Comparative genomics and metabolomics analysis of Riemerella anatipestifer strain CH-1 and CH-2

Abstract

Riemerella anatipestifer is a major pathogenic microorganism in poultry causing serositis with significant mortality. Serotype 1 and 2 were most pathogenic, prevalent, and liable over the world. In this study, the intracellular metabolites in R. anatipestifer strains RA-CH-1 (serotype 1) and RA-CH-2 (serotype 2) were identified by gas chromatography-mass spectrometer (GC-MS). The metabolic profiles were performed using hierarchical clustering and partial least squares discriminant analysis (PLS-DA). The results of hierarchical cluster analysis showed that the amounts of the detected metabolites were more abundant in RA-CH-2. RA-CH-1 and RA-CH-2 were separated by the PLS-DA model. 24 potential biomarkers participated in nine metabolisms were contributed predominantly to the separation. Based on the complete genome sequence database and metabolite data, the first large-scale metabolic models of iJL463 (RA-CH-1) and iDZ470 (RA-CH-2) were reconstructed. In addition, we explained the change of purine metabolism combined with the transcriptome and metabolomics data. The study showed that it is possible to detect and differentiate between these two organisms based on their intracellular metabolites using GC-MS. The present research fills a gap in the metabolomics characteristics of R. anatipestifer.

Figure 1

The metabolite profile of two RA species by GC–MS. (a) GC–MS total ion-chromatograms (TIC) of the typical metabolome samples from RA-CH-1 (blue) and RA-CH-2 (green) cultivated in TSB medium and sampled in the stationary growth phase. (b) Classification of 81 identified metabolites identified in RA.

Figure 2

Metabolite-metabolite correlation analysis. Blank squares: p > 0.05. Marked with red or blue (p < 0.05) are the significant metabolite-metabolite correlations. Positive correlations are shown in red; negative correlations are shown in blue.

Figure 3

Heatmap of total metabolites in RA-CH-1 and RA-CH-2. Colors represent fold-change values between each line and the samples. Red squares in the heat map indicate increases of intracellular metabolite concentration, while green squares indicate decreases of intracellular metabolite concentration. Fold-change values were log2-transformed. Both columns (samples) and rows (metabolites) were subjected to hierarchical clustering analysis (HCA).

Figure 4

Comparison of network utilization in RA-CH-1 and RA-CH-2. (a) HAC of 24 biomarkers in two RA strains. RA-CH-1 (8 samples) and RA-CH-2 (8 samples) belonged to two subgroups. (b) Activities of RA metabolic pathways according to comparisons between two RA strains. The activity scores (AS) for each pathway were calculated using the PAPi algorithm. PAPi calculates an AS for each metabolic pathway listed in the KEGG database based on the number of metabolites identified from each pathway and their relative abundances. Related pathways are grouped according to their cellular metabolism and only pathways with statistically significant differences in activity (p < 0.05 by ANOVA) are shown. (c) The pathway of Purine metabolism in RA.