Effect of Duration of High-Grain Feeding on Chewing, Feeding Behavior, and Salivary Composition in Cows with or without a Phytogenic Feed Supplement

Modular comparison of untargeted metabolomics processing steps

BACKGROUND

Untargeted metabolomics requires robust and reliable strategies for data processing to extract relevant information form the underlying raw data. Multiple platforms for data processing are available, but the choice of software tool can have an impact on the analysis. This study provides a comprehensive evaluation of four workflows based on commonly used metabolomics software tools: XCMS, Compound Discoverer, MS-DIAL, and MZmine. These tools were applied to a dataset derived from bovine saliva samples spiked with small polar molecules analyzed by anion exchange chromatography coupled to high resolution mass spectrometry.

RESULTS

The analysis revealed significant differences in the number and overlap of detected features, with only approximately 8 % of the features included in all four peak tables. Among the overlapping features, MS-DIAL demonstrated the greatest similarity to manual integration, while XCMS and MZmine also performed well. In contrast, Compound Discoverer had issues to reliably integrate high baseline peaks. This study also explores various post-processing strategies, including missing value imputation, transformation, scaling, and filtering. The assessment of missing values indicated that they primarily originated from low abundance, making imputation with small values the most effective approach. No clear evidence suggested that transformation is necessary for downstream statistical analyses. Auto scaling emerged as the most suitable strategy for data scaling. Low thresholds for blank filtering were found to be the most effective in enhancing data quality. The optimization of filtering thresholds required a careful balance to remove unnecessary information while retaining vital data.

SIGNIFICANCE AND NOVELTY

This work provides an overview of commonly applied strategies in untargeted metabolomics analysis, emphasizing the importance of careful workflow selection and optimization. It serves as a resource for refining data processing strategies to achieve accurate and reliable results, while also offering fresh insights into the challenges encountered throughout the untargeted metabolomics processing pipeline.

Elucidating the factors and consequences of the severity of rumen acidosis in first-lactation Holstein cows during transition and early lactation

First-lactation cows are particularly prone to subacute ruminal acidosis (SARA) during transition. Besides common risk factors of SARA, such as feeding of starch-rich diets, an individual severity of SARA in cows has been recently evidenced. Yet, the factors that play a role in SARA severity have not been elucidated. The main goal of this research was to evaluate the factors of SARA severity in first-lactation cows during transition and early lactation, which go beyond high-grain feeding, and to explore their impact on behavior, health, and fermentation in the rumen and hindgut. Twenty-four first-lactation Holstein cows with the same feeding regime were used starting from 3 wk before the expected calving day until 10 wk postpartum. Cows received a close-up diet (32% concentrate) until calving and were then transitioned to a lactation diet (60% concentrate) within 1 week. The SARA severity was assessed by cluster analysis of several rumen pH metrics, which revealed exceptionally longer and more severe SARA in cows denominated as high (n = 9), as compared to moderate (n = 9) and low (n = 6) SARA severity cows (P < 0.01). The logistic analysis showed that the length of close-up feeding, age at parturition, and the level of dry matter intake (DMI) were the main factors that influenced the cows' odds for high SARA severity (each P ≤ 0.01). Moreover, the ANOVA hinted differences in the metabolic activity of the ruminal microbiome to promote SARA severity, as indicated by highest ruminal propionate proportions (P = 0.05) in high SARA severity cows, also with similar DMI. The distinct SARA severity was marginally reflected in behavior and there were no effects of SARA severity or high-grain feeding on blood inflammation markers, which peaked at parturition regardless of SARA severity (P < 0.01). Still, ongoing high-grain feeding increased liver enzyme concentrations from 6 wk postpartum on, compared to weeks before (P < 0.01), yet irrespectively of SARA severity. In conclusion, first-lactation cows differed in SARA severity under the same feeding regime, which was ascribed to management factors and differences in ruminal fermentation. Further research is warranted to validate these findings and to understand the mechanisms behind differences in the metabolic function of rumen microbiome, in particular in terms of evaluating markers for various SARA severity, as well as to evaluate potential long-term effects on health, performance, fertility, and longevity of dairy cows.