Ratolins transgènics poden ajudar a la fecundació in vitro

Els programes de fecundació in vitro utilitzen sovint una hormona que secreta la glàndula pituïtària. Obtenir aquestes hormones resulta molt costós. Investigadors de la UAB estan treballant per tal d'obtenir més eficaçment aquesta hormona utilitzant ratolins transgènics per testar in vivo les construccions transgèniques.Los programas de fecundación in vitro utilizan a menudo una hormonaque secreta la glándula pituitaria. Obtener estas hormonas resulta muycostoso. Investigadores de la UAB están trabajando para conseguir máseficazmente esta hormona utilizando ratones transgénicos para testar invivo las construcciones transgénicas

DAG expression : high-throughput gene expression analysis of real-time PCR data using standard curves for relative quantification

Background: Real-time quantitative PCR (qPCR) is still the gold-standard technique for gene-expression quantification. Recent technological advances of this method allow for the high-throughput gene-expression analysis, without the limitations of sample space and reagent used. However, non-commercial and user-friendly software for the management and analysis of these data is not available. Results: The recently developed commercial microarrays allow for the drawing of standard curves of multiple assays using the same n-fold diluted samples. Data Analysis Gene (DAG) Expression software has been developed to perform highthroughput gene-expression data analysis using standard curves for relative quantification and one or multiple reference genes for sample normalization. We discuss the application of DAG Expression in the analysis of data from an experiment performed with Fluidigm technology, in which 48 genes and 115 samples were measured. Furthermore, the quality of our analysis was tested and compared with other available methods. Conclusions: DAG Expression is a freely available software that permits the automated analysis and visualization of highthroughput qPCR. A detailed manual and a demo-experiment are provided within the DAG Expression software at http:// www.dagexpression.com/dage.zip

VtaA8 and VtaA9 from Haemophilus parasuis delay phagocytosis by alveolar macrophages

Haemophilus parasuis, a member of the family Pasteurellaceae, is a common inhabitant of the upper respiratory tract of healthy pigs and the etiological agent of Glässer's disease. As other virulent Pasteurellaceae, H. parasuis can prevent phagocytosis, but the bacterial factors involved in this virulence mechanism are not known. In order to identify genes involved in phagocytosis resistance, we constructed a genomic library of the highly virulent reference strain Nagasaki and clones were selected by increased survival after incubation with porcine alveolar macrophages (PAM). Two clones containing two virulent-associated trimeric autotransporter (VtaA) genes, vtaA8 and vtaA9, respectively, were selected by this method. A reduction in the interaction of the two clones with the macrophages was detected by flow cytometry. Monoclonal antibodies were produced and used to demonstrate the presence of these proteins on the bacterial surface of the corresponding clone, and on the H. parasuis phagocytosis-resistant strain PC4-6P. The effect of VtaA8 and VtaA9 in the trafficking of the bacteria through the endocytic pathway was examined by fluorescence microscopy and a delay was detected in the localization of the vtaA8 and vtaA9 clones in acidic compartments. These results are compatible with a partial inhibition of the routing of the bacteria via the degradative phagosome. Finally, antibodies against a common epitope in VtaA8 and VtaA9 were opsonic and promoted phagocytosis of the phagocytosis-resistant strain PC4-6P by PAM. Taken together, these results indicate that VtaA8 and VtaA9 are surface proteins that play a role in phagocytosis resistance of H. parasuis

Differences in phagocytosis susceptibility in Haemophilus parasuis strains

Haemophilus parasuis is a colonizer of the upper respiratory tract of healthy pigs, but virulent strains can cause a systemic infection characterized by fibrinous polyserositis, commonly known as Glässer's disease. The variability in virulence that is observed among H. parasuis strains is not completely understood, since the virulence mechanisms of H. parasuis are largely unknown. In the course of infection, H. parasuis has to survive the host pulmonary defences, which include alveolar macrophages, to produce disease. Using strains from different clinical backgrounds, we were able to detect clear differences in susceptibility to phagocytosis. Strains isolated from the nose of healthy animals were efficiently phagocytosed by porcine alveolar macrophages (PAM), while strains isolated from systemic lesions were resistant to this interaction. Phagocytosis of susceptible strains proceeded through mechanisms independent of a specific receptor, which involved actin filaments and microtubules. In all the systemic strains tested in this study, we observed a distinct capsule after interaction with PAM, indicating a role of this surface structure in phagocytosis resistance. However, additional mechanisms of resistance to phagocytosis should be explored, since we detected different effects of microtubule inhibition among systemic strains

On the holobiont ‘predictome’ of immunocompetence in pigs

Background Gut microbial composition plays an important role in numerous traits, including immune response. Integration of host genomic information with microbiome data is a natural step in the prediction of complex traits, although methods to optimize this are still largely unexplored. In this paper, we assess the impact of different modelling strategies on the predictive capacity for six porcine immunocompetence traits when both genotype and microbiota data are available. Methods We used phenotypic data on six immunity traits and the relative abundance of gut bacterial communities on 400 Duroc pigs that were genotyped for 70 k SNPs. We compared the predictive accuracy, defined as the correlation between predicted and observed phenotypes, of a wide catalogue of models: reproducing kernel Hilbert space (RKHS), Bayes C, and an ensemble method, using a range of priors and microbial clustering strategies. Combined (holobiont) models that include both genotype and microbiome data were compared with partial models that use one source of variation only. Results Overall, holobiont models performed better than partial models. Host genotype was especially relevant for predicting adaptive immunity traits (i.e., concentration of immunoglobulins M and G), whereas microbial composition was important for predicting innate immunity traits (i.e., concentration of haptoglobin and C-reactive protein and lymphocyte phagocytic capacity). None of the models was uniformly best across all traits. We observed a greater variability in predictive accuracies across models when microbiability (the variance explained by the microbiome) was high. Clustering microbial abundances did not necessarily increase predictive accuracy. Conclusions Gut microbiota information is useful for predicting immunocompetence traits, especially those related to innate immunity. Modelling microbiome abundances deserves special attention when microbiability is high. Clustering microbial data for prediction is not recommended by default.info:eu-repo/semantics/publishedVersio

On the holobiont 'predictome' of immunocompetence in pigs

Gut microbial composition plays an important role in numerous traits, including immune response. Integration of host genomic information with microbiome data is a natural step in the prediction of complex traits, although methods to optimize this are still largely unexplored. In this paper, we assess the impact of different modelling strategies on the predictive capacity for six porcine immunocompetence traits when both genotype and microbiota data are available. We used phenotypic data on six immunity traits and the relative abundance of gut bacterial communities on 400 Duroc pigs that were genotyped for 70 k SNPs. We compared the predictive accuracy, defined as the correlation between predicted and observed phenotypes, of a wide catalogue of models: reproducing kernel Hilbert space (RKHS), Bayes C, and an ensemble method, using a range of priors and microbial clustering strategies. Combined (holobiont) models that include both genotype and microbiome data were compared with partial models that use one source of variation only. Overall, holobiont models performed better than partial models. Host genotype was especially relevant for predicting adaptive immunity traits (i.e., concentration of immunoglobulins M and G), whereas microbial composition was important for predicting innate immunity traits (i.e., concentration of haptoglobin and C-reactive protein and lymphocyte phagocytic capacity). None of the models was uniformly best across all traits. We observed a greater variability in predictive accuracies across models when microbiability (the variance explained by the microbiome) was high. Clustering microbial abundances did not necessarily increase predictive accuracy. Gut microbiota information is useful for predicting immunocompetence traits, especially those related to innate immunity. Modelling microbiome abundances deserves special attention when microbiability is high. Clustering microbial data for prediction is not recommended by default. The online version contains supplementary material available at 10.1186/s12711-023-00803-4

Author Correction : Expression-based GWAS identifies variants, gene interactions and key regulators affecting intramuscular fatty acid content and composition in porcine meat

Altres ajuts: "Personal Investigador en Formació" (PIF) grant from Universitat Autònoma de Barcelona (458-01-1/2011)Aquesta és una correcció a l'article 10.1038/srep3180

A gene co-association network regulating gut microbial communities in a Duroc pig population

Background: Analyses of gut microbiome composition in livestock species have shown its potential to contribute to the regulation of complex phenotypes. However, little is known about the host genetic control over the gut microbial communities. In pigs, previous studies are based on classical "single-gene-single-trait" approaches and have evaluated the role of host genome controlling gut prokaryote and eukaryote communities separately. Results: In order to determine the ability of the host genome to control the diversity and composition of microbial communities in healthy pigs, we undertook genome-wide association studies (GWAS) for 39 microbial phenotypes that included 2 diversity indexes, and the relative abundance of 31 bacterial and six commensal protist genera in 390 pigs genotyped for 70 K SNPs. The GWAS results were processed through a 3-step analytical pipeline comprised of (1) association weight matrix; (2) regulatory impact factor; and (3) partial correlation and information theory. The inferred gene regulatory network comprised 3561 genes (within a 5 kb distance from a relevant SNP-P < 0.05) and 738,913 connections (SNP-to-SNP co-associations). Our findings highlight the complexity and polygenic nature of the pig gut microbial ecosystem. Prominent within the network were 5 regulators, PRDM15, STAT1, ssc-mir-371, SOX9 and RUNX2 which gathered 942, 607, 588, 284 and 273 connections, respectively. PRDM15 modulates the transcription of upstream regulators of WNT and MAPK-ERK signaling to safeguard naive pluripotency and regulates the production of Th1- and Th2-type immune response. The signal transducer STAT1 has long been associated with immune processes and was recently identified as a potential regulator of vaccine response to porcine reproductive and respiratory syndrome. The list of regulators was enriched for immune-related pathways, and the list of predicted targets includes candidate genes previously reported as associated with microbiota profile in pigs, mice and human, such as SLIT3, SLC39A8, NOS1, IL1R2, DAB1, TOX3, SPP1, THSD7B, ELF2, PIANP, A2ML1, and IFNAR1. Moreover, we show the existence of host-genetic variants jointly associated with the relative abundance of butyrate producer bacteria and host performance

Gut eukaryotic communities in pigs : diversity, composition and host genetics contribution

Background. The pig gut microbiome harbors thousands of species of archaea, bacteria, viruses and eukaryotes such as protists and fungi. However, since the majority of published studies have been focused on prokaryotes, little is known about the diversity, host-genetic control, and contributions to host performance of the gut eukaryotic counterparts. Here we report the first study that aims at characterizing the diversity and composition of gut commensal eukaryotes in pigs, exploring their putative control by host genetics, and analyzing their association with piglets body weight. Results. Fungi and protists from the faeces of 514 healthy Duroc pigs of two sexes and two different ages were characterized by 18S and ITS ribosomal RNA gene sequencing. The pig gut mycobiota was dominated by yeasts, with a high prevalence and abundance of Kazachstania spp. Regarding protists, representatives of four genera (Blastocystis, Neobalantidium, Tetratrichomonas and Trichomitus) were predominant in more than the 80% of the pigs. Heritabilities for the diversity and abundance of gut eukaryotic communities were estimated with the subset of 60d aged piglets (N = 390). The heritabilities of α-diversity and of the abundance of fungal and protists genera were low, ranging from 0.15 to 0.28. A genome wide association study reported genetic variants related to the fungal α-diversity and to the abundance of Blastocystis spp. Annotated candidate genes were mainly associated with immunity, gut homeostasis and metabolic processes. Additionally, we explored the association of gut commensal eukaryotes with piglet body weight. Our results pointed to a positive contribution of fungi from the Kazachstania genus, while protists displayed both positive (Blastocystis and Entamoeba) and negative (Trichomitus) associations with piglet body weight. Conclusions. Our results point towards a minor and taxa specific genetic control over the diversity and composition of the pig gut eukaryotic communities. Moreover, we provide evidences of the associations between piglets' body weight after weaning and members from the gut fungal and protist eukaryote community. Overall, this study highlights the relevance of considering, along with that of bacteria, the contribution of the gut eukaryote communities to better understand host-microbiome association and their role on pig performance, welfare and health

Influence of dietary n-3 long-chain fatty acids on microbial diversity and composition of sows’ feces, colostrum, milk, and suckling piglets’ feces

Introduction: Very little is known about the impact of n-3 long-chain fatty acids (n-3 LCFAs) on the microbiota of sows and their piglets. The aim of this study was to evaluate the effect of n-3 LCFA in sow diets on the microbiota composition of sows’ feces, colostrum, and milk as well as that of piglets’feces. Methods: Twenty-two sows were randomly assigned to either a control or an n-3 LCFA diet from service to weaning. Sows’ and piglets’ performance was monitored. The gestating and lactating sows’microbiomes in feces, colostrum, and milk were characterized by 16s ribosomal RNA gene sequencing. The fecal microbiome from the two lowest (>800 g) and the two highest birth weight piglets per litter was also characterized, and the LPS levels in plasma were analyzed at weaning. Results and Discussion: n-3 LCFA increased microbiota alpha diversity in suckling piglets’ and gestating sows’ feces. However, no effects were observed in colostrum, milk, or lactating sows’ feces. Dietary n-3 LCFA modified the microbiota composition of gestating sows’ feces, milk, and suckling piglets’feces, without affecting lactating sows’ feces or colostrum. In gestating sows’ feces and milk, the decrease in genus Succinivibrio and the increase of Proteobacteria phylum, due to the increased genera Brenneria and Escherichia, respectively, stand out. In the feces of suckling piglets, the higher abundance of the beneficial genus Akkermansia and Bacteroides, and different species of Lactobacillus are highlighted. In addition, positive correlations for families and genera were found between lactating sows’ feces and milk, milk and suckling piglets’ feces, and lactating sows’ feces and suckling piglets’feces. To conclude, dietary n-3 LCFA had a positive impact on the microbiome of suckling piglet’s feces by increasing microbial diversity and some beneficial bacteria populations, had a few minor modifications on the microbiome of milk and gestating sows’ feces and did not change the microbiome in lactating sows’ feces or colostrum. Therefore, this study shows the effect of dietary n-3 LCFA on the microbiota of sows, colostrum, milk, and suckling piglets during the lactation period providing crucial information on the microbiota status at the early stages of life, which have an impact on the post-weaning.info:eu-repo/semantics/publishedVersio