Identification of differential duodenal gene expression levels and microbiota abundance correlated with differences in energy utilisation in chickens
Barbara M. Konsak A B D G , Dragana Stanley A D F G , Volker R. Haring A , Mark S. Geier B C D , Robert J. Hughes B C D , Gordon S. Howarth B , Tamsyn M. Crowley A D E and Robert J. Moore A D H
+ Author Affiliations
- Author Affiliations
A Australian Animal Health Laboratory, CSIRO Animal, Food and Health Sciences, Geelong, Vic. 3220, Australia.
B School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA 5371, Australia.
C South Australian Research and Development Institute, Pig and Poultry Production Institute, Nutrition Research Laboratory, Roseworthy, SA 5371, Australia.
D Poultry Cooperative Research Centre, PO Box U242, University of New England, Armidale, NSW 2315, Australia.
E School of Medicine, Deakin University, Geelong, Vic. 3220, Australia.
F Central Queensland University, Higher Education Division, Bruce Highway, Rockhampton, Qld, 4702.
G Made equal contributions: co-first authorship.
H Corresponding author. Email: rob.moore@csiro.au
Animal Production Science 53(12) 1269-1275 https://doi.org/10.1071/AN12426
Submitted: 13 December 2012 Accepted: 2 August 2013 Published: 1 October 2013
Journal Compilation © CSIRO Publishing 2013 Open Access CC BY-NC-ND
Abstract
Among the terrestrial production animals, chickens are the most efficient users of energy. Apparent metabolisable energy (AME) is a measure of energy utilisation efficiency representing the difference between energy consumed and energy lost via the excreta. There are significant differences in the energy utilisation capability of individual birds that have a similar genetic background and are raised under identical conditions. It would be of benefit to poultry producers if the basis of these differences could be understood and the differences minimised. We analysed duodenal gene expression and microbiota differences in birds with different energy utilisation efficiencies. Using microarray analysis, significant differences were found in duodenal gene expression between high- and low-AME birds, indicating that level of cell turnover may distinguish different groups of birds. High-throughput sequencing of bacterial 16S rRNA genes indicated that duodenal microbiota was dominated by Lactobacillus species and two operational taxonomic units, identified as lactobacilli species, were found to be more abundant (P < 0.05) in low-AME birds. The present study has identified gene expression and microbiota properties that correlate with differences in AME; further studies will be required to investigate the causal relationships.
References
Bedford M (2001) Enzymes improve broiler body weight uniformity for increased profitability. World Poultry 17, 16–19.Bolstad, BM (2010) ‘preprocessCore: a collection of pre-processing functions. R package, version 1.20.0.’ Available at http://www.bioconductor.org/packages//2.10/bioc/html/preprocessCore.html [Verified 23 August 2013]
Boulesteix AL, Slawski M (2009) Stability and aggregation of ranked gene lists. Briefings in Bioinformatics 10, 556–568.
| Stability and aggregation of ranked gene lists.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhtVamu7bN&md5=f99b9c858e714b2f1a88aab652fc1fd8CAS | 19679825PubMed |
Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R (2010a) PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267.
| PyNAST: a flexible tool for aligning sequences to a template alignment.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXntlGqug%3D%3D&md5=9b0864a16cf2e1f9d6c50796d3e652b4CAS | 19914921PubMed |
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010b) Qiime allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336.
| Qiime allows analysis of high-throughput community sequencing data.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXksFalurg%3D&md5=30357f63b4c045af34688e2e3cec0730CAS | 20383131PubMed |
Chu G, Goss V, Narasimhan B, Tibshirani R (2000) ‘SAM, significance analysis of microarrays. User guide and technical documentation.’ (Stanford University: Stanford, CA)
Chun J, Lee JH, Jung Y, Kim M, Kim S, Kim BK, Lim YW (2007) EzTaxon: a web-based tool for the identification of prokaryotes based on 16S ribosomal RNA gene sequences. International Journal of Systematic and Evolutionary Microbiology 57, 2259–2261.
| EzTaxon: a web-based tool for the identification of prokaryotes based on 16S ribosomal RNA gene sequences.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhsVanu73P&md5=54a3adbcdb09144d77ca442c761a4449CAS | 17911292PubMed |
FAO (2011) ‘World livestock 2011 – livestock in food security.’ (FAO: Rome) Available at http://www.fao.org/docrep/014/i2373e/i2373e.pdf [Verified 20 August 2013]
Farrell DJ (1999) In vivo and in vitro techniques for the assessment of the energy content of feed grains for poultry: a review. Australian Journal of Agricultural Research 50, 881–888.
| In vivo and in vitro techniques for the assessment of the energy content of feed grains for poultry: a review.Crossref | GoogleScholarGoogle Scholar |
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biology 5, R80
| Bioconductor: open software development for computational biology and bioinformatics.Crossref | GoogleScholarGoogle Scholar | 15461798PubMed |
Gentleman RC, Carey VJ, Huber W, Hahne F (2009) ‘Genefilter: methods for filtering genes from microarray experiments. R Package Version 1.24.2., Bioconductor 2.6.’ Available at http://bioc.ism.ac.jp/2.6/bioc/html/genefilter.html. [Verified 20 August 2013]
Havenstein G, Ferket P, Qureshi M (2003) Growth, livability, and feed conversion of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poultry Science 82, 1500–1508.
Henry R, Rothwell G (1995) ‘The world poultry industry. IFC Global agribusiness series.’ (World Bank: Washington DC)
Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4, 44–57.
| Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhsFCkurnI&md5=8fa4b4df3f538c76cbc23c3e24cfd22aCAS |
Hughes R (2003 )
Hughes R (2004 )
Hughes R Choct M (1997 )
Kauffmann A, Gentleman R, Huber W (2009) arrayQualityMetrics – a bioconductor package for quality assessment of microarray data. Bioinformatics 25, 415–416.
| arrayQualityMetrics – a bioconductor package for quality assessment of microarray data.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhtlOlsrw%3D&md5=61d363b00e0eb8316f536b969ab0fe8dCAS | 19106121PubMed |
Lane DJ (1991) 16S and 23S rRNA sequencing. In ‘Nucleic acid techniques in bacterial systematics’. (Eds E Stackebrandt, M Goodfellow) pp. 115–175. (Wiley: Chichester, UK)
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar , Buchner A, Lai T, Steppi S, Jobb G, Forster W, Brettske I, Gerber S, Ginhart AW, Gross O, Grumann S, Hermann S, Jost R, Konig A, Liss T, Lussmann R, May M, Nonhoff B, Reichel B, Strehlow R, Stamatakis A, Stuckmann N, Vilbig A, Lenke M, Ludwig T, Bode A, Schleifer KH (2004) ARB: a software environment for sequence data. Nucleic Acids Research 32, 1363–1371.
| ARB: a software environment for sequence data.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXhvFSqu7k%3D&md5=b35f94c7f3f34d214786c7ab8782b4d6CAS | 14985472PubMed |
Mulder HA, Hill WG, Vereijken A, Veerkamp RF (2009) Estimation of genetic variation in residual variance in female and male broiler chickens. Animal 3, 1673–1680.
| Estimation of genetic variation in residual variance in female and male broiler chickens.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC38vptVynsg%3D%3D&md5=ccfe88917f7c10f21ed103eebbb4e983CAS | 22443551PubMed |
NRC (1994) ‘Nutrient requirements of poultry.’ (National Academic Press: Washington, DC)
Owens B, Tucker L, Collins M, McCracken K (2008) Effects of different feed additives alone or in combination on broiler performance, gut microflora and ileal histology. British Poultry Science 49, 202–212.
| Effects of different feed additives alone or in combination on broiler performance, gut microflora and ileal histology.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXksFaqurk%3D&md5=40f8464345892393bdd99115ad926133CAS | 18409095PubMed |
Quinlan AR, Stewart DA, Stromberg MP, Marth GT (2008) Pyrobayes: an improved base caller for SNP discovery in pyrosequences. Nature Methods 5, 179–181.
| Pyrobayes: an improved base caller for SNP discovery in pyrosequences.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhsV2ksLo%3D&md5=5a4c66e47f773b29a2e87142e02bc04aCAS | 18193056PubMed |
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology 75, 7537–7541.
| Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXis1yltw%3D%3D&md5=b5fe3ac0c05235e886f325af78b7d4e1CAS | 19801464PubMed |
Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, Article3
| Linear models and empirical bayes methods for assessing differential expression in microarray experiments.Crossref | GoogleScholarGoogle Scholar | 16646809PubMed |
Snell-Castro R, Godon JJ, Delgenes JP, Dabert P (2005) Characterisation of the microbial diversity in a pig manure storage pit using small subunit rDNA sequence analysis. FEMS Microbiology Ecology 52, 229–242.
| Characterisation of the microbial diversity in a pig manure storage pit using small subunit rDNA sequence analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXisVCrtbk%3D&md5=f40f80c00d94db3d3f541721338ae87bCAS | 16329909PubMed |
Stamatakis A, Hoover P, Rougemont J (2008) A rapid bootstrap algorithm for the RAxML web servers. Systems Biology 57, 758–771.
| A rapid bootstrap algorithm for the RAxML web servers.Crossref | GoogleScholarGoogle Scholar |
Stanley D, Denman SE, Hughes RJ, Geier MS, Crowley TM, Chen H, Haring VR, Moore RJ (2012) Intestinal microbiota associated with differential feed conversion efficiency in chickens. Applied Microbiology and Biotechnology 96, 1361–1369.
| Intestinal microbiota associated with differential feed conversion efficiency in chickens.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xhs1ektL7P&md5=7922fe153cb425bb8500d4e3eb85ed82CAS | 22249719PubMed |
Stanley D, Geyer MS, Denman SE, Haring VR, Crowley TM, Hughes RJ, Moore RJ (2013) Identification of chicken intestinal microbiota correlated with the efficiency of energy extraction from feed. Veterinary Microbiology 164, 85–92.
| Identification of chicken intestinal microbiota correlated with the efficiency of energy extraction from feed.Crossref | GoogleScholarGoogle Scholar | 23434185PubMed |
Torok VA, Ophel-Keller K, Loo M, Hughes RJ (2008) Application of methods for identifying broiler chicken gut bacterial species linked with increased energy metabolism. Applied and Environmental Microbiology 74, 783–791.
| Application of methods for identifying broiler chicken gut bacterial species linked with increased energy metabolism.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhvVWjsr0%3D&md5=9ad23b085c83cf2927e07355256cfe35CAS | 18065621PubMed |
Toudic C (2006) ‘Evaluating uniformity in broilers – factors affecting variation. Hubbard Technical Bulletin.’ Available at http://www.hubbardbreeders.com/bulletins/index.php?id=28. [Verified 30 May 2013]
Yu Z, Morrison M (2004) Improved extraction of PCR-quality community DNA from digesta and fecal samples. BioTechniques 36, 808–812.
