Clostridium butyricum Restores Intestinal Barrier Integrity via the IL-22/Reg3 Pathway Following Traumatic Brain Injury in Mice

Abstract

Traumatic brain injury (TBI) disrupts the intestinal barrier, linking brain trauma to systemic inflammation and secondary complications. This study investigated the role of gut microbiota and its metabolites in intestinal barrier dysfunction following TBI, using a controlled cortical impact mouse model. TBI-induced gut dysbiosis was characterized by reduced microbial diversity and a loss of butyrate-producing bacteria, which led to decreased levels of short-chain fatty acids (SCFAs), particularly butyric acid. This disruption compromised the interleukin-22/regenerating islet-derived protein 3 (IL-22/Reg3) signaling pathway, which is essential for maintaining gut barrier integrity. Supplementation with Clostridium butyricum restored butyric acid production, enhanced IL-22/Reg3 expression, and alleviated TBI-induced intestinal permeability. These findings identify the SCFA/IL-22/Reg3 axis as a key mediator of gut barrier homeostasis after TBI and highlight the potential therapeutic role of butyrate-producing probiotics in managing TBI-associated intestinal complications.

Introduction

Traumatic brain injury (TBI), characterized by structural and functional brain damage due to physical trauma, affects over 50 million individuals annually worldwide, with China reporting the highest incidence at approximately 13 cases per 100,000 population.¹ TBI is a major public health concern, often associated with systemic complications such as intestinal barrier dysfunction,² which is implicated in adverse outcomes such as endotoxemia, bacterial translocation, and multiple organ dysfunction syndrome. TBI disrupts the intestinal epithelial barrier by inducing microstructural damage, mucosal blood flow disturbances, and inflammatory mediator release.^3

–6 An increasing number of studies have gained insight into the precise mechanisms underlying intestinal barrier dysfunction following TBI; most of these studies focused on the ileum and colon.^7
–9 The ileum possesses unique structural and functional properties distinct from other intestinal regions, including its roles in antimicrobial peptide (AMP) production, immune regulation, and microbial interactions. Given these regional differences, investigating TBI-induced changes in the ileum is crucial for understanding gut barrier dysfunction in a targeted and region-specific manner. However, a complete understanding of the intricate molecular pathways in TBI-induced ileal homeostasis disorder remains unclear.

The gut harbors a dense and diverse microbial community that generates metabolites exerting local and systemic effects on host physiology.^10,11 These metabolites regulate gut immunity,¹² metabolism,¹³ and even brain function,^14,15 forming the foundation of the brain-gut-microbiota axis—a bidirectional communication network between the brain and gut. This axis has garnered increasing attention in neurodegenerative diseases such as Alzheimer’s and Parkinson’s,^16

–19 as well as in models linking gut microbiota to neurotransmitter metabolism and behavior.²⁰ Nevertheless, the molecular pathways underlying TBI-induced intestinal barrier dysfunction via the brain-gut-microbiota axis remain poorly understood.

Short-chain fatty acids (SCFAs), key microbial metabolites derived from the fermentation of dietary fibers by the gut microbiota, have garnered significant attention due to their pivotal roles in maintaining gut, brain, and systemic homeostasis.^21,22 SCFAs, including acetate, propionate, and butyrate, act as essential signaling molecules that influence intestinal integrity,²³ immune function,²⁴ and neuroinflammation.²⁵ In the gut, SCFAs are crucial for maintaining epithelial barrier function, regulating mucosal immunity, and modulating gut motility.^26,27 Their systemic effects include metabolic regulation, anti-inflammatory responses, and epigenetic modifications.^28
–30 Importantly, SCFAs can cross the blood-brain barrier and directly affect neuroimmune interactions, neuronal plasticity, and oxidative stress responses, making them pivotal mediators in the gut-brain axis.^31
–33 Following TBI, alterations in gut microbiota composition and SCFA production have been reported, leading to increased intestinal permeability, systemic inflammation, and exacerbated neuroinflammatory cascades.^34

–38 These changes contribute to secondary brain injury and may hinder recovery. Understanding the mechanistic connections between SCFAs and TBI pathology could provide novel therapeutic approaches to mitigate gut dysbiosis, restore gut-brain homeostasis, and promote neuroprotection.

TBI induces widespread systemic effects beyond the central nervous system, including disruption of gut homeostasis and microbiota composition. Emerging evidence suggests that microbiota and its metabolites, particularly SCFAs, play a crucial role in maintaining intestinal barrier integrity and modulating immune responses. However, the precise mechanisms by which TBI-induced gut dysbiosis affects SCFA production and compromises intestinal barrier integrity remain poorly understood. Given the essential role of SCFAs in regulating interleukin-22/regenerating islet-derived protein 3 (IL-22/Reg3)-mediated antimicrobial defense and epithelial protection, we hypothesize that TBI disrupts SCFA-producing microbiota, leading to reduced butyrate levels and impaired IL-22/Reg3 signaling, ultimately contributing to ileal barrier dysfunction. This study aims to elucidate the mechanistic link between TBI-induced dysbiosis and gut barrier impairment, providing insights into potential microbiota-targeted therapeutic strategies.

Materials and Methods

Mice

Male C57BL/6 specific pathogen-free mice (to reduce variability due to hormonal fluctuations), aged 8 weeks and weighing 22 ± 4 g, were obtained from the Shanghai Research Center for Model Organisms (Shanghai, China, License No. SCXK-2014-0002). Mice were group-housed in separate cages according to their experimental group within the same room. Mice were housed in a controlled environment maintained at a stable temperature of 24°C, with a relative humidity of 50% and a 12-h light/dark cycle, all of which were upheld consistently throughout the entire experimental period. This experiment adhered to rigorous ethical standards and was conducted with approval from the Institutional Animal Care and Use Committee at Tongji University School of Medicine. The experiment was conducted following the guidelines provided by the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Various measures were taken, including humane euthanasia, when necessary, to minimize any potential suffering experienced by the animals and to reduce the overall number of animals used in the experiment.

Controlled Cortical Impact Model for TBI Induction

The controlled cortical impact (CCI) method was used to establish the TBI animal model.³⁹ The procedure involved the intraperitoneal injection of sodium pentobarbital (65 mg/kg) into the mice, and surgery was initiated once pedal reflexes were no longer present. Throughout the procedure, the core body temperature of the mice was maintained at 37°C using heating pads and rectal thermometers. The mice were securely positioned in a supine position within a stereotaxic frame. The skin was disinfected with iodophor, and the scalp was incised to expose the skull, after which 4-mm-diameter craniotomies were performed, centered at 2.0 mm lateral to the midline over the right hemisphere and 2.0 mm posterior to the bregma. A 3.0-mm rounded metal tip from the Pin-Point™ CCI devices (Model PCI 3000, Hatteras Instruments Inc., Cary, NC, USA) was directed vertically toward the brain surface to induce the animal CCI model. This procedure was performed with a consistent duration of 180 ms for all mice, using a strike velocity of 3.0 m/s and a deformation depth of 1.0 mm to mimic moderate TBI. Subsequently, the mice were gently removed from the stereotaxic holder, and their wounds were carefully sutured. The mice were maintained on heating pads until they regained consciousness from anesthesia and resumed their movements, all under careful observation. Mice in the control group were administered anesthesia and underwent craniotomy without cortical impact.

Experimental protocols

Experiment 1

To explore changes in the microbiota and ileal transcriptome after TBI, 10 mice were randomly divided into two groups: a sham group and a TBI group. Fecal samples and terminal ileum tissues were collected from each group 3 days after TBI or sham surgery (Fig. 1A). RNA sequencing (RNA-seq) of the terminal ileum and 16S rDNA sequencing of cecal contents were performed.

FIG. 1.

Traumatic brain injury (TBI)-induced inflammation and ileal barrier impairment. (A) The TBI animal model was induced using a controlled cortical impact (CCI) device. Sham-operated mice served as the control group. Mice were then euthanized to collect serum at 3 h and days 1 and 3 post-TBI for enzyme-linked immunosorbent assays (ELISAs); terminal ileum tissue, brain tissue, and cecal contents were harvested from control and TBI mice on day 3 post-injury for subsequent experiments. (B) Brain tissue images and Nissl-stained brain sections from control and TBI mice on day 3 post-injury. (C) Serum levels of corticosterone, IL-1β, IL-6, and TNF-α in control and TBI mice at 3 h and days 1 and 3 post-TBI, as measured by ELISA. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc multiple comparisons test (*p < 0.05; **p < 0.01; ***p < 0.001). (D) Representative images of hematoxylin and eosin (H&E)-stained terminal ileum sections of control and TBI mice on day 3 post-injury (scale bar, 400 μm). The degree of injury was evaluated using villi height and villous-to-crypt ratio. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using an unpaired, two-tailed t-test (*p < 0.05; ***p < 0.001). (E) Representative images of Alcian Blue–Periodic Acid-Schiff (AB-PAS)-stained terminal ileum tissue sections from control and TBI mice on day 3 post-injury (scale bars, 100 μm). Goblet cells (stained purple) are indicated by red arrows. Data are presented as mean ± SEM (n = 5). Statistical analysis of goblet cells per villus-crypt unit was performed using an unpaired, two-tailed t-test (ns, not significant). (F) Intestinal barrier permeability was assessed by measuring serum levels of 4 kDa fluorescein isothiocyanate (FITC)-dextran in control and TBI mice on day 3 post-injury. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using an unpaired, two-tailed t-test (***p < 0.001). (G) Real-time PCR analysis of relative mRNA expression levels of claudin 4 and ZO-1 in terminal ileum samples from control and TBI mice on day 3 post-injury. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using an unpaired, two-tailed t-test (*p < 0.05; **p < 0.01). (H) Western blot of claudin-4 and ZO-1 protein expression in terminal ileum samples from control and TBI mice on day 3 post-injury. (I) Statistical analysis of Western blot data. Results from at least three independent experiments are presented as mean ± SEM (n = 5). Statistical analysis was performed using an unpaired, two-tailed t-test (***p < 0.001). (J) Representative immunofluorescence images and quantification of Claudin-4- and ZO-1-positive cells in terminal ileum sections from control and TBI mice on day 3 post-injury (scale bar, 100 μm). Data are presented as mean ± SEM (n = 5). Statistical analysis of the percentage of Claudin-4- and ZO-1-positive cells was performed using an unpaired, two-tailed t-test (***p < 0.001).

Experiment 2

To evaluate the serum levels of corticosterone, IL-1β, IL-6, and TNF-α after TBI, serum samples from a separate group of 5 mice were collected at different time points: before TBI and at 3 h, 1 day, and 3 days post-TBI. Enzyme-linked immunosorbent assays (ELISAs) were performed to measure the levels of corticosterone, IL-1β, IL-6, and TNF-α.

Experiment 3

To assess the effects of butyrate-producing microbiota on ileal barrier function after TBI, 20 mice were randomly divided into four groups as follows: control group, TBI group, control + Clostridium butyricum (C. butyricum) group, and TBI + C. butyricum group. Mice in the C. butyricum groups received C. butyricum Miyairi 588 (>1.5 × 10⁷ CFU; Miyarisan, Tokyo, Japan) dissolved in sterile water ad libitum for 3 weeks, as previously described.⁴⁰ Probiotics were replenished daily. The remaining groups received sterile water only (Fig. 6A). All animals were group-housed according to their experimental group in separate cages within the same room. Water consumption was closely monitored to estimate the intake of C. butyricum. After 3 weeks of pretreatment, mice underwent either CCI or sham surgery.

Sample collection

Fecal samples, serum, brain, and ileum tissues were collected from experimental animals at designated time points.

Fecal collection

To minimize contamination, fecal samples were collected under sterile conditions following a standardized protocol.⁴¹ After euthanizing the mice under deep anesthesia, the cecum was aseptically isolated using sterile scissors and forceps. Cecal contents were gently expressed into sterile microcentrifuge tubes inside a laminar flow hood. A new set of sterile instruments was used for each animal to prevent cross-contamination. Samples were immediately transferred into prechilled sterile cryovials and snap-frozen at –80°C for subsequent DNA extraction. All tools and surfaces were sterilized using ethanol before and during collection.

Brain tissue collection

Brains were harvested following transcardial perfusion with cold phosphate-buffered saline (PBS) under deep anesthesia. Tissues were immersion-fixed in 4% paraformaldehyde (PFA) at 4°C for 24 h. Fixed tissues were subsequently processed for Nissl staining.

Ileum tissue collection

Following established protocols for intestinal tissue preparation in mice,⁴² ileal samples were collected and processed for downstream analyses. For histological and immunohistochemical analysis, mice were perfused transcardially with cold PBS under deep anesthesia. The terminal ileum, defined as the distal 2–3 cm segment proximal to the cecum, was immediately dissected without unfolding or pinning and immersion-fixed in freshly prepared 4% PFA at 4°C for 24 h. The fixed samples were then dehydrated through a graded ethanol series, cleared in xylene, and embedded in paraffin at 60°C. Paraffin blocks were sectioned at 5 μm using a rotary microtome (Leica RM2235, Germany). Sections were mounted on poly-L-lysine-coated slides, deparaffinized, rehydrated, and processed for hematoxylin and eosin (H&E) or immunohistochemistry staining according to standard protocols.⁴³ For gene and protein expression analysis, the terminal ileum (same anatomical region as above) was excised post-perfusion, snap-frozen in liquid nitrogen, and stored at –80°C until use. Whole tissue samples, including the mucosa, submucosa, and muscularis layers, were collected without separating the individual layers.

Blood collection

Blood was collected from mice via cardiac puncture under anesthesia into tubes without anticoagulant. Samples were immediately placed on ice and centrifuged within 30 min at 1,500 × g for 10 min at 4°C. The serum supernatant was carefully aliquoted to avoid contamination with cellular components and stored at −80°C until analysis. All samples were free from visible hemolysis and underwent only one freeze-thaw cycle before use in ELISA.

Nissl staining

The Nissl staining procedure was conducted following the manufacturer’s recommended protocol. Initially, 30-μm coronal paraffin sections of the mouse brain were immersed in deionized water to eliminate any residual paraffin. Subsequently, these paraffin sections underwent a series of ethanol baths to facilitate dehydration. The sections were then immersed in Nissl staining solution (C0117, Beyotime) and allowed to incubate for 5–10 min at 37°C. Once the slides were thoroughly dried, they were covered and left to air dry for 12 h. Finally, the slides were observed, and images were captured using a light microscope (BX51; Olympus).

Histology and immunohistochemistry analyses

Terminal ileum samples were fixed in 4% PFA, embedded in paraffin, sectioned at 5 μm thickness, mounted onto glass slides, and stained with H&E. Sections were observed under a light microscope (BX51, Olympus Corporation, Tokyo, Japan). The degree of intestinal injury was assessed by measuring villus height and the villus-to-crypt ratio, as previously described.^3,9,44 For each sample, villus height and crypt depth were measured at three distinct locations, and the average value was calculated to minimize measurement error.

Goblet cell quantification was performed on Alcian Blue-Periodic Acid-Schiff (AB-PAS)-stained sections of the terminal ileum. Images were captured under a light microscope (BX51; Olympus) at 400× magnification. Goblet cells were identified by their characteristic blue (acidic mucins) or magenta (neutral mucins) staining. For each sample, goblet cells were counted in 10 randomly selected villus-crypt units, and results were expressed as the number of goblet cells per villus-crypt unit.

For immunohistochemistry analysis, the tissue sections, measuring 10 μm in thickness, underwent deparaffinization and rehydration. The sections were incubated in a 3% hydrogen peroxide-methanol solution for 10 min to quench peroxidase activity. Antigens were retrieved by boiling the sections in a 10 mM sodium citrate solution (pH 6.0) for 10 min. Following a rinse with PBS, the sections were blocked with goat serum for 20 min. The samples were then exposed to primary antibodies, including anti-lysozyme (1:250, ab108508, Abcam, RRID: AB_10861277) and anti-IL-22 (1:200, PA1-21357, Thermo Fisher, RRID: AB_2248942), overnight at 4°C. Subsequently, the sections were incubated with the following secondary antibody: goat anti-rabbit IgG-HRP antibody (1:500, 111-035-003, Jackson ImmunoResearch, RRID: AB_2313567) for 30 min at 20–25°C. Staining was visualized using 3,3′-diaminobenzidine as the substrate and counterstained with hematoxylin. Q-imaging software and a light microscope (BX51; Olympus) were used to capture images at varying magnifications. For each sample, Paneth cells were counted in 10 randomly selected villus-crypt units, and results were expressed as Paneth cell (lysozyme positive) number per crypt.

Immunofluorescence staining

Immunofluorescence staining was performed on 6 μm sections of the ileum. Tissue sections were subjected to permeabilization using a solution containing Triton X-100 and then blocked with 5% Bovine Serum Albumin. Subsequently, sections were incubated overnight at 4°C with the following primary antibodies: anti-Claudin-4 (1:200, ab53156, Abcam, RRID: AB_869176), anti-ZO-1 tight junction (TJ) protein (1:200, ab96587, Abcam, RRID: AB_10680012), anti-REG3B (1:200, PA5-47700, Thermo Fisher, RRID: AB_2608916), and anti-REG3G (1:200, PA5-50450, Thermo Fisher, RRID: AB_2635903). The sections were then washed and treated with goat anti-rabbit IgG (H + L) (1:500, 111-165-003, Jackson ImmunoResearch, RRID: AB_2338000) and donkey anti-sheep IgG (H + L) (1:500, A-11015, Thermo Fisher, RRID: AB_2534082) for 1 h after being well washed. The sections were washed three times with PBS, and a mounting medium containing 4’,6-diamidino-2-phenylindole (DAPI) was used to stain the nuclei. Finally, the sections on the slides were covered with coverslips. Fluorescent images were acquired using an Olympus BX51 fluorescence microscope (Olympus, Tokyo, Japan) equipped with a Point Grey Grasshopper3 monochrome camera (GS3-U3-51S5M-C, FLIR Systems, USA) and appropriate filter sets. Images were captured at 20× or 40× magnification with 100% fluorescence intensity. Exposure times were fixed for all samples: 5 min for DAPI and 8 min for TxRed channels. These settings were applied consistently across all experimental groups to ensure comparability.

For each sample, three randomly selected nonoverlapping fields were imaged per slide. Image analysis was performed using ImageJ software (NIH, USA). A consistent intensity threshold for each fluorescence channel (DAPI: 10, Claudin-4: 30, ZO-1: 35, Reg3b: 20, Reg3G: 35) was determined using the “Threshold” function and manually adjusted based on negative control sections to exclude background signal. The “Analyze Particles” function was then applied to automatically quantify positive cells. The total number of cells per field was determined by DAPI counterstaining. The percentage of positive cells was calculated as the ratio of positive cells to total cells per field, and the average of three fields was used as the value for each biological sample.

Data are presented as mean ± SEM. Statistical comparisons were performed using unpaired, two-tailed Student’s t-test or two-way analysis of variance (ANOVA) followed by Tukey’s post hoc test, with p < 0.05 considered statistically significant.

Enzyme-linked immunosorbent assay

The serum and ileum of mice were collected at different time points after TBI; corticosterone, IL-1β, IL-6, and TNFα levels were measured by the QuicKey Pro Mouse CORT (Corticosterone) ELISA Kit (Elabscience, Wuhan, China), Mouse Interleukin 1β (IL-1β) ELISA Kit (Bioswamp, Wuhan, China), Mouse Interleukin 6 (IL-6) ELISA Kit (Bioswamp, Wuhan, China), and Mouse Tumor Necrosis Factor-α (TNF-α) ELISA Kit (Bioswamp, Wuhan, China) according to the manufacturer’s instructions. Serum samples for IL-1β, IL-6, and TNF-α measurements were diluted 1:4 in PBS before analysis.

Ileum tissues were homogenized in normal saline at a ratio of 900 μL saline per 100 mg of tissue. The homogenates were centrifuged at 3,000 × g for 10 min at 4°C, and the supernatants were collected. Protein concentrations were determined using a BCA protein quantification assay kit (KGPBCA, KeyGEN BioTECH, China). ELISAs were subsequently performed according to the manufacturer’s instructions.

Fluorescein isothiocyanate-dextran intestinal permeability assay

Intestinal permeability is measured by determining the serum concentration of 4-kDa fluorescein isothiocyanate-dextran (FITC-dextran). Three days after TBI, mice were fasted for 4 h and gavaged with 0.2 ml of 4-kDa FITC-dextran (MedChemExpress, New Jersey, USA) at a concentration of 60 mg per 100 g body weight. Then, 4 h later, approximately 400 μL of blood was collected from each mouse via cardiac puncture and allowed to clot at room temperature for 30 min. The samples were then centrifuged at 4,000 × g for 10 min at 4°C, and the resulting serum was collected. For fluorescence measurement, 100 μL of serum sample was diluted 1:1 with PBS, and FITC-dextran concentration was determined using a microplate reader (excitation/emission: 488/525 nm) against a standard curve.

Western blotting

Tissue samples were homogenized in a cell lysis buffer for Western and Immunoprecipitation (KeyGEN BioTECH, China), which was supplemented with protease and phosphatase inhibitor cocktails. Lysates were incubated on ice for 30 min and then centrifuged at 12,000 × g for 15 min at 4°C to remove debris. The supernatants were collected, and protein concentrations were determined using a BCA protein assay kit (KGPBCA, KeyGEN BioTECH, China). Equal amounts of protein (30 μg per lane) were separated by SDS-PAGE on 4–12% polyacrylamide gels and transferred onto PVDF membranes (Millipore, USA). Membranes were blocked with 5% nonfat dry milk in Tris-buffered saline containing 0.1% Tween-20 for an hour at room temperature and then incubated overnight at 4°C with primary antibodies diluted in blocking buffer.

After blocking, the membranes underwent probing with primary antibodies followed by horseradish-peroxidase-conjugated secondary antibodies. Signal detection was accomplished through enhanced chemiluminescence using the Tanon 5200CE detection system. The antibodies used in this study included β-actin (1:1,000 dilution, AF7018, Affinity, RRID: AB_2839420), anti-Claudin-4 (1:1,000 dilution, ab53156, Abcam, RRID: AB_869176), anti-ZO-1 TJ protein (1:1,000 dilution, ab96587, Abcam, RRID: AB_10680012), anti-REG3B (1:1,000 dilution, PA5-47700, Thermo Fisher, RRID: AB_2608916), anti-REG3G (1:1,000 dilution, PA5-50450, Thermo Fisher, RRID: AB_2635903), anti-IL-22 (1:500 dilution, PA5-47782, Thermo Fisher, RRID: AB_2577147), goat anti-rabbit IgG-HRP antibody (1:5,000 dilution, 111-035-003, Jackson ImmunoResearch, RRID: AB_2313567), rabbit anti-sheep IgG H&L (HRP) antibody (1:5,000, ab6747, Abcam, RRID: AB_955453), and donkey anti-goat IgG-HRP (1:5,000 dilution, A0181, Beyotime, RRID: AB_3073542). The intensity of Western blot bands was quantified using ImageJ software (NIH, USA). The relative protein levels were calculated by normalizing the target protein band intensity to the corresponding β-actin band intensity. Normalized values were used for statistical analysis.

Quantitative reverse transcription polymerase chain reaction

The TRIzol reagent (Thermo Fisher) was used to extract total RNA from terminal ileum tissues, following the manufacturer’s guidelines. A NanoDrop spectrophotometer (Thermo Fisher) was used to quantify the RNA concentration. The PrimeScript RT reagent kit and the SYBR Premix Ex Taq II kit from Takara were used to assess mRNA expression levels. The reaction system (10 μL) contained 5 μL of 2× PerfectStart™ Green qPCR SuperMix, 0.2 μL of 10 μM forward primer, 0.2 μL of 10 μM reverse primer, 1 μL of cDNA, and 3.6 μL of nuclease-free H₂O. The qPCR cycling conditions were as follows: Initial denaturation at 94°C for 30 s, followed by 45 cycles of 94°C for 5 s and 60°C for 30 s. After amplification, a melting curve analysis was performed by gradually increasing the temperature from 60°C to 97°C, with fluorescence signals collected five times per degree Celsius to verify product specificity. β-actin was used as an internal control for mRNA. Gene expression was normalized to the housekeeping gene, β-actin, and relative expression levels were calculated using the 2^−ΔΔCt method. All reactions were run in triplicate. The following is the list of primers used:

RNA-seq

RNA-seq was performed on the Illumina platform by Shanghai OE Biotech Co., Ltd. The MirVana miRNA isolation kit (Ambion) was used to accomplish the extraction of total RNA, strictly following the manufacturer’s instructions. Subsequently, an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) was used to assess the integrity of the RNA; only samples displaying an RNA integrity number of ≥7 were selected for further analysis. Libraries were constructed using the TruSeq Stranded Total RNA with Ribo-Zero Gold kit, adhering to the manufacturer’s protocols. Following library preparation, the samples were subjected to sequencing on the Illumina platform (HiSeq^TM 2500), producing paired-end reads of 150 bp/125 bp in length.

RNA-seq data analysis

The initial output of high-throughput sequencing consisted of FASTQ format sequences. It was imperative to perform a quality filtering step on the raw reads to ensure that the subsequent analysis would be based on high-quality data. For this purpose, the software tools Trimmomatic⁴⁵ and Hisat2⁴⁶ were used in the data preprocessing and genomic alignment stages. The EstimateSizeFactors function within the DESeq (2012) R package was used to normalize the read counts so as to further enhance the robustness of the analysis. In addition, the nbinomTest function was incorporated to calculate p values and fold-change values for the comparative analysis, considering significant differences as those with p values ≤0.05 and fold-change values ≥2.

Gene name	Primer sequence from 5′ to 3′
Reg3b	F-TATACCCTCCGCACGCATTA
	R-TCAAACCAGGTCTGTGGTATC
Reg3g	F-GACACTGGGCTATGAACCCAA
	R-CCTGAGGAAGAGGAAGGATT
ZO-1	F-TTTCCTCCTTGACCTCCCTA
	R-TGATTCTACAATGCGGCGATAA
Claudin-4	F-ATGGTCATCAGCATCATCG
	R-TGATCTTGGCCTTGACGG
β-actin	F-GGCTCCTAGCACCATGAAGA
	R-AGCTCAGTAACAGTCCGCC

16S rDNA sequencing of cecal contents

16S rDNA sequencing was performed by Shanghai REALGENE Biotech Co., Ltd. using the Illumina platform. Microbial DNA was extracted from cecal content samples using the MagPure Soil DNA LQ Kit (Magen, China; Cat. No. D6356-02) according to the manufacturer’s instructions.^47
–49 DNA concentration and purity were assessed using the Qubit dsDNA Assay Kit (Life Technologies, Cat. No. Q32854). The sequencing specifically targeted the hypervariable region of the 16S rDNA, focusing on the V3-V4 region. The forward primer (5′−3′) used in this process was CCTACGGGRSGCAGCAG (341F), while the reverse primer (5′−3′) used was GGACTACVVGGGTATCTAATC (806R). To initiate the analysis, PCR amplification was performed, followed by the purification, quantification, and homogenization of the resultant products to construct a sequencing library. The resulting library underwent an initial inspection for quality, ensuring that only libraries meeting the established criteria proceeded to the next stage. Subsequently, the qualified library was subjected to sequencing using the high-throughput Illumina NovaSeq PE250 platform, guaranteeing a robust and reliable data set.

16S rDNA sequencing data analysis

In terms of addressing the issue of overlapping relationships, we processed the paired reads acquired through bidirectional sequencing by using PANDAseq (version 2.9) software.⁵⁰ This step involved the meticulous assembly of the paired reads into a unified sequence, generating extended reads that encompassed the hypervariable region. The extended reads were subjected to clustering to form operational taxonomic units (OTUs) with a similarity threshold set at 0.97, so as to facilitate subsequent analysis of species diversity. Research software (version 7.0.1090) was used to perform this clustering process.⁵¹ A single representative sequence was chosen from each OTU and subjected to a comparative analysis against a 16S rDNA database of known species (RDP, http://rdp.cme.msu.edu) using the RDP method. This allowed for the classification of each OTU.

The culmination of this process was the acquisition of an OTU abundance table, categorized based on the number of sequences within each OTU.^52,53 Leveraging the species annotations, we computed the relative abundance of each sample across various classification levels, encompassing kingdom, phylum, class, order, family, and genus. A range of analytical techniques, including alpha diversity, beta diversity, principal coordinate analysis (PCoA), linear discriminant analysis effect size (LEfSe), Sankey diagrams, and the Wilcox test function from the stats package in R (Version 3.5.1), were used to delve deeper into the microbiota’s characteristics.

LC/MS-based SCFA analysis

Chemicals

All chemicals and solvents were analytical or HPLC grade. Water was purchased from Thermo Fisher Scientific (Thermo Fisher Scientific, Waltham, MA, USA). Propanol, pyridine, N-hexane, and propyl chloroformate were purchased from ANPEL Laboratory Technologies (Shanghai) Inc. Sodium hydroxide and anhydrous sodium sulfate were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). SCFA standards were from Sigma-Aldrich Trading Co., Ltd. (Shanghai, China).

Sample preparation

SCFAs were extracted from mouse cecal contents at 4°C under temperature-controlled conditions to minimize evaporation during sample preparation. The cecal contents from the mice were combined with a 50% acetonitrile solution (composed of a 50/50 ratio of water and acetonitrile, v/v) at a proportion of 1 mL per 100 mg of cecal contents. These samples were subjected to grinding for 3 min by an automatic sample rapid grinding instrument (JXFSTPRP-24/32, Shanghai Jingxin Industrial Development Co., Ltd.), followed by a 10-min sonication in an ultrasonic water bath. Afterward, the samples were centrifuged at 12,000 × g for 10 min. The supernatants, which contained the SCFAs, were carefully collected and then treated with 3-NPH and EDC-6% pyridine for 30 min at 40°C. Following the reaction, a 10% acetonitrile solution was added to the samples. The resulting upper organic layer, comprising SCFA derivatives, was separated and filtered through a 0.22-μm filter, and then it was preserved at −80°C and shielded from light until needed.

GC-MS analysis

Stock solutions of individual SCFA standards were prepared, and a mixed standard solution (MSS) was created to generate calibration curves across a range of concentrations (1−200 μg/mL). For derivatization, 500 μL of propanol-pyridine (3:2, v/v) and 100 μL of propyl chloroformate were added to the sample, vortexed, and sonicated. The derivatized metabolites were extracted in two steps using n-hexane, with the final extract dried over anhydrous sodium sulfate and analyzed by Gas Chromatography-Mass Spectrometry (GC-MS). Standard solutions underwent the same derivatization process. GC-MS analysis was performed using a 7890B-5977A Agilent system (Agilent Technologies Inc., CA, USA) with a DB-5MS capillary column (30 m × 0.25 mm × 0.25 μm), helium as the carrier gas at 1.0 mL/min, and a split injection (10:1) at 260°C. The temperature program included an initial 50°C for 5.2 min, followed by a ramp to 290°C at varying rates. Mass spectrometry was performed in electron impact ionization mode (70 eV), with the ion source and quadrupole temperatures set to 230°C and 150°C, respectively, and a scan range of m/z 30−600. Liquid Chromatography-Mass Spectrometry (LC-MS) analysis was conducted with a Thermo Fisher LC-MS system, using a C18 column with a gradient elution of water (0.1% formic acid) and acetonitrile at a flow rate of 0.3 mL/min. The MS system was operated in a negative ion mode, with a mass range of m/z 50–1,000 and a capillary voltage of 3.5 kV. Quality control was maintained using pooled QC samples. Data analysis was performed using GraphPad Prism 10, with statistical significance determined via Student’s t-test or one-way ANOVA with Tukey’s post hoc test, and a p value of <0.05 was considered statistically significant.

Statistical analyses

Statistical analyses were performed using GraphPad Prism software (version 10.1.2; GraphPad Software). Data are presented as mean ± SEM from at least three independent experiments. The Shapiro-Wilk test was used to assess the normality of data distributions before analysis. Comparisons between two groups were performed using the unpaired, two-tailed Student’s t-test. For multiple group comparisons following one-way or two-way ANOVA, Tukey’s post hoc multiple comparisons test was used to identify significant differences between groups. For data that did not meet normality assumptions, nonparametric tests (Mann-Whitney U test or Kruskal-Wallis test) were applied. The specific statistical test used for each experiment is indicated in the corresponding figure legend. A p value of <0.05 was considered statistically significant.

Results

TBI induces inflammation and ileal barrier impairment

The CCI brain injury model was successfully established in mice. Ileum tissue, brain tissue, and fecal samples were collected 3 days post-TBI, while serum was obtained at 3 hours (h), 1 day, and 3 days post-TBI (Fig. 1A). Brain imaging revealed substantial tissue damage following TBI, and Nissl staining demonstrated that, compared with sham-operated controls, the cortical tissue in TBI mice exhibited pronounced tissue loss (Fig. 1B). To investigate whether TBI induced elevated serum corticosterone levels and systemic inflammation, blood samples were collected before injury and 3 h, 1 day, and 3 days post-TBI. Inflammatory markers, including proinflammatory cytokines IL-1β, TNF-α, and IL-6, were quantified in serum using ELISA. Serum corticosterone levels increased significantly at 3 h post-TBI, followed by a slight decline on days 1 and 3. Consistent with the elevation in corticosterone, proinflammatory cytokine levels were also markedly increased after TBI and remained significantly higher than those in the control group up to 3 days postinjury (Fig. 1C). For histological examination of the terminal ilea, H&E staining was performed. In the control group, the ileal villi exhibited normal and consistent morphology with uniform villous height. Conversely, the TBI group displayed blunted villi with structural deformities and signs of necrosis. Infiltration of inflammatory cells was observed in both the mucosal and submucosal layers, with no significant differences between groups. Villus height and the villus-to-crypt ratio, which reflect the structural integrity of the intestinal mucosa, were significantly reduced in the TBI group relative to controls (Fig. 1D). The AB-PAS staining revealed a reduction in goblet cell phenotype in the TBI group on day 3; however, this change did not reach statistical significance (Fig. 1E). Intestinal barrier permeability was assessed by measuring FITC-dextran serum levels 3 days post-TBI. A marked increase in FITC-dextran serum levels was observed, suggesting impaired intestinal barrier function (Fig. 1F). TJs, comprising integral membrane proteins such as claudins and occludin, as well as cytoplasmic scaffolding proteins such as ZO-1, are essential for maintaining the physical intestinal barrier integrity. The expression levels of TJs were assessed to evaluate the impact of TBI on the mechanical barrier. The mRNA expression levels of claudin-4 and ZO-1 were all downregulated after TBI (Fig. 1G). This downregulation was also evident at the protein level for both claudin-4 and ZO-1 (Fig. 1H–J). These results collectively indicate that TBI induces inflammation and impairs the integrity of the ileal barrier, adding to the existing evidence that TBI compromises the mechanical barrier function of the ileal epithelium.

Ileal transcriptome changes after TBI

The expression profiles of differentially expressed genes (DEGs) in TBI mice and controls were examined. Principal component analysis and a gene heat map vividly displayed that three TBI mice formed a distinct cluster, markedly separated from the control group (Fig. 2A, B). These results underscored substantial variations in gene expression between the two groups. A volcano plot unveiled a total of 500 differentially expressed mRNAs when comparing the TBI and control groups (Fig. 2C). Among these, 341 were upregulated and 159 were downregulated (p < 0.05, log₂ fold change [FC] > 1). Tjp1 and Cldn4, coding genes of TJ proteins ZO-1 and claudin-4, were downregulated after TBI (Supplementary Fig. S1A). After the identification of DEGs, GO and KEGG enrichment analyses were conducted (Supplementary Fig. S1B). In the GO analyses, response to bacterium, defense response, antimicrobial humoral immune response mediated by AMP, immune response, and negative regulation of activated T cell proliferation pathways were downregulated (Fig. 2D). KEGG enrichment analyses showed the top 30 pathways, including cytokine-cytokine receptor interaction, p53 signaling pathway, TNF signaling pathway, JAK-STAT signaling pathway, IL-17 signaling pathway, and Toll-like receptor signaling pathway, which participate in gut immunity (Fig. 2E). Enrichment analyses indicated that TBI disrupted the gut immunity and antimicrobial response. Gene Set Enrichment Analysis showed differences in PPAR signaling pathway, primary bile acid biosynthesis, and taurine and hypotaurine metabolism between the TBI and control groups (Fig. 2F). These results suggested that TBI results in changes in the ileal transcriptome expression profile, especially those genes related to intestinal immune homeostasis, antimicrobial immune response, and metabolism.

FIG. 2.

Expression characteristics of the intestinal transcriptome. (A) Principal component analysis (PCA) of mRNA expression in the terminal ileum of control and traumatic brain injury (TBI) mice on day 3 post-injury (n = 3). (B) Heat map of mRNA expression in the terminal ileum of control and TBI mice on day 3 post-injury (n = 3). (C) Volcano plot filtering of mRNA expression in the terminal ileum of control and TBI mice on day 3 post-injury (n = 3, p < 0.05, log₂ fold change [FC] > 1). (D) GO analysis showing the top 30 enriched downregulated terms in the terminal ileum of control and TBI mice on day 3 post-injury (n = 3). (E) KEGG analysis showing the top 30 pathways affected by TBI in the terminal ileum of control and TBI mice on day 3 post-injury. (F) Gene Set Enrichment Analysis (GSEA) revealed significant differences in several pathways between control and TBI mice on day 3 post-injury.

TBI reduces diversity and alters the composition of gut microbiota

Gut microbiota was analyzed via 16S rDNA sequencing, identifying a total of 543 OTUs. Rarefaction analysis confirmed sufficient sequencing depth, indicating that microbial diversity within the samples was adequately captured (Supplementary Fig. S2A, B). A Venn diagram visually represented the overlap of OTUs between the two groups, illustrating that 227 of the 501 total OTUs were shared between groups, while 194 and 80 OTUs were unique to the control and TBI groups, respectively (Supplementary Fig. S2C). PCoA demonstrated clear separation between TBI and control groups, indicating distinct microbial community structures within each group (Supplementary Fig. S2D). Analyzing microbial alpha diversity using metrics such as the Simpson index, observed species index, PD whole tree index, Shannon index, and Chao1 index revealed a decrease in diversity following TBI (Fig. 3A). Furthermore, sample correlation analysis indicated the absence of any significant correlation between the microbiome profiles of the TBI and control groups (Supplementary Fig. S2E). Collectively, these outcomes underscore significant alterations in the diversity of the gut microbiome.

FIG. 3.

Profiles of gut microbiota in healthy controls and traumatic brain injury (TBI) mice. (A) Alpha diversity of the gut microbiota was assessed using the Simpson index, Observed species index, PD whole tree index, Shannon index, and Chao1 index. Data are presented as box plots showing minimum to maximum values (n = 5). Statistical analysis was performed using an unpaired, two-tailed t-test or Mann–Whitney test (**p < 0.01; ***p < 0.001). (B) Composition of the bacterial community at the phylum level in control and TBI mice on day 3 post-injury (n = 5). (C) Composition of the bacterial community (top 20 genera) at the genus level in control and TBI mice on day 3 post-injury (n = 5). (D, E) Linear discriminant analysis effect size (LEfSe) identified predominant bacterial taxa differentially abundant between control and TBI mice on day 3 post-injury (n = 5). (F) Sankey diagrams showing the major taxonomic proportion at the phylum and genus levels in control and TBI mice on day 3 post-injury (n = 5).

As for the abundance of gut microbiota, Bacteroidetes, Firmicutes, and Proteobacteria emerged as the predominant bacterial phyla, collectively constituting approximately 90% of the OTUs in each group. Notably, the TBI group exhibited an increase in the relative abundance of Bacteroidetes and Deferribacteres, coupled with a decrease in Firmicutes (Fig. 3B; Supplementary Fig. S3A). At the genus level, Figure 3C depicts the average composition of the top 20 bacterial communities. In comparison with the control group, there was a noticeable reduction in butyrate-producing bacteria in the TBI group. Specifically, genera such as Acetatifactor, Roseburia, Clostridium XlVa, Clostridium XlVb, Prevotella, and Saccharibacteria_genera_incertae_sedis showed decreased presence after TBI (Supplementary Fig. S3B). To identify high-dimensional biomarkers, LEfSe was conducted to determine the dominant bacterial taxa distinguishing the two groups. Compared with the control group, the TBI group exhibited a decrease in the phylum Firmicutes, as well as in the associated class Clostridia, order Clostridiales, and family Lachnospiraceae. At the genus level, Eisenbergiella, Lachnospiracea_incertae_sedis, and Acetatifactor were also found to be diminished in the TBI group in comparison with the control group. Enrichment in the phylum Bacteroidetes, class Bacteroidia, order Bacteroidales, family Bacteroidaceae, and genera Bacteroides and Parabacteroides was observed, contributing to gut microbiome dysbiosis in the TBI group when compared with the control group (Fig. 3D, E). The Sankey diagrams provide a clear visual representation of the dominant proportions of taxa, both at the phylum and genus levels, in the healthy control and TBI groups (Fig. 3F). Importantly, the proportion of Bacteroidetes in mice post-TBI increased significantly and became the predominant phylum, accompanied by changes in other bacterial groups. In summary, the core composition of the gut microbiota underwent significant changes after TBI, characterized by a reduction in beneficial bacteria, particularly SCFA-producing bacteria, and an increase in pathogenic bacteria, leading to gut microbiota dysbiosis.

Correlation between gut microbiota and host terminal ileal transcriptome

By integrating transcriptomic and cecal microbiota data, we conducted a Pearson’s correlation-based analysis to identify microbe-associated genes and evaluate the potential influence of gut microbiota on host transcriptional patterns following TBI. The analysis focused on the top 50 most abundant bacterial genera and the top 50 DEGs, ultimately revealing 33 genera and 50 genes that formed 442 significant bacteria–gene correlation pairs (Fig. 4A). Notably, Reg3b and Reg3g were positively correlated with Acetatifactor, Butyricicoccus, Clostridium XlVa, Helicobacter, Lachnospiracea_incertae_sedis, Prevotella, and Streptococcus. Conversely, they exhibited negative correlations with Bacteroides. Reg3 proteins, as AMPs, are essential components of the gut mucosal defense system and play a pivotal role in maintaining spatial separation between host tissue and the gut microbiota.⁵⁴ Enrichment analysis of differentially expressed microbiota-related genes revealed downregulation of pathways, including antimicrobial humoral immune response, defense against gram-negative bacteria, and negative regulation of inflammatory and immune response. Conversely, pathways associated with triglyceride and cholesterol homeostasis, lipoprotein metabolism, and high-density lipoprotein particle remodeling were upregulated (Fig. 4B). These results indicated that the ileal transcriptome was influenced by gut microbiota following TBI, and the expression of Reg3 genes was positively correlated with the abundance of butyrate-producing bacteria.

FIG. 4.

Correlation between gut microflora and host terminal ileum transcriptome. (A) Correlation between gut microflora (at the genus level) and host distal ileum transcriptome (top 50 differentially expressed genes) in control and traumatic brain injury (TBI) mice on day 3 post-injury. Red indicates a positive correlation; blue indicates a negative correlation. (B) GO analysis showing the top 30 upregulated and downregulated enrichment terms of microflora-related genes in control and TBI mice on day 3 post-injury (n = 3).

TBI impairs intestinal barrier function by reducing SCFA production

SCFAs are one of the major metabolites of gut microbiota and are important media in microbiome-host interactions. We investigated whether microbiota SCFAs contribute to the regulation of the gut transcriptome following TBI. A notable finding was the positive correlation between the expression of Reg3 genes and the abundance of butyrate-producing bacteria such as Clostridium XIVa, Roseburia, and Butyricicoccus, both of which were significantly reduced after TBI (Fig. 5A). In contrast, two representative opportunistic pathogens, Bacteroides and Escherichia/Shigella, increased in abundance and were negatively correlated with Reg3 expression (Fig. 5B). These findings raise the possibility that the gut microbiota may influence ileal barrier function by modulating Reg3 secretion, a hypothesis that is investigated further below.

FIG. 5.

Traumatic brain injury (TBI) acutely impairs the SCFA/IL-22/Reg2 pathway in the ileum. (A) Abundance of butyrate-producing microbiota at the genus level in control and TBI mice on day 3 post-injury (n = 5). Statistical analysis was performed using the Mann-Whitney test (ns, not significant; *p < 0.05; **p < 0.01; ***p < 0.001). (B) Abundance of conditional pathogens at the genus level in control and TBI mice on day 3 post-injury (n = 5). Statistical analysis was performed using the Mann–Whitney test (*p < 0.05; **p < 0.01). (C) Quantification of short-chain fatty acids (SCFAs) in the cecal contents of the control and TBI day 3 mice. Data are presented as mean ± SEM (n = 5). Statistical analyses were performed using an unpaired, two-tailed t-test (***p < 0.001). (D) Real-time PCR analysis of relative mRNA expression levels of regenerating islet-derived proteins Reg3b and Reg3g in terminal ileum samples from control and TBI mice on day 3 post-injury. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using an unpaired, two-tailed t-test (**p < 0.01; ***p < 0.001). (E) Western blot of Reg3b and Reg3g in terminal ileum samples from control and TBI mice on day 3 post-injury (n = 5). (F) Statistical analysis of Western blot data. Results from at least three independent experiments are presented as mean ± SEM (n = 5). Statistical analysis was performed using an unpaired, two-tailed t-test (**p < 0.01; ***p < 0.001). (G) Real-time PCR of relative mRNA expression and statistical analysis of Western blot results for interleukin-22 (IL-22) in terminal ileum samples from control and TBI mice on day 3 post-injury. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using the Mann-Whitney test (*p < 0.05). (H) Western blot of IL-22 in terminal ileum samples from control and TBI mice on day 3 post-injury (n = 5). (I) Representative immunofluorescence images of Reg3b (green), Reg3g (red), and immunohistochemistry images of IL-22 in terminal ileum sections from control and TBI mice on day 3 post-injury (scale bar, 100 μm). Reg3b- and Reg3g-positive signals are indicated by white arrows; IL-22-positive cells are indicated by red arrows. Quantification of Reg3b and Reg3g fluorescence intensities and IL-22-positive cell counts is presented as mean ± SEM (n = 5). Statistical analysis was performed using an unpaired, two-tailed t-test (**p < 0.01; ***p < 0.001).

FIG. 6.

Alterations in gut microbiota and short-chain fatty acid (SCFA) levels after Clostridium butyricum (C. butyricum) supplementation following traumatic brain injury (TBI). (A) Schematic diagram of the experimental design. (B) Alpha diversity of the gut microbiota in four experimental groups (Control, TBI day 3, Control + C. butyricum, and TBI day 3 + C. butyricum) was assessed using the Simpson index, Observed species index, PD whole tree index, Shannon index, and Chao1 index. Data are presented as box plots showing minimum to maximum values (n = 5). Statistical analysis was performed using the Kruskal-Wallis test (ns. not significant; *p < 0.05; **p < 0.01; ***p < 0.001). (C) The relative abundance of butyrate-producing microbiota in four groups. Data are presented as box plots showing minimum to maximum values (n = 5). Statistical analysis was performed using the Kruskal-Wallis test (ns. not significant; *p < 0.05; **p < 0.01). (D) Quantification of SCFAs in the cecal contents of four groups. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using one-way analysis of variance (ANOVA) or Kruskal-Wallis test (ns. not significant; *p < 0.05; **p < 0.01; ***p < 0.001).

To investigate further, LC-MS/MS analysis was performed to measure SCFA levels in cecal contents 3 days post-TBI. Levels of key SCFAs, including butyrate, acetate, propionate, and pentanoate, were markedly reduced in the TBI group (Fig. 5C). This reduction coincided with the downregulation of both mRNA and protein expressions of Reg3 (Fig. 5D–F, I). Notably, deficiencies in Reg3b or Reg3g, members of the Reg3 family, have previously been associated with an increase in mucosa-associated bacteria and enhanced bacterial translocation across the intestinal barrier.⁵⁵

IL-22, a cytokine crucial for maintaining tissue homeostasis, regulating inflammation, and mediating immune defense, has been identified as an upstream regulator of Reg3 expression in colonic epithelial cells.⁵⁶ Importantly, microbiota-derived SCFAs are known to stimulate IL-22 production by CD4⁺ T cells, innate lymphoid cells (ILCs), and enteric glial cells, thereby contributing to intestinal homeostasis and providing protection against inflammation.^57,58 In our study, both mRNA and protein levels of IL-22 in the ileum were significantly reduced after TBI, paralleling the decrease in Reg3 expression when compared with the control group (Fig. 5G–I).

Given that IL-22 signaling drives Reg3 expression under pathogenic or inflammatory conditions, these findings suggest that TBI-induced ileal barrier dysfunction may result from disruption of the SCFA/IL-22/Reg3 pathway, mediated by a reduction in butyrate-producing microbiota. This highlights a potential mechanistic link between TBI, gut microbiota, and ileal barrier integrity.

C. butyricum pretreatment is associated with improved ileal barrier function

To further confirm that the reduction of butyrate-producing microbiota following TBI contributes to ileal barrier impairment and that supplementation with butyrate-producing bacteria can restore the damaged ileal barrier of TBI, we pretreated mice with a commercially available strain of Clostriduim butyricum (C. butyricum) for 3 weeks before CCI or sham surgery. Both CCI and sham-operated mice were provided with sterile water either supplemented with or without C. butyricum strain Miyairi 588 (>15 × 10⁶ CFU; Miyarisan, Japan) to evaluate its protective effects against TBI-induced ileal barrier dysfunction (Fig. 6A). It is noteworthy that the Miyairi 588 strain of C. butyricum has a long-standing history as a probiotic used to treat both antibiotic-associated and nonantibiotic-associated diarrhea in humans. Microbiota analysis showed that C. butyricum supplementation did not reverse the TBI-induced decrease in microbiotal diversity (Fig. 6B). Furthermore, the relative abundance of other butyrate-producing microbiota remained unchanged in the TBI + C. butyricum group compared with the TBI group (Fig. 6C). Among the SCFAs analyzed, only butyric acid and pentanoic acid exhibited significant increases in the cecal contents following C. butyricum supplementation, relative to the TBI group (Fig. 6D).

Notably, the morphology of the ileal villi was significantly improved following C. butyricum supplementation, although the number of goblet cells showed no significant change (Fig. 7A). Serum levels of FITC-dextran showed that ileal barrier permeability decreased after supplementation with C. butyricum (Fig. 7B). Mice that had been pretreated with C. butyricum exhibited heightened expression of ZO-1, Claudin-4, Reg3g, Reg3b, and IL-22 compared with TBI mice treated with sterile water. This observation was further substantiated by the results of protein expression levels in Western blot (Fig. 7D, E), immunofluorescence, and immunohistochemistry experiments (Fig. 7F), which aligned with the findings from real-time PCR (Fig. 7C). Interestingly, while the expression of ZO-1 and Claudin-4 proteins was significantly upregulated in the TBI + C. butyricum group compared with the TBI group (Fig. 7C–E), the corresponding mRNA levels showed a less pronounced increase. Notably, C. butyricum treatment significantly improved the mRNA expression of ZO-1 and Claudin-4 genes in TBI mice, but the magnitude of change was smaller than that observed at the protein level, suggesting a potential regulatory mechanism beyond transcription. In addition, Paneth cell numbers, assessed by lysozyme staining, decreased after TBI and were restored following supplementation with C. butyricum, showing a pattern consistent with the changes observed in Reg3 expression (Fig. 7F). Furthermore, we assessed corticosterone and proinflammatory cytokine levels in both serum and ileum tissues. ELISA results showed that corticosterone levels were reduced in both compartments after C. butyricum supplementation. Similarly, proinflammatory cytokines, including IL-1β, TNF-α, and IL-6, were significantly downregulated. In contrast, the anti-inflammatory cytokine IL-22 was upregulated following C. butyricum treatment, suggesting that C. butyricum alleviates both local and systemic inflammatory responses (Fig. 8A, B).

FIG. 7.

Clostridium butyricum (C. butyricum) improves intestinal barrier function in TBI mice. (A) Representative images of hematoxylin and eosin (H&E)-stained and Alcian Blue–Periodic Acid-Schiff (AB-PAS)-stained terminal ileum sections following C. butyricum treatment (scale bar, 100 μm). Quantification of villus-to-crypt ratio and goblet cell number per villus-crypt unit is presented as mean ± SEM (n = 5). Statistical analysis was performed using two-way analysis of variance (ANOVA) followed by Tukey’s multiple comparisons test (ns. not significant; **p < 0.01; ***p < 0.001). (B) Intestinal barrier permeability assessed by serum levels of 4 kDa fluorescein isothiocyanate (FITC)-dextran. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using two-way ANOVA followed by Tukey’s multiple comparisons test (ns. not significant; *p < 0.05, ***p < 0.001). (C) Real-time PCR analysis of relative mRNA expression in terminal ileum samples from healthy controls and TBI mice with or without C. butyricum supplementation. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using two-way ANOVA followed by Tukey’s multiple comparisons test (ns. not significant; *p < 0.05; **p < 0.01; ***p < 0.001). (D, E) Western blot analysis of protein expression in terminal ileum samples from healthy controls and TBI mice with or without C. butyricum supplementation. Data from at least three independent experiments are presented as mean ± SEM (n = 5). Statistical analysis was performed using two-way ANOVA followed by Tukey’s multiple comparisons test (ns. not significant; *p < 0.05; **p < 0.01; ***p < 0.001). (F) Representative immunofluorescence images of ZO-1 (red), Claudin 4 (green), Reg3g (red), and Reg3b (green), and immunohistochemistry images of IL-22 and lysozyme in terminal ileum sections from healthy controls and TBI mice with or without C. butyricum supplementation (scale bar: 100 μm). IL-22-positive cells are indicated by red arrows. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using two-way ANOVA followed by Tukey’s multiple comparisons test (ns. not significant; *p < 0.05; **p < 0.01; ***p < 0.001).

FIG. 8.

Clostridium butyricum (C. butyricum) mitigates systemic and ileal inflammation following traumatic brain injury (TBI). (A) Serum levels of corticosterone, IL-1β, IL-6, TNF-α, and IL-22 were measured by enzyme-linked immunosorbent assay (ELISA) in four groups (Control, TBI day 3, Control + C. butyricum, and TBI day 3 + C. butyricum). (B) Ileal tissue levels of corticosterone, IL-1β, IL-6, TNF-α, and IL-22 were measured by ELISA across the same four groups. Data are presented as mean ± SEM (n = 5). Statistical analysis was performed using two-way ANOVA followed by Tukey’s multiple comparisons test (ns. not significant; *p < 0.05; **p < 0.01; ***p < 0.001).

Collectively, these results strongly support a potentially protective role of C. butyricum against TBI-induced intestinal barrier impairment, likely mediated through the upregulation of the IL-22/Reg3 pathway. In conclusion, our findings suggest that alterations in microbiota composition lead to reduced SCFA production, which in turn diminishes IL-22 expression and downstream Reg3 signaling in the intestine following TBI.

Discussion

The “brain-gut-microbiota axis” is a bidirectional communication network that plays a critical role in maintaining central nervous system and gut homeostasis after TBI. Intestinal barrier dysfunction is a common and severe complication of TBI, leading to increased intestinal permeability, bacterial translocation, endotoxemia, and systemic inflammation, all of which elevate mortality risk.^6,59
–61 Despite its clinical relevance, effective treatments for TBI-induced intestinal barrier disruption remain unavailable. In this study, we used a CCI model to investigate the interplay between gut microbiota and host ileal transcription following TBI. Our results revealed significant microbiota alterations, including reduced diversity and a decline in butyrate-producing bacteria, which were potentially linked to intestinal immune dysregulation and compromised barrier integrity. We identified microbiota-associated genes and revealed that microbiota alterations may influence ileal barrier function by the SCFA/IL-22/Reg3 pathway after TBI. Supplementation with C. butyricum restored SCFA levels, upregulated IL-22 and Reg3, and protected against ileal barrier dysfunction, providing novel insights into the pathogenesis of TBI through the lens of the brain-gut-microbiota axis (Fig. 9).

FIG. 9.

Impact of TBI on gut microbiota and intestinal barrier function, and the protective role of Clostridium butyricum. TBI triggers systemic inflammation, as indicated by elevated levels of corticosterone, TNF-α, IL-1β, and IL-6, which in turn disrupt gut immune responses. This dysregulation leads to a decrease in butyrate-producing bacteria and SCFAs, impairing IL-22 signaling and downregulation of Reg3 expression. Consequently, tight junction integrity is compromised, exacerbating gut barrier dysfunction. Supplementation with C. butyricum restores SCFA levels, promotes IL-22 production by CD4⁺ T cells and innate lymphoid cells (ILCs), and upregulates Reg3 proteins. These effects collectively strengthen tight junctions, enhance gut barrier integrity, and alleviate TBI-induced intestinal dysfunction. SCFAs, short-chain fatty acids; IL-22, interleukin-22; Reg3, regenerating islet-derived protein 3; TBI, traumatic brain injury.

TBI-induced mucosal injury was evident 3 days post-injury, consistent with previous findings of enhanced ileal permeability within hours⁶² to days⁶³ following TBI. The gut microbiota, dominated by Bacteroidetes, Firmicutes, and Proteobacteria in healthy individuals,⁶⁴ exhibited significant dysbiosis post-TBI. We observed a decline in Firmicutes and an increase in Bacteroides, resembling patterns of dysbiosis associated with inflammatory bowel diseases such as Crohn’s disease.^65,66 At the genus level, beneficial butyrate-producing bacteria decreased, while proinflammatory genera such as Bacteroides and Escherichia/Shigella increased. These changes likely exacerbate intestinal inflammation and contribute to barrier dysfunction via the brain-gut axis.

Transcriptomic analysis of the terminal ileum revealed marked alterations in genes involved in bacterial response, immune regulation, and AMP production. Notably, the expression of Reg3, a critical AMP that enhances gut barrier integrity, was significantly downregulated following TBI. Reg3 plays a key role in maintaining the spatial separation between luminal bacteria and the intestinal epithelium^55,67
–69 and is closely linked to the abundance of butyrate-producing bacteria. A reduction in SCFA levels, particularly butyrate, has been associated with diminished IL-22 expression in previous studies,⁵⁷ and our data support that this relationship may further impair AMP-mediated protection of the intestinal barrier.

SCFAs, primarily produced through bacterial fermentation of dietary fiber, are essential for intestinal immunity. They interact with G-protein-coupled receptors (GPR41, GPR43, and GPR109a)^70,71 on intestinal epithelial cells, activating signaling pathways that modulate cytokine and AMP production.^72,73 Butyrate, in particular, upregulates IL-22 expression via histone deacetylase inhibition and GPR signaling,^57,58 which promotes antimicrobial immunity, tissue repair, and epithelial barrier function.^74

–77 IL-22 is essential for preserving gut homeostasis, and its expression is regulated by the aryl hydrocarbon receptor (AhR) pathway. Activation of AhR in ILC3 enhances IL-22 secretion, which is critical for protecting against infections and inflammatory damage.^78
–80 Our findings confirm that TBI disrupts SCFA production, which is associated with reduced IL-22 expression and downstream Reg3 expression, ultimately compromising the integrity of the ileal barrier. However, a major limitation of our study is that we did not directly investigate the involvement of the AhR pathway in IL-22 regulation following TBI. Further research will be necessary to elucidate the precise role of the AhR pathway in this context.

Importantly, supplementation with C. butyricum, a butyrate-producing bacterium, restored SCFA levels, upregulated IL-22 and Reg3 expression, and improved TJ protein integrity, thereby effectively mitigating TBI-induced ileal barrier dysfunction. The observed discrepancy between mRNA and protein levels of TJ proteins (ZO-1, Claudin-4) in C. butyricum-treated mice suggests the involvement of posttranscriptional or translational regulation. Such divergence may result from altered mRNA stability, enhanced translation efficiency, or reduced protein degradation under microbial or inflammatory modulation. Previous studies have shown that gut microbiota or microbial metabolites can influence host protein synthesis pathways, including mTOR signaling and RNA-binding protein activity, thereby affecting TJ protein expression independently of mRNA abundance.^81,82 These possibilities warrant further investigation to delineate the precise mechanisms by which C. butyricum restores intestinal barrier function after TBI. Paneth cells, the primary source of Reg3 proteins, exhibited changes following TBI that closely mirrored alterations in Reg3 expression. This suggests that the observed fluctuations in Reg3 levels are likely due to impaired Paneth cell function. Notably, supplementation with C. butyricum alleviated these effects, restoring the expression patterns of both Reg3b and Reg3g. In addition, C. butyricum treatment significantly reduced proinflammatory cytokine levels and increased the anti-inflammatory cytokine IL-22 in both serum and ileal tissue, supporting its role in attenuating local and systemic inflammatory responses. While the potential of C. butyricum to influence systemic inflammation may contribute to improved neurological outcomes post-TBI, it is also important to consider the role of SCFAs produced by C. butyricum in modulating systemic inflammation. SCFAs can influence immune responses by regulating the balance of pro- and anti-inflammatory cytokines, and this could play a crucial role in protecting the brain after injury. However, further studies are needed to validate these effects and assess the full therapeutic potential of SCFAs in the context of TBI. Collectively, these findings underscore the therapeutic potential of butyrate-producing bacteria—such as the clinically available C. butyricum strain Miyairi 588—as promising candidates for the treatment of TBI-associated gastrointestinal dysfunction.

Several limitations of this study should be acknowledged. First, the experimental design involved pretreatment with C. butyricum for 3 weeks before TBI induction. While this approach allowed for the assessment of potential protective effects, it does not accurately reflect typical clinical scenarios, where TBI results from an acute impact and therapeutic interventions begin in the acute phase following injury. This limitation reduces the translational relevance of our findings. Future studies should focus on postinjury administration of C. butyricum to more effectively evaluate its therapeutic potential in the context of TBI.

Second, microbial profiling in this study was performed using 16S rDNA rather than 16S rRNA. While 16S rDNA sequencing is widely used for microbiota characterization, it does not differentiate between active and inactive microbial populations, as DNA can persist in both viable and dead cells. Consequently, the observed microbial shifts may not fully represent functionally relevant changes. RNA-based approaches, such as 16S rRNA sequencing or metatranscriptomics, could offer deeper insights into the metabolically active microbiota following TBI. Future studies using RNA-based techniques may help to better elucidate the dynamic gut microbiota responses to TBI.

Third, only male mice were used to minimize variability due to hormonal cycling. However, this precludes analysis of potential sex differences in response to TBI and microbial modulation. Inclusion of female mice in future studies will be important to assess sex-specific effects.

In addition, sham-operated animals were used as controls, which may confound the interpretation of gut and brain outcomes. Craniotomy alone can induce inflammatory and metabolic responses independent of TBI, potentially obscuring the specific effects of brain injury. Incorporating a naive (nonsurgical) control group in future experiments would help to better distinguish the effects of surgical procedures from those of TBI itself.

Finally, while C. butyricum supplementation was associated with improved intestinal barrier function, causality cannot be established. Further mechanistic studies, including the use of genetic or pharmacologic tools, are needed to define the pathways involved and assess the therapeutic viability of targeting the gut microbiota in TBI.

Conclusions

This study underscores the pivotal role of the brain-gut-microbiota axis in TBI-induced ileal barrier dysfunction and identifies the SCFA/IL-22/Reg3 signaling pathway as a potential therapeutic target. Restoring gut microbial homeostasis—through interventions such as C. butyricum supplementation—may represent a promising strategy to mitigate gut-related complications and improve clinical outcomes in patients with TBI.

Transparency, Rigor, and Reproducibility Summary

The study design and analytic plan were preregistered before initiating data collection with the Institutional Animal Care and Use Committee at Tongji University School of Medicine (TJAA11023101). A predetermined sample size of 5 mice per group was allocated for histological assessments and the detection of protein and mRNA expression levels and 16S rDNA sequencing of cecal contents, while three mice were designated for RNA-seq, based on insights gained from prior studies. In total, 45 mice were selectively bred for the experiments and were randomly assigned to either trauma or sham groups using a random number generator; however, two mice succumbed to TBI. The handling and analysis of histological materials were executed by team members who were blinded to both trauma status and medication conditions. Histological analyses were conducted in two separate batches, with sample assignments to batches performed randomly. Ongoing replication of the study by a collaborating laboratory is in progress.

All comprehensive data from this study are publicly accessible in Supplementary Materials. The RNA-seq data and 16S rDNA sequencing (16S-seq) data have been securely deposited in the NCBI Sequence Read Archive (SRA) under the BioProject identifier PRJNA765477. A limited number of histological samples from each experimental group are available for future analyses upon request. This article will be published under a Creative Commons Open Access license and, upon publication, will be freely accessible (https://www.liebertpub.com/loi/neu).

Footnotes

Acknowledgments

The authors thank Dr. Kazuko Saeki and Dr. Yuan Chi (Department of Biochemistry, Juntendo University School of Medicine) for their advice and helpful discussions. The authors also thank LetPub () for linguistic assistance and presubmission expert review.

Ethics Approval Statement

The study design and analytic plan were preregistered before initiating data collection with the Institutional Animal Care and Use Committee at Tongji University School of Medicine (TJAA11023101).

Data Availability Statement

Authors’ Contributions

Conceptualization: M.L. Data curation: Me.C. and J.Y. Formal analysis: L.X. and Me.C. Funding acquisition: C.Z., M.L., L.X., and S.X. Investigation: M.L. and Y.P. Methodology: T.J., Mu.C., B.H., and K.Z. Project administration: T.Y. Resources: C.Z. Software: T.J. Supervision: J.Z. Validation: Y.P. Visualization: S.X. Writing—original draft: Y.P. Writing—review and editing: L.X., D.H., and J.Z.

Author Disclosure Statement

The authors have no competing interest to disclose.

Funding Information

This study was supported by grants from the National Natural Science Foundation of China (No. 82070541 to M.L.; Nos. 82271406, 81771332, and 81571184, to C.Z.); the Natural Science Foundation of Shanghai (No. 22ZR1451200 to C.Z.); the Health Industry Clinical Research Project of Shanghai Municipal Health Commission (No. 20204125 to M.L.; No. 201840110 to S.X.); the Key Disciplines Group Construction Project of Shanghai Pudong New Area Health Commission (No. PWZxq2022-10 to C.Z.); the Medical Discipline Construction Project of Pudong Health Committee of Shanghai (No. PWYgy2021-07 to C.Z.); the Key Discipline Construction Project of Shanghai East Hospital (No. 2024-DFZD-003S to CZ); the Li Jieshou Intestinal Barrier Research Foundation (No. LJS-201901A to M.L.); the Japan China Sasakawa Medical Fellowship (M.L.); Wu Jieping Medical Foundation Special Fund for Clinical Scientific Research (No. 320.6750.2024-25-5 to C.Z.); and the Three-Year Action Plan for Discipline Construction of School of Nursing, Tongji University (No. JS2210320 to L.X.).

Supplementary Materials

Supplementary Figure S1

Supplementary Figure S2

Supplementary Figure S3

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