Arachidonate 5-lipoxygenase (ALOX5) gene polymorphism is associated with Alzheimer's disease and body mass index

Šerý, Omar; Hlinecká, Lýdia; Povová, Jana; Bonczek, Ondřej; Zeman, Tomáš; Janout, Vladimí­r; Ambrož, Petr; Khan, Naim Akhtar; Balcar, Vladimír J.

doi:10.1016/j.jns.2016.01.022

Cited by 26 publications

(5 citation statements)

References 59 publications

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“…Furthermore, SNPs in FLAP or LTA 4 ‐H are correlated with a two‐fold risk for myocardial infarction and stroke 65,66 . SNPs in 5‐LOX (ALOX5) have also been associated with modest increases in body mass index 67 . These studies highlight the importance of understanding genetic variants in the RvE1 response as numerous polymorphisms in the EPA metabolic pathway are common with potential clinical implications.…”

Section: Discussionmentioning

confidence: 99%

Resolvin E1 derived from eicosapentaenoic acid prevents hyperinsulinemia and hyperglycemia in a host genetic manner

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Resolvin E1 derived from eicosapentaenoic acid prevents hyperinsulinemia and hyperglycemia in a host genetic manner

et al. 2020

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“…Mutations on FGFR3 could prematurely trigger intracellular signaling to terminate cell proliferation [ 89 ]. ALOX5 is also related to the progression of neurodegenerative diseases such as Alzheimer’s disease [ 90 ]. The suppression of the expression of ALOX5 could reduce inflammation-induced neuro cell death [ 91 , 92 ].…”

Section: Resultsmentioning

confidence: 99%

A Comprehensive Study of De Novo Mutations on the Protein-Protein Interaction Interfaces Provides New Insights into Developmental Delay

et al. 2022

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Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DNMs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control. Next, we identified 302 DD-related PsychiPPIs, defined as PPIs harboring a statistically significant number of DNM missenses at their interface, and 42 DD candidate genes from PsychiPPI. We observed that PsychiPPIs preferentially affected the human protein interactome network hub proteins. When analyzing DD candidate genes using gene ontology and gene spatio-expression, we found that PsychiPPI genes carrying PPI interface mutations, such as FGFR3 and ALOX5, were enriched in development-related pathways and the development of the neocortex, and cerebellar cortex, suggesting their potential involvement in the etiology of DD. Our results demonstrated that DD patients carried an excess burden of PPI-truncating DNM, which could be used to efficiently search for disease-related genes and mutations in large-scale sequencing studies. In conclusion, our comprehensive study indicated the significant role of PPI interface DNMs in developmental delay pathogenicity.

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“…Pathways include immune system [83], adaptive immune system [84], neutrophil degranulation [85], cytokine signaling in immune system [86] and GPCR ligand binding [87] were responsible for progression of COVID-19 infection. A previous study demonstrated that the expression levels of AHSP (alpha hemoglobin stabilizing protein) [88], IL7R [89], FBXO7 [90], KLRB1 [91], PIP4K2A [92], NFE2 [93], CCR2 [94], CLEC12A [95], NLRP12 [96], PECAM1 [97], TRIM10 [98], ICAM3 [99], EEF1A1 [100], CCR4 [101], PTPRC (protein tyrosine phosphatase receptor type C) [102], CX3CR1 [103], TSPAN32 [104], EOMES (eomesodermin) [105], ATM (ATM serine/threonine kinase) [106], CD28 [107], LRRK2 [108], CCL5 [109], CD33 [110], FCRL3 [111], CCR3 [112], FGL2 [113], GZMA (granzyme A) [114], PICALM (phosphatidylinositol binding clathrin assembly protein) [115], ALOX5 [116], MME (membrane metalloendopeptidase) [117], VIM (vimentin) [118], CD93 [119], GCA (grancalcin) [120], CD226 [121], CD1D [122], TNFSF4 [123], LEF1 [124], TLR4 [125], CCR7 [126], DPP4 [127], NLRC4 [128], ITGB3 [129], RASGRP1 [130], TLR2 [131], DOCK2 [132], CSF1R [133], PRKCB (protein kinase C beta) [134], CAMK4 [135], CXCL5 [136], CD36 [137], P2RY12 [138], LILRB2 [139], CD5 [140], SLC25A37 [141], ADIPOR1 [142], PECAM1 [143], RGS10 [144], RGS18 [145], ANK1 [146], RNF182 [147], NPRL3 [148], NINJ2 [149], KCNA3 [150], ABCG2 [151], MS4A6A [152], WDR45 [153], RAB39B...…”

Section: Discussionmentioning

confidence: 99%

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

Vastrad¹,

Vastrad²

2022

Preprint

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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.

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Arachidonate 5-lipoxygenase (ALOX5) gene polymorphism is associated with Alzheimer's disease and body mass index

Cited by 26 publications

References 59 publications

Resolvin E1 derived from eicosapentaenoic acid prevents hyperinsulinemia and hyperglycemia in a host genetic manner

Resolvin E1 derived from eicosapentaenoic acid prevents hyperinsulinemia and hyperglycemia in a host genetic manner

A Comprehensive Study of De Novo Mutations on the Protein-Protein Interaction Interfaces Provides New Insights into Developmental Delay

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

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