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Abstract Bovine mastitis is one of the main inflammatory diseases that can affect the udder during lactation. Somatic cell counts and sometimes microbiological tests are routinely adopted during monitoring diagnostics in dairy herds. However, subclinical mastitis is challenging to identify, reducing the possibility of early treatments. The main aim of this study was to investigate the miRNome profile of extracellular vesicles isolated from milk as potential biomarkers of subclinical mastitis. Milk samples were collected from a total of 60 dairy cows during routine monitoring tests. Small RNA sequencing technology was applied to extracellular vesicles of milk samples collected from cows classified according to the somatic cell count to identify differences in the miRNome between mastitic and healthy cows. A total of 1997 miRNAs were differentially expressed between both groups. Among them, 68 miRNAs whose FDRs were

Over recent decades, there was a substantial evolution in the productive management of dairy animals worldwide with a consequent boost in individual milk yield. This evolution positioned the milk production as the central metabolic priority around which all other physiological functions are coordinated and partially minimised. This shift underscores the crucial role of effectively managing stressful phases in intensive dairy farming systems, also highlighting the resilience exhibited by the animals. Indeed, monitoring the nutritional and health status becomes paramount, aiming for an early detection of (sub)clinical health impairments. Given the mammary gland’s centrality in high-yielding dairy breeds, it’s unsurprising that the milk matrix provides insights about udder itself but also systemic metabolic function. The emerging field of milk phenomics explores links between milk components and animal health, holding great promise for studying dairy cow resilience. The use of infrared spectroscopy on milk to predict indicators and complex traits at the herd level is a promising approach. In the dairy sector, the available infrared instruments mainly implement the Fourier transform infrared (FTIR) spectroscopy. This method is widely employed in milk recording schemes worldwide for animal monitoring and breeding purposes. In addition, visible and near-infrared (NIR) spectroscopy is increasingly integrated into milking systems for daily on-farm monitoring of milk quality and animals’ physiological status. This review examines the topic of milk phenomics together with potential and challenges of infrared spectroscopy to predict indicators and complex traits related to health and nutritional disorders exploiting the biological bonds that exist between milk and blood in dairy animals.HIGHLIGHTS Milk is a valuable source of information for assessing nutritional and health proxies in dairy animals. Infrared spectroscopy is used within routine milk recording systems for the determination of major components, but other useful applications exist. This review summarises research on milk phenomics carried out by mean of infrared spectroscopy to predict complex traits related to animals’ health and nutritional disorders.

Abstract Background Various blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results. Results Results indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (−log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability. Conclusion Our results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.

Abstract Background Metabolic disturbances adversely impact productive and reproductive performance of dairy cattle due to changes in endocrine status and immune function, which increase the risk of disease. This may occur in the post-partum phase, but also throughout lactation, with sub-clinical symptoms. Recently, increased attention has been directed towards improved health and resilience in dairy cattle, and genomic selection (GS) could be a helpful tool for selecting animals that are more resilient to metabolic disturbances throughout lactation. Hence, we evaluated the genomic prediction of serum biomarkers levels for metabolic distress in 1353 Holsteins genotyped with the 100K single nucleotide polymorphism (SNP) chip assay. The GS was evaluated using parametric models best linear unbiased prediction (GBLUP), Bayesian B (BayesB), elastic net (ENET), and nonparametric models, gradient boosting machine (GBM) and stacking ensemble (Stack), which combines ENET and GBM approaches. Results The results show that the Stack approach outperformed other methods with a relative difference (RD), calculated as an increment in prediction accuracy, of approximately 18.0% compared to GBLUP, 12.6% compared to BayesB, 8.7% compared to ENET, and 4.4% compared to GBM. The highest RD in prediction accuracy between other models with respect to GBLUP was observed for haptoglobin (hapto) from 17.7% for BayesB to 41.2% for Stack; for Zn from 9.8% (BayesB) to 29.3% (Stack); for ceruloplasmin (CuCp) from 9.3% (BayesB) to 27.9% (Stack); for ferric reducing antioxidant power (FRAP) from 8.0% (BayesB) to 40.0% (Stack); and for total protein (PROTt) from 5.7% (BayesB) to 22.9% (Stack). Using a subset of top SNPs (1.5k) selected from the GBM approach improved the accuracy for GBLUP from 1.8 to 76.5%. However, for the other models reductions in prediction accuracy of 4.8% for ENET (average of 10 traits), 5.9% for GBM (average of 21 traits), and 6.6% for Stack (average of 16 traits) were observed. Conclusions Our results indicate that the Stack approach was more accurate in predicting metabolic disturbances than GBLUP, BayesB, ENET, and GBM and seemed to be competitive for predicting complex phenotypes with various degrees of mode of inheritance, i.e. additive and non-additive effects. Selecting markers based on GBM improved accuracy of GBLUP.

ABSTRACT: Udder health has a crucial role in sustainable milk production, and various reports have pointed out that changes in udder condition seem to affect milk mineral content. The somatic cell count (SCC) is the most recognized indicator for the determination of udder health status. Recently, a new parameter, the differential somatic cell count (DSCC), has been proposed for a more detailed evaluation of intramammary infection patterns. Specifically, the DSCC is the combined proportions of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) on the total SCC, with macrophages (MAC) representing the remainder proportion. In this study, we evaluated the association between DSCC in combination with SCC on a detailed milk mineral profile in 1,013 Holstein-Friesian cows reared in 5 herds. An inductively coupled plasma–optical emission spectrometry was used to quantify 32 milk mineral elements. Two different linear mixed models were fitted to explore the associations between the milk mineral elements and first, the DSCC combined with SCC, and second, DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the proportion of PMN-LYM and MAC by SCC. We observed a significant positive association between SCC and milk Na, S, and Fe levels. Differential somatic cell count showed an opposite behavior to the one displayed by SCC, with a negative association with Na and positive association with K milk concentrations. When considering DSCC as count, Na and K showed contrasting behavior when associated with PMN-LYM or MAC counts, with decreasing of Na content and increasing K when associated with increasing PMN-LYM counts, and increasing Na and decreasing K when associated with increasing MAC count. These findings confirmed that an increase in SCC is associated with altered milk Na and K amounts. Moreover, MAC count seemed to mirror SCC patterns, with the worsening of inflammation. Differently, PMN-LYM count exhibited patterns of associations with milk Na and K contents attributable more to LYM than PMN, given the nonpathological condition of the majority of the investigated population. An interesting association was observed for milk S content, which increased with increasing of inflammatory conditions (i.e., increased SCC and MAC count) probably attributable to its relationship with milk proteins, especially whey proteins. Moreover, milk Fe content showed positive associations with the PMN-LYM population, highlighting its role in immune regulation during inflammation. Further studies including individuals with clinical condition are needed to achieve a comprehensive view of milk mineral behavior during udder health impairment.

ABSTRACT: The causes of variation in the milk mineral profile of dairy cattle during the first phase of lactation were studied under the hypothesis that the milk mineral profile partially reflects the animals' metabolic status. Correlations between the minerals and the main milk constituents (i.e., protein, fat, and lactose percentages), and their associations with the cows' metabolic status indicators were explored. The metabolic status indicators (MET) that we used were blood energy-protein metabolites [nonesterified fatty acids, β-hydroxybutyrate (BHB), glucose, cholesterol, creatinine, and urea], and liver ultrasound measurements (predicted triacylglycerol liver content, portal vein area, portal vein diameter and liver depth). Milk and blood samples, and ultrasound measurements were taken from 295 Holstein cows belonging to 2 herds and in the first 120 d in milk (DIM). Milk mineral contents were determined by ICP-OES; these were considered the response variable and analyzed through a mixed model which included DIM, parity, milk yield, and MET as fixed effects, and the herd/date as a random effect. The MET traits were divided in tertiles. The results showed that milk protein was positively associated with body condition score (BCS) and glucose, and negatively associated with BHB blood content; milk fat was positively associated with BHB content; milk lactose was positively associated with BCS; and Ca, P, K and S were the minerals with the greatest number of associations with the cows' energy indicators, particularly BCS, predicted triacylglycerol liver content, glucose, BHB and urea. We conclude that the protein, fat, lactose, and mineral contents of milk partially reflect the metabolic adaptation of cows during lactation and within 120 DIM. Variations in the milk mineral profile were consistent with changes in the major milk constituents and the metabolic status of cows.

Abstract Background 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. Results A total of 1,682 and 2,427 differentially expressed genes (DEGs) were identified when comparing Prototheca spp. and S. agalactiae to healthy animals, respectively. Pathogen-specific pathway analyses evidenced that Prototheca’s infection upregulated antigen processing and lymphocyte proliferation pathways while S. agalactiae induced a reduction of energy-related pathways like the tricarboxylic acid cycle, and carbohydrate and lipid metabolism. The integrative analysis of commonly shared DEGs between the two pathogens (n = 681) referred to the core-mastitis response genes, and phenotypic data evidenced a strong covariation between those genes and the flow cytometry immune cells (r 2 = 0.72), followed by the udder health (r 2 = 0.64) and milk quality parameters (r 2 = 0.64). Variables with r ≥ 0.90 were used to build a network in which the top 20 hub variables were identified with the Cytoscape cytohubba plug-in. The genes in common between DIABLO and cytohubba (n = 10) were submitted to a ROC analysis which showed they had excellent predictive performances in terms of discriminating healthy and mastitis-affected animals (sensitivity > 0.89, specificity > 0.81, accuracy > 0.87, and precision > 0.69). Among these genes, CIITA could play a key role in regulating the animals’ response to subclinical IMI. Conclusions Despite some differences in the enriched pathways, the two mastitis-causing pathogens seemed to induce a shared host immune-transcriptomic response. The hub variables identified with the integrative approach might be included in screening and diagnostic tools for subclinical IMI detection.

Abstract Background Blood metabolic profiles can be used to assess metabolic disorders and to evaluate the health status of dairy cows. Given that these analyses are time-consuming, expensive, and stressful for the cows, there has been increased interest in Fourier transform infrared (FTIR) spectroscopy of milk samples as a rapid, cost-effective alternative for predicting metabolic disturbances. The integration of FTIR data with other layers of information such as genomic and on-farm data (days in milk (DIM) and parity) has been proposed to further enhance the predictive ability of statistical methods. Here, we developed a phenotype prediction approach for a panel of blood metabolites based on a combination of milk FTIR data, on-farm data, and genomic information recorded on 1150 Holstein cows, using BayesB and gradient boosting machine (GBM) models, with tenfold, batch-out and herd-out cross-validation (CV) scenarios. Results The predictive ability of these approaches was measured by the coefficient of determination (R2). The results show that, compared to the model that includes only FTIR data, integration of both on-farm (DIM and parity) and genomic information with FTIR data improves the R2 for blood metabolites across the three CV scenarios, especially with the herd-out CV: R2 values ranged from 5.9 to 17.8% for BayesB, from 8.2 to 16.9% for GBM with the tenfold random CV, from 3.8 to 13.5% for BayesB and from 8.6 to 17.5% for GBM with the batch-out CV, and from 8.4 to 23.0% for BayesB and from 8.1 to 23.8% for GBM with the herd-out CV. Overall, with the model that includes the three sources of data, GBM was more accurate than BayesB with accuracies across the CV scenarios increasing by 7.1% for energy-related metabolites, 10.7% for liver function/hepatic damage, 9.6% for oxidative stress, 6.1% for inflammation/innate immunity, and 11.4% for mineral indicators. Conclusions Our results show that, compared to using only milk FTIR data, a model integrating milk FTIR spectra with on-farm and genomic information improves the prediction of blood metabolic traits in Holstein cattle and that GBM is more accurate in predicting blood metabolites than BayesB, especially for the batch-out CV and herd-out CV scenarios.

ABSTRACT: Cheese-making traits in dairy cattle are important to the dairy industry but are difficult to measure at the individual level because there are limitations on collecting phenotypic information. Mid-infrared spectroscopy has its advantages, but it can only be used during monthly milk recordings. Recently, in-line devices for real-time analysis of milk quality have been developed. The AfiLab recording system (Afimilk) offers significant benefits as phenotypes can be collected from each cow at each milking session. The objective of this study was to assess the potential of integrating AfiLab real-time milk analyzer measures with the stacking ensemble learning technique using heterogeneous base learners for the in-line daily monitoring of cheese-making traits in Holstein cattle with a view to developing a precision livestock farming system for monitoring the technological quality of milk. Data and samples for wet-laboratory analyses were collected from 499 Holstein cows belonging to 2 farms where the AfiLab system was installed. The traits of concern were 9 milk coagulation traits [3 milk coagulation properties (MCP), and 6 curd firming traits (CFt)], and 7 cheese-making traits [3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits]. The near-infrared AfiLab spectral data and on-farm information (days in milk and parity) were used to assess the predictive ability of different statistical methods [elastic net (EN), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and artificial neural network (ANN)] across different cross-validation scenarios. These statistical methods were considered the base learners, which were then combined in a stacking ensemble learning. Results indicate that including information on the cows (days in milk and parity) in the AfiLab infrared prediction increased its accuracy by 10.3% for traditional MCP, 13.8% for curd firming, 9.8% for CY, and 11.2% for REC traits compared with those obtained from near-infrared AfiLab alone. The statistical approaches exhibited high prediction accuracies (R2) averaged across the cross-validation scenarios for traditional MCP (0.58 for ANN, 0.55 for EN and GBM, 0.52 for XGBoost, and 0.62 for stacking ensemble), CFt (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble), and similar R2 averages for CY and REC (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble). The ANN approach was more accurate than the other base learners (EN, GBM, and XGBoost) and improved accuracy across cross-validation scenarios on average by 7% for traditional MCP, 5% for CFt, 8% for CY, and 7% for REC. The stacking ensemble method improved prediction accuracy by 3% to 31% for traditional MCP, 2% to 26% for CFt, 1% to 38% for CY traits, and 2% to 27% for REC traits compared with the base learners. The prediction accuracies of the different approaches evaluated tended to decrease from the 10-fold cross-validation to the independent validation scenario, although there was a smaller reduction in prediction accuracy with the stacking ensemble learning technique across all the cross-validation scenarios. Our results show that combining in-line on-farm information with stacking ensemble machine learning represents an effective alternative for obtaining robust daily predictions of milk cheese-making traits.

Abstract Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.

Abstract Metabolic disorders, including hepatic lipidosis and ketosis, severely affect animal health status and welfare with a large economic burden in dairy herds. The gold standard for diagnosing hepatic lipidosis is the liver biopsy, which is impractical and invasive for the screening at farm level. Ultrasound (US) imaging is a promising technique for identifying liver dysfunction, but standardized specifications in physiological conditions are needed. Herein, we described the features of four US measurements, namely the liver predicted triacylglycerol (pTAG) content, liver depth (LD), and portal vein area (PVA) and depth (PVD) and we investigated their associations with a set of hematochemical (HC) indicators in 342 clinically healthy Holstein Friesian dairy cows. Liver pTAG content was negatively associated with hematocrit and positively with globulin, whereas PVA was negatively associated with thiol group levels, and LD positively with ceruloplasmin. We found significant interactions between some HC parameters and parity: in particular, creatinine, thiol groups and globulin for PVA, and aspartate aminotransferase, paraoxonase and ceruloplasmin for PVD. This study offers new insights on variations in liver function occurring after calving and pave the way for the potential use of minimally invasive techniques for prompt detection of metabolic disorders in dairy herds.

Osteosarcoma (OSA) represents the most common primary bone tumor in dogs and is characterized by a highly aggressive behavior. Cell lines represent one of the most suitable and reproducible pre-clinical models, and therefore the knowledge of their molecular landscape is mandatory to investigate oncogenic mechanisms and drug response. The present study aims at determining variants, putative driver genes, and gene expression aberrations by integrating whole-exome and RNA sequencing. For this purpose, eight canine OSA cell lines and one matched pair of primary tumor and normal tissue were analyzed. Overall, cell lines revealed a mean tumor mutational burden of 9.6 mutations/Mb (range 3.9–16.8). Several known oncogenes and tumor suppressor genes, such as ALK, MYC, and MET, were prioritized as having a likely role in canine OSA. Mutations in eight genes, previously described as human OSA drivers and including TP53, PTCH1, MED12, and PI3KCA, were retrieved in our cell lines. When variants were cross-referenced with human OSA driver mutations, the E273K mutation of TP53 was identified in the Wall cell line and tumor sample. The transcriptome profiling detected two possible p53 inactivation mechanisms in the Wall cell line on the one hand, and in D17 and D22 on the other. Moreover, MET overexpression, potentially leading to MAPK/ERK pathway activation, was observed in D17 and D22 cell lines. In conclusion, our data provide the molecular characterization of a large number of canine OSA cell lines, allowing future investigations on potential therapeutic targets and associated biomarkers. Notably, the Wall cell line represents a valuable model to empower prospective in vitro studies both in human and in dogs, since the TP53 driver mutation was maintained during cell line establishment and was widely reported as a mutation hotspot in several human cancers.

Dairy cows have high incidences of metabolic disturbances, which often lead to disease, having a subsequent significant impact on productivity and reproductive performance. As the milk fatty acid (FA) profile represents a fingerprint of the cow’s nutritional and metabolic status, it could be a suitable indicator of metabolic status at the cow level. In this study, we obtained milk FA profile and a set of metabolic indicators (body condition score, ultrasound liver measurements, and 29 hematochemical parameters) from 297 Holstein–Friesian cows. First, we applied a multivariate factor analysis to detect latent structure among the milk FAs. We then explored the associations between these new synthetic variables and the morphometric, ultrasonographic and hematic indicators of immune and metabolic status. Significant associations were exhibited by the odd-chain FAs, which were inversely associated with β-hydroxybutyrate and ceruloplasmin, and positively associated with glucose, albumin, and γ-glutamyl transferase. Short-chain FAs were inversely related to predicted triacylglycerol liver content. Rumen biohydrogenation intermediates were associated with glucose, cholesterol, and albumin. These results offer new insights into the potential use of milk FAs as indicators of variations in energy and nutritional metabolism in early lactating dairy cows.

The current nutrient recommendations focus on pigs fed ad libitum up to 140 kg in body weight (BW). It remains unclear whether this applies to pigs weighing above 140 kg in BW under different rearing conditions. This study aimed to estimate protein (Pd) and lipid (Ld) depositions and the metabolizable energy (ME), standardized ileal digestible lysine (SID lysine) requirement and partitioning in 224 C21 Goland pigs (90–200 kg in BW). The control pigs (C) received diets limiting ME up to 170 kg in slaughter weight (SW) at 9 months of age (SA); older (OA) pigs had restricted diets limiting ME and SID lysine up to 170 kg in SW at >9 months SA; younger (YA) pigs were fed nonlimited amounts of ME and SID lysine up to 170 kg in SW at 170 kg in SW. The estimated MEm averaged 1.03 MJ/kg0.60. An 11% increase in MEm was observed in OA pigs compared to the controls. Energy restriction had negligible effects on the estimated MEm. The marginal efficiency of SID lysine utilization for Pd averaged 0.725, corresponding to a SID lysine requirement of 9.8 g/100 g Pd.

Approximately 10–15% of worldwide human cancers are attributable to viral infection. When operating as carcinogenic elements, viruses may act with various mechanisms, but the most important is represented by viral integration into the host genome, causing chromosome instability, genomic mutations, and aberrations. In canine species, few reports have described an association between viral integration and canine cancers, but more comprehensive studies are needed. The advancement of next-generation sequencing and the cost reduction have resulted in a progressive increasing of sequencing data in veterinary oncology offering an opportunity to study virome in canine cancers. In this study, we have performed viral detection and integration analyses using VirusFinder2 software tool on available whole-genome and whole-exome sequencing data of different canine cancers. Several viral sequences were detected in lymphomas, hemangiosarcomas, melanomas, and osteosarcomas, but no reliable integration sites were identified. Even if with some limitations such as the depth and type of sequencing, a restricted number of available nonhuman genomes software, and a limited knowledge on endogenous retroviruses in the canine genome, results are compelling. However, further experiments are needed, and similarly to feline species, dedicated analysis tools for the identification of viral integration sites in canine cancers are required.

Slaughter weight (SW) is critical for dry-cured ham production systems with heavy pigs. A total of 159 C21 Goland pigs (gilts and barrows) at 95 ± 9.0 kg body weight (BW) from three batches were used to investigate the impact of ad libitum feeding on SW, growth performance, feed efficiency, and carcass and green ham characteristics. Diets contained 10 MJ/kg of net energy and 7.4 and 6.0 g/kg of SID-lysine. Slaughter weight classes (SWC) included 210 kg BW. In each batch, pigs were sacrificed at 230 or 258 d of age. Left hams were scored for round shape, fat cover thickness, marbling, lean colour, bicolour and veining. Data were analyzed with a model considering SWC, sex and SWC × Sex interactions as fixed factors and the batch as a random factor. The linear, quadratic and cubic effects of SWC were tested, but only linear effects were found. Results showed that pigs with greater SWC had greater average daily gain and feed consumption, with similar feed efficiency and better ham quality traits: greater ham weight, muscularity, and fat coveringin correspondence of semimembranosus muscle. Barrows were heavier and produced hams with slightly better characteristics than gilts.

The genomic landscape in human B-cell lymphoma has revealed several somatic mutations and potentially relevant germline alterations affecting therapy and prognosis. Also, mutations originally described as somatic aberrations have been shown to confer cancer predisposition when occurring in the germline. The relevance of mutations in canine B-cell lymphoma is scarcely known and gene expression profiling has shown similar molecular signatures among different B-cell histotypes, suggesting other biological mechanisms underlining differences. Here, we present a highly accurate approach to identify single nucleotide variants (SNVs) in RNA-seq data obtained from 62 completely staged canine B-cell lymphomas and 11 normal B-cells used as controls. A customized variant discovery pipeline was applied and SNVs were found in tumors and differentiated for histotype. A number of known and not previously identified SNVs were significantly associated to MAPK signaling pathway, negative regulation of apoptotic process and cell death, B-cell activation, NF-kB and JAK-STAT signaling. Interestingly, no significant genetic fingerprints were found separating diffuse large B-cell lymphoma from indolent lymphomas suggesting that differences of genetic landscape are not the pivotal causative factor of indolent behavior. We also detected several variants in expressed regions of canine B-cell lymphoma and identified SNVs having a direct impact on genes. Using this brand-new approach the consequence of a gene variant is directly associated to expression. Further investigations are in progress to deeply elucidate the mechanisms by which altered genes pathways may drive lymphomagenesis and a higher number of cases is also demanded to confirm this evidence.

In poultry production, gut microbiota (GM) plays a pivotal role and influences different host functions related to the efficiency of production performances. Antimicrobial (AM) use is one of the main factors affecting GM composition and functions. Although several studies have focused their attention on the role of AMs as growth promoters in the modulation of GM in broilers, the consequences of higher AM concentrations administered during prophylactic treatments need to be better elucidated. For this purpose, 16S rRNA gene sequencing was performed to evaluate the impact of different prophylactic AM protocols on the composition and diversity of the broiler GM. Diversity analysis has shown that AM treatment significantly affects alpha diversity in ileum and beta diversity in both ileum and caecum. In ileal samples, the Enterobacteriaceae family has been shown to be particularly affected by AM treatments. AMs have been demonstrated to affect GM composition in broiler. These findings indicate that withdrawal periods were not enough for the restoral of the original GM. Further studies are needed for a better elucidation of the negative effects caused by an altered GM in broilers.

Background: Diffuse large B-cell lymphoma (DLBCL), marginal zone lymphoma (MZL) and follicular lymphoma (FL) are the most common B-cell lymphomas (BCL) in dogs. Recent investigations have demonstrated overlaps of these histotypes with the human counterparts, including clinical presentation, biologic behavior, tumor genetics, and treatment response. The molecular mechanisms that underlie canine BCL are still unknown and new studies to improve diagnosis, therapy, and the utilization of canine species as spontaneous animal tumor models are undeniably needed. Recent work using human DLBCL transcriptomes has suggested that long non-coding RNAs (lncRNAs) play a key role in lymphoma pathogenesis and pinpointed a restricted number of lncRNAs as potential targets for further studies. Results: To expand the knowledge of non-coding molecules involved in canine BCL, we used transcriptomes obtained from a cohort of 62 dogs with newly-diagnosed multicentric DLBCL, MZL and FL that had undergone complete staging work-up and were treated with chemotherapy or chemo-immunotherapy. We developed a customized R pipeline performing a transcriptome assembly by multiple algorithms to uncover novel lncRNAs, and delineate genome-wide expression of unannotated and annotated lncRNAs. Our pipeline also included a new package for high performance system biology analysis, which detects high-scoring network biological neighborhoods to identify functional modules. Moreover, our customized pipeline quantified the expression of novel and annotated lncRNAs, allowing us to subtype DLBCLs into two main groups. The DLBCL subtypes showed statistically different survivals, indicating the potential use of lncRNAs as prognostic biomarkers in future studies. Conclusions: In this manuscript, we describe the methodology used to identify lncRNAs that differentiate B-cell lymphoma subtypes and we interpreted the biological and clinical values of the results. We inferred the potential functions of lncRNAs to obtain a comprehensive and integrative insight that highlights their impact in this neoplasm.