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

Host and rumen microbiome contributions to feed efficiency traits in Holstein cows

It is now widely accepted that dairy cow performance is influenced by both the host genome and rumen microbiome composition. The contributions of the genome and the microbiome to the phenotypes of interest are quantified by heritability (h2) and microbiability (m2), respectively. However, if the genome and microbiome are included in the model, then the h2 reflects only the contribution of the direct genetic effects quantified as direct heritability (hd2), and the holobiont effect reflects the joint action of the genome and the microbiome, quantified as the holobiability (ho2). The objectives of this study were to estimate h2, hd2,m2, and ho2 for dry matter intake, milk energy, and residual feed intake; and to evaluate the predictive ability of different models, including genome, microbiome, and their interaction. Data consisted of feed efficiency records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. Three kernel models were fit to each trait: one with only the genomic effect (model G), one with the genomic and microbiome effects (model GM), and one with the genomic, microbiome, and interaction effects (model GMO). The model GMO, or holobiont model, showed the best goodness-of-fit. The hd2 estimates were always 10% to 15% lower than h2 estimates for all traits, suggesting a mediated genetic effect through the rumen microbiome, and m2 estimates were moderate for all traits, and up to 26% for milk energy. The ho2 was greater than the sum of hd2 and m2, suggesting that the genome-by-microbiome interaction had a sizable effect on feed efficiency. Kernel models fitting the rumen microbiome (i.e., models GM and GMO) showed larger predictive correlations and smaller prediction bias than the model G. These findings reveal a moderate contribution of the rumen microbiome to feed efficiency traits in lactating Holstein cows and strongly suggest that the rumen microbiome mediates part of the host genetic effect.

Relationship between feed efficiency and gut microbiota in laying chickens under contrasting feeding conditions

The gut microbiota is known to play an important role in energy harvest and is likely to affect feed efficiency. In this study, we used 16S metabarcoding sequencing to analyse the caecal microbiota of laying hens from feed-efficient and non-efficient lines obtained by divergent selection for residual feed intake. The two lines were fed either a commercial wheat-soybean based diet (CTR) or a low-energy, high-fibre corn-sunflower diet (LE). The analysis revealed a significant line x diet interaction, highlighting distinct differences in microbial community composition between the two lines when hens were fed the CTR diet, and more muted differences when hens were fed the LE diet. Our results are consistent with the hypothesis that a richer and more diverse microbiota may play a role in enhancing feed efficiency, albeit in a diet-dependent manner. The taxonomic differences observed in the microbial composition seem to correlate with alterations in starch and fibre digestion as well as in the production of short-chain fatty acids. As a result, we hypothesise that efficient hens are able to optimise nutrient absorption through the activity of fibrolytic bacteria such as Alistipes or Anaerosporobacter, which, via their production of propionate, influence various aspects of host metabolism.

The potential of microbiota information to better predict efficiency traits in growing pigs fed a conventional and a high-fiber diet

BACKGROUND

Improving pigs' ability to digest diets with an increased dietary fiber content is a lever to improve feed efficiency and limit feed costs in pig production. The aim of this study was to determine whether information on the gut microbiota and host genetics can contribute to predict digestive efficiency (DE, i.e. digestibility coefficients of energy, organic matter, and nitrogen), feed efficiency (FE, i.e. feed conversion ratio and residual feed intake), average daily gain, and daily feed intake phenotypes. Data were available for 1082 pigs fed a conventional or high-fiber diet. Fecal samples were collected at 16 weeks, and DE was estimated using near‑infrared spectrometry. A cross-validation approach was used to predict traits within the same diet, for the opposite diet, and for a combination of both diets, by implementing three models, i.e. with only genomic (Gen), only microbiota (Micro), and both genomic and microbiota information (Micro+Gen). The predictive ability with and without sharing common sires and breeding environment was also evaluated. Prediction accuracy of the phenotypes was calculated as the correlation between model prediction and phenotype adjusted for fixed effects.

RESULTS

Prediction accuracies of the three models were low to moderate (< 0.47) for growth and FE traits and not significantly different between models. In contrast, for DE traits, prediction accuracies of model Gen were low (< 0.30) and those of models Micro and Micro+Gen were moderate to high (> 0.52). Prediction accuracies were not affected by the stratification of diets in the reference and validation sets and were in the same order of magnitude within the same diet, for the opposite diet, and for the combination of both diets. Prediction accuracies of the three models were significantly higher when pigs in the reference and validation populations shared common sires and breeding environment than when they did not (P < 0.001).

CONCLUSIONS

The microbiota is a relevant source of information to predict DE regardless of the diet, but not to predict growth and FE traits for which prediction accuracies were similar to those obtained with genomic information only. Further analyses on larger datasets and more diverse diets should be carried out to complement and consolidate these results.

The feasibility of partial replacement of berseem hay by spent mushroom (Pleurotus osteratus) substrate in rabbit diets on growth performance, digestibility, caecum fermentation, and economic efficiency

Nowadays, agricultural by-product disposal is a major concern. The mushroom by-products could be used as an alternative feed source in rabbit diets. Therefore, partial replacement of berseem hay (BH) with spent mushroom substrate (SMS) was conducted in four experimental groups as follows: 0, 20, 40, and 60% of SMS. Forty weaned New Zealand White (NZW) rabbits at 6th weeks of age with an initial body weight of 520.25 ± 70.01 g were fed the experimental diet for 8 weeks. The results showed that ash content and cell wall constituents in SMS were higher than in BH, while the other nutrient compounds in SMS were lower than in BH. Dietary SMS at a level of 60% increased the final body weight (p = 0.05) and feed conversion ratio (p ≤ 0.05). However, average daily gain and total feed intake were not affected by treatments. The replacement of SMS at levels of 40 and 60% significantly improved nutrient digestibility and total digestible nutrient value. SMS 60% decreased (p = 0.040) faecal N and improved (p = 0.006) retained nitrogen. The SMS replacement increased caecum length (p = 0.001), and full and empty caecum weight (p = 0.001 and 0.021, respectively) compared to the control. The SMS inclusion caused a decrease (p = 0.021 and 0.007) in the pH and NH3-N concentrations, respectively. Total VFA, acetic acid, butyric acid, and propionic acid proportions increased with the dietary inclusion of SMS in a level-dependent manner. Using SMS as a replacement for BH in growing rabbit diets reduced the total feed cost, and consequently improved net revenue, economic efficiency, and relative economic efficiency.

Diversity and Co-Occurrence Pattern Analysis of Cecal and Jejunal Microbiota in Two Rabbit Breeds

This study aimed to evaluate the productive performance and microbiota variation in the jejunum and cecum of two rabbit breeds with different growth rates. This study was carried out on Native Middle-Egypt Breed (NMER) and Giant Flanders (GF) rabbits from 5 weeks to 12 weeks of age. Twenty NMER (NM) and GF male rabbits were slaughtered, and the jejunum and cecum tracts were collected to assay gut microbiota composition via 16S ribosomal RNA (rRNA) gene sequencing and histology examination. At 12 weeks of age, daily weight gain, villus height in the jejunum, total protein, and albumin were higher in GF rabbits than in NMER rabbits. Also, the jejunal villi of GF were well arranged in their dense borders. The microbiota between the jejunum and cecum was significantly different in terms of Beta-diversity. A significant correlation between Enterococcus (jejunum NM samples) and Lactobacillus (cecum GF samples) with body weight and weight gain was found (p < 0.05). Moreover, Escherichia-Shigella in the cecum of NM was significantly correlated with weight gain (p < 0.05). The most abundant genera identified in the jejunal and cecal contents of GF were generally beneficial microbiota. They may also play a role in reducing the pathogenic effects of Escherichia coli in these rabbits.

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.

Predicting feed efficiency traits in growing lambs from their ruminal microbiota

Selecting feed-efficient sheep could improve the sustainability of this livestock production. However, most sheep breeding companies cannot afford to record feed intake to select feed-efficient animals. Past studies underlined the potential of omics data, including microbiota metabarcoding data, as proxies for feed efficiency. The study involved 277 Romane lambs from two lines divergently selected for residual feed intake (RFI). There were two objectives: check the consequences of selecting for feed efficiency over the rumen microbiota, and assess the predictive ability of the rumen microbiota for host traits. The study assessed two contrasting diets (concentrate diet and mixed diet) and two microbial groups (prokaryotes and eukaryotes). Discriminant analyses did not highlight any significant effect of sheep selection for residual feed intake on the rumen microbiota composition. Indeed, prokaryotic and eukaryotic microbiota compositions poorly discriminated the RFI lines, with averaged balanced error rates ranging from 45% to 55%. Correlations between host traits (feed efficiency and production traits) and their predictions from microbiota data varied between -0.07 and 0.56, depending on the trait, diet and sequencing. Feed intake was the most accurately predicted trait. However, predictions from fixed effects and BW were more accurate than or as accurate as predictions from the microbiota. Environmental effects can greatly affect the variability of microbiota compositions. Considering batch and environmental effects should be paramount when the predictive ability of the microbiota is assessed. This study argues why metabarcoding the rumen microbiota is not the best way to predict meat sheep production traits: fixed effects and BW were more cost-effective proxies and they led to similar or better predictive accuracies than microbiota metabarcoding (16S and 18S sequencing).

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 (ADG_AL) or restricted feeding (ADG_R).

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 ADG_AL and ADG_R, respectively. Many of these OTU had a negative effect on both traits. The largest effects on ADG_R 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 ADG_AL, 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.

Chlorogenic acid improves growth performance of weaned rabbits via modulating the intestinal epithelium functions and intestinal microbiota

This study was conducted to investigate the impacts of chlorogenic acid (CGA) on growth performance, intestinal permeability, intestinal digestion and absorption-related enzyme activities, immune responses, antioxidant capacity and cecum microbial composition in weaned rabbits. One hundred and sixty weaned rabbits were allotted to four treatment groups and fed with a basal diet or a basal diet supplemented with 400, 800, or 1,600 mg/kg CGA, respectively. After a 35-d trial, rabbits on the 800 mg/kg CGA-supplemented group had higher (p < 0.05) ADG and lower (p < 0.05) F/G than those in control (CON) group. According to the result of growth performance, eight rabbits per group were randomly selected from the CON group and 800 mg/kg CGA group to collect serum, intestinal tissue samples and cecum chyme samples. Results showed that compared with the CON group, supplementation with 800 mg/kg CGA decreased (p < 0.05) levels of D-lactate, diamine oxidase, IL-1β, IL-6, and malondialdehyde (MDA), and increased IL-10 concentration in the serum; increased (p < 0.05) jejunal ratio of villus height to crypt depth, enhanced (p < 0.05) activities of maltase and sucrase, increased (p < 0.05) concentrations of IL-10, T-AOC, MHCII and transforming growth factor-α, and decreased (p < 0.05) levels of TNF-α and MDA in the jejunum of weaned rabbits. In addition, results of high-throughput sequencing showed that CGA supplementation elevated (p < 0.05) microbial diversity and richness, and increased (p < 0.05) the abundances of butyrate-producing bacteria (including genera V9D2013_group, Monoglobus, Papillibacter, UCG-005, and Ruminococcus). These results indicated that dietary supplementation with 800 mg/kg CGA could improve the growth performance of weaned rabbits by enhancing intestinal structural integrity, improving the intestinal epithelium functions, and modulating the composition and diversity of gut microbiota.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00738-2.

The Effect of Exogenous Lysozyme Supplementation on Growth Performance, Caecal Fermentation and Microbiota, and Blood Constituents in Growing Rabbits

The effects of exogenous lysozyme supplementation (LYZ) on growth performance, caecal fermentation and microbiota, and blood characteristics were investigated in growing rabbits. A total of 420 growing male V-Line rabbits (30 d old; weighing 528 ± 16 g) were randomly divided into four groups of 105 rabbits each, and monitored for 42 days. Experimental groups included a control group (LYZ0) fed a basal diet without LYZ supplementation, and three treated groups fed the same basal diet supplemented with LYZ at 50, 100, and 150 mg/kg diet, respectively. The results showed a quadratic improvement in the final body weight, daily growth rate, FCR, and digestibility of DM, while the digestibility of OM, CP, EE, NDF, and ADF improved linearly when LYZ supplementation was increased. The dressing percentage increased quadratically when LYZ levels were increased in the rabbit diets. In rabbits fed LYZ diets, L. acidophilus counts increased linearly (p < 0.05) and L. cellobiosus, and Enterococcus sp. counts increased quadratically, whereas E. coli counts decreased. In the LYZ-supplemented groups, the caecal pH value and NH3-N concentration declined quadratically, whereas total VFA, acetic, and butyric acids increased. Total lipids decreased linearly, whilst triglycerides and cholesterol decreased quadratically with LYZ supplementation. Total antioxidant capacity, superoxide dismutase, glutathione S-transferase, and catalase increased quadratically, while malondialdehyde decreased linearly in the LYZ-supplemented groups. In conclusion, exogenous lysozyme administration improved rabbit growth performance and antioxidant status while lowering the blood lipid profile, altering the bacterial population, and regulating caecal fermentation. Therefore, LYZ up to 150 mg/kg can be used as a potential supplement in rabbit feed.