Neuroendocrine, inflammatory, and extracellular vesicle responses during the Navy Special Warfare Screener Selection Course

Abstract

Military operational stress is known to increase adrenal hormones and inflammatory cytokines, while decreasing hormones associated with the anabolic milieu and neuroendocrine system. Less is known about the role of extracellular vesicles (EVs), a form of cell-to-cell communication, in military operational stress and their relationship to circulating hormones. The purpose of this study was to characterize the neuroendocrine, cytokine, and EV response to an intense. 24-h selection course known as the Naval Special Warfare (NSW) Screener and identify associations between EVs and cytokines. Blood samples were collected the morning of and following the NSW Screener in 29 men (18-26 yr). Samples were analyzed for concentrations of cortisol, insulin-like growth factor I (IGF-I), neuropeptide-Y (NPY), brain-derived neurotrophic factor (BDNF), α-klotho, tumor necrosis factor-α (TNFα), and interleukins (IL) -1β, -6, and -10. EVs stained with markers associated with exosomes (CD63), microvesicles (VAMP3), and apoptotic bodies (THSD1) were characterized using imaging flow cytometry and vesicle flow cytometry. The selection event induced significant changes in circulating BDNF (−43.2%), IGF-I (−24.6%), TNFα (+17.7%), and IL-6 (+13.6%) accompanied by increases in intensities of THSD1⁺ and VAMP3⁺ EVs (all P < 0.05). Higher concentrations of IL-1β and IL-10 were positively associated with THSD1⁺ EVs (P < 0.05). Military operational stress altered the EV profile. Surface markers associated with apoptotic bodies were positively correlated with an inflammatory response. Future studies should consider a multiomics assessment of EV cargo to discern canonical pathways that may be mediated by EVs during military stress.

INTRODUCTION

The physiological impact of multifactorial military stress on the endocrine and immune systems is well documented (1–4). Military operational stress, comprising mental and physical stress compounded with energy and sleep deficits, increases adrenal hormones (i.e., cortisol) and inflammatory cytokines [i.e., interleukins (IL) and tumor necrosis factor (TNF)], while decreasing hormones associated with the anabolic milieu [i.e., insulin-like growth factor I (IGF-I)] as well as neuroendocrine markers [i.e., neuropeptide-Y (NPY), brain-derived neurotrophic factor (BDNF), and α-klotho] (1–3, 5–7). There are, however, other unexplored biomarkers that may provide insight into physiological and metabolic responses to stress. Specifically, extracellular vesicles (EVs) are increasingly recognized as another important mechanism through which organ and tissue cross talk occurs beyond conventional endocrine and neural communication.

EVs regulate physiological processes, such as tissue repair and immune regulation (8). Released by nearly all cells, EVs carry soluble mediators, such as cytokines, and aid in the disposal of cellular waste generated under stressful conditions (9–11). Collectively, EVs comprise three subpopulations based on size and biogenesis: 1) exosomes, 2) microvesicles, and 3) apoptotic bodies (9). Exosomes typically range in diameter from 30 to 150 nm and are formed through a series of invaginations of the cell plasma membrane, before being released into circulation (12, 13). In contrast, microvesicles are formed via outward budding of the plasma membrane and are slightly larger in diameter (100–1,000 nm) (14). Unlike exosomes and microvesicles, apoptotic bodies are only formed during cell death as a result of membrane blebbing and are among the largest-sized EVs (500–5,000 nm) (9, 15, 16). EVs contain cargo, such as nucleic acids and proteins, that is protected in circulation by the vesicle’s lipid bilayer (17). EVs deliver this cargo to target cells in various ways, which can influence posttranscriptional regulation of gene expression, protein turnover, and synchronization of circadian rhythm between the environment and individual cells (9, 14, 18–20). Thus, EVs may possess predictive and diagnostic capabilities not yet fully elucidated.

To date, EVs have not been investigated in a military field setting consisting of prolonged physical exertion, mental fatigue, energy deficit, and minimal sleep, accompanied by psychological stress. Considered one of the most elite in the US Department of Defense, US Navy Sea-Air-Land (SEAL) teams conduct special operations in some of the most austere environments (21). To become a Navy SEAL, candidates must complete an arduous 6-mo basic training program that is designed to test tolerance to a variety of physiological stressors while maintaining high physical and psychological function. Attrition rates range between 60%–85% (21–23). The rigorous training comprises high energy expenditure ranging from 16,700 kJ·day⁻¹ to 19,000 kJ·day⁻¹ and sleep deprivation that pushes candidates to their limits (24). Even qualifying for SEAL training is an arduous process. For example, the US Naval Academy conducts a screening process for their students (i.e., midshipmen) consisting of an intense 24-h selection event, known as the Naval Special Warfare (NSW) Screener. Successful completion of the NSW Screener is a requirement for midshipmen to be selected and considered for attendance at the NSW summer training known as SEAL Officer Assessment and Selection.

Therefore, the purpose of this study was to 1) characterize the neuroendocrine, cytokine, and EV response to the intense, 24-h NSW Screener and 2) identify associations between EVs and inflammatory cytokines. We hypothesized that the Screener would increase inflammatory cytokines and decrease anabolic and neuroendocrine biomarkers, as reported in similar military settings of longer durations. We also hypothesized there would be an increase in EVs associated with apoptotic bodies (i.e., EVs formed during cell death) due to the disruption to homeostasis and cellular damage resulting from the intensity and duration of the stress. Provided that cytokines can be released in soluble form or encapsulated within EVs (26), we hypothesized there would be positive associations between EVs and cytokines.

METHODS

Participants

Eligible participants were US Naval Academy midshipmen in their junior year, between the ages of 18–26, who participated in the NSW Screener. Midshipmen were briefed on the study by the research team 4–6 wk before the Screener. Each participant provided written informed consent before any study-related testing. Midshipmen with a current injury were excluded. The study protocol was part of a larger physiological resilience study titled “Physiological Biomarkers of Resilience and Musculoskeletal Readiness” (Award No. W81XWH-18-1-0452), approved by the University of Pittsburgh Institutional Review Board (IRB), US Naval Academy IRB, and the US Army Human Research Protection Office (HRPO).

Naval Special Warfare Screener

The NSW Screener takes place twice per year at the US Naval Academy, once in the fall and once in the spring. The Screener is designed to challenge midshipmen mentally and physically. Midshipmen are evaluated based on their physical performance and ability to lead and work as a team during high-stress situations. The Screener is administered by SEAL instructors and consists of rigorous physical activities including running, obstacle courses, ruck marching, calisthenics, small boat handling, and pool/open-water swims over a period of 24 h with little to no sleep. Midshipmen are continuously evaluated on their performance and able to voluntary withdraw at any time (i.e., “drop on request”). In addition, midshipmen may be withdrawn by SEAL instructors during the 24-h period due to medical concerns or lack of performance. The results of the Screener are used to determine the best-qualified midshipmen to attend the NSW Officer Assessment and Selection in the summer (25). Data were collected across four iterations of the Screener: Fall 2018 (n = 7), Spring 2019 (n = 3), Fall 2019 (n = 5), and Fall 2020 (n = 14). No Screener took place in Spring 2020 due to the COVID-19 pandemic. Pre-Screener blood samples (pre) were collected the morning of the Screener (0600–0900) in a rested state before the commencement of the Screener at ∼1700 h. The Screener concluded at ∼1700 h the following day. Post-Screener blood samples (post) were collected during medical check, which occurred ∼14 h later between 0700 and 0900.

Biological Specimens

Blood was collected from an upper extremity vein using standard venipuncture via a standard 21-g safety needle and vacutainer holder (BD Vacutainer Eclipse and Vacutainer one-use holder, Becton, Dickinson and Company, Franklin Lakes, NJ). Trained personnel using aseptic technique performed venipunctures. A total of 8 mL of blood (4 mL serum and 4 mL plasma) was collected on the morning of the Screener and again the morning following the Screener. All blood was collected into appropriate collection tubes (SST for serum and EDTA for plasma; BD Vacutainer Becton, Dickinson and Company, Franklin Lakes, NJ). Serum was obtained from the SST tubes by allowing the blood to clot for 30 min and centrifuging at 1,500 g for 10 min at room temperature. EDTA tubes were centrifuged at 1,500 g for 10 min at room temperature immediately after collection. Supernatant was aliquoted and stored at −80°C locally at the US Naval Academy, then transferred overnight on dry ice to the University of Pittsburgh Neuromuscular Research Laboratory, and subsequently stored at −80°C until further analysis.

ELISA assays were used to measure the following biomarkers using plasma samples from EDTA collection tubes: IGF-I (APLCO, Salem), α-klotho (Immuno-Biological Laboratories, Takasaki, Japan), and NPY (R&D Systems, Minneapolis, MN). BDNF and a high-sensitivity cytokine panel (i.e., TNF- α, IL-6, IL-1 β, and IL-10) were analyzed from blood plasma using MILLIPLEX Magnetic Bead Panels (EMD Millipore, Burlington, MA). Cortisol was analyzed using serum samples (Alpco Salem). Kit sensitivity is as follows: IGF-I: 0.09 ng/mL; α-klotho: 6.15 pg/mL; BDNF: 10 pg/mL; cytokine panel: 3.2 pg/mL; and cortisol: 2.5 pg/mL. This information was not available for NPY. All samples were run in duplicate with intra-assay coefficients of variation of 10% or less.

Size Exclusion Chromatography

The workflow for EV analysis is outlined in Fig. 1. EVs were isolated from plasma samples (ETDA collection tubes) using 70-nm size exclusion chromatography (SEC) columns, per manufacturer’s instructions (qEVoriginal, Izon, Medford, MA). Plasma samples were brought to room temperature and centrifuged at 1,500 g for 10 min to remove cell debris. SEC columns were flushed with 10 mL of EV-free phosphate-buffered saline (PBS, Sigma Aldrich) solution, after which 450 µL of the plasma sample was loaded into the column, and fractions were collected as they eluted. The first 3 mL of the eluate was discarded, and the subsequent 1.5 mL EV fraction was collected. The following 4.5 mL after the EV fraction, primarily plasma protein eluate, was discarded. Columns were flushed with 15 mL of PBS between samples, with the same column used for up to five samples. Isolated EV samples were stored at −80°C until subsequent analysis.

Figure 1.

Overview of extracellular vesicle (EV) analysis. A: EVs were isolated from plasma samples using size exclusion chromatography (SEC). B: EV concentrations and size were measured using vesicle flow cytometry (vFC). C: EV samples were stained with immunofluorescence markers associated with exosomes (CD63), microvesicles (VAMP3), and apoptotic bodies (THSD1) and then assed using imaging flow cytometry to collect multispectral images of each EV that passed through the system. D: gating strategies were applied to EV image files to identify populations of CD63⁺, VAMP3⁺, and THSD1⁺ EVs. (Figure created with BioRender.com.)

Vesicle Flow Cytometry

EVs isolated from plasma were analyzed by vesicle flow cytometry (vFC) to estimate EV concentration, size, and surface marker prevalence. Although nanoparticle tracking analysis (NTA) is a commonly used method to quantify nanoparticles based on light scatter, vFC is able to discriminate membrane-bound vesicles from other similar-sized protein aggregates by labeling EVs with a fluorescent lipid probe (1:100 vFRed, Cellarcus Biosciences, San Diego, CA) before single-particle analysis via high-sensitivity flow cytometry (27). For this study, EV samples were stained and analyzed at the Whiteside Laboratory within the University of Pittsburgh Cancer Institute using the vFC assay kit (vFC EV Analysis kit, Cellarcus Biosciences, San Diego, CA) and the CytoFlex flow cytometer (Beckman Coulter Life Sciences, Indianapolis, IN). Samples were run in duplicate with appropriate controls and standards per manufacturer’s instructions (28). Briefly, isolated EV samples were thawed and stained with the membrane stain vFRed (5 µL sample + 5 µL vFRed), then incubated for 1 h at room temperature with 5 µL of a cocktail of fluorescence-labeled antibodies against tetraspanins, CD63, CD9, and CD81, and markers associated with exosomes. Following incubation, samples were diluted and then detected based on fluorescence trigger at 488 nm (28, 29). Data from each sample were collected using the fast setting (60 µL/min) for a total of 120 s. Average EV diameter was determined based on membrane fluorescence calibrated to synthetic vesicle size standards (Lipo100, Cellarcus, Biosciences). Data analysis for vFC was conducted using FCS Express Version 6 (De Novo Software, Pasadena, CA).

Immunofluorescence Staining of EV Subpopulations for Imaging Flow Cytometry

Frozen EV samples were thawed to room temperature, vortexed, and 140 µL from each sample was placed into a new Eppendorf tube and fixed with an equal volume of 4% paraformaldehyde solution. Samples were incubated at room temperature for 10 min and then centrifuged at 16,000 g at 4°C for 30 min (Thermo Scientific Fiberlite F21-48x1.5/2/.0 rotor). Afterward, 140 µL of supernatant was extracted and discarded from each sample and 140 µL of blocking buffer (3% bovine serum albumin) was added. Samples were placed on a rocker plate and incubated at room temperature for 1 h, then centrifuged at 16,000 g for 30 min at 4°C, after which 140 µL of supernatant was removed and discarded. EV samples were then stained with fluorescently conjugated antibodies associated with EV subpopulation surface markers as follows (9): exosomes, CD63 (1:280 dilution, Novus Biologicals, NBP2-42225AF700); microvesicles, vesicle-associated membrane protein 3 (VAMP3) (1:280 dilution, Novus Biologicals, NBP1-97948AF405); and apoptotic bodies, thrombospondin type 1 domain 1 (THSD1) (1:1,000 dilution, Novus Biologicals, FAB5178T-100UG). Following overnight incubation in the dark at 4°C, samples were centrifuged at 16,000 g for 30 min at 4°C and 60 µL of supernatant was removed and discarded. EVs were resuspended with 20 µL of PBS and analyzed using imaging flow cytometry. Compensation beads (Invitrogen, UltraComp eBeads, 01–2222-42) and fluorescence minus one (FMO) controls for each EV surface marker were also stained following the same procedure, beginning with blocking buffer and using half the volume for antibody staining.

Imaging Flow Cytometry

EV samples were imaged on an ImageStream Mk II system (Luminex Corporation, Seattle, WA) and data were acquired using the INSPIRE control software. High gain mode, which has been demonstrated to enhance the detection of small EVs (30), was used in this study. Laser settings were set to maximum intensity, magnification set to ×60, and fluidics set to low speed/high sensitivity with a core size of 7 µm for optimal detection of nano-sized vesicles. Criterion for event detection was objects with a side scatter (SSC) intensity < 1e + 5 to remove SpeedBeads. SpeedBeads are polystyrene beads that run continuously during sample acquisition to focus and synchronize the two charge-coupled device cameras (32). All samples and FMOs were acquired for 3 min. Compensation beads for each antibody were collected to a threshold of 2,000 events. The INSPIRE acquisition software generates data in the form of a raw image file for all samples, controls, and compensation. Raw image files were extracted, color compensation was applied, and “features,” or quantitative information about the image, were exported using Image Data Exploration and Analysis Software (IDEAS).

Conventional gating methods were implemented to identify populations of CD63⁺ (exosomes), VAMP3⁺ (microvesicles), and THSD1⁺ (apoptotic bodies) EVs from the total EV population using FMO controls to remove background event detection. Objects detected for each EV sample were stratified by size based on the area of the bright-field image to identify changes that may occur within specific size ranges. Stratification was based on diameter cutoffs typically used in EV literature (16). Average intensities for CD63⁺, VAMP3⁺, and THSD1⁺ at each stratum (i.e., small, medium, and large), as well as for the total sample (i.e., without stratification), were determined for all samples. Data collected from EV samples are collectively referred to as the “EV profile.” Further details pertaining to gating and stratification methods are provided in the Supplemental Material.

In-Well Western Analysis

EVs were verified for tetraspanin markers, CD63, CD81, and CD9 by performing an in-well immunofluorescence western on three randomly picked plasma-derived EV samples from the experimental cohort. First, EVs were fixed in 4% paraformaldehyde solution at room temperature for 20 min, followed by a PBS wash. The samples were then blocked in 3% BSA for 1 h at room temperature and then incubated overnight at 4°C with Alexa Fluor-conjugated antibodies at 1:2,000 dilution (CD63: sc-5275, CD81: sc-23962, and CD9: sc-13118). Samples were washed with PBS for 5 min, resuspended in 150 µL of PBS, and then loaded to a 96-well plate. The fluorescent measurements were performed using LI-COR ODYSSEY CLx and LI-COR Image Studio Acquisition Software (LI-COR Biosciences, NE). Intensity values (arbitrary units; AU) were normalized to EV concentrations derived from the vFC kits. All centrifugation for the sample preparation was conducted at 16,100 g for 15 min at 4°C.

Statistical Analysis

Normality was assessed using Shapiro–Wilk tests. The P value was set a priori at 0.05 (two-sided) for all analyses. Sample size was determined a priori (G*Power, v. 3.1.9.3) using previous reports of neuroendocrine response to military training (1, 5) with the following inputs: two-tailed, effect size (d) = 0.6, α = 0.05, and power = 0.80. Paired sample t tests were used for normally distributed variables to evaluate changes among neuroendocrine markers, cytokines, and the EV profile in midshipmen able to complete the Screener. If assumptions for paired samples t test were not met, data transformations (logarithmic, square root, and reciprocal) were conducted. For instances where the results from the transformed data agreed with the results from the raw data, mean and standard deviations of raw data were reported. Hedges’ g values were calculated for significant outcomes from paired samples t test to measure the magnitude of the difference pre- to post-Screener. Hedges’ g uses the pooled standard deviation, rather than the population standard deviation used in Cohen’s d, to reduce overestimation of the population effect size that can occur with a small sample size (33). Spearman’s rho (r_s) correlation analyses were conducted to identify associations between cytokines and the EV profile. All statistical measures were obtained using IBM SPSS Statistics for Macintosh, Version 27 (IBM Corp., Armonk, NY).

Steps to Ensure Rigor

All sample conditions for a given subject were analyzed on the same day during data acquisition, including ELISA assays, EV isolation, immunofluorescence staining, vesicle flow cytometry, and imaging flow cytometry. Compensations and controls were stained and run for each day of flow cytometry data acquisition (i.e., compensations and controls were not reused). Investigators performing data acquisition were blinded to completion status of the participants.

RESULTS

Baseline Characteristics

Of the midshipmen enrolled in this study that attended the NSW Screener (n = 29), 20 midshipmen between the ages of 18–26 yr (178.3 ± 6.0 cm, 77.2 ± 6.5 kg, 9.1 ± 3.1% body fat) passed the Screener from Fall 2018 (n = 6), Spring 2019 (n = 2), Fall 2019 (n = 5), and Fall 2020 (n = 7). A total of nine midshipmen did not pass the Screener across the four iterations included in this study. Changes in neuroendocrine, cytokine, and EV responses were therefore assessed among the 20 midshipmen able to complete the Screener.

Neuroendocrine Response to Stress

Significant decreases in circulating BDNF and IGF-I concentrations were observed following completion of the Screener (Fig. 2). BDNF concentrations dropped by −43.2% (P = 0.032, Hedges’ g = 0.509) and IGF-I decreased by −24.5% (P < 0.001, Hedges’ g = 3.869) from pre- to post-Screener. No significant changes were detected in cortisol (P = 0.427), NPY (P = 0.805), or α-klotho (P = 0.711).

Figure 2.

Neuroendocrine biomarker concentrations before and after 24-h Screener (N = 20). Significant declines (*P < 0.05) in response to the stress of the 24-h Screener were observed in brain-derived neurotrophic factor (BDNF) (A) and insulin-like growth factor I (IGF-I) (B), whereas no significant difference was observed in cortisol (C), neuropeptide-Y (NPY) (D), or α-klotho (E). Bars indicate mean and standard deviation. Gray lines connect raw data points corresponding to each individual’s response. Statistical significance was analyzed by paired samples t test.

In contrast, the proinflammatory cytokine TNF-α increased by +17.7% (P < 0.001, Hedges’ g = 1.640) accompanied by a +13.6% increase in log-transformed IL-6 concentrations (P = 0.055; Fig. 3). No changes were observed in IL-1β (P = 0.253) or the anti-inflammatory marker IL-10 (P = 0.152) (Fig. 3).

Figure 3.

Inflammatory cytokine concentrations before and after 24-h Screener (N = 20). A significant increase (*P < 0.05) in response to the stress of the 24-h Screener was observed in tumor necrosis factor-α (TNF-α) (A), whereas no significant differences were observed in interleukin 6 (IL-6) (B), interleukin 1β (IL-1β) (C), or interleukin 10 (IL-10) (D). Bars indicate mean and standard deviation. Gray lines connect raw data points corresponding to each individual’s response. Statistical significance was analyzed by paired samples t test.

Extracellular Vesicle Response to Stress

The concentration of total EVs was unchanged following the 24-h Screener (P = 0.760; Fig. 4B). However, the mean diameter of EVs significantly increased 1.9% following the Screener (P < 0.001, Hedges’ g = 1.019; Fig. 4C). Although the proportion of THSD1⁺ EVs relative to the total number of EVs was similar from pre- to post-Screener (P = 0.169; Fig. 5A), the average intensity of THSD1⁺ EVs increased by 18.9% in response to stress (P = 0.015, Hedges’ g = 0.584; Fig. 5B). This increase persisted by 39.4% when the average intensity of THSD1⁺ EVs was normalized to the total number of EVs (P = 0.004, Hedges’ g = 0.728; Fig. 5C).

Figure 4.

Extracellular vesicle (EV) characterization pre- and post-Screener using vesicle flow cytometry (vFC). A: representative plot of estimated diameter of all particles. Extracellular vesicles (EVs) were stained with a lipid membrane marker (vFRed) and analyzed by vFC. Data from each sample were collected for 120 s. B: there was no significant difference in EV concentration from pre- to post-Screener (N = 20). C: EV size was determined based on EV membrane fluorescence calibrated to synthetic vesicle size standards (Lipo100). EV diameter significantly (*P < 0.05) increased in response to the stress. Bars indicate mean and standard deviation. Gray lines connect raw data points corresponding to each individual’s response. Statistical significance was analyzed by paired samples t test.

Figure 5.

Changes in THSD1+ (apoptotic bodies), VAMP3+ (microvesicles), and CD63+ (exosomes) extracellular vesicles (EVs) relative to total EVs in response to 24-h Screener (N = 20). A significant difference (*P < 0.05) in response to the stress of the 24-h Screener was not observed in the proportion of THSD1⁺ EVs relative to the total number of EVs (A); however, the average intensity of THSD1 among all THSD1⁺ EVs (B) and average intensity of THSD1+ EVs normalized to total EVs (C) significantly increased. No significant changes were observed in the proportion of VAMP3⁺ EVs relative to the total number of EVs (D), whereas the average intensity of VAMP3 among all VAMP3⁺ EVs (E) and the average intensity of VAMP3+ EVs normalized to total EVs (F) significantly increased. No significant differences were observed in the proportion of CD63⁺ EVs relative to the total number of EVs (G), the average intensity of CD63⁺ among all CD63⁺ EVs (H), or CD63+ EVs normalized to total EVs (I). Note that in H, all CD63 intensities were increased by 50 to be above 0 for figure interpretation. Note that in I, all CD63 intensities were increased by 0.001 to be above 0 for figure interpretation. Bars indicate mean and standard deviation in all figures. Statistical significance was analyzed by paired samples t test.

Proportions of VAMP3⁺ EVs relative to the EV total were similar pre- to post-Screener (P = 0.189, Fig. 5D); however, the overall average intensity of VAMP3⁺ EVs significantly increased by 31.4% following the stress (P = 0.029, Hedges’ g = 0.517, Fig. 5E). The increase in VAMP3 intensity was also observed when normalized to the total number of EVs (P = 0.019, Hedges’ g = 0.562, Fig. 5F). No significant changes in response to the Screener were observed in the proportion of CD63⁺ EVs (P = 0.139, Fig. 5G), average intensity of CD63⁺ EVs (P = 0.066, Fig. 5H), or average intensity of CD63⁺ EVs normalized to total number of all EVs (P = 0.149, Fig. 5).

Among large-sized EVs, there was a 21.6% increase in the average intensity of THSD1⁺ following the 24-h Screener (P = 0.015, Hedges’ g = 0.584; Fig. 6A), whereas the average intensity of large-sized VAMP3⁺ EVs did not change significantly (log-transformed P = 0.058; Fig. 6B). No significant changes were observed among medium-sized EVs when considering average intensity of THSD1⁺ (P = 0.571; Fig. 6C) or VAMP3⁺ (P = 0.188; Fig. 6D). Similarly, there were no significant changes in the average intensity of CD63⁺ among small-sized EVs (P = 0.777; Fig. 6E).

Figure 6.

Changes in THSD1+ (apoptotic bodies), VAMP3+ (microvesicles), and CD63+ (exosomes) extracellular vesicles (EVs) based on size stratification pre- to post-Screener (N = 20). Among large-sized EVs, a significant difference (*P < 0.05) in response to the stress of the 24-h Screener was observed in the average intensity of THSD1⁺ (A), but not the average intensity of VAMP3⁺ (B). No changes were observed among medium-sized EVs in average intensity of THSD1⁺ (C) or the average intensity of VAMP3⁺ (D). There was no difference in the average intensity of CD63⁺ among small-sized EVs (E). Note that intensity data for D and E were increased by 50 arbitrary units (AU) to be above zero for figure interpretation. Bars indicate mean and standard deviation in all figures. Statistical significance was analyzed by paired samples t test.

Despite no significant changes observed among CD63⁺ EVs using imaging flow cytometry, significant changes among EVs stained with the tetraspanin cocktail (i.e., CD63/CD9/CD81) were observed when using vesicle flow cytometry. Though the concentration of CD63⁺/CD9⁺/CD81⁺ EVs was similar pre- to post-Screener (P = 0.446), there was a significant 4.9% increase in the median fluorescent intensity (i.e., level of expression) of CD63⁺/CD9⁺/CD81⁺ EVs following the Screener (P < 0.001, Hedges’ g = 2.308) (Fig. 7). In-well immunofluorescence Western analysis verified the presence of EV surface marker intensities for CD9 (0.010 AU per EV), CD63 (0.003 AU per EV), and CD81 (0.015 AU per EV) (Supplemental Fig. S2). Vesicle flow cytometry raw data are included in the Supplemental Material.

Figure 7.

Changes in tetraspanin (TS) cocktail+ (CD63, CD81, and CD9) extracellular vesicles (EVs) pre- to post-Screener (N = 20). A and B: the concentration of TS cocktail+ EVs was similar pre- to post-Screener (A), whereas the median fluorescence intensity (MFI) of TS cocktail+ EVs significantly (*P < 0.05) increased in response to the stress (B). Bars indicate mean and standard deviation in all figures. AU, arbitrary units. Statistical significance was analyzed by paired samples t test.

Relationships between Inflammatory Cytokines and Extracellular Vesicles

Several significant positive correlations were identified between inflammatory cytokines and EV profiles, both at the pre-Screener and post-Screener timepoints. Before the Screener onset, higher concentrations of IL-1β and IL-10 were associated with a larger proportion of THSD1⁺ EVs relative to total EVs (r_s = 0.550, P = 0.012; r_s = 0.484, P = 0.030, respectively). Likewise, following the Screener, higher concentrations of IL-10 correlated with a larger proportion of THSD1⁺ EVs relative to total EVs (r_s = 0.440, P = 0.047), whereas IL-1 β did not reach statistical significance (P = 0.111). No other significant correlations were observed between inflammatory cytokines and extracellular vesicles at the pre- or post-Screener timepoints (all P > 0.05).

DISCUSSION

The primary aim of this study was to characterize the impact of an intense, 24-h military field-based stressor on circulating neuroendocrine biomarkers, cytokines, and EV profiles. Furthermore, we sought to examine the relationships between cytokines and various aspects of the EV profile. The study findings support our primary hypothesis by demonstrating an increase in inflammatory cytokines and average intensity of THSD1⁺ EVs, with a concurrent decrease in neurotrophic biomarkers. Our data also suggest that there is a positive relationship between inflammatory cytokines and proportion of TSHD1⁺ EVs relative to total EVs at both pre- and post-Screener timepoints.

Intense 24-h Military Operational Stress Reduces Circulating Neuroendocrine Biomarkers and Elevates Proinflammatory Cytokines

Our findings demonstrated decreases in IGF-I (−24.5%) and BDNF (−43.2%) concentrations following the 24-h NSW Screener course. These results are in agreement with previous reports of decreases in anabolic hormones and neurotrophins following intense military training of longer durations ranging from days to weeks (1, 6, 34). IGF-I concentrations have been reported to decline by ∼40%–50% during 8-wk Ranger training (1, 2, 34), −22% within the first 13 days of Finnish military field training (35), and by −24% after just 3 days of sustained military operations involving high energy expenditure and caloric deficit (36). Likewise, similar declines in BDNF have been reported after 24 h of simulated military operational stress (5) and following several weeks of military training (1, 6). The IGF-I system enhances protein synthesis and attenuates protein degradation (37, 38), whereas BDNF regulates energy homeostasis and enhances fat oxidation during exercise (39, 40), both of which are important and favorable physiological processes. The comparable proportional decline in IGF1 after just 24 h of military operational stress in the present study compared with 3 days of sustained military operations affirms the intensity, rigor, and energetic and metabolic demands of the NSW Screener.

The physiological and metabolic stress imposed by the Screener is also apparent by the increases in proinflammatory cytokines TNF-α (+17.7%) and IL-6 (+13.6%), responses that can occur following prolonged exercise, inadequate recovery, and/or excessive training stress (41, 42). TNF-α is secreted at the onset of the inflammatory response, followed by IL-6, which has been reported to be produced by myoblasts and regenerating myofibers in response to muscle injury (43). As demonstrated by Lundeland and colleagues (44), elevated concentrations of TNF-α were present 3 days after the onset of an intense 7-day Norwegian military ranger training, whereas increases in IL-1β and IL-6 were not significant. However, with prolonged exposure to military operational stress, elevations in IL-6 are more prominent as evident by a 217% increase in IL-6 immediately following 8 wk of US Army Ranger training, compared with a nonsignificant increase in IL-10 and nonsignificant decreases in TNF-α and IL-1β (1). Of note, IL-6 was the first identified “myokine,” a substance produced and released by skeletal muscle that exerts effects on other organs of the body (45, 46). Although microtraumas to muscle and connective tissue occur with exercise training and can elicit a mild inflammatory response, continued high-volume and high-intensity training with little rest can lead to systemic inflammation, producing large quantities of proinflammatory cytokines (43).

Contrary to other military operational stress scenarios (3, 7), we did not observe a significant increase in cortisol following the 24-h military stressor. Previous studies have reported serum cortisol concentrations increased by over +200% in military training consisting of ∼24 h of simulated captivity and interrogations (3, 7, 47). However, upon further investigation, the average cortisol concentration at the pre-Screener timepoint (29.93 µg/dL) was more similar to average concentrations following mock prisoner of war interrogations (27.23–33.6 µg/dL) than baseline concentrations in other military stressors (3, 7, 47). Therefore, it is plausible that midshipmen were already in a heightened stress state several hours before the onset of the Screener. A recent meta-analysis of the anticipatory stress response to sport competition supports this notion. Cortisol concentrations before competition were significantly higher than time-matched baseline concentrations on a noncompetition day, presumably in preparation for impending psychological and physiological demands (48).

THSD1 Abundance Increased among Large-Sized EVs following 24-h Military Operational Stress, with Minimal Change in VAMP3⁺ and CD63⁺ EVs

This is the first study to examine EVs in a military field setting consisting of prolonged physical exertion with minimal sleep, accompanied by mental fatigue. Furthermore, performance during the Screener determines midshipmen’s fate to attend NSW summer training, which augments the fear of failure. Provided that the Screener is multifactorial stress, interpreting the degree of impact from individual stressors on EVs is uncertain. However, due to the continuous physical activity over the 24-h period, it is likely that the response is predominately attributed to physical stress. In the present study, we observed a significant increase in average intensity of THSD1 normalized to the total number of EVs, as well as when normalized to large-sized EVs, corresponding to the typical size range of apoptotic bodies. THSD1 has been associated with apoptotic bodies and is considered a molecular bridge between phagocytic and apoptotic cells by aiding in the recognition and phagocytosis of cells undergoing apoptosis (9, 49). More broadly, THSD1 is involved in cell-to-cell communication and regulates tissue genesis, motility, proliferation, and repair (49). Provided that physical tasks during the Screener primarily involve running, ruck marching, and calisthenics, higher heart rates and eccentric muscle load may require a more prominent repair response to manage tissue homeostasis (10). Furthermore, strenuous exercise contributes to enhanced production of microparticles from platelets and neutrophils due to high endothelial shear stress (50), which may have contributed to the increase in VAMP3 abundance per EV (9).

We did not observe significant changes among CD63⁺ EVs in response to the Screener. Previous studies have reported a marked increase in EVs following exercise in various populations (51–53), specifically EVs enriched with tetraspanins CD63, CD9, and CD81 (10, 54). The lack of significant changes may be attributed in part to the timing of the post-Screener collection, as small EVs are released shortly after exercise onset and concentrations can remain elevated for up to 90 min following exercise (i.e., running), but return to baseline within 6–24 h (10, 55). Considering post-Screener blood draws were completed ∼14 h following completion of the Screener, it is possible that the optimal window during which CD63⁺ EVs would be elevated was missed. However, an increase in the level of intensity of CD63⁺/CD9⁺/CD81⁺ EVs was detected with vesicle flow cytometry, indicating that other markers associated with exosomes were elevated following the Screener. Furthermore, skeletal muscle may be a key contributor to circulating EVs following exercise and contribute to muscle homeostasis, repair, and regeneration (53). EVs are rarely identified by a single surface marker (54); therefore, casting a “wider net” of surface markers associated with each of the three EV subpopulations, as well as markers associated with a specific cell or tissue origin, is advantageous to capture surface marker heterogeneity.

Although physical exertion was the predominant stress during the Screener, sleep and caloric restriction are additional factors that were present and must be considered when interpreting these results. EVs facilitate circadian rhythm synchronization by acting as a bridge between the internal master clock located in the suprachiasmatic nucleus in the hypothalamus, which is calibrated by environmental time and the regulation of individual cells (19, 20). In a study by Khalyfa et al. (20), exosomes derived from mice exposed to inverted light-dark cycles altered canonical clock gene expression in white adipose tissue and subsequent metabolic function. These data suggest changes in EV profiles may be attributed to sleep derangement. With regard to feeding state, Newman et al. (56) reported that despite no difference in total EV particles and size between fasted or fed states, the abundance of CD9⁺ EVs was significantly greater in a fasted state. In contrast, several studies have reported significant postprandial increases in EV concentrations (57, 58). However, these studies used marker-less quantification methods for measuring EV concentration; therefore, at least part of the increase in concentrations may be attributed to an increase in co-isolates such as lipoproteins (56, 59). In agreement with Newman et al. (56), the present study demonstrated significant changes in the level of expression of CD63⁺/CD9⁺/CD81⁺ EVs, despite no changes in total EV concentration. The nature of the present field study precluded measurements of feeding state, energy expenditure, and sleep parameters during the Screener. Therefore, the extent to which each individual stressor contributed to alterations in the EV profile is unknown. Rather, this study contributes to ecological validity of the EV response to multistress environments.

Notable Relationships Are Present between Cytokines and THSD1⁺ EVs

Higher concentrations of IL-10 and IL-1β were significantly associated with a larger proportion of THSD1⁺ EVs at the pre-Screener timepoint. In the present study, THSD1 was used as a surface marker associated with apoptotic bodies, which are among the largest-sized EVs (9). Positive relationships between cytokine concentration and EV size have previously been reported (60). Higher concentrations of monocyte chemoattractant protein-1 (MCP-1), a cytokine that helps regulate migration of white blood cells, were positively associated with the largest-sized EV subpopulation, whereas associations were weaker among EVs of smaller sizes (60, 61). Similar associations were also identified between EV size and IL-1β, although the physiological relevance of this relationship is unknown (60). IL-10 has been reported to heighten apoptosis in various cell types associated with both innate (e.g., monocytes) and adaptive (e.g., type 1 T helper cells) immunity (62). Similar to the role of THSD1 in regulating proliferation and repair, IL-10 is able to repress proinflammatory gene expression and promote regeneration (49, 60). Though not examined in this study, IL-1α, another member of the IL-1 family, has been observed in endothelial cell-derived apoptotic bodies (11). Although several other cytokines have been associated with EVs, IL-1β remains among one of the most established associations (11), supporting the findings from the present study.

In addition to exchanging intercellular information via surface molecules, EVs are also known to mediate cytokine transport (11). Most notably, plasma membrane-derived EVs are a major secretory pathway for the rapid release of IL-1β as this cytokine lacks a peptide signal for secretion (11, 63, 64). A recent study by Tang et al. (65) demonstrated that IL-10-loaded EVs significantly mitigated renal injury and inflammation in a murine model of ischemic acute kidney injury. While it is evident that many mechanistic questions remain surrounding relationships between EVs and cytokines, studies such as Tang et al. will be important for future investigations of the utility of EVs as a primary vehicle for cytokine delivery and a potential therapeutic in combating the deleterious effects of multifactorial stress.

Considerations

Several limitations should be acknowledged when interpreting the findings of this study. Here, we used three surface marker proteins to broadly characterize changes across exosomes (CD63), microvesicles (VAMP3), and apoptotic bodies (THSD1) in response to military field training. However, EVs are rarely identified by a single surface marker and often are not unique to one subpopulation (54). For example, some vesicles containing well-established EV markers and within the exosomal range of 30–150 nm have been shown to bud from the plasma membrane, a form of biogenesis characteristic of microvesicles (46). Plasma-derived EVs comprise vesicles from various cells containing specific cell-type markers for platelets (e.g., CD41a, CD61), endothelial cells (e.g., CD62E, CD146b), and immune cells (e.g., CD40, CD69) (11, 66). Future research should consider including cell type-specific surface markers and measurements of nucleic and/or protein contents within EVs to further advance our understanding of cell cross talk during periods of intense stress. The general EV markers used in this study provide a broad assessment of EV subpopulation shifts that occur with intense, multifactorial stress.

It should also be noted that samples from the first Screener collected in Fall 2018 remained in storage at −80°C for ∼2.5 yr before analyses. Although most cytokines are stable for up to 2 yr when stored at −80°C, IL-1β, IL-6, and IL-10 are susceptible to degradation (67). Long-term storage of isolated EVs at −80°C may decrease EV concentration and increase particle size in both a time and freeze-thaw cycle-dependent manner (68). EVs recovered from frozen plasma appear to be less impacted than frozen isolated EVs, possibly a result of being preserved in the biological environment (68). Other reports suggest long-term storage of frozen samples and freeze-thaw cycles have minimal impact on EVs (69, 70). In the present study, EVs were isolated from frozen plasma samples and EV isolates were subsequently stored at −80°C for 4 wk. Nonetheless, the lengthy storage time of frozen plasma samples may be considered a potential limitation.

The timing of the post-Screener blood draw may have precluded discernable changes in EV concentration and CD63⁺ EVs. Small EVs remain elevated following an acute bout of exhaustive exercise, but return to baseline within 6 h (10). In addition, beyond 90 min of moderate, continuous exercise, the rate of EV clearance has been reported to exceed the rate of EV biogenesis (54). It is unclear to what extent 24 h of continuous, intense exercise may alter the rate of EV release and clearance, as many acute exercise protocols in EV research are <60 min of continuous exercise. Conducting research in military training scenarios is accompanied by inherent limitations given that the primary goal of operational trainings is to achieve military objectives, with less concern for research activity. This often contributes to potentially confounding factors, such as delayed sample collection times (47, 71).

Conclusions

Our results demonstrate that the arduous 24-h NSW Screener impacted neuroendocrine biomarkers and inflammatory cytokines similar to that previously observed in military training lasting days or weeks (1, 6, 34). We observed a significant increase in the abundance of THSD1 following stress. The proportion of THSD1⁺ EVs was positively associated with pro- and anti-inflammatory cytokines, IL-1β and IL-10, respectively. Provided that EVs are a key pathway for IL-1β secretion during the adaptive stress response, future studies should investigate EV cargo, including nucleic acids and proteins, as potential targets to mediate the neuroendocrine stress response. In addition, future studies should consider incorporating cell-type-specific EV markers as an indication of EV origin to discern how the unique multifactorial stress of military training impacts different physiological systems to varying degrees. Stress responses are essential for adaptation in challenging and demanding environments (72). Given the predictive and diagnostic capabilities EVs harbor through posttranscriptional regulation of gene expression (9, 14, 18), EVs may serve as a valuable biometric tool in military settings to elucidate key physiological adaptations to acute stress.

DATA AVAILABILITY

The data that support the findings of this study will be made available upon reasonable request from the corresponding author.

SUPPLEMENTAL DATA

Supplemental Figs. S1 and S2: https://doi.org/10.6084/m9.figshare.20020721.v1

GRANTS

This work was supported by the Department of Defense (“Physiological Biomarkers of Resilience and Musculoskeletal Readiness,” Award No. W81XWH-18-1-0452).

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the authors.

AUTHOR CONTRIBUTIONS

M.E.B., S.D.F., F.A., and B.C.N., conceived and designed research; M.E.B., W.R.C., B.M., and M.W. performed experiments; M.E.B., W.R.C., and Q.M. analyzed data; M.E.B., W.R.C., Q.M., A.S., and F.A. interpreted results of experiments; M.E.B. prepared figures; M.E.B. drafted manuscript; M.E.B., W.R.C., Q.M., B.M., A.S., S.D.F., A.K.L., M.W., A.Su, F.A., and B.C.N. edited and revised manuscript; M.E.B., W.R.C., Q.M., B.M., A.S., S.D.F., A.K.L., M.W., A.Su, F.A., and B.C.N. approved final version of manuscript.