Early bacterial colonization and succession within the gastrointestinal tract has been suggested to be crucial in the establishment of specific microbiota composition and the shaping of host phenotype. Here, the composition and dynamics of faecal microbiomes were studied for 31 healthy piglets across five age strata (days 14, 36, 48, 60 and 70 after birth) together with their mothers. Faecal microbiome composition was assessed by 16S rRNA gene 454-pyrosequencing. Bacteroidetes and Firmicutes were the predominant phyla present at each age. For all piglets, luminal secretory IgA concentration was measured at day 70, and body weight was recorded until day 70. The microbiota of suckling piglets was mainly represented by Bacteroides, Oscillibacter, Escherichia/Shigella, Lactobacillus and unclassified Ruminococcaceae genera. This pattern contrasted with that of Acetivibrio, Dialister, Oribacterium, Succinivibrio and Prevotella genera, which appeared increased after weaning. Lactobacillus fermentum might be vertically transferred via breast milk or faeces. The microbiota composition coevolved with their hosts towards two different clusters after weaning, primarily distinguished by unclassified Ruminococcaceae and Prevotella abundances. Prevotella was positively correlated with luminal secretory IgA concentrations, and body weight. Our study opens up new possibilities for health and feed efficiency manipulation via genetic selection and nutrition in the agricultural domain.

The ecological interactions within the gut microbial communities are complex and far from being fully understood. Here we report the first study that aims at defining the interaction network of the gut microbiota in pigs and comparing it with the enterotype-like clustering analysis. Fecal microbiota of 518 healthy piglets was characterized by 16S ribosomal RNA gene sequencing. Two networks were constructed at the genus and operational taxonomic unit levels. Within-network interactions mirrored the human gut microbiota relationships, with a strong co-exclusion between Prevotella and Ruminococcus genera, and were consistent with the two enterotype-like clusters identified in the pig microbiota. Remarkably, the cluster classification of the individuals was significantly associated with the body weight at 60 days of age (P=0.005) and average daily gain (P=0.027). To the best of our knowledge, this is the first study to provide an integrated overview of the porcine gut microbiota that suggests a conservation of the ecological community interactions and functional architecture between humans and pig. Moreover, we show that the microbial ecosystems and porcine growth traits are linked, which allows us to foresee that the enterotype concept may have an important role in the animal production industry.

Data were collected over the first 4 generations of a divergent selection experiment for residual feed intake of Large White pigs having ad libitum access to feed. This data set was used to obtain estimates of heritability for residual feed intake and genetic correlations (r(a)) between this trait and growth, carcass, and meat quality traits. Individual feed intake of group-housed animals was measured by single-space electronic feeders. Upward and downward selection lines were maintained contemporarily, with 6 boars and 35 to 40 sows per line and generation. Numbers of records were 793 for residual feed intake (RFI1) of boar candidates for selection issued from first-parity (P1) litters and tested over a fixed BW range (35 to 95 kg) and 657 for residual feed intake (RFI2) and growth, carcass, and meat quality traits of castrated males and females issued from second-parity (P2) litters and tested from 28 to 107 kg of BW. Variance and covariance components were estimated using REML methodology applied to a series of multitrait animal models, which always included the criterion for selection as 1 of the traits. Estimates of heritability for RFI1 and RFI2 were 0.14 +/- 0.03 and 0.24 +/- 0.03, respectively, whereas the estimate of r(a) between the 2 traits was 0.91 +/- 0.08. Estimates of r(a) indicated that selection for low residual feed intake has the potential to improve feed conversion ratio and reduce daily feed intake, with minimal correlated effect for ADG of P2 animals. Estimates of r(a) between RFI2 and body composition traits of P2 animals were positive for traits related to the amount of fat depots (r(a) = 0.44 +/- 0.16 for carcass backfat thickness) and negative for carcass lean meat content (r(a) = -0.55 +/- 0.14). There was a tendency for a negative genetic correlation between RFI2 and carcass dressing percent (r(a) = -0.36 +/- 0.21). Moreover, selection for low residual feed intake is expected, through lower ultimate pH and lighter color, to decrease pork quality (r(a) = 0.77 +/- 0.14 between RFI2 and a meat quality index intended to predict the ratio of the weight of ham after curing and cooking to the weight of defatted and boneless fresh ham).

This review summarizes the results from the INRA (Institut National de la Recherche Agronomique) divergent selection experiment on residual feed intake (RFI) in growing Large White pigs during nine generations of selection. It discusses the remaining challenges and perspectives for the improvement of feed efficiency in growing pigs. The impacts on growing pigs raised under standard conditions and in alternative situations such as heat stress, inflammatory challenges or lactation have been studied. After nine generations of selection, the divergent selection for RFI led to highly significant (P<0.001) line differences for RFI (−165 g/day in the low RFI (LRFI) line compared with high RFI line) and daily feed intake (−270 g/day). Low responses were observed on growth rate (−12.8 g/day, P<0.05) and body composition (+0.9 mm backfat thickness, P=0.57; −2.64% lean meat content, P<0.001) with a marked response on feed conversion ratio (−0.32 kg feed/kg gain, P<0.001). Reduced ultimate pH and increased lightness of the meat (P<0.001) were observed in LRFI pigs with minor impact on the sensory quality of the meat. These changes in meat quality were associated with changes of the muscular energy metabolism. Reduced maintenance energy requirements (−10% after five generations of selection) and activity (−21% of time standing after six generations of selection) of LRFI pigs greatly contributed to the gain in energy efficiency. However, the impact of selection for RFI on the protein metabolism of the pig remains unclear. Digestibility of energy and nutrients was not affected by selection, neither for pigs fed conventional diets nor for pigs fed high-fibre diets. A significant improvement of digestive efficiency could likely be achieved by selecting pigs on fibre diets. No convincing genetic or blood biomarker has been identified for explaining the differences in RFI, suggesting that pigs have various ways to achieve an efficient use of feed. No deleterious impact of the selection on the sow reproduction performance was observed. The resource allocation theory states that low RFI may reduce the ability to cope with stressors, via the reduction of a buffer compartment dedicated to responses to stress. None of the experiments focussed on the response of pigs to stress or challenges could confirm this theory. Understanding the relationships between RFI and responses to stress and energy demanding processes, as such immunity and lactation, remains a major challenge for a better understanding of the underlying biological mechanisms of the trait and to reconcile the experimental results with the resource allocation theory.

Litter characteristics at birth were recorded in 4 genetic types of sows with differing maternal abilities. Eighty-two litters from F(1) Duroc x Large White sows, 651 litters from Large White sows, 63 litters from Meishan sows, and 173 litters from Laconie sows were considered. Statistical models included random effects of sow, litter, or both; fixed effects of sow genetic type, parity, birth assistance, and piglet sex, as well as gestation length, farrowing duration, piglet birth weight, and litter size as linear covariates. The quadratic components of the last 2 factors were also considered. For statistical analyses, GLM were first considered, assuming a binomial distribution of stillbirth. Hierarchical models were also fitted to the data to take into account correlations among piglets from the same litter. Model selection was performed based on deviance and deviance information criterion. Finally, standard and robust generalized estimating equations (GEE) procedures were applied to quantify the importance of each effect on a piglet's probability of stillbirth. The 5 most important factors involved were, in decreasing order (contribution of each effect to variance reduction): difference between piglet birth weight and the litter mean (2.36%), individual birth weight (2.25%), piglet sex (1.01%), farrowing duration (0.99%), and sow genetic type (0.94%). Probability of stillbirth was greater for lighter piglets, for male piglets, and for piglets from small or very large litters. Probability of stillbirth increased with sow parity number and with farrowing duration. Piglets born from Meishan sows had a lower risk of stillbirth (P < 0.0001) and were little affected by the sources of variation mentioned above compared with the 3 other sow genetic types. Standard and robust GEE approaches gave similar results despite some disequilibrium in the data set structure highlighted with the robust GEE approach.