SSs that are used more in the fio1-3 mutant and their cognate 5’SSs that are used less. As a control to show that the strong difference in sequence preference caused by loss of U6 snRNA methylation is unusual, we included sequence logos and heatmaps for alternative 5’SSs that are used more at high temperatures and cognate sites that are preferred at low temperatures (compare Figure 4 —figure supplement 1C-D in the revised manuscript). These two sets of matched 5’SSs show no overall difference in sequence preference as reported on page 11 of our submitted manuscript.

7. Figure 5. Why only show out to pos -2 in the exon? More positions than that can pair with U5.

It is true that more positions can base pair with U5 snRNA loop 1. In the original submitted version of our manuscript, we tried to consider this carefully with regard to both the sequence logos and the heatmaps of U5 and U6 interacting sequences. Although the -3 position of the 5’SS can pair with U5, we found that the information content of the -3 position in annotated Arabidopsis 5’SSs is low with only 0.10 bits of entropy compared to 0.49 and 0.85 bits for the -2 and -1 positions respectively. The entropy of the -4 position is even lower (0.04 bits) suggesting almost no sequence preference at this position. We found that when aggregating data into U5 and U6 heatmaps, the -3 position did not contain important information, making the heatmaps more complex to interpret (see Author response image 3 for an example). Hence, we chose to exclude the -3 position from heatmaps and sequence logos in our submitted manuscript. However, we recognise that it is standard practice to display 5’SS sequence logos from the -3 position to +5 position, and this does not overly complicate sequence logo interpretation. We have therefore redrawn sequence logo figures to include the -3 position of the 5’SS in Figures throughout the revised manuscript.

Author response image 3

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Heatmaps showing the distribution of U5 snRNA and U6 snRNA interacting sequence classes for 5’SSs of introns which have increased (left) and decreased (right) retention in *fio1-3*. U5 classes are based upon the distance of the —3 to —1 positions of the 5’SS from the consensus motif AAG. U6 classes are based upon the distance of the +3 to +5 positions of the 5’SS from the consensus motif RAG.

8. Some of the fonts in the figures are so small as to be unreadable or nearly unreadable, e.g. Figure 4-Figure sup 1 and Figure 5-Figure sup 1.

The figures are scaled to the size of the page in the submitted PDF, but we also submitted full resolution figures matching eLife recommendations (>10cm, >300dpi) with uniform font sizes across all figures (8pt). If our manuscript is accepted for publication, we will consult the production editor for direction on how to appropriately resolve this issue.

9. ALKBH10B is a demethylase that affects early flower phenotype. Does this protein also mediate the demethylation of U6 m6A?

We are grateful for this interesting suggestion, and we have incorporated it into the revised Discussion section. It is true that alkbh10b mutants have been reported to be late flowering (Duan et al., 2017). It is plausible that ALKBH10B could demethylate U6 snRNA, since a related human protein, FTO, has been shown to remove TMG-cap adjacent m6Am modifications from snRNAs (Mauer et al., 2019). To experimentally test this, however, would require an alkbh10b mutant, which we currently do not have. This line of research is therefore not feasible in a 2-month period but it will be interesting to examine in the future.

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https://doi.org/10.7554/eLife.78808.sa2

Article and author information

Author details

Matthew T Parker

School of Life Sciences, University of Dundee, Dundee, United Kingdom

Contribution
Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-0891-8495
Beth K Soanes

Centre for Plant Sciences, School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom

Contribution
Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

Competing interests
No competing interests declared
Jelena Kusakina

Centre for Plant Sciences, School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom

Contribution
Conceptualization, Formal analysis, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

Competing interests
No competing interests declared
Antoine Larrieu

Centre for Plant Sciences, School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom

Contribution
Conceptualization, Resources, Formal analysis, Validation, Investigation, Methodology, Project administration, Writing – review and editing

Competing interests
No competing interests declared
Katarzyna Knop

School of Life Sciences, University of Dundee, Dundee, United Kingdom

Present address
Cancer Research UK Beatson Institute, Glasgow, United Kingdom

Contribution
Conceptualization, Resources, Formal analysis, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing – review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-2636-9450
Nisha Joy

School of Life Sciences, University of Dundee, Dundee, United Kingdom

Contribution
Conceptualization, Formal analysis, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing – review and editing

Competing interests
No competing interests declared
Friedrich Breidenbach
1. School of Life Sciences, University of Dundee, Dundee, United Kingdom
2. RNA Biology and Molecular Physiology, Faculty of Biology, Bielefeld University, Bielefeld, Germany
Contribution
Conceptualization, Formal analysis, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing – review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-9610-1927
Anna V Sherwood

School of Life Sciences, University of Dundee, Dundee, United Kingdom

Present address
Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark

Contribution
Formal analysis, Validation, Investigation, Methodology, Project administration, Writing – review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0001-7153-8420
Geoffrey J Barton

School of Life Sciences, University of Dundee, Dundee, United Kingdom

Contribution
Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing – review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-9014-5355
Sebastian M Fica

Department of Biochemistry, University of Oxford, Oxford, United Kingdom

Contribution
Conceptualization, Formal analysis, Funding acquisition, Visualization, Writing – original draft, Project administration, Writing – review and editing

Competing interests
No competing interests declared
Brendan H Davies

Centre for Plant Sciences, School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom

Contribution
Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

For correspondence
b.h.davies@leeds.ac.uk

Competing interests
No competing interests declared
Gordon G Simpson
1. School of Life Sciences, University of Dundee, Dundee, United Kingdom
2. Cell & Molecular Sciences, James Hutton Institute, Invergowrie, United Kingdom
Contribution
Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

For correspondence
g.g.simpson@dundee.ac.uk

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0001-6744-5889

Funding

Biotechnology and Biological Sciences Research Council (BB/M000338/1)

Brendan H Davies

Biotechnology and Biological Sciences Research Council (BB/W007967/1)

Brendan H Davies

Biotechnology and Biological Sciences Research Council (BB/T007222/1)

Beth K Soanes

HORIZON EUROPE Marie Sklodowska-Curie Actions (799300)

Katarzyna Knop

HORIZON EUROPE Marie Sklodowska-Curie Actions (896598)

Nisha Joy

Wellcome Trust (220212/Z/20/Z)

Sebastian M Fica

Global Challenges Research Fund (University of Dundee Global Challenges Research Fund)

Geoffrey J Barton
Gordon G Simpson

Biotechnology and Biological Sciences Research Council (BB/W007673/1)

Gordon G Simpson

Biotechnology and Biological Sciences Research Council (BB/V010662/1)

Geoffrey J Barton
Gordon G Simpson

Biotechnology and Biological Sciences Research Council (BB/M010066/1)

Geoffrey J Barton
Gordon G Simpson

Biotechnology and Biological Sciences Research Council (BB/M004155/1)

Geoffrey J Barton
Gordon G Simpson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. For the purpose of Open Access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

Acknowledgements

We thank Mary McKay (School of Biology, University of Leeds) for excellent technical assistance with the EMS mutant screen. We are indebted to Oliver Manners for help in establishing m⁶A immunoprecipitation. We thank Abdelmadjid Atrih (Centre for Advanced Scientific Technologies) for the m⁶A LC-MS/MS analysis. We thank David Lilley, Tim Wilson and Adrian Whitehouse for helpful discussions. We thank Alper Akay, Martin Balcerowicz, James Lloyd, Anjil Srivastava and Carey Metheringham for comments on the manuscript. This work was supported by awards from the BBSRC (BB/V010662/1; BB/M010066/1; BB/M004155/1; BB/W007673/1) and the University of Dundee Global Challenges Research Fund to GGS and GJB; and awards from BBSRC (BB/M000338/1; BB/W007967/1) and ERA-CAPS FLOWPLAST to BD. BS is funded through the BBSRC DTP3 award, BB/T007222/1. KK and NJ were funded through the European Union Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreements 799300 and 896598, respectively. SMF is a Wellcome Trust and Royal Society Sir Henry Dale Fellow (grant number 220212/Z/20/Z). The FingerPrints Proteomics Facility of the University of Dundee is supported by a Wellcome Trust Technology Platform Award (097945/B/11/Z).

Senior Editor

James L Manley, Columbia University, United States

Reviewing Editor

Jonathan P Staley, University of Chicago, United States

Publication history

Received: March 21, 2022
Preprint posted: April 5, 2022 (view preprint)
Accepted: November 20, 2022
Accepted Manuscript published: November 21, 2022 (version 1)
Version of Record published: December 30, 2022 (version 2)

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.