Impact of prolonged continuous exposure to stress on immune function and gut microbiome in a perpetual avoidance of water on a wheel mouse model

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Tourlousse, Nanako Itoh, Yuji Sekiguchi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7484981/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Response to stress involves a complex interplay between the immune system and the gut microbiome, yet studies on long-term continuous stress remain scarce due to the lack of suitable models. In this study, we employed our previously developed Perpetual Avoidance of Water on a Wheel (PAWW) model to investigate the effects of prolonged stress on immune-related cytokine expression and gut microbiome composition in mice. We demonstrated that PAWW stress can be sustained for six weeks without signs of behavioral or physiological adaptation. Circadian locomotor rhythms remained disrupted throughout the exposure period, accompanied by elevated plasma norepinephrine and epinephrine. Quantitative real-time PCR revealed significant upregulation of inflammation-related cytokine genes, including TNF-α in the brain and IL-6 and IL-1β in intestinal Peyer’s patches (PPs). Microbiome profiling by 16S rRNA gene sequencing showed that stressed mice underwent more pronounced compositional changes than controls, particularly in members of the Lachnospiraceae family. These findings indicate that the PAWW model provides a robust platform for investigating the effects of chronic stress on the brain–immune–gut axis. Prolonged PAWW stress was associated with both inflammatory immune responses and microbiome alterations, highlighting a potential role for PPs in mediating intestinal immune regulation under stress conditions. Stress mice model chronic stress cytokines immune function gut microbiome Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Stress is a major health concern globally and has been linked to increased prevalence and severity of a myriad of health problems, including psychiatric, gastrointestinal, and cardiovascular diseases [1-3]. The role of the gut-brain axis in mediating the body’s response to stress has long been recognized. In the last decade, accumulating evidence has further revealed the central role of the gut microbiome in regulating the gut-brain axis [4]. This entails interactions between the gut microbiome and multiple pathways involved in communication between the gut and brain, particularly the immune system [5]. Further, numerous studies have demonstrated the potential of beneficial microbes to improve stress responses [6]. Gaining a deeper understanding of the interplay between the gut microbiome and immune system under stress thus holds promise for developing next-generation microbiome-based therapies to alleviate stress and its associated health complications [7]. Animal models, particularly rodents, have been extensively used to investigate the effects of stress on the immune system and gut microbiome [8]. Such studies showed that exposure to a myriad of stressors, both chronic and acute, affects the gut microbiome in a variety of animal models [9]. More specifically, stress-induced dysbiosis of the gut microbiome is characterized by changes in microbial diversity and shifts in the abundance of particular species, namely the depletion of beneficial microorganisms and/or the overgrowth of pathobionts, as observed in both animal models and humans [10, 11]. Most previous studies on the impact of stress on the immune system-gut microbiome axis have focused on intermittently applied stressors [12]. The effects of continuous stressors on both the gut microbiome and immune system remain largely unexplored, mainly due to a lack of suitable models. Since different types of stressors may induce distinct changes in the gut microbiome, understanding how continuous stressors influence the gut microbiome, and the immune system, could improve our understanding of their roles in stress responses. The Perpetual Avoidance of Water on a Wheel (PAWW) model, which we previously developed [13], offers a promising system to investigate the impact of long-term stress exposure on the immune system-gut microbiome axis. Compared to conventional water avoidance stress models, which impose stress for only one hour per day [10], the PAWW model exposes mice to continuous stress by placing them on a running wheel suspended above water, thereby forcing the mice to remain active to prevent their tails from contacting the water surface. We previously showed that mice can be exposed to PAWW stress for up to three weeks, without adaptation and sustained disruption of their circadian rhythms. The purpose of this study was to investigate the role of the immune system–gut microbiome axis under long-term stress exposure. To achieve this, we extended the PAWW model to six weeks by confirming that stressed mice showed sustained disruption of their circadian rhythms and elevated plasma levels of stress hormones. We then applied this extended model to investigate the impact of long-term stress on: (i) the expression of immune-related cytokine genes, and (ii) the diversity and composition of the gut microbiome. For this purpose, gene expression in the brain and multiple peripheral organs of stressed mice by quantitative real-time PCR and compared to expression levels in unstressed controls. Evaluating cytokine gene expression in multiple organs allows for a more precise understanding of how chronic stress disrupts neuroimmune, mucosal and systemic immune homeostasis. For microbiome analysis, fecal samples were collected every two weeks and analyzed by high throughput 16S rRNA gene amplicon sequencing to determine how stress affected microbiome diversity and the abundance of specific bacterial lineages. Materials and methods 2.1 Animals experiment All experiments involving animals were conducted in accordance with the guidelines for the Care and Use of Laboratory Animals at the National Institute of Advanced Industrial Science and Technology (AIST, Japan) and the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines 2.0. The study protocol was approved by AIST’s Animal Care and Use Committee (approval no. 2021-0318). Six-week-old male C3H/HeN mice (n=12), obtained from Japan SLC (Hamamatsu, Japan), were housed individually (that is, one mouse per cage) throughout the entire course of the study. Mice had unrestricted access to a standard diet (diet CE-2; CLEA Japan, Tokyo, Japan) and running wheels (model SW-15; Melquest, Toyama, Japan). Mice were maintained under a 12-hr light/dark cycle (LD 12:12; lights on at 8:00 Zeitgeber time [ZT] 0) with a white, fluorescent lamp at cage level providing 350 lux of light. All mice were acclimated to these conditions for two weeks prior to the initiation of PAWW stress exposure. After the two-week acclimatization period, mice were randomly assigned to one of two groups (n=6 per group). The control group remained under the same conditions. The mice in the stress group were placed individually in cages with restricted running wheels. As previously described, they were exposed to continuous psychological stress through perpetual water avoidance by replacing the paper-chip bedding with a 1.5-cm layer of water [13]. Wheel-running activity was continuously monitored at one-minute intervals using the Chronobiology Kit (Stanford Software Systems, Stanford, CA, USA). 2.2 Sample collection Freshly evacuated fecal pellets were collected on autoclaved paper by briefly transferring the animals to a new cage lined with autoclaved paper (for mice in the control group) or by replacing the water layer beneath the running wheels with autoclaved paper (for mice in the stress group). Feces of mice were collected at 0-, 2-, 4- and 6-weeks and stored immediately at -80°C until processing. At the conclusion of the six-week experiment, mice were euthanized using 2-3% isoflurane anesthesia. Blood was collected by cardiac puncture and transferred into EDTA-coated tubes (Terumo Corporation, Tokyo, Japan) [14]. Blood samples were centrifuged at 4500 g at 4°C for 10 min, and recovered plasma stored at -20°C. Subsequently, brain, liver, mesenteric lymph node (MLNs), Peyer’s patches (PPs) and spleen were surgically collected, snap-frozen in liquid nitrogen, and immediately stored at -80°C until further analysis. The collection of all samples was conducted at 9:00 Zeitgeber time [ZT] 1 to prevent confounding due to circadian rhythms. 2.3 Enzyme-linked immunosorbent assays The levels of plasma corticosterone (Corticosterone Competitive ELISA Kit, Invitrogen, USA) epinephrine and norepinephrine (Epinephrine/Norepinephrine ELISA Kit, Abnova, Taiwan) were determined by enzyme-linked immunosorbent assays according to the manufacturer’s instructions. All samples were measured duplicate, and the average used as the final value for each mouse. 2.4 Total RNA extraction and quantitative real-time PCR Total RNA was extracted from tissues using RNAiso total RNA extraction reagent (Takara Bio, Otsu, Japan). The cDNA was generated from 1 µg of total RNA using PrimeScript RT Master Mix (Takara Bio, Otsu, Japan). Quantitative real-time PCR was performed using SYBR Premix Ex Taq II (Takara Bio, Otsu, Japan) on a LightCycler instrument (Roche Diagnostics, Mannheim, Germany). Sequences of the qPCR primers (Thermo Fisher Scientific) are listed in Table S1 [15]. PCR conditions were as follows: 95 °C for 10 s, followed by 45 cycles of 95 °C for 5 s, 58 °C for 10 s and at 72 °C for 10 s. A standard curve was generated using a four-fold serial dilution of cDNA pooled across samples, and used to calculate gene expression levels relative to the expression of the housekeeping gene 36B4 [16]. 2.5 Fecal DNA extraction, 16S rRNA gene amplicon PCR and sequencing Extraction of DNA from fecal pellets (approximately 50 to 100 mg, wet weight) was performed using the ISOSPIN Fecal DNA kit (Nippon Gene, Toyama, Japan), following our previously described protocol [17]. Amplicon libraries of the V4 hypervariable region of the 16S rRNA gene were constructed using Illumina’s two-step tailed PCR method, as we described previously [18]. More specifically, first-round PCRs (20 μl) contained 1× KAPA HiFi HotStart ReadyMix, 500 nM each of tailed forward (5’-TCGTCGGCAGCGTCAGATGTGTATAAGA GACAGGTGYCAGCMGCCGCGGTAA-3’) and reverse primer (5’-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACN VGGGTWTCTAAT-3’), and 5 ng of fecal DNA. Thermal cycling conditions were as follows: 95°C for 3 min; 16 cycles of 95°C for 30 s, 55°C for 30 s and 72°C for 30 s; and finally 72°C for 5 min. Amplicons were purified using the Agencourt AMPure XP PCR Purification system (bead-to-sample ratio of 1:1) and eluted in 25 μl of 10 mM Tris-HCl (pH 8.5). Dual indexing of amplicon libraries was performed using Illumina’s Nextera XT Index Kit, in PCRs (25 μl) containing 1× KAPA HiFi HotStart ReadyMix, 2.5 μl each of Index 1 and 2 primers, and 2.5 μl of purified first-round PCR products. Thermal cycling conditions were as follows: 95°C for 3 min; 8 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s; 72°C for 5 min. Amplicons were purified as described above and quantified using the D1000 ScreenTape Assay system and 2200 TapeStation instrument (Agilent). Amplicon libraries were combined at equimolar concentrations, supplemented with PhiX control DNA, and sequenced on an Illumina MiSeq instrument using V2 chemistry (2×251 bp paired-end reads). Across samples ( n =48), a total of 135,448±28,878 (mean±SD) read pairs were obtained, of which 116,038±27,594 were retained the final ASV count table (see below for details). 2.6 Sequence data processing and reconstruction of amplicon sequence variants Primers in the sequence reads were trimmed using Cutadapt v4.2 [19], with parameters -g ^GTGYCAGCMGCCGCGGTRA -G ^GGACTACNVGGGTWTCTAAK --pair-adapters --no-indels --error-rate 1 --discard-untrimmed --minimum-length 225. Primer-trimmed reads were further processed using the DADA2 v1.26 [20] filterAndTrim function, with parameters truncLen = c(180,150), = c(0, 0), maxN = c(0,0), maxEE = c(2,2), rm.phix = TRUE. Subsequent reconstruction of amplicon sequence variants (ASVs) followed DADA2’s standard workflow for big data (https://benjjneb.github.io/dada2/bigdata_paired.html), including learning of error models (function learnErrors , with options randomize = TRUE, nbases = 2e+08), denoising (function dada2 ), and merging of denoised forward and reverse reads (function mergePairs ). Data from different sequencing runs were processed separately and resultant sequence tables merged using DADA2’s mergeSequenceTables function, followed by removing bimeras using DADA2’s removeBimeraDenovo function, with method = "consensus". ASV sequences (features) were imported into QIIME 2 (version qiime2-amplicon-2024.5 [21]) and taxonomically classified against the Greengenes2 database (version 2022.10 [22]) using the q2-feature-classifier plugin (command: qiime feature-classifier classify-sklearn, with default confidence level of 0.7) using a publicly available pre-trained model (2022.10.backbone.v4.nb.sklearn-1.4.2.qza, obtained from https://data.qiime2.org/classifiers/sklearn-1.4.2/greengenes2). A final sequence table was obtained after removing ASVs with an aberrant length (254 bp) and ASVs that were not assigned at the phylum level. 2.7 Microbiome data analysis Data were imported into R v4.2.2 [23], run using RStudio v2023.03.1+446, for analysis and visualization, mainly using packages part of the tidyverse ecosystem v2.0.0 [24], including ggplot2 v3.5.1 and dplyr v1.1.4, and the community ecology package vegan v2.6 [25]. Unless stated otherwise, all analyses were performed using compositionality-aware techniques, without subsampling of the count data. Prior to other analyses, rare ASVs that were detected in <10 samples (out of 48) and/or with a <100 total reads were removed. To mitigate undefined logarithms, zeros in the count table were imputed by Bayesian-multiplicative replacement, using the function cmultRepl (method = "CZM", output = "p-counts") of the R package zCompositions v1.5.0 [26]. Centered-log ratio transformation of the zero-imputed count tables was performed using vegan’s decostand function (method = "clr"). Aitchison distances were calculated as the Euclidean distance of the CLR-transformed counts using vegan’s vegdist function (method = "euclidean"). Principal components analysis, based on the CLR-transformed count data, was performed R stats’ prcomp function. Alpha diversity (ASV-level Shannon diversity and richness) was calculated using vegan’s diversity and specnumber functions, based on count data randomly subsampled to even depth (35,000) using vegan’s rrarefy function. 2.8 Statistical analysis. Mean levels of stress hormones and cytokines between experimental groups were compared using Welch's t-test, with R stats’ t.test function with option var.equal = FALSE. For wheel-running activity, total wheel revolutions per day were averaged across 7 days to generate weekly values for downstream analysis [13]. Wheel-running activity the week prior to assignment of mice to the two experimental groups was taken as the baseline. Linear mixed-effects model analysis was performed using the function mixed of the R package afex v1.3 [27], involving the experimental group, time (that is, weeks post-baseline), and their interaction as main effects and subject as a random effect. For the microbiome data, group centroids were compared by permutational analysis of variance (PERMANOVA, vegan’s adonis2 function) based on Aitchison distances calculated based on imputed ASV count tables. To account for repeated measures, restricted permutations were set using the function permute of the R package permute v0.9. For pairwise comparisons, we used Welch’s t-test on the distances, using the functions and script available from https://github.com/alekseyenko/Tw2. Microbial differential abundance analysis was performed using the function linda of the R package LinDA v0.2.0 [28]. In short, LinDA uses linear regression of CLR-transformed data along with compositional bias correction [28]. The zero-imputed count table was used as input, and, as above, the model included the experimental group (categorical variable), time (numerical variable) as well as their interaction as the main effects and subject (categorical variable) as the random effect. Based on the LinDA’s output, ASVs with a significant interaction term (false discovery rate adjusted p-values of ≤0.1) were retained. Results and discussion 3.1 Effect of six-week PAWW stress on circadian rhythm of wheel-running activity To enable studying the effect of long-term continuous stress on the brain–immune–gut axis, we assessed whether PAWW stress can be applied for six weeks, compared to the 3-week period evaluated in our original study [13]. Mice were housed in individual wheel-running cages, and their locomotor activity was monitored. After two weeks of acclimatization, mice were randomly assigned to a control and stress group. For the stress group, continuous psychological stress (i.e. PAWW stress) was induced by replacing the paper-chip bedding in the cages with a layer of water. As shown in Fig. 1A , mice exposed to PAWW stress exhibited a rapid and marked disruption of their circadian locomotor rhythm. More specifically, stressed mice showed a decrease in total daily activity ( Fig. 1B ) and an increase in daytime activity ( Fig. 1C ) over the entire course of the experiment. These changes are consistent with our previous observations [13] ,and demonstrate that PAWW stress disrupted the mice’s circadian locomotor rhythms without signs of adaptation over the six-week exposure period. This confirms the utility of the PAWW model for investigating the long-term effects of chronic stress, and establishes it, to the best of our knowledge, as one of the most prolonged continuous stress models reported to date. 3.2 Effect on plasma levels of corticosterone, epinephrine and norepinephrine After confirming sustained disruption of the mice’s circadian rhythms, we next analyzed stress hormone levels to verify that the mice maintained a physiological stress response after the six-week exposure period. The levels of the stress hormones corticosterone, epinephrine, and norepinephrine were measured in plasma samples collected at the end of the six-week experiment. As shown in Fig. 2A , both epinephrine and norepinephrine concentrations were significantly elevated (p-value <0.05, Welch’s t-test) in stressed mice compared to controls. Corticosterone levels were also increased in the stress group, but the difference compared to the control group did not reach statistical significance (p-value = 0.063). These results indicate that exposure to PAWW stress for six weeks leads to a sustained increase in stress hormones in the plasma. Our previous study showed that epinephrine and norepinephrine levels significantly increased, whereas corticosterone levels declined after one week of PAWW stress [13]. Typically, acute stress triggers the rapid release of stress-related hormones such as epinephrine, norepinephrine, and cortisol (corticosterone in rodents) to initiate immediate physiological responses [29, 30]. However, under chronic stress conditions, prolonged activation of these pathways can lead to sustained elevations in stress hormone levels [31]. Our findings suggest that continued exposure to PAWW stress for six weeks successfully captured features of chronic stress, as evidenced by the sustained elevation of these stress hormones. 3.3 Effect on the expression of immune-related cytokine genes Exposure to stress is well known to impact the immune system and inflammatory responses [32]. Specifically, stress can alter the balance between pro- and anti-inflammatory cytokines, leading to changes in immune function and increased risk of immune-related diseases [33]. Whether PAWW stress leads to similar physiological response was not characterized in our previous work. To understand how chronic stress influences immune responses, it is essential to evaluate cytokine expression in both central and peripheral immune-related organs. The brain is the central regulator of the stress response and is particularly sensitive to proinflammatory cytokines. Chronic stress triggers glial activation and neuroinflammation, which contributes to behavioral alterations [34-36]. Meanwhile, key peripheral immune organs, including PPs, MLNs, spleen, and the liver, play critical roles in mucosal and systemic immune regulation. Stress can impair their function, promote inflammation, and alter immune responses [37, 38]. To investigate these effects, we used real-time quantitative PCR to measure the expression of multiple pro-inflammatory cytokines (namely, tumor necrosis factor-alpha (TNF-α), interleukin (IL)-6, IL-1β, IL-1RA, nuclear factor-kB (NF-κB)) and the anti-inflammatory cytokine IL-10 in the brain and peripheral organs (liver, MLNs, PPs, and spleen) of stressed and control mice. As shown in Fig. 2B , expression of the TNF-α gene was significantly upregulated in the brain of stressed mice compared to controls (p-value = 0.037, Welch’s t-test). In addition to the inflammatory response in the brain, PAWW stress also led to significant upregulation of the expression of IL-1β (p-value = 0.009) and IL-6 (p-value = 0.034) gene expression in intestinal PPs ( Fig. 2B ). Previous studies have shown that chronic stress induces neuroinflammation in rodent models and humans [36]. This neuroinflammation is characterized by changes in inflammatory mediators, such as NF-κB, Toll-like receptors, and proinflammatory cytokines, including TNF-α, IL-1β, and IL-6 in the brain and serum [39, 40]. TNF-α is a major mediator of neuroinflammation and is consistently elevated in the brain of rodents subjected to chronic stress [35] and in patients with depression [41]. While IL-6 is pleiotropic, it is primarily pro-inflammatory and has been linked to sleep disturbances in animal models [42]. The absence of IL-6 has also been shown to protect against stress-induced intestinal injury and apoptosis [43]. IL-1β plays a central role in the effects of chronic stress, including depressive-like behavior and impaired neurogenesis [44]. IL-1β is commonly elevated in inflammatory bowel disease (IBD) and colitis models, particularly in IBD patients experiencing sleep disturbances [45]. These studies indicate that IL-6 and IL-1β are key mediators of sleep- and gastrointestinal-related dysfunction under stress conditions. Taken together, our results suggest that IL-6 and IL-1β may mediate the gastrointestinal effects of PAWW stress, potentially through mechanisms involving sleep disturbances. To our knowledge, the upregulation of proinflammatory cytokines (namely, IL-6 and IL-1β) in stress models has not been reported specifically in intestinal PPs. However, inflammation in PPs has been implicated in the pathogenesis of Crohn's disease, a chronic inflammatory disease of the gastrointestinal tract [37]. Chronic stress has been shown to compromise the functional integrity of the follicle-associated epithelium, a key component of PPs, thereby increasing the uptake of luminal antigens and bacteria. This increased antigen exposure in PPs may exacerbate immune activation and suggests a potential role of chronic stress in the initiation of pro-inflammatory immune responses within the intestinal mucosa [38]. These findings suggest that PAWW stress promotes inflammatory cytokines expression in PPs, contributing to intestinal mucosal immune response. As a whole, our findings indicate that PAWW stress promotes neuroinflammation and upregulates key proinflammatory cytokines in intestinal PPs, contributing to mucosal immune dysregulation. 3.4 Effect of PAWW stress on gut microbiome diversity After having demonstrated that sustained PAWW stress was accompanied by upregulation of the expression of pro-inflammatory cytokine genes as part of the brain–immune–gut axis, we next investigated whether PAWW stress led to changes in the gut microbiome. Here, we first observed that stressed mice produced harder feces than control mice ( Fig. S1 ), although the total fecal output remained comparable between the groups (data not shown). These changes in fecal consistency or hardness may reflect stress-induced alterations in gut motility, water absorption, or mucus secretion [46]. Clinical studies have shown that elevated stress hormones, such as cortisol and norepinephrine, are associated with harder stools [47]. Together, this suggests that higher levels of stress hormones may contribute to harder stool production in PAWW-stressed mice. We characterized the gut microbiome of mice in both experimental groups by 16S rRNA gene amplicon sequencing of fecal samples collected at week 0, representing the baseline community, and after 2, 4, and 6 weeks. As shown in Fig. S2 , Firmicutes_D (IQR: 30.6-54.5%), Firmicutes_A (24.3-51.6%), and Bacteroidota (5.8-18.9%) represented the most abundant phyla across mice. At higher taxonomic ranks, the families Lactobacillaceae (27.5-51.6%) and Lachnospiraceae (21.3-46.1%), along with Bacteroidaceae (2.2-7.5%) and Muribaculaceae (2.2-6.2%), were the most abundant. The most abundant genera included Ligilactobacillus , Lactobacillus , and Limosilactobacillus within the family of the Lactobacillaceae . Within the family, Lachnospiraceae , the hitherto uncultured genus COE1 was the most abundant, along with the genus Kineothrix . We first explored gut microbiome community structure and composition by principal component analysis (PCA) based on Aitchison distances. As shown in Fig. 3A and Fig. S3 , samples of the stress and control groups clustered together at baseline but differed at subsequent time points. Along with the first principal component, which captured 26.9% of the variance in microbiome compositions, the trajectories of the control and stress groups were significantly different (linear mixed effects model, p-value < 0.01 for the interaction term) ( Fig. 3B ). Further, analysis of community differences among groups by PERMANOVA indicated that experimental treatment, time as well as their interaction significantly affected microbiome compositions (p-value < 0.01). Based on pairwise comparisons, the control and stress groups were not significantly different at baseline (p-value = 0.178) but showed significant differences for subsequent time points (p-value < 0.05). These data suggested that exposure to PAWW stress resulted in compositional changes in the mice’s gut microbiome. To further quantify this effect, we calculated per-subject community dissimilarities to the baseline (that is, the week-0 samples), an approach previously described as volatility analysis in the context of stress [48]. Using the Aitchison distance as above, this analysis revealed that dissimilarity-to-baseline for the stress group differed significantly from the control group at all time points (p-value < 0.05; Mann-Whitney test; Fig. 3C ). Further, comparison of first distances (that is, dissimilarities between consecutive samples) showed that stressed mice also showed significant differences in changes between weeks 2 and 4 compared to the control mice, whereas dissimilarities between week-4 and week-6 samples for both experimental groups were comparable ( Fig. S4 ). This suggested that the gut microbiome stabilized after continued exposure to stress, with a composition different from that of the control group. These data showed that chronic exposure to stress led to significant changes in gut microbiome composition, with differences that exceeded changes due to natural progression of the gut microbiome in the control group. In contrast to beta diversity, alpha diversity (Shannon index and ASV richness) did not show any significant differences between experimental groups ( Fig. S5 ). 3.5 Differentially abundant taxa, in stress group compared to control group Having observed that the gut microbiome undergoes changes due to PAWW stress, we next sought to identify which microbiome features (i.e. ASVs) differed significantly between the stress and control groups. To this end, we performed compositionality-aware differential abundance analysis using linear mixed effect models, as implemented in LinDA (see Methods). We then retained on ASVs with a significant interaction term (adjusted p-value of <0.1) between treatment and time in the fitted model, as these represent ASVs that responded differently in the control and stress groups. A total of 47 ASVs (out of) were found to exhibit a significantly different response in the stress group compared to the control group ( Fig. 4A ). Of these, 15 and 32 ASVs showed a positive and negative interaction term, respectively. As shown in Fig. 4B and Fig. S6 , ASVs with a positive interaction term represented ASVs that showed an increase in their relative abundance following exposure to stress. Within the top 10 ASVs with the largest effect size, this included 7 ASVs belonged to the family Lachnospiraceae , including four ASVs assigned to the genus COE1 (asv19 and asv29) and Ventrimonas (asv96 and asv126), as well as ASVs belonging to the genera Enterococcus_D (asv140), Borkfalkia (asv199), and Enterenecus (asv 147). ASVs with the top 10 largest negative effect sizes all belonged to the family Lachnospiraceae ( Fig. 4B and Fig. S7 ). A a whole, these results showed that PAWW stress led to considerable changes in the abundances of specific phylotypes. The majority of these were affiliated with the family Lachnospiraceae within the Greengenes2 taxonomic framework, with the recognition that Lachnospiraceae is the second-most abundant family across samples in our study, with the highest ASV richness (mean: 82; IQR: 73-95, at a subsampling depth of 35,000) ( Fig. S8 ). Sorted by effect size, two ASVs (i.e. asv29 and ASV19) belonging to the genus COE1 within the family Lachnospiraceae showed the strongest increase in abundance; these two ASVs were virtually absent in control mice but reached abundances exceeding 1% after 6 weeks of PAWW stress. In addition to members of the COE1 genus, two ASVs (i.e. asv96 and ASV126) assigned to the recently proposed genus Ventrimonas [49] also showed a significant increase due to PAWW stress. However, little is known about the potential role of these two genera in the murine gut. In addition, we also found that other phylotypes within the genus COE1 showed decreased abundance after stress exposure, and future studies are needed to better understand these dynamics. Named (at the genus level) ASVs that showed the strongest decrease included two ASVs belonging to the genus Kineothrix (i.e. asv73 and asv41) as well as two ASVs related to Eubacterium (i.e. asv4 and asv163 assigned to the genera Eubacterium _J and Eubacterirum _F, respectively). Currently, a single species within the genus Kineothrix has been validly published, namely K. alysoides [50], which was described as a saccharolytic butyrate-producer. Given that butyrate is an important metabolite with anti-inflammatory and immune-regulating properties, this suggest that decreased abundance Kineothrix may be associated with increased inflammation in stressed mice . In similar fashion, Eubacterium spp. generally represents beneficial microbes that contribute to homeostasis through production of butyrate as well as cholesterol and bile acid metabolism [51]. This suggests loss of beneficial microbes, although higher-resolution microbiome characterization using shotgun metagenomics would be needed to substantiate this, especially considering that other ASVs belong to the genus Kineothrix showed increased abundance. As a whole, our microbiome analysis points to a significant association between members of the family Lachnospiraceae and long-term continuous stress exposure. Changes in Lachnospiraceae have previously also been observed in several animal models of stress [52, 53]. Similarly, various types of stress have been associated with changes in the abundance of Lachnospiracea in human studies [12, 54]. Most human studies have shown that Lachnospiraceae are associated with ulcerative colitis and Crohn’s disease, which are chronic immune-mediated inflammatory diseases of the gastrointestinal tract [55-57]. Previously, it has been suggested that PPs play a role in regulating the gut-brain axis through interactions with the gut microbiome [58]. Disruptions in the composition of the gut microbiome have been linked to neurodegenerative and neuroinflammatory diseases [59]. PPs engage in bidirectional communication with the microbiome and may influence gut-brain signaling [60]. However, the mechanisms by which stress-induced PP dysfunction affect brain function via microbiome alterations remain poorly understood and require further investigation. Our results show that PAWW stress significantly alters gut microbiome composition and induces inflammation in brain and PPs. As a whole, our findings revealed the link between microbial alterations and stress-related immune responses relevant to the gut-brain axis. The application of the PAWW model to germ-free mice has the potential to provide more clarity on this interaction. Selective introduction of specific microbiome, particularly Lachnospiraceae , might reveal their role in PPs-mediated immune signaling and its impact on gut-brain axis. Conclusion Our results show that prolonged continuous stress induced by the PAWW model over a six-week period effectively captures characteristics of chronic stress, as demonstrated by sustained disruption of circadian locomotor rhythms and persistent elevation of stress hormone levels without signs of physiological adaptation. Moreover, chronic PAWW stress significantly altered both immune-related gene expression and gut microbiome composition in mice. The upregulation of pro-inflammatory cytokines indicates that chronic PAWW stress promotes inflammation in both the brain and PPs, highlighting a potential link between sustained stress and mucosal immune responses. Additionally, shifts in the gut microbiome, particularly those involving members of the Lachnospiraceae family, suggest that the gut microbiome may contribute to stress-induced immune changes. As a whole, our findings indicate that the PAWW model is suitable for investigating the brain–immune–gut axis under chronic stress conditions, as it reveals concurrent changes in immune responses and gut microbiome composition, warranting further exploration of their potential interactions. Declarations CRediT authorship contribution statements Papawee Saiki: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Funding acquisition, Investigation, Data curation, Formal analysis, Conceptualization. Dieter M. Tourlousse: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Data curation. Nanako Itoh: Methodology, Investigation. Yuji Sekiguchi: Writing – review & editing, Visualization, Supervision. Koyomi Miyazaki: Visualization, Supervision. Declaration of competing interest The authors declare that there are no financial or personal relationships that could have inappropriately influenced the research presented in this study. Data availability Raw sequencing data have been deposited in NCBI’s Sequence Read Archive under BioProject PRJNA1190504. Funding This study was supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Early-Career Scientists (grant number 20K19700) to P. Saiki. References Agid, O., Y. Kohn, and B. Lerer, Environmental stress and psychiatric illness. Biomedicine & Pharmacotherapy, 2000. 54 (3): p. 135-141. Mayer, E.A., The neurobiology of stress and gastrointestinal disease. Gut, 2000. 47 (6): p. 861-869. Steptoe, A. and M. Kivimäki, Stress and cardiovascular disease. Nature Reviews Cardiology, 2012. 9 (6): p. 360-370. Pang, S., J. Wen-Yi, and W. Zi, The interplay between the gut microbiome and neurological disorders: Exploring the gut-brain Axis. Neurology Letters, 2023. 2 (1): p. 25-29. Morys, J., A. Małecki, and M. Nowacka-Chmielewska, Stress and the gut-brain axis: an inflammatory perspective. Frontiers in Molecular Neuroscience, 2024. 17 : p. 1415567. Liu, R.T., The microbiome as a novel paradigm in studying stress and mental health. American Psychologist, 2017. 72 (7): p. 655. Zhang, R.Y., et al., The gut microbiome, immune modulation, and cognitive decline: insights on the gut-brain axis. Frontiers in Immunology, 2025. 16 . Leigh, S.J., et al., The impact of acute and chronic stress on gastrointestinal physiology and function: a microbiota-gut-brain axis perspective. Journal of Physiology-London, 2023. 601 (20): p. 4491-4538. Karl, J.P., et al., Effects of Psychological, Environmental and Physical Stressors on the Gut Microbiota. Frontiers in Microbiology, 2018. 9 . Choi, S.I., et al., The Protective Effect of Roseburia faecis Against Repeated Water Avoidance Stress-induced Irritable Bowel Syndrome in a Wister Rat Model. J Cancer Prev, 2023. 28 (3): p. 93-105. Krupa-Kotara, K., et al., The Role of Microbiota Pattern in Anxiety and Stress Disorders—A Review of the State of Knowledge. Psych, 2023. 5 (3): p. 602-618. Bellach, L., et al., The Effects of Caloric Restriction and Clinical Psychological Intervention on the Interplay of Gut Microbial Composition and Stress in Women. Nutrients, 2024. 16 (16). Miyazaki, K., et al., Continuous exposure to a novel stressor based on water aversion induces abnormal circadian locomotor rhythms and sleep-wake cycles in mice. PLoS One, 2013. 8 (1): p. e55452. Suabjakyong, P., et al., Polyphenol extract from Phellinus igniarius protects against acrolein toxicity in vitro and provides protection in a mouse stroke model. PLoS One, 2015. 10 (3): p. e0122733. Saiki, P., et al., The anti-inflammatory effect of Agaricus brasiliensis is partly due to its linoleic acid content. Food Funct, 2017. 8 (11): p. 4150-4158. Saiki, P., et al., Purified Gymnemic Acids from Gymnema inodorum Tea Inhibit 3T3-L1 Cell Differentiation into Adipocytes. Nutrients, 2020. 12 (9): p. 2851. Tourlousse, D.M., et al., Validation and standardization of DNA extraction and library construction methods for metagenomics-based human fecal microbiome measurements. Microbiome, 2021. 9 (1). Tourlousse, D.M., et al., Characterization and Demonstration of Mock Communities as Control Reagents for Accurate Human Microbiome Community Measurements. Microbiology Spectrum, 2022. 10 (2). Martin, M., Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. journal, 2011. 17 (1): p. 10-12. Callahan, B.J., et al., DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 2016. 13 (7): p. 581-583. Bolyen, E., et al., Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 (vol 37, pg 852, 2019). Nature Biotechnology, 2019. 37 (9): p. 1091-1091. McDonald, D., et al., Greengenes2 unifies microbial data in a single reference tree. Nat Biotechnol, 2024. 42 (5): p. 715-718. Team, R.C., RA language and environment for statistical computing, R Foundation for Statistical. Computing, 2020. Wickham, H., et al., Welcome to the Tidyverse. Journal of open source software, 2019. 4 (43): p. 1686. Oksanen, J., et al., vegan: Community Ecology Package. R package version 2.6-4. 2022 . 2023. Palarea-Albaladejo, J. and J.A. Martín-Fernández, zCompositions - R Package for multivariate imputation of left-censored data under a compositional approach. Chemometrics and Intelligent Laboratory Systems, 2015. 143 : p. 85-96. Singmann, H., et al., Evaluating the robustness of parameter estimates in cognitive models: A meta-analytic review of multinomial processing tree models across the multiverse of estimation methods. Psychological Bulletin, 2024. Zhou, H., et al., LinDA: linear models for differential abundance analysis of microbiome compositional data. Genome Biol, 2022. 23 (1): p. 95. Shukla, P.K., et al., Chronic stress and corticosterone exacerbate alcohol-induced tissue injury in the gut-liver-brain axis. Scientific Reports, 2021. 11 (1). Flak, J.N., et al., Role of paraventricular nucleus-projecting norepinephrine/epinephrine neurons in acute and chronic stress. European Journal of Neuroscience, 2014. 39 (11): p. 1903-1911. Sic, A., et al., Neurobiological Implications of Chronic Stress and Metabolic Dysregulation in Inflammatory Bowel Diseases. Diseases, 2024. 12 (9). Huang, C.J., et al., Psychological stress during exercise: immunoendocrine and oxidative responses. Exp Biol Med (Maywood), 2010. 235 (12): p. 1498-504. Agarwal, S.K. and G.D. Marshall, Jr., Stress effects on immunity and its application to clinical immunology. Clin Exp Allergy, 2001. 31 (1): p. 25-31. Ravi, M., A.H. Miller, and V. Michopoulos, The immunology of stress and the impact of inflammation on the brain and behaviour. BJPsych advances, 2021. 27 (3): p. 158-165. Clark, I.A., L.M. Alleva, and B. Vissel, The roles of TNF in brain dysfunction and disease. Pharmacology & Therapeutics, 2010. 128 (3): p. 519-548. White, A.G., et al., Chronic Stress-Induced Neuroinflammation: Relevance of Rodent Models to Human Disease. International Journal of Molecular Sciences, 2024. 25 (10). Gullberg, E. and J.D. Söderholm, Peyer's patches and M cells as potential sites of the inflammatory onset in Crohn's disease. Inflammatory Bowel Disease: Genetics, Barrier Function, Immunologic Mechanisms, and Microbial Pathways, 2006. 1072 : p. 218-232. Velin, Å.K., et al., Increased antigen and bacterial uptake in follicle associated epithelium induced by chronic psychological stress in rats. Gut, 2004. 53 (4): p. 494-500. Wang, Y., et al., TLR4-NF-kappaB Signal Involved in Depressive-Like Behaviors and Cytokine Expression of Frontal Cortex and Hippocampus in Stressed C57BL/6 and ob/ob Mice. Neural Plast, 2018. 2018 : p. 7254016. Tang, Y., et al., miR-182 mediated the inhibitory effects of NF-κB on the GPR39/CREB/BDNF pathway in the hippocampus of mice with depressive-like behaviors. Behavioural brain research, 2022. 418 : p. 113647. Pandey, G.N., et al., Abnormal protein and mRNA expression of inflammatory cytokines in the prefrontal cortex of depressed individuals who died by suicide. Journal of Psychiatry & Neuroscience, 2018. 43 (6): p. 376-385. Periasamy, S., et al., Sleep Deprivation-Induced Multi-Organ Injury: Role of Oxidative Stress and Inflammation. Excli Journal, 2015. 14 : p. 672-683. Zhang, Y., et al., Knockout of IL-6 mitigates cold water-immersion restraint stress-induced intestinal epithelial injury and apoptosis. Front Immunol, 2022. 13 : p. 936689. Goshen, I., et al., Brain interleukin-1 mediates chronic stress-induced depression in mice via adrenocortical activation and hippocampal neurogenesis suppression. Molecular Psychiatry, 2008. 13 (7): p. 717-728. Ali, T., et al., Sleep, immunity and inflammation in gastrointestinal disorders. World Journal of Gastroenterology, 2013. 19 (48): p. 9231-9239. Camilleri, M., et al., Intestinal barrier function in health and gastrointestinal disease. Neurogastroenterology and Motility, 2012. 24 (6): p. 503-512. Lemay, D.G., et al., Technician-Scored Stool Consistency Spans the Full Range of the Bristol Scale in a Healthy US Population and Differs by Diet and Chronic Stress Load. Journal of Nutrition, 2021. 151 (6): p. 1443-1452. Bastiaanssen, T.F., et al., Volatility as a concept to understand the impact of stress on the microbiome. Psychoneuroendocrinology, 2021. 124 : p. 105047. Hitch, T.C., et al., Broad diversity of human gut bacteria accessible via a traceable strain deposition system. bioRxiv, 2024. Haas, K.N. and J.L. Blanchard, Kineothrix alysoides, gen. nov., sp. nov., a saccharolytic butyrate-producer within the family Lachnospiraceae. International journal of systematic and evolutionary microbiology, 2017. 67 (2): p. 402-410. Mukherjee, A., et al., Gut microbes from the phylogenetically diverse genus Eubacterium and their various contributions to gut health. Gut Microbes, 2020. 12 (1). Li, S.Y., et al., shift in the microbial community of mice faecal sample effects on water immersion restraint stress. Amb Express, 2017. 7 . Wang, R., et al., The effects of chronic unpredicted mild stress on maternal negative emotions and gut microbiota and metabolites in pregnant rats. Peerj, 2023. 11 . Ma, L., et al., Psychological Stress and Gut Microbiota Composition: A Systematic Review of Human Studies. Neuropsychobiology, 2023. 82 (5): p. 247-262. Sankarasubramanian, J., et al., Gut Microbiota and Metabolic Specificity in Ulcerative Colitis and Crohn's Disease. Frontiers in Medicine, 2020. 7 . Mancabelli, L., et al., Identification of universal gut microbial biomarkers of common human intestinal diseases by meta-analysis. Fems Microbiology Ecology, 2017. 93 (12). He, X.X., et al., Relationship between clinical features and intestinal microbiota in Chinese patients with ulcerative colitis. World Journal of Gastroenterology, 2021. 27 (28). Asgari, R., et al., Peyer’s Patch: possible target for modulating the Gut-Brain-Axis through microbiota. Cellular Immunology, 2024. 401 : p. 104844. Khatoon, S., et al., Effects of gut microbiota on neurodegenerative diseases. Frontiers in Aging Neuroscience, 2023. 15 . Abo-Shaban, T., et al., Issues for patchy tissues: defining roles for gut-associated lymphoid tissue in neurodevelopment and disease. Journal of Neural Transmission, 2023. 130 (3): p. 269-280. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7484981","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":600902761,"identity":"625a1248-3179-490d-8125-e3bd62bdad0e","order_by":0,"name":"Papawee Saiki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYHADxmaGhAoGHiiPjVgtZ4Ba2IjXwsDMwNhGhGJz9rMHP/NU2DDItx9uNng4z05Gfn4D84sPDHx5uLRY9uQlS/OcSWMwOJPYnJC4LZnH4BgDm+UMBrZiXFoMDuQYSOe2Ha7fwJDYfCBxGzOPARsDmzHQR4kNuLScf2P8O/fffwb5/odALXPqeeTbCGm5kWMmndtwgIHhBshhDYd5GI4xMD/Gp8Vyxrs06z/HkoF6HzYbJBw7DvRLYhvjDAPcfjHnzz18c0aNHdBh6Y8lf9RU28s3Hz784UPFMZwhZgCPbgRgbJNgMDiWQIoWBuYPDAw1OLWMglEwCkbBiAMAvGFUF4QkILgAAAAASUVORK5CYII=","orcid":"","institution":"National Institute of Advanced Industrial Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Papawee","middleName":"","lastName":"Saiki","suffix":""},{"id":600902762,"identity":"4ded2fcd-0542-47e3-b9a2-6a3ef5079020","order_by":1,"name":"Dieter M. Tourlousse","email":"","orcid":"","institution":"National Institute of Advanced Industrial Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Dieter","middleName":"M.","lastName":"Tourlousse","suffix":""},{"id":600902763,"identity":"9a98635b-ff6a-4f5d-b207-344522de3255","order_by":2,"name":"Nanako Itoh","email":"","orcid":"","institution":"National Institute of Advanced Industrial Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Nanako","middleName":"","lastName":"Itoh","suffix":""},{"id":600902764,"identity":"b7eb37a2-a4c2-4dc8-b7a5-5478f30c9e35","order_by":3,"name":"Yuji Sekiguchi","email":"","orcid":"","institution":"National Institute of Advanced Industrial Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuji","middleName":"","lastName":"Sekiguchi","suffix":""},{"id":600902765,"identity":"7014f187-7d55-4906-b666-b53c35a3b180","order_by":4,"name":"Koyomi Miyazaki","email":"","orcid":"","institution":"National Institute of Advanced Industrial Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Koyomi","middleName":"","lastName":"Miyazaki","suffix":""}],"badges":[],"createdAt":"2025-08-29 05:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7484981/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7484981/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104268075,"identity":"d4795782-1b4b-40bd-bbe7-5231c6d1229e","added_by":"auto","created_at":"2026-03-09 20:59:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":451810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eRepresentative double-plot actograms of wheel-running activity for mice in the control (top panel) and stress (bottom panel) groups under 12-hour light: dark cycles. Days, relative to the starting point of PAWW stress exposure, are shown on the left axis. Relative total daily wheel-running activity, compared to the pre-stress baseline (indicated as “pre”) \u003cstrong\u003e(B)\u003c/strong\u003e and daytime-to-total activity \u003cstrong\u003e(C)\u003c/strong\u003efor both experimental groups (colored as indicated in the legend). For panels B and C, data are shown as the mean (symbols) and SEM (error bars) for six animals per group. Statistical testing was performed using a linear mixed effects model and p-values for the effect of treatment is indicated (*p \u0026lt; 0.01, **p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7484981/v1/8fbb97127feb8dac28c5253d.jpg"},{"id":104404579,"identity":"8c91b65c-fb6e-48ff-a19f-36ab65514eb5","added_by":"auto","created_at":"2026-03-11 12:20:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":736713,"visible":true,"origin":"","legend":"\u003cp\u003ePlasma concentration of the stress hormones corticosterone (pg/mL), epinephrine (ng/mL) and norepinephrine levels (ng/mL) \u003cstrong\u003e(A)\u003c/strong\u003e and relative gene expression levels of IL-10, IL-1β, IL-1RA, IL-6, NFkβ, and TNF-α in various tissues, as indicated in the facet labels \u003cstrong\u003e(B)\u003c/strong\u003e. Measurements were performed at ZT1 at the end of the experiment (that is, week 6). Data are shown as the mean (bars) and SEM (error bars) for six animals per group (C: control group; S: stressed group). Black symbols represent individual datapoints. P-values were obtained using two-tailed Welch’s t-tests and considered significant at a threshold of 0.05 (marked with an asterisk).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7484981/v1/4e0c9ffc5ff62e6842e64f6c.jpg"},{"id":104268072,"identity":"8efb5d2f-0b94-4160-9541-da58f9aaa1cc","added_by":"auto","created_at":"2026-03-09 20:59:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003ePrincipal components analysis of gut microbiome compositions. Each symbol represents a distinct sample, color-coded according to the experimental group and time point as indicated in the legend (control: blue; stress: red). \u003cstrong\u003e(B)\u003c/strong\u003eTrajectories of samples along the first principal component of the ordination plot shown in panel A. \u003cstrong\u003e(C)\u003c/strong\u003e Gut microbiome volatility expressed as the dissimilarity (Aitchison distance) of week-2, week-4 and week-6 samples to subject-matched baseline communities (that is, week-0 samples). Thin lines in panels B and C show repeated measures of individual animals. Symbol and error bars represent the mean and SEM of six animals per group. P-values in panels B and C were calculated using the Mann–Whitney U-test and considered significant at a threshold of 0.05 (marked with an asterisk). Colors in panels B and C reflect the color scheme in panel A (i.e. stress group: dark red and control group: dark blue).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7484981/v1/e26e3e8b0d653bbafd12758e.jpg"},{"id":104404689,"identity":"f7acc47e-94d6-40f3-86b8-fe732958e00c","added_by":"auto","created_at":"2026-03-11 12:20:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":950082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eBar charts of effect sizes for the interaction term of the LinDA linear mixed effect model. Fill colors represent signed FDR-adjusted p-values as indicated in the legend. Colored squares on the left indicate the family-level taxonomic assignment of the ASVs; genus-level taxonomic assignments are shown in the \u003cem\u003ey\u003c/em\u003e-axis labels (unnamed genera in the Greengenes2 taxonomy framework are shown as “g__unnamed”, and ASVs not assigned at the genus level are shown as “g__unclassified”). \u003cstrong\u003e(B)\u003c/strong\u003eRelative abundances of the ASVs shown on the left panel. Circles and error bars indicate the geometric mean and standard deviation of six animals per group (control: dark blue; stress: dark red).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7484981/v1/622f4501600df400d9970dde.jpg"},{"id":104779855,"identity":"715b0eab-9422-4fc3-9d34-be2d920fe23b","added_by":"auto","created_at":"2026-03-17 07:46:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3307792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7484981/v1/82198194-1fe3-495c-835b-1b7a798167a5.pdf"},{"id":104268071,"identity":"be623c7e-78b3-4dd8-8b34-63389a4909c3","added_by":"auto","created_at":"2026-03-09 20:59:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1387690,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementdata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7484981/v1/bbbdd707d0a9f6b90440c4fd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of prolonged continuous exposure to stress on immune function and gut microbiome in a perpetual avoidance of water on a wheel mouse model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStress is a major health concern globally and has been linked to increased prevalence and severity of a myriad of health problems, including psychiatric, gastrointestinal, and cardiovascular diseases [1-3]. The role of the gut-brain axis in mediating the body\u0026rsquo;s response to stress has long been recognized. In the last decade, accumulating evidence has further revealed the central role of the gut microbiome in regulating the gut-brain axis [4]. This entails interactions between the gut microbiome and multiple pathways involved in communication between the gut and brain, particularly the immune system [5]. Further, numerous studies have demonstrated the potential of beneficial microbes to improve stress responses [6]. Gaining a deeper understanding of the interplay between the gut microbiome and immune system under stress thus holds promise for developing next-generation microbiome-based therapies to alleviate stress and its associated health complications [7].\u003c/p\u003e\n\n\u003cp\u003eAnimal models, particularly rodents, have been extensively used to investigate the effects of stress on the immune system and gut microbiome [8]. Such studies showed that exposure to a myriad of stressors, both chronic and acute, affects the gut microbiome in a variety of animal models [9]. More specifically, stress-induced dysbiosis of the gut microbiome is characterized by changes in microbial diversity and shifts in the abundance of particular species, namely the depletion of beneficial microorganisms and/or the overgrowth of pathobionts, as observed in both animal models and humans [10, 11]. \u003c/p\u003e\n\n\u003cp\u003eMost previous studies on the impact of stress on the immune system-gut microbiome axis have focused on intermittently applied stressors [12]. The effects of continuous stressors on both the gut microbiome and immune system remain largely unexplored, mainly due to a lack of suitable models. Since different types of stressors may induce distinct changes in the gut microbiome, understanding how continuous stressors influence the gut microbiome, and the immune system, could improve our understanding of their roles in stress responses.\u003c/p\u003e\n\n\u003cp\u003eThe Perpetual Avoidance of Water on a Wheel (PAWW) model, which we previously developed [13], offers a promising system to investigate the impact of long-term stress exposure on the immune system-gut microbiome axis. Compared to conventional water avoidance stress models, which impose stress for only one hour per day [10], the PAWW model exposes mice to continuous stress by placing them on a running wheel suspended above water, thereby forcing the mice to remain active to prevent their tails from contacting the water surface. We previously showed that mice can be exposed to PAWW stress for up to three weeks, without adaptation and sustained disruption of their circadian rhythms.\u003c/p\u003e\n\n\u003cp\u003eThe purpose of this study was to investigate the role of the immune system\u0026ndash;gut microbiome axis under long-term stress exposure. To achieve this, we extended the PAWW model to six weeks by confirming that stressed mice showed sustained disruption of their circadian rhythms and elevated plasma levels of stress hormones. We then applied this extended model to investigate the impact of long-term stress on: (i) the expression of immune-related cytokine genes, and (ii) the diversity and composition of the gut microbiome. For this purpose, gene expression in the brain and multiple peripheral organs of stressed mice by quantitative real-time PCR and compared to expression levels in unstressed controls. Evaluating cytokine gene expression in multiple organs allows for a more precise understanding of how chronic stress disrupts neuroimmune, mucosal and systemic immune homeostasis. For microbiome analysis, fecal samples were collected every two weeks and analyzed by high throughput 16S rRNA gene amplicon sequencing to determine how stress affected microbiome diversity and the abundance of specific bacterial lineages.\u003c/p\u003e\n"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Animals experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments involving animals were conducted in accordance with the guidelines for the Care and Use of Laboratory Animals at the National Institute of Advanced Industrial Science and Technology (AIST, Japan) and the ARRIVE (Animal Research: Reporting of \u003cem\u003eIn Vivo\u003c/em\u003e Experiments) guidelines 2.0. The study protocol was approved by AIST\u0026rsquo;s Animal Care and Use Committee (approval no. 2021-0318). \u003c/p\u003e\n\n\u003cp\u003eSix-week-old male C3H/HeN mice (n=12), obtained from Japan SLC (Hamamatsu, Japan), were housed individually (that is, one mouse per cage) throughout the entire course of the study. Mice had unrestricted access to a standard diet (diet CE-2; CLEA Japan, Tokyo, Japan) and running wheels (model SW-15; Melquest, Toyama, Japan). Mice were maintained under a 12-hr light/dark cycle (LD 12:12; lights on at 8:00 Zeitgeber time [ZT] 0) with a white, fluorescent lamp at cage level providing 350 lux of light. All mice were acclimated to these conditions for two weeks prior to the initiation of PAWW stress exposure.\u003c/p\u003e\n\n\u003cp\u003eAfter the two-week acclimatization period, mice were randomly assigned to one of two groups (n=6 per group). The control group remained under the same conditions. The mice in the stress group were placed individually in cages with restricted running wheels. As previously described, they were exposed to continuous psychological stress through perpetual water avoidance by replacing the paper-chip bedding with a 1.5-cm layer of water [13]. Wheel-running activity was continuously monitored at one-minute intervals using the Chronobiology Kit (Stanford Software Systems, Stanford, CA, USA). \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.2 Sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFreshly evacuated fecal pellets were collected on autoclaved paper by briefly transferring the animals to a new cage lined with autoclaved paper (for mice in the control group) or by replacing the water layer beneath the running wheels with autoclaved paper (for mice in the stress group). Feces of mice were collected at 0-, 2-, 4- and 6-weeks and stored immediately at -80\u0026deg;C until processing. \u003c/p\u003e\n\n\u003cp\u003eAt the conclusion of the six-week experiment, mice were euthanized using 2-3% isoflurane anesthesia. Blood was collected by cardiac puncture and transferred into EDTA-coated tubes (Terumo Corporation, Tokyo, Japan) [14]. Blood samples were centrifuged at 4500 \u003cem\u003eg\u003c/em\u003e at 4\u0026deg;C for 10 min, and recovered plasma stored at -20\u0026deg;C. Subsequently, brain, liver, mesenteric lymph node (MLNs), Peyer\u0026rsquo;s patches (PPs) and spleen were surgically collected, snap-frozen in liquid nitrogen, and immediately stored at -80\u0026deg;C until further analysis. The collection of all samples was conducted at 9:00 Zeitgeber time [ZT] 1 to prevent confounding due to circadian rhythms.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.3 Enzyme-linked immunosorbent assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe levels of plasma corticosterone (Corticosterone Competitive ELISA Kit, Invitrogen, USA) epinephrine and norepinephrine (Epinephrine/Norepinephrine ELISA Kit, Abnova, Taiwan) were determined by enzyme-linked immunosorbent assays according to the manufacturer\u0026rsquo;s instructions. All samples were measured duplicate, and the average used as the final value for each mouse.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.4 Total RNA extraction and quantitative real-time PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from tissues using RNAiso total RNA extraction reagent (Takara Bio, Otsu, Japan). The cDNA was generated from 1 \u0026micro;g of total RNA using PrimeScript RT Master Mix (Takara Bio, Otsu, Japan). Quantitative real-time PCR was performed using SYBR Premix Ex Taq II (Takara Bio, Otsu, Japan) on a LightCycler\u003csup\u003e \u003c/sup\u003einstrument (Roche Diagnostics, Mannheim, Germany). Sequences of the qPCR primers (Thermo Fisher Scientific) are listed in Table S1 [15]. PCR conditions were as follows: 95 \u0026deg;C for 10 s, followed by 45 cycles of 95 \u0026deg;C for 5 s, 58 \u0026deg;C for 10 s and at 72 \u0026deg;C for 10 s. A standard curve was generated using a four-fold serial dilution of cDNA pooled across samples, and used to calculate gene expression levels relative to the expression of the housekeeping gene 36B4 [16].\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.5 Fecal DNA extraction, 16S rRNA gene amplicon PCR and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtraction of DNA from fecal pellets (approximately 50 to 100 mg, wet weight) was performed using the ISOSPIN Fecal DNA kit (Nippon Gene, Toyama, Japan), following our previously described protocol [17].\u003c/p\u003e\n\n\u003cp\u003eAmplicon libraries of the V4 hypervariable region of the 16S rRNA gene were constructed using Illumina\u0026rsquo;s two-step tailed PCR method, as we described previously [18]. More specifically, first-round PCRs (20 \u0026mu;l) contained 1\u0026times; KAPA HiFi HotStart ReadyMix, 500 nM each of tailed forward (5\u0026rsquo;-TCGTCGGCAGCGTCAGATGTGTATAAGA GACAGGTGYCAGCMGCCGCGGTAA-3\u0026rsquo;) and reverse primer (5\u0026rsquo;-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACN VGGGTWTCTAAT-3\u0026rsquo;), and 5 ng of fecal DNA. Thermal cycling conditions were as follows: 95\u0026deg;C for 3 min; 16 cycles of 95\u0026deg;C for 30 s, 55\u0026deg;C for 30 s and 72\u0026deg;C for 30 s; and finally 72\u0026deg;C for 5 min. Amplicons were purified using the Agencourt AMPure XP PCR Purification system (bead-to-sample ratio of 1:1) and eluted in 25 \u0026mu;l of 10\u0026thinsp;mM Tris-HCl (pH 8.5). Dual indexing of amplicon libraries was performed using Illumina\u0026rsquo;s Nextera XT Index Kit, in PCRs (25\u0026thinsp;\u0026mu;l) containing 1\u0026times; KAPA HiFi HotStart ReadyMix, 2.5\u0026thinsp;\u0026mu;l each of Index 1 and 2 primers, and 2.5\u0026thinsp;\u0026mu;l of purified first-round PCR products. Thermal cycling conditions were as follows: 95\u0026deg;C for 3\u0026thinsp;min; 8 cycles of 95\u0026deg;C for 30 s, 55\u0026deg;C for 30 s, and 72\u0026deg;C for 30 s; 72\u0026deg;C for 5\u0026thinsp;min. Amplicons were purified as described above and quantified using the D1000 ScreenTape Assay system and 2200 TapeStation instrument (Agilent). Amplicon libraries were combined at equimolar concentrations, supplemented with PhiX control DNA, and sequenced on an Illumina MiSeq instrument using V2 chemistry (2\u0026times;251 bp paired-end reads). Across samples (\u003cem\u003en\u003c/em\u003e=48), a total of 135,448\u0026plusmn;28,878 (mean\u0026plusmn;SD) read pairs were obtained, of which 116,038\u0026plusmn;27,594 were retained the final ASV count table (see below for details).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.6 Sequence data processing and reconstruction of amplicon sequence variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimers in the sequence reads were trimmed using Cutadapt v4.2 [19], with parameters -g ^GTGYCAGCMGCCGCGGTRA -G ^GGACTACNVGGGTWTCTAAK --pair-adapters --no-indels --error-rate 1 --discard-untrimmed --minimum-length 225. Primer-trimmed reads were further processed using the DADA2 v1.26 [20] \u003cem\u003efilterAndTrim\u003c/em\u003e function, with parameters truncLen = c(180,150), = c(0, 0), maxN = c(0,0), maxEE = c(2,2), rm.phix = TRUE. Subsequent reconstruction of amplicon sequence variants (ASVs) followed DADA2\u0026rsquo;s standard workflow for big data (https://benjjneb.github.io/dada2/bigdata_paired.html), including learning of error models (function \u003cem\u003elearnErrors\u003c/em\u003e, with options randomize = TRUE, nbases = 2e+08), denoising (function \u003cem\u003edada2\u003c/em\u003e), and merging of denoised forward and reverse reads (function \u003cem\u003emergePairs\u003c/em\u003e). Data from different sequencing runs were processed separately and resultant sequence tables merged using DADA2\u0026rsquo;s \u003cem\u003emergeSequenceTables\u003c/em\u003e function, followed by removing bimeras using DADA2\u0026rsquo;s \u003cem\u003eremoveBimeraDenovo\u003c/em\u003e function, with method = \u0026quot;consensus\u0026quot;. ASV sequences (features) were imported into QIIME 2 (version qiime2-amplicon-2024.5 [21]) and taxonomically classified against the Greengenes2 database (version 2022.10 [22]) using the q2-feature-classifier plugin (command: qiime feature-classifier classify-sklearn, with default confidence level of 0.7) using a publicly available pre-trained model (2022.10.backbone.v4.nb.sklearn-1.4.2.qza, obtained from https://data.qiime2.org/classifiers/sklearn-1.4.2/greengenes2). A final sequence table was obtained after removing ASVs with an aberrant length (\u0026lt;252 bp or \u0026gt;254 bp) and ASVs that were not assigned at the phylum level.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.7 Microbiome data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were imported into R v4.2.2 [23], run using RStudio v2023.03.1+446, for analysis and visualization, mainly using packages part of the tidyverse ecosystem v2.0.0 [24], including ggplot2 v3.5.1 and dplyr v1.1.4, and the community ecology package vegan v2.6 [25]. Unless stated otherwise, all analyses were performed using compositionality-aware techniques, without subsampling of the count data. Prior to other analyses, rare ASVs that were detected in \u0026lt;10 samples (out of 48) and/or with a \u0026lt;100 total reads were removed. To mitigate undefined logarithms, zeros in the count table were imputed by Bayesian-multiplicative replacement, using the function \u003cem\u003ecmultRepl\u003c/em\u003e (method = \u0026quot;CZM\u0026quot;, output = \u0026quot;p-counts\u0026quot;) of the R package zCompositions v1.5.0 [26]. Centered-log ratio transformation of the zero-imputed count tables was performed using vegan\u0026rsquo;s \u003cem\u003edecostand\u003c/em\u003e function (method = \u0026quot;clr\u0026quot;). Aitchison distances were calculated as the Euclidean distance of the CLR-transformed counts using vegan\u0026rsquo;s \u003cem\u003evegdist\u003c/em\u003e function (method = \u0026quot;euclidean\u0026quot;). Principal components analysis, based on the CLR-transformed count data, was performed R stats\u0026rsquo; \u003cem\u003eprcomp\u003c/em\u003e function. Alpha diversity (ASV-level Shannon diversity and richness) was calculated using vegan\u0026rsquo;s \u003cem\u003ediversity\u003c/em\u003e and \u003cem\u003especnumber\u003c/em\u003e functions, based on count data randomly subsampled to even depth (35,000) using vegan\u0026rsquo;s \u003cem\u003errarefy\u003c/em\u003e function.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.8 Statistical analysis. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean levels of stress hormones and cytokines between experimental groups were compared using Welch\u0026apos;s t-test, with R stats\u0026rsquo; t.test function with option var.equal = FALSE. For wheel-running activity, total wheel revolutions per day were averaged across 7 days to generate weekly values for downstream analysis [13]. Wheel-running activity the week prior to assignment of mice to the two experimental groups was taken as the baseline. Linear mixed-effects model analysis was performed using the function \u003cem\u003emixed\u003c/em\u003e of the R package afex v1.3 [27], involving the experimental group, time (that is, weeks post-baseline), and their interaction as main effects and subject as a random effect. For the microbiome data, group centroids were compared by permutational analysis of variance (PERMANOVA, vegan\u0026rsquo;s adonis2 function) based on Aitchison distances calculated based on imputed ASV count tables. To account for repeated measures, restricted permutations were set using the function permute of the R package permute v0.9. For pairwise comparisons, we used Welch\u0026rsquo;s t-test on the distances, using the functions and script available from https://github.com/alekseyenko/Tw2. Microbial differential abundance analysis was performed using the function \u003cem\u003elinda\u003c/em\u003e of the R package LinDA v0.2.0 [28]. In short, LinDA uses linear regression of CLR-transformed data along with compositional bias correction [28]. The zero-imputed count table was used as input, and, as above, the model included the experimental group (categorical variable), time (numerical variable) as well as their interaction as the main effects and subject (categorical variable) as the random effect. Based on the LinDA\u0026rsquo;s output, ASVs with a significant interaction term (false discovery rate adjusted p-values of \u0026le;0.1) were retained.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Effect of six-week PAWW stress on circadian rhythm of wheel-running activity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo enable studying the effect of long-term continuous stress on the brain\u0026ndash;immune\u0026ndash;gut axis, we assessed whether PAWW stress can be applied for six weeks, compared to the 3-week period evaluated in our original study [13]. Mice were housed in individual wheel-running cages, and their locomotor activity was monitored. After two weeks of acclimatization, mice were randomly assigned to a control and stress group. For the stress group, continuous psychological stress (i.e. PAWW stress) was induced by replacing the paper-chip bedding in the cages with a layer of water.\u003c/p\u003e\n\n\u003cp\u003eAs shown in \u003cstrong\u003eFig. 1A\u003c/strong\u003e, mice exposed to PAWW stress exhibited a rapid and marked disruption of their circadian locomotor rhythm. More specifically, stressed mice showed a decrease in total daily activity (\u003cstrong\u003eFig. 1B\u003c/strong\u003e) and an increase in daytime activity (\u003cstrong\u003eFig. 1C\u003c/strong\u003e) over the entire course of the experiment. These changes are consistent with our previous observations [13] ,and demonstrate that PAWW stress disrupted the mice\u0026rsquo;s circadian locomotor rhythms without signs of adaptation over the six-week exposure period. This confirms the utility of the PAWW model for investigating the long-term effects of chronic stress, and establishes it, to the best of our knowledge, as one of the most prolonged continuous stress models reported to date.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e3.2 Effect on plasma levels of corticosterone, epinephrine and norepinephrine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter confirming sustained disruption of the mice\u0026rsquo;s circadian rhythms, we next analyzed stress hormone levels to verify that the mice maintained a physiological stress response after the six-week exposure period. The levels of the stress hormones corticosterone, epinephrine, and norepinephrine were measured in plasma samples collected at the end of the six-week experiment. As shown in \u003cstrong\u003eFig. 2A\u003c/strong\u003e, both epinephrine and norepinephrine concentrations were significantly elevated (p-value \u0026lt;0.05, Welch\u0026rsquo;s t-test) in stressed mice compared to controls. Corticosterone levels were also increased in the stress group, but the difference compared to the control group did not reach statistical significance (p-value = 0.063). These results indicate that exposure to PAWW stress for six weeks leads to a sustained increase in stress hormones in the plasma.\u003c/p\u003e\n\n\u003cp\u003eOur previous study showed that epinephrine and norepinephrine levels significantly increased, whereas corticosterone levels declined after one week of PAWW stress [13]. Typically, acute stress triggers the rapid release of stress-related hormones such as epinephrine, norepinephrine, and cortisol (corticosterone in rodents) to initiate immediate physiological responses [29, 30]. However, under chronic stress conditions, prolonged activation of these pathways can lead to sustained elevations in stress hormone levels [31]. Our findings suggest that continued exposure to PAWW stress for six weeks successfully captured features of chronic stress, as evidenced by the sustained elevation of these stress hormones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Effect on the expression of immune-related cytokine genes \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExposure to stress is well known to impact the immune system and inflammatory responses [32]. Specifically, stress can alter the balance between pro- and anti-inflammatory cytokines, leading to changes in immune function and increased risk of immune-related diseases [33]. Whether PAWW stress leads to similar physiological response was not characterized in our previous work.\u003c/p\u003e\n\n\u003cp\u003eTo understand how chronic stress influences immune responses, it is essential to evaluate cytokine expression in both central and peripheral immune-related organs. The brain is the central regulator of the stress response and is particularly sensitive to proinflammatory cytokines. Chronic stress triggers glial activation and neuroinflammation, which contributes to behavioral alterations [34-36]. Meanwhile, key peripheral immune organs, including PPs, MLNs, spleen, and the liver, play critical roles in mucosal and systemic immune regulation. Stress can impair their function, promote inflammation, and alter immune responses [37, 38]. To investigate these effects, we used real-time quantitative PCR to measure the expression of multiple pro-inflammatory cytokines (namely, tumor necrosis factor-alpha (TNF-\u0026alpha;), interleukin (IL)-6, IL-1\u0026beta;, IL-1RA, nuclear factor-kB (NF-\u0026kappa;B)) and the anti-inflammatory cytokine IL-10 in the brain and peripheral organs (liver, MLNs, PPs, and spleen) of stressed and control mice.\u003c/p\u003e\n\n\u003cp\u003eAs shown in \u003cstrong\u003eFig. 2B\u003c/strong\u003e, expression of the TNF-\u0026alpha; gene was significantly upregulated in the brain of stressed mice compared to controls (p-value = 0.037, Welch\u0026rsquo;s t-test). In addition to the inflammatory response in the brain, PAWW stress also led to significant upregulation of the expression of IL-1\u0026beta; (p-value = 0.009) and IL-6 (p-value = 0.034) gene expression in intestinal PPs (\u003cstrong\u003eFig. 2B\u003c/strong\u003e).\u003c/p\u003e\n\n\u003cp\u003ePrevious studies have shown that chronic stress induces neuroinflammation in rodent models and humans [36]. This neuroinflammation is characterized by changes in inflammatory mediators, such as NF-\u0026kappa;B, Toll-like receptors, and proinflammatory cytokines, including TNF-\u0026alpha;, IL-1\u0026beta;, and IL-6 in the brain and serum [39, 40]. TNF-\u0026alpha; is a major mediator of neuroinflammation and is consistently elevated in the brain of rodents subjected to chronic stress [35] and in patients with depression [41]. While IL-6 is pleiotropic, it is primarily pro-inflammatory and has been linked to sleep disturbances in animal models [42]. The absence of IL-6 has also been shown to protect against stress-induced intestinal injury and apoptosis [43]. IL-1\u0026beta; plays a central role in the effects of chronic stress, including depressive-like behavior and impaired neurogenesis [44]. IL-1\u0026beta; is commonly elevated in inflammatory bowel disease (IBD) and colitis models, particularly in IBD patients experiencing sleep disturbances [45]. These studies indicate that IL-6 and IL-1\u0026beta; are key mediators of sleep- and gastrointestinal-related dysfunction under stress conditions. Taken together, our results suggest that IL-6 and IL-1\u0026beta; may mediate the gastrointestinal effects of PAWW stress, potentially through mechanisms involving sleep disturbances.\u003c/p\u003e\n\n\u003cp\u003eTo our knowledge, the upregulation of proinflammatory cytokines (namely, IL-6 and IL-1\u0026beta;) in stress models has not been reported specifically in intestinal PPs. However, inflammation in PPs has been implicated in the pathogenesis of Crohn\u0026apos;s disease, a chronic inflammatory disease of the gastrointestinal tract [37]. Chronic stress has been shown to compromise the functional integrity of the follicle-associated epithelium, a key component of PPs, thereby increasing the uptake of luminal antigens and bacteria. This increased antigen exposure in PPs may exacerbate immune activation and suggests a potential role of chronic stress in the initiation of pro-inflammatory immune responses within the intestinal mucosa [38]. These findings suggest that PAWW stress promotes inflammatory cytokines expression in PPs, contributing to intestinal mucosal immune response. \u003c/p\u003e\n\n\u003cp\u003eAs a whole, our findings indicate that PAWW stress promotes neuroinflammation and upregulates key proinflammatory cytokines in intestinal PPs, contributing to mucosal immune dysregulation.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e3.4 Effect of PAWW stress on gut microbiome diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter having demonstrated that sustained PAWW stress was accompanied by upregulation of the expression of pro-inflammatory cytokine genes as part of the brain\u0026ndash;immune\u0026ndash;gut axis, we next investigated whether PAWW stress led to changes in the gut microbiome. Here, we first observed that stressed mice produced harder feces than control mice (\u003cstrong\u003eFig. S1\u003c/strong\u003e), although the total fecal output remained comparable between the groups (data not shown). These changes in fecal consistency or hardness may reflect stress-induced alterations in gut motility, water absorption, or mucus secretion [46]. Clinical studies have shown that elevated stress hormones, such as cortisol and norepinephrine, are associated with harder stools [47]. Together, this suggests that higher levels of stress hormones may contribute to harder stool production in PAWW-stressed mice.\u003c/p\u003e\n\n\u003cp\u003eWe characterized the gut microbiome of mice in both experimental groups by 16S rRNA gene amplicon sequencing of fecal samples collected at week 0, representing the baseline community, and after 2, 4, and 6 weeks. As shown in \u003cstrong\u003eFig. S2\u003c/strong\u003e, \u003cem\u003eFirmicutes_D\u003c/em\u003e (IQR: 30.6-54.5%), \u003cem\u003eFirmicutes_A\u003c/em\u003e (24.3-51.6%), and \u003cem\u003eBacteroidota \u003c/em\u003e(5.8-18.9%) represented the most abundant phyla across mice. At higher taxonomic ranks, the families \u003cem\u003eLactobacillaceae\u003c/em\u003e (27.5-51.6%) and \u003cem\u003eLachnospiraceae\u003c/em\u003e (21.3-46.1%), along with \u003cem\u003eBacteroidaceae\u003c/em\u003e (2.2-7.5%) and \u003cem\u003eMuribaculaceae\u003c/em\u003e (2.2-6.2%), were the most abundant. The most abundant genera included \u003cem\u003eLigilactobacillus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eLimosilactobacillus\u003c/em\u003e within the family of the \u003cem\u003eLactobacillaceae\u003c/em\u003e. Within the family, \u003cem\u003eLachnospiraceae\u003c/em\u003e, the hitherto uncultured genus COE1 was the most abundant, along with the genus \u003cem\u003eKineothrix\u003c/em\u003e.\u003c/p\u003e\n\n\u003cp\u003eWe first explored gut microbiome community structure and composition by principal component analysis (PCA) based on Aitchison distances. As shown in \u003cstrong\u003eFig. 3A\u003c/strong\u003e and \u003cstrong\u003eFig. S3\u003c/strong\u003e, samples of the stress and control groups clustered together at baseline but differed at subsequent time points. Along with the first principal component, which captured 26.9% of the variance in microbiome compositions, the trajectories of the control and stress groups were significantly different (linear mixed effects model, p-value \u0026lt; 0.01 for the interaction term) (\u003cstrong\u003eFig. 3B\u003c/strong\u003e). Further, analysis of community differences among groups by PERMANOVA indicated that experimental treatment, time as well as their interaction significantly affected microbiome compositions (p-value \u0026lt; 0.01). Based on pairwise comparisons, the control and stress groups were not significantly different at baseline (p-value = 0.178) but showed significant differences for subsequent time points (p-value \u0026lt; 0.05). \u003c/p\u003e\n\n\u003cp\u003eThese data suggested that exposure to PAWW stress resulted in compositional changes in the mice\u0026rsquo;s gut microbiome. To further quantify this effect, we calculated per-subject community dissimilarities to the baseline (that is, the week-0 samples), an approach previously described as volatility analysis in the context of stress [48]. Using the Aitchison distance as above, this analysis revealed that dissimilarity-to-baseline for the stress group differed significantly from the control group at all time points (p-value \u0026lt; 0.05; Mann-Whitney test; \u003cstrong\u003eFig. 3C\u003c/strong\u003e). Further, comparison of first distances (that is, dissimilarities between consecutive samples) showed that stressed mice also showed significant differences in changes between weeks 2 and 4 compared to the control mice, whereas dissimilarities between week-4 and week-6 samples for both experimental groups were comparable (\u003cstrong\u003eFig. S4\u003c/strong\u003e). This suggested that the gut microbiome stabilized after continued exposure to stress, with a composition different from that of the control group. These data showed that chronic exposure to stress led to significant changes in gut microbiome composition, with differences that exceeded changes due to natural progression of the gut microbiome in the control group. In contrast to beta diversity, alpha diversity (Shannon index and ASV richness) did not show any significant differences between experimental groups (\u003cstrong\u003eFig. S5\u003c/strong\u003e).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e3.5 Differentially abundant taxa, in stress group compared to control group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaving observed that the gut microbiome undergoes changes due to PAWW stress, we next sought to identify which microbiome features (i.e. ASVs) differed significantly between the stress and control groups. To this end, we performed compositionality-aware differential abundance analysis using linear mixed effect models, as implemented in LinDA (see Methods). We then retained on ASVs with a significant interaction term (adjusted p-value of \u0026lt;0.1) between treatment and time in the fitted model, as these represent ASVs that responded differently in the control and stress groups. \u003c/p\u003e\n\n\u003cp\u003eA total of 47 ASVs (out of) were found to exhibit a significantly different response in the stress group compared to the control group (\u003cstrong\u003eFig. 4A\u003c/strong\u003e). Of these, 15 and 32 ASVs showed a positive and negative interaction term, respectively. As shown in \u003cstrong\u003eFig. 4B\u003c/strong\u003e and \u003cstrong\u003eFig. S6\u003c/strong\u003e, ASVs with a positive interaction term represented ASVs that showed an increase in their relative abundance following exposure to stress. Within the top 10 ASVs with the largest effect size, this included 7 ASVs belonged to the family \u003cem\u003eLachnospiraceae\u003c/em\u003e, including four ASVs assigned to the genus COE1 (asv19 and asv29) and \u003cem\u003eVentrimonas\u003c/em\u003e (asv96 and asv126), as well as ASVs belonging to the genera \u003cem\u003eEnterococcus_D\u003c/em\u003e (asv140), \u003cem\u003eBorkfalkia\u003c/em\u003e (asv199), and Enterenecus (asv 147). ASVs with the top 10 largest negative effect sizes all belonged to the family \u003cem\u003eLachnospiraceae (\u003c/em\u003e\u003cstrong\u003eFig. 4B\u003c/strong\u003e and \u003cstrong\u003eFig. S7\u003c/strong\u003e). A a whole, these results showed that PAWW stress led to considerable changes in the abundances of specific phylotypes. The majority of these were affiliated with the family \u003cem\u003eLachnospiraceae \u003c/em\u003ewithin the Greengenes2 taxonomic framework, with the recognition that \u003cem\u003eLachnospiraceae\u003c/em\u003e is the second-most abundant family across samples in our study, with the highest ASV richness (mean: 82; IQR: 73-95, at a subsampling depth of 35,000) (\u003cstrong\u003eFig. S8\u003c/strong\u003e). \u003c/p\u003e\n\n\u003cp\u003eSorted by effect size, two ASVs (i.e. asv29 and ASV19) belonging to the genus COE1 within the family \u003cem\u003eLachnospiraceae\u003c/em\u003e showed the strongest increase in abundance; these two ASVs were virtually absent in control mice but reached abundances exceeding 1% after 6 weeks of PAWW stress. In addition to members of the COE1 genus, two ASVs (i.e. asv96 and ASV126) assigned to the recently proposed genus \u003cem\u003eVentrimonas\u003c/em\u003e [49] also showed a significant increase due to PAWW stress. However, little is known about the potential role of these two genera in the murine gut. In addition, we also found that other phylotypes within the genus COE1 showed decreased abundance after stress exposure, and future studies are needed to better understand these dynamics.\u003c/p\u003e\n\n\u003cp\u003eNamed (at the genus level) ASVs that showed the strongest decrease included two ASVs belonging to the genus \u003cem\u003eKineothrix\u003c/em\u003e (i.e. asv73 and asv41) as well as two ASVs related to \u003cem\u003eEubacterium\u003c/em\u003e (i.e. asv4 and asv163 assigned to the genera \u003cem\u003eEubacterium\u003c/em\u003e_J and \u003cem\u003eEubacterirum\u003c/em\u003e_F, respectively). Currently, a single species within the genus \u003cem\u003eKineothrix \u003c/em\u003ehas been validly published, namely \u003cem\u003eK. alysoides \u003c/em\u003e[50], which was described as a saccharolytic butyrate-producer. Given that butyrate is an important metabolite with anti-inflammatory and immune-regulating properties, this suggest that decreased abundance \u003cem\u003eKineothrix\u003c/em\u003e may be associated with increased inflammation in stressed mice\u003cem\u003e. \u003c/em\u003eIn similar fashion, \u003cem\u003eEubacterium\u003c/em\u003e spp. generally represents beneficial microbes that contribute to homeostasis through production of butyrate as well as cholesterol and bile acid metabolism [51]. This suggests loss of beneficial microbes, although higher-resolution microbiome characterization using shotgun metagenomics would be needed to substantiate this, especially considering that other ASVs belong to the genus \u003cem\u003eKineothrix \u003c/em\u003eshowed increased abundance. \u003c/p\u003e\n\n\u003cp\u003eAs a whole, our microbiome analysis points to a significant association between members of the family \u003cem\u003eLachnospiraceae \u003c/em\u003eand long-term continuous stress exposure. Changes in \u003cem\u003eLachnospiraceae\u003c/em\u003e have previously also been observed in several animal models of stress [52, 53]. Similarly, various types of stress have been associated with changes in the abundance of \u003cem\u003eLachnospiracea \u003c/em\u003ein human studies\u003cem\u003e \u003c/em\u003e[12, 54]. Most human studies have shown that \u003cem\u003eLachnospiraceae\u003c/em\u003e are associated with ulcerative colitis and Crohn\u0026rsquo;s disease, which are chronic immune-mediated inflammatory diseases of the gastrointestinal tract [55-57].\u003c/p\u003e\n\n\u003cp\u003ePreviously, it has been suggested that PPs play a role in regulating the gut-brain axis through interactions with the gut microbiome [58]. Disruptions in the composition of the gut microbiome have been linked to neurodegenerative and neuroinflammatory diseases [59]. PPs engage in bidirectional communication with the microbiome and may influence gut-brain signaling [60]. However, the mechanisms by which stress-induced PP dysfunction affect brain function via microbiome alterations remain poorly understood and require further investigation. Our results show that PAWW stress significantly alters gut microbiome composition and induces inflammation in brain and PPs. \u003c/p\u003e\n\n\u003cp\u003eAs a whole, our findings revealed the link between microbial alterations and stress-related immune responses relevant to the gut-brain axis. The application of the PAWW model to germ-free mice has the potential to provide more clarity on this interaction. Selective introduction of specific microbiome, particularly\u003cem\u003e Lachnospiraceae\u003c/em\u003e, might reveal their role in PPs-mediated immune signaling and its impact on gut-brain axis.\u003c/p\u003e\n"},{"header":"Conclusion","content":"\u003cp\u003eOur results show that prolonged continuous stress induced by the PAWW model over a six-week period effectively captures characteristics of chronic stress, as demonstrated by sustained disruption of circadian locomotor rhythms and persistent elevation of stress hormone levels without signs of physiological adaptation. Moreover, chronic PAWW stress significantly altered both immune-related gene expression and gut microbiome composition in mice. The upregulation of pro-inflammatory cytokines indicates that chronic PAWW stress promotes inflammation in both the brain and PPs, highlighting a potential link between sustained stress and mucosal immune responses. Additionally, shifts in the gut microbiome, particularly those involving members of the \u003cem\u003eLachnospiraceae\u003c/em\u003e family, suggest that the gut microbiome may contribute to stress-induced immune changes. As a whole, our findings indicate that the PAWW model is suitable for investigating the brain\u0026ndash;immune\u0026ndash;gut axis under chronic stress conditions, as it reveals concurrent changes in immune responses and gut microbiome composition, warranting further exploration of their potential interactions.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePapawee Saiki:\u0026nbsp;\u003c/strong\u003eWriting – review \u0026amp; editing, Writing – original draft, Visualization, Validation, Methodology, Funding acquisition, Investigation, Data curation, Formal analysis, Conceptualization.\u003cstrong\u003e\u0026nbsp;Dieter M. Tourlousse:\u0026nbsp;\u003c/strong\u003eWriting – review \u0026amp; editing, Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Data curation. \u003cstrong\u003eNanako Itoh:\u003c/strong\u003e Methodology, Investigation. \u003cstrong\u003eYuji Sekiguchi:\u0026nbsp;\u003c/strong\u003eWriting – review \u0026amp; editing, Visualization, Supervision. \u003cstrong\u003eKoyomi Miyazaki:\u0026nbsp;\u003c/strong\u003eVisualization, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no financial or personal relationships that could have inappropriately influenced the research presented in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequencing data have been deposited in NCBI’s Sequence Read Archive under BioProject PRJNA1190504.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Early-Career Scientists (grant number 20K19700) to P. Saiki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgid, O., Y. Kohn, and B. Lerer, \u003cem\u003eEnvironmental stress and psychiatric illness.\u003c/em\u003e Biomedicine \u0026amp; Pharmacotherapy, 2000. \u003cstrong\u003e54\u003c/strong\u003e(3): p. 135-141.\u003c/li\u003e\n\u003cli\u003eMayer, E.A., \u003cem\u003eThe neurobiology of stress and gastrointestinal disease.\u003c/em\u003e Gut, 2000. \u003cstrong\u003e47\u003c/strong\u003e(6): p. 861-869.\u003c/li\u003e\n\u003cli\u003eSteptoe, A. and M. Kivim\u0026auml;ki, \u003cem\u003eStress and cardiovascular disease.\u003c/em\u003e Nature Reviews Cardiology, 2012. \u003cstrong\u003e9\u003c/strong\u003e(6): p. 360-370.\u003c/li\u003e\n\u003cli\u003ePang, S., J. Wen-Yi, and W. Zi, \u003cem\u003eThe interplay between the gut microbiome and neurological disorders: Exploring the gut-brain Axis.\u003c/em\u003e Neurology Letters, 2023. \u003cstrong\u003e2\u003c/strong\u003e(1): p. 25-29.\u003c/li\u003e\n\u003cli\u003eMorys, J., A. Małecki, and M. Nowacka-Chmielewska, \u003cem\u003eStress and the gut-brain axis: an inflammatory perspective.\u003c/em\u003e Frontiers in Molecular Neuroscience, 2024. \u003cstrong\u003e17\u003c/strong\u003e: p. 1415567.\u003c/li\u003e\n\u003cli\u003eLiu, R.T., \u003cem\u003eThe microbiome as a novel paradigm in studying stress and mental health.\u003c/em\u003e American Psychologist, 2017. \u003cstrong\u003e72\u003c/strong\u003e(7): p. 655.\u003c/li\u003e\n\u003cli\u003eZhang, R.Y., et al., \u003cem\u003eThe gut microbiome, immune modulation, and cognitive decline: insights on the gut-brain axis.\u003c/em\u003e Frontiers in Immunology, 2025. \u003cstrong\u003e16\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLeigh, S.J., et al., \u003cem\u003eThe impact of acute and chronic stress on gastrointestinal physiology and function: a microbiota-gut-brain axis perspective.\u003c/em\u003e Journal of Physiology-London, 2023. \u003cstrong\u003e601\u003c/strong\u003e(20): p. 4491-4538.\u003c/li\u003e\n\u003cli\u003eKarl, J.P., et al., \u003cem\u003eEffects of Psychological, Environmental and Physical Stressors on the Gut Microbiota.\u003c/em\u003e Frontiers in Microbiology, 2018. \u003cstrong\u003e9\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eChoi, S.I., et al., \u003cem\u003eThe Protective Effect of Roseburia faecis Against Repeated Water Avoidance Stress-induced Irritable Bowel Syndrome in a Wister Rat Model.\u003c/em\u003e J Cancer Prev, 2023. \u003cstrong\u003e28\u003c/strong\u003e(3): p. 93-105.\u003c/li\u003e\n\u003cli\u003eKrupa-Kotara, K., et al., \u003cem\u003eThe Role of Microbiota Pattern in Anxiety and Stress Disorders\u0026mdash;A Review of the State of Knowledge.\u003c/em\u003e Psych, 2023. \u003cstrong\u003e5\u003c/strong\u003e(3): p. 602-618.\u003c/li\u003e\n\u003cli\u003eBellach, L., et al., \u003cem\u003eThe Effects of Caloric Restriction and Clinical Psychological Intervention on the Interplay of Gut Microbial Composition and Stress in Women.\u003c/em\u003e Nutrients, 2024. \u003cstrong\u003e16\u003c/strong\u003e(16).\u003c/li\u003e\n\u003cli\u003eMiyazaki, K., et al., \u003cem\u003eContinuous exposure to a novel stressor based on water aversion induces abnormal circadian locomotor rhythms and sleep-wake cycles in mice.\u003c/em\u003e PLoS One, 2013. \u003cstrong\u003e8\u003c/strong\u003e(1): p. e55452.\u003c/li\u003e\n\u003cli\u003eSuabjakyong, P., et al., \u003cem\u003ePolyphenol extract from Phellinus igniarius protects against acrolein toxicity in vitro and provides protection in a mouse stroke model.\u003c/em\u003e PLoS One, 2015. \u003cstrong\u003e10\u003c/strong\u003e(3): p. e0122733.\u003c/li\u003e\n\u003cli\u003eSaiki, P., et al., \u003cem\u003eThe anti-inflammatory effect of Agaricus brasiliensis is partly due to its linoleic acid content.\u003c/em\u003e Food Funct, 2017. \u003cstrong\u003e8\u003c/strong\u003e(11): p. 4150-4158.\u003c/li\u003e\n\u003cli\u003eSaiki, P., et al., \u003cem\u003ePurified Gymnemic Acids from Gymnema inodorum Tea Inhibit 3T3-L1 Cell Differentiation into Adipocytes.\u003c/em\u003e Nutrients, 2020. \u003cstrong\u003e12\u003c/strong\u003e(9): p. 2851.\u003c/li\u003e\n\u003cli\u003eTourlousse, D.M., et al., \u003cem\u003eValidation and standardization of DNA extraction and library construction methods for metagenomics-based human fecal microbiome measurements.\u003c/em\u003e Microbiome, 2021. \u003cstrong\u003e9\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eTourlousse, D.M., et al., \u003cem\u003eCharacterization and Demonstration of Mock Communities as Control Reagents for Accurate Human Microbiome Community Measurements.\u003c/em\u003e Microbiology Spectrum, 2022. \u003cstrong\u003e10\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eMartin, M., \u003cem\u003eCutadapt removes adapter sequences from high-throughput sequencing reads.\u003c/em\u003e EMBnet. journal, 2011. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 10-12.\u003c/li\u003e\n\u003cli\u003eCallahan, B.J., et al., \u003cem\u003eDADA2: High-resolution sample inference from Illumina amplicon data.\u003c/em\u003e Nature Methods, 2016. \u003cstrong\u003e13\u003c/strong\u003e(7): p. 581-583.\u003c/li\u003e\n\u003cli\u003eBolyen, E., et al., \u003cem\u003eReproducible, interactive, scalable and extensible microbiome data science using QIIME 2 (vol 37, pg 852, 2019).\u003c/em\u003e Nature Biotechnology, 2019. \u003cstrong\u003e37\u003c/strong\u003e(9): p. 1091-1091.\u003c/li\u003e\n\u003cli\u003eMcDonald, D., et al., \u003cem\u003eGreengenes2 unifies microbial data in a single reference tree.\u003c/em\u003e Nat Biotechnol, 2024. \u003cstrong\u003e42\u003c/strong\u003e(5): p. 715-718.\u003c/li\u003e\n\u003cli\u003eTeam, R.C., \u003cem\u003eRA language and environment for statistical computing, R Foundation for Statistical.\u003c/em\u003e Computing, 2020.\u003c/li\u003e\n\u003cli\u003eWickham, H., et al., \u003cem\u003eWelcome to the Tidyverse.\u003c/em\u003e Journal of open source software, 2019. \u003cstrong\u003e4\u003c/strong\u003e(43): p. 1686.\u003c/li\u003e\n\u003cli\u003eOksanen, J., et al., \u003cem\u003evegan: Community Ecology Package. R package version 2.6-4. 2022\u003c/em\u003e. 2023.\u003c/li\u003e\n\u003cli\u003ePalarea-Albaladejo, J. and J.A. Mart\u0026iacute;n-Fern\u0026aacute;ndez, \u003cem\u003ezCompositions - R Package for multivariate imputation of left-censored data under a compositional approach.\u003c/em\u003e Chemometrics and Intelligent Laboratory Systems, 2015. \u003cstrong\u003e143\u003c/strong\u003e: p. 85-96.\u003c/li\u003e\n\u003cli\u003eSingmann, H., et al., \u003cem\u003eEvaluating the robustness of parameter estimates in cognitive models: A meta-analytic review of multinomial processing tree models across the multiverse of estimation methods.\u003c/em\u003e Psychological Bulletin, 2024.\u003c/li\u003e\n\u003cli\u003eZhou, H., et al., \u003cem\u003eLinDA: linear models for differential abundance analysis of microbiome compositional data.\u003c/em\u003e Genome Biol, 2022. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 95.\u003c/li\u003e\n\u003cli\u003eShukla, P.K., et al., \u003cem\u003eChronic stress and corticosterone exacerbate alcohol-induced tissue injury in the gut-liver-brain axis.\u003c/em\u003e Scientific Reports, 2021. \u003cstrong\u003e11\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eFlak, J.N., et al., \u003cem\u003eRole of paraventricular nucleus-projecting norepinephrine/epinephrine neurons in acute and chronic stress.\u003c/em\u003e European Journal of Neuroscience, 2014. \u003cstrong\u003e39\u003c/strong\u003e(11): p. 1903-1911.\u003c/li\u003e\n\u003cli\u003eSic, A., et al., \u003cem\u003eNeurobiological Implications of Chronic Stress and Metabolic Dysregulation in Inflammatory Bowel Diseases.\u003c/em\u003e Diseases, 2024. \u003cstrong\u003e12\u003c/strong\u003e(9).\u003c/li\u003e\n\u003cli\u003eHuang, C.J., et al., \u003cem\u003ePsychological stress during exercise: immunoendocrine and oxidative responses.\u003c/em\u003e Exp Biol Med (Maywood), 2010. \u003cstrong\u003e235\u003c/strong\u003e(12): p. 1498-504.\u003c/li\u003e\n\u003cli\u003eAgarwal, S.K. and G.D. Marshall, Jr., \u003cem\u003eStress effects on immunity and its application to clinical immunology.\u003c/em\u003e Clin Exp Allergy, 2001. \u003cstrong\u003e31\u003c/strong\u003e(1): p. 25-31.\u003c/li\u003e\n\u003cli\u003eRavi, M., A.H. Miller, and V. Michopoulos, \u003cem\u003eThe immunology of stress and the impact of inflammation on the brain and behaviour.\u003c/em\u003e BJPsych advances, 2021. \u003cstrong\u003e27\u003c/strong\u003e(3): p. 158-165.\u003c/li\u003e\n\u003cli\u003eClark, I.A., L.M. Alleva, and B. Vissel, \u003cem\u003eThe roles of TNF in brain dysfunction and disease.\u003c/em\u003e Pharmacology \u0026amp; Therapeutics, 2010. \u003cstrong\u003e128\u003c/strong\u003e(3): p. 519-548.\u003c/li\u003e\n\u003cli\u003eWhite, A.G., et al., \u003cem\u003eChronic Stress-Induced Neuroinflammation: Relevance of Rodent Models to Human Disease.\u003c/em\u003e International Journal of Molecular Sciences, 2024. \u003cstrong\u003e25\u003c/strong\u003e(10).\u003c/li\u003e\n\u003cli\u003eGullberg, E. and J.D. S\u0026ouml;derholm, \u003cem\u003ePeyer\u0026apos;s patches and M cells as potential sites of the inflammatory onset in Crohn\u0026apos;s disease.\u003c/em\u003e Inflammatory Bowel Disease: Genetics, Barrier Function, Immunologic Mechanisms, and Microbial Pathways, 2006. \u003cstrong\u003e1072\u003c/strong\u003e: p. 218-232.\u003c/li\u003e\n\u003cli\u003eVelin, \u0026Aring;.K., et al., \u003cem\u003eIncreased antigen and bacterial uptake in follicle associated epithelium induced by chronic psychological stress in rats.\u003c/em\u003e Gut, 2004. \u003cstrong\u003e53\u003c/strong\u003e(4): p. 494-500.\u003c/li\u003e\n\u003cli\u003eWang, Y., et al., \u003cem\u003eTLR4-NF-kappaB Signal Involved in Depressive-Like Behaviors and Cytokine Expression of Frontal Cortex and Hippocampus in Stressed C57BL/6 and ob/ob Mice.\u003c/em\u003e Neural Plast, 2018. \u003cstrong\u003e2018\u003c/strong\u003e: p. 7254016.\u003c/li\u003e\n\u003cli\u003eTang, Y., et al., \u003cem\u003emiR-182 mediated the inhibitory effects of NF-\u0026kappa;B on the GPR39/CREB/BDNF pathway in the hippocampus of mice with depressive-like behaviors.\u003c/em\u003e Behavioural brain research, 2022. \u003cstrong\u003e418\u003c/strong\u003e: p. 113647.\u003c/li\u003e\n\u003cli\u003ePandey, G.N., et al., \u003cem\u003eAbnormal protein and mRNA expression of inflammatory cytokines in the prefrontal cortex of depressed individuals who died by suicide.\u003c/em\u003e Journal of Psychiatry \u0026amp; Neuroscience, 2018. \u003cstrong\u003e43\u003c/strong\u003e(6): p. 376-385.\u003c/li\u003e\n\u003cli\u003ePeriasamy, S., et al., \u003cem\u003eSleep Deprivation-Induced Multi-Organ Injury: Role of Oxidative Stress and Inflammation.\u003c/em\u003e Excli Journal, 2015. \u003cstrong\u003e14\u003c/strong\u003e: p. 672-683.\u003c/li\u003e\n\u003cli\u003eZhang, Y., et al., \u003cem\u003eKnockout of IL-6 mitigates cold water-immersion restraint stress-induced intestinal epithelial injury and apoptosis.\u003c/em\u003e Front Immunol, 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 936689.\u003c/li\u003e\n\u003cli\u003eGoshen, I., et al., \u003cem\u003eBrain interleukin-1 mediates chronic stress-induced depression in mice via adrenocortical activation and hippocampal neurogenesis suppression.\u003c/em\u003e Molecular Psychiatry, 2008. \u003cstrong\u003e13\u003c/strong\u003e(7): p. 717-728.\u003c/li\u003e\n\u003cli\u003eAli, T., et al., \u003cem\u003eSleep, immunity and inflammation in gastrointestinal disorders.\u003c/em\u003e World Journal of Gastroenterology, 2013. \u003cstrong\u003e19\u003c/strong\u003e(48): p. 9231-9239.\u003c/li\u003e\n\u003cli\u003eCamilleri, M., et al., \u003cem\u003eIntestinal barrier function in health and gastrointestinal disease.\u003c/em\u003e Neurogastroenterology and Motility, 2012. \u003cstrong\u003e24\u003c/strong\u003e(6): p. 503-512.\u003c/li\u003e\n\u003cli\u003eLemay, D.G., et al., \u003cem\u003eTechnician-Scored Stool Consistency Spans the Full Range of the Bristol Scale in a Healthy US Population and Differs by Diet and Chronic Stress Load.\u003c/em\u003e Journal of Nutrition, 2021. \u003cstrong\u003e151\u003c/strong\u003e(6): p. 1443-1452.\u003c/li\u003e\n\u003cli\u003eBastiaanssen, T.F., et al., \u003cem\u003eVolatility as a concept to understand the impact of stress on the microbiome.\u003c/em\u003e Psychoneuroendocrinology, 2021. \u003cstrong\u003e124\u003c/strong\u003e: p. 105047.\u003c/li\u003e\n\u003cli\u003eHitch, T.C., et al., \u003cem\u003eBroad diversity of human gut bacteria accessible via a traceable strain deposition system.\u003c/em\u003e bioRxiv, 2024.\u003c/li\u003e\n\u003cli\u003eHaas, K.N. and J.L. Blanchard, \u003cem\u003eKineothrix alysoides, gen. nov., sp. nov., a saccharolytic butyrate-producer within the family Lachnospiraceae.\u003c/em\u003e International journal of systematic and evolutionary microbiology, 2017. \u003cstrong\u003e67\u003c/strong\u003e(2): p. 402-410.\u003c/li\u003e\n\u003cli\u003eMukherjee, A., et al., \u003cem\u003eGut microbes from the phylogenetically diverse genus Eubacterium and their various contributions to gut health.\u003c/em\u003e Gut Microbes, 2020. \u003cstrong\u003e12\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eLi, S.Y., et al., \u003cem\u003eshift in the microbial community of mice faecal sample effects on water immersion restraint stress.\u003c/em\u003e Amb Express, 2017. \u003cstrong\u003e7\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eWang, R., et al., \u003cem\u003eThe effects of chronic unpredicted mild stress on maternal negative emotions and gut microbiota and metabolites in pregnant rats.\u003c/em\u003e Peerj, 2023. \u003cstrong\u003e11\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eMa, L., et al., \u003cem\u003ePsychological Stress and Gut Microbiota Composition: A Systematic Review of Human Studies.\u003c/em\u003e Neuropsychobiology, 2023. \u003cstrong\u003e82\u003c/strong\u003e(5): p. 247-262.\u003c/li\u003e\n\u003cli\u003eSankarasubramanian, J., et al., \u003cem\u003eGut Microbiota and Metabolic Specificity in Ulcerative Colitis and Crohn\u0026apos;s Disease.\u003c/em\u003e Frontiers in Medicine, 2020. \u003cstrong\u003e7\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eMancabelli, L., et al., \u003cem\u003eIdentification of universal gut microbial biomarkers of common human intestinal diseases by meta-analysis.\u003c/em\u003e Fems Microbiology Ecology, 2017. \u003cstrong\u003e93\u003c/strong\u003e(12).\u003c/li\u003e\n\u003cli\u003eHe, X.X., et al., \u003cem\u003eRelationship between clinical features and intestinal microbiota in Chinese patients with ulcerative colitis.\u003c/em\u003e World Journal of Gastroenterology, 2021. \u003cstrong\u003e27\u003c/strong\u003e(28).\u003c/li\u003e\n\u003cli\u003eAsgari, R., et al., \u003cem\u003ePeyer\u0026rsquo;s Patch: possible target for modulating the Gut-Brain-Axis through microbiota.\u003c/em\u003e Cellular Immunology, 2024. \u003cstrong\u003e401\u003c/strong\u003e: p. 104844.\u003c/li\u003e\n\u003cli\u003eKhatoon, S., et al., \u003cem\u003eEffects of gut microbiota on neurodegenerative diseases.\u003c/em\u003e Frontiers in Aging Neuroscience, 2023. \u003cstrong\u003e15\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eAbo-Shaban, T., et al., \u003cem\u003eIssues for patchy tissues: defining roles for gut-associated lymphoid tissue in neurodevelopment and disease.\u003c/em\u003e Journal of Neural Transmission, 2023. \u003cstrong\u003e130\u003c/strong\u003e(3): p. 269-280.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stress mice model, chronic stress, cytokines, immune function, gut microbiome","lastPublishedDoi":"10.21203/rs.3.rs-7484981/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7484981/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Response to stress involves a complex interplay between the immune system and the gut microbiome, yet studies on long-term continuous stress remain scarce due to the lack of suitable models. In this study, we employed our previously developed Perpetual Avoidance of Water on a Wheel (PAWW) model to investigate the effects of prolonged stress on immune-related cytokine expression and gut microbiome composition in mice. We demonstrated that PAWW stress can be sustained for six weeks without signs of behavioral or physiological adaptation. Circadian locomotor rhythms remained disrupted throughout the exposure period, accompanied by elevated plasma norepinephrine and epinephrine. Quantitative real-time PCR revealed significant upregulation of inflammation-related cytokine genes, including TNF-α in the brain and IL-6 and IL-1β in intestinal Peyer’s patches (PPs). Microbiome profiling by 16S rRNA gene sequencing showed that stressed mice underwent more pronounced compositional changes than controls, particularly in members of the Lachnospiraceae family. These findings indicate that the PAWW model provides a robust platform for investigating the effects of chronic stress on the brain–immune–gut axis. Prolonged PAWW stress was associated with both inflammatory immune responses and microbiome alterations, highlighting a potential role for PPs in mediating intestinal immune regulation under stress conditions.","manuscriptTitle":"Impact of prolonged continuous exposure to stress on immune function and gut microbiome in a perpetual avoidance of water on a wheel mouse model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 20:59:29","doi":"10.21203/rs.3.rs-7484981/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-27T15:37:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176563661488253356559548401116627138078","date":"2026-04-23T18:43:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213306248434039276896728324509690904961","date":"2026-04-18T06:37:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110719714802801241741935995574140202624","date":"2026-04-16T15:23:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-02T08:39:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-30T12:59:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-02T13:05:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T03:09:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-02T03:06:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d2e6415-502e-40e4-bca2-b709965d4fd5","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T20:59:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 20:59:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7484981","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7484981","identity":"rs-7484981","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}