Distinct transcriptomic and exomic abnormalities within myelodysplastic syndrome marrow cells

Abstract

To provide biologic insights into mechanisms underlying myelodysplastic syndromes (MDS) we evaluated the CD34 marrow cells transcriptome using high-throughput RNA sequencing (RNA-Seq). We demonstrated significant differential gene expression profiles (GEPs) between MDS and normal and identified 41 disease classifier genes. Additionally, two main clusters of GEPs distinguished patients based on their major clinical features, particularly between those whose disease remained stable versus patients who transformed into acute myeloid leukemia within 12 months. The genes whose expression was associated with disease outcome were involved in functional pathways and biologic processes highly relevant for MDS. Combined with exomic analysis we identified differential isoform usage of genes in MDS mutational subgroups, with consequent dysregulation of distinct biologic functions. This combination of clinical, transcriptomic and exomic findings provides valuable understanding of mechanisms underlying MDS and its progression to a more aggressive stage and also facilitates prognostic characterization of MDS patients.

Transcriptional regulation of HSCs in Aging and MDS reveals DDIT3 as a Potential Driver of Dyserythropoiesis

“…To our knowledge, possibly due to the technical complexity of studying such scarce subpopulation, this is the first time that purified human HSCs have been analyzed in MDS. Notably, many of the genes and processes found deregulated in MDS were different from those previously described [32][33][34][35][36][37][38][39][40][41] , which may be due to the fact that preceding studies analyzed HSCs-enriched populations, from which HSCs are only a small fraction. Secondly, we have analyzed the molecular lesions of these cells in the context of aging, allowing for the identification of complex alterations in aging and MDS development that include not only specific lesions of aging and MDS, but also continuous alterations, and even reversal of the aging transcriptome, which are findings not previously described.…”

“…In this work, we have taken two novel approaches in the study of transcriptional alterations in MDS that we believe are relevant for understanding the molecular pathogenesis of this disease. Firstly, as MDS is considered an HSC disease, we focused our analysis on highly purified HSCs, which may explain the identification of several genes deregulated in MDS not previously described [15][16][17][18][19][20][21][22][23][24] .…”

“…It is possible that transcriptional mechanisms, which can act as integrators of different alterations, also play a relevant role. A handful of studies have recently started to identify specific transcriptional alterations with prognostic value or a potential role in the pathogenesis and phenotype of MDS [32][33][34][35][36][37][38][39][40][41] . Although different cell types are phenotypically altered in MDS, previous studies suggest that HSCs are key to understanding this disease due to their privileged position at the apex of the differentiation process [3][4][5][6][7] .…”

“…Recent studies have demonstrated that, as in other hematological malignancies 13,14 , transcriptional mechanisms may also play a relevant role. In this sense, several works aiming to elucidate gene expression aberrations in MDS patients have started to identify, in HSCs-enriched populations, specific lesions with prognostic value or a potential role in the phenotype of the disease [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] . Moreover, certain mutations have been associated with specific transcriptional profiles in patients with MDS 17,[21][22][23] , supporting the role gene expression profiles as common integrators of different alterations, being affected by lesions such as mutations or aberrant signaling from the environment, among others.…”

SplicingFactory – Splicing diversity analysis for transcriptome data

“…Myelodysplastic syndrome dataset processing RNA-seq datasets from two papers (Im et al, 2018;Pellagatti et al, 2018) were uniformly reprocessed. We downloaded the data from SRA (SRP133442, SRP149374) using SRA-tools (version 2.9.6).…”