The gut microbiota has an important role in animal health and performance, but its contribution is difficult to determine, in particular given the effects of host genetic factors. Here, whole-genome sequencing of the hosts and 16S rRNA gene sequencing of the microbiota were performed to separate the effects between host genetics and the microbiota in the duodenum, jejunum, ileum, caecum and faeces on fat deposition in 206 yellow broilers reared under identical conditions. Despite the notable spatial variation in the diversity, composition and potential function of the gut microbiota, host genetics exerted limited effects on the gut microbial community. The duodenal and caecal microbiota made greater contributions to fat deposition and could separately account for 24% and 21% of the variance in the abdominal fat mass after correcting for host genetic effects. We further identified two caecal microbial taxa, Methanobrevibacter and Mucispirillum schaedleri, which were significantly correlated with fat deposition. Chickens with a lower Methanobrevibacter abundance had significantly lower abdominal fat content than those with a higher abundance of Methanobrevibacter (35.51 vs. 55.59 g), and the body weights of these chickens did not notably differ. Chickens with a higher M. schaedleri abundance exhibited lower abdominal fat accumulation (39.88 vs. 55.06 g) and body weight (2.23 vs. 2.41 kg) than those with a lower abundance of this species. These findings may aid the development of strategies for altering the gut microbiota to control fat deposition during broiler production.

Despite the convenience and non-invasiveness of fecal sampling, the fecal microbiota does not fully represent that of the gastrointestinal (GI) tract, and the efficacy of fecal sampling to accurately represent the gut microbiota in birds is poorly understood. In this study, we aim to identify the efficacy of feces as a gut proxy in birds using chickens as a model. We collected 1,026 samples from 206 chickens, including duodenum, jejunum, ileum, cecum, and feces samples, for 16S rRNA amplicon sequencing analyses. In this study, the efficacy of feces as a gut proxy was partitioned to microbial community membership and community structure. Most taxa in the small intestine (84.11–87.28%) and ceca (99.39%) could be identified in feces. Microbial community membership was reflected with a gut anatomic feature, but community structure was not. Excluding shared microbes, the small intestine and ceca contributed 34.12 and 5.83% of the total fecal members, respectively. The composition of Firmicutes members in the small intestine and that of Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria members in the ceca could be well mirrored by the observations in fecal samples (ρ = 0.54–0.71 and 0.71–0.78, respectively, P < 0.001). However, there were few significant correlations for each genus between feces and each of the four gut segments, and these correlations were not high (ρ = −0.2–0.4, P < 0.05) for most genera. Our results suggest that fecal microbial community has a good potential to identify most taxa in the chicken gut and could moderately mirror the microbial structure in the intestine at the microbial population level with phylum specificity. However, it should be interpreted with caution by using feces as a proxy to study associations for microbial structure at individual microorganism level.

BackgroundResidual feed intake (RFI) is a powerful indicator for energy utilization efficiency and responds to selection. Low RFI selection enables a reduction in feed intake without affecting growth performance. However, the effective variants or major genes dedicated to phenotypic differences in RFI in quality chickens are unclear. Therefore, a genome-wide association study (GWAS) and RNA sequencing were performed on RFI to identify genetic variants and potential candidate genes associated with energy improvement.ResultsA lower average daily feed intake was found in low-RFI birds compared to high-RFI birds. The heritability of RFI measured from 44 to 83 d of age was 0.35. GWAS showed that 32 of the significant single nucleotide polymorphisms (SNPs) associated with the RFI (P < 10−4) accounted for 53.01 % of the additive genetic variance. More than half of the effective SNPs were located in a 1 Mb region (16.3–17.3 Mb) of chicken (Gallus gallus) chromosome (GGA) 12. Thus, focusing on this region should enable a deeper understanding of energy utilization. RNA sequencing was performed to profile the liver transcriptomes of four male chickens selected from the high and low tails of the RFI. One hundred and sixteen unique genes were identified as differentially expressed genes (DEGs). Some of these genes were relevant to appetite, cell activities, and fat metabolism, such as CCKAR, HSP90B1, and PCK1. Some potential genes within the 500 Kb flanking region of the significant RFI-related SNPs detected in GWAS (i.e., MGP, HIST1H110, HIST1H2A4L3, OC3, NR0B2, PER2, ST6GALNAC2, and G0S2) were also identified as DEGs in chickens with divergent RFIs.ConclusionsThe GWAS findings showed that the 1 Mb narrow region of GGA12 should be important because it contained genes involved in energy-consuming processes, such as lipogenesis, social behavior, and immunity. Similar results were obtained in the transcriptome sequencing experiments. In general, low-RFI birds seemed to optimize energy employment by reducing energy expenditure in cell activities, immune responses, and physical activity compared to eating.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2861-5) contains supplementary material, which is available to authorized users.

Feed consumption accounts for the major cost of broiler production. Improving the efficiency of feed utilization is a primary goal in breeding strategies, although few studies have focused on slower growing broilers. Here, we recorded the feed intake (FI) during the fast-growing period (d 56 to 76) and measured the live weight, body measurements, carcass characteristics, and intramuscular fat (IMF) content of Chinese yellow broilers. Then, the residual feed intake (RFI) and feed conversion ratio (FCR) were calculated for each individual. Pair-wise phenotypic correlations were subsequently calculated between feed efficiency traits and others. Finally, we separately selected the more efficient individuals based on RFI and FCR values to evaluate the impacts on the traits of FI, growth, carcass characteristics, and meat quality. The results showed higher correlations between FCR and production traits than with RFI, while RFI showed a moderate and positive phenotypic correlation with abdominal fat. FCR was weakly correlated with FI and slightly positively correlated with IMF content. The correlation coefficient between RFI and FI was 0.62, and that between RFI and IMF content was close to zero. Without increasing FI, decreasing FCR could effectively enhance the growth rate and market weight with no adverse effect on meat quality. In contrast, by improving RFI, FI and abdominal fat mass were significantly reduced and thus increased the yield with no unfavorable effects on meat quality. In consideration of consumer preference and overall economical benefits, RFI is a more suitable index to improve feed efficiency in slower growing broilers.

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BackgroundThe feed conversion ratio (FCR) and residual feed intake (RFI) are common indexes in measuring feed efficiency for livestock. RFI is a feed intake adjusted for requirements for maintenance and production so these two traits are related. Similarly, FCR is related to feed intake and weight gain because it is their ratio. Cholecystokinin type A receptor (CCKAR) plays an important role in animal digestive process. We examined the interplay of these three parameters in a local Chinese chicken population.ResultsThe feed intake (FI) and body weights (BW) of 1,841 individuals were monitored on a daily basis from 56 to 105 d of age. There was a strong correlation between RFI and average daily feed intake (ADFI) and a negative correlation between the FCR and daily gain (rg = − 0.710). Furthermore, we identified 51 single nucleotide polymorphisms (SNPs) in the CCKAR and 4 of these resulted in amino acid mutations. The C334A mutation was specifically associated with FI and the expected feed intake (EFI) (P <  0.01) and significantly associated with the average daily gain (ADG) (P <  0.05). G1290A was significantly associated with FI and EFI (P < 0.05).ConclusionFCR is apply to weight selecting, and RFI is more appropriate if the breeding focus is feed intake. And C334A and G1290A of the CCKAR gene can be deemed as candidate markers for feed intake and weight gain.Electronic supplementary materialThe online version of this article (10.1186/s40104-018-0261-1) contains supplementary material, which is available to authorized users.

In China, the low egg production rate is a major challenge to Muscovy duck farmers. Hypothalamus and ovary play essential role in egg production of birds. However, there are little or no reports from these tissues to identify potential candidate genes responsible for egg production in White Muscovy ducks. A total of 1,537 laying ducks were raised; the egg production traits which include age at first egg (days), number of eggs at 300 d, and number of eggs at 59 wk were recorded. Moreover, 4 lowest ( LP ) and 4 highest producing ( HP ) were selected at 59 wk of age, respectively. To understand the mechanism of egg laying regulation, we sequenced the hypothalamus and ovary transcriptome profiles in LP and HP using RNA-Seq. The results showed that the number of eggs at 300 d and number of eggs at 59 wk in the HP were significantly more ( P < 0.001) than the LP ducks. In total, 106.98G clean bases were generated from 16 libraries with an average of 6.68G clean bases for each library. Further analysis showed 569 and 2,259 differentially expressed genes ( DEGs ) were identified in the hypothalamus and ovary between LP and HP, respectively. The KEGG pathway enrichment analysis revealed 114 and 139 pathways in the hypothalamus and ovary, respectively which includes Calcium signaling pathway, ECM-receptor interaction, Focal adhesion, MAPK signaling pathway, Apoptosis and Apelin signaling pathways that are involved in egg production. Based on the GO terms and KEGG pathways results, 10 potential candidate genes (P2RX1, LPAR2, ADORA1, FN1, AKT3, ADCY5, ADCY8, MAP3K8, PXN, and PTTG1) were identified to be responsible for egg production. Further, protein-protein interaction was analyzed to show the relationship between these candidate genes. Therefore, this study provides useful information on transcriptome of hypothalamus and ovary of LP and HP Muscovy ducks.

1Background: Despite the convenience and noninvasiveness of fecal sampling, the fecal 1 2 microbiota does not fully represent that of the gastrointestinal (GI) tract, and the efficacy of fecal 1 3 sampling to accurately represent the gut microbiota in birds is poorly understood. In this study, we 1 4 aim to identify the efficacy of feces as a gut proxy in birds using chickens as a model. We 1 5 collected 1,026 samples from 206 chickens, including duodenum, jejunum, ileum, cecum and 1 6 feces samples, for 16S rRNA amplicon sequencing analyses.