Metagenomic analysis of intestinal microbiota in geese from different farming systems in Gunungpati, Semarang

R Susanti(1*), Ari Yuniastuti(2), Fitri Arum Sasi(3), Muchamad Dafip(4)

(1) Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Sekaran Gunungpati, Semarang, Jawa Tengah 50229
(2) Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Sekaran Gunungpati, Semarang, Jawa Tengah 50229
(3) Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Sekaran Gunungpati, Semarang, Jawa Tengah 50229
(4) Master Degree Program in Biomedical Sciences, Faculty of Medicine, Universitas Diponegoro, Jl. Prof. Sudarto SH, Tembalang, Semarang, Jawa Tengah 50275
(*) Corresponding Author


The diversity of intestinal bacteria in geese correlates with environmental conditions, rearing methods, and consumed feeds. The intestinal bacteria composition is useful for the absorption of nutrition, improving the metabolism, and may be related to the immune system. This study was conducted to examine the intestinal bacteria composition and the diversity of maintained goose in aviaries and barns. This research was an observational exploratory. Five geese were taken purposively from local breeders in Gunungpati District, Semarang City. A total of 5 g of intestinal contents from each sample was used for microbial genome isolation. Then, the genome was amplified to collect 16S rRNA gene region V3-V4. The amplicons were then sequenced using the next generation sequencing (NGS) method (Illumina high-throughput sequencing; paired-end reads) and analyzed using QIIME2 to identify bacterial species. In addition, GC-MS was performed to identify and measure fatty acid contents in the intestinal. The results showed that both rearing and caged goose contained nine phyla of intestinal bacteria. The number of intestinal bacteria of barn geese (SU) reached 32,748 Operational Taxonomy Units (OTU); higher than aviary geese (SK), which was 11,646 OTU. The intestinal bacteria community in barn geese was approved by Phylum TM7 (Saccharibacteria candidate) (53.18%), followed by Firmicutes (32.51%) and Bacteriodetes (5.42%). Whereas on SK Firmicutes was compiled 49.3 4% of total OTU, TM7 (S. candidate) up to 21.17%, and Actinobacteria up to 15.99 %. The abundance of TM7 may contribute to high 9,12-octadecadienoic acid production, while Firmicutes was related to the high production of oleic acid. Based on these data, the reared geese had a more abundant diversity of bacteria than the caged one.


Aviary; barn; goose; intestinal bacteria; metagenomics; rearing management

Full Text:



Altizer S, Bartel R, Han BA. 2011. Animal migration and infectious disease risk. Science. 331(6015):296–302. doi:10.1126/science.1194694.

Batta AK, Salen G, Batta P, Tint GS, Alberts DS, Earnest DL. 2002. Simultaneous quantitation of fatty acids, sterols and bile acids in human stool by cap­illary gas­liquid chromatography. J Chromatogr B Analyt Technol Biomed Life Sci. 775(2):153–161. doi:10.1016/S1570­0232(02)00289­1.

Beckmann L, Simon O, Vahjen W. 2006. Isolation and identification of mixed linked β­glucan de­ grading bacteria in the intestine of broiler chickens and partial characterization of respective 1,3­1,4­β­ glucanase activities. J Basic Microbiol. 46(3):175– 185. doi:10.1002/jobm.200510107.

Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holman SP. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 13(7):581–583. doi:10.1038/nmeth.3869.DADA2.

Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, et al. 2010. QI­ IME allows analysis of high­throughput commu­nity sequencing data. Nat Methods. 7(5):335–336. doi:10.1038/nmeth.f.303.QIIME.

Chao A. 1984. Nonparametric Estimation of the Number of Classes in a Population. Scand Stat Theory Appl. 11(4):265.

den Besten G, Van Eunen K, Groen AK, Venema K, Rei­ jngoud DJ, Bakker BM. 2013. The role of short­ chain fatty acids in the interplay between diet, gut mi­ crobiota, and host energy metabolism. J Lipid Res. 54(9):2325–2340. doi:10.1194/jlr.R036012.

Dennis KL, Wang Y, Blatner NR, Wang S, Saadalla A, Trudeau E, Roers A, Weaver CT, Lee JJ, Gilbert JA, et al. 2013. Adenomatous polyps are drivenbymicrobe­instigated focal inflammation and are controlled by IL­10­producing T cells. Can­cer Res. 73(19):5905–5913. doi:10.1158/0008­ 5472.CAN­13­1511.

DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Ander­ sen GL. 2006. Greengenes, a chimera­checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 72(7):5069–5072. doi:10.1128/AEM.03006­05.

Dominguez­Bello MG, Costello EK, Contreras M, Ma­gris M, Hidalgo G, Fierer N, Knight R. 2010. De­ livery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci USA. 107(26):11971– 11975. doi:10.1073/pnas.1002601107.

Dominianni C, Sinha R, Goedert JJ, Pei Z, Yang L, Hayes RB, Ahn J. 2015. Sex, body mass in­dex, and dietary fiber intake influence the hu­man gut microbiome. PLoS ONE. 10(4):1–14. doi:10.1371/journal.pone.0124599.

Eneroth P, Hellstrom K, Sjovall J. 1968. A Method for Quantitative Determination of bile acid in human fe­ ces. Acta Chem Scand. 22(6):1720–1744.

Grond K. 2017. Development and dynamics of gut microbial communities of migratory shorebirds in the West­ ern Hemisphere. Ph.D. thesis, Kansas State Univer­sity. doi:10.1017/CBO9781107415324.004.

Harris MT, Brown JD, Goekjian VH, Luttrell MP, Poul­ son RL, Wilcox BR, Swayne DE, Stallknecht DE. 2010. Canada geese and the epidemiology of avian influenza viruses. J Wildl Dis. 46(3):981–987. doi:10.7589/0090­3558­46.3.981.

He X, McLean JS, Edlund A, Yooseph S, Hall AP, Liu SY, Dorrestein PC, Esquenazi E, Hunter RC, Cheng G, et al. 2015. Cultivation of a human­associated TM7 phylotype reveals a reduced genome and epibi­ otic parasitic lifestyle. Proc Natl Acad Sci USA. 112(1):244–249. doi:10.1073/pnas.1419038112.

Holm JB, Humphrys MS, Robinson CK, Settles ML, Ott S, Fu L, Yang H, Gajer P, He X, McComb E, et al. 2019. Ultrahigh ­Throughout Multiplexing and Se­quencing of >500­Base­Pair Amplicon Regions on the Illumina HiSeq 2500 Platform. mSystems. 4(1):1– 10. doi:10.1128/msystems.00029­19.

Hunter JD. 2007. Matplotlib: A 2D graphics environment. Comput Sci Eng. 9(3):99–104. doi:10.1109/MCSE.2007.55.

Jamroz D, Jakobsen K, Bach Knudsen KE, Wiliczkiewicz A, Orda J. 2002. Digestibility and energy value of non­starch polysaccharides in young chickens, ducks and geese, fed diets containing high amounts of barley. Comp Biochem Physiol A Mol In­tegr Physiol. 131(3):657–668. doi:10.1016/S1095­ 6433(01)00517­7.

Kim SH. 2018. Challenge for one health: Co­circulation of zoonotic h5n1 and h9n2 avian influenza viruses in Egypt. Viruses. 10(3):1–16. doi:10.3390/v10030121.

Leung TL, Koprivnikar J. 2016. Nematode parasite di­versity in birds: the role of host ecology, life his­ tory and migration. J Anim Ecol. 85(6):1471–1480. doi:10.1111/1365­2656.12581.

Li Y, Xu Q, Huang Z, Lv L, Liu X, Yin C, Yan H, Yuan J. 2016. Effect of Bacillus subtilis CGMCC 1.1086 on the growth performance and intestinal mi­crobiota of broilers. J Appl Microbiol. 120(1):195– 204. doi:10.1111/jam.12972.

Liu YJ, Liu SJ, Drake HL, Horn MA. 2011. Al­phaproteobacteria dominate active 2­methyl­4­ chlorophenoxyacetic acid herbicide degraders in agricultural soil and drilosphere. Environ Microbiol. 13(4):991–1009. doi:10.1111/j.1462­ 2920.2010.02405.x.

Magurran AE. 2004. Measuring biological diversity. Ox­ ford: Blackwell.

Mandl JN, Ahmed R, Barreiro LB, Daszak P, Epstein JH, Virgin HW, Feinberg MB. 2015. Reservoir host immune responses to emerging zoonotic viruses. Cell. 160(1­2):20–35. doi:10.1016/j.cell.2014.12.003.

McDonald D, Price MN, Goodrich J, Nawrocki EP, De­ santis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P. 2012. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 6(3):610– 618. doi:10.1038/ismej.2011.139.

Pan D, Yu Z. 2014. Intestinal microbiome of poultry and its interaction with host and diet. Gut Microbes. 5(1):108–119. doi:10.4161/gmic.26945.

Phuong DQ, Dung NT, Jørgensen PH, Handberg KJ, Vinh NT, Christensen JP. 2011. Susceptibil­ity of Muscovy (Cairina Moschata) and mallard ducks (Anas Platyrhynchos) to experimental infections by different genotypes of H5N1 avian in­fluenza viruses. Vet Microbiol. 148(2­4):168–174. doi:10.1016/j.vetmic.2010.09.007.

Pielou EC. 1966. The measurement of diversity in dif­ferent types of biological collections. J Theor Biol. 13:131–144. doi:10.1016/0022­5193(66)90013­0.

Shannon CE, Weaver W. 1949. The Mathematical Theory of Communication. Champaign, IL.

Simpson EH. 1949. Measurement of Diversity. Nature. 163(1):688. doi:10.1038/163688a0.

Singh KM, Shah TM, Reddy B, Deshpande S, Rank DN, Joshi CG. 2014. Taxonomic and gene­centric metage­nomics of the fecal microbiome of low and high feed conversion ratio (FCR) broilers. J Appl Genet. 55(1):145–154. doi:10.1007/s13353­013­0179­4.

Susanti R, Fibriana F, Sasi FA. 2018. Genotype of ja­vanese backyard waterfowl based on antiviral myxovirus gene. Warasan Songkhla Nakharin. 40(3):498– 505. doi:10.14456/sjst­psu.2018.74.

Thomas F, Hehemann JH, Rebuffet E, Czjzek M, Michel G. 2011. Environmental and gut Bacteroidetes: The food connection. Front Microbiol. 2(1):1–16. doi:10.3389/fmicb.2011.00093.

Wang W, Cao J, Yang F, Wang X, Zheng S, Sharshov K, Li L. 2016. High­throughput sequencing reveals the core gut microbiome of Bar­headed goose (Anser indicus) in different wintering areas in Tibet. Microbiology­ open. 5(2):287–295. doi:10.1002/mbo3.327.

Yamak US, Sarica M, Boz MA, Ucar A. 2016. The effect of production system (barn and free­ range), slaughtering age and gender on carcass traits and meat quality of partridges (Alec­toris chukar). Br Poult Sci. 57(2):185–192. doi:10.1080/00071668.2016.1144920.

Yang H, Xiao Y, Gui G, Li J, Wang J, Li D. 2018. Micro­bial community and short­chain fatty acid profile in gastrointestinal tract of goose. Poult Sci. 97(4):1420– 1428. doi:10.3382/ps/pex438.

Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W, Schleifer KH, Whitman WB, Euzéby J, Amann R, Rosselló­Móra R. 2014. Uniting the classification of cultured and uncultured bacteria and archaea us­ ing 16S rRNA gene sequences. Nat Rev Microbiol. 12(9):635–645. doi:10.1038/nrmicro3330.

Yeoman CJ, Chia N, Jeraldo P, Sipos M, Goldenfeld ND, White BA. 2012. The microbiome of the chicken gas­trointestinal tract. Anim Health Res Rev. 13(1):89– 99. doi:10.1017/S1466252312000138.

Zhao Y, Li X, Sun S, Chen L, Jin J, Liu S, Song X, Wu C, Lu L. 2019. Protective role of dryland rearing on netting floors against mortality through gut microbiota­associated immune performance in Shaoxing ducks. Poult Sci. 98(10):4530–4538. doi:10.3382/ps/pez268.

Zheng A, Luo J, Meng K, Li J, Bryden WL, Chang W, Zhang S, Wang LX, Liu G, Yao B. 2016. Probi­ otic (Enterococcus faecium) induced responses of the hepatic proteome improves metabolic efficiency of broiler chickens (Gallus gallus). BMC Genomics. 17(1):1–12. doi:10.1186/s12864­016­2371­5.

Zhou JY, Shen HG, Chen HX, Tong GZ, Liao M, Yang HC, Liu JX. 2006. Characterization of a highly pathogenic H5N1 influenza virus derived from bar­ headed geese in China. J Gen Virol. 87(7):1823–1833. doi:10.1099/vir.0.81800­0.

Zhu C, Song W, Tao Z, Liu H, Zhang S, Xu W, Li H. 2020. Analysis of microbial diversity and com­ position in small intestine during different develop­ment times in ducks. Poult Sci. 99(2):1096–1106. doi:10.1016/j.psj.2019.12.030.


Article Metrics

Abstract views : 2393 | views : 2468


  • There are currently no refbacks.

Copyright (c) 2020 The Author(s)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.