GCâMS Metabolomics Identifies Metabolite Alterations That Precede

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GC-MS metabolomics identifies metabolite alterations that precede subclinical mastitis in the blood of transition dairy cows ELDA DERVISHI, Guanshi Zhang, Suzanna M Dunn, Rupasri Mandal, David S. Wishart, and Burim N. Ametaj J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00538 • Publication Date (Web): 04 Nov 2016 Downloaded from http://pubs.acs.org on November 8, 2016

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Journal of Proteome Research

GC-MS metabolomics identifies metabolite alterations that precede subclinical mastitis in the blood of transition dairy cows Elda Dervishi,†⊥ Guanshi Zhang,†⊥ Suzanna M Dunn,† Rupasri Mandal,‡ David S Wishart,‡ and Burim N Ametaj*†. †

Department of Agricultural, Food and Nutritional Science, University of Alberta,

Edmonton, AB T6G 2P5, Canada ‡

Departments of Biological Sciences and Computing Science, University of Alberta,

Edmonton, AB T6G 2E9, Canada

*

Corresponding Author

Tel: 780-492-9841. Fax: 780-492-4265. E- mail: [email protected].

ACS Paragon Plus Environment

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ABSTRACT The objectives of this study were to determine alterations in the serum metabolites related to amino acid (AA), carbohydrate, and lipid metabolism in transition dairy cows before diagnosis of subclinical mastitis (SCM), during, and after diagnosis of disease. A subclinical mastitis case was determined as a cow having somatic cell count (SCC) > 200,000/mL of milk for two or more consecutive reports. Blood samples were collected from 100 Holstein dairy cows at 5 time points at -8 and -4 wks before parturition, at the week of SCM diagnosis, +4, and +8 wks after parturition. Twenty healthy control cows (CON) and 6 cows that were diagnosed with SCM were selected for serum analysis with GC-MS. At -8 wks a total of 13 metabolites were significantly altered in SCM cows. In addition, at the wk of SCM diagnosis 17 metabolites were altered in these cows. Four wks after parturition 10 metabolites were altered in SCM cows and at + 8 wks 11 metabolites were found to be different between the two groups. Valine (Val), serine (Ser), tyrosine (Tyr) and phenylalanine (Phe) had very good predictive abilities for SCM and could be used at 8 wks and -4 wks before calving. Combination of Val, isoleucine (Ile), Ser and proline (Pro) can be used as diagnostic biomarkers of SCM during early stages of lactation at +4 to +8 wks after parturition. In conclusion, SCM is preceded and followed by alteration in AA metabolism.

KEYWORDS: subclinical mastitis, dairy cow, GC-MS, metabolomics, amino acids, carbohydrates


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INTRODUCTION Subclinical mastitis (SCM) is one of the most prevalent forms of mastitis in dairy cows with incidence rate ranging between 20-50% of the cows in a herd.1,2 Presently, SCM is diagnosed by the number of somatic cell count (SCC) and California mastitis test (CMT). The latter gives a positive reaction only when SCC is above 400,000, whereas infection is present once the SCC is over 200,000. A threshold of 200,000 cells/ mL has been shown to have a high sensitivity and specificity for identifying SCM.3,4 In addition, it has been reported that a SCC of less than 100,000 cells/ mL is a healthy level of somatic cells.5 Metabolomics, an emerging 'omics' science, is increasingly being used to investigate disease etiology, develop screening biomarkers of disease as well as monitor and predict treatment of complex diseases.6-9 Metabolomics is related to the study of chemical compounds or metabolites generated by cells and organisms during normal or pathogenic conditions. Changes in metabolite composition in the body fluids give insights into the health or disease state of an animal.10 Metabolomics approach has been employed for investigations of metabolic alterations and identification of diagnostic biomarkers with regards to periparturient diseases of dairy cattle.11-13 In addition, Sun et al.12 reported plasma metabolomics in cows affected by milk fever. Metabolomics has been utilized in studying alterations during hepatic steatosis and displaced abomasum in dairy cows.14,15 In two recent articles we reported that several serum metabolites including carnitine, propionyl


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carnitine, and lysophosphatidylcholine acyl C14:0A could be used to predict the risk of cows being affected by periparturient diseases (i.e. retain placenta, mastitis, metritis and laminitis), up to 4-6 wks prior to occurrence of clinical signs. Results of these studies suggest that metabolomics can be used for studying pathobiology of disease as well as for identification of diagnostic and screening biomarkers of disease in transition dairy cows.9,16 Several studies have been published in relation to mastitis and milk composition in dairy cows.17-20 In one of these studies metabolites lactate, butyrate, isoleucine, acetate and BHB showed significant increase in the milk with high SCC.19 However, most of these studies are more related to diagnosis of disease rather than screening and they have not used serum analyses. Metabolomics investigations for prediction of SCM have not been previously reported. Using a simpler test like metabolomics and testing blood prior to development of disease is of major significance to dairy industry for prevention of SCM. Clearly, an early diagnosis of the potential risk of developing SCM can lower the incidence of SCM in dairy herds and prompt development and application of new strategies for their prevention. In this study we used GC-MS in conjunction with multivariate statistical analysis to detect alterations in the profiles of serum metabolite in dairy cows before, during, and after they were diagnosed with SCM. The objectives of the current study were: 1) to determine if there were alterations in the blood metabolites related to amino acid (AA), lipid and carbohydrate metabolism in transition dairy cows, before, during, and after diagnosis of SCM, and 2) to identify metabolite biomarkers in the blood that


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might be useful as screening, diagnostic, and predictive biomarkers of SCM in dairy cows. MATERIALS AND METHODS This research was part of a prospective study designed to study the pathobiology of several periparturient diseases, including their characterization and identification of potential screening and predictive biomarkers of those diseases in dairy cows. All experimental procedures were approved by the University of Alberta Animal Policy and Welfare Committee for Livestock, and animals were cared for in accordance with the guidelines of the Canadian Council on Animal Care.21 The metabolomics analyses were performed at the Metabolomics Innovation Centre, University of Alberta, Edmonton, AB, Canada. Animals and Diets One hundred pregnant Holstein dairy cows at the Dairy Research and Technology Centre, University of Alberta (Edmonton, AB, Canada), were used in this study. Cows were selected based on their parity. Although a total of 100 cows were sampled only 6 were selected as having SCM with > 200,000 cells/mL of milk (all other cows diagnosed with SCM and having concurrent diseases were excluded from analysis) and 20 as healthy controls (CON). To avoid overlaps of diseases cows that were not affected by multiple diseases only were selected. If a cow with SCM was diagnosed having another disease it was excluded from the experiment. Healthy cows had no clinical signs of any diseases including metritis, lameness, milk fever, mastitis, retained placenta, or ketosis and had SCC lower than 200,000 cells/mL. In this study,


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SCM was diagnosed according to the farm standard operating procedures. A subclinical mastitis case was diagnosed if a cow had an elevated SCC but milk appeared normal. Milk also was evaluated for presence of clots and/or flakes. Somatic cell count > 200,000 cells/mL, for two or more consecutive reports (i.e., weekly), were used for confirmation of SCM. Cows in this study were diagnosed with SCM at +2 and +3 wks after calving. Six pregnant multiparous (parity: 3.1 ± 0.4, Mean ± SEM) Holstein dairy cows with SCM and 20 CON cows that were similar in parity (3.2 ± 0.3) and body condition score (BCS), were selected for this study. The total experimental period for each cow lasted 17 wks starting from -8 wks before parturition until +8 wks postpartum. All cows were fed the same close-up diet prepartum and were gradually switched to a fresh lactation diet with a greater proportion of grain during the first 7 d after parturition to meet the energy demands for high milk production. Daily ration was offered as TMR for ad libitum intake once daily at 0800 h to allow approximately 5% refusals throughout the experiment. All TMR were formulated to meet or exceed the nutrient requirements of a 680 kg lactating cows as per NRC guidelines.22 The compositions of pre- and post- calving diet are shown in table 1 and 2. Health status of cows during the entire experimental period was monitored daily based on their daily feeding (i.e., DMI) and milking behavior (i.e., milk yield) as well as for clinical symptoms of disease by trained staff and on a weekly basis by a veterinary practitioner. All clinical signs of diseases and veterinary treatments were


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recorded for each cow. Collection of Blood Samples Blood samples were collected from 100 Holstein dairy cows in order to have at least 20 CON and 6 sick cows with SCM. Twenty healthy CON cows and six cows that were diagnosed with SCM (SCC > 200,000 cells/mL) were selected for serum analysis with GC-MS. Blood samples were obtained from the coccygeal veins once per week at 0700 before the morning feeding. GC-MS analysis were conducted at 5 time points at -8 (53-59 d) and -4 (25-31 d) wks before parturition, during disease wk (2-3 wks postpartum; 11-24 d), and at +4 (25-31 d) and +8 (53-59 d) wks after calving for each cow in the experiment. More specifically, for the disease week, samples collected during 11-17 d postpartum for healthy CON cows and those collected between 11- 24 d postpartum for the SCM cows were used for analysis. All blood samples were collected into 10-mL vacutainer tubes (Becton Dickinson, Franklin Lakes, NJ, USA) and allowed for clotting. The tubes were centrifuged at 2,090 x g at 4 ℃ for 20 min to separate the serum (Rotanta 460 R centrifuge, Hettich Zentrifugan, Tuttlingen, Germany). Subsequently, the separated serum was aspirated from the supernatant gradually by transfer pipets (Fisher Scientific, Toronto, ON, Canada) into a 10-mL sterile test tube (Fisher Scientific, Toronto, ON, Canada). Serum samples were stored at -80 ℃ freezer until analyses to avoid loss of bioactivity and contamination and were thawed on ice for approximately 2 h before use. Compound Identification and Quantification Prior to analysis by GC-MS, the serum samples were extracted to separate polar


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metabolites. The extraction and derivatization protocol was adapted from a previously reported method to deproteinize and achieve broad metabolite coverage of polar metabolites.23 Briefly, an aliquot of 100 µL of serum sample containing 10 µL of ribitol in water (0.4 mg/mL) as an internal standard was extracted with 800 µL of cold HPLC grade methanol:HPLC water (8:1 vol/vol) and vortexed for 1 min. The samples were kept at 4 ℃ for 20 min and then centrifuged at 10,000 rpm for 10 min. After centrifugation, 200 µL of the supernatant was transferred into a glass vial insert (250 µL, Agilent, Santa Clara, CA, USA) in a 1.5 mL glass vial with screw cap (Agilent, Santa Clara, CA, USA) and evaporated to dryness using a Speedvac concentrator (Savant Instruments Inc., SDC-100-H, Farmingdale, NY) for 4 h and then using the lyophilizer (LABCONCO, Kansas City, MO, USA) for 2 h until completely dry. After drying, a common protocol for carbonyl methoximation and hydroxyl, primary amine and thiol silylation was used for these polar metabolites. Extracted residues were reconstituted with 40 µL methoxyamine hydrochloride (20 mg/mL, Sigma-Aldrich, Oakvile, ON, Canada) in American Chemical Society (ACS) grade pyridine and incubated at room temperature for 16 h. Then 50 µL of MSTFA (NMethyl-N-trifluoroacetamide) with 1% TMCS (trimethylchlorosilane) derivatization agent (Thermo Fisher Scientific, Pierce Biotechnology, Rockford, IL, USA) was added and incubated at 80 °C for 1.5 to 2 h on a hotplate. The samples were vortexed 3 times during incubation to ensure complete dissolution. Samples were stored for less than 48 h at 4 ºC until analysis. Derivated extracts were injected by Agilent 7683 Series Autosampler (Agilent


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Technologies, Palo Alto, CA, USA) followed by the analysis employing Agilent 6890N GC system coupled with electron impact (EI) ionization mode 5973N mass selective detector (Agilent Technologies, Palo Alto, CA, USA). A 2 µL aliquot was injected with a 5:1 split ratio onto a 30 m × 0.25 mm × 0.25 µm DB-5 column (Agilent Technologies). The injector port temperature was held at 250 °C and the helium carrier gas flow rate was set to 1 mL/min at an initial oven temperature of 50 °C. The oven temperature was increased at 10 °C/min to 310 °C for a final run time of 26 min. Full scan spectra (50-500 m/z; 1.7 scans/sec) were acquired after a 6 min solvent delay, with an MS ion source temperature of 200 °C. The quality control (QC) were prepared by mixing amino acids Ala, Val, Ile Gly, Ser, and Lys; then, they were treated and analyzed in the same way as serum samples to investigate the reproducibility and repeatability of the methods. A QC was run every 10 samples to monitor the stability and reproducibility of the method. In addition, hexane and a blank sample were run as well for the elution of residual impurities and analytes from the glass liner and the capillary column at the beginning of the sequence. All the derivatized samples were run within 24 h after preparation. After running all the samples, a mixture of alkane standard solution C8-C20 and C21C40 (1:1 vol/vol, Sigma-Aldrich, Oakvile, ON, Canada) was injected to acquire the retention times of n-alkanes for the calculation of the Kovat’s retention index of metabolites instantly. Raw MS data (“.D” file format) were first transformed into CDF format by the software Data Analysis prior to data pretreatment. Identification and quantification of


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polar metabolites was performed following the method described previously.24 Briefly, the Automated Mass Spectral Deconvolution and Identification System (AMDIS) spectral deconvolution software (Version 2.70) from NIST (National Institute of Standards and Technology) was used to process the total ion chromatogram and the EI-MS spectra of each GC peak. After deconvolution, the purified mass spectrum of each of the trimethylsilylated metabolites was identified using the National Institute of Standards and Technology (NIST) MS Search program (version 2.0d) linked to the 2008 NIST mass spectral library (2008). Retention Indices (RIs) were calculated using a C8-C20 and C21-C40 alkane mixture solution (Fluka, Sigma-Aldrich) which served as an external alkane standard. Metabolites were identified by matching the EI-MS spectra with those of reference compounds from the (NIST) library. In AMDIS, each search produces a list of library spectra (“hits”), which is ranked by the similarity to the target spectrum according to a mathematically computed “match factor”. The match factor indicates the likelihood that our spectrum and the reference NIST spectrum arose from the same compound. In the current study, we considered hits with a match factor of > 60% and a probability > 20%. In addition, authenticity checks were performed by using additional published retention index libraries.25 RIs and Electron Ionization (EI) spectra were subsequently used for producing external five-point calibration curves (for absolute quantification). Statistical Analysis Univariate analysis of continuous data was performed using Wilcoxon-MannWhitney (rank sum) test provided by R (Version 3.0.3, R Development Core Team,


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2008). All metabolomics data were processed statistically by the MetaboAnalyst software.26 Recommended statistical procedures for metabolomics analysis were followed according to the previously published protocols.26 Metabolites that were frequently (> 50%) below the limit of detection or with at least 50% missing values were removed from consideration. Otherwise, missing values were replaced by a value of one-half of the minimum positive value in the original data. Data normalization of metabolite concentration was done prior to statistical analysis and pathway analysis to create a Gaussian distribution.26 In this study, we used logtransformation and auto scaling of metabolite values. To perform a standard cross-sectional 2-group study, we compared healthy cows (CON) and the group of SCM at each time point (-8, -4 wks prepartum as well as at disease wk, +4, and +8 wks postpartum) separately. Principle component analysis (PCA), partial least squares – discriminant analysis (PLS-DA) and pathway analysis were performed via MetaboAnalyst. In the PLS-DA model, a VIP (variable importance in the projection) plot was used to rank the metabolites based on their importance in discriminating SCM group from CON group of cows. Metabolites with the highest VIP values are the most powerful group discriminators. Typically, VIP values > 1 are significant and VIP values > 2 are highly significant. A 2000 permutation test was implemented to validate the reliability of the model because it used random resampling of SCM and CON cows to determine the probability that the SCM and CON groups are a result of chance.27


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Biomarker profiles and the quality of the biomarker sets were determined using receiver-operator characteristic (ROC) curves as calculated by MetaboAnalyst 3.0.28 Paired sensitivity and false-positive ratios (1-specificity) at different classification decision boundaries were calculated. A ROC curve is plotted with sensitivity values on the Y-axis and the corresponding false-positive rates (1-specificity) on the X-axis. ROC curves are often summarized into a single metric known as the area under the ROC curve (AUC), which indicates the accuracy of a test for correctly distinguishing one group such as SCM cows from CON ones. If all positive samples are ranked before negative ones, the AUC is 1.0, which indicates a perfect discriminating test. The 95% confidence interval (CI) and P values also were calculated. A rough guide for assessing the utility of a biomarker set based on its AUC is 0.9~1.0 = excellent; 0.8~0.9 = good; 0.7~0.8 = fair; 0.6~0.7 = poor; 0.5~0.6 = fail. Permutation test was conducted for each ROC curve at different time points with 1,000 permutations.

RESULTS Metabolomic data were obtained from 26 transition dairy cows at 5 different time points including - 8, -4, disease diagnosis wk, +4 and +8 wks around parturition. A total of 29 metabolites were identified and quantified using an in-house massspectrometry library for each cow (Table 3). These metabolites can be grouped under amino acids (AA), carbohydrate, and lipid metabolism. Univariate Statistical Analysis By using a univariate analysis, we compared the SCM group with the CON


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group at five time points separately. The results of significantly regulated metabolites (P < 0.05) in CON group versus SCM cows at -8, -4, wk of diagnosis of disease, +4, and +8 wks around parturition are shown in Table 3 and 4. Results showed that concentrations of 13 metabolites were significantly altered in SCM cows at -8 wks. Specifically, concentrations of Val, Gly, Leu, Pro, Ser, Phe, Tyr, phosphoric acid, ornithine, glutamic acid, D-mannose, myoinositol, and linoleic acid, were greater in SCM cows as compared to cows in the CON group. The most up-regulated metabolites were myo-inositol (10.65-fold) and Val 9.34-fold greater as compared to CON cows. Moreover, an increasing tendency for Ile and Lys was observed in SCM cows (P = 0.06). At -4 wks before parturition concentrations of 15 metabolites including Val, Gly, Leu, Ile, Ser, Phe, Lys, urea, phosphoric acid, creatinine, ornithine, glutamic acid, Dmannose, galactose, and linoleic acid were increased in cows with SCM. Animals with SCM were characterized by an increase of 20.43-fold in Ser concentration and 13.54-fold in Val concentration as compared to CON group. In addition, at the wk of diagnosis of SCM, 17 metabolites were altered in SCM cows. Concentration of Val, Leu, Ile, Ser, Phe, Lys, Tyr, phosphoric acid, aspartic acid, pyroglutamic acid, creatinine, ornithine, glutamic acid, myo-inositol, linoleic acid, and stearic acid were greater in SCM cows compared to cows in the CON group. Meanwhile, concentration of galactose was lower in SCM cows versus those in the CON group (Table 3). Results of the univariate analysis indicated that a total of 10 and 11 metabolites


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in the serum were significantly different at +4 and +8 wks between the two groups. Specifically, at +4 wks after parturition, concentrations of Val, Gly, Pro, Ile, Ser, Lys, phosphoric acid, creatinine, palmitic acid, and myo-inositol were significantly greater in SCM cows as compared to cows in the CON group. Concentration of Phe and oleic acid tended to be greater in SCM cows but did not reach significance (P= 0.06 and P=0.07, respectively). The same alterations were observed at +8 wks after parturition; concentrations of Val, Gly, Pro, Ile, Phe, Ser, phosphoric acid, Tyr, D-mannose, palmitic acid, and myo-inositol were greater in SCM cows compared to those in the CON group. The mean ± SEM concentration values, P values along with the fold change, and direction of change in SCM cases relative to CON cows are provided in Table 4. In addition, concentrations of oxalate and oleic acid tended to be greater in the serum of SCM cows but the difference did not reach significance. Intriguingly, concentrations of Val, Ser, and phosphoric acid were greater in cows diagnosed with SCM in all time points evaluated in this study. Multivariate Analysis of Serum Metabolites at -8 and -4 Weeks before Parturition Multivariate analysis are showed that when CON cows were compared with notyet-diseased cows (those that eventually developed SCM) at -8 and -4 wks, PLS-DA had two separate clusters at the 2 time points (Figure 1A, B; Figure 2A, B). At -8 wks prepartum, 3 metabolites accounted for the observed separation between the two groups including Val, Ser, and Tyr. At -4 wks before parturition, 3 metabolites accounted for most observed separation including Phe, Ser, and Val. Permutation testing revealed that the observed


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separation was not by chance (P < 0.05). A VIP plot of the PLS-DA from -8 and -4 wks prior to the expected day of parturition in which the metabolites were ranked based on their contribution to discriminate the SCM cows from CON ones are shown in Figure 1C and Figure 2C. The top 15 most important metabolites that separated the two groups are shown in the VIP plots (Figure 1C and 2C). The heat map on the right side of two VIP plots indicates that all 15 metabolites were greater in SCM cows relative to the CON ones. Top 3 metabolites with greater VIP score were used for ROC curve analysis. A ROC curve plot showing the performance of the top 3 metabolites in predicting which cows will develop SCM at -8 and -4 wks prior to parturition, using a standard support vector machine model, are shown in Figures 1D and 2D. The Areas Under the Curve (AUC) for the two curves were 0.993 for the -8 wks (combination of Val, Ser, and Tyr, empirical P = 0.001) and 0.903 at -4 wks (combination of Val, Ser, and Phe, empirical P = 0.001) indicated that these biomarkers have very good predictive abilities for SCM. Metabolite enrichment analysis showed that at -8 wks before parturition, propanoate metabolism (L-Val) and sphingolipid metabolism (L-Ser), protein biosynthesis (L-Tyr; L-Phe, L-Pro; L-Thre, L-Lys, L- Glu, L-Leu, L-Val), Val, Leu, and Ile degradation (L-Leu and L-Val), and methionine metabolism were the top 5 most enriched pathways (Figure 3A). At -4 wks before parturition, sphingolipid (LSer), Phe and Tyr metabolism (L-Tyr, and L-Phen), propanoate metabolism (L-Val), protein biosynthesis (L-Tyr; L-Phe, L-Pro; L-Thre, L-Lys, L-Leu, L-Val), and finally Val, Leu, and Ile degradation (L-Leu and L-Val) were the top 5 most enriched


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pathways (Figure 3B). Multivariate analysis and modeling showed that 4 plasma metabolites corresponding to AA metabolism like Val, Ser, Tyr, and Phe could be used to generate highly reliable biomarker models for predicting which cows will develop SCM. Our data support the idea that these biomarkers could be used as early as -8 wks (combination of Val, Ser, and Tyr) and -4 wks (combination of Val, Ser, and Phe) before calving. Multivariate Analysis of Plasma Metabolites at the Week of Disease Diagnosis When SCM cows were compared with CON cows at the diagnosis wk, unsupervised multivariate analysis (i.e., PCA) and supervised multivariate analysis (i.e., PLS-DA) once again revealed a distinctive separation between the two groups of cows (Figure 4A & 4B). In this case, 3 metabolites (i.e., Val, Ile, and Ser) with the greatest VIP scores contributed most significantly to the observed separation (Figure 4C). The ROC curve (Figure 4D) indicated that combination of these metabolites was a highly significant biomarker of SCM, with AUC, 0.99 (95% CI, 0.906-1) (empirical P = 0.001). The pathway enrichment analysis showed that on the diagnosis week of SCM propanoate metabolism (L- Val); protein biosynthesis (L-Tyr;L-Phe, L-Thre, LLys, L-Leu, and L-Val); sphingolipid metabolism (L-Ser); Val, Leu, and Ile degradation (L-Leu and L-Val); and aspartate metabolism (D-Aspartic acid) were the top 5 most enriched pathways affected (Figure 3C). Multivariate analysis and modeling showed that 3 plasma metabolites corresponding to AA metabolism such as Val, Ile, and Ser could be used to generate highly reliable biomarker models to


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diagnose cows with SCM. Multivariate Analysis of Plasma Metabolites at +4 and +8 weeks After Parturition When CON cows were compared with SCM cows at +4 and +8 wks postpartum, both PCA and PLS-DA showed a clear separation between SCM and CON cows (Figure 5A and 5B; and Figure 6A and 6B). The corresponding VIP plots for these 2 time points, shown in Figures 5C and 6C, indicated that Gly, Ile, and Pro were the most discriminating metabolites at +4 weeks and Val, Ile, and Pro were the top 3 metabolites for separation of clusters at +8 wk. Multivariate models (ROC curves) combing 3 discriminating metabolites (i.e., Gly, Ile, and Pro) at +4 wks and 3 metabolites (i.e., Val, Pro, and Ile) at +8 weeks produced areas under the receiveroperating curves of AUC=1 (95% CI: 1-1, empirical P < 0.05; Figure 5D) and AUC=1 (95% CI: 1-1, empirical P < 0.05; Figure 6D), respectively. Moreover, pathway enrichment analysis showed that at +4 wks after parturition ammonia recycling (Gly and L-Ser); methionine metabolism (Gly, and L-Ser); glutathione metabolism (Gly); porphyrin metabolism (Gly); and bile acid synthesis (Gly); were the top 5 most enriched pathways (Figure 7A). At +8 wks after parturition propanoate (L-Val), Val, Leu, and Ile degradation (L-Val); protein biosynthesis (L-Tyr; L-Phe, LPro, L-Lys, and L-Val); Gly, Ser, and Thre metabolism (Gly, L-Ser); and ammonia recycling (Gly and L-Ser) were the top 5 most enriched pathways (Figure 7B).

DISCUSSION In the present study we used high throughput targeted GC-MS metabolomics


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profiling to identify metabolites related to pathobiology of SCM in the blood of transition dairy cows before occurrence of disease, during the disease week, and after diagnosis of disease. We also used this approach to discover screening, diagnostic, and prognostic biomarkers of SCM. It should be noted that cows diagnosed with SCM in this study were in a state of systemic inflammation during the dry off period and before diagnosis of disease as indicated previously in a companion article by greater concentrations of serum amyloid A and TNF measured around calving.29 The number of serum metabolites that were significantly different between the two groups of cows was 13, 15, 17, 10, and 11 metabolites at -8 wks, -4 wks before parturition, at the week of SCM diagnosis, at +4 wks, and at +8 wks postpartum, respectively. The most important finding of the present study was that there were multiple alterations in AA metabolism and protein biosynthesis in the serum of pre-SCM cows starting at -8 and -4 wks before the expected day of parturition. For example, all branched chain amino acids (BCAAs) including Val, Leu, and Ile were greater before and during occurrence of disease. Increased BCAAs in the blood is an indication of poor metabolic health.30 The main source of BCAAs after calving is muscle protein. Branched chain amino acids need two organs to be degraded, the muscle and the liver. In the muscles BCAAs are deaminated and degraded into BCKA (branched chain αketo acids), and the latter enter TCA in the liver and are used to produce glucose and energy.31 There is also evidence that Val, Leu, and Ile play important roles in controlling the redox state. In a recent study D'Antona et al. 32 demonstrated that diets


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enriched with BCAAs increased longevity of mice by inducing mitochondrial biogenesis and lowering of oxidative stress involving mechanisms that increase defenses against oxidative stress. Moreover, supplements enriched with BCAAs were shown to trigger a marked decrease of oxidative stress complemented by a positive effect on protein synthesis and glucose metabolism.33,34 Branched chain amino acids also are known to be involved in the regulation of protein synthesis by immune cells and the release of cytokines. Waithe et al.

35

31

For example,

demonstrated that lack of Val or Ile in culture medium resulted in

absence of protein synthesis or proliferation of lymphocytes in response to mitogens. The immune system has a high dependence upon protein synthesis, since mounting an immune response requires generation of new cells and synthesis of antigen-presenting structures, immunoglobulins, cytokines, cytokine receptors, and acute phase proteins.36,31 Indeed, pre-SCM cows in this study had greater concentrations of serum amyloid A and TNF in the serum compared to control healthy ones, as reported previously by us.29 This was also supported by the finding that protein biosynthesis was one of the most enriched pathways in pre-SCM cows, which demonstrates that those cows had greater necessity for protein synthesis in order to mount a successful immune response. Our results are in agreement with Calder

33

and Li et al.

31

which

state that mounting an immune response requires BCAAs for synthesis of proteins, cytokines, and acute phase proteins. Another important finding of this study was that pre-SCM cows had elevated concentrations of Ser at both -8 and -4 wks precalving and at the wk of disease


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diagnosis as compared to healthy cows. Serine plays an important role in the process of methylation ranging from synthesis of key metabolic intermediates such as creatine, phosphatidylcholine, and epinephrine to methylation of proteins, DNA, and RNA.37 Serine also is essential for synthesis of ceramide and phosphatidylserine, known as structural components and signaling molecules of various body cells including T and B lymphocytes.38,39 In addition, one of the serine's functions is to help synthesis of phospholipids, which are part of cell membranes. Konashi et al.

40

demonstrated that inadequate intake of dietary Ser lowers the immune response in chickens. It is speculated that elevated concentrations of Ser at the wk of disease diagnosis and postpartum is an indicator of activation of immune response in cows with SCM. Tyrosine, Phe, and Pro were also greater in pre-SCM cows. Tyrosine is an essential component for the production of thyroid hormones, melanine pigment, catecholamines epinephrine, norepinephrine, and dopamine.39 Norepinephrine is a key messenger released from the sympathetic nervous system, which among other functions, acts on the immune system.41 Phenylalanine is required to maintain a sufficient provision of tetrahydrobiopterin for the production of NO by iNOS in activated macrophages and other leukocytes.42 It has been reported that deficiency of Phe and Tyr impairs immune responses in chickens, which could be reversed by their supplementation to the diet.40 Proline has been reported as an important AA in protection of lymphocytes from apoptosis, stimulating cell growth, and promoting antibody production.43 One product of Pro oxidation, H2O2, is a signaling molecule


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and a cytotoxic agent against pathogenic bacteria. 44,39 The results further support the idea that pre-SCM cows exhibit activation of immune response starting at -8 wks prior to parturition, which corresponds with the starting of dry off period. The same cows also had greater serum amyloid A, TNF, and lactate during the same time period.29 In cows, the dry period is initiated by cessation of milking at around 60 days prior to parturition; however, the mammary gland still continues to synthesize and secrete milk which accumulates in the gland, causing swelling of the mammary gland.45 Moreover, the process of mammary gland involution is characterized by activation of an immune response, during which the concentration of all immunoglobulin classes, cytokines, acute phase proteins, lactoferrin, macrophages, and lymphocytes increases substantially.46,47 If the duration of the immune response is longer than necessary it suggests presence of an infection in the mammary gland, which has been shown to be of particular importance in regards to health of the udder in subsequent lactations.48,47 There is evidence that the rate of new intramammary infections is significantly greater in the dry period and most of the new intramammary infections develop during 1-3 wks after drying off and during the final stages of the dry period.47-49 The data suggest that SCM in dairy cows is proceeded by activation of the immune response prior to what is considered transition period (i.e., -3 to +3 wks around calving). In a recent study, Zhou et al.50 investigated amino acid profiles between cows with high liver functionality index (HLFI) and those with low liver functionality index (LLFI) but no differences in the concentration of BCAAs between the HLFI and LLFI cows, during prepartum period, were reported. However, during postpartum period


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HLFI cows had greater concentration of total amino acids, BCAAs, and essential amino acids. The authors suggest that greater BCAA in the HLFI cows might indicate a better capacity of the immune system to respond to an inflammatory challenge postpartum. In our study, a plausible possibility is that the alteration in the AA concentrations in the serum of cows during the dry off period and the postpartum suggests presence of inflammation and intramammary infection starting at -8 wks before parturition. During infection there is a marked increase in the demand for substrates including AAs and proteins by the immune system. The systemic inflammatory response stimulates, among others, protein catabolism,51,52 and the release of AAs from muscle protein providing substrates for the synthesis of antibodies, cytokines, acute phase proteins, and proteins by immune system,53 which could result in a general increase in the concentration of serum AAs. Interestingly alterations of AA and carbohydrate metabolism continued even after diagnosis of disease, until +8 wks after parturition. Post-SCM cows were characterized by perturbations of metabolic pathways including ammonia recycling and methionine and glutathione metabolism at +4 wks after calving. Moreover, at +8 wks postpartum post-SCM cows continued to show alterations in ammonia recycling metabolism and protein biosynthesis which suggest that post-SCM fluctuations of metabolites continue even at +8 wks after parturition. In a previous study, we reported that the same cows with SCM had greater somatic cell count as compared to healthy animals during the week of SCM diagnosis and throughout the postpartum period.29 In dairy cows, transition period has been defined as -3 to +3 wks around calving.54 The


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fact that alteration of protein and carbohydrate metabolism starts from -8 wks before parturition and continues up to +8 wks postpartum suggests that the length of the transition period should be reconsidered.

CONCLUSIONS In conclusion, data from this study indicated that multiple perturbations of AA metabolism preceded development of SCM in transition dairy cows. Alterations of AAs metabolism also were detected during the week of disease diagnosis and after diagnosis of disease. The identified metabolites and pathways can help in better understanding the pathobiology of SCM and also as predictive and diagnostic biomarkers of the disease. Four AA including Val, Ser, Tyr, and Phe in the serum were identified as the main metabolites distinguishing the healthy from SCM cows and have the potential to be used for screening cows for susceptibility to SCM as early as -8 wks and -4 wks prior to parturition. Biomarkers identified after calving might be used to evaluate how long the disease continues and predict the efficiency of a treatment. It is acknowledged that the number of sick cows affected by SCM in this study was low and that these data should be considered as preliminary and validation in a larger cohort of animals in the future is warranted.

AUTHOR INFORMATION *

Corresponding Author

Tel: 780-492-9841. Fax: 780-492-4265. E- mail: [email protected]. Author Contributions


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⊥ E.D and G.ZH contributed equally to this work. The manuscript was written through contributions of all authors. ED wrote the manuscript, collected samples, did clinical monitoring and evaluation of the cows, and maintained the database of the project. GZ contributed in collection of samples, lab analysis, and data statistical analysis and writing the manuscript. S.M.D contributed in collection of samples, lab analysis. R.M and D.S.W contributed in sample analysis. B.N.A contributed in conceiving the idea and designing of the experiments and supervised the experiment, lab analyses, statistical processing as well as writing of the manuscript.

ACKNOWLEDGEMENTS This study was supported by research grants awarded to principal investigators Dr. Burim N. Ametaj and Dr. David S. Wishart from Genome Alberta (Calgary, Alberta, Canada), Alberta Livestock and Meat Agency Ltd. (Edmonton, Alberta, Canada), and the Natural Sciences and Engineering Research Council of Canada (Ottawa, Ontario, Canada). We acknowledge the help of D. Hailemariam, S. A. Goldansaz, Q. Deng, and J. F. Odhiambo in collection of samples from cows. We are also grateful to the staff at Metabolomics Innovation Centre, University of Alberta, Edmonton, AB, Canada, especially the help of P. Liu in preparation of the DI/LC-MS/MS kit, and the support of B. Han with the data analysis. We are also grateful to the technical staff at Dairy Research and Technology Center, University of Alberta, for their help and care with the cows.

ABBREVIATIONS


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SCM, subclinical mastitis retain placenta; CON, control cows; SCC, somatic cell counts; SEM: standard error of the mean; GC-MS, gas chromatography- mass spectrometry; AA, amino acids; Val, valine; Ser, serine; Tyr, tyrosine; Phe, phenylalanine; PCA, principal component analysis; PLS-DA VIP: variable importance in the projection; PLS-DA, partial least squares – discriminant analysis; CI, confidence interval; AUC, area under the ROC curve; ROC, receiver-operator characteristic.

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2001, 60, 389–397. (54) Drackley, J. K. Biology of dairy cows during the transition period: The final frontier.

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Table 1. Prepartum diet for the dry off cows Item Close-up diet (CUD) Ingredient % of DM Alfalfa hay 10.0 Barley silage 60.0 CUD grain 30.0 Nutrient composition of CUD grain % in 100 kg of mix 1 Ruminant TM Pak 0.2775 Selenium 1000 mg/kg (UNscr FineCr) 0.2 Custom TM Complex Premix2 0.33 Vitamin A/D3-1000-2003 0.006 Barley grain, rolled 39.5815 Flo-bond mycotoxin binder 0.5 Limestone 3.7 Magnesium chloride 1.645 Mag Ox-56%4 0.54 Scale Molasses (60:40) 2.5 Canola meal 17.0 Vitamin E 50% Ads5 0.18 Soybean hulls, ground 33.0 Salt 0.54 1 Ruminant TM Pak: a premix containing cobalt, copper, iodine, manganese, and zinc. 2 Custom TM complex premix: a custom product supplying organic sources of cobalt, copper, manganese, and zinc. 3 Vitamin A/D3-1000-2003: Vitamin A acetate (retinyl acetate) and Vitamin D3 (cholecalciferol). 4 Mag Ox 56%: 56% magnesium guarantee. 5 Vitamin E 50% Ads contains 226,800 IU of Vitamin E per pound.


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Table 2. Ingredients of TMR fed to cows during early lactation Early lactation diet Item Ingredient % of DM %, DM Alfalfa Hay 9.59 Barley Silage 30.24 Alfalfa Silage 9.64 High 16% dairy ration 50.53 Nutrient composition of dairy ration % amount per kg ADE Vit Pak-30 Natural E1 0.05 0.11 Ruminant TM Pak2 Selenium, 1,000 mg/kg (UNscr FineCr) 0.07 Custom TM Complex premix3 0.07 AminoShure - L4 0.33 Blood meal 3.50 Barley grain, rolled 39.90 Barley grain, ground 27.50 Di-calcium phosphate 21% 1.00 Vit D-10,000 KIU/kg 0.02 Diamond V XPC5 0.13 Dairy Xtract 0.02 Energizer RP10 2.75 Limestone 1.70 6 Mag Ox-56% 0.43 Scale Molasses (60:40) 1.25 Nutri A-Z C Dry 0.10 Amino Plus (High bypass soy)7 8.00 8 Vitamin E 50% Ads 0.01 Soy bean meal-47.5% 1.25 Sodium bicarbonate 0.80 Salt 0.50 Poultry-Tallow 0.50 9 Biotin 2%-Rovimix H-2 0.01 Wheat distillers grain (50:50) 10.00 1 ADE Vit Pak-30 Natural E: a premix containing vitamins A, D3, and E. 2 Ruminant TM Pak: a premix containing cobalt, copper, iodine, manganese, and zinc. 3 Custom TM complex premix: a custom product supplying organic sources of cobalt, copper, manganese, and zinc. 4 AminoShure - L: hydrogenated vegetable oil, and L-lysine monohydrochloride (Halchemix, Port Perry, ON, Canada). 5 Diamond V XPC: concentrated yeast (Diamond V Mills, Cedar Rapids, IA). 6 Mag Ox 56%: 56% magnesium guarantee. 7 Amino Plus: a high by-pass soy meal. 8 Vitamin E 50% Ads contains 226,800 IU of Vitamin E per pound. 9 DSM Nutritional Products (Parsippany, NJ).


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Table 3. Concentrations of serum metabolites (mean (SEM)) in healthy control (CON) and SCM cows at 3 time points (-8 wks, -4 wks, and the week of diagnosis of disease) as determined by GS-MS. 8 wks before parturition

4 wks before parturition

SM diagnosis wk

Metabolite, mM

SCM

CON

P-value

Fold change

SCM

CON

p-value

Fold change

SCM

CON

p-value

Fold change

Number of cases L-Lactic acid Valine Alanine Glycine Oxalate Leucine Proline Isoleucine Urea Serine Phosphoric acid Threonine Aspartic acid Pyroglutamic acid Creatinine Ornithine Glutamic acid Phenylalanine Lysine Citric acid Tyrosine D-Mannose Galactose Palmitic acid Myo-Inositol Linoleic acid Stearic acid Oleic acid Lactose

6 1.99 (1.03) 2.09 (1.72) 0.47 (0.15) 0.39 (0.11)

20 1.44 (1.03) 0.22 (0.14) 0.41 (0.250) 0.26 (0.251)

0.12 < 0.001 0.51 0.03

1.38 9.34 1.15 1.5

0.25 (0.32) 5.68 (7.15) 1.40 (1.41) 3.96 (2.90) 1.26 (0.88) 0.71 (0.38) 0.18 (0.18)

0.09 (0.075) 1.03 (1.004) 0.11 (0.040) 3.15 (2.619) 0.15 (0.118) 0.40 (0.277) 0.11 (0.036)

0.02 0.008 0.07 0.22 0.006 0.01 0.31

2.71 5.5 12.18 1.26 8.41 1.77 1.65

6 1.96 (0.51) 3.11 (5.59) 0.41 (0.03) 3.47 (6.49) 6.09 (5.42) 1.60 (3.11) 0.79 (0.51) 0.36 (0.32) 12.06 (14.67) 2.74(5.39) 2.09 (2.91) 0.24 (0.23)

20 2.23 (1.83) 0.23 (0.18) 0.43 (0.35) 0.43 (0.49) 4.72 (2.30) 0.16 (0.18) 0.36 (0.20) 0.13 (0.08) 3.42 (2.60) 0.13 (0.15) 0.41 (0.24) 0.14 (0.06)

0.85 < 0.0001 0.45 0.02 0.92 0.01 0.06 0.00 0.04 0.0008 0.001 0.15

-1.14 13.54 -1.04 7.99 1.29 9.69 2.15 2.65 3.53 20.43 5.05 1.72

6 1.10 (0.43) 0.849 (0.41) 0.401 (0.003) 1.018 (0.94) 5.291 (3.65) 0.096 (0.02)

20 1.47(1.21) 0.21 (0.06) 0.39 (0.38) 0.38 (0.38) 3.68 (2.17) 0.07 (0.02)

0.82 0.0007 0.14 0.18 0.18 0.02

-1.33 3.95 1.01 2.62 1.44 1.28

0.43 (0.13) 0.33 (0.26) 0.31 (0.36) 0.37 (0.43) 0.40 (0.32) 0.22 (0.32) 1.05 (0.54) 0.20 (0.26) 2.39 (1.15) 0.12 (0.06) 0.18 (0.22) 0.33 (0.31) 0.33 (0.12) 0.63 (0.67) 0.14 (0.05)

0.32 (0.08) 0.18 (0.05) 0.13 (0.04) 0.12 (0.03) 0.18 (0.09) 0.08 (0.03) 0.46 (0.12) 0.05 (0.07) 3.14 (1.74) 0.09 (0.03) 0.01 (0.02) 0.17 (0.06) 0.25 (0.16) 0.22 (0.13) 0.17 (0.14)

0.11 0.01 0.026 0.005 0.063 0.360 0.04 0.001 0.36 0.10 0.006 0.01 0.23 0.13 1.00

1.33 1.87 2.41 3.05 2.17 2.73 2.27 3.94 -1.31 1.35 10.65 2 1.31 2.81 -1.18

1.30 (1.82) 1.16 (1.65) 0.52 (0.48) 0.88 (1.15) 0.53 (0.65) 3.27 (7.39) 0.20 (0.33) 2.01 (2.54) 0.04 (0.01) 3.90 (4.68) 0.22 (0.20) 0.08 (0.14) 0.42 (0.15) 0.70 (0.61) 0.22 (0.14)

0.37 (0.27) 0.36 (0.13) 0.18 (0.05) 0.18 (0.12) 0.11 (0.04) 0.42 (0.42) 0.08 (0.05) 0.55 (0.22) 0.04 (0.04) 2.97 (1.77) 0.13 (0.09) 0.01 (0.01) 0.33 (0.25) 0.40 (0.41) 0.14 (0.08)

0.02 0.004 0.005 0.035 0.0004 0.48 0.92 0.006 0.01 0.65 0.11 0.003 0.25 0.28 0.21

3.47 3.24 2.89 4.8 4.8 7.79 2.3 3.64 1.16 1.31 1.65 8.79 1.27 1.75 1.52

0.783 (0.40) 4.090 (1.83) 0.454 (0.20) 0.810 (0.52) 0.111 (0.04) 0.763 (0.37) 0.166 (0.09) 0.55 (0.18) 0.26 (0.08) 0.19 (0.13) 0.22 (0.17) 0.26 (0.14) 0.08 (0.01) 0.79 (0.31) 0.02 (0.00) 1.94 (0.95) 0.36 (0.25) 0.12 (0.15) 0.46 (0.41) 1.18 (0.74) 1.10 (0.92) 0.19 (0.10)

0.15 (0.06) 3.38 (2.70) 0.12 (0.09) 0.36 (0.22) 0.12(0.03) 0.15 (0.11) 0.07 (0.028) 0.34 (0.08) 0.160(0.03) 0.10 (0.02) 0.11 (0.03) 0.15 (0.13) 0.08 (0.04) 0.46 (0.09) 0.02 (0.01) 3.800 (1.95) 0.179 (0.10) 0.008 (0.01) 0.187 (0.08) 0.571 (0.49) 0.591 (0.54) 0.123 (0.09)

0.01 0.24 0.0002 0.009 0.97 0.001 0.003 0.03 0.003 0.05 0.041 0.020 0.64 0.05 0.87 0.03 0.13 0.0007 0.05 0.05 0.26 0.06

5.07 1.21 3.69 2.24 -1.09 5.01 2.21 1.61 1.66 1.8 1.92 1.65 -1.07 1.69 1 -1.95 2.05 14.28 2.47 2.07 1.87 1.6

ACS Paragon Plus 35 Environment


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Table 4. Concentrations of serum metabolites in mM (mean (SEM)) in healthy control (CON) and SCM cows at 3 time points (+4 wks, and +8 wks, after parturition) as determined by GS-MS. Metabolite, mM Number of cases L-Lactic acid Valine Alanine Glycine Oxalate Leucine Proline Isoleucine Urea Serine Phosphoric acid Threonine Aspartic acid Pyroglutamic acid Creatinine Ornithine Glutamic acid Phenylalanine Lysine Citric acid Tyrosine D-Mannose Galactose Palmitic acid Myo-Inositol Linoleic acid Stearic acid Oleic acid

4 wks after parturition

8 wks after parturition

SCM

CON

p-value

Fold change

6 1.701 (0.29) 2.295 (2.65) 0.983 (0.59) 2.524 (1.95) 6.474 (4.54) 0.604 (0.65) 1.163 (0.88) 1.160 (1.17) 12.396 (11.19) 0.784 (1.01) 4.781 (6.39) 0.479 (0.46) 1.072 (1.44)

6 1.942 (0.88) 0.214 (0.11) 0.585 (0.26) 0.464 (0.32) 5.199 (2.17) 0.334 (0.20) 0.207 (0.27) 0.154 (0.24) 5.824 (2.73) 0.123 (0.10) 0.616 (0.49) 0.130 (0.04) 0.771 (0.36)

0.803 0.008 0.166 0.002 0.808 0.213 0.029 0.019 0.215 0.012 0.008 0.1282 0.280

-1.14 10.73 1.68 5.44 1.25 1.81 5.62 7.52 2.13 6.38 7.76 3.68 1.39

SCM / CON Down Up Up Up Up Up Up Up Up Up Up Up Up

1.201 (1.56) 1.019 (1.54)

0.319 (0.12) 0.597 (0.32)

0.041 0.806

3.77 1.71

0.639 (0.78) 0.916 (0.30) 0.219 (0.26) 2.562 (3.72)

0.084 (0.01) 0.576 (0.26) 0.131 (0.06) 1.436 (0.87)

0.065 0.056 0.818 0.678

3.346 (4.78) 0.652 (0.61) 0.105 (0.15)

1.145 (1.05) 0.125 (0.03) 0.052 (0.13)

2.313 (2.71) 2.553 (2.51)

0.404 (0.24) 0.260 (0.18)

SCM

CON

p-value

Fold change

SCM / CON

6 1.578 (0.729) 1.080 (0.221)

6 2.518 (2.171) 0.216 (0.050)

0.8065 0.0001

-1.6 5.01

Down Up

1.257 (0.508) 2.054 (1.822)

0.397 (0.300) 6.640 (5.055)

0.0087 0.0795

3.17 -3.23

Up Down

2.151 (0.815) 0.914 (0.213) 10.217 (2.580) 0.536 (0.272) 1.977 (1.079)

0.188 (0.206) 0.133 (0.192) 6.521 (3.728) 0.122 (0.079) 0.807 (0.757)

0.0050 0.0022 0.0738 0.0121 0.0547

11.45 6.86 1.57 4.38 2.45

Up Up Up Up Up

Up Up

0.407 (0.154) 0.379 (0.163)

0.329 (0.136) 0.291 (0.068)

0.3763 0.1147

1.24 1.3

Up Up

7.61 1.59 1.67 1.78

Up Up Up Up

0.179 0.015 0.045

2.92 5.22 2.01

Up Up Up

0.347 (0.318) 0.554 (0.408) 0.121 (0.004) 1.267 (0.254) 0.168 (0.066) 1.890 (0.457) 0.233 (0.107) 0.045 (0.010)

0.086 (0.018) 0.545 (0.209) 0.127 (0.073) 0.834 (0.353) 0.049 (0.047) 1.425 (1.500) 0.127 (0.067) -0.002 (0.008)

0.0450 0.5683 1.0000 0.0278 0.0161 0.3939 0.0450 < 0.0001

4.05 1.02 -1.04 1.52 3.4 1.33 1.84 0.04

Up Up Down Up Up Up Up Up

0.145 0.076

5.72 9.8

Up Up

0.516 (0.245) 0.873 (0.515)

0.527 (0.555) 0.387 (0.515)

0.6884 0.0649

-1.02 2.26

Down Up


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FIGURE LEGENDS Fig. 1 (a) PCA and (b) PLS-DA, permutation test: P < 0.05) of 20 control (CON) and 6 SCM cows at -8 wks before parturition. (c) Variables ranked by variable importance in projection (VIP), and (d) Receiver-operator characteristic (ROC) curve. Fig. 2 (a) PCA and (b) PLS-DA (Permutation test: P < 0.05) of 20 CON and 6 SCM cows at 4 wks before parturition. (c) VIP, and (d) ROC curve of 20 CON and 6 lameness cows at -4 wks before parturition. Fig. 3 Summary plots for quantitative enrichment analysis at (a) -8 wks, (b) -4 wks before parturition and (c) wk of the diagnosis of disease. Fig. 4 (a) PCA and (b) PLS-DA (Permutation test: P < 0.05) of 20 CON and 6 SCM at disease diagnosis wk. (c) VIP, and (d) ROC curve of 20 CON and 6 SCM cows at disease diagnosis wk. Fig. 5 (a) PCA and (b) PLS-DA (Permutation test: P < 0.05) of 6 CON and 6 SCM cows at +4 wks after parturition. (c) VIP, and (d) ROC curve of 6 CON and 6 SCM cows at +4 wks after parturition. Fig. 6 (a) PCA and (b) PLS-DA of 6 CON (Permutation test: P < 0.05) and 6 SCM cows at +8 wks after parturition. (c) VIP, and (d) ROC curve of 6 CON and 6 SCM cows at +8 wks after parturition. Fig. 7 Summary plots for quantitative enrichment analysis at (a) +4 wks, (b) +8 wks after parturition


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Figure 1.


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Figure 2


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Figure 3


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Figure 4


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Figure 5


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Figure 6


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Figure 7


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“For TOC only”

Photograph courtesy of Guanshi Zhang and Elda Dervishi. Copyright 2016.


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GCâMS Metabolomics Identifies Metabolite Alterations That Precede

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GCâMS Metabolomics Identifies Metabolite Alterations That Precede