APLAID complicated with arrhythmogenic dilated cardiomyopathy caused by a novel PLCG2 variant

Wang, Tianjiao; Wang, Xinyu; Teng, Yiqun; Wu, Lifang; Zhu, Feng; Ma, Danjie; Wang, Hua; Liu, Xiaolin

doi:10.1007/s12026-024-09455-y

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…ROBO2 [760], VCAM1 [761], GRP (gastrin releasing peptide) [762], FABP4 [763], ANO1 [764], SOX6 [765], TFAP2C [766], RAMP3 [767], PLA2G7 [768], MMP12 [769], FAIM2 [770], APOD (apolipoprotein D) [771], LAG3 [772], SOX18 [773], F2RL2 [774], CCR1 [775], FLT1 [776], FABP5 [629], TRPC3 [777]. THSD7A [778], DKK2 [779], PRKCB (protein kinase C beta) [780], DHCR24 [781], PDE3B [782], BMP4 [783], IL1RL1 [784], MYPN (myopalladin) [785], PLCG2 [786], PRL (prolactin) [787], WNT5A [788], MEOX1 [789], TIMP3 [790], FRZB (frizzled related protein) [791], CPE (carboxypeptidase E) [792], ADAMTS9 [793], NDNF (neuron derived neurotrophic factor) [794], PDGFB (platelet derived growth factor subunit B) [795], PIK3CG [796], LDLR (low density lipoprotein receptor) [797], CD4 [798], TRPA1 [799], F2RL3 [800], C1QL1 [801], ADAMTS5 [802], PDE4B [803], NES (nestin) [804], TH (tyrosine hydroxylase) [805], MMP8 [806], KDR (kinase insert domain receptor) [807], ADRB2 [808], ACKR3 [809], PTPRC (protein tyrosine phosphatase receptor type C) [810], KL (klotho) [811], KL (klotho) [812], PLAU (plasminogen activator, urokinase) [813], CCND2 [814], PTGS1 [815], INSIG1 [816], IRX2 [817], SIGLEC1 [818], UCN2 [819], CYP2J2 [820], CYP1A1 [821], ASTN2 [822], NTN1 [823], PDGFD (platelet derived growth factor D) [824], MSTN (myostatin) [663], LEPR (leptin receptor) [664], IL15 [825], CACNA1H [826], BMP2 [827], SYT7 [<...…”

Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Next generation sequencing (NGS) dataset GSE243039 was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein-protein interaction (PPI) network was constructed and modules were analysed using the Human Integrated Protein-Protein Interaction rEference (HIPIE) database and Cytoscape software, and hub genes were identified. Subsequantely, a network between miRNAs and hub genes, and network between TFss and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve (ROC) analysis was used to validate the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including VCAM1, SNCA, PRKCB, ADRB2, FOXQ1, MDFI, ACTBL2, PRKD1, DAPK1 and ACTC1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including TCF3 and CLOCK, were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment, and monitoring of endometriosis.

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Section: Discussionmentioning

confidence: 99%

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Characterization of a Novel Pathogenic PLCG2 Variant Leading to APLAID Syndrome Responsive to a TNF Inhibitor

Yang,

Tao,

Han

et al. 2024

Arthritis & Rheumatology

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ObjectiveAutoinflammation and PLCγ2‐associated antibody deficiency and immune dysregulation (APLAID) syndrome is an autoinflammatory disease caused by gain‐of‐function variants in PLCG2. This study investigates the pathogenic mechanism of a novel variant of PLCG2 in a patient with APLAID syndrome.MethodsWhole exome sequencing and Sanger sequencing were used to identify the pathogenic variant in the patient. Single‐cell RNA sequencing, immunoblotting, luciferase assay, IP‐one ELISA, calcium flux assay, quantitative PCR, and immunoprecipitation were used to define inflammatory signatures and evaluate the effects of the PLCG2 variant on protein functionality and immune signaling.ResultsWe identified a novel de novo variant, PLCG2 p.D993Y, in a patient with colitis, pansinusitis, skin rash, edema, recurrent respiratory infections, B cell deficiencies, and hypogammaglobulinemia. The single‐cell transcriptome revealed exacerbated inflammatory responses in the patient's PBMCs. Expression of the D993Y variant in HEK293T, COS‐7, and PLCG2 knock‐out THP‐1 cell lines showed heightened PLCγ2 phosphorylation, elevated IP3 production and intracellular Ca2+ release, and activation of the MAPK, NFκB, and NFAT signaling pathways compared to control‐transfected cells. In vitro experiments indicated that the D993Y variant altered amino acid properties, disrupting the interaction between the catalytic and auto‐inhibitory domains of PLCγ2, resulting in PLCγ2 auto‐activation.ConclusionsOur findings demonstrated that the PLCG2 D993Y variant is a gain‐of‐function mutation via impairing its auto‐inhibition, activating multiple inflammatory signaling pathways, thus leading to APLAID syndrome. This study further broadens the molecular underpinnings and phenotypic spectrum of PLCγ2‐related disorders.

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Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

Vastrad,

Vastrad

2024

Egypt J Med Hum Genet

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Background Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Methods Next-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes. Results A total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. Conclusions This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

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APLAID complicated with arrhythmogenic dilated cardiomyopathy caused by a novel PLCG2 variant

Cited by 3 publications

References 35 publications

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

Characterization of a Novel Pathogenic PLCG2 Variant Leading to APLAID Syndrome Responsive to a TNF Inhibitor

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

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