Identification of mutations in SLC4A1, GP1BA and HFE in a family with venous thrombosis of unknown cause by next‑generation sequencing

Chang, Wei‐An; Sheu, Chau‐Chyun; Liu, Kuan‐Ting; Shen, Jheng‐Heng; Yen, Meng‐Chi; Kuo, Po‐Lin

doi:10.3892/etm.2018.6693

Cited by 8 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was further supported by the findings that mutation in GPIBA was present in a family with venous thrombosis. 17 Based on these works, we found that GPIBA and vWF were aberrantly expressed in DVT models, further showing their role in occurrence of thrombosis. Furthermore, we also showed the alteration in proteins critical in platelet adhesion, suggesting participation of GPIBA in thrombus formation.…”

Section: Discussionmentioning

confidence: 70%

Synergic effect of GPIBA and von Willebrand factor in pathogenesis of deep vein thrombosis

Zhang

et al. 2020

Vascular

View full text Add to dashboard Cite

Objectives In cardiovascular disease, deep vein thrombosis is one of the vital symptoms causing pulmonary thromboembolism. However, the pathogenesis of deep vein thrombosis is still not clear. One of the critical factors leading to deep vein thrombosis is the platelet aggregation that is mediated by a set of key genes including platelet membrane protein coded by platelet glycoprotein Ib alpha chain (GPIBA). Methods Deep vein thrombosis model was established according to the previous protocol, and venous blood and thrombi were collected for further analysis. Results The dynamic changes of GPIBA and coagulation factor, von Willebrand factor, were observed in deep vein thrombosis models. Meanwhile, critical proteins participating in adhesion and binding of platelets such as epithelial membrane protein 2 (EMP2), vascular cell adhesion protein 1 (VCAM1), immunoreceptor tyrosine-based activation motif 1 (ITAM1), integrin subunit alpha M (ITGAM), or fibronectin were also differentially expressed in deep vein thrombosis models. Conclusions Application of heparin could reverse these dynamic changes in deep vein thrombosis models. Thus, we explained the potential synergic role of GPIBA and von Willebrand factor in regulating the occurrence of deep vein thrombosis and provide therapeutic target against cardiovascular disease.

show abstract

Section: Discussionmentioning

confidence: 70%

Synergic effect of GPIBA and von Willebrand factor in pathogenesis of deep vein thrombosis

Zhang

et al. 2020

Vascular

View full text Add to dashboard Cite

show abstract

“…Of these proteins, ADH1B may improve the cognitive function profile of those who drink moderately ( 27 ). Genetic variants of GP1BA may be associated with venous thrombosis in Asian ancestry ( 28 ). KLKB1 causes a dose-dependent enhancement of the anticoagulant effects of plasma thrombin generation (TG) and coagulation regulatory protein (TM) ( 29 ).…”

Section: Discussionmentioning

confidence: 99%

DIA-based technology explores hub pathways and biomarkers of neurological recovery in ischemic stroke after rehabilitation

Wei

Li²,

Zeng³

et al. 2023

Front. Neurol.

View full text Add to dashboard Cite

ObjectiveIschemic stroke (IS) is a common disease that causes severe and long-term neurological disability in people worldwide. Although rehabilitation is indispensable to promote neurological recovery in ischemic stroke, it is limited to providing a timely and efficient reference for developing and adjusting treatment strategies because neurological assessment after stroke treatment is mostly performed using scales and imaging. Therefore, there is an urgent need to find biomarkers that can help us evaluate and optimize the treatment plan.MethodsWe used data-independent acquisition (DIA) technology to screen differentially expressed proteins (DEPs) before and after ischemic stroke rehabilitation treatment, and then performed Gene Ontology (GO) and pathway enrichment analysis of DEPs using bioinformatics tools such as KEGG pathway and Reactome. In addition, the protein–protein interaction (PPI) network and modularity analysis of DEPs were integrated to identify the hub proteins (genes) and hub signaling pathways for neurological recovery in ischemic stroke. PRM-targeted proteomics was also used to validate some of the screened proteins of interest.ResultsAnalyzing the serum protein expression profiles before and after rehabilitation, we identified 22 DEPs that were upregulated and downregulated each. Through GO and pathway enrichment analysis and subsequent PPI network analysis constructed using STRING data and subsequent Cytoscape MCODE analysis, we identified that complement-related pathways, lipoprotein-related functions and effects, thrombosis and hemostasis, coronavirus disease (COVID-19), and inflammatory and immune pathways are the major pathways involved in the improvement of neurological function after stroke rehabilitation.ConclusionComplement-related pathways, lipoprotein-related functions and effects, thrombosis and hemostasis, coronavirus disease (COVID-19), and inflammation and immunity pathways are not only key pathways in the pathogenesis of ischemic stroke but also the main pathways of action of rehabilitation therapy. In addition, IGHA1, LRG1, IGHV3-64D, and CP are upregulated in patients with ischemic stroke and downregulated after rehabilitation, which may be used as biomarkers to monitor neurological impairment and recovery after stroke.

show abstract

“…IL7R [202], S1PR1 [203], NFE2 [204], CCR2 [205], NLRP12 [206], PECAM1 [207], PIM1 [208], AP1S2 [209], ITK (IL2 inducible T cell kinase) [210], CCR4 [211], CX3CR1 [212], FOXO3 [213], CD28 [214], GPX1 [215], PRDX6 [216], LRRK2 [217], CCL5 [218], ETS1 [219], CCR3 [220], SELPLG (selectin P ligand) [221], VIM (vimentin) [222], CD1D [223], SIGLEC9 [224], TLR4 [225], FLNA (filamin A) [226], CCR7 [227], DPP4 [228], NLRC4 [229], TLR2 [230], DOCK2 [231], CD40LG [232], CD36 [233], MAPK1 [234], TGFBR2 [235], LTA4H [236], PECAM1 [237], VCAN (versican) [238], NPRL3 [239], CDC27 [240], PINK1 [241], TGFBI (transforming growth factor beta induced) [242], CR1 [243], AKT3 [244], STAT4 [245], CXCL10 [246], CRP (C-reactive protein) [247], IL17F [248], CD34 [249], BCL3 [250], TSLP (thymic stromal lymphopoietin) [251], CCL2 [252], F10 [253], STC1 [254], POSTN (periostin) [255], IL17A [256], SMOC2 [257], SFTPA2 [258], FAIM2 [259], LHX9 [260], HRG (histidine rich glycoprotein) [261] and CA9 [262] have been reported to be related to lung diseases, but these genes might be involved in the progression of COVID-19. SLC4A1 [263], CCR2 [264], FGL2 [265], CD84 [266], TLR4 [267], ITGB3 [268], TLR2 [267], CD36 [269], MFSD2B [270], PLXDC2 [271], KLF15 [272], C6 [273] and HRG (histidine rich glycoprotein) […”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of next-generation sequencing data in COVID-19 and its secondary complications

Vastrad¹,

Vastrad²

2022

Preprint

View full text Add to dashboard Cite

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has made a serious public health threat globally. To discover key molecular changes in COVID-19 and its secondary complications, we analyzed next-generation sequencing (NGS) data of COVID-19. NGS data (GSE163151) was screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were identified in the present study, using DESeq2 package in R programming software. Gene ontology (GO) and pathway enrichment analysis were performed, and the protein-protein interaction (PPI) network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network were established. Subsequently, receiver operating characteristic curve (ROC) analysis was used to validate the diagonostics valuesof the hub genes. Firstly, 954 DEGs (477 up regulated and 477 down regulated) were identified from the four NGS dataset. GO enrichment analysis revealed enrichment of DEGs in genes related to the immune system process and multicellular organismal process, and REACTOME pathway enrichment analysis showed enrichment of DEGs in the immune system and formation of the cornified envelope. Hub genes were identified from the PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, the ROC analysis indicate that COVID-19 and its secondary complications with following hub genes, namely, RPL10, FYN, FLNA, EEF1A1, UBA52, BMI1, ACTN2, CRMP1, TRIM42 and PTCH1, had good diagnostics values. This study identified several genes associated with COVID-19 and its secondary complications, which improves our knowledge of the disease mechanism.

show abstract

Identification of mutations in SLC4A1, GP1BA and HFE in a family with venous thrombosis of unknown cause by next‑generation sequencing

Cited by 8 publications

References 40 publications

Synergic effect of GPIBA and von Willebrand factor in pathogenesis of deep vein thrombosis

Synergic effect of GPIBA and von Willebrand factor in pathogenesis of deep vein thrombosis

DIA-based technology explores hub pathways and biomarkers of neurological recovery in ischemic stroke after rehabilitation

Comprehensive analysis of next-generation sequencing data in COVID-19 and its secondary complications

Contact Info

Product

Resources

About