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The consequences of climate change are becoming a relevant issue also for the livestock industry in temperate countries. Transgenerational studies outlined the effect of the ancestors’ calving season, considered as an indicator of the period during which pregnancy occurs, on their female progeny performance. The aim of this study was to evaluate the effect of great granddam (F0) calving month on their great granddaughters (F3) Test Day (TD) records for milk production. A total of 414,069 TD records of milk, fat, and protein yields from 27,224 Italian Simmental F3 cows was analysed with a mixed linear model with year of calving, parity, age at parity, F0 calving month, days in milk class, the interactions between DIM and F0 calving month and between parity and F0 calving month, F3 calving date and the random effects of herd-test date and animal. The F0 calving month significantly influenced F3 performance for the three considered traits; largest F3 TD records were obtained for F0 calvings occurring in October, smallest for February. The largest milk production of F3 cows were observed when their F1 conceptions occurred in winter. Results were confirmed by a further analysis considering the calving seasons of F0, F1 and F2. The effect of F0 calving month (associated with the conception of F1) on milk production decreased as F3 parity increased. Results of this study suggest that the period of ancestor pregnancy and conception affects milk production traits of further generations also in temperate climates.

Livestock expresses complex traits influenced by several factors. The response of animals to variations in climatic factors, such as increases in temperature, may induce heat stress conditions. In this study, animals living at different temperatures were compared using the genome-wide Wright fixation index (FST). A total of 825 genotypes of Sarda breed ewes were divided into two groups based on the flocks’ average temperature over a 20-year period to compute the FST: 395 and 430 sheep were represented in colder and hotter groups, respectively. After LOWESS regression and CONTROL CHART application, 623 significant markers and 97 selection signatures were found. A total of 280 positional candidate genes were retrieved from a public database. Among these genomic regions, we found 51 annotated genes previously associated with heat stress/tolerance in ruminants (FCGR1A, MDH1, UGP2, MYO1G, and HSPB3), as well as immune response and cellular mechanisms related to how animals cope with thermal stress (RIPK1, SERPINB1, SERPINB9, and PELI1). Moreover, other genes were associated with milk fat (SCD, HERC3, SCFD2, and CHUK), body weight, body fat, and intramuscular fat composition (AGPAT2, ABCD2, MFAP32, YTHDC1, SIRT3, SCD, and RNF121), which might suggest the influence of environmental conditions on the genome of Sarda sheep.

Visna/Maedi virus (VMV) is lentiviral disease of sheep responsible for severe production losses. Multiple genomic regions associated with infection were reported indicating genetic complexity. In this study, a combined genome-wide approach using a high-density SNP array has been performed, comparing VMV-infected (n = 78) and non-infected (n = 66) individuals of the Valle del Belice breed. The serological tests showed a seroprevalence of 26%. The comparison among results from different approaches (GWAS, Fisher’s exact test and the FST analysis) revealed two association signals: on OAR03 close to the GRIN2B gene and on OAR05 close to the TMEM232 gene. To the best of our knowledge, there has been no previous association between these genes and lentiviral infection in any species. The GRIN2B gene plays a role in pain response, synaptic transmission, and receptor clustering, while TMEM232 is involved in the development of immune-related disorders. The results highlighted new aspects of the genetic complexity related to the resistance/susceptibility to VMV in sheep, confirming that studies on different breeds can lead to different results. The ideal approach for validation of the markers identified in our study is to use samples from a population independent from the discovery population with the same phenotype used in the discovery stage.

Abstract Heterozygosity-rich regions (HRR) are genomic regions of high heterozygosity, which may harbor loci related to key functional traits such as immune response, survival rate, fertility, and other fitness traits. This study considered 30 Italian and 19 worldwide goat breeds genotyped with the Illumina GoatSNP50k BeadChip. The aim of the work was to study inter-breed relationships and HRR patterns using Sliding Window (SW) and Consecutive Runs (CR) detection methods. Genetic relationships highlighted a clear separation between non-European and European breeds, as well as the north–south geographic cline within the latter. The Pearson correlation coefficients between the descriptive HRR parameters obtained with the SW and CR methods were higher than 0.9. A total of 166 HRR islands were detected. CHI1, CHI11, CHI12 and CHI18 were the chromosomes harboring the highest number of HRR islands. The genes annotated in the islands were linked to various factors such as productive, reproductive, immune, and environmental adaptation mechanisms. Notably, the Montecristo feral goat showed the highest number of HRR islands despite the high level of inbreeding, underlining potential balancing selection events characterizing its evolutionary history. Identifying a species-specific HRR pattern could provide a clearer view of the mechanisms regulating the genome modelling following anthropogenic selection combined with environmental interaction.

The aim of this study was to estimate genetic parameters and investigate the genomic background of scrotal circumference and semen parameters in the Italian Simmental bulls. Scrotal circumference, number of normal spermatozoa, ejaculate volume, spermatozoa motility, and total spermatozoa were measured on 622 bulls, of which 603 had genotypes for 42,141 SNP. Variance components of scrotal circumference were estimated with an animal model that included the fixed effects of birth year, animal age, and measurement method, and the random effects of day of measurement and animal. In the model for the other traits, the scrotal circumference was added as a covariate to account for its influence on the semen parameters. A genome-wide association study was carried out using the ssGBLUP-approach. Heritabilities ranged from 0.07 ± 0.05 (spermatozoa motility) to 0.50 ± 0.14 (scrotal circumference). A total of 13 SNP passed the Bonferroni correction threshold and the number of significantly associated markers ranged from 1 (ejaculate volume and spermatozoa motility) to 5 (total spermatozoa). Genes already associated with reproduction parameters were retrieved close to the significant SNP. Results of the present study gave preliminary insights about the genetic determinism of semen quality in Italian Simmental bulls.Highlights Low to moderate heritabilities were estimated for semen traits. Few markers were associated with the phenotypes, suggesting their polygenic determinism. Candidate genes already associated in literature with sperm traits were found.

ABSTRACT: Heat tolerance is a key feature of resilient animals. Offspring of animals that suffer environmental stress during pregnancy could show physiological, morphological, and metabolic modifications. This is due to a dynamic reprogramming of the epigenetics of the mammalian genome that occurs in the early life cycle. Thus, the aim of this study was to investigate the extent of the transgenerational effect of heat stress during the pregnancy of Italian Simmental cows. The effects of dam and granddam birth months (as indicator of pregnancy period) on their daughter and granddaughter estimated breeding values (EBV) for some dairy traits as well as of the temperature-humidity index (THI) during the pregnancy were tested. A total of 128,437 EBV (milk, fat, and protein yields, and somatic cell score) were provided by the Italian Association of Simmental Breeders. The best birth months (of both dam and granddam) for milk yield and protein yield were May and June, whereas the worst were January and March. Great-granddam pregnancies developed during the winter and spring seasons positively affected the EBV for milk and protein yields of their great-granddaughters; in contrast, pregnancies during summer and autumn had negative effects. These findings were confirmed by the effects of maximum and minimum THI in different parts of the great-granddam pregnancy on the performances of their great-granddaughters. Thus, a negative effect of high temperatures during the pregnancy of female ancestors was observed. Results of the present study suggest a transgenerational epigenetic inheritance in Italian Simmental cattle due to environmental stressors.

This study investigated the effects of maternal linseed supplementation during gestation and lactation on muscle, brain and liver tissues composition and fatty acid (FA) profile in lambs. In a 2 × 2 factorial design, a total of 36 Sarda dairy ewes were fed a control diet (CON, n = 18) or a diet containing linseed (LIN, n = 18) during the last 8 weeks of gestation. After lambing, 9 ewes per group changed to the other diet, moving from CON to LIN and vice-versa. The single-born lambs (n = 36) were reared exclusively on milk and were slaughtered at 4 weeks of age and samples of muscle, brain, and liver tissues were collected. Data were analysed with a general linear model to test the effects of mothers’ gestation and lactation diets, their interaction and the effect of lamb sex. Experimental results evidenced that lambs from mothers fed LIN diet during lactation had a greater content of almost all C18:1 and conjugated linoleic acid (CLA) isomers, both in muscle (P

Single-step genomic best linear unbiased predictor (ssGBLUP) is a methodology for estimating breeding values jointly for genotyped and non-genotyped animals. Since its development in the early 2010s, ssGBLUP faced challenges like modelling missing pedigrees, efficiently computing accuracies, ensuring the compatibility between genomic and pedigree information, implementing large-scale genetic evaluations, and using non-genotyped animals for genome-wide association studies, among others. Because of the extensive research and the availability of efficient software packages, those challenges for ssGBLUP were solved. Nowadays, ssGBLUP is the chosen methodology estimating values in almost all livestock populations. This review aims to report the progress of ssGBLUP, outline the current state of the art, and hypothesise about the future of this methodology.Highlights Single-step genomic BLUP is the most popular methodology for genetic evaluations including genotyped and non-genotyped animals. The development of theories and efficient software allows to use single-step for virtually any real dataset. Continuous research in single-step will allow the use of massive amount of data like video recording, omics, among others.

Single-step genomic best linear unbiased predictor (ssGBLUP) is a method for jointly estimating breeding values (BV) for genotyped and non-genotyped animals. Genomic information in the Italian Mediterranean Buffalo (IMB) is now available and its inclusion in the genetic evaluation system could increase both accuracy and genetic progress of the traits of interest of the breed. The aim of this study was to test the feasibility of ssGBLUP and to present the first results of the implementation of a genomic evaluation for production and type traits in the IMB. Phenotypic information on production (270-day milk, mozzarella yield (MY), protein and fat kg and %, respectively) and morphology: feet and legs (FL) and mammary system (MS) were used for this study. Production records included 743,904 lactations from 276,451 buffalo cows born from 1984 to 2019. Morphological traits were from 91,966 buffalo cows from 2004 to 2022. Regarding the genotypes, a total of 2,017 buffalo cows and 133 bulls were used. Data were analysed fitting two multitrait animal models, a 6-trait model for production data and a 2-trait model for morphology data. According to the relationship matrix used, two models were fitted: (i) the BLUP with the numerator relationship matrix (A); (ii) the ssGBLUP where A and the genomic relationship matrix (G) are blended into H. BV were estimated with BLUP and ssGBLUP models. Three different scenarios were used, according to the cut-off year used to create the partial datasets, namely 2012, 2015 and 2017. In each scenario, correlation, accuracy, dispersion, and bias statistics were calculated (LR method). Both bulls (N=49) and cows (N=1288) were used for validations. On average, correlation between EBVs from partial and whole datasets estimated with BLUP and ssGBLUP increased from 6 to 49% and from 14 to 17% for production and type traits, respectively. Among the traits analysed, the ones most affected by the change were protein/fat content, MY, and AM. The accuracy increase for these traits was above 20% when using the ssGBLUP. All LR statistics improved also for non-genotyped females. These results showed that implementing ssGBLUP in the breeding program can generate more accurate predictions for important traits in dairy IMB than traditional BLUP.

Single-step genomic best linear unbiased predictor (ssGBLUP) is a method for jointly estimating breeding values (BV) for genotyped and non-genotyped animals. Genomic information in the Italian Mediterranean Buffalo (IMB) is now available. Its inclusion in the genetic evaluation system could increase both the accuracy and genetic progress of the traits of interest of the breed. The study aimed to test the feasibility of ssGBLUP and show the first results of implementing a genomic evaluation for production and type traits in the IMB. Phenotypic information on production (270-day milk, mozzarella yield (MY), protein and fat kg and %, respectively) and morphology: feet and legs (FL) and mammary system (MS) were used for this study. Production records included 743,904 lactations from 276,451 buffalo cows born from 1984 to 2019. Morphological traits were from 91,966 buffalo cows from 2004 to 2022. Regarding the genotypes, 2,017 buffalo cows and 133 bulls were used. Data were analyzed fitting two multi-trait animal models, a 6-trait model for production data and a 2-trait model for morphology data. According to the relationship matrix used, two models were fitted: (i) the BLUP with the numerator relationship matrix (A) and (ii) the ssGBLUP where A and the genomic relationship matrix (G) are blended into H. BVs were estimated with BLUP and ssGBLUP models. The cutoff year used to create the partial data set was 2012. The correlation, accuracy, dispersion, and bias statistics were calculated (LR method). Both bulls (N=49) and cows (N=1288) were used for validations. On average, the correlation between EBVs from partial and whole datasets estimated with BLUP and ssGBLUP increased from 6 to 49% and from 14 to 17% for production and type traits, respectively. Among the traits analyzed, the most affected by the change were protein/fat content, MY, and AM. The accuracy increase for these traits was above 20% when using the ssGBLUP. All LR statistics also improved for non-genotyped females. These results showed that implementing ssGBLUP in the breeding program can generate more accurate predictions for essential traits in dairy IMB than traditional BLUP.

Biochar can be useful for the functional recovery of soils contaminated with potentially toxic elements (PTEs), even if its effectiveness is variable and sometimes limited, and conflicting results have been recently reported. To shed some light on this regard, softwood-derived biochar was added at 2.5 (2.5-Bio) and 5.0% w/w (5.0-Bio) rates to an acidic (pH 5.74) soil contaminated by Cd (28 mg kg−1), Pb (10,625 mg kg−1), and Zn (3407 mg kg−1). Biochar addition increased soil pH, available P and CEC, and reduced labile Cd, Pb, and Zn (e.g., by 27, 37, and 46% in 5.0-Bio vs. the unamended soil). The addition of biochar did not change the number of total heterotrophic bacteria, actinomycetes, and fungi, while it reduced the number of Pseudomonas spp. and soil microbial biomass. Dehydrogenase activity was reduced in amended soils (e.g., by ~60 and 75% in 2.5- and 5.0-Bio, respectively), while in the same soils, urease increased by 48 and 78%. Approximately 16S rRNA gene amplicon sequencing and the Biolog community-level physiological profile highlighted a significant biochar impact (especially at a 5% rate) on soil bacterial diversity. Tomato (but not triticale) yield increased in the amended soils, especially in 2.5-Bio. This biochar rate was also the most effective at reducing Cd and Pb concentrations in shoots. Overall, these results demonstrate that 2.5% (but not 5.0%) biochar can be useful to restore the soil chemical fertility of PTE-polluted soils with limited (or null) impact on soil microbial and biochemical parameters.

The agro-livestock sector produces about one third of global greenhouse gas (GHG) emissions. Since more energy is needed to meet the growing demand for food and the industrial revolution in agriculture, renewable energy sources could improve access to energy resources and energy security, reduce dependence on fossil fuels, and reduce GHG emissions. Hydrogen production is a promising energy technology, but its deployment in the global energy system is lagging. Here, we analyzed the theoretical and practical application of green hydrogen generated by electrolysis of water, powered by renewable energy sources, in the agro-livestock sector. Green hydrogen is at an early stage of development in most applications, and barriers to its large-scale deployment remain. Appropriate policies and financial incentives could make it a profitable technology for the future.

Abstract Background Water buffalo is one of the most important livestock species in the world. Two types of water buffalo exist: river buffalo (Bubalus bubalis bubalis) and swamp buffalo (Bubalus bubalis carabanensis). The buffalo genome has been recently sequenced, and thus a new 90 K single nucleotide polymorphism (SNP) bead chip has been developed. In this study, we investigated the genomic population structure and the level of inbreeding of 185 river and 153 swamp buffaloes using runs of homozygosity (ROH). Analyses were carried out jointly and separately for the two buffalo types. Results The SNP bead chip detected in swamp about one-third of the SNPs identified in the river type. In total, 18,116 ROH were detected in the combined data set (17,784 SNPs), and 16,251 of these were unique. ROH were present in both buffalo types mostly detected (~ 59%) in swamp buffalo. The number of ROH per animal was larger and genomic inbreeding was higher in swamp than river buffalo. In the separated datasets (46,891 and 17,690 SNPs for river and swamp type, respectively), 19,760 and 10,581 ROH were found in river and swamp, respectively. The genes that map to the ROH islands are associated with the adaptation to the environment, fitness traits and reproduction. Conclusions Analysis of ROH features in the genome of the two water buffalo types allowed their genomic characterization and highlighted differences between buffalo types and between breeds. A large ROH island on chromosome 2 was shared between river and swamp buffaloes and contained genes that are involved in environmental adaptation and reproduction.

The aim of this study was to test the feasibility of genomic selection in the Italian Mediterranean water buffalo, which is farmed mainly in the south Italy for milk, and mozzarella, production. A total of 498 animals were genotyped at 49,164 loci. Test day records (80,417) of milk (MY), fat (FY) and protein (PY) yields from 4127 cows, born between 1975 and 2009, were analysed in a three-trait model. Cows born in 2008 and 2009 with phenotypes and genotypes were selected as validation animals (n = 50). Variance components (VC) were estimated with BLUP and ssGBLUP. Heritabilities for BLUP were 0.25 ± 0.02 (MY), 0.16 ± 0.01 (FY) and 0.25 ± 0.01 (PY). Breeding values were computed using BLUP and ssGBLUP, using VC estimated from BLUP. ssGBLUP was applied in five scenarios, each with a different number of genotypes available: (A) bulls (35); (B) validation cows (50); (C) bulls and validation cows (85); (D) all genotyped cows (463); (E) all genotypes (498). Model validation was performed using the LR method: correlation, accuracy, dispersion, and bias statistics were calculated. Average correlations were 0.71 ± 0.02 and 0.82 ± 0.01 for BLUP and ssGBLUP-E, respectively. Accuracies were also higher in ssGBLUP-E (0.75 ± 0.03) compared to BLUP (0.57 ± 0.03). The best dispersions (i.e. closer to 1) were found for ssGBLUP-C. The use of genotypes only for the 35 bulls did not change the validation values compared to BLUP. Results of the present study, even if based on small number of animals, showed that the inclusion of genotypes of females can improve breeding values accuracy in the Italian Buffalo.Highlights The genotypes of males did not improve the predictions. Genotypes of females improve breeding values accuracy. Slight increase in prediction accuracy with weighted ssGBLUP.

Milk coagulation ability is crucial for the dairy sheep industry since the whole amount of milk is processed into cheese. Non-coagulating milk (NCM) is defined as milk not forming a curd within the testing time. In sheep milk, it has been reported in literature that up to 20% of milk is NCM. Although the clotting properties of individual milk have been widely studied, little attention has been given to NCM and genomic dissection of this trait. Mid-infrared (MIR) spectra can be exploited both to predict cheese-making aptitude and to discriminate between coagulating milk and NCM. The main goals of this work were (i) to assess the predictivity of MIR spectra for NCM classification and (ii) to conduct a genome-wide association study on coagulation ability. Milk samples from 949 Sarda ewes genotyped and phenotyped for milk coagulation properties (MCPs) served as the training dataset. The validation dataset included 662 ewes. Three classical MCPs were measured: rennet coagulation time (RCT), curd firmness (a30), and curd firming time (k20). Moreover, MIR spectra were acquired and stored in the region between 925.92 and 5,011.54 cm−1. The probability of a sample to be NCM was modeled by step-wise logistic regression on milk spectral information (LR-W), logistic regression on principal component (LR-PC), and canonical discriminant analysis of spectral wave number (DA-W). About 9% of the samples did not coagulate at 30 min. The use of LR-W gave a poorer classification of NCM. The use of LR-PC improved the percentage of correct assignment (45 ± 9%). The DA-W method allows us to reach 75.1 ± 10.3 and 76.5 ± 18.4% of correct assignments of the inner and external validation datasets, respectively. As far as GWA of NCM, 458 SNP associations and 45 candidate genes were detected. The genes retrieved from public databases were mostly linked to mammary gland metabolism, udder health status, and a milk compound also known to affect the ability of milk to coagulate. In particular, the potential involvement of CAPNs deserves further investigation.

Individual dry matter intake (DMI) is a relevant factor for evaluating feed efficiency in livestock. However, the measurement of this trait on a large scale is difficult and expensive. DMI, as well as other phenotypes, can be predicted from milk spectra. The aim of this work was to predict DMI from the milk spectra of 24 lactating Sarda dairy sheep ewes. Three models (Principal Component Regression, Partial Least Squares Regression, and Stepwise Regression) were iteratively applied to three validation schemes: records, ewes, and days. DMI was moderately correlated with the wavenumbers of the milk spectra: the largest correlations (around ±0.30) were observed at ~1100–1330 cm−1 and ~2800–3000 cm−1. The average correlations between real and predicted DMI were 0.33 (validation on records), 0.32 (validation on ewes), and 0.23 (validation on days). The results of this preliminary study, even if based on a small number of animals, demonstrate that DMI can be routinely estimated from the milk spectra.

A total of 112 dogs (49 males and 63 females) belonging to different breeds (i.e., Boxer, Cirneco dell’Etna, Fonni’s Dog, Labrador, Crossbreed, German Shepherd, Pit Bull, Shar-Pei, Yorkshire) were analyzed to compare the serum concentration of serotonin, dopamine, norepinephrine, prolactin, beta-endorphins, thyroxine (T4), triiodothyronine (T3), thyroid-stimulating hormone (TSH), and assess whether these parameters can be correlated with the behavioral phenotype of the investigated breeds. T4 was above or below the threshold in 61% and 14% of dogs, respectively; T3, in contrast, 41% of dogs showed values below the limit, while 26% above it. TSH was within the reference range in 58% of dogs; 94% of the dogs had prolactin in the reference range and only five animals showed values above the limit. For beta-endorphins, 49% of dogs had values above the limit, while 46% had values within the reference range. Serotonin and dopamine values below physiological limits were found in 62% and 70% of dogs, respectively. Finally, 61% of the dogs showed norepinephrine values within the reference range. The study confirmed that the assessment of the serum values of hormones and neurotransmitters in dogs could be useful to better understand the behavioral phenotype of the animal and could be useful for breeders and trainers for the selection of the most suitable subjects for specific tasks.

In this study, we investigated the sorption–desorption behavior of imazamox (IMZ) and 2,4-DB (DB) in two typical acidic Mediterranean agricultural soils and the impact of these herbicides on culturable soil bacterial populations, enzyme activities and functional diversity when applied at concentrations higher than recommended doses (10×, 50×, 500×). Herbicide sorption was similar in both soils and IMZ was less retained compared to DB (~0.5 vs. 40 µg g−1 soil, respectively). IMZ desorption was remarkable (70–100%) while that of DB was more limited, i.e., ~40%. Three days after spiking (DAS), IMZ and DB significantly increased the number of soil-culturable heterotrophic bacteria, actinomycetes and Pseudomonas spp., soil respiration and the potential catabolic capacity of soil microbial communities. Soil dehydrogenase activity increased by ~56–70% in IMZ-treated soils while being reduced by ~33–41% in DB-treated ones. β-glucosidase activity showed a soil-dependent behavior, while the pattern of C source utilization suggested a change of soil microbial community structure after herbicide (especially DB) spiking. At 30 DAS, the herbicides’ impact on soil microorganisms, enzyme activity and functional diversity was still visible. Moreover, a toxic effect of DB (at 50× and 500×), but not IMZ, was recorded vs. Rhizobium sullae, the bacterial symbiont of Hedysarum coronarium. The obtained results indicated that IMZ and DB are poorly sorbed and highly desorbed by both soils. Moreover, at the tested concentrations, IMZ and DB can have short- and medium-term impacts on the microbial component and the related activity of the investigated soils, likely affecting a range of ecosystem services provided by soil microorganisms.

The inbreeding coefficient is an important parameter for livestock management. Small ruminant breeders and associations mainly rely on pedigree information, but genomic tools are gaining relevance, overcoming possible pedigree inconsistencies. This study investigates the relationship between pedigree-based and genomic inbreeding in two goat and four sheep dairy breeds. Pedigree and genomic data (medium-density SNPchip) were obtained for 3107 goats and 2511 sheep. We estimated pedigree depth (number of fully traced generations, FullGen) and inbreeding (FPED), as well as two genomic inbreeding indexes, using runs of the homozygosity (FROH) and genomic relationship matrix (FGRM). The correlation between the inbreeding coefficients was assessed. A linear regression model (LRM) was fitted for estimating FPED from FROH. After quality control on genomic data, we retained 5085 animals. Mean inbreeding values were low, with higher FROH than FPED and FGRM. Breed differences can partially depend on different managements. The correlation between FPED and FROH was the highest and directly related to pedigree depth. The best LRM was chosen for FullGen ≥4 and ≥6 for goats and sheep, respectively; after excluding animals with extreme residuals, a new refined regression equation was calculated. Since massive genotyping is not affordable to small ruminant breeders, it is important to understand the distinction and relationship between differently calculated inbreeding coefficients, also in view of the introduction of genomic enhanced breeding values. Our study highlights the importance of accurate pedigree information and, especially if not obtainable, of calculating genomic-based inbreeding coefficients. A better estimation of animals’ relatedness contributes to improve animal breeding and conservation.

Copper (Cu) is a ubiquitous trace element in the aquatic environment, and is usually found at low levels. Copper environmental concentrations can be altered as a result anthropogenic activities. Shellfish are useful bioindicators to ensure adequate environmental monitoring. Thus, the aim of the present study was as follows: (a) determine the LC50 of copper(I) chloride in grooved carpet shell (Ruditapes decussatus) collected in the Santa Gilla lagoon (Sardinia, Italy), and (b) analyze the antioxidant biomarkers in digestive gland and gills of same specimens exposed to different concentrations of the above-mentioned metal (0.045, 0.45, and 0.90 mg/L) for 96 h. A withdrawal period of 96 h was considered for the treated clam, carrying out the same biochemical analyses, superoxide dismutase (SOD), catalase (CAT), selenium-dependent glutathione peroxidase (Se-GPx), glutathione S-transferases (GSTs), and total glutathione (GSH+2GSSG) in the two tissues. Different time and dose responses of the antioxidant biomarkers were recorded in the digestive glands and gills. Oxidative stress biomarkers highlighted the ability of Cu to induce oxidative stress in R. decussatus. Clam, following the withdrawal period of 96 h, has not been able to achieve the control levels of all biochemical markers in the digestive gland and gills. R. decussatus can be a suitable model to assess the ecotoxicity of copper in aquatic ecosystems. These findings may advance knowledge on the role and the effects of copper on oxidative stress biomarkers in grooved carpet shell. The metal ecotoxicity response can be useful to perform accurate biomarker-based monitoring programs using this bivalve species.

Defining optimal nutrition in animals and humans remains a main scientific challenge. The objective of the work was to develop a dynamic model of reactive oxygen species (ROS)–polyunsaturated fatty acid (PUFA)–antioxidant homeostasis using the rabbit as a model. The problem entity was to evaluate the main metabolites generated from interactions between traits included in the conceptual model and identified by three main sub–models: (i) ROS generation, (ii) PUFA oxidation and (iii) antioxidant defence. A mathematical model (VENSIM software) that consisted of molecular stocks (INPUTs, OUTPUTs), exchange flows (intermediate OUTPUTs) and process rates was developed. The calibration was performed by using standard experimental data (Experiment 1), whereas the validation was carried out in Experiments 2 and 3 by using supra–nutritional dietary inputs (VIT E+ and PUFA+). The accuracy of the models was measured using 95% confidence intervals. Analytical OUTPUTs (ROS, PUFA, Vit E, Ascorbic acid, Iso–/NeuroProstanes, Aldehydes) were well described by the standard model. There was also good accuracy for the VIT E+ scenario, whereas some compensatory rates (Kc1–Kc4) were added to assess body compensation when high levels of dietary PUFA were administered (Experiment 3). In conclusion, the model can be very useful for predicting the effects of dietary treatments on the redox homeostasis of rabbits.

Annual and interannual dynamics of shellfish toxins and associated harmful algal species (HAS) were analyzed from 2015 to 2020 in Tortolì Lagoon (Sardinia, west Mediterranean Sea). Analysis of seasonal occurrence of different harmful algae, such as Dinophysis spp., Prorocentrum spp., Pseudo-nitzschia spp. and Alexandrium minutum, was performed. The species Dinophysis acuminata and Dinophysis sacculus were responsible for the accumulation of lipophilic toxins belonging to the okadaic acid group (OAs) and pectenotoxins2 (PTX2) in bivalve mollusks. The highest HAS detection was recorded in the winter months; in particular, Dinophysis spp. was mostly present in January–February. Out of 1090 analyzed mollusk samples, 39 were non-compliant, exceeding the legal limits (160 μg OA eq/kg e.p.) reported in Regulation 853/2004 of the European Commission. A statistical analysis related to the presence of OA and PTX2 in mollusks with various environmental parameters (pH, water temperature, dissolved oxygen, algal density) was implemented, proving a clear winter seasonality. The present study highlights the necessity to better understand the different factors able to influence the production and accumulation of toxins in bivalve mollusks bred in an important Sardinian production area. The contribution of this research is important not only from an environmental and productive point of view but also from the view of implementing management in order to mitigate any harm to human health.

The concept of welfare applied to farm animals has undergone a remarkable evolution. The growing awareness of citizens pushes farmers to guarantee the highest possible level of welfare to their animals. New perspectives could be opened for animal welfare reasoning around the concept of domestic, especially farm, animals as partial human artifacts. Therefore, it is important to understand how much a particular behavior of a farm animal is far from the natural one of its ancestors. This paper is a contribution to better understand the role of genetics of the farm animals on their behavior. This means that the naïve approach to animal welfare regarding returning animals to their natural state should be challenged and that welfare assessment should be considered.

Genetic diversity was assessed in five subpopulations (Negra-MORB and Cárdena-MORC for Morucha breed; Jarda-JAR, Bociblanca-BOC, and Avileña-Negra Ibérica breed-ANI for Avileña Negra Ibérica main breed) belonging to two local Spanish cattle breeds (Avileña Negra Ibérica-AVI and Morucha-MOR) by using univariate and multivariate statistical approaches. A total of 4,137 animals (2,785 ANI, 333 BOC, 127 JAR, 263 MORB and 629 MORC) were genotyped with the Affymetrix Beadchip Microarray HD. After quality control, 30,391 common SNPs were retained. In the univariate approach, the Wright fixation index (FST) was computed between and within breeds. In the multivariate approach, the stepwise discriminant analysis and the canonical discriminant analysis (CDA) were applied. The highest weighted FST value (0.05) was computed between BOC and MORB. Instead, the lowest values were found between ANI and JAR and between ANI and BOC. In the multivariate approach, CDA significantly discriminated the five breeds by using 1,144 markers. Of the four Canonical functions (CAN1, CAN2, CAN3 and CAN4) extracted by CDA, CAN1 fully separated AVI and MOR whereas CAN2 disentangled the two subpopulations of MOR. Considering CAN3 and CAN4, the CDA separated AVI subpopulations. Among markers selected by both FST and CDA, the top 10 discriminant SNPs were mapped to genomic regions harbouring genes involved in coat colour, behaviour, and morphological traits. This study confirmed that the two breeds, different at phenotypic level, are different also at genetic level. Moreover, the results showed that MOR subpopulations are more different from each other than the AVI ones.

The ultimate goal of genetic selection is to improve genetic progress by increasing favorable alleles in the population. However, with selection, homozygosity, and potentially harmful recessive alleles can accumulate, deteriorating genetic variability and hampering continued genetic progress. Such potential adverse side effects of selection are of particular interest in populations with a small effective population size like the Romosinuano beef cattle in Mexico. The objective of this study was to evaluate the genetic background and inbreeding depression in Mexican Romosinuano cattle using pedigree and genomic information. Inbreeding was estimated using pedigree (FPED) and genomic information based on the genomic relationship matrix (FGRM) and runs of homozygosity (FROH) of different length classes. Linkage disequilibrium (LD) was evaluated using the correlation between pairs of loci, and the effective population size (Ne) was calculated based on LD and pedigree information. The pedigree file consisted of 4875 animals born between 1950 and 2019, of which 71 had genotypes. LD decreased with the increase in distance between markers, and Ne estimated using genomic information decreased from 610 to 72 animals (from 109 to 1 generation ago), the Ne estimated using pedigree information was 86.44. The reduction in effective population size implies the existence of genetic bottlenecks and the decline of genetic diversity due to the intensive use of few individuals as parents of the next generations. The number of runs of homozygosity per animal ranged between 18 and 102 segments with an average of 55. The shortest and longest segments were 1.0 and 36.0 Mb long, respectively, reflecting ancient and recent inbreeding. The average inbreeding was 2.98 ± 2.81, 2.98 ± 4.01, and 7.28 ± 3.68% for FPED, FGRM, and FROH, respectively. The correlation between FPED and FGRM was −0.25, and the correlations among FPED and FROH of different length classes were low (from 0.16 to 0.31). The correlations between FGRM and FROH of different length classes were moderate (from 0.44 to 0.58), indicating better agreement. A 1% increase in population inbreeding decreased birth weight by 0.103 kg and weaning weight by 0.685 kg. A strategy such as optimum genetic contributions to maximize selection response and manage the long-term genetic variability and inbreeding could lead to more sustainable breeding programs for the Mexican Romosinuano beef cattle breed.

Arthrogryposis and macroglossia are congenital pathologies known in several cattle breeds, including Piemontese. As variations in single genes were identified as responsible for arthrogryposis in some breeds, we decided: (i) to test the hypothesis of a similar genetic determinism for arthrogryposis in the Piemontese breed by genotyping affected and healthy animals with a high-density chip and applying genome-wide association study (GWAS), FST and canonical discriminant analysis (CDA) procedures, and (ii) to investigate with the same approach the genetic background of macroglossia, for which no genetic studies exist so far. The study included 125 animals (63 healthy, 30 with arthrogryposis, and 32 with macroglossia). Differently from what reported for other breeds, the analysis did not evidence a single strong association with the two pathologies. Rather, 23 significant markers on different chromosomes were found (7 associated to arthrogryposis, 11 to macroglossia, and 5 to both pathologies), suggesting a multifactorial genetic mechanism underlying both diseases in the Piemontese breed. In the 100-kb interval surrounding the significant SNPs, 20 and 26 genes were identified for arthrogryposis and macroglossia, respectively, with 12 genes in common to both diseases. For some genes (NTN3, KCNH1, KCNH2, and KANK3), a possible role in the pathologies can be hypothesized, being involved in processes related to muscular or nervous tissue development. The real involvement of these genes needs to be further investigated and validated.

Genome-wide association studies (GWAS) are traditionally carried out by using the single marker regression model that, if a small number of individuals is involved, often lead to very few associations. The Bayesian methods, such as BayesR, have obtained encouraging results when they are applied to the GWAS. However, these approaches, require that an a priori posterior inclusion probability threshold be fixed, thus arbitrarily affecting the obtained associations. To partially overcome these problems, a multivariate statistical algorithm was proposed. The basic idea was that animals with different phenotypic values of a specific trait share different allelic combinations for genes involved in its determinism. Three multivariate techniques were used to highlight the differences between the individuals assembled in high and low phenotype groups: the canonical discriminant analysis, the discriminant analysis and the stepwise discriminant analysis. The multivariate method was tested both on simulated and on real data. The results from the simulation study highlighted that the multivariate GWAS detected a greater number of true associated single nucleotide polymorphisms (SNPs) and Quantitative trait loci (QTLs) than the single marker model and the Bayesian approach. For example, with 3000 animals, the traditional GWAS highlighted only 29 significantly associated markers and 13 QTLs, whereas the multivariate method found 127 associated SNPs and 65 QTLs. The gap between the two approaches slowly decreased as the number of animals increased. The Bayesian method gave worse results than the other two. On average, with the real data, the multivariate GWAS found 108 associated markers for each trait under study and among them, around 63% SNPs were also found in the single marker approach. Among the top 118 associated markers, 76 SNPs harbored putative candidate genes.