Central and Peripheral Changes in FOS Expression in Schizophrenia Based on Genome-Wide Gene Expression

Identification of hub genes and construction of diagnostic nomogram model in schizophrenia

Schizophrenia (SCZ), which is characterized by debilitating neuropsychiatric disorders with significant cognitive impairment, remains an etiological and therapeutic challenge. Using transcriptomic profile analysis, disease-related biomarkers linked with SCZ have been identified, and clinical outcomes can also be predicted. This study aimed to discover diagnostic hub genes and investigate their possible involvement in SCZ immunopathology. The Gene Expression Omnibus (GEO) database was utilized to get SCZ Gene expression data. Differentially expressed genes (DEGs) were identified and enriched by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and disease ontology (DO) analysis. The related gene modules were then examined using integrated weighted gene co-expression network analysis. Single-sample gene set enrichment (GSEA) was exploited to detect immune infiltration. SVM-REF, random forest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to identify hub genes. A diagnostic model of nomogram was constructed for SCZ prediction based on the hub genes. The clinical utility of nomogram prediction was evaluated, and the diagnostic utility of hub genes was validated. mRNA levels of the candidate genes in SCZ rat model were determined. Finally, 24 DEGs were discovered, the majority of which were enriched in biological pathways and activities. Four hub genes (NEUROD6, NMU, PVALB, and NECAB1) were identified. A difference in immune infiltration was identified between SCZ and normal groups, and immune cells were shown to potentially interact with hub genes. The hub gene model for the two datasets was verified, showing good discrimination of the nomogram. Calibration curves demonstrated valid concordance between predicted and practical probabilities, and the nomogram was verified to be clinically useful. According to our research, NEUROD6, NMU, PVALB, and NECAB1 are prospective biomarkers in SCZ and that a reliable nomogram based on hub genes could be helpful for SCZ risk prediction.

Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement

Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.

Screening and verification of potential gene targets in esophageal carcinoma by bioinformatics analysis and immunohistochemistry

Background

To evaluate the potential of candidate proteins as diagnostic markers or drug targets in esophageal carcinoma (ESCA).

Methods

GSE20347, GSE17351, and GSE45670 were downloaded from Gene Expression Omnibus (GEO). Differently expressed genes (DEGs) between ESCA and normal esophageal tissues from patients were obtained. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. The genes commonly featured in ESCA were screened by least absolute shrinkage and selection operator (LASSO) logistic regression and Boruta feature selection algorithm. The transcriptome data and corresponding clinical data of ESCA were downloaded from The Cancer Genome Atlas (TCGA) public database. Kaplan-Meier survival analysis was used to explore the core genes related to the prognosis of patients. A protein-protein interaction (PPI) network was generated by GeneMANIA to visualize the functional network between genes. Expressions of CRIP2, FOS, and HOXA10 genes in ESCA cells were verified by immunohistochemistry (IHC).

Results

Out of 11,207 genes, 430 DEGs were identified, including 210 up-regulated genes and 220 down-regulated genes. After taking the intersection of LASSO regression and Boruta algorithm, 15 core genes were identified. Survival analyses demonstrated that low expression of CRIP2 (P=2.643e-02), as well as high expression of FOS (P=4.837e-02) and HOXA10 (P=4.97e-02), was significantly associated with the worse prognosis of ESCA patients. The 3 genes were strongly correlated with the content of immune cells and the stage of tumors. The expression of CRIP2 was correlated with the sensitivity of patients to dasatinib; FOS expression was correlated with the sensitivity of patients to erlotinib, and HOXA10 expression affected the sensitivity of patients to cisplatin, dasatinib, erlotinib, and gefitinib. The cBioportal database showed that 56 patients (31%) had the above core gene mutations: CRIP2 (8%), FOS (10%), and HOXA10 (17%). The IHC showed that there were differences in the expressions of these core genes between ESCA patients and the normal population (P<0.05), with ESCA patients showing higher expression.

Conclusions

The low CRIP2 expression and high expressions of FOS and HOXA10 are associated with more advanced tumor stage, which may have the potential to be novel biomarkers for treatment selection in ESCA.

Bioinformatics analysis of long non-coding RNA-associated competing endogenous RNA network in schizophrenia

Schizophrenia (SCZ) is a serious psychiatric condition with a 1% lifetime risk. SCZ is one of the top ten global causes of disabilities. Despite numerous attempts to understand the function of genetic factors in SCZ development, genetic components in SCZ pathophysiology remain unknown. The competing endogenous RNA (ceRNA) network has been demonstrated to be involved in the development of many kinds of diseases. The ceRNA hypothesis states that cross-talks between coding and non-coding RNAs, including long non-coding RNAs (lncRNAs), via miRNA complementary sequences known as miRNA response elements, creates a large regulatory network across the transcriptome. In the present study, we developed a lncRNA-related ceRNA network to elucidate molecular regulatory mechanisms involved in SCZ. Microarray datasets associated with brain regions (GSE53987) and lymphoblasts (LBs) derived from peripheral blood (sample set B from GSE73129) of SCZ patients and control subjects containing information about both mRNAs and lncRNAs were downloaded from the Gene Expression Omnibus database. The GSE53987 comprised 48 brain samples taken from SCZ patients (15 HPC: hippocampus, 15 BA46: Brodmann area 46, 18 STR: striatum) and 55 brain samples taken from control subjects (18 HPC, 19 BA46, 18 STR). The sample set B of GSE73129 comprised 30 LB samples (15 patients with SCZ and 15 controls). Differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified using the limma package of the R software. Using DIANA-LncBase, Human MicroRNA Disease Database (HMDD), and miRTarBase, the lncRNA- associated ceRNA network was generated. Pathway enrichment of DEmRNAs was performed using the Enrichr tool. We developed a protein-protein interaction network of DEmRNAs and identified the top five hub genes by the use of STRING and Cytoscape, respectively. Eventually, the hub genes, DElncRNAs, and predictive miRNAs were chosen to reconstruct the subceRNA networks. Our bioinformatics analysis showed that twelve key DEmRNAs, including BDNF, VEGFA, FGF2, FOS, CD44, SOX2, NRAS, SPARC, ZFP36, FGG, ELAVL1, and STARD13, participate in the ceRNA network in SCZ. We also identified DLX6-AS1, NEAT1, MINCR, LINC01094, DLGAP1-AS1, BABAM2-AS1, PAX8-AS1, ZFHX4-AS1, XIST, and MALAT1 as key DElncRNAs regulating the genes mentioned above. Furthermore, expression of 15 DEmRNAs (e.g., ADM and HLA-DRB1) and one DElncRNA (XIST) were changed in both the brain and LB, suggesting that they could be regarded as candidates for future biomarker studies. The study indicated that ceRNAs could be research candidates for investigating SCZ molecular pathways.

The Etiology of Auditory Hallucinations in Schizophrenia: From Multidimensional Levels

Enormous efforts have been made to unveil the etiology of auditory hallucinations (AHs), and multiple genetic and neural factors have already been shown to have their own roles. Previous studies have shown that AHs in schizophrenia vary from those in other disorders, suggesting that they have unique features and possibly distinguishable mechanisms worthy of further investigation. In this review, we intend to offer a comprehensive summary of current findings related to AHs in schizophrenia from aspects of genetics and transcriptome, neurophysiology (neurometabolic and electroencephalogram studies), and neuroimaging (structural and functional magnetic resonance imaging studies and transcriptome–neuroimaging association study). Main findings include gene polymorphisms, glutamate level change, electroencephalographic alterations, and abnormalities of white matter fasciculi, cortical structure, and cerebral activities, especially in multiple regions, including auditory and language networks. More solid and comparable research is needed to replicate and integrate ongoing findings from multidimensional levels.

The retinoid X receptor: a nuclear receptor that modulates the sleep-wake cycle in rats

RATIONALE

The nuclear receptor retinoid X receptor (RXR) belongs to a nuclear receptor superfamily that modulates diverse functions via homodimerization with itself or several other nuclear receptors, including PPARα. While the activation of PPARα by natural or synthetic agonists regulates the sleep-wake cycle, the role of RXR in the sleep modulation is unknown.

OBJECTIVES

We investigated the effects of bexarotene (Bexa, a RXR agonist) or UVI 3003 (UVI, a RXR antagonist) on sleep, sleep homeostasis, levels of neurochemical related to sleep modulation, and c-Fos and NeuN expression.

METHODS

The sleep-wake cycle and sleep homeostasis were analyzed after application of Bexa or UVI. Moreover, we also evaluated whether Bexa or UVI could induce effects on dopamine, serotonin, norepinephrine epinephrine, adenosine, and acetylcholine contents, collected from either the nucleus accumbens or basal forebrain. In addition, c-Fos and NeuN expression in the hypothalamus was determined after Bexa or UVI treatments.

RESULTS

Systemic application of Bexa (1 mM, i.p.) attenuated slow-wave sleep and rapid eye movement sleep. In addition, Bexa increased the levels of dopamine, serotonin, norepinephrine epinephrine, adenosine, and acetylcholine sampled from either the nucleus accumbens or basal forebrain. Moreover, Bexa blocked the sleep rebound period after total sleep deprivation, increased in the hypothalamus the expression of c-Fos, and decreased NeuN activity. Remarkably, UVI 3003 (1 mM, i.p.) induced opposite effects in sleep, sleep homeostasis, neurochemicals levels, and c-Fos and NeuN activity.

CONCLUSIONS

The administration of RXR agonist or antagonist significantly impaired the sleep-wake cycle and exerted effects on the levels of neurochemicals related to sleep modulation. Moreover, Bexa or UVI administration significantly affected c-Fos and NeuN expression in the hypothalamus. Our findings highlight the neurobiological role of RXR on sleep modulation.

Integrating genome-wide association study with regulatory SNP annotation information identified candidate genes and pathways for schizophrenia

BACKGROUND

Schizophrenia is a complex mental disorder. The genetic mechanism of schizophrenia remains elusive now.

METHODS

We conducted a large-scale integrative analysis of two genome-wide association studies of schizophrenia with functional annotation datasets of regulatory single-nucleotide polymorphism (rSNP). The significant SNPs identified by the two genome-wide association studies were first annotated to obtain schizophrenia associated rSNPs and their target genes and proteins, respectively. We then compared the integrative analysis results to identify the common rSNPs and their target regulatory genes and proteins, shared by the two genome-wide association studies of schizophrenia. Finally, DAVID tool was used to conduct gene ontology and pathway enrichment analysis of the identified targets genes and proteins.

RESULTS

We detected 53 schizophrenia-associated target genes for rSNP, such as FOS (P value = 2.18×10^-20), ATXN1 (P value = 5.22×10^-21) and HLA-DQA1 (P value = 1.98×10^-10). Pathway enrichment analysis identified 24 pathways for transcription factors binding regions, chromatin interacting regions, long non-coding RNAs, topologically associated domains, circular RNAs and post-translational modifications, such as hsa05034:Alcoholism (P value = 2.57×10^-7) and hsa04612:Antigen processing and presentation (P value = 6.82×10^-8).

CONCLUSION

We detected multiple candidate genes, gene ontology terms and pathways for schizophrenia, supporting the functional importance of rSNPs, and providing novel clues for understanding the genetic architecture of schizophrenia.