Journal: Journal of Animal Science and Biotechnology

Data publication: July 2023 

Autors: Vittoria Bisutti, Núria Mach, Diana Giannuzzi, Alice Vanzin, Emanuele Capra, Riccardo Negrini, Maria Elena Gelain, Alessio Cecchinato, Paolo Ajmone-Marsan & Sara Pegolo 

Abstract

Subclinical intramammary infection (IMI) represents a significant problem in maintaining dairy cows’ health. Disease severity and extent depend on the interaction between the causative agent, environment, and host. To investigate the molecular mechanisms behind the host immune response, we used RNA-Seq for the milk somatic cells (SC) transcriptome profiling in healthy cows (n = 9), and cows naturally affected by subclinical IMI from Prototheca spp. (n = 11) and Streptococcus agalactiae (S. agalactiae; n = 11). Data Integration Analysis for Biomarker discovery using Latent Components (DIABLO) was used to integrate transcriptomic data and host phenotypic traits related to milk composition, SC composition, and udder health to identify hub variables for subclinical IMI detection. 

Journal: Journal of Animal Science and Biotechnology

Data publication: September 2023 

Autors: S. Pegolo , D. Giannuzzi , F. Piccioli-Cappelli , L. Cattaneo , M. Gianesella , P.L. Ruegg , E. Trevisi , A. Cecchinato  

Abstract

The aim of this study was to investigate the associations between subclinical intramammary infection (IMI) from different pathogens combined with inflammation status and a set of blood biochemical traits including energy-related metabolites, indicators of liver function or hepatic damage, oxidative stress, inflammation, innate immunity, and mineral status in 349 lactating Holstein cows. Data were analyzed with a linear model including the following fixed class effects: days in milk, parity, herd, somatic cell count (SCC), bacteriological status (positive and negative), and the SCC × bacteriological status interaction. Several metabolites had significant associations with subclinical IMI or SCC. Increased SCC was associated with a linear decrease in cholesterol concentrations which ranged from −2% for the class ≥50,000 and <200,000 cells/mL to −11% for the SCC class ≥400,000 cells/mL compared with the SCC class <50,000 cells/mL. A positive bacteriological result was associated with an increase in bilirubin (+24%), paraoxonase (+11%), the ratio paraoxonase/cholesterol (+9%), and advanced oxidation protein product concentration (+23%). Increased SCC were associated with a linear decrease in ferric reducing antioxidant power concentrations ranging from −3% for the class ≥50,000 and <200,000 cells/mL to −9% for the SCC class ≥400,000 cells/mL (respect to the SCC class <50,000 cells/mL). A positive bacteriological result was associated with an increase in haptoglobin concentrations (+19%). Increased SCC were also associated with a linear increase in haptoglobin concentrations, which ranged from +24% for the class ≥50,000 and <200,000 cells/mL (0.31 g/L) to +82% for the SCC class ≥400,000 cells/mL (0.45 g/L), with respect to the SCC class <50,000 cells/mL (0.25 g/L). Increased SCC were associated with a linear increase in ceruloplasmin concentrations (+15% for SCC ≥50,000 cells/mL). The observed changes in blood biochemical markers, mainly acute phase proteins and oxidative stress markers, suggest that cows with subclinical IMI may experience a systemic involvement.

Journal: Journal of Animal Science and Biotechnology

Data publication: May 2023 

Autors: Diana Giannuzzi, Lucio Flavio Macedo Mota , Sara Pegolo, Franco Tagliapietra , Stefano Schiavon , Luigi Gallo , Paolo Ajmone Marsan,  Erminio Trevisi , Alessio Cecchinato 

Abstract

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CVr) and herd-out (CVh). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R2 value from 5% to 75% for CVr and 2% to 139% for CVh, whereas the stacking ensemble increased the R2 value from 4% to 70% for CVr and 4% to 150% for CVh. Considering the best model, with the CVr scenario, good prediction accuracies were obtained for glucose (R2 = 0.81), urea (R2 = 0.73), albumin (R2 = 0.75), total reactive oxygen metabolites (R2 = 0.79), total thiol groups (R2 = 0.76), ceruloplasmin (R2 = 0.74), total proteins (R2 = 0.81), globulins (R2 = 0.87), and Na (R2 = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.

Journal: Meat Science

Data publication: October 2023  

Autors: Alessandro Toscano , Diana Giannuzzi, Isaac Hyeladi Malgwi , Veronika Halas , Paolo Carnier , Luigi Gallo , Stefano Schiavon 

Abstract

To explore the influence of 4 feeding strategies on dry-cured ham quality, 336 barrows and gilts (3 batches, 112 pigs/batch) of 90 kg body weight (BW), were divided into 4 groups and housed in 8 pens with automated feeders. In the control group (C), the pigs were fed restrictively medium-protein feeds and slaughtered at 170 kg BW (SW) and 265 d of slaughter age (SA). With the older age (OA) treatment, the pigs were restrictively fed low protein feeds and slaughtered at 170 kg SW and 278 d SA. The other two groups were fed ad libitum high protein feeds, the younger age (YA) group was slaughtered at 170 kg SW and 237 d SA, the greater weight (GW) at 265 d of SA and 194 kg SW. The hams were dry-cured and seasoned for 607 d, weighed before and after seasoning and deboning. Sixty hams were sampled and sliced. The lean and the fat tissues were separated and analyzed for proximate composition and fatty acid profile. The model of analysis considered sex and treatment as fixed factors. With respect to C: i) OA lowered the ham weight, the lean protein content, increased marbling and decreased the PUFA proportion in intramuscular and subcutaneous fat; ii) YA hams had thicker fat cover with lower PUFA in intramuscular and subcutaneous fat; iii) GW increased the deboned ham weight, fat cover depth and marbling, reduced PUFA in intramuscular and subcutaneous fat, without alteration of the lean moisture content. Sex had a negligible impact.