Protein inhibitor of activated STAT1 (PIAS1), a small ubiquitin-like modifier (SUMO) E3 ligase, was considered to be an inhibitor of STAT1 by inhibiting the DNA-binding activity of STAT1 and blocking STAT1-mediated gene transcription in response to cytokine stimulation. PIAS1 has been determined to be involved in modulating several biological processes such as cell proliferation, DNA damage responses, and inflammatory responses, both in vivo and in vitro . However, the role played by PIAS1 in regulating neurodegenerative diseases, including Alzheimer’s disease (AD), has not been determined. In our study, significantly different expression levels of PIAS1 between normal controls and AD patients were detected in four regions of the human brain. Based on a functional analysis of Pias1 in undifferentiated mouse hippocampal neuronal HT-22 cells, we observed that the expression levels of several AD marker genes could be inhibited by Pias1 overexpression. Moreover, the proliferation ability of HT-22 cells could be promoted by the overexpression of Pias1 . Furthermore, we performed RNA sequencing (RNA-seq) to evaluate and quantify the gene expression profiles in response to Pias1 overexpression in HT-22 cells. As a result, 285 significantly dysregulated genes, including 79 upregulated genes and 206 downregulated genes, were identified by the comparison of Pias1 /+ cells with WT cells. Among these genes, five overlapping genes, including early growth response 1 ( Egr1 ), early growth response 2 ( Egr2 ), early growth response 3 ( Egr3 ), FBJ osteosarcoma oncogene ( Fos ) and fos-like antigen 1 ( Fosl1 ), were identified by comparison of the transcription factor binding site (TFBS) prediction results for STAT1, whose expression was evaluated by qPCR. Three cell cycle inhibitors, p53, p18 and p21, were significantly downregulated with the overexpression of Pias1 . Analysis of functional enrichment and expression levels showed that basic region leucine zipper domain-containing transcription factors including zinc finger C2H2 (zf-C2H2), homeobox and basic/helix-loop-helix (bHLH) in several signaling pathways were significantly involved in PIAS1 regulation in HT-22 cells. A reconstructed regulatory network under PIAS1 overexpression demonstrated that there were 43 related proteins, notably Nr3c2, that directly interacted with PIAS1.
The ubiquitin-proteasome system (UPS) plays crucial roles in numerous cellular functions. Dysfunction of the UPS shows certain correlations with the pathological changes in Alzheimer’s disease (AD). This study aimed to explore the different impairments of the UPS in multiple brain regions and identify hub ubiquitin ligase (E3) genes in AD. The brain transcriptome, blood transcriptome and proteome data of AD were downloaded from a public database. The UPS genes were collected from the Ubiquitin and Ubiquitin-like Conjugation Database. The hub E3 genes were defined as the differentially expressed E3 genes shared by more than three brain regions. E3Miner and UbiBrowser were used to predict the substrate of hub E3. This study shows varied impairment of the UPS in different brain regions in AD. Furthermore, we identify seven hub E3 genes (CUL1, CUL3, EIF3I, NSMCE1, PAFAH1B1, RNF175, and UCHL1) that are downregulated in more than three brain regions. Three of these genes (CUL1, EIF3I, and NSMCE1) showed consistent low expression in blood. Most of these genes have been reported to promote AD, whereas the impact of RNF175 on AD is not yet reported. Further analysis revealed a potential regulatory mechanism by which hub E3 and its substrate genes may affect transcription functions and then exacerbate AD. This study identified seven hub E3 genes and their substrate genes affect transcription functions and then exacerbate AD. These findings may be helpful for the development of diagnostic biomarkers and therapeutic targets for AD.
Transcriptome differences between Hodgkin's lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), and mantle cell lymphoma (MCL), which are all derived from B cell, remained unclear. This study aimed to construct lymphoma-specific diagnostic models by screening lymphoma marker genes. Transcriptome data of HL, DLBCL, and MCL were obtained from public databases. Lymphoma marker genes were screened by comparing cases and controls as well as the intergroup differences among lymphomas. A total of 9 HL marker genes, 7 DLBCL marker genes, and 4 MCL marker genes were screened in this study. Most HL marker genes were upregulated, whereas DLBCL and MCL marker genes were downregulated compared to controls. The optimal HL-specific diagnostic model contains one marker gene (MYH2) with an AUC of 0.901. The optimal DLBCL-specific diagnostic model contains 7 marker genes (LIPF, CCDC144B, PRO2964, PHF1, SFTPA2, NTS, and HP) with an AUC of 0.951. The optimal MCL-specific diagnostic model contains 3 marker genes (IGLV3-19, IGKV4-1, and PRB3) with an AUC of 0.843. The present study reveals the transcriptome data-based differences between HL, DLBCL, and MCL, when combined with other clinical markers, may help the clinical diagnosis and prognosis.
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