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. 2020 May;25(5):918-938.
doi: 10.1038/s41380-019-0370-z. Epub 2019 Mar 12.

Towards precision medicine for stress disorders: diagnostic biomarkers and targeted drugs

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Free PMC article

Towards precision medicine for stress disorders: diagnostic biomarkers and targeted drugs

H Le-Niculescu et al. Mol Psychiatry. 2020 May.
Free PMC article

Abstract

The biological fingerprint of environmental adversity may be key to understanding health and disease, as it encompasses the damage induced as well as the compensatory reactions of the organism. Metabolic and hormonal changes may be an informative but incomplete window into the underlying biology. We endeavored to identify objective blood gene expression biomarkers for psychological stress, a subjective sensation with biological roots. To quantify the stress perception at a particular moment in time, we used a simple visual analog scale for life stress in psychiatric patients, a high-risk group. Then, using a stepwise discovery, prioritization, validation, and testing in independent cohort design, we were successful in identifying gene expression biomarkers that were predictive of high-stress states and of future psychiatric hospitalizations related to stress, more so when personalized by gender and diagnosis. One of the top biomarkers that survived discovery, prioritization, validation, and testing was FKBP5, a well-known gene involved in stress response, which serves as a de facto reassuring positive control. We also compared our biomarker findings with telomere length (TL), another well-established biological marker of psychological stress and show that newly identified predictive biomarkers such as NUB1, APOL3, MAD1L1, or NKTR are comparable or better state or trait predictors of stress than TL or FKBP5. Over half of the top predictive biomarkers for stress also had prior evidence of involvement in suicide, and the majority of them had evidence in other psychiatric disorders, providing a molecular underpinning for the effects of stress in those disorders. Some of the biomarkers are targets of existing drugs, of potential utility in patient stratification, and pharmacogenomics approaches. Based on our studies and analyses, the biomarkers with the best overall convergent functional evidence (CFE) for involvement in stress were FKBP5, DDX6, B2M, LAIR1, RTN4, and NUB1. Moreover, the biomarker gene expression signatures yielded leads for possible new drug candidates and natural compounds upon bioinformatics drug repurposing analyses, such as calcium folinate and betulin. Our work may lead to improved diagnosis and treatment for stress disorders such as PTSD, that result in decreased quality of life and adverse outcomes, including addictions, violence, and suicide.

Conflict of interest statement

ABN is listed as inventor on a patent application being filed by Indiana University and is a co-founder of MindX Sciences. The other authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Steps 1–3: Discovery, prioritization and validation. a Cohorts used in study, depicting flow of discovery, prioritization, and validation of biomarkers from each step. b Discovery cohort longitudinal within-subject analysis. Phchp### is study ID for each subject. V# denotes visit number. c Discovery of possible subtypes of stress based on High Stress visits in the discovery cohort. Subjects were clustered using measures of mood and anxiety (from Simplified Affective State Scale (SASS)) [7], as well as psychosis (PANNS Positive). d Differential gene expression in the Discovery cohort—number of genes identified with differential expression (DE) and absent–present (AP) methods with an internal score of ≥2. Red—increased in expression in High Stress, blue—decreased in expression in High Stress. At the discovery step, probesets are identified based on their score for tracking stress with a maximum of internal points of 6 (33% (2 pt), 50% (4 pt) and 80% (6 pt)). e Prioritization with Convergent Functional Genomics (CFG) for prior evidence of involvement in stress. In the prioritization step, probesets are converted to their associated genes using Affymetrix annotation and GeneCards. Genes are prioritized and scored using CFG for stress evidence with a maximum of 12 external points. Genes scoring at least 6 points out of a maximum possible of 18 total internal and external scores points are carried to the validation step. f Validation in an independent cohort of psychiatric patients with clinically severe trait stress and high-state stress. In the validation step, biomarkers are assessed for stepwise change from the discovery groups of subjects with Low Stress, to High Stress, to Clinically Severe Stress, using analysis of variance. N = number of testing visits. Two hundred and thirty-two biomarkers were nominally significant, NUB1 and ASCC1 were the most significant increased and decreased biomarkers, respectively, and 1130 biomarkers were stepwise changed
Fig. 2
Fig. 2
Best predictive biomarkers. From among top candidate biomarkers (n = 285) from Steps 1–3 (Discovery—39, Prioritization—21, Validation—232 bolded). Bar graph shows best predictive biomarkers in each group. *Nominally significant for predictions p < 0.05. **Bonferroni significant for the 285 biomarkers tested. Table underneath the figures displays the actual number of biomarkers for each group whose area under the receiver-operating characteristic curve p values (a, b) and Cox odds ratio p values (c) are at least nominally significant. Some gender and diagnosis groups are missing from the graph as they did not have any significant biomarkers. Cross-sectional is based on levels at one visit. Longitudinal is based on levels at multiple visits (integrates levels at most recent visit, maximum levels, slope into most recent visit, and maximum slope). Dividing lines represent the cutoffs for a test performing at chance levels (white) and at the same level as the best biomarkers for all subjects in cross-sectional (gray) and longitudinal (black) based predictions. All biomarkers perform better than chance. Biomarkers performed better when personalized by gender and diagnosis

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