The cell cycle-related genes as biomarkers for schizophrenia

Okazaki, Kenji; Boku, Shuken; Otsuka, Ikuo; Mouri, Kentaro; Aoyama, Shigeru; Shiroiwa, Kyoichi; Sora, Ichiro; Fujita, Aiko; Shirai, Yutaka; Sato, Osamu; Kokai, Masahiro; Hishimoto, Akitoyo

doi:10.1016/j.pnpbp.2016.05.005

Cited by 25 publications

(19 citation statements)

References 28 publications

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“…We found that top prioritized genes (e.g., N=43 with Captum score > 0.01) are enriched with gene regulation, DNA damage and cell cycle functions ( Figure 1C), suggesting them as potential biomarkers for schizophrenia. This result was also supported by experimental studies [12][13][14].…”

Section: Usage Casesupporting

confidence: 77%

Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes

Nguyen

Wang

2020

Preprint

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Population studies such as GWAS have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g., gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics is still challenging. To address this, we develop an interpretable deep learning method, Varmole to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via drop-connect without needing prior feature selections. Varmole is available as a Python package on GitHub at https://github.com/daifengwanglab/Varmole .

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Section: Usage Casesupporting

confidence: 77%

Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes

Nguyen

Wang

2020

Preprint

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“…A small number of studies have shown differing gene expression levels in cell cycle genes in patients with schizophrenia, compared to healthy controls (Gassó et al, 2017;Lin et al, 2016;Okazaki et al, 2016;Wang et al, 2010;Yang et al, 2017). A similar connection with antipsychotic drug effects has not been reported before.…”

Section: Discussionmentioning

confidence: 62%

Integrative genomic strategies applied to a lymphoblast cell line model reveal specific transcriptomic signatures associated with clozapine response

Ori

Wang

Pulit

et al. 2020

Preprint

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Clozapine is an important antipsychotic drug. However, its use is often accompanied by metabolic adverse effects and, in rare instances, agranulocytosis. The molecular mechanisms underlying these adverse events are unclear. To gain more insights into the response to clozapine at the molecular level, we exposed lymphoblastoid cell lines (LCLs) to increasing concentrations of clozapine and measured genome-wide gene expression and DNA methylation profiles. We observed robust and significant changes in gene expression levels due to clozapine (n = 463 genes at FDR < 0.05) affecting cholesterol and cell cycle pathways. At the level of DNA methylation, we find significant changes upstream of the LDL receptor, in addition to global enrichments of regulatory, immune and developmental pathways. By integrating these data with human tissue gene expression levels obtained from the Genotype-Tissue Expression project (GTEx), we identified specific tissues, including liver and several tissues involved in immune, endocrine and metabolic functions, that clozapine treatment may disproportionately affect. Notably, differentially expressed genes were not enriched for genome-wide disease risk of schizophrenia or for known psychotropic drug targets. However, we did observe a nominally significant association of genetic signals related to total cholesterol and low-density lipoprotein levels. Together, these results shed light on the biological mechanisms through which clozapine functions. The observed associations with cholesterol pathways, its genetic architecture and specific tissue effects may be indicative of the metabolic adverse effects observed in clozapine users. LCLs may thus serve as a useful tool to study these molecular mechanisms further.

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“…The possibility of cell cycle-related genes to function as biomarkers was tested by measuring and comparing messenger RNA (mRNA) levels in the peripheral blood of SCZ patients and healthy controls [102]. It was found that the combination of cyclin-dependent kinase 4 (CDK4), minichromosome maintenance complex component 7 (MCM7) and DNA polymerase delta 4 accessory subunit (POLD4) mRNA expression might be a potential useful biomarker for SCZ [102].…”

Section: Biomarkers and Geneticsmentioning

confidence: 99%

“…It was found that the combination of cyclin-dependent kinase 4 (CDK4), minichromosome maintenance complex component 7 (MCM7) and DNA polymerase delta 4 accessory subunit (POLD4) mRNA expression might be a potential useful biomarker for SCZ [102]. The latter three genes were significantly decreased in acute SCZ [102]. However, CDK4 expression levels recovered significantly in the remission state [102].…”

Section: Biomarkers and Geneticsmentioning

confidence: 99%

See 1 more Smart Citation

Biomarkers and schizophrenia: a qualitative review

Silva¹,

Pinto²

2017

IJCNMH

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Schizophrenia (SCZ) is a psychiatric disorder with a broad spectrum of biological and clinical manifestations of yet not completely clear pathophysiological mechanisms. Several lines of evidence have been supporting the idea that immunoinflammatory, oxidative, hormonal and cellular dysfunctions are implicated on the biomolecular basis of SCZ. However, accurate diagnosis and selection of appropriate treatments remains challenging as a result of the scarcity of objective tests. Furthermore, there is a compelling need to find biomarkers that could predict drug response and tailor pharmacological treatment, particularly in drug-naïve first episode psychosis (FEP). Hence, numerous technologies have been employed in order to search for SCZ biological markers, but evidence relating them to treatment efficacy is lacking. In this regard, some preliminary data suggest promising results. The current review provides information on: (1) potential biomarkers associated with biological disturbances and (2) biological markers associated with treatment response.

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The cell cycle-related genes as biomarkers for schizophrenia

Cited by 25 publications

References 28 publications

Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes

Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes

Integrative genomic strategies applied to a lymphoblast cell line model reveal specific transcriptomic signatures associated with clozapine response

Biomarkers and schizophrenia: a qualitative review

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