Role of gut microbiota in idiopathic inflammatory myopathies: insights from 16S rRNA sequencing and cluster analysis

Abstract

Background

The disturbed gut microbiota has been found involved in the immunopathogenesis of various autoimmune diseases (AIDs). However, the clinical and pathogenic role of gut microbiota in Idiopathic inflammatory myopathies (IIMs) remains unclear. This study employs 16 S rRNA gene sequencing combined with unsupervised cluster analysis to explore the alterations in gut micro-biota in IIMs patients, aiming to elucidate the association between gut microbial composition and the clinical manifestations of IIMs.

Results

Analysis of fecal samples from 77 IIMs patients and 32 healthy controls revealed significant reductions in both the richness and diversity of the gut bacterial community in the IIM group. IIMs patients exhibited a significantly elevated microbial dysbiosis index (MDI) and a decreased abundance of Bacteroidetes. Clustering analysis classified IIMs patients into three distinct microbiota-based clusters, each with unique bacterial compositions and clinical features. Cluster 1 showed a lower incidence of skin symptoms and Cluster 2 had a lower disease activity score and relatively normal immunity indicators. Conversely, Cluster 3 patients exhibited a higher prevalence of interstitial lung disease (ILD). Correlation analyses highlighted specific gut bacteria associated with clinical features, particularly strong negative correlations between core short-chain fatty acids (SCFAs)-producing bacteria, such as Faecalibacterium, Subdoligranulu, and Ruminococcus, and skin manifestations, disease activity, and inflammation indicators.

Conclusions

Our study revealed significant alterations in the gut microbiota of IIMs patients along with the distinct microbial signatures identified in different patient clusters, underscoring the potential role of gut microbiota in the pathogenesis and clinical heterogeneity of IIMs. These findings provide a novel perspective on the pathogenesis of IIMs and highlight the importance of gut microbiota as a potential therapeutic target in managing IIMs.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12866-026-05401-0.

Background

Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of autoimmune connective tissue diseases (CTDs) characterized by chronic inflammation of skeletal muscles, with potential involvement of additional organs including the skin, joints, lungs, heart, and gastrointestinal tract [1]. Patients with IIMs can be classified into subtypes such as dermatomyositis (DM), inclusion body myositis (IBM), anti-synthetase syndrome (ASS), immune-mediated necrotizing myopathy (IMNM), and polymyositis (PM), based on distinctive clinical, serological, and pathological features [2, 3].

As with other types of autoimmune diseases (AIDs), IIMs are thought to be caused by a complex interplay between genetic and environmental risk factors, although the precise mechanisms that initiate autoimmunity in IIMs remain unclear [4]. Recent studies have highlighted the significant role of the gut microbiota in the pathogenesis of AIDs, such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and primary Sjögren’s syndrome (pSS) [5–8]. The gastrointestinal tract hosts the largest microbial population in the human body and is constantly exposed to environmental factors. Host–microbiota interface in the gut, including mucin, epithelial tight junction, and the gut-associated lymphoid tissue (GALT), are crucial in shaping microbiota [9, 10]. Conversely, the gut microbiota influences the development and maturation of the host immune system [11, 12]. With emerging mechanistic studies of microbes and their metabolites, we had a better understanding of how disturbed gut microbiota contributes to AIDs [13, 14]. Due to the dysfunctional gut barrier in patients with AIDs, the live gut microbes translocated into gut tissues and promoted overactivation of innate and adaptive immunity in the local tissue, fostering systemic autoimmunity [15–17]. Innate immune cells, such as dendritic cells (DCs), are activated and secrete inflammatory cytokines, including type I interferons (IFNs), IL-12, and IL-23 [18, 19]. Microbial antigens can be presented to CD4 + T cells by DCs and macrophages, leading to the differentiation of inflammatory T cell subtypes. B cells are also directly activated by microbial antigens with the assistance of T follicular helper (Tfh) cells [20]. Furthermore, a decrease in beneficial commensal microbes results in reduced production of important microbial metabolites, such as short-chain fatty acids (SCFAs), which are critical for activating anti-inflammatory immune cells and cytokines [21].

Despite the growing interest in the role of the gut microbiome in AIDs, specifically investigating the association between gut microbiota dysbiosis and IIMs, particularly its subtypes, remains limited. Bae et al. enrolled 36 patients with DM and found they had lower microbial diversity and a distinct taxonomic composition compared with healthy controls (HCs) [22]. Zhufeng et al. [23] and Luo et al. [24] also reported significant reductions in gut microbiota diversity and richness in IIMs patients compared to HCs. Although these studies identified different bacterial taxa associated with IIMs, they consistently found correlations with serum inflammatory cytokine levels. Furthermore, Li et al. discovered associations between gut microbiota diversity, autoantibody levels, and clinical symptoms in IIMs patients [25]. Their research also highlighted significant differences in the gut microbiota between myositis patients with rapidly progressive interstitial lung disease (RP-ILD) and those with chronic interstitial lung disease (C-ILD), with R.intestinalis being a key differentiator between the two groups.

However, previous studies typically involved small sample sizes and did not provide detailed analyses of microbiota characteristics across different myositis subtypes with varying clinical presentations. In this study, we employed 16 S rRNA gene sequencing technology to explore alterations in the gut microbiota of patients with IIMs. By elucidating the changes in gut microbial composition associated with the clinical manifestations of IIMs, we aim to offer a novel perspective on the role of gut microbiota in the pathogenesis of IIMs.

Materials and methods

Patient participation

We recruited IIM patients admitted to West China Hospital of Sichuan University from May 2020 to November 2022. All patients met the 2017 EULAR/ACR criteria for IIM classification [2]. Antisynthetase syndrome (ASS) was diagnosed according to the ACR/EULAR classification, by the presence of anti-ARS autoantibodies complicated with clinical features [26]. Age- and gender-matched healthy controls without disease history of muscular diseases were also recruited from family members of hospitalized patients. Exclusion criteria for both groups were as follows: (1) current extreme diets (e.g., strict vegetarians); (2) a history of receiving antibiotics, antivirals, or antifungals within the last three months; (3) major organ dysfunction, a serious infection or malignancy; (5) diagnosed with other rheumatic or autoimmune diseases; (6) under 18 years old. We enrolled 77 IIM patients and 32 HCs and all subjects provided written informed consent before participation. This study was approved by the Biomedical Research Ethics Committee, West China Hospital of Sichuan University (no. 695 in 2020).

Clinical data and sample collection

Fecal samples were freshly collected from each participant within one hour after excretion at their first admission to the Department of Rheumatology and Immunology at West China Hospital of Sichuan University. All fecal samples were immediately frozen and stored at -80℃ for further analyses. All patients’ medical charts were retrospectively reviewed for demographic characteristics, clinical features, and laboratory data, which were collected at their first admission to the hospital. IIM-associated interstitial lung disease (IIM-ILD) was diagnosed according to the definition of connective tissue disease-associated interstitial lung disease (CTD-ILD), based on chest high-resolution computed tomography (HRCT) findings and clinical evaluation by two experienced rheumatologist and pulmonologist [27, 28]. Rapidly progressive interstitial lung disease (RP-ILD) was considered as progressive dyspnea and/or hypoxemia, together with worsening interstitial abnormalities on chest HRCT, occurring within 3 months from the onset of respiratory symptoms [29]. On the same day, a rheumatology specialist used the Myositis Disease Activity Assessment Tool (MDAAT) to assess disease activity and computed the MITAX and MYOACT scores [30].

Microbial DNA extraction and PCR amplification

Microbial DNA extraction and PCR amplification were conducted using the E.Z.N.A.^® soil DNA Kit (Omega Bio-Tek, Norcross, GA, US). DNA quality and concentration were verified by 1.0% agarose gel electrophoresis and NanoDrop^® ND-2000 spectrophotometry (Thermo Scientific Inc., USA), respectively. Purified PCR products of the hypervariable region V3–V4 of the bacterial 16S rRNA gene were obtained using primer pairs 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCT-AAT-3’) on an ABI GeneAmp^® 9700 PCR thermocycler (ABI, CA, USA). The AxyPre DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) was used to purify the PCR products, which were then quantified using a Quantus™ Fluorometer (Promega, USA) and subsequently stored at − 80 °C.

Illumina MiSeq sequencing and data processing

For Illumina MiSeq sequencing, equimolar pools of purified amplicons underwent paired-end sequencing using the Illumina MiSeq PE300 platform (Illumina, San Diego, USA). This process was carried out by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Raw sequencing data underwent demultiplexing and quality filtering using Fastq version 0.19.6, followed by merging with FLASH version 1.2.1. Sequences were then processed with UPARSE 7.1, clustering them into operational taxonomic units (OTUs) at a 97% similarity threshold. For each OTU, the sequence with the highest abundance was chosen as the representative. Taxonomic classification utilized RDP Classifier version 2.11 against the Silva v138 16 S rRNA gene database (confidence threshold of 0.7). Sequencing files were deposited in the NCBI Sequence Read Archive.

Bioinformatical analysis

The analysis of gut microbiota using bioinformatics was carried out through the Majorbio Cloud platform (https://cloud.majorbio.com). Alpha diversity metrics (observed OTUs, Chao1 richness, Ace index, Simpson index) and rarefaction curves were computed with Mothuv1.30.2. Principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarity and ANOSIM assessed community similarity among samples. Welch’s t-test identified significantly abundant taxa (phylum and genera) between groups, while linear discriminant analysis effect size (LefSe) highlighted discriminatory taxa (LDA score cutoff of 4.0). Associations between gut microbiota and clinical indicators were examined using Spearman correlation analysis, with a statistical significance threshold set at P < 0.05.

Statistical analyses

IBM SPSS (Armonk, New York, United States: IBM Corp) version 26.0 and Graph Prism (Version 9.0) was used to perform statistical analyses. Descriptive statistics are reported as mean (standard deviation) or median (Q25, Q75) for continuous variables and as numbers and frequencies for binary and categorical variables.

The two-step cluster analysis was performed to obtain the clusters of patients using the top 10 relative abundance genera as predictor variables: Blautia, Escherichia-Shigella, Streptococcus, Lactobacillus, Faecalibacterium, Bifidobacterium, Bacteroides, Eubacterium_hallii_group, Subdoligranulum, and Anaerostipes. Schwarz’s Bayesian criterion (BIC) and the silhouette coefficient were used to compare cluster solutions. Silhouette measures of less than 0.2 were classified as poor; between 0.2 and 0.5 as fair; greater than 0.5 was good solution quality with fair or higher considered acceptable clustering. Characteristics were compared among the clusters using the chi-square, ANOVA, and Kruskal‒Wallis tests as appropriate. The level of statistical significance was set at p < 0.05.

Results

Gut microbiota alteration between IIMs patients and healthy control

A total of 109 fecal samples were collected from 32 healthy controls (HC group) and 77 IIMs patients (IIM group). The IIM group included 42 individuals with DM, 16 with IMNM and 19 with ASS. Basic demographics were similar between the two groups (Table 1). Analysis of the fecal microbiota via 16 S rRNA sequencing revealed a significant reduction in bacterial community richness (Ace index and Chao index) and diversity (Shannon index) in the IIM group compared to the HC group (Fig. 1A). Beta diversity, assessed using partial least-squares discriminant analysis (PLS-DA), showed a marked difference in the bacterial community between the IIM and HC groups (Fig. 1B, Adonis P = 0.001). We next calculated the microbial dysbiosis index (MDI) at the OTU level and found it significantly elevated in the IIM group (Fig. 1C, p < 0.001).

Table 1.

Basic demographics of the study population

Variable	HC group (n = 32) (n = 32)	IIM group (n = 77) (n = 77)	P value
Female, n (%)	20 (62.50)	59 (76.60)	0.133
Age, year (mean, SD)	45.56 ± 12.38	48.26 ± 11.96	0.291
DM, n (%)	/	42 (54.55)
IMNM, n (%)	/	16 (20.78)
ASS, n (%)	/	19 (24.68)
IIM-ILD, n (%)	/	48 (62.34)

HC healthy control, IIM idiopathic inflammatory myopathies, DM dermatomyositis, IMNM immune-mediated necrotizing myopathy, ASS anti-synthetase syndrome, IIM-ILD IIM-associated interstitial lung disease. The meaning of the symbol “/“ was “untested”

Fig. 1.

Gut microbiota alteration in IIMs patients compared to healthy controls. (A) Comparison of community richness (Ace index and Chao index) and microbial diversity (Shannon index) in HC and IIM groups. (B) Partial least-squares discriminant analysis (PLS-DA) at the OTU level between HC and IIM groups. (C) Comparison of microbial dysbiosis index (MDI) at the OTU level between HC and IIM groups. (D) Community bar plot analysis at the phylum level in HC and IIM groups. (E) Differential genera between HC and IIM groups confirmed by Wilcoxon rank-sum test, with only the top 15 genera being shown. HC, healthy control; IIM, idiopathic inflammatory myopathies. *p<0.05, **p<0.01,and ***p<0.001

Further examination of gut microbiota composition at the phylum level showed that both groups had similar major phyla, including Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria (Fig. 1D). Despite this similarity, Bacteroidetes were significantly reduced in the IIM group compared to the HC group (Fig. 1D, p < 0.001). At the genus level, the relative abundances of Faecalibacterium (p = 0.001), Bacteroides (p < 0.001), Agathobacter (p < 0.001), Ruminococcus (p = 0.003), etc. were significantly decreased in the IIM group. In contrast, Streptococcus (p < 0.001) and Lactobacillus (p = 0.004) were increased in IIM group (Fig. 1E).

Clustering Analysis Reveals Distinct Gut Microbiota Profiles in IIMs Patients

Based on the observed differences in gut microbiota between the IIM and HC groups, we further conducted a clustering analysis of gut microbiota in IIMs patients, irrespective of their initial clinical diagnoses. The two-step cluster analysis classified the patients into three clusters according to the relative abundance of the top 10 genera: cluster 1 (n = 30, 39.0%), cluster 2 (n = 18, 23.4%), and cluster 3 (n = 29, 37.7%) (Table 2). The ratio of sizes (largest cluster to smallest cluster) was 1.67, which was adequate since it is generally preferred to be below 3. The Silhouette measure of cohesion and separation was 0.3 (i.e., cluster quality between fair and good) with the highest ratio of distance measure 1.669 and the BIC value of 618.277.

Table 2.

Clinical manifestations and laboratory data of three clusters

Variable	Cluster			P value
	1 (n = 30)	2 (n = 18)	3 (n = 29)
General information
Female, n (%)	22 (73.3)	13 (72.2)	24 (82.8)	0.652
Age, mean ± SD, years	42.50 ± 11.63	54.06 ± 11.49	50.81 ± 11.26	0.001*
BMI, mean ± SD, Kg/m2	22.25 ± 3.05	21.95 ± 2.90	22.05 ± 2.91	0.936
Disease course, median (IQR), months	10.00 (3.75,39.00)	12.00 (7.50,48.00)	9.5 (2.50,27.00)	0.400
Smoking history, n (%)	4 (13.3)	2 (11.1)	3 (10.3)	1.000
Disease diagnoses
DM, n (%)	16 (53.3)	12 (66.67)	14 (48.28)	0.462
IMNM, n (%)	4 (13.33)	3 (16.67)	9 (31.03)	0.218
ASS, n (%)	10 (33.33)	3 (16.67)	6 (20.69)	0.354
IIM-ILD, n (%)	15 (50.00)	10 (55.56)	23 (79.31)	0.053b
Chronic-ILD, n (%)†	14 (93.33)	10 (100.00)	20 (86.96)	0.805
RP-ILD, n (%)†	1 (6.67)	0	3 (13.04)	0.805
Clinical manifestations
Fever, n (%)	1 (3.33)	2 (11.1)	2 (6.90)	0.567
Fatigue, n (%)	13 (43.33)	4 (22.20)	17 (58.62)	0.050*
Arthritis, n (%)	6 (20.00)	5 (27.80)	7 (24.14)	0.821
Myalgia, n (%)	9 (30.00)	7 (38.90)	9 (31.03)	0.799
Myasthenia, n (%)	11 (36.67)	7 (38.90)	14 (48.28)	0.642
Skin rash, n (%)	8 (26.67)	10 (55.60)	15 (51.72)	0.069a
Heliotrope rash, n (%)	2 (6.67)	3 (16.70)	5 (17.24)	0.419
“V” sign, n (%)	5 (16.67)	3 (16.70)	5 (17.24)	0.998
Gottron papule, n (%)	4 (13.33)	6(33.30)	11 (37.93)	0.084c
Mechanic’s hands, n (%)	5 (16.67)	3 (16.70)	3 (10.34)	0.745
Periungual capillary changes, n (%)	1 (3.33)	2 (11.1)	5 (17.24)	0.215
Disease activity
MITAX score, mean ± SD	0.17 ± 0.12	0.15 ± 0.14	0.22 ± 0.14	0.044*
MYOACT score, mean ± SD	0.19 ± 0.09	0.18 ± 0.09	0.25 ± 0.11	0.031*
MYOACT Constitutional, mean ± SD	3.43 ± 1.98	2.28 ± 1.49	3.76 ± 2.06	0.036*
MYOACT Cutaneous, mean ± SD	1.77 ± 2.03	3.94 ± 3.47	2.83 ± 2.49	0.069a
MYOACT Skeletal, mean ± SD	1.07 ± 2.24	1.78 ± 3.19	1.28 ± 2.49	0.719
MYOACT Gastrointestinal, mean ± SD	1.17 ± 2.00	0.89 ± 1.78	0.97 ± 2.03	0.891
MYOACT Pulmonary, mean ± SD	2.43 ± 2.96	2.22 ± 2.76	4.24 ± 3.36	0.038*
MYOACT Cardiovascular, mean ± SD	1.67 ± 2.55	1.44 ± 2.43	1.76 ± 2.66	0.960
MYOACT Muscle, mean ± SD	4.03 ± 3.39	3.28 ± 3.18	3.86 ± 3.37	0.745
Laboratory features
HGB, mean ± SD, g	131.14 ± 14.06	131.33 ± 17.38	132.55 ± 15.41	0.934
PLT, mean ± SD, ×109/L	220.53 ± 81.47	230.72 ± 136.5	205.76 ± 53.42	0.694
WBC, mean ± SD, ×109/L	7.71 ± 3.01	7.35 ± 3.00	8.89 ± 4.63	0.312
PMN, mean ± SD, ×109/L	5.31 ± 2.16	5.07 ± 2.37	7.06 ± 4.20	0.048*
LYM, mean ± SD, ×109/L	1.41 ± 0.73	1.51 ± 0.79	1.47 ± 0.73	0.909
MONO, mean ± SD, ×109/L	0.61 ± 0.24	0.51 ± 0.17	0.58 ± 0.24	0.310
CD3, mean ± SD, mm3	1015 ± 575.4	1082 ± 394.6	1107 ± 530.3	0.864
CD4, mean ± SD, mm3	577.3 ± 254.5	786.9 ± 221.3	621.5 ± 253.8	0.026*
CD8, mean ± SD, mm3	395.0 ± 274.6	329.2 ± 133.6	426.2 ± 273.0	0.615
CD4/CD8, mean ± SD	1.99 ± 0.53	2.90 ± 0.88	2.45 ± 1.23	0.009*
ALT, mean ± SD	77.10 ± 73.26	49.67 ± 49.53	81.11 ± 88.01	0.377
AST, mean ± SD	64.31 ± 56.99	34.25 ± 22.95	55.61 ± 48.66	0.188
GGT, mean ± SD	42.33 ± 38.35	22.89 ± 12.29	53.88 ± 54.62	0.042*
C3, mean ± SD, g/L	0.91 ± 0.15	0.85 ± 0.10	0.96 ± 0.15	0.182
C4, mean ± SD, g/L	0.21 ± 0.08	0.22 ± 0.06	0.25 ± 0.09	0.308
ESR, mean ± SD	38.21 ± 24.13	45.92 ± 31.48	39.19 ± 35.48	0.745
CRP, mean ± SD, mg/L	4.51 ± 3.12	4.481 ± 3.39	6.991 ± 4.52	0.107b
CK, median (IQR), IU/L	625 (93,1997)	250 (68.25,1440)	76 (46.5,753.5)	0.099c
LDH, median (IQR), IU/L	321.0 (192.0,507.0)	289.0 (213.0,471.0)	292.0 (215.0,493.5)	0.904
HBDH, median (IQR), IU/L	242.0 (152.0,377.0)	217.5 (156.8,365.3)	206.0 (167.0,378.0)	0.964
Autoantibody
ANA (+), n (%)	12 (40.00)	5 (27.78)	13 (44.83)	0.502
Anti-Ro52 (+), n (%)	16 (53.33)	10 (44.44)	17 (58.62)	0.639
Anti-MDA5 (+), n (%)	8 (26.67)	3 (16.67)	10 (34.48)	0.409
Anti-SRP/HMGCR (+), n (%)	1 (3.33)	2 (11.11)	6 (20.69)	0.119
Anti-ARS (+), n (%)	12 (40.00)	5 (27.78)	9 (31.03)	0.636
Anti-Jo-1 (+), n (%)††	7 (58.33)	3 (60.00)	5 (55.56)	0.985
non-Jo-1 anti-ARS (+), n (%)††	5 (41.67)	2 (40.00)	4 (44.44)	0.985
Anti-NXP2 (+), n (%)	1 (3.33)	0	0	/
Anti-Mi2 (+), n (%)	1 (3.33)	1 (5.56)	0	0.703
Anti-TIF1γ (+), n (%)	2 (6.67)	1 (5.56)	1 (3.45)	> 0.999

BMI body mass index, DM dermatomyositis IMNM immune-mediated necrotizing myopathy, ASS anti-synthetase syndrome, IIM-ILD IIM-associated interstitial lung disease, RP-ILD rapidly progressive interstitial lung disease, MITAX Myositis Intention to Treat Activities Index, MYOACT Myositis Intention to Treat Activities Index, HGB hemoglobin, PLT platelet, WBC white blood cell, PMN polymorphonuclear neutrophils, LYM lymphocytes, MONO monocytes, ALT alanine transaminase, AST aspartate aminotransferase, GGT Gamma-glutamyltransferase, C3 Complement 3, C4 Complement 4, ESR erythrocyte sedimentation rate, CRP C-reactive protein, CK creatine kinase, LDH lactate dehydrogenase, HBDH hydroxybutyrate dehydrogenase, ANA antinuclear antibody, Ro52 anti-cytoplasmic ribonucleoprotein of 52 kDa, MDA5 melanoma differentiation-associated gene 5, SRP signal recognition particle, HMGCR 3-hydroxy-3-methyl glutaryl coenzyme A reductase, ARS aminoacyl-tRNA synthetase, NXP2 anti-nuclear matrix protein 2, TIF1γ transcription intermediary factor-1gamma

†For Chronic-ILD and RP-ILD groups, percentages were calculated among IIM-ILD patients in each cluster. †† For anti-Jo-1 positive and non-Jo-1 anti-ARS groups, percentages were calculated among ARS-positive patients in each cluster. Non-Jo-1 anti-ARS antibodies included anti-PL-7, anti-PL-12, anti-EJ, anti-Zo, and anti-Ha antibodies. The detailed anti-ARS antibody subtype counts in clusters 1, 2, and 3, respectively, were as follows: anti-PL-7, 3/2/2; anti-PL-12, 1/0/1; anti-EJ, 1/0/1; anti-Zo, 0/0/1; and anti-Ha, 1/0/0. *p < 0.05 among three clusters; a, p < 0.05 between cluster1 and 2; b, p < 0.05 between cluster2 and 3; c, p < 0.05 between cluster1 and 3. The meaning of the symbol “/“ was “untested”

The PLS-DA score plots showed good separation among the three clusters and ANOSIM also revealed significant differences (Fig. 2A, R = 0.31, p = 0.001). Interestingly, we found bacterial community richness and diversity in cluster 3 were significantly decreased compared to the other clusters (Fig. 2B). Further analysis of the microbiota differences indicated that, despite all samples originating from IIMs patients, the microbial community structure within each cluster displayed highly distinct characteristics. In terms of phylum level, we found that the proportion of Bacteroidota and Desulfobacterota was highest in cluster 1 and Actinobacteriota in cluster 2 (Fig. 2C). At the genus level, we observed Faecalibacterium, Bacteroides, Subdoligranulum, and Agathobacter enriched in cluster 1, Blautia, Bifidobacterium, Eubacterium_hallii_group, Anaerostipes, etc. in cluster 2, and Streptococcus, Lactobacillus, unclassified_f__Enterobacteriaceae and Klebsiella in cluster 3 (Fig. 2D). To further specifically identify genera associated with different clusters, we used LEfSe to compare bacterial abundances with linear discriminant analysis (LDA) score > 4 and obtained results consistent with the above (Fig. 2E).

Fig. 2.

Gut microbiota alteration among cluster 1, cluster 2, and cluster 3. (A) Comparison of community richness (Ace index) and microbial diversity (Shannon index) among three clusters. (B) Partial least-squares discriminant analysis (PLS-DA) at the OTU level among three clusters. (C) Community bar plot analysis at the phylum level among three clusters. (D) Differential genera among three clusters confirmed by the Wilcoxon rank-sum test, with only the top 15 genera being shown. (E) The greatest differences at genus levels among the three clusters. Linear discriminant analysis, LDA score (log10), LDA score > 4. LDA, linear discriminant analysis; LEfSe, linear discriminant analysis (LDA) effect size; *p<0.05, **p<0.01, and ***p<0.001

Distinct clinical and laboratory profiles among gut microbiota-based clusters

IIMs encompass a clinically heterogeneous group of diseases, where disease manifestations vary even among individuals diagnosed with the same subtype. We attempted to explore the relationship between gut microbiota and clinical features in IIMs patients by analyzing the clinical profiles of the three clusters (Table 2).

Compared to clusters 2 and 3, the patients in cluster 1 were much younger and there was no difference in gender ratio. Considering the clinical manifestations across the three clusters, patients in Cluster 1 were the least likely to exhibit skin symptoms, with a notably lower incidence of skin rashes (26.67% vs. 55.60% vs. 51.72%) and Gottron’s papules (13.33% vs. 33.30% vs. 37.93%). Additionally, the MYOACT Cutaneous scores for Cluster 1 were significantly lower compared to the other two clusters (1.77 ± 2.03, p = 0.069). Cluster 1 patients had notably elevated CK levels, while Cluster 2 patients exhibited the lowest levels of ALT, AST, GGT, and IgM, alongside the highest levels of CD4 + T cell, CD4/CD8 ratios, and IgG. Meanwhile, Cluster 3 patients showed increased levels of PMN, GGT and CRP. Regarding other phenotypic differences, Cluster 3 patients had a significantly higher prevalence of IIM-ILD (50% vs. 56% vs. 79%, p = 0.053) and poorer MYOACT pulmonary scores (4.24 ± 3.36, p = 0.038). This factor likely contributed to the notable increases in total MITAX scores (0.22 ± 0.14, p = 0.044) and overall MYOACT scores (0.25 ± 0.11, p = 0.031) observed in Cluster 3.

Additionally, we found the distribution of the categorical diagnosis was homogeneous within the clusters and there is no significant difference in the positivity of the MSA. Considering the heterogeneity of anti-ARS antibodies, we further analyzed the relationship between anti-ARS antibody subtypes and gut microbial signatures. The distribution of anti-Jo-1-positive and non-Jo-1 anti-ARS-positive patients did not differ significantly among the three clusters. In addition, no significant differences were observed in alpha diversity, beta diversity, or the relative abundance of the top 10 genera used for clustering between anti-Jo-1-positive and non-Jo-1 anti-ARS-positive patients (Supplementary Figure S1 and Supplementary Table S1). Further analysis of drug usage among the three clusters revealed no significant differences in the use of glucocorticoids and DMARDs (Supplementary Table S2).

Microbial Interactions with Clinal Features in IIMs patients

The above analysis results reveal substantial differences in gut microbiota composition and clinical features across the three clusters. To further explore these relationships, we performed a correlation analysis between the genera with differential abundance among the clusters and the specific clinical features identified.

As illustrated in Fig. 3, Faecalibacterium, Subdoligranulum, and Agathobacter showed a strong negative correlation with age. Anaerostipes demonstrated a significant negative correlation with overall physical conditions, including fatigue and myositis disease activity scores (MITAX and MYOACT scores). Additionally, Faecalibacterium, Ruminococus, and Subdoligranulum were negatively correlated with the presence of skin rash. Furthermore, Lactobacillus was positively associated with the combined occurrence of ILD in IIMs patients, whereas Faecalibacterium and Agathobacter exhibited a negative correlation with the MYOACT pulmonary score.

Fig. 3.

The correlation analysis between gut microbiota and clinical features in IIMs patients. Heatmap of Spearman correlation analysis between the top 15 differential microbial genera among three clusters and 18 clinical indicators in IIMs patients. *p<0.05, **p<0.01, and ***p<0.001

Regarding laboratory features, Bifidobacterium and the Eubacterium_hallii_group showed negative correlations with liver function-related laboratory indicators (ALT, AST, and GGT), while Streptococcus exhibited positive correlations with these indicators. Concerning inflammation and immunity markers, Anaerostipes, Eubacterium_hallii_group, and Subdoligranulum demonstrated significant negative correlations with CRP. Additionally, Eubacterium_hallii_group exhibited a positive correlation with CD4 + T cell count, and Bifidobacterium was negatively correlated with CK levels. Besides, we found no significant correlations between MSA and differential genera (Supplementary Figure S2).

Discussion

In our study, we analyzed gut microbiota alterations using 16s rRNA sequencing for a deeper understanding of the relationship between gut microecology and IIMs. We observed significantly reduced bacterial community richness and diversity in IIMs patients, accompanied by a notable decrease of core short-chain fatty acids (SCFAs) -producing bacteria and a higher microbial dysbiosis index compared to healthy controls. These findings are consistent with previous reports on gut microbiota alterations in IIMs, suggesting that disease progression in IIMs is indeed associated with gut microbial dysbiosis [22–25].

To further investigate the correlation between the gut microbiome and disease features of IIMs, we conducted an unsupervised clustering analysis based on gut microbiota profiles. We identified three distinct gut microbiota clusters with unique clinical characteristics (Fig. 4): Cluster 1, characterized by higher levels of genera Faecalibacterium, Bacteroides, Subdoligranulum, and Agathobacter, were relatively young and exhibited minor skin manifestations. Cluster 2, enriched in Blautia, Bifidobacterium, Eubacterium_hallii_group, and Anaerostipes, exhibited enhanced liver and immune function. Cluster 3, enriched in Streptococcus and Lactobacillus, showed higher inflammation levels and a greater prevalence of IIM-ILD. Notably, the distribution of myositis subtypes across these clusters was consistently aligned, suggesting that clustering based on bacteriophage could provide insights into the underlying mechanisms governing the occurrence of their clinical phenotypes, irrespective of their diagnostic categories.

Fig. 4.

The gut microbiota clustering reveals three distinct clinical phenotypes. Created with BioRender.com

Correlation analysis further linked specific genera with clinical features, particularly highlighting strong negative correlations between core SCFA-producing bacteria and skin manifestations, disease activity, and inflammation levels. SCFAs, key metabolites produced by specific gut bacteria, play critical roles in health and disease, including regulating gut homeostasis [31]. Among these, acetate (C2), propionate (C3), and butyrate (C4) are the most represented [32, 33]. SCFAs enter the circulation and exert systemic effects through G-protein-coupled transmembrane receptors: GPR43 (FFAR2) and GPR41 (FFAR3) recognize acetate, butyrate, and propionate, while only butyrate activates GPR109a [34]. Indeed, they regulate gut homeostasis and their deficiency is identified in the pathogenesis of several disorders, including inflammatory bowel diseases (IBD), RA, and SLE [35, 36]. In our study, we found the genera belonging to the butyrate-producing families such as Ruminococcaceae (e.g., Faecalibacterium and Subdoligranulum) and Lachnospiraceae (e.g., Ruminococcus) were negatively correlated with skin rash incidence. Similar reductions in these SCFA-producing taxa have been observed in other inflammatory skin diseases such as symptomatic dermographism, psoriasis, and rosacea, suggesting that the decrease of SCFA-producing bacteria reduced the capacity to produce SCFA, particularly butyrate, contributes to skin barrier dysfunction [37, 38]. Some reports have confirmed gut microbiota and the metabolites can transfer to the skin. In a mouse model of atopic dermatitis (AD), Aurélien et al. have found that orally administered SCFAs, particularly butyrate, enhanced skin barrier function by altering the mitochondrial metabolism of epidermal keratinocytes and increasing the production of key structural components such as cholesterol and ceramides in the stratum corneum [39]. Similarly, butyrate supplementation alleviated skin lesions and reduced autoantibodies in SLE mouse models [40, 41].

In addition to the skin, the lungs are also frequently affected by IIMs, with ILD being a significant complication with a prevalence of approximately 40% [42]. Despite improvements in survival rates for IIMs, ILD continues to impact survival negatively [43–45]. Consistent with the relationship between skin manifestation and SCFA-producing genera, we found the incidence of IIM-ILD and MYOACT pulmonary score negative with Faecalibacterium and Agathobacter. Bae et al. also discovered the depletion of the Ruminococcaceae family in DM-ILD patients compared with controls [22]. Park et al. verified that sodium butyrate, which is the counterpart of butyrate, has an ameliorative effect on lung fibrosis in a model of bleomycin-induced lung fibrosis in mice [46]. Furthermore, they explored the potential mechanisms that may be mediated by the indirect and direct antifibrotic effects on fibroblasts, which are exhibited through the modulation of macrophage differentiation and the inhibition of histone deacetylase (HDAc). In addition to the strong correlation between SCFA-producing genera and IIM-ILD, we also found that Lactobacillus was positive with IIM-ILD. Lactobacillus has been identified as one probiotic and has an excellent immunomodulatory function in improving gastrointestinal and respiratory disorders [47, 48]. However, same to our results, Su et al. found the genus Lactobacillus to be associated with an increased risk of pulmonary arterial hypertension (PAH), the pathogenesis of which is very similar to ILD in terms of endothelial cell hyperproliferation and endothelial-to-mesenchymal transition (EndoMT) [49]. These controversial results indicate that the effect of Lactobacillus on the respiratory tract is strain-dependent. Given the inability to further analyze the species level in our study, further investigation is required to elucidate this mechanism.

The present results highlight the significant role of gut microflora, especially SCFA-producing genera, in regulating the gut-skin and gut-lung axis. Correlation analysis of laboratory indicators suggests this regulatory function may be mediated through modulation of the immune system. In accordance with the findings observed in SLE, our results indicate that elevated levels of Streptococcus spp. were linked to increased CRP and GGT levels, indicating a potential pathogenic role in AIDs [50, 51]. Furthermore, we observed that hepatic impairment markers (AST, ALT, GGT) and inflammation indexes (CRP, CK) were decreased with higher abundances of SCFA-producing bacteria, including Anaerostipes, Eubacterium_hallii_group, Subdoligranulum, and Bifidobacterium. In line with our findings, the typical butyrate-producing bacteria Faecalibacterium prausnitzii was found to be reduced in IBD and negatively correlated with CRP levels [52]. Moreover, the administration of butyrate in collagen-induced arthritis (CIA) mouse model resulted in the downregulation of proinflammatory cytokines, including IL-1β, IL-6, and IL-17 A [53, 54]. Furthermore, the abundance of Eubacterium_hallii_group was positively correlated with CD4 + T cell levels in peripheral blood. Accumulating data has demonstrated that aberrant CD4 + T cell responses contribute to the development of AIDs [55–57]. Shimojima et al. found decreased CD4+, CD8+, and CD3 + T cells within the peripheral blood lymphocytes of PM/DM patients with active disease [58]. Studies have highlighted the potential role of propionate and butyrate in Treg production and function by interacting directly with naïve T cells to increase acetylation of the Foxp3 transcription factor promoter, and indirectly through inhibition of HDAC activity to increase TGFβ1 production to promote Treg differentiation [59–61]. Furthermore, studies have proved that SCFAs, especially butyrate, inhibit the activity of factor kappa b (NF-κB) in mononuclear cells, which is a transcriptional factor controlling the expression of multiple genes involved in the inflammatory response, and enhancing the expression of anti-inflammatory cytokines such as IL-10 [62–64]. Regarding innate immunity, SCFAs can reduce neutrophil production of reactive oxygen species (ROS) and myeloperoxidase (MPO), as well as decrease chemotaxis of inflammatory cells by downregulating the expression of monocyte chemoattractant protein-1 (MCP-1), vascular cell adhesion molecule-1 (VCAM1), and chemokines [65, 66]. These results indicate that gut microbiota may regulate the immune system and inflammatory response in IIMs patients through SCFA.

Additionally, we observed a negative correlation between SCFA-producing genera and the age of IIMs patients. This trend was also identified in studies on healthy older people and age-related diseases, which indicates that age-related and disease-related deterioration in the gut microbiome of older people reflects overlapping interactions [67, 68]. Surprisingly, no significant relationship was found between myositis-specific autoantibodies (MSAs) and the gut microbiota in IIMs patients. Traditionally, autoantibodies found in patients with myositis are described as being myositis-specific autoantibodies and are largely mutually exclusive. Certain MSAs are more closely associated with extra muscular manifestations, such as skin and lung disease, than with myositis itself. The existing literature on the relationship between MSA and intestinal flora is limited. Li et al. suggested that anti-PL-12 antibody, anti-Ro-52 antibody, and anti-EJ antibody positively correlated with bacterial diversity, while Liang et al. found Lactobacillus increased in anti-SRP antibodies positive IMNM patients [25, 69]. In consideration of the extant research outcomes, it is proposed that the generation of MSA may not be associated with the breakdown of the intestinal mucosal barrier, but rather with the skin or lung barrier [70].

The results of this study should be evaluated in the context of some potential limitations. First, due to the low prevalence and marked heterogeneity of IIMs, the sample size of each clinical subtype was limited, particularly for IMNM and ASS patients which may have introduced sample selection bias that potentially affects our results, and we will try to increase the sample size for further validation and investigation in the future. Second, although patients with extreme diets and those who had taken antibiotics in the past three months to account for the potential impact of antibiotics on the gut microbiota, some of the included patients were treated with glucocorticoids or DMARDs, which may have influenced the analysis of the gut microbiota in comparison to healthy individuals. We will endeavor to screen untreated IIMs patients for further verification. Third, long-term survival data were not systematically collected in this study. Therefore, the association between microbiota-based clusters and ILD-related mortality could not be reliably evaluated. Fourth, the measurement of proinflammatory cytokines and microbial metabolites in plasma was not included in the original study design, and appropriate samples were not collected. Therefore, the potential mechanistic links between gut microbiota, microbial metabolites, and immune dysregulation in IIMs require further investigation. Last, the results of 16SrDNA gene sequencing did not provide species-level analysis, thus precluding a more detailed examination. Therefore, a more detailed investigation into the impact of specific strains on disease activity is essential.

Conclusions

To date, a cluster approach characterizing the impact of gut microbiota on IIMs manifestation has not been reported. Our findings emphasize the potential impact of microbial dysbiosis on IIMs disease manifestations, particularly within SCFA-producing genera. These findings provide new evidence that clinical modulation of the gut microbiota, tailored to the specific clinical phenotypes of IIM patients, may be a complementary approach to traditional IIMs treatments.

Abbreviations

AIDs

Autoimmune diseases

ARS

Aminoacyl-tRNA synthetase

ASS

Anti-synthetase Syndrome

BMI

Body Mass Index

CK

Creatine Kinase

CRP

C-Reactive Protein

CTDs

Connective Tissue Diseases

DM

Dermatomyositis

GALT

Gut-Associated Lymphoid Tissue

HLA

Human Leukocyte Antigen

IIMs

Idiopathic Inflammatory Myopathies

IBD

Inflammatory Bowel Disease

ILD

Interstitial Lung Disease

IMNM

Immune-Mediated Necrotizing Myopathy

MDAAT

Myositis Disease Activity Assessment Tool

MDI

Microbial Dysbiosis Index

MITAX

Myositis Intention to Treat Activities Index

MSA

Myositis-Specific Antibodies

PM

Polymyositis

PRRs

Pattern Recognition Receptors

RP-ILD

Rapidly Progressive Interstitial Lung Disease

SCFA

Short-Chain Fatty Acid

STAT

Signal Transducer and Activator of Transcription

TGP

Total Glucosides of Paeony

Authors’ contributions

T.W., Y.Li, and Y.Liu did conceptualization; T.W. and Y.Li prepared the original draft, implemented software, and conducted formal analysis; D.H., X.L., L.C., and J.Z. helped with the sample and clinical data collection; Y.W. and T.W. performed data curation and created visualization; Y. Liu reviewed and edited the manuscript and is also the guarantor and funding recipient of the work. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Clinical Research Innovation Project, West China Hospital, Sichuan University, grant number 2019HXCX01.

Data availability

The fastq files were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA1207276).

Declarations

Ethics approval and consent to participate

This study was approved by the Biomedical Research Ethics Committee, West China Hospital of Sichuan University (no. 695 in 2020). All subjects provided written informed consent before participation.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.