Metabolomics meets machine learning: Longitudinal metabolite profiling in serum of normal versus overconditioned cows and pathway analysis

Abstract

This study aimed to investigate the differences in the metabolic profiles in serum of dairy cows that were normal or overconditioned when dried off for elucidating the pathophysiological reasons for the increased health disturbances commonly associated with overconditioning. Fifteen weeks antepartum, 38 multiparous Holstein cows were allocated to either a high body condition (HBCS; n = 19) group or a normal body condition (NBCS; n = 19) group and were fed different diets until dry-off to amplify the difference. The groups were also stratified for comparable milk yields (NBCS: 10,361 ± 302 kg; HBCS: 10,315 ± 437 kg; mean ± standard deviation). At dry-off, the cows in the NBCS group (parity: 2.42 ± 1.84; body weight: 665 ± 64 kg) had a body condition score (BCS) <3.5 and backfat thickness (BFT) <1.2 cm, whereas the HBCS cows (parity: 3.37 ± 1.67; body weight: 720 ± 57 kg) had BCS >3.75 and BFT >1.4 cm. During the dry period and the subsequent lactation, both groups were fed identical diets but maintained the BCS and BFT differences. A targeted metabolomics (AbsoluteIDQ p180 kit, Biocrates Life Sciences AG, Innsbruck, Austria) approach was performed in serum samples collected on d −49, +3,

Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering

“…In the last few years, ML research and techniques have improved as large datasets generated by modern analytical lab instruments become available. Therefore, in recent reports we are starting to see ML-based research in identifying weight loss biomarkers [ 72 ], the discovery of food identity markers [ 73 ] farm animal metabolism [ 74 ] and many other applications in untargeted metabolomics [ 75 , 76 ]. In metabolic engineering, several areas are starting to take advantage of ML and systems biology integration including pathways identification and analysis, modeling of metabolisms and growth, and 3D protein modeling ( Figure 3 ).…”

Distinct serum metabolomic signatures of multiparous and primiparous dairy cows switched from a moderate to high-grain diet during early lactation

“…These observations were previously reported to be associated with lipid mobilization in early postpartum cows, where primiparous cows showed higher concentrations of serine, methionine-sulfoxide and trans -4-hydroxyproline compared to multiparous cows (Humer et al 2016a ). The latter metabolite was identified as a significant metabolite on day 21 after parturition in normal and over-conditioned cows (Ghaffari et al 2019 ). Both metabolites were also decreased in cows experiencing ruminal acidosis, particularly at H-wk1.…”

Machine learning approach to integrated endometrial transcriptomic datasets reveals biomarkers predicting uterine receptivity in cattle at seven days after estrous

“…However, the emergence of machine learning (ML), as a big data science tool, is less explored in livestock functional genomics in general, and bovine species in particular. ML refers to the use of self-learning algorithms to make sense of big data and is a branch of artificial intelligence that holds great potential for pattern recognition in complex datasets 3 , 4 , such as the ones derived from the “omics” technologies. In transcriptomic data (captured by either microarrays or RNA-sequencing platforms), expression pattern analysis is central to find functionally relevant groups of genes under different treatment conditions or phenotypic categories.…”