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Constitutive and variable 2'-O-methylation (Nm) in human ribosomal RNA.

Yuri MotorinMarc QuinternetWassim RhalloussiVirginie Marchand
Published in: RNA biology (2021)
Epitranscriptomic modifications of stable RNAs are dynamically regulated and specific profiles of 2'-O-methylation in rRNA have been associated with distinct cancer types. However, these observations pointed out the existence of at least two distinct groups: a rather large group with constitutive rRNA Nm residues exhibiting a stable level of methylation and a more restricted set of variable modifications, giving rise to the concept of 'specialized ribosomes'. These heterogeneous ribosomes can modulate their translational properties and be key regulatory players, depending on the physiological state of the cell. However, these conclusions were drawn from a limited set of explored human cell lines or tissues, mostly related to cancer cells of the same type. Here, we report a comprehensive analysis of human rRNA Nm modification variability observed for >15 human cell lines grown in different media and conditions. Our data demonstrate that human Nm sites can be classified into four groups, depending on their observed variability. About ⅓ of rRNA 2'-O-methylations are almost invariably modified at the same level in all tested samples (stable modifications), the second group of relatively invariant modifications (another ½ of the total) showing a slightly higher variance (low variable group) and two variable groups, showing an important heterogeneity. Mapping of these four classes on the human ribosome 3D structure shows that stably modified positions are preferentially located in the important ribosome functional sites, while variable and highly variable residues are mostly distributed to the ribosome periphery. Possible relationships of such stable and variable modifications to the ribosome functions are discussed.
Keyphrases
  • endothelial cells
  • squamous cell carcinoma
  • high resolution
  • machine learning
  • genome wide
  • stem cells
  • young adults
  • bone marrow
  • big data
  • lymph node metastasis
  • artificial intelligence