Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows

Ceciliani, Fabrizio; Lecchi, Cristina; Urh, C.; Sauerwein, H.

doi:10.1016/j.jprot.2017.10.010

Cited by 69 publications

(64 citation statements)

References 131 publications

(154 reference statements)

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“…Interestingly, we further improved the prediction accuracy of the model by between 0.04 and 0.10 by incorporating the MIR spectra (model 3), implying that MIR spectra capture variation in fertility beyond milk fatty acids and blood metabolic profiles. Using milk metabolomic or proteomic approaches may elucidate some of these compounds (Goldansaz et al, 2017;Ceciliani et al, 2018;Xu et al, 2018;Greenwood and Honan, 2019). The removal of MIR-derived traits from model 3 did not change prediction accuracy, which means that the useful information obtained from the MIR prediction equations of milk fatty acids, blood metabolic profiles, and milk composition is already included in the MIR spectra.…”

Section: Resultsmentioning

confidence: 99%

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

Bonfatti

Luke

et al. 2019

Journal of Dairy Science

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The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-byherd external validation, using basic on-farm information (model 1) was not sufficient to classify good-and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or ...

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Section: Resultsmentioning

confidence: 99%

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

Bonfatti

Luke

et al. 2019

Journal of Dairy Science

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“…There is a multitude of studies about the metabolic backgrounds of such socalled production diseases like ketosis, fatty liver, or hypocalcemia, although the investigations aiming to assess the complexity of the pathophysiological reactions are largely focused on gene expression, that is, transcriptomics. For extending the knowledge toward the proteome and the metabolome, the respective technologies are of increasing importance (Vignoli et al 2019) and can provide an overall view of how dairy cows react to metabolic stress, which is needed for an in-depth understanding of the molecular mechanisms of the related diseases (Ceciliani et al 2018). Displaced abomasum occurs simultaneously with fatty liver, but the relationship between the diseases are not clear (Ingvartsen 2006).…”

Section: Introductionmentioning

confidence: 99%

Nuclear magnetic resonance (NMR)-based metabolome profile evaluation in dairy cows with and without displaced abomasum

Basoglu

Başpınar

Tenori

et al. 2020

Veterinary Quarterly

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Erdem Gulersoy (2020) Nuclear magnetic resonance (NMR)-based metabolome profile evaluation in dairy cows with and without displaced abomasum, Veterinary Quarterly, 40:1, 1-15, ABSTRACT Background: Displaced abomasum (DA) is a condition of dairy cows that severely impacts animal welfare and causes huge economic losses. Objective: To assess the metabolic status of the disease using metabolomics in serum, urine and liver samples aimed at both water soluble and lipid soluble fractions. Methods: Fifty Holstein multiparous cows with DA (42 left, 8 right) and 20 clinically healthy Holstein multiparous cows were used. Left DA was associated with concomitant ketosis in 19 animals and right in two. NMR-based metabolomics approach and hematological and biochemical analyses were performed. Statistical analysis was carried out on 1 H-NMR data after they have been normalized using PQN method. Results: Contrary to generated PCA score plots the OPLS-supervised method revealed differences between healthy animals and diseased ones based on serum water-soluble samples. While water and lipid soluble metabolites decreased in serum samples, fatty acid fractions and cholesterol were increased in liver samples in DA affected cows. The metabolomic and chemical profiles clearly revealed that cows with DA (especially with LDA) were at risk of ketosis and fatty liver. Serum hippuric acid concentration was significantly higher in healthy cows in comparison with LDA, whereas serum glycine concentration was reported higher for healthy when compared to RDA affected animals. Conclusion: A biochemical network and pathway mapping revealed 'valine, leucine and isoleucine biosynthesis' and 'phenylalanine, tyrosine and tryptophan biosynthesis' as the most probable altered metabolic pathway in DA condition. Serum was advocated as the optimal biological matrix for the 1 H-NMR analysis. ARTICLE HISTORY

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“…Thus, treatment with oral calcium can be considered an approach to prevent not only hypocalcemia but also other disorders of dairy cows during the post-partum period. After parturition, a negative energy balance favors the occurrence of subclinical or clinical ketosis, as observed by Ceciliani et al (2018) in dairy cows during the transition period. Although no clinical signs associated with ketosis were observed in this study, it is important to highlight a tendency (P=0.08) toward increased ketone bodies in the control group, with a trend toward fewer ketone bodies in the treated group than in the control group.…”

Section: Discussionmentioning

confidence: 95%

Metaphylactic effect of calcium on milk composition and animal health in post-partum dairy cows

Albani

Lopes

Campigotto

et al. 2019

An. Acad. Bras. Ciênc.

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The aim of this study was to evaluate the effects of subcutaneous calcium administration in postpartum dairy cows with respect to carbohydrate, protein and lipids metabolism, as well as its effects on milk composition. Twenty post-partum dairy cows were randomly divided into two groups (n=10): a control and a treated group with a single dose (150 mL) of calcium. Blood collection was performed on post-partum days 1, 2, 3, 7 and 10. In addition, we measured serum levels of total calcium, total proteins, albumin, globulins, glucose, cholesterol, triglycerides, as well as creatine kinase (CK) activity. We determined milk composition on post-partum days 5, 10 and 20. Serum total calcium levels was higher on post-partum days 3 and 7 in the treated group. Serum urea (day 3) and triglycerides (days 7 and 10) levels were higher in the treated group, as were CK activity levels at post-partum days 2 and 3. Serum glucose and cholesterol levels were lower on post-partum day 2 in treated animals. Fat (day 20) and lactose (day 10) content in milk samples were higher than in the treated animals. We concluded that treatment with calcium in cows during the post-partum period changes biochemical variables related to metabolism.

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Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows

Cited by 69 publications

References 131 publications

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

Nuclear magnetic resonance (NMR)-based metabolome profile evaluation in dairy cows with and without displaced abomasum

Metaphylactic effect of calcium on milk composition and animal health in post-partum dairy cows

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