To gain insight into the importance of carefully selecting the sampling area for intestinal microbiota studies, cecal and fecal microbial communities of Caldes meat rabbit were characterized. The animals involved in the study were divided in two groups according to the feed intake level they received during the fattening period; ad libitum (n = 10) or restricted to 75% of ad libitum intake (n = 11). Cecum and internal hard feces were sampled from sacrificed animals. Assessment of bacterial and archaeal populations was performed by means of Illumina sequencing of 16S rRNA gene amplicons in a MiSeq platform. A total of 596 operational taxonomic units (OTUs) were detected using QIIME software. Taxonomic assignment revealed that microbial diversity was dominated by phyla Firmicutes (76.42%), Tenericutes (7.83%), and Bacteroidetes (7.42%); kingdom Archaea was presented at low percentage (0.61%). No significant differences were detected between sampling origins in microbial diversity or richness assessed using two alpha-diversity indexes: Shannon and the observed number of OTUs. However, the analysis of variance at genus level revealed a higher presence of genera Clostridium, Anaerofustis, Blautia, Akkermansia, rc4-4, and Bacteroides in cecal samples. By contrast, genera Oscillospira and Coprococcus were found to be overrepresented in feces, suggesting that bacterial species of these genera would act as fermenters at the end of feed digestion process. At the lowest taxonomic level, 83 and 97 OTUs in feces and cecum, respectively, were differentially represented. Multivariate statistical assessment revealed that sparse partial least squares discriminant analysis (sPLS-DA) was the best approach for this purpose. Interestingly, the majority of the most discriminative OTUs selected by sPLS-DA were found to be differentially represented between sampling origins in univariate analysis. Our study provides evidence that the choice of intestinal sampling area is relevant due to important differences in some taxa’s relative abundance that have been revealed between rabbits’ cecal and fecal microbiota. An appropriate sampling intestinal area should be chosen in each microbiota assessment.

BackgroundTo date, the molecular mechanisms that underlie residual feed intake (RFI) in pigs are unknown. Results from different genome-wide association studies and gene expression analyses are not always consistent. The aim of this research was to use machine learning to identify genes associated with feed efficiency (FE) using transcriptomic (RNA-Seq) data from pigs that are phenotypically extreme for RFI.MethodsRFI was computed by considering within-sex regression on mean metabolic body weight, average daily gain, and average backfat gain. RNA-Seq analyses were performed on liver and duodenum tissue from 32 high and 33 low RFI pigs collected at 153 d of age. Machine-learning algorithms were used to predict RFI class based on gene expression levels in liver and duodenum after adjusting for batch effects. Genes were ranked according to their contribution to the classification using the permutation accuracy importance score in an unbiased random forest (RF) algorithm based on conditional inference. Support vector machine, RF, elastic net (ENET) and nearest shrunken centroid algorithms were tested using different subsets of the top rank genes. Nested resampling for hyperparameter tuning was implemented with tenfold cross-validation in the outer and inner loops.ResultsThe best classification was obtained with ENET using the expression of 200 genes in liver [area under the receiver operating characteristic curve (AUROC): 0.85; accuracy: 0.78] and 100 genes in duodenum (AUROC: 0.76; accuracy: 0.69). Canonical pathways and candidate genes that were previously reported as associated with FE in several species were identified. The most remarkable pathways and genes identified were NRF2-mediated oxidative stress response and aldosterone signalling in epithelial cells, the DNAJC6, DNAJC1, MAPK8, PRKD3 genes in duodenum, and melatonin degradation II, PPARα/RXRα activation, and GPCR-mediated nutrient sensing in enteroendocrine cells and SMOX, IL4I1, PRKAR2B, CLOCK and CCK genes in liver.ConclusionsML algorithms and RNA-Seq expression data were found to provide good performance for classifying pigs into high or low RFI groups. Classification was better with gene expression data from liver than from duodenum. Genes associated with FE in liver and duodenum tissue that can be used as predictive biomarkers for this trait were identified.Electronic supplementary materialThe online version of this article (10.1186/s12711-019-0453-y) contains supplementary material, which is available to authorized users.

Nitrogen dynamics and its association to metabolically active microbial populations were assessed in two vertical subsurface vertical flow (VF) wetlands treating urban wastewater. These VF wetlands were operated in parallel with unsaturated (UVF) and partially saturated (SVF) configurations. The SVF wetland exhibited almost 2-fold higher total nitrogen removal rate (5 g TN m d) in relation to the UVF wetland (3 g TN m d), as well as a low NO-N accumulation (1 mg L vs. 26 mg L in SVF and UVF wetland effluents, respectively). After 6 months of operation, ammonia oxidizing prokaryotes (AOP) and nitrite oxidizing bacteria (NOB) displayed an important role in both wetlands. Oxygen availability and ammonia limiting conditions promoted shifts on the metabolically active nitrifying community within 'nitrification aggregates' of wetland biofilms. Ammonia oxidizing archaea (AOA) and Nitrospira spp. overcame ammonia oxidizing bacteria (AOB) in the oxic layers of both wetlands. Microbial quantitative and diversity assessments revealed a positive correlation between Nitrobacter and AOA, whereas Nitrospira resulted negatively correlated with Nitrobacter and AOB populations. The denitrifying gene expression was enhanced mainly in the bottom layer of the SVF wetland, in concomitance with the depletion of NO-N from wastewater. Functional gene expression of nitrifying and denitrifying populations combined with the active microbiome diversity brought new insights on the microbial nitrogen-cycling occurring within VF wetland biofilms under different operational conditions.

Background The effect of the production environment and different management practices in rabbit cecal microbiota remains poorly understood. While previous studies have proved the impact of the age or the feed composition, research in the breeding farm and other animal management aspects, such as the presence of antibiotics in the feed or the level of feeding, is still needed. Characterization of microbial diversity and composition of growing rabbits raised under different conditions could help better understand the role these practices play in cecal microbial communities and how it may result in different animal performance. Results Four hundred twenty-five meat rabbits raised in two different facilities, fed under two feeding regimes (ad libitum or restricted) with feed supplemented or free of antibiotics, were selected for this study. A 16S rRNA gene-based assessment through the MiSeq Illumina sequencing platform was performed on cecal samples collected from these individuals at slaughter. Different univariate and multivariate approaches were conducted to unravel the influence of the different factors on microbial alpha diversity and composition at phylum, genus and OTU taxonomic levels. The animals raised in the facility harboring the most stable environmental conditions had greater, and less variable, microbial richness and diversity. Bootstrap univariate analyses of variance and sparse partial least squares-discriminant analyses endorsed that farm conditions exerted an important influence on rabbit microbiota since the relative abundances of many taxa were found differentially represented between both facilities at all taxonomic levels characterized. Furthermore, only five OTUs were needed to achieve a perfect classification of samples according to the facility where animals were raised. The level of feeding and the presence of antibiotics did not modify the global alpha diversity but had an impact on some bacteria relative abundances, albeit in a small number of taxa compared with farm, which is consistent with the lower sample classification power according to these factors achieved using microbial information. Conclusions This study reveals that factors associated with the farm effect and other management factors, such as the presence of antibiotics in the diet or the feeding level, modify cecal microbial communities. It highlights the importance of offering a controlled breeding environment that reduces differences in microbial cecal composition that could be responsible for different animal performance.

Gut microbiota plays an important role in nutrient absorption and could impact rabbit feed efficiency. 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 efficiency. 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 cage-average traits. The inclusion of microbial information into certain mixed models increased their predictive ability up to 20% and 46% for cage-average feed efficiency 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 efficiency traits, although specific OTUs taxonomically assigned to 5 different phyla have a higher weight. Rabbit growth and feed efficiency are influenced by host cecal microbiota, thus considering microbial information in models improves the prediction of these complex phenotypes.