Diminished serum repetin levels in patients with schizophrenia and bipolar disorder

Wang, Shuai; Ren, Hai‐Peng; Xu, Jie; Yu, Yang; Han, Shui-Ping; Qiao, Hui; Cheng, Shaoli; Xu, Chang; An, Shu-Cheng; Ju, Bomiao; Yu, Chengyuan; Wang, Chanyuan; Wang, Tao; Yang, Zhenjun; Taylor, Ethan Will; Zhao, Liyun

doi:10.1038/srep07977

Cited by 4 publications

(6 citation statements)

References 59 publications

(74 reference statements)

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“…RPTN is commonly expressed in both mouse and human brains; it is highly expressed in the choroid plexus, hippocampal region, and prefrontal cortex of the brain. In patients with psychotic disorders, such as schizophrenia and bipolar disorder, serum RPTN levels are reduced 37 …”

Section: Discussionmentioning

confidence: 99%

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Elucidation of common molecular diagnostic biomarkers between chronic periodontitis and Parkinson's disease via bioinformatics analyses

Zehui,

Mengting,

Pengfei

et al. 2023

J of Periodontal Research

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Background and ObjectivesParkinson's disease (PD) and chronic periodontitis (CP) are both inflammatory diseases; a correlation between the two diseases has been reported, but the underlying mechanisms of this association have not been investigated. We investigated the common molecular mechanisms between PD and CP and the role of immune cells in the pathogenesis of them using bioinformatics analyses to elucidate the association between the two diseases.MethodsWe obtained gene expression data from the Gene Expression Omnibus (GEO) database: GSE10334, GSE16134, and GSE23586 for CP gingival samples and GSE20146 for PD brain samples. Subsequently, we conducted an enrichment analysis of the differentially expressed genes (DEGs) using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Moreover, all DEGs were analysed for protein–transcription factor interactions and protein–immune cell co‐expression. We constructed protein–transcription factor, protein–protein interaction (PPI), and protein–immune cell co‐expression networks using the Cytoscape software. Moreover, we identified the hub genes and investigated them for potential diagnostic value.Results and ConclusionWe identified 99 DEGs in the three CP datasets, 520 DEGs in the PD dataset and found five common DEGs in the CP and PD datasets, namely CXCR4, CXCL8, CD19, RPTN, and SLC16A9. These common DEGs identified in our study may have a potential impact on disease pathogenesis through the involvement of CXCR4‐CXCL8‐CD19 protein‐complexes in dendritic cells. Therefore, CD19, LCP2, CXCR4, and LYN could be used as target molecules for the clinical diagnosis of both diseases.

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Section: Discussionmentioning

confidence: 99%

“…In patients with psychotic disorders, such as schizophrenia and bipolar disorder, serum RPTN levels are reduced. 37 F I G U R E 8 GO (A) and KEGG (B) enrichment of hub genes.…”

Section: Discussionmentioning

confidence: 99%

Elucidation of common molecular diagnostic biomarkers between chronic periodontitis and Parkinson's disease via bioinformatics analyses

Zehui,

Mengting,

Pengfei

et al. 2023

J of Periodontal Research

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“…Immune system [51], axon guidance [52], metabolism [53], diseases of metabolism [54] and neuronal system [55] were lined with advancement of BD. Altered expression of SLC6A9 [56] and RHD (Rh blood group D antigen) [57], CHRFAM7A [58], ANXA3 [59], SLC1A5 [60], KCNH2 [61], HP (haptoglobin) [62], SELENBP1 [63], SNCA (synuclein alpha) [64], TGM2 [65], PINK1 [66], B2M [67], QPCT (glutaminyl-peptide cyclotransferase) [68], CBS (cystathionine beta-synthase) [69], NQO2 [70], GLRX5 [71], BASP1 [72], GAS7 [73], GPX1 [74], OLIG2 [75], RPTN (repetin) [76], IL33 [77], SOX10 [78], GRIK1 [79], ZFPM2 [80], SHANK3 [81], ERBB3 [82], ARC (activity regulated cytoskeleton associated protein) [83], GFAP (glial fibrillary acidic protein) [84], PLAT (plasminogen activator, tissue type) [85], GRIK5 [86], CACNB2 [87], NRXN2 [88], TERT (telomerase reverse transcriptase) [89], GNB3 [90], L1CAM [91], EGR3 [92], CAV1 [93], CACNA1B [94], MAGI1 [95], KIR2DL1 [96], PAH (phenylalanine hydroxylase) [97] and CYP3A5 [98] have been shown in schizophrenia. Recent studies showed that SLC6A9 [99], FKBP1B [100], S100A12 [101], SLC6A19 [102], TXN (thioredoxin) [103], TLR9 [104], HP (haptoglobin) [105], ARG1 [106], PINK1 [107], B2M [108], C5AR1 [109], MYADM (myeloid associated differentiation marker) [110], CBS (cystathionine beta-synthase) [111], GPX1 [112], SIAH2 [113], PRDX2 [114], RDH8 [115], CYP11B2 [116], RARRES2 [117], NOX1 [118], IL33 [119], OTC (ornithine transcarbamylase) [120], CYP1A1 […”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A protein–protein interaction (PPI) network and module analysis were used to identify hub genes. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed. Receiver operating characteristic curve (ROC) analysis was used to validate hub genes. In this work, 957 DEGs, including 477 up regulated and 480 down regulated, were obtained from NGS data. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. Finally, through analyzing the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 by the Cytoscape software. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.

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“…showed that RPTN is ubiquitously expressed with relatively high levels in the choroid plexus, hippocampus, and prefrontal cortex in both mouse and the human brain. This study was the first to report RPTN expression in the brain [ 51 ]. RPTN protein contains two EF-hands, which structurally resemble the calcium-binding domain of Parvalbumin (PV).…”

Section: Discussionmentioning

confidence: 99%

“…Calcium-binding proteins play important roles in the brain and in psychiatric disorders in which PV is regarded as a neuronal marker and has a potential key role in the pathophysiology of schizophrenia. Thus, RPTN plays a potential role in emotional and cognitive processing in which it is involved in cornified cell envelope formation and has an important role in the pathogenesis of schizophrenia and bipolar disorder [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

Recursive Feature Elimination-based Biomarker Identification for Open Neural Tube Defects

Karthik

Aruna

Shivashankar

et al. 2022

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Background: Open spina bifida (myelomeningocele) is the result of the spinal cord to close completely and is the second most common and severe birth defect. Open neural tube defects are multifactorial, and the exact molecular mechanism of the pathogenesis is not clear due to disease complexity for which prenatal treatment options remain limited worldwide. Artificial intelligence techniques like machine learning tools have been increasingly used in precision diagnosis. Objective: The primary objective of this study is to identify key genes for open neural tube defects using machine learning approach that provides additional information about myelomeningocele in order to obtain more accurate diagnosis. Materials and methods: Our study reports differential gene expression analysis from multiple datasets (GSE4182 and GSE101141) of amniotic fluid samples with open neural tube defects. The sample outliers in the datasets were detected using principal component analysis (PCA). We report a combination of the differential gene expression analysis with recursive feature elimination (RFE), a machine learning approach to get 4 key genes for open neural tube defects. The features selected were validated using five binary classifiers for diseased and healthy samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF) and K-nearest neighbour (KNN) with 5-fold cross validation. Results: Growth Associated Protein 43 (GAP43), Glial fibrillary acidic protein (GFAP), Repetin (RPTN) and CD44 are the important genes identified in the study. These genes are known to be involved in axon growth, astrocyte differentiation in the central nervous system, post traumatic brain repair, neuroinflammation and inflammation linked neuronal injuries. These key genes represent a promising tool for further studies in diagnosis and early detection of open neural tube defects. Conclusion: These key biomarkers help in diagnosis and early detection of open neural tube defects, thus evaluating its progress and seriousness in diseases condition. This study strengthens previous literature sources of confirming these biomarkers linked with open NTD’s. Thus, among other prenatal treatment options present until now, these biomarkers help in early detection of open neural tube defects which provides success in both treatment and prevention of these defects in advance stage itself.

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Diminished serum repetin levels in patients with schizophrenia and bipolar disorder

Cited by 4 publications

References 59 publications

Elucidation of common molecular diagnostic biomarkers between chronic periodontitis and Parkinson's disease via bioinformatics analyses

Elucidation of common molecular diagnostic biomarkers between chronic periodontitis and Parkinson's disease via bioinformatics analyses

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Recursive Feature Elimination-based Biomarker Identification for Open Neural Tube Defects

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