Integrative Genomic Analysis Predicts Regulatory Role of N 6-Methyladenosine-Associated SNPs for Adiposity

A genetic variant in the TAPBP gene enhances cervical cancer susceptibility by increasing m6A modification

Genetic variants can affect gene expression by altering the level of N6-methyladenosine (m6A) modifications. A better understanding of the association of these genetic variants with susceptibility to cervical cancer (CC) can promote advances in disease screening and treatment. Genome-wide identification of m6A-associated functional SNPs for CC was performed using the TCGA and JENGER databases, incorporating the data from RNA-seq and MeRIP-seq. The screened risk-associated SNP rs1059288 (A>G), which is located in the 3' UTR of TAPBP, was further validated in a case-control study involving 921 cases and 1077 controls. The results revealed a significant association between rs1059288 and the risk of CC (OR 1.48, 95% CI 1.13-1.92). Mechanistically, the presence of the risk G allele of rs1059288 was associated with increased m6A modification of TAPBP compared with the A allele. This modification was facilitated by the m6A methyltransferase METTL14 and the reading protein YTHDF2. Immunohistochemical staining of tissue microarrays containing 61 CC and 45 normal tissues showed an overexpression of TAPBP in CC. Furthermore, the upregulation of TAPBP promoted the growth and migration of CC cells as well as tumor-forming ability, inhibited apoptosis, and conferred increased resistance to commonly used chemotherapeutic drugs such as bleomycin, cisplatin, and doxorubicin. Knockdown of TAPBP inhibited the JAK/STAT/MICB signaling pathway in CC cells and upregulated certain immune genes including ISG15, IRF3, PTPN6, and HLA-A. These findings offer insights into the involvement of genetic variations in TAPBP in the development and progression of CC.

Elucidating sleep disorders: a comprehensive bioinformatics analysis of functional gene sets and hub genes

Background

Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood.

Methods

The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses.

Results

The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis.

Conclusion

In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results.

epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation

Recent technological advances in sequencing DNA and RNA modifications using high-throughput platforms have generated vast epigenomic and epitranscriptomic datasets whose power in transforming life science is yet fully unleashed. Currently available in silico methods have facilitated the identification, positioning and quantitative comparisons of individual modification sites. However, the essential challenge to link specific 'epi-marks' to gene expression in the particular context of cellular and biological processes is unmet. To fast-track exploration, we generated epidecodeR implemented in R, which allows biologists to quickly survey whether an epigenomic or epitranscriptomic status of their interest potentially influences gene expression responses. The evaluation is based on the cumulative distribution function and the statistical significance in differential expression of genes grouped by the number of 'epi-marks'. This tool proves useful in predicting the role of H3K9ac and H3K27ac in associated gene expression after knocking down deacetylases FAM60A and SDS3 and N6-methyl-adenosine-associated gene expression after knocking out the reader proteins. We further used epidecodeR to explore the effectiveness of demethylase FTO inhibitors and histone-associated modifications in drug abuse in animals. epidecodeR is available for downloading as an R package at https://bioconductor.riken.jp/packages/3.13/bioc/html/epidecodeR.html.

In silico identification and in vitro expression analysis of breast cancer-related m6A-SNPs

Research on m6A-associated SNPs (m6A-SNPs) has emerged recently due to their possible critical roles in many key biological processes. In this sense, several investigations have identified m6A-SNPs in different diseases. In order to gain a more complete understanding of the role that m6A-SNPs can play in breast cancer, we performed an in silico analysis to identify the m6A-SNPs associated with breast cancer and to evaluate their possible effects. For this purpose, we downloaded SNPs related to breast cancer and a list of m6A-SNPs from public databases in order to identify which ones appear in both. Subsequently, we assessed the identified m6A-SNPs in silico by expression quantitative trait loci (eQTL) analysis and differential gene expression analysis. We genotyped the m6A-SNPs found in the in silico analysis in 35 patients with breast cancer, and we carried out a gene expression analysis experimentally on those that showed differences. Our results identified 981 m6A-SNPs related to breast cancer. Four m6A-SNPs showed an eQTL effect and only three were in genes that presented an altered gene expression. When the three m6A-SNPs were evaluated in the tissue sample of our breast cancer patients, only the m6A-SNP rs76563149 located in ZNF354A gene presented differences in allele frequencies and a low gene expression in breast cancer tissues, especially in luminal B HER2+ subtype. Future investigations of these m6A-SNPs should expand the study in different ethnic groups and increase the sample sizes to test their association with breast cancer and elucidate their molecular function.

Exploring the Epigenetic Regulatory Role of m6A-Associated SNPs in Type 2 Diabetes Pathogenesis

Purpose

Genetic factors in type 2 diabetes (T2D) pathogenesis have been widely explored by the genome-wide association studies (GWAS), identifying a great amount of susceptibility loci. With the development of high-resolution sequencing, the N(6)-methyladenosine (m6A) RNA modification has been proved to be affected by genetic variation. In this study, we identified the T2D-associated m6A-SNPs from T2D GWAS data and explored the underlying mechanism of the pathogenesis of T2D.

Methods

We examined the association of m6A-SNPs with T2D among large-scale T2D GWAS summary statistics and further performed multi-omics integrated analysis to explore the potential role of the identified m6A-SNPs in T2D pathogenesis.

Results

Among the 15,124 T2D-associated m6A-SNPs, 71 of them reach the genome-wide significant threshold (5.0e-05). The leading SNP rs4993986 (C>G), which is located near the m6A modification site at the 3' end of the HLA-DQB1 transcript, is expected to participate in the pathogenesis of T2D by influencing m6A modification to regulate the HLA-DQB1 expression.

Conclusion

The current study has suggested a potential correlation between m6A-SNPs and T2D pathogenesis and also provided new insights into the pathogenic mechanism of the T2D susceptibility loci identified by GWAS.

Genome-Wide Identification of m6A-Associated Single-Nucleotide Polymorphisms in Colorectal Cancer

Background

N6-methyladenosine (m6A)-associated single-nucleotide polymorphisms (SNPs) play important roles in cancers, with previous research suggesting potential associations between m6A-SNPs and cancer. However, the relationship between the genetic determinants of m6A modification and colorectal cancer (CRC) remains unclear.

Methods

An integrative method combining raw data and summary statistics of genome-wide association studies with expression quantitative trait loci (eQTL) and differential expression data was applied to screen potential candidate CRC-associated m6A-SNPs.

Results

A total of 402 m6A-SNPs were identified as being associated with CRC (p < 0.001), with 98 showing eQTL signals. In particular, three genes were found to harbor CRC-associated m6A-SNPs: rs178184 in NOVA1, rs35782901 in HTR4, and rs60571683 in SLCO1B3. These genes were differentially expressed in at least one publicly available dataset (p < 0.05), with NOVA1 (p = 3.41×10^-11) and HTR4 (p = 5.56×10^-7) being significantly downregulated in CRC (dataset: GSE89076), and SLCO1B3 was significantly overexpressed (datasets: GSE32323 [p = 3.27×10^-5], GSE21510 [p = 1.09×10^-6], and GSE89076 [p = 7.63×10^-6]).

Conclusion

This study identified three m6A-SNPs (rs178184, rs35782901, and rs60571683) that may be associated with CRC. However, the lack of analysis of primary CRC samples in order to further elucidate the underlying pathogenesis is a major limitation of this study. Future investigations are needed to validate these CRC-associated m6A-SNPs and explore the m6A-mediated pathogenic mechanism in CRC.

Recent advances in functional annotation and prediction of the epitranscriptome

RNA modifications, in particular N⁶-methyladenosine (m⁶A), participate in every stages of RNA metabolism and play diverse roles in essential biological processes and disease pathogenesis. Thanks to the advances in sequencing technology, tens of thousands of RNA modification sites can be identified in a typical high-throughput experiment; however, it remains a major challenge to decipher the functional relevance of these sites, such as, affecting alternative splicing, regulation circuit in essential biological processes or association to diseases. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, we review here recent progress in functional annotation and prediction of RNA modification sites from a bioinformatics perspective. The review covers naïve annotation with associated biological events, e.g., single nucleotide polymorphism (SNP), RNA binding protein (RBP) and alternative splicing, prediction of key sites and their regulatory functions, inference of disease association, and mining the diagnosis and prognosis value of RNA modification regulators. We further discussed the limitations of existing approaches and some future perspectives.