Relationship between feed efficiency and gut microbiota in laying chickens under contrasting feeding conditions.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
08 Apr 2024
08 Apr 2024
Historique:
received:
20
06
2023
accepted:
28
03
2024
medline:
9
4
2024
pubmed:
9
4
2024
entrez:
8
4
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38589474
doi: 10.1038/s41598-024-58374-3
pii: 10.1038/s41598-024-58374-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8210Informations de copyright
© 2024. The Author(s).
Références
OECD-FAO Agricultural Outlook (Edition 2021). OECD https://doi.org/10.1787/4bde2d83-en .
Noblet, J., Wu, S.-B. & Choct, M. Methodologies for energy evaluation of pig and poultry feeds: A review. Anim. Nutr. 8, 185–203 (2022).
pubmed: 34977388
doi: 10.1016/j.aninu.2021.06.015
Zerjal, T. et al. Assessment of trade-offs between feed efficiency, growth-related traits, and immune activity in experimental lines of layer chickens. Genet. Sel. Evol. 53, 44 (2021).
pubmed: 33957861
pmcid: 8101249
doi: 10.1186/s12711-021-00636-z
Marchesi, J. A. P. et al. Exploring the genetic architecture of feed efficiency traits in chickens. Sci. Rep. 11, 4622 (2021).
pubmed: 33633287
pmcid: 7907133
doi: 10.1038/s41598-021-84125-9
Gabarrou, J.-F., Geraert, P. A., Williams, J., Ruffier, L. & Rideau, N. Glucose–insulin relationships and thyroid status of cockerels selected for high or low residual food consumption. Br. J. Nutr. 83, 645–651 (2000).
pubmed: 10911773
doi: 10.1017/S0007114500000829
de Verdal, H. 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. 12, 59 (2011).
pubmed: 21733156
pmcid: 3141568
doi: 10.1186/1471-2156-12-59
Bindari, Y. R. & Gerber, P. F. Centennial review: Factors affecting the chicken gastrointestinal microbial composition and their association with gut health and productive performance. Poultry Sci. 101, 101612 (2022).
doi: 10.1016/j.psj.2021.101612
Oakley, B. B. et al. The chicken gastrointestinal microbiome. FEMS Microbiol. Lett. 360, 100–112 (2014).
pubmed: 25263745
doi: 10.1111/1574-6968.12608
Kraimi, N. et al. Influence of the microbiota-gut-brain axis on behavior and welfare in farm animals: A review. Physiol. Behav. 210, 112658 (2019).
pubmed: 31430443
doi: 10.1016/j.physbeh.2019.112658
Stanley, D. et al. Intestinal microbiota associated with differential feed conversion efficiency in chickens. Appl. Microbiol. Biotechnol. 96, 1361–1369 (2012).
pubmed: 22249719
doi: 10.1007/s00253-011-3847-5
Yan, W., Sun, C., Yuan, J. & Yang, N. Gut metagenomic analysis reveals prominent roles of Lactobacillus and cecal microbiota in chicken feed efficiency. Sci. Rep. 7, 45308 (2017).
pubmed: 28349946
pmcid: 7365323
doi: 10.1038/srep45308
Khan, S., Moore, R. J., Stanley, D. & Chousalkar, K. K. The gut microbiota of laying hens and its manipulation with prebiotics and probiotics to enhance gut health and food safety. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.00600-20 (2020).
doi: 10.1128/AEM.00600-20
pubmed: 33310718
pmcid: 7531966
Morrison, D. J. & Preston, T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes 7, 189–200 (2016).
pubmed: 26963409
pmcid: 4939913
doi: 10.1080/19490976.2015.1134082
Mahmood, T. & Guo, Y. Dietary fiber and chicken microbiome interaction: Where will it lead to?. Anim. Nutr. 6, 1–8 (2020).
pubmed: 32211522
doi: 10.1016/j.aninu.2019.11.004
Stanley, D. et al. Identification of chicken intestinal microbiota correlated with the efficiency of energy extraction from feed. Vet. Microbiol. 164, 85–92 (2013).
pubmed: 23434185
doi: 10.1016/j.vetmic.2013.01.030
Stanley, D., Hughes, R. J., Geier, M. S. & Moore, R. J. Bacteria within the gastrointestinal tract microbiota correlated with improved growth and feed conversion: Challenges presented for the identification of performance enhancing probiotic bacteria. Front. Microbiol. 7, 8. https://doi.org/10.3389/fmicb.2016.00187 (2016).
doi: 10.3389/fmicb.2016.00187
Siegerstetter, S.-C. et al. Intestinal microbiota profiles associated with low and high residual feed intake in chickens across two geographical locations. PLOS ONE 12, e0187766 (2017).
pubmed: 29141016
pmcid: 5687768
doi: 10.1371/journal.pone.0187766
Borey, M. et al. Broilers divergently selected for digestibility differ for their digestive microbial ecosystems. PLOS ONE 15, e0232418 (2020).
pubmed: 32421690
pmcid: 7233591
doi: 10.1371/journal.pone.0232418
Wen, C. et al. Joint contributions of the gut microbiota and host genetics to feed efficiency in chickens. Microbiome 9, 126 (2021).
pubmed: 34074340
pmcid: 8171024
doi: 10.1186/s40168-021-01040-x
Buzala, M. & Janicki, B. Review: Effects of different growth rates in broiler breeder and layer hens on some productive traits. Poultry Sci. 95, 2151–2159 (2016).
doi: 10.3382/ps/pew173
Kers, J. G. et al. Host and environmental factors affecting the intestinal microbiota in chickens. Front. Microbiol. 9, 322066 (2018).
doi: 10.3389/fmicb.2018.00235
Bordas, A., Tixier-Boichard, M. & Merat, P. Direct and correlated responses to divergent selection for residual food intake in Rhode island red laying hens. Br. Poult. Sci. 33, 741–754 (1992).
pubmed: 1393669
doi: 10.1080/00071669208417515
El-Kazzi, M., Bordas, A., Gandemer, G. & Minvielle, F. Divergent selection for residual food intake in Rhode Island red egg-laying lines: Gross carcase composition, carcase adiposity and lipid contents of tissues. Br. Poult. Sci. 36, 719–728 (1995).
pubmed: 8746973
doi: 10.1080/00071669508417816
Gabarrou, J. F., Géraert, P. A., Picard, M. & Bordas, A. Diet-induced thermogenesis in cockerels is modulated by genetic selection for high or low residual feed intake. J Nutr 127, 2371–2376 (1997).
pubmed: 9405588
doi: 10.1093/jn/127.12.2371
Gabarrou, J. F. et al. Energy balance of laying hens selected on residual food consumption. Br. Poult. Sci. 39, 79–89 (1998).
pubmed: 9568303
doi: 10.1080/00071669889439
Tixier-Boichard, M., Boichard, D., Groeneveld, E. & Bordas, A. Restricted maximum likelihood estimates of genetic parameters of adult male and female Rhode Island red chickens divergently selected for residual feed consumption. Poult. Sci. 74, 1245–1252 (1995).
pubmed: 7479501
doi: 10.3382/ps.0741245
Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From dietary fiber to host physiology: Short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).
pubmed: 27259147
doi: 10.1016/j.cell.2016.05.041
Dong, X. Y., Azzam, M. M. M. & Zou, X. T. Effects of dietary threonine supplementation on intestinal barrier function and gut microbiota of laying hens. Poult. Sci. 96, 3654–3663 (2017).
pubmed: 28938780
doi: 10.3382/ps/pex185
Geng, S. et al. Alterations and correlations of the gut microbiome, performance, egg quality, and serum biochemical indexes in laying hens with low-protein amino acid-deficient diets. ACS Omega 6, 13094–13104 (2021).
pubmed: 34056459
pmcid: 8158825
doi: 10.1021/acsomega.1c00739
Videnska, P. et al. Succession and replacement of bacterial populations in the caecum of egg laying hens over their whole life. PLOS ONE 9, e115142 (2014).
pubmed: 25501990
pmcid: 4264878
doi: 10.1371/journal.pone.0115142
Jha, R. & Mishra, P. Dietary fiber in poultry nutrition and their effects on nutrient utilization, performance, gut health, and on the environment: A review. J. Anim. Sci. Biotechnol. 12, 51 (2021).
pubmed: 33866972
pmcid: 8054369
doi: 10.1186/s40104-021-00576-0
Cantu-Jungles, T. M. & Hamaker, B. R. Tuning expectations to reality: Don’t expect increased gut microbiota diversity with dietary fiber. The Journal of Nutrition 153, 3156–3163 (2023).
pubmed: 37690780
doi: 10.1016/j.tjnut.2023.09.001
Hamaker, B. R. & Tuncil, Y. E. A perspective on the complexity of dietary fiber structures and their potential effect on the gut microbiota. Journal of Molecular Biology 426, 3838–3850 (2014).
pubmed: 25088686
doi: 10.1016/j.jmb.2014.07.028
Tap, J. et al. Gut microbiota richness promotes its stability upon increased dietary fibre intake in healthy adults. Environ. Microbiol. 17, 4954–4964 (2015).
pubmed: 26235304
doi: 10.1111/1462-2920.13006
Martinez-Guryn, K. et al. Small intestine microbiota regulate host digestive and absorptive adaptive responses to dietary lipids. Cell Host Microbe 23, 458-469.e5 (2018).
pubmed: 29649441
pmcid: 5912695
doi: 10.1016/j.chom.2018.03.011
Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).
pubmed: 17183312
doi: 10.1038/nature05414
Velasco-Galilea, M., Piles, M., Ramayo-Caldas, Y. & Sánchez, J. P. The value of gut microbiota to predict feed efficiency and growth of rabbits under different feeding regimes. Sci. Rep. 11, 19495 (2021).
pubmed: 34593949
pmcid: 8484599
doi: 10.1038/s41598-021-99028-y
Svihus, B. Limitations to wheat starch digestion in growing broiler chickens: A brief review. Anim. Prod. Sci. 51, 583–589 (2011).
doi: 10.1071/AN10271
Tiwari, U. P., Singh, A. K. & Jha, R. Fermentation characteristics of resistant starch, arabinoxylan, and β-glucan and their effects on the gut microbial ecology of pigs: A review. Anim. Nutr. 5, 217–226 (2019).
pubmed: 31528722
pmcid: 6737498
doi: 10.1016/j.aninu.2019.04.003
Klostermann, C. E. et al. Presence of digestible starch impacts in vitro fermentation of resistant starch. Food Funct. 15, 223–235 (2024).
pubmed: 38054370
doi: 10.1039/D3FO01763J
Martínez, I., Kim, J., Duffy, P. R., Schlegel, V. L. & Walter, J. Resistant starches types 2 and 4 have differential effects on the composition of the fecal microbiota in human subjects. PLoS ONE 5, e15046 (2010).
pubmed: 21151493
pmcid: 2993935
doi: 10.1371/journal.pone.0015046
Regmi, P. R., Metzler-Zebeli, B. U., Gänzle, M. G., van Kempen, T. A. T. G. & Zijlstra, R. T. Starch with high amylose content and low in vitro digestibility increases intestinal nutrient flow and microbial fermentation and selectively promotes bifidobacteria in pigs. J. Nutr. 141, 1273–1280 (2011).
pubmed: 21628635
doi: 10.3945/jn.111.140509
Ryan, S. M., Fitzgerald, G. F. & van Sinderen, D. Screening for and identification of starch-, amylopectin-, and pullulan-degrading activities in bifidobacterial strains. Appl. Environ. Microbiol. 72, 5289–5296 (2006).
pubmed: 16885278
pmcid: 1538741
doi: 10.1128/AEM.00257-06
Rivière, A., Selak, M., Lantin, D., Leroy, F. & De Vuyst, L. Bifidobacteria and butyrate-producing colon bacteria: Importance and strategies for their stimulation in the human gut. Front. Microbiol. https://doi.org/10.3389/fmicb.2016.00979 (2016).
doi: 10.3389/fmicb.2016.00979
pubmed: 27446020
pmcid: 4923077
Takada, T., Kurakawa, T., Tsuji, H. & Nomoto, K. Fusicatenibacter saccharivorans gen. nov., sp. Nov., isolated from human faeces. Int. J. Syst. Evol. Microbiol. 63, 3691–3696 (2013).
pubmed: 23625266
doi: 10.1099/ijs.0.045823-0
Wongkuna, S. et al. Taxono-genomics description of Olsenella lakotia SW165T sp. nov., a new anaerobic bacterium isolated from cecum of feral chicken. F1000Res 9, 1103 (2020).
pubmed: 33024551
pmcid: 7520715
Lundberg, R., Scharch, C. & Sandvang, D. The link between broiler flock heterogeneity and cecal microbiome composition. Anim. Microbiome 3, 54 (2021).
pubmed: 34332648
pmcid: 8325257
doi: 10.1186/s42523-021-00110-7
Zhang, Y. et al. Dietary resistant starch modifies the composition and function of caecal microbiota of broilers. J. Sci. Food Agric. 100, 1274–1284 (2020).
pubmed: 31721238
doi: 10.1002/jsfa.10139
Holmstrøm, K., Collins, M. D., Møller, T., Falsen, E. & Lawson, P. A. Subdoligranulum variabile gen. nov., sp. nov. from human feces. Anaerobe 10, 197–203 (2004).
pubmed: 16701519
doi: 10.1016/j.anaerobe.2004.01.004
Khan, M. T. et al. The gut anaerobe Faecalibacterium prausnitzii uses an extracellular electron shuttle to grow at oxic–anoxic interphases. ISME J. 6, 1578–1585 (2012).
pubmed: 22357539
pmcid: 3400418
doi: 10.1038/ismej.2012.5
Moens, F., Weckx, S. & De Vuyst, L. Bifidobacterial inulin-type fructan degradation capacity determines cross-feeding interactions between bifidobacteria and Faecalibacterium prausnitzii. Int. J. Food Microbiol. 231, 76–85 (2016).
pubmed: 27233082
doi: 10.1016/j.ijfoodmicro.2016.05.015
Ziemer, C. J. Newly cultured bacteria with broad diversity isolated from eight-week continuous culture enrichments of cow feces on complex polysaccharides. Appl. Environ. Microbiol. 80, 574–585 (2014).
pubmed: 24212576
pmcid: 3911107
doi: 10.1128/AEM.03016-13
Zhou, Q. et al. Genetic and microbiome analysis of feed efficiency in laying hens. Poult. Sci. 102, 102393. https://doi.org/10.1016/j.psj.2022.102393 (2022).
doi: 10.1016/j.psj.2022.102393
pubmed: 36805401
pmcid: 9958098
Zhang, Y. K. et al. Characterization of the rumen microbiota and its relationship with residual feed intake in sheep. Animal 15, 100161 (2021).
pubmed: 33785185
doi: 10.1016/j.animal.2020.100161
Louis, P. & Flint, H. J. Formation of propionate and butyrate by the human colonic microbiota. Environ. Microbiol. 19, 29–41 (2017).
pubmed: 27928878
doi: 10.1111/1462-2920.13589
Torok, V. A. et al. Identification and characterization of potential performance-related gut microbiotas in broiler chickens across various feeding trials. Appl. Environ. Microbiol. 77, 5868–5878 (2011).
pubmed: 21742925
pmcid: 3165380
doi: 10.1128/AEM.00165-11
Singh, K. M. 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 39, 10595–10602 (2012).
pubmed: 23053958
doi: 10.1007/s11033-012-1947-7
Gardiner, G. E., Metzler-Zebeli, B. U. & Lawlor, P. G. Impact of intestinal microbiota on growth and feed efficiency in pigs: a review. Microorganisms 8, 1886 (2020).
pubmed: 33260665
pmcid: 7761281
doi: 10.3390/microorganisms8121886
De Maesschalck, C. et al. Amorphous cellulose feed supplement alters the broiler caecal microbiome. Poult. Sci. 98, 3811–3817 (2019).
pubmed: 31065709
doi: 10.3382/ps/pez090
Polansky, O. et al. Important metabolic pathways and biological processes expressed by chicken cecal microbiota. Appl. Environ. Microbiol. 82, 1569–1576 (2016).
pmcid: 4771310
doi: 10.1128/AEM.03473-15
den Besten, G. et al. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J. Lip. Res. 54, 2325–2340 (2013).
doi: 10.1194/jlr.R036012
Van Soest, P. J., Robertson, J. B. & Lewis, B. A. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74, 3583–3597 (1991).
pubmed: 1660498
doi: 10.3168/jds.S0022-0302(91)78551-2
Bedu-Ferrari, C. et al. In-depth characterization of a selection of gut commensal bacteria reveals their functional capacities to metabolize dietary carbohydrates with prebiotic potential. Systems https://doi.org/10.1128/msystems.01401-23 (2024).
doi: 10.1128/msystems.01401-23
Godon, J. J., Zumstein, E., Dabert, P., Habouzit, F. & Moletta, R. Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis. Appl. Environ. Microbiol. 63, 2802–2813 (1997).
pubmed: 9212428
pmcid: 168577
doi: 10.1128/aem.63.7.2802-2813.1997
Lluch, J. et al. The characterization of novel tissue microbiota using an optimized 16S metagenomic sequencing pipeline. PLoS ONE 10, e0142334 (2015).
pubmed: 26544955
pmcid: 4636327
doi: 10.1371/journal.pone.0142334
Nadkarni, M. A., Martin, F. E., Jacques, N. A. & Hunter, N. Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set. Microbiology 148, 257–266 (2002).
pubmed: 11782518
doi: 10.1099/00221287-148-1-257
Escudié, F. et al. FROGS: find, rapidly, OTUs with galaxy solution. Bioinformatics 34, 1287–1294 (2018).
pubmed: 29228191
doi: 10.1093/bioinformatics/btx791
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: A fast and accurate illumina paired-end reAd mergeR. Bioinformatics 30, 614–620 (2014).
pubmed: 24142950
doi: 10.1093/bioinformatics/btt593
Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).
pubmed: 23202435
doi: 10.1038/nmeth.2276
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).
pubmed: 23193283
pmcid: 3531112
doi: 10.1093/nar/gks1219
Douglas, G. M. et al. PICRUSt2: An improved and extensible approach for metagenome inference. bioRxiv https://doi.org/10.1101/672295 (2019).
doi: 10.1101/672295
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
pubmed: 23630581
pmcid: 3632530
doi: 10.1371/journal.pone.0061217
Oksanen, J. et al. Vegan: Community ecology package (2022).
Fox, J. et al. Car: Companion to Applied Regression (2022).
Lenth, R. V. et al. Emmeans: Estimated marginal means, aka least-squares means (2022).
Wheeler, B. & Torchiano, M. lmPerm: permutation tests for linear models (2016).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Darzi, Y., Letunic, I., Bork, P. & Yamada, T. iPath30: Interactive pathways explorer v3. Nucleic Acids Res. 46, 510–513 (2018).
doi: 10.1093/nar/gky299
Revelle, W. psych: Procedures for psychological, psychometric, and personality research (2023).
Gu, Z. Complex heatmap visualization. iMeta 1, e43 (2022).
doi: 10.1002/imt2.43