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

2021 ◽  
Vol 12 ◽  
Author(s):  
Vipul Vilas Wagh ◽  
Parin Vyas ◽  
Suchita Agrawal ◽  
Tejaswini A. Pachpor ◽  
Vasudeo Paralikar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Peripheral Blood ◽  
Functional Enrichment ◽  
Antipsychotic Treatment ◽  
Drug Status ◽  
Expression Studies ◽  
Biomarker Analysis ◽  
Follow Up Studies ◽  
Gene Expression Studies

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.

A Meta-Analysis of Gene Expression Studies in Severe Sepsis and Septic Shock

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2741-2741 ◽  
Author(s):  
Maiara M L Fiusa ◽  
Benilton S Carvalho ◽  
Rodolfo M E Hubert ◽  
Welliton Souza ◽  
Iscia Lopes-Cendes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Severe Sepsis ◽  
Peripheral Blood ◽  
Meta Analysis ◽  
Inclusion Criteria ◽  
Comprehensive List ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Sepsis And Septic Shock

Abstract Introduction: Sepsis represents a complex inflammatory response to infection. Gene expression studies based on microarrays have shown that this response can affect more than 80% of cellular functions and pathways, in what has been termed a “genomic storm”. For several years, sepsis was regarded as a pro-inflammatory condition, and this concept resulted in several experimental treatment strategies aimed to block inflammation. However, systematic failure of these therapies and recent evidence demonstrating that anti-inflammatory pathways are also activated during sepsis illustrate the complexity and our incomplete knowledge about the pathogenesis of this condition. In the last decade, microarray-based gene expression studies have been used in attempts to improve our understanding about sepsis. Raw data from most of these studies are now collected in public archives, thus offering a unique opportunity to combine the information from different studies by meta-analysis. It has been shown that by analyzing data from multiple experiments, biases and artifacts between datasets can be cancelled out, potentially allowing true relationships to stand out. Accordingly, an increasing number of bioinformatics protocols and guidelines about meta-analysis of gene expression studies have been published in the last years. In the context of sepsis, several high-quality microarray-based gene expression studies are available. However, no systematic meta-analysis of these studies has been performed. In order to identify genes and pathways robustly associated with the pathogenesis of sepsis, we performed a meta-analysis of gene expression studies in human severe sepsis and septic shock. Material and methods: Microarray data were identified by searching two public databases (Gene Expression Omnibus and Array-Express) using the following search criteria: (“sepsis or “septic shock”) AND (“peripheral blood” or “leukocytes”) AND (“homo sapiens”). Inclusion criteria were: studies in humans with severe sepsis or septic shock; RNA obtained from peripheral blood leukocytes; availability of raw data; and matched healthy controls from the same study. To improve consistency, only studies using similar platforms were compared. We used the R/BioConductor environment to preprocess the datasets using the Robust Multi-array Average algorithm (RMA) implemented in the ‘oligo’ package and to perform meta-analysis through the ‘RankProd’ package implementation. This is a non-parametric statistical method that utilizes ranks of the log-ratio statistics for all genes across different studies to generate a list of differentially expressed (DE) genes between two conditions, and considered superior to alternative methodologies. For this study, we selected genes with fold-change of expression above 2 and false discovery rate below 0.01, calculated based on 10,000 permutations. Gene set analysis was initially performed using WebGestalt and confirmed in similar tools (KEGG, Pathway Commons, WikiPathways). Only pathways identified by more than one tool were considered. Results: Forty-five studies were identified, of which five fulfilled inclusion criteria. Our meta-analysis included data from 259 patients and 132 controls. Out of 22,216 probesets, we observed 352 as candidates for DE, 215 of which were up-regulated and 137 down-regulated. Top 5 up-regulated genes were CD177, MMP8, HP, ARG1 and ANXA3. Top 5 down-regulated genes were FCER1A, YME1L1, TRDV3, LRRN3 and MYBL1. The gene ontology term associated with the set of DE genes in both analysis with higher statistical significance was "immune response” (adjP=2.85e-27), and the most significant pathways identified were “Hematopoietic cell lineage” (adjP=8.69e-13), “TCR signaling pathway” (adjP=3.04e-10) and “immune system” (adjP=1.08e-19). Discussion and conclusion: The combined analysis of data generated by high-throughput experiments is an attractive and validated strategy to improve the sensitivity and specificity of genome-wide expression data. This meta-analysis provides a comprehensive list of genes, pathways and expression signatures associated with severe sepsis and septic shock, confirming several results from individual studies. In addition, our meta-analysis potentially provides new biological insights about sepsis, by listing a comprehensive list of new candidate genes with robust associations with this condition. Disclosures No relevant conflicts of interest to declare.

Identification of endogenous control genes for gene expression studies in peripheral blood of patients with coronary artery disease

2013 ◽  
Vol 47 (2) ◽  
pp. 192-196 ◽  
Author(s):  
Siew Wai Fong ◽  
Suhairi Ibrahim ◽  
Mohd Sapawi Mohamed ◽  
Ling Ling Few ◽  
Wei Cun See Too ◽  
...  
Keyword(s):  
Gene Expression ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Peripheral Blood ◽  
Endogenous Control ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Artery Disease

scholarly journals Assessment of common housekeeping genes as reference for gene expression studies using RT-qPCR in mouse choroid plexus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kim Hoa Ho ◽  
Annarita Patrizi
Keyword(s):  
Gene Expression ◽  
Choroid Plexus ◽  
Sensory Deprivation ◽  
Housekeeping Genes ◽  
Housekeeping Gene ◽  
Experimental Conditions ◽  
Brain Repair ◽  
Expression Studies ◽  
Testing Algorithms ◽  
Gene Expression Studies

AbstractChoroid plexus (ChP), a vascularized secretory epithelium located in all brain ventricles, plays critical roles in development, homeostasis and brain repair. Reverse transcription quantitative real-time PCR (RT-qPCR) is a popular and useful technique for measuring gene expression changes and also widely used in ChP studies. However, the reliability of RT-qPCR data is strongly dependent on the choice of reference genes, which are supposed to be stable across all samples. In this study, we validated the expression of 12 well established housekeeping genes in ChP in 2 independent experimental paradigms by using popular stability testing algorithms: BestKeeper, DeltaCq, geNorm and NormFinder. Rer1 and Rpl13a were identified as the most stable genes throughout mouse ChP development, while Hprt1 and Rpl27 were the most stable genes across conditions in a mouse sensory deprivation experiment. In addition, Rpl13a, Rpl27 and Tbp were mutually among the top five most stable genes in both experiments. Normalisation of Ttr and Otx2 expression levels using different housekeeping gene combinations demonstrated the profound effect of reference gene choice on target gene expression. Our study emphasized the importance of validating and selecting stable housekeeping genes under specific experimental conditions.

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