| Literature DB >> 29061162 |
Vasiliki Chondrou1, Petros Kolovos2, Argyro Sgourou3, Alexandra Kourakli4, Alexia Pavlidaki1,5, Vlasia Kastrinou1, Anne John6, Argiris Symeonidis4, Bassam R Ali6,7, Adamantia Papachatzopoulou8, Theodora Katsila1, George P Patrinos9,10,11.
Abstract
BACKGROUND: Human erythropoiesis is characterized by distinct gene expression profiles at various developmental stages. Previous studies suggest that fetal-to-adult hemoglobin switch is regulated by a complex mechanism, in which many key players still remain unknown. Here, we report our findings from whole transcriptome analysis of erythroid cells, isolated from erythroid tissues at various developmental stages in an effort to identify distinct molecular signatures of each erythroid tissue.Entities:
Keywords: Beta-thalassemia; Biomarkers; Erythroid cell differentiation; Ontogenesis; Pharmacogenomics; Sickle cell disease; Transcriptomics; VEGFA
Mesh:
Substances:
Year: 2017 PMID: 29061162 PMCID: PMC5654038 DOI: 10.1186/s40246-017-0120-8
Source DB: PubMed Journal: Hum Genomics ISSN: 1473-9542 Impact factor: 4.639
Fig. 1Differential gene expression when adult peripheral blood is compared to cord blood and fetal liver. A total of 264 genes were up- (FC > 2) or downregulated (FC < 2), when tissues with low HbF expression levels were compared to their counterparts with high HbF expression levels. Columns represent samples; rows are genes. Genes that were upregulated are depicted in red and genes that were downregulated are depicted in green
Fig. 2Common and unique up- and downregulated genes during ontogenesis. a The number in each circle represents the number of differentially expressed genes among the groups in question. Common and unique genes are shown. b PANTHER analysis outcomes (GO; molecular function and biological process)
Summary of our genotype analysis of β-type hemoglobinopathy patients of Hellenic origin and healthy (non-thalassemic) individuals
| Study population | Genotype frequency (%) | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| rs3024997 (G>A) | rs2146323 (C>A) | rs10434 (A>G) | |||||||
| G/G | A/A | G/A | C/C | A/A | C/A | A/A | G/G | A/G | |
| Healthy individuals | 34 | 21 | 45 | 60 | 9 | 31 | 18 | 31 | 51 |
| β-thalassemia major patients | 26 | 16 | 58 | 51 | 3 | 46 | 21 | 33 | 46 |
| NTDT patients | 47 | 6 | 47 | 33 | 11 | 56 | 21 | 29 | 50 |
| HU responders | 26 | 16 | 58 | 43 | 10 | 48 | 26 | 26 | 48 |
| HU non-responders | 35 | 12 | 53 | 63 | 17 | 20 | 25 | 29 | 46 |
The number of individuals per group (n) is indicated in parentheses per tagSNP: rs3024997 healthy individuals (n = 112), NTDT patients (n = 17), β-thalassemia major patients (n = 112), HU responders (n = 19), HU non-responders (n = 26); rs2146323 healthy individuals (n = 115), NTDT patients (n = 18), β-thalassemia major patients (n = 114), HU responders (n = 21), HU non-responders (n = 30); rs10434 healthy individuals (n = 72), NTDT patients (n = 14), β-thalassemia major patients (n = 105), HU responders (n = 19), HU non-responders (n = 28)
HU hydroxyurea, NTDT non-transfusion-dependent thalassemia
Fig. 3TagSNPs across the VEGFA gene are associated with disease phenotype (a) and HU treatment efficacy (b). a disease phenotype, rs3024997 (G>A; healthy individuals vs. NTDT patients p = 0.005; β-thalassemia major patients vs. NTDT patients p = 0.003) and rs2146323 (C>A; healthy individuals vs. β-thalassemia major patients p = 0.03; healthy individuals vs. NTDT patients p = 0.0005; β-thalassemia major patients vs. NTDT patients p = 0.009). b HU treatment efficacy, rs2146323 (C>A; HU responders vs. HU non-responders p = 0.0002). HU: hydroxyurea
Fig. 4Pairwise linkage disequilibrium (LD) calculations for the tagSNPs of interest across the VEGFA gene (CEU). LD is measured as D′ and R 2 (see also Additional file 1: Table S2)
Fig. 5In silico analysis of the role of rs3024997 on splicing. For rs3024997 (G > A), the Human Splicing Finder prediction algorithm supports the activation of an intronic cryptic acceptor site and thus, potential alteration of splicing. Yet, the RESCUE ESE, EIEs, PESE Octamers, ESE Finder-SRp40, and ESR Sequences prediction algorithms suggest the creation of an exonic splicing enhancer, probably, with no impact on splicing. The graphic representation of the region confirms individual outcomes per algorithm considered, each one corresponding at a different color and length. For more information on the data, please visit http://www.umd.be/HSF3/technicaltips.html