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Megakaryocytes (MKs) are the unique non-pathological cells that undergo polyploidization in mammals. The polyploid formation is critical for understanding the MK biology, and transcriptional regulation is involved in the differentiation and maturation of MKs. However, little is known about the functions of transcriptional elongation factors in the MK polyploidization. In this study, we investigated the role of transcription elongation factor EloA in the polyploidy formation during the MK differentiation. We found that EloA was highly expressed in the erythroleukemia cell lines HEL and K562. Knockdown of EloA in HEL cell line was shown to impair the phorbol myristate acetate (PMA) induced polyploidization process, which was used extensively to model megakaryocytic differentiation. Selective over-expression of EloA mutants with Pol II elongation activity partially restored the polyploidization. RNA-sequencing revealed that knockdown of EloA decelerated the transcription of genes enriched in the ERK1/2 cascade pathway. The phosphorylation activity of ERK1/2 decreased upon the EloA inhibition, and the polyploidization process of HEL was hindered when ERK1/2 phosphorylation was inhibited by PD0325901 or SCH772984. This study evidenced a positive role of EloA in HEL polyploidization upon PMA stimulation through enhanced ERK1/2 activity. [ABSTRACT FROM AUTHOR]

Inherited thrombocytopenia 2 (THC2) is difficult to diagnose due to the lack of specific clinical characteristics and diagnostic methods. To identify potential plasma protein biomarkers for THC2, we collected the plasma samples from a THC2 family (9 THC2 and 15 non-THC2 members), enriched the medium and low abundant proteins using Proteominer and analyzed the protein profiles using the liquid chromatography-mass spectrometry in data independent acquisition mode. Initially, we detected 784 proteins in the plasma samples of this family and identified 27 up-regulated and 36 down-regulated in the THC2 group compared to the non-THC2 group (|log2 ratio| >1 and p-value <0.05). To improve the predictive power, top eight dysregulated proteins (B7Z2B4, LTF, HP, ERN1, IGHV1-8, A0A0X9V9C4, VH6DJ, and D3JV41) were selected by an area under the curve-based random forest process to construct a clinical model. Multivariate analysis with random forest and support vector machine showed that the prediction model provided high discrimination ability for THC2 diagnosis (AUC: 1.000 and 0.967, respectively). The potential plasma protein biomarkers will be tested in more THC2 patients and other thrombocytopenia patients to further validate their specificity and sensitivity. [ABSTRACT FROM AUTHOR]