NRG1, PIP4K2A, and HTR2C as Potential Candidate Biomarker Genes for Several Clinical Subphenotypes of Depression and Bipolar Disorder

Levchenko, Аnastasia; Vyalova, N.; Nurgaliev, Timur; Pozhidaev, Ivan V; Симуткин, Г. Г.; Бохан, Н. А.; Иванова, Светлана

doi:10.3389/fgene.2020.00936

Cited by 16 publications

(13 citation statements)

References 132 publications

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“…PI4K2A also regulates VAMP3 involved in vesicular trafficking and GH secretion, which is already reported in rat pituicytes [38]. Protein PIP4K2A is involved in neuronal signal transduction and is a critical regulator for intracellular cholesterol transport [39], which has been reported as a biomarker gene candidate in bipolar disorder and depression [40]. Nucleotide‐binding proteins such as PI4K2A, RHOG, RAC1 and VCP (‘Valosin‐containing protein’) have been linked with genetic susceptibility of pituitary tumours [41].…”

Section: Resultsmentioning

confidence: 95%

Evaluation of autoantibody signatures in pituitary adenoma patients using human proteome arrays

Banerjee

Ray

Barpanda

et al. 2022

Proteomics Clinical Apps

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Purpose To identify the specific diagnostic biomarkers related to pituitary adenomas (PAs), we performed serological antibody profiles for three types of PAs, namely Acromegaly, Cushing's and Nonfunctional Pituitary Adenomas (NFPAs), using the human proteome (HuProt) microarray. This is the first study describing the serum autoantibody profile of PAs. Experimental Design We performed serological autoantibody profiling of four healthy controls, four Acromegaly, three Cushing's and three NFPAs patient samples to obtain their autoantibody profiles, which were used for studying expression, interaction and altered biological pathways. Further, significant autoantibodies of PAs were compared with data available for glioma, meningioma and AAgAtlas for their specificity. Results Autoantibody profile of PAs led to the identification of differentially expressed significant proteins such as AKNAD1 (AT‐Hook Transcription Factor [AKNA] Domain Containing 1), NINJ1 (Nerve injury‐induced protein 1), L3HYPDH (Trans‐3‐hydroxy‐L‐proline dehydratase), RHOG (Rho‐related GTP‐binding protein) and PTP4A1 (Protein Tyrosine Phosphatase Type IVA 1) in Acromegaly. Protein ABR (Active breakpoint cluster region‐related protein), ST6GALNAC6 (ST6 N‐acetylgalactosaminide alpha‐2, 6‐sialyltransferase 6), NOL3 (Nucleolar protein 3), ANXA8 (Annexin A8) and POLR2H (RNA polymerase II, I and III subunit H) showed an antigenic response in Cushing's patient's serum samples. Protein dipeptidyl peptidase 3 (DPP3) and reticulon‐4 (RTN4) exhibited a very high antigenic response in NFPA patients. These proteins hold promise as potential autoantibody biomarkers in PAs.

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Section: Resultsmentioning

confidence: 95%

Evaluation of autoantibody signatures in pituitary adenoma patients using human proteome arrays

Banerjee

Ray

Barpanda

et al. 2022

Proteomics Clinical Apps

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“…SNPeff identified 8 as downstream variants, 46 as intergenic, 14 within introns, 5 upstream of genes and 2 in the 3’ UTR for WLI; 3 downstream variants, 51 within intergenic regions, 10 within introns, 4 upstream of genes, 1 in the 3’UTR, and 1 on a splice site/intron for WMI. Of these, two intron variants in WLI fell within HTR2C, a gene associated with depression 54 , 55 schizophrenia 56 , 57 , and stress response 58 , 59 . One intron variant on WLI fell within Il1rapl1, a gene associated with autism 60 , 61 and schizophrenia 62 .…”

Section: Resultsmentioning

confidence: 99%

Whole genome sequencing of nearly isogenic WMI and WLI inbred rats identifies genes potentially involved in depression and stress reactivity

Jong

Kim

Guryev

et al. 2021

Sci Rep

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The WMI and WLI inbred rats were generated from the stress-prone, and not yet fully inbred, Wistar Kyoto (WKY) strain. These were selected using bi-directional selection for immobility in the forced swim test and were then sib-mated for over 38 generations. Despite the low level of genetic diversity among WKY progenitors, the WMI substrain is significantly more vulnerable to stress relative to the counter-selected WLI strain. Here we quantify numbers and classes of genomic sequence variants distinguishing these substrains with the long term goal of uncovering functional and behavioral polymorphism that modulate sensitivity to stress and depression-like phenotypes. DNA from WLI and WMI was sequenced using Illumina xTen, IonTorrent, and 10X Chromium linked-read platforms to obtain a combined coverage of ~ 100X for each strain. We identified 4,296 high quality homozygous SNPs and indels between the WMI and WLI. We detected high impact variants in genes previously implicated in depression (e.g. Gnat2), depression-like behavior (e.g. Prlr, Nlrp1a), other psychiatric disease (e.g. Pou6f2, Kdm5a, Reep3, Wdfy3), and responses to psychological stressors (e.g. Pigr). High coverage sequencing data confirm that the two substrains are nearly coisogenic. Nonetheless, the small number of sequence variants contributes to numerous well characterized differences including depression-like behavior, stress reactivity, and addiction related phenotypes. These selected substrains are an ideal resource for forward and reverse genetic studies using a reduced complexity cross.

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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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NRG1, PIP4K2A, and HTR2C as Potential Candidate Biomarker Genes for Several Clinical Subphenotypes of Depression and Bipolar Disorder

Cited by 16 publications

References 132 publications

Evaluation of autoantibody signatures in pituitary adenoma patients using human proteome arrays

Evaluation of autoantibody signatures in pituitary adenoma patients using human proteome arrays

Whole genome sequencing of nearly isogenic WMI and WLI inbred rats identifies genes potentially involved in depression and stress reactivity

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

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