Differential Transcriptional Response Following Glucocorticoid Activation in Cultured Blood Immune Cells: a Novel Approach to PTSD Biomarker Development Michael S

Breen et al. Translational Psychiatry (2019) 9:201 https://doi.org/10.1038/s41398-019-0539-x Translational Psychiatry

ARTICLE Open Access Differential transcriptional response following glucocorticoid activation in cultured blood immune cells: a novel approach to PTSD biomarker development Michael S. Breen 1,2,3, Linda M. Bierer1,4,5, Nikolaos P. Daskalakis 6, Heather N. Bader4,5, Iouri Makotkine1,4,5, Mitali Chattopadhyay1,4,5,ChangxinXu1,4,5, Ariela Buxbaum Grice1, Anna S. Tocheva 7, Janine D. Flory1,4,5, Joseph D. Buxbaum1,2,3,8,MichaelJ.Meaney9,10,KristenBrennand 2,8,11,12 and Rachel Yehuda1,4,5

Abstract Post-traumatic stress disorder (PTSD) is a condition of stress reactivity, whose clinical manifestations are evident when patients are triggered following exposure to a traumatic event. While baseline differences in gene expression of glucocorticoid signaling and inﬂammatory cytokines in peripheral blood mononuclear cells (PBMCs) have been associated with PTSD, these alterations do not fully recapitulate the molecular response to physiological triggers, such as stress hormones. Therefore, it is critical to develop new techniques that will capture the dynamic transcriptional response associated with stress-activated conditions relative to baseline conditions. To achieve this goal, cultured PBMCs from combat-exposed veterans with PTSD(+)(n = 10) and without PTSD(−)(n = 10) were incubated with increasing concentrations (vehicle, 2.5 nM, 5 nM, 50 nM) of dexamethasone (DEX). Across diagnosis and dosage,

1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; several genes and gene networks were reliable markers of glucocorticoid stimulation (FDR < 5%), including enhanced expression of FKPB5, VIPR1, NR1I3, and apoptosis-related pathways, and reduced expression of NR3C1, STAT1, IRF1, and related inflammatory and cellular stress-responsive pathways. Dose-dependent differential transcriptional changes in several genes were also identified between PTSD+ and PTSD−. Robust changes in expression were observed at 2.5 nM DEX in PTSD− but not PTSD+ participants; whereas, with increasing concentrations (5 nM and 50 nM), several genes were identified to be uniquely up-regulated in PTSD+ but not PTSD− participants. Collectively, these preliminary findings suggest that genome-wide gene expression profiling of DEX-stimulated PBMCs is a promising method for the exploration of the dynamic differential molecular responses to stress hormones in PTSD, and may identify novel markers of altered glucocorticoid signaling and responsivity in PTSD.

Introduction pituitary-adrenal (HPA) axis dysregulation and func- Biological studies of post-traumatic stress disorder tional alterations of the glucocorticoid receptor (GR) as (PTSD) have consistently pointed to hypothalamic- major contributors to the development and progression of – the disorder1 4. Changes in GR sensitivity and alterations in peripheral blood gene expression profiles, including Correspondence: Michael S. Breen ([email protected])or Rachel Yehuda ([email protected]) genes implicated in glucocorticoid signaling and inflam- 1Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, matory cytokine production – whether derived from a – NY 10029, USA candidate5 or genome-wide exploratory approaches6 8— 2Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA reflect some of the most robust laboratory findings for Full list of author information is available at the end of the article.

© The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a linktotheCreativeCommons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Breen et al. Translational Psychiatry (2019) 9:201 Page 2 of 13

PTSD. However, it has been challenging to define the induce profound changes in gene expression, in vitro exact nature of these alterations and their functional DEX-stimulation was expected to produce greater differ- implications because the HPA axis is a self-regulating ences in gene expression patterns in PTSD than pre- system and glucocorticoid actions are binding site spe- viously documented under basal conditions. cific9,10. Moreover, since PTSD is a condition of stress The primary goal of the current exploratory investiga- reactivity, whose clinical effects are most evident when tion was to compare DEX-stimulated changes in PBMC trauma survivors are triggered by the environment, there gene expression between combat-exposed veterans with are likely distinct HPA axis alterations and gene expres- and without PTSD. To this end, we developed a novel sion responses associated with stress-activated conditions in vitro DEX dose-dependent challenge test and genome- versus basal conditions. To date, knowledge of the wide RNA-sequencing data was generated from molecular basis of PTSD has been limited by a lack of participant-derived PBMCs that were incubated with information regarding the dynamic response of gene 0 nM (vehicle), 2.5 nM, 5 nM, and 50 nM of DEX. A expression to stress hormones in live, stimulated multi-step analytic approach was used that specifically blood cells. sought to identify: (1) DEX-stimulated genes and co- Early studies used dexamethasone (DEX) challenge tests regulatory networks that change with increasing DEX in cultured peripheral blood mononuclear cells (PBMCs) concentrations; and (2) individual genes and networks from combat-exposed veterans with and without PTSD that differ in response to DEX between PTSD cases and and observed that DEX-induced inhibition of lysozyme trauma-exposed controls, providing a basis for putative activity was greater in veterans with PTSD11. A significant diagnostic biomarkers for the disorder. correlation was observed between the lysozyme IC50-DEX and the cortisol response to in vivo DEX administration in Materials and methods PTSD. Lipopolysaccharide-induced cytokine production Participants and measures from leukocytes, a measure of monocyte responsiveness, Participants were combat-exposed veterans with and has also been reported to be more sensitive to DEX in without PTSD (n = 10, respectively) who provided writ- combat veterans with PTSD compared to controls4.In ten, informed consent and for whom sufficient RNA for studies of major depression, in vivo assessments of GR genome-wide expression analyses was extracted. Elig- function have been conducted by analyzing gene expres- ibility for participation was determined on the basis of a sion profiles following DEX administration and report psychological evaluation using the Structured Clinical robust and reproducible changes in peripheral blood gene Interview for DSM-5 (SCID) and the Clinician Adminis- expression as well as decreased glucocorticoid sensitivity tered PTSD Scale (CAPS) for determination of PTSD – in depression12 14. Interestingly, DEX-induced gene diagnosis and severity24,25. Diagnostic and clinical exclu- expression revealed significantly increased FKBP5 mRNA sions included the presence of moderate or severe sub- expression, which was dependent on FKBP5 risk variants stance use disorder within the past 6 months, lifetime in depressed patients15,16. history of primary psychotic or Bipolar I disorders, neu- There are, however, several intrinsic difficulties that rological disorder or major systemic illness, and treatment accompany in vivo glucocorticoid challenges, specifically with systemic steroids; for PTSD (−) only, current or pharmacokinetic and individual variability in drug recurrent major depressive disorder were exclusionary. – absorption, distribution, and metabolic profiles17 19. Participants with self-reported history of moderate or Moreover, glucocorticoid actions are highly dose- severe traumatic brain injury (TBI) were excluded as this – dependent20 22 and examination of genome-wide tran- group is most likely to suffer long term cognitive pro- scriptional responses across a broad range of concentra- blems related to the TBI. However, mild TBI (mTBI) is a tions of DEX would provide significantly greater very common historical exposure in veterans who served information about the responsiveness of genes and gene in Iraq and/or Afghanistan, referred to as the “signature networks to glucocorticoid stimulation in PTSD. It is for injury” in this group of warfighters, and were included in these reasons that in vitro GR challenge tests provide a the current study in order to lead to a study population complimentary and powerful framework to overcome that is well representative of OEF/OIF veterans. Notably, complications of in vivo administrations. Nevertheless, the majority of veterans exposed to a mTBI (loss of there are no in vitro studies of glucocorticoid-induced consciousness < 30 min) often do not report any obser- gene expression in PBMCs, a critical gap that was recently vable cognitive problems beyond a few days/weeks fol- highlighted in the literature23, and represents an impor- lowing the injury. Participants stabilized on psychotropic tant next step to measure HPA axis activity and GR medications were included. Participants also completed function in PTSD patients. several self-report questionnaires including the Childhood Since glucocorticoid dysfunction is a well-replicated Trauma Questionnaire (CTQ)26, which is comprised of 25 signature of combat-related PTSD, and glucocorticoids questions that ask individuals to record their impressions Breen et al. Translational Psychiatry (2019) 9:201 Page 3 of 13

of childhood physical abuse, physical neglect, sexual 2.5, 5, and 50 nM. Cultures were incubated at 37 °C, abuse, emotional abuse and emotional neglect, using a 5- 5%(vol/vol) CO2 for 72 hours. Following, the plates were point Likert-type scale. Higher scores reflect higher levels centrifuged at 900×g for 15 min at 4 °C and 160 µl of the of exposure to childhood adversity. supernatant was collected and pooled from each DEX concentration well. For RNA isolation, the cell pellet on Peripheral blood mononuclear cell isolation the bottom of each well was re-suspended in 100 µl of Sixty ml of fasting blood was collected at 8 am by rou- TRIzol reagent. Cell lysates for each DEX dose were tine venipuncture in ethylenediaminetetraacetic acid pooled, aliquoted and stored at −80 °C until RNA (EDTA)-containing vacutainer tubes (VWR, West Che- isolation. ster, Pennsylvania). Platelet-rich plasma was separated by low-speed centrifugation at 120×g for 15 min at 22 °C. RNA isolation, library preparation, and quantification of After collecting plasma, the remaining cells were diluted gene expression with an equal volume of Hank’s Balanced Salt Solution RNA was extracted from TRIzol-lysed PBMCs using the (HBSS; Gibco, Grand Island, New York) and PBMCs were miRNAeasy Mini Kit (Qiagen). RNA quantity was mea- isolated by density gradient centrifugation using Ficoll- sured on the Nano Drop 2000 Spectrophotometer − Paque (GE Healthcare). The PBMC layer was collected, (Thermo Scientific) (56.6 ± 16.7 ng μl 1) and the quality washed twice in HBSS and mononuclear cells were and integrity measured with the Agilent 2100 Bioanalyzer counted manually using a Cellometer Disposable Count- (Agilent, Santa Clara, CA, USA). All RNA integrity ing Chambers (Nexcelom Bioscience LLC. Lawrence, numbers (RINs) were greater than 6 (RIN: 7.66 ± 0.79). MA). The cells were re-suspended in complete RPMI, The Illumina TruSeq Stranded Total RNA kit (Ilumina, containing RPMI-1640 (Gibco), 10% fetal bovine serum, San Diego, CA, USA) was used for library preparation 50 U/ml penicillin–streptomycin mixture (Gibco) at a accordingly to manufacturer instructions without any density of 1.75–2.00 × 106 cells/ml of the medium. modifications. A total of 80 indexed RNA libraries were pooled and sequenced using long read paired-end Dexamethasone treatment chemistry (2 × 150 bp) at a read depth of 30 M reads per Preliminary studies were conducted to identify optimal sample using the Illumina HiSeq2500. Adapter sequences culture and glucocorticoid stimulation conditions for were clipped and low quality reads were discarded using PBMCs. The current approach was based on our pre- Trimmomatic27 using parameters: ILLUMINACLIP, viously published functional assay, which measures the MINLEN:140, CROP:140. All high-quality trimmed reads responsiveness to glucocorticoids11. In brief, the assay were then mapped to UCSC Homo sapiens reference incubates PBMCs under 8 distinct doses (0.0, 0.5, 1.0, 2.5, genome (build hg37) using default STAR v2.4.0 para- 5, 10, 50, 100 nM) of DEX (Sigma-Aldrich) stock solution, meters28 (percentage of mapped reads: 92.1% ± 1.6%). which has high affinity to the glucocorticoid receptor. Samtools was used to convert bamfiles to samfiles and After a 3-day culture, lysozyme activity can be measured featureCounts29 was used to quantify gene expression in the supernatant by turbidimetry using micrococcus levels for each individual sample using default paired-end lysodeikticus as a substrate. Since lysozyme synthesis is parameters. inhibited by DEX, reduction of lysozyme activity in our system reflects functional responsiveness to glucocorti- Statistical analyses coids (Fig. S1). Total RNA from the cell pellet of the Genome-wide RNA-sequencing data underwent exten- culture from 14 subjects was used mRNA expression sive data pre-processing and quality-control, and nor- analysis using quantitative polymerase chain reaction malized using the VOOM transformation method in the R (qPCR) experiments on several immune/glucocorticoid package limma30. First, linear mixed effect models related genes (Fig. S1, B-J). Based on these results, doses implemented through the R package varianceParition31 2.5 nM and 5 nM displayed the highest variance across were applied to decompose the transcriptome into the genes and could be suitable as intermediate doses. For the percentage attributable to multiple biological and tech- selection of max dose, results indicated that 50 nM nical sources of variation. By properly attributing multiple induces a max genomic response in all the samples. sources of expression variation in this fashion, it is pos- Subsequently, 20 µl of DEX at concentration of 0, 27.5, sible to identify and partially correct for some con- 55, and 550 nM were pipetted in a flat bottom 96-well founding variables. For each gene, the percentage of gene plate. To increase RNA yield, a total of 18 wells were expression variation attributable to individual as a repe- prepared for each DEX concentration (~9.0 × 106 cells per ated measure (i.e., donor effects), RIN, BMI, DEX con- dose) in complete RPMI. PBMCs were prepared at 2.5 × centration, individual age, clinical diagnosis, childhood 106 cells/ml in complete RPMI and 200 µl were pipetted trauma exposure, and variation in basal immune cell type into each well, bringing the final concentration of DEX to 0, frequencies was computed. Second, differential gene Breen et al. Translational Psychiatry (2019) 9:201 Page 4 of 13

expression analyses were conducted using a moderated t- IRF7, IFIT2-3 and transcription factor ATF5, as well as test from the R package limma30. All analyses adjusted for several other genes strongly implicated in cytokine and the possible confounding influence of the following cov- interferon signaling (Fig. S2, F–H). Notably, when samples ariates: individual as a repeated measure, individual age, were parsed by diagnosis, donor as a repeated measure RIN, BMI, CTQ and basal cell type frequencies. In one explained a larger percentage of observed transcriptome instance (see Fig. 1), group status (PTSD+ versus PTSD variation within PTSD+ participants (12.2%) compared to − −) was also included in the model to identify DEX- PTSD- (5.5%; p = 2.2 × 10 16) (Figure S2,I), suggestive induced gene expression effects that were independent of of a broader, more varied glucocorticoid response in PTSD status. Due to the repeated measures study design, PTSD+ PBMCs. where individuals are represented across distinct incuba- tions of DEX (vehicle, 2.5 nM, 5 nM, 50 nM), the dupli- Baseline differences in gene expression cateCorrelation function was applied in the limma Baseline gene expression profiles (vehicle; 0 nM DEX) analysis and gene level significance values were adjusted were compared between PTSD+ and PTSD− groups for multiple testing using the Benjamini and Hochberg while adjusting for the possible influence of donor as a method to control the false discovery rate. Third, unsu- repeated measure, age, prior-deployment, RIN, BMI, pervised weighted gene co-expression network analysis32 childhood trauma exposure, and basal cell type fre- was used to construct a signed co-expression network and quencies (see Supplemental Information). No significant identify DEX-stimulated changes in gene co-expression differences in gene expression were observed (q-value < modules. Finally, all significantly differentially expressed 0.05) and a distribution of PTSD-related P-values, which genes and all network modules with genes passing intra- was no different from the expected uniform distribution, modular membership (kME) > 0.6 were subjected to was identified (Fig. S3; λ mean = 0.69). functional annotation using the ToppFun module of ToppGene Suite Software33. DEX-stimulated genes and modules as markers of Full details on materials and methods are described in glucocorticoid activation the SI Material and Methods. Dose-dependent transcriptional responses were examined following each increasing concentration of DEX Results relative to baseline and adjusted for the same variables as Clinical features and demographics: characterizing known above. To focus on the dose-dependent effects of DEX sources of expression variation and to identify reliable markers of glucocorticoid activa- Genome-wide RNA-sequencing profiles were generated tion that are independent of PTSD status, an additional from live cultured PBMCs incubated with increasing covariate of diagnosis (e.g., PTSD+ and PTSD−) was concentrations of DEX from a primary cohort of combat- included into these analyses. exposed veterans with PTSD (PTSD+; n = 10) and with- Relative to vehicle (0 nM DEX), a total of 1291, 15,197, out PTSD (PTSD−; n = 10) (Table S1). The two groups and 19,439 genes were significantly differentially expres- did not differ in age, gender, nicotine, race, prior- sed (q-value < 0.05) following incubation with 2.5 nM, deployment and childhood trauma, but did differ in 5 nM, and 50 nM of DEX, respectively (Fig. S4); repre- body mass index (BMI) and whether they screened posi- senting a total of 21,117 unique genes. To better under- tive for the Department of Veterans Affairs screening for stand the functional aspects of these DEX-stimulated possible traumatic brain injury (i.e., possible mTBI). To changes in gene expression, the common unsupervised test the influence of various factors on gene expression analytical tool weighted gene co-expression network profiles, for each gene, the percentage of gene expression analysis was applied to all 21,117 genes. Seven co- variation attributable to each clinical and technical factor expression modules were identified and each displayed a was computed (Fig. S2). Collectively, these variables unique module eigengene (ME) profile that significantly explained ~40% of transcriptome variation, with donor as changed with increasing concentrations of DEX (Fig. 1, a repeated measure having the largest genome-wide effect Table S2). ME values for modules 1–5 (M1–5) decreased that explained a median 8.7% of the observed variation, in expression while ME values for modules 6-7 (M6-7) followed by RIN (3.9%), childhood trauma (3.7%) DEX increased in expression. Modules M4 and M6 represent concentration (3.7%) (Fig. S2, A–E). The remaining fac- well-known reliable markers of glucocorticoid activation, tors explained smaller fractions of overall transcriptome and, compared to other modules, displayed clear dose- variation, including BMI (0.8%) and possible mTBI response effects. Module M4 decreased in expression and (<0.1%). Expression variation due to diagnosis (i.e., PTSD+ was found to be significantly enriched for processes and PTSD−) had a detectable effect in a smaller number related to inflammatory and cytokine signaling and har- of genes. Genes with expression that varied most across bored several relevant hub genes, including CSF1, IL1B, different concentrations of DEX, included STAT1-2, IRF1, IL1A, IL1RN, IL2RB, IL-2RG, several chemokine ligands Breen et al. Translational Psychiatry (2019) 9:201 Page 5 of 13

P -6 GO term Adj. -value Example genes DEX dosage 0.3 p=8.35 10 0.2 Oxidative phosphorylation 1.18e-18 NR3C1, ATP5A1, ATP5C1,, 0nM 0.1 Mitochondrial translation 1.08e-13 ATP5E, ATP5F1, CWC15, Ribosome biogenesis 1.89e-10 DHX16, DHX8, EIF4A3, 2.5nM 0.0 Spliceosome pathway 5.67e-10 HNRNPA1, NDUFA1, -0.1 5nM

Module 1 NDUFA10 806 genes -0.2 50nm -0.3 p=0.002 RNA binding 2.46e-44 CCNC, KAT2B, MED1, 0.2 mRNA processing 3.79e-22 MED10, MED13, RORA, 0.1 Protein ubiquitination 1.41e-19 EEF1A1, EEF2, RPL10, 0.0 Cell cycle 1.04e-14 RPL10A, RPL11, RPL13 -0.1 Translation 1.47e-13 Module 2

2821 genes -0.2 -0.3 -8 0.3 p=8.43 10 Antigen processing 1.63e-19 KEAP1, PSMA1, PSMA7, 0.2 FC receptor signaling 1.65e-17 GTF2E2, HIST1H2BJ, 0.1 Ubiquitin-dependent 3.22e-21 HIST1H3H, HIST1H4D, 0.0 degradation of cyclin D KPNB1, NMT1, NRAS, Degradation of AXIN 6.13e-20 RAN, SLC25A5, TCEB2,

Module 3 -0.1 292 genes Proteasome 1.75e-20 UBB, UBC -0.2

p=4.45 10-10 Inflammatory response 2.52e-16 CCL19, CCL2, CCL4, 0.2 Defense response 2.52e-16 CCL8, CSF1, CSF2, 0.1 Response to cytokine 1.58e-14 FLT1, IL15RA, IL1A, Cytokine signalling 3.33e-09 IL1B, IL2RG, 0.0 INHBA, LIF, LTA, Module 4 165 genes -0.1 OSM, TNFRSF18

-7 0.3 p=1.98 10 Cellular response to stress 7.25e-05 AKT1-2, TYK2, STAT6, 0.2 PTP1B signalling 5.05e-04 ARHGEF2, PDCD11, 0.1 Protein transport 5.05e-04 PSMD2, RAD23A, CS, ST3GAL2, PI4KB, 0.0 CHFR,YWHAG, SF3B2, Module eigengene (ME) of gene expression Module 5 -0.1 5817 genes TNFRSF10A-B, GNB1 -0.2

p=1.38 10-9 0.2 G-protein coupled receptor 1.72e-03 VIPR1, MERTK, PDK4, activity FKPB5, DEPTOR, MAFF, 0.1 Apoptotic process 4.38e-02 TSC22D3, PDK4, WNT7A, 0.0 Cell death 4.38e-02 ITSN1, NUPR1, TNFRSF21 RLN1, TXNIP, THBS1, Module 6 142 genes -0.1 NRBP2, VNN1, FZD5, -0.2 TNFSF8 -5 0.2 p=5.23 10 Synaptic signalling 2.02e-31 NFASC, NTRK3, CSMD2, 0.1 ECM matrix 1.24e-29 SHANK2, MUC16, 0.0 Trans-synaptic signalling 1.77e-25 RP3-323A16.1, DNAH2, STAB2, CACNA1D, -0.1 LRP2, RYR3, ARFGEF3, Module 7 -0.2 SLIT3, SDK1, UNC80 11,074 genes 11,074 -0.3 GRIN2B, COL27A1

Fig. 1 DEX-stimulated gene co-expression modules. Genes that were found to be significantly differentially expression between vehicle and 2.5 nM, 5 nM, and 50 nM of DEX (FDR < 5%), which were independent of PTSD status, were subjected to WGCNA analysis. A total of 21,117 genes were used as input. Seven modules were identified and an analysis of variance (ANOVA) was used to assess changes in module eigengene (ME) values with increasing concentration of DEX (p-values are labeled above each boxplot). Each module was subjected to gene ontology enrichment analysis and the top most significant enrichment terms and their associated Benjamini-Hochberg adjusted P-values are displayed. Further, we also display some of the top hub genes (kME > 0.6) within each module for quick interpretation of GR-stimulated gene co-expression modules and candidate individual genes.

(e.g., CCL2, CCL4, CCL8) and transcription factors ATF3, apoptosis, and cell death related pathways. Module M6 ATF5 and MAFF. Module M6 was increased in expression also harbored several key hub genes, including FKBP5, and implicated G-coupled protein receptor activity, DEPTOR, PIK3IP1, NUPR1 and transcription factors Breen et al. Translational Psychiatry (2019) 9:201 Page 6 of 13

PER1 and RUNX2. Interestingly, modules M4 and M6 Differential transcriptional responses to DEX in PTSD also contain a significant fraction of genes with well Next, the dose-dependent transcriptional response to validated glucocorticoid binding sites (p = 0.009, p = DEX was assessed separately in PTSD+ and PTSD- par- 0.001, respectively), which are known to have significant ticipants (Table S3). At 2.5 nM of DEX, PTSD− partici- glucocorticoid-inducible gene regulatory activity10 pants displayed 1544 downregulated genes and 1135 (Fig. S5). upregulated genes (q-value < 0.05), while PTSD + parti- Module M1 decreased in expression and was implicated cipants displayed four up-regulated genes and no single in oxidative phosphorylation and mitochondrial transla- gene was downregulated (Fig. 2a, b). With increasing tion and included the glucocorticoid receptor gene, concentrations of DEX, 2326 genes were up-regulated and NR3C1. Modules M2, M3, and M5 were also down- 3680 genes were downregulated in PTSD− participants regulated were respectively enriched for translation, following stimulation with 5 nM of DEX, whereas 7698 ubiquitin-proteasomal pathway, and protein transport, genes were up-regulated and 7049 genes down-regulation representing pathways, which have been previously following stimulation with 50 nM of DEX (Fig. 2a). A reported to be downregulated with low doses of gluco- significant proportion of DEX-stimulated changes in gene corticoids across various experimental contexts. expression was found in common between 5 nM and Finally, no module showed distinct differences in co- 50 nM DEX in the PTSD− group ( ∩ = 5484, OR = 21.3 p − regulatory patterns between PTSD+ and PTSD− groups < 2.0 × 10 16) (Fig. S7). In parallel, PTSD+ group dis- (Figure S6), indicating that lack of a clear dose-response is played upregulation of 3996 genes and down-regulation of not confounded by PTSD status. 6516 genes with 5 nM of DEX, and with 50 nM of DEX,

Fig. 2 Differential transcriptional response to DEX in PTSD. DEX-stimulated changes in gene expression were evaluated separately for PTSD− and PTSD+ participants. A clear distinction for gene expression changes that were either suppressed or enhanced for DEX were identified for a PTSD− and b PTSD+ participants. c Quantile-quantile (QQ)-plots demonstrate an observed distribution of p-values, which greatly deviates from the expected uniform distribution across all three concentrations of DEX. The genomic inflation factor (λ, also defined as median X2) was computed to measure deviations of the observed genome-wide distribution of the test statistic from with the expected null distribution. A mean λ of 1 indicates no difference from the expected null distribution, while λ > 1 indicates marked shifts from the expected null distribution. d Concordance of genome- wide log2 fold-changes for all differentially expressed genes (Adj. P < 0.05) were computed in a dose-dependent manner and a linear regression model assessed the overall correspondence between PTSD− and PTSD+ participants. Gold points indicate gene expression changes that are unique to PTSD+ participants. Blue shading indicates a density distribution, whereby an excess of data points are depicted by a denser shading. Breen et al. Translational Psychiatry (2019) 9:201 Page 7 of 13

3239 genes were up-regulated and 6516 genes were down- which strengthened with increasing concentrations to regulation. Similarly, a substantial fraction of these DEX- 5 nM and 50 nM DEX (R2 = 0.64, R2 = 0.75, respectively). stimulated changes in gene expression overlapped Genes that were uniquely and significantly (q-value < between 5 nM and 50 nM DEX for PTSD+ participants 0.05) downregulated in the PTSD- group following − ( ∩ = 6818, OR = 15.9 p < 2.0 × 10 16) (Fig. S7). Impor- 2.5 nM of DEX were strongly enriched for several mole- tantly, compared to the p-value distributions derived from cular pathways, including cytokine signaling, signaling by baseline gene expression analysis, quantile-quantile (QQ)- interleukins and TNF signaling pathways (Fig. 3a), while plots demonstrate an observed distribution of p-values, genes that were uniquely and significantly upregulated which greatly deviates from the expected uniform dis- were implicated in the complement pathway, GPCR sig- tribution across all three concentrations of DEX, whereby naling and ECM-associated genes/proteins (Fig. 3b, Table PTSD- participants displayed a larger fraction of genes S4). As these biological processes demonstrate significant with an abundance of low p-values (Fig. 2c). To further differential response in PTSD following low doses of quantify these results, the concordance between direc- DEX, these gene sets were further interrogated for dif- tionality of change statistics (log fold-change) was mea- ferential responses following higher doses of DEX using sured for the DEX-stimulated genes in PTSD- and PTSD+ gene set preservation analysis (see Supplemental Infor- groups in a dose-dependent manner (Fig. 2d). Low-to- mation). While most gene sets did not show significant moderate levels of concordance were observed for DEX- differential responses to increasing concentrations DEX stimulated gene expression profiles at 2.5 nM (R2 = 0.37), and were highly preserved between PTSD- and PTSD+

Fig. 3 Differential response to DEX within functional gene sets. Gene ontology enrichment was performed on the signiﬁcantly differentially expressed genes (Adj. p < 0.05) from vehicle to 2.5 nM DEX in PTSD- participants and were parsed by (a) downregulated genes and (b) up-regulated genes. (c) Gene set preservation analysis was performed on all gene sets with signiﬁcant enrichment results (Table S4) to identify gene sets with the most differential response to DEX between PTSD− and PTSD+ participants. Randomly selected groupings of genes matching the same number of genes within each gene set were also permuted to provide n preservation-based estimate of what is expect by chance. Six gene sets displayed no

preservation (Zsummary < 2) between PTSD+ compared to PTSD- participants. d–f Z-scaled expression data examines the average expression proﬁles across vehicle, 2.5 nM, 5 nM, and 50 nM concentrations of DEX for three gene sets with differential responses to DEX, including d norepinephrine neurotransmitter release, e glucocorticoid biosynthesis and f IL-7 signaling. Increased response at 2.5 nM of DEX was observed for norepinephrine neurotransmitter release and glucocorticoid biosynthesis while decreased response at 2.5 nM DEX was observed for IL-7 signaling. Red lines indicate genes that increase with expression and blue lines indicate genes that decrease in expression. Dots represent averages across all samples. Breen et al. Translational Psychiatry (2019) 9:201 Page 8 of 13

participants (e.g., cell cycle, apoptosis, TLR cascades), 30 genes were uniquely downregulated in PTSD+ parti- several other gene sets showed differential transcriptional cipants (Fig. 5b). A significant fraction of genes that were responses to DEX between PTSD+ and PTSD- partici- uniquely upregulated following 5 nM of DEX were also pants, including norepinephrine neurotransmitter upregulated following 50 nM exposures of DEX ( ∩ = 27, – release, glucocorticoid biosynthesis and IL-1, IL-6 and IL- OR = 17.8 p = 5.0 × 10 33) (Fig. 5c). These genes included 7 signaling pathways (Zsummary <2) (Fig. 3c, Table S5). CCL25, FAM25E, GHSR, GRP88, IFIT1B, MDK,ORIN2, Genes mapping to these gene sets revealed marked dif- O6C75, and LEFTY and several lncRNAs (~45%) (Table ferences in expression profiles following 2.5 nM, 5 nM S7). We validated these nine genes displaying differential and 50 nM of DEX between PTSD- and PTSD+ partici- response to DEX in a subset of samples by RT-qPCR (see pants (Fig. 3d–f). Supplemental Information). Strong concordance was observed for directionality of change statistics (log fold- DEX-induced changes in glucocorticoid regulatory genes change) between RT-qPCR results and RNA-sequencing Subsequently, our exploratory investigation focused on results for these eight mRNAs at 2.5 nM (R2 = 0.88), 5 nM a well-curated list of 75 GR regulatory network genes, (R2 = 0.89) and 50 nM (R2 = 0.92), indicating strong inde- which have been the intense focus of GR activation and pendent technical validation (Fig. 5d). Furthermore, these PTSD biomarker research. These genes were collected RT-qPCR targets also validate the significant differences through the Pathway Interaction Database (set no. M115) identified using RNA-seq between PTSD+ and PTSD− and then were manually examined to ensure that only participants at 2.5 nM and 50 nM of DEX (Fig. 5e). GR-related genes were included within the final list. Gene expression levels for these GR-related genes were exam- Discussion ined for PTSD-related differential response to DEX, and Here, we report the first demonstration of genome-wide revealed that GR-related genes were either strongly up- gene expression effects in response to in vitro gluco- regulated or downregulated by DEX (Fig. 4a, b, Table S6). corticoid treatment in PTSD. By exposing cultured A total of 19 GR-related genes were found to be sig- PBMCs to increasing concentrations of the synthetic nificantly increased in response to DEX in both PTSD− glucocorticoid DEX and examining genome-wide tran- and PTSD+ participants, including genes FKPB5, NR1I3, scriptional responses, our findings capture the dynamic VIPR1, PBX1, and FGG (Fig. 4c). The remaining 55 genes response of gene expression to glucocorticoid stimulation were found to be downregulated by DEX in both PTSD- and reveal several novel findings of glucocorticoid func- and PTSD+ participants, including genes STAT1, ICAM1, tion and responsivity in PTSD. First, these results illus- IRF1, and TP53 (Fig. 4c). Notably, IL-5 gene expression trate that different concentrations of DEX activate very displayed a difference in response to DEX whereby different genes and gene networks, illuminating a well- expression increased for PTSD+ participants and known, but not often acknowledged the complexity of decreased for PTSD− participants. dose-dependent glucocorticoid effects. Second, DEX- Subsequently, we examined these genes, which were stimulated changes in gene expression achieve enhanced increasing and decreasing in expression following DEX effect sizes, which are required to leverage gene expres- treatment, for significant differences in their magnitude of sion profiles as actionable, clinical biomarkers for PTSD, change between diagnostic groups. Of the 55 DEX even with limited sized samples. Finally, these findings induced downregulated GR-related genes, a greater illustrate that in vitro stimulation of blood immune cells decrease was observed for PTSD- compared to PTSD+ with DEX permits for a more effective discrimination participants at the lowest treatment dose (2.5 nM of DEX) between PTSD+ and PTSD− participants than baseline − (p = 4.3 × 10 7) (Fig. 4d), particularly for glucocorticoid gene expression with the potential for discovery of PTSD receptor NR3C1, IFN-γ, IL4, IRF1, transcription factor biomarkers. All of these points are discussed in JUN and others (Fig. 4g). No significant differences in turn below. overall levels of DEX-induced change in gene expression DEX-responsive gene co-expression modules were were observed in GR-related between PTSD+ and PTSD identified indicating widespread effects of DEX on diverse − participants following treatment with higher treatment cellular signaling systems, many of which have been doses (5 nM and 50 nM of DEX) (Fig. 4e, f). linked to glucocorticoid activity and/or PTSD. The gene modules with the most distinct dose-response to DEX RT-qPCR validation of differential responses to DEX were modules M4 and M6. Module M4 decreased in Under close inspection, 332 genes were uniquely upre- expression and was enriched for processes related to the gulated and 31 genes were uniquely downregulated in inflammatory response and cytokine signaling while M6 PTSD+ participants following 5 nM of DEX (q-value < increased in expression and was implicated in apoptosis- 0.05) (Fig. 5a, shaded in gold in Fig. 2d). In response to related processes. Both modules contained genes har- 50 nM of DEX, 89 genes were uniquely upregulated and boring a significant fraction of glucocorticoid binding Breen et al. Translational Psychiatry (2019) 9:201 Page 9 of 13

A B C Down-regulated Down-regulated Up-regulated PTSD- Up-regulated PTSD+ DEX-induced up-regulated GR genes AFP,AFFP, B BGLAP,GLAP, C CDK5R1,DK5R1, CG CGA,A, CS CSN2,N2, VIPR1 VIPR1 1.0 1.0 EPEP300,300, FGG FGG,, FKBP FKBP5,5, FOS FOS,, K KRT14,RT14, FKBP5 FKBP5 KKRT17,RT17, MAPK10,MAPK10, NR1I3,NR1I3, PBX1 PBX1,, POU2F1,POU2F1, NR1I3 IL5 PRKACG, SELE, SFN, VIPRVIPR11 0.5 BGLAP 0.5 CGA PBX1 BGLAP DEX-induced down-regulated GR genes FGG SFN 0.0 0.0 AKT1,AKT1, BAX,C CDK5,DK5, C CDKN1A,DKN1A, C CREB1,REB1, PCK2 PCK2 CSF2,CSF2, F FKBP4,KBP4, G GATA3,ATTA3, GS GSK3B,K3B, HDA HDAC1,C1, TP53 NFKB1 HDAC2,HDAC2, H HSP90AA1,SP90AA1, CAM1 CAM1,, I IFNG,FNG, IL IL13,13, -0.5 IRF1 -0.5 TP53 IL2,IL2, I IL4,L4, IL5 IL5,5, I IL6,L6, I IRF1,RF1, JUN JUN,N, K KRT5,RT55, -scaled expression SPI1 -scaled expression ICAM1

Z MAPK1,MAPK1, MAPK MAPK11,11, MAPK14 MAPK14,, MAPK3 MAPK3,, Z [GR regulatory genes]

ICAM1 [GR regulatory genes] IRF1 -1.0 MAPK8, MAPK9,MAPK9, MDM2 MDM2,, M MMP1,MP1, NCOA1, STAT1 -1.0 STAT1 NCOA2,NCOA2, NFATC1,NFATC1, N NFKB1,FKB1, N NR3C1,R3C1, PCK2,PCK2, 0nM 2.5nM 5nM 50nM 0nM 2.5nM 5nM 50nM POMC,POMC, P PPP5C,PP5C, P PRKACA,RKACA, PRKA PRKACB,CB, Dexamethasone concentration Dexamethasone concentration RELA,RELA, S SGK1,GK1, S SMARCA4,MARCA4, S SMARCC1,MARCC1, SMARCC2,SMARCC2, S SMARCD1,MARCD1, S SPI1,PI1, S STAT1,TAT1, STAT5A,STAT5A, STAT5B,STAT5B, SUMO2, T TBP,BP, TBX21,TBX21, TP53,TP53, TSG101 TSG101,, YWHAH

DEF 1.5 2.0 2.5nM PTSD+ enhanced 5nM 1.5 50nM PTSD- enhanced PTSD+ supp. 1.5 PTSD- supp. 1.0 1.0 p=0.04 p=4.3 10-7 1.0 p=0.26

Density p=0.35 0.5 0.5 p=0.67 p=0.64 0.5

0.0 0.0 0.0 -1 0 1 -2 -1 0 1 -2 -1 0 1 log2FC [0nML vs. 2.5nM] log2FC [0nM vs. 5nM] log2FC [0nM vs. 50nM] G 1.0 PTSD+ * PTSD- 0.5 * * 0.0 * * * * * * * -0.5 * * * * * * * Change in expression

(%) [2.5nM-0nM / 0nM] * * -1.0 *

IL4 IL6 JUN IL13 TP53 CGA PBX1 CSF2 IFNG IRF1 RELA HDAC2 FKBP5 NCOA1NR3C1 PPP5C ICAM1 NFKB1 TBX21 SMARCD1 GR signalling Immune cell signalling Fig. 4 DEX-stimulated effects on glucocorticoid regulatory genes. We examined the effects of DEX on a curated list of 75 well-known glucocorticoid regulatory genes. A clear distinction for gene expression changes that were either suppressed or enhanced for DEX were identiﬁed for a PTSD− and b PTSD + participants. Dark solid lines indicate average splines across all DEX-induced increased (red) and decreased (blue) genes. c A total of 19 genes were consistently enhanced by DEX while 55 genes were consistently suppressed by DEX. Difference in the magnitude of gene for DEX-suppressed and –enhanced genes were evaluated for PTSD− and PTSD+ participants d at 2.5 nM, e 5 nM, and f 50 nM of DEX. A Wilcox-rank

sum test was used to compare differences in distribution of log2 fold-changes (FC) between groups. g GR-related genes that were significantly (Adj. p < 0.05) and uniquely differentially expressed in PTSD- participants following 2.5 nM of DEX. A full list across all dosages can be found in Table S6. sites with glucocorticoid-inducible gene regulatory activ- in the down-regulation of inflammatory and cytokine ity. After ligand binding, the GR trans-locates to the signaling genes36, which likely explains the clear DEX- nucleus where it binds to glucocorticoid binding sites and induced dose-responses in modules M4 and M6. glucocorticoid response element sequences in genes Decreases in PTP1B signaling and protein transport (M5) resulting in transactivation10. Indeed, such were also observed, and previous research has identified glucocorticoid-induced transactivation has been pro-inflammatory cytokines, such as TNF-α, as positive mechanistically linked to the induction of pro-apoptotic regulators of PTP1B expression in diverse cell lines and genes34,35, however, ligand binding to the GR also results tissues37. Likewise, reduced expression of oxidative Breen et al. Translational Psychiatry (2019) 9:201 Page 10 of 13

Fig. 5 Validating differential responses to DEX in PTSD+ participants. a A total of 363 genes were significantly and uniquely responsive following 5 nM of DEX and b a total of 118 genes were uniquely responsive following 50 nM of DEX in PTSD+ participants. c Overlap analysis of DEX stimulated genes following 5 nM and 50 nM. d Real-Time quantitative PCR (RT-qPCR) was used to validate top performing mRNA targets that were uniquely upregulated in PTSD+. For these genes, the concordance between log2 fold-change statistics was assessed using a linear regression model between RT-qPCR and RNA-sequencing results for PTSD+ (red) and PTSD- (gray) participants. e Log2 fold-changes for RT-qPCR results validate a unique transcriptional response to PTSD+ participants. Asterisks (*) indicate changes that are significantly different from vehicle and were computed using a moderated t-test. phosphorylation and mitochondrial translation (M1) was suppressive and -enhancing effects of glucocorticoids on also observed whereby similar investigations have repor- a number of cellular signaling cascades. ted reduced efficiency of mitochondrial oxidative phos- Moreover, while several molecular pathways and gene phorylation in response to low doses of DEX across sets display strong responses to DEX, there are several various experimental conditions and tissues38. Similar genes within these gene sets with either minimal or no reports have described the inhibitory effects of DEX on change in expression. For example, while a significant protein-ubiquination (M2) and cyclin D and axin fraction of inflammatory and cytokine signaling genes were expression (M3), that occur on a component of the robustly downregulated with increasing concentrations of mitogen signaling cascade parallel to the ERK pathway39. DEX, a substantial fraction of these was genes also Together, these results highlight both the immuno- unchanged in expression, including C-reactive protein Breen et al. Translational Psychiatry (2019) 9:201 Page 11 of 13

(CRP), IFNA2, IL5, IL9, IL20, several chemokine ligands receptor43. Moreover, these data indicate a weaker (e.g., CCL11, CCL21) and chemokine receptors (e.g., CCR3, glucocorticoid-induced transcriptional response in PTSD+ CCR7). Similarly, while several apoptosis-related genes were relative to PTSD− following a low dose of DEX (2.5 nM) up-regulated, others also were unchanged in gene expres- suggesting enhanced functional ‘sensitivity’ to DEX. This sion, including AIFM3, BCL2L10, CREBBP, ERBB2, several striking group difference was absent at higher doses. It is caspases (e.g., CASP2, CASP6). Follow-up functional studies important to note that the term ‘sensitivity’ is oper- integrating additional unbiased genome-scale data, includ- ationally defined. Glucocorticoids can affect transcription ing DNA methylation and ChIP-Seq are likely required to through multiple mechanisms not all of which involved fully clarify these differences. direct GR binding to DNA (e.g., protein-protein interac- The current study further demonstrates that in vitro tions). However, the emergence of the difference in DEX- stimulation of blood immune cells via DEX significantly induced transcription at a concentration well within the amplifies gene expression effect sizes into a range which range of the Kd of DEX for the GR is consistent with a may facilitate actionable, clinical biomarkers for PTSD. GR-mediated effect. Likewise, both co-expression mod- Small sample sizes have almost universally been inade- ules (Fig. 1) and multiple gene sets (Fig. 3d) reveal clear quate for biomarker discovery, especially in the context of dose (or concentration) – response functions reflecting a cross-sectional post-trauma baseline gene expression primary effect at 2.5 nM DEX. studies of PTSD, which have consistently produced small The findings of increased DEX-related GR ‘sensitivity’ of – effect sizes6 8. Further, similar results were also derived PMBCs from the PTSD- contrasts with previous research from the largest PTSD peripheral blood transcriptomic from our group. Our previous research revealed that study conducted to date (Fig. S4, A-B)7, and provide trauma-exposed combat veterans with and without PTSD additional support for the weak effects observed at base- displayed a similar density of GR in leukocytes, but only line (vehicle; 0 nM) between PTSD− and PTSD+ parti- combat veterans with PTSD showed a decrease in leukocyte – cipants in the current study. In fact, current sample size GR number following low dose DEX challenge1 3,44.A estimates indicate that >5000 samples are required to higher GR-‘sensitivity’ in PTSD was also observed following reach clinically meaningful effect sizes (power = 0.8) for DEX-induced inhibition of lysozyme activity in monocytes4. blood-based gene expression biomarker discovery in This discrepancy should not be surprising. Glucocorticoid PTSD7. The lack of robust changes in gene expression and effects on both DNA binding, as revealed in ChIP-seq the equivocal results produced from baseline peripheral analyses, and transcriptional regulation are highly tissue blood gene expression profiles in PTSD are not surprising, specific45. DNase hypersensitivity assays, which reflect given that baseline measurements of neuroendocrine chromatin accessibility, together with GR ChIP sequencing markers have also not been as well replicated as those that reveal striking differences in both targets and transcriptional have involved the response to glucocorticoid challenge, effects across cell types46. Tissue-specificity may thus be – such as the low dose dexamethasone suppression test40 42. conferred by diversity in chromatin accessibility as well as The ability to examine the dynamic gene expression by multiple tissue-specific modulators of glucocorticoid response to provocation not only provides more textured action (e.g., FKBP5, 11ßHSD 1 or 2, etc.) or tissue-specific and relevant information about PTSD, it may increase expression of GR variants (i.e., GRa and GRß) that can have reproducibility across studies. Indeed, PTSD is a disorder opposing effects47. Differences in tissue responses to glu- of stress reactivity, most often observed clinically when cocorticoidsmaybemediatedbytissue-specificgroupdif- patients are triggered. The results underscore the need to ferences at multiple levels. Likewise, environmental develop new measures for examining transcriptional regulation of the methylation status of the GR gene is responses immediately following glucocorticoid activa- similarly tissue-specific48. Additionally, GR regulation of tion. Moreover, the data presented here provide an transcription is also determined by the form of GR binding important proof-of-principle approach and show that to DNA. GR’s bind DNA as monomers or homodimers, DEX-stimulated gene expression changes in an in vitro with resulting differences in transcriptional effects. Finally, preparation of blood immune cells permits for a more the GR effects on transcription are also regulated by cellular powerful discrimination between PTSD+ and PTSD− context: GR’s interact with the binding of other transcrip- participants than does examination of gene expression tion factors (e.g.,AP-1,CREB, etc.) with DNA, which can levels of quiescent ex vivo PBMCs. alter transcriptional effects45.Insum,tissue-specific differ- Our genome-wide approach has also highlighted several ences in GR ‘sensitivity’ between PTSD + and PTSD- sub- dose-dependent differences in the transcriptional jects are both unsurprising and a highly attractive target for response to DEX in PTSD. The dose-response relation molecular analysis. between DEX concentration and transcriptional activity is The transcriptional responses captured by stimulating consistent with previous pharmacological studies as well cultured PBMCs with DEX, and discussed here, provide as with the known affinity (~5 nM) of DEX for the GR interesting candidates for further glucocorticoid and/or Breen et al. Translational Psychiatry (2019) 9:201 Page 12 of 13

PTSD studies. For example, the identification of several Acknowledgements lncRNAs with differential transcriptional responses to This work was supported by the Office of the Assistant Secretary of Defense for + Health Affairs through the USAMRMC BAA for Extramural Medical Research DEX in PTSD participants align with a growing body of under Award No. W81XWH-15-1-0706. Opinions, interpretations, conclusions, literature linking ncRNAs with key aspects of PTSD and recommendations are those of the author and are not necessarily pathophysiology49. Therefore, a potential application for endorsed by the Department of Defense. This material is the result of work fi supported with resources and the use of facilities at the James J. Peters VAMC this type of work is the identi cation of genes and in Bronx, NY. The contents of this manuscript do not represent the views of the molecular processes that are highly and differentially U.S. Department of Veterans Affairs or the United States Government. influenced by glucocorticoids. Towards this end, and to Authors’ contributions promote the exchange of this information, we developed a R.Y., J.D.F., M.C., I.M., H.N.B. and N.P.D. originated the study design and ran pilot web application with an easily searchable interface to act experiments. M.C. performed cell culture work. C.X. performed qPCR work. M.S. as a companion to this paper and is available from the B. and A.B.G. performed computational analyses. J.D.B., M.J.M., K.B. and A.S.T. aided in interpretation of the data for the work. M.S.B. drafted the manuscript following URL, https://breenms.shinyapps.io/DEXPTSD/. and all authors revised it for important intellectual content and approved the Using the application, researchers can quickly query any final version. gene of interest to evaluate the dose-dependent effect of DEX on gene expression levels. Author details This study also presents some limitations. First, these 1Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, results require replication and further follow-up in lar- NY 10029, USA. 2Department of Genetics and Genomic Sciences, Icahn School ger independent cohorts composed of mixed biological of Medicine at Mount Sinai, New York, NY 10029, USA. 3Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New sexes that have been well-characterized clinically. York, NY 10029, USA. 4Division of Traumatic Stress Studies, Icahn School of However, it is worth noting that while the majority of Medicine at Mount Sinai, New York, NY 10029, USA. 5Mental Health Care Center, James J. Peters Veterans Affairs Medical Center, Bronx, NY 10468, USA. baseline PBMC transcriptome investigations in PTSD, 6 7 Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, including our own large-scale transcriptome work , MA 02478, USA. 7Columbia Center for Translational Immunology, Columbia produce gene level results with weak effect sizes (Fig. University Medical Center, New York, NY 10032, USA. 8Friedman Brain Institute, S4), these preliminary results are novel in demonstrating Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. 9Douglas Mental Health University Institute, McGill Group for Suicide Studies, McGill robust group differences in response to DEX even University, Montreal, Quebec H3A 0G4, Canada. 10Ludmer Centre for among a small sample (i.e., some aspects of a stress- Neuroinformatics and Mental Health, Department of Psychiatry, McGill related disorder may only be observable under condi- University, Montreal, Quebec H3A 0G4, Canada. 11Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY tions of elevated arousal/stress). Second, it is likely that 10029, USA. 12Department of Neuroscience, Icahn School of Medicine at future work examining such transcriptional responses at Mount Sinai, New York, NY 10029, USA an individual single cell level in a well-defined, geno- typed population will further establish these methods Conflict of interest The authors declare that they have no conflict of interest. while uncovering a landscape of PTSD biomarkers that will serve as definitive guides for the study of PTSD stress responsivity, resiliency and vulnerability. Third, it Publisher’s note will be important to relate the panel of transcriptional Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. findings to neuroendocrine and in vitro measures of GR sensitivity that have been well studied in trauma- Supplementary Information accompanies this paper at (https://doi.org/ exposed samples. Finally, clarifying and testing the 10.1038/s41398-019-0539-x). relationships between DEX-responsive genes in cultured peripheral blood samples versus patient-derived neu- Received: 20 February 2019 Revised: 23 June 2019 Accepted: 7 July 2019 ronal samples, will help to inform new treatment targets and the development of next-generation mechanism- based therapeutics. References In sum, these results provide support for the utility of 1. Yehuda,R.&Lowy,M.T.Lymphocyteglucocorticoid receptor number in glucocorticoid-stimulated gene expression profiles as posttraumatic stress disorder. Am.J.Psychiatry148, 499 (1991). 2. Yehuda, R., Boisoneau, D., Mason, J. W. & Giller, E. L. Glucocorticoid receptor potent tools for the study of PTSD pathophysiology, for number and cortisol excretion in mood, anxiety, and psychotic disorders. Biol. the detection of target treatments, and for the develop- Psychiatry 34,18–25 (1993). ment of blood biomarkers for PTSD-related GR sensi- 3. 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