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Impact of Selection for Digestive Efficiency on Microbiota Composition in the Chicken

Impact of Selection for Digestive Efficiency on Microbiota Composition in the Chicken

  • Sandrine Mignon-Grasteau, 
  • Agnès Narcy, 
  • Nicole Rideau, 
  • Céline Chantry-Darmon, 
  • Marie-Yvonne Boscher, 
  • Nadine Sellier, 
  • Marie Chabault, 
  • Barbara Konsak-Ilievski, 
  • Elisabeth Le Bihan-Duval, 
  • Irène Gabriel
PLOS
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Abstract

Objectives

Feed efficiency and its digestive component, digestive efficiency, are key factors in the environmental impact and economic output of poultry production. The interaction between the host and intestinal microbiota has a crucial role in the determination of the ability of the bird to digest its food and to the birds’ feed efficiency. We therefore investigated the phenotypic and genetic relationships between birds’ efficiency and the composition of the cecal microbiota in a F2 cross between broiler lines divergently selected for their high or low digestive efficiency.

Methods

Analyses were performed on 144 birds with extreme feed efficiency values at 3 weeks, with feed conversion values of 1.41±0.05 and 2.02±0.04 in the efficient and non-efficient groups, respectively. The total numbers of Lactobacillus, L. salivarius, L. crispatus, C. coccoides, C. leptum and E. coli per gram of cecal content were measured.

Results

The two groups mainly differed in larger counts of Lactobacillus, L. salivarius and E. coli in less efficient birds. The equilibrium between bacterial groups was also affected, efficient birds showing higher C. leptum, C. coccoides and L. salivarius to E. coli ratios. The heritability of the composition of microbiota was also estimated and L. crispatus, C. leptum, and C. coccoides to E. coli ratios were moderately but significantly heritable (0.16 to 0.24). The coefficient of fecal digestive use of dry matter was genetically and positively correlated with L. crispatus, C. leptum, C. coccoides (0.50 to 0.76) and negatively with E. coli (-0.66). Lipid digestibility was negatively correlated with E. coli (-0.64), and AMEn positively correlated with C. coccoides and with the C. coccoides to Lactobacillus ratio (0.48 to 0.64). We also detected 14 Quantitative Trait Loci (QTL) for microbiota on the host genome, mostly on C. leptum and Lactobacillus. The QTL for C. leptum on GGA6 was close to genome-wide significance. This region mainly includes genes involved in anti-inflammatory responses and in the motility of the gastrointestinal tract.

Introduction

Feed efficiency is the major component of both economic profitability and environmental impact of poultry production. It has been shown that when birds are fed a challenging diet (for their hardness and viscosity characteristics) their digestive efficiency has a significant role in feed efficiency, and that is highly heritable [1]. After 8 generations of divergent selection on digestive efficiency, we obtained two genotypes of chickens with 30 to 40% difference between good digesters (D+) and poor digesters (D-). In a preliminary study, Gabriel et al. [2] showed that the composition and homogeneity of microbiota varied widely between these two lines, which suggests that the genetics of the host influence the composition of its microbiota. This difference in microbiota composition is not surprising as biotopes of the digestive tract have probably been modified between these two lines due to differences in anatomy of the gastrointestinal tract and digestive physiology [3]. Moreover, the microbiota is in constant interaction with the host and has been shown to influence several major functions such as the immunological, physiological and nutritional status of birds [4,5]. Several factors originating from the host can impact its microbiota, such as those due to digestive physiology (turnover of the intestinal epithelium, quantity of mucus, motility of gut, gut secretions), the nutrient composition of the bowel which depends on the composition of the diet and on the bird’s capacity to digest feed, and the presence of antibacterial compounds of the immune system [6].

Several studies have suggested the existence of the influence of the host’s genetics on the composition of the chicken microbiota as it differs between individuals [7,8], between lines selected on growth or digestive efficiency [2,4,912] and between birds within a genotype with high or low feed or digestive efficiency [9,11,13]. A few studies have gone further than group comparisons, to propose estimates of genetic parameters, genetic or phenotypic correlations between growth performance and microbiota composition or QTL detection for microbiota. Heritability of the quantity of 16S rRNA copies has been estimated in only two studies on high and low body weight chicken lines [4,10]. Despite the relatively low numbers of birds (60 to 132 chickens per study), they indicated that some species or genera such as Lactobacillus spp. or Streptococcae seem to be heritable and correlated with body weight. However, no correlation with feed or digestive efficiency was available, and these studies relied on fecal samples, the composition of which varies widely within a day due to emptying of the ceca [14]. However, studies performed in mammals have indicated that the host’s genetics influence its digestive microbiota [15].

The aim of our study was therefore to provide a complete set of information on the genetic basis of the host’s influence on microbiota composition through i) comparison of microbiota composition between high and low efficiency groups to establish how far selection on digestive efficiency affected microbiota composition, ii) estimation of phenotypic and genetic relationships between feed efficiency, digestive efficiency and microbiota composition to estimate which proportion of microbiota composition is due to the overall genetic background of birds and iii) Quantitative Trait Loci (QTL) detection of microbiota composition to identify regions in which a variation of the DNA sequence will affect microbiota composition, through a modification of the environment provided to bacteria (e.g., physico-chemical conditions, nutrient concentrations, immune system activity, …). As previous studies showed that the difference in microbiota composition between efficient and non-efficient birds was greater in cecal content than in other intestinal compartments [2,11,16], we focused our study on this specific digestive segment.

Materials and Methods

Animals and rearing

All animal care and experimental procedures reported in this paper were in accordance with French and European regulations concerning animal experimentation, including authorizations to experiment on live birds no. 37–100, 006290, 37–123, 37–005, A37-162, 04726, 7275 for scientists and those delivered at 30/09/1996, 26/02/2007, and 09/09/2005 for technicians from the French Ministry of Agriculture. The Experimental Unit where birds were kept is registered by the ministry of Agriculture with license number C-37-175-1 for animal experimentation. Measure of digestive efficiency in individual cages, blood sampling procedures for genotyping, euthanasia procedures by injection of pentobarbital, scientific justification, evidence for a lack of alternatives and endpoints were approved by the ethics committee in Animal Experimentation of Val de Loire (00886.02 and 01047.02). This ethics committee is registered by the National Committee under the number C2EA-19. The personal license number from the French Veterinary Service for this study is 548.

Data were collected on chickens from a F2 population obtained by crossing two medium-growth broiler lines divergently selected on their high (D+) or low (D-) digestive efficiency determined by metabolizable energy corrected to zero nitrogen retention at 3 weeks (AMEn) [1]. The divergent selection experiment started from a pure male line from the SASSO breeding company, used as the father of the medium-growth crossbred commercial chickens. The F2 population, created to detect the QTLs for digestive efficiency, has been described in Tran et al. [17]. Before crossing, the D+ and D- populations presented wide differences in feed and digestive efficiency (30 to 40%) [18,19]. Using an F2 population instead of the initial divergent lines allowed us to compare efficient and non-efficient birds in a population with a common genetic background.

Five males and fourteen females per line (D+ and D-) were used as F0 grand-parents. Males of the D+ and D- lines were mated respectively to females from the D- and D+ line to produce the F1 generation (half D+×D- and half D-×D+). Six F1 sires (3 D+×D- and 3 D-×D+) were mated to sixty F1 females of the reciprocal cross (i.e., D+×D- females for D-×D+ males, D-×D+ females for D+×D- males) to produce a total of 864 F2 birds (male and female). The F2 birds were reared in 4 batches (between January and June 2009) on the floor from hatching to 8 d to allow normal development of intestinal microbiota and subsequently transferred to individual cages in three rearing cells until slaughter at 23 d to measure feed and digestive efficiency. Birds were fed a diet similar to the diet used during the selection experiment [17], including 52.5% Rialto wheat and 6% soybean oil, 3110 kcal.kg-1 DM and 21.1% CP. Clinacox (0.02%) was used as anticoccidial agent, as it has a limited effect on the development of intestinal microbiota [20,21].

Phenotypes

Birds were weighed at 0, 9, 14, 17, 20 and 23 d. Their feed intake was individually recorded between 9 and 14 d, 14 and 17 d, 17 and 20 d and between 20 and 23 d. A balance trial with a total collection of excreta was performed between 20 and 23 d to measure fecal digestive efficiency traits as AMEn, coefficients of fecal digestive use of dry matter, starch, lipids and proteins (CDUDM, CDUS, CDUL, CDUP). These digestive efficiency traits were determined through near infrared spectroscopy following the method of Bastianelli et al. [22].

Due to time constraints, it was not possible to select birds used for microbiota study on their digestive efficiency. Instead, the subsample of 144 birds used for microbiota determination were selected on their feed efficiency between 17 and 20 d, as feed efficiency and digestive efficiency had been previously shown to be strongly genetically correlated (-0.70, [1]). The mean values of feed efficiency, estimated through the feed conversion ratio (FCR, i.e. the ratio of feed intake to weight gain) were 1.41±0.05 in the low FCR group (FCR_L) and 2.02±0.04 in the high FCR group (FCR_H), respectively. Initial and final body weight of birds in the 2 groups were similar (Table 1), as could be expected from the absence of genetic correlation between AMEn and body weight.

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Table 1. Least square means (± standard errors) of feed and digestive efficiency, body weight and feed intake in the high (FCR_H) and poor (FCR_L) feed efficiency groups.

https://doi.org/10.1371/journal.pone.0135488.t001

Microbiota determination

Previous studies in these chicken lines showed that the greatest difference in microbiota composition between D+ and D- was in the cecal content [2]. This study was therefore focused on this digestive segment. At 23 d, after 2h30 of feeding following 8 hours’ fasting, birds were killed by pentobarbital injection, and their ceca immediately removed. Ceca were opened and their content gently removed in order to obtain only the content and not the mucosa, frozen in liquid nitrogen and stored at -80°C until further processing.

Microbial DNA was then extracted from cecal samples using the QIAamp DNA mini-kit (QIAGEN, cat#51306). We used a combination of the methods of Yu et al. [23] and Stanley et al. [11]. Briefly, 25 mg of cecal content were transferred to a tube with lysis buffer [11] and sterile zirconium beads. Samples were homogenized at maximum speed (Frequency 30.sec-1) Retsch MM301 for 3 min, followed by heating at 70°C for 5 min. Following centrifugation (5 min, 16 000 g, 4°C), a second extraction step was carried out. The two supernatants were pooled for the DNA purification step. Proteinase K was added and the sample was heated at 70°C for 10 min to remove proteins. Ethanol was then added and the sample was purified using a QIAamp column as described by the manufacturer. The sample was eluted in Tris-EDTA buffer AE (Qiagen). DNA quantity and quality were measured on a Nanodrop spectrophotometer.

16S rDNA was quantified by qPCR to determine the number of copies of the main bacterial groups in the chicken gut within the Firmicutes phylum (lactobacillus genus, Lactobacillus salivarius and Lactobacillus crispatus species, Clostridium coccoides and Clostridium leptum groups) and within the Proteobacteria phylum (Escherichia coli). The primers used for Lactobacillus were those described by Walter et al. [24] and Heilig et al. [25] for forward and reverse primers, respectively. The primers used for L. crispatus, L. salivarius, C. leptum, C. coccoides and E. coli were those proposed by De Backer et al. [26], Harrow et al. [27], Matsuki et al. [28], Matsuki et al. [29] and Huijsdens et al. [30], respectively.

Reactions were run in triplicate in 384 well plates in a final volume of 10 μl. The EpMotion 5070 liquid handling robot (Eppendorf, Le Pecq, France) was used to distribute the master mix and DNA to the 384 well plates. The L. salivarius reaction consisted of 5 μl of TaqMan Universal PCR 2 × Master Mix (Applied Biosystems, Courtaboeuf, France), 0.2 μl of both 10 μM primers (Eurogentec, Angers, France) and minor groove binder probe (Applied Biosystems), 1.9 μl of nuclease-free water and 2.5 μl of template DNA at the appropriate dilution. Amplification was carried out with a Light Cycler 480 (Roche, Meylan, France) as follows: 10 min at 95°C, followed by 45 cycles of denaturation (10 s at 95°C), annealing (30 s at 60°C) and extension (30 s at 72°C). Reactions for the other bacterial groups consisted of 5 μl of Light Cycler 480 SYBR Green I Master Mix (Roche, Meylan, France), 0.5 μl of 10 μM primers (Eurogentec), 1.5 μl of nuclease-free water and 2.5 μl of template DNA at the appropriate dilution. The cycling conditions were as follows: 10 min at 95°C, then 45 cycles of denaturation (10 s at 95°C), annealing (20 s at 60°C) and extension (30 s at 72°C). Following amplification, melting curve analysis was included in order to assess the specificity of the amplified product. Standard curves were generated by amplification of serial 10-fold dilutions of E. coli (K12-1 strain, CIRM-BP 371), L. plantarum (DSM 20174, ATCC 14917, CIRM-BIA 466) genomic DNA (International Center of Microbial Resources, CIRM, INRA, France), L. Salivarus (ATTC 11741, DSMZ 20555) and L. crispatus (ATCC 33820, DSMZ 20584) and used for the quantification of total bacteria and E. coli and lactobacillus spp. L. salivarus and L. crispatus, respectively. Reference clones EF445158 and EF445150 [31] were used to generate the standard curves for the quantification of C. leptum and C. coccoides, respectively. The copy number for each reaction was calculated from the standard curves and determined by the second derivative maximum method [32]. Results are presented as number of 16S rDNA copies expressed per gram of fresh sample (Table 2).

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Table 2. Least square means (± standard errors) of cecal microbiota counts (qPCR) in group with low (FCR_L) or high (FCR_H) feed conversion ratio.

https://doi.org/10.1371/journal.pone.0135488.t002

As preliminary studies showed that the Bacteroides genus was not detected in F0 birds from D+ and D- lines [2], this group was not included in the present study.

Markers and genotyping

All F0, F1 and F2 birds were genotyped with a dedicated Illumina Infinium custom array including 6,000 single nucleotide markers (SNP) markers chosen for their informativity in our design and for their distribution across the genome [33]. The markers presenting deviations from the Hardy-Weinberg equilibrium within families, inconsistent genotyping relative to pedigree or genetic map information or poor quality of markers were discarded from the analysis in order to reduce the risk of erroneous results [33]. Finally, 3,379 markers were used. The genetic map was deduced from the physical position of the SNP markers and from the genetic consensus reference map published by Groenen et al. [34]. This set of markers covers 3,099.1 cM.

Statistical analyses

Phenotypic analyses.

We first tested whether the composition of microbiota (count of each category or ratio of counts between categories) and digestive efficiency parameters were different between the feed efficiency groups. Due to their non-normal distributions, raw bacterial counts were log-transformed before analysis and the ratios calculated with the log-transformed counts of each category. The analysis of variance was performed with the GLM procedure of SAS/STAT Version 9.4 and model 1: (1) where yijklmn is the performance of animal n (N = 144), μ the general mean, Hi the fixed effect of hatch i (i = 1 to 5), Sj the fixed effect of sex j (j = males or females), Ck the fixed effect of rearing cell k (N = 3), RCl the fixed effect of the raw of cage within the cell (N = 3), Gm the fixed effect of FCR group (FCR_H, FCR_L), and eijklmn the residual pertaining to animal n.

In order to determine which microbiota characteristics might be related to digestive efficiency, we carried out multifactorial correspondence analysis with the SPAD 7.0 software. For each trait of microbiota and digestive efficiency, we determined three categories with equal frequencies, i.e. the third with the lowest values, the third with the median values and the third with the highest values (noted L, M and H, respectively). The analysis was performed using microbiota as active traits (i.e. contributing to construction of axes) and projecting digestive efficiency categories on the graph. The impact of active variables was assessed through their relative contribution to each axis. A t-test was then performed to determine whether digestive efficiency traits were significantly associated with composition of microbiota.

Genetic analyses.

In order to estimate both heritability of microbiota traits and their genetic correlations with digestive efficiency, we added an animal genetic effect into the model used for the analysis of variance (model 1) and estimated genetic parameters with VCE 6.0.2 software [35]. The pedigree used to construct the relationship matrix included 1571 animals.

QTL analysis.

QTL detection was carried out with the QTLMap software [36] using a half-sib model [37,38] with interval mapping based on maximum likelihood estimations [39]. This model does not make assumptions on the number of QTL alleles segregating in the design. The traits were analyzed separately. Depending on preliminary analysis of variance, data were pre-corrected for fixed effects of batch (4 levels), sex (2 levels), rearing cell (3 levels) and cage row (3 levels). QTL analyses were performed by comparing the hypothesis of one QTL (H1) versus no QTL (H0) to test the segregation of a QTL on each linkage group. For chromosome Z, separate analyses were performed for males and females.

For each trait on each chromosome, the significance threshold at the chromosome-wide level was calculated from the results of 5,000 simulations of performance under the null hypothesis. For the most significant QTLs, 20,000 simulations were made to derive the genome-wide p-value (PG) from the chromosome-wide p-value (PC) using an approximate Bonferroni correction: (2) where r is the ratio of the length of a specific chromosome to the length of the genome considered for QTL detection, as in Tilquin et al. [40]. Confidence intervals for QTLs (95%) were estimated using the LOD drop-off method as proposed by Lander and Botstein [39].

The significance of the QTL effects within each sire family was tested using a Student test, by assuming an equal distribution of the QTL alleles in the progeny. A QTL effect was retained as significant for Student test p-values <0.05, and the corresponding sire families were assumed to segregate for this QTL. These familial substitution effects were estimated in families found to significantly segregate for the QTL.

Results and Discussion

Differences between groups and multifactorial analysis

The main differences in digestive and feed efficiency and in microbiota composition between the FCR_L and FCR_H groups are presented in Tables 1 and 2, respectively. Differences in FCR between the two groups were due to higher feed intake in FCR_H, as no difference of initial of final body weight was observed. Due to the incomplete genetic correlations between FCR and digestive efficiency, the difference in digestive efficiency between the two groups (17.3%) was lower than the expected 30 to 40% [18,41]. Differences between the two groups for the coefficients of fecal digestive use of starch, lipids and proteins were similar to differences previously observed between the D+ and D- lines [42], the difference being lower for proteins (10.4%) and higher for lipids (37.7%).

Differences between the two FCR groups regarding microbiota composition are shown in Table 2. The groups mainly differed by higher counts for Lactobacillus, L. salivarius and E. coli in the FCR_H group (+24.6%, +58.4% and +50.7%, respectively) than in the FCR_L group. These variations in some bacterial groups resulted in differences in equilibrium between bacteria. As L. salivarius but not L. crispatus differed between FCR_H and FCR_L, their ratio to Lactobacillus was also different between the two groups, with relatively more L. salivarius and less L. crispatus in less efficient birds (P = 0.03 and 0.06, respectively). The ratios of clostridia to lactobacillus and to E. coli were also higher in efficient than in less efficient birds (P<0.02). The association between the composition of cecal microbiota and feed conversion ratio in chicken has already been reported by Torok et al. [16] and Stanley et al. [11]. In the latter study, contrary to our study, a higher L. crispatus count was associated with poor efficiency, but their study was based on a very different genotype and diet, which might explain the difference in results.

The phenotypic association between microbiota composition and digestive efficiency was seen with the multifactorial analysis (Table 3, Fig 1). It first illustrated that the first factor of variation in microbiota composition was E. coli, as together E. coli and the ratio of E. coli to Lactobacillus, L. crispatus, C. leptum and C. coccoides represented 52.9% of the variability on the first axis. The second axis reflected the equilibrium between Lactobacillus, L. crispatus and clostridia, with 70.5% of the variability of the axis explained by Lactobacillus, C. leptum, the ratios of L. crispatus to Lactobacillus, C. leptum and C. coccoides and the ratio of C. coccoides to Lactobacillus. All traits of digestive efficiency were significantly associated with these two axes, better digestive efficiency being associated with a lower E. coli count, and thus higher ratios of other bacteria types to E. coli. On the second axis, better digestive efficiency was also associated with lower Lactobacillus and C. leptum counts, higher ratios of C. coccoides and L. crispatus to Lactobacillus and a lower ratio of C. leptum to Lactobacillus.

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Table 3. Results of multiple correspondence analysis between cecal microbiota composition and digestive efficiency.

https://doi.org/10.1371/journal.pone.0135488.t003

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Fig 1. Multiple correspondence analysis of digestive microbiota in cecal contents and digestive efficiency traits.

AMEn: metabolisable energy corrected to zero nitrogen retention; CDUDM, CDUL, CDUP, CDUS: coefficients of fecal digestive use of dry matter, lipids, proteins, and starch; Lacto.: Lactobacillus count; L. saliv.: L. salivarius count in cecal content; L. crisp.: L. crispatus count in cecal content; C. lept.: C. leptum count in cecal content; C. cocco.: C. coccoides count in cecal content; H, M, L: third of individuals with the highest, medium, and lowest values within a trait. The size of the squares is proportional to the relative contribution of the category of active variables to the axes. The size of the circles is proportional to the cos² between the axes and the category of the illustrative variable.

https://doi.org/10.1371/journal.pone.0135488.g001

These associations between digestive efficiency and microbiota composition are consistent with literature reports. Indeed, it was shown in previous studies that E. coli and Lactobacillus were more frequent in the chicken ceca when birds were fed a wheat and barley diet, leading to a lower AMEn, than when they were fed a corn diet leading to a higher AMEn [43,44].

The digestive microbiota in the ceca is dependent on the characteristics of this biotope such as the nature and quantity of available substrate and transit time. It has previously been reported that the transit time is much shorter in D- than in D+ birds [45]. This implies that D+ birds have more time to absorb nutrients in the intestine, which promotes the development of bacterial species able to survive in harsh conditions, whereas the development of bacteria able to proliferate fast is favored in D- birds with shorter transit times.

The quantity and nature of nutrients in the ceca indicate that cecal function is more developed in D+ than in D- birds, the former having heavier cecal weights and cecal content than the latter [45]. This may contribute to the better capacity of the FCR_L birds in our study to extract energy and nitrogen from the diet, which thus leads to a higher AMEn and a lower quantity of nitrogen available to be used by bacteria in uric acid fermentation. Moreover, the relationship between cecal digestive microbiota and feed efficiency may be explained in part by the large number of bacteria present in this digestive segment and by their high metabolic capacity to produce volatile fatty acids by fermentation, which provides an additional energy source for birds [46].

The development of cecal bacteria is also influenced by the nature of the substrates that come both from the end of the small intestine and from the retro-peristaltic flux from the cloaca, the latter including urinary products. Undigested compounds from the ileum in our FCR_H and FCR_L groups were probably different as a consequence of their difference in the digestion of proteins, starch and lipids. Moreover, urinary compounds mainly composed of uric acid may also differ between the two groups [47]. Cecal contents may thus be relatively rich in protein and uric acids in FCR_L birds, and conversely relatively rich in starch in FCR_H birds. These different conditions may be responsible for the different bacterial development, for example the positive relationship between digestive efficiency and the C. coccoides to Lactobacillus ratio might partly be due to the ability of Clostridium spp. to utilize uric acid [48].

On the other hand, the relationship between digestive efficiency and microbiota composition may be due to the effects of microbiota on its host. The cecal digestive microbiota may have an effect on digestive physiology via its production of metabolites such as butyrate, secretion of neuroendocrine hormones and its interactions with the nervous system that innervates the gastrointestinal tract or via neuropeptides [4952]. Another contribution of cecal microbiota to fecal digestibility is its contribution to the fecal biomass. However, in the case of the D+ and D- lines, this contribution is probably minor as D+ animals with the highest fecal digestibility appeared to have more developed cecal microbiota [2].

Heritability

Heritability of microbiota composition reflects the proportion of this composition that can be attributed to the genetics of the host. As they were calculated on a low number of animals, the genetic parameters presented in Table 4 have to be taken as indicative values. Heritability estimates were generally low. However, among the bacterial groups or ratios that differed between FCR_H and FCR_L birds, the ratios of L. crispatus, C. leptum and C. coccoides to E. coli presented significant heritability estimates (between 0.16 and 0.24). E. coli and the ratio of E. coli to Lactobacillus presented similar heritability estimates but, due to higher standard error estimates, they were not significant. By comparing the composition of fecal microbiota in lines selected for high or low body weight, Zhao et al. [4] and Meng et al. [10] also found that microbiota differed between the two lines and that several species presented heritability. As in our study, they found that L. salivarius was not heritable.

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Table 4. Heritability (± standard errors) of microbiota composition and genetic correlations (± standard errors) between microbiota composition and digestive efficiency.

https://doi.org/10.1371/journal.pone.0135488.t004

The presence of significant heritability for some microbiota components and of genetic correlations between microbiota and digestive efficiency is consistent with the expected effects of host genetics on microbiota. By selecting more and less efficient birds, we first changed the quantity of undigested nutrients in the ceca that are used as growth substrates by bacteria. Moreover, several earlier studies on these lines showed that the biotope had been considerably modified in the small intestine, which also affected the conditions of microbiota development. Indeed, we showed that acid secretions and bile acid secretions [17], gut motility [45] and the structure of the intestinal epithelium [18,53] were very different between the two lines. All these parameters may explain an indirect effect of host genotype on microbiota through modification of the microbiota biotope [4]. Finally, the presence of several QTLs for digestive and feed efficiency which are located on GGA16, that also carries the major histocompatibility complex, highlights the fact that the birds’ immune system had probably been affected during the selection process, which would also directly affect the relationship between host and bacteria.

Both positive and negative genetic correlations were found between digestive efficiency and microbiota composition. As for phenotypic correlations, we found negative genetic correlations between Lactobacillus and starch and protein digestibility, and between E. coli and digestibility of dry matter and lipids. The negative correlation between Lactobacillus and starch digestibility may be due to the fact that the ceca of FCR_H group contain more undigested starch that can be used by lactobacillus in fermentation in the ceca, thus favoring its development [54]. Similarly, the negative correlation between digestibility of lipids and E. coli, L. crispatus/E. coli or C. coccoides/E. coli may be due to the negative effect of E. coli on lipid digestibility in the small intestine. A greater amount of E. coli decreases the digestibility of lipids and thus increases the quantity of undigested lipids in the ceca, that can in turn be used by E. coli for its growth in this segment [55,56]. This negative relationship between fecal lipid digestibility and the presence of E. coli in the ceca has already been reported by Rodriguez et al. [43]. The negative correlation may also originate from the effects of bacteria on the host, through the production of unfavorable metabolites, their secretion of neuroendocrine hormones and their interactions with the enteric nervous system [52].

However, not all relationships between microbiota and digestive efficiency were negative. Indeed, several positive correlations were observed between AMEn and C. coccoides and the C. coccoides to Lactobacillus ratio. This may be explained by the caloric extraction from undigestible polysaccharides by this Clostridium spp. which may increase energy available to the host [6]. The positive genetic correlations between protein digestibility and the ratio of C. leptum to lactobacillus (and C. coccoides to Lactobacillus although not significant) could be explained by the ability of Clostridium spp. to metabolize amino acids [57] and to degrade uric acid into ammonia that can be used by the host to synthesize amino acids [58]. Clostridium spp. and Lactobacillus may also be involved in regulation of the digestive physiology via the production of beneficial active biological compounds [59,60].

QTL for microbiota

We detected a total of 14 QTLs that influence the composition of the microbiota (Table 5). They were all significant only on the chromosome-wide scale. However, the QTL for C. leptum count on chromosome 6, which was significant in all six sire families, was quite close to the genome-wide significance level (P = 0.09). For 10 of these 14 QTLs, C. leptum and lactobacillus were involved. This higher frequency of QTL in these two groups is consistent with their frequency in cecal microbiota in this study [61]. Moreover, Clostridium spp. are able to degrade the non-starch polysaccharides of wheat [62,63], and it is thus possible that our selection on digestive efficiency with Rialto wheat specifically affected these bacteria. The effects of these QTLs were quite high, which is partly due to the fact that we used birds with extreme FCR values for detection.

Several of these QTLs co-localized with QTLs for other traits detected in the same experiment. First, co-localizations were found with the anatomy of the gut, such as intestine and particularly ileum weight and length (with QTLs 2, 7, 13 and 14 [17,64]) and proventriculus weight (with QTLs 4 and 6, [17]). Secondly, QTLs 6 and 8 co-localized with the QTL for breast yield [64]. Finally, QTLs for L. crispatus and L. crispatus to C. coccoides ratio co-localized with a QTL for feeding behavior (unpublished data).

The QTL for C. leptum count on chromosome 6 was close to genome-wide significance, and we therefore looked for potential candidate genes in this region. Relevant genes present in this region are mainly linked to the inflammatory response of the intestine, which is consistent with previous results in mammals showing the contribution of host genetics to the digestive microbiota community [15]. Indeed, most of the genes shown to have an impact on the composition of gut microbiota are components of the immune system. In this study, candidate genes appeared in the Toll-Like Receptor (TLR) and the transforming growth factor β (TGF-β) pathways. This is consistent with the role of intestinal bacteria in the stimulation of immune system development and induction of a continuous anti-inflammatory response in the host [61,65].

The TLR receptors present in the intestinal epithelium are able to recognize molecular patterns present in microbial cell walls and are the first line of defense in the immune response of the host to microbes. They have also been shown to be involved in the control of microbiota in mice [66]. They activate transcription factor NFκB, that in turn regulates the expression of genes of both innate and adaptive immunity, including inflammatory cytokines [67,68]. Most commensal bacteria, including C. leptum, are able to limit the immuno-regulatory NFkB pathway by their production of butyrate [6,69]. The genes linked to NFκB in our QTL zone were i) DMTB1 which activates NFκB and has been shown to limit intracellular invasion by Salmonella enterica [70], ii) C6H10ORF46, the neddylation of which can be reduced by bacteria, which in turn blocks the NFκB pathway [69], iii) GRK5 the expression of which inhibits the expression of NFκB [68], and iv) PRDX3 which interacts with MAP3KIA to regulate expression of NFκB [71].

TGF-β is a cytokine involved in the secretion of interleukin 17 (IL-17) by Th17 and Treg cells which contribute to the inflammatory process in the intestine [72], and its production is stimulated by the presence of C. leptum [6,73]. Within the TGF-β pathway, our QTL zone contained i) HTRA1 which inhibits signals from TGF-β family members [74], ii) RAB11FIP2 which is involved in the limitation of inflammatory responses to commensal bacteria through its action of compartmentalization of Toll-Like receptors [75], iii) ADAM12 which limits the production of IL-17 by Th17 cells [72], and iv) C10ORF88 which is also involved in the secretion of IL-17 by Th17 cells through its effect on vitamin D metabolism [76]. Vitamin D also regulates the innate immune response to microbiota [77].

In addition, the PSTK gene within the QTL for C. leptum (GGA6), which contributes to the selenocysteine secretion pathway, is used by bacteria to fix selenium, a trace element that has an anti-apoptotic function in the colonic crypts and contributes to the integrity of the intestinal mucosa [78]. This gene is also involved in the regulation of calcium metabolism which is required for normal early muscle development, which is also consistent with the fact that this QTL co-localizes with a QTL for breast yield found in the same experiment [64].

Finally, our QTL region for C. leptum on GGA6 also includes genes involved in the regulation of intestinal motility such as VMAT2 and BAG3, which to our knowledge has not been reported before. BAG3 is expressed in the muscular layer of the intestine and has a probable function in the regulation of motility of the intestine [79]. VMAT2 is a transporter of catecholamines such as dopamine and norepinephrine that affect both immunity and motility of the intestine and have been shown to be stimulated by the presence of Clostridium spp., including C. leptum [6]. This is also consistent with our previous results on the D+ and D- lines showing wide differences in transit times between the two lines, particularly in the ceca [45], and this factor may be involved in modification of digestive microbiota (including clostridia), as has been reported in the mouse [80].

Conclusion

Our study clearly demonstrates the existence of genetic control by the host of its microbiota, and the link between host genetics, microbiota composition and feed and digestive efficiency. Lactobacillus, C. leptum and E. coli were identified as the most important factors in this interaction. Moreover, the equilibrium between the different categories of bacteria is also an important element of this interaction. The QTL for C. leptum on chromosome 6 indicates that the inflammatory response of the gut and the motility of the digestive tract are the most probable processes involved. Transcriptomic analyses are underway to confirm the involvement of these candidate genes in determination of microbiota.

Acknowledgments

The authors thank M. Leconte (INRA, Nouzilly, France) for assistance provided during sample collection, and her contribution to laboratory analyses.

Author Contributions

Conceived and designed the experiments: SMG NR IG NS MC ELBD AN. Performed the experiments: SMG NR IG NS MC ELBD BKI AN MYB CCD. Analyzed the data: SMG. Wrote the paper: SMG NR IG NS MC ELBD BKI AN MYB CCD. Microbiota determination: IG BKI. Genotyping: MYB CCD.

References

  1. 1. Mignon-Grasteau S, Muley N, Bastianelli D, Gomez J, Péron A, Sellier N, et al. Heritability of Digestibilities and Divergent Selection for Digestion Ability in Growing Chicks Fed a Wheat Diet. Poult Sci. 2004, 83: 860–867. pmid:15206610
  2. 2. Gabriel I, Guardia S, Konsak B, Leconte M, Rideaud P, Moreau-Vauzelle C, et al. Comparaison du microbiote bactérien digestif de poulets sélectionnés sur leur énergie métabolisable. In: ITAVI, editor; Proceedings of the 9th Journées de la Recherche Avicole; 2011 Mar 29–30; Tours, France. France: WPSA, 2011. p. 760–764. French.
  3. 3. Guardia S, Konsak B, Combes S, Levenez F, Cauquil L, Guillot JF, et al. Effects of stocking density on the growth performance and digestive microbiota of broiler chickens. Poult Sci. 2011, 90: 1878–1889. pmid:21844251
  4. 4. Zhao L, Wang G, Siegel P, He C, Wang H, Zhao W, et al. Quantitative genetic background of the host influences gut microbiomes in chickens. Sci Rep. 2013, 3: 1–6.
  5. 5. Kohl KD. Diversity and function of the avian gut microbiota. J Comp Physiol B. 2012, 182: 591–602. pmid:22246239
  6. 6. Lopetuso LR, Scaldaferri F, Petito V, Gasbarrini A. Commensal Clostridia: leading players in the maintenance of gut homeostasis. Gut Pathogens. 2013, 5: 23. pmid:23941657
  7. 7. Gabriel I, Leconte M, Guillon J, Rideaud P, Moreau-Vauzelle C, Dupont C. Variabilité individuelle de la flore digestive du poulet observée par empreinte moléculaire. In: ITAVI, editor. Proceedings of the 7th Journées de la Recherche Avicole; 2007 Mar 29–30; Tours, France. France: WPSA; 2007. p. 224–228.
  8. 8. Guardia S, Recoquillay F, Juin H, Lessire M, Leconte M, Rideaud P, et al. Variabilité inter-individuelle de la flore digestive du poilet de chair analysée par empreinte moléculaire: conséquences pour l'étude de l'effet de ses facteurs de variation In: ITAVI, editor. Proceedings of the 8th Journées de la Recherche Avicole; 2009 Mar 25–26; Saint-Malo, France. France: WPSA; 2009. p. 135–139. French.
  9. 9. Stanley D, Geier MS, Denman SE, Haring VR, Crowley TM, Hughes RJ, et al. Identification of chicken intestinal microbiota correlated with the efficiency of energy extraction from feed. Vet Microbiol. 2013, 164: 85–92. pmid:23434185
  10. 10. Meng H, Zhang Y, Zhao L, Zhao W, He C, Honaker CF, et al. Body weight selection affect quantitative genetic correlated responses in gut microbiota. Plos One. 2014, 9: e89862. pmid:24608294
  11. 11. Stanley D, Denman SE, Hughes RJ, Geier MS, Crowley TM, Chen H, et al. Intestinal microbiota associated with differential feed conversion efficiency in chickens. Appl Microbiol Biotechnol. 2012, 96: 1361–1369. pmid:22249719
  12. 12. Lumpkins BS, Batal AB, Lee MD. Evaluation of the bacterial community and intestinal development of different genetic lines of chickens. Poult Sci. 2010, 89: 1614–1621. pmid:20634515
  13. 13. Singh KM, Shah T, Deshpande S, Jakhesara SJ, Koringa PG, Rank DN, et al. High through put 16S rRNA gene-based pyrosequencing analysis of the fecal microbiota of high FCR and low FCR broiler growers. Mol Biol Rep. 2012, 39: 10595–10602. pmid:23053958
  14. 14. Sekelja M, Rud I, Knutsen SH, Denstadli V, Westereng B, Naes T, et al. Abrupt temporal fluctuations in the chicken fecal microbiota are explained by its gastrointestinal origin. Appl Env Microbiol. 2012, 78: 2941–2948.
  15. 15. Spor A, Koren O, Ley R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat Rev Microbiol. 2011, 9: 279–290. pmid:21407244
  16. 16. Torok VA, Hughes RJ, Mikkelsen LL, Perez-Maldonado R, Balding K, Macalpine R, et al. Identification and characterization of potential performance-related gut microbiotas in broiler chickens across various feeding trials. Appl Env Microbiol. 2011, 77: 5868–5878.
  17. 17. Tran TS, Narcy A, Carré B, Gabriel I, Rideau N, Gilbert H, et al. Detection of QTL controlling digestive efficiency and anatomy of the digestive tract in chicken fed a wheat-based diet. Genet Sel Evol. 2014, 46: 25. pmid:24708200
  18. 18. De Verdal H, Mignon-Grasteau S, Jeulin C, Le Bihan-Duval E, Leconte M, Mallet S, et al. Digestive tract measurements and histological adaptation in broiler lines divergently selected for digestive efficiency. Poult Sci. 2010, 89: 1955–1961. pmid:20709981
  19. 19. Mignon-Grasteau S, Juin H, Sellier N, Bastianelli D, Gomez J, Carré B (2010) Genetic Parameters of Wheat- or Corn-based Diets in Chickens. In: Erhardt G, editor. Proceedings of the 9th World Congress Applied to Livestock Production; 2010 Aug 1–6;. Leipzig, Germany. Germany: German Society for Animal Science; 2010. 0249.
  20. 20. Administration UFaD. Environmental assessment for ClinacoxTM type. A medicated article for chicken and turkey (Diclazuril). Silver Springs (MD): Administration UFaD; 1996. pp. 64.
  21. 21. Sauvant D, Pérez JM, Tran G, editors. Tables de composition et valeur nutritionnelle des matières premières destinées aux animaux d'élevage. 2nd edition. Paris: INRA-AFZ, 2004. French.
  22. 22. Bastianelli D, Bonnal L, Juin H, Mignon-Grasteau S, Davrieux F, Carré B (2010) Prediction of the chemical composition of poultry excreta by near infrared spectroscopy. J NIRS. 2010, 18: 69–77.
  23. 23. Yu Z, Morrison M. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques. 2004, 36: 808–812. pmid:15152600
  24. 24. Walter J, Hertel C, Tannock GW, Lis CM, Munro K, Hammes WP. Detection of Lactobacillus, Pediococcus, Leuconostoc, and Weissella species in human feces by using group-specific PCR primers and denaturing gradient gel electrophoresis. Appl Env Microbiol. 2001, 67: 2578–2585.
  25. 25. Heilig HG, Zoetendal EG, Vaughan EE, Marteau P, Akkermans AD, de Vos WM. Molecular diversity of Lactobacillus spp. and other lactic acid bacteria in the human intestine as determined by specific amplification of 16S ribosomal DNA. Appl Env Microbiol. 2002, 68: 114–123.
  26. 26. De Backer E, Verhelst R, Verstraelen H, Alqumber MA, Burton JP, Tagg JR, et al. Quantitative determination by real-time PCR of four vaginal Lactobacillus species, Gardnerella vaginalis and Atopobium vaginae indicates an inverse relationship between L. gasseri and L. iners. BMC Microbiol. 2007, 7: 115. pmid:18093311
  27. 27. Harrow SA, Ravindran V, Butler RC, Marshall JW, Tannock GW. Real-time quantitative PCR measurement of ileal Lactobacillus salivarius populations from broiler chickens to determine the influence of farming practices. Appl Env Microbiol. 2007, 73: 7123–7127.
  28. 28. Matsuki T, Watanabe K, Fujimoto J, Takada T, Tanaka R. Use of 16S rRNA gene-targeted group-specific primers for real-time PCR analysis of predominant bacteria in human feces. Appl Env Microbiol. 2004, 70: 7220–7228.
  29. 29. Matsuki T, Watanabe K, Fujimoto J, Miyamoto Y, Takada T, Matsumoto K, et al. Development of 16S rRNA-gene-targeted group-specific primers for the detection and identification of predominant bacteria in human feces. Appl Env Microbiol. 2002, 68: 5445–5451.
  30. 30. Huijsdens XW, Linskens RK, Mak M, Meuwissen SG, Vandenbroucke-Grauls CM, Savelkoul PH. Quantification of bacteria adherent to gastrointestinal mucosa by real-time PCR. J Clin Microbiol. 2002, 40: 4423–4427. pmid:12454130
  31. 31. Monteils V, Cauquil L, Combes S, Godon JJ, Gidenne T. Potential core species and satellite species in the bacterial community within the rabbit caecum. FEMS Microbiol Ecol. 2008, 66: 620–629. pmid:19049656
  32. 32. Tichopad A, Dilger M, Schwarz G, Pfaffl MW. Standardized determination of real-time PCR efficiency from a single reaction set-up. Nucleic Acids Res. 2003, 31: e122. pmid:14530455
  33. 33. Tran TS, Narcy A, Carré B, Gabriel I, Rideau N, Gilbert H, et al. Detection of QTL controlling digestive efficiency and anatomy of the digestive tract in chicken fed a wheat-based diet. Genet Sel Evol. 2014, 46.
  34. 34. Groenen MAM, Wahlberg P, Foglio M, Cheng HH, Megens HJ, Crooijmans RPMA, et al. A high-density SNP-based linkage map of the chicken genome reveals sequence features correlated with recombination rate. Gen Res. 2009, 19: 510–519.
  35. 35. Kovac M, Groeneveld E, Garcia-Cortez A. VCE 6 User's Manual. Version 6.0.2. 2008.
  36. 36. Gilbert H, Le Roy P, Moreno C, Robelin D, Elsen JM (2008) QTLMAP, a software for QTL detection in outbred populations. Ann Hum Genet. 2008, 72: 694–694.
  37. 37. Elsen JM, Mangin B, Goffinet B, Boichard D, Le Roy P. Alternative models for QTL detection in livestock. I. General introduction. Genet Sel Evol. 1999, 31: 213–224.
  38. 38. Le Roy P, Elsen JM, Boichard D, Mangin M, Bidanel JP, Goffinet B. An algorithm for QTL detection in mixture of full and half sib families. In: University of New England, CSIRO, editors. Proceedings of the 6th World Congress of Genetics Applied to Livestock; 1998 Jan 11–16; Armidale, Australia. Australia: University of New England; 1998. p. 257–260.
  39. 39. Lander ES, Botstein D. Mapping Mendelian Factors Underlying Quantitative Traits Using Rflp Linkage Maps. Genetics. 1989, 121: 185–199. pmid:2563713
  40. 40. Tilquin P, Barrow PA, Marly J, Pitel F, Plisson-Petit F, Velge P, et al. A genome scan for quantitative trait loci affecting the Salmonella carrier-state in the chicken. Genet Sel Evol. 2005, 37: 539–561. pmid:16093014
  41. 41. Rougière N, Gomez J, Mignon-Grasteau S, Carré B. Effects of diet particle size on digestive parameters in D(+) and D(-) genetic chicken lines selected for divergent digestion efficiency. Poult Sci. 2009, 88: 1206–1215. pmid:19439632
  42. 42. Mignon-Grasteau S, Juin H, Sellier N, Bastianelli D, Gomez J, Carré B. Genetic Parameters of Wheat- or Corn-based Diets in Chickens. In: Erhardt G, editor. Proceedings of the 9th World Congress Applied to Livestock Production; 2010 Aug 1–6;. Leipzig, Germany. Germany: German Society for Animal Science; 2010. 0249.
  43. 43. Rodriguez ML, Rebolé A, Velasco S, Ortiz LT, Trevino J, Alzueta C. Wheat- and barley-based diets with or without additives influence broiler chicken performance, nutrient digestibility and intestinal microflora. J Sci Food Agric. 2012, 92: 184–190. pmid:21780133
  44. 44. Mathlouti N, Mallet S, Saulnier L, Quemener B, Larbier M. Effects of xylanase and b-xylanase addition on performance, nutrient digestibility, and physico-chemical conditions in the small intestine contents and caecal microflora of broiler chickens fed a wheat and barley-based diet. Anim Res. 2002, 51: 395–406.
  45. 45. Rougière N, Malbert CH, Rideau N, Cognié J, Carré B. Comparison of gizzard activity between chickens from genetic D+ and D- lines selected for divergent digestion efficiency. Poult Sci. 2002, 91: 460–467.
  46. 46. Jozefiak D, Rutkowski A, Martin SA. Carbohydrate fermentation in the avian ceca: a review. Anim Feed Sci Technol. 2004, 113: 1–15.
  47. 47. De Verdal H, Narcy A, Bastianelli D, Chapuis H, Même N, Urvoix S, et al. Improving the efficiency of feed utilization in poultry by selection. 1. Genetic parameters of anatomy of the gastro-intestinal tract and digestive efficiency. BMC Genet. 2011, 12: 9. pmid:21733156
  48. 48. Mead GC. Microbes of the avian cecum: types present and substrates utilized. J Exp Zool Supplement. 1989, 3: 48–54.
  49. 49. Tappenden KA, McBurney MI. Systemic short-chain fatty acids rapidly alter gastrointestinal structure, function, and expression of early response genes. Dig Dis Sci. 1998, 43: 1526–1536. pmid:9690391
  50. 50. Mroz Z, Koopmans SJ, Bannink A, Partanen K, Krasucki W, Overland M, et al. Carboxylic acids as bioregulators and gut growth promoters in nonruminants. In: Mosenthin R, Zentek J, Zebrowska T, editors. Biology of nutrition in animals. Edinburgh: Elsevier; 2006. pp. 81–133.
  51. 51. Holzer P, Reichmann F, Farzi A. Neuropeptide Y, peptide YY and pancreatic polypeptide in the gut-brain axis. Neuropeptides. 2012, 46: 261–274. pmid:22979996
  52. 52. Lyte M. Microbial endocrinology as a basis for improved L-DOPA bioavailability in Parkinson's patients treated for Helicobacter pylori. Med Hyp. 2010, 74: 895–897.
  53. 53. Rideau N, Godet E, Combémorel C, Chaudeau M, Carré B, Mignon-Grasteau S. The gastric isthmus from D+ and D- broiler lines divergently selected for digestion efficiency shows histological and morphological differences. Poult Sci. 2014, 93: 1245–1250. pmid:24795318
  54. 54. Szylit O, Champ M, Ait-Abdelkader N, Raibaud P.Role of five Lactobacillus strains on carbohydrate degradation in monoxenic chickens. Reprod Nut Dev. 2014, 20: 1701–1706.
  55. 55. Nantel G, Proulx P. Lipase activity in E. coli. Biochim Biophys Acta. 1973, 316: 156–161. pmid:4582442
  56. 56. Campbell JW, Morgan-Kiss RM, Cronan JE Jr.. A new Escherichia coli metabolic competency: growth on fatty acids by a novel anaerobic β-oxidation pathway. Mol Biol. 2003, 47: 793–805.
  57. 57. Elsden SR, Hilton MG, Waller JM. The end products of the metabolism of aromatic amino acids by Clostridia. Archiv Microbiol. 1976, 107: 283–288.
  58. 58. Braun EJ, Campbell CE. Uric acid decomposition in the lower gastrointestinal tract. J Exp Zool Supplement. 1989, 3: 70–74.
  59. 59. Asano Y, Hiramoto T, Nishino R, Aiba Y, Kimura T, Yoshihara K, et al. Critical role of gut microbiota in the production of biologically active, free catecholamines in the gut lumen of mice. Am J Physiol Gastroint Liver Physiol. 2012, 303: G1288–1295.
  60. 60. Hemarajata P, Versalovic J. Effects of probiotics on gut microbiota: mechanisms of intestinal immunomodulation and neuromodulation. Ther Adv Gastroenterol. 2013, 6: 39–51.
  61. 61. Stanley D, Hughes RJ, Moore RJ. Microbiota of the chicken gastrointestinal tract: influence on health, productivity and disease. Appl Microbiol Biotechnol. 2014, 98: 4301–4310. pmid:24643736
  62. 62. Singh KM, Shah T, Deshpande S, Jakhesara SJ, Koringa PG, Rank DN, et al. High throughput 16S rRNA gene-based pyrosequencing analysis of the fecal microbiota of high FCR and low FCR broiler growers. Mol Biol Rep. 2012, 39: 10595–10602. pmid:23053958
  63. 63. Cummings JH, Macfarlane GT. The control and consequences of bacterial fermentation in the human colon. J Appl Bacteriol. 1991, 70: 443–459. pmid:1938669
  64. 64. Mignon-Grasteau S, Rideau N, Gabriel I, Chantry-Darmon C, Boscher MY, Sellier N, et al. Detection of QTL controlling feed efficiency and excretion in chicken fed a wheat-based diet. Genet Sel Evol. 2015, in press.
  65. 65. Kohl KD. Diversity and function of the avian gut microbiota. J Comp Physiol B. 2012, 182: 591–602. pmid:22246239
  66. 66. Vijay-Kumar M, Carvalho FA, Aitken JD, Fifadara NH, Gewirtz AT. TLR5 or NLRC4 is necessary and sufficient for promotion of humoral immunity by flagellin. Eur J Immunol. 1991, 40: 3528–3534.
  67. 67. Jacquier V. Approches génomiques des interactions entre l'implantation du microbiote chez le jeune lapin et la maturation du système immunitaire. PhD Thesis, Institut National Polytechnique de Toulouse. 2014.
  68. 68. Sorriento D, Ciccarelli M, Santulli G, Campanile A, Altobelli GG, Cimini V, et al. The G-protein-coupled receptor kinase 5 inhibits NFkappaB transcriptional activity by inducing nuclear accumulation of IkappaB alpha. Proc Nat Acad Sci USA. 2008, 105: 17818–17823. pmid:19008357
  69. 69. Kumar A, Wu H, Collier-Hyams LS, Hansen JM, Li T, Yamoah K, et al. Commensal bacteria modulate cullin-dependent signaling via generation of reactive oxygen species. EMBO J. 2007, 26: 4457–4466. pmid:17914462
  70. 70. Rosenstiel P, Sina C, End C, Renner M, Lyer S, Till A, et al. Regulation of DMBT1 via NOD2 and TLR4 in intestinal epithelial cells modulates bacterial recognition and invasion. J Immunol. 2007, 178: 8203–8211. pmid:17548659
  71. 71. Hiroi M, Nagahara Y, Miyauchi R, Misaki Y, Goda T, Kasezawa N, et al. The combination of genetic variations in the PRDX3 gene and dietary fat intake contribute to obesity risk. Obesity (Silver Springs). 2011, 19: 882–887.
  72. 72. Zhou AX, El Hed A, Mercer F, Kozhaya L, Unutmaz D. The metalloprotease ADAM12 regulates the effector function of human Th17 cells. Plos One. 2013, 8: e81146. pmid:24363794
  73. 73. Li YN, Huand F, Cheng HJ, Li SY, Liu L, Wang LY. Intestine-derived Clostridium leptum induces murine tolerogenic dendritic cells and regulatory T cells in vitro. Hum Immunol. 2014, 75: 1232–1238. pmid:25300998
  74. 74. Kramer J, Malek M, Lamont SJ. Association of twelve candidate gene polymorphisms and response to challenge with Salmonella enteritidis in poultry. Anim Genet. 2003, 34: 339–348. pmid:14510669
  75. 75. Yu S, Nie Y, Knowles B, Sakamori R, Stypulkowski E, Patel C, et al. TLR sorting by Rab11 endosomes maintains intestinal epithelial-microbial homeostasis. EMBO J. 2014, 33: 1882–1895. pmid:25063677
  76. 76. Madsen K. Western diets as a trigger for inflammatory bowel disease. In: Gussler J, Graham MA, editors. Proceedings of the 111th Abbott nutrition research conference, Inflammation and nutrition in chronic disease; 2010 Jun 29-Jul 1; Columbus, USA. USA: Abbott Nutrition; 2011. p. 1–11.
  77. 77. Cantorna MT, McDaniel K, Bora S, Chen J, James J. Vitamin D, immune regulation, the microbiota, and inflammatory bowel disease. Exp Biol Med (Maywood). 2014, 239: 1524–1530.
  78. 78. Rayman MP. Selenium and human health. Lancet. 2012, 379: 1256–1268. pmid:22381456
  79. 79. Lee YD, Yoon JS, Yoon HH, Youn HJ, Kim J, Lee JH. Expression of Bis in the mouse gastrointestinal system. Anat Cell Biol. 2012, 45: 160–169. pmid:23094204
  80. 80. Kashyap PC, Marcobal A, Ursell LK, Larauche M, Duboc H, Earle KA, et al. Complex interactions among diet, gastrointestinal transit, and gut microbiota in humanized mice. Gastroenterol. 2013, 144: 967–977.