Introduction

AD is a common, chronic inflammatory skin disease, and patients with AD typically exhibit recurrent eczema and severe itching of the skin1. In the United States, approximately one-quarter of adults with AD develop symptoms in adulthood2. The pathogenesis of AD is quite elusive and complex. Some suspect that this may be caused by a combination of genetic and environmental factors that lead to skin barrier dysfunction and abnormal immune responses3,4. Metabolites are intermediates or end products of metabolic reactions that affect disease pathogenesis. Recent studies have shown that AD may be associated with metabolic disorders, including central obesity, diabetes, dyslipidemia and hypertension5,6.

Amino acid metabolites are related to the onset of AD. Tryptophan metabolites facilitate the resolution of skin inflammation in AD by restoring the barrier function of the epithelium and regulating immune and inflammatory responses by modulating the activation, survival, proliferation and differentiation of immune cells7. Bifidobacterium longum-mediated tryptophan metabolism relieves AD symptoms by activating the AHR-driven immune response8.

Metabolites often play a role in the immune response by affecting related inflammatory factors and inflammatory proteins. An association between metabolism and immunity has been reported since the 1960s9. In recent years, some studies have further demonstrated the interaction between metabolic pathways and the immune response10. For example, glutamate participates in multiple immune responses by promoting the polarization of macrophages in response to IL-4 stimulation11.

Circulating inflammatory proteins participate in the pathogenesis of various diseases such as autoimmune diseases by mediating abnormal inflammatory responses. Therefore, by analyzing the genetic variants associated with protein abundance, protein quantitative trait loci (pQTLs) could be used to identify proteins associated with the development of AD.

The mechanism by which serum metabolites affect the progression of AD at the genetic level has not been investigated. MR is a statistical method that utilizes genetic variants as instrumental variables (IVs) to test for causal relationships between exposures and outcomes12. Through MR and mediation analysis, we explored the genetic causality between serum metabolites and AD and revealed the underlying mechanism by which serum metabolites influence AD from the inflammatory protein perspective.

Methods

Study design

Figure 1 shows the detailed flowchart of this study.

Fig. 1
figure 1

Research process for identifying metabolites and inflammatory proteins causally associated with AD. Schematic diagram in the lower right corner: the design of mediation Mendelian randomization analyses. First of all, a two-sample MR was performed to investigate the causal relationships between blood metabolites (exposure) and atopic dermatitis (outcome). Secondly, inflammatory proteins (mediator) were selected for subsequent mediation analyses. And lastly, the two-step MR analysis was conducted to detect potential mediating inflammatory proteins (Step 1, the effect of blood metabolites on inflammatory proteins; Step 2, the effect of inflammatory proteins on atopic dermatitis). The instrumental variables used for MR need to satisfy 3 assumptions. First, the genetic variants must be robustly associated with blood metabolites. Second, the genetic variants must not associate with any confounders. Third, the genetic variants only affect the outcome through the exposure and not through any direct casual pathway. Abbreviations: MR, Mendelian Randomization; AD, Atopic dermatitis; SNP, Single nucleotide polymorphism; MAF, Minor allele frequency.

Full size image

Exposure data and IV selection

The genetic variants for 91 circulating inflammatory proteins were obtained from a GWAS involving 14,824 European ancestry participants13. In the ideal case, SNPs with a P value < 5e−8 should be selected for MR analysis. When only a few SNPs were available, we utilized a more relaxed threshold with an SNP P value < 1e−5. To identify independent SNPs, we performed LD pruning with a window size of 10,000 kb and r2 threshold of 0.001 in PLINK and used the European 1000 Genomes Project as a reference panel14. Because the P value threshold used in the significance test appeared loose, we set the following criteria to obtain more rigorous SNPs significantly associated with exposure: F statistics > 10 and minor allele frequency (MAF) > 0.01, which provides sufficient information MR analysis15. Finally, we excluded the SNPs significantly associated with outcome.

We obtained the human metabolome data from a large GWAS with 8,299 participants, which included 309 metabolite ratios and 1091 blood metabolites16. The exposure screening criteria used for MR causal analysis were as follows: (I) genome-wide significant association (P value < 1e−5); (II) F statistics > 10 and MAF > 0.01 were used to correct weak IV bias; (III) kb = 500 and r2 = 0.1; and (IV) relevant SNPs significantly associated with outcome were removed.

The GWAS data of immune cells were obtained from GWAS catalog (GCST90001391–GCST90002121) including 731 immune immunophenotypes17.When calculating the causal relationship between immune cells and AD, the exposure screening criteria used for MR causal analysis were as follows: (I) genome-wide significant association (P value < 5e−8); (II) F statistics > 10 and MAF > 0.01 were used to correct weak IV bias; (III) kb = 10,000 and r2 = 0.001; and (IV) relevant SNPs significantly associated with outcome were removed.

Outcome data and GWAS meta-analysis

To comprehensively evaluate the heritable loci for AD in Europeans. We conducted a meta-analysis of two GWAS datasets of AD using METAL software: FinnGen (n case = 13,473; n control = 336,589) and the UK Biobank (n case eur = 2904; n control eur = 412,489)18,19.

Mendelian randomization analysis

We utilized the TwoSampleMR (R package, v0.5.8) package to perform MR analysis between exposures and outcomes. The random-effect inverse variance–weighted (IVW) method and MR-Egger were our main analysis methods, and P values < 0.05 were considered to indicate statistical significance. The random-effect IVW method allows for heterogeneity for SNPs20.

For sensitivity analysis, heterogeneity was assessed by Cochran’s Q test, and a P value > 0.05 is indicative of no heterogeneity. Next, we utilized MR-PRESSO21 and MR-Egger regression tests to detect horizontal pleiotropic effects with MRPRESSO (R package, v1.0) package. The MR-PRESSO global test can detect horizontal pleiotropy. The MR-PRESSO outlier test was used to remove outliers and correct for horizontal pleiotropy. This process was repeated until all the statistical tests were not significant with a P value > 0.05. After removing the pleiotropic SNPs, the remaining SNP list was used for further MR analysis. Leave-one-out analysis was used to reveal whether a single SNP influenced the MR results. In addition, we used the PhenoScanner database to investigate whether the SNPs were associated with confounders (including nitrogen dioxide air pollution, particulate matter air pollution (PM10), particulate matter air pollution (PM2.5), nitrogen dioxide air pollution, carbon monoxide and ultraviolet radiation, etc.) and removed potentially pleiotropic SNPs22. This analysis is conducted using the following parameters: the catalog was selected as GWAS, and the r2 was set to 0.8.

Mediation analysis

For metabolites that causally associate with both AD and inflammatory proteins, we conducted a mediation analysis to quantify the effects of these metabolites on AD through inflammatory proteins. The “total” effect of exposure on the outcome encompasses both “direct” effects and any “indirect” effects mediated through one or more intermediary variables. In this study, the total effect was obtained through a standard univariate MR analysis, also known as the primary MR. To disentangle direct and indirect effects, we utilized the results from a two-step MR approach, selecting the product method to estimate the β value for the indirect effect and the Delta method to estimate the standard error (SE) and 90% confidence interval (CI). We constructed a two-sample MR framework by combining two primary MR analyses (for which the metabolome was exposed and the AD was the outcome). Two-step MR (the first step: the metabolome served as the exposure, inflammatory proteins were the outcome; the second step: inflammation was the exposure, and AD was the outcome). Mediation analysis suggested that the total effect of exposure on outcomes included both direct and mediated indirect effects. We determined the overall effect of the metabolome on AD via univariate MR analysis. The product of the beta values from the two-step MR analysis was used as the “indirect” effect, whereas the direct effect was the total effect minus the indirect effect.

Reverse causality detection

To assess reverse causality, the genetic instruments used to assess AD from the meta-analysis used the same screening criteria as above. We tested the causal association of AD with two outcomes (inflammatory proteins and metabolome) using MR-IVW, MR-Egger, the weighted median, the simple mode and the weighted mode. In addition, we applied Steiger directionality to verify the causal relationship between exposures and outcomes.

Transcriptome analysis by RNA-seq

We downloaded the transcriptome sequencing data from the GSE193309 dataset of AD patients with lesional skin (n = 111) and healthy human skin (n = 112). The differential gene expression analysis of different groups was conducted using the edgeR (R package, v3.32.1) package. The infiltration levels of different groups of immune cells were quantified via ssGSEA via the GSVA (R package, v1.38.2) package.

Quality control and analysis of scRNA-seq of skin in AD

We obtained single-cell sequencing immune atlases of the skin from AD patients and healthy individuals (7 healthy people and 7 AD patients)23 from the Human Cell Atlas database. Firstly, we conducted quality control (QC) on the sequencing data, adhering to the same parameters used in the original study with Seurat (R package, v4.3.0)24. Cells with over 20% mitochondrial gene percentages were excluded, as well as those expressing fewer than 100 or more than 6000 genes. Doublets were removed using the scDblFinder (R package, v1.5.7) package25. The NormalizeData and ScaleData functions in the Seurat package were then applied for data normalization and scaling. Highly variable genes were identified using the FindVariableFeatures function, and the Harmony (R package, v1.2.0) package26 was utilized to remove batch effects between samples and integrate the data. Finally, principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were employed for dimensionality reduction and clustering of cells.

The Human Protein Atlas (HPA) database

The HPA database (Human Protein Atlas) is based on proteomic, transcriptomic, and systems biology data, providing access to experimental images related to tissues, cells, organs, and more. We obtained immunohistochemical images of S100A12 in different tissues from HPA database.

Drug targets analysis and molecular docking for potential compounds

We utilized the Drug-Gene Interaction Database (DGIdb) to retrieve druggable proteins and search for potential drugs for AD-related proteins. In addition, we used the Chinese medicine database (HERB) to retrieve potential herbal compounds of AD-associated proteins and herbal medicines. To verify the molecular docking of traditional Chinese medicine (TCM) compounds related to S100A12 with the protein S100A12, we retrieved the 3D crystal structure of the S100A12 protein from the RCSB Protein Data Bank (PDB) database (https://www.rcsb.org/) and used Pymol software (version 2.4.0) to remove ligand molecules and water molecules from the crystal structure. We downloaded the structure file of the TCM compound, converted the file from its original format to the mol2 format using OpenBabel software (version 2.3.2). Then, we uploaded the processed receptor (S100A12 protein) and ligands (TCM compounds in mol2 format) to SwissDock (http://www.swissdock.ch/). We utilized SwissDock to perform the docking simulations, including removal of water molecules, addition of hydrogen atoms, assignment of charges, and setting rotatable bonds. Finally, we used Pymol again to visualize the docking results.

Results

Two-sample MR and bidirectional MR analyses of metabolites and AD risk

To conduct a more comprehensive analysis of GWAS summary data for AD, we integrated GWAS data on AD from both the FinnGen database and UK Biobank, the meta-integrated GWAS data was then used as the outcome for a MR analysis (Fig. S1). We assessed the causal relationship between the metabolite levels and AD risk. After MR-PRESSO and MR‒Egger intercept stepwise removal of genetic instruments with horizontal pleiotropic effects, twenty-two significant AD-associated metabolites (P value < 0.05) were detected. In addition to unknown metabolites (X-12112, X-24494, X-11787 and X-11843), the identified metabolites involved several metabolic pathways: amino acids (citramalate, 3-methyl-2-oxobutyrate, 4-methyl-2-oxopentanoate, 3-(4-hydroxyphenyl)lactate, N6-methyllysine, N2-acetyl,N6,N6-dimethyllysine, cys-gly,oxidized, N6,N6-dimethyllysine, spermidine and methylsuccinoylcarnitine), lipids (cortolone glucuronide (1) and 1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4)), xenobiotics (2-hydroxyhippurate) and partially characterized molecules (metabolonic lactone sulfate). The identified metabolite ratios included the Aspartate to asparagine ratio, the Androsterone glucuronide to etiocholanolone glucuronide ratio, the Phenylalanine to phosphate ratio and the Spermidine to N-acetylputrescine ratio. Among these metabolites, seven were protective factors against atopic dermatitis (Metabolonic lactone sulfate, X-11787, Cortolone glucuronide (1), 2-hydroxyhippurate, Methylsuccinoylcarnitine, X-24494 and the Aspartate to asparagine ratio), and all the other metabolites were risk factors for disease (Fig. 2A,B). There was no evidence of reverse causality. The sensitivity analysis demonstrated that the associated metabolites had a consistent direction.

Fig. 2
figure 2

Heatmap of 1400 serum metabolites with causal effects on AD. (A) Beta effect value of MR analysis results for metabolites and AD. (B) P value of MR analysis results for metabolites and AD.

Full size image

Two-sample and bidirectional MR analyses of inflammatory proteins and AD

Similarly, we performed two-sample MR analysis to examine the association between inflammatory proteins and AD. IVW analysis indicated that S100A12 was positively associated with AD (beta = 0.006, p = 0.03) (Fig. 3), and the analysis also revealed a positive correlation between natural killer cell receptor 2B4 levels and AD (beta = 0.002, p = 0.02). However, the reverse analysis showed that AD had genetic causality on the natural killer cell receptor 2B4. Then we applied Steiger algorithm directionality to verify the causal relationship between S100A12 and AD, the Steiger test for all genetic instrumental variables is less than 0.05, this indicates that the instrumental variables selected for MR analysis are correct. S100A12 is a member of the small calcium-binding S100 protein family27, and S100A12 can activate NF-κB signaling and induce the recruitment of neutrophils, monocytes and lymphocytes, thus causing inflammation and autoimmune diseases28.

Fig. 3
figure 3

The inflammatory protein S100A12 is significantly associated with the development of AD.

Full size image

AD-related blood metabolites affect the expression of S100A12

To identify blood metabolites associated with the inflammatory protein S100A12, we further observed the causal relationships between metabolites and inflammatory factors. As shown in Figs. 4 and 5, multiple metabolites, N6-methyllysine (OR [95% CI] 1.038 [1.025, 1.052]; p = 4.68E−08), N2-acetyl,N6,N6-dimethyllysine (OR [95% CI] 1.038 [1.025, 1.051]; p = 1.04E−07), X-24494 (OR [95% CI] 0.925 [0.886, 0.966]; p = 1.21E−03), X-12112 (OR [95% CI] 1.032 [1.018, 1.045]; p = 7.49E−06) and N6,N6-dimethyllysine (OR [95% CI] 1.033 [1.019, 1.046]; p = 6.63E−06), affected the expression of S100A12, which was evaluated as a risk factor for AD. In addition, MR-Egger intercept analysis revealed no horizontal pleiotropy, and reverse analysis revealed no effect of inflammation on the metabolites. According to the sensitivity analyses, all five metabolites demonstrated consistent trends.

Fig. 4
figure 4

The six AD-related metabolites had causal effects on the expression of S100A12. (A) Scatter plot for the effect of N6-dimethyllysine on AD. (B) Scatter plot for the effect of N2-acetyl,N6,N6-dimethyllysine on AD. (C) Scatter plot for the effect of N6,N6-dimethyllysine on AD. (D) Scatter plot for the effect of X-24494 on AD. (E) Scatter plot for the effect of X-12112 on AD.

Full size image
Fig. 5
figure 5

Forest plot of the influence of metabolites on S100A12 expression.

Full size image

Amino acid metabolites influence AD through the inflammatory factor S100A12

Serum amino acid metabolites (N6,N6-dimethyllysine, N6-methyllysine, N2-acetyl,N6,N6-dimethyllysine) have previously been found to be risk factors for AD. However, the detailed mechanism by which metabolites influence AD is not well understood. To explain the mediating mechanisms and factors of amino acid metabolites affecting AD, we used a two-step MR approach to identify the mediating factors of metabolites affecting AD (Fig. 6).

Fig. 6
figure 6

Schematic diagram of the mediation effect analysis. β represents the causal effect value (disease risk) of the exposure variable on the outcome variable in the results of the MR analysis. βC Total effect value βC of metabolites on AD; βA effects of exposure on mediator; βB effects of mediator on outcome; βC′ value of direct effect between metabolites and AD; βA × βC. Effect value mediated by the mediator.

Full size image

The results showed that N6-methyllysine; N2-acetyl,N6,N6-dimethyllysine and N6,N6-dimethyllysine promoted AD progression by upregulating the expression of S100A12 (Fig. 7). The total effect between N6,N6-dimethyllysine and AD disease was 0.001, and the proportion of the mediation effect via S100A12 was approximately 19.2% (Fig. 7A). The total effect between N6 methyllysine and AD disease is 0.001, and the proportion of the mediation effect via S100A12 was approximately 22.8% (Fig. 7B). The total effect beta value between N2-acetyl, N6, N6-dimethyllysine and AD disease is 0.001, and the proportion of the mediation effect via S100A12 was 22.2% (Fig. 7C).

Fig. 7
figure 7

Effects of metabolites on AD via S100A12. (A) N6,N6-dimethyllysine effect on AD mediated by S100A12. (B) N6-methyllysine effect on AD mediated by S100A12. (C) N2-acetyl,N6,N6-dimethyllysine effect on AD mediated by S100A12.

Full size image

Amino acid metabolites influence AD disease by decreasing the proportion of naive CD4 T cells

Importantly, we also investigated whether 731 immune cell phenotypes mediated the causal relationship between amino acid metabolites and AD. Interestingly, we found that only one type of immune cell (HVEM on naïve CD4+ T cell) mediated the causal effects of these three metabolites, and this immune cell was a protective factor for AD (Fig. S3). This type of immune cell typically represents a naive CD4+ T cell state and is a cell type that does not contribute to skin inflammation in AD patients.

S100A12 is associated with immune stimulation in AD lesion skin

We first analyzed the differential expression of genes in the skin of the healthy and lesion groups (LS) and found that genes such as C19orf38, LILRA2, DOK2, TNF, COL6A5, and S100A12 were upregulated in patients in the LS group (Fig. 8A), and S100A12 was significantly upregulated in the LS group (Fig. 8C). We next assessed the relationship between S100A12 and immune activation and found a strong positive correlation between S100A12 and a variety of immune cells (activated CD4 T cells (r = 0.75), neutrophils (r = 0.72), and pDCs (r = 0.65)) (Fig. 8B). There was greater infiltration of multiple immune cells in the S100A12 high-expression group. The S100A12 high-expression group exhibited infiltration of multiple immune cells at relatively high levels (activated CD8+ T cells, activated CD4+ T cells, and effector memory CD4+ T cells) (Fig. 8D).

Fig. 8
figure 8

S100A12 is significantly upregulated in lesional AD skin and significantly correlates with the infiltration of multiple activated immune cells. (A) Heatmap of differentially expressed genes between healthy and lesional AD skin samples based on the edgeR algorithm; (B) correlation of S100A12 with immune cell infiltration in AD lesional skin; (C) volcano plot of S100A12 significantly upregulated in AD lesional skin; (D) heatmap of the degree of immune cell infiltration between AD patients in the S100A12 high-expression group and AD patients in the S100A12 low-expression group.

Full size image

S100A12 specifically expressed by monocytes can activate immune cells through AGER/TLR4 receptors

We obtained immunohistochemical images of S100A12 expression in various tissues from the HPA database, and the results showed that S100A12 expression was relatively pronounced in bone marrow and spleen, while its expression was weaker in skin (Fig. S2). Understanding the specific expression cells and functions of S100A12 protein can reveal its contributions to AD. To investigate the role of the S100A12 gene in immune cells within the skin tissues of patients with AD, we collected single-cell transcriptomic data from these patients. Using cell marker genes provided by the authors, we identified a total of 41 cellular subpopulations, which were primarily categorized into T cells, B cells, natural killer (NK) cells, Langerhans cells (LCs), dendritic cells, monocytes, macrophages, and mast cells (Fig. 9A, B). Importantly, S100A12 is specifically expressed by monocytes and macrophages (Fig. 9C), indicating its primary source and cellular specificity. Furthermore, studies have established that AGER and TLR4 are the primary receptors for S100A12 ligands. Thus, we delved deeper into the expression patterns of AGER and TLR4 across various cell types. Notably, AGER is predominantly expressed in lymphocyte subsets (Fig. 9D), whereas TLR4 is mainly expressed in macrophage subsets (Fig. 9E). To gain a more detailed insight into the expression of the S100A12-AGER/TLR4 receptor-ligand complex across cell types, we found that S100A12 is mainly released by monocytes and macrophages. The S100A12 further activate migratory memory classes T cells (Tmm3) and dendritic cells (DC1) through the AGER receptor, and also act on macrophages (Mac4) through TLR4.

Fig. 9
figure 9

S100A12 specifically expressed by monocytes can act on immune cells through AGER/TLR4 receptors. (A) UMAP plot of immune cells from patients with AD and healthy individuals. (B) Marker gene plot annotated with cell types. (C, D, E) Expression patterns of S100A12, AGER, and TLR4 genes in the UMAP plot of immune cells. (F) Heatmap showing the expression of S100A12, AGER, and TLR4 genes across different cell types. Abbreviation: central memory cells (Tcm), tissue-resident memory T cells (Trm), Cytotoxic T cells (CTL-c), cytotoxic T lymphocytes exhaustion cluster (CTL-ex), cytotoxic T lymphocytes activated cluster (CTL-ax), CD3D+ T cell cluster (Tet), central memory Tregs (cmTreg), naive T cell (Tn), derived dendritic cells (DCs), langerhans cell populations (LCs), inflammatory Monos (InfMono), monocyte (mono), innate lymphoid cell population (ILC), migratory DC (migDC).

Full size image

Drug prediction of the targets and molecular docking for potential compounds

S100A12 is a mediator of AD triggered by amino acid metabolites. To block the mediating factors linking blood amino acid metabolites to the onset of AD, we searched two databases (Drug-Gene Interaction Database (DGIdb) and Chinese medicine database (HERB)) for potential drugs (Tables S1 and S2). The clinically approved drugs RIMEGEPANT, UBROGEPANT, METHOTREXATE, ATOGEPANT and EPTINEZUMAB target S100A12 and exert an inhibitory and antagonistic effect on it. The potential TCM compound targeting S100A12 is citric acid.

To investigate the potential interactions between the candidate compound and the S100A12 protein, we performed computational molecular docking simulations with citric acid and S100A12. The results revealed a hydrogen bond interaction between citric acid and the ASP-61 residue of S100A12, which could potentially explain the inhibitory effect of citric acid on the function of the S100A12 protein (Fig. 10).

Fig. 10
figure 10

Visualization of the molecular docking results between citric acid and S100A12 using Pymol software. The orange structure represents citric acid, while the cyan structure represents the S100A12 protein. The hydrogen bonds between the molecules are marked in yellow.

Full size image

Therefore, these S100A12-targeted drugs and compound are expected to limit the progression of AD pathology.

Discussion

In this study, we tested for the relationship between metabolites and AD and found that a variety of amino acid metabolites (citramalate, 3-methyl-2-oxobutyrate, 4-methyl-2-oxopentanoate, 3-(4-hydroxyphenyl)lactate, N6-methyllysine, N2-acetyl,N6,N6-dimethyllysine, cys-gly,oxidized, N6,N6-dimethyllysine, spermidine and methylsuccinoylcarnitine), lipids (cortolone glucuronide (1) and 1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4)) and xenobiotics (2-hydroxyhippurate)) have potential causal relationships with AD. However, the detailed mechanism of these metabolites in AD is not fully understood. To date, few metabolomics studies on AD have been reported. Several studies have demonstrated that 3-(4-hydroxyphenyl)lactate shares common genetic regulation with hepatic steatosis and hepatic fibrosis29. In addition, 3-(4-hydroxyphenyl)lactate was also connected with the risk of severe bronchiolitis30.

The relationships between the amino acid metabolites (N6-methyllysine, N2-acetyl,N6,N6-dimethyllysine and N6,N6-dimethyllysine) and AD have not yet been reported. Ottas reported the serum metabolome of AD patients, which showed differences in the levels and proportions of acylcarnitine, phosphatidylcholine and the cleavage product of fibrinogen A-α31. Previous studies have shown that the levels of amino acids, biogenic amines, acylcarnitine, sphingomyelins and phosphatidylcholines, including metabolites such as glutamine, asparagine and asymmetric dimethylarginine, are greater in the lesional skin than in the normal skin of AD patients32. Tryptophan was also identified as a crucial pathogenic metabolite for AD inflammation and pruritus8,33.

Importantly, we found the inflammatory protein S100A12 mediates the causal effect between amino acid metabolites (N6-methyllysine, N2-acetyl,N6,N6-dimethyllysine and N6,N6-dimethyllysine) and AD. This indicates that N6-methyllysine; N2-acetyl,N6,N6-dimethyllysine and N6,N6-dimethyllysine promote the development of AD by affecting the expression of S100A12 in blood. Furthermore, we explored the role of S100A12 and its receptors TLR4/AGER in activating immune cells. Utilizing the immunohistochemical results from the HPA database, we found that S100A12 is prominently expressed in bone marrow and spleen, with weaker expression in skin. To delve deeper, we acquired single-cell sequencing data from skin tissues of patients with AD. Our analysis revealed that S100A12 is specifically expressed by monocytes and macrophages, and it exerts immune-activating effects on memory T cells, macrophages, and dendritic cells via TLR4/AGER. This finding underscores the immuno-activating role of S100A12 in skin tissues.The expression of S100A12 by neutrophils and monocytes promotes inflammation through binding to the receptor for advanced glycation and subsequently activating the nuclear factor kappa B pathway34. In addition to its intracellular activity, S100A12 has various extracellular activities that mediate innate immune responses35. S100A12 expression is upregulated in inflamed synovial tissue, and the serum level of S100A12 is correlated with rheumatoid arthritis (RA) activity36. Serum S100A12 levels were reported to be significantly upregulated in AD patients compared to healthy controls and correlated with AD severity. Therefore, the antimicrobial protein S100A12 may be a potential autoantigen for AD patients37.

Additionally, we investigated whether 731 immune cell phenotypes mediate the causal effect between amino acid metabolites (N6-methyllysine, N2-acetyl,N6,N6-dimethyllysine and N6,N6-dimethyllysine) and atopic dermatitis. Our findings revealed that a decrease in HVEM expression on naïve CD4+ T cells mediates this effect, suggesting that amino acid metabolites can significantly reduce the proportion of naïve CD4+ T cells. Furthermore, this finding also indicates a potential activation of naïve CD4+ T cells, driving their differentiation into effector CD4+ T cells. We further queried multiple compound databases for drugs targeting S100A12, which blocks the mediating effect of amino acid metabolites on the onset of AD, including clinically approved medications (e.g. RIMEGEPANT, UBROGEPANT, METHOTREXATE) and herbal compounds (citric acid). METHOTREXATE is a biologic therapy approved by the US Food and Drug Administration for moderate to severe AD38. A 5-year follow-up study on the treatment of severe AD with methotrexate demonstrated that methotrexate was effective and safe39. Methotrexate is more persistent and less costly than cyclosporine (a calcineurin inhibitor approved for the treatment of AD)40. Consequently, we believe that these drugs could be beneficial for managing AD, and further clinical trials will be necessary to substantiate these findings in the future.

Conclusion

In our study, we found that metabolites such as 3-methyl-2-oxobutyrate, 4-methyl-2-oxopentanoate, 3-(4-hydroxyphenyl)lactate, Citramalate, N6-methyllysine, N2-acetyl,N6,N6-dimethyllysine, Cys-gly,oxidized, N6,N6-dimethyllysine, 1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) and Spermidine promote the development of diseases. However, metabolites such as Cortolone glucuronide (1), 2-hydroxyhippurate and Methylsuccinoylcarnitine inhibit disease development. Furthermore, N6-methyllysine, N6,N6-dimethyllysine and N2-acetyl,N6,N6-dimethyllysine influence the onset of AD by mediating the inflammatory protein S100A12. S100A12 was also associated with multiple immune cells (e.g., activated CD4 T cells and neutrophils). Therefore, metabolites may play a therapeutic role in AD by influencing immune cell infiltration via S100A12.