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TRNA m1A modification regulate hematopoietic stem cell maintenance via mTORC1 signaling [mim-tRNAseq]

ABSTRACT: tRNA m1A modification regulate hematopoietic stem cell maintenance via mTORC1 signaling [mim-tRNAseq]

PROVIDER: PRJNA1063221 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR27482566.fastq.gz	Fastqsanger.gz
	SRR27482567.fastq.gz	Fastqsanger.gz
	SRR27482568.fastq.gz	Fastqsanger.gz
	SRR27482569.fastq.gz	Fastqsanger.gz

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tRNA m1A modification regulate hematopoietic stem cell maintenance via mTORC1 signaling [mim-tRNAseq]

Project description:High-resolution quantitative profiling of tRNA abundance and modification status in hematopoietic cells upon Trmt6 deletion by mim-tRNAseq

2024-06-03 | GSE252912 | GEO

tRNA m1A modification regulate hematopoietic stem cell maintenance via mTORC1 signaling

Project description:tRNA m1A modification regulate hematopoietic stem cell maintenance via mTORC1 signaling

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High-resolution quantitative profiling of tRNA abundance and modification status in eukaryotes by mim-tRNAseq

Project description:Measurements of cellular tRNA abundance are hampered by pervasive blocks to cDNA synthesis at modified nucleosides and the extensive similarity among tRNA genes. We overcome these limitations with modification-induced misincorporation tRNA sequencing (mim-tRNAseq), which combines a workflow for full-length cDNA library construction from mature tRNA with a simple-to-use computational analysis toolkit. Our method accurately captures tRNA abundance and modification status in multiple eukaryotes and is applicable to any organism with a known genome.

2021-02-12 | GSE152621 | GEO

tRNA m1A modification regulate hematopoietic stem cell maintenance via mTORC1 signaling [scRNA-seq]

Project description:Hematopoietic stem cells (HSCs) have some unique physiological adaptations to maintain blood cell production and deal with stress responses throughout life. To maintain such adaptations, HSCs are particularly dependent on maintaining a tightly controlled protein translation rate. However, how HSCs fine tune protein translation has not been fully described. In this study, we report the role of TRMT6 in regulating HSC function in adult hematopoiesis. Trmt6-null mice became moribund due to hematopoietic failure with pancytopenia in the blood and bone marrow 4 to 8 weeks after Trmt6 deletion. Trmt6-deleted bone marrow cells failed to repopulate hemoablated recipients in competitive transplantation experiments. Loss of TRMT6 resulted in an abnormally high rate of recruitment of quiescent hematopoietic stem cells (HSCs) into the cell cycle. Cycling HSCs produced progenitors at the expense of self-renewal, which led to the exhaustion of the HSC pool. Mechanistically, mTORC1 signaling pathway was highly upregulated in HSC-enriched cell populations after Trmt6 deletion by single cell RNA-seq analysis. Furthermore, TRMT6 was required for tRNA m1A modification required for HSC quiescence and self-renewal. Our data indicate that TRMT6 promotes the expression of TSC1, fine-tuning mTORC1 signaling level which is essential for HSC maintenance and self-renewal in adult hematopoiesis.

2024-06-03 | GSE230711 | GEO

tRNA m1A modification regulate hematopoietic stem cell maintenance via mTORC1 signaling [scRNA-seq]

Project description:tRNA m1A modification regulate hematopoietic stem cell maintenance via mTORC1 signaling [scRNA-seq]

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Prokaryotic N1-methyladenosine erasers maintain tRNA m1A modification levels in Streptomyces venezuelae.

Project description:tRNA modifications help maintain tRNA structure and facilitate stress responses. Found in all three kingdoms of life, m1A tRNA modification occurs in the T loop of many tRNAs and stabilizes tertiary tRNA structure and impacts translation. M1A in T loop is known to be reversible by three mammalian demethylase enzymes, which bypasses the need of turning over the tRNA molecule to adjust their m1A levels in cells. However, no prokaryotic tRNA demethylase enzyme has been identified. Using Streptomyces venezuelae as a model organism, we confirmed the presence and quantitative m1A tRNA signatures using mass spectrometry and high throughput tRNA sequencing. We identified two RNA demethylases that can remove m1A in tRNA and confirmed the activity of a previously annotated tRNA m1A writer. Using single gene knockouts of these erasers and the m1A writer, we subjected these strains to stress conditions and found dynamic changes to m1A levels in many tRNAs. Phenotypic characterization highlighted changes to their growth and altered antibiotic production. Our identification of the first prokaryotic tRNA demethylase enzyme paves the way for investigating new mechanisms in global translational regulation in bacteria.

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Chemical manipulation of m1A mediates its detection in human tRNA

Project description:N1-methyl adenosine (m1A) is a wide-spread RNA modification present in tRNA, rRNA and mRNA. m1A modification sites in tRNAs are evolutionary conserved and its formation on tRNA is catalyzed by methyltransferase TRMT61A and TRMT6 complex. m1A promotes translation initiation and elongation. Due to its positive charge under physiological conditions, m1A can notably modulate RNA structure. It also blocks Watson-Crick base pairing and causes mutation and truncation during reverse transcription. Several misincorporation-based high throughput sequencing methods have been developed to sequence m1A. In this study, we introduce a reduction-based m1A sequencing (red-m1A-seq). We report that NaBH4 reduction of m1A can improve the mutation and readthrough rates using commercially available RT enzymes to give better positive signature, while alkaline-catalyzed Dimroth rearrangement can efficiently convert m1A to m6A to provide good controls, allowing the detection of m1A with higher sensitivity and accuracy. We applied red‑m1A-seq to sequence human small RNA and we not only detected all the previously reported tRNA m1A sites, but also new m1A sites in mt-tRNAAsn-ATT and 5.8S rRNA.

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Project description:Transfer RNAs (tRNAs) are small adaptor RNAs essential for mRNA translation. Alterations in the cellular tRNA population can directly affect mRNA decoding rates and translational efficiency during cancer development and progression. To evaluate changes in the composition of the tRNA pool, multiple sequencing approaches have been developed to overcome reverse transcription blocks caused by the stable structures of these molecules and their numerous base modifications. It remains however unclear whether current sequencing protocols faithfully capture tRNAs existing in cells or tissues. This is specifically challenging for clinical tissue samples that often present variable RNA qualities. For this reason, we developed ALL-tRNAseq, which combines the highly processive MarathonRT and RNA demethylation, for the robust assessment of tRNA expression, together with a randomized adapter ligation strategy prior to reverse transcription, to assess tRNA fragmentation levels in both cell lines and tissues. Incorporation of tRNA fragments not only informed on sample integrity, but also significantly improved tRNA profiling of tissue samples. Our data showed that our profiling strategy effectively improves classification of oncogenic signatures in glioblastoma and diffuse large B-cell lymphoma tissues, particularly for samples presenting higher levels of RNA fragmentation, further highlighting the utility of ALL-tRNAseq for translational research.

2023-02-24 | GSE186736 | GEO

High-resolution quantitative profiling of tRNA abundance and modification status in eukaryotes by mim-tRNAseq

Project description:High-resolution quantitative profiling of tRNA abundance and modification status in eukaryotes by mim-tRNAseq

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tRNA quantification of five human cell lines by hydro-tRNAseq and small RNA-seq

Project description:We analyze whether tRNA quantifications based on small RNA-seq data are informative enough to distinguish between different human cell lines covering multiple tissue types. We therefore apply both small RNA-seq and Hydro-tRNAseq to HEK293 (kidney), HCT116 (colon), HeLa (cervix), MDA-MB-231 (breast), and BJ fibroblasts. First, the correlations between the two methods of identical samples and computational mapping pipeline range between 0.93 and 0.96 for all cell lines. tRNA quantifications from both protocols are compared and significantly higher Spearman correlations are obtained within matching samples versus mismatching cell lines. In consequence, we demonstrate that small RNA-seq quantifications of sample-specific tRNA profiles show a good agreement with conventional tRNA-seq.

2020-02-28 | GSE137834 | GEO

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