Dynamic Ribosomal RNA Methylation Regulates Translation in the Hematopoietic System and is Essential for Stem Cell Fitness
Rabany, O.; Ben-Dror, S.; Arafat, M.; Aharoni, H.; Halperin, Y.; Marchand, V.; Romanovski, N.; Ussishkin, N.; Livneh, M.; Reches, A.; Wexler, J.; Mayorek, N.; Monderer-Rothkoff, G.; Shifman, S.; Mammer, W.; VanInsberghe, M.; Pauli, C.; Muller-Tidow, C.; Karmi, O.; Livneh, Y.; van Oudenaarden, A.; Motorin, Y.; Nachmani, D.
Show abstract
Self-renewal and differentiation are at the basis of hematopoiesis. While it is known that tight regulation of translation is vital for hematopoietic stem cells (HSCs) biology, the mechanisms underlying translation regulation across the hematopoietic system remain obscure. Here we reveal a novel mechanism of translation regulation in the hematopoietic hierarchy, which is mediated by ribosomal RNA (rRNA) methylation dynamics. Using ultra-low input ribosome-profiling, we characterized cell-type-specific translation capacity during erythroid differentiation. We found that translation efficiency changes progressively with differentiation and can distinguish between discrete cell populations as well as to define differentiation trajectories. To reveal the underlying mechanism, we performed comprehensive mapping of the most abundant rRNA modification - 2-O-methyl (2OMe). We found that, like translation efficiency, 2OMe dynamics followed a distinct trajectory during erythroid differentiation. Genetic perturbation of individual 2OMe sites demonstrated their distinct roles in modulating proliferation and differentiation. By combining CRISPR screening, molecular and functional analyses, we identified a specific methylation site, 28S-Gm4588, which is progressively lost during differentiation, as a key regulator of HSC self-renewal. We showed that low methylation at this site led to translational skewing, mediated mainly by codon frequency, which promoted differentiation. Functionally, HSCs with diminished 28S-Gm4588 methylation exhibited impaired self-renewal capacity ex-vivo, and loss of fitness in-vivo in bone marrow transplantations. Extending our findings beyond the hematopoietic system, we also found distinct dynamics of 2OMe profiles during differentiation of non-hematopoietic stem cells. Our findings reveal rRNA methylation dynamics as a general mechanism for cell-type-specific translation, required for cell function and differentiation. KEY POINTSO_LIHematopoietic differentiation is associated with rRNA methylation dynamics to control cell-type-specific translation. C_LIO_LITranslation efficiency can distinguish discrete cell types and define differentiation trajectories. C_LIO_LIHSC fitness is regulated by a single rRNA methylation. C_LI
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics 98%
- Leukemic stem cells hijack lineage inappropriate signalling pathways to promote their growth 97%
- Expression of terminal deoxynucleotidyl transferase (TdT) identifies lymphoid-primed progenitors in human bone marrow 97%
Similar papers in this journal
- Chromatin state barriers enforce an irreversible mammalian cell fate decision 97%
- A single cell framework identifies functionally and molecularly distinct multipotent progenitors in adult human hematopoiesis 97%
- Concurrent stem- and lineage-affiliated chromatin programs precede hematopoietic lineage restriction 96%
Similar papers in this journal
- Inactive Parp2 causes Tp53-dependent lethal anemia by blocking replication-associated nick ligation in erythroblasts 97%
- Definition of a Small Core Transcriptional Circuit Regulated by AML1-ETO 95%
- Enhancer-promoter interactions are reconfigured through the formation of long-range multiway chromatin hubs as mouse ES cells exit pluripotency 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.