Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder.

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.

Reversion of AHRR Demethylation Is a Quantitative Biomarker of Smoking Cessation.

Smoking is the largest preventable cause of morbidity and mortality in the world. Although there are effective pharmacologic and behavioral treatments for smoking cessation, our inability to objectively quantify smokers' progress in decreasing smoking has been a barrier to both clinical and research efforts. In prior work, we and others have shown that DNA methylation at cg05575921, a CpG residue in the aryl hydrocarbon receptor repressor (AHRR), can be used to determine smoking status and infer cigarette consumption history. In this study, we serially assessed self-report and existing objective markers of cigarette consumption in 35 subjects undergoing smoking cessation therapy, then quantified DNA methylation at cg05575921 at study entry and three subsequent time points. Five subjects who reported serum cotinine and exhaled carbon monoxide verified smoking abstinence for the 3 months prior to study exit averaged a 5.9% increase in DNA methylation at cg05575921 (p < 0.004) over the 6-month study. Although the other 30 subjects did not achieve smoking cessation at the 6-month time point, their self-reported reduction of cigarette consumption (mean = 6 cigarettes/day) was associated with a 2.8% increase DNA methylation at cg05575921 (p < 0.05). Finally, a survey of subjects as they exited the study demonstrated strong support for the clinical use of epigenetic biomarkers. We conclude that AHRR methylation status is a quantifiable biomarker for progress in smoking cessation that could have substantial impact on both smoking cessation treatment and research.

A quantitative epigenetic approach for the assessment of cigarette consumption.

Smoking is the largest preventable cause of morbidity and mortality in the world. Despite the development of numerous preventive and treatment interventions, the rate of daily smoking in the United States is still approximately 22%. Effective psychosocial interventions and pharmacologic agents exist for the prevention and treatment of smoking. Unfortunately, both approaches are hindered by our inability to accurately quantify amount of cigarette consumption from the point of initial experimentation to the point of total dependency. Recently, we and others have demonstrated that smoking is associated with genome-wide changes in DNA methylation. However, whether this advance in basic science can be employed as a reliable assay that is useful for clinical diagnosis and treatment has not been shown. In this communication, we determine the sensitivity and specificity of five of the most consistently replicated CpG loci with respect to smoking status using data from a publically available dataset. We show that methylation status at a CpG locus in the aryl hydrocarbon receptor repressor, cg05575921, is both sensitive and specific for smoking status in adults with a receiver operated curve characteristic area under the curve of 0.99. Given recent demonstrations that methylation at this locus reflects both intensity of smoking and the degree of smoking cessation, we conclude that a methylation-based diagnostic at this locus could have a prominent role in understanding the impact of new products, such as e-cigarettes on initiation of cigarette smoking among adolescents, while improving the prevention and treatment of smoking, and smoking related disorders.

A pilot examination of the genome-wide DNA methylation signatures of subjects entering and exiting short-term alcohol dependence treatment programs.

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.

Quality-controlled measurement methods for quantification of variations in transcript abundance in whole blood samples from healthy volunteers.

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 <5% of total combined variation and was primarily preanalytical. Interindividual biological sample variation was larger than technical variation. For 12 of 19 tests, the distribution of measured values was gaussian (Shapiro-Wilks test). 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 alpha = 0.05 statistical test for mean differences.