FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Microorganisms2022; 10(6); 1166; doi: 10.3390/microorganisms10061166

Comparative Analysis of Microbiome Metagenomics in Reintroduced Wild Horses and Resident Asiatic Wild Asses in the Gobi Desert Steppe.

Abstract: The gut microbiome offers important ecological benefits to the host; however, our understanding of the functional microbiome in relation to wildlife adaptation, especially for translocated endangered species, is lagging. In this study, we adopted a comparative metagenomics approach to test whether the microbiome diverges for translocated and resident species with different adaptive potentials. The composition and function of the microbiome of sympatric Przewalski's horses and Asiatic wild asses in desert steppe were compared for the first time using the metagenomic shotgun sequencing approach. We identified a significant difference in microbiome composition regarding the microbes present and their relative abundances, while the diversity of microbe species was similar. Furthermore, the functional profile seemed to converge between the two hosts, with genes related to core metabolism function tending to be more abundant in wild asses. Our results indicate that sympatric wild equids differ in their microbial composition while harboring a stable microbial functional core, which may enable them to survive in challenging habitats. A higher abundance of beneficial taxa, such as , and genes related to metabolism pathways and enzymes, such as lignin degradation, may contribute to more diverse diet choices and larger home ranges of wild asses.
Get Full Text
Publication Date: 2022-06-07 PubMed ID: 35744684PubMed Central: PMC9229091DOI: 10.3390/microorganisms10061166Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research explored the differences in gut microorganisms in two species of wild equids living in the same habitat: the reintroduced Przewalski’s horses and the resident Asiatic wild asses. Through genomic analysis, the study identified a variation in microbiome composition among the animals but detected similarities in microbial species diversity and functionality which could be crucial for survival in rough environments.

Objective and Methodology

  • The study aimed to understand the composition and functionality of the gut microbiome in animals and its relationship to the adaptive potentials of translocated endangered species and resident species.
  • The research focused on Przewalski’s horses and Asiatic wild asses, both living in the harsh environment of the Gobi Desert Steppe.
  • To achieve this, the scientists employed a comparative metagenomics approach, a method which uses genetic material recovered directly from environmental samples. The researchers performed a metagenomic shotgun sequencing, a comprehensive method aimed at characterizing all genes from all the microbes present in a sample.

Findings

  • The team identified significant differences in microbiome composition between the two species, regarding the presence and relative abundance of different microbes.
  • Despite these differences, the two species had similar amounts of microbe species diversity.
  • The study found a commonality in their functional profiles. Genes related to core metabolic functions were more plentiful in Asiatic wild asses.
  • The researchers hypothesized that these shared functionalities might aid both species in thriving in demanding habitats.

Implications

  • The results showed that although these sympatric (living in the same geographical region and often interacting with each other) species have different microbial compositions, they maintain a stable functional core of microorganisms.
  • The presence of certain gut microbes, along with genes related to specific metabolic pathways and enzymes, may contribute to their adaptability to their environment. For instance, the higher abundance of microbes that can degrade plant fibers might be related to the more diverse diet choices and larger home ranges of wild asses.
  • These findings may help understand how animals adapt and survive in challenging habitats, potentially providing insights into conservation efforts for endangered, reintroduced species.

Cite This Article

APA
Tang L, Gao Y, Yan L, Jia H, Chu H, Ma X, He L, Wang X, Li K, Hu D, Zhang D. (2022). Comparative Analysis of Microbiome Metagenomics in Reintroduced Wild Horses and Resident Asiatic Wild Asses in the Gobi Desert Steppe. Microorganisms, 10(6), 1166. https://doi.org/10.3390/microorganisms10061166

Publication

Microorganisms
ISSN: 2076-2607
NlmUniqueID: 101625893
Country: Switzerland
Language: English
Volume: 10
Issue: 6
PII: 1166

Researcher Affiliations

Tang, Liping
  • School of Ecology and Nature Conservation, Beijing Forestry University, 35 Tsinghua East Road, Beijing 100083, China.
Gao, Yunyun
  • School of Ecology and Nature Conservation, Beijing Forestry University, 35 Tsinghua East Road, Beijing 100083, China.
Yan, Liping
  • School of Ecology and Nature Conservation, Beijing Forestry University, 35 Tsinghua East Road, Beijing 100083, China.
Jia, Huiping
  • School of Ecology and Nature Conservation, Beijing Forestry University, 35 Tsinghua East Road, Beijing 100083, China.
Chu, Hongjun
  • Institute of Forestry Ecology, Xinjiang Academy of Forestry Sciences, Urumqi 830002, China.
Ma, Xinping
  • Xinjiang Kalamaili Mountain Ungulate Nature Reserve Management Center, Urumqi 830000, China.
He, Lun
  • China Wildlife Conservation Association, Beijing 100714, China.
Wang, Xiaoting
  • China Wildlife Conservation Association, Beijing 100714, China.
Li, Kai
  • School of Ecology and Nature Conservation, Beijing Forestry University, 35 Tsinghua East Road, Beijing 100083, China.
Hu, Defu
  • School of Ecology and Nature Conservation, Beijing Forestry University, 35 Tsinghua East Road, Beijing 100083, China.
Zhang, Dong
  • School of Ecology and Nature Conservation, Beijing Forestry University, 35 Tsinghua East Road, Beijing 100083, China.

Grant Funding

  • 2019JQ03018 / Beijing Forestry University Outstanding Young Talent Cultivation Project
  • 31872964 / National Science Foundation of China

Conflict of Interest Statement

The authors declare no conflict of interest.

References

This article includes 58 references
  1. Belkaid Y, Hand TW. Role of the microbiota in immunity and inflammation.. Cell 2014 Mar 27;157(1):121-41.
    doi: 10.1016/j.cell.2014.03.011pmc: PMC4056765pubmed: 24679531google scholar: lookup
  2. Ma M, Tu C, Luo J, Lu M, Zhang S, Xu L. Metabolic and immunological effects of gut microbiota in leaf beetles at the local and systemic levels.. Integr Zool 2021 May;16(3):313-323.
    doi: 10.1111/1749-4877.12528pubmed: 33704889google scholar: lookup
  3. Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease.. Nat Rev Microbiol 2021 Jan;19(1):55-71.
    doi: 10.1038/s41579-020-0433-9pubmed: 32887946google scholar: lookup
  4. Li Y, Zhang T, Shi M, Zhang B, Hu X, Xu S, Ding J, Liu S, Hu D, Rubenstein D. Characterization of intestinal microbiota and fecal cortisol, T3, and IgA in forest musk deer (Moschus berezovskii) from birth to weaning.. Integr Zool 2021 May;16(3):300-312.
    doi: 10.1111/1749-4877.12522pmc: PMC8248411pubmed: 33452844google scholar: lookup
  5. Cryan JF, Dinan TG. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour.. Nat Rev Neurosci 2012 Oct;13(10):701-12.
    doi: 10.1038/nrn3346pubmed: 22968153google scholar: lookup
  6. Rosenberg E, Zilber-Rosenberg I. The hologenome concept of evolution after 10 years.. Microbiome 2018 Apr 25;6(1):78.
    doi: 10.1186/s40168-018-0457-9pmc: PMC5922317pubmed: 29695294google scholar: lookup
  7. Hurst GDD. Extended genomes: symbiosis and evolution.. Interface Focus 2017 Oct 6;7(5):20170001.
    doi: 10.1098/rsfs.2017.0001pmc: PMC5566813pubmed: 28839925google scholar: lookup
  8. Henry LP, Bruijning M, Forsberg SKG, Ayroles JF. The microbiome extends host evolutionary potential.. Nat Commun 2021 Aug 26;12(1):5141.
    doi: 10.1038/s41467-021-25315-xpmc: PMC8390463pubmed: 34446709google scholar: lookup
  9. Greyson-Gaito CJ, Bartley TJ, Cottenie K, Jarvis WMC, Newman AEM, Stothart MR. Into the wild: microbiome transplant studies need broader ecological reality.. Proc Biol Sci 2020 Feb 26;287(1921):20192834.
    doi: 10.1098/rspb.2019.2834pmc: PMC7062022pubmed: 32097591google scholar: lookup
  10. Xu L, Liu Y, Xu S, Lu M. Gut commensal bacteria in biological invasions.. Integr Zool 2019 Nov;14(6):613-618.
    doi: 10.1111/1749-4877.12385pubmed: 30811842google scholar: lookup
  11. Xia C, Cao J, Zhang H, Gao X, Yang W, Blank D. Reintroduction of Przewalski’s horse (Equus ferus przewalskii) in Xinjiang, China: The status and experience.. Biol. Conserv. 2014;177:142–147.
    doi: 10.1016/j.biocon.2014.06.021google scholar: lookup
  12. Jiang Z, Zong H. Reintroduction of the przewalski’s horse in China: Status quo and outlook.. Nat. Conserv. Res. 2019;4:15–22.
    doi: 10.24189/ncr.2019.045google scholar: lookup
  13. Zhang XC, Shao CL, Ge Y, Chen C, Xu WX, Yang WK. [Suitable summer habitat of the khulan in the Mt. Kalamaili Ungulate Nature Reserve and estimation of its population].. Ying Yong Sheng Tai Xue Bao 2020 Sep 15;31(9):2993-3004.
    pubmed: 33345500doi: 10.13287/j.1001-9332.202009.032google scholar: lookup
  14. . IUCN Red List of Threatened Species.. .
  15. Van Dierendonck M.C., Wallis De Vries M.F.. Ungulate reintroductions: Experiences with the takhi or Przewalski horse (Equus ferus przewalskii) in Mongolia.. Conserv. Biol. 1996;10:728–740.
    doi: 10.1046/j.1523-1739.1996.10030728.xgoogle scholar: lookup
  16. Bahloul K, Pereladova O.B., Soldatova N, Fisenko G, Sidorenko E, Sempéré A.J.. Social organization and dispersion of introduced kulans (Equus hemionus kulan) and Przewalski horses (Equus przewalski) in the Bukhara Reserve, Uzbekistan.. J. Arid Environ. 2001;47:309–323.
    doi: 10.1006/jare.2000.0714google scholar: lookup
  17. Huang Y, Chu H, Lan W, Shi K, Tao Y, Shao C. Trophic Niche Width and Overlap of Equus przewalskii, E. hemionus and Gazella subgutturosa in Autumn.. Arid Zone Res. 2011;47:1045–1050.
  18. Burnik Šturm M, Ganbaatar O, Voigt CC, Kaczensky P. Sequential stable isotope analysis reveals differences in multi-year dietary history of three sympatric equid species in SW Mongolia.. J Appl Ecol 2017 Aug;54(4):1110-1119.
    doi: 10.1111/1365-2664.12825pmc: PMC5510718pubmed: 28717255google scholar: lookup
  19. Kaczensky P, Ganbaatar O, Von Wehrden H, Walzer C. Resource selection by sympatric wild equids in the Mongolian Gobi.. J. Appl. Ecol. 2008;45:1762–1769.
    doi: 10.1111/j.1365-2664.2008.01565.xgoogle scholar: lookup
  20. Xu W, Xia C, Yang W, Blank D.A., Qiao J, Liu W. Seasonal diet of Khulan (Equidae) in Northern Xinjiang, China.. Ital. J. Zool. 2012;79:92–99.
    doi: 10.1080/11250003.2011.620635google scholar: lookup
  21. Garber A, Hastie P, Murray JA. Factors Influencing Equine Gut Microbiota: Current Knowledge.. J Equine Vet Sci 2020 May;88:102943.
    doi: 10.1016/j.jevs.2020.102943pubmed: 32303307google scholar: lookup
  22. Poinar HN, Schwarz C, Qi J, Shapiro B, Macphee RD, Buigues B, Tikhonov A, Huson DH, Tomsho LP, Auch A, Rampp M, Miller W, Schuster SC. Metagenomics to paleogenomics: large-scale sequencing of mammoth DNA.. Science 2006 Jan 20;311(5759):392-4.
    doi: 10.1126/science.1123360pubmed: 16368896google scholar: lookup
  23. Wang WL, Xu SY, Ren ZG, Tao L, Jiang JW, Zheng SS. Application of metagenomics in the human gut microbiome.. World J Gastroenterol 2015 Jan 21;21(3):803-14.
    doi: 10.3748/wjg.v21.i3.803pmc: PMC4299332pubmed: 25624713google scholar: lookup
  24. Dodd C.S., Grueber C.E.. Functional Diversity within Gut Microbiomes: Implications for Conserving Biodiversity.. Conservation. 2021;1:311–326.
    doi: 10.3390/conservation1040024google scholar: lookup
  25. Hu D, Yang J, Qi Y, Li B, Li K, Mok KM. Metagenomic Analysis of Fecal Archaea, Bacteria, Eukaryota, and Virus in Przewalski's Horses Following Anthelmintic Treatment.. Front Vet Sci 2021;8:708512.
    doi: 10.3389/fvets.2021.708512pmc: PMC8416479pubmed: 34490397google scholar: lookup
  26. Tang L, Li Y, Srivathsan A, Gao Y, Li K, Hu D, Zhang D. Gut Microbiomes of Endangered Przewalski's Horse Populations in Short- and Long-Term Captivity: Implication for Species Reintroduction Based on the Soft-Release Strategy.. Front Microbiol 2020;11:363.
    doi: 10.3389/fmicb.2020.00363pmc: PMC7081077pubmed: 32226419google scholar: lookup
  27. Yan L, Tang L, Zhou Z, Lu W, Wang B, Sun Z, Jiang X, Hu D, Li J, Zhang D. Metagenomics reveals contrasting energy utilization efficiencies of captive and wild camels (Camelus ferus).. Integr Zool 2022 May;17(3):333-345.
    doi: 10.1111/1749-4877.12585pubmed: 34520120google scholar: lookup
  28. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data.. Bioinformatics 2014 Aug 1;30(15):2114-20.
    doi: 10.1093/bioinformatics/btu170pmc: PMC4103590pubmed: 24695404google scholar: lookup
  29. Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K, Yamashita H, Lam TW. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices.. Methods 2016 Jun 1;102:3-11.
    doi: 10.1016/j.ymeth.2016.02.020pubmed: 27012178google scholar: lookup
  30. Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification.. BMC Bioinformatics 2010 Mar 8;11:119.
    doi: 10.1186/1471-2105-11-119pmc: PMC2848648pubmed: 20211023google scholar: lookup
  31. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data.. Bioinformatics 2012 Dec 1;28(23):3150-2.
    doi: 10.1093/bioinformatics/bts565pmc: PMC3516142pubmed: 23060610google scholar: lookup
  32. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J, Bork P, Ehrlich SD, Wang J. A human gut microbial gene catalogue established by metagenomic sequencing.. Nature 2010 Mar 4;464(7285):59-65.
    doi: 10.1038/nature08821pmc: PMC3779803pubmed: 20203603google scholar: lookup
  33. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND.. Nat Methods 2015 Jan;12(1):59-60.
    doi: 10.1038/nmeth.3176pubmed: 25402007google scholar: lookup
  34. Huson DH, Mitra S, Ruscheweyh HJ, Weber N, Schuster SC. Integrative analysis of environmental sequences using MEGAN4.. Genome Res 2011 Sep;21(9):1552-60.
    doi: 10.1101/gr.120618.111pmc: PMC3166839pubmed: 21690186google scholar: lookup
  35. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes.. Nucleic Acids Res 2000 Jan 1;28(1):27-30.
    doi: 10.1093/nar/28.1.27pmc: PMC102409pubmed: 10592173google scholar: lookup
  36. Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics.. Nucleic Acids Res 2009 Jan;37(Database issue):D233-8.
    doi: 10.1093/nar/gkn663pmc: PMC2686590pubmed: 18838391google scholar: lookup
  37. Chong J, Liu P, Zhou G, Xia J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data.. Nat Protoc 2020 Mar;15(3):799-821.
    doi: 10.1038/s41596-019-0264-1pubmed: 31942082google scholar: lookup
  38. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation.. Genome Biol 2011 Jun 24;12(6):R60.
    doi: 10.1186/gb-2011-12-6-r60pmc: PMC3218848pubmed: 21702898google scholar: lookup
  39. Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles.. Bioinformatics 2014 Nov 1;30(21):3123-4.
    doi: 10.1093/bioinformatics/btu494pmc: PMC4609014pubmed: 25061070google scholar: lookup
  40. Paulson J.N., Pop M, Bravo H.C.. Metastats: An improved statistical method for analysis of metagenomic data.. Genome Biol. 2011;12:P17.
    doi: 10.1186/1465-6906-12-S1-P17google scholar: lookup
  41. Zhang Z, Xu D, Wang L, Hao J, Wang J, Zhou X, Wang W, Qiu Q, Huang X, Zhou J, Long R, Zhao F, Shi P. Convergent Evolution of Rumen Microbiomes in High-Altitude Mammals.. Curr Biol 2016 Jul 25;26(14):1873-9.
    doi: 10.1016/j.cub.2016.05.012pubmed: 27321997google scholar: lookup
  42. Manor O, Dai CL, Kornilov SA, Smith B, Price ND, Lovejoy JC, Gibbons SM, Magis AT. Health and disease markers correlate with gut microbiome composition across thousands of people.. Nat Commun 2020 Oct 15;11(1):5206.
    doi: 10.1038/s41467-020-18871-1pmc: PMC7562722pubmed: 33060586google scholar: lookup
  43. Jerbi H, Rejeb A, Erdoǧan S, Pérez W. Anatomical and morphometric study of gastrointestinal tract of donkey (Equus africanus asinus). J. Morphol. Sci. 2014;31:18–22.
    doi: 10.4322/jms.ao055613google scholar: lookup
  44. Godon JJ, Arulazhagan P, Steyer JP, Hamelin J. Vertebrate bacterial gut diversity: size also matters.. BMC Ecol 2016 Mar 23;16:12.
    doi: 10.1186/s12898-016-0071-2pmc: PMC4804487pubmed: 27008566google scholar: lookup
  45. Zoelzer F, Burger AL, Dierkes PW. Unraveling differences in fecal microbiota stability in mammals: from high variable carnivores and consistently stable herbivores.. Anim Microbiome 2021 Nov 4;3(1):77.
    doi: 10.1186/s42523-021-00141-0pmc: PMC8567652pubmed: 34736528google scholar: lookup
  46. Shade A. Diversity is the question, not the answer.. ISME J 2017 Jan;11(1):1-6.
    doi: 10.1038/ismej.2016.118pmc: PMC5421358pubmed: 27636395google scholar: lookup
  47. Edwards JE, Shetty SA, van den Berg P, Burden F, van Doorn DA, Pellikaan WF, Dijkstra J, Smidt H. Multi-kingdom characterization of the core equine fecal microbiota based on multiple equine (sub)species.. Anim Microbiome 2020 Feb 12;2(1):6.
    doi: 10.1186/s42523-020-0023-1pmc: PMC7807809pubmed: 33499982google scholar: lookup
  48. Moeller AH, Peeters M, Ndjango JB, Li Y, Hahn BH, Ochman H. Sympatric chimpanzees and gorillas harbor convergent gut microbial communities.. Genome Res 2013 Oct;23(10):1715-20.
    doi: 10.1101/gr.154773.113pmc: PMC3787267pubmed: 23804402google scholar: lookup
  49. Librado P, Orlando L. Genomics and the Evolutionary History of Equids.. Annu Rev Anim Biosci 2021 Feb 16;9:81-101.
    doi: 10.1146/annurev-animal-061220-023118pubmed: 33197207google scholar: lookup
  50. Ransom-Jones E, Jones DL, McCarthy AJ, McDonald JE. The Fibrobacteres: an important phylum of cellulose-degrading bacteria.. Microb Ecol 2012 Feb;63(2):267-81.
    doi: 10.1007/s00248-011-9998-1pubmed: 22213055google scholar: lookup
  51. Boulund F, Pereira M.B., Jonsson V, Kristiansson E. Computational and Statistical Considerations in the Analysis of Metagenomic Data.. Metagenomics Perspect. Methods Appl. 2018;63:81–102.
    doi: 10.1016/B978-0-08-102268-9.00004-5google scholar: lookup
  52. Liu X, Fan H, Ding X, Hong Z, Nei Y, Liu Z, Li G, Guo H. Analysis of the gut microbiota by high-throughput sequencing of the V5-V6 regions of the 16S rRNA gene in donkey.. Curr Microbiol 2014 May;68(5):657-62.
    doi: 10.1007/s00284-014-0528-5pubmed: 24452427google scholar: lookup
  53. Liu H, Zhao X, Han X, Xu S, Zhao L, Hu L, Xu T, Zhao N, Zhang X, Chen D, He F, Chen X. Comparative study of gut microbiota in Tibetan wild asses (Equus kiang) and domestic donkeys (Equus asinus) on the Qinghai-Tibet plateau.. PeerJ 2020;8:e9032.
    doi: 10.7717/peerj.9032pmc: PMC7276150pubmed: 32547852google scholar: lookup
  54. Ouwerkerk JP, Aalvink S, Belzer C, de Vos WM. Akkermansia glycaniphila sp. nov., an anaerobic mucin-degrading bacterium isolated from reticulated python faeces.. Int J Syst Evol Microbiol 2016 Nov;66(11):4614-4620.
    doi: 10.1099/ijsem.0.001399pubmed: 27499019google scholar: lookup
  55. Xu Y, Wang N, Tan HY, Li S, Zhang C, Feng Y. Function of Akkermansia muciniphila in Obesity: Interactions With Lipid Metabolism, Immune Response and Gut Systems.. Front Microbiol 2020;11:219.
    doi: 10.3389/fmicb.2020.00219pmc: PMC7046546pubmed: 32153527google scholar: lookup
  56. Blikslager A, Gonzalez L. Equine Intestinal Mucosal Pathobiology.. Annu Rev Anim Biosci 2018 Feb 15;6:157-175.
    doi: 10.1146/annurev-animal-030117-014748pmc: PMC7769316pubmed: 29144770google scholar: lookup
  57. Smith D.G., Burden F.A.. Practical donkey and mule nutrition.. In: Geor R., Coenen M., Harris P., editors. Equine Applied and Clinical Nutrition: Health, Welfare and Performance. Sauders; Philadelphia, PA, USA: 2013. pp. 304–316.
    doi: 10.1016/C2009-0-39370-8google scholar: lookup
  58. Jakeer S, Varma M, Sharma J, Mattoo F, Gupta D, Singh J, Kumar M, Gaur N.A.. Metagenomic analysis of the fecal microbiome of an adult elephant reveals the diversity of CAZymes related to lignocellulosic biomass degradation.. Symbiosis. 2020;81:209–222.
    doi: 10.1007/s13199-020-00695-8google scholar: lookup

Citations

This article has been cited 2 times.
  1. Guo R, Zhang W, Shen W, Zhang G, Xie T, Li L, Jinmei J, Liu Y, Kong F, Guo B, Li B, Sun Y, Liu S. Analysis of gut microbiota in chinese donkey in different regions using metagenomic sequencing. BMC Genomics 2023 Sep 5;24(1):524.
    doi: 10.1186/s12864-023-09575-zpubmed: 37670231google scholar: lookup
  2. Zhou Y, Wu Y, Ma C, Ruan X, Cha M, Zhou Y, Li T, Sun W, Liu H. Comparative Profiling of the Fecal Bacteriome, Mycobiome, and Protist Community in Wild Versus Captive (Cervus canadensis). Animals (Basel) 2025 Dec 24;16(1).
    doi: 10.3390/ani16010044pubmed: 41514732google scholar: lookup
Subscribe to our newsletter
Join 99,658 horse owners like you who receive our email updates on horse health and nutrition.