Protein profiling of plasma proteins in dairy cows with subclinical hypocalcaemia

Fan, Zhipeng; Sheng, Shi; Xu, Chuchu; Xiao, Xinhuan; Wang, Gang; Bai, Yunlong; Xia, Cheng; Wu, Lina; Zhang, Hongyou; Xu, Chuang; Yang, Wei

doi:10.1186/s13620-017-0082-0

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…Similarly, MIR spectral data has been used to estimate the Ca and P concentration of milk with reasonable accuracy (Toffanin et al, 2015); however, its ability to predict serum Ca and Mg concentrations was poor. Studies have demonstrated that animals suffering from subclinical hypocalcemia showed significant changes in their serum proteome (Wang et al, 2016;Fan et al, 2017). Given that changes in serum albumin and globulin concentrations could not be identified with milk MIR spectra, it follows that changes in serum proteome associated with hypocalcemia are also not detectable using milk MIR spectral analysis.…”

Section: Pls Regression Models For Predicting Serum Metabolite Concenmentioning

confidence: 99%

Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra

Luke

Rochfort

Wales

et al. 2019

Journal of Dairy Science

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Metabolic disorders in early lactation have negative effects on dairy cow health and farm profitability. One method for monitoring the metabolic status of cows is metabolic profiling, which uses associations between the concentrations of several metabolites in serum and the presence of metabolic disorders. In this cross-sectional study, we investigated the use of midinfrared (MIR) spectroscopy of milk for predicting the concentrations of these metabolites in serum. Between July and October 2017, serum samples were taken from 773 early-lactation Holstein Friesian cows located on 4 farms in the Gippsland region of southeastern Victoria, Australia, on the same day as milk recording. The concentrations in sera of β-hydroxybutyrate (BHB), fatty acids, urea, Ca, Mg, albumin, and globulins were measured by a commercial diagnostic laboratory. Optimal concentration ranges for each of the 7 metabolites were obtained from the literature. Animals were classified as being either affected or unaffected with metabolic disturbances based on these ranges. Milk samples were analyzed by MIR spectroscopy. The relationships between serum metabolite concentrations and MIR spectra were investigated using partial least squares regression. Partial least squares discriminant analyses (PLS-DA) were used to classify animals as being affected or not affected with metabolic disorders. Calibration equations were constructed using data from a randomly selected subset of cows (n = 579). Data from the remaining cows (n = 194) were used for validation. The coefficient of determination (R 2) of serum BHB, fatty acids, and urea predictions were 0.48, 0.61, and 0.90, respectively. Predictions of Ca, Mg, albumin, and globulin concentrations were poor (0.06 ≤ R 2 ≤ 0.17). The PLS-DA models could predict elevated fatty acid and urea concentrations with an accuracy of approximately 77 and 94%, respectively. A second independent validation data set was assembled in March 2018, comprising blood and milk samples taken from 105 autumn-calving cows of various breeds. The accuracies of BHB and fatty acid predictions were similar to those obtained using the first validation data set. The PLS-DA results were difficult to interpret due to the low prevalence of metabolic disorders in the data set. Our results demonstrate that MIR spectroscopy of milk shows promise for predicting the concentration of BHB, fatty acids, and urea in serum; however, more data are needed to improve prediction accuracies.

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Section: Pls Regression Models For Predicting Serum Metabolite Concenmentioning

confidence: 99%

Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra

Luke

Rochfort

Wales

et al. 2019

Journal of Dairy Science

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“…Many studies have found that BHBA is a predominant and stable blood ketone body in ruminant ketosis [4,10], which, therefore, has been widely used for clinically diagnosing and classifying ketosis in dairy cows [11]. Furthermore, a large number of molecular biomarkers have been also identified to be associated with ketosis, such as milk fatty acids [12], serum hepatokines [13], inflammatory biomarkers [14], methylglyoxal [2], metabolites [15], mineral elements [16], protein profiling [17], and amino acids [18] in blood.…”

Section: Introductionmentioning

confidence: 99%

Clinical Ketosis-Associated Alteration of Gene Expression in Holstein Cows

Chen

Qin

et al. 2020

Genes

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Ketosis is one of the most prevalent transition metabolic disorders in dairy cows, and has been intrinsically influenced by both genetic and nutritional factors. However, altered gene expression with respective to dairy cow ketosis has not been addressed yet, especially at the genome-wide level. In this study, we recruited nine Holsteins diagnosed with clinical ketosis and ten healthy controls, for which whole blood samples were collected at both prepartum and postpartum. Four groups of blood samples were defined: from cows with ketosis at prepartum (PCK, N = 9) and postpartum (CK, N = 9), respectively, and controls at prepartum (PHC, N = 10) and postpartum (HC, N = 10). RNA-Seq approach was used for investigating gene expression, by which a total of 27,233 genes were quantified with four billion high-quality reads. Subsequently, we revealed 75 and four differentially expressed genes (DEGs) between sick and control cows at postpartum and prepartum, respectively, which indicated that sick and control cows had similar gene expression patterns at prepartum. Meanwhile, there were 95 DEGs between postpartum and prepartum for sick cows, which showed depressed changes of gene expression during this transition period in comparison with healthy cows (428 DEGs). Functional analyses revealed the associated DEGs with ketosis were mainly involved in biological stress response, ion homeostasis, AA metabolism, energy signaling, and disease related pathways. Finally, we proposed that the expression level of STX1A would be potentially used as a new biomarker because it was the only gene that was highly expressed in sick cows at both prepartum and postpartum. These results could significantly help us to understand the underlying molecular mechanisms for incidence and progression of ketosis in dairy cows.

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“…Moyes et al (2013) used isobaric tags for relative and absolute quantitation based quantitative profiling of cow liver tissue and found potential hepatic biomarkers for different degrees of physiological imbalance of dairy cows in early and mid-lactation. Using the same method, Fan et, al (Fan et al, 2017) identified differentially expressed proteins related to a metabolic disorder of subclinical hypocalcemia. Moreover, proteomic analysis using liver tissues (Xu and Wang, 2008) and adipose tissues (Xu et al, 2019) has also been used in cow ketosis studies, which would provide novel opportunities to unravel the complex biology of the disease.…”

Section: Introductionmentioning

confidence: 99%

Metabolomic and Proteomic Profiles Associated With Ketosis in Dairy Cows

Chen

et al. 2020

Front. Genet.

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Ketosis is a common metabolic disease in dairy cows during early lactation. However, information about the metabolomic and proteomic profiles associated with the incidence and progression of ketosis is still limited. In this study, an integrated metabolomics and proteomics approach was performed on blood serum sampled from cows diagnosed with clinical ketosis (case, ≥ 2.60 mmol/L plasma β-hydroxybutyrate; BHBA) and healthy controls (control, < 1.0 mmol/L BHBA). Samples were taken 2 weeks before parturition and 2 weeks after parturition from 19 animals (nine cases, 10 controls). All serum samples (n = 38) were subjected to Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomic analysis, and 20 samples underwent Data-Independent Acquisition (DIA) LC-MS based proteomic analysis. A total of 97 metabolites and 540 proteins were successfully identified, and multivariate analysis revealed significant differences in both metabolomic and proteomic profiles between cases and controls. We investigated clinical ketosis-associated metabolomic and proteomic changes using statistical analyses. Correlation analysis of statistically significant metabolites and proteins showed 78 strong correlations (correlation coefficient, R ≥ 0.7) between 38 metabolites and 25 proteins, which were then mapped to pathways using IMPaLA. Results showed that ketosis altered a wide range of metabolic pathways, such as metabolism, metabolism of proteins, gene expression and post-translational protein modification, vitamin metabolism, signaling, and disease related pathways. Findings presented here are relevant for identifying molecular targets for ketosis and biomarkers for ketosis detection during the transition period.

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Protein profiling of plasma proteins in dairy cows with subclinical hypocalcaemia

Cited by 7 publications

References 27 publications

Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra

Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra

Clinical Ketosis-Associated Alteration of Gene Expression in Holstein Cows

Metabolomic and Proteomic Profiles Associated With Ketosis in Dairy Cows

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