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Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based

Song-Yao Zhang^1,2, Shao-Wu Zhang¹, Xiao-Nan Fan¹

¹Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, China.

Plos Computational Biology

|January 3, 2019

Abstract:

N6-methyladenosine (m6A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m6A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m6A levels are controlled and whether and how regulation of m6A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m6A-regulated genes and m6A-associated disease, which includes Deep-m6A, the first model for detecting condition-specific m6A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m6A, a new network-based pipeline that prioritizes functional significant m6A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m6A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m6A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m6A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m6A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m6A regulatory functions and its roles in diseases.

Insights

N6-methyladenosine (m6A) regulates mRNA metabolism and disease. This study introduces computational tools to identify m6A-regulated genes and associated diseases, revealing m6A

Area of Science:

Epigenetics and RNA Biology
Computational Biology and Bioinformatics
Cancer Genomics

Background:

N6-methyladenosine (m6A) is the most prevalent mRNA modification, implicated in mRNA metabolism and various diseases, including cancer.
The precise control mechanisms and disease relevance of m6A regulation remain largely unelucidated.
Understanding m6A's role is crucial for advancing cancer research and therapeutic strategies.

Purpose of the Study:

To develop and validate a computational framework for predicting m6A-regulated genes and their associated diseases.
To identify key genes and pathways influenced by m6A modification in human samples.
To uncover novel m6A-associated diseases and their underlying molecular mechanisms.

Main Methods:

Development of Deep-m6A, a deep learning model for single-base resolution detection of condition-specific m6A sites from MeRIP-Seq data.
Implementation of Hot-m6A, a network-based pipeline utilizing Protein-Protein Interaction (PPI) and gene-disease networks to prioritize functional m6A genes and diseases.
Application of the computational scheme to 75 human MeRIP-seq samples.

Main Results:

Identification of 709 functionally significant m6A-regulated genes and nine enriched subnetworks.
Functional enrichment analysis revealed m6A targets critical genes in transcription, cell organization, proliferation, and cancer pathways (e.g., Wnt pathway).
Prioritization of five significantly associated diseases, including leukemia and renal cell carcinoma.

Conclusions:

The proposed computational scheme effectively identifies m6A-regulated genes and associated diseases.
m6A plays a significant role in fundamental biological processes and cancer development.
These findings provide valuable insights into m6A regulatory functions and disease implications, opening avenues for future research.

Read full analysis

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