Biomarker and pathway analyses of urine metabolomics in dairy cows when corn stover replaces alfalfa hay

BACKGROUND

Alfalfa hay and corn stover are different type of forages which can significantly impact a cow's lactation performance, but the underlying metabolic mechanism has been poorly studied. We used biomarker and pathway analyses to characterize related biomarkers and pathways based on urine metabolomics data from different forage treatments. Urine was collected from 16 multiparous Holstein dairy cows fed alfalfa hay (AH, high-quality forage, n = 8) and corn stover (CS, low-quality forage, n = 8) respectively. Gas chromatography-time of flight/mass spectrometry (GC-TOF/MS) was performed to identify metabolites in urine and the metaboanalyst online platform was used to do biomarker and pathway analysis.

RESULTS

Hippuric acid (HUA) and N-methyl-glutamic (NML-Glu) indicated the most significant difference between the two diets, when statistically validated by biomarker analysis. HUA was also validated by standard compound quantitative method and showed significant higher concentration in CS group than AH group (2.8282 vs. 0.0005 mg/mL; P < 0.01). The significant negative correlation between milk yield and HUA (R(2) = 0.459; P < 0.01) and significant positive correlation between milk yield and NML-Glu (R(2) = 0.652; P < 0.01) were characterized. The pathway analysis revealed that these different metabolites were involved in 17 pathways including 7 influential pathways (pathway impact value > 0): Tyr metabolism, starch and sucrose metabolism, amino sugar and nucleotide sugar metabolism, galactose metabolism, Phe, Tyr and Try biosynthesis, purine metabolism, and glycerolipid metabolism. Based on the metabolome view map, the Phe, Tyr and Try biosynthesis pathway exhibited the highest impact value (0.50), and the Holm-Bonferroni multiple testing-based analysis revealed the most significant difference in the Tyr metabolism pathway (Holm P = 0.048).

CONCLUSIONS

The identified HUA and NML-Glu may serve as potential biomarkers for discriminating CS and AH diets and could be used as candidates for milk yield related mechanistic investigations. Integrated network pathways associated with related metabolites provide a helpful perspective for discovering the effectiveness of forage quality in lactation performance and provides novel insights into developing strategies for better utilization of CS and other low-quality forage in China.

High-throughput metabolomic approach revealed the acupuncture exerting intervention effects by perturbed signatures and pathways

Metabolomics can capture global changes and the overall physiological status in biochemical networks and pathways in order to elucidate sites of perturbations. High-throughput metabolomics and acupuncturology have similar characteristics such as entirety, comprehensiveness and dynamic changes, and can identify potential candidates for acupuncture effects and provide valuable information towards understanding therapy mechanisms. Saliva has recently gained popularity as a potential tool for biomarker monitoring, as its composition may potentially reflect plasma metabolite levels and, therefore, may be used as an indicator of the physiological state. However, the underlying mechanism of acupuncture, remains largely unknown, which hinders its widespread use. Acupuncture would produce unique characterization of metabolic perturbations. In this study, UPLC/ESI-HDMS in high-accuracy mode coupled with pattern recognition analysis was carried out to investigate the mechanism and saliva metabolite biomarkers for acupuncture treatment at 'Zusanli' acupoint (ST-36) as a case study. Putative metabolite identifications for these ions were obtained through a mass-based database search. As a result, the top canonical pathways including phenylalanine metabolism, alanine, aspartate and glutamate metabolism, d-glutamine and d-glutamate metabolism, and steroid hormone biosynthesis pathways were acutely perturbed. 26 differential metabolites were identified by chemical profiling, and may be useful to clarify the physiological basis and mechanism of ST-36. More importantly, network construction has led to the integration of metabolites associated with the multiple perturbation pathways. These results provide useful insights into biomarker discovery utilizing metabolomics as an efficient and cost effective platform. This study opens new possibilities for the selection of saliva as a source of metabolite biomarkers representative of specific disorders.