The Ribomethylome Landscape of Hematopoietic System
Authors: Wang Yanyan , Rohde Christian , Zhou Fengbiao , Hennrich Marco , Poisa-Beiro Laura , Eckstein Volker , Pabst Caroline , Gavin Anne-Claude , Ho Anthony D , Müller-Tidow Carsten 
Published: February 2021

Introduction:
RNA modifications are emerging as important determinants of cell identity and cell fate. Small nucleolar RNAs (snoRNA) guide pseudouridylation and 2'-O-methylation of RNA species. C/D box snoRNAs are essential for AML1/ETO-induced leukemia (Zhou et al. Nat Cell Biol 2017). Dynamics and relevance of these modifications in hematopoiesis are unknown. Here, we aimed to determine the plasticity of ribosomal 2'-O-methylation (Ribomethylome) patterns in hematopoietic cell populations and the interdependence with snoRNA expression, transcriptomics and proteomics.
Methods:
Healthy donors (19-86yrs) donated bone marrow and six cell populations were sorted or prepared: Hematopoietic stem/progenitor cell (HPC), Monocyte/macrophage precursor (MON), Granulocytic precursor (GRA), Erythroid precursor (ERY), Lymphocyte progenitor (LYM), and Mesenchymal stem/stromal cell (MSC). Small RNA sequencing and Ribometh-seq data were obtained for 65 and 55 samples, respectively. Data were analyzed together with accompanying RNA-seq and Mass-spec proteomics data which were available for all the specimens. Bioinformatics analyses were based on PCA, tSNE, spearman correlation, paired t-test, GSEA and ANOVA.
Results:
The analyses of 2'-O-methylation (Ribomethylome) in six bone marrow cell types from healthy donors revealed that ribosomal modifications occurred different during the process of hematopoietic differentiation. Among these sides, HPC and Myeloid lineage showed significant variability between different cell populations. Ribomethylome patterns differed between cell types and PCA analyses indicated that cellular identity was matched with a specific Ribomethylome pattern. Plasticity in Ribomethylomes were most evident for HPC, LYM, GRA and MON which showed high levels of 2'-O-methylation (almost 100% of rRNA methylated) whereas methylation levels in MSC cells were much lower (Spearman correlation&lt;0.4). These findings indicated that Ribomethylome patterns were cell type specific. Using snoRNA sequencing, we showed that snoRNA expression levels differed between the different cell types. C/D box snoRNAs were variably expressed, and the expression differences for SNORD68 and SNORD87 were associated with respective Ribomethylome changes of predicted target sites. We next analyzed the association between specific 2'-O-methylation levels and the levels of protein expression. Only those proteins were included for whom no association between mRNA and total protein levels were observed. Spearman rank analyses suggested that RAB7A, PSME1 involved "antigen processing and presentation" and FLNA, RCC2 involved "cell migration" correlated closely with 2'-O-methylation of the dynamically regulated sites 28S_3723_SNORD87 and 5.8S_14_SNORD71.
Conclusion:
Our finding based on multi-omics analyses identifies cell type specific Ribomethylomes. Myeloid differentiation is associated with specific Ribomethylome changes. Distinct Ribomethylomes may contribute to cellular identity by directing translation of specific sets of mRNAs.
Figure 1: The effects of ribomethylome and protein translation were evident and separated by different cell populations(tSNE).
Figure 1

Disclosures
Müller-Tidow: Daiichi Sankyo: Research Funding; BiolineRx: Research Funding; Janssen-Cilag GmbH: Speakers Bureau; Pfizer: Research Funding, Speakers Bureau.