Serum metabolomics in chickens infected with Cryptosporidium baileyi

Abstract

Background: Cryptosporidium baileyi is an economically important zoonotic pathogen that causes serious respiratory symptoms in chickens for which no effective control measures are currently available. An accumulating body of evidence indicates the potential and usefulness of metabolomics to further our understanding of the interaction between pathogens and hosts, and to search for new diagnostic or pharmacological biomarkers of complex microorganisms. The aim of this study was to identify the impact of C. baileyi infection on the serum metabolism of chickens and to assess several metabolites as potential diagnostic biomarkers for C. baileyi infection.

Methods: Ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) and subsequent multivariate statistical analysis were applied to investigate metabolomics profiles in the serum samples of chickens infected with C. baileyi, and to identify potential metabolites that can be used to distinguish chickens infected with C. baileyi from non-infected birds.

Results: Multivariate statistical analysis identified 138 differential serum metabolites between mock- and C. baileyi-infected chickens at 5 days post-infection (dpi), including 115 upregulated and 23 downregulated compounds. These metabolites were significantly enriched into six pathways, of which two pathways associated with energy and lipid metabolism, namely glycerophospholipid metabolism and sphingolipid metabolism, respectively, were the most enriched. Interestingly, some important immune-related pathways were also significantly enriched, including the intestinal immune network for IgA production, autophagy and cellular senescence. Nine potential C. baileyi-responsive metabolites were identified, including choline, sirolimus, all-trans retinoic acid, PC(14:0/22:1(13Z)), PC(15:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PE(16:1(9Z)/24:1(15Z)), phosphocholine, SM(d18:0/16:1(9Z)(OH)) and sphinganine.

Conclusions: This is the first report on serum metabolic profiling of chickens with early-stage C. baileyi infection. The results provide novel insights into the pathophysiological mechanisms of C. baileyi in chickens.

Keywords: Chicken; Cryptosporidium baileyi; Metabolomics; Pathway analysis; Serum sample.

Fig. 1

Score plots of multivariate statistical analysis. a Principal component analysis (PCA) score plots for all samples. Case 1 Cryptosporidium baileyi-infected chickens, Con 1 phosphate buffer saline-inoculated chickens, QC quality control. b Partial least squares-discriminant analysis (PLS-DA) score plots for Case 1 and Con 1 samples, c orthogonal partial least squares-discriminant analysis (OPLS-DA) score plots for Case 1 and Con 1 samples, d results of 200-times response permutation testing of OPLS-DA. Q2 and R2 represent the intercepts of the regression curve and y-axis generated by the linear regression between the R2 and Q2 values of "permuted” model and the R2Y and Q2Y values of the "real" OPLS-DA model, respectively

Fig. 2

Expression levels of metabolites between the experimental (Case 1, E1–E9) and mock (Con 1, N1–N9) samples. a Volcano plot for all differential metabolites. Each dot represents one metabolite with detectable expression in both conditions, with the colored dots marking the threshold [false discovery rate (FDR) < 0.05] for defining a metabolite as differentially expressed. Red and blue points represent the significantly upregulated and significantly downregulated metabolites, respectively; gray points indicate non-significant differential metabolites. b Hierarchical cluster analysis of all differential metabolites (FDR < 0.05). Each sample is visualized in a single column and each metabolite is represented by a single row. Red coloration indicates significantly increased metabolite levels, while green coloration indicates low expression (see color scale on figure)

Fig. 3

KEGG pathway enrichment analysis of differential serum metabolites following C. baileyi infection. a Significantly enrichments pathways with FDR (q-value) < 0.05. b Relationships between metabolic pathways and differential serum metabolites enriched. Each oval denotes one metabolic pathway. Triangles denote differentially abundant metabolites, with red representing upregulated metabolites

Fig. 4

Identification of potential biomarkers response to C. baileyi infection. a Potential biomarker metabolites detected in ESI+ mode based on receiver operating characteristic curve analysis, b potential biomarker metabolites detected in ESI− mode based on ROC analysis. ESI Electrospray ionization