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.
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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. 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. Stress disorders, such as post-traumatic stress disorder PTSD , are prevalent, disabling, and underdiagnosed in both the military and civilian realm [ 1 , 2 , 3 ].
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Stress disorders consist of mental and physical over-reaction to environmental cues that are perceived as potentially harmful, engendered by past exposure to traumatic events. The persistence, intensity, dis-congruence from the environment, or congruence with excessive response are all hallmarks of clinical illness.
Owing to stigma and lack of objective tests, they are often underdiagnosed, sub-optimally treated, and can lead to self-medication with alcohol and drugs. They may culminate in some cases with violence and suicide. Psychiatric patients may have an increased vulnerability to stress, regardless of their primary diagnosis, as well as increased reasons for stress disorders, due to their often adverse life trajectory.
As such, they may be a particularly suitable population in which to try to identify blood biomarkers for stress that are generalizable and trans-diagnostic. First, we used a powerful longitudinal within-subject design in individuals with psychiatric disorders to discover blood gene expression changes between self-reported low- and high-stress states.
Second, we prioritized the list of candidate biomarkers with a Convergent Functional Genomics CFG approach, comprehensively integrating previous published human and animal model evidence in the field and directly citing it. Third, we validated our top biomarkers from discovery and prioritization in an independent cohort of psychiatric subjects with high scores on a clinical stress rating scale.
Fourth, we tested whether the candidate biomarkers from the first three steps are able to predict high-stress state, and future psychiatric hospitalizations with stress, in another independent cohort of psychiatric subjects. We tested the biomarkers in all subjects in the independent test cohort, as well as in a more personalized fashion by gender and psychiatric diagnosis, showing increased accuracy with the personalized approach.
Fifth, we assessed whether our biomarkers have evidence for involvement in other psychiatric and related disorders, as well as analyzed the biological pathways and networks they are involved in. Sixth, we identified which of our biomarkers are targets of existing drugs and thus can be used for pharmacogenomic population stratification and measuring of response to treatment.
Given the negative impact of untreated stress on quality and quantity of life, the current lack of objective measures to determine appropriateness of treatment, and the mixed results with existing medications, the importance of approaches such as ours cannot be overstated. We used three independent cohorts: discovery major psychiatric disorders with changes in state stress , validation major psychiatric disorders with clinically severe trait and state stress , and testing an independent major psychiatric disorders cohort for predicting state stress and for predicting trait future hospitalization visits with stress as the primary reason Fig.
Steps 1—3: Discovery, prioritization and validation. Phchp is study ID for each subject. V denotes visit number. Red—increased in expression in High Stress, blue—decreased in expression in High 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.
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. Two hundred and thirty-two biomarkers were nominally significant, NUB1 and ASCC1 were the most significant increased and decreased biomarkers, respectively, and biomarkers were stepwise changed. Similar to our previous studies [ 4 , 5 , 6 ], the psychiatric subjects are part of a larger longitudinal cohort of adults that we are continuously collecting.
All subjects understood and signed informed consent forms detailing the research goals, procedure, caveats, and safeguards, per Institutional Review Board-approved protocol. Subjects completed diagnostic assessments by an extensive structured clinical interview—Diagnostic Interview for Genetic Studies—and up to six testing visits, 3—6 months apart or whenever a new psychiatric hospitalization occurred. At each testing visit, they received a series of rating scales, including a self-report visual analog scale 1— for quantitatively assessing state stress at that particular moment in time Simplified Stress Scale , which has 4 items Life Stress, Financial Stress, Health Stress, and Social Stress.
We also required that at least one of the other items Health Stress, Financial Stress, or Social Stress must have concording low or high score with the Life Stress Fig.
There were 5 subjects with 4 visits each, 9 subjects with 3 visits each, and 22 subjects with 2 visits each resulting in a total of 91 blood samples for subsequent gene expression microarray studies Fig. Our independent test cohort for predicting state high stress consisted of 95 male and 27 female subjects with psychiatric disorders, demographically matched with the discovery cohort, with one or multiple testing visits in our laboratory, with either Low Stress, Intermediate Stress, or High Stress Fig.
Our test cohort for predicting trait future hospitalization visits with stress symptoms, in the first year of follow-up, and all future hospitalization visits with stress symptoms Fig. Their medications were listed in their electronic medical records and documented by us at the time of each testing visit. Medications can have a strong influence on gene expression.
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However, our discovery of differentially expressed genes was based on within-subject analyses, which factor out not only genetic background effects but also minimizes medication effects, as the subjects rarely had major medication changes between visits. Moreover, there was no consistent pattern of any particular type of medication, as our subjects were on a wide variety of different medications, psychiatric and non-psychiatric.
Furthermore, the independent validation and testing cohort gene expression data was Z -scored by gender and diagnosis before being combined, to normalize for any such effects. Some subjects may be non-compliant with their treatment and may thus have changes in medications or drug of abuse not reflected in their medical records. That being said, our goal is to find biomarkers that track stress, regardless if the reason for it is endogenous biology or driven by substance abuse or medication non-compliance.
In fact, one would expect some of these biomarkers to be direct or indirect targets of medications, as we show in this paper. Overall, the discovery, validation, and replication by testing in independent cohorts of the biomarkers, with our design, occurs despite the subjects having different genders, diagnoses, being on various different medications, and other lifestyle variables.
Whole blood 2. PaxGene tubes contain proprietary reagents for the stabilization of RNA. RNA was extracted and processed as previously described, including standard globin clear and RNA quality assessment steps [ 4 , 5 , 6 ]. Microarray work was carried out using previously described methodology [ 4 , 5 , 6 , 7 ].
TL was determined using a relative quantitative real-time PCR method [ 8 ]. Two assays were carried out, one for the Human albumin gene ALB , which is a single copy gene, and the other assay with primers specific to the repetitive telomeric TEL sequence.
We analyzed gene expression differences between visits with Low Stress defined as a score of 0—33 and visits with High Stress defined as a score of 67— Fig. We analyzed the data in two ways: an absent—present AP approach, and a differential expression DE approach, as in previous work by us on suicide biomarkers [ 4 , 5 , 6 ]. The AP approach may capture turning on and off of genes, and the DE approach may capture gradual changes in expression.
We used a powerful within-subject design, then an across-subjects summation score for probesets. Analyses were performed as previously described [ 5 , 6 , 7 ]. In brief, we imported all Affymetrix microarray data as CEL. Using only the perfect match values, we ran a robust multi-array analysis RMA by gender and diagnosis, background corrected with quantile normalization and a median polish probeset summarization of all chips, to obtain the normalized expression levels of all probesets for each chip. Then, to establish a list of differentially expressed probesets we conducted a within-subject analysis, using a fold change in expression of at least 1.
Probesets that have a 1. These values were then summed for each probeset across all the comparisons and subjects, yielding a range of raw scores. The probesets above the We have developed in our laboratories R scripts to automate and conduct all these large dataset analyses in bulk, checked against human manual scoring [ 7 ]. Genes were then scored using our manually curated CFG databases as described below Fig. We have established in our laboratory Laboratory of Neurophenomics, www.
Only findings deemed significant in the primary publication, by the study authors, using their particular experimental design and thresholds, are included in our databases. Our databases include only primary literature data and do not include review papers or other secondary data integration analyses to avoid redundancy and circularity.
These large and constantly updated databases have been used in our CFG cross-validation and prioritization platform Fig. Analyses were performed as previously described [ 5 , 6 ].https://niotbusencol.ml
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The AP- and DE-derived lists of genes were combined, and the gene expression data corresponding to them was used for the validation analysis. We transferred the log-transformed expression data to an Excel sheet, and non-log transformed the data by taking 2 to the power of the transformed expression value. We then Z -scored the values by gender and diagnosis. We then imported the Excel sheets with the Z -scored by gender and diagnosis expression data into Partek, and statistical analyses were performed using a one-way analysis of variance ANOVA for the stepwise changed probesets and also attempted a stringent Bonferroni corrections for all the probesets tested Fig.
We also wrote an R script that automatically analyzes the data directly from the Excel sheet and used that to confirm our calculations. We carried forward into testing the top biomarkers from each of the Steps 1—3. In Step 4, testing, the test cohort for predicting High Stress state and the test cohort for predicting Future Hospitalizations with Stress trait , were assembled out of data that was RMA normalized by gender and diagnosis.
The cohort was completely independent from the discovery and validation cohorts; there was no subject overlap with them. Phenomic clinical and gene expression markers used for predictions were Z -scored by gender and diagnosis, to be able to combine different markers into panels and to avoid potential artifacts due to different ranges of expression in different gender and diagnoses. Markers were combined by simple summation of the increased risk markers minus the decreased risk markers. Predictions were performed using R-studio. For cross-sectional analyses, we used marker expression levels, Z -scored by gender and diagnosis.
For longitudinal analyses, we combined four measures: marker expression levels, slope defined as ratio of levels at current testing visit vs.