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This study developed potential blood-based biomarker tests for diagnosing and differentiating schizophrenia (SZ), bipolar disorder type I (BD), and normal control (NC) subjects using mRNA gene expression signatures. A total of 90 subjects (n = 30 each for the three groups of subjects) provided blood samples at two visits. The Affymetrix exon microarray was used to profile the expression of over 1.4 million probesets. We selected potential biomarker panels using the temporal stability of the probesets and also back-tested them at two different visits for each subject. The 18-gene biomarker panels, using logistic regression modeling, correctly differentiated the three groups of subjects with high accuracy across the two different clinical visits (83–88% accuracy). The results are also consistent with the actual data and the “leave-one-out” analyses, indicating that the models should be predictive when applied to independent data cohorts. Many of the SZ and BD subjects were taking antipsychotic and mood stabilizer medications at the time of blood draw, raising the possibility that these drugs could have affected some of the differential transcription signatures. Using an independent Illumina data set of gene expression data from antipsychotic medication-free SZ subjects, the 18-gene biomarker panels produced a receiver operating characteristic curve accuracy greater than 0.866 in patients that were less than 30 years of age and medication free. We confirmed select transcripts by quantitative PCR and the nCounter® System. The episodic nature of psychiatric disorders might lead to highly variable results depending on when blood is collected in relation to the severity of the disease/symptoms. We have found stable trait gene panel markers for lifelong psychiatric disorders that may have diagnostic utility in younger undiagnosed subjects where there is a critical unmet need. The study requires replication in subjects for ultimate proof of the utility of the differential diagnosis.

Alcoholism has a profound impact on millions of people throughout the world. However, the ability to determine if a patient needs treatment is hindered by reliance on self-reporting and the clinician’s capability to monitor the patient’s response to treatment is challenged by the lack of reliable biomarkers. Using a genome-wide approach, we have previously shown that chronic alcohol use is associated with methylation changes in DNA from human cell lines. In this pilot study, we now examine DNA methylation in peripheral mononuclear cell DNA gathered from subjects as they enter and leave short-term alcohol treatment. When compared with abstinent controls, subjects with heavy alcohol use show widespread changes in DNA methylation that have a tendency to reverse with abstinence. Pathway analysis demonstrates that these changes map to gene networks involved in apoptosis. There is no significant overlap of the alcohol signature with the methylation signature previously derived for smoking. We conclude that DNA methylation may have future clinical utility in assessing acute alcohol use status and monitoring treatment response.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Background: Transcript abundance (TA) measurement in whole blood frequently is conducted to identify potential biomarkers for disease risk and to predict or monitor drug response. Potential biomarkers discovered in this way must be validated by quantitative technology. In this study we assessed the use of standardized reverse transcription PCR (StaRT-PCR™) to validate potential biomarkers discovered through whole blood TA profiling. Methods: For each of 15 healthy volunteers, 6 blood samples were obtained, including 3 samples at each of 2 separate visits. Total variation in TA for each gene was partitioned into replicate, sample, visit, study participant, and residual components. Results: Variation originating from technical processing was Conclusion: For control or diseased population groups with variation rates as low as those observed in this control group, 17 individuals per group would be required to detect 1 SD change with 80% power with a 2-sided α = 0.05 statistical test for mean differences.