N-methyladenosine (mA) is the most common internal modification of mRNAs in higher eukaryotic. This process is performed by methyltransferase. Methyltransferase-like 3 (METTL3) is the best known mA methyltransferase that functions in the reversible epi-transcriptome modulation of mA modification. Besides acting as a mA methyltransferase, METTL3 also regulates mRNA translation and other biological processes. In recent years, studies have identified numerous roles and molecular mechanisms associated with METTL3 in multiple biological processes. However, these findings have not been summarized. In this review, we have systematically summarized the most recent important roles of METTL3 in various biological processes, including cell cycle progression, cell proliferation, cell apoptosis, cell migration and invasion, cell differentiation and inflammatory response. In addition, we discuss the prospect of using a METTL3 as a new diagnostic biomarker and therapeutic target for human cancers.
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Health Psychol
January 2025
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles.
Objective: Although sexual minority men experience substantial discrimination, in addition to increased risk for several serious mental and somatic health problems, the biological mechanisms underlying these effects are unclear. To address this issue, we examined how experiences of social safety (i.e.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Centro Nacional de Análisis Genómico, Barcelona, Spain.
The recent development of genetic lineage recorders, designed to register the genealogical history of cells using induced somatic mutations, has opened the possibility of reconstructing complete animal cell lineages. To reconstruct a cell lineage tree from a molecular recorder, it is crucial to use an appropriate reconstruction algorithm. Current approaches include algorithms specifically designed for cell lineage reconstruction and the repurposing of phylogenetic algorithms.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Sorbonne Université, Institut du Cerveau (Paris Brain Institute) ICM, Inserm, CNRS, Hôpital de la Pitié Salpêtrière, Paris, France.
Somatic mosaic variants, and especially somatic single nucleotide variants (sSNVs), occur in progenitor cells in the developing human brain frequently enough to provide permanent, unique, and cumulative markers of cell divisions and clones. Here, we describe an experimental workflow to perform lineage studies in the human brain using somatic variants. The workflow consists in two major steps: (1) sSNV calling through whole-genome sequencing (WGS) of bulk (non-single-cell) DNA extracted from human fresh-frozen tissue biopsies, and (2) sSNV validation and cell phylogeny deciphering through single nuclei whole-genome amplification (WGA) followed by targeted sequencing of sSNV loci.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Measurements of cell phylogeny based on natural or induced mutations, known as lineage barcodes, in conjunction with molecular phenotype have become increasingly feasible for a large number of single cells. In this chapter, we delve into Quantitative Fate Mapping (QFM) and its computational pipeline, which enables the interrogation of the dynamics of progenitor cells and their fate restriction during development. The methods described here include inferring cell phylogeny with the Phylotime model, and reconstructing progenitor state hierarchy, commitment time, population size, and commitment bias with the ICE-FASE algorithm.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Allen Discovery Center for Lineage Tracing and Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
Mutations are acquired frequently, such t`hat each cell's genome inscribes its history of cell divisions. Loss of heterozygosity (LOH) accumulates throughout the genome, offering large encoding capacity for phylogenetic inference of cell lineage.In this chapter, we demonstrate a method, using single-cell RNA sequencing, for reconstructing cell lineages from inferred LOH events in a Bayesian manner, annotating the lineage with cell phenotypes, and marking developmental time points based on X-chromosome inactivation.
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