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.

PRGdb 4.0: an updated database dedicated to genes involved in plant disease resistance process.

The Plant Resistance Genes database (PRGdb; http://prgdb.org/prgdb4/) has been greatly expanded, keeping pace with the increasing amount of available knowledge and data (sequenced proteomes, cloned genes, public analysis data, etc.). The easy-to-use style of the database website has been maintained, while an updated prediction tool, more data and a new section have been added. This new section will contain plant resistance transcriptomic experiments, providing additional easy-to-access experimental information. DRAGO3, the tool for automatic annotation and prediction of plant resistance genes behind PRGdb, has been improved in both accuracy and sensitivity, leading to more reliable predictions. PRGdb offers 199 reference resistance genes and 586.652 putative resistance genes from 182 sequenced proteomes. Compared to the previous release, PRGdb 4.0 has increased the number of reference resistance genes from 153 to 199, the number of putative resistance genes from 177K from 76 proteomes to 586K from 182 sequenced proteomes. A new section has been created that collects plant-pathogen transcriptomic data for five species of agricultural interest. Thereby, with these improvements and data expansions, PRGdb 4.0 aims to serve as a reference to the plant scientific community and breeders worldwide, helping to further study plant resistance mechanisms that contribute to fighting pathogens.