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. 2024 Jul 6;30(7):e13822. doi: 10.1111/srt.13822

Metagenomics reveals unique gut mycobiome biomarkers in psoriasis

Xuan Wang 1, Jiaxin Sun 1, Xiandan Zhang 2, Wei Chen 1, Jing Cao 1, Huimin Hu 3,✉
PMCID: PMC11227279  PMID: 38970783

Abstract

Purpose

In present, the diagnosis of psoriasis is mainly based on the patient's typical clinical manifestations, dermoscopy and skin biopsy, and unlike other immune diseases, psoriasis lacks specific indicators in the blood. Therefore, we are required to search novel biomarkers for the diagnosis of psoriasis.

Methods

In this study, we analyzed the composition and the differences of intestinal fungal communities composition between psoriasis patients and healthy individuals in order to find the intestinal fungal communities associated with the diagnosis of psoriasis. We built a machine learning model and identified potential microbial markers for the diagnosis of psoriasis.

Results

The results of AUROC (area under ROC) showed that Aspergillus puulaauensis (AUROC = 0.779), Kazachstania africana (AUROC = 0.750) and Torulaspora delbrueckii (AUROC = 0.745) had high predictive ability (AUROC > 0.7) for predicting psoriasis, While Fusarium keratoplasticum (AUROC = 0.670) was relatively lower (AUROC < 0.7).

Conclusion

The strategy based on the prediction of intestinal fungal communities provides a new idea for the diagnosis of psoriasis and is expected to become an auxiliary diagnostic method for psoriasis.

Keywords: biomarkers, gut, mycobiome, psoriasis

1. INTRODUCTION

Psoriasis is a chronic autoimmune inflammatory skin disease and affected by a complex interplay between the immunologic factors, Ps‐associated susceptibility loci and various environmental factors. 1 The global prevalence is about 2%, but varies regionally from a low prevalence in Asian and some African populations to a high prevalence of 11% in Caucasian and Scandinavian populations. 2 , 3 Psoriasis is characterized by the excessive proliferation and abnormal differentiation of keratinocytes, resulting in skin erythema, plagues, and constant scales. 4 , 5 Psoriasis is often comorbid with a range of complications such as metabolic syndrome, cardiovascular problems, diabetes, chronic inflammatory bowel disease, and chronic kidney disease. 6 In addition, patients with psoriasis have a significantly higher risk of anxiety, depression and even suicide. 7

Currently, the diagnosis of psoriasis is mainly based on the patient's typical clinical manifestations, dermoscopy, and skin biopsy. 8 , 9 Histopathological examination is an invasive method and has not become routine test for psoriasis. Moreover, unlike other autoimmune diseases, psoriasis lacks specific blood markers. Therefore, it is important to find specific biomarkers for the diagnosis of psoriasis. 10 , 11

In recent years, with the continuous progress of metagenomics, metatranscriptomics and other technologies, we are able to explore more deeply the relationship between gut microbial communities and human health, especially to reveal the role of gut flora in the pathogenesis of autoimmune diseases. 12 , 13 , 14 Some studies have shown that psoriasis is characterized by a low diversity of intestinal bacterial flora. 15 , 16 The abundance of Ruminococcus and Megasphaera of the phylum Firmicutes were markedly increased in patients with psoriasis. However, the phylum Bacteroidetes was significantly reduced in the intestinal tracts of patients with psoriasis. 17 Animal experiments showed that both mice reared under germ‐free conditions for several generations and conventional mice treated with broad‐spectrum antibiotic were more resistant to imiquimod‐induced skin inflammation than conventional mice. 18 All these evidences show that there is an inextricable link between psoriasis and gut microbes.

Despite the abundance of metagenomics data, the link between the gut microbiota and human health and disease remains elusive. 19 It is important to search for and identify specific microbial communities associated with disease. Due to the extreme complexity of the composition and function of the gut microbiota, including its species, numbers, and complex interactions, traditional statistical analyses have been considered incapable of elucidating the relevance to human disease. 20 , 21 In addition, there is growing research evidence that artificial intelligence techniques such as machine learning algorithms can be used to assist in the diagnosis and prediction of human diseases, such as premature birth, colorectal cancer, alcoholic hepatitis, and even biological age and death. The success of the construction of these predictive models hints at the great potential of the application of artificial intelligence techniques to gut microbes and human health. 22 , 23 , 24 Machine learning is a branch of artificial intelligence which builds a model to make a prediction or decision by learning from data. Machine learning can help us to better process and analyze microbial datasets. 25 , 26 Previous studies have mostly focused on gut bacterial communities and neglected the relationship between fungal communities and psoriasis. In this study, we mainly analyzed the differences of the intestinal fungal communities between psoriasis and healthy groups. A predictive model was built using machine learning algorithms to explore potential microbial markers for psoriasis diagnosis. In short, we hope that the machine learning model built based on gut fungal communities can be one of the methods for psoriasis diagnosis.

2. METHODS

2.1. Data collection

In order to obtain comprehensive information about the gut microbiota as well as to avoid biases arising from different data processing methods, we chose the sequence read archive (SRA) of raw sequencing metadata rather than the results of data processing from existing research platforms. In this study we used metagenomic shotgun sequencing data from NCBI (SRA accession number: PRJNA634145). There was a total of 15 healthy and 32 psoriasis groups in the dataset.

2.2. Processing of the data

Since SRA data is a non‐textual data, it cannot be directly used for subsequent analysis. First, the data were converted from SRA format to FASTQ format using fastq‐dump software. The quality of all sequencing reads was assessed using FASTQC. Low‐quality reads, adapters, human DNA contamination, etc. were removed using Knead data and Trimmomatic software based on the quality report. Then, based on the fungi database, the clean reads were annotated using Kraken2. Finally, Bracken was used to estimate the abundance of various microbial communities in each sample. All software calls were executed on a Linux/Ubuntu system using the bash command.

2.3. Visualization of data

All downstream data analysis of the metagenomes was performed in R software (version 4.3.1). The sample data were analyzed using the phyloseq package for smoothing, while some low abundance OTUs were filtered out, and the smoothed OTUs could be used for subsequent diversity analysis. Alpha diversity analysis was performed on the psoriasis and the healthy groups using the vegan package, P < 0.05 was considered statistically significant, and the ggplot2 and ggpubr packages were used for the visualization of Alpha diversity. Beta diversity analysis was performed using Permute, lattice, vegan and ape packages and principal coordinate analysis (PCoA) was plotted based on Bray–Curtis distance. Differences in fungal community composition between the psoriasis groups and healthy controls were analyzed using statistical analysis of metagenomic profiles (STAMP) (Welch t‐test). Finally, SVM and LASSO models were utilized to screen for fungal communities with high specificity and sensitivity for distinguishing psoriasis patients from healthy individuals.

3. RESULTS

3.1. Diversity of gut mycobiome in psoriasis and healthy controls

In this study, we utilized whole‐metagenome shotgun sequencing to compare the fungal composition of psoriasis (n = 32) from healthy controls (n = 15). After metagenomic sequencing, a total of 4,108,836,286 raw reads (36,011,206–142,714,552 per pool) were obtained from 47 libraries. Among these libraries, 14,537,540 reads were associated with fungi.

To illustrate whether the sample size of this experiment is reasonable, we performed species accumulation curve analysis. Analysis of species accumulation curve indicated that the samples were sufficient to reveal the characterization of fungal microbiome, as the species accumulation curves tended to be saturated (Figure 1A). Alpha diversity is the diversity of species within a particular region or ecosystem. Shannon‐Wiener index, an index to measure alpha diversity: an increase in the Shannon‐Wiener index value represents an increase in species diversity. The results indicated that the psoriasis groups did not show statistically significant differences in Shannon index compared to healthy controls (P = 0.077, Wilcoxon test) (Figure 1B).

FIGURE 1.

FIGURE 1

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Alpha diversity analysis of the psoriasis and healthy groups. (A) Species accumulation curve. The abscissa represents samples and the ordinate represents the number of the cumulative number of species found. (B) Comparison of fungal alpha diversity (Shannon index) between psoriasis groups and healthy controls. Upper and lower whiskers extend to data no more than 1.5× the interquartile range from the upper and lower edge of the box, respectively. The sample sizes in this study are as follows: psoriasis patients (n = 32), healthy controls (n = 15).

Unlike alpha diversity analysis, beta diversity is a comparison of diversity between samples and reflects the differences between samples in community composition. We calculated the beta diversity index between each group based on Bray–Curtis distance and plotted the principal coordinate analysis (PCoA). Interestingly, although some overlap exists, unweighted UniFrac analysis suggested that the PCoA can distinguish the healthy controls from the psoriasis groups at the class, order, family, genus, and species levels (Figure 2B–F). Unfortunately, we did not observe a significant difference between the two groups at the phylum level (Figure 2A).

FIGURE 2.

FIGURE 2

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Beta diversity analysis of psoriasis patients and healthy controls at the (A) phylum, (B) class, (C) order, (D) family (E) genus, and (F) species levels.

3.2. Fungal biomarkers for discriminating psoriasis from healthy controls

Statistical analysis of metagenomic profiles (STAMP) was used to identify the key gut fungal communities responsible for distinguishing the psoriasis patients from healthy controls. In total, 51 differential fungal communities were identified at the phylum, class, order, family, genus and the species levels. Five fungal communities including Sordariomycetes, Hypocreales, Nectriaceae, Fusarium, and Fusarium pseudograminearum were significantly reduced in psoriasis groups versus healthy controls, while 46 fungal communities including Schizosaccharomycetales, Schizosaccharomyces pombe, Schizosaccharomycetes, Schizosaccharomycetaceae, Schizosaccharomyces, Eurotiomycetes, Aspergillus, Clavicipitaceae, Aspergillaceae, and Eurotiales, etc. were significantly enriched in psoriasis groups versus healthy controls (Figure 3A). The abundance of our differential species is then presented as a heat map, and again we can clearly see that most of the differential fungal communities are significantly more abundant in psoriasis than in the healthy groups (Figure 3B).

FIGURE 3.

FIGURE 3

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Analysis of differences in the composition of intestinal fungal communities in psoriasis patients and healthy controls. (A) Analysis of differences between groups using STAMP. Significance values shown were calculated using two‐sided Welch t‐tests. (B) Heat map of the 51 differential fungal communities.

Potential microbial markers that can serve as the diagnostic indicators for psoriasis were screened from differential fungal communities by two different algorithms. First, we used the LASSO algorithm with ten‐fold cross‐validation to screen for characteristic fungal communities. From this model, we obtained four representative fungal communities including Aspergillus puulaauensis, Torulaspora delbrueckii, Kazachstania africana, and Fusarium keratoplasticum. For the SVM‐RFE algorithm, when the feature number was 51, the classifier had the minimum error. A Venn diagram was used to ultimately identify the four characterized fungal communities (Aspergillus puulaauensis, Kazachstania africana, Torulaspora delbrueckii, Fusarium keratoplasticum) overlapped by LASSO and SVM‐RFE algorithms (Figure 4A).

FIGURE 4.

FIGURE 4

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Lasso and SVM‐REF models for screening out fungal biomarkers that distinguish psoriasis patients from healthy individuals. (A) Venn diagram identified fungal communities that were shared by 2 feature selection algorithms. (B–E) ROC curves estimating the diagnostic performance of fungal communities. (F) Heat map of characteristic fungal biomarkers.

Finally, we evaluated the predictive performance of fungal communities in the diagnosis of psoriasis by ROC curves. The AUC values of the ROC curves were 0.779 of Aspergillus puulaauensis (95% CI: 0.637–0.901), 0.750 of Kazachstania africana (95% CI: 0.592–0.874), and, 0.745 of Torulaspora delbrueckii (95% CI: 0.590–0.869) demonstrating that these characteristic fungal communities enabled to serve as one of the reference indicators for the diagnosis of psoriasis (Figure 4B–D). Unfortunately, the AUC values of Fusarium keratoplasticum (95% CI: 0.498–0.814) showed that it could not be a predictive biomarker for psoriasis (Figure 4E). In the heatmap we can likewise observe the difference in the abundance of the four fungal communities between the psoriasis and the healthy groups (Figure 4F). Thus, we can conclude that characteristic fungal communities have excellent predicting performance in psoriasis diagnosis.

4. DISCUSSION

The microbial community is a ubiquitous group in the human body, equivalent to a symbiotic “organ” in the human body, responsible for the functions that cannot be realized by human cells. The intestine is the natural habitat for a large and dynamic microbial community in the human body. 27 , 28 , 29 , 30 There are more than 100 trillion microorganisms in the human gut, consisting of bacteria, archaea, parasites, fungi and viruses. 31 Most of the gut microorganisms are mutually beneficial symbionts that play an integral role in nutrient metabolism, immune system development, colonization resistance, and epithelial barrier function. 32 The microbial community inhabiting the human gut is constantly changing at the individual level, and the symbiotic or competitive relationship between gut microbes and the host depends on this dynamic balance of the microbial community. The term “microbial disorders” refers to changes in microbial composition that disrupt this balance, thereby inducing the onset of disease or correlating with disease progression. Gut microbial disorders also contribute to the development and progression of many autoimmune diseases, including psoriasis. 33 , 34

Psoriasis is an immune‐mediated inflammatory skin disease that affects 1%–3% of the world's population. 35 Psoriasis onset is influenced by a variety of factors, with both genetic and environmental factors having key roles. 36 Studies have shown that the gut microbial community is strongly associated with the onset and progression of psoriasis. 37 However, the relevance of psoriasis to the gut microbiota remains unclear. In this study, we analyzed the gut fungal community of psoriasis patients and healthy individuals for potential microbial biomarkers to assist in the diagnosis of psoriasis. The composition of gut microbial community is dynamic and complex, with a high degree of intra‐ and inter‐individual variability. 38 Traditional statistical methods are unable to differentiate the gut microbial community between healthy individuals and psoriasis patients. Therefore, the use of machine learning algorithms to explore biomarkers of gut microbes in psoriasis is of great practical value. 39

Recent reports have indicated that psoriasis is characterized by a low diversity of intestinal bacterial flora. 40 Faecalibacterium prausnitzii of the phylum Firmicutes, Bacteroidetes, and the intestinal commensal Bifidobacterium are significantly reduced in the intestinal tracts of patients with psoriasis. 41 Faecalibacterium prausnitzii is one of the most common microorganisms in the large intestine and it is an important source of butyric acid. Butyric acid is a short‐chain fatty acid (SCFA), which is the preferred energy source for the colonic epithelium and is a key factor in maintaining the integrity of the intestinal barrier. A decrease in butyric acid‐producing bacteria may disrupt the integrity of the intestinal mucosal barrier in patients with psoriasis, inducing infections that exacerbate the inflammatory response. 42 , 43 Previous studies showed that the proportion of various bacteria with SCFA production capacity, such as Faecalibacterium, Clostridium butyricum, Faecalibacterium prausnitzii, was reduced in the intestines of patients with psoriasis. 44 , 45 And SCFA plays an irreplaceable role in several immune functions such as reducing oxidative stress, inhibiting Th17 proliferation and IL‐17A secretion as well as regulating Th1/Th2 and Th17/Treg balance. 46 G protein‐coupled receptor 43 (GPR43) is highly expressed in neutrophils, eosinophils, and monocytes, and SCFA participates in neutrophil chemotaxis by activating GPR43. 47 , 48 These studies emphasize the importance of gut microbes for the development and progression of psoriasis.

In this study, we first analyzed the alpha diversity of fungal communities in the psoriasis and healthy groups, which showed no significant differences between the psoriasis and healthy groups. Several studies of gut microbes in psoriasis have similarly found no significant changes in alpha diversity between psoriasis patients and healthy individuals, which is consistent with the results of our analysis. 49 , 50 Beta diversity is a statistical analysis based on the Bray–Curtis distance, is used to compare microbial composition between groups in order to identify community differences between groups. To evaluate the variability of microbial communities between the two groups, beta diversity was assessed using the principal coordinates analysis (PCoA). 51 The results of beta diversity showed significant differences between the psoriasis and healthy groups at the class, order, family, genus, and species level. The results of several studies showed significant differences in the Beta diversity of gut microbial communities between psoriasis patients and healthy individuals. 52 , 53 These results suggest that the human gut fungal communities changes significantly from a healthy state to the development of psoriasis.

STAMP is a tool that provides extensive hypothesis testing, exploratory plots, effect size measures and confidence intervals for aiding in the identification of biologically relevant differences. 54 To further screen for fungal communities that differed between the psoriasis and the healthy groups, we performed a STAMP analysis. It was found that a total of 51 different fungal communities were screened out, among which 46 fungal communities had significantly higher abundance in the psoriasis groups than in the healthy groups, and five fungal communities had significantly lower abundance in the psoriasis groups than in the healthy groups. All of these significantly different microbial communities have the potential to be biomarkers for psoriasis prediction.

In order to improve the disease prediction capability, we constructed two types of deep learning models, namely LASSO and Support vector machine (SVM). LASSO refers to a machine learning approach based on regression that can actively select from various potential multicollinear variables. We classified variables by looking up the lambda parameter to find the smallest error. 55 SVM is a powerful binary classifier that establishes a classification hyperplane as a decision surface. SVM‐RFE is an SVM‐based machine learning method that reduces the eigenvectors generated by SVM in order to optimize the variables in a prediction model. 56 By using the LASSO algorithm, four relatively important fungal communities were screened out, including Aspergillus puulaauensis, Kazachstania africana, Torulaspora delbrueckii, and Fusarium keratoplasticum. When using the SVM‐RFE algorithm to identify characteristic microbial markers, the classifier error was minimized when the number of features was 51. To improve the accuracy of the prediction model, we took the intersection of the two models and obtained four characteristic fungal communities. Finally, we evaluated the performance of fungal communities in psoriasis diagnosis by ROC curves. Aspergillus puulaauensis, Kazachstania africana, and Torulaspora delbrueckii had a good predictive efficacy with an AUROC > 0.7. However, unfortunately, the AUROC < 0.7 of Fusarium keratoplasticum showed poor specificity and sensitivity and could not be a predictive biomarker for psoriasis.

In conclusion, we analyzed the characteristics of gut fungal community in psoriatic and healthy individuals, identified fungal biomarkers, and demonstrated the potential of fungal biomarkers as a non‐invasive diagnostic indicators for psoriasis. Our study results provide new directions for the diagnosis of psoriasis. However, due to the lack of sufficient intestinal metagenomic data for psoriasis at this stage, more data analysis is needed to confirm our predictive model.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

ACKNOWLEDGEMENTS

This research received no external funding.

Wang X, Sun J, Zhang X, Chen W, Cao J, Hu H. Metagenomics reveals unique gut mycobiome biomarkers in psoriasis. Skin Res Technol. 2024;30:e13822. 10.1111/srt.13822

DATA AVAILABILITY STATEMENT

The data that support the findings of this study were derived from the following resources available in the public domain: https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA634145.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study were derived from the following resources available in the public domain: https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA634145.

Articles from Skin Research and Technology are provided here courtesy of International Society of Biophysics and Imaging of the Skin, International Society for Digital Imaging of the Skin, and John Wiley & Sons Ltd

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