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Article

Transport Stress Induces Neuroimmune Dysregulation and Exacerbates Mycobacterium Tuberculosis Infection in Mice

by
Xiaojuan Gou
1,2,†,
Yingying Lei
1,2,†,
Bing Yang
1,2,
Wei Zhou
1,2,
Xi Chen
1,2,
Keji Yan
1,2,
Qinghua Zhang
3,
Hu Zheng
1,2,
Lulu Deng
1,2,
Ting Guo
1,2,
Siqi Zhang
1,2,
Xiangru Wang
1,2,
Jinxia Dai
1,2,
Gang Cao
1,2,4,* and
Ke Xiao
5,6,*
1
National Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
2
College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China
3
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
4
Faculty of Life and Health Sciences, Shenzhen University of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518107, China
5
The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
6
Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2026, 27(4), 1919; https://doi.org/10.3390/ijms27041919
Submission received: 27 December 2025 / Revised: 13 February 2026 / Accepted: 15 February 2026 / Published: 17 February 2026
(This article belongs to the Section Molecular Immunology)
Browse Figures
Versions Notes

Abstract

Transport stress (TS) significantly impacts immune function and infection, of which the cellular and molecular mechanisms remain elusive. In this study, we investigated the effects of TS on immune responses and its role in exacerbating Mycobacterium tuberculosis (Mtb) infection. TS induced anxiety-like behaviors, disrupted hypothalamic–pituitary–adrenal (HPA) axis homeostasis, altered hormone secretion patterns, and led to dysregulation of stress response genes in mice. Single-cell RNA sequencing of the spleen revealed severe immune dysfunction upon TS, characterized by inhibition of Th17 differentiation, IL-17 signaling, and antigen presentation. Importantly, TS affects the crosstalk between the HPA axis and the spleen through ligand–receptor interactions. Furthermore, TS exacerbated the severity of Mtb infection and disturbed infection-induced gene regulation. TS impairs Th1/Th2 differentiation through neuroendocrine immune dysregulation, and also compromises macrophage immune function, further weakening host defense. These findings could greatly contribute to understanding TS-induced neuroendocrine immune regulation and deteriorated infection.

1. Introduction

TS profoundly affects animal health, productivity, and economic outcomes. It arises from multiple stressors encountered during transit, including temperature fluctuations, restricted space, prolonged fasting, and mechanical vibration [1,2]. These stressors can induce varying degrees of tissue and organ damage [3,4], potentially progressing to pathological states marked by increased morbidity and mortality [5]. In addition to these physiological disruptions, different types of stress such as acute restraint, chronic mild stress, and social defeat stress can provoke anxiety-like behaviors. These behavioral alterations are associated with neural changes involving dysfunction of the red nucleus and reduced expression of the serotonin transporter [6,7,8]. These findings demonstrate that TS causes not only organ damage and immune dysfunction but also psychological and behavioral disorders in livestock, resulting in multiple adverse effects on animal husbandry. Therefore, clarifying the molecular mechanisms of TS and developing effective interventions are urgent priorities for improving livestock production and welfare.
Stress regulates neural function largely through its effects on the HPA axis. Activation of the HPA axis triggers a hormonal cascade involving corticotropin-releasing hormone (CRH), adrenocorticotropic hormone, and glucocorticoids (GCs). Chronic stress disrupts this negative feedback loop, leading to sustained HPA axis dysregulation [9,10]. There is continuous communication between the immune system and neuroendocrine pathways. Immune challenges such as infection, tissue injury, or autoimmune activation stimulate cytokine release. Cytokines can further activate the HPA axis and promote GC secretion [11,12]. In return, GCs inhibit cytokine production and regulate adaptive immunity by controlling immune cell trafficking and adjusting the balance between Th1 and Th2 responses. As major effector molecules of the HPA axis, GCs inhibit dendritic cell maturation, suppress antigen presentation, disrupt NF-κB and NFAT signaling, and selectively suppress Th1 and Th17 responses. These actions weaken both innate and adaptive immunity [13]. This immunosuppressive effect is also evident at the organ level: chronic restraint stress also causes structural alterations in the spleen, including expansion of the white pulp, degeneration of the red pulp, and reduced spleen index [14,15].
The immunosuppressive effects of TS provide a favorable environment for opportunistic infections. Tuberculosis caused by Mtb remains a major global public health threat [16]. Its immunopathogenesis depends on interactions between Mtb and host macrophages and T cells [17]. Stress impairs macrophage function and disrupts T cell responses, resulting in increased susceptibility to Mtb [18,19,20]. Clearance of Mtb relies heavily on Th1-mediated immunity and macrophage activation, which are highly sensitive to GC-induced suppression. Because Mtb infection progresses slowly and persists for long periods, it represents an ideal model to examine the prolonged neuroendocrine and immune consequences of TS. This is of particular importance because TS is common in livestock transportation and wildlife translocation.
Many earlier studies have focused on a single mechanism, either emphasizing HPA axis abnormalities or stress-mediated immunosuppression, while overlooking that TS often impacts both neuroendocrine and immune systems simultaneously. To address this, our study integrates HPA axis bulk RNA-seq, splenic single-cell RNA-seq, behavioral assessments, and Mtb infection outcomes to systematically characterize the TS-induced neuroimmune imbalance and its detrimental effects on host immune defense.

2. Results

2.1. TS Induces Anxiety-like Behavior in Mice

To assess how TS influences mouse behavior, male mice were exposed to the TS paradigm (Figure 1A). Anxiety-related phenotypes were assessed using the open field test (OFT) and the elevated plus maze (EPM). In the OFT, TS-exposed mice displayed markedly reduced exploration of the central area relative to controls (Figure 1B–D). A similar pattern emerged in the EPM, where TS mice showed diminished occupancy of the open arms and a corresponding increase in time spent in the closed arms (Figure 1E–G). In addition to these behavioral alterations, repeated TS exposure led to decreases in both survival rate and body weight when compared with the control group (Figure 1H,I). These data suggest that repeated TS exposure induces an anxiety-like phenotype in mice. In addition, HE staining of the spleen revealed a clear demarcation between the white and red pulp, intact blood sinus structures, and the presence of lymphocytes, macrophages, and other immune cells with normal morphology in the control group. In the TS group, the spleen exhibited marked pathological alterations, including disrupted red pulp architecture, and damaged splenic sinusoids accompanied by enlarged lumen size, increased sinusoidal permeability, and hemorrhage. These changes led to pronounced inflammatory responses and structural remodeling within the spleen (Figure 1J). Together, these findings demonstrate that repeated TS exposure results in anxiety-like behaviors and systemic physiological alterations in mice.

2.2. TS Alters HPA Axis Gene Expression and Cross-Organ Molecular Signaling

We performed RNA-seq on the hypothalamus, pituitary, and adrenal glands from control and TS mice to profile differential expression across the HPA axis. After TS, hypothalamic upregulated genes were dominated by interferon-inducible genes (e.g., Gbp4, Gbp2), while stress- and cellular-function-related genes (e.g., Egr1, Fos) were downregulated; in the pituitary, upregulated genes were mainly linked to circadian rhythm and hormone precursors (e.g., Dbp, Ciart, Pomc), whereas downregulated genes were associated with clock control and stress; in the adrenal gland, circadian genes (e.g., Dbp, Per3) were upregulated, and genes related to immune response, stress signaling, and cell cycle/clock regulation (e.g., Bcl3, Gadd45b, Cdkn1a) were downregulated (Figure 2A; DEGs in Table S2). GO analysis showed enrichment of upregulated genes in immune activation/regulation and neural processes, while downregulated genes were enriched in regulation of neuron death, receptor serine/threonine kinase signaling, hormone synthesis and metabolism, and stress/metabolic regulation (Figure 2B). Specifically, Npy and Agrp were significantly upregulated in the hypothalamus within the positive regulation of behavior; pituitary hormone-secretion genes including Nr1d1, Doc2b, Per2 were significantly upregulated; and adrenal Ddit4, Bcl3, Cdkn1a were significantly downregulated in the intrinsic apoptotic pathway responding to p53-mediated DNA damage (Figure 2C).
Receptor–ligand network analysis revealed cross-organ coupling between hypothalamic Npy/Agrp and their receptors in the pituitary and between pituitary Pomc and its receptor in the adrenal gland, alongside intraorgan connections (Figure 2D). Transcription factor analysis indicated enrichment of the interferon regulatory factor family in the hypothalamus, and immune- and cell-cycle-related factors in the pituitary and adrenal glands (Figure 2E). Overall, TS broadly reprogrammed gene expression and cross-organ communication along the HPA axis, with prominent effects on immune regulation, metabolism, and circadian control.

2.3. TS Disrupts Neuroimmune Crosstalk and Immune Response in the Spleen

To assess the effects of TS on the splenic immune microenvironment at the single-cell level, we performed scRNA-seq on spleen tissues from control and TS-treated mice. After stringent quality control (Figure S1A), 20,937 high-quality cells (9128 control and 11,809 TS) were retained and integrated. Dimensionality reduction by PCA followed by UMAP visualization identified 18 major immune cell types based on canonical marker genes (Figure 3A and Figure S1B,C). DEG analysis revealed that TS predominantly induced gene downregulation in the spleen (Figure 3B, see DEGs in Table S3). KEGG enrichment of downregulated genes highlighted immune- and inflammation-related pathways, including Th17 cell differentiation, IL-17 signaling, and antigen processing and presentation (Figure 3C). These genes were consistently suppressed across multiple cell types after TS (Figure S1D).
Cell–cell communication analysis using CellChat showed a global reduction in both interaction strength and number following TS (Figure S1E). Monocytes, neutrophils, NKT cells, Tregs, and naïve CD4+ T cells exhibited fewer outgoing and incoming signals in the TS group (Figure 3D). At the pathway level, COMPLEMENT, CXCL, and MIF signaling were more active in the control group, whereas IL1 and GRN pathways were relatively enhanced after TS (Figure S1F). Notably, contributions of multiple cell types to the COMPLEMENT pathway were markedly reduced after TS, while CXCL signaling showed increased input from NKT cells and cDCs but decreased input from neutrophils (Figure 3E).
Further analysis demonstrated that COMPLEMENT signaling from monocytes and neutrophils observed in controls was largely abolished following TS (Figure S2A), accompanied by reduced interactions such as C3-(Itgax+Itgb2), C3-(Itgam+Itgb2), and C3-Cr2 (Figure S2B). MIF signaling from naïve CD4+ T cells, NKT cells, and Tregs was also substantially decreased, driven by reduced Mif-(Cd74+Cxcr2), Mif-(Cd74+Cxcr4), and Mif-(Cd74+Cd44) interactions (Figure S2C,D). In contrast, GRN signaling to neutrophils was enhanced after TS, particularly through the Grn-Sort1 ligand–receptor pair (Figure S2E,F).
Finally, we constructed a ligand–receptor crosstalk network linking HPA axis signals to splenic DEGs (Figure 3F). Potential neuroendocrine–immune crosstalk was identified, including hypothalamic Npy–pituitary Npy1r and Agrp-Mc3r crosstalk, as well as pituitary Pomc–adrenal Mc2r crosstalk. Pomc may also influence splenic receptors (Oprm1, Oprl1, Oprk1, Oprd1, and Mc1r), while Angptl2, Igf, and Gdf15 were associated with regulation of Pirb/Tlr4, Igf1r, and Tgfbr2, respectively. Consistently, key intrasplenic immune interactions such as C3-Cr2 were significantly downregulated following TS (Figure 3G). Together, these results indicate that TS disrupts HPA-axis-mediated neuroimmune communication and impairs coordinated immune signaling within the spleen, potentially compromising immune function.

2.4. TS Affects Infection-Related Gene Regulation and Exacerbates Mtb Infection

To evaluate the impact of TS on Mtb infection, TS was applied after pathogen challenge. Mice infected with Mtb alone were assigned to the Mtb group, whereas mice subjected to both Mtb infection and TS constituted the Mtb+TS group (Figure 4A). Anxiety-like behaviors were assessed using the OFT and EPM. In the OFT, Mtb+TS mice showed significantly reduced center exploration compared with Mtb mice (Figure 4B,C). Consistently, EPM analysis revealed decreased time in the open arms and increased time in the closed arms in the Mtb+TS group (Figure 4D,E).
Beyond behavioral changes, mice exposed to both Mtb and TS exhibited lower survival rates and greater body weight loss relative to the Mtb group alone (Figure 4F,G). TS also exacerbated disease severity, as reflected by higher bacterial burdens in the lungs and spleen (Figure 4H). Lung histopathology revealed that Mtb alone caused interstitial thickening, inflammatory infiltration (grade 3), mild hemorrhage (grade 1), and structural destruction (grade 1). Compared with the Mtb group, the Mtb+TS group showed superimposed injuries: severe structural destruction (grade 2), persistent inflammatory infiltration (grade 2), and concurrent interstitial thickening and wall thickening, resulting in more severe lung dysfunction than either single factor. Spleen histopathology showed that Mtb alone induced obvious granulomatous inflammatory lesions in the spleen, characterized by structureless pale areas with loss of cell outlines. Compared to the Mtb group, the Mtb+TS group exhibited vacuolation of the periarteriolar lymphoid sheath, lymphocyte necrosis, enlarged splenic nodules, and increased multinucleated giant cells. The boundary between red and white pulp was indistinct (Figure 4I).
Finally, qPCR validation confirmed that TS profoundly altered infection-induced gene expression. Under normal conditions, Tnfα was significantly upregulated in both spleen and lung following Mtb infection, whereas this response was markedly attenuated under TS conditions (Figure 4J). Consistent trends were observed for other immune-related genes, including Il1β, Il12b, Il10, and Tgfβ in the lung, as well as Il12b and Il10 in the spleen, whose infection-induced upregulation was diminished by TS (Figure S3A,B).

2.5. TS Exacerbates Mtb Infection by Disrupting HPA Axis–Spleen Immune Crosstalk

To further investigate the molecular mechanisms underlying the exacerbation of Mtb infection following TS, we performed RNA-seq analysis of the HPA axis in mice from the Mtb and Mtb+TS groups. As shown in Figure 5A, the number of downregulated DEGs in the Mtb+TS group relative to the TS group was markedly higher than that observed in the Mtb group relative to controls, including genes such as Npy and Agrp. In the hypothalamus, comparison of Mtb vs. control and Mtb+TS vs. TS revealed 37 commonly upregulated genes, whereas 52 and 41 genes were uniquely upregulated in the Mtb vs. control and Mtb+TS vs. TS comparisons, respectively. Notably, only one gene was downregulated in the Mtb vs. control comparison, whereas 30 genes were downregulated in the Mtb+TS vs. TS comparison (Figure 5B).
Functional enrichment analysis indicated that these DEGs were primarily associated with immune-related pathways, including antigen processing and presentation (Figure S3C,D). Interestingly, genes uniquely upregulated in the Mtb+TS group were enriched in neurotransmitter and ion transport processes (Figure 5C), whereas downregulated genes were mainly involved in hormone- and neuropeptide-mediated regulation of metabolism and behavior (Figure 5D). Under normal conditions, Mtb infection significantly upregulated genes associated with adaptive immune responses; however, this response was markedly attenuated under TS. In contrast, neuropeptide signaling pathway genes were selectively downregulated following infection under TS conditions. Genes related to neurotransmitter transport and interferon-γ responses were induced by infection under normal conditions and further enhanced by TS (Figure 5E). The shared DEGs between the pituitary and adrenal glands also showed significant enrichment in immune-related processes, including interferon response (Figure S3E–H).
To further dissect immune alterations, we performed scRNA-seq on spleens from Mtb and Mtb+TS mice. After quality control, 44,414 cells from four groups were integrated and visualized by PCA and UMAP, revealing 18 major cell types based on canonical markers (Figure S4A–D). Compared with the Mtb vs. control comparison, downregulated DEGs in the Mtb+TS vs. TS comparison exhibited a reduced magnitude of suppression (Figure 5F). KEGG analysis showed that upregulated DEGs in the Mtb group were enriched in Th1, Th2, and Th17 differentiation pathways (Figure 5G), while downregulated genes were enriched in MAPK, IL-17, and B cell receptor signaling pathways. In contrast, fewer cell types exhibited enrichment in these immune pathways in the Mtb+TS vs. TS comparison (Figure S4E).
Cell–cell interaction analysis demonstrated increased interaction number and strength in both Mtb vs. control and Mtb+TS vs. TS comparisons (Figure S4F). Notably, macrophages, neutrophils, and monocytes displayed more pronounced alterations in outgoing and incoming signaling in the Mtb+TS vs. TS comparison than in the Mtb vs. control comparison (Figure 5H). Finally, crosstalk analysis between the HPA axis and splenic immune cells revealed that Mtb infection primarily activated ligand–receptor pairs involved in immune cell migration and inflammation, such as Cxcl9/10-Cxcr3 and Ccl2-Ccr2 (Figure 5I). In contrast, the Mtb+TS vs. TS comparison additionally revealed interactions related to neuroendocrine homeostasis, including Crh-Crhr1, Agrp-Mc3r, Gh-Ghr, Tac1-Tacr1, and Lep-Lepr (Figure 5J).

2.6. TS Alters Splenic Myeloid Cell Function During Mtb Infection

In the cell interaction analysis, significant differences in the overall information flow of multiple signaling pathways were observed between the Mtb and control groups and between the Mtb+TS and TS groups, respectively. Specifically, the COMPLEMENT pathway was activated in both comparisons, while pathways such as LIGHT and VISFATIN were uniquely observed in the Mtb-infected group (Figure S4G). Notably, by comparing the changes in the contribution of various cell pathways in the Mtb+TS vs. TS group and the Mtb vs. control group, it was found that the changes in macrophages, neutrophils, and monocytes were greater than those in other cell types (Figure 6A). Regarding COMPLEMENT-pathway-related genes, macrophages in the Mtb group exhibited upregulated gene expression compared to the control group; however, this upregulation was significantly diminished in the Mtb+TS group vs. TS comparison (Figure 6B).
Based on these findings, we extracted the DEGs specific to macrophages, neutrophils, and monocytes for further analysis. Comparing DEGs in macrophages between the Mtb vs. control and Mtb+TS vs. TS groups, we identified 85 shared upregulated genes, 79 uniquely upregulated genes in the Mtb vs. control group, and 73 uniquely upregulated genes in the Mtb+TS vs. TS group (Figure 6C). GO enrichment analysis revealed that the 79 uniquely upregulated genes in the Mtb vs. control group were primarily associated with processes such as interferon-gamma response, CD4-positive alpha-beta T cell differentiation, and myeloid cell differentiation (Figure 6D).
Similar analyses for neutrophils and monocytes demonstrated that the genes uniquely upregulated in the Mtb vs. control group were closely related to antiviral immune responses and immune homeostasis regulation (Figure S5A–D). Furthermore, we examined the expression patterns of the genes enriched in the identified biological processes in macrophages. Interestingly, the expression of these genes was increased in the Mtb group compared to the control group, but the extent of upregulation was attenuated in the Mtb+TS vs. TS comparison. This attenuation was particularly pronounced for genes such as Eif6, Klf13, Relb, and Ubash3b (Figure 6E,F and Figure S5E). Finally, we constructed crosstalk networks between the HPA axis and macrophages for both the Mtb vs. control and Mtb+TS vs. TS comparisons. Similar to the results shown in Figure 5I,J, the Mtb+TS vs. TS comparison included not only ligand–receptor pairs involved in immune regulation, as observed in the Mtb vs. control group, but also revealed additional ligand–receptor interactions related to neuroendocrine homeostasis (Figure 6G,H).

3. Discussion

The present study systematically investigates the multifaceted effects of TS in mice. We found that TS induces both anxiety-like behaviors and HPA-axis-mediated hormonal changes, highlighting its role in stress response mechanisms. TS disrupts splenic immune function, particularly impairing macrophage activity, which exacerbates the severity of Mtb infection through immune dysregulation. These findings deepen our understanding of the interplay among stress, the HPA axis, and immunity, offering novel strategies to mitigate stress-induced immune damage and to enhance the prevention and treatment of tuberculosis.

3.1. TS Alters Gene Expression Within the HPA Axis and Impairs Immune Responses in the Spleen

Our findings demonstrate that TS upregulates hypothalamic Npy and Agrp expression, consistent with previous studies showing that stress enhances the expression of these neuropeptides [21,22]. NPY and AgRP are critical regulators of energy homeostasis and stress responses, and their dysregulation has been implicated in stress-related disorders such as depression and anxiety [23,24,25,26]. Additionally, TS alters Pomc expression in the pituitary, which corroborates earlier research linking stress to increased Pomc transcription [27]. These observations underscore the pivotal role of TS in modulating the HPA axis, thereby influencing systemic stress responses and homeostasis.
scRNA-seq analysis further reveals that TS disrupts neuroimmune communication by impairing HPA axis regulation and interfering with critical immune cell interactions in the spleen. TS leads to downregulation of genes associated with immune-related pathways, including Th17 cell differentiation, IL-17 signaling, and antigen processing and presentation, while also reducing cellular interactions, indicative of severe immune dysregulation. This aligns with existing research linking Th17 cell alterations to mood disorders such as depression [28]. Furthermore, IL-17A, a Th17-derived cytokine, has been shown to be associated with anxiety and depressive symptoms in both humans and animal models [29,30,31]. These findings collectively suggest that targeting Th17 cells may hold potential for antidepressant therapies, with IL-17 serving as a promising biomarker or therapeutic target [32].
Additionally, ligand–receptor analysis highlights TS-induced disruption of neuroimmune communication via HPA-axis-mediated spleen regulation. Chronic HPA axis activation has been shown to compromise immune function [33], while PVN CRH neurons regulate splenic activity in mice [34]. Moreover, HPA axis activation contributes to stress-induced leukocyte redistribution, with the spleen playing a key role in modulating peripheral circulatory changes during stress [35]. Collectively, these findings provide a comprehensive understanding of how TS alters neuroimmune interactions, leading to immune dysregulation, and highlight the potential for therapeutic interventions targeting the HPA axis and immune pathways to mitigate the adverse effects of TS.

3.2. TS-Induced Immune Dysregulation Aggravates Mtb Infection

Comparative analysis between Mtb-infected and Mtb+TS groups revealed that TS amplifies bacterial proliferation and tissue damage by disrupting immune responses. This aligns with previous studies demonstrating that stress exacerbates the severity of mycobacterial infections [36,37]. While previous studies have shown that stress-induced Npy expression in the brain may correlate with PTSD vulnerability and that AgRP neuronal activity controls TNF-α release during endotoxemia [38,39], our findings extend these observations by demonstrating that TS synergizes with Mtb infection to suppress hypothalamic Npy and Agrp gene expression. This highlights a potential neuroendocrine–immune axis through which TS and Mtb infection exacerbate metabolic and immune disorders.
Furthermore, our analysis of the HPA axis–spleen interaction network revealed that Mtb infection under TS conditions altered ligand–receptor pairs, mainly associated with neuroendocrine homeostasis, such as Crh-Crhr1, Agrp-Mc3r, and Gh-Ghr. These findings suggest that TS may modulate neuroendocrine signaling between the HPA axis and spleen, thereby influencing immune responses to Mtb infection. The central role of CRH in regulating stress-induced immune responses is well-established [40]. CRHR1 antagonists have demonstrated therapeutic potential in mitigating stress-induced immunosuppression and depressive-like behaviors [41,42]. Similarly, growth hormone (GH) and its receptor (GHR) exhibit dual immunomodulatory and cognitive functions [43]. GH reduces systemic inflammation by regulating cytokines and inflammatory markers [44] and enhances immune function through thymic regeneration [45]. Collectively, these findings underscore the potential for targeting CRH, AGRP, GH, and their receptors as novel therapeutic strategies to alleviate TS-induced immune dysfunction and improve host defense against infection.
In summary, TS-induced immune dysregulation promotes Mtb infection severity by disturbing neuroendocrine–immune pathways and the HPA axis–spleen interaction network. This highlights the importance of understanding and addressing stress-related immune dysfunction in developing effective interventions to combat tuberculosis and other infectious diseases exacerbated by environmental stressors.

3.3. TS Induces Immune Dysregulation of Splenic Myeloid Cells, Exacerbating Mtb Infection

Our study demonstrates that TS significantly alters the signaling dynamics of macrophages, neutrophils, and monocytes, potentially influencing Mtb infection progression. This aligns with previous research showing that chronic stress modulates myeloid cell inflammatory responses through its inflammatory priming effects, impacting both mouse and human health [46]. Macrophages play a dual pivotal role in host defense against Mtb, serving as both the primary reservoir for the pathogen and crucial effector cells for infection clearance [47]. Activation of the IFN-γ pathway is essential for macrophages to counteract mycobacterial survival strategies and eliminate intracellular pathogens [48]. Our data revealed a critical finding: in Mtb-infected mice without TS, there was a significant upregulation of genes associated with the “IFN-γ response” and “myeloid cell differentiation” compared to controls. However, this protective immune response was attenuated in mice subjected to both Mtb infection and TS, suggesting that TS may counteract Mtb-induced protective immune responses through neuroendocrine pathways, thereby compromising host defense mechanisms.
Furthermore, our analysis of the interaction network between the HPA axis and splenic macrophages demonstrated that, under TS conditions, Mtb infection induces ligand–receptor pairs associated with neuroendocrine homeostasis, such as Crh-Crhr1, Agrp-Mc3r, and Gh-Ghr. These interactions may modulate the immune function of splenic macrophages, highlighting the complex interplay between stress, neuroendocrine signaling, and immune regulation during Mtb infection. These findings enhance our understanding of the immune regulatory mechanisms in Mtb and provide a theoretical foundation for developing innovative intervention strategies targeting stress-associated immune dysfunction.
This study has certain limitations. First, a tissue pooling strategy was employed at the sample collection stage. Although this approach helps reduce technical variability and minimize the impact of random individual differences, it may obscure interindividual biological heterogeneity, thereby limiting the independent assessment of variability among animals. Second, animal mortality during the experimental period may have introduced potential biological bias. Individuals that did not survive could represent more severe phenotypes in terms of disease or stress response, potentially leading to a degree of survivorship bias. These factors should therefore be carefully considered when interpreting the results. Nevertheless, the overall trends observed across experimental groups were consistent, supporting the robustness of the study’s conclusions.

4. Materials and Methods

4.1. Bacterial Strains

Mtb H37Ra was grown statically at 37 °C in Middlebrook 7H9 broth (Becton Dickinson, Franklin Lakes, NJ, USA; Cat. 271310) supplemented with 10% oleic albumin dextrose catalase (OADC, Becton Dickinson, Franklin Lakes, NJ, USA), 0.05% Tween 80, and 0.5% glycerol. Alternatively, the bacteria were cultured on solid Middlebrook 7H11 agar plates (Becton Dickinson, Franklin Lakes, NJ, USA) supplemented with 10% OADC and 0.5% glycerol. For mouse infection, H37Ra cultures were centrifuged (3000× g, 10 min) to collect bacteria pellets. These pellets were then resuspended in phosphate-buffered saline (PBS) to a density of 3 × 106 bacteria/mL and repeatedly passed through an insulin syringe several times to disperse the bacteria [49].

4.2. Animals

Male C57BL/6 mice (4–6 weeks old, 18–20 g body weight) were purchased from Huazhong Agricultural University Experimental Animal Center, Wuhan, China. The mice were group-housed (5 per cage) in polycarbonate cages with free access to food and water for one week prior to use in the experiments under a 12 h light/dark cycle at 22–25 °C and 50–70% relative humidity. The C57BL/6 mice were randomly divided into four groups (control, TS, Mtb, and Mtb+TS groups), with 10 mice in each group. The control group was infected intravenously (lateral tail vein) with PBS and not subjected to TS. The Mtb group was infected intravenously with H37Ra (1 × 106 CFU/mice) and not subjected to TS. The TS group was injected intravenously with PBS and subjected to TS. The Mtb+TS group was infected intravenously with H37Ra and subjected to TS. All behavioral tests were conducted by the same experimenter, blinded to group assignment, to minimize experimenter-related variability and bias. Animals were randomized to experimental groups, and sample sizes were determined based on our previous work to ensure statistical power. We minimized the number of animals used and their suffering, and tissue samples were collected from randomly selected animals within each group. All procedures were approved by the Huazhong Agricultural University Ethics Committee (HZAUMO-2023-0234) and conducted in accordance with NIH guidelines (NIH Publication No. 85-23), with animals continuously monitored and experiments terminated immediately upon reaching predefined humane endpoints.

4.3. TS in Mice

TS is a complex stimulus involving vibration, high temperature, noise, hunger/thirst, crowding, and collision. Our protocol employs a shaker (90–120 rpm) to simulate road vibration, 35–36 °C to mimic summer heat exposure, and combines shaker noise, crowding, and fasting/water deprivation to replicate all key stressors. Mice were subjected to simulated TS daily (3 h; 8:30 to 11:30) for 8 consecutive days according to the previous study with some modifications [9,50]. Minimal handling was used to place a group of 10 mice into the restraint device, which consisted of a closed, transparent 500 mL plastic bottle, to restrict their activity. Large breathing holes around the bottle provided adequate ventilation. The bottles containing the mice were transferred to a temperature-controlled shaker and shaken at 90–120 rpm at 35–36 °C. The mice were dried and placed in their original cages with free access to food and water after TS.

4.4. Behavioral Tests

Behavioral tests were conducted by experimenters blinded to the group allocation. All behavioral tests were conducted in a behavioral testing room with ambient temperature, lighting, pressure and sound. Mice were habituated to the behavior room in their home cages 1 h prior to testing. Mice were tested in a random order. After each test session, the experimental apparatus was cleaned between sessions with 75% alcohol and dried with toilet paper to prevent the odors of different mice from affecting the results of the subsequent experiments. The movements of the test mice were tracked and evaluated using the PanLab SMART Video Tracking software from PanLab/Harvard Apparatus. All behavioral data were obtained from individual animals, with each data point representing a single mouse.

4.5. Open Field Test (OFT)

The OFT was used to measure physical condition and anxiety-like behavior in the open field apparatus, as described previously [51]. The open box apparatus was 40 cm × 40 cm × 40 cm. The center zone was defined as a 20 cm × 20 cm square in the middle, and four 10 cm × 10 cm corners were defined as corner zones. Mice were individually placed into the center of the arena, facing the side wall at the same position, and were allowed to explore freely for 5 min. Their behaviors, including the total distance traveled (cm), time spent in the center zone (seconds), and time spent in the corner zones (seconds), were automatically recorded for the entire 10 min period after mice were placed in the apparatus.

4.6. Elevated Plus Maze (EPM) Test

The EPM test was performed according to the previous study [51]. The EPM test consists of four arms (5 cm × 30 cm). Two closed arms had 20-cm-high walls and the other open arms without walls. The maze was elevated 40 cm above the floor. Mice were placed in the center of the maze facing an open arm and allowed free access to four arms for 5 min. The mice were video recorded, and distance traveled (cm), time spent in the open arms ([time in open arms]/[time in total arms] × 100%) and time spent in the closed arms ([time in closed arms]/[time in total arms] × 100%) were analyzed using the SMART V2.0 software.

4.7. RNA-seq Protocols

4.7.1. Total RNA Isolation

We randomly selected six samples each from the hypothalamus, pituitary, and adrenal glands and eight samples each from the lung and spleen from mice in the control, TS, Mtb, and Mtb+TS groups. Total RNA was extracted using a total RNA extraction reagent (TaKaRa, Beijing, China; Cat. 9109 ) according to the manufacturer’s protocol. RNA purity and quantification were evaluated using the NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). RNA integrity was assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA).

4.7.2. RNA-seq Library Preparation and Sequencing

We extracted total RNA from six hypothalamic, pituitary, and adrenal tissues from each group of mice. Three RNA samples from each tissue were randomly pooled at equimolar concentrations, resulting in two samples per tissue for RNA sequencing library preparation. We prepared the mRNA-seq libraries using the VAHTS Universal V6 RNA-seq Library Prep Kit for MGI (Vazyme, Nanjing, China; Cat. NRM604) according to the manufacturer’s instructions. The prepared libraries were sequenced on the MGISEQ-2000 platform (BGI, Shenzhen, China) with 150 bp paired-end reads.

4.8. Colony-Forming Unit (CFU) Assay

Homogenates of spleen and lung tissues from the Mtb and Mtb+TS groups were serially diluted 1:10 in PBS. A 100 μL aliquot of each dilution was then plated onto Middlebrook 7H11 agar plates supplemented with OADC enrichment and PENTA antibiotics (BBL/MGIT; Becton, Dickinson and Company, Franklin Lakes, NJ, USA). The plates were incubated at 37 °C for approximately three weeks to enumerate bacterial load, and CFUs were manually counted. All assays were conducted in triplicate, and colonies were counted manually.

4.9. Hematoxylin and Eosin (HE) Staining

Lungs and spleens of the mice were fixed with 4% paraformaldehyde, dehydrated, and embedded in paraffin. Subsequently, they were cut into slices with 5 μm thickness. The slices were dewaxed with xylene, rehydrated with graded ethanol, and stained with hematoxylin and eosin sequentially. The cytomorphology of the lung and spleen tissues in each group was observed under an optical microscope.

4.10. Scoring System for Lung Injury Based on HE Staining

4.10.1. Alveolitis Scoring

Alveolitis is one of the important manifestations of lung injury. According to the infiltration degree and distribution of inflammatory cells, alveolitis is classified into the following four grades based on the HE staining lung injury scoring standard:
Grade 0: No manifestations of alveolitis. Grade 1: Scattered infiltration of inflammatory cells, involving less than 25% of the alveolar area. Grade 2: Infiltration of inflammatory cells, occupying 25% to 50% of the alveolar area. Grade 3: Infiltration of inflammatory cells, occupying more than 50% of the alveolar area.

4.10.2. Alveolar Hemorrhage Scoring

Alveolar hemorrhage is a common manifestation of lung injury. According to the range and degree of hemorrhage, alveolar hemorrhage is classified into the following three grades based on the HE staining lung injury scoring standard:
Grade 0: No alveolar hemorrhage. Grade 1: Alveolar hemorrhage, limited to a single alveolus. Grade 2: Alveolar hemorrhage, involving multiple alveoli.

4.10.3. Alveolar Structural Destruction Scoring

Alveolar structural destruction is one of the important manifestations of lung injury. According to the degree of alveolar structural destruction, alveolar structural destruction is classified into the following three grades based on the HE staining lung injury scoring standard:
Grade 0: No alveolar structural destruction. Grade 1: Alveolar structural destruction, limited to a single alveolus. Grade 2: Alveolar structural destruction, involving multiple alveoli.

4.11. Quantitative Real-Time PCR (qRT-PCR)

We randomly selected eight lungs and eight spleens from mice in the control, TS, Mtb, and Mtb+TS groups, respectively, for RNA extraction. cDNA synthesis was performed using ABScript II RT Mix for quantitative polymerase chain reaction (qPCR) with gDNA Remover (RK20403, ABclonal Biotech Co., Wuhan, China) according to the manufacturer’s instructions. The qRT-PCRs were carried out on the QuantStudio5 system (Applied Biosystems, Foster City, CA, USA) utilizing SYBR Green Master Mix (RK21203, ABclonal Biotech Co, Wuhan, China) and following the manufacturer’s instructions. The expression level of each gene was normalized to the reference gene (GAPDH). Relative gene expression was calculated by the 2−ΔΔCt method [52,53]. Three independent experiments were conducted for statistical analysis. Primers used for qRT-PCR are listed in Table S1. All qRT-PCR data were obtained from individual animals, with each data point corresponding to a single mouse.

4.12. Flow Cytometry

Single-cell suspensions from mouse lungs were prepared following tissue-specific digestion protocols. Lung tissue was mechanically homogenized and filtered through a 70 μm nylon mesh (Becton Dickinson) to isolate cellular components. All cell populations were immunostained with a panel of antibodies (anti-mouse CD45, Cat## 1031557; anti-mouse CD3, Cat##100213; anti-mouse CD4, Cat##100408; anti-mouse CD8, Cat##100803; anti-mouse CD11b, Cat##101207; anti-mouse Ly6G, Cat##127613; anti-mouse Ly6C, Cat##128005; all from Biolegend) at optimized dilutions (1:100–1:50) for 30 min at 4 °C. After staining, the samples were washed three times in PBS containing 0.1% formaldehyde and analyzed on a Beckman Cytoflex S flow cytometer using CytExpert software 2.7, with lymphocyte gates defined by forward/side scatter parameters.

4.13. Single-Cell RNA-seq Sample Preparation

For single-cell transcriptomic analysis, spleen tissues were collected from each experimental group. We randomly selected three spleens per group (n = 3), and single-cell suspensions were generated following standard mechanical dissociation and filtration procedures. To obtain a representative sample for each group while minimizing individual variability, single-cell suspensions from the three biological replicates were pooled at equimolar cell concentrations, resulting in one pooled sample per group for downstream sequencing. This pooling strategy is widely used to capture population-level cellular heterogeneity while reducing individual-animal bias, consistent with previously published single-cell studies [54,55].

4.14. RNA-seq Analysis

Quality control of the raw RNA-seq data was first performed using FastQC. Reads containing low-quality regions or adapter contamination were then filtered out with Trimmomatic [56]. After preprocessing, the remaining reads were mapped to the mouse reference genome (mm10) using HISAT2 [57]. Gene-level count data were subsequently processed with DESeq2 (v1.36.0) [58] for normalization and differential expression analysis.

4.15. Motif Analysis

The findMotifs.pl parameter in HOMER [59] was used to identify transcription factor binding motifs enriched in the DEGs. The specific command was findMotifs.pl genes.txt mouse MotifOutput, where genes.txt was a list of DEGs.

4.16. scRNA-seq Transcriptome Analysis

4.16.1. scRNA-seq Data Preprocessing

Raw scRNA-seq reads were preprocessed using the BD Rhapsody™ Whole Transcriptome Analysis (WTA) pipeline (v1.11). The GRCm38-PhiX-gencodevM19-20181206.tar file was used for genome alignment, and the gencodevM19-20181206.gtf file was used for annotation. The pipeline was run with default parameters. The resulting “_RSEC_MolsPerCell.csv” file was used for downstream transcriptome analysis.

4.16.2. scRNA-seq Quality Control

scRNA-seq data processing was carried out mainly with the Seurat package (v4.4.0) [60]. Seurat objects were generated using the “CreateSeuratObject” function, followed by normalization of gene expression levels using “NormalizeData.” Cells were filtered out when they contained fewer than 200 or more than 5000 detected genes or when mitochondrial transcripts exceeded 20 percent of total expression. Potential doublets were subsequently identified and removed with DoubletFinder (v4.0.3) [61].

4.16.3. Clustering of scRNA-seq Transcriptome

To correct for batch effects between the four samples, the IntegrateData function in Seurat was used to integrate the four datasets, followed by the standard Seurat cluster analysis workflow. Each cell population was then manually annotated, and a total of 18 cell populations, including mature BCs, were identified.

4.16.4. Ligand–Receptor Expression Network Diagram

To construct the ligand–receptor expression network, we first identified DEGs encoding ligands and determined whether their corresponding receptor genes were also expressed in the dataset; these pairs were retained for network visualization. Conversely, DEGs encoding receptors were also examined for the expression of their corresponding ligand genes in the data. Ligand–receptor pairs identified by either approach were included in the network diagram.

4.16.5. Cell–Cell Communication Analysis

Cell–cell communication analysis was performed using CellChat (v1.6.1) [62]. After importing the normalized expression matrix, a CellChat object was generated with the createCellChat function. The dataset was subsequently processed by identifying overexpressed genes and ligand–receptor interactions through the identifyOverExpressedGenes and identifyOverExpressedInteractions functions. Communication probabilities were then estimated with computeCommunProb and refined using filterCommunication. Signaling-pathway-level communication was inferred with computeCommunProbPathway, and the overall interaction network was summarized using the aggregateNet function.

4.17. Statistical Analyses

Statistical analysis was performed using GraphPad Prism 10 (GraphPad Software Inc., CA, USA). Data showing a normal distribution were tested using unpaired t-tests for two-group comparisons or a one- or two-way ANOVA followed by post hoc multiple comparison tests for more than two groups. Details regarding statistical analyses are described in the figure legends. Data are presented as means ± standard error of means (SEMs) with a p < 0.05 required for results to be considered as statistically significant. Statistical significance was indicated as * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001, ns, not significant, p > 0.05.

5. Conclusions

This study suggests that TS may, to some extent, impair host defense against Mtb by disrupting the HPA axis–spleen neuroendocrine–immune pathways. TS was associated with increased anxiety-like behavior, altered hormone secretion, and impaired key immune defenses, including Th17 responses, IFN-γ signaling, and myeloid cell function, accompanied by dysregulation of neuropeptide-related genes (Npy, Agrp, Pomc). These effects may be mediated, at least in part, through disruption of critical neuroimmune ligand–receptor communications (e.g., Npy-Npy1r, Crh-Crhr1) and dysfunction of splenic innate immune cells (macrophages, neutrophils, and monocytes), providing a partial explanation for the observed immune regulation changes during Mtb infection. Overall, these results provide mechanistic and trend-supporting evidence indicating a consistent and reproducible association between transportation stress and impaired stress–immune axis function, offering a reference for stress management and potential strategies to mitigate TS-associated immune dysregulation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms27041919/s1.

Author Contributions

K.X., Y.L. and X.G. initiated and designed the study. Y.L., B.Y., W.Z., X.C., T.G. and S.Z. were involved in the construction and validation of the mouse model. K.Y. and Q.Z. prepared and performed sequencing of the RNA-seq and scRNA-seq libraries. X.G., H.Z. and L.D. conducted RNA-seq and scRNA-seq data analyses. K.X., X.G. and Y.L. drafted the manuscript. G.C., K.X., X.W. and J.D. reviewed and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 32221005 and U21A20259 to G.C.; 32371201 to K.X.) and the Shenzhen Medical Research Special Fund Project (Grant No. D2401006 to K.X.).

Institutional Review Board Statement

This study was carried out in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. The protocol was approved by the Scientific Ethic Committee of Huazhong Agricultural University (HZAUMO-2023-0234; date of approval: 12 September 2023).

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw and processed RNA-seq data, as well as the processed single-cell RNA-seq data from this study, have been deposited in GEO under the accession number GSE284564.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. TS induces anxiety-like behavior in mice. (A) Experimental procedures for anxiety-like behavioral tests, including the open field test (OFT) and the elevated plus maze (EPM). (B) Representative OFT images of control and TS mice. (C,D) Summary plots of time spent in corner zones (C) and center zones (D) in the OFT. Control group, n = 10 mice; TS group, n = 10 mice. (E) Representative EPM images of control and TS mice. (F,G) Summary plots of time spent in open arms (F) and closed arms (G). Same sample sizes as in B. (H) Percent survival rates of control and TS mice. Control group, n = 20 mice; TS group, n = 22 mice. (I) Monitoring body weight changes in control and TS mice over time. Control group, n = 20 mice; TS group, n = 22 mice. (J) HE staining of spleen sections from control and TS mice. The arrow indicates splenic sinusoids. The pentagram indicates the hemorrhage. The triangle indicates the splenic nodule. Control group, n = 3 mice; TS group, n = 3 mice. ** p < 0.01, *** p < 0.001, **** p < 0.0001, by unpaired Student’s t-test (C,D,F,G) and two-way ANOVA (I), data represent means ± SEMs. All behavioral data were derived from individual animals, with each data point corresponding to a single mouse.
Figure 1. TS induces anxiety-like behavior in mice. (A) Experimental procedures for anxiety-like behavioral tests, including the open field test (OFT) and the elevated plus maze (EPM). (B) Representative OFT images of control and TS mice. (C,D) Summary plots of time spent in corner zones (C) and center zones (D) in the OFT. Control group, n = 10 mice; TS group, n = 10 mice. (E) Representative EPM images of control and TS mice. (F,G) Summary plots of time spent in open arms (F) and closed arms (G). Same sample sizes as in B. (H) Percent survival rates of control and TS mice. Control group, n = 20 mice; TS group, n = 22 mice. (I) Monitoring body weight changes in control and TS mice over time. Control group, n = 20 mice; TS group, n = 22 mice. (J) HE staining of spleen sections from control and TS mice. The arrow indicates splenic sinusoids. The pentagram indicates the hemorrhage. The triangle indicates the splenic nodule. Control group, n = 3 mice; TS group, n = 3 mice. ** p < 0.01, *** p < 0.001, **** p < 0.0001, by unpaired Student’s t-test (C,D,F,G) and two-way ANOVA (I), data represent means ± SEMs. All behavioral data were derived from individual animals, with each data point corresponding to a single mouse.
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Figure 2. TS-induced dysregulation of the HPA axis. (A) DEGs in hypothalamus (left), pituitary (middle), and adrenal glands (right): TS vs. control. Red: upregulated; blue: downregulated; gray: non-significant. (B) GO enrichment of DEGs (TS vs. control). Red: upregulated gene-enriched pathways; blue: downregulated. (C) Cnetplots depict gene networks for selected key pathways identified in panel B. (D) Ligand–receptor crosstalk network (TS vs. control). Arrows: hypothalamus–pituitary (orange-red), pituitary–adrenal (light blue), intratissue (gray). Gene colors: orange-red (up), light blue (down), gray (ns). (E) Motif analysis of DEGs in the hypothalamus (left), pituitary (middle), and adrenal glands (right) compares the TS group to the control group, identifying three specific motifs and their associated transcription factors in each tissue. RNA-seq data were obtained from pooled samples, with each library constructed by combining tissues from 3 mice per group.
Figure 2. TS-induced dysregulation of the HPA axis. (A) DEGs in hypothalamus (left), pituitary (middle), and adrenal glands (right): TS vs. control. Red: upregulated; blue: downregulated; gray: non-significant. (B) GO enrichment of DEGs (TS vs. control). Red: upregulated gene-enriched pathways; blue: downregulated. (C) Cnetplots depict gene networks for selected key pathways identified in panel B. (D) Ligand–receptor crosstalk network (TS vs. control). Arrows: hypothalamus–pituitary (orange-red), pituitary–adrenal (light blue), intratissue (gray). Gene colors: orange-red (up), light blue (down), gray (ns). (E) Motif analysis of DEGs in the hypothalamus (left), pituitary (middle), and adrenal glands (right) compares the TS group to the control group, identifying three specific motifs and their associated transcription factors in each tissue. RNA-seq data were obtained from pooled samples, with each library constructed by combining tissues from 3 mice per group.
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Figure 3. TS disrupts immune response in the spleen. (A) UMAP plot of splenocyte scRNA-seq data (unsupervised clustering, 18 immune cell types). Abbreviations: BC, B cells; TC, T cells; Treg, regulatory T cells; γδ TC, gamma delta T cells; NKT, natural killer T cells; NK, natural killer cells; cDC, conventional dendritic cells; pDC, plasmacytoid dendritic cells. (B) Differential gene expression across clusters (TS vs. control). Color scale: red, upregulated; blue, downregulated. (C) KEGG enrichment analysis of downregulated DEGs across all clusters in the TS and control groups. In the bar plot, gradient colors represent p-values, while bubble size corresponds to Gene Ratio, with larger bubbles indicating a higher proportion of genes involved in the pathway. (D) Interaction strength of outgoing and incoming signals for clusters in the control group (left) and TS group (right). The horizontal axis is the outgoing interaction strength, while the vertical axis is the incoming interaction strength. (E) Heatmap of signaling pathway contributions to cell groups. Top bar: total outgoing/incoming signals per cell group; right bar: total signals per pathway. (F) Ligand–receptor crosstalk network between HPA axis and splenocytes. Edge color: log-scaled interaction strength. (G) Key ligand–receptor crosstalk. Arrows: hypothalamus–pituitary (orange-red), pituitary–adrenal/spleen (light blue), adrenal–spleen (cyan-green). Gene labels: orange-red (up), light blue (down), black (ns). Single-cell RNA-seq data were obtained from a pooled sample, generated by combining single-cell suspensions from three mice per group at equal cell concentrations.
Figure 3. TS disrupts immune response in the spleen. (A) UMAP plot of splenocyte scRNA-seq data (unsupervised clustering, 18 immune cell types). Abbreviations: BC, B cells; TC, T cells; Treg, regulatory T cells; γδ TC, gamma delta T cells; NKT, natural killer T cells; NK, natural killer cells; cDC, conventional dendritic cells; pDC, plasmacytoid dendritic cells. (B) Differential gene expression across clusters (TS vs. control). Color scale: red, upregulated; blue, downregulated. (C) KEGG enrichment analysis of downregulated DEGs across all clusters in the TS and control groups. In the bar plot, gradient colors represent p-values, while bubble size corresponds to Gene Ratio, with larger bubbles indicating a higher proportion of genes involved in the pathway. (D) Interaction strength of outgoing and incoming signals for clusters in the control group (left) and TS group (right). The horizontal axis is the outgoing interaction strength, while the vertical axis is the incoming interaction strength. (E) Heatmap of signaling pathway contributions to cell groups. Top bar: total outgoing/incoming signals per cell group; right bar: total signals per pathway. (F) Ligand–receptor crosstalk network between HPA axis and splenocytes. Edge color: log-scaled interaction strength. (G) Key ligand–receptor crosstalk. Arrows: hypothalamus–pituitary (orange-red), pituitary–adrenal/spleen (light blue), adrenal–spleen (cyan-green). Gene labels: orange-red (up), light blue (down), black (ns). Single-cell RNA-seq data were obtained from a pooled sample, generated by combining single-cell suspensions from three mice per group at equal cell concentrations.
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Figure 4. TS exacerbates the severity of Mtb infection in mice. (A) Experimental design outlining Mtb infection, TS, and anxiety-like behavior testing in mice. (B) Representative images from the open field test (OFT) of Mtb-infected and Mtb+TS mice. (C) Time spent in the corner region (left) and the center region (right) during the OFT. Mtb group: n = 10 mice; Mtb+TS group: n = 10 mice. (D) Representative images from the elevated plus maze (EPM) of Mtb-infected and Mtb+TS mice. (E) Time spent in the open arms (left) and closed arms (right) during the EPM. Sample sizes are consistent with those in panel (C). (F) Survival rates of Mtb-infected and Mtb+TS mice. Mtb group: n = 20 mice; Mtb+TS group: n = 20 mice. (G) Monitoring of body weight changes in Mtb-infected and Mtb+TS mice. Mtb group: n = 20 mice; Mtb+TS group: n = 20 mice. (H) Bacterial load in the lung (left) and spleen (right) of Mtb-infected and Mtb+TS mice. Mtb group: n = 7 mice; Mtb+TS group: n = 7 mice. (I) HE staining of lung (left) and spleen (right) sections from the Mtb group (top) and Mtb+TS group (bottom). Mtb group: n = 3 mice; Mtb+TS group: n = 3 mice. (J) qRT-PCR analysis of Tnfα expression levels in the lung (left) and spleen (right), comparing four groups: control, Mtb, TS, and Mtb+TS. Mtb group: n = 8 mice; Mtb+TS group: n = 8 mice. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, by unpaired Student’s t-test (C,E,H), two-way ANOVA (G) and ordinary one-way ANOVA (J), data represent means ± SEMs. Lung tissue: the arrow indicates alveolar wall, the pentagram indicates alveolar septum; Spleen tissue: the triangle indicates splenic nodule, the pentagram indicates white pulp. All behavioral, bacterial load, and qRT-PCR data were derived from individual animals, with each data point corresponding to a single mouse.
Figure 4. TS exacerbates the severity of Mtb infection in mice. (A) Experimental design outlining Mtb infection, TS, and anxiety-like behavior testing in mice. (B) Representative images from the open field test (OFT) of Mtb-infected and Mtb+TS mice. (C) Time spent in the corner region (left) and the center region (right) during the OFT. Mtb group: n = 10 mice; Mtb+TS group: n = 10 mice. (D) Representative images from the elevated plus maze (EPM) of Mtb-infected and Mtb+TS mice. (E) Time spent in the open arms (left) and closed arms (right) during the EPM. Sample sizes are consistent with those in panel (C). (F) Survival rates of Mtb-infected and Mtb+TS mice. Mtb group: n = 20 mice; Mtb+TS group: n = 20 mice. (G) Monitoring of body weight changes in Mtb-infected and Mtb+TS mice. Mtb group: n = 20 mice; Mtb+TS group: n = 20 mice. (H) Bacterial load in the lung (left) and spleen (right) of Mtb-infected and Mtb+TS mice. Mtb group: n = 7 mice; Mtb+TS group: n = 7 mice. (I) HE staining of lung (left) and spleen (right) sections from the Mtb group (top) and Mtb+TS group (bottom). Mtb group: n = 3 mice; Mtb+TS group: n = 3 mice. (J) qRT-PCR analysis of Tnfα expression levels in the lung (left) and spleen (right), comparing four groups: control, Mtb, TS, and Mtb+TS. Mtb group: n = 8 mice; Mtb+TS group: n = 8 mice. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, by unpaired Student’s t-test (C,E,H), two-way ANOVA (G) and ordinary one-way ANOVA (J), data represent means ± SEMs. Lung tissue: the arrow indicates alveolar wall, the pentagram indicates alveolar septum; Spleen tissue: the triangle indicates splenic nodule, the pentagram indicates white pulp. All behavioral, bacterial load, and qRT-PCR data were derived from individual animals, with each data point corresponding to a single mouse.
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Figure 5. TS leads to immune disturbance and aggravates Mtb infection in mice. (A) Volcano plots of hypothalamic DEGs: (left), Mtb vs. control; (right), Mtb+TS vs. TS. Red: upregulated; blue: downregulated; gray: non-significant. (B) Venn diagram of hypothalamic DEG overlap between Mtb vs. control and Mtb+TS vs. TS comparisons. (C,D) GO enrichment of hypothalamic-specific DEGs in Mtb+TS vs. TS: (C) 41 upregulated genes; (D) 30 downregulated genes. (E) Heatmap showing expression levels of key genes in the hypothalamus across four groups. (F) Differential expression analysis: (left), Mtb vs. control; (right), Mtb+TS vs. TS. (G) KEGG pathway enrichment: (top), Mtb vs. control; (bottom), Mtb+TS vs. TS. The color gradient in the histogram represents the p-value, and the bubble size corresponds to the Gene Ratio. (H) Differences in the interaction intensities of outgoing and incoming signals among cell clusters: (left), Mtb vs. control; (right), Mtb+TS vs. TS. Axes: outgoing (x-axis) vs. incoming (y-axis) signal strength. (I,J) Ligand–receptor crosstalk networks: (I) Mtb vs. control; (J) Mtb+TS vs. TS. Arrows: hypothalamus–pituitary (orange-red), pituitary–adrenal/spleen (light blue), adrenal–spleen (cyan-green), intraspleen (gray). Gene colors: orange-red (up), light blue (down), black (ns).
Figure 5. TS leads to immune disturbance and aggravates Mtb infection in mice. (A) Volcano plots of hypothalamic DEGs: (left), Mtb vs. control; (right), Mtb+TS vs. TS. Red: upregulated; blue: downregulated; gray: non-significant. (B) Venn diagram of hypothalamic DEG overlap between Mtb vs. control and Mtb+TS vs. TS comparisons. (C,D) GO enrichment of hypothalamic-specific DEGs in Mtb+TS vs. TS: (C) 41 upregulated genes; (D) 30 downregulated genes. (E) Heatmap showing expression levels of key genes in the hypothalamus across four groups. (F) Differential expression analysis: (left), Mtb vs. control; (right), Mtb+TS vs. TS. (G) KEGG pathway enrichment: (top), Mtb vs. control; (bottom), Mtb+TS vs. TS. The color gradient in the histogram represents the p-value, and the bubble size corresponds to the Gene Ratio. (H) Differences in the interaction intensities of outgoing and incoming signals among cell clusters: (left), Mtb vs. control; (right), Mtb+TS vs. TS. Axes: outgoing (x-axis) vs. incoming (y-axis) signal strength. (I,J) Ligand–receptor crosstalk networks: (I) Mtb vs. control; (J) Mtb+TS vs. TS. Arrows: hypothalamus–pituitary (orange-red), pituitary–adrenal/spleen (light blue), adrenal–spleen (cyan-green), intraspleen (gray). Gene colors: orange-red (up), light blue (down), black (ns).
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Figure 6. TS induces immune dysregulation of splenic myeloid cells in mice, exacerbating Mtb infection. (A) Heatmaps show outgoing/incoming signaling activity in Mtb vs. control (left) and Mtb+TS vs. TS (right). Red/blue indicate increased/decreased activity, respectively. (B) Violin plots depict the expression profiles of genes associated with the COMPLEMENT pathway across corresponding cell clusters in the control, Mtb, TS, and Mtb+TS groups. (C) Venn diagram of upregulated/downregulated macrophage genes overlapping between Mtb vs. control and Mtb+TS vs. TS comparisons. (D) Bar graph showing GO enrichment analysis of 79 uniquely upregulated DEGs in macrophages for the comparison between the Mtb group and the control group. (E,F) Boxplots display gene expression patterns linked to enriched GO biological processes in macrophages (four groups). (E) Interferon-gamma response genes. (F) Myeloid differentiation genes. Statistical significance was assessed using the Wilcoxon rank-sum test: ns (not significant), ** p < 0.01, *** p < 0.001, **** p < 0.0001. (G) A ligand–receptor interaction network between the HPA axis and splenic macrophages was constructed using key DEGs from the Mtb vs. control comparison. Interactions are color-coded: orange-red arrows (hypothalamus–pituitary), light blue arrows (pituitary–adrenal gland and pituitary–macrophages), and cyan-green arrows (adrenal gland–macrophages). Gene names are colored by expression change: orange-red (upregulated), light blue (downregulated), and black (no significant change). (H) A similar network analysis based on key DEGs from the Mtb+TS vs. TS comparison, using the same crosstalk coding system as in (G).
Figure 6. TS induces immune dysregulation of splenic myeloid cells in mice, exacerbating Mtb infection. (A) Heatmaps show outgoing/incoming signaling activity in Mtb vs. control (left) and Mtb+TS vs. TS (right). Red/blue indicate increased/decreased activity, respectively. (B) Violin plots depict the expression profiles of genes associated with the COMPLEMENT pathway across corresponding cell clusters in the control, Mtb, TS, and Mtb+TS groups. (C) Venn diagram of upregulated/downregulated macrophage genes overlapping between Mtb vs. control and Mtb+TS vs. TS comparisons. (D) Bar graph showing GO enrichment analysis of 79 uniquely upregulated DEGs in macrophages for the comparison between the Mtb group and the control group. (E,F) Boxplots display gene expression patterns linked to enriched GO biological processes in macrophages (four groups). (E) Interferon-gamma response genes. (F) Myeloid differentiation genes. Statistical significance was assessed using the Wilcoxon rank-sum test: ns (not significant), ** p < 0.01, *** p < 0.001, **** p < 0.0001. (G) A ligand–receptor interaction network between the HPA axis and splenic macrophages was constructed using key DEGs from the Mtb vs. control comparison. Interactions are color-coded: orange-red arrows (hypothalamus–pituitary), light blue arrows (pituitary–adrenal gland and pituitary–macrophages), and cyan-green arrows (adrenal gland–macrophages). Gene names are colored by expression change: orange-red (upregulated), light blue (downregulated), and black (no significant change). (H) A similar network analysis based on key DEGs from the Mtb+TS vs. TS comparison, using the same crosstalk coding system as in (G).
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Gou, X.; Lei, Y.; Yang, B.; Zhou, W.; Chen, X.; Yan, K.; Zhang, Q.; Zheng, H.; Deng, L.; Guo, T.; et al. Transport Stress Induces Neuroimmune Dysregulation and Exacerbates Mycobacterium Tuberculosis Infection in Mice. Int. J. Mol. Sci. 2026, 27, 1919. https://doi.org/10.3390/ijms27041919

AMA Style

Gou X, Lei Y, Yang B, Zhou W, Chen X, Yan K, Zhang Q, Zheng H, Deng L, Guo T, et al. Transport Stress Induces Neuroimmune Dysregulation and Exacerbates Mycobacterium Tuberculosis Infection in Mice. International Journal of Molecular Sciences. 2026; 27(4):1919. https://doi.org/10.3390/ijms27041919

Chicago/Turabian Style

Gou, Xiaojuan, Yingying Lei, Bing Yang, Wei Zhou, Xi Chen, Keji Yan, Qinghua Zhang, Hu Zheng, Lulu Deng, Ting Guo, and et al. 2026. "Transport Stress Induces Neuroimmune Dysregulation and Exacerbates Mycobacterium Tuberculosis Infection in Mice" International Journal of Molecular Sciences 27, no. 4: 1919. https://doi.org/10.3390/ijms27041919

APA Style

Gou, X., Lei, Y., Yang, B., Zhou, W., Chen, X., Yan, K., Zhang, Q., Zheng, H., Deng, L., Guo, T., Zhang, S., Wang, X., Dai, J., Cao, G., & Xiao, K. (2026). Transport Stress Induces Neuroimmune Dysregulation and Exacerbates Mycobacterium Tuberculosis Infection in Mice. International Journal of Molecular Sciences, 27(4), 1919. https://doi.org/10.3390/ijms27041919

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