Currently, gene expression studies have also moved to more accessible tissues such as peripheral cells and blood due to the relative ease of acquisition and the potential utility of such samples including the possibility to collect larger sample sizes with a minimally invasive procedure. The peripheral blood transcriptome shares >80% homology with genes expressed in the brain [28
], heart, liver, spleen, colon, kidney, prostate and stomach [41
]. The intensity of peripheral blood transcriptome gene expression for a number of biological processes is comparable to that of the prefrontal cortex [42
]. Recently, Rollins et al.
] demonstrated considerable overlap between gene expression in brain and peripheral blood, from the same individual, using two independent populations and different high-throughput array platforms.
The use of blood cells to perform microarray studies has other advantages. The use of blood, from living subjects, to study gene expression avoids the influence of confounding variables associated with post-mortem brain studies, such as the post-mortem interval, low pH and other factors that decrease the integrity of mRNA and which must be accounted for in subsequent analyses [43
]. A recent gene expression study among psychiatric patients demonstrated the possibility of discriminating between schizophrenia and bipolar disorder using a blood-based protocol [16
]. This same group has also confirmed findings implicating the selenium-binding protein 1 gene in schizophrenia using both brain and blood samples [8
Peripheral studies in SZ have been increasing in number as recently reviewed [30
]. Highlights of blood-derived biomarker studies are briefly presented. Zhang and colleagues [44
] explored the plausibility of neuregulin-1 (NRG-1) gene expression as a state and trait dependent biomarker. They showed that patients with SZ had lower levels of NRG-1 in lymphocytes in comparison to both sibling and non-sibling controls. Interestingly, a longitudinal investigation of NRG-1 levels following antipsychotic treatment induction demonstrates gradual increases in gene expression, which could be used as a treatment biomarker following validation in a larger sample (SZ, treated N
= 31). Kuzman et al.
] performed a similar study, looking at array-based gene expression in whole blood of first-episode SZ compared to controls. They identified 180 probe sets having significantly altered gene expression in SZ and validated four genes.
Yao and colleagues [45
] performed a study testing the applicability of utilizing peripheral gene expression and verifying if differences found in previous peripheral gene expression studies can translate to new samples. The authors’ were unable to replicate findings for all the genes tested. However, this should not discount the plausible utility of these genes as biomarkers since the sample size tested in was small. Bousman et al.
] performed a study in a sample of 19 SZ and bipolar disorder (BD) patients with psychosis, to assess correlations between previously found SZ-associated ubiquitin gene peripheral expression with positive and negative symptoms. This study presents encouraging evidence for the utility of peripheral gene expression to assess specific aspects and more robust phenotypes within each disorder.
Takahashi and collaborators [47
] took a slightly different approach although still starting from array-based peripheral gene expression data. Their bioinformatics-based approach identified a 14 probe set biomarker with high diagnostic accuracy using artificial neural networks analysis. However, when employing an unsupervised hierarchical clustering–with the 14 probe sets diagnostic separation was not attained.
Recently, there have been investigations into the use of peripheral blood microRNA expression as possible biomarkers for SZ. Lai and colleagues [48
] used a TaqMan array composed of 365 human microRNAs, in a learning set of 30 cases and controls. Their significant findings, when comparing cases and controls, were subsequently tested in an independent case–control sample of 60 SZ and 30 control subjects. The authors were able to identify a disease signature, producing adequate sensitivity and specificity, with seven microRNAs. Beveridge et al.
], previously found evidence for microRNA mediated regulation of expression in post-mortem SZ brain. As an independent follow-up in peripheral blood, Gardiner and colleagues [50
] investigated microRNA expression in 112 SZ and 76 controls, and were able to identify 33 microRNAs passing false discovery rate correction. These studies demonstrate the utility of microRNAs in discriminating disease state.
These reports however show little overlap between the results of each study, most likely due to disease heterogeneity, medications, different preparations of blood cells, array platform differences small samples and other confounders [8
]. Nevertheless the studies are encouraging in that they provide positive evidence that some peripheral markers are comparable to those from post-mortem studies, thus lending support to the use of peripheral blood samples as an advantageous alternative in the quest for the biological markers of brain-based disorders. However, there is room for additional improved methodologies, utilizing various levels of genomic information to identify biomarkers, one of which is investigating the genetics of gene expression—specifically eQTLs and associated SNPs.
Suggestions that we offer for selection of a predictive biomarker: (i) The gene should have differential expression in SZ in LCL (or blood or peripheral blood mononuclear cell (PBMC)) in at least one or more prior studies, (ii) the gene ideally should be abundant in cell lines and whole blood, (iii) the gene should show differential expression within the SZ sample in brain [34
] and (iv) the gene should not represent treatment effects (unless this is an aim of the study being undertaken), this criterion can be achieved through the collection of peripheral blood from first-episode SZ prior to the induction of treatment. As a working example of these selection criteria, the multidimensional scatter plot, in , shows that the resulting gene list can be refined to using only four probe sets from the above criteria for complete classification between SZ and controls. These probe sets located in four different genes reliably predict subjects with SZ from controls in LCLs in our particular data sets, and predictions are based upon independent criteria applied to independent data sets.
Figure 1: There were four exons from different genes (BAT2L, DEK, GSR and DBC1) that significantly discriminated first-episode individuals with SZ (blue) and controls (red). The individual exons with bootstrap P-values <10−8 were plotted in multidimensional (more ...)