The value of gut microbiota to predict feed efficiency and growth of rabbits under different feeding regimes

Gut microbiota plays an important role in nutrient absorption and could impact rabbit feed efciency. This study aims at investigating such impact by evaluating the value added by microbial information for predicting individual growth and cage phenotypes related to feed efciency. The dataset comprised individual average daily gain and cage-average daily feed intake from 425 meat rabbits, in which cecal microbiota was assessed, and their cage mates. Despite microbiota was not measured in all animals, consideration of pedigree relationships with mixed models allowed the study of cageaverage traits. The inclusion of microbial information into certain mixed models increased their predictive ability up to 20% and 46% for cage-average feed efciency and individual growth traits, respectively. These gains were associated with large microbiability estimates and with reductions in the heritability estimates. However, large microbiabililty estimates were also obtained with certain models but without any improvement in their predictive ability. A large proportion of OTUs seems to be responsible for the prediction improvement in growth and feed efciency traits, although specifc OTUs taxonomically assigned to 5 diferent phyla have a higher weight. Rabbit growth and feed efciency are infuenced by host cecal microbiota, thus considering microbial information in models improves the prediction of these complex phenotypes.info:eu-repo/semantics/publishedVersio

Use of Bayes factors to evaluate the effects of host genetics, litter and cage on the rabbit cecal microbiota

The rabbit cecum hosts and interacts with a complex microbial ecosystem that contributes to the variation of traits of economic interest. Although the influence of host genetics on microbial diversity and specific microbial taxa has been studied in several species (e.g., humans, pigs, or cattle), it has not been investigated in rabbits. Using a Bayes factor approach, the aim of this study was to dissect the effects of host genetics, litter and cage on 984 microbial traits that are representative of the rabbit microbiota. Analysis of 16S rDNA sequences of cecal microbiota from 425 rabbits resulted in the relative abundances of 29 genera, 951 operational taxonomic units (OTU), and four microbial alpha-diversity indices. Each of these microbial traits was adjusted with mixed linear and zero-inflated Poisson (ZIP) models, which all included additive genetic, litter and cage effects, and body weight at weaning and batch as systematic factors. The marginal posterior distributions of the model parameters were estimated using MCMC Bayesian procedures. The deviance information criterion (DIC) was used for model comparison regarding the statistical distribution of the data (normal or ZIP), and the Bayes factor was computed as a measure of the strength of evidence in favor of the host genetics, litter, and cage effects on microbial traits. According to DIC, all microbial traits were better adjusted with the linear model except for the OTU present in less than 10% of the animals, and for 25 of the 43 OTU with a frequency between 10 and 25%. On a global scale, the Bayes factor revealed substantial evidence in favor of the genetic control of the number of observed OTU and Shannon indices. At the taxon-specific level, significant proportions of the OTU and relative abundances of genera were influenced by additive genetic, litter, and cage effects. Several members of the genera Bacteroides and Parabacteroides were strongly influenced by the host genetics and nursing environment, whereas the family S24-7 and the genus Ruminococcus were strongly influenced by cage effects. This study demonstrates that host genetics shapes the overall rabbit cecal microbial diversity and that a significant proportion of the taxa is influenced either by host genetics or environmental factors, such as litter and/or cage

Use of Bayes factors to evaluate the effects of host genetics, litter and cage on the rabbit cecal microbiota

Background The rabbit cecum hosts and interacts with a complex microbial ecosystem that contributes to the variation of traits of economic interest. Although the influence of host genetics on microbial diversity and specific microbial taxa has been studied in several species (e.g., humans, pigs, or cattle), it has not been investigated in rabbits. Using a Bayes factor approach, the aim of this study was to dissect the effects of host genetics, litter and cage on 984 microbial traits that are representative of the rabbit microbiota. Results Analysis of 16S rDNA sequences of cecal microbiota from 425 rabbits resulted in the relative abundances of 29 genera, 951 operational taxonomic units (OTU), and four microbial alpha-diversity indices. Each of these microbial traits was adjusted with mixed linear and zero-inflated Poisson (ZIP) models, which all included additive genetic, litter and cage effects, and body weight at weaning and batch as systematic factors. The marginal posterior distributions of the model parameters were estimated using MCMC Bayesian procedures. The deviance information criterion (DIC) was used for model comparison regarding the statistical distribution of the data (normal or ZIP), and the Bayes factor was computed as a measure of the strength of evidence in favor of the host genetics, litter, and cage effects on microbial traits. According to DIC, all microbial traits were better adjusted with the linear model except for the OTU present in less than 10% of the animals, and for 25 of the 43 OTU with a frequency between 10 and 25%. On a global scale, the Bayes factor revealed substantial evidence in favor of the genetic control of the number of observed OTU and Shannon indices. At the taxon-specific level, significant proportions of the OTU and relative abundances of genera were influenced by additive genetic, litter, and cage effects. Several members of the genera Bacteroides and Parabacteroides were strongly influenced by the host genetics and nursing environment, whereas the family S24-7 and the genus Ruminococcus were strongly influenced by cage effects. Conclusions This study demonstrates that host genetics shapes the overall rabbit cecal microbial diversity and that a significant proportion of the taxa is influenced either by host genetics or environmental factors, such as litter and/or cage.info:eu-repo/semantics/publishedVersio

The Anomalous Temperatures of Cu and Their Physical Significance. (II, 3)

The experimental evidences concerning anomalous changes of various properties of Cu, with temperature, were studied in literature, and it was found that four anomalous temperatures, including the melting point, exist, the values being 503°, 553°, 823° and 1356°K (mp), and that in these temperatures and the absolute zero, there exists a regurality as shown in the following table where the numbers in parentheses show the ratios of the intervals between the adjacent temperatures in each group. This regurality was understood by the writer to be identical ; in nature, with Lande\u27s interval rule in atomic spectra in the case of odd multiplicity, and, accordingly, it was inferred that the temperatures in each of the groups, II and I, would correspond to the components of the fine structures of the energy levels, E_1 and E_2 respectively. These levels, as well as E_3 and E_4 had been determined from experimental data, as those associated with the valence electron, their energy positions being in the order of E_1, E_2, E_3 and E_4. Further, it was considered that each of E_1 and E_2 is associated with two electrons and two atoms, hence, they may be denoted as, E_1 : (A_1, B_1) ^3D1, 2, 3 E_2 : (A_2, B_2) ^3D3, 2, 1 where A_1, B_1 and A_1, B_2 denote two pairs of atoms which associate respectively with the levels, E_1 and E_2, forming the diatomic molecules, (A_1, B_1) and (A_2, B_2). Concerning E_3 and E_4, it was assumed that, as in the case of Zn, there exist two groups of anomalies in low temperature range, which show the multiple structures of E_3 and E_4, respectively. Further it was assumed that the electrons associating with E_3 and E_4 are identical with those which associate to E_l and E_2, respectively, and they oscillate between E_1 and E_3, E_2 and E_4, respectively. Furthermore, that these oscillations take place, in resonance, in the group of the above molecules of the same kind, and, accordingly the molecules in the above group are bound mutually by the energy of the resonance exchange. On the other hand, it was proved previously that, when the electron is in E_3 or E_4, it plays the role of electric conduction, but, in E_1 or E_2, it binds the atoms firmly, and so, with the above idea the important properties of metals were explained consistently

Disentangling the causal relationship between rabbit growth and cecal microbiota through structural equation models

Background The effect of the cecal microbiome on growth of rabbits that were fed under different regimes has been studied previously. However, the term “effect” carries a causal meaning that can be confounded because of potential genetic associations between the microbiome and production traits. Structural equation models (SEM) can help disentangle such a complex interplay by decomposing the effect on a production trait into direct host genetics effects and indirect host genetic effects that are exerted through microbiota effects. These indirect effects can be estimated via structural coefficients that measure the effect of the microbiota on growth while the effects of the host genetics are kept constant. In this study, we applied the SEM approach to infer causal relationships between the cecal microbiota and growth of rabbits fed under ad libitum (ADGAL) or restricted feeding (ADGR). Results We identified structural coefficients that are statistically different from 0 for 138 of the 946 operational taxonomic units (OTU) analyzed. However, only 15 and 38 of these 138 OTU had an effect greater than 0.2 phenotypic standard deviations (SD) on ADGAL and ADGR, respectively. Many of these OTU had a negative effect on both traits. The largest effects on ADGR were exerted by an OTU that is taxonomically assigned to the Desulfovibrio genus (− 1.929 g/d, CSS-normalized OTU units) and by an OTU that belongs to the Ruminococcaceae family (1.859 g/d, CSS-normalized OTU units). For ADGAL, the largest effect was from OTU that belong to the S24-7 family (− 1.907 g/d, CSS-normalized OTU units). In general, OTU that had a substantial effect had low to moderate estimates of heritability. Conclusions Disentangling how direct and indirect effects act on production traits is relevant to fully describe the processes of mediation but also to understand how these traits change before considering the application of an external intervention aimed at changing a given microbial composition by blocking/promoting the presence of a particular microorganism.info:eu-repo/semantics/publishedVersio

kernInt : A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets

The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i.e., prediction of a phenotype of interest) from taxonomic abundances has become increasingly common in the microbiome field. However, a gap exists between supervised and classical unsupervised analyses, based on computing ecological dissimilarities for visualization or clustering. Despite this, both approaches face common challenges, like the compositional nature of next-generation sequencing data or the integration of the spatial and temporal dimensions. Here we propose a kernel framework to place on a common ground the unsupervised and supervised microbiome analyses, including the retrieval of microbial signatures (taxa importances). We define two compositional kernels (Aitchison-RBF and compositional linear) and discuss how to transform non-compositional beta-dissimilarity measures into kernels. Spatial data is integrated with multiple kernel learning, while longitudinal data is evaluated by specific kernels. We illustrate our framework through a single point soil dataset, a human dataset with a spatial component, and a previously unpublished longitudinal dataset concerning pig production. The proposed framework and the case studies are freely available in the kernInt package at https://github.com/elies-ramon/kernInt

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

Integrating genome-wide co-association and gene expression to identify putative regulators and predictors of feed efficiency in pigs

International audienceAbstractBackgroundFeed efficiency (FE) has a major impact on the economic sustainability of pig production. We used a systems-based approach that integrates single nucleotide polymorphism (SNP) co-association and gene-expression data to identify candidate genes, biological pathways, and potential predictors of FE in a Duroc pig population.ResultsWe applied an association weight matrix (AWM) approach to analyse the results from genome-wide association studies (GWAS) for nine FE associated and production traits using 31K SNPs by defining residual feed intake (RFI) as the target phenotype. The resulting co-association network was formed by 829 SNPs. Additive effects of this SNP panel explained 61% of the phenotypic variance of RFI, and the resulting phenotype prediction accuracy estimated by cross-validation was 0.65 (vs. 0.20 using pedigree-based best linear unbiased prediction and 0.12 using the 31K SNPs). Sixty-eight transcription factor (TF) genes were identified in the co-association network; based on the lossless approach, the putative main regulators were COPS5, GTF2H5, RUNX1, HDAC4, ESR1, USP16, SMARCA2 and GTF2F2. Furthermore, gene expression data of the gluteus medius muscle was explored through differential expression and multivariate analyses. A list of candidate genes showing functional and/or structural associations with FE was elaborated based on results from both AWM and gene expression analyses, and included the aforementioned TF genes and other ones that have key roles in metabolism, e.g. ESRRG, RXRG, PPARGC1A, TCF7L2, LHX4, MAML2, NFATC3, NFKBIZ, TCEA1, CDCA7L, LZTFL1 or CBFB. The most enriched biological pathways in this list were associated with behaviour, immunity, nervous system, and neurotransmitters, including melatonin, glutamate receptor, and gustation pathways. Finally, an expression GWAS allowed identifying 269 SNPs associated with the candidate genes’ expression (eSNPs). Addition of these eSNPs to the AWM panel of 829 SNPs did not improve the accuracy of genomic predictions.ConclusionsCandidate genes that have a direct or indirect effect on FE-related traits belong to various biological processes that are mainly related to immunity, behaviour, energy metabolism, and the nervous system. The pituitary gland, hypothalamus and thyroid axis, and estrogen signalling play fundamental roles in the regulation of FE in pigs. The 829 selected SNPs explained 61% of the phenotypic variance of RFI, which constitutes a promising perspective for applying genetic selection on FE relying on molecular-based prediction

Hierarchy Establishment in Growing Finishing Pigs: Impacts on Behavior, Growth Performance, and Physiological Parameters

In recent years, an increased number of studies have dealt with the analysis of social dominance related to animal behavior, physiology, and performance. This study aimed to investigate whether hierarchical ranking affects the coping style, non-social behavior during open field and novel object tests, performance, and physiological parameters of pigs. A total of 48 growing pigs (24 barrows and 24 females) were mixed three times during the growing–finishing period. The social and non-social behaviors of pigs were directly noted, and three behavioral tests were performed during the experimental period. Performance and physiological parameters were also recorded. Statistical analysis considered hierarchical classification (dominant vs. intermediary vs. subordinate) and p-values ≤ 0.05 were considered significant. After three regroupings, the pigs in different hierarchical classifications showed no change in hair cortisol values and open-field and novel object tests. Mean corpuscular hemoglobin concentration and leukocyte values increased in intermediary pigs, and the lowest counts were found in pigs classified as dominants. Furthermore, dominant pigs visited the feeder more but spent shorter time there compared to subordinate and intermediary pigs. Our results suggest that hierarchical classification influenced feeding behavior and physiological parameters without affecting cortisol values and growth performance, demonstrating a possible compensation skill.info:eu-repo/semantics/publishedVersio