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Gut microbiota dysbiosis orchestrates vitiligo-related oxidative stress through the metabolite hippuric acid

Microbiome volume 13, Article number: 112 (2025) Cite this article

Abstract

Background

Vitiligo, a depigmenting autoimmune skin disease characterized by melanocyte dysfunction or death, is known to be associated with an imbalance in gut microbiota. Oxidative stress plays a critical role in the pathogenesis of vitiligo. However, the complex promising interaction between abnormal accumulation of reactive oxygen species (ROS) in the skin and gut microbiota has remained unclear.

Results

Here, we compared transcriptome data of vitiligo lesions and normal skin and identified a high expression of oxidative stress-related genes in vitiligo lesions. We also established a vitiligo mouse model and found that the presence of gut microbiota influenced the expression of ROS-related genes. Depletion of gut microbiota reduced abnormal ROS accumulation and mitochondrial abnormalities in melanocytes, significantly improving depigmentation. Our findings from manipulating gut microbiota through cohousing, fecal microbiota transplantation (FMT), and probiotic supplementation showed that transferring gut microbiota from mice with severe vitiligo-like phenotypes exacerbated skin depigmentation while probiotics inhibited its progression. Targeted metabolomics of fecal, serum, and skin tissues revealed gut microbiota-dependent accumulation of hippuric acid, mediating excessive ROS in the skin. Elevated serum hippuric acid levels were also confirmed in vitiligo patients. Additionally, a microbiota-dependent increase in intestinal permeability in vitiligo mice mediated elevated hippuric acid levels, and we found that hippuric acid could directly bind to ROS-related proteins (NOS2 and MAPK14).

Conclusions

Our results suggested the important role of gut microbiota in regulating vitiligo phenotypes and oxidative stress. We identified hippuric acid, a gut microbiota-host co-metabolite, as a critical mediator of oxidative stress in vitiligo skin and its binding targets (NOS2 and MAPK14), resulting in oxidative stress. Validation in a small human cohort suggested that hippuric acid could serve as a novel diagnostic biomarker and therapeutic target for vitiligo. These findings provided new insights into how gut microbiota regulates skin oxidative stress in vitiligo and suggested potential treatment strategies for the disease.

Video Abstract

Introduction

Vitiligo is a prevalent autoimmune skin disorder characterized by depigmented patches resulting from the destruction of melanocytes [1,2,3]. It affects 0.5 to 2% of the global population, significantly diminishing the life quality of patients [4, 5]. Current therapeutic approaches primarily rely on nonspecific treatments such as topical or systemic steroids, phototherapy, and autologous epidermal grafting, hampered by recurrence and prolonged treatment regimens [2, 4]. It underscores the need to deepen our understanding of vitiligo’s pathogenesis and devise more durable therapeutic strategies.

Studies indicate oxidative stress is a pivotal player in the onset and progression of vitiligo [6,7,8]. The eventual demise of epidermal melanocytes constitutes a critical mechanism underlying the localized or generalized depigmentation of the skin and mucous membranes [9]. Despite advances, the intrinsic factors triggering oxidative stress remain elusive, and interventions targeting local oxidative stress alone often fail to halt disease progression, suggesting broader origins of lesion-associated oxidative stress beyond the skin microenvironment [3, 9].

The “gut-skin axis” concept offers a promising avenue for addressing these challenges [10, 11]. Recent research has illuminated the significant role of gut microbiota, central to this axis, in the pathogenesis of various skin disorders such as atopic dermatitis [12, 13], psoriasis [14], and urticaria [15]. Our preliminary investigations have identified substantial differences in gut microbiota composition between vitiligo patients and healthy controls [16]. Studies by Bzioueche et al. [17] and Wu et al. [18] followed similar findings in patients with vitiligo. Additionally, Dellacecca et al. observed alterations in pigmentation in vitiligo mice following oral antibiotic treatment [19]. These disparities may modulate microbial-related metabolomic profiles or influence intestinal mucosal environments, thereby elevating concentrations of gut-derived metabolites in serum and contributing to the aberrant skin immune microenvironment.

This study introduces a novel framework for comprehending the intricate interplay between gut microbiota and skin. It sheds new light on how microbiome-associated and vitiligo-associated transcriptional signatures contribute to local skin homeostasis imbalance through a melanoma/regulatory T cell (Treg)-induced vitiligo induction model. Furthermore, our findings not only highlight the potential of probiotic therapy in ameliorating vitiligo-like skin phenotypes, a promising avenue for future treatment, but also pinpoint hippuric acid as a pivotal metabolite in gut-host co-metabolism, elucidating its molecular role via interactions with ROS-related proteins (NOS2 and MAPK14).

Results

Oxidative stress-related transcriptome signature in lesions of vitiligo patients

Oxidative stress in the skin is recognized as a primary trigger in the onset and progression of vitiligo [6,7,8]. To thoroughly investigate whether oxidative stress pathways are activated in the skin lesions of vitiligo patients, we conducted a comprehensive re-analysis of microarray transcriptome data from Regazzetti et al. [20] and Singh et al. [21] (Fig. 1A). Principal component analysis (PCA) revealed distinct gene expression profiles between lesional and non-lesional skin (Fig. 1B), with group information detailed in table S1. Our analysis identified 213 upregulated genes and 158 downregulated genes (Fig. 1C and Fig. S1), highlighting significant differences in genes related to oxidative stress, immune function, pigment deposition, and metabolic functions (Padj < 0.01). Oxidative stress-related genes such as ECT2, HDAC2, RWDD1, and TXN showed significantly elevated expression levels in lesion skin and increased expression of immune-related genes like ANXA1 and BRCC3. Conversely, pigment deposition-related genes (DCT, TYR) were notably downregulated (Fig. 1D). Further gene ontology (GO) analysis of differentially expressed genes (DEGs) between the lesional and non-lesional skin confirmed biological processes consistent with disease progression, particularly the enrichment of multiple pathways related to oxidative stress and ROS (Fig. 1E). These findings indicated that oxidative stress and ROS accumulation were crucial biological processes in vitiligo lesional skin.

Fig. 1
figure 1

Integrative transcriptomic analysis of lesional vs. non-lesional skin in vitiligo patients from the GEO Database. A Schematic diagram of workflow of transcriptomic analysis from datasets GSE65127 and GSE75819. B Principal component analysis (PCA) was performed on normalized gene counts comparing lesional and non-lesional skin samples from vitiligo patients (n = 25). C Number of upregulated and downregulated DEGs identified in lesional versus non-lesional skin across the vitiligo patients. D Heatmap displaying a subset of DEGs (Padj < 0.05 show with a red circle) in lesional versus non-lesional skin. The list of genes with symbols (left) indicates their functional annotation (top left). Each column represents a biological replicate, and each row represents a gene. E Significant Gene Ontology (GO) terms enriched with the respective − log10(FDR). The list of terms with symbols (left) indicates their functional annotation (bottom left)

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Enrichment of oxidative stress-related pathways in the skin transcriptome of the vitiligo mouse model

To explore the underlying mechanisms of oxidative stress formation in vitiligo skin, we utilized the mouse model as reported by Xu et al. [22, 23], where vitiligo was induced by inoculating B16F10 melanoma cells and depleting Treg cells, mimicking the pathologies observed in human vitiligo patients (Fig. 2A). By day 35 post-induction, visible depigmentation appeared on the backs of the mice, with signs of depigmentation also noted on the tail skin (Fig. 2B and fig. S2A); by day 60, the median depigmented area on the back reached 47.42% (Fig. 2C). Histological sections of mouse tail skin via bright field vision revealed localized depigmentation. Representative whole-mount immunofluorescence staining of Melan-A+ melanocytes and CD8+ T cells demonstrated distinct patterns of CD8+ T cell infiltration and melanocyte loss post-induction (Fig. 2, D–F), consistent with the autoimmune pathogenesis observed in vitiligo [22, 24].

Fig. 2
figure 2

Tail skin transcriptome in the melanoma-regulatory T cell (Treg)-induced vitiligo mouse model. A Schematic diagram of the melanoma/Treg-induced vitiligo mouse model (created with BioRender.com). B Representative hair coat images from control and vitiligo-induced mice post-induction at days 35 and 60. C Quantification of the percentage of depigmented skin area on the backs of vitiligo mice. Mean and SEM, 3 independent experiments including control (n = 9) and vitiligo mice (n = 9). Padj = 0.0428 for no induction versus vitiligo day 35, Padj < 0.0001 for no induction versus vitiligo day 35, and Padj = 0.0428 for vitiligo day 35 versus vitiligo day 60. (D) Representative epidermal of tail skin images of vitiligo mice at day 35 post-induction, including bright field images and wholemount immunofluorescent staining showing Melan-A+ melanocytes and CD8+ T cells. E, F Quantification of Melan-A+ melanocytes and CD8+ T cells in the epidermis of control (n = 9) and vitiligo mice (n = 9). G Principal component analysis (PCA) on normalized gene counts of tail skin samples isolated from control (n = 9) and vitiligo-induced (n = 9) mice. H Number of upregulated and downregulated differentially expressed genes (DEGs) in vitiligo-induced versus control mice. I Heatmap displaying a subset of DEGs in vitiligo-induced versus control mice. Each column represents a biological replicate, and each row represents a gene. J Significant Gene Ontology (GO) terms enriched with the respective − log10(FDR) and gene percent contained. The number and percentage of genes per term are shown. Statistical significance was determined using (C) Kruskcal-Wallis followed by Dunn’s post hoc test or (E and F) unpaired Student’s t-test. *P < 0.05, ****P < 0.0001

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Following this, we performed bulk RNA-seq on depigmented skin from day 35 induced vitiligo mouse tails. The results revealed significant differences in gene expression profiles between vitiligo-induced and non-induced mouse skin (Fig. 2G). Differential expression analysis identified 427 upregulated genes and 101 downregulated genes (Fig. 2H and Fig. S2B), highlighting the top 70 differentially expressed genes (Padj < 0.01) involved in oxidative stress, immune function, and metabolic processes, including oxidative stress-related genes such as Lep and Mtarc1, and immune-related genes such as Tshr and Lipe (Fig. 2I). GO analysis of DEGs indicated enrichment in various pathways related to immune response and stress, particularly significant enrichment in pathways such as “response to stress,” “response to stimulus,” and “response to oxygen-containing compound” (Fig. 2J), suggesting oxidative stress processes similar to disease onset in the depigmented skin of the vitiligo mouse model.

Depletion of gut microbiota eliminates vitiligo-related ROS accumulation in the skin

Previous studies have reported significant ROS accumulation in the skin of vitiligo patients [25]. Our assessment of ROS levels in the live tail skin of vitiligo mice at day 35 (Fig. 3A) revealed a marked increase in ROS accumulation compared with untreated mice (Fig. 3B,C). This finding was supported by electron microscopy of perilesional skin in vitiligo mice, which showed evident cellular damage and mitochondrial morphological abnormalities in melanocytes compared with non-induction mice (Fig. 3E).

Fig. 3
figure 3

Gut microbiota contributes to vitiligo-related oxidative stress in the skin and mitochondrial dysfunction in melanocytes. A Schematic diagram showing oral administration of antibiotics (ABX) for 2 weeks followed by the vitiligo induction procedure. Subsequent experiments were conducted on day 35 (created with BioRender.com). B Representative confocal images showing dihydroethidium (DHE) fluorescence in tail skin sections from vitiligo mice treated with water and ABX. Scale bars, 200 µm. C Quantification of mean DHE fluorescence intensities indicating reactive oxygen species (ROS) levels. Mean and SEM, 3 independent experiments including vitiligo mice treated with water (n = 6) and ABX (n = 6). Padj = 0.0372 for non-induction-water versus vitiligo-water, Padj = 0.0001 for non-induction-ABX versus vitiligo-water, and Padj = 0.0481 for vitiligo-water versus vitiligo-ABX. D Quantification of depigmentation in back skin. Mean and SEM, 3 independent experiments including vitiligo mice treated with water (n = 9) and ABX (n = 9). E Representative electron micrographs of tail skin from vitiligo mice treated with water and ABX. Blue arrowheads, healthy; red arrowheads, abnormal. Scale bar, 500 nm. F, G Principal component analysis (PCA) on normalized gene counts of tail skin samples isolated from non-induction and vitiligo mice treated with water (n = 6, 6) and ABX (n = 6, 6). H Number of upregulated and downregulated differentially expressed genes (DEGs) in vitiligo mice treated with water versus ABX. I Heatmap showing ROS-related genes in the tail skin of non-induction and vitiligo mice treated with water and ABX. Each column represents a biological replicate. Statistical significance was determined using. C Two-way ANOVA followed by Tukey’s post hoc test or D unpaired Student’s t-test. *P < 0.05, ***P < 0.001

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It has been suggested that gut microbiota may contribute to the pathophysiology of extraintestinal organs, including skin diseases, through the gut-skin axis [10, 11, 26]. Building on significant differences in gut microbiota composition observed in vitiligo patients [16,17,18], we investigated the impact of gut microbiota depletion using antibiotics (ABX) on vitiligo mice’s depigmentation phenotype and skin ROS accumulation. Mice were treated with a broad-spectrum ABX cocktail (1 g/L ampicillin, 1 g/L metronidazole, 1 g/L neomycin, and 0.5 g/L vancomycin) known for effectively depleting indigenous gut microbiota and facilitating the colonization of donor-derived gut bacteria (Fig. 3A and Table S2). We observed a significant reduction in ROS accumulation at the lesion site of vitiligo mice following ABX treatment compared with untreated vitiligo mice, with no notable damage to melanocytes or mitochondria (Fig. 3, B, C, and E). Additionally, there was a significant reduction in vitiligo lesion area post-ABX treatment (P = 0.0002, Fig. 3D). Bulk RNA-seq analysis of the tail skin post-ABX treatment revealed distinct transcriptomic profiles between untreated and treated-vitiligo mice, as confirmed by principal component analysis (Fig. 3, F and G). Specifically, ABX treatment in vitiligo mice led to 900 upregulated and 626 downregulated genes (Fig. 3H and Fig. S3), indicating a decisive influence of gut microbiota changes on gene expression profiles in vitiligo skin lesions.

We further focused on ROS-related genes in the transcriptomic profile post-ABX treatment, identifying several upregulated ROS-related genes such as Abcd2, Car3, and Aimf2 in vitiligo mice, regulated by gut microbiota (Fig. 3I). Collectively, these findings underscore the role of gut microbiota in regulating extraintestinal organ-skin interactions, particularly in oxidative stress status and ROS accumulation in vitiligo disease states.

Probiotic treatment inhibits the progression of vitiligo-like lesions by correcting specific gut dysbiosis in vitiligo mice

Given our findings that gut microbiota significantly influenced the development and progression of vitiligo-like depigmentation in mice, we analyzed the gut microbiota of vitiligo mice using 16S rRNA sequencing. Compositional differences in gut microbiota were observed between induced model mice and non-induction mice, characterized by distinct alpha and beta diversity indices (Fig. 4, A,B). Although there was no significant difference in the Bacteroidetes: Firmicutes ratio at the phylum level, there was a notable decrease in the Verrucomicrobia phylum in vitiligo mice (Fig. S4, A, C, D). At the class level, we observed a significant increase in c_Clostridia abundance (P = 0.0496), accompanied by decreased levels of c_Verrucomicrobiae (P = 0.0004) and c_Gammaproteobacteria (P = 0.0076, Fig. 4C and Fig. S4B). Further analysis at the order level indicated a marked increase in o_Clostridiales within the c_Clostridia class (Fig. 4C,D). We focused on the proportion of o_Clostridiales in vitiligo mice, revealing that Lachnospiraceae and Ruminococcaceae collectively accounted for 48% of o_Clostridiales and 31% of Firmicutes, whereas in non-induction mice, Lachnospiraceae accounted for 81% and Ruminococcaceae for 15%, contributing 5% to Firmicutes (Fig. S4, B and E). This result suggested that the elevated presence of Ruminococcaceae in the o_Clostridiales composition may be a critical factor in vitiligo mice. LEfSe [27] analysis identified species such as Lachnospiraceae_bacterium_COE1, Clostridiales_bacterium_CIEAF_020, and unidentified_rumen_bacterium_JW32 with significantly higher relative abundances in vitiligo mice (LDA > 3, Fig. 4E and Fig. S4C).

Fig. 4
figure 4

Signature and regulation of gut microbiota in the melanoma-Treg-induced vitiligo mouse. A Alpha diversity indices of the genus level for non-induction (n = 9) and vitiligo (n = 9) mice, assessed using the Shannon index. B Principal coordinate analysis (PCoA) of overall gut microbiota based on unweighted UniFrac distance for non-induction (n = 6) and vitiligo (n = 9) mice. C Relative abundance of the top 3 taxa at the class and order levels for non-induction (n = 6) and vitiligo (n = 9) mice. D Bar chart showing the relative abundance of gut microbiota at the order level in fecal samples. Each column represents a biological replicate. E Linear discriminant analysis effect size (LEfSe) analysis indicating significantly different bacterial taxa abundance between non-induction and vitiligo mice. F Schematic diagram of the co-housing and fecal microbiota transplantation (FMT) experiments (created with BioRender.com). G Schematic diagram of the probiotic supplement treatment experiments (created with BioRender.com). H Bar chart showing the relative concentration of taxa at the class level in FMT (n = 6) and donor mice (n = 4). Each column represents a biological replicate. I Analysis of depigmentation of the back skin from sep-2 M and co-2 M mice after the vitiligo induction procedure (from days 14 to 42, n = 5). Data are presented in kinetic line plots showing mean and SEM. J Analysis of depigmentation of the back skin from control and FMT mice after the vitiligo induction procedure (from days 14 to 42, n = 5). Data are presented in kinetic line plots showing mean and SEM. K Analysis of depigmentation of the back skin from vehicle and probiotic-supplement-treated mice after the vitiligo induction procedure (from days 35 to 63, n = 5). Data are presented in kinetic line plots showing mean and SEM. Statistical significance was determined using (D, I, J, and K) Mann–Whitney U test. *P < 0.05, **P < 0.01, ***P < 0.001

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To explore the impact of regulating gut microbiota on the vitiligo skin phenotype, we conducted cohousing and fecal microbiota transplantation (FMT) experiments (Fig. 4F). Compared with separated housed vitiligo mice (sep-2w) at day 14, cohoused mice (co-2w) exhibited more severe depigmentation after 4 weeks (P = 0.0317, Fig. 4I). FMT is an effective method for establishing gut microbiota. We divided pre-induced mice into FMT and control groups. We administrated fecal microbiota filtrate from vitiligo mice (donor mice) on day 35 to the FMT group while administering PBS to the control group (Fig. 4F). After 35 days of colonization, 16S rRNA analysis revealed that after FMT, the microbiota composition in the FMT group mice was highly like that of donor mice (Fig. 4H). In addition, the mice in the FMT group showed more severe depigmentation than the control group, although the difference was not significant (Fig. 4J). Together, these results indicated that gut microbiota in vitiligo mice with severe depigmentation might exacerbate vitiligo-like depigmentation in mildly affected mice. However, colonization of microbiota from vitiligo mice into pre-induced mice did not significantly exacerbate the vitiligo-like depigmentation phenotype, suggesting that gut microbiota may play a more significant role in promoting disease progression after autoimmune triggers.

Next, we attempted to alleviate mice with vitiligo-like depigmentation by correcting gut dysbiosis through probiotic treatment (Fig. 4G). Based on previous results, we initiated treatment on day 35, when the autoimmune phenotype was most pronounced. The results showed that compared with vehicle treatment, oral administration of the probiotic supplement BIFICO® for 4 weeks significantly inhibited the progression of vitiligo-like depigmentation. Starting from the third week of treatment (day 49), the depigmentation area in treated mice was controlled to less than 30% compared with controls (P = 0.0159, Fig. 4K). These findings preliminarily suggested that modulating gut microbiota imbalance could inhibit the progression of depigmentation and that probiotics had potential therapeutic effects on the vitiligo-like phenotype in mice.

Accumulation of hippuric acid in lesions of vitiligo mice is dependent on gut microbiota

We identified that gut microbiota could regulate oxidative stress in vitiligo skin. Next, we aimed to elucidate the mechanisms of this gut-skin axis regulation. Our focus was primarily on metabolites derived from gut microbiota, as small molecule metabolites can traverse the intestinal mucosal barrier, circulate through the bloodstream, and exert functions on the skin. Using ultraperformance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS), we quantitatively analyzed the metabolomics of feces, serum, and skin tissue from non-induced and vitiligo-induced mice (Fig. 5A and Fig. S5, A and B). Our analysis revealed similar patterns of metabolite composition between non-induced and vitiligo-induced mice (Fig. 5B). Notably, the proportions of metabolite species composition differed significantly among feces, serum, and skin. Short-chain fatty acids (SCFAs) were predominant in feces (average 64.10% in non-induced mice and 64.20% in vitiligo mice). By contrast, their proportions decreased to less than 6% in serum and skin (Table S3 to S5). Organic acids were the most abundant metabolites in serum (average 66.43% in non-induced mice and 63.35% in vitiligo mice), followed by amino acids (average 24.99% in non-induced mice and 24.37% in vitiligo mice). In the skin, amino acids predominated (average 60.40% in non-induced mice and 62.80% in vitiligo mice), followed by organic acids (average 26.21% in non-induced mice and 21.37% in vitiligo mice), with SCFAs ranking third (Fig. 5B and Table S3 to S5). These data suggested that fecal metabolite composition was determined mainly by intestinal digestion and microbial metabolism, influencing the composition of circulating metabolites, while skin metabolites showed a lower proportion of gut-related metabolites. We conducted pathway enrichment analyses using selected pathway-associated metabolite sets (SMPDB) [28] to examine differential metabolites in feces, circulation, and skin tissues of vitiligo mice. Skin metabolites exhibited significantly enriched biological functions distinct from those of fecal and circulating metabolites, notably enriching pathways related to carbohydrate metabolism such as “amino sugar metabolism,” “gluconeogenesis,” and the “Warburg effect.” The most significantly enriched pathways in circulating metabolites were the “urea cycle,” followed by the “Warburg effect,” while fecal metabolites showed significant enrichment in “alpha-linolenic acid and linoleic acid metabolism,” followed by the “urea cycle” (Fig. S5C).

Fig. 5
figure 5

Microbiota and vitiligo-related regulation of fecal, serum, and skin metabolites. A Schematic diagram illustrating the targeted quantitative metabolomics analysis conducted on fecal, serum, and skin tissue from non-induction and vitiligo mice treated with water and ABX (created with BioRender.com). B Bar chart showing the relative concentration of metabolite classes in fecal, serum, and skin tissue from non-induction (n = 6) and vitiligo mice (n = 6). C Volcano plots depicting differentially abundant metabolites from targeted quantitative metabolomics analysis of fecal (left: n = 6, 6), serum (middle: n = 6, 6), and skin tissue (right: n = 6, 6) samples from control and vitiligo mice. x-axis: − log10(FC). y-axis: − log10(P). Purple, upregulated; green, downregulated. D Venn diagram of differentially abundant metabolites from fecal (67), serum (99), and skin (40) samples, P < 0.05; intercept (8). E Heatmap of intercepted differentially abundant metabolites in skin samples from non-induction and vitiligo mice treated with water (n = 6, 6) and ABX (n = 3, 3). Each column represents a biological replicate. F Bar chart showing the relative concentration of 2-phenylpropionate, hippuric acid, and indoleacetic acid in fecal, serum, and skin samples from non-induction (n = 6) and vitiligo mice (n = 6). G Quantification of 2-phenylpropionate, hippuric acid, and indoleacetic acid in skin tissues of vitiligo mice treated with water (n = 6) and ABX (n = 3). H Quantification of 2-phenylpropionate, hippuric acid, and indoleacetic acid in fecal samples of vitiligo mice treated with water (n = 6) and ABX (n = 3). Statistical significance was determined using (G and H) Mann–Whitney U test. *P < 0.05

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Further analysis of significant differential metabolites in feces, circulation, and skin of vitiligo mice using fold change and statistical tests revealed notable increases in bile acid-related metabolites such as taurohyodeoxycholic acid (THDCA) and bromodichloroacetic acid (bDCA), with decreases in ursodeoxycholic acid (UDCA) and chenodeoxycholic acid (CDCA) compared with non-induced mice. Amino acids such as alpha-linolenic acid and hippuric acid also showed elevated concentrations (Fig. 5C,D). Given our focus on metabolites derived from gut microbiota that acted on the skin via the bloodstream, we analyzed metabolites significantly elevated in feces, circulation, and skin of vitiligo mice, identifying eight metabolites, including 2-phenylpropionate, hippuric acid, and hydrocinnamic acid. Metabolomic studies following water or ABX treatment in non-induced and vitiligo mice revealed that functional gut microbiota was essential for the increased presence of 2-phenylpropionate (P = 0.0238 in fecal and P = 0.0238 in skin), hippuric acid (P = 0.0238 in fecal and P = 0.0238 in skin), and indoleacetic acid (P = 0.0238 in fecal and P = 0.0238 in skin) in vitiligo feces and lesion skin (Fig. 5, E, G, and H). We calculated the relative concentration ratios of these three metabolites in feces, serum, and skin tissue, observing a gradual increase in hippuric acid concentration ratio from feces to serum and then to the skin in both non-induced and vitiligo mice. At the same time, ratios of 2-phenylpropionate and indoleacetic acid decreased gradually (Fig. 5F). Unlike non-induced mice, vitiligo mice exhibited a higher proportion of 2-phenylpropionate in feces (7.94% in non-induced mice and 2.99% in vitiligo mice), mainly showing a hippuric acid concentration ratio as high as 92.48% (concentration value 11.94 nmol/g) in the vitiligo skin, compared with 84.68% (concentration value 2.23 nmol/g) in non-induced mice (Fig. 5F–H and Fig. S5, D and E). These findings suggest that hippuric acid, among the vitiligo-specific metabolites regulated by gut microbiota, may play a crucial role in skin function.

Hippuric acid increases skin ROS accumulation associated with vitiligo

We sought to determine the cumulative effect of gut-associated metabolites on skin ROS levels in vivo, focusing on three candidate metabolites—2-phenylpropionate, hippuric acid, and indoleacetic acid—that were specifically elevated in the feces, circulation, and skin of vitiligo mice. To ascertain which metabolite contributes to increased ROS generation in the skin of vitiligo mice and to avoid potential artifacts from differential gut-to-circulation absorption kinetics, we administered intraperitoneal injections of 2-phenylpropionate, hippuric acid, and indoleacetic acid daily from day 12 to day 35 of modeling in both non-induced and vitiligo-induced mice (Fig. 6A). Hippuric acid treatment partially reproduced the observed ROS accumulation in diseased mouse skin (Padj = 0.0251 versus vehicle and Padj = 0.0431 versus indoleacetic acid, Fig. 6B,C and Fig. S6B). However, the results indicated that 2-phenylpropionate and indoleacetic acid had no effect on skin ROS production (Fig. 6C, Fig. S6A and S6B). Specifically, concurrent intraperitoneal hippuric acid injections during vitiligo induction increased skin ROS levels (Fig. 6B,C). By day 35, measurements of vitiligo mice’s depigmented areas revealed increased depigmentation following hippuric acid treatment (P = 0.0436, Fig. 6D). Furthermore, skin concentrations in non-induction mice treated with hippuric acid reached levels comparable to those in vitiligo mice (7.97 nmol/g, P = 0.0016, Fig. 6E).

Fig. 6
figure 6

Hippuric acid leading to microbiota-modulated skin ROS accumulation in vitiligo. A Schematic diagram illustrating the metabolite treatment protocol. Non-induction and vitiligo mice were treated intraperitoneally with 2-phenylpropionate (2-PP), hippuric acid (HA), or indoleacetic acid (IAA) daily from days 12 to 35 during the vitiligo induction procedure (created with BioRender.com). B, C Representative confocal images and quantification of mean dihydroethidium (DHE) fluorescence intensities showing reactive oxygen species (ROS) levels in tail skin sections from non-induction and vitiligo mice treated with vehicle, 2-PP, HA, or IAA, n = 4 mice/group. Scale bars, 200 µm. Mean and SEM, 3 independent experiments. D Quantification of depigmentation of the back skin from vitiligo mice treated with vehicle, 2-PP, HA, or IAA, n = 4 mice/group. Mean and SEM, 3 independent experiments. E Quantification of HA levels in skin tissues of wild-type mice treated intraperitoneally with vehicle (n = 6) and HA (n = 6). F Heatmap of ROS-related genes in the tail skin of wild-type mice treated intraperitoneally with vehicle (n = 3) and HA (n = 5). Each column represents a biological replicate. G Significant Gene Ontology (GO) terms enriched with the respective − log10(FDR) and gene percent contained. The number and percentage of genes per term are shown. H Volcano plot of differentially expressed genes (DEGs) of tail skin from non-induction versus vitiligo mice in Fig. 2 with HA-specific genes (orange labeled). Statistical significance was determined using (C and D) one-way ANOVA followed by Tukey’s post hoc test or E unpaired Student’s t-test. *P < 0.05, **P < 0.01

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Hippuric acid (HA), a benzoic acid derivative and one of the mammalian urine’s most abundant organic acids, is a crucial gut microbiota-host co-metabolite [29, 30]. Previously, we confirmed the significant elevation of hippuric acid in water-treated vitiligo mouse skin (average concentration 11.94 nmol/g, P < 0.01, Fig. 5G), which was reduced to an average of 5.62 nmol/g with ABX-treated, approaching levels in non-induction mice skin (water 2.23 nmol/g, ABX 3.39 nmol/g). This indicated the essential role of gut microbiota in elevating hippuric acid concentrations in vitiligo mice skin. Notably, intraperitoneal hippuric acid injections in non-induction mice significantly increased skin ROS accumulation (Fig. 6B,C, P < 0.05), partially replicating the phenomenon observed in vitiligo mice skin. Moreover, hippuric acid injections in vitiligo mice exacerbated ROS accumulation (P < 0.05), suggesting its role in exacerbating disease-related oxidative stress. However, we did not find any promotion of melanocyte loss or CD8+ T cell increase after exogenous hippuric acid treatment in vitiligo-induced mice (Fig. S6, C and D). This finding, in combination with the elevated hippuric acid-induced skin ROS levels (Fig. 6B,C), suggested that hippuric acid acted primarily by directly causing elevated levels of oxidative stress in skin tissues rather than by causing melanocyte loss or CD8+ T cell increase.

Next, we assessed the gene expression profile in the tail skin of non-induction mice following intraperitoneal injections of hippuric acid. Skin RNA-seq analysis demonstrated upregulation of ROS-related genes such as Tnf, Clec7a, Cybb, Itgb2, Pml, Syk, and Trim30a (Fig. 6F and Fig. S6F). GO analysis further revealed the enrichment of multiple pathways related to stimuli, stress response, and ROS regulation, including “regulation of response to stimulus, response to stress, and response to reactive oxygen species,” indicating hippuric acid’s significant role in activating oxidative stress and related pathways in the skin (Fig. 6G). Subsequently, analysis of DEGs in vitiligo versus non-induction mice skin (Fig. S2F) associated with hippuric acid revealed upregulation of ROS-related genes such as Ptgs2, Cxcl1, and Trim30a, suggesting that these genes may be regulated upstream by hippuric acid (Fig. 6H).

We also collected and analyzed serum samples from 15 patients with progressive vitiligo and 15 healthy volunteers to validate the targeted metabolite hippuric acid (Table S6). The results showed that the median concentration of hippuric acid in the vitiligo patient group was 2.4 μmol/l, significantly higher than that in the healthy volunteer group (P = 0.0186, 1.3 μmol/l, Fig. S6E and Table S6), suggesting that similar metabolic dysregulation may exist in vitiligo patients.

Vitiligo gut microbiota enhances hippuric acid levels by affecting the intestinal mucosal barrier

Given our finding that disease-state levels of hippuric acid and skin ROS accumulation depend on the presence of gut microbiota and significant differences in gut microbiota between vitiligo mice and non-induced mice, we examined intestinal tissue morphology and the expression of intestinal barrier-related proteins in vitiligo mice (Fig. 6A–C, and Fig. S6, G to I). Histological examination via HE staining revealed no significant differences or inflammation in the intestinal tissue of vitiligo mice compared with non-induced mice (Fig. 7A and Fig. S6G). However, measurement of small intestinal mucosal thickness showed a significant decrease in vitiligo mice (P = 0.0039, Fig. 7A,B), and alcian blue staining indicated a significant reduction in the number of goblet cells per unit length of the small intestine in vitiligo mice (P = 0.0003, Fig. 7A,C). We also assessed the expression of intestinal epithelial barrier-related proteins zonula occludens-1 (ZO-1) and occludin, finding no significant difference in expression levels between non-induced and vitiligo mice (Fig. S6, H and I).

Fig. 7
figure 7

Disruption of the gut–blood barrier in vitiligo aggravates HA rise in the skin and direct binding to protein NOS2 and MAPK14. A Representative histological images of the small intestine using H&E and alcian blue staining in non-induction and vitiligo mice. Scale bars, 50 µm. B Quantification of small intestine thickness in non-induction (n = 6) and vitiligo mice (n = 6). Mean and SEM. C Quantification of the number of goblet cells per unit length in the small intestine from non-induction (n = 6) and vitiligo mice (n = 6). Mean and SEM. D Intestinal permeability was assessed by measuring the percentage of fluorescent FITC-dextran (4 kDa) translocation into the circulation following oral gavage. Mean ± SEM from three independent experiments, including non-induction and vitiligo mice treated with water (n = 4) or ABX (n = 4). E Measurement of HA levels translocated into circulation 4 h post-oral gavage in non-induction and vitiligo mice treated with water and ABX, n = 4 mice/group. Mean and SEM. Padj = 0.0004 for non-induction-water versus vitiligo-water, Padj < 0.0001 for non-induction-ABX versus vitiligo-water, and Padj < 0.0001 for vitiligo-water versus vitiligo-ABX. F Schematic diagram illustrating the reverse virtual molecular docking using whole and part databases (created with BioRender.com). G Ridgeline plot showing the smoothed density distribution of binding proteins with their binding scores. H Significant Gene Ontology (GO) terms enriched with the respective − log10(FDR). I The proteins with binding scores (absolute value > 7) from the whole and part databases based on simulated molecular docking are shown. J Network diagram illustrating oxidative stress-related proteins with binding scores (absolute value > 7) from the simulated molecular docking. K Differential scanning fluorimetry (DSF) analysis showing the effects of various concentrations of hippuric acid on the melting temperature (Tm) of proteins (NOS2, MPO, and MAPK14) with protein concentrations ranging from 0 to 500 µM. L Surface plasmon resonance (SPR) analysis demonstrated the direct interaction between hippuric acid and the proteins (NOS2, MPO, and MAPK14). Statistical significance was determined using unpaired t test (B, C) or two-way ANOVA test (D, E) followed by Tukey’s post hoc test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Full size image

Previous studies have indicated that certain bacterial genes are associated with mucin degradation and disruption of the gut barrier [31,32,33]. In conjunction with the taxa identified from our mentioned 16S rRNA sequencing analysis (Fig. 4A–E), we statistically and graphically illustrated the relative abundance of mucin degradation-related taxa, as shown in Fig. S6J. We observed an increase in the relative abundance of specific taxa that degrade intestinal mucin in vitiligo mice, particularly c_Clostridia and g_Ruminococcus. These findings suggested that the abnormal gut microbes present in the vitiligo model might contribute to the compromised integrity of the intestinal barrier. These results suggested that the intestinal epithelial barrier was not significantly compromised in vitiligo, consistent with the absence of inflammation observed in histological examinations. However, reducing goblet cells in the small intestine of vitiligo mice may weaken the mucosal barrier due to reduced mucus secretion. Moreover, we observed high gut permeability in water-treated vitiligo mice, whereas the barrier function in ABX-treated vitiligo mice was comparable to that of non-induced mice, whether treated with water or ABX (Fig. 7D). Subsequently, we tested the translocation rate into the bloodstream following oral administration of hippuric acid in mice (Fig. S6K). We found that the translocation rate of hippuric acid was highest in vitiligo mice, significantly higher than in vitiligo mice and non-induction mice, both treated with ABX (Fig. 7E). This result might be due to a weakened mucosal barrier associated with gut microbiota under vitiligo conditions rather than impaired intestinal epithelial barrier function.

Identification of NOS2 and MAPK14 as direct hippuric acid-binding proteins

To elucidate the potential molecular mechanisms by which hippuric acid induces oxidative stress and significant ROS accumulation in the skin, we initially employed reverse virtual molecular docking to predict proteins that may interact with hippuric acid. Utilizing the reverse virtual screening platform established by Zhang et al. [34] and protein databases (whole and partial databases), we conducted calculations (Fig. 7F). Each database yielded the top 500 proteins based on their potential binding scores (higher absolute scores indicating stronger binding), concentrated within the range of − 7 to − 8 (Fig. 7G). We then selected proteins from both databases with absolute values greater than 7 and subjected them to GO analysis. Surprisingly, we found enrichment in multiple pathways related to oxidative stress and ROS (Fig. 7H), consistent with our earlier observations of hippuric acid significantly increasing ROS levels in the skin. We also conducted a protein–protein interaction (PPI) analysis of ROS-related proteins among the screened candidates. We identified mitogen-activated protein kinase 14 (MAPK14), nitric oxide synthase 2 (NOS2), and myeloperoxidase (MPO) at central network positions (Fig. 7I,J), indicating significant associations with other ROS-related proteins.

Given our hypothesis that hippuric acid, as a gut microbiota-related metabolite, directly enhances intracellular ROS levels in the skin, we selected NOS2, MPO, and MAPK14—known for their crucial roles in oxidative stress—as candidate proteins to assess their binding capacity with hippuric acid. NOS2 catalyzes nitric oxide (NO) production, pivotal in oxidative stress [35], while MPO links inflammation and oxidative stress, catalyzing various ROS derivatives and leading to redox imbalance [36]. MAPK14, a member of the mitogen-activated protein kinase family, activates during oxidative stress and participates in diverse biological functions [37]. We used differential scanning fluorimetry (DSF) to assess molecular-level interactions. The results indicated that hippuric acid binds to NOS2, MPO, and MAPK14 to varying degrees (Fig. 7K). Notably, the ΔTm for HA and MAPK14 increased dose-dependently, reaching up to 10.89 °C, suggesting strong binding affinity. The ΔTm for HA with NOS2 and MPO also exhibited dose-dependent increases, with maxima of 2.94 °C and 3.98 °C, respectively (Table S7). To further validate the interactions between NOS2, MPO, MAPK14, and hippuric acid, we used surface plasmon resonance (SPR) to assess molecular-level interactions. In kinetic experiments, SPR found that NOS2 had a binding affinity (KD) of 32.05 μM for hippuric acid, while MAPK14 had a KD of 14.24 μM (Fig. 7L, Fig. S7C and Table S8). No binding was detected between MPO and hippuric acid (Fig. 7L). These results preliminarily confirmed that hippuric acid could directly bind to NOS2 and MAPK14.

Discussion

Vitiligo is an autoimmune skin disorder characterized by depigmented patches, with oxidative stress playing a crucial role in its pathogenesis. Although several studies have implicated the gut microbiota in vitiligo, the mechanisms by which gut microbiota mediate disease progression remain unclear. In this study, we established a vitiligo mouse model and modulated gut microbiota through ABX treatment, cohousing experiments, and FMT to investigate the contribution of gut microbiota to vitiligo-related depigmentation and skin oxidative stress. Moreover, we identified abnormal accumulation of ROS in the skin and gut dysbiosis in vitiligo mice. Notably, we found that probiotic supplementation significantly alleviated the progression of vitiligo-like depigmentation. By combining quantitative metabolomics analysis of feces, serum, and skin tissues with experiments manipulating microbial communities, we observed the accumulation of hippuric acid in the skin of vitiligo mice. This phenomenon was attributed to the dysbiosis of vitiligo-specific gut microbiota and increased intestinal permeability.

We reanalyzed the skin expression profiles of vitiligo patients and confirmed the upregulation of oxidative stress genes in lesional skin. Additionally, we observed abnormal accumulation of ROS and mitochondrial damage in the melanocytes of the skin of vitiligo mice, which was absent in the mice treated with ABX. This result suggested that vitiligo-specific oxidative stress was linked to the upregulation of ROS-related genes in vitiligo mice. Transcriptomic analysis revealed significant differences between the skin of vitiligo mice and controls, with many ROS-related genes not similarly upregulated in ABX-treated vitiligo mice. These findings indicated that abnormal gut microbiota in vitiligo dictate the oxidative stress status in the skin.

Current research underscores the significant role of oxidative stress in both the initiation and autoimmune progression of vitiligo. Studies have shown that oxidative stress-induced mitochondrial dysfunction in melanocytes is a critical contributor to melanocyte death. Excessive accumulation of ROS in melanocytes alters their structure, induces various oxidation products, including oxidized proteins and glycation products [38, 39], and triggers endoplasmic reticulum stress and the unfolded protein response. These processes disrupt DNA and other cellular structures, ultimately leading to melanocyte apoptosis and programmed cell death [9, 40,41,42]. Our findings demonstrated that the vitiligo mouse model mimics the oxidative stress processes observed in human vitiligo skin, with clear evidence of mitochondrial damage in melanocytes. Notably, melanocyte damage was significantly attenuated in vitiligo mice treated with ABX, highlighting the critical role of gut microbiota in developing vitiligo.

Therefore, we aimed to comprehensively understand the mechanistic relationship between gut microbiota and oxidative stress in vitiligo lesional skin. While previous studies have suggested the potential of gut microbiota modulation in regulating vitiligo [19], to our knowledge, our study is the first study to combine quantitative metabolomics and 16S rRNA sequencing. Through experiments including antibiotic-induced depletion of gut microbiota, cohousing trials, FMT, and assessment of intestinal-related markers, we detailed how gut microbiota influence depigmentation phenotypes and oxidative stress in vitiligo-like mice. Our vitiligo mouse model effectively simulated the autoimmune progression, skin depigmentation, and skin oxidative stress observed clinically, alongside dysbiosis resembling that found in vitiligo-specific gut microbiota. Microbiota depletion notably reduced depigmentation and oxidative stress levels in vitiligo-like mice, whereas cohousing exacerbated depigmentation. These findings highlight the significant, albeit often overlooked, role of gut microbiota in vitiligo-related autoimmune processes and reinforce the therapeutic potential of microbiota modulation. Additionally, the observed alleviation of depigmentation in vitiligo mice with probiotic supplementation provides strong evidence for the therapeutic potential of probiotics.

However, our study has limitations. Future research should use germ-free mice to determine the specific effects of bacterial strains on vitiligo, whether acting alone or in synergy. More in-depth mechanistic studies are needed to elucidate these interactions fully. We employed an immune-induced mouse model to effectively simulate the pathophysiological state of vitiligo. The pathogenesis of vitiligo is intricate and encompasses a multitude of factors, including genetic predispositions, environmental influences, and trauma, all of which contribute to the onset of autoimmunity [1,2,3, 22]. By concentrating on the immune-induced vitiligo model, our research specifically targets the autoimmune pathways linked to vitiligo and their critical interaction with the gut microbiota. It is crucial to integrate this model with genetic and other diverse mouse models to thoroughly investigate the profound connections between vitiligo and gut microbiome across various mechanistic pathways. Moreover, our study utilized a probiotic intervention (BIFICO®) that contains multiple strains, making it difficult to determine which specific bacteria have protective effects. Our results indicated a significant reduction in the relative abundance of probiotics such as Akkermansia and Lactobacillus reuteri in the gut of vitiligo mice. Therefore, it is important to explore the potential use of single-strain probiotics (e.g., Akkermansia muciniphila and Lactobacillus spp.) in future research to address oxidative stress related to vitiligo.

By integrating quantitative metabolomics of feces, serum, and skin, we systematically delineated differences in important metabolite compositions among these compartments in vitiligo-like mice, focusing mainly on the impact of microbiota depletion on vitiligo-associated differential metabolites. We identified hippuric acid as a metabolite highly correlated with vitiligo-related oxidative stress regulation. Given that various transport systems and receptors in endothelial cells facilitate the penetration of different metabolites into the skin, a concentration gradient of specific metabolites is established and maintained between circulation and skin. Only hippuric acid replicated the excessive ROS accumulation observed in vitiligo-affected skin. Considering the observed weakening of the mucosal barrier in the intestines of vitiligo mice, the hypothesis that gut microbiota-related metabolites and other factors influence skin function is histologically supported. Hippuric acid is a host-gut microbiota co-metabolite [29, 30], and our metabolomic results showed simultaneous increases in hippuric acid concentrations in the feces, serum, and skin tissue of vitiligo mice. However, the elevated skin concentrations of hippuric acid in vitiligo may not solely originate from gut microbiota, which supports the hypothesis that weakened mucosal barriers in vitiligo mouse intestines facilitate easier penetration of excessive hippuric acid into skin tissues. Our findings show significant decreases in hippuric acid content in the feces, plasma, and skin of ABX-treated vitiligo-like mice. Additionally, we collected preliminary serum samples from a small cohort of vitiligo patients, which initially validated this conclusion. Our study had limitations. Further mechanistic insights can be obtained through single-cell RNA sequencing of melanocytes to determine whether hippuric acid-related oxidative stress directly contributes to their depletion. We have yet to identify the specific gut bacteria that play a crucial role in metabolizing hippuric acid in the context of vitiligo. To advance our understanding of this complex relationship, future research utilizing gnotobiotic or mono-colonized mouse models is essential. Such studies will provide critical insights into the direct influence of specific gut microbes on the dysregulation of hippuric acid, potentially paving the way for innovative therapeutic strategies.

The chronic nature of vitiligo progression, affecting widespread skin areas including apparent sites like the face and hands, imposes substantial economic and psychological burdens on patients. Aside from localized skin therapies and surgical interventions, effective treatment strategies for progressive vitiligo remain limited. Our findings preliminarily suggested that modulation of gut microbiota, particularly through probiotic supplementation, significantly slowed the disease progression and depigmentation process. Additionally, our results indicate that hippuric acid has the potential to serve as a novel diagnostic biomarker for vitiligo and as a therapeutic target within its metabolic pathway. However, these findings require validation in large-scale human samples, particularly the integration of disease stage, treatment history, dietary intake, systemic inflammation, and other pertinent factors to thoroughly assess the levels and potential roles of disease-specific metabolites in vitiligo patients and further in-depth research into the regulatory mechanisms of its metabolic pathway. Our study demonstrated that hippuric acid can directly bind to NOS2 and MAPK14 through molecular interaction experiments. To further elucidate this relationship, future in vivo and ex vivo biological studies, including mutagenesis approaches, are imperative to disrupt the hippuric acid binding sites on NOS2 and MAPK14. Future experiments could include using genetically modified mouse models with NOS2 or MAPK14 knockouts to investigate the potential role of host genetic factors in gut dysbiosis and oxidative stress in vitiligo.

Conclusions

In conclusion, our study identifies and validates the critical role of gut microbiota and its derivative metabolite, hippuric acid, in mediating oxidative stress within vitiligo skin. Importantly, these findings suggest that utilizing probiotics to improve vitiligo-associated gut microbiota dysbiosis or targeting hippuric acid synthesis can offer a promising intervention strategy for the personalized treatment of vitiligo patients. This research can potentially lead to the development of novel, more effective treatments for vitiligo.

Materials and methods

Sample collection from vitiligo patients

Advanced vitiligo patients (n = 15, 8 males and 7 females) and healthy volunteers (n = 15, 12 males and 3 females) were recruited from the Department of Dermatology at the Air Force Medical Center, Fourth Military Medical University. All patients had developed new lesions within the past 3 months and exhibited significant disease activity for over 3 months, with a median disease duration of 12 months. Peripheral venous blood samples were drawn from participants in the morning following admission, using standard venipuncture procedures. The blood samples were processed to prepare serum samples for downstream validations. Detailed basic information about the participants is provided in table S6.

Vitiligo mouse model of melanoma–Treg induction

We employed a melanoma–Treg-induced protocol developed by Xu et al. [22]. In brief, we used B16F10 melanoma inoculation coupled with regulatory T (Treg) cell depletion to activate endogenous auto-reactive CD8+ T cells targeting epidermal melanocytes, thereby inducing vitiligo in mice. First, 8–9-week-old C57BL/6 J mice were intradermally inoculated with 2 × 105 B16F10 melanoma cells in the dorsal skin of the right flank (day 0). CD4 depletion antibodies (cat# BE0003-1, BioXcell) were then injected on days 4 and 10. Only mice that developed primary tumors were used for further analysis. Primary tumors were surgically removed on day 12. No spontaneous tumor metastasis was observed in this B16F10 cell line, and mice with recurrence of primary tumors after surgery were excluded from further study.

Detection of reactive oxygen species in mouse tail skin

Reactive oxygen species (ROS) was detected in mouse tail skin using Dihydroethidium (DHE, cat#HY-D0079, MedChemExpress). Live tail skin tissue was embedded in 5% agarose gel and fixed in an automatic vibrating microtome (7000smz-2 Vibratome, Campden Instruments, England). Sections were cut longitudinally at 100 μm. Samples were washed three times before DHE staining. For DHE staining, samples were incubated for 30 min with 10 μM DHE at 37 °C in the dark. After staining, the slides were washed in PBS for 30 s and mounted with 50% glycerol containing DAPI. All procedures were completed within 6 h. The sections were then imaged immediately with identical settings using the PI (red) channel on the FV3000 confocal laser scanning microscope (Olympus, Japan). Image quantification was performed using ImageJ software (version 1.54d). For quantification of total ROS levels, pixel intensities of Z stacks (20 µm) were used. The mean of the summed DHE intensities averaged from each tissue was used for statistical analysis. The mean of the summed pixel intensities averaged from each tissue and normalized to the area of selection was used for statistical analysis.

Bulk RNA-seq and bioinformatic analysis

Total RNA was extracted using the Trizol reagent kit (Invitrogen) according to the manufacturer’s protocol. RNA quality was assessed on an Agilent 2100 Bioanalyzer (Agilent Technologies, USA) and verified using RNase-free agarose gel electrophoresis. After extraction, eukaryotic mRNA was enriched using Oligo (dT) beads. The enriched mRNA was then fragmented into short fragments using a fragmentation buffer and reverse-transcribed into cDNA with random primers. Second-strand cDNA was synthesized using DNA polymerase I, RNase H, dNTP, and buffer. The cDNA fragments were purified with the QiaQuick PCR extraction kit (Qiagen, German), end-repaired, poly(A)-tailed, and ligated to Illumina sequencing adapters. The ligation products were size-selected by agarose gel electrophoresis, PCR-amplified, and sequenced using the Illumina NovaSeq 6000 (USA) by Gene Denovo Biotechnology Co. (China).

Differential expression analysis was performed with DESeq2 v.1.32.0. Differentially expressed genes (DEGs) were identified using the DESeq2 model with an adjusted P < 0.05 (Wald test) and an absolute fold change > 1.5. Heatmaps were generated using the R package pheatmap v.1.0.8, calculated from scaled (z-scores) normalized read counts of DEGs with hierarchical clustering of the rows (complete method). Gene Ontology (GO) enrichment analysis of the genes was conducted using goseq v.1.44.0. Overenriched GO categories were extracted using a 0.1 false discovery rate (FDR) cutoff. Lists of ROS-related genes were extracted from Mouse and Human MSigDB v2023.2.Hs and MSigDB v2023.2.Mm. Other bioinformatic analyses were performed using Omicsmart, a dynamic real-time interactive online platform for data analysis (http://www.omicsmart.com).

Statistical analysis

Statistical analyses were performed using GraphPad Prism (v.10.1.2), SPSS (v.29.0.1.0), R (v.3.5.1), and RStudio (v.1.1.456). Data are presented as means ± standard error of the mean (SEM) or as individual data points with bar graphs representing means and SEM. Significant differences between the two groups were calculated using a two-tailed unpaired Student’s t-test or Mann–Whitney U test, with a significance threshold of P < 0.05. For comparisons among three or more groups, One-way ANOVA followed by Tukey’s post hoc test, Kruskal–Wallis followed by Dunn’s post hoc test, or Two-way ANOVA followed by Tukey’s post hoc test was employed, with a significance threshold of P < 0.05. The chi-square test was used to compare the distribution of categorical variables between groups. The specific statistical test used for each experiment is detailed in the corresponding figure legend, along with the sample size and number of replicates. All data presented are representative of at least three independent experiments.

Data availability

Mouse tail skin bulk RNA-seq data are available at the Gene Expression Omnibus (GEO) under accession number GSE273242. The 16S rRNA sequencing data of feces from vitiligo mice have been deposited on NCBI’s SRA database (BioProject PRJNA1138542).

Abbreviations

Treg:

Regulatory T cell

ROS:

Reactive oxygen species

MPO:

Myeloperoxidase

NOS2:

Nitric oxide synthase 2

PCA:

Principal component analysis

GO:

Gene ontology

DEGs:

Differentially expressed genes

ABX:

Antibiotics

DHE:

Dihydroethidium

FMT:

Fecal microbiota transplantation

UPLC-MS/MS:

Ultraperformance liquid chromatography coupled to tandem mass spectrometry

SCFAs:

Short-chain fatty acids

SMPDB:

Small molecule pathway database

THDCA:

Taurohyodeoxycholic acid

bDCA:

Bromodichloroacetic acid

UDCA:

Ursodeoxycholic acid

CDCA:

Chenodeoxycholic acid

ZO-1:

Zonula occludens-1

PPI:

Protein-protein interaction

MAPK14:

Mitogen-activated protein kinase 14

DSF:

Differential scanning fluorimetry

SPR:

Surface plasmon resonance

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Acknowledgements

We acknowledge the assistance of Prof. Chunying Li and Prof. Shuli Li of the Department of Dermatology of Xijing Hospital at the Fourth Military Medical University, which helped provide B16F10 cell line for this research. We are grateful for the valuable and constructive suggestions given by Dr. Jianru Chen of the Department of Dermatology of Xijing hospital at the Fourth Military Medical University.

Funding

This article was supported by the National Natural Science Foundation of China (Grant Nos. 82203909 and 82100513), the Postdoctoral Fellowship Program of CPSF (Grant No. GZC20233592), the support funding from Fourth Military Medical University (2020AXJHHJ), the Youth Talent Support Program of Air Force Medical Center (Grant No. 22BJQN003), the Boost Program for Young Doctor of Air Force Medical Center (Grant No. 21ZT10), and the Science and Technology Foundation of Liaoning Province guided by the central government in 2023 (Grant No. 2023JH6/100100034).

Author information

Author notes

  1. Qingrong Ni and Lin Xia contributed equally to this work.

Authors and Affiliations

  1. Department of Dermatology, Air Force Medical Center, Fourth Military Medical University, Beijing, 100142, China

    Qingrong Ni, Ye Huang, Xiaoying Yuan, Weijie Gu, Yijin Wang & Hong Cai

  2. Department of Neurobiology, Basic Medical Science Academy, Fourth Military Medical University, Xi’an, 710032, China

    Qingrong Ni, Meng Nian, Shengxi Wu & Jing Huang

  3. Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, Shenyang, 110016, China

    Lin Xia

  4. Department of Orthopedics, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China

    Yueqi Chen

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Contributions

This study was designed by Q.N., J.H., and H.C. Data were collected by Q.N., L.X., Y.H., X.Y., W.G., Y.W. and M.N. Funding was acquired by Q.N. and L.X. The original draft of the manuscript was written by J.H., Q.N. and L.X. Reviewing and editing of the manuscript was done by S.W., J.H., H.C., and Y.C. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Qingrong Ni, Hong Cai or Jing Huang.

Ethics declarations

Ethics approval and consent to participate

The experimental animals used in this study were adult male mice with a pure C57BL/6 J background, acquired from the Experimental Animal Center of the Fourth Military Medical University. All procedures were approved by the Fourth Military Medical University and adhered to all relevant ethical regulations. The mice were housed under a 12-h light/dark cycle at 22–25 °C with free access to water and food in environmentally controlled conditions.

The UPLC-MS/MS analysis and ELISA analysis of serum from both advanced vitiligo patients and healthy volunteers were reviewed and approved by the Ethics Committee of the Air Force Medical Center (No. 2022–188-PJ01). All participants provided written informed consent for sample collection and data analysis. Prior to giving consent, participants were informed about the goals and related experimental procedures of the study.

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All authors have approved the publication of this manuscript.

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The authors declare no competing interests.

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Supplementary Information

40168_2025_2102_MOESM1_ESM.pdf

Additional file 1. Supplementary materials and methods. In this file are reported the detailed methods used in this study: 1) Cell culture; 2) Preparation of mouse samples; 3) Measuring depigmentation; 4) H&E staining; 5) AB staining; 6) IHC analysis; 7) Elisa; 8) Transmission electron microscope (TEM); 9) Whole-mount immunofluorescence staining; 10) 16S rDNA sequencing and bioinformatic analysis; 11) Targeted quantitative metabolomics; 12) Cocktail antibiotic treatment; 13) Co-housing experiment; 14) Fecal microbiota transplantation (FMT); 15) Supplementation with probiotics; 16) Supplementation with metabolites; 17) In vivo intestinal permeability test; 18) Reverse virtual screening calculation and bioinformatic analysis; 19) Differential Scanning Fluorimetry (DSF); 20) Surface Plasmon Resonance (SPR) [22, 27, 43,44,45,46,47,48].

40168_2025_2102_MOESM2_ESM.pdf

Additional file 2. Fig. S1. Lesional skin signature in vitiligo patients. Heatmap of the top 190 differentially expressed genes (DEGs) in lesional versus non-lesional skin. Each column represents a biological replicate, and each row represents a gene. Fig. S2. Tail Skin transcriptome signature in the melanoma-Treg-induced vitiligo mouse model. (A) Representative images of tail skin hair from mice at Day 0, Day 35, and Day 60 post melanoma/Treg-induced vitiligo induction procedure. (B) Heatmap of the top 150 differentially expressed genes (DEGs) in the tail skin of non- induction (n = 9) and vitiligo (n = 9) mice. Each column represents a biological replicate. Fig. S3. Tail skin signature between vitiligo mice treated with water and ABX. Heatmap of the top 150 differentially expressed genes (DEGs) in the tail skin of non- induction (n = 6) and vitiligo (n = 6) mice. Each column represents a biological replicate. Fig. S4. Gut dysbiosis signature of vitiligo mice after melanoma-Treg-induced vitiligo model. (A) Bar chart showing the relative abundance of microbiota at the family level in fecal samples from non-induction (n = 6) and vitiligo (n = 9) mice. Each column represents a biological replicate. (B) Krona [43] circle diagram illustrating the mean percent of taxa at the different levels of Clostridiales in non- induction (n = 6) and vitiligo (n = 9) mice. (C) Linear discriminant analysis effect size (LEfSe) [27] analysis showing significantly different bacterial taxa at various levels in control mice relative to vitiligo mice. (D) Relative abundance of differentially abundant taxa at the phylum level for control (n = 6) and vitiligo (n = 9) mice. Mean and SEM. (E) Relative abundance of differentially abundant taxa at the genus level for control (n = 6) and vitiligo (n = 9) mice. Mean and SEM. Statistical significance was determined using (E and F) Mann–Whitney U test. *P < 0.05, **P < 0.01, ***P < 0.001. Fig. S5. Metabolomics signature of vitiligo mice in fecal, serum, and skin after melanoma-Treg-induced vitiligo model. (A and B) Multivariate control chart (MCC) and principal component analysis (PCA) of metabolites in fecal, serum, and skin tissues from non-induction and vitiligo mice treated with water and ABX. Batch correction with pooled quality controls (QCs) was performed to normalize batch-to- batch variability before further data analysis. Each dot in the MCC represents an individual sample. (C) Pathway enrichment analysis bar plot using pathway- associated metabolite sets (SMPDB): comparison of non-induction versus vitiligo mice in fecal, serum, and skin tissues. (D and E) Quantification of 2- phenylpropionate, hippuric acid, and indoleacetic acid in fecal and skin tissues of non- induction (n = 6) and vitiligo (n = 6) mice. Mean and SEM. Statistical significance was determined using (D and E) Mann–Whitney U test. *P < 0.05, **P < 0.01. Fig. S6. Hippuric acid-related oxidative stress genes signature and gut-blood barrier signature in vitiligo mice. (A) Representative confocal images and quantification of mean dihydroethidium (DHE) fluorescence intensities showing reactive oxygen species (ROS) levels in tail skin sections from non-induction and vitiligo mice treated with 2-phenylpropionate (2-PP) or indoleacetic acid (IAA), n = 4 mice/group. Scale bars, 200 µm. (B) Combined data showing the quantification of mean DHE fluorescence intensities showing ROS levels in tail skin sections from non-induction and vitiligo mice treated with vehicle, 2-PP, HA, or IAA. Data were pooled from 3 independent experiments with n = 4 mice per group. (C) Representative wholemount immunofluorescent staining of tail epidermis images of vitiligo mice showing Melan-A+ melanocytes and CD8+ T cells treated intraperitoneally with the vehicle and HA daily from Day 12 to Day 35 during the vitiligo induction procedure. (D) Quantification of Melan-A+ melanocytes and CD8+ T cells in the epidermis of vitiligo mice treated intraperitoneally with vehicle (n = 4) and HA (n = 4). (E) Quantification of HA concentration in human serum samples from healthy volunteers (n = 15) and vitiligo patients (n = 15) using targeted metabolite analysis. (F) Heatmap of ROS-related genes in the tail skin of wild-type mice treated intraperitoneally with vehicle (n = 3) and HA (n = 5). Each column represents a biological replicate. (G) Representative histology of the large intestine using H&E and alcian blue staining of non-induction and vitiligo mice. Scale bars, 50 µm. (H and I) Representative immunohistochemical staining images and quantification of mean H-score of Zonula Occludens-1 (ZO-1) and Occludin in the small intestine of non-induction (n = 3) and vitiligo mice (n = 4). Scale bars, 50 µm. (J) Relative abundance of gut mucin degradation-related taxa for non-induction (n = 9) and vitiligo (n = 9) mice. Mean and SEM. (K) Schematic diagram illustrating the translocation of hippuric acid into the circulation 4 h post-oral gavage in non-induction and vitiligo mice treated with water and ABX. Statistical significance was determined using using (B) one-way ANOVA followed by Tukey’s post-hoc test, using (C and D) unpaired Student’s t-test, using (I) Mann–Whitney U test and. *P < 0.05, **P < 0.01, ***P < 0.001. Fig. S7. Quality control results and steady-state analysis of proteins combined with hippuric acid for DSF and SPR. (A) Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis image showing myeloperoxidase (MPO), mitogen-activated protein kinase 14 (MAPK14[1] ), and nitric oxide synthase 2 (NOS2) protein quality. (B) The bicinchoninic acid (BCA) protein assay standard curve was used to determine protein concentrations. (C) Steady-state analysis based on response values detected at equilibrium. The fitted saturation curves demonstrate that as the concentration of the small molecule increases, the response values for MPO and NOS2 proteins also increase, indicating an increased amount of ligand captured by the proteins. CE: This citation is not found in floats.Please check if it is citation or not and proceed further

40168_2025_2102_MOESM3_ESM.pdf

Additional file 3. Supplementary Table S1. Group information of GSE65127 and GSE75819. Table S2. Relative abundance of vitiligo mice and no-induction mice with ABX treated. Table S3. The mean concentration of fecal metabolite classes in vitiligo and no-induction mice via targeted qualified metabolomics (nmol/g). Table S4. The mean concentration of serum metabolite classes in vitiligo and no-induction mice via targeted qualified metabolomics (μmol/l). Table S5. The mean concentration of skin metabolite classes in vitiligo and no-induction mice via targeted qualified metabolomics (nmol/g). Table S6. Baseline characteristics of vitiligo patients versus healthy volunteers. Table S7. Tm Values of Proteins (NOS2, MPO and MAPK14) Bound to Hippuric Acid. Table S8. Affinity data of proteins (NOS2 and MAPK14) with small molecule Hippuric acid.

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Ni, Q., Xia, L., Huang, Y. et al. Gut microbiota dysbiosis orchestrates vitiligo-related oxidative stress through the metabolite hippuric acid. Microbiome 13, 112 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40168-025-02102-0

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Microbiome

ISSN: 2049-2618

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