Lupus and inflammatory bowel disease share a common set of microbiome features distinct from other autoimmune disorders

Abstract

Objectives: This study aims to elucidate the microbial signatures associated with autoimmune diseases, particularly systemic lupus erythematosus (SLE) and inflammatory bowel disease (IBD), compared with colorectal cancer (CRC), to identify unique biomarkers and shared microbial mechanisms that could inform specific treatment protocols.

Methods: We analysed metagenomic datasets from patient cohorts with six autoimmune conditions-SLE, IBD, multiple sclerosis, myasthenia gravis, Graves' disease and ankylosing spondylitis-contrasting these with CRC metagenomes to delineate disease-specific microbial profiles. The study focused on identifying predictive biomarkers from species profiles and functional genes, integrating protein-protein interaction analyses to explore effector-like proteins and their targets in key signalling pathways.

Results: Distinct microbial signatures were identified across autoimmune disorders, with notable overlaps between SLE and IBD, suggesting shared microbial underpinnings. Significant predictive biomarkers highlighted the diverse microbial influences across these conditions. Protein-protein interaction analyses revealed interactions targeting glucocorticoid signalling, antigen presentation and interleukin-12 signalling pathways, offering insights into possible common disease mechanisms. Experimental validation confirmed interactions between the host protein glucocorticoid receptor (NR3C1) and specific gut bacteria-derived proteins, which may have therapeutic implications for inflammatory disorders like SLE and IBD.

Conclusions: Our findings underscore the gut microbiome's critical role in autoimmune diseases, offering insights into shared and distinct microbial signatures. The study highlights the potential importance of microbial biomarkers in understanding disease mechanisms and guiding treatment strategies, paving the way for novel therapeutic approaches based on microbial profiles.

Trial registration number: NCT02394964.

Keywords: ankylosing; autoimmune diseases; lupus erythematosus; machine learning; spondylitis; systemic.

Figure 1.. Taxonomic associations observed in metagenomic data from CRC and autoimmune disease cohorts.

(A) Each study charts the quantity of samples collected from both healthy and diseased individuals, with some individuals contributing multiple samples from various visits. (B) The area under the receiver operator curve (AUROC) for random forest models trained on the taxonomic composition from one cohort and used to predict labels (healthy/diseased) based on the taxonomic composition of individuals within the test cohort. For individuals providing multiple samples from different visits, the sample from the first visit was selected for use in both intra-cross-validation and cross-studies validation. Median values were calculated from multiple evaluations, encompassing both cross-study testing and 5-fold cross-validation iterations. (C) Species with q-values < 0.1 in two or more studies are plotted across all studies. Asterisks represent those studies in which the q-value < 0.1.

Figure 2.. Microbial functional genes and CAZymes exhibit comparable predictive performance to species for autoimmune diseases.

(A) The area under the receiver operator curve (AUROC) for random forest models trained on the protein family abundances, determined using Pfam, from one cohort and used to predict labels (healthy/diseased) based on the protein family abundances of individuals within the test cohort. (B) AUROC for random forest models trained on the CAZyme profiles from one cohort and used to predict labels (healthy/diseased) based on the CAZyme profiles of individuals within the test cohort. (C) Boxplot comparison of predictive performance when taxonomic or functional abundance profiles are used in microbiome datasets. The AUROC was estimated through a five-fold cross-validation experiment, conducted five times. Welch’s t-test was used to determine the statistical significance between the two approaches.

Figure 3.. Host-microbiome PPIs provide insight into disease processes associated with IBD and SLE.

(A) AUROC for random forest models trained on the summed abundance of bacterial interactors which target each human protein interactor from one cohort and used to predict labels (healthy/diseased) based on the same information from individuals within the test cohort. (B) Comparison of PPI-associated genes and the percentage of these genes identified as disease-associated in DisGeNet, in relation to the predicted host-microbiome PPIs (HB-net) and all reviewed human proteins from Uniprot. (C) Venn diagrams showing the overlap of enriched pathways relevant to IBD and SLE, based on genes associated with these diseases according to DisGeNet or disease-associated PPIs (q < 0.05). (D) Enriched pathways associated with human protein interactors identified as important features in each disease type are plotted. Only those pathways associated with three or more diseases are plotted in the heatmap according to their Benjamini-Hochberg-adjusted p-values. Asterisks indicate B-H adjusted p-values under 0.05. Pathways known to play a role in IBD and SLE are marked in red and labeled by a hashtag and/or asterisk, respectively. The total number of human proteins identified as important features within each pathway are plotted according to the disease.

Figure 4.. Predicted human-microbiome PPIs linked to IBD and SLE.

(A) Mapping of NR3C1-associated human-microbiome PPIs relevant to disease. Displayed are only those PPIs with significant associations and concurrent bacterial protein clusters linked to disease. q values represent p-values adjusted using the Benjamini-Hochberg (BH) method. The metagenomics datasets’ genera predicted to contain these UniProt clusters are annotated. (B) Mapping of CXCL8-associated human-microbiome PPIs pertinent to disease. (C) HEK293T cells expressing FLAG-NR3C1 and c-Myc-tagged controls/bacterial proteins, FLAG-NR3C1 alone, or nothing were subject to co-immunoprecipitation using an anti-c-Myc Co-IP kit. Whole cell lysates and the Co-IP elutions were subject to Western blotting using an anti-FLAG antibody to identify FLAG-NR3C1. (1) and (2) indicate the first and second representatives from the same UniRef50 cluster.