In search of a candidate pathogen for giant cell arteritis: sequencing-based characterization of the giant cell arteritis microbiome

Abstract

Objective: To characterize the microbiome of the temporal artery in patients with giant cell arteritis (GCA), and to apply an unbiased and comprehensive shotgun sequencing-based approach to determine whether there is an enrichment of candidate pathogens in the affected tissue.

Methods: Temporal artery biopsy specimens were collected from patients at a single institution over a period of 4 years, and unbiased DNA sequencing was performed on 17 formalin-fixed, paraffin-embedded specimens. Twelve of the 17 patients fulfilled the clinical and histopathologic criteria for GCA, and the other 5 patients served as controls. Using PathSeq software, human DNA sequences were computationally subtracted, and the remaining non-human DNA sequences were taxonomically classified using a comprehensive microbial sequence database. The relative abundance of microbes was inferred based on read counts assigned to each organism. Comparison of the microbial diversity between GCA cases and controls was carried out using hierarchical clustering and linear discriminant analysis of effect size.

Results: Propionibacterium acnes and Escherichia coli were the most abundant microorganisms in 16 of the 17 samples, and Moraxella catarrhalis was the most abundant organism in 1 control sample. Pathogens previously described to be correlated with GCA were not differentially abundant in cases compared to controls. There was not a significant burden of likely pathogenic viruses.

Conclusion: DNA sequencing of temporal artery biopsy specimens from GCA cases, in comparison with non-GCA controls, showed no evidence of previously identified candidate GCA pathogens. A single pathogen was not clearly and consistently associated with GCA in this case series.

Figure 1. Whole genome sequence analysis of temporal artery biopsies from giant cell arteritis and control cases

(A) Whole genome sequencing reveals a similar number of human, microbial (bacteria, viruses, archaea, and fungi), and unmapped reads between cases and controls. The median number of reads and their distribution is depicted. (B) Species level comparison of the three most abundant organisms. P. acnes was the most abundant organism in sixteen cases and E. coli was typically the second most abundant. M. catarrhalis was identified in high abundance in one control.

Figure 2. Heatmap representation of the most abundant organisms in the tissue microbiome of GCA and control TA biopsies

Whole genome sequencing of TA biopsies was followed by microbial taxonomic classification of reads using the PathSeq computational platform. As demonstrated, PathSeq analysis of the GCA microbiome does not show an enrichment of candidate pathogens or other microbes in cases compared to controls. The forty-nine most abundant organisms in cases and controls are shown in the heatmap. The heatmap indicates the relative abundance value for each bacterium listed in each sample (RA values were normalized based on the number of total human reads per sample). Red shading indicates a relatively higher abundance of the given bacterium, white shading indicates intermediate abundance, and blue shading indicates a relatively low abundance of the given bacterium. Hierarchical clustering of cases (noted by a purple box in the top row) and controls (noted by a green box in the top row) was conducted using pairwise average linkage and Pearson’s correlation.