The authors are accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Xi'an Jiaotong University (2021032). All study subjects signed informed consent forms.

Moderate-to-severe (PASI > 10 or BSA > 10) psoriatic patients successfully treated by 5-month secukinumab treatment (n = 11; AT group), which was defined by achieving a PASI 90 response, were selected, and age-, sex-, and disease severity-matched (1:3) psoriatic patients without therapy (BT group) were identified. The control group (H group) compromised age- and sex-matched (1:3 or 1:4) healthy donors (Supplementary Table 1). All participants with 18–25 BMI were selected from the Second Affiliated Hospital of Xi'an Jiaotong University, and they did not receive antibiotics, probiotics, glucocorticoids, or immunosuppression during the 6 previous months. Besides, they have not suffered from diseases of the digestive system, immune system, or other severe illnesses. Finally, we collected 11 samples from the AT group, 32 samples from the BT group (one patient was lost), and 35 samples from the H group for 16S rRNA gene sequencing.

Then 24 samples with high-quality DNA from the AT (8), BT (8), and H (8) groups were selected for shotgun metagenomic sequencing (Supplementary Table 2).

Fresh fecal samples were collected in a specimen collection kit and stored at −80°C before further manipulation in the laboratory. All fecal samples were transferred to the laboratory in an ice box.

DNA from stool samples was extracted using the DNA Isolation Kit (MoBio, Carlsbad, CA, USA) according to the manufacturer's instructions. Using NanoDrop 2000 (Thermo Fisher Scientific, Waltham, USA), the concentration and purity of bacterial genomic DNA were tested. Total DNA quality was detected by electrophoresis using a 1% agarose gel. All the genomic DNA was stored at −20 and −80°C for short-term and long-term uses, respectively.

The V3 + V4 regions of the 16S rDNA genes were amplified by PCR with the universal primers (F: 5′-ACTCCTACGGGAGGCAGCA-3′, R: 5′-GGACTACHVGGGTWTCTAAT-3′). Next, 16S rDNA gene sequencing was performed on fecal samples from our 78 samples on the Illumina Hiseq 2500 sequencing platform (Biomarker Technologies Corporation, Beijing, China) in PE250 mode (2 × 250 bp paired ends). The analysis of microbiota was carried out on the BMK Cloud platform (www.biocloud.net).

Particularly, the paired-end reads were assembled using the FLASH software to produce Raw Tags. To get clean tags, Trimmomatic (version 0.33) was used to perform a quality filter. Using UCHIME (version 8.1), we screened for and removed putative chimeric sequences. Operational taxonomic units (OTUs) were clustered with more than 97% identity by USEARCH (version 10.0), while the OTUs whose proportions were < 0.005% of the total OTUs were removed.

A total of 24 samples (n = 8 for the AT group, n = 8 for the BT group, and n = 8 for the H group) were sequenced for the metagenomes. For each sample, 10 ng of genomic DNA was used for PE library preparation with the VAHTS^® Universal Plus DNA Library Prep Kit for Illumina. Libraries were sequenced using the Illumina NovaSeq 6000 (PE 150) platform and NovaSeq 6000 S4 Reagent Kit at Biomarker Technologies Corporation (Beijing, China). After being filtered with fastp, the rest of the reads were assembled and predicted with MEGAHIT and MetaGeneMark, respectively. MMseqs2 was utilized to construct non-redundant gene sets at a threshold with a shared sequence identity of over 95% and overlap of over 90%. The metagenomeSeq function of the R software was used to screen different species at different levels. The gene abundance profile was calculated according to Wen et al. (2017). The clean non-redundant gene sequences were queried against the CARD database (RGI model, strict and perfect hits only), the CAZy database (if alignment > 80 amino acids, use E-value < 1e-5; otherwise, use E-value < 1e-3; a covered fraction of HMM > 0.3), the KEGG database (at the threshold of E-value < 1e-5), eggNOG database (at the threshold of E-value < 1e-5), and the GO database. We used the R package pheatmap to draw the heat map of these pathways.

Using the Mothur software, alpha diversity based on OTU level, including indices to measure evenness (Simpson) and richness (ACE), Good's coverage, and species accumulation curves, was investigated. The difference in alpha diversity between groups was examined by Student's t-test. Using the QIIME software, the Non-MetricMulti-Dimensional Scaling (NMDS) analysis based on binary_jaccard and bray-curtis distances was performed to assess the beta diversity of the bacterial community and functional gene composition, respectively. To test whether beta diversity is statistically different among different samples, permutational analysis of variance (PERMANOVA) was performed using the R software. Linear discriminant analysis (LDA) effect size (LEfSe) was used to further determine the specific significantly different bacterial taxa among cohorts. LDA values > 2.0 with a p-value of < 0.05 were considered significantly enriched. The functional abundance spectrum of the Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog functional profile was predicted by the PICRUSt procedure from the 16S rRNA gene data. Besides, the phenotype prediction of the gut microbiota was analyzed by BugBase using Pairwise Mann-Whitney-Wilcoxon tests. Differential pathway analysis was carried out by metagenomic sequencing based on the Kruskal-Wallis H-test and the one-way ANOVA test.

We enrolled 78 participants, including 11 psoriatic patients after 5-month secukinumab treatment, 32 psoriatic patients without treatment, and 35 healthy people as controls. There were no significant differences in demographic information between the AT, BT, and H groups. A summary of related characteristics of the above three groups is presented in Table 1, and detailed related information about participants is shown in Supplementary Table 1.

Then, 78 fecal samples were collected, containing 5,841,838 raw sequences with a mean length of 456 bases, for 16S rDNA sequencing and analysis. After quality trimming and chimera checking, we obtained 5,585,894 high-quality sequences in total for further analysis. Four hundred and twenty-four OTUs in all samples were identified, and 382, 412, and 420 OTUs were confirmed in the AT, BT, and H groups, respectively. The data summary is shown in Table 2. All sequences in the three groups had high coverage values of over 99%. The species accumulation curves at the genus level had reached a plateau in all groups (Figure 1A).

For the shotgun metagenomic sequencing, a total of 60.0 GB of data were generated. Also, 5,785,683 genes were predicted using MetaGeneMark. The species accumulation curves at the genus level had reached a plateau in all groups (Supplementary Figure 1).

The above results illustrate that most fecal microbiota species were detected, which provided good sequencing depth for fecal microbiota exploration in the follow-up analysis.

We set out to investigate the richness of fecal microbiota and structural differences among these three groups, and the α-diversity (ACE index and Simpson index) of gut microbiota was calculated (Table 2). We discovered that secukinumab therapy induced significantly increased microbiota richness compared to that of the other two groups by ACE indices (Figure 1B) and Simpson indices (Figure 1C). However, there were no significant differences in the α-diversity of gut microbiota between the BT and H groups, as indicated by both the ACE and Simpson indices.

Furthermore, to assess the similarity of gut microbial communities among these three groups, beta diversity was analyzed. NMDS based on binary_jaccard was performed, which suggested that the fecal microbiota of the AT group was different from the other two groups (stress = 0.1480 < 0.2; Figure 1D). The separation trend of the AT and BT groups is more obvious, which is validated significantly (p = 0.002, PERMANOVA; Figures 1D, E).

The taxonomy of fecal microbiota was evaluated using the RDP classifier. By 16S rRNA gene sequencing, two dominant phyla, namely, Firmicutes and Bacteroidota, based on different relative abundances among the three groups, were identified. Firmicutes (70.12%) increased and Bacteroidota (19.38%) decreased in the AT group when compared to the BT group (Firmicutes 51.24%, Bacteroidota 40.15%) and the H group (Firmicutes 56.94%, Bacteroidota 33.95%; Figure 2A). At the genus level, the abundance of Faecalibacterium (15.31%) was higher, and the abundance of Bacteroides (14.91%) and Prevotella_9 (0.03%) was lower in the AT group than in the other two groups (Figure 2B).

To further explore the microbiota composition, shotgun metagenomic sequencing was used. It is effective to visualize relationships between the microbiota and samples by using the Circos graph. As shown in Figure 2C, the AT group showed a decline in Bacteroides and Prevotella compared to the BT group and a decrease in Dialister compared to the H group at the genus level. But the level of Ruminococcus was higher in the AT group. The changes in the rest of the groups were unnoticeable (Figure 2C). Bacteroides and Firmicutes were predominant in the gut microbiota of all subjects. Firmicutes/Bacteroides values were higher in the AT group than in the BT group, as shown in Figure 2D. At the species level, Figure 2E shows a heat map of microbiota composition based on the one-way ANOVA test. More detailed information on microbiota composition at the phylum, family, and taxonomy levels is shown in Supplementary Figures 2–4.

To further evaluate the differences in the gut microbiota composition between the two groups, a metastatistical analysis was performed. On a phylum level, there was a significant increase in Firmicutes and a decrease in Bacteroidota in the AT group compared to the other two groups (Table 3). At the genus level, when compared to the H group, the AT group presented obviously decreased genera Bradyrhizobium, unclassified_Firmicutes and uncultured_Clostridiales_bacterium, but increased genera Hydrogenophaga, Pantoea, and unclassified_Comamonadaceae (Table 3). In addition, when compared to the BT group, the AT group showed statistically decreased genera Bradyrhizobium, Hydrogenophaga, and Lactococcus but increased genera Pantoea and unclassified_Comamonadaceae (Table 3).

Consequently, we investigated the specific significantly different bacterial taxa among these three groups by using LEfSe analysis at the genus level (LDA score > 2, p < 0.05). Several genera, including Faecalibacterium, Agathobacter, Subdoligrnulum, Bifidobacterium, and Dialister, were significantly enriched in the AT group, while the genera Succinivibrio and Paraprevotella were obviously enriched in the BT group (Figure 3A). Besides, we detected an alteration in gut microbiota composition between the AT group and the H group. We identified that genera, such as Faecalibacterium, Subdoligrnulum, and Ruminococcus, were significantly increased in the AT group, whereas Limosilactobacillus, Desulfovibrio, and Paraprevotella were significantly enriched in the H group (Figure 3B). Finally, in Supplementary Figure 5, we discovered that the genera Prevotella_7, Weissella, and Lactococcus were increased in the BT group, while Megasphaera, Dialister, Romboutsia, and other genera were enriched in the H group.

The potential prediction for phenotypic functions in complex communities among these three groups was analyzed by BugBase. Five predicted phenotypic functions (contains mobile elements, forms biofilms, gram-negative, gram-positive, and potentially pathogenic) exhibited significant differences in abundance (Figure 4). The AT group showed an obviously increased abundance in the mobile elements phenotype than the other two groups, which manifested as increased Lachnospiraceae abundance and Ruminococcaceae but decreased Veillonellaceae abundance (Figure 4A). Besides, the gut microbiota of the AT group demonstrated a more significant abundance in forming biofilms phenotype, having an elevated abundance of Bifidobacteriaceae, Coriobacteriaceae, and Verrucomicrobiaceae, and reduced abundance of Enterobacteriaceae and Alcaligenaceae than the H group (Figure 4B). Furthermore, we observed that the abundance of gut microbiota in the AT group was significantly lower in gram-negative phenotype (Veillonellaceae, Prevotellaceae, Enterobacteriaceae, and Bacteroidaceae; Figure 4C), while a statistically higher abundance of gut microbiota in the gram-positive phenotype (Lachnospiraceae and Ruminococcaceae) was confirmed than in the other two groups (Figure 4D). Finally, a potentially pathogenic phenotype was evaluated. The gut microbiota abundance of the AT group and the H group was obviously reduced in this phenotype compared to the BT group. The AT group suggested massively decreased Veillonellaceae, Prevotellaceae, and Bacteroidaceae, while the H group demonstrated obviously reduced Prevotellaceae and Bacteroidaceae (Figure 4E).

To better understand how biological functions in secukinumab treatment may be affected, we used PICRUSt2 to evaluate the composition of functional genes in the fecal microbiota of our samples. As a result, a total of five significantly different functional COGs between the AT group and the H group were found, as well as seven COGs between the AT group and the BT group were predicted (Supplementary Figure 6).

Moreover, further analysis in the context of the KEGG database was performed to better clarify metabolic function changes in the community samples. In the AT group, the functional genes for the metabolism of terpenoids and polyketides, membrane transport, substance dependence, and infectious diseases: viral were highly enriched, whereas metabolism of cofactors and vitamins, glycan biosynthesis and metabolism, transport and catabolism, and the circulatory system were significantly reduced in comparison with the H group (Figure 5A). Detailed information is shown in Supplementary Table 3A. Additionally, compared to the BT group, eight KEGG pathways (including infectious diseases, such as viral, and infectious diseases, such as parasitic, substance dependence, and others) were significantly enriched and five KEGG pathways (cardiovascular diseases, circulatory system, glycan biosynthesis and metabolism, amino acid metabolism, and metabolism of cofactors and vitamins) were statistically reduced in the AT group (Figure 5B and Supplementary Table 3B).

To further identify functional changes that occur in response to secukinumab therapy, shotgun metagenomic analysis was also used. NMDS based on bray_curtis was performed, which suggested that the fecal microbiota of the BT group was different from the other two groups (stress = 0.0988 < 0.2; Figure 6A). As shown in Figure 6B, the UPGMA method of beta diversity generated two major clusters.

Moreover, to better clarify metabolic function changes in samples, the KEGG database was used. Pathways that changed in the AT group were related to upregulated glycerophospholipid metabolism, glycerolipid metabolism, N-glycan biosynthesis, and the phosphatidylinositol signaling system (Figure 6C). In the BT group, KEGG metabolic pathways indicated that the gut microbiome was enriched in oxidative phosphorylation and carbon fixation in photosynthetic organisms but decreased in glycerophospholipid metabolism and glycerolipid metabolism (Figure 6C). Interestingly, the KEGG metabolic pathways of the H group also showed higher expression of glycerophospholipid metabolism and glycerolipid metabolism, which is similar to that of the secukinumab therapy group (Figure 6C).

GO analysis showed that following treatment with secukinumab, metabolic process and cellular process were the dominant terms in the biological process category, whereas structural molecule activity and binding were the most enriched in the molecular function category (Figure 6D). More detailed information and a heat map on the CARD database (Supplementary Figure 7), eggNOG database (Supplementary Figure 8), and CAZy database (Supplementary Figure 9) are shown in Supplementary material.