Peripheral Blood-Based Gene Expression Studies in Schizophrenia: A Systematic Review

Abstract

Schizophrenia is a disorder that is characterized by delusions, hallucinations, disorganized speech or behavior, and socio-occupational impairment. The duration of observation and variability in symptoms can make the accurate diagnosis difficult. Identification of biomarkers for schizophrenia (SCZ) can help in early diagnosis, ascertaining the diagnosis, and development of effective treatment strategies. Here we review peripheral blood-based gene expression studies for identification of gene expression biomarkers for SCZ. A literature search was carried out in PubMed and Web of Science databases for blood-based gene expression studies in SCZ. A list of differentially expressed genes (DEGs) was compiled and analyzed for overlap with genetic markers, differences based on drug status of the participants, functional enrichment, and for effect of antipsychotics. This literature survey identified 61 gene expression studies. Seventeen out of these studies were based on expression microarrays. A comparative analysis of the DEGs (n = 227) from microarray studies revealed differences between drug-naive and drug-treated SCZ participants. We found that of the 227 DEGs, 11 genes (ACOT7, AGO2, DISC1, LDB1, RUNX3, SIGIRR, SLC18A1, NRG1, CHRNB2, PRKAB2, and ZNF74) also showed genetic and epigenetic changes associated with SCZ. Functional enrichment analysis of the DEGs revealed dysregulation of proline and 4-hydroxyproline metabolism. Also, arginine and proline metabolism was the most functionally enriched pathway for SCZ in our analysis. Follow-up studies identified effect of antipsychotic treatment on peripheral blood gene expression. Of the 27 genes compiled from the follow-up studies AKT1, DISC1, HP, and EIF2D had no effect on their expression status as a result of antipsychotic treatment. Despite the differences in the nature of the study, ethnicity of the population, and the gene expression analysis method used, we identified several coherent observations. An overlap, though limited, of genetic, epigenetic and gene expression changes supports interplay of genetic and environmental factors in SCZ. The studies validate the use of blood as a surrogate tissue for biomarker analysis. We conclude that well-designed cohort studies across diverse populations, use of high-throughput sequencing technology, and use of artificial intelligence (AI) based computational analysis will significantly improve our understanding and diagnostic capabilities for this complex disorder.

Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities

“…Although KEGG is a highly cited pathway resource, pathways contained in this resource may not adequately capture mechanisms within the psychiatric domain. In the future, we foresee using other modalities (e.g., proteomics) or samples from other cells (e.g., blood samples) in place of tissue samples as recent studies have shown that blood biomarkers can be effectively used to detect schizophrenia and bipolar disorder (Wagh et al ., 2021; Munkholm et al ., 2015).…”

Negative Regulation of the IL-1 System by IL-1R2 and IL-1R8: Relevance in Pathophysiology and Disease

“…In line with these results, the treatment with Anakinra of patients with cryopyrin-associated periodic syndrome (CAPS), in addition to reduce signs and symptoms of IL-1-dependent inflammation, reversed mental defects in patients ( 169 ). A further evidence of the relevance of IL-1R8 in the brain is provided by genetic studies on schizophrenia, which identified SIGIRR as one of the genes associated with genetic alterations in this psychiatric condition ( 170 ).…”

RNA biomarkers for alcohol use disorder

“…However, blood and saliva are easily obtained, routinely sampled in clinical practice through minimally invasive means, posing little harm to the patient. Importantly, blood-and saliva-derived biomarkers show promise for guiding the diagnosis, prognosis, or treatment for behavioral disorders and psychiatric illnesses (e.g., Munkholm et al, 2015;Hicks et al, 2018;Hess et al, 2020;Le-Niculescu et al, 2021;Wagh et al, 2021; see Supplementary Table 1 for more references). These examples demonstrate that molecular data in peripheral tissues can be clinically useful as biomarkers for psychiatric and behavioral disorders.…”