There are to date no clinical laboratory blood tests for mood disorders. Given the complex nature of mood disorders, the current reliance on patient self-report of symptoms and the clinician's impression on interview of patient is a rate limiting step in delivering the best possible care with existing treatment modalities, as well as in developing new and improved treatment approaches, including new medications. We propose, and provide proof of principle for, an approach to help identify state blood biomarkers for mood state. Such biomarkers can serve as a basis for objective clinical laboratory tests. Our approach is based on the integration of human and animal model data, as a way of reducing the false-positives inherent in each approach and helping identify true biomarker molecules. We propose to measure gene expression differences in fresh blood samples from subjects with DIGS- diagnosed bipolar disorders that have low mood scores vs. those that have high mood scores at the time of the blood draw. We will then integrate our human blood gene expression data with human postmortem data, human genetic linkage/association data and animal model gene expression data (using data from two different animal models studied in our group, one pharmacogenomic and one transgenic), an approach called Convergent Functional Genomics, as a Bayesian strategy for cross-validating and prioritizing findings. In pilot studies for this project, we have identified a series of high probability blood candidate biomarker genes for mood state that deserve future scrutiny. Topping our list of candidate biomarker genes for mood state we have five genes involved in myelination (Mbp, Edg2, Mag, Pmp22 and Ugt8), and six genes involved in growth factor signaling (Fgfr1, Fzd3, Erbb3, Igfbp4, Igfbp6, and Ptprm). All of these genes have prior evidence of differential expression in human postmortem brains from mood disorder subjects. A predictive score developed based on a panel of ten top candidate biomarkers (five for high mood, five for low mood) shows sensitivity and specificity for high mood and low mood states, in two independent cohorts. Our preliminary studies suggest that blood biomarkers may offer an unexpectedly informative window into brain functioning and disease state, specifically in regards to mood disorders. This exciting preliminary work needs to be carefully extended and replicated in a larger independent cohort, as well as compared to normal controls to derive additional normative data, which is what we propose to do in this project.

Public Health Relevance

Potential Impact on Veterans Health Care: A more objective assessment of mood state with biomarker profiling will lead to more targeted treatments for veterans affected by psychiatric disorders involving mood dysregulation primarily, such as in bipolar disorder or depression, and secondarily, such as in PTSD, with improved efficacy and decreased side-effects. This will have an impact on patient health, well- being, safety, quality of life, and independent functioning, as well as decrease hospitalizations and overall health-care costs. Moreover, biomarker profiling may help with assessing response to treatment, risk of relapse, and early intervention efforts to prevent the full-blown development of illness in susceptible individuals.

Agency
National Institute of Health (NIH)
Institute
Veterans Affairs (VA)
Type
Non-HHS Research Projects (I01)
Project #
5I01CX000139-03
Application #
8195976
Study Section
Mental Health and Behavioral Science A (MHBA)
Project Start
2009-10-01
Project End
2012-09-30
Budget Start
2011-10-01
Budget End
2012-09-30
Support Year
3
Fiscal Year
2012
Total Cost
Indirect Cost
Name
Rlr VA Medical Center
Department
Type
DUNS #
608434697
City
Indianapolis
State
IN
Country
United States
Zip Code
46202
Levey, D F; Niculescu, E M; Le-Niculescu, H et al. (2016) Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment. Mol Psychiatry 21:768-85
Niculescu, A B (2016) A brief proposal for improving clinical trials. Mol Psychiatry 21:736-7
Rangaraju, S; Levey, D F; Nho, K et al. (2016) Mood, stress and longevity: convergence on ANK3. Mol Psychiatry 21:1037-49
Niculescu, A B; Levey, D F; Phalen, P L et al. (2015) Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach. Mol Psychiatry 20:1266-85
Niculescu, A B; Levey, D; Le-Niculescu, H et al. (2015) Psychiatric blood biomarkers: avoiding jumping to premature negative or positive conclusions. Mol Psychiatry 20:286-8
Levey, D F; Le-Niculescu, H; Frank, J et al. (2014) Genetic risk prediction and neurobiological understanding of alcoholism. Transl Psychiatry 4:e391
Le-Niculescu, H; Levey, D F; Ayalew, M et al. (2013) Discovery and validation of blood biomarkers for suicidality. Mol Psychiatry 18:1249-64
Niculescu, A B (2013) Convergent functional genomics of stem cell-derived cells. Transl Psychiatry 3:e305
Ayalew, M; Le-Niculescu, H; Levey, D F et al. (2012) Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction. Mol Psychiatry 17:887-905
Le-Niculescu, H; Balaraman, Y; Patel, S D et al. (2011) Convergent functional genomics of anxiety disorders: translational identification of genes, biomarkers, pathways and mechanisms. Transl Psychiatry 1:e9