Metabolomic changes in Mycobacterium avium subsp. paratuberculosis (MAP) inoculated Holstein-Friesian cattle suggest metabolites indicative of MAP exposure

Application: Identified metabolites successfully differentiated between Mycobacterium avium subspecies paratuberculosis (MAP) inoculated and control cattle during the incubation stage of infection. If validated, these metabolites would enable farmers to identify MAP exposed cattle before shedding or clinical signs develop.
Introduction: Paratuberculosis, commonly known as Johne’s disease, is a chronic intestinal infection of ruminants, caused by MAP. Clinical signs, including reduced milk yields, weight loss and diarrhoea, are typically absent until 2 to 6 years post-infection [1]. This study aimed to examine the effects of MAP inoculation on the metabolomic profile of serum from Holstein-Friesian cattle and correlate affected metabolites to haematological parameters.
Materials and methods: At approximately 6-weeks of age, 35 calves received an inoculation with MAP (clinical isolate CIT003) 3.8 × 10⁹ on 2 consecutive days. An additional 20 calves formed the control group. Cattle were housed indoors in two separate buildings 500 metres apart, according to their inoculation status. Serum samples were collected pre-, 2-, 3-, 6-, 10-, 12-, 16-, 20-, 24-, 28-, 31- and 33-months post MAP inoculation. Cattle were euthanised 12-, 24- or 33– months post MAP inoculation. Sera from 18 cattle euthanised 33-months post MAP inoculation were utilised. Sera were assessed using flow infusion electrospray high resolution mass spectrometry on a Q Exactive hybrid quadrupole- Orbitrap mass spectrometer for high throughput, sensitive, non-targeted metabolite fingerprinting. Following principal component analyses (PCA), time series analyses used false discovery rate adjusted two-way ANOVA tests to identify mass-ions (m/z) which significantly (p-values < 0.05) differed between experimental classes. Correlation analysis between the identified metabolites and haematology parameters, including erythrocytes, lymphocytes and monocytes, was performed using Pearson’s correlation coefficient. Metabolites were identified using the DIMEdb database of metabolites for Direct Infusion/Injection
Metabolomics (https://dimedb.ibers.aber.ac.uk/) based on their mass-ion (m/z) values.
Results: PCA of the metabolomes discriminated between MAP inoculated and control cattle (Fig. 1). Repeated measures ANOVA highlighted the effect of time on metabolite levels. These results were reinforced by area under the curve (AUC) assessments which indicated that identified metabolites represented sensitive and specific changes occurring at discrete time points. Metabolite set enrichment analysis (MSEA) using over representation analysis (ORA) demonstrated that phosphatidylcholine biosynthesis was significantly affected by MAP inoculation (P <0.05). Additionally, significant correlations were seen between identified metabolites, such as 2-oxosuccinamate, and haematology parameters, particularly monocytes (%) (P < 0.05) (one example is given in Fig. 2).
Conclusion: Metabolomic analysis showed clear differentiation between MAP inoculated and control cattle, in addition to significant correlations between selected metabolites and haematological parameters. Future work could include assessing the relative accumulation of these metabolites in lactating dairy cattle.