Repolarization of HSC attenuates HSCs failure in Shwachman-Diamond syndrome
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ABSTRACT:
PROVIDER: PRJNA664115 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12660800_1.fastq.gz | Fastqsanger.gz | |||
| SRR12660800_2.fastq.gz | Fastqsanger.gz | |||
| SRR12660801_1.fastq.gz | Fastqsanger.gz | |||
| SRR12660801_2.fastq.gz | Fastqsanger.gz | |||
| SRR12660802_1.fastq.gz | Fastqsanger.gz |
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Project description:Shwachman-Diamond syndrome (SDS) is a bone marrow failure (BMF) syndrome associated with an increased risk of myelodysplasia and leukemia. The molecular mechanisms of SDS are not fully understood. We report that primitive hematopoietic cells from SDS patients present with a reduced activity of the small Rho GTPase Cdc42 and concomitantly a reduced frequency of HSCs polar for polarity proteins. The level of apolarity of SDS HSCs correlated with the magnitude of HSC depletion in SDS patients. Importantly, exogenously provided Wnt5a or GDF11 that elevates the activity of Cdc42 restored polarity in SDS HSCs and increased the number of HSCs in SDS patient samples in surrogate ex vivo assays. Single cell level RNA-Seq analyses of SDS HSCs and daughter cells demonstrated that SDS-HSC treated with GDF11 are transcriptionally more similar to control than to SDS-HSCs. Treatment with GDF11 reverted pathways in SDS HSCs associated with rRNA processing and ribosome function, but also viral infection and immune function, p53-dependent DNA damage, spindle checkpoints, and metabolism, further implying a role of these pathways in HSC failure in SDS. Our data suggest that HSC failure in SDS is driven at least in part by low Cdc42 activity in SDS HSCs. Our data thus identify novel rationale approaches to attenuate HSCs failure in SDS.
2020-09-18 | GSE158140 | GEO
Project description:Shwachman-Diamond syndrome (SDS) is a bone marrow failure (BMF) syndrome associated with an increased risk of myelodysplasia and leukemia. The molecular mechanisms of SDS are not fully understood. We report that primitive hematopoietic cells from SDS patients present with a reduced activity of the small RhoGTPase Cdc42 and concomitantly a reduced frequency of HSCs polar for polarity proteins. The level of apolarity of SDS HSCs correlated with the magnitude of HSC depletion in SDS patients. Importantly, exogenously provided Wnt5a or GDF11 that elevates the activity of Cdc42 restored polarity in SDS HSCs and increased the number of HSCs in SDS patient samples in surrogate ex vivo assays. Single cell level RNA-Seq analyses of SDS HSCs and daughter cells demonstrated that SDS HSC treated with GDF11 are transcriptionally more similar to control than to SDS HSCs. Treatment with GDF11 reverted pathways in SDS HSCs associated with rRNA processing and ribosome function, but also viral infection and immune function, p53-dependent DNA damage, spindle checkpoints, and metabolism, further implying a role of these pathways in HSC failure in SDS. Our data suggest that HSC failure in SDS is driven at least in part by low Cdc42 activity in SDS HSCs. Our data thus identify novel rationale approaches to attenuate HSCs failure in SDS.
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Project description:Transcription profiles of fate-restricted HSCs [Bulk RNAseq HSC]
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Project description:We have developed a new conditional transgenic mouse showing that MLL-ENL, at an endogenous-like expression level, induces leukemic transformation selectively in LT-HSCs. To investigate the molecular mechanism of leukemic transformation in LT-HSCs conditionally expressing MLL-ENL, we preliminarily performed comprehensive gene expression profiling of CreER-transduced LT-HSCs and ST-HSCs using cDNA microarray analysis. For initial screening of candidate genes invloved in the leukemic transformation, total RNA was extracted from colony-forming cells derived from LT-HSCs and ST-HSCs transduced with CreER or mock. Four samples were analyzed, and CreER-transduced LT/ST-HSC-derived cells were compared with mock-transduced LT/ST-HSC-derived cells, while CreER/mock-transduced LT-HSC-derived cells were compared with CreER/mock-transduced ST-HSC-derived cells.
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A profile of 79 differentially expressed lncRNAs in HSC-MPPs was identified suggesting a role for these RNA species in HSC/progenitor biology. Expression clusters of transcription factors and cell adhesion molecules are identified between the different cell populations. Dormant HSCs, as identified by label-retaining assays, showed a highly differential expression profile compared to active HSCs. In addition to >200 differentially expressed cell surface receptors and lncRNAs, processes including metabolism, development, immune response, signaling (TGFb, Kit, senescence/autophagy) are distinct between the two types of HSCs. In addition, using whole cell proteome analysis of FACS-sorted HSCs and MPP1, >6,000 proteins were identified by quantitative tandem mass spectrometry. Quantification of these proteins confirmed the close relationship between these cell types also seen in their transcript profile and revealed processes such as energy metabolism, immune response and cell cycle to be modulated along early lineage progression. While MPP1/2 still show multilineage potential in reconstitution experiments, a strong lineage bias and low self-renewal potential is observed in mice reconstituted with MPP3/4. These functional differences are accompanied by complex changes in their transcriptome and is also revealed by principal component analysis. In summary, the global mRNA, lncRNA and proteome signatures uncovered here and which are complemented by functional assays, provide a comprehensive and searchable resource of the molecular make-up of the entire HSC/progenitor population present in the bone marrow. These data will provide the basis for a global understanding of stem cell biology in the adult blood system. The uploaded dataset corresponds to the quantitative proteomic comparison of HSC and MPP1, which was done in three biological replicates. Data analysis: MS raw data files were processed with MaxQuant (version 1.3.0.5) (Cox and Mann 2008). Enzyme specificity was set to trypsin/P and a maximum of two missed cleavages were allowed. Cysteine carbamidomethylation and methionine oxidation were selected as fixed and variable modifications, respectively. The derived peak list was searched using the built-in Andromeda search engine (version 1.3.0.5) in MaxQuant against the Uniprot mouse database (2013.02.20) containing 75,721 proteins to which 247 frequently observed contaminants as well as reversed sequences of all entries had been added. Initial maximal allowed mass tolerance was set to 20 ppm for peptide masses, followed by 6 ppm in the main search, and 0.5 Dalton for fragment ion masses. The minimum peptide length was set to six amino acid residues and three labeled amino acid residues were allowed. A 1% false discovery rate (FDR) was required at both the protein level and the peptide level. In addition to the FDR threshold, proteins were considered identified if they had at least one unique peptide. The protein identification was reported as an indistinguishable “protein group” if no unique peptide sequence to a single database entry was identified. The ‘match between runs’ was enabled for consecutive peptide fractions with a 2 minutes time window. The iBAQ algorithm was used for estimation of the abundance of different proteins within a single sample (proteome) (Schwanhausser 2011). For evaluation of differential protein expression between HSC and MPP1, statistical analysis was performed for the proteins quantified in all three replicates using the Limma package in R/Bioconductor (Gentleman 2004, Smyth 2004). After fitting a linear model to the data, an empirical Bayes moderated t-test was used for the protein ratios, which were weighted on log10(summed peptide intensities) in order to capture the effect that the statistical spread of unregulated proteins is much more focused for highly abundant proteins than for low abundance ones (Cox 2008). P-values were then adjusted for multiple testing with Benjamini and Hochberg's method and proteins with an adjusted p-value lower than 0.1 were considered to be differentially expressed between HSC and MPP1. Associated transcriptomics data has been deposited at ArrayExpress with accession E-MTAB-2262.
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