Comprehensive analysis across mammalian tissues and cells decipher the underlying mechanism of m6A specificity

Chai, G.; Chen, H.; Ma, D.; Ren, Z.; Liu, X.; Luo, G.

doi:10.1101/2024.04.19.590363

Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.04.19.590363

Comprehensive analysis across mammalian tissues and cells decipher the underlying mechanism of m6A specificity

Chai, G.·Chen, H.·Ma, D.·Ren, Z.·Liu, X.·Luo, G.

View original source ↗Export metadata ↓

Abstract

N6-methyladenine (m6A) stands out as the most prevalent internal chemical modification on mammalian mRNA, playing a vital role in diverse biological processes. Despite considerable advancements in individual cell line studies, the characteristics of m6A sites across distinct cell lines or tissues remain elusive. In this study, we have successfully identified approximately 1.5 million high-confidence m6A sites in human and mouse cell lines or tissues using published m6A-seq data. By categorizing m6A sites into different consistency levels, we observe that those of high consistency are notably enriched near the stop codon. Furthermore, they exhibit a higher likelihood of interaction with known m6A binding proteins such as YTHDF1-3, RBM15, YTHDC1, and IGF2BP1, thereby influencing gene expression homeostasis. Additionally, these sites display a higher CpG density in the promoter region of the genes they mark, with METTL3 demonstrating a preference for binding to the promoter region of its marked genes. m6A sites of low consistency levels, including unique m6A sites, show a significant enrichment near the start codon. These sites are more prone to binding by newly discovered m6A-binding proteins such as DDX3X, PRPF8, and EIF3G. The identification of these distinct features of m6A sites lays a foundational understanding for unraveling the functional roles of m6A.

Explore related subjects

bioinformatics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Chai, G., Chen, H., Ma, D., Ren, Z., Liu, X., Luo, G.. 2024-04-23. Comprehensive analysis across mammalian tissues and cells decipher the underlying mechanism of m6A specificity. https://doi.org/10.1101/2024.04.19.590363

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Senescence-associated KRAS upregulation in peripheral T cells links to premature coronary artery disease

Aims: Premature coronary artery disease (PCAD) lacks specific molecular drivers, and the role of immunosenescence is unclear. We investigated whether aging-related gene dysregulation in T cells contributes to PCAD. Methods: We combined bulk transcriptomics of PBMCs from 12 PCAD patients and 21 controls, single-cell RNA sequencing of PBMCs and human atherosclerotic plaques, weighted gene co-expression network analysis, gene perturbation network analysis, and molecular docking. Results: KRAS was identified as a hub gene intersecting PCAD-associated genes and aging-related genes. Single-cell analysis showed KRAS upregulation predominantly in effector CD8+ T cells, which exhibited the highest senescence scores that were further elevated in disease. Network perturbation of KRAS strongly impacted the cell killing pathway. KRAS-high effector CD8+ T cells were detected in coronary and carotid plaques, displaying enhanced cytotoxicity, exhaustion, and senescence features. Additionally, a candidate small molecule was computationally predicted to bind inactive KRAS. Conclusions: Elevated KRAS expression in senescent, cytotoxic CD8+ T cells is associated with PCAD, bridging immunosenescence and premature atherosclerosis. This finding provides a novel biomarker candidate and potential therapeutic entry point, awaiting further functional validation.

bioinformatics↗

bioRxiv2026

Targeted finetuning enables co-folding models to learn ligand-induced protein conformational states

Advances in protein structure prediction have enabled all-atom protein-ligand co-folding models that predict bound conformations directly from sequence and small-molecule structure. However, these models often fail to generalize to novel binding sites or alternative protein conformational states, limiting their utility for chemical biology and drug discovery. Here we show this limitation reflects training data bias rather than architectural constraints and can be overcome through targeted finetuning. Using ten previously unseen X-ray structures of Werner (WRN) helicase from a drug discovery program, we finetune Boltz-1 to learn both an allosteric binding site and a large conformational change locking the enzyme in an inactive state, while preserving accuracy on the ATP-bound state. The finetuned model generalizes to different chemical series and transfers the conformational logic across RecQ-family helicases in a binding-site sequence-dependent manner. This approach provides a blueprint for adapting foundation models as new structural and mechanistic data emerge, enabling co-folding networks to capture ligand-induced conformational switches and binding poses absent from their training data but central to biological regulation and therapeutic intervention.

bioinformatics↗

bioRxiv2026

Benchmarking single-cell foundation models for aging biology

Single cell foundation models (scFMs) provide representations of cellular states, but their utility across biological questions in aging research remains unclear. We established a benchmark of cellular representations for aging research, evaluating ten general-purpose scFMs, three aging-specific models and conventional methods across five biological questions using more than 2.5 million single cell transcriptomes. Using frozen pretrained representations, Geneformer performed best among scFMs for chronological age prediction and age pseudotime concordance, although 2,000 highly variable genes achieved higher mean performance. Several scFMs captured positive molecular age shifts across three disease contexts, consistent with reported aging-associated changes. SCimilarity performed well for rare cellular state identification across out-of-distribution datasets, exceeding aging specific models and conventional baselines. At the gene level, scGPT showed the highest recovery of reference TF target interactions, including aging-related regulatory hubs. Overall, scFMs supported diverse aging analyses, but performance depended on the biological question, highlighting their utility for rare cellular state identification and regulatory analysis.

bioinformatics↗