Novel homozygous stop-gain pathogenic variant of PPP1R13L gene leads to arrhythmogenic cardiomyopathy

Kalayinia, Samira; Mahdavi, Mohammad; Houshmand, Golnaz; Hesami, Mohsen; Pourirahim, Maryam; Maleki, Majid

doi:10.1186/s12872-022-02802-7

Cited by 4 publications

(5 citation statements)

References 32 publications

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“…Panel (8 trio) – – PPP1R13L NF-κB and p53 inhibitor DCM 6 (refs. 9 , 10 , 94 – 97 ) 11 15 PTV/missense Exome (6 trio, 5 proband) – Severe and rapidly progressing DCM RPL3L Muscle-specific ribosomal protein DCM 4 (refs. 10 , 98 – 100 ) 6 9 Missense Exome (6 trio) AF – SLC30A5 Zinc transporter CM 1 (ref.…”

Section: Resultsmentioning

confidence: 99%

Exploring the complex spectrum of dominance and recessiveness in genetic cardiomyopathies

Lipov,

Jurgens,

Mazzarotto

et al. 2023

Nat Cardiovasc Res

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Discrete categorization of Mendelian disease genes into dominant and recessive models often oversimplifies their underlying genetic architecture. Cardiomyopathies (CMs) are genetic diseases with complex etiologies for which an increasing number of recessive associations have recently been proposed. Here, we comprehensively analyze all published evidence pertaining to biallelic variation associated with CM phenotypes to identify high-confidence recessive genes and explore the spectrum of monoallelic and biallelic variant effects in established recessive and dominant disease genes. We classify 18 genes with robust recessive association with CMs, largely characterized by dilated phenotypes, early disease onset and severe outcomes. Several of these genes have monoallelic association with disease outcomes and cardiac traits in the UK Biobank, including LMOD2 and ALPK3 with dilated and hypertrophic CM, respectively. Our data provide insights into the complex spectrum of dominance and recessiveness in genetic heart disease and demonstrate how such approaches enable the discovery of unexplored genetic associations.

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Section: Resultsmentioning

confidence: 99%

Exploring the complex spectrum of dominance and recessiveness in genetic cardiomyopathies

Lipov,

Jurgens,

Mazzarotto

et al. 2023

Nat Cardiovasc Res

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“…DOCK4 [58], CEACAM1 [59], STAT1 [60], ARG1 [61], TLR4 [62], ADAM9 [63], VNN1 [64], ABCA1 [65], STAT2 [66], TLR5 [67], PTGS2 [68], RNF213 [69], ZDHHC17 [70], JAK2 [71], TLR8 [72], NOTCH2 [73], PDGFC (platelet derived growth factor C) [74], CMPK2 [75], TLR2 [76], CYP1B1 [77], CCR1 [78], HDAC9 [79], IL1RN [80], GCH1 [81], LYST (lysosomal trafficking regulator) [82], PELI1 [83], EGR1 [84], SNX10 [85], CA2 [86], ZEB2 [87], HIF1A [88], PLA2G7 [89], CCR2 [90], GAB1 [91], IRAK3 [92], LDLR (low density lipoprotein receptor) [93], TLR6 [94], SIRT1 [95], NOD2 [96], ATP10D [97], ELOVL6 [98], VCAN (versican) [99], TET2 [100], TET3 [101], ZBTB20 [102], HS3ST1 [103], PF4 [104], DNAJC2 [105], NFIA (nuclear factor I A) [106], CCR7 [107], PRDX2 [108], ADK (adenosine kinase) [109], TCF7 [110], LGALS3 [111], OLFM2 [112], HDAC11 [113] and ARPC1B [114] are significantly related to the atherosclerosis. Studies have revealed that KCNJ2 [115], TLR4 [116], JAK2 [117], TLR2 [118], EGR1 [119], GAB1 [120], ZBTB11 [121], BIN1 [122], TCF4 [123], PPP1R13L [124], TRPM4 [125], LGALS3 [126] and SNTA1 [127] plays a key role in cardiac arrhythmia. Recently, increasing evidence demonstrated that KCNJ2 [128], TLR4 [129], CYP2D6 [130], TLR2 [129], SNX10 [131], SIRT1 [132<...…”

Section: Discussionmentioning

confidence: 99%

“…Recently, increasing evidence demonstrated that KCNJ2 [128], TLR4 [129], CYP2D6 [130], TLR2 [129], SNX10 [131], SIRT1 [132], PF4 [133], PCYT2 [134] and LGALS3 [135] were altered expression in atrial fibrillation. Studies had shown that KCNJ2 [136], ARG1 [137], TLR4 [138], IFIH1 [139], FBN2 [140], PDK4 [141], TLR8 [142], PDGFC (platelet derived growth factor C) [143], FNIP1 [144], TLR2 [145], PTPN11 [146], LATS2 [147], GCH1 [148], ARFGEF2 [149], CA2 [150], PTPRC (protein tyrosine phosphatase receptor type C) [151], CCR2 [152], GAB1 [153], VEGFA (vascular endothelial growth factor A) [154], UBR1 [155], PHLPP1 [156], MTM1 [157], FMR1 [158], SIRT1 [159], NOD2 [160], MYOF (myoferlin) [161], OSBPL11 [162], ZBTB11 [121], UTRN (utrophin) [163], ZNF593 [164], CCR7 [165], PRDX2 [166], BIN1 [167], NFIC (nuclear factor I C) [168], TCF4 [169], PPP1R13L [124], NDUFB11 [170], TAX1BP3 [171], TRPM4 [172], NMRAL1 [173], LGALS3 [126] and NAA10 [174] were associated with cardiomyopathy. CD274 [175], CEACAM1 [176], STAT1 [177], ARG1 [178], TLR4 [179], LRRK2 [180], ABCA1 [181], IFIH1 [182], TLR5 [183], PTGS2 [184], CYP2D6 [185], RNF213 [186], C9ORF72 [187], JAK2 [188], TLR8 [189], NOTCH2 [190], PDGFC (platelet derived growth factor C) [191], TLR2 [192], PRKAB2 [193], HDAC9 [194], NCOA4 [195], LATS2 [196], DICER1 […”

Section: Discussionmentioning

confidence: 99%

“…We discovered that that EGR1 was targeted by 53 TFs (ex; LMO2); DDX5 was targeted by 51 TFs (ex; MYCN); NCOA3 was targeted by 48 TFs (ex; SIN3B); STAT1 was targeted by 42 TFs (ex; IRF8); HIF1A was targeted by 40 TFs (ex; GATA1); RPS14 was targeted by 45 TFs (ex; RCOR3); RPL3 was targeted by 44 TFs (ex; DMRT1); RPS19 was targeted by 42 TFs (ex; GCH1 [81], LYST (lysosomal trafficking regulator) [82], PELI1 [83], EGR1 [84], SNX10 [85], CA2 [86], ZEB2 [87], HIF1A [88], PLA2G7 [89] [105], NFIA (nuclear factor I A) [106], CCR7 [107], PRDX2 [108], ADK (adenosine kinase) [109], TCF7 [110], LGALS3 [111], OLFM2 [112], HDAC11 [113] and ARPC1B [114] are significantly related to the atherosclerosis. Studies have revealed that KCNJ2 [115], TLR4 [116], JAK2 [117], TLR2 [118], EGR1 [119], GAB1 [120], ZBTB11 [121], BIN1 [122], TCF4 [123], PPP1R13L [124], TRPM4 [125], LGALS3 [126] and SNTA1 [127] plays a key role in cardiac arrhythmia. Recently, increasing evidence demonstrated that KCNJ2 [128], TLR4 [129], CYP2D6 [130], TLR2 [129], SNX10 [131], SIRT1 [132], PF4 [133], PCYT2 [134] and LGALS3 [135] were altered expression in atrial fibrillation.…”

Section: Construction Of the Tf-hub Gene Regulatory Networkmentioning

confidence: 99%

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Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA hub gene regulatory network and TF hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research.

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“…Next-generation sequencing (NGS) provides a cost-effective and accurate diagnostic method to identify variants in patients with DCM [ 12 – 14 ]. In the present study, via whole-exome sequencing (WES), we detected a novel deletion, c.884_886del: p.Lys295del, in FBXO32 that may play a role in DCM pathogenesis.…”

Section: Introductionmentioning

confidence: 99%

A novel likely pathogenic variant in the FBXO32 gene associated with dilated cardiomyopathy according to whole‑exome sequencing

et al. 2022

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Background Familial dilated cardiomyopathy (DCM) is a genetic heart disorder characterized by progressive heart failure and sudden cardiac death. Over 250 genes have been reported in association with DCM; nonetheless, the genetic cause of most DCM patients has been unknown. The goal of the present study was to determine the genetic etiology of familial DCM in an Iranian family. Methods Whole-exome sequencing was performed to identify the underlying variants in an Iranian consanguineous family with DCM. The presence of the candidate variant was confirmed and screened in available relatives by PCR and Sanger sequencing. The pathogenic effect of the candidate variant was assessed by bioinformatics analysis, homology modeling, and docking. Results One novel likely pathogenic deletion, c.884_886del: p.Lys295del, in F-box only protein 32 (muscle-specific ubiquitin-E3 ligase protein; FBXO32) was identified. Based on bioinformatics and modeling analysis, c.884_886del was the most probable cause of DCM in the studied family. Conclusions Our findings indicate that variants in FBXO32 play a role in recessive DCM. Variants in FBXO32 may disturb the degradation of target proteins in the ubiquitin–proteasome system and lead to severe DCM. We suggest considering this gene variants in patients with recessively inherited DCM.

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Novel homozygous stop-gain pathogenic variant of PPP1R13L gene leads to arrhythmogenic cardiomyopathy

Cited by 4 publications

References 32 publications

Exploring the complex spectrum of dominance and recessiveness in genetic cardiomyopathies

Exploring the complex spectrum of dominance and recessiveness in genetic cardiomyopathies

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

A novel likely pathogenic variant in the FBXO32 gene associated with dilated cardiomyopathy according to whole‑exome sequencing

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