Integrated multi-omics revealed that dysregulated lipid metabolism played an important role in RA patients with metabolic diseases

Abstract

Objectives: Patients with rheumatoid arthritis (RA) commonly experience a high prevalence of multiple metabolic diseases (MD), leading to higher morbidity and premature mortality. Here, we aimed to investigate the pathogenesis of MD in RA patients (RA_MD) through an integrated multi-omics approach.

Methods: Fecal and blood samples were collected from a total of 181 subjects in this study for multi-omics analyses, including 16S rRNA and internally transcribed spacer (ITS) gene sequencing, metabolomics, transcriptomics, proteomics and phosphoproteomics. Spearman's correlation and protein-protein interaction networks were used to assess the multi-omics data correlations. The Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm were used to identify disease-specific biomarkers for RA_MD diagnosis.

Results: Our results found that RA_MD was associated with differential abundance of gut microbiota such as Turicibacter and Neocosmospora, metabolites including decreased unsaturated fatty acid, genes related to linoleic acid metabolism and arachidonic acid metabolism, as well as downregulation of proteins and phosphoproteins involved in cholesterol metabolism. Furthermore, a multi-omics classifier differentiated RA_MD from RA with high accuracy (AUC: 0.958). Compared to gouty arthritis and systemic lupus erythematosus, dysregulation of lipid metabolism showed disease-specificity in RA_MD.

Conclusions: The integration of multi-omics data demonstrates that lipid metabolic pathways play a crucial role in RA_MD, providing the basis and direction for the prevention and early diagnosis of MD, as well as new insights to complement clinical treatment options.

Keywords: Lipid metabolism; Metabolic diseases; Multi-omics; Rheumatoid arthritis.

Fig. 1

The flowchart of our study design. RA, rheumatoid arthritis, RA_MD, RA patients with metabolic diseases, RA_T2D, RA patients with type 2 diabetes, RA_HLP, RA patients with hyperlipidaemia, RA_AS, RA patients with atherosclerosis, GA, gouty arthritis, GA_MD, GA patients with metabolic diseases, SLE, systemic lupus erythematosus, SLE_MD, SLE patients with metabolic diseases

Fig. 2

The alteration of the structural composition and diversity of microbial communities among control, RA and RA_MD. The differences of α-diversity by Kruskal-Wallis H test for Chao index in fungi (A) and Shannon index in bacteria (B). * p < 0.05. (C) The Beta diversity of bacteria shown by principal co-ordinates analysis (PCOA) based on Bray Curtis distance analysis. Relative abundance of fungi at phylum (D) and genus(E) levels. Relative abundance of bacteria at phylum (F) and genus(G) levels. The Wilcoxon rank-sum test bar plot between RA and RA_MD at the fungal (H) and bacterial (I) genus levels. Values represented mean and standard error. (J) Spearman’s correlation heatmap of Ko pathways and significant bacteria at genus level

Fig. 3

Down-regulation of unsaturated fatty acids (UFAs) in RA_MD patients. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) displayed the differential metabolites of anionic (A) and cationic (B) modes between RA_MD and RA. (C) Volcano plot showed that the differentially expressed metabolites between RA and RA_MD. (D) The KEGG enrichment analysis of 42 significant metabolites between RA_MD and RA. (E-H) The relative abundance of galactonic acid, 20-HETE, 9-OxoDE and all cis-(6, 9, 12)-linolenic acid among control, RA and RA_MD. (I) Spearman’s correlation heatmap of differential metabolites and clinical characteristics

Fig. 4

Identification and functional analysis of differentially expressed genes profiles. (A, B) The Gene Set Enrichment Analysis. (C) Bubble plot depicted KEGG enrichment analysis of 355 differentially expressed genes. (D-I) The relative abundance of 6 significantly expressed genes. (J) The correlated relationship of 8 different genes that were associated with metabolic diseases

Fig. 5

Combined multi-omics analysis revealed dysregulation of lipid metabolism. (A) Correlation network among differential metabolites, gut microbiota and clinical indicators. (B) The shared metabolic pathways between metabolomics and transcriptomics. (C) The Roc curve of significantly expressed gut microbiota and metabolites. (D) Protein-protein interaction (PPI) network map among differential genes, proteins and phosphoproteins involved in the cholesterol metabolism. (E) 3 types of metabolism pathways displayed the connections among metabolites, genes, proteins and phosphoproteins

Fig. 6

Specific alterations in gut microbiota and plasma metabolites among GA, SLE and RA. The differences of α-diversity by Kruskal-Wallis H test for Shannon index in fungi (A) and Chao in bacteria (B) index among control, GA, GA_MD, SLE and SLE_MD groups. (C) The plot depicted the composition of the differential bacterial genera in the three types disease comparison groups. G1, RA_MD vs. RA, G2, GA_MD vs. GA, G3, SLE_MD vs. SLE, (D) The classification of differential metabolites in G1, G2 and G3 groups. (E) Stacked bar charts showed metabolism pathways and classification of differential metabolites enrichment in three groups