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Erythroid mRNA translation in RPS14 deficiency
in the interactions with mRNAs and tissue-selective trans- lation.5 In a third model, the erythroid transcription factor GATA1 is selectively targeted by translation impairment in DBA or post-translational cleavage by caspase in 5q- syndrome.6,7 DBA and 5q- myelodysplastic syndromes (MDS) have insufficient globin production leading to excess of free heme, accumulation of reactive oxygen species and cell death.8 Furthermore, free heme stops GATA1 synthesis and its suppression of the heme-regulat- ed inhibitor (HRI) activity and subsequent eIF2a phospho- rylation is inefficient to rescue globin translation.9,10
Cellular models of DBA that were developed by expressing a shRNA to RPS19 and of somatic 5q- syn- drome through the same silencing mechanism for RPS146,11,12 support a role for the unbalanced production of ribosome subunits in the translational decrease of GATA1 transcript which could be dependent on the structure of its 5'UTR (untranslated region).6,12 A global assessment is thus needed of the rules governing translation specificity when ribosome production is diminished. In our current study, we investigated mechanisms that regulate translation under conditions of limited ribosome availability and assessed their contribution to the normal human erythroid differentiation process. We analyzed the characteristics of transcripts occupied by ribosomes under conditions of RPS14 downregulation in both cell lines and primary cells. Our results indicate that the transcript length, codon usage and 3’UTR structure are key factors governing the transla- tion selectivity.
Methods
Cell culture
Human erythroblasts were derived from CD34+ cord blood pro- genitors and infected by non-inducible GFP-pLenti X1 vector con- taining shRPS14 or shSCR. The UT-7/EPO cell line was trans- duced with a scrambled (SCR) or RPS14 shRNA cloned into a pLKO.1 Tet-On vector, selected with puromycine (1 mg/mL) and induced with doxycycline (0.2 mg/mL) for 3 days. Cord blood and other patient samples were obtained from the Centre d’Investigation Clinique Paris Descartes Necker Cochin through the Programme Hospitalier de Recherche Clinique (PHRC MDS- 04; INCa-DGOS-5480; IRB IdF 2753).
Quantitative proteomics and data analysis
Label free quantification (LFQ) proteomic experiments were performed as described previously.13 For data and statistical analy- ses, the MS data were processed with MaxQuant version 1.5.2.8 using human sequences from the Uniprot-Swiss-prot database (Uniprot, release 2015-02) with a false discovery rate (FDR) below 1% for both peptides and proteins. LFQ results from MaxQuant were imported into Perseus software (version 1.5.1.6). Protein copy numbers per cell were then calculated using the Protein ruler plugin of Perseus by standardization to the total histone MS sig- nal.14 Raw data were deposited, and processed data are provided in the Online Supplementary Table S1. The abundances of erythroid progenitor, precursor and mature stage proteins were obtained from our two previous studies.13,15
Oligonucleotide microarrays: transcriptome and translatome analysis
RNA was extracted from total cell lysates or from purified heavy polysomes for gene expression profiling on an Affymetrix GeneChip Human transcriptome 2.0 (HTA 2.0, Thermo Fisher
Scientific, Waltham, MA, USA). Differential expression analysis was then carried out using a Student t-test corrected by signifi- cance analysis microarrays (SAM). Differentially expressed genes were selected using a P-value cut-off <0.05, a calculated q-value ≤0.05 and a minimum fold-change of >1.5 or <1/1.5. These genes were annotated using the Gene Ontology consortium software (www.geneontology.org/). Cytoscape (v3.2.1) and Enrichment Map plug-in were used to generate networks for gene sets enriched with an FDR <0.1.
Codon usage, upstream open reading frames and structure prediction analysis
The codon adaptation index annotation and relative synony- mous codon usage (RSCU) analysis were performed using the CAIcal tool (www.genomes.urv.es/CAIcal/).16
Flow cytometry, western blotting and real-time-quantitative polymerase chain reaction
All antibodies and primers are listed in the Online Supplementary Appendix.
Statistical analysis
The statistical analysis of each plot is described above or in the corresponding figure legend. All grouped data values are presented as a mean±standard deviation (SD) or standard error of the mean (SEM). All boxes and whisker plots of expression data are present- ed as medians ± interquartile range. P-values were calculated using a two-sided Mann-Whitney U-test, Student t-test or Kruskal- Wallis ANOVA test with GraphPadPrism software (GraphPad Software, San Diego, CA, USA). Gene set enrichment analysis (GSEA) was based on the Kolmogorov-Smirnov test.
Data accessibility
The raw and preprocessed HTA 2.0 microarray data are publicly available at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (GEO; GSE108822). The raw and preprocessed proteomic data are avail- able via ProteomeXchange with identifiers PXD008650 and PXD009258. Several published datasets were used: (GEO GSE126523; GSE15061; GSE89183; GSE85864; GSE95854).
Results
Limited ribosome availability leads to a translational defect of GATA1
We first investigated the expression of the GATA1 gene in the context of RPS14 downregulation by infecting human primary erythroblasts with a non-inducible pLenti X1 shRPS14 vector (Figure 1A). At 3 days post infection, the expression of GATA1 protein was decreased together with a lower percentage of differentiated glycophorin A (GPA)+ cells (Figure 1B and Online Supplementary Figure S1A). Consistently, immuno-histochemistry analysis of BM biopsy sections and immunofluorescence microscopy of cultured erythroblasts confirmed that GATA1 was less abundant in del(5q) MDS compared to control erythrob- lasts (Figure 1C and Online Supplementary Figure S1B). By contrast, publicly available transcriptome data for del5q patients indicated that GATA1 transcript levels were nor- mal in MDS with del(5q) (Online Supplementary Figure S1C).17 This suggests that GATA1 gene expression is main- ly regulated at a post-transcriptional level.
To investigate this regulatory scenario further, we estab- lished stable UT-7/EPO shRPS14 cell lines via an inducible
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