Yong Zeng

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.

Cancer stem cells (CSCs) are responsible for tumor progression, recurrence, and drug resistance. To identify genetic vulnerabilities of colon cancer, we performed targeted CRISPR dropout screens comprising 657 Drugbank targets and 317 epigenetic regulators on two patient-derived colon CSC-enriched spheroids. Next-generation sequencing of pooled genomic DNAs isolated from surviving cells yielded therapeutic candidates. We unraveled 44 essential genes for colon CSC-enriched spheroids propagation, including key cholesterol biosynthetic genes (HMGCR, FDPS, and GGPS1). Cholesterol biosynthesis was induced in colon cancer tissues, especially CSC-enriched spheroids. The genetic and pharmacological inhibition of HMGCR/FDPS impaired self-renewal capacity and tumorigenic potential of the spheroid models in vitro and in vivo. Mechanistically, HMGCR or FDPS depletion impaired cancer stemness characteristics by activating TGF-β signaling, which in turn downregulated expression of inhibitors of differentiation (ID) proteins, key regulators of cancer stemness. Cholesterol and geranylgeranyl diphosphate (GGPP) rescued the growth inhibitory and signaling effect of HMGCR/FDPS blockade, implying a direct role of these metabolites in modulating stemness. Finally, cholesterol biosynthesis inhibitors and 5-FU demonstrated antitumor synergy in colon CSC-enriched spheroids, tumor organoids, and xenografts. Taken together, our study unravels novel genetic vulnerabilities of colon CSC-enriched spheroids and suggests cholesterol biosynthesis as a potential target in conjunction with traditional chemotherapy for colon cancer treatment.

Gene expression as an intermediate molecular phenotype has been a focus of research interest. In particular, studies of expression quantitative trait loci (eQTL) have offered promise for understanding gene regulation through the discovery of genetic variants that explain variation in gene expression levels. Existing eQTL methods are designed for assessing the effects of common variants, but not rare variants. Here, we address the problem by establishing a novel analytical framework for evaluating the effects of rare or private variants on gene expression. Our method starts from the identification of outlier individuals that show markedly different gene expression from the majority of a population, and then reveals the contributions of private SNPs to the aberrant gene expression in these outliers. Using population-scale mRNA sequencing data, we identify outlier individuals using a multivariate approach. We find that outlier individuals are more readily detected with respect to gene sets that include genes involved in cellular regulation and signal transduction, and less likely to be detected with respect to the gene sets with genes involved in metabolic pathways and other fundamental molecular functions. Analysis of polymorphic data suggests that private SNPs of outlier individuals are enriched in the enhancer and promoter regions of corresponding aberrantly-expressed genes, suggesting a specific regulatory role of private SNPs, while the commonly-occurring regulatory genetic variants (i.e., eQTL SNPs) show little evidence of involvement. Additional data suggest that non-genetic factors may also underlie aberrant gene expression. Taken together, our findings advance a novel viewpoint relevant to situations wherein common eQTLs fail to predict gene expression when heritable, rare inter-individual variation exists. The analytical framework we describe, taking into consideration the reality of differential phenotypic robustness, may be valuable for investigating complex traits and conditions.