Serum metabolic fingerprinting of pre-lameness dairy cows by GC–MS reveals typical profiles that can identify susceptible cows

Journal of Proteomics 213 (2020) 103620

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Serum metabolic fingerprinting of pre-lameness dairy cows by GC–MS reveals typical profiles that can identify susceptible cows

T

Elda Dervishia, Guanshi Zhanga, Grzegorz Zwierzchowskia, Rupasri Mandalb, David S. Wishartb, ⁎ Burim N. Ametaja, a b

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, Alberta T6G 2E9, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Dairy cow Serum GC–MS Metabolites Lameness

The objectives of this study were to identify metabolite fingerprints in the serum related to amino acid (AA), carbohydrate, and lipid metabolism in transition dairy cows at −8 and −4 wks prior to parturition, at +2 wks postpartum during lameness diagnosis as well as at +4 and +8 wks after parturition. All cases of lameness occurred at around +2 wks after parturition. Out of 100 dairy cows included in this nested case-control study only 6 pregnant multiparous (parity: 3.0 ± 0.6, Mean ± SEM) Holstein dairy cows with lameness only and 20 healthy control cows (CON) were selected for serum GC–MS metabolomics analysis. All cows selected were not injured mechanically and had similar parity (3.3 ± 0.6) and body condition score (BCS). A total of 29 metabolites were identified and quantified in the serum. Results showed that 18 and 15 metabolites differentiated pre-lame cows from CON ones at −8 and −4 wks prior to parturition. Ten metabolites were found altered at the week of lameness diagnosis. Of note: pre-lame cows were characterized by greater concentrations of several amino acids including Gly, Leu, Phe, Ser, Val, D-mannose, Myo-inositol, and phosphoric acid (PA) at −8 and −4 wks prior to lameness and at the week of lameness diagnosis. At +4 wks after parturition 11 metabolites were altered in lameness cows, and at +8 wks there were 13 metabolites that differentiated the two groups. The high accuracy of the top 6 metabolites at −8 wks prior to parturition or approximately 9–11 wks before lameness diagnosis (Glu, Orn, Phe, Ser, Val, and PA) and another 5 metabolites at −4 wks before parturition, or approximately 5–7 wks before lameness diagnosis (Leu, Orn, Phe, Ser, and D-mannose) suggest that those metabolites may serve as potential monitoring biomarkers of lameness prior to lameness diagnosis. Data also showed multiple alterations during the week of lameness as well as at +4 and +8 wks postpartum suggesting lame cows are not metabolically healthy several weeks after the incidence of lameness. Significance: Lameness is one of the top three health issues of dairy cows in Canada that influences early culling of dairy cows. Despite a few efforts, there is scarcity of data regarding metabolic alterations that precede, associate, and follow lameness. We investigated whether alterations in the metabolite signatures prior, during, and after development of lameness can be used to screen cows for susceptibility to lameness, characterize lameness from the metabolic prospective, and predict the outcome of this economically important health issue of dairy cows. The results demonstrate typical metabotypes as shown by increased serum concentrations of Val, Gly, Ser, Leu, Phe, D-mannose, myo-inositol, and phosphoric acid at −8 and −4 wks prior to parturition (or −6 to −10 wks prior to occurrence of lameness) and at the week of lameness diagnosis.

1. Introduction Lameness is the third most important health issue of dairy cows in Canada that influences early culling of dairy cows, after infertility and mastitis. Lameness is a multifactorial health issue related to the type of diet provided (high grain diets), body condition score (BCS), days in milk (DIM), and parity with older cows being more susceptible [1].

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Lameness also has significant economic implication costing dairy producers between $121 and $216 per case [2]. The prevalence of lameness in USA and United Kingdom varies between 20 and 25% [3,4]. In Canada, Solano et al. [1] reported that at herd-level lameness prevalence ranged from 0 to 69% with an average of 21%. In the province of British Colombia, it has been reported a lameness incidence of 35%, under a free stall system [5].

Corresponding author. E-mail address: [email protected] (B.N. Ametaj).

https://doi.org/10.1016/j.jprot.2019.103620 Received 24 September 2019; Received in revised form 5 December 2019; Accepted 13 December 2019 Available online 14 December 2019 1874-3919/ © 2019 Published by Elsevier B.V.

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this nested case-control study. In this study cows were diagnosed with lameness between 1 and 3 wks postpartum with an average of +2 wks after calving. Lameness was diagnosed by trained staff based on a locomotion score system according to the farm standard operating procedure [15]. All six lameness cows used in this experiment had a score of 5, which showed severe lameness with pronounced arching of the back, reluctant to move, and complete weight transfer off the affected limb. The 20 healthy cows had a lameness score of 1. Cows with lameness were treated by trimming and medication [16]. Lame cows were administered either Excenel® RTU (Zoetis Canada, Kirkland, QC, Canada) at 1 mL per 50 kg IM, once a day for 3 d, or Procaine Penicillin Gr (Dominion Veterinary Laboratories Ltd., Winnipeg, MB, Canada) at 2 mL per 45 kg IM twice a day for 3 d. The total experimental period for each cow was 16 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 gradual increase of barley grain during the first 7 d after parturition to meet the energy demands for high milk production [12]. Daily ration was offered as TMR for ad libitum intake once daily at 0800 h to allow approximately 5% refusals throughout the experiment. The TMR was formulated to meet or exceed nutrient requirements of a 680 kg lactating cows as per NRC guidelines (2001) [17].

Environmental factors also have been reported as risk factors including the type of flooring, its slipperiness and cleanliness as well as the type of stall bedding [2,4,6,7]. The evolving of “omics” technologies and biosensors can make possible development of lameness detection assays by integrating physiological variables in addition to environmental conditions. A number of methods have been employed in diagnosing cases of lameness in dairy cattle including mobility scoring [8] and infrared tomography [9]. In addition, various authors including us have reported that measurement of pro-inflammatory cytokines and acute-phase proteins (APPs) can be used as biomarkers of lameness [10–12]. Recent advances in genomics, transcriptomics, proteomics, and metabolomics sciences have facilitated development of novel diagnostic and monitoring technologies [13]. Metabolomics is the newest of the “omics” sciences and has the great potential to identify early monitoring biomarkers associated with lameness. To the best of our knowledge, only one study by Zheng et al. [13] has reported utilization of 1H NMR-based metabolomics to assess metabolic differences between dairy cows with acute footrot and healthy cows. The authors identified a total of 21 metabolites to be differently expressed between footrot cows and healthy controls. The top 5 pathways associated with footrot included glycine, serine, and threonine metabolism, ketone body synthesis and degradation, methane metabolism, valine, leucine, and isoleucine biosynthesis, and pyruvate metabolism [13]. Despite a few efforts, there is scarcity of data regarding metabolic alterations that precede, associate, and follow lameness. Recently, we reported that lameness affected serum concentrations of several variables related to innate immunity and carbohydrate metabolism starting from −8 wks prior to parturition. In that article we demonstrated that concentrations of lactate in the serum of cows with lameness were greater than those in the CON cows at all 5 time points around parturition (−8 wks prior to and up to +8 wks after calving) [12]. Screening of dairy cows for susceptibility to lameness as well as early diagnosis and timely treatment might prevent development of the pathology, improve welfare and wellbeing of cows, and increase longevity, in addition to minimizing the economic loss to dairy producers. The objective of this study was to investigate whether alterations in the metabolite signatures prior, during, and after the development of lameness can be used to screen cows for susceptibility to lameness, characterize lameness from the metabolic pathway prospective, and predict the outcome of this economically important health issue of dairy cows.

2.2. Blood sample collection For this study, blood samples were obtained from the coccygeal veins of 100 transition dairy cows from −8 to +8 wks around calving and 26 of them were retrospectively selected for further analyses. Cows that were affected by mastitis, metritis, ketosis, milk fever, or retained placenta or had several periparturient diseases at the same time were excluded from further analyses as were cows that had mechanical injury of the legs or foot. Twenty cows out of a total of 26 selected were completely healthy cows, with no clinical signs of any disease (CON cows) and 6 cows were diagnosed postpartum as lame cows. Cows with lameness had no other concurrent diseases. Blood samples were obtained once per week at 0700 before feeding at −8 wks (53–59 d), −4 wks (25–31 d), disease week (+1 to +3 wks; 8–21 d), +4 wks (25–31 d) and +8 wks (53–59 d) around calving. 2.3. GC–MS compound identification and quantification

2. Materials and methods

Prior to analysis by GC–MS, the serum samples were extracted to separate polar metabolites. The extraction and derivatization protocol was adapted from a previously reported method [18] to deproteinize and achieve broad metabolite coverage of polar metabolites. Detailed procedures of metabolite extraction, derivatization, quality control (QC) parameters, raw data acquisition, and raw data processing have been published previously [19,20]. Briefly, an aliquot of 100 μL of serum sample containing 10 μL of ribitol in water (0.4 mg mL-1) 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 °C 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 another 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-1; Sigma-Aldrich, Oakvile, ON, Canada) in ACS grade pyridine and incubated at room temperature for

This study was part of a large project designed to study the pathobiology of periparturient diseases of transition dairy cows and also to identify potential screening biomarkers of those diseases. 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 [14]. Data related to concentrations of serum cytokines and acute phase proteins, dry matter intake (DMI), milk production and milk composition for this experiment have been reported previously [12]. The metabolomics analyses were performed at the Metabolomics Innovation Centre, University of Alberta, Edmonton, AB, Canada. 2.1. Animals and diets One hundred pregnant Holstein dairy cows were used in this study. We excluded all cases with other periparturient diseases including cows that were lame because of mechanical injury. All cases with lameness were diagnosed by an experienced veterinary practitioner. Six pregnant multiparous (parity: 3.0 ± 0.6, Mean ± SEM) Holstein dairy cows with lameness and 20 healthy control cows (CON) that were similar in parity (3.3 ± 0.6) and body condition score (BCS), were selected for 2

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16 h. Then 50 μL of MSTFA (N-Methyl-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. Derivatized samples were stored for < 48 h at 4 °C until analysis. Derivatized extracts were injected by Agilent 7683 Series autosampler (Agilent Technologies, Palo Alto, CA, USA) followed by the analysis employing Agilent 6890 N GC system coupled with electron impact (EI) ionization mode 5973 N 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-1 at an initial oven temperature of 50 °C. The oven temperature was increased at 10 °C min1 to 310 °C for a final run time of 26 min. Full scan spectra (50–500 m/ z; 1.7 scans/s) 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 (i.e., alanine, valine, isoleucine, glycine, serine, and lysine) then 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 C21-C40 (1:1 vol/vol, Sigma-Aldrich, Oakvile, ON, Canada) was injected to get 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 polar metabolites was performed following the method as previously described (Wishart et al., 2008). 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 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 case, 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 (Psychogios et al., 2011). RIs and EI spectra were subsequently used for producing external 5-point calibration curves (for absolute quantification).

Univariate analysis was performed using t-test (i.e., the parameter follows normal distribution) or the Wilcoxon Mann Whitney test (i.e., the parameter does not follow normal distribution) provided by R (Version 3.0.3, R Development Core Team, 2008) [21]. Statistical significance was declared at P < .05. A nested case-control study was performed by comparing the healthy cows group and the group of cows that developed lameness after parturition at each time point (−8, −4, disease diagnosis, +4, and +8 wks around calving) separately. Multivariate analyses: Partial least squares – discriminant analysis (PLS-DA), variable importance in the projection (VIP), receiver-operator characteristic (ROC) and pathway analysis were performed using MetaboAnalyst 3.0 [22]. For the supervised PLS-DA model, a permutation testing with 2000 random re-samplings was implemented to validate the reliability of the PLS-DA model and to determine the probability that the metabolites distinguishing the lame and CON groups are a result of chance. In the PLS-DA model, a VIP plot was used to rank the metabolites based on their importance in discriminating cows with lameness from CON group. 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 [23]. Biomarker profiles and the quality of the biomarker sets were determined using receiver-operator characteristic (ROC) curves as calculated by the ROCCET web server [24]. A ROC curve shows how the sensitivity and specificity of a test or biomarker profile change as the classification decision boundary is varied across the range of available biomarker scores. Because ROC curves depict the performance of a biomarker test over the complete range of possible decision boundaries, it allows the optimal specificity and associated sensitivity to be determined post hoc. Receiver-operator characteristic curves are often summarized into a single metric known as the area under the curve (AUC). The AUC can be interpreted as the probability that a diagnostic test or a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. If all positive samples are ranked before negative ones (i.e., a perfect classifier), the AUC is 1.0. An AUC of 0.5 is equivalent to randomly classifying subjects as either positive or negative (i.e., the classifier is of no practical utility). A rough guide for assessing the utility of a biomarker based on its AUC is 0.9 to 1.0 = excellent; 0.8 to 0.9 = good; 0.7 to 0.8 = fair; 0.6 to 0.7 = poor; 0.5 to 0.6 = fail [24]. We also conducted metabolite set enrichment analysis (MSEA), performing over-representation analysis (ORA). For ORA we used the list of significant of metabolites and pathway-associated metabolite sets were selected as metabolite library. Details and recommended statistical procedures for metabolomics analysis were followed according to previously published protocols [22–24]. The relationship among significant metabolites and their interrelationship with other metabolites in lame cows were visualized using Metscape plugin in cytoscape [25,26]. 3. Results The results of our study show that pre-lame cows were characterized by multiple metabolite alterations. In this study cows were diagnosed with lameness between 1 and 3 wks postpartum with an average of +2 wks after parturition and the results are presented relative to the week of diagnosis of lameness. These metabolic alterations start as early as −10 wks prior to lameness diagnosis and all changes of serum metabolome are described for each time point separately. A total of 29 metabolites were identified and quantified in the serum samples at 5 time points. Those metabolites included mainly amino acids, fatty acids, carbohydrates, urea, pyroglutamic and phosphoric acid (PA).

2.4. Statistical analysis Metabolites that were frequently (> 20%) below the limit of detection or with at least 20% missing values were removed from consideration. Otherwise, missing values were replaced by a value of onehalf 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. In this study, we used log-transformation and auto-scaling of metabolite values.

3.1. Changes of serum metabolome before lameness diagnosis At −10 wks prior to lameness diagnosis pre-lame cows had greater concentrations of 5 glucogenic amino acids (Ala, Gly, Pro, Ser, and Val), 3

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that was down-regulated in pre-lame cows vs CON ones. Multivariate analysis including PLS-DA showed distinct score plots for the serum samples between healthy cows and pre-lame cows at week −10 and −6 prior to lameness (Figs. 1a and 2a) suggesting early changes in the metabolomic profile in pre-lame cows. The top 15 most important metabolites that separated the two groups of cows are represented in the VIP plots (Figs. 1b and 2b). At −10 wks prior to disease metabolites Glu, Orn, Phen, Ser, Val and PA had the highest VIP score. Moreover, at −6 wks prior to lameness the same metabolites displayed again the same trend but different ranking. The compounds with the highest VIP score were selected in order to determine whether they would serve as potential predictive markers. Plots for ROC curves showing the performance of the top 6 metabolites at −10 wks (Phe, Val, Ser, Orn, Glu, and PA) before lameness and the performance of the top 5 metabolites at −6 wks prior to lameness (Orn, Phen, Leu, Ser, and D-mannose) are shown in Figs. 1c and 2c, (empirical p = .001). The AUC for the curves were 0.999 (95% CI 1–1) at −10 wks and 1.0 (95% CI 1–1) at −6 wks prior to lameness, which suggest that those compounds may be used as potential biomarkers of lameness starting at −10 and −6 wks prior to clinical lameness. Furthermore, pathway analysis revealed that at −10 wks before lameness several metabolic pathways including Phe and Tyr metabolism, propanoate metabolism, and cysteine metabolism were the top 3 most enriched pathways. Moreover, at −6 wks before lameness, spermine and spermidine biosynthesis, fructose and mannose degradation, and Phe and Tyr metabolism, were the top 3 most enriched pathways (Figs. 1d and 2d).

Table 1 Concentrations of serum metabolites [mean (SD)] in healthy control (CON) and pre-lameness (LAM) cows at −10 wks before diagnosis of lameness, as determined by GC–MS. Name

Mean (SD) of LAM

Mean (SD) of CON

P-Value

Fold change

Valine Alanine Glycine Proline Serine Lysine Leucine Phenylalanine Ornithine Linoleic acid Stearic acid Cholesterol D-Mannose Myo-Inositol Phosphoric acid Pyroglutamic acid Urea Palmitic acid

0.820 0.679 0.820 0.309 0.395 0.263 0.498 0.426 0.908 0.397 0.801 1.367 0.335 0.061 1.689 0.518 4.290 0.220

0.209 0.429 0.274 0.180 0.116 0.171 0.107 0.123 0.211 0.175 0.275 0.373 0.055 0.009 0.431 0.149 3.163 0.094

0.02 0.01 (W) 0.005 (W) 0.01 (W) 0.001 (W) 0.01 (W) 0.003 (W) 0.003 0.001 (W) 0.02 (W) 0.02 (W) 0.001 (W) 0.001 (W) 0.01 0.001 (W) 0.002 (W) 0.05 (W) 0.05 (W)

3.93 1.58 2.99 1.72 3.41 1.53 4.66 3.46 4.30 2.27 2.91 3.67 6.06 7.15 3.92 3.47 1.36 2.36

(0.486) (0.127) (0.44) (0.09) (0.29) (0.06) (0.35) (0.14) (0.76) (0.33) (0.86) (0.52) (0.24) (0.04) (0.42) (0.39) (1.37) (0.22)

(0.10) (0.25) (0.25) (0.13) (0.05) (0.09) (0.09) (0.04) (0.09) (0.07) (0.17) (0.23) (0.08) (0.01) (0.29) (0.08) (2.62) (0.04)

and 2 ketogenic amino acids (Leu and Lys) when compared to CON cows (Table 1; p < .05). Among the amino acids, Leu was increased +4.66-fold when compared with CON cows, followed by Orn (+4.3fold increase). In addition, pre-lame cows had greater concentrations of cholesterol and fatty acids like linoleic and stearic acid (p < .05). Interestingly myo-inositol was the most increased metabolite in the serum of pre-lame cows with +7.15-fold change, followed by D-mannose with +6.06-fold change (p < .05). Alterations in the serum of pre-lame cows continued at −6 wks prior to lameness diagnosis (Table 2). A total of 16 metabolites, were found to be altered in pre-lame cows (Table 2; p < .05). For example, glucogenic amino acids (Asp, Glu, Gly, Ser, and Val) and the ketogenic amino acid (Leu) were significantly greater in pre-lame cows when compared to CON ones (p < .05). Among glucogenic amino acids, Glu was the most up-regulated (+4.78-fold) followed by Asp (+3.01-fold). At −6 wks prior to lameness, Leu remained increased with +4.48-fold change when compared with CON cows followed by Orn (+3.47-fold). Additionally, D-mannose and myo-inositol had the most increased concentrations in the serum of pre-lame cows at −6 wks prior to lameness diagnosis, with +7.65- and +7.2-fold change, respectively. At −6 wks prior to lameness diagnosis, oxalate was the only compound

3.2. Changes of serum metabolome at the week of lameness diagnosis At the week of lameness diagnosis concentrations of 10 metabolite species were greater in lame cows vs CON (Table 3; p < .05). More specifically, glucogenic amino acids Gly, Ser, and Val and ketogenic amino acid Leu were greater in lame cows. In addition, those cows were characterized by greater concentrations of cholesterol, PA, and Dmannose (p < .05). Furthermore, the most up-regulated compounds at this week were D-mannose with +13.47-fold change, followed by MyoInositol with +8.09-fold change, PA with +6.44-fold change and cholesterol with +4-fold change. Pathway analysis revealed that at the week of lameness diagnosis, fructose and mannose degradation and propanoate metabolism remained the top 2 most enriched pathways. Multivariate PCA did not show a clear separation between the two groups of cows; however, PLS-DA analysis showed a distinct score plot for the serum samples between healthy cows and the lame ones at the week of lameness event (Fig. 3a). The reason for the lack of separation between the two groups at PCA is related to the fact that all metabolites (significantly and non-significantly different ones) were included in the analysis. The permutation test showed a p value of 0.10. The top 15 most important metabolites that separated the two groups of cows are presented in the VIP plots (Fig. 3b). Compounds such as Val, D-mannose, PA, cholesterol, and Leu had the highest VIP scores. Metabolites with the highest VIP score were selected in order to determine their diagnostic potential. A ROC curve plot showing the performance of the top metabolites is shown in Fig. 3c, (empirical p = .001). The AUC for the curve was 1.0 (95% CI 1–1) which suggest that those metabolites and the pathways (Fig. 3d) affected may serve to characterize lameness metabolically. Of note, certain compounds such as amino acids Gly, Leu, Ser, Phe, and Val as well as D-mannose, myo-inositol and PA were consistently altered at −10 and −6 wks prior to lameness diagnosis and at the week of clinical signs in lame cows (Fig. 4). At all three aforementioned time point pre-lame and lame cows had greater concentration of those metabolites than the CON group.

Table 2 Concentrations of serum metabolites [mean (SD)] in healthy control (CON) and pre-lameness (LAM) cows at −6 wks before diagnosis of lameness, as determined by GC–MS. Name

Mean (SD) of LAM

Mean (SD) of CON

P-Value

Fold change

Valine Glycine Serine Aspartic acid Glutamic acid Leucine Phenylalanine Ornithine Phosphoric acid Oxalate Pyroglutamic acid Tyrosine D-Mannose Myo-Inositol Creatinine Cholesterol

0.589 0.825 0.373 0.531 0.879 0.462 0.416 0.650 1.272 2.551 0.408 1.158 0.373 0.062 0.740 0.968

0.230 0.403 0.106 0.177 0.184 0.103 0.110 0.187 0.405 4.406 0.146 0.491 0.049 0.009 0.360 0.358

0.001 (W) 0.03 (W) 0.001 (W) 0.02 (W) 0.005 (W) 0.001 (W) 0.03 0.001 (W) 0.001 (W) 0.04 (W) 0.02 (W) 0.004 (W) 0.001 (W) 0.001 (W) 0.05 (W) 0.01 (W)

2.56 2.05 3.52 3.01 4.78 4.48 3.78 3.47 3.14 −1.73 2.80 2.36 7.65 7.2 2.06 2.70

(0.33) (0.57) (0.30) (0.45) (0.96) (0.31) (0.27) (0.32) (0.73) (1.48) (0.36) (0.82) (0.34) (0.06) (0.54) (070)

(0.19) (0.46) (0.06) (0.10) (0.12) (0.08) (0.04) (0.06) (0.24) (2.31) (0.07) (0.13) (0.05) (0.01) (0.13) (0.19)

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Fig. 1. (A) PLS-DA, permutation test: (P < .05) of 20 control (CON) and 6 pre-lameness cows at −10 wks before lameness diagnosis, (B) Variables ranked by variable importance in projection (VIP), (C) Receiver-operator characteristic (ROC) curve and (D) Pathway enrichment overview.

myo-inositol, and palmitic acid had the highest VIP score at +6 wks after lameness diagnosis (Fig. 6b). A ROC curve plot showing the performance of the top 5 metabolites at +2 and +6 wks after diagnosis of lameness is shown in Figs. 5c and 6c. The AUC for the curves were 0.99 (95% CI 0.75–1) at +6 wks and 0.99 (95% CI 1–1) at +10 wks after diagnosis. Pathway analysis revealed that at +2 wks and +6 wks after lameness diagnosis methionine metabolism as well as phenylalanine and tyrosine metabolism were the most enriched pathways (Figs. 5d and 6d). Again, certain compounds such as amino acids Gly, Phe, palmitic acid, stearic acid, oleic acid, and galactose were consistently altered during postpartum in such a way that lame cows had greater concentration when compared with healthy ones (Fig. 7). For more information about analysis of all time points together see Supplementary Figs. 1-3.

3.3. Changes of serum metabolome after lameness diagnosis Serum alterations in post-lame cows continued at +2 and +6 wks after lameness diagnosis (i.e., +4 and +8 wks after parturition). Mean values of serum amino acids (Ile, Gly, Phe, Ser, Val, and Thr) were significantly higher in cows with lameness (Table 4; p < .05). In addition, those cows had higher concentration of fatty acids including stearic, palmitic, and oleic acids (p < .0.5). The most up-regulated compounds at +2 wks after lameness diagnosis were oleic acid (+9.93fold increase) followed by the amino acid Phe and stearic acid. Furthermore, at +6 wks after diagnosis of lameness a total of 13 metabolite species were up-regulated in cows with lameness (Table 5; p < .05). The amino acid Pro, cholesterol, and PA were metabolites with the highest concentrations in the serum of post-lame cows when compared to healthy CON. Interestingly multivariate analysis (PLS-DA) showed a clear separation between the two groups at +2 wks after lameness diagnosis (Fig. 5a and 6a). The analysis showed a distinct score plot for the serum samples between healthy cows and lameness cows. Compounds such as Gly, Phe, Ser, galactose, and Thr had the highest VIP score at +2 wks after lameness diagnosis (Fig. 5b), meanwhile Pro, Phe, cholesterol,

4. Discussion The incidence rate of lameness in dairy cows in Canada ranges between 10 and 27% (avg. 21%) [1]. It should be noted that lameness is not a disease in itself but a sign of pain and discomfort originating from an ongoing pathological process in the foot or leg area of dairy cows. 5

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Fig. 2. (A) PLS-DA, permutation test: (P < .05) of 20 control (CON) and 6 pre- lameness cows at −6 wks before lameness diagnosis, (B) Variables ranked by variable importance in projection (VIP), (C) Receiver-operator characteristic (ROC) curve, and (D) Pathway enrichment overview.

including ruminal histamine and endotoxin as well as local metalloproteinases activated by gastrointestinal Streptococcus bovis [27,28]. Results of this study throw light on the metabolic alterations prior to, during, and after clinical signs of lameness. We hypothesized that metabolomic fingerprinting of dairy cows prior to, during, and after diagnosis of lameness could identify metabolomic alterations that precede, associate, and follow clinical manifestation of lameness. Those changes could help to better understand the etiopathology of lameness and can be used as biomarkers for monitoring cows for susceptibility to lameness as well as to characterize lameness metabolically and understand the impact of lameness on metabolic status of dairy cows, weeks after appearance of clinical signs. Indeed, the results of this study showed that cows with lameness experienced multiple metabolite alterations in the serum starting from 9 to 11 wks prior to appearance of clinical signs. Results showed that prelame cows were characterized by greater concentration of several amino acids including Val, Gly, Ser, Leu, and Phe as well as D-mannose, myo-inositol and PA 6–10 wks prior to diagnosis of the disorder and during lameness event (Fig. 4). Besides serving as building blocks for protein synthesis, amino acids play important roles as a source of energy and for supporting mounting of an immune cells [29]. Amino acids are metabolized in the liver and

Table 3 Concentrations of serum metabolites [mean (SD)] in healthy control (CON) and pre-lameness (LAM) cows at diagnosis of lameness, as determined by GC–MS. Name

Mean (SD) of LAM

Mean (SD) of CON

p-Value

Fold change

Valine Glycine Serine Leucine Threonine Phenylalanine Phosphoric acid D-Mannose Myo-Inositol Cholesterol

0.747 1.109 0.401 0.294 0.250 0.232 2.322 0.570 0.044 1.345

0.215 0.390 0.123 0.086 0.120 0.118 0.361 0.042 0.005 0.336

0.001 (W) 0.01 (W) 0.001 (W) 0.002 (W) 0.003 (W) 0.001 (W) 0.001 (W) 0.001 (W) 0.002 (W) 0.0001 (W)

3.47 2.84 3.26 3.41 2.07 1.97 6.44 13.47 8.09 4.00

(0.52) (0.75) (0.33) (0.21) (0.16) (0.13) (2.31) (0.54) (0.04) (0.86)

(0.07) (0.38) (0.09) (0.03) (0.03) (0.03) (0.23) (0.04) (0.01) (0.18)

Culling of cows with regards to foot issues in Canada is ranked third after infertility and mastitis with very significant impact on the profitability of dairy operations. There are several reasons why cows limp; however, the most important risk factors include postpartum diet as well as mechanical and environmental factors. With regards to dietary factors three main potential causal agents have been suggested 6

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Fig. 3. (A) PLS-DA, permutation test: (P < .05) of 20 control (CON) and 6 lame cows at the week of lameness diagnosis, (B) Variables ranked by variable importance in projection (VIP), (C) Receiver-operator characteristic (ROC) curve and (D) Pathway enrichment overview.

published article, we reported that the same cows with lameness at −4 wks prior to parturition, showed an overall tendency for greater BHBA concentration [12]. We also observed that cows with lameness experienced lower dry matter intake (DMI), lower milk production, and milk fat depression during the experimental period. In addition, concentrations of lactate in the serum of cows with lameness were greater than those in the CON cows at all-time points in the experiment [12]. During all experimental time points especially at −6 wks before lameness diagnosis, pre-lame and lame cows seemed to utilize amino acids to produce lactate and ketone bodies rather than for gluconeogenesis. It is speculated that such increase in serum amino acids concentration may be caused by increased protein breakdown in skeletal muscles to meet the energy demand and to mount an inflammatory response against a potential pathogen or its toxic membrane components including endotoxins. Fatty acids are an important metabolic fuel as well as an integral part of cell membranes, and precursors to prostaglandins, thromboxanes, and leukotrienes collectively called eicosanoids. These molecules have the ability to regulate cell activation, immune responses, and inflammation. In our study an interesting finding was that pre-lame cows had greater concentration of linoleic acid (C18:2 n-6). Linoleic acid is a

other tissues where alpha amino groups are removed and the residual carbon skeletons are degraded to form several intermediate metabolites of TCA cycle. These intermediates are oxidized into carbon dioxide, in the TCA cycle, generating energy. The amino acids Leu and Val are branched chain amino acids and are involved in the regulation of protein synthesis by immune cells and the release of cytokines [29]. In macrophages, it was observed that serine deprivation diminishes lipopolysaccharide (LPS) induction of the pro-inflammatory cytokine IL-1β, suggesting that the amino acid Ser is required for optimal LPS induction of IL-1β mRNA [30]. Meanwhile, Gly and Phe have been reported to have immunosuppressive effects causing inhibition of proliferation of T lymphocytes [31,32]. Several reports in human and animals have described elevated serum or plasma amino acids concentrations in various inflammatory conditions including sepsis, subclinical mastitis, metritis and retain placenta [30,33–35]. In addition, Leu, a ketogenic amino acid, when it is broken down leads to production of acetyl CoA. This molecule can be used to generate fatty acids and/or ketone bodies. Meanwhile, Phe is both a glucogenic and ketogenic amino acid, which is used to produce fumarate (TCA cycle) or acetoacetyl CoA, which leads to formation of ketone bodies. Ketone bodies include acetoacetate, acetone, and hydroxybutyric acid (BHBA). In a previous 7

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Fig. 4. The network of significant metabolites that were consistently changed (up-regulated) in pre- lame cows (at wks −10 and −6 before lameness diagnosis) and lame cows (at the week of lameness diagnosis). Table 4 Concentrations of serum metabolites [mean (SD)] in healthy control (CON) and pre-lameness (LAM) cows at +2 wks after diagnosis of lameness, as determined by GC–MS.

Table 5 Concentrations of serum metabolites [mean (SD)] in healthy control (CON) and pre-lameness (LAM) cows at +6 wks after diagnosis of lameness, as determined by GC–MS.

Name

Mean (SD) of LAM

Mean (SD) of CON

p-Value

Fold change

Name

Mean (SD) of LAM

Mean (SD) of CON

p-Value

Fold change

Valine Glycine Isoleucine Serine Threonine Phenylalanine Palmitic acid Stearic acid Oleic acid Galactose Pyroglutamic acid Phosphoric acid

1.068 2.231 0.106 0.782 0.413 0.628 0.546 2.498 2.587 3.463 0.567 4.064

0.214 0.464 0.058 0.123 0.130 0.084 0.125 0.404 0.260 1.145 0.332 0.616

0.04 0.01 0.01 (W) 0.004 (W) 0.002 (W) 0.05 0.01 (W) 0.01 (W) 0.01 (W) 0.01 (W) 0.05 (W) 0.06

5.00 4.81 1.82 6.36 3.17 7.48 4.38 6.18 9.93 3.02 1.71 6.60

Glycine Proline Phenylalanine Creatinine Palmitic acid Stearic acid Oleic acid Cholesterol Phosphoric acid Citric acid D-Mannose Galactose Glucose

2.506 2.942 0.839 1.851 0.619 2.984 2.403 8.736 9.211 0.567 0.505 4.049 0.948

0.397 0.188 0.086 0.329 0.127 0.527 0.387 0.702 0.807 0.127 0.117 1.425 2.281

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.03 0.04 0.01 0.01 0.01

6.32 15.65 9.8 5.62 4.89 5.66 6.22 12.44 11.42 4.48 4.32 2.84 −2.41

(0.81) (1.29) (0.04) (0.57) (0.29) (0.53) (0.55) (2.79) (2.96) (0.86) (0.28) (3.54)

(0.12) (0.32) (0.014) (0.10) (0.04) (0.01) (0.03) (0.25) (0.18) (1.06) (0.18) (0.49)

precursor of omega-6 (n-6) series and has been reported to contribute to inflammation [36] by increasing the formation of arachidonic acid (C20:4 n-6). Synthesis of oxygenation products of arachidonic acid such as prostaglandin E2, thromboxane A2, and leukotriene B4 are potent mediators of inflammation [37]. In humans, elevated pro-inflammatory eicosanoid products could drive up other markers of inflammation such as interleukin- 6 (IL-6) and tumor necrosis factor (TNF) [36]. Moreover, pre-lame and lame cows had greater concentration of palmitic acid and stearic acid at −10 wks before lameness diagnosis and at +6 and +10

(1.61) (1.51) (0.54) (2.16) (0.31) (1.67) (1.33) (6.31) (7.06) (0.40) (0.18) (0.71) (0.53)

(0.30) (0.21) (0.02) (0.14) (0.10) (0.55) (0.51) (0.40) (0.76) (0.07) (0.15) (1.50) (0.69)

(W) (W) (W) (W) (W) (W)

(W) (W)

wks after diagnosis when compared with CON group. These two fatty acids have been reported to be pro-inflammatory, triggering the release of TNF and IL-6 [38]. In agreement with these findings, we previously reported that the same cows affected by lameness were in a chronic inflammatory state and had greater concentrations of IL-6 and serum amyloid alpha (SAA) in the serum vs CON cows at −10 and −6 wks prior to diagnosis of lameness. In addition, concentrations of TNF tended to be greater in cows with lameness compared with the CON 8

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Fig. 5. (A) PLS-DA, permutation test: P < .05) of 20 control (CON) and 6 lame cows at +2 wks after lameness diagnosis, (B) Variables ranked by variable importance in projection (VIP), (C) Receiver-operator characteristic (ROC) curve and (D) Pathway enrichment overview.

potential bacterial infection or lower their antibacterial immune responses. Another important finding of our study was that serum D-mannose was higher in pre-lame cows starting at −10 and −6 wks before diagnosis of lameness and continued to be higher until +6 wks after diagnosis. Fructose and mannose degradation pathway was one of the main enriched pathways identified in pre-lame (−10 and −6 wks before diagnosis) and in lame cows (at the week of diagnosis). In case of infections, bacteria adhere to the surface of the epithelial cells and they do so by attaching to certain sugars within the cell wall glycoproteins. Previously, King et al. [42] reported that mannose might be effective in lowering bacterial infection in the equine endometrium. In another study by Sheldon et al. [43], it was reported that mannose could prevent E. coli from adhering to the uterine mucosa. The source of Dmannose is internal since no treatment was applied to the cows prior to parturition. Elevation of these metabolites in pre-lame and lame cows suggest that myo-inositol and D-mannose might be potential compounds which can help cows in fighting infections or inflammation at very early stages after calving. In addition, our result showed that pre-lame cows had greater concentrations of PA in the serum compared with CON cows. Elevated

[12]. Another interesting finding of this study was that both myo-inositol and D-mannose were greater in the serum of pre-lame cows at −10 and −6 wks before diagnosis of lameness and at the week of lameness event. Myo-inostitol plays an important role in various cellular processes, including cell growth and survival, development and function of peripheral nerves, osteogenesis, reproduction, and glucose homeostasis [39]. Reports have indicated that myo-inositol is also involved in the process of phagocytosis [40]. Recently, it was reported that low and intermediate doses (2.5–5.0 mM twice per day for 3 d) of myo-inositol enhance phagocytic capabilities of macrophages against antibiotic-resistant E. coli by depolarizing the membrane potential of macrophages [41]. However, high doses (20 mM) of myo-inositol injected into mice lowered the ability to eliminate antibiotic-resistance E. coli [41]. The authors proposed that low and intermediate doses of exogenous myoinositol have the ability to enhance host immunity, especially phagocytosis by macrophages, and eliminate the pathogen. In dairy cows in our study we don't know yet whether myo-inositol influences macrophage activity and at what blood concentration enhances phagocytic capability of the host macrophages, leaving us to speculate that either the increased myo-inositol in pre-lame cows contributes to fight the 9

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Fig. 6. (A) PLS-DA, permutation test: P < .05) of 20 control (CON) and 6 lame cows at +6 wks after lameness diagnosis, (B) Variables ranked by variable importance in projection (VIP), (C) Receiver-operator characteristic (ROC) curve and (D) Pathway enrichment overview.

palmitic, stearic, and oleic acids as well as galactose when compared with healthy cows. The only metabolite that was down-regulated after lameness diagnosis (at +6 wks) was glucose. Glucose can be a limiting factor to milk secretion. An increase in glucose availability has been reported to positively affect milk yield [49,50]. This might explain our previous observation that cows with lameness had lower milk production during the experimental period [12]. The monosaccharides D-glucose alone or combined with D-galactose are precursors to the synthesis of the milk disaccharide lactose. It should be noted that although we did not observe differences in the concentration of lactose in the milk between the two groups of cows this should be explained by the finding we reported previously that lame cows dropped milk yield by almost 25% [12]. In addition, enhanced galactose might have played a role in immune cell activation. Chang et al. [51] demonstrated that both Band T-cells can use alternative energy sources including galactose for growth and proliferation and production of cytokines like IFN-γ. The impact of lameness on fertility has been previously documented [52,53]. Our results showed that alterations in serum metabolome in lame cows continue until +6 wks after lameness diagnosis (approximately +8 wks after parturition). It is a common practice that in this period the cows are inseminated. It is obvious that dairy cows affected

concentrations of PA were present in pre-lame, lame, and post-lame periods. Inorganic phosphorous (Pi) is the second most abundant mineral element found in the body of dairy cows. The main Pi storage site in the body of dairy cows is the skeleton, where almost 80% of the Pi is stored as hydroxyapatite and released together with Ca, and the remaining is distributed in soft tissue and body fluids [15,44,45]. Despite known functions of Pi in the formation of bones and in other important enzymes and mediators little is known about the function of Pi in immunity. Interestingly, a few studies indicate that high levels of Pi in blood (hyperphosphatemia) are supportive of an inflammatory response. Thus, Goodson et al. [46], Yamada et al. [47] and NavarroGonzales et al. [48] reported that Pi loading induces inflammation and increases serum TNF and other proinflammatory cytokines. It seems like hyperphosphatemia might be a host response to support immune cells to mount a more efficient immune response. However, chronic hyperphosphatemia might be deleterious because it might prolongate the chronic inflammatory state, as was the case with our dairy cows. Alterations in amino acids, fatty acids, and carbohydrate signatures continued to be present even during the post-lame period at +2 and +6 wks after lameness diagnosis. Those cows had greater concentrations of several amino acids like Gly and Phe and fatty acids including 10

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Fig. 7. The network of significant metabolites that were consistently changed in lame cows (at +2 and +6 wks after lameness diagnosis).

interventions. Further studies with a larger cohort of animals are warranted to validate the identified biomarkers. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jprot.2019.103620.

by lameness are not metabolically healthy at that period of time. The implications of this might be that if cows are not metabolically healthful this might affect their reproduction outcome. 5. Conclusions

Funding

In conclusion, data from this study demonstrated typical metabolite fingerprints as shown by increased serum concentrations of Val, Gly, Ser, Leu, Phe, D-mannose, myo-inositol, and PA at −8 and−4 wks prior to parturition (or −6 to −10 wks prior to occurrence of lameness) and at the week of lameness diagnosis. Multiple metabolites changed during the pre-lame period which suggest that those cows go through typical metabolic alterations that can be tracked and used to identify susceptible cows. Metabolites identified in blood might play various roles in supporting cow's needs for energy purposes and for mounting an immune response to potential unidentified inflammatory agents. Additionally, cows affected by lameness had typical alterations in several serum metabolites related to amino acids, fatty acids, and carbohydrate metabolism. Metabolic alterations were present in post-lame cows even after they were free of clinical signs at +4 and +8 wks postpartum. The high accuracy of the top 5 metabolites at −8 wks prior to lameness diagnosis (i.e., Glu, Orn, Phe, Ser, Val, and PA) and another 5 metabolites at −4 wks prior to lameness diagnosis (Leu, Orn, Phe, Ser, and D-mannose) suggest that those metabolites may serve as potential monitoring biomarkers of lameness prior to appearance of clinical signs. The results support the idea that metabolomics approach can give insights into the etiopathology of lameness and might serve to better understand the disease process and develop potential therapeutic

This research was funded by Genome Alberta (Calgary, AB, Canada) and ALMA (Alberta Livestock and Meat Agency Ltd., Edmonton, AB, Canada), grant number AARI2008A100R.

Declaration of Competing Interest None

Acknowledgments We thank Genome Alberta (Calgary, AB, Canada) and ALMA (Alberta Livestock and Meat Agency Ltd., Edmonton, AB, Canada) for financial support of the project. We acknowledge that Drs. B.N. Ametaj and D.S. Wishart were the Principal Investigators of this research work. We also thank the full or partial contribution of D. Hailemariam, S.A. Goldansaz, Q. Deng, and J.F. Odhiambo in collection of samples from the cows. Elda Dervishi and Guanshi Zhang contributed equally to this work. 11

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