Background The therapeutic strategy of bovine respiratory disease (BRD) often involves a combination of an antibiotic with an anti-inflammatory agent. Aim of this study was to evaluate the clinical effect of a new combination product containing tulathromycin and ketoprofen for the treatment of naturally occurring BRD. Methods Two hundred and eighty animals were randomized upon diagnosis of BRD. One hundred forty animals each were treated once subcutaneously with tulathromycin-ketoprofen or tulathromycin. Rectal temperature of each animal was measured at 1, 2, 4, 6, 8, 10, 12 and 24 h post-treatment. Individual respiration and depression scores were determined at 6 h post-treatment. Daily rectal temperature, respiration and depression scores were recorded from day 2 to 14 and on day 21. Results The tulathromycin-ketoprofen and tulathromycin treatment group demonstrated a treatment success rate of 94.2% and 95.0%, respectively and a relapse rate of 3.8% and 4.0%, respectively. Tulathromycin-ketoprofen demonstrated superior pyrexia control compared to tulathromycin within the first 24 h following treatment. Tulathromycin-ketoprofen-treated animals demonstrated faster improvement of their clinical symptoms (respiration and depression score). Conclusion Efficacy of tulathromycin-ketoprofen for the treatment of BRD was non-inferior to tulathromycin. The combination product clearly exhibited more pronounced fever control than tulathromycin which is considered beneficial for animal welfare.

Among the dairy sector's current concerns, the assessment of global animal health status is a complex challenge. Its multidimensionality means that global monitoring tools are rarely considered. Instead, specific disease detection is often studied separately and, due to financial and ethical issues, uses small-scale data sets focusing on few biomarkers. Several studies have already been conducted using milk Fourier transform mid-infrared (FT-MIR) spectroscopy to detect mastitis and lameness or to quantify health-related biomarkers in milk or blood. Those studies are relevant but they focus mainly on one biomarker or disease. To solve this issue and the small-scale data set, in this study, we proposed a holistic approach using big data obtained from milk recording, including milk yield, somatic cell count, and 27 FT-MIR-based predictors related to milk composition and animal health status. Using 740,454 records collected from 114,536 first-parity Holstein cows in southern Belgium, we performed repeated unsupervised learning algorithms based on Ward's agglomerative hierarchical clustering method to find potential interesting patterns. A divide-and-conquer approach was used to overcome the limitation of computational resources in clustering a relatively large data set. Five groups of records were identified. Differences observed in the fourth group suggested a relationship to metabolic disorders. The fifth group seemed to be related to mastitis. In a second step, we performed a partial least squares discriminant analysis (PLS-DA) to predict the probability of belonging to those specific groups for the entire data set. The obtained global accuracy was 0.77 and the balanced accuracy (i.e., the mean between sensitivity and specificity) of discriminating the fourth and fifth groups was 0.88 and 0.96, respectively. Then, a validation of the interpretation of those groups was performed using 204 milk and blood reference records. The predicted probability associated with the metabolic disorders issue had significant correlations of 0.54 with blood β-hydroxybutyrate, 0.44 with blood nonesterified fatty acids, -0.32 with blood glucose, -0.23 with milk glucose-6-phosphate, and 0.38 with milk isocitrate. In contrast, the predicted probability of belonging to the mastitis group had correlations of 0.69 with milk lactate dehydrogenase, 0.46 with milk N-acetyl-β-d-glucosaminidase, -0.18 with milk free glucose, and 0.16 with milk glucose-6-phosphate. Consequently, these results suggest that the obtained quantitative traits indirectly reflect some of the main health disorders in dairy farming and could be used to monitor dairy cows on a large scale. By using unsupervised learning on large-scale milk recording data and then validating the pattern using reference laboratory measures, we propose a new approach to quickly assess dairy cow health status.

Overconditioning is a risk factor for upregulated pre- and postpartum fat mobilization. Therefore, we hypothesized that overconditioning at the end of pregnancy leads to the accumulation of lipids in the liver and modifications of the hepatic gene expression pattern. The aim of this study was to evaluate the effect of normal- versus overconditioning on the hepatic transcriptomic profile of dairy cows at the end of pregnancy. Ten dry multiparous Holstein cows were killed 2 wk before expected calving. Body condition score (BCS) and backfat thickness (BFT) were evaluated, and blood samples for nonesterified fatty acids (NEFA) were taken before cows were killed. After cows were killed, liver biopsy samples were collected for further assessment of total lipids and RNA sequencing. Five cows were classified as normal-conditioned (median BCS = 3, range 2.75-3.5) and 5 as overconditioned (median BCS = 4, range 4-5). Regression models confirmed that normal-conditioned cows had lower BFT (1.29 ± 0.29 cm; least squares means ± standard error) and serum NEFA (0.16 ± 0.04 mmol/L) in comparison to overconditioned cows (3.14 ± 0.43 cm and 0.38 ± 0.07 mmol/L for BFT and NEFA, respectively). Total liver lipid percentage tended to be lower in normal- versus overconditioned cows (4.63 ± 0.40% and 6.06 ± 0.44%, respectively). In comparison to the mean liver lipid percentage of the normal- and overconditioned cows, 1 overconditioned cow had a relatively low (5.21%) and 1 normal-conditioned cow had a relatively high (6.07%) liver lipid percentage. Differentially expressed genes analysis (edgeR quasi-likelihood method) showed that normal-conditioned cows presented 11 upregulated and 12 downregulated genes in comparison to overconditioned cows. Linear discriminant analysis effects size revealed 133 differentially expressed genes between normal- versus overconditioned cows. Notably, the liver of normal-conditioned cows had upregulated genes associated with liver functionality (ALB, SELENOP, IGF1, and IGF2). On the other hand, overconditioned cows had upregulated genes associated with the acute-phase response (C3, HPX, and, LBP). High basal lipolysis in overconditioned cows at the end of pregnancy increased liver lipid content, and this may alter the hepatic gene expression pattern to a pro-inflammatory state.

The current studies aimed to evaluate the pharmacokinetic (PK) and pharmacodynamic (PD) profile and to establish a PK-PD model for ketoprofen in a new fixed combination product containing tulathromycin (2.5 mg/kg) and ketoprofen (3 mg/kg) to treat bovine respiratory disease associated with pyrexia in cattle. Firstly, the effect of different ketoprofen doses as mono-substance (1, 3, and 6 mg/kg subcutaneous) on lipopolysaccharide-induced fever was evaluated which indicated that rectal temperature reduction lasted longer in the calves receiving 3 and 6 mg/kg ketoprofen. Secondly, the PK profile of the combination product was compared with mono-substance products (3 mg/kg subcutaneous and intramuscular). The PK profile of ketoprofen in the combination product was characterized by longer t1/2 , lower Cmax and increased AUC in comparison with mono-substance products. Due to prolonged ketoprofen exposure in the combination product, the pyrexia reducing effect of the combination product lasted longer in a second lipopolysaccharide challenge study in comparison with mono-substance products. Finally, a PK-PD model for the anti-pyretic effect of ketoprofen was developed based on the data from the different studies. The PK-PD model eliminated the need for additional animal experiments and indicated that a 3 mg/kg ketoprofen dose in the combination product provided optimal efficacy.

The main objective of this study was to test the efficiency of a management system combining metabolic clustering of cows based on Fourier-transform mid-infrared (FT-MIR) spectra of milk and targeted treatment of metabolically imbalanced cows with propylene glycol drench. We hypothesized that cows identified in a metabolically imbalanced status during early lactation were associated with subsequent impaired health, reproduction, and production, and that treatment with propylene glycol treatment would improve health, reproduction, and production relatively more in these cows than in control cows. We completed a prospective, randomized controlled trial with 356 early-lactation cows in 2 private dairy herds in Denmark from December 2017 to April 2018. Milk samples of cows were collected before treatment, from 4 to 9 d in milk, and after treatment, from 22 to 27 d in milk. Milk samples were analyzed using FT-MIR spectroscopy. We also measured 4 milk metabolites (β-hydroxybutyrate, isocitrate, malate, and glutamate) and fat and protein contents. Based on FT-MIR spectra and cluster analyses, cows were clustered into groups of metabolically imbalanced and healthy cows. Within each group, cows were allocated randomly to treatment with propylene glycol (500 mL for 5 d) or no treatment. We analyzed the effect of the treatment on cow-level variables: metabolic cluster, milk metabolites, fat and protein contents, and fat-to-protein ratio at a milk sampling after the treatment. Furthermore, we analyzed daily milk yield, calving to first service interval, and disease occurrence. Results showed only a few effects of propylene glycol treatment and few interactions between treatment and metabolic clusters. We found no significant main effects of propylene glycol treatment in any of these analyses. A negative effect of the imbalanced metabolic cluster was found for the outcome of calving to first service interval for multiparous cows. In conclusion, we found a longer calving to first service interval in metabolically imbalanced cows, but we were not able to demonstrate overall benefits from the applied detection of cows in imbalanced metabolic status in early lactation and follow-up by treatment with propylene glycol.

Oxidized linoleic acid metabolites (OXLAM) are products of adipocyte lipolysis with the potential to modulate adipose tissue (AT) lipid metabolism and inflammation. In periparturient cows, linoleic acid is preferentially mobilized from AT during lipolysis by hormone-sensitive lipase (HSL) compared with other polyunsaturated fatty acids. Enzymatic and nonenzymatic reactions generate OXLAM from linoleic acid. Among OXLAM, 9-, 10-, and 12-hydroxy-octadecadienoic acids (HODE) are associated with pro-inflammatory responses, whereas 9- and 13-oxo-octadecadienoic acids (oxoODE) and 13-HODE can facilitate inflammation resolution and promote lipogenesis. This study evaluated the effect of HSL activity on OXLAM biosynthesis using subcutaneous AT explants collected from multiparous dairy cows at 10 d before and again at 10 and 24 d after calving. Explants were treated for 3 h without or with the β-adrenergic agonist isoproterenol (ISO; 1 µM; MilliporeSigma, Burlington, MA) to induce HSL activity. The contribution of HSL to OXLAM biosynthesis was determined by inhibiting its activity with CAY10499 (2 µM; Cayman Chemical, Ann Arbor, MI). After treatments, media and explants were collected for lipidomic analysis using HPLC-tandem mass spectroscopy. Results indicated that ISO increased the biosynthesis of 9-, 12-, and 13-HODE and 9-oxoODE, and this effect was reduced at 24 d after calving. Inhibiting HSL activity partially reversed ISO effects on HODE and 9-oxoODE. Our ex vivo model demonstrated for the first time a direct effect of HSL activity on the biosynthesis of OXLAM in AT, especially at 10 d before and 10 d after calving. The biosynthesis of anti-inflammatory OXLAM is limited during the first weeks after parturition and may promote AT inflammation and lipolytic responses to negative energy balance. These results indicate that HSL activity releases linoleic acid for OXLAM biosynthesis in concentrations of a magnitude that may bypass the need for the activation of phospholipases linked with the inflammatory cascade and thus supports, in part, lipolysis-driven inflammation within AT of periparturient cows.

AbstractThe aim of this study was to identify genomic regions associated with 305‐day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single‐step genomic BLUP approach and imputed high‐density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305‐day milk yield and the parameters of lactation curve in primi‐ and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.

The aim of this study was to detect the genomic region or regions associated with metabolic clusters in early-lactation Holstein cows. This study was carried out in 2 experiments. In experiment I, which was carried out on 105 multiparous Holstein cows, animals were classified through k-means clustering on log-transformed and standardized concentrations of blood glucose, insulin-like growth factor I, free fatty acids, and β-hydroxybutyrate at 14 and 35 d in milk (DIM), into metabolic clusters, either balanced (BAL) or other (OTR). Forty percent of the animals were categorized in the BAL group, and the remainder were categorized as OTR. The cows were genotyped for a total of 777,962 SNP. A genome-wide association study was performed, using a case-control approach through the GEMMA software, accounting for population structure. We found 8 SNP (BTA11, BTA23, and BTAX) associated with the predicted metabolic clusters. In experiment II, carried out on 4,267 second-parity Holstein cows, milk samples collected starting from the first week until 50 DIM were used to determine Fourier-transform mid-infrared (FT-MIR) spectra and subsequently to classify the animals into the same metabolic clusters (BAL vs. OTR). Twenty-eight percent of the animals were categorized in the BAL group, and the remainder were classified in the OTR category. Although daily milk yield was lower in BAL cows, we found no difference in daily fat- and protein-corrected milk yield in cows from the BAL metabolic cluster compared with those in the OTR metabolic cluster. In the next step, a single-step genomic BLUP was used to identify the genomic region(s) associated with the predicted metabolic clusters. The results revealed that prediction of metabolic clusters is a highly polygenic trait regulated by many small-sized effects. The region of 36,258 to 36,295 kb on BTA27 was the highly associated region for the predicted metabolic clusters, with the closest genes to this region (ANK1 and miR-486) being related to hematopoiesis, erythropoiesis, and mammary gland development. The heritability for metabolic clustering was 0.17 (SD 0.03), indicating that the use of FT-MIR spectra in milk to predict metabolic clusters in early-lactation across a large number of cows has satisfactory potential to be included in genetic selection programs for modern dairy cows.

The aim of the present study was to identify genomic region(s) associated with the length of the calving interval in primiparous (n = 6866) and multiparous (n = 5071) Holstein cows. The single nucleotide polymorphism (SNP) solutions were estimated using a weighted single-step genomic best linear unbiased prediction (WssGBLUP) approach and imputed high-density panel (777 k) genotypes. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. The results showed that the accuracies of GEBVs with WssGBLUP improved by +5.4 to +5.7, (primiparous cows) and +9.4 to +9.7 (multiparous cows) percent points over accuracies from the pedigree-based BLUP. The most accurate genomic evaluation was provided at the second iteration of WssGBLUP, which was used to identify associated genomic regions using a windows-based GWAS procedure. The proportion of additive genetic variance explained by windows of 50 consecutive SNPs (with an average of 165 Kb) was calculated and the region(s) that accounted for equal to or more than 0.20% of the total additive genetic variance were used to search for candidate genes. Three windows of 50 consecutive SNPs (BTA3, BTA6, and BTA7) were identified to be associated with the length of the calving interval in primi- and multiparous cows, while the window with the highest percentage of explained genetic variance was located on BTA3 position 49.42 to 49.52 Mb. There were five genes including ARHGAP29, SEC24D, METTL14, SLC36A2, and SLC36A3 inside the windows associated with the length of the calving interval. The biological process terms including alanine transport, L-alanine transport, proline transport, and glycine transport were identified as the most important terms enriched by the genes inside the identified windows.

The aim of this study was to describe metabolism of early-lactation dairy cows by clustering cows based on glucose, insulin-like growth factor I (IGF-I), free fatty acid, and β-hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using 3 sets of milk biomarkers (milk metabolites and enzymes, glycans on the immunogamma globulin fraction of milk, and Fourier-transform mid-infrared spectra of milk). Metabolic clusters are used to identify dairy cows with a balanced or imbalanced metabolic profile. Around 14 and 35 d in milk, serum or plasma concentrations of BHB, free fatty acids, glucose, and IGF-I were determined. Cows with a favorable metabolic profile were grouped together in what was referred to as the "balanced" group (n = 43) and were compared with cows in what was referred to as the "other balanced" group (n = 64). Cows with an unfavorable metabolic profile were grouped in what was referred to as the "imbalanced" group (n = 19) and compared with cows in what was referred to as the "other imbalanced" group (n = 88). Glucose and IGF-I were higher in balanced compared with other balanced cows. Free fatty acids and BHB were lower in balanced compared with other balanced cows. Glucose and IGF-I were lower in imbalanced compared with other imbalanced cows. Free fatty acids and BHB were higher in imbalanced cows. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat- and protein-corrected milk yield in balanced cows, whereas that of imbalanced cows was higher. Dry matter intake and the daily increase in dry matter intake were higher in balanced cows and lower in imbalanced cows. Energy balance was continuously higher in balanced cows and lower in imbalanced cows. Weekly or twice-weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6-phosphate, BHB, lactate dehydrogenase, N-acetyl-β-d-glucosaminidase, isocitrate), immunogamma globulin glycans (19 peaks), and Fourier-transform mid-infrared spectra (1,060 wavelengths reduced to 15 principal components) were determined. Milk biomarkers with or without additional cow information (days in milk, parity, milk yield features) were used to create predictive models for the metabolic clusters. Accuracy for prediction of balanced (80%) and imbalanced (88%) cows was highest using milk metabolites and enzymes combined with days in milk and parity. The results and models of the present study are part of the GplusE project and identify novel milk-based phenotypes that may be used as predictors for metabolic and performance traits in early-lactation dairy cows.

Extracellular vesicles (EVs) play a possible role in cell–cell communication and are found in various body fluids and cell conditioned culture media. The aim of this study was to isolate and characterize EVs in culture medium conditioned by bovine embryos in group and to verify if these EVs are functionally active. Initially, ultracentrifuged bovine serum albumin (BSA) containing medium was selected as suitable EV-free embryo culture medium. Next, EVs were isolated from embryo conditioned culture medium by OptiPrepTM density gradient ultracentrifugation. Isolated EVs were characterized by nanoparticle tracking analysis, western blotting, transmission, and immunoelectron microscopy. Bovine embryo-derived EVs were sizing between 25–230 nm with an average concentration of 236.5 ± 1.27 × 108 particles/mL. Moreover, PKH67 EV pre-labeling showed that embryo-secreted EVs were uptaken by zona-intact bovine embryos. Since BSA did not appear to be a contaminating EV source in culture medium, EV functionality was tested in BSA containing medium. Individual embryo culture in BSA medium enriched with EVs derived from conditioned embryo culture medium showed significantly higher blastocyst rates at day 7 and 8 together with a significantly lower apoptotic cell ratio. In conclusion, our study shows that EVs play an important role in inter embryo communication during bovine embryo culture in group.

The objective of this study was to develop a generic risk management system based on the Hazard Analysis and Critical Control Point (HACCP) principles for the prevention of critical negative energy balance (NEB) in dairy herds using an expert panel approach. In addition, we discuss the advantages and limitations of the system in terms of implementation in the individual dairy herd. For the expert panel, we invited 30 researchers and advisors with expertise in the field of dairy cow feeding and/or health management from eight European regions. They were invited to a Delphi-based set-up that included three inter-correlated questionnaires in which they were asked to suggest risk factors for critical NEB and to score these based on 'effect' and 'probability'. Finally, the experts were asked to suggest critical control points (CCPs) specified by alarm values, monitoring frequency and corrective actions related to the most relevant risk factors in an operational farm setting. A total of 12 experts (40 %) completed all three questionnaires. Of these 12 experts, seven were researchers and five were advisors and in total they represented seven out of the eight European regions addressed in the questionnaire study. When asking for suggestions on risk factors and CCPs, these were formulated as 'open questions', and the experts' suggestions were numerous and overlapping. The suggestions were merged via a process of linguistic editing in order to eliminate doublets. The editing process revealed that the experts provided a total of 34 CCPs for the 11 risk factors they scored as most important. The consensus among experts was relatively high when scoring the most important risk factors, while there were more diverse suggestions of CCPs with specification of alarm values and corrective actions. We therefore concluded that the expert panel approach only partly succeeded in developing a generic HACCP for critical NEB in dairy cows. We recommend that the output of this paper is used to inform key areas for implementation on the individual dairy farm by local farm teams including farmers and their advisors, who together can conduct herd-specific risk factor profiling, organise the ongoing monitoring of herd-specific CCPs, as well as implement corrective actions when CCP alarm values are exceeded.

Reproductive diseases affect 25% of dairy cows in the US and often develop from retention of the placenta. It is well established that expulsion of the placenta is a highly regulated inflammatory process, but the mechanisms by which dysregulation of uterine immune responses impair this process are poorly understood. In healthy non-ruminants, pro-inflammatory M1 macrophages are predominant in uterine tissue after parturition. However, macrophage phenotype in the postpartum bovine uterus is unknown. Our study compared macrophage phenotypes in the uterine caruncles of multiparous dairy cows that during the first day postpartum either retained (RET, n = 5) or had normal expulsion (NOR, n = 5) of placenta. Immune cells were sorted magnetically from the caruncular endometrial cell fraction using the CD172a marker and monocyte/macrophage population was characterized using flow cytometry. Transcriptional and protein expression studies were performed on uterine caruncles. Compared to NOR, RET samples showed a lower CD14+/CD16+ expression (P < 0.05) in caruncle monocyte/macrophage population. As opposed to NOR, RET further demonstrated greater expression of anti-inflammatory M2 macrophage associated genes CD206, C-type lectin domain family 7 member A (CLEC7A), and RNASE6. In addition, caruncles from RET showed decreased signal transducer and activator of transcription 3 (STAT3) activation, an important promoter of proteolytic activity, compared to NOR. Our studies demonstrate that there is an overall lower number of macrophage populations in the caruncle of cows with RET placenta and these are polarized towards M2 phenotype. Excessive accumulation of M2 macrophages may lead to reduced trafficking of immune cells into the caruncle thus impairing the inflammatory, phagocytic and proteolytic processes that lead to placental expulsion.

A contributor to the GplusE consortium, Cinzia Marchitelli, was inadvertently omitted from the list on the title page. The complete list should read as follows: *List of authors in the GplusE consortium: Niamh McLoughlin, Alan Fahey, Elizabeth Matthews, Andreia Santoro, Colin Byrne, Pauline Rudd, Roisin O'Flaherty, Sinead Hallinan, Claire Wathes, Zhangrui Cheng, Ali Fouladi, Geoff Pollott, Dirk Werling, Beatriz Sanz Bernardo, Alistair Wylie, Matt Bell, Mieke Vaneetvelde, Kristof Hermans, Geert Opsomer, Sander Moerman, Jenne De Koster, Hannes Bogaert, Jan Vandepitte, Leila Vandevelde, Bonny Vanranst, Johanna Hoglund, Susanne Dahl, Soren Ostergaard, Janne Rothmann, Mogens Krogh, Else Meyer, Charlotte Gaillard, Jehan Ettema, Tine Rousing, Federica Signorelli, Francesco Napolitano, Bianca Moioli, Alessandra Crisà, Luca Buttazzoni, Jennifer McClure, Daragh Matthews, Francis Kearney, Andrew Cromie, Matt McClure, Shujun Zhang, Xing Chen, Huanchun Chen, Junlong Zhao, Liguo Yang, Guohua Hua, Chen Tan, Guiqiang Wang, Michel Bonneau, Andrea Pompozzi, Armin Pearn, Arnold Evertson, Linda Kosten, Anders Fogh, Thomas Andersen, Matthew Lucy, Chris Elsik, Gavin Conant, Jerry Taylor, Nicolas Gengler, Michel Georges, Frédéric Colinet, Marilou Ramos Pamplona, Hedi Hammami, Catherine Bastin, Haruko Takeda, Aurelie Laine, Anne-Sophie Van Laere, Martin Schulze, Sergio Palma Vera, and Cinzia Marchitelli. The authors regret the error. Prediction of metabolic clusters in early-lactation dairy cows using models based on milk biomarkersJournal of Dairy ScienceVol. 102Issue 3PreviewThe aim of this study was to describe metabolism of early-lactation dairy cows by clustering cows based on glucose, insulin-like growth factor I (IGF-I), free fatty acid, and β-hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using 3 sets of milk biomarkers (milk metabolites and enzymes, glycans on the immunogamma globulin fraction of milk, and Fourier-transform mid-infrared spectra of milk). Metabolic clusters are used to identify dairy cows with a balanced or imbalanced metabolic profile. Full-Text PDF Open Archive

Intense lipolysis triggers an inflammatory response within adipose tissue characterized by adipose tissue macrophage (ATM) infiltration; however, the mechanisms triggering this process are poorly characterized in transition dairy cows. The aim of this study was to determine the association between ATM infiltration and body fat mobilization in the transition period, markers of excessive lipolysis, and adipose tissue expression of genes related to chemotactic and inflammatory responses. Subcutaneous adipose tissue samples were taken from the tailhead of 9 multiparous Holstein cows, 27 ± 2.2 d (far-off) and 10 ± 1.5 d (close-up) before and 9 ± 0.3 d after calving (fresh). Blood samples were collected by coccygeal venipuncture 2 h before adipose sample collections. Body condition score (BCS) was assessed independently by 3 experienced technicians at every time point. Based on BCS loss intensity between the close-up and fresh period, cows were divided into 2 groups: low BCS loss (LBCSL, change in BCS <0.25 units, n = 5) and high BCS loss (HBCSL, change in BCS >0.25 units, n = 4). Although none of the LBCSL cows had a health event, all cows in the HBCSL group suffered from one or more clinical disorder (retained placenta, milk fever, or ketosis) in the transition period. The number of ATM was determined by immunohistochemistry, and expression of selected chemotactic and inflammatory genes was determined by reverse-transcription quantitative real-time PCR in subcutaneous adipose tissue samples. The proportion of ATM in subcutaneous adipose tissue increased in HBCSL during the postpartum period. The proportion of ATM was not associated with serum β-hydroxybutyrate or free fatty acid concentrations on the day of adipose tissue collection. The ATM infiltration in the fresh period was associated with local expression of the chemotactic genes, C-C motif chemokine ligand 22 (CCL22), osteopontin (SPP1), and the receptor for SPP1, cluster of differentiation 44 (CD44). This supports a potential chemotactic role of CCL22 and SPP1 for ATM in bovine adipose tissue. None of the genes encoding pro- or anti-inflammatory mediators, tumor necrosis factor (TNF), IL6, and IL10 were associated with the proportion of ATM. Our results indicate that ATM infiltration of subcutaneous adipose tissue is associated with body fat mobilization in early-lactation dairy cows and supports a role for ATM in the adaptation of adipose tissues to the metabolic challenges of the transition period.

In the present study, we tested the hypothesis that overconditioning in dairy cows at the end of the dry period leads to infiltration of fat and alterations of the insulin secretory capacity of the pancreas. Pregnant Holstein Friesian dairy cows were selected based on body condition score (BCS) at the start of the dry period. Body condition score varied between cows to have optimal conditioned (2.5 < BCS ≤3.5, n = 5) and overconditioned (3.5 < BCS ≤5, n = 5) cows. All animals underwent an intravenous glucose tolerance test (IVGTT) at an average of 260 d of gestation to measure the pancreatic insulin secretory capacity and assess peripheral insulin sensitivity regarding glucose metabolism. Eight days after the IVGTT, animals were slaughtered. The pancreas was dissected and weighed and tissue samples were taken for histological analysis. Results revealed that overconditioning in dairy cows led to fat infiltration in the pancreas and an increase in size of pancreatic islets expressed relative to the total area of pancreatic tissue. In addition, results revealed a positive correlation between serum fatty acid concentration and peak insulin concentration and area and number of pancreatic islets expressed relative to the total area of pancreatic tissue. The IVGTT revealed that overconditioned animals have a higher insulin secretory capacity of the pancreas, as demonstrated by higher peak insulin concentration, higher acute insulin response to glucose, and higher area under the curve (AUC) for insulin compared with optimal conditioned cows. A higher AUC for glucose during the first 60 min following administration of the glucose bolus in overconditioned cows indicates an insulin-resistant state regarding glucose metabolism. Our results suggest that the pancreas of overconditioned dairy cows at the end of gestation compensates for the concomitantly elevated level of peripheral insulin resistance by greater secretion of insulin.

Vets often use key performance indicators (KPIs) to evaluate the actual performance status of a dairy herd. Basic knowledge of data analysis is necessary to interpret these KPIs correctly, but unfortunately vets often lack the training and knowledge required to do this. This article aims to make vets aware of common data interpretation errors and discusses four data misinterpretation pitfalls – variation, momentum, lag and bias – as well as introduces the concept of data visualisation, such as the use of graphs and charts, to help vets avoid these pitfalls.

Hormone sensitive lipase (HSL) activation is part of the metabolic adaptations to the negative energy balance common to the mammalian periparturient period. This study determined HSL contribution to adipose tissue (AT) lipolysis and how insulin regulates its activity in periparturient dairy cows. Subcutaneous AT (SCAT) samples were collected at 11 d prepartum (dry) and 11 (fresh) and 24 d (lactation) postpartum. Basal and stimulated lipolysis (ISO) responses were determined using explant cultures. HSL contribution to lipolysis was assessed using an HSL inhibitor (CAY). Basal lipolysis was higher in SCAT at dry compared with fresh. CAY inhibited basal lipolysis negligibly at dry, but at fresh and lactation it reduced basal lipolysis by 36.1 ± 4.51% and 43.1 ± 4.83%, respectively. Insulin inhibited lipolysis more pronouncedly in dry compared to fresh. Results demonstrate that HSL contribution to basal lipolysis is negligible prepartum. However, HSL is a major driver of SCAT lipolytic responses postpartum. Lower basal lipolysis postpartum suggests that reduced lipogenesis is an important contributor to fatty acid release from SCAT. Loss of adipocyte sensitivity to the antilipolytic action of insulin develops in the early lactation period and supports a state of insulin resistance in AT of cows during the first month postpartum.

The aim of this study was to determine the number of adipose tissue macrophages (ATM) and the mRNA expression of adipokines [adiponectin (ADIPOQ), leptin (LEP), interleukin 6 (IL6), tumor necrosis factor (TNF), and interleukin 10 (IL10)] in different adipose depots from cows with a variable body condition score (BCS) at the end of the dry period. We hypothesized that the number of ATM and the expression of these adipokines depend on adipocyte size and the anatomical location of the adipose depot. Subcutaneous, omental, mesenteric, perirenal, and intrapelvic adipose tissue samples were taken immediately after euthanasia of 10 Holstein Friesian dairy cows (upcoming parity 2 to 5, age 3.9 ± 1.4 yr; mean ± standard deviation) at the end of pregnancy (actual days of pregnancy at the moment of euthanasia: 269 ± 5 d). During the dry period, all animals received similar diets to meet but not exceed requirements. Five animals were considered to have a normal BCS (2.5-3.5) and 5 animals were considered to be over-conditioned (BCS = 3.75-5). Body weight of the animals at the moment of euthanasia was 717 ± 77 kg. Expression of the different genes was determined by reverse transcription quantitative real-time PCR. Adipocyte size was determined by measuring the area of 100 adipocytes on histological sections. Average adipocyte area was 10,475 ± 1,019, 8,500 ± 780, 10,383 ± 1,227, 11,466 ± 1,039, and 11,087 ± 1,632 µm2 for the subcutaneous, mesenteric, omental, intrapelvic, and perirenal adipose depot, respectively. Immunohistochemistry using anti-bovine CD172a antibodies was performed to determine the proportion of ATM (the number of CD172a-positive cells per 100 adipocytes, given as a percentage). Expression of LEP, IL6, and TNF was positively associated with adipocyte size, whereas no association could be detected between ADIPOQ and IL10 with the size of the adipocytes. The omental adipose depot was especially infiltrated with ATM (1.92 ± 0.55, 1.10 ± 0.33, and 8.28 ± 2.24% for the subcutaneous, mesenteric, and omental adipose depot, respectively). The proportion of ATM was positively associated with the size of the adipocytes in the omental and mesenteric adipose depot. Expression of ADIPOQ, LEP, IL6, TNF, and IL10 differed among depots, which suggests differences in inflammatory characteristics depending on the anatomical location of depots. In conclusion, the results of the present study confirm the adipose tissue as a potential source of inflammatory mediators and demonstrate ATM infiltration, especially in the omental adipose depot.