Multi-country metabolic signature discovery for chicken health classification.

INTRODUCTION: To decrease antibiotic resistance, their use as growth promoters in the agricultural sector has been largely abandoned. This may lead to decreased health due to infectious disease or microbiome changes leading to gut inflammation. OBJECTIVES: We aimed to generate a m/z signature classifying chicken health in blood, and obtain biological insights from the resulting m/z signature. METHODS: We used direct infusion mass-spectrometry to determine a machine-learned metabolomics signature that classifies chicken health from a blood sample. We then challenged the resulting models by investigating the classification capability of the signature on novel data obtained at poultry houses in previously unseen countries using a Leave-One-Country-Out (LOCO) cross-validation strategy. Additionally, we optimised the number of mass/charge (m/z) values required to maximise the classification capability of Random Forest models, by developing a novel ranking system based on combined univariate t-test and fold-change analyses and building models based on this ranking through forward and reverse feature selection. RESULTS: The multi-country and LOCO models could classify chicken health. Both resulting 25-m/z and 3784-m/z signatures reliably classified chicken health in multiple countries. Through mummichog enrichment analysis on the large m/z signature, we found changes in amino acid metabolism, including branched chain amino acids and polyamines. CONCLUSION: We reliably classified chicken health from blood, independent of genetic-, farm-, feed- and country-specific confounding factors. The 25-m/z signature can be used to aid development of a per-metabolite panel. The extended 3784-m/z version can be used to gain a deeper understanding of the metabolic causes and consequences of low chicken health. Together, they may facilitate future treatment, prevention and intervention.

Metabolic fingerprinting reveals extensive consequences of GLS hyperactivity.

BACKGROUND: High glutaminase (GLS;EC3.5.1.2) activity is an important pathophysiological phenomenon in tumorigenesis and metabolic disease. Insight into the metabolic consequences of high GLS activity contributes to the understanding of the pathophysiology of both oncogenic pathways and inborn errors of glutamate metabolism. Glutaminase catalyzes the conversion of glutamine into glutamate, thereby interconnecting many metabolic pathways. METHODS: We developed a HEK293-based cell-model that enables tuning of GLS activity by combining the expression of a hypermorphic GLS variant with incremental GLS inhibition. The metabolic consequences of increasing GLS activity were studied by metabolic profiling using Direct-Infusion High-Resolution Mass-Spectrometry (DI-HRMS). RESULTS AND CONCLUSIONS: Of 12,437 detected features [m/z], 109 features corresponding to endogenously relevant metabolites were significantly affected by high GLS activity. As expected, these included strongly decreased glutamine and increased glutamate levels. Additionally, increased levels of tricarboxylic acid (TCA) intermediates with a truncation of the TCA cycle at the level of citrate were detected as well as increased metabolites of transamination reactions, proline and ornithine synthesis and GABA metabolism. Levels of asparagine and nucleotide metabolites showed the same dependence on GLS activity as glutamine. Of the nucleotides, especially metabolites of the pyrimidine thymine metabolism were negatively impacted by high GLS activity, which is remarkable since their synthesis depend both on aspartate (product of glutamate) and glutamine levels. Metabolites of the glutathione synthesizing γ-glutamyl-cycle were either decreased or unaffected. GENERAL SIGNIFICANCE: By providing a metabolic fingerprint of increasing GLS activity, this study shows the large impact of high glutaminase activity on the cellular metabolome.