Genome-wide mRNA Expression Analysis of Acute Psychological Stress Responses

Abstract

Introduction:

Most of previous studies have examined effects of acute psychological stress in humans on selected panels of genes. The genomic approach may help identify novel genes that underline biological mechanisms of acute psychological stress responses.

Objective:

This exploratory study aimed to investigate genome-wide transcriptional activity changes in response to acute psychological stress.

Methods:

The sample included 40 healthy women (mean age 31±11.6 years). Twenty-two participants had a stress experience induced by the Trier Social Stress Test (TSST) (experimental group) and 18 did not (control group). Psychological stress levels and hemodynamic changes were assessed before and after the TSST. The peripheral blood samples obtained before and after the TSST were processed for mRNA-sequencing.

Results:

The psychological and hemodynamic stress parameters indicated that the TSST induced moderate levels of stress in the experimental group. Six genes (HCG26, HCP5, HLA-F, HLA-F-AS1, LOC1019287, and SLC22A16) were up-regulated, and five genes (CA1, FBXO9, SNCA, STRADB, and TRMT12) were down-regulated, among those who experienced the stress induction, compared with the control group. Nine genes out of 11 were found in the network of endocrine system disorders, neurological disease, and organismal injury and abnormalities.

Conclusion:

The identified genes such as HCP5, SLC22A16, and SNCA have previously been proposed as the therapeutic targets of cancer and Parkinson’s disease. Future studies are suggested to examine pathological mechanisms by which the identified genes may mediate the association between psychological stress and adverse health outcomes. Such studies may ultimately identify therapeutic targets that enhance biological resilience to the adverse effects of psychological stress.

Introduction

Psychological stress is linked with adverse health outcomes, particularly depression, cardiovascular disease, and cancer.¹ While many studies have demonstrated that chronic stress is prospectively related to altered levels of inflammatory cytokines, immune dysfunctions and delayed neuronal recovery,^2,3 it is still unclear how acute stress is processed at transcriptional levels. Recently, a study conducted in mice demonstrated that a single stress event left the long-lasting changes in the expressions of microRNA, messenger RNA, and proteins in the subregion of the amygdada.⁴ Although many animal studies have attempted to determine the underlying mechanisms of stress pathophysiology, human studies investigating subcellular level responses to acute psychological stress are limited. In addition, it is recognized that gene expression in animal models is not readily translated to humans.⁵ Available data collected in humans demonstrate that acute psychological stress is a potent trigger of inflammatory, neuroendocrine, and metabolic responses, resulting in predisposition to disease. For example, acute stress increased expression of proinflammatory genes, including IL-6, IL-1β, TH1/TH2 cytokine, catecholamine receptor, nuclear factor (NF)-кB (NF-kB), IкB, or tumor necrosis factor-alpha (TNF-α);^6–10 the hypothalamic-pituitary-adrenal (HPA) axis related genes;¹¹ immune activity;^12,13 and glucose metabolism.¹⁴ However, most of studies have investigated the expression of selected genes and only a limited number of studies have examined genome-wide transcriptional activity in response to acute psychological stress using a microarray.^15,16 The RNA-sequencing genomic approach to identifying differentially expressed genes may provide us with a broader perspective to understanding biological mechanisms of acute psychological stress responses. It may also identify novel genes that influence previously known and unknown stress response pathways. To this end, our study investigated the effect of acute psychological stress on genome-wide transcriptome profile in whole blood samples using RNA-sequencing and explored what genes may be differentially expressed after the stress induction using a randomized controlled design. We have used the Trier Social Stress Test (TSST) which is a standard laboratory procedure to reliably induce stress in human research participants.^17,18 This exploratory approach may identify genes that can then be studied in future confirmatory work.

Methods

Participants

All the measurements and blood samples were obtained during a previous study which aimed to evaluate changes in arterial stiffness after stress induction.¹⁹ After the approval by the institutional review board for the previous study, a convenience sample of 85 women were recruited from Charlottesville communities in VA, using flyer, email, and word of mouth. The study included female adults aged 18 to 55 years. This selected sex and age range reduced heterogeneity of the sample and obviated the need to control for potential confounding effect that old age and menopause may impose on cardiovascular function. To minimize potential confounding effects, women who were taking medications for psychological issues (e.g., anxiolytics and antipsychotics), who had any diagnosed cardiovascular disease (e.g., hypertension, diabetes mellitus, or hyperlipidemia), or who had gone through menopause were excluded. In the previous study, participants were randomized by coin flips into either the intervention (stress induction) or control (no stress induction) group. Financial considerations prohibited genetic sequencing on all 85 participants. Because the previous study aimed to test changes of transcriptome profile in relation to arterial stiffness from before to after a stress induction, only 40 participants who had exhibited changes in arterial stiffness were selected for genetic sequencing; n=22 who received the experiment (experimental group) and n=18 who did not (control group). This sample was used for the current study to analyze transcriptome changes in relation to acute psychological stress induction.

Measures

Contextual factors

Age, race, per capita income, and education level were self-reported by the participants.

Psychological factors

Subjective acute psychological stress level was measured by the Subjective Units of Distress Scale and the state anxiety subscale of the Spielberger State-Trait Anxiety Inventory.

Subjective Units of Distress Scale (SUDS).

Current and peak distress was measured by the SUDS, which is a widely-used one-item scale.²⁰ The question asked before the experiment (for the experimental group) or the sitting period (for the control group) was: “On a scale of 0 to 10, with 0 ‘not distressed at all’ and 10 ‘the most distressed’, what is your distress level now?” The question asked after the experiment (or after the sitting period) was: “On a scale of 0 to 10, with 0 ‘not distressed at all’ and 10 ‘the most distressed’, what was your highest level of distress during the experiment or the sitting period?”

State anxiety subscale of the Spielberger State-Trait Anxiety Inventory (STAI).

State anxiety was also measured before and after the experiment (or after the sitting period). The 20-item state anxiety subscale uses a 4-point Likert scale to assess the intensity of anxiety tied to “how one feels right now, that is, at this moment” (e.g., feelings of worry, tension). The total score is derived from the sum of the items with higher scores indicating greater anxiety.²¹ This scale has been used extensively and has strong construct and divergent validity and internal consistency in large samples.²² In the current study, the internal consistency of the baseline state anxiety scale was excellent (Cronbach’s α = .90).

Physiological factors

Mean arterial pressure (MAP) and heart rate (HR).

Blood pressure (BP) and HR are commonly tested to assess physiological response to stress. In this study, BP and HR were included as indicators of acute psychological stress, along with the SUDS and the state anxiety subscale of the STAI. BP and HR were measured using WelchAllyn Vital Signs Monitor 300 Series. After measuring the mid-section circumference of the nondominant upper arm, the proper-sized cuff was applied snuggly with the artery marker on the cuff placed over the brachial artery.²³ BP was measured on the arm kept at the level of the heart without movement. Given that the concurrent use of systolic BP and diastolic BP may cause multicollinearity issue, MAP was used by calculating diastolic BP +1/3 (systolic BP - diastolic BP).

Body mass index (BMI).

BMI is an anthropometric measurement that may affect physiological response to stress;²⁴ thus, BMI was included as a covariate in transcriptome analysis. Height (m) and Weight (kg) were measured to determine BMI in kilogram divided by square meter (kg/m²). Height was measured by a wall stadimeter (Accu-Hite, USA), and weight was measured with an electronic scale (Penn Scale, USA).

Procedure

Participants’ physiological conditions were standardized according to the European Society of Cardiology’s recommendations for measuring cardiovascular functions.^25,26 To minimize variance caused by circadian patterns, the study procedure was conducted in a quiet room between 1pm and 3pm. All participants refrained from vigorous exercise and consuming coffee, tea, bananas, chocolate, cocoa, citrus fruits, and vanilla for one day before data collection, because vigorous exercise and consumption of those food items may change cardiovascular hemodynamics. The participants had the same breakfast containing cereal (35g), milk (8oz), and orange juice (8oz) at 8am followed by fasting until data collection was completed in the afternoon.

Pre-test data collection

For baseline measures of stress, participants completed the SUDS and the STAI subscale. Next, height (m) and weight (kg) were measured. After 10 minutes resting in the supine position, BP was measured. Blood was drawn by an experienced nurse-phlebotomist (J.L.). Participants were then randomized by the flip of a coin to either the experimental group or the control group.

Trier Social Stress Test (TSST)

For the experimental group, acute psychological stress was induced by using the TSST which is a valid and reliable tool to induce acute psychological stress and to study biological responses to stress in laboratory settings.^17,18 The intention of the TSST is to create perceived uncontrollability and fears of negative social evaluation. These two components are considered central to activate biological stress reactivity, such as activation of the HPA axis and sympathetic-adrenal medullary axis.²⁷ The TSST requires speech performance and verbal arithmetic performance in front of an audience, and is known to induce considerable changes in corticotropin,²⁸ cortisol,^29,30 blood pressure,²⁹ and heart rate.³¹

The TSST was conducted in an intervention room located across the hall from the room where the baseline measures were collected. Individuals who were assigned to the experimental group were given instructions for the upcoming TSST task. They had 10 minutes to prepare for a 5-minute speech in which they were asked to convince two interviewers that they were a strong candidate for their dream job. The participants were told that their performances would be videotaped and evaluated by the interviewers. The participants stood in front of a video camera and two interviewers (research staff dressed in lab coats). During the presentation, the interviewers maintained neutral expressions and periodically took notes. If participants stopped early, they were encouraged to continue. If the participants repeatedly looked at the interviewers or stopped talking for more than 10 seconds, they were told that “you have X minutes left but you can let me know if you wish to stop”. The presentation was immediately followed by the mental arithmetic test, which involved sequentially subtracting the number 13 from 1022. If a mistake was made, they were asked to start again from the beginning. This continued until the participant had completed the task for 5 minutes.¹⁷

The participants in the control group spent about 20 minutes quietly sitting in the same intervention room. They did not undergo the TSST.

Post-test data collection

Immediately after the TSST period or the sitting period (depending on the condition), participants in both groups completed the SUDS a second time. Next, they laid down in a supine position, BP was measured, and peripheral blood was also drawn again from the peripheral catheter, which had been placed during the earlier phlebotomy. Finally, participants were asked to complete the STAI subscale again before being fully debriefed and compensated for their participation.

RNA extraction, library preparation and sequencing

Peripheral blood (2.5 ml) was collected in a PAXgene ^™ RNA tube (QIAGEN, Frederick, MD) and stored at −80°C until ready for RNA extraction. RNA was extracted from whole blood using the PAXgene ^™ Blood RNA system (QIAGEN). The quality of the RNA samples was evaluated using an Agilent 4200 Tapestation (Agilent Technologies, US) by the RNA Integrity Number (RIN), and the quantity of the RNA was measured using a Qubit (Life Technologies, US). All of the samples used for this study had excellent purity (A260/A280≥1.9; A260/A230≥2) and showed no visible signs of degradation (RIN ≥ 9). We used the TruSeq Stranded mRNA library prep kit (Illumina, San Diego, US) to generate mRNA-seq libraries. These kits generated high quality libraries for sequencing by fragmentizing the RNA, performing reverse transcription and ligating the indexed adapters. This allowed the individual libraries to be pooled in equimolar fashion, minimizing potential technical bias of run variation. The pooled libraries were then sequenced with an Illumina NextSeq 500 instrument.

Plan for analyses

Characteristics of study participants are described by the mean and standard deviation (SD) for continuous variables and by frequency and percent for categorical variables. Condition differences in participants’ characteristics at baseline were examined using independent samples t-tests. To compare psychological and physiological responses between the experimental and control groups, repeated measures of analysis of covariance (RM-ANCOVAs) were conducted, controlling for age, MAP, and BMI that may affect physiological responses to stress. The within-subjects factor was time (pre and post stressor), and the between-subjects factor was stressor (TSST and control). The interaction of the within-subjects factor and between-subjects factor was tested. All statistical analyses were performed with SPSS Statistics 25 for Windows (SPSS, Chicago, IL, USA).

Bioinformatic analysis of RNA-sequencing data

We performed bioinformatics quality control using FastQC, version 0.11.7. Poor quality reads and adapter sequences were filtered out by running CutAdapt (V2.5). To confirm the quality of the library and sequencing, we used RNA-SeQC for quality control specific to RNA-sequencing and assessed total number of reads, depth of reads, average read length, average coverage across the gene, number of genes identified, PCR duplication rate, ribosomal content, and exon/intron representation. We aligned the raw reads to GRCh38 reference genome using STAR, version 2.6.1a. We counted number of reads mapped to genes using htseq, version 0.11.0. We performed differential gene expression analysis between the experimental and control groups using DESeq2, version 1.30.1 while controlling for age, MAP, and BMI as potential covariates. The gene count table was imported to DESeq2. The distribution of the reads was modeled as a negative binomial distribution with the mean and the variance estimated from the data. P-values were calculated by using Wald test. Multiple testing correction was performed with Benjamini-Hochberg’s False Discovery Rate (FDR) adjusted by Independent Hypothesis Weighting method with the cutoff of 0.05 on (FDR), accounting for age, BMI, and mean arterial pressure as covariate variables. R version 4.0.3 (2020-10-10) and BiocManager verion 3.12 were used.

Construction of pathway-gene-process network.

Biological pathways and networks related to stress induction were identified by using Ingenuity Pathway Analysis (Ingenuity® Systems, Redwood City, CA). Genes were selected as input for pathway testing when FDR was equal or less than 0.05. Right-tailed Fisher’s exact test was conducted to calculate significance value of the pathways fitting.

Results

Characteristics of participants

The characteristics of the study participants are summarized in table 1. The average age of the participants was 31 years (SD 11.6). Most of the study participants were Asian (90%), and ethnicity was not assessed. The average BMI was in the normal range with a mean of 23.16 (SD 4.69) kg/m². While the sample tended to be well educated, the average per capita income was low with a mean of $19,298.39 (SD 12,921.45) because many participants were graduate/undergraduate students. There were no significant differences in these demographic characteristics between the experimental and control group.

Table 1.

Characteristics of the Study Participants

	All	Experimental Group	Control Group	p-value
N (%)	40 (100%)	22 (55.0%)	18 (45.0%)
Age (year)	31.43 ± 11.57	29.64 ± 10.04	33.61 ± 13.17	0.285
Race				0.978
Asian	36 (90.0%)	20 (90.9%)	16 (88.9%)
Black	4 (10.0%)	2 (9.1%)	2 (11.1%)
Body mass index (kg/m2)	23.16 ± 4.69	22.62 ± 4.25	23.81 ± 5.25	0.433
Per Capita Income ($)	19,298.39 ± 12,921.45	20,089.74 ± 15,037.39	18,726.85 ± 11,584.51	0.777
Education				0.171
High school graduate	7 (17.5%)	2 (9%)	5 (27.8%)
Associate degree	10 (25.0%)	8 (36.4%)	2 (11.1%)
College degree	13 (32.5%)	6 (27.3%)	7 (38.9%)
Graduate degree	10 (25.0%)	6 (27.3%)	4 (22.2%)

Number (Percent) or Mean ± Standard Deviation

Effects of the TSST on psychological stress and physiological measures

Table 2 displays the results of the RM-ANCOVA. The interaction between the within-subjects factor (time) and the between-subjects factor (TSST) was significant, showing that the participants who completed the TSST had significantly higher scores of the SUDS (F_{1, 32}=59.89, p=.000, and η²=0.65) and the state anxiety (F_{1, 35}=10.62, p=.002, and η²=0.23) after the stress induction compared to those in the control group, after controlling for age, MAP, and BMI. The participants in the experimental group also showed significantly higher systolic BP (F_{1, 35}=26.53, p=.000, and η²=0.43), diastolic BP (F_{1, 35}=18.76, p=.000, and η²=0.34), and HR (F_{1, 35}=7.49, p=.010, and η²=0.18) after the stress induction compared with those in the control group, after controlling for age and BMI.

Table 2.

Effects of the TSST on Psychological Stress and Physiological Measures

	Mean (SD)				p-value for ANCOVA (condition × time interaction)
	Pre-test		Post-test
	Exp.	Control	Exp.	Control
SUDS	2.05 ± 1.84	2.73 ± 1.98	5.46 ± 1.89	1.67 ± 1.54	0.000*
SA	31.64 ± 7.29	31.89 ± 7.91	35.50 ± 10.34	29.06 ± 8.78	0.002*
Systolic BP	101.18 ± 6.56	104.11 ± 10.15	110.27 ± 9.62	104.94 ± 8.91	0.000&
Diastolic BP	60.50 ± 5.99	62.83 ± 8.35	65.36 ± 6.91	61.78 ± 8.33	0.000&
Heart Rate	58.41±7.84	60.61±7.06	61.86±8.41	59.78±6.71	0.010*

TSST = Trier social stress test

BP = blood pressure

Exp. = experimental group

SUDS = subjective units of distress scale

SA = Spielberger Sate-Trait Anxiety Inventory – state anxiety subscale

ANCOVA = analysis of covariance

^*Age, body mass index, and mean arterial pressure were controlled

^&Age and body mass index were controlled.

Differentially expressed genes after stress induction in the experimental group compared with the control group

The Illumina’s NextSeq 500 sequencer generated around 30 million paired end reads with read length of 75 bp (2 × 75 bp) per sample/library. A total of 22021 genes were expressed. Tables 3 and 4 present the significantly up- and down-regulated genes associated with stress induction. Figure 1 shows normalized mRNA expressions in terms of read counts for the differentially regulated genes under the acute stress condition (TSST). The IPA identified one related network of “Endocrine System Disorders, Neurological Disease, Organismal Injury and Abnormalities” with a score of 27 (Figure 2). Nine molecules out of 11 were found in this network.

Table 3.

Up-regulated genes after stress induction in the experimental group compared with the control group

Gene symbol	Gene name	log2 (Fold_Change)	p-value	Adjusted p-value (FDR)
HCG26	human leukocyte antigen complex group 26	0.36	5.44E-08	0.001
HCP5	HLA complex P5	0.31	7.14E-06	0.043
HLA-F	major histocompatibility complex class I, F	0.29	3.13E-05	0.049
HLA-F-AS1	HLA-F antisense RNA 1	0.33	2.37E-06	0.006
LOC1019287	undefined	0.82	2.95E-07	0.001
SLC22A16	solute carrier family 22 member 16	0.78	1.29E-05	0.041

Table 4.

Down-regulated genes after stress induction in the experimental group compared with the control group

Gene symbol	Gene name	log2 (Fold_Change)	p-value	Adjusted p-value (FDR)
CA1	carbonic anhydrase1	−1.08	1.93E-07	0.001
FBXO9	F-box protein 9	−0.27	7.87E-07	0.004
SNCA	synuclein alpha	−0.86	2.17E-05	0.048
STRADB	STE20 related adaptor beta	−0.71	7.51E-06	0.043
TRMT12	tRNA methyltransferase 12 homolog	−0.28	1.22E-05	0.030

Figure 1.

Normalized read counts for differentially expressed genes under the experimental group (stress) compared with the control group (no stress). (a) Up-regulated genes, (b) down-regulated genes in stress.

Figure 2.

The network identified by the Ingenuity Pathway Analysis

Discussion

The present exploratory study investigated genes that were differently expressed between two conditions that varied in whether or not they were exposed to the TSST, an acute psychological stressor. The psychological and physiological stress parameters including SUDS, state anxiety, BP, and HR indicated that the TSST successfully induced moderate but statistically significant levels of acute psychological stress in the participants in the experimental group. The results showed the significant up-regulation of six genes (HCG26, HCP5, HLA-F, HLA-F-AS1, LOC1019287, and SLC22A16) and down-regulation of five genes (CA1, FBXO9, SNCA, STRADB, and TRMT12) in the stress-induced group, compared with the control group. IPA analysis demonstrated that nine genes out of 11 are implicated in endocrine system disorders, neurological disease, organismal injury and abnormalities. The current study revealed novel genes that have not been previously reported in relation to psychological stress. Notably, this study is exploratory so needs to be followed by confirmatory research, but ultimately, these genes may help elucidate pathophysiological mechanisms by which psychological stress is linked to disease conditions.

Upregulation of genes linked to a psychological stress induction

Among six upregulated genes, four genes (HCG26, HCP5, HLA-F, and HLA-F-AS1) are affiliated with the Human Leukocyte Antigen (HLA) complex, which is also referred to the Major Histocompatibility Complex (MHC) in humans. The HLA complex is a group of proteins on cell surface and known to play a critical role in the immune system.³² All these four genes are also long non-coding RNAs (lncRNA) that are defined as RNA with more than 200 nucleotides having no protein-coding capacity. The crucial function of lncRNA and its regulatory role in the occurrence and progression of tumors has been recognized in accumulated studies.³³ Histocompatibility leukocyte antigen complex p5 (HCP5) is known to play important roles in proliferation, migration, and invasion of cancer cells in many types of cancer.^34,35 With anti-tumor effect of downregulation of HCP5,³⁶ HCP5 is considered a promising biomarker and therapeutic target in cancer. On the other hand,³⁴ only a small number of studies have been conducted on HCG26, HLA-F, and HLA-F-AS1, and molecular mechanisms by which they are involved in biological processes remain largely unknown. HCG26 is human leukocyte antigen (HLA) complex group 26. One study exploring the roles of lncRNAs in follicular development demonstrated that HCG26 expression was upregulated in patients with polycystic ovary syndrome and was associated with follicle count and cell proliferation.³⁷ HLA-F encodes HLA Class I Histocompatibility Antigen, Alpha Chain F.³⁸ A previous study on patients with breast cancer showed that HLA-F expression was positively associated with tumor size and poorer clinical outcomes.³⁹ HLA-F-AS1 (HLA-F antisense RNA 1) has been reported to be up-regulated in colorectal cancer cell tissues and could promote proliferation of colorectal cancer cells.⁴⁰ It has been well documented that acute stress induces redistribution of leukocytes and increases expression of cellular adhesion molecule and chemotaxis that are critical in immune cell recruitment and migration.¹² While reflecting the effects of acute psychological stress on leukocytosis, our findings suggest that acute psychological stress may dysregulate these four lncRNAs in the HLA system, which are known to play an important role in immune responses.

Whereas LOC1019287 has not been characterized, the function of SLC22A16 has been well documented⁴¹. Membrane transporters are proteins that transport molecules across the cell membrane. Solute carrier (SLC) transporters are one of two large groups of membrane transporters. SLC22 family contains cation and carnitine transporters, which include SLC22A16.⁴¹ Previous studies have suggested that SLC22A16 may be a novel target for cancer treatment. For example, a study on acute myeloid leukemia demonstrated the SLC22A16 showed the greatest differential expression in acute myeloid leukemia cells among different carnitine transporters, compared with normal cells.⁴² Another study identified SLC22A16 as one of 13 hub genes that are involved in carcinogenesis or progression of nasopharyngeal carcinoma, and suggested that it may be considered as a diagnostic biomarker for nasopharyngeal carcinoma.⁴³ It was also reported that SLC22A16 upregulation is an independent unfavorable prognostic indicator in gastric cancer.⁴⁴ While our results from exploratory analyses need to be interpreted cautiously, the previous reports on the high correlation between SLC22A16 and different types of cancer, along with our study findings, inform future studies that may elucidate the causal relationship between stress and cancer.

Downregulation of genes linked to a psychological stress induction

SNCA has previously been associated with psychological stress in an animal study.⁴⁵ SNCA encodes α-synuclein, one of three families of synuclein that are soluble proteins found in nervous system tissue. Variants within SNCA have been extensively studied due to SNCA’s link to Parkinson’s disease.⁴⁶ α-synuclein is known to play roles in the cycling of synaptic vesicles,⁴⁷ and overexpression of SNCA has demonstrated a detrimental effect on neuron function.⁴⁶ Plasma and serum α-synuclein is suggested as a potential biomarker of diagnosis in patients with Parkinson’s disease, the second most common neurodegenerative disease.⁴⁸ There is interest in the association between psychological stress and Parkinson’s disease, and one study showed that chronic mild stress accelerates aggregation of α-synuclein in male mice,⁴⁵ seemingly suggesting a different directional effect than observed in the current study. Given our result that acute stress downregulated SNCA, it appears that acute and chronic psychological stress may exert different effects on SNCA expression, but clearly both the animal research and the current test need to be replicated and extended in more human samples.

Down-regulated genes after stress induction also included the genes CA1, FBXO9, STRADB, and TRMT12. CA1 encodes carbonic anhydrases1 (CA1), which belongs to a family of zinc metalloenzymes. CA1 is known to catalyze the reversible hydration of carbon dioxide and be involved in the regulation of the affinity of hemoglobin for oxygen.⁴⁹ A previous study on patients with colon cancer showed that higher expression levels of CA1 had a higher survival probability than patients with lower expression levels of CA1, and identified CA1 as one of the potential biomarkers due to its predictive role in the status and survival time of colon cancer.⁵⁰ A recent study showed that CA1 is upregulated in septic patients, suggesting their protective response of white cells in severe hostile environments such as sepsis.⁵¹ Downregulation of CA1 in our study suggests that the gene may respond differently to severe stress conditions. F-box only protein 9 (FBXO9) is a member of the F-box protein family, which constitutes one of the four subunits of the ubiquitin protein ligase. A study showed that primary tumors with loss of FBXO9 highly expressed proteins associated with metastasis and invasion.⁵² STRADB encodes STE20-related kinase adapter protein beta enzyme which is involved in cell cycle and apoptosis.⁵³ Reduced expression of STRADB is associated with increased cell cycle length and consequent slowing down of the cell cycle.⁵⁴ TRMT12 is one of tRNA methyltransferases that catalyze RNA methylation. Studies have shown that TRMT12 is highly expressed in a large cohort of primary tumors⁵⁵ and different cancer cell lines.⁵⁶ Although not extensively studied, previous studies suggest that CA1, FBXO9, and TRMT12 are implicated in cancer development. Future studies are suggested to explore how various types of stress (e.g., acute vs. chronic psychological stress, or psychological vs. physiological stress) may differently regulate expressions of these genes.

The strengths of the current study include a genomic approach to exploring stress responses in healthy human subjects, use of RNA-sequencing which is superior in gene expression profiling to microarray,⁵⁷ and successful induction of psychological stress in a laboratory setting using the standardized protocol of the TSST. Nevertheless, this study has several limitations. First, relatively few genes were differentially expressed in two groups. Further, inflammatory genes, such as cytokines, that are commonly noticed in previous studies, have not been expressed differently in two groups. Potential contributing factors include the convenience sampling. Selection of participants that exhibited change in arterial stiffness after stress induction may generate the results that reduce generalizability of the study findings, given that the participants may be more likely to demonstrate hemodynamic changes compared to others. Secondly, because this sample is small including only females and majority of whom being Asians, potential impact of sex, race, and other confounders on the human transcriptome should also be considered. Thirdly, while use of gene expression patterns in whole blood cells is convenient and may have advantages for translational research, RNA-seq studies of homogeneous cell populations or specific tissues can be more informative.⁵⁸ Fourthly, people with high levels of trait anxiety are more likely to respond in stressful situations with increased anxiety. Therefore, future studies are suggested to explore whether higher scores on trait anxiety play a role in the transcriptional activity related to acute stress situations. Fifthly, the findings from this exploratory study should be replicated in a study conducted with a larger sample that includes RT-qPCR. Lastly, the potential confounding effects of lymphocyte subset redistribution were not controlled in the present analyses. Lymphocyte redistribution in response to acute stress and sympathetic nervous system activation has been well described;⁵⁹ thus, analysis of isolated leukocyte subpopulations can be considered for future studies.

Conclusion

This study highlights previously unreported associations of 11 genes with acute psychological stress and provides further evidence of stress-induced alterations of the gene expression profile. At present, it remains difficult to explain molecular mechanisms of these genes in the stress response. Future follow-up studies should validate the identified genes and explicate the underlying mechanisms among psychological stress, the genes, and associated disease conditions, including cancer and neurodegenerative disease. Such studies will clarify pathological mechanisms by which vulnerabilities to the diseases may be initiated or aggravated by psychological stress and may ultimately identify therapeutic targets that will enhance biological resilience to the adverse effects of psychological stress.

Importance.

This study highlights a need of examining roles of HCP5, SLC22A16, and SNCA in the link of psychological stress with cancer and Parkinson’s disease.