Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5' sites

Abstract

N6-methyladenosine (m6A) is a common modification of mRNA with potential roles in fine-tuning the RNA life cycle. Here, we identify a dense network of proteins interacting with METTL3, a component of the methyltransferase complex, and show that three of them (WTAP, METTL14, and KIAA1429) are required for methylation. Monitoring m6A levels upon WTAP depletion allowed the definition of accurate and near single-nucleotide resolution methylation maps and their classification into WTAP-dependent and -independent sites. WTAP-dependent sites are located at internal positions in transcripts, topologically static across a variety of systems we surveyed, and inversely correlated with mRNA stability, consistent with a role in establishing "basal" degradation rates. WTAP-independent sites form at the first transcribed base as part of the cap structure and are present at thousands of sites, forming a previously unappreciated layer of transcriptome complexity. Our data shed light on the proteomic and transcriptional underpinnings of this RNA modification.

Figure 1. Proteomic identification of methyltransferase complex components

(A-B) Proteins associated with HIS-tagged METTL3 (A) and V5-tagged METTL14 (B). Fold-changes quantifying enrichment versus control (Methods) across two biological replicates. (C) Proteins associated with biotinylated, methylated RNA baits, compared to biotinylated, non-methylated counterparts, in two biological replicates. (D) Network of associations with the bait proteins (shaded grey nodes) identified in the different mass-spec experiments. An edge from bait A to target B indicates that B was enriched (fold change > 1.5) when performing IPs on A. Only target nodes with an incoming degree ≥2 are displayed. Edge width distinguishes association between baits (thick lines) and with non-baits (thin lines). Edges are colored in dark red if they were enriched by a protein bait involving an m6a ‘writer’ (METTL3 / METTL14 / WTAP), cyan if they were enriched by a ‘reader’ (YTHDF1 / YTHDF2 / YTHDF3), or dark blue if they were enriched with a methylated bait. The node for KIAA1429, further analyzed functionally, is marked by a thicker border.

Figure 2. Perturbation of methyltransferase complex components identifies complex-dependent and -independent methylation sites

(A) K-means clustering of log2 transformed Peak Over Input scores (yellow: high, blue: low) across 16,487 sites (rows) detected in mouse embryonic fibroblasts (MEFs). Only sites detected in 2 or more of the presented samples (columns), with a maximal Peak Over Input score greater than 8, and within genes expressed above the 60^th percentile are shown. Sites were initially clustered into 5 clusters using k-means, and three of the clusters were subsequently manually merged with existing clusters, based on similarity of characteristics, to yield 3 clusters (separated by black lines). For visualization purposes, the log₂(POI) scores were capped at 4.5, and then transformed into Z scores. The internal shRNA identifiers are indicated in parentheses. (B) Distributions of distance from the nearest consensus site of the sites in the three clusters in (A). (C) Distribution of the sites in (A) grouped by the clusters in (A) across five gene segments, defined as in (Dominissini et al., 2012). (D) Distribution of length of internal exons comprising the sites identified in (A) across the identified clusters. (E) K-means clustering of log2 transformed Peak Over Input scores across 17,331 sites (rows) detected in human A549 cells, visualized as in A. (F) K-means clustering of log2 transformed Peak Over Input scores for set of experiments involving perturbations of A549 cells with siRNAs, as indicated. Only WTAP-dependent sites (rows) are plotted within genes expressed above the 60^th percentile across all conditions. (G) Relative m6A/A ratios across A549 cells treated with siRNA as indicated, as measured by HPLC-M/S. Values are plotted for both the poly(A) fraction (blue) and the flowthrough (orange). (H) Examples of coverage from IP (blue) and input (red) experiments across selected genes in A549 cells treated with shGFP (top row) or shWTAP (bottom row).

Figure 3. A human and mouse catalogue of methylation dynamics

(A) Scheme depicting the four experimental systems profiled in this study. Top left: Doxycycline-inducible OKMS (OCT4, KLF4, MYC and SOX2) fibroblast reprogramming to iPSCs. Bottom left: Human embryonic stem cells undergoing differentiation to neural progenitor cells. Top right: Bone marrow derived dendritic cells stimulated by lipopolysaccharide (LPS). Bottom right: Embryonic and adult mice brain. (B,C) Heatmaps of POI scores (visualized as in Fig. 1A) of all WTAP-dependent sites (rows) identified in one or more sample (columns), within genes expressed above the 60^th percentile across all conditions (Methods) in human (B) or mouse (C). (D, E) Number of conditions (X axis, of a total of 7, as in B and C respectively, excluding any genetic perturbations) in human (D) and mouse (E), in which sites are identified as present (POI > 4). Sites were grouped into three equally sized groups, based on POI score.

Figure 4. Methylations are depleted from housekeeping genes, and associated with decreased transcript stability

(A) Association between gene expression and methylation density in MEFs. (B) Top GO categories harboring non-methylated genes in human (top), mouse (middle), and yeast (yeast). Shown is the proportion of non-methylated genes in each category. Only genes expressed >60^th percentile and longer than 500nt and only GO categories harboring >30 matches genes were considered for this analysis. (C) Correlations (Spearman ρ, X axis) between different gene attributes (Y axis) and methylation density. Transcript half-lives were obtained from (Schwanhausser et al., 2011). We used only sites defined in A549 cells (Fig. 2E), in protein coding genes longer than 500nt and expressed above the 60^th quantile, with a maximum POI score >8, with at least 50% increased POI score in non-perturbed cells compared to WTAP-depleted cells, and which were in segments other than the TSS segment. (D) Analysis examining the difference in variability explained (R², X axis) upon elimination of each of the indicated variables from a linear regression model predicting methylation density on the basis of all of them. (E) Association between mRNA half lives (Schwanhausser et al., 2011) and methylation densities. Half lives were binned into 10 equally sized bins, and mean methylation densities for each bin were calculated. Error bars - SEM.

Figure 5. Identification of methylated transcription start sites (mTSSs)

(A) Proportion of sites harboring an adenosine (Y axis) at the detected position (dark green bars) or the position immediately upstream (light green bars) in 33,714 sites with >10 reads within 50 nt of annotated TSSs. Sites were binned based on fold changes (IP over input; X axis), using thresholds as indicated. (B) Sequence logo for a set of 3,445 putative mTSSs with a log2 fold-change exceeding 4.5. The position of the putative methylated mTSS is indicated, and of a TATA box signal located ∼30 bp upstream. (C) Proportion of putative mTSSs supported by 2 or more CAGE tags (Y axis) for a subset of 4,879 sites, in which the ‘Adenosine’ position precedes the ‘Stack’ position. Proportions are shown both for the adenosine position (pink bar), the stack position (cyan), and 4,879 positions randomly assigned to the same set of genes within the first 50 nt (grey bar). (D) Distributions of the log₁₀ transformed number of CAGE tags observed for the three groups defined in (C). (E, F) Examples of genes with multiple internal mTSSs in human iPSCs. Number of reads beginning at each of the first 50 annotated positions in the IP (blue) and input (red) sample are shown. Detected mTSSs are highlighted in grey, and marked with a black arrow; Putative penultimate-base RT-dropoffs are indicated with a purple arrow.