Ask about this productRelated genes to: PNPLA5 Blocking Peptide
- Gene:
- PNPLA5 NIH gene
- Name:
- patatin like phospholipase domain containing 5
- Previous symbol:
- -
- Synonyms:
- dJ388M5.4, GS2L
- Chromosome:
- 22q13.31
- Locus Type:
- gene with protein product
- Date approved:
- 2004-09-06
- Date modifiied:
- 2015-11-23
Related products to: PNPLA5 Blocking Peptide
Related articles to: PNPLA5 Blocking Peptide
- Numerous variables that regulate the metabolism of Sertoli cells and sperm have been identified, one of which is sex steroid hormones. These hormones play a vital role in maintaining energy homeostasis, influencing the overall metabolic balance of the human body. The proper functioning of the reproductive system is closely linked to energy status, as the reproductive axis responds to metabolic signals. The aim of this study was to investigate the gene expression patterns of metabolite interconversion enzymes in testicular cells (Sertoli cells and spermatogonia) of non-obstructive azoospermia (NOA) patients, as compared to normal controls, to understand the molecular mechanisms contributing to NOA. We used microarray and bioinformatics techniques to analyze 2912 genes encoding metabolite interconversion enzymes, including methyltransferase, monooxygenase, transmembrane reductase, and phosphohydrolase, in both testicular cells and normal samples. In sperm, the upregulation of MOXD1, ACAD10, PCYT1A, ARG1, METTL6, GPLD1, MAOA, and CYP46A1 was observed, while ENTPD2, CPT1C, ADC, and CYB5B were downregulated. Similarly, in the Sertoli cells of three NOA patients, RPIA, PIK3C3, LYPLA2, CA11, MBOAT7, and HDHD2 were upregulated, while NAA25, MAN2A1, CYB561, PNPLA5, RRM2, and other genes were downregulated. Using STRING and Cytoscape, we predicted the functional and molecular interactions of these proteins and identified key hub genes. Pathway enrichment analysis highlighted significant roles for G1/S-specific transcription, pyruvate metabolism, and citric acid metabolism in sperm, and the p53 signaling pathway and folate metabolism in Sertoli cells. Additionally, Weighted Gene Co-expression Network Analysis (WGCNA) and single-cell RNA sequencing (scRNA-seq) were performed to validate these findings, revealing significant alterations in gene expression and cellular distribution in NOA patients. Together, these results provide new insights into the molecular mechanisms underlying NOA and identify potential therapeutic targets. - Source: PubMed
Publication date: 2024/10/29
Hashemi Karoii DanialBaghaei HamoonAbroudi Ali ShakeriDjamali MelikaHasani Mahforoozmahalleh ZahraAzizi HosseinSkutella Thomas - The overdiagnosing of papillary thyroid carcinoma (PTC) in China necessitates the development of an evidence-based diagnosis and prognosis strategy in line with precision medicine. A landscape of PTC in Chinese cohorts is needed to provide comprehensiveness. - Source: PubMed
Publication date: 2023/09/19
Liu WeiZhu JunkanWu ZhenYin YongxiangWu QiaoWu YimingZheng JiaojiaoWang CongChen HongyanQazi Talal JamilWu JunZhang YuqingLiu HoubaoYang JingminLu DaruZhang XuminAi Zhilong - Patatin-like phospholipase domain containing 5 (PNPLA5) is a newly-discovered lipase. Although the PNPLA family plays critical roles in diverse biological processes, the biological functions of PNPLA5 mostly unknown. We previously found that the deletion of Pnpla5 in rats causes a variety of phenotypic abnormalities. In this study, we further explored the effects of Pnpla5 knockout (KO) on male rats. - Source: PubMed
Publication date: 2022/08/12
Liu Zhi-GuoHu Yan-QingLi KuiMu Yu-LianWu Tian-Wen - Ovarian carcinoma (OV) is one of the most lethal gynecological malignancies globally, and the overall 5-year survival rate of OV was 47% in 2018 according to American data. To increase the survival rate of patients with OV, many researchers have sought to identify biomarkers that act as both prognosis-predictive markers and therapy targets. However, most of these have not been suitable for clinical application. The present study aimed at constructing a predictive prognostic nomogram of OV using the genes identified by combining The Cancer Genome Atlas (TCGA) dataset for OV with the immune score calculated by the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Firstly, the algorithm was used to calculate the immune score of patients with OV in the TCGA-OV dataset. Secondly, differentially expressed genes (DEGs) between low and high immune score tissues were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed to predict the functions of these DEGs. Thirdly, univariate, multivariate and Lasso Cox's regression analyses were carried out step by step, and six prognosis-related DEGs were identified. Then, Kaplan-Myer survival curves were generated for these genes and validated by comparing their expression levels to further narrow the range of DEGs and to calculate the risk score. Two genes were identified, cell division cycle 20B and patatin-like phospholipase domain containing 5, which were both shown to have higher expression levels in OV tissues and to be significantly associated with the prognosis of OV. Next, a nomogram was created using these two genes and age, and using the receiver operating characteristic (ROC) curve and calibration curve, the effectiveness of the nomogram was validated. Finally, an external validation was conducted for this nomogram. The ROC showed that the areas under the curve (AUCs) of the 3- and 5-year overall survival predictions for the nomogram were 0.678 and 0.62, respectively. Moreover, the ROC of the external validation model showed that the AUCs of the 3- and 5-year were 0.699 and 0.643, respectively, demonstrating the effectiveness of the generated nomogram. In conclusion, the present study has identified two immune-related genes as biomarkers that reliably predict overall survival in OV. These biomarkers might also be potential molecular targets of immune therapy to treat patients with OV. - Source: PubMed
Publication date: 2020/09/08
Lin HanWang JiaminWen XiaohuiWen QidanHuang ShiyaMai ZhefenLu LingjingLiang XingyanPan HaixiaLi ShunaHe YuhongMa Hongxia - This review addresses the contribution of some genes to the phenotype of familial hypercholesterolemia. At present, it is known that the pathogenesis of this disease involves not only a pathological variant of low-density lipoprotein receptor and its ligands (apolipoprotein B, proprotein convertase subtilisin/kexin type 9 or low-density lipoprotein receptor adaptor protein 1), but also lipids, including sphingolipids, fatty acids, and sterols. The genetic cause of familial hypercholesterolemia is unknown in 20%-40% of the cases. The genes (signal transducing adaptor family member 1), (cytochrome P450 family 7 subfamily A member 1), (lipase A, lysosomal acid type), (ATP binding cassette subfamily G member 5), (ATP binding cassette subfamily G member 8), and (patatin like phospholipase domain containing 5), which can cause aberrations of lipid metabolism, are being evaluated as new targets for the diagnosis and personalized management of familial hypercholesterolemia. - Source: PubMed
Publication date: 2019/11/29
Mikhailova SvetlanaIvanoshchuk DinaraTimoshchenko OlgaShakhtshneider Elena