BACKGROUND & AIMS: RNA N 6-methyladenosine (m 6 A) modification has recently emerged as a new regulatory mechanism in cancer progression. We aimed to explore the role of the m 6 A regulatory enzyme METTL3 in colorectal cancer (CRC) pathogenesis and its potential as a therapeutic target. METHODS: The expression and clinical implication of METTL3 were investigated in multiple human CRC cohorts. The underlying mechanisms of METTL3 in CRC were investigated by integrative m 6 A sequencing, RNA sequencing, and ribosome profiling analyses. The efficacy of targeting METTL3 in CRC treatment was elucidated in CRC cell lines, patient-derived CRC organoids, and Mettl3-knockout mouse models. RESULTS: Using targeted clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 dropout screening, we identified METTL3 as the top essential m 6 A regulatory enzyme in CRC. METTL3 was overexpressed in 62.2% (79/127) and 88.0% (44/50) of primary CRCs from 2 independent cohorts. High METTL3 expression predicted poor survival in patients with CRC (n ¼ 374, P < .01). Functionally, silencing METTL3 suppressed tumorigenesis in CRC cells, human-derived primary CRC organoids, and Mettl3-knockout mouse models. We discovered the novel functional m 6 A methyltransferase domain of METTL3 in CRC cells by domain-focused CRISPR screening and mutagenesis assays. Mechanistically, METTL3 directly induced the m 6 A-GLUT1-mTORC1 axis as identified by integrated m 6 A sequencing, RNA sequencing, ribosome sequencing,

N6-Methyladenosine (m6A) accounts for approximately 0.2% to 0.6% of all adenosine in mammalian mRNA, representing the most abundant internal mRNA modifications. m6A RNA immunoprecipitation followed by high-throughput sequencing (MeRIP-seq) is a powerful technique to map the m6A location transcriptome-wide. However, this method typically requires 300 μg of total RNA, which limits its application to patient tumors. In this study, we present a refined m6A MeRIP-seq protocol and analysis pipeline that can be applied to profile low-input RNA samples from patient tumors. We optimized the key parameters of m6A MeRIP-seq, including the starting amount of RNA, RNA fragmentation, antibody selection, MeRIP washing/elution conditions, methods for RNA library construction, and the bioinformatics analysis pipeline. With the optimized immunoprecipitation (IP) conditions and a postamplification rRNA depletion strategy, we were able to profile the m6A epitranscriptome using 500 ng of total RNA. We identified approximately 12,000 m6A peaks with a high signal-to-noise (S/N) ratio from 2 lung adenocarcinoma (ADC) patient tumors. Through integrative analysis of the transcriptome, m6A epitranscriptome, and proteome data in the same patient tumors, we identified dynamics at the m6A level that account for the discordance between mRNA and protein levels in these tumors. The refined m6A MeRIP-seq method is suitable for m6A epitranscriptome profiling in a limited amount of patient tumors, setting the ground for unraveling the dynamics of the m6A epitranscriptome and the underlying mechanisms in clinical settings.

Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1 , but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2-5 . It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be fully recovered from whole-genome sequence (WGS) data on 21,620 unrelated individuals of European ancestry. We assigned 47.1 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned variation accordingly. The estimated heritability was 0.79 (SE 0.09) for height and 0.40 (SE 0.09) for BMI, consistent with pedigree estimates. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein altering variants, consistent with negative selection thereon. Cumulatively variants in the MAF range of 0.0001 to 0.1 explained 0.54 (SE 0.05) and 0.51 (SE 0.11) of heritability for height and BMI, respectively. Our results imply that the still missing heritability of complex traits and disease is accounted for by rare variants, in particular those in regions of low LD.

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