gender-diagnosis: F-SZA vas stress score (1-100): Independent Test cohort blood gene expression data
Extracted molecule
total RNA
Extraction protocol
Whole blood (2.5 ml) RNA extraction: 2.5-5 ml of whole blood was collected into each PaxGene tube by routine venipuncture. PaxGene tubes contain proprietary reagents for the stabilization of RNA. The cells from whole blood will be concentrated by centrifugation, the pellet washed, resuspended and incubated in buffers containing Proteinase K for protein digestion. A second centrifugation step will be done to remove residual cell debris. After the addition of ethanol for an optimal binding condition the lysate is applied to a silica-gel membrane/column. The RNA bound to the membrane as the column is centrifuged and contaminants are removed in three wash steps. The RNA is then eluted using DEPC-treated water.
Label
Biotin
Label protocol
Sample Labeling: Sample labeling is performed using the Ambion MessageAmp II-BiotinEnhanced aRNA amplification kit. The procedure is briefly outlined below and involves the following steps: 1. Reverse Transcription to Synthesize First Strand cDNA is primed with the T7 Oligo(dT) Primer to synthesize cDNA containing a T7 promoter sequence. 2. Second Strand cDNA Synthesis converts the single-stranded cDNA into a double-stranded DNA (dsDNA) template for transcription. The reaction employs DNA Polymerase and RNase H to simultaneously degrade the RNA and synthesize second strand cDNA. 3. cDNA Purification removes RNA, primers, enzymes, and salts that would inhibit in vitro transcription. 4. In Vitro Transcription to Synthesize aRNA with Biotin-NTP Mix generates multiple copies of biotin-modified aRNA from the double- stranded cDNA templates; this is the amplification step. 5. aRNA Purification removes unincorporated NTPs, salts, enzymes, and inorganic phosphate to improve the stability of the biotin-modified aRNA.
Arrays are stained using standard Affymetrix protocols for antibody signal amplification and scanned on an Affymetrix GeneArray 2500 scanner with a target intensity set at 250. Present/Absent calls are determined using GCOS software with thresholds set at default values
Description
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) Subjects were recruited from the patient population at the Indianapolis VA Medical Center. All subjects understood and signed informed consent forms detailing the research goals, procedure, caveats and safeguards, per IRB 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 (1-100) for quantitatively assessing state stress at that particular moment in time (Simplified Stress Scale- SSS), which has 4-items (Life Stress, Financial Stress, Health Stress and Social Stress). We also administered the PTSD Checklist- Civilian Version (PCL-C) scale, which measures clinical severity of trait stress symptoms over the month preceding testing. We collected whole blood (10 ml) in two RNA-stabilizing PAXgene tubes, labeled with an anonymized ID number, and stored at -80 degrees C in a locked freezer until the time of future processing
Data processing
The AP derived and DE derived lists of genes were combined, and the gene expression data corresponding to them was used for the validation analysis. The cohorts (Validation Clinically Severe Stress, alongside the Low Stress and High Stress groups in the Discovery cohort) were assembled out of Affymetrix .cel data that was RMA normalized by gender and diagnosis. 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 ANOVA for the stepwise changed probesets, and also attempted a stringent Bonferroni corrections for all the probesets tested (Figure 1F). We also wrote an R script that automatically analyzes the data directly from the Excel sheet, and used that to confirm our calculations.
