Metabotyping reveals distinct metabolic alterations in ketotic cows and identifies early predictive serum biomarkers for the risk of disease

Zhang, Guanshi; Dervishi, Elda; Dunn, Suzanna M.; Mandal, Rupasri; Liu, Philip L.‐F.; Han, Byoung-Sung; Wishart, David S.; Ametaj, Burim N.

doi:10.1007/s11306-017-1180-4

Cited by 39 publications

(55 citation statements)

References 34 publications

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“…Based on our citation analysis for 2017, MetaboAnalyst has been used in at least one-fourth of all metabolomics publications for that year, attesting to its status as one of the preferred tools for metabolomic data analysis. MetaboAnalyst has been used to elucidate key metabolic differences in breast cancer of African-American and Caucasian women ( 8 ), to identify highly predictive biomarkers of ketosis in dairy cows ( 9 ), to understand alterations in the intestinal metabolome during enteric infections ( 10 ), as well as to study many other biological processes and complex diseases ( 11–14 ).…”

Section: Introductionmentioning

confidence: 99%

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

Chong

Soufan

et al. 2018

Nucleic Acids Research

3,167

2,585

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We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

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Section: Introductionmentioning

confidence: 99%

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

Chong

Soufan

et al. 2018

Nucleic Acids Research

3,167

2,585

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“…Dairy cows are most susceptible to naturally occurring CM within the first weeks after calving; however, the infection may occur during the close-up period or around calving (Rollin et al, 2015), when milk sample are not available (Hurley and Theil, 2011). Multiple studies have become available during recent years that used blood samples collected during the last two month before and after calving for the discovery of predictive biomarker of various diseases in early lactation dairy cows (Imhasly et al, 2015; Dervishi et al, 2016; Zhang et al, 2017); however, to the best of our knowledge, none looked at predictive serum indicators of CM. Dervishi et al reported that cows that subsequently developed SCM had 4 weeks before calving elevated serum concentrations of inflammatory markers, monosaccharides, and AA and their metabolites (Dervishi et al, 2015, 2016).…”

Section: Introductionmentioning

confidence: 99%

Metabotypes with elevated protein and lipid catabolism and inflammation precede clinical mastitis in prepartal transition dairy cows

Zandkarimi

Vanegas

Fern

et al. 2018

Journal of Dairy Science

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Clinical mastitis (CM), the most prevalent and costly disease in dairy cows, is diagnosed most commonly shortly after calving. Current indicators do not satisfactorily predict CM. This study aimed to develop a robust and comprehensive mass spectrometry-based metabolomic and lipidomic workflow using untargeted ultra-performance liquid chromatography high-resolution mass spectrometry for predictive biomarker detection. Using a nested case-control design, we measured weekly during the prepartal transition period differences in serum metabolites, lipids, inflammation markers, and minerals between clinically healthy Holstein dairy cows diagnosed with mastitis postcalving (CMP; n = 8; CM diagnosis d 1 = 3 cows, d 2 = 2 cows, d 4 = 1 cow; d 25 = 1 cow, and d 43 = 1 cow that had subclinical mastitis since d 3) or not (control; n = 9). The largest fold differences between CMP and control cows during the prepartal transition period were observed for 3'-sialyllactose in serum. Seven metabolites (N-methylethanolamine phosphate, choline, phosphorylcholine, free carnitine, trimethyl lysine, tyrosine, and proline) and 3 metabolite groups (carnitines, AA metabolites, and water-soluble phospholipid metabolites) could correctly classify cows for their future CM status at both 21 and 14 d before calving. Biochemical analysis using lipid and metabolite-specific commercial diagnostic kits supported our mass spectrometry-based omics results and additionally showed elevated inflammatory markers (serum amyloid A and visfatin) in CMP cows. In conclusion, metabolic phenotypes (i.e., metabotype) with elevated protein and lipid metabolism and inflammation may precede CM in prepartal transition dairy cows. The discovered serum metabolites and lipids may assist in predictive diagnostics, prevention strategies, and early treatment intervention against CM, and thereby improve cow health and welfare.

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“…The kit assay (AbsoluteIDQ p180; Biocrates Life Sci., Innsbruck, Austria), used with an ABI 4000 Q-Trap mass spectrometer (Applied Biosystems/MDS Sciex, Foster City, CA, USA), enables the quantification of 145 metabolites in as little of 10 µL of biofluid. Detailed information and description of the method has been reported elsewhere [14,31]. Mass spectrometric analysis was performed using an API4000 Qtrap ® tandem mass spectrometry instrument (Applied Biosystems/MDS Analytical Technologies, Foster City, CA, USA) that is equipped with a solvent delivery system.…”

Section: Methodsmentioning

confidence: 99%

“…Previously, our group applied high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR)-based metabolomics of plasma to identify plasma metabolites such as acetic acid, arginine, ethanol, alanine, methylhistidine, and proline as biomarkers of aflatoxin ingestion in dairy cows fed no sequestering agents [12]. However, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) offers several advantages including high sensitivity and specificity and the potential to analyze disease-associated metabolic changes and quantify hundreds of metabolites in one run [13,14]. Thus, the objective of this study was to evaluate the effects of supplementing clay with or without Saccharomyces cerevisiae fermentation product (SCFP) on the plasma metabolomics profile of dairy cows challenged with aflatoxin B 1 using a combination of direct injection and tandem mass spectrometry (DI-MS/MS) with a reverse-phase LC-MS/MS.…”

Section: Introductionmentioning

confidence: 99%

DI/LC–MS/MS-Based Metabolome Analysis of Plasma Reveals the Effects of Sequestering Agents on the Metabolic Status of Dairy Cows Challenged with Aflatoxin B1

Ogunade

Jiang

Pech-Cervantes

2019

Toxins

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The study applied a targeted metabolomics approach that uses a direct injection and tandem mass spectrometry (DI–MS/MS) coupled with a liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based metabolomics of plasma to evaluate the effects of supplementing clay with or without Saccharomyces cerevisiae fermentation product (SCFP) on the metabolic status of dairy cows challenged with aflatoxin B1. Eight healthy, lactating, multiparous Holstein cows in early lactation (64 ± 11 DIM) were randomly assigned to one of four treatments in a balanced 4 × 4 duplicated Latin square design with four 33 d periods. Treatments were control, toxin (T; 1725 µg aflatoxin B1 (AFB1)/head/day), T with clay (CL; 200 g/head/day), and CL with SCFP (YEA; 35 g of SCFP/head/day). Cows in T, CL, and YEA were dosed with aflatoxin B1 (AFB1) from days 26 to 30. The sequestering agents were top-dressed from day 1 to 33. On day 30 of each period, 15 mL of blood was taken from the coccygeal vessels and plasma samples were obtained from blood by centrifugation and analyzed for metabolites using a kit that combines DI–MS/MS with LC–MS/MS-based metabolomics. The data were analyzed using the GLIMMIX procedure of SAS. The model included the effects of treatment, period, and random effects of cow and square. Significance was declared at p ≤ 0.05. Biomarker profiles for aflatoxin ingestion in dairy cows fed no sequestering agents were determined using receiver–operator characteristic (ROC) curves, as calculated by the ROCCET web server. A total of 127 metabolites such as amino acids, biogenic amines, acylcarnitines, glycerophospholipids, and organic acids were quantified. Compared with the control, T decreased (p < 0.05) plasma concentrations of alanine, leucine, and arginine and tended to decrease that of citrulline. Treatment with CL had no effects on any of the metabolites relative to the control but increased (p ≤ 0.05) concentrations of alanine, leucine, arginine, and that of citrulline (p = 0.07) relative to T. Treatment with YEA resulted in greater (p ≤ 0.05) concentrations of aspartic acid and lysine relative to the control and the highest (p ≤ 0.05) plasma concentrations of alanine, valine, proline, threonine, leucine, isoleucine, glutamic acid, phenylalanine, and arginine compared with other treatments. The results of ROC analysis between C and T groups revealed that the combination of arginine, alanine, methylhistidine, and citrulline had sufficient specificity and sensitivity (area under the curve = 0.986) to be excellent potential biomarkers of aflatoxin ingestion in dairy cows fed no sequestering agents. This study confirmed the protective effects of sequestering agents in dairy cows challenged with aflatoxin B1.

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Metabotyping reveals distinct metabolic alterations in ketotic cows and identifies early predictive serum biomarkers for the risk of disease

Cited by 39 publications

References 34 publications

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

Metabotypes with elevated protein and lipid catabolism and inflammation precede clinical mastitis in prepartal transition dairy cows

DI/LC–MS/MS-Based Metabolome Analysis of Plasma Reveals the Effects of Sequestering Agents on the Metabolic Status of Dairy Cows Challenged with Aflatoxin B1

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