Mastitis is an inflammatory response of mammary tissue caused by physical trauma or infectious agents, representing one of the main diseases in dairy cows’ farms leading to significant economic losses due to reduced milk production, treatment costs, milk wastage due to drug withdrawal times and prematurely reformed animals [1][2]. This study was part of the LATSAN project performed in three Friesian-Holstein Italian selected dairy farms with the presence of contagious mastitis. The aim of this study was to evaluate serum metabolome differences in animals affected by subclinical intramammary infection (sIMI) caused by Streptococcus agalactiae and Prototheca spp. After an initial blood sampling and bacteriological milk screening (T0) performed on all animals (n = 450), aimed at detecting cows with sIMI and at obtaining serum (n.75), the animals were followed up after two (T1) weeks from T0. For this study 40 out of 75 sIMI animals were selected and grouped according to the infectious status confirmed in both period (T0 and T1): G0 for negative animals (n=16), G1 for animals affected by Streptococcus agalactiae mastitis (n=17), G2 for animals affected by Prototheca spp. (n=7). Serum samples of T0 were processed to perform metabolomic analysis through 1H-NMR. Statistical differences between groups of the main characteristics as parity, somatic cell count (SCC), milk composition and blood metabolites were evaluated by one-way ANOVA. A p-value < 0.05 was considered as significant, while a trend for significance was considered with 0.05 ≤ p-value ≤ 0.10. According to the statistical analysis, SCC significantly differed among the 3 groups: in G1 the highest value was observed, in G0 the lowest instead. From serum samples, 43 metabolites were identified, of which 10 (3-Hydroxybutyrate, Acetate, Acetone, Allantoin, Asparagine, Carnitine, Citrulline, Ethanol, Lactose, Methylguanidine) showed significant differences among groups and 4 (Citrate, Dimethylamine, Histidine, Valine) a trend for significance. In conclusion, sIMI may influence blood metabolome also according to different etiological pathogens.
DIFFERENCES IN THE SERUM METABOLOME PROFILE OF DAIRY COWS AFFECTED BY SUBCLINICAL MASTITIS
Anastasia Lisuzzo;Chiara Tommasoni;Enrico Fiore;Alessio Cecchinato;Vittoria Bisutti;Matteo Gianesella
2023
Abstract
Mastitis is an inflammatory response of mammary tissue caused by physical trauma or infectious agents, representing one of the main diseases in dairy cows’ farms leading to significant economic losses due to reduced milk production, treatment costs, milk wastage due to drug withdrawal times and prematurely reformed animals [1][2]. This study was part of the LATSAN project performed in three Friesian-Holstein Italian selected dairy farms with the presence of contagious mastitis. The aim of this study was to evaluate serum metabolome differences in animals affected by subclinical intramammary infection (sIMI) caused by Streptococcus agalactiae and Prototheca spp. After an initial blood sampling and bacteriological milk screening (T0) performed on all animals (n = 450), aimed at detecting cows with sIMI and at obtaining serum (n.75), the animals were followed up after two (T1) weeks from T0. For this study 40 out of 75 sIMI animals were selected and grouped according to the infectious status confirmed in both period (T0 and T1): G0 for negative animals (n=16), G1 for animals affected by Streptococcus agalactiae mastitis (n=17), G2 for animals affected by Prototheca spp. (n=7). Serum samples of T0 were processed to perform metabolomic analysis through 1H-NMR. Statistical differences between groups of the main characteristics as parity, somatic cell count (SCC), milk composition and blood metabolites were evaluated by one-way ANOVA. A p-value < 0.05 was considered as significant, while a trend for significance was considered with 0.05 ≤ p-value ≤ 0.10. According to the statistical analysis, SCC significantly differed among the 3 groups: in G1 the highest value was observed, in G0 the lowest instead. From serum samples, 43 metabolites were identified, of which 10 (3-Hydroxybutyrate, Acetate, Acetone, Allantoin, Asparagine, Carnitine, Citrulline, Ethanol, Lactose, Methylguanidine) showed significant differences among groups and 4 (Citrate, Dimethylamine, Histidine, Valine) a trend for significance. In conclusion, sIMI may influence blood metabolome also according to different etiological pathogens.
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