Genomic Variation Affecting MPV and PLT count in association with development of Ischemic Stroke and its Subtypes

Genomic Variation Affecting MPV and PLT count in association with development of Ischemic Stroke and its Subtypes | 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 Genomic Variation Affecting MPV and PLT count in association with development of Ischemic Stroke and its Subtypes Abhilash Ludhiadch, Sulena Sulena, Sandeep Singh, Sudip Chakraborty, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2333866/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jul, 2023 Read the published version in Molecular Neurobiology → Version 1 posted 5 You are reading this latest preprint version Abstract Background Platelets play a significant role in pathophysiology of ischemic stroke since they are involved in the formation of intravascular thrombus after erosion or rupture of the atherosclerotic plaques. Platelet (PLT) count and Mean platelet volume (MPV) are the two significant parameters that affect functions of the platelets. Methods In the current study MPV and PLT count was evaluated using flow cytometry and cell counter. SonoClot analysis was carried out to evaluate Activated Clot Timing (ACT), Clot Rate (CR) and Platelet Function (PF). Genotyping was carried out GSA and Sanger sequencing and expression analysis was carried out using RT-PCR. In silico analysis was carried out using GROMACS tool and UNAFold. The interaction of significant proteins with other proteins was predicted using STRING database. Results 96 genes were analyzed and a significant association of THPO (rs6141) and ARHGEF3 (rs1354034) was observed with the disease and its subtypes. Altered genotypes were associated significantly with increased MPV, decreased PLT count and CR. Expression analysis revealed a higher expression in patients bearing the variant genotypes of both the genes. In silico analysis revealed that mutation in THPO gene leads to the reduced compactness of protein structure. mRNA encoded by mutated ARHGEF3 gene increases the half-life of mRNA. The two significant proteins interact with many other proteins especially the ones involved in the platelet activation, aggregation, erythropoiesis, megakaryocyte maturation, and cytoskeleton rearrangements suggesting that they could be important player in determination of MPV values. Conclusions In conclusion the current study demonstrated the role of higher MPV affected by genetic variation in the development of IS and its subtypes. The results of the current study also indicate that higher MPV can be used as a biomarker for the disease and altered genotypes and higher MPV can be targeted for better therapeutic outcomes. Platelets MPV Ischemic Stroke Genotypes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Introduction Stroke or ‘brain attack’ is one of the major causes of mortality and morbidity worldwide after myocardial infraction [1]. Stroke is mainly divided into two types: ischemic and hemorrhagic stroke [2]. 87% of the cases are of Ischemic stroke (IS) type, 10% Intracerebral hemorrhage (ICH) and 3% subarachnoid hemorrhage (SAH) [3]. IS occurs by obstruction in blood supply to the brain which is usually caused by formation of a thrombus leading to cell death in the brain [4]. Various modifiable and non-modifiable risk factors are associated with the development of stroke. The modifiable risk factors including hypertension, obesity, hyperglycemia, hyperlipidemia, atherosclerosis, thrombosis, renal dysfunction, and Sickle Cell Disease attribute to 87% risk of stroke. The non-modifiable risk factors include family history, age, and ethnicity. It has also been established that stroke has a strong genetic component [5–7]. Genes involved in various pathways including homocysteine metabolism, coagulation and hemostasis, rennin angiotensin aldosterone system, cAMP degradation pathway, inflammation, extracellular matrix, lipid metabolism are known to be associated with stroke susceptibility [7–9]. Platelet traits Platelet traits such as Mean Platelet Volume (MPV) and Platelet Count (PLT) and pathways involved in recruitment of platelets have also been implicated in the disease pathophysiology [10, 11] [11–13]. Platelets are known to play an important role in pathophysiology of IS by virtue of their capability in the formation of intravascular thrombus after the erosion or rupture of atherosclerotic plaques [14]. We have already established the association of increased MPV with degree of disability and rate of clot formation in IS patients [15]. PLT count and MPV are markers of platelet function and activation and are positively associated with platelet reactivity and aggregation [16–18]. An increase in MPV occurs when platelets become activated and swollen spheres instead of quiescent discs. Large platelets are more adhesive and likely to aggregate more than smaller ones [19]. These traits and other platelet functions have been reported to be highly influenced by genetic variation. Various Genome wide association Studies (GWAS) involving different populations have demonstrated that the genomic alterations are associated with PLT count and MPV [10, 20, 21]. Variation involved in the genes involved in significant processes such as megakaryopoiesis, megakaryocyte/platelet adhesion, platelet formation and cell cycle regulation has been reported to influence platelet physiology [11, 22, 23]. PLT count and MPV altered by genetic profile has not been evaluated in association with the development of IS and its subtypes. Therefore, the current study has been carried out with an aim to explore the alterations in genes affecting MPV and PLT count and their functional implications, associated with IS and its subtypes. Material And Methods Study Population Two hundred IS patients were recruited from Guru Gobind Singh Medical College and Hospital, Faridkot, Punjab, India. The study was approved by the ethical committee of Central University of Punjab as well as the study hospital. Patients confirmed to have suffered an IS as diagnosed by CT scan or MRI were included in this study. Patients having HS or TIA were excluded from this study. Patients with major secondary problems like renal, hepatic skeletal and other neurological disorders were also excluded from the study. As a control group 200 age and sex matched healthy individuals without history of any other medical condition especially the cardiovascular and neurological diseases were also employed in the study. Written informed consent was obtained from all the recruited subjects. Stroke subtypes were stratified as per TOAST classification [24]. Blood Sample Collection A total 5 ml of blood was collected in EDTA and sodium citrate containing vacutainers with the written informed consent of the participants. Measurements of Platelet Count and Mean Platelet Volume MPV and PLT count were evaluated using automated cell counter (ABX Micros 60 Hematology System). The values were confirmed using BD Accuri C6 flow cytometer in 50% of the patients as reported previously [15]. Sonoclot Signature Analysis The assessment of clot timing in IS patients was carried out using Sonoclot Coagulation and Platelet Function Analyzer (Sienco Inc.: Model no. SCP1) as described in our previous study [15]. DNA isolation DNA isolation was carried out using organic method (phenol-chloroform method) Russell and Sambrook (2001). Evaluation of Genomic alterations affecting MPV and PLT The Screening for genetic variations affecting MPV and PLT count was carried out in 17% IS patients to figure out the gene variants occurring at a higher frequency. This screening was carried out using Global Screening Array (GSA) v3.0 microchip (Illumina Inc.). After analyzing the GSA results, variants of three genes THPO (rs6141), WDR66 (rs7961894) and ARHGEF3 (rs1354034) were filtered out and all the samples i.e. 200 patients and 200 controls were screened for these variants using Sanger Sequencing. Expression Analysis RNA was isolated from the platelets using Trizol method. cDNA synthesis was carried out using cDNA synthesis kit (iScript™ cDNA Synthesis Kit Bio-Rad) as per manufacturer’s instructions with equal amount of RNA from each sample. 18s rRNA was used as a housekeeping gene. Statistical Analysis All significant variants were tested for Hardy-Weinberg equilibrium. The association of genotypes and alleles with IS (univariate analysis) was estimated by the odds ratio with 95% confidence interval (CI) and χ2 analysis using OpenEpi software (version 2.3.1; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA). The association of gene variants with the disease was confirmed by Multiple Logistic Regression analysis (MLR). The independent variables were decided as the following dummy variables: MPV, 0 for standard and 1 for elevated; hypertension, 0 for normotension and 1 for hypertension; diabetes, 0 for normal and 1 for diabetic; tobacco use, 0 for no tobacco use and 1 for tobacco use; alcohol consumption, 0 for nonalcoholics and 1 for alcohol consumers; family history, 0 for no family history of IS and 1 for family history of IS. The dummy variables for gene variants were 0 for normal homozygous and 1 for heterozygous and mutant homozygote. All the statistical analysis was carried out using SPSS [Version: 28.0.1.1 (15)]. Statistical significance was defined as p < 0.05. Molecular Modelling and Simulation of ARHGEF3 (Q9NR81) and THPO (P40225) The amino acid sequence of the proteins were retrieved from UniProt [25]. The AlphaFold Protein Structure Database was used to predict the protein structures [26]. GROMACS 2018.3 (Abraham et al., 2015), modules were used to simulate the system of proteins as well as for the analysis to identify the potential role of mutation on structural integrity of the two proteins with accession numbers Q9NR81 (Rho guanine nucleotide exchange factor 3, ARHGEF3) and P40225 (Thrombopoietin, THPO). In order to identify any structural deviation and/or stability, we have calculated the RMSD (root mean squared deviation) for the wild and mutated proteins along with Radius of Gyration (Rg) that again tells us about any changes in the folded structure of the protein or conformational jump during the course of simulation. As far as rs1354034 variant of the ARHGEF3 gene is concerned, it is an intronic variant, it was difficult to simulate the mutated ARHGEF3 protein as it does not show any change in amino acid sequence. Therefore, we used UNAFold to predict the changes caused by this SNP at mRNA level [27]. Protein-protein interaction networks The STRING web based server was used to generate protein-protein interaction network of THPO and ARHGEF3 proteins at highest confidence score of 0.700 [28]. Results In our recent study we already established the association of various risk factors with IS and its subtypes [15]. A total of 106 variants affecting MPV, PLT count, and latelet reactivity were screened using GSA in 17% IS patients (Fig. 1 ). Three genes ARHGEF3 (rs1354034, T > C), THPO (rs6141, C > T), and WDR66 (rs7961894, C > T) showed variation based on the results of GSA (Fig. 1 ). These were further validated by subjecting all the samples (200 patients and 200 controls) to Sanger sequencing. No significant association of WDR66 (rs7961894) was found with the disease. In case of THPO, rs6141 (C > T) (Fig. 2 and Tables 1 and 2 ) polymorphism a significant difference was observed in genotypic distribution between IS patients and controls [for TT vs CC, X 2 = 37.09; p < 0.001, OR = 6.98 (95% CI; 3.60-13.22)]. TT and CT genotypes showed a significant association with the disease [for TT vs CC + CT, X 2 = 6.323; p < 0.005, OR = 1.89 (95% CI; 1.17–3.07)] (Table). However, we did not find any significant difference in the distribution of T and C alleles between IS patients and controls. Even after controlling all the confounding risk factors using MLR, a significant association of TT genotype with IS was found [(p = 0.008; adjusted odds ratio- 3.834; 95% CI; 1.431–10.277)] (Table 5 ). Table 1 Distribution of THPO (rs6141), genotypes and allelic frequencies in ischemic stroke patients Gene Genotype Patients Controls Allele Patients Controls CC 21 (10.50) 89 (44.50) C 165 (0.41) 255 (0.63) THPO (rs6141) CT 123 (61.50) 77 (38.50) TT 56 (28) 34 (17) T 235 (0.58) 145 (0.36) Table 2 Chi-square (X 2 ), crude odds ratio and p -value for THPO (rs6141) gene variant Gene Patients vs Controls Chi-square (X2) Odds Ratio (95% CI) p value THPO (rs6141) TT vs CC 37.09 6.98 (3.6-13.22) < 0.001 TT vs CC + CT 6.323 1.89 (1.17–3.07) < 0.005 T vs C 0.064 1.049 (0.78–1.39) < 0.3 Table 3 Distribution of ARHGEF3 (rs1344034), genotypes and allelic frequencies in ischemic stroke patients and controls Gene Genotype Patients Controls Allele Patients Controls TT 15 (7.50) 84 (42) T 130 (0.325) 261 (0.652) ARHGEF3 (rs1354034) TC 100 (50) 93 (46.5) CC 85 (42.5) 23 (11.5) C 270 (0.675) 139 (0.347) Table 4 Chi-square (X2), crude odds ratio and p-value for ARHGEF3 (rs1344034) gene variant Gene Patients vs Controls Chi-square (X2) Odds Ratio (95% CI) p value CC vs TT 81.02 20.7 (10.1-42.39) < 0.001 ARHGEF3 (rs1354034) CC vs TT + TC 47.2 5.688 (3.392–9.539) < 0.001 C vs T 84.54 3.9 (2.908–5.23) < 0.001 Table 5 Independent association of genotypes with IS Gene Risk factor (Genotype/s) Adjusted odds ratio p value THPO (rs6141) CT + TT genotype (Heterozygous + mutant homozygotes) 3.834 (1.431–10.277 p < 0.008 ARHGEF3 (rs1354034) TC + CC (heterozygous + mutant homozygotes) 4.419 (1.657– 11.785) p C) gene with the disease, a significant difference was observed in genotypic distribution of CC and TT genotype between IS patients and controls [for CC vs TT, X 2 = 81.02; p < 0.001, OR = 20.7 (95% CI; 10.1-42.39)] (Fig. 3 ). A significant difference between CC and TT + TC genotypes between IS patients and Controls [for CC vs TT + TC, X 2 = 47.2; p < 0.001, OR = 5.688 (95% CI; 3.392–9.39)] was also observed (Tables 3 and 4 ). The C allele also associated significantly with the disease [C vs T, X 2 = 84.54; p < 0.001, OR = 3.9 (95% CI; 2.908–5.23)]. After controlling all the confounding risk factors using MLR significant association of CC genotype with IS was observed [( p = 0.003; adjusted odds ratio- 4.419; 95% CI; 1.657– 11.785)] (Table 5 ). Evaluating the association of these two variants with IS subtypes a significant association of TT genotypes of THPO gene whereas for ARHGEF3 gene significant association was observed with C allele as well as CC genotypes ( p < 0.001) (Tables 6 and 7 ). Table 6 Chi-square (X 2 ), odds ratio and p -value for THPO (rs6141) gene variant in IS subtypes Subtype THPO (rs6141) Chi-square (X2) Odds Ratio (95% CI) p value TT vs CC 21.59 5.399 (2.639–11.05) 0.001 Large Artery atherosclerosis T vs C 25.46 2.274 (1.657–3.121) 0.001 TT vs CC 10.89 8.725 (2.264–33.63) 0.001 Small artery occlusion T vs C 13.12 2.528 (1.541–4.147) 0.001 TT vs CC 13 13.09 (2.727–62.83) 0.001 Cardioembolism T vs C 1.03 0.2512 (0.03061-2.062) 0.156 Table 7 Chi-square (X2), odds ratio and p -value for ARHGEF3 (rs1344034) gene variant in IS subtypes Subtype ARHGEF3 (rs1354034) Chi-square (X2) Odds Ratio (95% CI) p value Large Artery Atherosclerosis CC vs TT 51.09 15.27 (6.839, 34.1) < 0.001 C vs T 50.75 3.219 (2.33, 4.447) < 0.001 CC vs TT 32.87 19.17 (5.984, 61.43) < 0.001 Small artery occlusion C vs T 35.57 4.78 (2.801, 8.156) < 0.001 CC vs TT - - - Cardioembolism C vs T 37.5 8.919 (4.049, 19.65) < 0.001 Acute stroke of other determined etiology CC vs TT - - - C vs T 7.34 13.14 (1.601, 107.9) < 0.003 The association of variants of THPO (rs6141) and ARHGEF3 (rs1354034) genes with MPV and PLT count was also evaluated. The altered genotypes of THPO (rs6141) and ARHGEF3 (rs1354034) gene showed a significant association with increased MPV whereas for PLT count an association of the variant genotypes with decreased PLT count was observed although it did not reach statistical significance (Table 8 and Figs. 4 and 5 ).This is also in consensus with our observation where an inverse correlation was observed between these two variables [15]. Table 8 Association of mean MPV and PLT count with THPO (rs6141) and ARHGEF3 (rs1354034) genotypes Genotype Mean MPV p value Mean PLT count p value CC 10.41 ± 1.789 Reference 321.3 ± 126.931 < 0.3351 THPO (rs6141) CT 11.00 ± 1.720 < 0.001 313.55 ± 90.025 < 1.0000 TT 11.76 ± 1.794 < 0.001 310.66 ± 90.531 Reference TT 10.04 ± 1.422 Reference 298.8 ± 87.682 Reference ARHGEF3 (rs1354034) TC 11.13 ± 1.871 P < 0.001 315.88 ± 97.854 < 0.0667 CC 11.19 ± 1.767 P < 0.001 313.44 ± 91.988 < 0.1041 The clotting parameters including ACT, CR and PF were also compared among various genotypes of THPO (rs6141) and ARHGEF3 (rs1354034) genes. A significant increase in CR was observed in the patients bearing the altered TT genotype of THPO and CC genotype of ARHGEF3 gene in comparison with normal genotypes of both these genes, CC and TT respectively. However, we did not find significant difference in ACT and PF values among the variant genotypes of both the genes (Tables 9 , 10 and Fig. 7 ). Table 9 Association of different genotypes of THPO (rs6141) with ACT, CR and PF THPO (rs6141) ACT P value CR p value PF p value CC 104.5 ± 22.86 - 28.25 ± 13.32 - 3.47 ± 1.132 - CT 140 ± 90.24 0.003 34.7 ± 16.28 0.01 2.36 ± 1.36 < 0.0001 TT 105.62 ± 34.48 0.834 59 ± 25.90 < 0.001 3.3 ± 0.96 < 0.3768 Table 10 Association of different genotypes of ARHGEF3 (rs1354034) with ACT, CR and PF ARHGEF3 (rs1354034) ACT p value CR p value PF p value TT 93.5 ± 5.5 - 25.5 ± 1.5 - 2.25 ± 0.25 - TC 133.58 ± 83.98 0.0003 38.41 ± 19.71 < 0.001 2.55 ± 1.257 0.07 CC 121 ± 64.57 0.0013 42.73 ± 26.31 < 0.001 2.94 ± 1.40 0.0003 Expression analysis carried out by qPCR of THPO (rs6141) and ARHGEF3 (rs1354034) genes revealed higher expression of both the genes in patients bearing altered genotypes (Fig. 38). The altered genotypes showed significantly higher expression in comparison with heterozygous and normal genotypes ( p < 0.05). Further the heterozygous genotypes showed a significantly higher expression as compared to the normal genotypes ( p < 0.05). Similarly the expression of CC genotype of ARHGEF3 (rs1354034) gene was significantly higher in comparison with the heterozygous and normal genotypes (p < 0.05). After comparing the heterozygous genotype with normal genotype it showed significantly higher expression ( p < 0.05) (Fig. 8 ). The results were normalized against 18s rRNA which was used as a housekeeping gene to evaluate the expression of both the genes. The amino acid sequence of the proteins were retrieved from UniProt [1]. The AlphaFold Protein Structure Database was used to predict the protein structures of both THPO and ARHGEF3 genes (Fig. 39). RMSD for both the Proteins (wild and mutated) were calculated using GROMACS module gmx rms module with respect to a crystal structure as a reference. The stability of a protein relative to reference structure can be determined by measuring the deviation produced during the simulation. The smaller the deviations, represents the more stable simulated structure. RMSD values for the all atoms of the mentioned three proteins were calculated for 100ns simulation. It can be observed for P40225 (Thrombopoietin) the wild type structure was stable around 2.0 nm (3.5 ns) with respect to its crystal structure, but the mutated (R38C) structure showed a sudden jump to higher rmsd value (2.5 nm) at 4.0 ns and maintained a constant elevation of RMSD value during the course of its simulation (Figs. 9 – 11 ). Similar indication was observed in case of Radius of gyration calculation that suggests P40225 wild type protein structure is stable during the course of simulation, whereas in case of mutated structure moderate fluctuations were observed. However, the mutated protein shows reduced compactness in the protein structure with respect to the wild variant till the end of the simulation. As far as rs1354034 variant of the ARHGEF3 gene is concerned, it is an intronic variant, it was difficult to simulate the mutated ARHGEF3 protein as it does not show any change in amino acid sequence. Therefore, we used UNAFold to predict the changes caused by this SNP at mRNA level. UNAFold software package is used to create the simulations of folding, hybridization, and melting pathways for one or two single-stranded RNA or DNA molecule. It combines free energy minimization, partition function calculations and stochastic sampling to predict the folding of single stranded RNA or DNA. Further, for melting simulations, the package computes entire melting profiles, not just the melting temperatures [27]. The results showed that the mutant (rs1354034) variant RNA exhibits qualitatively greater stability wrt the free energy associated with the secondary structure. This increased stability manifests in reduced decay rate of the mutant RNA and as a consequence, into its increased processing into mRNA and its translation into the protein. The examination of the secondary structure of the mutant variant clearly indicates more organized structure (Fig. 12 ) with 54 helices as compared to the wild type sequence (50 helices). The average stem-loop size is also less for the mutant variant vis-à-vis wild type. This reduces the probability of its decay by RNAses. The protein-protein interaction network of THPO showed that it interacts closely with ten proteins including Signal transducer and activator of transcription 5A (STAT5A), Signal transducer and activator of transcription 3 (STAT3), SHC-transforming protein 1 (SHC1), Tyrosine-protein kinase (JAK2), Erythropoietin (EPO), Signal transducer and activator of transcription 5B (STAT5B), Granulocyte colony-stimulating factor (CSF3), Kit ligand (KITLG), Thrombopoietin receptor (MPL) and Interleukin-3 (IL3) (Fig. 13 a). These proteins are mainly involved in cell growth, development, differentiation, mediating cellular responses to cytokines and other growth factors. Furthermore, relationship was noticed with the increased level of THPO protein which increases both platelet size and platelet count (Balcik et al. , 2013; Kapur et al. , 2020). The Rho guanine nucleotide exchange factor 3 (ARHGEF3) acts as guanine nucleotide exchange factor for RhoA and RhoB GTPases and it showed interaction with five proteins based on STRING analysis including Ring finger protein 145 (RNF145), Rho-related GTP-binding protein RhoB (RHOB), Rsa homology gene family (RHOA), Rho-related GTP-binding protein RhoC (RHOC) and Catenin beta-1 (CTNNB1) (Fig. 13 b). Discussion On account of the ability to form intravascular thrombus following the erosion or rupture of atherosclerotic plaques, platelets are known to play an essential role in the pathophysiology of IS [14]. Platelet parameters like MPV and PLT count are considered as significant determinants of platelet function [29]. There is evidence that increased platelet size and count reflects increased platelet activity and are useful predictive and prognostic biomarkers for cerebrovascular events. In a recent study published from our lab an elevated MPV was found to be significantly associated with increased risk of IS and also higher clot rate and higher degree of disability based on mRS. [15]. These results are in accordance with previous studies where an increase in MPV has been associated significantly with increased risk of IS [12, 29–31]. In the current study we screened the genetic variants involved in PLT count, MPV and platelet reactivity in IS patients. Based on the previous reports a total of 106 variants in 96 genes involved in PLT count, MPV and Platelet reactivity were initially screened using GSA in 17% patients. Out of these, 62 variants have been reported to affect PLT count; 33 were found to affect MPV and 11 variants reported to affect platelet reactivity (Tables 10 , 11 and 12 ). Most of these genes were found to be either normal homozygous or showed very minor frequency of heterozygosity except for variants of two genes ARHGEF3 (rs1354034, T > C), and THPO (rs6141, C > T). Therefore, these were evaluated further by Sanger Sequencing after amplifying the specific regions of these genes bearing the variation in all the subjects. Table 11 SNPs associated with PLT Count SNP Gene SNP Gene rs2336384 MFN2 rs373121156 CDKN2A rs10914144 DNM3 rs117899880 BRD3 rs1668871 TMCC2 rs505404 PSMD13 rs7550918 LOC148824 rs4246215 FEN1 rs3811444 TRIM58 rs4938642 CBL rs12603268 GCKR rs7342306 CD9-VWF rs625132 EHD3 rs941207 BAZ2A rs17030845 THADA rs3184504 SH2B3 rs76160061 SYN2 rs17824620 RPH3A-PTPN11 rs7641175 SATB1 rs7961894 WDR66 rs1354034 ARHGEF3 rs4148441 ABCC4 rs3792366 PDIA5 rs8022206 RAD51L1 rs7694379 HSD17B13 rs8006385 ITPK1 rs17568628 F2R rs7149242 C14orf70-DLK1 rs700585 MEF2C rs11628318 RCOR1 rs2070729 IRF1 rs2297067 C14orf73 rs441460 LRRC16 rs3809566 TPM1 rs3819299 HLA-B rs1719271 ANKDD1A rs399604 HLA-DOA rs6065 GP1BA rs210134 BAK1 rs397969 AKAP10 rs9399137 HBS1L-MYB rs55997232 TAOK1 rs342275 PIK3CG rs10512472 SNORD7-AP2B1 rs4731120 WASL rs708382 FAM171A2-ITGA2B rs6995402 PLEC1 rs11082304 CABLES1 rs409801 AK3 rs8109288 TMP4 rs13300663 RCL1 rs17356664 EXOC3L2 rs1034566 ARVCF rs12526480 LRRC16A rs6141 THPOII rs6490294 ACAD10 rs9494145 HBS1L-MYB rs477895 BAD rs7896518 JMD1C rs13236689 CD36 rs151361 LRRC16A rs342293 PIK3CG Table 12 SNPs associated with MPV SNP Gene SNP Gene rs7961894 WDR66 rs10512627 KALRN rs8109288 TPM4 rs117341321 KIAA0232 rs1354034 ARHGEF3 rs2227831 F2R rs342293 PIK3CG rs4521516 MEF2C rs7075195 JMD1C rs10076782 RNF145 rs8076739 TAOK1 rs10813766 DOCK8 rs117213068 TMCC2 rs7075195 JMJD1C rs17655730 PSMD13 rs17655730 NLRP6 rs4812048 CTSZ-TUBB1 rs1558324 CD9-VWF rs342296 PIK3CG rs2015599 MTSTD1 rs11653144 TAOK1 rs10876550 COPZ1-NFE2-CBX5 rs17396340 KIF1B rs2950390 PTGES3 rs10914144 DNM3 rs7317038 GRTP1 rs649729 EHD3 rs944002 C14orf73 rs4305276 ANKMY1 rs3000073 BRF1 rs1354034 ARHGEF3 rs16971217 AP2B1 rs12969657 CD226 Table 13 SNPs associated with PLT Reactivity SNP Gene rs1613662 GP6 rs3557 FCER1G rs3737224 PEAR1 rs11264579 PEAR1 rs3729931 RAF1 rs147212241 P2RY12 rs3788337 GNAZ rs10496541 CD36 rs35091628 MAP2K2 rs12566888 PEAR1 rs7940646 MRVI1 Thrombopoietin (also known as THPO, TPO) is a major cytokine that plays a crucial role in platelet production. This humoral substance controls MK proliferation and differentiation to maintain normal thrombopoiesis [32]. The SNP rs6141 (C > T) situated at 3’-UTR region of the THPO gene is reported to be involved in the post transcriptional control of the gene expression mainly affecting mRNA splicing [33]. Studies have also shown that microdeletions involving this SNP in THPO gene cause mild congenital thrombocytopenia [34, 35]. Further two gain-of-function mutations in THPO gene G > C transversion and a G > T transversion have been reported to produce mRNAs with shortened 5′–untranslated regions (UTR) that are more efficiently translated in comparison with transcripts produced by wild type THPO. These transcripts with gain of function mutation result in elevated PLT count which might lead to thrombosis and bleeding [36, 37]. THPO variants with bi-allelic loss-of-function cause multilineage bone marrow failure and severely reduced platelet counts [38–40]. Another study identified a one-base deletion in the 5′-untranslated region of the THPO gene. In vitro experiments showed that this mutation increased TPO production and suggested that this region of the THPO gene may play a crucial role in regulating THPO expression [41]. Based on the results of different GWAS studies it has been established that rs6141 of THPO gene is a significant determinant of MPV and PLT count [33, 42, 43]. As far as the role of THPO in IS is concerned, it has been reported that elevated levels of TPO are associated with increased MPV and PLT counts in these patients [44–46]. In the current study evaluating the association of rs6141 (THPO) with IS we found a significant association of TT genotype with the disease which was confirmed by MLR analysis showing an independent association of TT genotype with the disease ( p < 0.05). However, we did not find a significant difference in the distribution of T and C alleles between patients and controls. As far as the association of this variant with IS subtypes is concerned, the T allele showed a significant association with LAA, Small artery occlusion, and cardioembolism. We also evaluated the association of variant genotype with MPV, Clot rate, and PLT count. The TT and CT genotypes showed a significant association with elevated MPV, higher clot rate and reduced PLT count in comparison with the CC genotype bearing patients. This association was also confirmed by MLR controlling all other confounding factors. Since the focus of the current study was on platelet parameters, therefore, the expression analysis of THPO (rs6141) was carried out using mRNA isolated from platelets of IS patients. Patients bearing TT genotype showed highest expression followed by CT and CC genotypes. Aged platelets induce the production of TPO in the hepatocytes. TPO increases the number of circulating platelets once released into the bloodstream. Most of the studies have linked higher TPO levels with increased platelet activity [46]. A study carried out by Balcik et al (2013) reported that patients with IS have higher TPO and MPV levels and concluded that increased TPO levels elevate both PLT count and MPV resulting in higher thrombotic capacity of platelets [44]. Another study carried out in acute myocardial infarction (AMI) patients and unstable angina pectoris also reported that increased TPO and MPV levels are positively associated with each other in AMI patients [47]. Yang et al (2008) evaluated the role of Severe Acute Respiratory Syndrome (SARS) in affecting normal functions of hematopoietic stem cells and megakaryocytic cells. They found that increased TPO levels in the plasma of these patients lead to thrombocytosis and hyperactive platelets [48]. Recently a study demonstrated that platelets in COVID-19 patients aggregate faster and showed increased spreading on both fibrinogen and collagen during clot retraction. It was also found that TPO levels were elevated in the serum of SARS-CoV-2 patients [49]. Previous studies have mostly explored the association of THPO with PLT count. However, its role in MPV has not been studied much. Since MPV and PLT count are inversely correlated therefore it is obvious that studies showing its association with increased PLT count might not have observed its impact on MPV [50]. The proposed mechanism by which TT genotype (rs6141) might lead to the higher expression of THPO gene in bone marrow producing hyperactive platelets has been depicted in Fig. 14 . TPO binds to the megakaryocytes or platelets and controls their production through a feedback mechanism. During normal hemostasis TPO concentrations remain normal. Platelets experience mechanical stress that shortens their life span under the conditions like atherosclerosis which indirectly activates platelet biogenesis [51] [44]. In vivo injection of recombinant adenoviral vectors, or transgenes resulted in variable thrombocytosis [52–55]. In addition, studies have also demonstrated that patients bearing gain of function mutations in THPO gene, enhance TPO mRNA translation which elevates its expression inducing lineage-selective effects in patients affected with thrombocytosis and polyclonal hematopoiesis. TPO levels were also observed to be higher in the serum [37, 41, 56]; [57]. It has been reported that around 1000 to 3000 platelets are produced from a single MK [58]. The mechanism leading to the production of platelets through MKs involves lot of reorganization of cytoskeletal components like actin and tubulin. THPO is the major watch dog of this process [59, 60]. Although the process of thrombopoiesis is understood well there are still many unanswered questions to how some transcription factors like GATA and FOG1 affect the size of the platelets size [61]. The impact of rs6141 of THPO gene on protein structure was evaluated by protein dynamics studies using GROMACS. It showed that the wild type structure of THPO was stable around 2.0 nm (3.5 ns) with respect to its crystal structure. On the other hand mutated (R38C) structure showed a sudden jump to higher RMSD value (2.5 nm) at 4.0 ns and maintained a constant higher RMSD value during the course of its simulation. Mutant protein encoded by THPO (rs6141) gene also showed a reduced compactness in structure in comparison with the wild type protein. This suggested that a minor deviation between the wild and mutated RMSD values affects the original protein structure. ARHGEF3, also known as XPLN is an exchange factor found in platelets, leukemics, and neuronal tissues [62]. It was first identified as RhoGEF (Rho guanine nucleotide exchange factor) for Rho GTPases through an expressed sequence tag database search, using the diffused B-cell lymphoma (Dbl) homology (DH) domain query in the BLASTN system [62]. Skeletal muscles and the brain have the highest levels of ARHGEF3 protein expression, followed by the heart, kidneys, platelets, and macrophages [63]. It plays a non-canonical role by inhibiting mTORC2 kinase activity through Akt signaling. [63, 64]. It is also involved in various primary cellular functions including cell adhesion, motility, polarity, growth, cell diferentiation and cytoskeleton rearrangements [63, 65]. GWAS studies have identified newer roles of ARHGEF3 gene in modulating bone mineral density (BMD), platelet differentiation and Hirschsprung disease [22, 66]. Another GWAS carried out to evaluate the association of significant variants with platelet traits reported the association of rs1354034 with MPV, in association with other genes including WDR66 , TAOK1, and Phospatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma [22]. A meta-analysis including the results of various GWAS studies on 66, 867 European individuals also demonstarted that rs1354034 located at 3p14.3 is assocaited significantly with PLT count and MPV [50]. Zou et al (2017), found that this SNP is present in the regulatory region (non-coding region upstream of the transcription start site) of ARHGEF3 gene and proposed that it may influence the binding of certain trasnscription factors like RUNX1, MEIS1, GATA2, GATA1, and FLI1 duirng MK maturation (Fig. 15 ). However, it is not clear if this SNP is directly involved in influencing the binding sites of these transcription factors [66]. The C allele of rs1354034 has been associated with lower ARHGEF3 mRNA expression, higher PLT count and lower MPV in humans (Zou et al. , 2017). In a very recent report researchers tried to investigate the genetic overlap of platelet parameters with an endophenotype of Parkinson’s Disease. They found that various genes including ARHGEF3 are associated with MPV as well as age of onset and Parkinson’s Disease suceptibility [67]. In the current study evaluating the association of rs1354034 (ARHGEF3) with IS and its subtypes, we found a significant association of CC genotype and C allele with the disease which was confirmed by MLR analysis showing an independent association of CC genotype and C allele with the disease. As far as association of this variant with IS subtypes is concerned, CC genotype and C allele were found to be associated significantly with LAA, cardioembolism, small artery occlusion and stroke of undetermined etiology. We also evaluated the association of variant genotypes of ARHGEF3 gene with MPV, Clot rate and PLT count. The variants CC and TC genotypes showed a significant association with elevated MPV, higher clot rate and reduced PLT count in comparison with TT genotype. This association was also confimred by MLR. A GWAS reported that the same SNP (rs1354034) is associated in trans with expression of vWF, which is an important factor in blood coagulation pathway in humans [68]. Expression analysis revealed that patients bearing CC genotype of ARHGEF3 gene showed highest expression followed by TC genotype in comparison with the TT genotype in platelets. Since this variant is an intronic variant its impact on ARHGEF3 mRNA was evaluated using UNAFold. This analysis revealed that the mRNA encoded by CC genotype of ARHGEF3 gene (rs1354034) leads to qualitatively greater stability with respect to the free energy associated with the secondary structure as compared to the mRNA encoded by normal genotype. This SNP present upstream to the ARHGEF3 gene has been associated significantly with higher expression of ARHGEF3 during MK maturation both in murines and humans [66, 69, 70]. Based on the previous studies it has been reported that ARHGEF3 is invloved in platelet shape change and function. It has also been demonstrated that ARHGEF3 might be a missing link between ADP mediated platelet shape change and activation via P2Y1 and P2Y2 receptors [66]. The proposed mechanism by which CC genotype (rs1354034) might lead to the higher expression of ARHGEF3 gene activating the MK maturation in bone marrow producing enlarged platelets has been depicted in Fig. 16 . Studies on mice lacking P2Y1 receptor do not show shape change of platelets in response to ADP suggesting that the ADP signaling is associated with shape change mechanisms in these blood cells [71]. This observation was further aided by another study that reported platelet shape change occurs through Rho signaling and actin reorganization [70, 72]. As mentioned previously THPO is a glycoprotein produced primarily in the liver that stimulates the formation of megakaryocytes and platelets. THPO protein was found to interact with 10 other proteins mainly involved in platelet activation (c-MPL, IL3), platelet aggregation (IL3), erythropoiesis (EPO, STAT5B), megakaryocyte development (c-MPL, JAK2, STAT3), cytoskeleton organization (CSF3), cell survival and proliferation (KITLG, STAT5A) (Fig. 17 ) [73–75]. For the production of platelets MKs undergo a series of remodeling events that result in thousands of platelets being released from a single cell [76]. All the proteins found to be interact with THPO protein are known to regulate the platelet formation and functions [77–79]. The interaction of THPO protein with other proteins suggested that there might be potential alternate mechanisms that could affect platelet production, morphology and function. The THPO along with other interacting proteins might be explored as significant biomarker affecting platelet parameters and functions and thereby a potential therapeutic target. ARHGEF3 activates two members of the Rho family GTPases, RHOA and RHOB, which are involved in osteoblast maintenance [70]. Various other cellular processes including cytoskeleton reorganization are activated and inactivated by Rho-like GTPases as discussed previously [80, 81]. By catalyzing the release of bound GDP, guanine nucleotide exchange factors (GEFs) accelerate Rho GTPase activity. ARHGEF3 inhibits mTORC2 kinase activity, primarily for Akt, by binding the mTORC2 complex. (Arthur et al ., 2002; Rossman et al. , 2005). ARHGEF3 protein has been found to interact with five other proteins involved in cytoskeleton organization (RHOA, RHOC), cell adhesion, migration (RHOA, RHOC, AHOB, and CTNNB1), apoptosis (RHOB) and cholesterol homeostasis (RNF145) (Fig. 18 ). Many physiological and pathological functions of platelets are mediated by Rho GTPase proteins [80]. Actin cytoskeleton regulation is one of the main functions of Rho GTPases, although they also participate in several other biochemical pathways [82]. When platelets interact with vWF and collagen via the cell-surface receptors GpIb-IX-V and GPVI, respectively, a dramatic change in shape occurs due to the reorganization of the actin cytoskeleton. When platelet morphology is altered, more surface area is available for interactions with the ECM and other cells [83, 84]. Initial shape changes include discoid loss, sphering, and filopodia extension. The interaction of ARHGEF3 and THPO with other proteins significantly involved in various platelet parameters and functions suggests that the genotype-phenotype correlation should not be based on one protein but rather than complete network should be analysed to explore their role as biomarkers or therapeutic targets. Conclusion We observed an association of ARHGEF3 (rs1354034) and THPO (rs6141) genes with higher MPV, higher rate of clot formation and risk of developing IS. Further we also observed that MPV and PLT count showed an inverse relationship with mutant alleles of both the genes. Expression analysis of both THPO and ARHGEF3 genes revealed a higher expression of variant genotypes in the platelets. In silico analysis carried out for THPO (rs6141) gene showed that the mutated protein has reduced compactness in the protein structure in comparison with the wild type which might be resulting in the higher expression of THPO gene in the platelets. We used UNAFold to predict the changes caused by the variant ARHGEF3 (rs1354034) at mRNA level because it is difficult to simulate the mutated ARHGEF3 (rs1354034) since it is an intronic variant. It showed that the mutant (rs1354034) variant RNA exhibits qualitatively greater stability with respect to the free energy associated with the secondary structure as compared to normal ARHGEF3 (rs1354034), which might lead to a higher expression in the platelets. Based on the STRING analysis it was observed that these two significant proteins interact with other proteins which are involved in various pathways such as platelet activation, aggregation, erythropoiesis, megakaryocyte development, cytoskeleton organization, cell adhesion. Cell migration, vascular development, apoptosis, cell proliferation and cholesterol homeostasis. The current study is a step forward to establish MPV as a diagnostic or prognostic marker for IS. There is a need to develop the specific treatment strategies that can particularly reduce MPV. Further, establishing the specific genotype-phenotype correlation of markers affecting MPV in a particular population might help in devising better or specific treatment strategies. Declarations Acknowledgements Financial assistance from Council for Scientific and Industrial Research (CSIR) India and DST-FIST is highly acknowledged. Funding Financial assistance from DST-FIST (SR/FST/LS-I/2017/49) is acknowledged with thanks. Financial support to Mr. Abhilash Ludhiadch (Award No-09/ 1051(0029)/2 019-EMR-1) from the Council for Scientific and Industrial Research (CSIR) India is highly acknowledged. Availability of Data and Material Not applicable. Consent to Participate Consent from each participant was taken prior to the sample collection. Consent for Publication Not applicable. Conflict of Interest The authors declare no conflict of 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-2333866","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":158051925,"identity":"7210942f-7ebc-4951-8cc8-a325bce2e921","order_by":0,"name":"Abhilash Ludhiadch","email":"","orcid":"","institution":"Central University of Punjab","correspondingAuthor":false,"prefix":"","firstName":"Abhilash","middleName":"","lastName":"Ludhiadch","suffix":""},{"id":158051926,"identity":"f86d5f2a-e0a9-4238-994e-77e0720510d3","order_by":1,"name":"Sulena Sulena","email":"","orcid":"","institution":"Baba Farid University of Health 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Pradesh","correspondingAuthor":false,"prefix":"","firstName":"Dixit","middleName":"","lastName":"Sharma","suffix":""},{"id":158051930,"identity":"9aaaabf4-6abe-497c-a25d-705727655a48","order_by":5,"name":"Mahesh Kulharia","email":"","orcid":"","institution":"Central University of Himachal Pradesh","correspondingAuthor":false,"prefix":"","firstName":"Mahesh","middleName":"","lastName":"Kulharia","suffix":""},{"id":158051931,"identity":"302100dc-9977-4bab-a1f0-1ba284be5e22","order_by":6,"name":"Paramdeep Singh","email":"","orcid":"","institution":"AIIMS Bathinda: All India Institute of Medical Sciences Bathinda","correspondingAuthor":false,"prefix":"","firstName":"Paramdeep","middleName":"","lastName":"Singh","suffix":""},{"id":158051932,"identity":"81adb640-4b05-4a4e-9e6d-28f2e97477ca","order_by":7,"name":"Anjana Munshi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYHCCBBiDjSGhAsKSACPitJwhTgscsDEwtsG14Ab8sxsefi6oYYg2uN3+7MHDeXaJ2xmYD97mYbDIw6VF4s6BZOkZxxhyN9w5Y26QuC05cWcDW7I1D4NEMU5rbiQkSPOwAbXcyGGTSNx2IHHDAR4zaaCWxAYcOuRvJCT/5vkH0pL+TCJxDkgL/ze8WgxuJKRJ87aBtCSYAZWBbWHDq8UQqMWat08id+aNHDOJhGPJxjub2Ywt5xjg1iJ3Iyf5Ns83m9w+oMMkf9TYyW5nb354401FHU4tDAw8CQwoEWHADCZxqgcC9gNovsOneBSMglEwCkYkAADfr1aG2xr6KAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4033-9124","institution":"Central University of Punjab","correspondingAuthor":true,"prefix":"","firstName":"Anjana","middleName":"","lastName":"Munshi","suffix":""}],"badges":[],"createdAt":"2022-12-01 13:30:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2333866/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2333866/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12035-023-03460-2","type":"published","date":"2023-07-15T01:08:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":30225566,"identity":"ed84b98c-6c13-4fa9-9d82-9b54b255ffa6","added_by":"auto","created_at":"2022-12-12 19:32:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29941,"visible":true,"origin":"","legend":"\u003cp\u003eGenes involved in MPV, PLT Count, and Platelet Reactivity analysed by GSA.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/a727eee33c0724501313181a.png"},{"id":30225570,"identity":"0181b496-a04c-4902-bd16-6f3a16dcb4ff","added_by":"auto","created_at":"2022-12-12 19:32:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20813,"visible":true,"origin":"","legend":"\u003cp\u003eChromatogram showing CC TT and CT genotypes of THPO gene (rs6141)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/eca9bc927aa515152dd1eb71.png"},{"id":30226351,"identity":"c2078a83-fbe0-4ab5-9786-6cf0ec53e48a","added_by":"auto","created_at":"2022-12-12 19:56:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":212051,"visible":true,"origin":"","legend":"\u003cp\u003eChromatogram showing TT, TC and CC genotypes of ARHGEF3 gene (rs1354034).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/ce44dab374355d5d2a08857b.png"},{"id":30225745,"identity":"65beb14e-f2a0-4d02-9262-3efb04fc99b4","added_by":"auto","created_at":"2022-12-12 19:40:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35099,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of Mean MPV with different genotypes of THPO (rs6141) and ARHGEF3 (rs1354034) genes\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/979a7c265d58c5808b90f440.png"},{"id":30225568,"identity":"36221962-eb46-4ee7-84d9-9c6a185331ef","added_by":"auto","created_at":"2022-12-12 19:32:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41849,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of\u003cstrong\u003e \u003c/strong\u003eMean PLT count, and different genotypes of THPO (rs6141) and ARHGEF3 (rs1354034) genes\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/6534ad215740be241bcaa82b.png"},{"id":30226042,"identity":"2b597eb7-280c-4248-8b93-77e9c3755038","added_by":"auto","created_at":"2022-12-12 19:48:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eThis image is not available with this version.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/71125e37a3b8814bd55c26bf.png"},{"id":30225567,"identity":"6e67e61a-c990-4c9f-8130-89c9403e1c25","added_by":"auto","created_at":"2022-12-12 19:32:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":31649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) \u003c/strong\u003eACT, CR and PF values in patients bearing CC, CT and TT genotypes of\u003cstrong\u003e \u003c/strong\u003eTHPO gene \u003cstrong\u003eb) \u003c/strong\u003eACT, CR and PF values in patients bearing TT TC and CC genotypes of ARHGEF3\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/d508db808bfc2fe2ddea088c.png"},{"id":30225569,"identity":"661cb281-6bd7-4e2a-8469-409fb8e301b3","added_by":"auto","created_at":"2022-12-12 19:32:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":40395,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis of different genotypes\u003cstrong\u003e \u003c/strong\u003eof\u003cstrong\u003e a) \u003c/strong\u003eTHPO (rs6141) and \u003cstrong\u003eb) \u003c/strong\u003eARHGEF3 (rs1354034) genes\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/b297155f6b6d7e464270aca1.png"},{"id":30226353,"identity":"1a560dc1-10eb-4b56-955d-64abca9be9bb","added_by":"auto","created_at":"2022-12-12 19:56:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1187032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003e RMSD values \u003cstrong\u003eb)\u003c/strong\u003e Rg evolutions of THPO (rs6141) (Accession number: P40225) with simulation time in wild (black) and mutated (red) proteins\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/970f21ece5242b35359c7298.png"},{"id":30226038,"identity":"891c23f8-5d29-468a-b3d1-027c4bd9f29f","added_by":"auto","created_at":"2022-12-12 19:48:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":119061,"visible":true,"origin":"","legend":"\u003cp\u003eRibbon model of wild type protein of Thrombopoitin (Accession number: P40225) during the course of a 100 ns simulation\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/049a72e69f653e130d87a50d.png"},{"id":30225753,"identity":"1af9fbe9-9c1c-4c53-bbe0-6da5bafecb87","added_by":"auto","created_at":"2022-12-12 19:40:21","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":91179,"visible":true,"origin":"","legend":"\u003cp\u003eRibbon model of mutant protein of Thrombopoitin (Accession number: P40225) during the course of a 100 ns simulation\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/40f59afb1ce078270a949bb1.png"},{"id":30226641,"identity":"a7ca1f61-076a-484a-bd27-face9f2c76fe","added_by":"auto","created_at":"2022-12-12 20:04:20","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":197344,"visible":true,"origin":"","legend":"\u003cp\u003eSimulations of folding, hybridization, and melting pathways of \u003cstrong\u003ea)\u003c/strong\u003e rs1354034 wild type and \u003cstrong\u003eb)\u003c/strong\u003e rs1354034 (mutant) at 37\u003csup\u003eO\u003c/sup\u003e C\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/ba3cb77e7fd0934e560c5b4e.png"},{"id":30225752,"identity":"d1be9d0f-f1a0-4988-b6db-c78d29869f7e","added_by":"auto","created_at":"2022-12-12 19:40:21","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":193460,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-protein interaction network analysis of THPO and ARHGEF3 proteins. (A)\u003c/strong\u003eRepresents interaction pattern of THPO, \u003cstrong\u003e(B)\u003c/strong\u003eRepresents interaction pattern of ARHGEF3. The network of THPO and ARHGEF3 proteins was generated with high confidence level (0.700) using STRING. The selected proteins form large clusters with other functional proteins. The node of the network represent proteins and functional association among proteins was represented by edge. The association of edge was shown by different colored lines i.e., red line-gene fusions, sky blue line-curated database, purple line-experimentally determined, green line-gene neighborhood, black line-co-expression, dark blue line-gene co-occurrence, light blue line-protein homology and light green line-text-mining.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/f8f670b451e67fb0dba9c3fd.png"},{"id":30225580,"identity":"7f150af0-5f2b-416c-93e1-fda325a78dcd","added_by":"auto","created_at":"2022-12-12 19:32:20","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":113231,"visible":true,"origin":"","legend":"\u003cp\u003eDesialylated platelets from circulation activates the JAK-STAT cascade in the hepatocytes leading to the activation og THPO gene, which further induces the MKs to produce platelets through c-mpl receptor in the bone marrow.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/53e2ee81789c057dae825f8b.png"},{"id":30225751,"identity":"b987b718-f808-42a1-bbad-ffb6ec5dc9ff","added_by":"auto","created_at":"2022-12-12 19:40:20","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":103001,"visible":true,"origin":"","legend":"\u003cp\u003eARHGEF3 upregulated during MK maturation, genomic region of ARHGEF3 where rs1354034 located may influence the binding of certain transcription factors promoting MK maturation to form platelets\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/83662a3d3eb0ecd0296bb473.png"},{"id":30225582,"identity":"0f65c1c0-8818-4560-be23-4b827b783f88","added_by":"auto","created_at":"2022-12-12 19:32:21","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":192235,"visible":true,"origin":"","legend":"\u003cp\u003eMutated TPHO leads to an over expressed TPO cytokine, which activates the JAK-STAT pathway through its receptor (c-MPL) in Megakaryocytes. This pathway leads to the formation of platelets and also controls this formation as a feedback loop. Overexpression of THPO hampers this feedback loop and thereby results in the continuous formation of enlarged platelets. Overexpression of ARHGEF3 activates RAS-GTP through Rho GEF after the activation of platelet receptor (P2Y1). RAS pathway in turn leads to the changes in cytoskeleton of platelets and thereby controls the platelet shape.\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/2f73082d75aaf30820ce5a7d.png"},{"id":30225578,"identity":"27663beb-9649-420c-b310-7a1c10e88944","added_by":"auto","created_at":"2022-12-12 19:32:20","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":173311,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network analysis of THPO showing its interaction with ten other proteins involved in different cellular functions\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/56233ab5f316eb6e9312acf4.png"},{"id":30225577,"identity":"5f42ee31-3a41-4963-b2c4-cf8cab9e941a","added_by":"auto","created_at":"2022-12-12 19:32:20","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":137876,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network analysis of ARHGEF3 showing its interaction with five other proteins involved in different cellular functions.\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/57507572af918787de50df01.png"},{"id":41736890,"identity":"76e80c95-d8b1-4e59-a8fb-81be262f63dc","added_by":"auto","created_at":"2023-08-18 04:13:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2924221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2333866/v1/631f3dcb-32d2-4588-9ed1-e642e0adb70d.pdf"}],"financialInterests":"","formattedTitle":"Genomic Variation Affecting MPV and PLT count in association with development of Ischemic Stroke and its Subtypes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke or \u0026lsquo;brain attack\u0026rsquo; is one of the major causes of mortality and morbidity worldwide after myocardial infraction [1]. Stroke is mainly divided into two types: ischemic and hemorrhagic stroke [2]. 87% of the cases are of Ischemic stroke (IS) type, 10% Intracerebral hemorrhage (ICH) and 3% subarachnoid hemorrhage (SAH) [3]. IS occurs by obstruction in blood supply to the brain which is usually caused by formation of a thrombus leading to cell death in the brain [4]. Various modifiable and non-modifiable risk factors are associated with the development of stroke. The modifiable risk factors including hypertension, obesity, hyperglycemia, hyperlipidemia, atherosclerosis, thrombosis, renal dysfunction, and Sickle Cell Disease attribute to 87% risk of stroke. The non-modifiable risk factors include family history, age, and ethnicity. It has also been established that stroke has a strong genetic component [5\u0026ndash;7]. Genes involved in various pathways including homocysteine metabolism, coagulation and hemostasis, rennin angiotensin aldosterone system, cAMP degradation pathway, inflammation, extracellular matrix, lipid metabolism are known to be associated with stroke susceptibility [7\u0026ndash;9].\u003c/p\u003e \u003cp\u003ePlatelet traits Platelet traits such as Mean Platelet Volume (MPV) and Platelet Count (PLT) and pathways involved in recruitment of platelets have also been implicated in the disease pathophysiology [10, 11] [11\u0026ndash;13]. Platelets are known to play an important role in pathophysiology of IS by virtue of their capability in the formation of intravascular thrombus after the erosion or rupture of atherosclerotic plaques [14]. We have already established the association of increased MPV with degree of disability and rate of clot formation in IS patients [15]. PLT count and MPV are markers of platelet function and activation and are positively associated with platelet reactivity and aggregation [16\u0026ndash;18]. An increase in MPV occurs when platelets become activated and swollen spheres instead of quiescent discs. Large platelets are more adhesive and likely to aggregate more than smaller ones [19]. These traits and other platelet functions have been reported to be highly influenced by genetic variation. Various Genome wide association Studies (GWAS) involving different populations have demonstrated that the genomic alterations are associated with PLT count and MPV [10, 20, 21]. Variation involved in the genes involved in significant processes such as megakaryopoiesis, megakaryocyte/platelet adhesion, platelet formation and cell cycle regulation has been reported to influence platelet physiology [11, 22, 23]. PLT count and MPV altered by genetic profile has not been evaluated in association with the development of IS and its subtypes. Therefore, the current study has been carried out with an aim to explore the alterations in genes affecting MPV and PLT count and their functional implications, associated with IS and its subtypes.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eTwo hundred IS patients were recruited from Guru Gobind Singh Medical College and Hospital, Faridkot, Punjab, India. The study was approved by the ethical committee of Central University of Punjab as well as the study hospital. Patients confirmed to have suffered an IS as diagnosed by CT scan or MRI were included in this study. Patients having HS or TIA were excluded from this study. Patients with major secondary problems like renal, hepatic skeletal and other neurological disorders were also excluded from the study. As a control group 200 age and sex matched healthy individuals without history of any other medical condition especially the cardiovascular and neurological diseases were also employed in the study. Written informed consent was obtained from all the recruited subjects. Stroke subtypes were stratified as per TOAST classification [24].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBlood Sample Collection\u003c/h2\u003e \u003cp\u003e A total 5 ml of blood was collected in EDTA and sodium citrate containing vacutainers with the written informed consent of the participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements of Platelet Count and Mean Platelet Volume\u003c/h2\u003e \u003cp\u003eMPV and PLT count were evaluated using automated cell counter (ABX Micros 60 Hematology System). The values were confirmed using BD Accuri C6 flow cytometer in 50% of the patients as reported previously [15].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSonoclot Signature Analysis\u003c/h2\u003e \u003cp\u003eThe assessment of clot timing in IS patients was carried out using Sonoclot Coagulation and Platelet Function Analyzer (Sienco Inc.: Model no. SCP1) as described in our previous study [15].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDNA isolation\u003c/h2\u003e \u003cp\u003eDNA isolation was carried out using organic method (phenol-chloroform method) Russell and Sambrook (2001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of Genomic alterations affecting MPV and PLT\u003c/h2\u003e \u003cp\u003eThe Screening for genetic variations affecting MPV and PLT count was carried out in 17% IS patients to figure out the gene variants occurring at a higher frequency. This screening was carried out using Global Screening Array (GSA) v3.0 microchip (Illumina Inc.). After analyzing the GSA results, variants of three genes THPO (rs6141), WDR66 (rs7961894) and ARHGEF3 (rs1354034) were filtered out and all the samples i.e. 200 patients and 200 controls were screened for these variants using Sanger Sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eExpression Analysis\u003c/h2\u003e \u003cp\u003eRNA was isolated from the platelets using Trizol method. cDNA synthesis was carried out using cDNA synthesis kit (iScript\u0026trade; cDNA Synthesis Kit Bio-Rad) as per manufacturer\u0026rsquo;s instructions with equal amount of RNA from each sample. 18s rRNA was used as a housekeeping gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll significant variants were tested for Hardy-Weinberg equilibrium. The association of genotypes and alleles with IS (univariate analysis) was estimated by the odds ratio with 95% confidence interval (CI) and χ2 analysis using OpenEpi software (version 2.3.1; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA). The association of gene variants with the disease was confirmed by Multiple Logistic Regression analysis (MLR). The independent variables were decided as the following dummy variables: MPV, 0 for standard and 1 for elevated; hypertension, 0 for normotension and 1 for hypertension; diabetes, 0 for normal and 1 for diabetic; tobacco use, 0 for no tobacco use and 1 for tobacco use; alcohol consumption, 0 for nonalcoholics and 1 for alcohol consumers; family history, 0 for no family history of IS and 1 for family history of IS. The dummy variables for gene variants were 0 for normal homozygous and 1 for heterozygous and mutant homozygote. All the statistical analysis was carried out using SPSS [Version: 28.0.1.1 (15)]. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Modelling and Simulation of ARHGEF3 (Q9NR81) and THPO (P40225)\u003c/h2\u003e \u003cp\u003eThe amino acid sequence of the proteins were retrieved from UniProt [25]. The AlphaFold Protein Structure Database was used to predict the protein structures [26]. GROMACS 2018.3 (Abraham et al., 2015), modules were used to simulate the system of proteins as well as for the analysis to identify the potential role of mutation on structural integrity of the two proteins with accession numbers Q9NR81 (Rho guanine nucleotide exchange factor 3, ARHGEF3) and P40225 (Thrombopoietin, THPO). In order to identify any structural deviation and/or stability, we have calculated the RMSD (root mean squared deviation) for the wild and mutated proteins along with Radius of Gyration (Rg) that again tells us about any changes in the folded structure of the protein or conformational jump during the course of simulation. As far as rs1354034 variant of the ARHGEF3 gene is concerned, it is an intronic variant, it was difficult to simulate the mutated ARHGEF3 protein as it does not show any change in amino acid sequence. Therefore, we used UNAFold to predict the changes caused by this SNP at mRNA level [27].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein interaction networks\u003c/h2\u003e \u003cp\u003eThe STRING web based server was used to generate protein-protein interaction network of THPO and ARHGEF3 proteins at highest confidence score of 0.700 [28].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn our recent study we already established the association of various risk factors with IS and its subtypes [15]. A total of 106 variants affecting MPV, PLT count, and latelet reactivity were screened using GSA in 17% IS patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Three genes ARHGEF3 (rs1354034, T\u0026thinsp;\u0026gt;\u0026thinsp;C), THPO (rs6141, C\u0026thinsp;\u0026gt;\u0026thinsp;T), and WDR66 (rs7961894, C\u0026thinsp;\u0026gt;\u0026thinsp;T) showed variation based on the results of GSA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These were further validated by subjecting all the samples (200 patients and 200 controls) to Sanger sequencing. No significant association of WDR66 (rs7961894) was found with the disease. In case of THPO, rs6141 (C\u0026thinsp;\u0026gt;\u0026thinsp;T) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) polymorphism a significant difference was observed in genotypic distribution between IS patients and controls [for TT vs CC, X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;37.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;6.98 (95% CI; 3.60-13.22)]. TT and CT genotypes showed a significant association with the disease [for TT vs CC\u0026thinsp;+\u0026thinsp;CT, X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.323; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, OR\u0026thinsp;=\u0026thinsp;1.89 (95% CI; 1.17\u0026ndash;3.07)] (Table). However, we did not find any significant difference in the distribution of T and C alleles between IS patients and controls. Even after controlling all the confounding risk factors using MLR, a significant association of TT genotype with IS was found [(p\u0026thinsp;=\u0026thinsp;0.008; adjusted odds ratio- 3.834; 95% CI; 1.431\u0026ndash;10.277)] (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of THPO (rs6141), genotypes and allelic frequencies in ischemic stroke patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAllele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (10.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (44.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e165 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e255 (0.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTHPO\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(rs6141)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (61.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (38.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e235 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e145 (0.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChi-square (X\u003csup\u003e2\u003c/sup\u003e), crude odds ratio and \u003cem\u003ep\u003c/em\u003e-value for THPO (rs6141) gene variant\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients vs Controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square (X2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTHPO\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(rs6141)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT vs CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.98 (3.6-13.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT vs CC\u0026thinsp;+\u0026thinsp;CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.89 (1.17\u0026ndash;3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT vs C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.049 (0.78\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of ARHGEF3 (rs1344034), genotypes and allelic frequencies in ischemic stroke patients and controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAllele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e130\u003cb\u003e(0.325)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e261 (0.652)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eARHGEF3\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(rs1354034)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e270 (0.675)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e139 (0.347)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChi-square (X2), crude odds ratio and p-value for ARHGEF3 (rs1344034) gene variant\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients vs Controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square (X2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC vs TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.7 (10.1-42.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eARHGEF3\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(rs1354034)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC vs TT\u0026thinsp;+\u0026thinsp;TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.688 (3.392\u0026ndash;9.539)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC vs T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9 (2.908\u0026ndash;5.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndependent association of genotypes with IS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk factor\u003c/p\u003e \u003cp\u003e(Genotype/s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted odds ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHPO (rs6141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;TT genotype\u003c/p\u003e \u003cp\u003e(Heterozygous\u0026thinsp;+\u0026thinsp;mutant homozygotes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.834 (1.431\u0026ndash;10.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARHGEF3 (rs1354034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTC\u0026thinsp;+\u0026thinsp;CC (heterozygous\u0026thinsp;+\u0026thinsp;mutant homozygotes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.419 (1.657\u0026ndash; 11.785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEvaluating the association of ARHGEF3 (rs1354034, T\u0026thinsp;\u0026gt;\u0026thinsp;C) gene with the disease, a significant difference was observed in genotypic distribution of CC and TT genotype between IS patients and controls [for CC vs TT, X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;81.02; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;20.7 (95% CI; 10.1-42.39)] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A significant difference between CC and TT\u0026thinsp;+\u0026thinsp;TC genotypes between IS patients and Controls [for CC vs TT\u0026thinsp;+\u0026thinsp;TC, X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;47.2; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;5.688 (95% CI; 3.392\u0026ndash;9.39)] was also observed (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The C allele also associated significantly with the disease [C vs T, X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;84.54; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;3.9 (95% CI; 2.908\u0026ndash;5.23)].\u003c/p\u003e \u003cp\u003eAfter controlling all the confounding risk factors using MLR significant association of CC genotype with IS was observed [(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; adjusted odds ratio- 4.419; 95% CI; 1.657\u0026ndash; 11.785)] (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvaluating the association of these two variants with IS subtypes a significant association of TT genotypes of THPO gene whereas for ARHGEF3 gene significant association was observed with C allele as well as CC genotypes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChi-square (X\u003csup\u003e2\u003c/sup\u003e), odds ratio and \u003cem\u003ep\u003c/em\u003e-value for THPO (rs6141) gene variant in IS subtypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHPO\u003c/p\u003e \u003cp\u003e(rs6141)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square (X2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT vs CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.399 (2.639\u0026ndash;11.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge Artery atherosclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT vs C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.274 (1.657\u0026ndash;3.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT vs CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.725 (2.264\u0026ndash;33.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall artery occlusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT vs C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.528 (1.541\u0026ndash;4.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT vs CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.09 (2.727\u0026ndash;62.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardioembolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT vs C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2512 (0.03061-2.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChi-square (X2), odds ratio and \u003cem\u003ep\u003c/em\u003e-value for ARHGEF3 (rs1344034) gene variant in IS subtypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARHGEF3\u003c/p\u003e \u003cp\u003e(rs1354034)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square (X2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge Artery Atherosclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC vs TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.27 (6.839, 34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC vs T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.219 (2.33, 4.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC vs TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.17 (5.984, 61.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall artery occlusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC vs T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.78 (2.801, 8.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC vs TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardioembolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC vs T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.919 (4.049, 19.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute stroke of other determined etiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC vs TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC vs T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.14 (1.601, 107.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe association of variants of THPO (rs6141) and ARHGEF3 (rs1354034) genes with MPV and PLT count was also evaluated. The altered genotypes of THPO (rs6141) and ARHGEF3 (rs1354034) gene showed a significant association with increased MPV whereas for PLT count an association of the variant genotypes with decreased PLT count was observed although it did not reach statistical significance (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).This is also in consensus with our observation where an inverse correlation was observed between these two variables [15].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of mean MPV and PLT count with THPO (rs6141) and ARHGEF3 (rs1354034) genotypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean MPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean PLT count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e321.3\u0026thinsp;\u0026plusmn;\u0026thinsp;126.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.3351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTHPO (rs6141)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e313.55\u0026thinsp;\u0026plusmn;\u0026thinsp;90.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e310.66\u0026thinsp;\u0026plusmn;\u0026thinsp;90.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e298.8\u0026thinsp;\u0026plusmn;\u0026thinsp;87.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eARHGEF3\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(rs1354034)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e315.88\u0026thinsp;\u0026plusmn;\u0026thinsp;97.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e313.44\u0026thinsp;\u0026plusmn;\u0026thinsp;91.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.1041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe clotting parameters including ACT, CR and PF were also compared among various genotypes of THPO (rs6141) and ARHGEF3 (rs1354034) genes. A significant increase in CR was observed in the patients bearing the altered TT genotype of THPO and CC genotype of ARHGEF3 gene in comparison with normal genotypes of both these genes, CC and TT respectively. However, we did not find significant difference in ACT and PF values among the variant genotypes of both the genes (Tables\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of different genotypes of THPO (rs6141) with ACT, CR and PF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHPO (rs6141)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e104.5\u0026thinsp;\u0026plusmn;\u0026thinsp;22.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e28.25\u0026thinsp;\u0026plusmn;\u0026thinsp;13.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e140\u0026thinsp;\u0026plusmn;\u0026thinsp;90.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e34.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e105.62\u0026thinsp;\u0026plusmn;\u0026thinsp;34.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;25.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.3768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of different genotypes of ARHGEF3 (rs1354034) with ACT, CR and PF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARHGEF3 (rs1354034)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e93.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e133.58\u0026thinsp;\u0026plusmn;\u0026thinsp;83.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e38.41\u0026thinsp;\u0026plusmn;\u0026thinsp;19.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e121\u0026thinsp;\u0026plusmn;\u0026thinsp;64.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e42.73\u0026thinsp;\u0026plusmn;\u0026thinsp;26.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExpression analysis carried out by qPCR of THPO (rs6141) and ARHGEF3 (rs1354034) genes revealed higher expression of both the genes in patients bearing altered genotypes (Fig.\u0026nbsp;38). The altered genotypes showed significantly higher expression in comparison with heterozygous and normal genotypes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Further the heterozygous genotypes showed a significantly higher expression as compared to the normal genotypes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly the expression of CC genotype of ARHGEF3 (rs1354034) gene was significantly higher in comparison with the heterozygous and normal genotypes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After comparing the heterozygous genotype with normal genotype it showed significantly higher expression (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The results were normalized against 18s rRNA which was used as a housekeeping gene to evaluate the expression of both the genes.\u003c/p\u003e \u003cp\u003eThe amino acid sequence of the proteins were retrieved from UniProt [1]. The AlphaFold Protein Structure Database was used to predict the protein structures of both THPO and ARHGEF3 genes (Fig.\u0026nbsp;39). RMSD for both the Proteins (wild and mutated) were calculated using GROMACS module gmx rms module with respect to a crystal structure as a reference. The stability of a protein relative to reference structure can be determined by measuring the deviation produced during the simulation. The smaller the deviations, represents the more stable simulated structure. RMSD values for the all atoms of the mentioned three proteins were calculated for 100ns simulation. It can be observed for P40225 (Thrombopoietin) the wild type structure was stable around 2.0 nm (3.5 ns) with respect to its crystal structure, but the mutated (R38C) structure showed a sudden jump to higher rmsd value (2.5 nm) at 4.0 ns and maintained a constant elevation of RMSD value during the course of its simulation (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Similar indication was observed in case of Radius of gyration calculation that suggests P40225 wild type protein structure is stable during the course of simulation, whereas in case of mutated structure moderate fluctuations were observed. However, the mutated protein shows reduced compactness in the protein structure with respect to the wild variant till the end of the simulation.\u003c/p\u003e \u003cp\u003eAs far as rs1354034 variant of the ARHGEF3 gene is concerned, it is an intronic variant, it was difficult to simulate the mutated ARHGEF3 protein as it does not show any change in amino acid sequence. Therefore, we used UNAFold to predict the changes caused by this SNP at mRNA level. UNAFold software package is used to create the simulations of folding, hybridization, and melting pathways for one or two single-stranded RNA or DNA molecule. It combines free energy minimization, partition function calculations and stochastic sampling to predict the folding of single stranded RNA or DNA. Further, for melting simulations, the package computes entire melting profiles, not just the melting temperatures [27].\u003c/p\u003e \u003cp\u003eThe results showed that the mutant (rs1354034) variant RNA exhibits qualitatively greater stability wrt the free energy associated with the secondary structure. This increased stability manifests in reduced decay rate of the mutant RNA and as a consequence, into its increased processing into mRNA and its translation into the protein. The examination of the secondary structure of the mutant variant clearly indicates more organized structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e) with 54 helices as compared to the wild type sequence (50 helices). The average stem-loop size is also less for the mutant variant vis-\u0026agrave;-vis wild type. This reduces the probability of its decay by RNAses.\u003c/p\u003e \u003cp\u003eThe protein-protein interaction network of THPO showed that it interacts closely with ten proteins including Signal transducer and activator of transcription 5A (STAT5A), Signal transducer and activator of transcription 3 (STAT3), SHC-transforming protein 1 (SHC1), Tyrosine-protein kinase (JAK2), Erythropoietin (EPO), Signal transducer and activator of transcription 5B (STAT5B), Granulocyte colony-stimulating factor (CSF3), Kit ligand (KITLG), Thrombopoietin receptor (MPL) and Interleukin-3 (IL3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003ea). These proteins are mainly involved in cell growth, development, differentiation, mediating cellular responses to cytokines and other growth factors. Furthermore, relationship was noticed with the increased level of THPO protein which increases both platelet size and platelet count (Balcik \u003cem\u003eet al.\u003c/em\u003e, 2013; Kapur \u003cem\u003eet al.\u003c/em\u003e, 2020).\u003c/p\u003e \u003cp\u003eThe Rho guanine nucleotide exchange factor 3 (ARHGEF3) acts as guanine nucleotide exchange factor for RhoA and RhoB GTPases and it showed interaction with five proteins based on STRING analysis including Ring finger protein 145 (RNF145), Rho-related GTP-binding protein RhoB (RHOB), Rsa homology gene family (RHOA), Rho-related GTP-binding protein RhoC (RHOC) and Catenin beta-1 (CTNNB1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003eb).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOn account of the ability to form intravascular thrombus following the erosion or rupture of atherosclerotic plaques, platelets are known to play an essential role in the pathophysiology of IS [14]. Platelet parameters like MPV and PLT count are considered as significant determinants of platelet function [29]. There is evidence that increased platelet size and count reflects increased platelet activity and are useful predictive and prognostic biomarkers for cerebrovascular events. In a recent study published from our lab an elevated MPV was found to be significantly associated with increased risk of IS and also higher clot rate and higher degree of disability based on mRS. [15]. These results are in accordance with previous studies where an increase in MPV has been associated significantly with increased risk of IS [12, 29\u0026ndash;31]. In the current study we screened the genetic variants involved in PLT count, MPV and platelet reactivity in IS patients. Based on the previous reports a total of 106 variants in 96 genes involved in PLT count, MPV and Platelet reactivity were initially screened using GSA in 17% patients. Out of these, 62 variants have been reported to affect PLT count; 33 were found to affect MPV and 11 variants reported to affect platelet reactivity (Tables\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, \u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and \u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Most of these genes were found to be either normal homozygous or showed very minor frequency of heterozygosity except for variants of two genes ARHGEF3 (rs1354034, T\u0026thinsp;\u0026gt;\u0026thinsp;C), and THPO (rs6141, C\u0026thinsp;\u0026gt;\u0026thinsp;T). Therefore, these were evaluated further by Sanger Sequencing after amplifying the specific regions of these genes bearing the variation in all the subjects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSNPs associated with PLT Count\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2336384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eMFN2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers373121156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCDKN2A\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10914144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDNM3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers117899880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eBRD3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1668871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTMCC2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers505404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePSMD13\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7550918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLOC148824\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4246215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eFEN1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3811444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTRIM58\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4938642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCBL\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers12603268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eGCKR\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7342306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCD9-VWF\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers625132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eEHD3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers941207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eBAZ2A\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers17030845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTHADA\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers3184504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eSH2B3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers76160061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eSYN2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers17824620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRPH3A-PTPN11\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7641175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eSATB1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7961894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eWDR66\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1354034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eARHGEF3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4148441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eABCC4\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3792366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePDIA5\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers8022206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRAD51L1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7694379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eHSD17B13\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers8006385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eITPK1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers17568628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eF2R\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7149242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eC14orf70-DLK1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers700585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eMEF2C\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11628318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRCOR1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2070729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eIRF1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2297067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eC14orf73\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers441460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLRRC16\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers3809566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTPM1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3819299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eHLA-B\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1719271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eANKDD1A\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers399604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eHLA-DOA\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eGP1BA\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers210134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eBAK1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers397969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eAKAP10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9399137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eHBS1L-MYB\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers55997232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTAOK1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers342275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePIK3CG\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10512472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eSNORD7-AP2B1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4731120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eWASL\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers708382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eFAM171A2-ITGA2B\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6995402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePLEC1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11082304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCABLES1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers409801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eAK3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers8109288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTMP4\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers13300663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRCL1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers17356664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eEXOC3L2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1034566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eARVCF\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers12526480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLRRC16A\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTHPOII\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6490294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eACAD10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9494145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eHBS1L-MYB\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers477895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eBAD\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7896518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eJMD1C\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers13236689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCD36\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers151361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLRRC16A\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers342293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePIK3CG\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSNPs associated with MPV\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7961894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eWDR66\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10512627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eKALRN\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers8109288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTPM4\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers117341321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eKIAA0232\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1354034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eARHGEF3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2227831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eF2R\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers342293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePIK3CG\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4521516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eMEF2C\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7075195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eJMD1C\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10076782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRNF145\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers8076739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTAOK1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10813766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDOCK8\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers117213068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTMCC2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7075195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eJMJD1C\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers17655730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePSMD13\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers17655730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eNLRP6\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4812048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCTSZ-TUBB1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1558324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCD9-VWF\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers342296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePIK3CG\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2015599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eMTSTD1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11653144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eTAOK1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10876550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCOPZ1-NFE2-CBX5\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers17396340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eKIF1B\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2950390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePTGES3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10914144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDNM3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7317038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eGRTP1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers649729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eEHD3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers944002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eC14orf73\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4305276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eANKMY1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers3000073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eBRF1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1354034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eARHGEF3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers16971217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eAP2B1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers12969657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCD226\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSNPs associated with PLT Reactivity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1613662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eGP6\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eFCER1G\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3737224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePEAR1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11264579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePEAR1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3729931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRAF1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers147212241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP2RY12\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3788337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eGNAZ\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10496541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eCD36\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers35091628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eMAP2K2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers12566888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePEAR1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7940646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eMRVI1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThrombopoietin (also known as THPO, TPO) is a major cytokine that plays a crucial role in platelet production. This humoral substance controls MK proliferation and differentiation to maintain normal thrombopoiesis [32]. The SNP rs6141 (C\u0026thinsp;\u0026gt;\u0026thinsp;T) situated at 3\u0026rsquo;-UTR region of the THPO gene is reported to be involved in the post transcriptional control of the gene expression mainly affecting mRNA splicing [33]. Studies have also shown that microdeletions involving this SNP in THPO gene cause mild congenital thrombocytopenia [34, 35]. Further two gain-of-function mutations in THPO gene G\u0026thinsp;\u0026gt;\u0026thinsp;C transversion and a G\u0026thinsp;\u0026gt;\u0026thinsp;T transversion have been reported to produce mRNAs with shortened 5\u0026prime;\u0026ndash;untranslated regions (UTR) that are more efficiently translated in comparison with transcripts produced by wild type THPO. These transcripts with gain of function mutation result in elevated PLT count which might lead to thrombosis and bleeding [36, 37]. THPO variants with bi-allelic loss-of-function cause multilineage bone marrow failure and severely reduced platelet counts [38\u0026ndash;40]. Another study identified a one-base deletion in the 5\u0026prime;-untranslated region of the \u003cem\u003eTHPO\u003c/em\u003e gene. In vitro experiments showed that this mutation increased TPO production and suggested that this region of the \u003cem\u003eTHPO\u003c/em\u003e gene may play a crucial role in regulating THPO expression [41]. Based on the results of different GWAS studies it has been established that rs6141 of THPO gene is a significant determinant of MPV and PLT count [33, 42, 43]. As far as the role of THPO in IS is concerned, it has been reported that elevated levels of TPO are associated with increased MPV and PLT counts in these patients [44\u0026ndash;46].\u003c/p\u003e \u003cp\u003eIn the current study evaluating the association of rs6141 (THPO) with IS we found a significant association of TT genotype with the disease which was confirmed by MLR analysis showing an independent association of TT genotype with the disease (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, we did not find a significant difference in the distribution of T and C alleles between patients and controls. As far as the association of this variant with IS subtypes is concerned, the T allele showed a significant association with LAA, Small artery occlusion, and cardioembolism. We also evaluated the association of variant genotype with MPV, Clot rate, and PLT count. The TT and CT genotypes showed a significant association with elevated MPV, higher clot rate and reduced PLT count in comparison with the CC genotype bearing patients. This association was also confirmed by MLR controlling all other confounding factors.\u003c/p\u003e \u003cp\u003eSince the focus of the current study was on platelet parameters, therefore, the expression analysis of THPO (rs6141) was carried out using mRNA isolated from platelets of IS patients. Patients bearing TT genotype showed highest expression followed by CT and CC genotypes. Aged platelets induce the production of TPO in the hepatocytes. TPO increases the number of circulating platelets once released into the bloodstream. Most of the studies have linked higher TPO levels with increased platelet activity [46]. A study carried out by Balcik \u003cem\u003eet al\u003c/em\u003e (2013) reported that patients with IS have higher TPO and MPV levels and concluded that increased TPO levels elevate both PLT count and MPV resulting in higher thrombotic capacity of platelets [44]. Another study carried out in acute myocardial infarction (AMI) patients and unstable angina pectoris also reported that increased TPO and MPV levels are positively associated with each other in AMI patients [47]. Yang \u003cem\u003eet al\u003c/em\u003e (2008) evaluated the role of Severe Acute Respiratory Syndrome (SARS) in affecting normal functions of hematopoietic stem cells and megakaryocytic cells. They found that increased TPO levels in the plasma of these patients lead to thrombocytosis and hyperactive platelets [48]. Recently a study demonstrated that platelets in COVID-19 patients aggregate faster and showed increased spreading on both fibrinogen and collagen during clot retraction. It was also found that TPO levels were elevated in the serum of SARS-CoV-2 patients [49].\u003c/p\u003e \u003cp\u003ePrevious studies have mostly explored the association of THPO with PLT count. However, its role in MPV has not been studied much. Since MPV and PLT count are inversely correlated therefore it is obvious that studies showing its association with increased PLT count might not have observed its impact on MPV [50].\u003c/p\u003e \u003cp\u003eThe proposed mechanism by which TT genotype (rs6141) might lead to the higher expression of THPO gene in bone marrow producing hyperactive platelets has been depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e14\u003c/span\u003e. TPO binds to the megakaryocytes or platelets and controls their production through a feedback mechanism. During normal hemostasis TPO concentrations remain normal. Platelets experience mechanical stress that shortens their life span under the conditions like atherosclerosis which indirectly activates platelet biogenesis [51] [44]. \u003cem\u003eIn vivo\u003c/em\u003e injection of recombinant adenoviral vectors, or transgenes resulted in variable thrombocytosis [52\u0026ndash;55]. In addition, studies have also demonstrated that patients bearing gain of function mutations in THPO gene, enhance TPO mRNA translation which elevates its expression inducing lineage-selective effects in patients affected with thrombocytosis and polyclonal hematopoiesis. TPO levels were also observed to be higher in the serum [37, 41, 56]; [57]. It has been reported that around 1000 to 3000 platelets are produced from a single MK [58]. The mechanism leading to the production of platelets through MKs involves lot of reorganization of cytoskeletal components like actin and tubulin. THPO is the major watch dog of this process [59, 60]. Although the process of thrombopoiesis is understood well there are still many unanswered questions to how some transcription factors like GATA and FOG1 affect the size of the platelets size [61].\u003c/p\u003e \u003cp\u003eThe impact of rs6141 of THPO gene on protein structure was evaluated by protein dynamics studies using GROMACS. It showed that the wild type structure of THPO was stable around 2.0 nm (3.5 ns) with respect to its crystal structure. On the other hand mutated (R38C) structure showed a sudden jump to higher RMSD value (2.5 nm) at 4.0 ns and maintained a constant higher RMSD value during the course of its simulation. Mutant protein encoded by THPO (rs6141) gene also showed a reduced compactness in structure in comparison with the wild type protein. This suggested that a minor deviation between the wild and mutated RMSD values affects the original protein structure.\u003c/p\u003e \u003cp\u003eARHGEF3, also known as XPLN is an exchange factor found in platelets, leukemics, and neuronal tissues [62]. It was first identified as RhoGEF (Rho guanine nucleotide exchange factor) for Rho GTPases through an expressed sequence tag database search, using the diffused B-cell lymphoma (Dbl) homology (DH) domain query in the BLASTN system [62]. Skeletal muscles and the brain have the highest levels of ARHGEF3 protein expression, followed by the heart, kidneys, platelets, and macrophages [63]. It plays a non-canonical role by inhibiting mTORC2 kinase activity through Akt signaling. [63, 64]. It is also involved in various primary cellular functions including cell adhesion, motility, polarity, growth, cell diferentiation and cytoskeleton rearrangements [63, 65].\u003c/p\u003e \u003cp\u003eGWAS studies have identified newer roles of ARHGEF3 gene in modulating bone mineral density (BMD), platelet differentiation and Hirschsprung disease [22, 66]. Another GWAS carried out to evaluate the association of significant variants with platelet traits reported the association of rs1354034 with MPV, in association with other genes including \u003cem\u003eWDR66\u003c/em\u003e, TAOK1, and Phospatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma [22]. A meta-analysis including the results of various GWAS studies on 66, 867 European individuals also demonstarted that rs1354034 located at 3p14.3 is assocaited significantly with PLT count and MPV [50]. Zou \u003cem\u003eet al\u003c/em\u003e (2017), found that this SNP is present in the regulatory region (non-coding region upstream of the transcription start site) of ARHGEF3 gene and proposed that it may influence the binding of certain trasnscription factors like RUNX1, MEIS1, GATA2, GATA1, and FLI1 duirng MK maturation (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e). However, it is not clear if this SNP is directly involved in influencing the binding sites of these transcription factors [66]. The C allele of rs1354034 has been associated with lower ARHGEF3 mRNA expression, higher PLT count and lower MPV in humans (Zou \u003cem\u003eet al.\u003c/em\u003e, 2017). In a very recent report researchers tried to investigate the genetic overlap of platelet parameters with an endophenotype of Parkinson\u0026rsquo;s Disease. They found that various genes including ARHGEF3 are associated with MPV as well as age of onset and Parkinson\u0026rsquo;s Disease suceptibility [67].\u003c/p\u003e \u003cp\u003eIn the current study evaluating the association of rs1354034 (ARHGEF3) with IS and its subtypes, we found a significant association of CC genotype and C allele with the disease which was confirmed by MLR analysis showing an independent association of CC genotype and C allele with the disease. As far as association of this variant with IS subtypes is concerned, CC genotype and C allele were found to be associated significantly with LAA, cardioembolism, small artery occlusion and stroke of undetermined etiology.\u003c/p\u003e \u003cp\u003eWe also evaluated the association of variant genotypes of ARHGEF3 gene with MPV, Clot rate and PLT count. The variants CC and TC genotypes showed a significant association with elevated MPV, higher clot rate and reduced PLT count in comparison with TT genotype. This association was also confimred by MLR. A GWAS reported that the same SNP (rs1354034) is associated \u003cem\u003ein trans\u003c/em\u003e with expression of vWF, which is an important factor in blood coagulation pathway in humans [68].\u003c/p\u003e \u003cp\u003eExpression analysis revealed that patients bearing CC genotype of \u003cem\u003eARHGEF3\u003c/em\u003e gene showed highest expression followed by TC genotype in comparison with the TT genotype in platelets.\u003c/p\u003e \u003cp\u003eSince this variant is an intronic variant its impact on ARHGEF3 mRNA was evaluated using UNAFold. This analysis revealed that the mRNA encoded by CC genotype of \u003cem\u003eARHGEF3\u003c/em\u003e gene (rs1354034) leads to qualitatively greater stability with respect to the free energy associated with the secondary structure as compared to the mRNA encoded by normal genotype.\u003c/p\u003e \u003cp\u003eThis SNP present upstream to the ARHGEF3 gene has been associated significantly with higher expression of ARHGEF3 during MK maturation both in murines and humans [66, 69, 70]. Based on the previous studies it has been reported that ARHGEF3 is invloved in platelet shape change and function. It has also been demonstrated that ARHGEF3 might be a missing link between ADP mediated platelet shape change and activation via P2Y1 and P2Y2 receptors [66]. The proposed mechanism by which CC genotype (rs1354034) might lead to the higher expression of ARHGEF3 gene activating the MK maturation in bone marrow producing enlarged platelets has been depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e16\u003c/span\u003e. Studies on mice lacking P2Y1 receptor do not show shape change of platelets in response to ADP suggesting that the ADP signaling is associated with shape change mechanisms in these blood cells [71]. This observation was further aided by another study that reported platelet shape change occurs through Rho signaling and actin reorganization [70, 72].\u003c/p\u003e \u003cp\u003eAs mentioned previously THPO is a glycoprotein produced primarily in the liver that stimulates the formation of megakaryocytes and platelets. THPO protein was found to interact with 10 other proteins mainly involved in platelet activation (c-MPL, IL3), platelet aggregation (IL3), erythropoiesis (EPO, STAT5B), megakaryocyte development (c-MPL, JAK2, STAT3), cytoskeleton organization (CSF3), cell survival and proliferation (KITLG, STAT5A) (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e17\u003c/span\u003e) [73\u0026ndash;75]. For the production of platelets MKs undergo a series of remodeling events that result in thousands of platelets being released from a single cell [76]. All the proteins found to be interact with THPO protein are known to regulate the platelet formation and functions [77\u0026ndash;79]. The interaction of THPO protein with other proteins suggested that there might be potential alternate mechanisms that could affect platelet production, morphology and function. The THPO along with other interacting proteins might be explored as significant biomarker affecting platelet parameters and functions and thereby a potential therapeutic target.\u003c/p\u003e \u003cp\u003eARHGEF3 activates two members of the Rho family GTPases, RHOA and RHOB, which are involved in osteoblast maintenance [70]. Various other cellular processes including cytoskeleton reorganization are activated and inactivated by Rho-like GTPases as discussed previously [80, 81]. By catalyzing the release of bound GDP, guanine nucleotide exchange factors (GEFs) accelerate Rho GTPase activity. ARHGEF3 inhibits mTORC2 kinase activity, primarily for Akt, by binding the mTORC2 complex. (Arthur \u003cem\u003eet al\u003c/em\u003e., 2002; Rossman \u003cem\u003eet al.\u003c/em\u003e, 2005). ARHGEF3 protein has been found to interact with five other proteins involved in cytoskeleton organization (RHOA, RHOC), cell adhesion, migration (RHOA, RHOC, AHOB, and CTNNB1), apoptosis (RHOB) and cholesterol homeostasis (RNF145) (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e18\u003c/span\u003e). Many physiological and pathological functions of platelets are mediated by Rho GTPase proteins [80]. Actin cytoskeleton regulation is one of the main functions of Rho GTPases, although they also participate in several other biochemical pathways [82]. When platelets interact with vWF and collagen via the cell-surface receptors GpIb-IX-V and GPVI, respectively, a dramatic change in shape occurs due to the reorganization of the actin cytoskeleton. When platelet morphology is altered, more surface area is available for interactions with the ECM and other cells [83, 84]. Initial shape changes include discoid loss, sphering, and filopodia extension. The interaction of ARHGEF3 and THPO with other proteins significantly involved in various platelet parameters and functions suggests that the genotype-phenotype correlation should not be based on one protein but rather than complete network should be analysed to explore their role as biomarkers or therapeutic targets.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe observed an association of ARHGEF3 (rs1354034) and THPO (rs6141) genes with higher MPV, higher rate of clot formation and risk of developing IS. Further we also observed that MPV and PLT count showed an inverse relationship with mutant alleles of both the genes. Expression analysis of both THPO and ARHGEF3 genes revealed a higher expression of variant genotypes in the platelets. In silico analysis carried out for THPO (rs6141) gene showed that the mutated protein has reduced compactness in the protein structure in comparison with the wild type which might be resulting in the higher expression of THPO gene in the platelets. We used UNAFold to predict the changes caused by the variant ARHGEF3 (rs1354034) at mRNA level because it is difficult to simulate the mutated ARHGEF3 (rs1354034) since it is an intronic variant. It showed that the mutant (rs1354034) variant RNA exhibits qualitatively greater stability with respect to the free energy associated with the secondary structure as compared to normal ARHGEF3 (rs1354034), which might lead to a higher expression in the platelets. Based on the STRING analysis it was observed that these two significant proteins interact with other proteins which are involved in various pathways such as platelet activation, aggregation, erythropoiesis, megakaryocyte development, cytoskeleton organization, cell adhesion. Cell migration, vascular development, apoptosis, cell proliferation and cholesterol homeostasis. The current study is a step forward to establish MPV as a diagnostic or prognostic marker for IS. There is a need to develop the specific treatment strategies that can particularly reduce MPV. Further, establishing the specific genotype-phenotype correlation of markers affecting MPV in a particular population might help in devising better or specific treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinancial assistance from Council for Scientific and Industrial Research (CSIR) India and DST-FIST is highly acknowledged.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial assistance from DST-FIST (SR/FST/LS-I/2017/49) is acknowledged with thanks. Financial support to Mr. Abhilash Ludhiadch (Award No-09/ 1051(0029)/2 019-EMR-1) from the Council for Scientific and Industrial Research (CSIR) India is highly acknowledged.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent from each participant was taken prior to the sample collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by Institutional ethics committee of the University (CUPB/CC/RO/18/2316) as well as study hospital (GGS/IEC/56).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFeigin, V.L., et al., \u003cem\u003eWorld Stroke Organization (WSO): global stroke fact sheet 2022.\u003c/em\u003e International Journal of Stroke, 2022. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 18-29.\u003c/li\u003e\n \u003cli\u003eAmarenco, P., et al., \u003cem\u003eClassification of stroke subtypes.\u003c/em\u003e Cerebrovascular diseases, 2009. \u003cstrong\u003e27\u003c/strong\u003e(5): p. 493-501.\u003c/li\u003e\n \u003cli\u003eVirani, S.S., et al., \u003cem\u003eHeart disease and stroke statistics\u0026mdash;2021 update: a report from the American Heart Association.\u003c/em\u003e Circulation, 2021. \u003cstrong\u003e143\u003c/strong\u003e(8): p. e254-e743.\u003c/li\u003e\n \u003cli\u003eOrellana-Urz\u0026uacute;a, S., et al., \u003cem\u003ePathophysiology of ischemic stroke: role of oxidative stress.\u003c/em\u003e Current Pharmaceutical Design, 2020. \u003cstrong\u003e26\u003c/strong\u003e(34): p. 4246-4260.\u003c/li\u003e\n \u003cli\u003eMunshi, A., et al., \u003cem\u003ePhosphodiesterase 4D (PDE4D) gene variants and the risk of ischemic stroke in a South Indian population.\u003c/em\u003e Journal of the Neurological Sciences, 2009. \u003cstrong\u003e285\u003c/strong\u003e(1-2): p. 142-145.\u003c/li\u003e\n \u003cli\u003eMunshi, A., et al., \u003cem\u003eAssociation of LPL gene variant and LDL, HDL, VLDL cholesterol and triglyceride levels with ischemic stroke and its subtypes.\u003c/em\u003e Journal of the neurological sciences, 2012. \u003cstrong\u003e318\u003c/strong\u003e(1-2): p. 51-54.\u003c/li\u003e\n \u003cli\u003eTraylor, M., et al., \u003cem\u003eGenetic basis of lacunar stroke: a pooled analysis of individual patient data and genome-wide association studies.\u003c/em\u003e The Lancet Neurology, 2021. \u003cstrong\u003e20\u003c/strong\u003e(5): p. 351-361.\u003c/li\u003e\n \u003cli\u003eBoehme, A.K., C. 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Sondek, \u003cem\u003eGEF means go: turning on RHO GTPases with guanine nucleotide-exchange factors.\u003c/em\u003e Nature reviews Molecular cell biology, 2005. \u003cstrong\u003e6\u003c/strong\u003e(2): p. 167-180.\u003c/li\u003e\n \u003cli\u003eSnyder, J.T., et al., \u003cem\u003eStructural basis for the selective activation of Rho GTPases by Dbl exchange factors.\u003c/em\u003e Nature structural biology, 2002. \u003cstrong\u003e9\u003c/strong\u003e(6): p. 468-475.\u003c/li\u003e\n \u003cli\u003eZou, S., et al., \u003cem\u003eSNP in human ARHGEF3 promoter is associated with DNase hypersensitivity, transcript level and platelet function, and Arhgef3 KO mice have increased mean platelet volume.\u003c/em\u003e PloS one, 2017. \u003cstrong\u003e12\u003c/strong\u003e(5): p. e0178095.\u003c/li\u003e\n \u003cli\u003eTirozzi, A., et al., \u003cem\u003eGenomic Overlap between Platelet Parameters Variability and Age at Onset of Parkinson Disease.\u003c/em\u003e Applied Sciences, 2021. \u003cstrong\u003e11\u003c/strong\u003e(15): p. 6927.\u003c/li\u003e\n \u003cli\u003eZhang, X., et al., \u003cem\u003eGenetic associations with expression for genes implicated in GWAS studies for atherosclerotic cardiovascular disease and blood phenotypes.\u003c/em\u003e Human molecular genetics, 2014. \u003cstrong\u003e23\u003c/strong\u003e(3): p. 782-795.\u003c/li\u003e\n \u003cli\u003eSimon, L.M., et al., \u003cem\u003eHuman platelet microRNA-mRNA networks associated with age and gender revealed by integrated plateletomics.\u003c/em\u003e Blood, The Journal of the American Society of Hematology, 2014. \u003cstrong\u003e123\u003c/strong\u003e(16): p. e37-e45.\u003c/li\u003e\n \u003cli\u003eKhaliq, S.A., Z. Umair, and M.-S. Yoon, \u003cem\u003eRole of ARHGEF3 as a GEF and mTORC2 Regulator.\u003c/em\u003e Frontiers in Cell and Developmental Biology, 2021. \u003cstrong\u003e9\u003c/strong\u003e: p. 806258-806258.\u003c/li\u003e\n \u003cli\u003eB. Kim, J.J., Carol Dangelmaier, James L. Daniel, Satya P. Kunapuli, Young, \u003cem\u003eThe P2Y1 receptor is essential for ADP-induced shape change and aggregation in mouse platelets.\u003c/em\u003e Platelets, 1999. \u003cstrong\u003e10\u003c/strong\u003e(6): p. 399-406.\u003c/li\u003e\n \u003cli\u003eKlages, B., et al., \u003cem\u003eActivation of G12/G13 results in shape change and Rho/Rho-kinase\u0026ndash;mediated myosin light chain phosphorylation in mouse platelets.\u003c/em\u003e The Journal of cell biology, 1999. \u003cstrong\u003e144\u003c/strong\u003e(4): p. 745-754.\u003c/li\u003e\n \u003cli\u003eDrachman, J.G., J.D. Griffin, and K. Kaushansky, \u003cem\u003eThe c-Mpl Ligand (Thrombopoietin) Stimulates Tyrosine Phosphorylation of Jak2, Shc, and c-Mpl (\u003c/em\u003e\u003cem\u003e\u0026lowast;).\u003c/em\u003e Journal of Biological Chemistry, 1995. \u003cstrong\u003e270\u003c/strong\u003e(10): p. 4979-4982.\u003c/li\u003e\n \u003cli\u003eHoffmann, O., et al., \u003cem\u003eThrombopoietin contributes to neuronal damage in experimental bacterial meningitis.\u003c/em\u003e Infection and immunity, 2011. \u003cstrong\u003e79\u003c/strong\u003e(2): p. 928-936.\u003c/li\u003e\n \u003cli\u003eBaker, J.E., et al., \u003cem\u003eHuman thrombopoietin reduces myocardial infarct size, apoptosis, and stunning following ischaemia/reperfusion in rats.\u003c/em\u003e Cardiovascular research, 2008. \u003cstrong\u003e77\u003c/strong\u003e(1): p. 44-53.\u003c/li\u003e\n \u003cli\u003eSaintillan, D., \u003cem\u003ePhysical mechanisms of platelet formation.\u003c/em\u003e Proceedings of the National Academy of Sciences, 2020. \u003cstrong\u003e117\u003c/strong\u003e(36): p. 21841-21843.\u003c/li\u003e\n \u003cli\u003eMbiandjeu, S., A. Balduini, and A. Malara, \u003cem\u003eMegakaryocyte cytoskeletal proteins in platelet biogenesis and diseases.\u003c/em\u003e Thrombosis and Haemostasis, 2021.\u003c/li\u003e\n \u003cli\u003eThon, J.N. and J.E. Italiano. \u003cem\u003ePlatelet formation\u003c/em\u003e. in \u003cem\u003eSeminars in hematology\u003c/em\u003e. 2010. Elsevier.\u003c/li\u003e\n \u003cli\u003eMachlus, K.R. and J.E. Italiano Jr, \u003cem\u003eThe incredible journey: From megakaryocyte development to platelet formation.\u003c/em\u003e Journal of Cell Biology, 2013. \u003cstrong\u003e201\u003c/strong\u003e(6): p. 785-796.\u003c/li\u003e\n \u003cli\u003eGoggs, R., et al., \u003cem\u003ePlatelet Rho GTPases\u0026ndash;a focus on novel players, roles and relationships.\u003c/em\u003e Biochemical Journal, 2015. \u003cstrong\u003e466\u003c/strong\u003e(3): p. 431-442.\u003c/li\u003e\n \u003cli\u003eUlu, A. and J.A. Frost, \u003cem\u003eRegulation of RhoA activation and cytoskeletal organization by acetylation.\u003c/em\u003e Small GTPases, 2016. \u003cstrong\u003e7\u003c/strong\u003e(2): p. 76-81.\u003c/li\u003e\n \u003cli\u003eHotta, K., et al., \u003cem\u003eInteraction of the Rho family small G proteins with kinectin, an anchoring protein of kinesin motor.\u003c/em\u003e Biochemical and biophysical research communications, 1996. \u003cstrong\u003e225\u003c/strong\u003e(1): p. 69-74.\u003c/li\u003e\n \u003cli\u003eHensler, M., et al., \u003cem\u003ePlatelet morphologic changes and fibrinogen receptor localization. Initial responses in ADP-activated human platelets.\u003c/em\u003e The American journal of pathology, 1992. \u003cstrong\u003e141\u003c/strong\u003e(3): p. 707.\u003c/li\u003e\n \u003cli\u003eMaxwell, M.J., et al., \u003cem\u003eShear induces a unique series of morphological changes in translocating platelets: effects of morphology on translocation dynamics.\u003c/em\u003e Arteriosclerosis, thrombosis, and vascular biology, 2006. \u003cstrong\u003e26\u003c/strong\u003e(3): p. 663-669.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Platelets, MPV, Ischemic Stroke, Genotypes","lastPublishedDoi":"10.21203/rs.3.rs-2333866/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2333866/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePlatelets play a significant role in pathophysiology of ischemic stroke since they are involved in the formation of intravascular thrombus after erosion or rupture of the atherosclerotic plaques. Platelet (PLT) count and Mean platelet volume (MPV) are the two significant parameters that affect functions of the platelets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn the current study MPV and PLT count was evaluated using flow cytometry and cell counter. SonoClot analysis was carried out to evaluate Activated Clot Timing (ACT), Clot Rate (CR) and Platelet Function (PF). Genotyping was carried out GSA and Sanger sequencing and expression analysis was carried out using RT-PCR. \u003cem\u003eIn silico\u003c/em\u003e analysis was carried out using GROMACS tool and UNAFold. The interaction of significant proteins with other proteins was predicted using STRING database.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e96 genes were analyzed and a significant association of THPO (rs6141) and ARHGEF3 (rs1354034) was observed with the disease and its subtypes. Altered genotypes were associated significantly with increased MPV, decreased PLT count and CR. Expression analysis revealed a higher expression in patients bearing the variant genotypes of both the genes. \u003cem\u003eIn silico\u003c/em\u003e analysis revealed that mutation in THPO gene leads to the reduced compactness of protein structure. mRNA encoded by mutated ARHGEF3 gene increases the half-life of mRNA. The two significant proteins interact with many other proteins especially the ones involved in the platelet activation, aggregation, erythropoiesis, megakaryocyte maturation, and cytoskeleton rearrangements suggesting that they could be important player in determination of MPV values.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn conclusion the current study demonstrated the role of higher MPV affected by genetic variation in the development of IS and its subtypes. The results of the current study also indicate that higher MPV can be used as a biomarker for the disease and altered genotypes and higher MPV can be targeted for better therapeutic outcomes.\u003c/p\u003e","manuscriptTitle":"Genomic Variation Affecting MPV and PLT count in association with development of Ischemic Stroke and its Subtypes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-12 19:32:15","doi":"10.21203/rs.3.rs-2333866/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-04-10T03:25:42+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-01-15T02:31:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Molecular Neurobiology","date":"2023-01-06T19:29:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-12-07T04:00:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2022-12-01T23:27:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c5a01135-2d8f-422d-8514-9d1bcb90542f","owner":[],"postedDate":"December 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-08-18T04:00:57+00:00","versionOfRecord":{"articleIdentity":"rs-2333866","link":"https://doi.org/10.1007/s12035-023-03460-2","journal":{"identity":"molecular-neurobiology","isVorOnly":false,"title":"Molecular Neurobiology"},"publishedOn":"2023-07-15 01:08:24","publishedOnDateReadable":"July 15th, 2023"},"versionCreatedAt":"2022-12-12 19:32:15","video":"","vorDoi":"10.1007/s12035-023-03460-2","vorDoiUrl":"https://doi.org/10.1007/s12035-023-03460-2","workflowStages":[]},"version":"v1","identity":"rs-2333866","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2333866","identity":"rs-2333866","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}