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.

Screening of Key Components for Melanogenesis Inhibition of Polygonum cuspidatum Extract Based on the Spectrum–Effect Relationship and Molecular Docking

“…The variable importance (VIP value) can reflect the importance of the X variable and its ability to explain the Y variable, while the variables with a VIP value greater than 1 are considered to have a more important ability than variables with a VIP value less than 1 [ 23 ]. As shown in Figure 4 B, the VIP values of P7, P11, P10, and P1 were all greater than 1.…”

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

“…Metabolomics is the joint to connect genotypes with phenotypes [6], so their relationships are currently interpreted by the metabolome genome-wide association study (mG-WAS) using metabolites as the metabolic phenotypes. The integrated genomic−metabolomic analysis is considered as a critical supplement to biology and physiology, as the metabolites provide the details of physiological state that can drive genetic variant-associated metabolites to display larger effect sizes, and then the quantitative trait loci (QTLs) affecting metabolite concentrations can be identified [53, [59][60][61][62][63].…”

“…The genome-metabolite network has been constructed in bacterial species [59,60]; for example, large-scale metabolic models of iJL463 and iDZ470 were constructed for Riemerella anatipestifer wild type strain CH-1 (RA-CH-1, serotype 1) and Riemerella anatipestifer wild type strain CH-2 (RA-CH-2, serotype 2), respectively [59]. In beef cattle, Li et al (2020) [61] detected three significant SNP associations (rs109862186, rs81117935 and rs42009425) for betaine, l-alanine and l-lactic acid, respectively; in addition, Wang and Kadarmideen (2020) found 152 genome-wide significant SNPs associated with 17 metabolites in pigs [53].…”