RUNX3 is a Potential Marker for Vaso-Occlusive Crises by a Whole Blood Transcriptomic Analysis in Sickle Cell Disease

RUNX3 is a Potential Marker for Vaso-Occlusive Crises by a Whole Blood Transcriptomic Analysis in Sickle Cell Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article RUNX3 is a Potential Marker for Vaso-Occlusive Crises by a Whole Blood Transcriptomic Analysis in Sickle Cell Disease Safa Taha, Hawra Abdulwahab, Muna aljishi, Ameera Sultan, Moiz Bakheit, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5397806/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jun, 2025 Read the published version in International Journal of Molecular Sciences → Version 1 posted You are reading this latest preprint version Abstract Sickle cell disease is the commonest hemoglobinopathy which results from a mutation in the β-globin gene of hemoglobin and predisposes to painful Vaso-occlusive crisis (VOC) and multi-organ dysfunctions. The huge phenotypical variation makes it challenging to define and predict the disease severity and outcomes. This study aimed to characterize the whole blood gene expression profile using Microarray technology in Bahraini patients with sickle cell disease determining the differentially expressed genes in steady-state (n=10) and during VOC (n=10) in comparison to healthy controls (n=8). The analysis identified 2073 and 3363 genes that were dysregulated during steady-state and VOC, respectively, compared to healthy controls. Additionally, 1078 genes were differentially expressed during VOC compared to steady state. The GO terms enrichment analysis revealed significant deregulation in terms related to immune and hematopoietic regulation, with downregulation in pathways critical for immune modulation and hematopoietic balance; one of the interesting genes identified among the 668 down-regulated genes was RUNX3. The RUNX3 gene was four folds down-regulated in microarray, three-fold in polymerase chain reaction, and a mean protein concentration of 11.13 pg/ml was observed in enzyme-linked immunosorbent assay during VOC compared to steady-state (457.93 pg/ml) (p<0.01). In conclusion, the RUNX3 gene plays a role in immune cell differentiation and inflammation predisposing to tissue inflammation and injury. It may serve as a potential biomarker for VOC, and future large-scale validation and proteomic assessments are recommended. VOC SCD Microarray hemolysis inflammation biomarker blood disease pain. Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Sickle cell disease (SCD) is caused by a mutation in the hemoglobin resulting in the formation of hemoglobin S (HbS) predisposing to hemolytic anemia, painful attacks, and multiple end-organ complications [1,2]. It was the first identified genetic disease [1] and it is the most common hemoglobinopathy in Kingdome of Bahrain. The earliest presentation of SCD is the acute painful vaso-occlusive crisis (VOC). VOC results from a complex mechanism involving adhesive interactions among sickle cells, leukocytes, endothelium, other blood cells, and plasma factors [3]. Microvascular occlusions occur through aggregation of red blood cells, leukocytes, and platelets predisposing to inflammation and intravascular and extravascular hemolysis [4]. There is a continuous interaction between hemostatic and inflammatory systems in SCD which further disseminates the hypercoagulability and inflammatory changes [5] as it’s well established that innate immune cells contribute to the formation of inflammation, adhesion, and painful attacks [6]. Moreover, SCD patients have increased susceptibility to infection with encapsulated bacteria such as Streptococcus Pneumoniae, Hemophilus Influenzae, and Salmonella due to splenic infarction together with recurrent VOC resulting in multiple organ infarctions and end-organ damages leading to various complications [7]. In general, complications can occur in any tissue such as acute chest syndrome (ACS), pneumonia, stroke, splenic infarction, avascular necrosis of bone, and osteomyelitis [7]. However, some individuals develop certain complications while others do not regardless of the number of VOC and the degree of anemia [8]. This huge phenotypical variation makes it challenging to define and predict the disease severity and outcomes [9]. This study aimed to determine the differentially expressed genes in SCD patients in steady-state and in VOC compared to healthy control using microarray. As well as, to investigate the effect of VOC on SCD gene expression analysis to detect genetic markers associated with the VOC. Such a study may enhance our knowledge of the disease pathophysiology and may aid in the development of targeted therapy to treat and prevent VOC. 2 Results 2.1 Characteristics of Participants The study enrolled twenty sickle cell disease patients of which ten patients had VOC. The summary of the patient’s characteristics is shown in Table 1. The statistical analysis showed no significant differences between both groups in baseline characteristics (p-value > 0.05). 2.2 Determination of the differentially expressed genes The analysis identified 2073 dysregulated genes in which 736 genes were up-regulated (p 2, and 1337 genes were down-regulated (p<0.05) with a fold change of < -2 in SCD patients in steady-state compared to healthy controls, Figure 1 (A) . Whereas in SCD patients in VOC compared to healthy controls, 3,363 genes were differentially regulated including 1080 genes that were up-regulated (p 2 and 2,283 genes were down-regulated (p<0.05) with a fold change of < -2, Figure 1 (B) . In addition, 1078 genes were differentially expressed including 410 up-regulated genes and 668 down-regulated genes in SCD patients in VOC compared to steady-state (p 2 and < -2, respectively, Figure 1 (C) . Furthermore, the assessment of down-regulated genes revealed 47 genes to be down-regulated in SCD patients in steady-state compared to healthy controls at a p-value of 4, while in SCD patients in VOC compared to healthy controls 255 genes were down-regulated, Figure 2.Table 2. Additionally, 79 genes were down-regulated in SCD patients in VOC compared to steady-state 2.3 Potential genetic marker for Vaso-occlusive crisis To identify potential genetic markers for VOC, a GO term enrichment analysis was conducted to reveal key biological processes affected during this condition. The analysis showed a significant up-regulation of genes associated with processes like cellular localization, signal transduction regulation, and ubiquitin ligase complex involvement (Figure 4). These terms suggest an increase in cellular activity and regulatory functions, likely indicative of heightened cellular demands and stress responses associated with the inflammatory and hypoxic environment during VOC. This increased activity may be central to the mechanisms of cell damage and adaptation underlying the Vaso-occlusive state. In contrast, down-regulated genes were notably associated with terms related to immune system processes, MHC protein complex binding and T cell receptor binding (Figure 4). These findings suggest a suppression of immune response pathways and a reduction in cellular activities essential for maintaining immune cell function and cellular homeostasis. Such down-regulation of immune-related and hematopoietic pathways could impact immune signaling and cellular stability, possibly contributing to impaired cellular responses in SCD patients experiencing VOC. In focusing further on genes with significant deregulation, particularly those implicated in immune and hematopoietic regulation, we identified a subset of 79 down-regulated genes. To refine our search for a robust VOC marker, we selected those genes that were consistently down-regulated with a four-fold change in SCD patients during VOC compared to steady-state and healthy controls (p-value < 0.001) but not more than two-fold up-regulated in steady-state patients compared to healthy controls (p-value < 0.05). This filtering left 26 genes (Table 3) with a high likelihood of true down-regulation during VOC. Among these, RUNX3 (RUNX Family Transcription Factor 3) emerged as a promising candidate for further analysis. RUNX3 showed a four-fold down-regulation in SCD patients during VOC compared to steady-state levels (p=2.33×10⁻⁸), as validated by qRT-PCR and ELISA (Figure 3A). 2.4 Validation of RUNX3 through qRT-PCR and ELISA The analysis of RUNX3 gene expression by qRT-PCR showed statistically significant down-regulation in SCD patients in VOC compared to SCD patients in steady-state with three-fold changes (p=5.517×10-6), Figure 3 (A) and (C) . Moreover, the measurement of RUNX3 protein concentration using ELISA showed a significant reduction in the concentration in SCD patients in VOC (11.13 pg/ml) compared to SCD patients in steady-state (457.93 pg./ml) (p=5.957×10-11) Figure 3 B) . 3 Discussion In SCD, gene expression meta-analysis studies done on the West African population identified several biological pathways to be associated with SCD through enrichment analysis. The innate immunity pathway was recognized among the major pathways [12, 13]. The IL7R (Interleukin 7 Receptor) and TRAT1 (T Cell Receptor Associated Transmembrane Adaptor 1) genes were among the top down-regulated in the previous meta-analysis studies as well as in our study. The IL7R protein is involved in the adaptive immune system and has a crucial part in V(D)J recombination during lymphocyte development [14], also it has a relation with other pathways such as hematopoietic cell lineage and extracellular signal-regulated kinases [15, 16]. Whereas the TRAT1 gene is a protein-coding gene that plays a role in the adaptive immune system as it stabilizes the T-cell antigen receptor (TCR)-CD3 complex at the surface of T-cells and is related to pathways in downstream signaling [17]. Moreover, the CD3E (CD3e Molecule) gene which coded for CD3-epsilon polypeptide was remarkably down-regulated in the steady-state and VOC group. This protein is involved in the activation of downstream signaling pathways, it has an important role in T-cell development and initiation of TCR-CD3 complex assembly [18]. Furthermore, our study was the first to enroll Arabs with SCD in whole-blood gene expression analysis. Only in our study, the RUNX3 (RUNX Family Transcription Factor 3) was significantly down-regulated in SCD patients in VOC compared to SCD patients in steady-state and this was further confirmed by qRT-PCR and ELISA. These results suggest that the RUNX3 gene may serve as a potential genetic marker for the development of VOC in SDC patients. Additionally, the RUNX3 was not identified in previous studies characterizing the gene expression in SCD patients as well and no study assessed its role at the transcriptional level in SCD patients. RUNX3 is a member of the runt domain-containing family of transcription factors located on chromosome 1p36.11 and formed of 65,647 bases [19]. It functions in activating or suppressing transcription through binding of its heterodimer protein form to the core DNA sequence 5'-TGTGGT-3' in some enhancers and promoters after forming a complex with beta subunit forms (heterodimeric complex core-binding factor with Core-Binding Factor Subunit Beta (CBFB)), as well as it interacts with other transcription factors [20]. Also, it works as a tumor suppressor as it is found to be silenced or deleted in cancer [21, 22]. Additionally, it may have a role in controlling cellular proliferation and differentiation [21]. Several studies assessed the role of RUNX3 in malignancies such as gastric, colon, and lung cancers [23, 24]. Besides, dysfunction of RUNX3 resulted in defects of transcriptional regulation and DNA repair leading to bone marrow failure which may progress to leukemia [25]. Moreover, Yanyan and co-workers reviewed data from animal and human studies which related RUNX3 to the pathogenesis of bronchial asthma part of it due to its involvement in the regulation of Th1/Th2 balance and the other part associated with its effect in modifying the several immune cell differentiations such as innate lymphoid cells, Treg cells, and dendritic cells [26]. In some reports, it was suggested that oxidative stress induced phosphorylation of RUNX3 leading to its cytoplasmic localization and subsequently inactivation mediating carcinogenesis along with hypersensitivity [27, 28]. The effect of RUNX3 on Th1/Th2 balance was through its involvement in class I Major Histocompatibility Complex assortment of T cells particularly CD8 + during their development and it was found that silencing of RUNX3 expression may inhibit Th1 cytokine release while facilitating Th2 cytokines secretion [29]. Additionally, it has a critical function in the differentiation of innate lymphoid cells by altering CD8 + T cells, Th1, and spleen natural killer cells differentiation [30]; which in turn can predispose to the development of inflammatory disease [31]. Moreover, RUNX3 was found to be protective against acute lung injury in rats with severe acute pancreatitis as up-regulation of RUNX3 resulted in increasing polymorphonuclear neutrophil apoptosis and inhibition of Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) phosphorylation which in turn decreases the progression of inflammatory response and subsequently organ damage [32]. Furthermore, Das et al. investigated the role of microRNA in modulating the HbF induction pathway by comparing the gene expression between individuals with high HbF levels including subjects with hereditary persistence of fetal hemoglobin and β-thalassemia minor with those having normal HbF. The study identified 931 differentially regulated genes and 19 differentially expressed miRNAs. In which two miRNAs were inversely correlated with RUNX3 mRNA: high expression of miRNA-301b and miRNA-582-5p resulted in decreased expression of RUNX3 [33]. Interestingly, up-regulation of miRNA-301b was suggested to be triggered by hemolysis and hypoxia [34, 35]. Add together, decreased expression of RUNX3 disrupts the inflammatory balance predisposing to tissue inflammation and injury which forms the basis of VOC pathophysiology. In conclusion, analysis of the transcriptional changes in SCD during steady-state and VOC resulted in the detection of genes for the first time to be associated with SCD and were significantly differentially expressed. Amongst these genes, RUNX3 plays a role in immune cell differentiation and inflammation. It may serve as a potential genetic biomarker and further validation in a larger sample size is recommended. Additionally, studying the RUNX3 pathway in relation to the disease pathogenesis may aid in the discovery of novel therapeutic targets and enable the development of personalized medicine in managing SCD patients. 4 Materials and Methods 4.1 Study population Twenty Bahraini patients with SCD (10 at steady state and 10 with VOC) and eight healthy participants from Salmaniya Medical Complex were recruited in this cross-sectional study in September 2019. Approval from the Research and Ethics Committee of the Arabian Gulf University, and the Research Technical Support Team of the Ministry of Health, Kingdom of Bahrain were obtained. Written informed written consents were obtained from each study participant. All experiments and methods were performed in accordance with relevant guidelines and regulations. The healthy volunteers were confirmed to have the Hemoglobin AA genotype by High-Performance Liquid Chromatography (HPLC), while all patients with SCD were confirmed to have the HbSS genotype. SCD patients were divided into two groups of ten participants each: SCD patients in a steady state defined as participants without any history of VOC that required neither evaluation in an emergency department nor hospital admission 12 weeks prior to the study enrollment; and SCD patients during VOC defined as participants with a history of acute, severe pain at the time of enrollment (self-rated score of ≥ 7 out of 10 on a Numerical Rating Scale (NRS)) [10, 11]. All SCD patients were not under treatment with hydroxyurea. 4.2 Sample collection For all subjects, blood samples were collected in two separate tubes. For the VOC group, the samples were collected within the first 48 hours of the crisis. First, 5 milliliters of venous blood were collected in a serum-separating tube and kept for 30 minutes at room temperature for clot formation and then centrifuged at 3,500 rpm for 15 minutes. The separated serum was stored at -80 °C until the analysis. In the second container, 2.5 milliliters of venous blood were collected in PAXgene® Blood RNA Tube (PreAnalytiX GmbH, Hombrechtikon, Switzerland) for immediate stabilization of intracellular RNA and was kept for a minimum of 2 hours at room temperature to allow for complete lysis of blood cells, and then stored at 4 °C until the analysis that was carried out within 3 days. 4.3 RNA extraction and gene expression analysis RNA was extracted from whole blood samples using a PAXgene® Blood RNA kit (PreAnalytiX GmbH, Hombrechtikon, Switzerland) following the manufacturer’s instructions. The quantity and purity of RNA samples were determined using the NanoDrop 1000 Spectrophometer (Thermo Fisher Scientific, Inc., Waltham, MA, USA) and the acceptable RNA purity of A260/A280 was 1.8-2.2. The RNA integrity was assessed using 1.2% agarose gel electrophoresis. All RNA samples were stored at −80 °C until further analysis. The assessment of gene expression was carried out using Affymetrix ClariomTM S Assays for human and GeneChip™ WT PLUS Hybridization, Wash and Stain Kit (Applied Biosystems™, California, USA) according to the manufacturer’s protocol. In brief, reverse transcription of 100 ng of total RNA of each sample was converted to double-stranded cDNA using the T7 promoter sequence primer. Followed by the synthesis and amplification of cRNA by an in vitro transcription of the second-stranded cDNA using T7 RNA polymerase. Then through reverse transcription of cRNA, the second cycle of single-stranded cDNA was synthesized, which contains dUTP. After hydrolyzing the RNA, 5.5 μg of purified single-stranded cDNA was fragmented using uracil-DNA glycosylase and apurinic/apyrimidinic endonuclease 1. Next, by terminal deoxynucleotidyl transferase, the fragmented cDNA was labeled with DNA Labeling Reagent which binds to biotin. The fragmented and biotin-labeled single-stranded cDNA samples were hybridized to GeneChip™ WT PLUS for sixteen hours in Affymetrix GeneChip® Hybridization Oven 645. Followed by washing and staining using the Affymetrix GeneChip® Fluidics Station 450 and Affymetrix® GeneChip® Command Console™ (AGCC) software. Finally, the arrays were scanned using Affymetrix GeneChip® Scanner 3000 7G. 4.4 Quantitative real-time polymerase chain reaction Real-time polymerase chain reaction (PCR) was performed to measure the expression of the RUNX Family Transcription Factor 3 (RUNX3) gene and normalized to GAPDH as a housekeeping gene. The reaction mixture for the SYBR Green assay contained 2× SYBR™ Select Master Mix (Applied Biosystems™, California, USA), 10 pmol of each forward and reverse primers (metabion international AG, Planegg, Germany), and 50 ng of cDNA. The sequences of the primers for RUNX3 and GAPDH were as follows: RUNX3 forward primer, 5'- GGCAATGACGAGAACTACTCCG -3', and RUNX3 reverse primer 5'- GATGGTCAGGGTGAAACTCTTCC -3'; GAPDH forward primer 5'-TCCCTGAGCTGAACGGGAAG-3', and GAPDH reverse primer 5'- GGAGTGGGTGTCGCTGT -3'. The reaction was carried out in 20 µL capillaries and incubated in Light Cycler® 2.0 (Roche). The used LightCycler run protocol was as follows: Denaturation program at 95˚C for 10 min, amplification, and quantification program repeated 45 times at 95˚C for 10 sec, 60˚C for 30 sec, and 72˚C for 30 sec, and finally a cooling step at 40˚C for 30 sec. The accumulation of PCR products during each cycle was determined by observing the rise in fluorescence of DNA-binding SYBR Green. Afterward, the crossing point of each sample was detected and normalized to the expression of the housekeeping gene. Then the fold change of expression was calculated using the 2^-ΔΔCt method. 4.5 Enzyme-linked immunosorbent assay The protein produced by the RUNX3 gene was measured by Enzyme-Linked Immunosorbent Assay (ELISA) (MyBioSource, California, USA) according to the manufacturer’s instructions. All standards and samples were assigned in duplicate to the plate, and the assay was run by adding 50 µL per well of diluted standard to the standard well. Then, 40 μL of Sample Diluent was pipetted to each sample well and combined with 10 μL of sample. After incubation and washing, 50 µL of HRP-Conjugated detection antibody was added, incubated, and washed. After that, 50 µL of each chromogen solution A and B were pipetted into each well, incubated, and washed. Then, a 50 µL of Stop Solution was added for reaction termination when the desired blue color intensity was attenuated. Immediately, the optical density (OD) at 450 nm was measured for each well using Synergy™ HTX Multi-Mode Microplate Reader (BioTek®) (BioTek Instruments, Inc., Winooski, VT, USA) and analyzed by Gen5 2.07.17. 4.6 Statistical analysis The demographic data were analyzed for their differences in SCD patients in steady state and during VOC. The values of continuous data were analyzed by Student’s two-sided unpaired t-test and presented in mean ± standard deviation (SD). The categorical variables were presented in numbers (percentage) and were analyzed using Fisher’s exact probability test. A p-value of < 0.05 was considered significant. The Transcriptome Analysis Console (TAC) software version 4.0.0.25 by Thermo Fisher Scientific was used to define the differential expression profile within the different groups, perform statistical analysis, and provide a list of differentially expressed genes. Genes with a fold change of >2 or <-2 and with a t-test or ANOVA P‐value of <0.05 were considered significantly altered between the conditions of each group. The Light Cycler Software version 4.1.1.21 was used for the analysis of qRT-PCR results by identifying the crossing points for the target and the reference gene in each sample. The average of crossing points for each target gene was calculated in relative to the housekeeping gene GAPDH in all the groups using relative Mono-Color Relative Quantification assay. Then the fold change was calculated using the delta Ct (2^-ΔΔCt) method. 4.7 Gene Ontology (GO) Term Enrichment Analysis The GO term enrichment analysis was performed to identify differentially deregulated GO terms between SCD patients in steady state and during VOC using the g: Profiler (version: e111_eg58_p18_f463989d). g: Profiler enables the comprehensive analysis of Gene Ontology (GO) terms, covering the biological process (BP), cellular component (CC), and molecular function (MF) domains to investigate pathway enrichment across differentially expressed genes. Input gene lists were based on significant differential expression results, with criteria set for adjusted p-values (threshold: FDR < 0.05) to ensure a high-confidence identification of GO terms (36). The enrichment results were visualized as bar plots, illustrating the distribution and -log10 transformed adjusted p-values for upregulated and downregulated GO terms, thereby highlighting terms with significant deregulation between the VOC and SCD samples. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Declarations Ethical approval and consent to participant: Ethical approval obtained from Research and Ethics Committee of the Arabian Gulf University reference number E003-PI-04/18 and the Research Technical Support Team of the Ministry of Health reference number AURS/156/2019. An informed consent was obtained from all participants. Consent for publication: not applicable. Author Contributions: Conceptualization, S.T. and H.A.; methodology, S.T. and H.A.; software, S.T. and H.A.; formal analysis, S.T. and H.A.; investigation, H.A., M.A and A.S.; data curation, S.A.; writing—original draft preparation, H.A.; writing—review and editing, S.A., M.A, A.S and M.B; supervision, S.T. and M.B.; project administration, M.B. Acknowledgment: not applicable. Funding: This research was funded by the COLLEGE OF MEDICINE AND MEDICAL SCIENCES, ARABIAN GULF UNIVERSITY, grant number E003-PI-04/18. Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Availability of data: Data will be available upon request. References Mason VR. SICKLE CELL ANEMIA. JAMA . 1922 Oct; 79: 1318. Rees DC, Williams TN, Gladwin MT. Sickle-cell disease. The Lancet . 2010 Dec; 376: 2018–2031. Manwani D, Frenette PS. Vaso-occlusion in sickle cell disease: pathophysiology and novel targeted therapies. Blood . 2013 Dec; 122: 3892–3898. Pinto VM, Balocco M, Quintino S, Forni GL. Sickle cell disease: a review for the internist. Internal and emergency medicine . 2019 Oct; 14(7): 1051–1064. de O. Toledo SL, Guedes JVM, Alpoim PN, Rios DRA, de B. Pinheiro M. Sickle cell disease: Hemostatic and inflammatory changes, and their interrelation. Clinica Chimica Acta . 2019 Jun; 493: 129–137. Allali S, Maciel T, Hermine O, de Montalembert M. Innate immune cells, major protagonists of sickle cell disease pathophysiology. haematologica . 2020 Feb; 105(2): 273. Benenson I, Porter S. Sickle Cell Disease: Bone, Joint, Muscle, and Motor Complications. Orthopedic nursing . 2018 ; 37(4): 221–227. Renoux C, Joly P, Faes C, Mury P, Eglenen B, Turkay M, et al. Association between Oxidative Stress, Genetic Factors, and Clinical Severity in Children with Sickle Cell Anemia. The Journal of pediatrics . 2018 Apr; 195: 228–235. Coelho A, Dias A, Morais A, Nunes B, Faustino P, Lavinha J. Sickle cell disease severity scoring: a yet unsolved problem. European Journal of Haematology . 2012 Oct; 89: 501–502. World Health Organization. WHO Guidelines on Pharmacological Treatment of Persisting Pain in Children with Medical Illnesses: World Health Organization; 2012. Karcioglu O, Topacoglu H, Dikme O, Dikme O. A systematic review of the pain scales in adults: Which to use? The American Journal of Emergency Medicine . 2018 Apr; 36: 707–714. Hounkpe BW, Fiusa MML, Colella MP, da Costa LNG, de Oliveira Benatti R, Saad STO, et al. Role of innate immunity-triggered pathways in the pathogenesis of Sickle Cell Disease: a meta-analysis of gene expression studies. Scientific Reports . 2015 Dec; 5. Hamda CB, Sangeda R, Mwita L, Meintjes A, Nkya S, Panji S, et al. A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study. PLOS ONE . 2018 Jul; 13: e0199461. Schlissel MS, Durum SD, Muegge K. The Interleukin 7 Receptor Is Required for T Cell Receptor γ Locus Accessibility to the V(D)j Recombinase. The Journal of Experimental Medicine . 2000 Mar; 191: 1045–1050. Martin CE, Spasova DS, Frimpong-Boateng K, Kim HO, Lee M, Kim KS, et al. Interleukin-7 Availability Is Maintained by a Hematopoietic Cytokine Sink Comprising Innate Lymphoid Cells and T Cells. Immunity . 2017 Jul; 47: 171–182.e4. Leung GA, Cool T, Valencia CH, Worthington A, Beaudin AE, Forsberg EC. The lymphoid-associated interleukin 7 receptor (IL7R) regulates tissue-resident macrophage development. Development . 2019 Jul; 146: dev176180. Wang M, Windgassen D, Papoutsakis ET. Comparative analysis of transcriptional profiling of CD3+, CD4+ and CD+ T cells identifies novel immune response players in T-Cell activation. BMC Genomics . 2008 ; 9: 225. de la Hera A, Müller U, Olsson C, Isaaz S, Tunnacliffe A. Structure of the T cell antigen receptor (TCR): two CD3 epsilon subunits in a functional TCR/CD3 complex. The Journal of Experimental Medicine . 1991 Jan; 173: 7–17. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Current protocols in bioinformatics . 2016 June; 54(1): 1-30. Maglott D, Ostell J, Pruitt K, Tatusova T. Entrez Gene: gene-centered information at NCBI. Nucleic acids research . 2005 Jan; 33(suppl\_1): D54--D58. The UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research . 2021 Jan; 49(D1): D480–D489. Mabuchi M, Kataoka H, Miura Y, Kim T, Kawaguchi M, Ebi M, et al. Tumor suppressor, AT motif binding factor 1 (ATBF1), translocates to the nucleus with runt domain transcription factor 3 (RUNX3) in response to TGF-β signal transduction. Biochemical and biophysical research communications . 2010 Jul; 398(2): 321--325. Xue M, Chen Ly, Wang Wj, Su Tt, Shi Lh, Wang L, et al. HOTAIR induces the ubiquitination of Runx3 by interacting with Mex3b and enhances the invasion of gastric cancer cells. Gastric Cancer . 2018 Feb; 21: 756–764. Liang Y, He L, Yuan H, Jin Y, Yao Y. Association between RUNX3 promoter methylation and non-small cell lung cancer: a meta-analysis. Journal of thoracic disease . 2014 Jun; 6(6): 694–705. Wang CQ, Krishnan V, Tay LS, Chin DWL, Koh CP, Chooi JY, et al. Disruption of Runx1 and Runx3 Leads to Bone Marrow Failure and Leukemia Predisposition due to Transcriptional and DNA Repair Defects. Cell Reports . 2014 Aug; 8: 767–782. Yanyan Y, Wang L, Guixiong G. The correlation between Runx3 and bronchial asthma. Clinica Chimica Acta . 2018 Dec; 487: 75–79. Yang H, Fu J, Yao L, Hou A, Xue X. Runx3 is a key modulator during the epithelial-mesenchymal transition of alveolar type II cells in animal models of BPD. International Journal of Molecular Medicine . 2017 Sep; 40: 1466–1476. Zhou X, Zhu J, Bian T, Wang R, Gao F. Mislocalization of Runt-related transcription factor 3 results in airway inflammation and airway hyper-responsiveness in a murine asthma model. Experimental and Therapeutic Medicine . 2017 Jul; 14: 2695–2701. Serroukh Y, Gu-Trantien C, Kashani BH, Defrance M, Manh TPV, Azouz A, et al. The transcription factors Runx3 and ThPOK cross-regulate acquisition of cytotoxic function by human Th1 lymphocytes. eLife . 2018 Feb; 7. Ebihara T, Song C, Ryu SH, Plougastel-Douglas B, Yang L, Levanon D, et al. Runx3 specifies lineage commitment of innate lymphoid cells. Nature Immunology . 2015 Sep; 16: 1124–1133. Tanriver Y, Diefenbach A. Transcription factors controlling development and function of innate lymphoid cells. International Immunology . 2014 Feb; 26: 119–128. Li S, Cui HZ, Xu CM, Sun ZW, Tang ZK, Chen HL. RUNX3 protects against acute lung injury by inhibiting the JAK2/STAT3 pathway in rats with severe acute pancreatitis. European Review for Medical and Pharmacological Sciences . 2019 Jun; 23: 5382–5391. Das SS, Sinha R, Chakravorty N. Integrative microRNA and gene expression analysis identifies new drug repurposing candidates for fetal hemoglobin induction in β-hemoglobinopathies. Gene . 2019 Jul 20;706:77-83. Kang B, Tan F, Samit G, Archer D, Sutliff R, Ofori-Acquah S, et al. microRNA-301b reduces PPARγ expression in transgenic sickle mice and in hemin-treated human pulmonary artery endothelial cells (1089.22). The FASEB Journal . 2014 ; 28: 1089–1111. Guo YJ, Liu JX, Guan YW. Hypoxia induced upregulation of miR-301a/b contributes to increased cell autophagy and viability of prostate cancer cells by targeting NDRG2. European review for medical and pharmacological sciences . 2016 ; 20(1): 101–108. Liis Kolberg, Uku Raudvere, Ivan Kuzmin, Priit Adler, Jaak Vilo, Hedi Peterson: g: Profiler—interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update) Nucleic Acids Research, May 2023; doi:10.1093/nar/gkad347 Tables Table 1. Baseline Characteristics of The Study Participants with SCD (n=20). Parameters Steady-State VOC p-values Gender [n (%)] Male 9 (90) 9 (90) 1 Female 1 (10) 1 (10) Age Mean in years ± SD 33± 10.82 34.9± 9.3 0.68 No. of VOC per year ± SD 9.7± 6.16 8.3± 5.38 0.59 No. of Hospital admissions per year Mean ± SD 3.3± 1.83 3.7± 1.64 0.61 White Blood Cell counts Mean in ×10 9 /L ± SD 5.4± 2.95 6.05± 3.91 0.68 Red Blood Cell counts Mean in ×10 12 /L ± SD 4.89± 0.94 4.07± 0.95 0.07 Hemoglobin Mean in g/dL ± SD 11.17± 1.14 10.48± 1.51 0.27 Platelet Mean in ×10 9 /L ± SD 309.19± 205.84 201.2± 118.04 0.17 Hemoglobin F Mean in % ± SD 13.88± 8.3 18.26± 6.02 0.2 Hemoglobin S Mean in % ± SD 79.81± 7.97 76.25± 5.41 0.26 Table 2. Top Ten Down-regulated Genes at p-values of 4. ID Gene Symbol Chromosome Group p-values Fold Change SCD Patients in Steady-State Compared to Healthy Controls TC1100010092.hg.1 EIF4G2; SNORD97 chr11 Multiple_Complex 1.31E-10 -16.33 TC0200007835.hg.1 ACTR2 chr2 Multiple_Complex 3.00E-11 -8.22 TC1500009865.hg.1 ANP32A chr15 Multiple_Complex 1.79E-09 -6.67 TC1700007383.hg.1 RPL23A; SNORD4B; SNORD42B; SNORD42A chr17 Multiple_Complex 1.93E-11 -6.6 TSUnmapped00000264.hg.1 RPL7A Coding 2.88E-12 -6.47 TC0700010538.hg.1 HNRNPA2B1 chr7 Multiple_Complex 3.42E-12 -6.07 TC0600007378.hg.1 HIST1H4J chr6 Coding 1.34E-08 -5.81 TC1500008312.hg.1 IQGAP1 chr15 Multiple_Complex 1.18E-05 -5.64 TC0800010667.hg.1 PDE7A chr8 Multiple_Complex 2.26E-08 -5.64 TC0600011227.hg.1 HIST1H4K chr6 Coding 1.66E-08 -5.6 SCD Patients in VOC Compared to Healthy Controls TC0500007138.hg.1 IL7R chr5 Multiple_Complex 2.75E-10 -12.76 TC1100009200.hg.1 CD3E chr11 Multiple_Complex 2.61E-12 -11.28 TC1700012052.hg.1 ACTG1 chr17 Multiple_Complex 2.01E-07 -10.4 TC1200007758.hg.1 HNRNPA1 chr12 Multiple_Complex 6.70E-10 -9.75 TC0600011173.hg.1 GUSBP2 chr6 Multiple_Complex 2.47E-07 -9.28 TC1700010447.hg.1 CCL5 chr17 Coding 4.51E-08 -9.24 TC0500013430.hg.1 GNB2L1; SNORD95; SNORD96A chr5 Multiple_Complex 6.41E-09 -8.96 TC0100007676.hg.1 LCK chr1 Multiple_Complex 8.11E-10 -8.92 TC0400011548.hg.1 LEF1 chr4 Multiple_Complex 7.22E-09 -8.88 TC0600011517.hg.1 HLA-DPA1 chr6 Multiple_Complex 6.94E-07 -8.79 Table 3. Differentially Regulated Genes in SCD Patients in VOC Compared to SCD Patients in Steady-State. ID Gene Symbol Chromo-some p-values Fold Change VOC vs Steady state VOC vs Healthy Steady state vs Healthy TC0100017110.hg.1 FCMR chr1 2.75E-07 -10.98 -9.24 -1.98 TC0200008268.hg.1 GNLY chr2 1.76E-05 -7.37 -8.92 -1.48 TC1200010850.hg.1 TESPA1 chr12 2.22E-05 -6.3 -8.72 -1.48 TC1700010447.hg.1 CCL5 chr17 7.39E-07 -6.24 -7.92 -1.46 TC0600007657.hg.1 HLA-DQA1 chr6 3.97E-05 -6.1 -6.5 -1.41 TC1900011774.hg.1 EMP3 chr19 2.17E-07 -5.99 -6 -1.34 TC0500012470.hg.1 CD74 chr5 1.06E-06 -5.89 -5.9 -1.32 TC1200006738.hg.1 KLRG1 chr12 0.0001 -5.72 -5.83 -1.19 TC1200012571.hg.1 ITFG2 chr12 8.44E-09 -5.52 -5.82 -1.15 TC2200008641.hg.1 RAC2 chr22 9.48E-07 -5.43 -5.8 -1.07 TC0200011075.hg.1 PTMA chr2 3.16E-08 -5.41 -5.76 -1.07 TC1000011904.hg.1 ABLIM1 chr10 5.35E-07 -5.22 -5.69 -1.06 TC1600006888.hg.1 CIITA chr16 1.76E-07 -5.21 -5.69 1 TC1200012801.hg.1 CS chr12 2.63E-06 -5.19 -5.34 1.01 TC1200012583.hg.1 CD27 chr12 2.89E-05 -5.14 -4.99 1.03 TC1100013190.hg.1 CFL1 chr11 6.80E-07 -4.97 -4.59 1.05 TC0600007650.hg.1 HLA-DRA chr6 7.56E-06 -4.96 -4.53 1.1 TC1900007839.hg.1 FXYD5 chr19 2.52E-08 -4.95 -4.53 1.12 TC1200010616.hg.1 TUBA1B chr12 1.20E-07 -4.8 -4.36 1.14 TC0100007676.hg.1 LCK chr1 6.31E-07 -4.52 -4.31 1.14 TC0300006993.hg.1 CRTAP chr3 2.83E-06 -4.4 -4.27 1.15 TC1100007790.hg.1 CD5 chr11 8.94E-08 -4.39 -4.18 1.16 TC1400006656.hg.1 OXA1L chr14 1.78E-08 -4.36 -4.16 1.18 TC0100013339.hg.1 RUNX3 chr1 2.33E-08 -4.12 -4.08 1.22 TC0100018246.hg.1 LRRC8C chr1 1.94E-06 -4.09 -4.04 1.3 TC0800009819.hg.1 DOK2 chr8 3.24E-06 -4.07 -4.03 1.32 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5397806","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374530050,"identity":"48b82c7a-eabf-4eb9-b97c-0e05e072a0cc","order_by":0,"name":"Safa Taha","email":"","orcid":"","institution":"Arabian Gulf University","correspondingAuthor":false,"prefix":"","firstName":"Safa","middleName":"","lastName":"Taha","suffix":""},{"id":374530488,"identity":"aae85c8a-ff59-45f0-9421-3679eab87fc1","order_by":1,"name":"Hawra Abdulwahab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYDCCAwxsYJqfmfkAkJKQIV6LZDtbAkgLD/FaDM7zGIBowlr4bh9ge/Cz7Z68wWGez69u1FjwMLAfProBnxbJcwnshr1txYYzD/Nus845BnQYT1raDXxaDM4wsEnwbktg7ANqMc5hA2qR4DEjqEXy77YE+4bDPM+Mc/4RqUUaaEvihMM8zI9z24jQInmGsU1a9l9C8sxmNjPm3D4JHjZCfuE7w3xM8s2ZBNt+/sOPP+d8q5PjZz98DK8WBgbGBhiLTQJM4leOCpg/kKJ6FIyCUTAKRg4AAAZgRRxDUCUNAAAAAElFTkSuQmCC","orcid":"","institution":"Arabian Gulf University","correspondingAuthor":true,"prefix":"","firstName":"Hawra","middleName":"","lastName":"Abdulwahab","suffix":""},{"id":374530489,"identity":"82e88484-5cde-46d2-a546-ce5ccdb76998","order_by":2,"name":"Muna aljishi","email":"","orcid":"","institution":"Arabian Gulf University","correspondingAuthor":false,"prefix":"","firstName":"Muna","middleName":"","lastName":"aljishi","suffix":""},{"id":374530490,"identity":"48ee9009-6979-4263-8fcd-00766f7071af","order_by":3,"name":"Ameera Sultan","email":"","orcid":"","institution":"Arabian Gulf University","correspondingAuthor":false,"prefix":"","firstName":"Ameera","middleName":"","lastName":"Sultan","suffix":""},{"id":374530491,"identity":"36e0fbd3-9034-4ae6-b0fc-92de69c0ea29","order_by":4,"name":"Moiz Bakheit","email":"","orcid":"","institution":"Arabian Gulf University","correspondingAuthor":false,"prefix":"","firstName":"Moiz","middleName":"","lastName":"Bakheit","suffix":""},{"id":374530492,"identity":"b13b5381-f6c2-4771-ae49-80aedc0a957f","order_by":5,"name":"Mohamed Belhocine","email":"","orcid":"","institution":"Arabian Gulf University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Belhocine","suffix":""}],"badges":[],"createdAt":"2024-11-05 19:36:01","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5397806/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5397806/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3390/ijms26136338","type":"published","date":"2025-06-30T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68384313,"identity":"a7e0100a-6cdb-425f-8a64-77fdf4a06bd3","added_by":"auto","created_at":"2024-11-06 17:13:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92403,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical Clustering of the differentially regulated genes at a \u003cem\u003ep-value\u003c/em\u003eof \u0026lt;0.05 and a fold change of \u0026lt; -2 or \u0026gt; 2: (\u003cstrong\u003eA\u003c/strong\u003e) 2,073 differentially regulated genes in SCD patients in steady-state compared to healthy controls; (\u003cstrong\u003eB\u003c/strong\u003e) 3,363 differentially regulated genes in SCD patients in VOC compared to healthy controls; (\u003cstrong\u003eC\u003c/strong\u003e) 1,078 differentially regulated genes in SCD patients in VOC compared to SCD patients in steady-state\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5397806/v1/429925dbed895160d67b67fc.png"},{"id":68384310,"identity":"d2dd6ee5-43e6-400a-ba62-d0909ed15f5f","added_by":"auto","created_at":"2024-11-06 17:13:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24709,"visible":true,"origin":"","legend":"\u003cp\u003eSample signals of RUNX3 gene showing four-folds down-regulation in SCD patients in VOC compared to SCD patients in steady-state.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5397806/v1/cb1c21b370bee916cb213ca4.png"},{"id":68384762,"identity":"5994d734-8fdd-4ba6-ac22-b6d8450ab6f1","added_by":"auto","created_at":"2024-11-06 17:21:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42314,"visible":true,"origin":"","legend":"\u003cp\u003eRUNX3 gene expression level and protein concentration: (\u003cstrong\u003eA\u003c/strong\u003e) Average folds change of RUNX3 gene through real-time polymerase chain reaction (qRT-PCR) showing three-folds down-regulation in SCD patients in VOC compared to SCD patients in steady-state at a p-value of 5.517×10\u003csup\u003e-6\u003c/sup\u003e; (\u003cstrong\u003eB\u003c/strong\u003e) The average RUNX3 protein concentration was reduced in SCD patients in VOC compared to SCD patients in steady-state at a p-value of 5.957×10\u003csup\u003e-11\u003c/sup\u003e; (\u003cstrong\u003eC\u003c/strong\u003e) Correlation of fold change of RUNX3 gene expression measured by microarray and qRT-PCR. X-axis represents log2-fold change determined by microarrays; y-axis represents log2-fold change determined by qRT-PCR.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5397806/v1/bbdfb6c940728585dbb7d1e6.png"},{"id":68384311,"identity":"9e42e0f3-1c21-493e-ae21-edcfcf93c969","added_by":"auto","created_at":"2024-11-06 17:13:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":141309,"visible":true,"origin":"","legend":"\u003cp\u003eGO Terms Enrichment Analysis for Genes Upregulated and Downregulated in VOC vs. SCD samples.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5397806/v1/b9ae18f66a84a86e269ba0d0.png"},{"id":85863276,"identity":"602740cc-778e-4fdf-b6d5-bc07100d0c5a","added_by":"auto","created_at":"2025-07-02 12:38:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1244569,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5397806/v1/ecbca1b7-6f7c-4dc8-9222-f2ef517958e2.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eRUNX3 is a Potential Marker for Vaso-Occlusive Crises by a Whole Blood Transcriptomic Analysis in Sickle Cell Disease\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSickle cell disease (SCD) is caused by a mutation in the hemoglobin resulting in the formation of hemoglobin S (HbS) predisposing to hemolytic anemia, painful attacks, and multiple end-organ complications [1,2]. It was the first identified genetic disease [1] and it is the most common hemoglobinopathy in Kingdome of Bahrain. The earliest presentation of SCD is the acute painful vaso-occlusive crisis (VOC). VOC results from a complex mechanism involving adhesive interactions among sickle cells, leukocytes, endothelium, other blood cells, and plasma factors [3]. Microvascular occlusions occur through aggregation of red blood cells, leukocytes, and platelets predisposing to inflammation and intravascular and extravascular hemolysis [4]. There is a continuous interaction between hemostatic and inflammatory systems in SCD which further disseminates the hypercoagulability and inflammatory changes [5] as it\u0026rsquo;s well established that innate immune cells contribute to the formation of inflammation, adhesion, and painful attacks [6].\u003c/p\u003e \u003cp\u003eMoreover, SCD patients have increased susceptibility to infection with encapsulated bacteria such as Streptococcus Pneumoniae, Hemophilus Influenzae, and Salmonella due to splenic infarction together with recurrent VOC resulting in multiple organ infarctions and end-organ damages leading to various complications [7]. In general, complications can occur in any tissue such as acute chest syndrome (ACS), pneumonia, stroke, splenic infarction, avascular necrosis of bone, and osteomyelitis [7]. However, some individuals develop certain complications while others do not regardless of the number of VOC and the degree of anemia [8]. This huge phenotypical variation makes it challenging to define and predict the disease severity and outcomes [9].\u003c/p\u003e \u003cp\u003eThis study aimed to determine the differentially expressed genes in SCD patients in steady-state and in VOC compared to healthy control using microarray. As well as, to investigate the effect of VOC on SCD gene expression analysis to detect genetic markers associated with the VOC. Such a study may enhance our knowledge of the disease pathophysiology and may aid in the development of targeted therapy to treat and prevent VOC.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003e2.1 Characteristics of Participants\u003c/p\u003e\n\u003cp\u003eThe study enrolled twenty sickle cell disease patients of which ten patients had VOC. The summary of the patient\u0026rsquo;s characteristics is shown in Table 1. The statistical analysis showed no significant differences between both groups in baseline characteristics (p-value \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e2.2 Determination of the differentially expressed genes\u003c/p\u003e\n\u003cp\u003eThe analysis identified 2073 dysregulated genes in which 736 genes were up-regulated (p \u0026lt;0.05) with a fold change of \u0026gt; 2, and 1337 genes were down-regulated (p\u0026lt;0.05) with a fold change of \u0026lt; -2 in SCD patients in steady-state compared to healthy controls, Figure 1\u003cstrong\u003e(A)\u003c/strong\u003e. Whereas in SCD patients in VOC compared to healthy controls, 3,363 genes were differentially regulated including 1080 genes that were up-regulated (p\u0026lt;0.05) with a fold change of \u0026gt; 2 and 2,283 genes were down-regulated (p\u0026lt;0.05) with a fold change of \u0026lt; -2, Figure 1\u003cstrong\u003e(B)\u003c/strong\u003e. In addition, 1078 genes were differentially expressed including 410 up-regulated genes and 668 down-regulated genes in SCD patients in VOC compared to steady-state (p\u0026lt;0.05) with a fold change of \u0026gt; 2 and \u0026lt; -2, respectively, Figure 1\u003cstrong\u003e(C)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, the assessment of down-regulated genes revealed 47 genes to be down-regulated in SCD patients in steady-state compared to healthy controls at a p-value of \u0026lt;0.001 and a fold change of \u0026gt; 4, while in SCD patients in VOC compared to healthy controls 255 genes were down-regulated, Figure 2.Table 2. Additionally, 79 genes were down-regulated in SCD patients in VOC compared to steady-state\u003c/p\u003e\n\u003cp\u003e2.3 Potential genetic marker for Vaso-occlusive crisis\u003c/p\u003e\n\u003cp\u003eTo identify potential genetic markers for VOC, a GO term enrichment analysis was conducted to reveal key biological processes affected during this condition. The analysis showed a significant up-regulation of genes associated with processes like cellular localization, signal transduction regulation, and ubiquitin ligase complex involvement (Figure 4). These terms suggest an increase in cellular activity and regulatory functions, likely indicative of heightened cellular demands and stress responses associated with the inflammatory and hypoxic environment during VOC. This increased activity may be central to the mechanisms of cell damage and adaptation underlying the Vaso-occlusive state.\u003c/p\u003e\n\u003cp\u003eIn contrast, down-regulated genes were notably associated with terms related to immune system processes, MHC protein complex binding and T cell receptor binding (Figure 4). These findings suggest a suppression of immune response pathways and a reduction in cellular activities essential for maintaining immune cell function and cellular homeostasis. Such down-regulation of immune-related and hematopoietic pathways could impact immune signaling and cellular stability, possibly contributing to impaired cellular responses in SCD patients experiencing VOC.\u003c/p\u003e\n\u003cp\u003eIn focusing further on genes with significant deregulation, particularly those implicated in immune and hematopoietic regulation, we identified a subset of 79 down-regulated genes. To refine our search for a robust VOC marker, we selected those genes that were consistently down-regulated with a four-fold change in SCD patients during VOC compared to steady-state and healthy controls (p-value \u0026lt; 0.001) but not more than two-fold up-regulated in steady-state patients compared to healthy controls (p-value \u0026lt; 0.05). This filtering left 26 genes (Table 3) with a high likelihood of true down-regulation during VOC.\u003c/p\u003e\n\u003cp\u003eAmong these, RUNX3 (RUNX Family Transcription Factor 3) emerged as a promising candidate for further analysis. RUNX3 showed a four-fold down-regulation in SCD patients during VOC compared to steady-state levels (p=2.33\u0026times;10⁻⁸), as validated by qRT-PCR and ELISA (Figure 3A).\u003c/p\u003e\n\u003cp\u003e2.4 Validation of RUNX3 through qRT-PCR and ELISA\u003c/p\u003e\n\u003cp\u003eThe analysis of RUNX3 gene expression by qRT-PCR showed statistically significant down-regulation in SCD patients in VOC compared to SCD patients in steady-state with three-fold changes (p=5.517\u0026times;10-6), Figure 3\u003cstrong\u003e(A)\u003c/strong\u003e and \u003cstrong\u003e(C)\u003c/strong\u003e. Moreover, the measurement of RUNX3 protein concentration using ELISA showed a significant reduction in the concentration in SCD patients in VOC (11.13 pg/ml) compared to SCD patients in steady-state (457.93 pg./ml) (p=5.957\u0026times;10-11) Figure 3\u003cstrong\u003eB)\u003c/strong\u003e.\u003c/p\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eIn SCD, gene expression meta-analysis studies done on the West African population identified several biological pathways to be associated with SCD through enrichment analysis. The innate immunity pathway was recognized among the major pathways [12, 13]. The IL7R (Interleukin 7 Receptor) and TRAT1 (T Cell Receptor Associated Transmembrane Adaptor 1) genes were among the top down-regulated in the previous meta-analysis studies as well as in our study. The IL7R protein is involved in the adaptive immune system and has a crucial part in V(D)J recombination during lymphocyte development [14], also it has a relation with other pathways such as hematopoietic cell lineage and extracellular signal-regulated kinases [15, 16]. Whereas the TRAT1 gene is a protein-coding gene that plays a role in the adaptive immune system as it stabilizes the T-cell antigen receptor (TCR)-CD3 complex at the surface of T-cells and is related to pathways in downstream signaling [17]. Moreover, the CD3E (CD3e Molecule) gene which coded for CD3-epsilon polypeptide was remarkably down-regulated in the steady-state and VOC group. This protein is involved in the activation of downstream signaling pathways, it has an important role in T-cell development and initiation of TCR-CD3 complex assembly [18].\u003c/p\u003e \u003cp\u003eFurthermore, our study was the first to enroll Arabs with SCD in whole-blood gene expression analysis. Only in our study, the RUNX3 (RUNX Family Transcription Factor 3) was significantly down-regulated in SCD patients in VOC compared to SCD patients in steady-state and this was further confirmed by qRT-PCR and ELISA. These results suggest that the RUNX3 gene may serve as a potential genetic marker for the development of VOC in SDC patients. Additionally, the RUNX3 was not identified in previous studies characterizing the gene expression in SCD patients as well and no study assessed its role at the transcriptional level in SCD patients.\u003c/p\u003e \u003cp\u003eRUNX3 is a member of the runt domain-containing family of transcription factors located on chromosome 1p36.11 and formed of 65,647 bases [19]. It functions in activating or suppressing transcription through binding of its heterodimer protein form to the core DNA sequence 5'-TGTGGT-3' in some enhancers and promoters after forming a complex with beta subunit forms (heterodimeric complex core-binding factor with Core-Binding Factor Subunit Beta (CBFB)), as well as it interacts with other transcription factors [20]. Also, it works as a tumor suppressor as it is found to be silenced or deleted in cancer [21, 22]. Additionally, it may have a role in controlling cellular proliferation and differentiation [21].\u003c/p\u003e \u003cp\u003eSeveral studies assessed the role of RUNX3 in malignancies such as gastric, colon, and lung cancers [23, 24]. Besides, dysfunction of RUNX3 resulted in defects of transcriptional regulation and DNA repair leading to bone marrow failure which may progress to leukemia [25]. Moreover, Yanyan and co-workers reviewed data from animal and human studies which related RUNX3 to the pathogenesis of bronchial asthma part of it due to its involvement in the regulation of Th1/Th2 balance and the other part associated with its effect in modifying the several immune cell differentiations such as innate lymphoid cells, Treg cells, and dendritic cells [26]. In some reports, it was suggested that oxidative stress induced phosphorylation of RUNX3 leading to its cytoplasmic localization and subsequently inactivation mediating carcinogenesis along with hypersensitivity [27, 28]. The effect of RUNX3 on Th1/Th2 balance was through its involvement in class I Major Histocompatibility Complex assortment of T cells particularly CD8\u0026thinsp;+\u0026thinsp;during their development and it was found that silencing of RUNX3 expression may inhibit Th1 cytokine release while facilitating Th2 cytokines secretion [29]. Additionally, it has a critical function in the differentiation of innate lymphoid cells by altering CD8\u0026thinsp;+\u0026thinsp;T cells, Th1, and spleen natural killer cells differentiation [30]; which in turn can predispose to the development of inflammatory disease [31].\u003c/p\u003e \u003cp\u003eMoreover, RUNX3 was found to be protective against acute lung injury in rats with severe acute pancreatitis as up-regulation of RUNX3 resulted in increasing polymorphonuclear neutrophil apoptosis and inhibition of Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) phosphorylation which in turn decreases the progression of inflammatory response and subsequently organ damage [32].\u003c/p\u003e \u003cp\u003eFurthermore, Das et al. investigated the role of microRNA in modulating the HbF induction pathway by comparing the gene expression between individuals with high HbF levels including subjects with hereditary persistence of fetal hemoglobin and β-thalassemia minor with those having normal HbF. The study identified 931 differentially regulated genes and 19 differentially expressed miRNAs. In which two miRNAs were inversely correlated with RUNX3 mRNA: high expression of miRNA-301b and miRNA-582-5p resulted in decreased expression of RUNX3 [33]. Interestingly, up-regulation of miRNA-301b was suggested to be triggered by hemolysis and hypoxia [34, 35]. Add together, decreased expression of RUNX3 disrupts the inflammatory balance predisposing to tissue inflammation and injury which forms the basis of VOC pathophysiology.\u003c/p\u003e \u003cp\u003eIn conclusion, analysis of the transcriptional changes in SCD during steady-state and VOC resulted in the detection of genes for the first time to be associated with SCD and were significantly differentially expressed. Amongst these genes, RUNX3 plays a role in immune cell differentiation and inflammation. It may serve as a potential genetic biomarker and further validation in a larger sample size is recommended. Additionally, studying the RUNX3 pathway in relation to the disease pathogenesis may aid in the discovery of novel therapeutic targets and enable the development of personalized medicine in managing SCD patients.\u003c/p\u003e"},{"header":"4 Materials and Methods","content":"\u003cp\u003e4.1 Study population\u003c/p\u003e\n\u003cp\u003eTwenty Bahraini patients with SCD (10 at steady state and 10 with VOC) and eight healthy participants from Salmaniya Medical Complex were recruited in this cross-sectional study in September 2019. Approval from the Research and Ethics Committee of the Arabian Gulf University, and the Research Technical Support Team of the Ministry of Health, Kingdom of Bahrain were obtained. Written informed written consents were obtained from each study participant. All experiments and methods were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003eThe healthy volunteers were confirmed to have the Hemoglobin AA genotype by High-Performance Liquid Chromatography (HPLC), while all patients with SCD were confirmed to have the HbSS genotype. SCD patients were divided into two groups of ten participants each: SCD patients in a steady state defined as participants without any history of VOC that required neither evaluation in an emergency department nor hospital admission 12 weeks prior to the study enrollment; and SCD patients during VOC defined as participants with a history of acute, severe pain at the time of enrollment (self-rated score of \u0026ge; 7 out of 10 on a Numerical Rating Scale (NRS)) [10, 11]. All SCD patients were not under treatment with hydroxyurea.\u003c/p\u003e\n\u003cp\u003e4.2 Sample collection\u003c/p\u003e\n\u003cp\u003eFor all subjects, blood samples were collected in two separate tubes. For the VOC group, the samples were collected within the first 48 hours of the crisis. First, 5 milliliters of venous blood were collected in a serum-separating tube and kept for 30 minutes at room temperature for clot formation and then centrifuged at 3,500 rpm for 15 minutes. The separated serum was stored at -80 \u0026deg;C until the analysis. In the second container, 2.5 milliliters of venous blood were collected in PAXgene\u0026reg; Blood RNA Tube (PreAnalytiX GmbH, Hombrechtikon, Switzerland) for immediate stabilization of intracellular RNA and was kept for a minimum of 2 hours at room temperature to allow for complete lysis of blood cells, and then stored at 4 \u0026deg;C until the analysis that was carried out within 3 days.\u003c/p\u003e\n\u003cp\u003e4.3 RNA extraction and gene expression analysis\u003c/p\u003e\n\u003cp\u003eRNA was extracted from whole blood samples using a PAXgene\u0026reg; Blood RNA kit (PreAnalytiX GmbH, Hombrechtikon, Switzerland) following the manufacturer\u0026rsquo;s instructions. The quantity and purity of RNA samples were determined using the NanoDrop 1000 Spectrophometer (Thermo Fisher Scientific, Inc., Waltham, MA, USA) and the acceptable RNA purity of A260/A280 was 1.8-2.2. The RNA integrity was assessed using 1.2% agarose gel electrophoresis. All RNA samples were stored at \u0026minus;80 \u0026deg;C until further analysis.\u003c/p\u003e\n\u003cp\u003eThe assessment of gene expression was carried out using Affymetrix ClariomTM S Assays for human and GeneChip\u0026trade; WT PLUS Hybridization, Wash and Stain Kit (Applied Biosystems\u0026trade;, California, USA) according to the manufacturer\u0026rsquo;s protocol. In brief, reverse transcription of 100 ng of total RNA of each sample was converted to double-stranded cDNA using the T7 promoter sequence primer. Followed by the synthesis and amplification of cRNA by an in vitro transcription of the second-stranded cDNA using T7 RNA polymerase. Then through reverse transcription of cRNA, the second cycle of single-stranded cDNA was synthesized, which contains dUTP. After hydrolyzing the RNA, 5.5 \u0026mu;g of purified single-stranded cDNA was fragmented using uracil-DNA glycosylase and apurinic/apyrimidinic endonuclease 1. Next, by terminal deoxynucleotidyl transferase, the fragmented cDNA was labeled with DNA Labeling Reagent which binds to biotin. The fragmented and biotin-labeled single-stranded cDNA samples were hybridized to GeneChip\u0026trade; WT PLUS for sixteen hours in Affymetrix GeneChip\u0026reg; Hybridization Oven 645. Followed by washing and staining using the Affymetrix GeneChip\u0026reg; Fluidics Station 450 and Affymetrix\u0026reg; GeneChip\u0026reg; Command Console\u0026trade; (AGCC) software. Finally, the arrays were scanned using Affymetrix GeneChip\u0026reg; Scanner 3000 7G.\u003c/p\u003e\n\u003cp\u003e4.4 Quantitative real-time polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eReal-time polymerase chain reaction (PCR) was performed to measure the expression of the RUNX Family Transcription Factor 3 (RUNX3) gene and normalized to GAPDH as a housekeeping gene. The reaction mixture for the SYBR Green assay contained 2\u0026times; SYBR\u0026trade; Select Master Mix (Applied Biosystems\u0026trade;, California, USA), 10 pmol of each forward and reverse primers (metabion international AG, Planegg, Germany), and 50 ng of cDNA.\u003c/p\u003e\n\u003cp\u003eThe sequences of the primers for RUNX3 and GAPDH were as follows: RUNX3 forward primer, 5\u0026apos;- GGCAATGACGAGAACTACTCCG -3\u0026apos;, and RUNX3 reverse primer 5\u0026apos;- GATGGTCAGGGTGAAACTCTTCC -3\u0026apos;; GAPDH forward primer 5\u0026apos;-TCCCTGAGCTGAACGGGAAG-3\u0026apos;, and GAPDH reverse primer 5\u0026apos;- GGAGTGGGTGTCGCTGT -3\u0026apos;.\u003c/p\u003e\n\u003cp\u003eThe reaction was carried out in 20 \u0026micro;L capillaries and incubated in Light Cycler\u0026reg; 2.0 (Roche). The used LightCycler run protocol was as follows: Denaturation program at 95˚C for 10 min, amplification, and quantification program repeated 45 times at 95˚C for 10 sec, 60˚C for 30 sec, and 72˚C for 30 sec, and finally a cooling step at 40˚C for 30 sec. The accumulation of PCR products during each cycle was determined by observing the rise in fluorescence of DNA-binding SYBR Green. Afterward, the crossing point of each sample was detected and normalized to the expression of the housekeeping gene. Then the fold change of expression was calculated using the 2^-\u0026Delta;\u0026Delta;Ct method.\u003c/p\u003e\n\u003cp\u003e4.5 Enzyme-linked immunosorbent assay\u003c/p\u003e\n\u003cp\u003eThe protein produced by the RUNX3 gene was measured by Enzyme-Linked Immunosorbent Assay (ELISA) (MyBioSource, California, USA) according to the manufacturer\u0026rsquo;s instructions. All standards and samples were assigned in duplicate to the plate, and the assay was run by adding 50 \u0026micro;L per well of diluted standard to the standard well. Then, 40 \u0026mu;L of Sample Diluent was pipetted to each sample well and combined with 10 \u0026mu;L of sample. After incubation and washing, 50 \u0026micro;L of HRP-Conjugated detection antibody was added, incubated, and washed. After that, 50 \u0026micro;L of each chromogen solution A and B were pipetted into each well, incubated, and washed. Then, a 50 \u0026micro;L of Stop Solution was added for reaction termination when the desired blue color intensity was attenuated. Immediately, the optical density (OD) at 450 nm was measured for each well using Synergy\u0026trade; HTX Multi-Mode Microplate Reader (BioTek\u0026reg;) (BioTek Instruments, Inc., Winooski, VT, USA) and analyzed by Gen5 2.07.17.\u003c/p\u003e\n\u003cp\u003e4.6 Statistical analysis\u003c/p\u003e\n\u003cp\u003eThe demographic data were analyzed for their differences in SCD patients in steady state and during VOC. The values of continuous data were analyzed by Student\u0026rsquo;s two-sided unpaired t-test and presented in mean \u0026plusmn; standard deviation (SD). The categorical variables were presented in numbers (percentage) and were analyzed using Fisher\u0026rsquo;s exact probability test. A p-value of \u0026lt; 0.05 was considered significant.\u003c/p\u003e\n\u003cp\u003eThe Transcriptome Analysis Console (TAC) software version 4.0.0.25 by Thermo Fisher Scientific was used to define the differential expression profile within the different groups, perform statistical analysis, and provide a list of differentially expressed genes. Genes with a fold change of \u0026gt;2 or \u0026lt;-2 and with a t-test or ANOVA P‐value of \u0026lt;0.05 were considered significantly altered between the conditions of each group.\u003c/p\u003e\n\u003cp\u003eThe Light Cycler Software version 4.1.1.21 was used for the analysis of qRT-PCR results by identifying the crossing points for the target and the reference gene in each sample. The average of crossing points for each target gene was calculated in relative to the housekeeping gene GAPDH in all the groups using relative Mono-Color Relative Quantification assay. Then the fold change was calculated using the delta Ct (2^-\u0026Delta;\u0026Delta;Ct) method.\u003c/p\u003e\n\u003cp\u003e4.7 Gene Ontology (GO) Term Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eThe GO term enrichment analysis was performed to identify differentially deregulated GO terms between SCD patients in steady state and during VOC using the g: Profiler (version: e111_eg58_p18_f463989d). g: Profiler enables the comprehensive analysis of Gene Ontology (GO) terms, covering the biological process (BP), cellular component (CC), and molecular function (MF) domains to investigate pathway enrichment across differentially expressed genes. Input gene lists were based on significant differential expression results, with criteria set for adjusted p-values (threshold: FDR \u0026lt; 0.05) to ensure a high-confidence identification of GO terms (36).\u003c/p\u003e\n\u003cp\u003eThe enrichment results were visualized as bar plots, illustrating the distribution and -log10 transformed adjusted p-values for upregulated and downregulated GO terms, thereby highlighting terms with significant deregulation between the VOC and SCD samples.\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participant:\u003c/strong\u003e Ethical approval obtained from Research and Ethics Committee of the Arabian Gulf University reference number E003-PI-04/18 and the Research Technical Support Team of the Ministry of Health reference number AURS/156/2019. An informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, S.T. and H.A.; methodology, S.T. and H.A.; software, S.T. and H.A.; formal analysis, S.T. and H.A.; investigation, H.A., M.A and A.S.; data curation, S.A.; writing—original draft preparation, H.A.; writing—review and editing, S.A., M.A, A.S and M.B; supervision, S.T. and M.B.; project administration, M.B.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by the COLLEGE OF MEDICINE AND MEDICAL SCIENCES, ARABIAN GULF UNIVERSITY, grant number E003-PI-04/18.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data:\u003c/strong\u003e Data will be available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMason VR. SICKLE CELL ANEMIA. \u003cem\u003eJAMA\u003c/em\u003e. \u003cstrong\u003e1922\u003c/strong\u003e Oct; 79: 1318.\u003c/li\u003e\n \u003cli\u003eRees DC, Williams TN, Gladwin MT. Sickle-cell disease. \u003cem\u003eThe Lancet\u003c/em\u003e. \u003cstrong\u003e2010\u003c/strong\u003e Dec; 376: 2018\u0026ndash;2031.\u003c/li\u003e\n \u003cli\u003eManwani D, Frenette PS. Vaso-occlusion in sickle cell disease: pathophysiology and novel targeted therapies. \u003cem\u003eBlood\u003c/em\u003e. \u003cstrong\u003e2013\u003c/strong\u003e Dec; 122: 3892\u0026ndash;3898.\u003c/li\u003e\n \u003cli\u003ePinto VM, Balocco M, Quintino S, Forni GL. Sickle cell disease: a review for the internist. \u003cem\u003eInternal and emergency medicine\u003c/em\u003e. \u003cstrong\u003e2019\u003c/strong\u003e Oct; 14(7): 1051\u0026ndash;1064.\u003c/li\u003e\n \u003cli\u003ede O. Toledo SL, Guedes JVM, Alpoim PN, Rios DRA, de B. Pinheiro M. Sickle cell disease: Hemostatic and inflammatory changes, and their interrelation. \u003cem\u003eClinica Chimica Acta\u003c/em\u003e. \u003cstrong\u003e2019\u003c/strong\u003e Jun; 493: 129\u0026ndash;137.\u003c/li\u003e\n \u003cli\u003eAllali S, Maciel T, Hermine O, de Montalembert M. Innate immune cells, major protagonists of sickle cell disease pathophysiology. \u003cem\u003ehaematologica\u003c/em\u003e. \u003cstrong\u003e2020\u003c/strong\u003e Feb; 105(2): 273.\u003c/li\u003e\n \u003cli\u003eBenenson I, Porter S. Sickle Cell Disease: Bone, Joint, Muscle, and Motor Complications. \u003cem\u003eOrthopedic nursing\u003c/em\u003e. \u003cstrong\u003e2018\u003c/strong\u003e; 37(4): 221\u0026ndash;227.\u003c/li\u003e\n \u003cli\u003eRenoux C, Joly P, Faes C, Mury P, Eglenen B, Turkay M, et al. Association between Oxidative Stress, Genetic Factors, and Clinical Severity in Children with Sickle Cell Anemia. \u003cem\u003eThe Journal of pediatrics\u003c/em\u003e. \u003cstrong\u003e2018\u003c/strong\u003e Apr; 195: 228\u0026ndash;235.\u003c/li\u003e\n \u003cli\u003eCoelho A, Dias A, Morais A, Nunes B, Faustino P, Lavinha J. Sickle cell disease severity scoring: a yet unsolved problem. \u003cem\u003eEuropean Journal of Haematology\u003c/em\u003e. \u003cstrong\u003e2012\u003c/strong\u003e Oct; 89: 501\u0026ndash;502.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. WHO Guidelines on Pharmacological Treatment of Persisting Pain in Children with Medical Illnesses: World Health Organization; 2012.\u003c/li\u003e\n \u003cli\u003eKarcioglu O, Topacoglu H, Dikme O, Dikme O. A systematic review of the pain scales in adults: Which to use? \u003cem\u003eThe American\u003c/em\u003e\u003cem\u003eJournal of Emergency Medicine\u003c/em\u003e. \u003cstrong\u003e2018\u003c/strong\u003e Apr; 36: 707\u0026ndash;714.\u003c/li\u003e\n \u003cli\u003eHounkpe BW, Fiusa MML, Colella MP, da Costa LNG, de Oliveira Benatti R, Saad STO, et al. Role of innate immunity-triggered pathways in the pathogenesis of Sickle Cell Disease: a meta-analysis of gene expression studies. \u003cem\u003eScientific Reports\u003c/em\u003e. \u003cstrong\u003e2015\u003c/strong\u003e Dec; 5.\u003c/li\u003e\n \u003cli\u003eHamda CB, Sangeda R, Mwita L, Meintjes A, Nkya S, Panji S, et al. A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cstrong\u003e2018\u003c/strong\u003e Jul; 13: e0199461.\u003c/li\u003e\n \u003cli\u003eSchlissel MS, Durum SD, Muegge K. The Interleukin 7 Receptor Is Required for T Cell Receptor \u0026gamma; Locus Accessibility to the V(D)j Recombinase. \u003cem\u003eThe Journal of Experimental Medicine\u003c/em\u003e. \u003cstrong\u003e2000\u003c/strong\u003e Mar; 191: 1045\u0026ndash;1050.\u003c/li\u003e\n \u003cli\u003eMartin CE, Spasova DS, Frimpong-Boateng K, Kim HO, Lee M, Kim KS, et al. Interleukin-7 Availability Is Maintained by a Hematopoietic Cytokine Sink Comprising Innate Lymphoid Cells and T Cells. \u003cem\u003eImmunity\u003c/em\u003e. \u003cstrong\u003e2017\u003c/strong\u003e Jul; 47: 171\u0026ndash;182.e4.\u003c/li\u003e\n \u003cli\u003eLeung GA, Cool T, Valencia CH, Worthington A, Beaudin AE, Forsberg EC. The lymphoid-associated interleukin 7 receptor (IL7R) regulates tissue-resident macrophage development. \u003cem\u003eDevelopment\u003c/em\u003e. \u003cstrong\u003e2019\u003c/strong\u003e Jul; 146: dev176180.\u003c/li\u003e\n \u003cli\u003eWang M, Windgassen D, Papoutsakis ET. Comparative analysis of transcriptional profiling of CD3+, CD4+ and CD+ T cells identifies novel immune response players in T-Cell activation. \u003cem\u003eBMC Genomics\u003c/em\u003e. \u003cstrong\u003e2008\u003c/strong\u003e; 9: 225.\u003c/li\u003e\n \u003cli\u003ede la Hera A, M\u0026uuml;ller U, Olsson C, Isaaz S, Tunnacliffe A. Structure of the T cell antigen receptor (TCR): two CD3 epsilon subunits in a functional TCR/CD3 complex. \u003cem\u003eThe Journal of Experimental Medicine\u003c/em\u003e. \u003cstrong\u003e1991\u003c/strong\u003e Jan; 173: 7\u0026ndash;17.\u003c/li\u003e\n \u003cli\u003eStelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. \u003cem\u003eCurrent protocols in bioinformatics\u003c/em\u003e. \u003cstrong\u003e2016\u003c/strong\u003e June; 54(1): 1-30.\u003c/li\u003e\n \u003cli\u003eMaglott D, Ostell J, Pruitt K, Tatusova T. Entrez Gene: gene-centered information at NCBI. \u003cem\u003eNucleic acids research\u003c/em\u003e. \u003cstrong\u003e2005\u003c/strong\u003e Jan; 33(suppl\\_1): D54--D58.\u003c/li\u003e\n \u003cli\u003eThe UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. \u003cem\u003eNucleic Acids Research\u003c/em\u003e. \u003cstrong\u003e2021\u003c/strong\u003e Jan; 49(D1): D480\u0026ndash;D489.\u003c/li\u003e\n \u003cli\u003eMabuchi M, Kataoka H, Miura Y, Kim T, Kawaguchi M, Ebi M, et al. Tumor suppressor, AT motif binding factor 1 (ATBF1), translocates to the nucleus with runt domain transcription factor 3 (RUNX3) in response to TGF-\u0026beta; signal transduction. \u003cem\u003eBiochemical and biophysical research communications\u003c/em\u003e. \u003cstrong\u003e2010\u003c/strong\u003e Jul; 398(2): 321--325.\u003c/li\u003e\n \u003cli\u003eXue M, Chen Ly, Wang Wj, Su Tt, Shi Lh, Wang L, et al. HOTAIR induces the ubiquitination of Runx3 by interacting with Mex3b and enhances the invasion of gastric cancer cells. \u003cem\u003eGastric Cancer\u003c/em\u003e. \u003cstrong\u003e2018\u003c/strong\u003e Feb; 21: 756\u0026ndash;764.\u003c/li\u003e\n \u003cli\u003eLiang Y, He L, Yuan H, Jin Y, Yao Y. Association between RUNX3 promoter methylation and non-small cell lung cancer: a meta-analysis. \u003cem\u003eJournal of thoracic disease\u003c/em\u003e. \u003cstrong\u003e2014\u003c/strong\u003e Jun; 6(6): 694\u0026ndash;705.\u003c/li\u003e\n \u003cli\u003eWang CQ, Krishnan V, Tay LS, Chin DWL, Koh CP, Chooi JY, et al. Disruption of Runx1 and Runx3 Leads to Bone Marrow Failure and Leukemia Predisposition due to Transcriptional and DNA Repair Defects. \u003cem\u003eCell Reports\u003c/em\u003e. \u003cstrong\u003e2014\u003c/strong\u003e Aug; 8: 767\u0026ndash;782.\u003c/li\u003e\n \u003cli\u003eYanyan Y, Wang L, Guixiong G. The correlation between Runx3 and bronchial asthma. \u003cem\u003eClinica Chimica Acta\u003c/em\u003e. \u003cstrong\u003e2018\u003c/strong\u003e Dec; 487: 75\u0026ndash;79.\u003c/li\u003e\n \u003cli\u003eYang H, Fu J, Yao L, Hou A, Xue X. Runx3 is a key modulator during the epithelial-mesenchymal transition of alveolar type II cells in animal models of BPD. \u003cem\u003eInternational Journal of Molecular Medicine\u003c/em\u003e. \u003cstrong\u003e2017\u003c/strong\u003e Sep; 40: 1466\u0026ndash;1476.\u003c/li\u003e\n \u003cli\u003eZhou X, Zhu J, Bian T, Wang R, Gao F. Mislocalization of Runt-related transcription factor 3 results in airway inflammation and airway hyper-responsiveness in a murine asthma model. \u003cem\u003eExperimental and Therapeutic Medicine\u003c/em\u003e. \u003cstrong\u003e2017\u003c/strong\u003e Jul; 14: 2695\u0026ndash;2701.\u003c/li\u003e\n \u003cli\u003eSerroukh Y, Gu-Trantien C, Kashani BH, Defrance M, Manh TPV, Azouz A, et al. The transcription factors Runx3 and ThPOK cross-regulate acquisition of cytotoxic function by human Th1 lymphocytes. \u003cem\u003eeLife\u003c/em\u003e. \u003cstrong\u003e2018\u003c/strong\u003e Feb; 7.\u003c/li\u003e\n \u003cli\u003eEbihara T, Song C, Ryu SH, Plougastel-Douglas B, Yang L, Levanon D, et al. Runx3 specifies lineage commitment of innate lymphoid cells. \u003cem\u003eNature Immunology\u003c/em\u003e. \u003cstrong\u003e2015\u003c/strong\u003e Sep; 16: 1124\u0026ndash;1133.\u003c/li\u003e\n \u003cli\u003eTanriver Y, Diefenbach A. Transcription factors controlling development and function of innate lymphoid cells. International \u003cem\u003eImmunology\u003c/em\u003e. \u003cstrong\u003e2014\u003c/strong\u003e Feb; 26: 119\u0026ndash;128.\u003c/li\u003e\n \u003cli\u003eLi S, Cui HZ, Xu CM, Sun ZW, Tang ZK, Chen HL. RUNX3 protects against acute lung injury by inhibiting the JAK2/STAT3 pathway in rats with severe acute pancreatitis. \u003cem\u003eEuropean Review for Medical and Pharmacological Sciences\u003c/em\u003e. \u003cstrong\u003e2019\u003c/strong\u003e Jun; 23: 5382\u0026ndash;5391.\u003c/li\u003e\n \u003cli\u003eDas SS, Sinha R, Chakravorty N. Integrative microRNA and gene expression analysis identifies new drug repurposing candidates for fetal hemoglobin induction in \u0026beta;-hemoglobinopathies. \u003cem\u003eGene\u003c/em\u003e. \u003cstrong\u003e2019\u003c/strong\u003e Jul 20;706:77-83.\u003c/li\u003e\n \u003cli\u003eKang B, Tan F, Samit G, Archer D, Sutliff R, Ofori-Acquah S, et al. microRNA-301b reduces PPAR\u0026gamma; expression in transgenic sickle mice and in hemin-treated human pulmonary artery endothelial cells (1089.22). \u003cem\u003eThe FASEB Journal\u003c/em\u003e. \u003cstrong\u003e2014\u003c/strong\u003e; 28: 1089\u0026ndash;1111.\u003c/li\u003e\n \u003cli\u003eGuo YJ, Liu JX, Guan YW. Hypoxia induced upregulation of miR-301a/b contributes to increased cell autophagy and viability of prostate cancer cells by targeting NDRG2. \u003cem\u003eEuropean review for medical and pharmacological sciences\u003c/em\u003e. \u003cstrong\u003e2016\u003c/strong\u003e; 20(1): 101\u0026ndash;108.\u003c/li\u003e\n \u003cli\u003eLiis Kolberg, Uku Raudvere, Ivan Kuzmin, Priit Adler, Jaak Vilo, Hedi Peterson: g: Profiler\u0026mdash;interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update) Nucleic Acids Research, May 2023; doi:10.1093/nar/gkad347\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Baseline Characteristics of The Study Participants with SCD (n=20).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"75%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 52%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSteady-State\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVOC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 18%;\"\u003e\n \u003cp\u003eGender [n (%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e9 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e9 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e1 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e1 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52%;\"\u003e\n \u003cp\u003eAge Mean in years \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e33\u0026plusmn; 10.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e34.9\u0026plusmn; 9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52%;\"\u003e\n \u003cp\u003eNo. of VOC per year \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e9.7\u0026plusmn; 6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e8.3\u0026plusmn; 5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52%;\"\u003e\n \u003cp\u003eNo. of Hospital admissions per year Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3.3\u0026plusmn; 1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e3.7\u0026plusmn; 1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52%;\"\u003e\n \u003cp\u003eWhite Blood Cell counts Mean in \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e5.4\u0026plusmn; 2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e6.05\u0026plusmn; 3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52%;\"\u003e\n \u003cp\u003eRed Blood Cell counts Mean in \u0026times;10\u003csup\u003e12\u003c/sup\u003e/L \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e4.89\u0026plusmn; 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e4.07\u0026plusmn; 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52%;\"\u003e\n \u003cp\u003eHemoglobin Mean in g/dL \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e11.17\u0026plusmn; 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e10.48\u0026plusmn; 1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52%;\"\u003e\n \u003cp\u003ePlatelet Mean in \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e309.19\u0026plusmn; 205.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e201.2\u0026plusmn; 118.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52%;\"\u003e\n \u003cp\u003eHemoglobin F Mean in % \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e13.88\u0026plusmn; 8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e18.26\u0026plusmn; 6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 52%;\"\u003e\n \u003cp\u003eHemoglobin S Mean in % \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e79.81\u0026plusmn; 7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15%;\"\u003e\n \u003cp\u003e76.25\u0026plusmn; 5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eTop Ten Down-regulated Genes at p-values of \u0026lt; 0.001 and a fold change of \u0026gt; 4.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"702\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Symbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-values\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFold Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCD Patients in Steady-State Compared to Healthy Controls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC1100010092.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eEIF4G2; SNORD97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e1.31E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-16.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0200007835.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eACTR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e3.00E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-8.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC1500009865.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eANP32A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e1.79E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC1700007383.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eRPL23A; SNORD4B; SNORD42B; SNORD42A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e1.93E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTSUnmapped00000264.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eRPL7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eCoding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e2.88E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0700010538.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eHNRNPA2B1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e3.42E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0600007378.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eHIST1H4J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eCoding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e1.34E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC1500008312.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eIQGAP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e1.18E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0800010667.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003ePDE7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e2.26E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0600011227.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eHIST1H4K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eCoding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e1.66E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCD Patients in VOC Compared to Healthy Controls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0500007138.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eIL7R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e2.75E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-12.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC1100009200.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eCD3E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e2.61E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-11.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC1700012052.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eACTG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e2.01E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC1200007758.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eHNRNPA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e6.70E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0600011173.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eGUSBP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e2.47E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC1700010447.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eCCL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eCoding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e4.51E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-9.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0500013430.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eGNB2L1; SNORD95; SNORD96A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e6.41E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-8.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0100007676.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eLCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e8.11E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0400011548.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eLEF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e7.22E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-8.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eTC0600011517.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5299%;\"\u003e\n \u003cp\u003eHLA-DPA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3846%;\"\u003e\n \u003cp\u003echr6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.5128%;\"\u003e\n \u003cp\u003eMultiple_Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9658%;\"\u003e\n \u003cp\u003e6.94E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.094%;\"\u003e\n \u003cp\u003e-8.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eDifferentially Regulated Genes in SCD Patients in VOC Compared to SCD Patients in Steady-State.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Symbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromo-some\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-values\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 31%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFold Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVOC vs Steady state\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVOC vs Healthy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSteady state \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;vs Healthy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eTC0100017110.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eFCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003echr1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2653%;\"\u003e\n \u003cp\u003e2.75E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-10.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e-9.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e-1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eTC0200008268.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eGNLY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003echr2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2653%;\"\u003e\n \u003cp\u003e1.76E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e-8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e-1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eTC1200010850.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eTESPA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003echr12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2653%;\"\u003e\n \u003cp\u003e2.22E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e-8.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e-1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eTC1700010447.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eCCL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003echr17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2653%;\"\u003e\n \u003cp\u003e7.39E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e-6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e-7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e-1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4694%;\"\u003e\n \u003cp\u003eTC0600007657.hg.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 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biomarker, blood disease, pain. ","lastPublishedDoi":"10.21203/rs.3.rs-5397806/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5397806/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSickle cell disease is the commonest hemoglobinopathy which results from a mutation in the β-globin gene of hemoglobin and predisposes to painful Vaso-occlusive crisis (VOC) and multi-organ dysfunctions. The huge phenotypical variation makes it challenging to define and predict the disease severity and outcomes. This study aimed to characterize the whole blood gene expression profile using Microarray technology in Bahraini patients with sickle cell disease determining the differentially expressed genes in steady-state (n=10) and during VOC (n=10) in comparison to healthy controls (n=8). The analysis identified 2073 and 3363 genes that were dysregulated during steady-state and VOC, respectively, compared to healthy controls. Additionally, 1078 genes were differentially expressed during VOC compared to steady state. \u0026nbsp;The GO terms enrichment analysis revealed significant deregulation in terms related to immune and hematopoietic regulation, with downregulation in pathways critical for immune modulation and hematopoietic balance; one of the interesting genes identified among the 668 down-regulated genes was RUNX3. The RUNX3 gene was four folds down-regulated in microarray, three-fold in polymerase chain reaction, and a mean protein concentration of 11.13 pg/ml was observed in enzyme-linked immunosorbent assay during VOC compared to steady-state (457.93 pg/ml) (p\u0026lt;0.01). In conclusion, the RUNX3 gene plays a role in immune cell differentiation and inflammation predisposing to tissue inflammation and injury. It may serve as a potential biomarker for VOC, and future large-scale validation and proteomic assessments are recommended.\u003c/p\u003e","manuscriptTitle":"RUNX3 is a Potential Marker for Vaso-Occlusive Crises by a Whole Blood Transcriptomic Analysis in Sickle Cell Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 17:13:24","doi":"10.21203/rs.3.rs-5397806/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67c82794-a0b3-47c5-a0c0-8306ada97462","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-02T12:38:15+00:00","versionOfRecord":{"articleIdentity":"rs-5397806","link":"https://doi.org/10.3390/ijms26136338","journal":{"identity":"international-journal-of-molecular-sciences","isVorOnly":true,"title":"International Journal of Molecular Sciences"},"publishedOn":"2025-06-30 00:00:00","publishedOnDateReadable":"June 30th, 2025"},"versionCreatedAt":"2024-11-06 17:13:24","video":"","vorDoi":"10.3390/ijms26136338","vorDoiUrl":"https://doi.org/10.3390/ijms26136338","workflowStages":[]},"version":"v1","identity":"rs-5397806","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5397806","identity":"rs-5397806","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}