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Journal of Microbiology

, Volume 57, Issue 7, pp 575–586 | Cite as

Carbohydrate metabolism genes dominant in a subtropical marine mangrove ecosystem revealed by metagenomics analysis

  • Huaxian Zhao
  • Bing Yan
  • Shuming Mo
  • Shiqing Nie
  • Quanwen Li
  • Qian Ou
  • Bo Wu
  • Gonglingxia Jiang
  • Jinli Tang
  • Nan LiEmail author
  • Chengjian JiangEmail author
Microbial Ecology and Environmental Microbiology

Abstract

Mangrove sediment microorganisms play a vital role in the energy transformation and element cycling in marine wetland ecosystems. Using metagenomics analysis strategy, we compared the taxonomic structure and gene profile of the mangrove and non-mangrove sediment samples at the subtropical estuary in Beibu Gulf, South China Sea. Proteobacteria, Bacteroidetes, and Firmicutes were the most abundant bacterial phyla. Archaeal family Methanosarcinaceae and bacterial genera Vibrio and Dehalococcoides were significantly higher in the mangrove sediments than in the nonmangrove sediments. Functional analysis showed that “Carbohydrate metabolism” was the most abundant metabolic category. The feature of carbohydrate-active enzymes (CZs) was analyzed using the Carbohydrate-Active EnZymes Database. The significant differences of CZs between mangrove and non-mangrove sediments, were attributed to the amounts of polyphenol oxidase (EC 1.10.3.-), hexosyltransferase (EC 2.4.1.-), and β-N-acetylhexosaminidase (EC 3.2.1.52), which were higher in the mangrove sediment samples. Principal component analysis indicated that the microbial community and gene profile between mangrove and non-mangrove sediments were distinct. Redundancy analysis showed that total organic carbon is a significant factor that affects the microbial community and gene distribution. The results indicated that the mangrove ecosystem with massive amounts of organic carbon may promote the richness of carbohydrate metabolism genes and enhance the degradation and utilization of carbohydrates in the mangrove sediments.

Keywords

metagenomics analysis mangrove ecosystem microbial community gene profile carbohydrate metabolism 

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Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant no. 31760437), the Science and Technology Basic Resources Investigation Program of China (Grant no. 2017FY100704), the Open Research Fund Program of Guangxi Key Lab of Mangrove Conservation and Utilization (Grant no. GKLMC-201702), the Distinguished Employment Offered Unit of Guangxi for Conservation and Ecological Monitoring of Mangroves and Seagrasses, the Natural Science Foundation of Guangxi Zhuang Autonomous Region of China (Grant no. 2017GXNSFAA198081, 2017-JJB130020).

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References

  1. Alias, S.A., Kuthubutheen, A.J., and Jones, E.B.G. 1995. Frequency of occurrence of fungi on wood in Malaysian mangroves. Hydrobiologia 295, 97–106.CrossRefGoogle Scholar
  2. Alongj, D.M., Boto, K.G., and Tirendi, F. 1989. Effect of exported mangrove litter on bacterial productivity and dissolved organic carbon fluxes in adjacent tropical nearshore sediments. Mar. Ecol. Prog. Ser. 56, 133–144.CrossRefGoogle Scholar
  3. Alongj, D.M., Christoffersen, P., and Tirendi, F. 1993. The influence of forest type on microbial-nutrient relationships in tropical mangrove sediments. J. Exp. Mar. Biol. Ecol. 171, 201–223.CrossRefGoogle Scholar
  4. Alzubaidy, H., Essack, M., Malas, T.B., Bokhari, A., Motwalli, O., Kamanu, F.K., Jamhor, S.A., Mokhtar, N.A., Antunes, A., Simoes, M.F., et al. 2016. Rhizosphere microbiome metagenomics of gray mangroves (Avicennia marina) in the Red Sea. Gene 576, 626–636.CrossRefGoogle Scholar
  5. Andreote, F.D., Jimenez, D.J., Chaves, D., Dias, A.C.F., Luvizotto, D.M., Dini-Andreote, F., Fasanella, C.C., Lopez, M.V., Baena, S., Taketani, R.G., et al. 2012. The microbiome of Brazilian mangrove sediments as revealed by metagenomics. PLoS One 7, e38600.CrossRefGoogle Scholar
  6. Arfi, Y., Chevret, D., Henrissat, B., Berrin, J.G., Levasseur, A., and Record, E. 2013. Characterization of salt-adapted secreted lignocellulolytic enzymes from the mangrove fungus Pestalotiopsis sp. Nat. Commun. 4, 1810.CrossRefGoogle Scholar
  7. Artimo, P., Jonnalagedda, M., Arnold, K., Baratin, D., Csardi, G., de Castro, E., Duvaud, S., Flegel, V., Fortier, A., Gasteiger, E., et al. 2012. ExPASy: SIB bioinformatics resource portal. Nucleic Acids Res. 40, W597–W603.CrossRefGoogle Scholar
  8. Badhai, J., Ghosh, T.S., and Das, S.K. 2016. Composition and functional characterization of microbiome associated with mucus of the coral Fungia echinata collected from Andaman Sea. Front. Microbiol. 7, 936.Google Scholar
  9. Badur, A.H., Jagtap, S.S., Yalamanchili, G., Lee, J.K., Zhao, H., and Rao, C.V. 2015. Alginate lyases from alginate-degrading Vibrio splendidus 12B01 are endolytic. Appl. Environ. Microbiol. 81, 1865–1873.CrossRefGoogle Scholar
  10. Bano, N., Nisa, M., Khan, N., Saleem, M., Harrison, P., Ahmed, S., and Azam, F. 1997. Significance of bacteria in the flux of organic matter in the tidal creeks of the mangrove ecosystem of the Indus River delta, Pakistan. Mar. Ecol. Prog. Ser. 157, 1–12.CrossRefGoogle Scholar
  11. Barbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C., and Silliman, B.R. 2011. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193.CrossRefGoogle Scholar
  12. Bashan, Y. and Holguin, G. 2002. Plant growth-promoting bacteria: a potential tool for arid mangrove reforestation. Trees 16, 159–166.CrossRefGoogle Scholar
  13. Beloqui, A., Pita, M., Polaina, J., Martinez-Arias, A., Golyshina, O.V., Zumarraga, M., Yakimov, M.M., Garcia-Arellano, H., Alcalde, M., Fernandez, V.M., et al. 2006. Novel polyphenol oxidase mined from a metagenome expression library of bovine rumen. J. Biol. Chem. 281, 22933–22942.CrossRefGoogle Scholar
  14. Bouillon, S., Koedam, N., Raman, A., and Dehairs, F. 2002. Primary producers sustaining macro-invertebrate communities in inter-tidal mangrove forests. Oecologia 130, 441–448.CrossRefGoogle Scholar
  15. Bouma, T.J., van Belzen, J., Balke, T., Zhu, Z., Airoldi, L, Blight, A.J., Davies, A.J., Galvan, C., Hawkins, S.J., Hoggart, S.P.G., et al. 2014 Identifying knowledge gaps hampering application of intertidal habitats in coastal protection: Opportunities & steps to take. Coast. Eng 87, 147–157.CrossRefGoogle Scholar
  16. Brune, A. 2018. Elusimicrobia, pp. 1–3. In Whitman, W.B., Rainey, E., Kampfer, P., Trujillo, M., Chun, J., DeVos, P., Hedlund, B., and Dedysh, S. (eds.), Bergey’s manual of systematics of archaea and bacteria-2015. John Wiley & Sons Ltd., Chichester, UK.Google Scholar
  17. Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K, and Madden, T.L. 2009. BLAST+: architecture and applications. BMC Bioinformatics 10, 421.CrossRefGoogle Scholar
  18. Cantarel, B.L., Coutinho, P.M., Rancurel, C., Bernard, T., Lombard, V., and Henrissat, B. 2009. The carbohydrate-active enzymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res. 37, D233–D238.CrossRefGoogle Scholar
  19. Costa, P.S., Reis, M.P., Avila, M.P., Leite, L.R., de Araujo, F.M.G., Salim, A.C.M., Oliveira, G., Barbosa, F., Chartone-Souza, E., and Nascimento, A.M.A. 2015. Metagenome of a microbial community inhabiting a metal-rich tropical stream sediment. PLoS One 10, e0119465.Google Scholar
  20. Dias, A.C.F., Andreote, F.D., Dini-Andreote, F., Lacava, P.T., Sa, A.LB., Melo, I.S., Azevedo, J.L., and Araujo, W.L. 2009. Diversity and biotechnological potential of culturable bacteria from Brazilian mangrove sediment. World J. Microbiol. Biotechnol. 25, 1305–1311.CrossRefGoogle Scholar
  21. Dixon, P. 2003. VEGAN, a package of R functions for community ecology. J. Veg Sci. 14, 927–930.CrossRefGoogle Scholar
  22. Fang, H., Cai, L., Yang, Y., Ju, F., Li, X., Yu, Y., and Zhang, T. 2014. Metagenomic analysis reveals potential biodegradation pathways of persistent pesticides in freshwater and marine sediments. Sci. Total Environ. 470–471, 983–992.Google Scholar
  23. Fernandez, N.F., Gundersen, G.W., Rahman, A., Grimes, MX., Ri-kova, K., Hornbeck, P., and Ma’ayan, A. 2017. Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data. Sci. Data 4, 170151.CrossRefGoogle Scholar
  24. Fu, L., Niu, B., Zhu, Z, Wu, S., and Li, W. 2012. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152.CrossRefGoogle Scholar
  25. Ganesh, S., Parris, D.J., DeLong, E.F., and Stewart, F.J. 2014. Metagenomic analysis of size-fractionated picoplankton in a marine oxygen minimum zone. ISME J. 8, 187–211.CrossRefGoogle Scholar
  26. Giri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., Masek, J., and Duke, N. 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 20, 154–159.CrossRefGoogle Scholar
  27. Hamady, M., Lozupone, C. and Knight, R. 2010. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and Phylo-Chip data. ISME J. 4, 17–27.CrossRefGoogle Scholar
  28. Hattenschwiler, S. and Vitousek, P.M. 2000. The role of polyphenols in terrestrial ecosystem nutrient cycling. Trends Ecol. Evol. 15, 238–243.CrossRefGoogle Scholar
  29. Hedderich, R. and Whitman, W.B. 2013. Physiology and biochemistry of the methane-producing archaea, pp. 635–662. In Rosenberg, E., DeLong, E.F., Lory, S., Stackebrandt, E., and Thompson, F. (eds.), The prokaryotes-2013. Springer Berlin Heidelberg, Berlin, Heidelberg, Germany.CrossRefGoogle Scholar
  30. Holguin, G., Vazquez, P., and Bashan, Y. 2001. The role of sediment microorganisms in the productivity, conservation, and rehabilitation of mangrove ecosystems: an overview. Biol. Fertil. Soils 33, 265–278.CrossRefGoogle Scholar
  31. Hong, K., Gao, A.H., Xie, Q.Y., Gao, H.G., Zhuang, L., Lin, H.P., Yu, H.P., Li, J., Yao, X.S., Goodfellow, M., et al. 2009. Actinomycetes for marine drug discovery isolated from mangrove soils and plants in China. Mar. Drugs 7, 24–44.CrossRefGoogle Scholar
  32. Huang, X.F., Bakker, M.G., Judd, T.M., Reardon, K.F., and Vivanco, J.M. 2013. Variations in diversity and richness of gut bacterial communities of termites (Reticulitermes flavipes) fed with grassy and woody plant substrates. Microb. Ecol. 65, 531–536.CrossRefGoogle Scholar
  33. Huang, H., Lv, J., Hu, Y, Fang, Z., Zhang, K., and Bao, S. 2008. Micromonospora rifamycinica sp. nov., a novel actinomycete from mangrove sediment. Int. J. Syst. Evol. Microbiol. 58, 17–20.CrossRefGoogle Scholar
  34. Huson, D.H., Beier, S., Flade, I., Gorska, A., El-Hadidi, M., Mitra, S., Ruscheweyh, H.J., and Tappu, R. 2016. MEGAN community edition — Interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957.CrossRefGoogle Scholar
  35. Imchen, M., Kumavath, R., Barh, D., Vaz, A., Goes-Neto, A., Tiwari, S., Ghosh, P., Wattani, A.R., and Azevedo, V. 2018. Comparative mangrove metagenome reveals global prevalence of heavy metals and antibiotic resistome across different ecosystems. Sci. Rep. 8, 11187.CrossRefGoogle Scholar
  36. Jiang, X.T., Peng, X., Deng, G.H., Sheng, H.F., Wang, Y., Zhou, H.W., and Tarn, N.F.Y. 2013. Illumina sequencing of 16S rRNA tag revealed spatial variations of bacterial communities in a mangrove wetland. Microb. Ecol. 66, 96–104.CrossRefGoogle Scholar
  37. Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M. 2014. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205.CrossRefGoogle Scholar
  38. Kanokratana, P., Uengwetwanit, T., Rattanachomsri, U., Bunterngsook, B., Nimchua, T., Tangphatsornruang, S., Plengvidhya, V., Champreda, V., and Eurwilaichitr, L. 2011. Insights into the phy-logeny and metabolic potential of a primary tropical peat swamp forest microbial community by metagenomic analysis. Microb. Ecol. 61, 518–528.CrossRefGoogle Scholar
  39. Karlsson, F.H., Fak, F., Nookaew, I., Tremaroli, V., Fagerberg, B., Petranovic, D., Backhed, F., and Nielsen, J. 2012. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat. Commun. 3, 1245.CrossRefGoogle Scholar
  40. Karlsson, F.H., Tremaroli, V., Nookaew, I., Bergstrom, G., Behre, C.J., Fagerberg, B., Nielsen, J., and Backhed, F. 2013. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103.CrossRefGoogle Scholar
  41. Kersters, K., De Vos, P., Gillis, M., Swings, J., Vandamme, P., and Stackebrandt, E. 2006. Introduction to the Proteobacteria, pp. 3–37. In Dworkin, M., Falkow, S., Rosenberg, E., Schleifer, K.H., and Stackebrandt, E. (eds.), The Prokaryotes-2006. Springer New York, New York, USA.CrossRefGoogle Scholar
  42. Kristensen, E., Bouillon, S., Dittmar, T., and Marchand, C. 2008. Organic carbon dynamics in mangrove ecosystems: A review. Aquat. Bot. 89, 201–219.CrossRefGoogle Scholar
  43. Lee, S., Cantarel, B., Henrissat, B., Gevers, D., Birren, B.W., Huttenhower, C., and Ko, G. 2014. Gene-targeted metagenomic analysis of glucan-branching enzyme gene profiles among human and animal fecal microbiota. ISME J. 8, 493–503.CrossRefGoogle Scholar
  44. Li, J., Jia, H., Cai, X., Zhong, H., Feng, Q., Sunagawa, S., Arumugam, M., Kultima, J.R., Prifti, E., Nielsen, T., etal. 2014. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841.CrossRefGoogle Scholar
  45. Mangrola, A.V., Dudhagara, P., Koringa, P., Joshi, C.G., and Patel, R.K. 2015. Shotgun metagenomic sequencing based microbial diversity assessment of Lasundra hot spring, India. Genomics Data 4, 73–75.CrossRefGoogle Scholar
  46. Marcial Gomes, N.C., Borges, L.R., Paranhos, R., Pinto, F.N., Mendonca-Hagler, L.C.S., and Smalla, K. 2008. Exploring the diversity of bacterial communities in sediments of urban mangrove forests. FEMS Microbiol. Ecol. 66, 96–109.CrossRefGoogle Scholar
  47. Mende, D.R., Waller, A.S., Sunagawa, S., Jarvelin, A.I., Chan, M.M., Arumugam, M., Raes, J., and Bork, P. 2012. Assessment of metagenomic assembly using simulated next generation sequencing data. PLoS One 7, e31386.CrossRefGoogle Scholar
  48. Meneghine, A.K., Nielsen, S., Varani, A.M., Thomas, T., and Cara-reto Alves, L.M. 2017. Metagenomic analysis of soil and freshwater from zoo agricultural area with organic fertilization. PLoS One 12, e0190178.CrossRefGoogle Scholar
  49. Ministry of Agriculture of the People’s Republic of China. 2006. Method for determination of soil organic matter. NY/T 1121.6-2006. (in Chinese).Google Scholar
  50. Ministry of Agriculture of the People’s Republic of China. 2007. Determination of pH in soil. NY/T 1377-2007. (in Chinese).Google Scholar
  51. Ministry of Ecology and Environment of the People’s Republic of China. 1997. Soil quality-determination of copper, zinc-flame atomic absorption spectrophotometry. GBT 17138–1997. (in Chinese).Google Scholar
  52. Ministry of Ecology and Environment of the People’s Republic of China. 2016. Soil and sediment-determination of aqua regia extracts of 12 metal elements-inductively coupled plasma mass spectrometry. HJ 803-2016. (in Chinese).Google Scholar
  53. Nagelkerken, I., Blaber, S.J.M., Bouillon, S., Green, P., Haywood, M., Kirton, LG., Meynecke, J.O., Pawlik, J., Penrose, H.M., Sasekumar, A., et al. 2008. The habitat function of mangroves for terrestrial and marine fauna: A review. Aquat. Bot. 89, 155–185.CrossRefGoogle Scholar
  54. Nielsen, H.B., Almeida, M., Juncker, AS., Rasmussen, S., Li, J., Sunagawa, S., Plichta, D.R., Gautier, L., Pedersen, A.G., Le Chatelier, E., et al. 2014. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828.CrossRefGoogle Scholar
  55. Olutona, G.O., Olatunji, S.O., and Obisanya, J.F. 2016. Downstream assessment of chlorinated organic compounds in the bed-sediment of Aiba Stream, Iwo, South-Western, Nigeria. Springerplus 5, 67.CrossRefGoogle Scholar
  56. Opsahl, S. and Benner, R. 1999. Characterization of carbohydrates during early diagenesis of five vascular plant tissues. Org. Geochem. 30, 83–94.CrossRefGoogle Scholar
  57. Oren, A. 2014. The family methanosarcinaceae, pp. 259–281. In Rosenberg, E., DeLong, E.F., Lory, S., Stackebrandt, E., and Thompson, F. (eds.), The prokaryotes-2014. Springer Berlin Heidelberg, Berlin, Heidelberg, Germany.Google Scholar
  58. Parks, D.H., Tyson, G.W., Hugenholtz, P., and Beiko, R.G. 2014. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124.CrossRefGoogle Scholar
  59. Paul, R.S., Ghormade, V., and Deshpande, M.V. 2000. Chitinolytic enzymes: an exploration. Enzyme Microb. Technol. 26, 473–483.CrossRefGoogle Scholar
  60. Powell, S., Forslund, K., Szklarczyk, D., Trachana, K., Roth, A, Huerta-Cepas, J., Gabaldon, T., Rattei, T., Creevey, C., Kuhn, M., et al. 2014. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res. 42, D231–D239.CrossRefGoogle Scholar
  61. Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K.S., Manichanh, C. Nielsen, T., Pons, N., Levenez, F., Yamada, T., et al. 2010. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65.CrossRefGoogle Scholar
  62. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., and Glockner, F.O. 2012. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596.CrossRefGoogle Scholar
  63. Rajendran, N., Saravanakumar, K., and Kathiresan, K. 2016. Mangroves: A potent source of polysaccharides, pp. 393–399. In Kim, S.K. (ed.), Marine glycobiology: principles and applications. CRC Press, Taylor & Francis Group, Boca Raton, USA.CrossRefGoogle Scholar
  64. Ray, R., Majumder, N., Das, S., Chowdhury, C., and Jana, T.K. 2014. Biogeochemical cycle of nitrogen in a tropical mangrove ecosystem, east coast of India. Mar. Chem. 167, 33–43.CrossRefGoogle Scholar
  65. Ronnback, P. 1999. The ecological basis for economic value of seafood production supported by mangrove ecosystems. Ecol. Econ. 29, 235–252.CrossRefGoogle Scholar
  66. Sahoo, K. and Dhal, N.K. 2009. Potential microbial diversity in mangrove ecosystems:A review. Indian J. Mar. Sci. 38, 249–256.Google Scholar
  67. Simoes, M.F., Antunes, A., Ottoni, C.A., Amini, M.S., Alam, I., Alzu-baidy, H., Mokhtar, N.A., Archer, J.A.C., and Bajic, V.B. 2015. Soil and rhizosphere associated fungi in gray mangroves (Avicennia marina) from the Red Sea — A metagenomic approach. Genomics Proteomics Bioinformatics 13, 310–320.CrossRefGoogle Scholar
  68. Soares, F., Marcon, J., Pereira e Silva, M., Khakhum, N., Cerdeira, L, Ottoni, J., Domingos, D., Taketani, R., de Oliveira, V.M., Lima, A.O.S., et al. 2017. A novel multifunctional β-N-acetylhexosa-minidase revealed through metagenomics of an oil-spilled mangrove. Bio engineering 4, 62.Google Scholar
  69. Standardization Administration of the People’s Republic of China. 2008. Soil quality — Analysis of total mercury, arsenic and lead contents in soils — Atomic fluorescence spectrometry — Part 1: analysis of total mercury contents in soils. GB/T 22105.1-2008. (in Chinese).Google Scholar
  70. Standardization Administration of the People’s Republic of China. 2008. Soil quality — Analysis of total mercury, arsenic and lead contents in soils — Atomic fluorescence spectrometry — Part 2: Analysis of total arsenic contents in soils. GB/T 22105.2-2008. (in Chinese).Google Scholar
  71. Thatoi, H., Behera, B.C., Mishra, R.R., and Dutta, S.K. 2013. Biodiversity and biotechnological potential of microorganisms from mangrove ecosystems: a review. Ann. Microbiol. 63, 1–19.CrossRefGoogle Scholar
  72. Thompson, C., Beys-da-Silva, W., Santi, L., Berger, M., Vainstein, M., Guima raes, J., and Vasconcelos, A.T. 2013. A potential source for cellulolytic enzyme discovery and environmental aspects revealed through metagenomics of Brazilian mangroves. AMB Express 3, 65.CrossRefGoogle Scholar
  73. Thompson, F.L., Iida, T., and Swings, J. 2004. Biodiversity of vibrios. Microbiol. Mol. Biol. Rev. 68, 403–431.CrossRefGoogle Scholar
  74. Walters, B.B., Ronnback, P., Kovacs, J.M., Crona, B., Hussain, S.A., Badola, R., Primavera, J.H., Barbier, E., and Dahdouh-Guebas, F. 2008. Ethnobiology, socio-economics and management of mangrove forests: A review. Aquat. Bot. 89, 220–236.CrossRefGoogle Scholar
  75. Wong, M.T., Wang, W., Lacourt, M., Couturier, M., Edwards, E.A., and Master, E.R. 2016. Substrate-driven convergence of the microbial community in lignocellulose-amended enrichments of gut microflora from the Canadian beaver (Castor canadensis) and north American moose (Alces americanus). Front. Microbiol. 7, 961.Google Scholar
  76. Yang, Q., Sun, F., Yang, Z., and Li, H. 2014. Comprehensive transcriptome study to develop molecular resources of the copepod Calanus sinicus for their potential ecological applications. Biomed Res. Int. 2014, 1–12.Google Scholar

Copyright information

© The Microbiological Society of Korea 2019

Authors and Affiliations

  • Huaxian Zhao
    • 1
  • Bing Yan
    • 2
  • Shuming Mo
    • 1
  • Shiqing Nie
    • 1
  • Quanwen Li
    • 1
  • Qian Ou
    • 1
  • Bo Wu
    • 1
  • Gonglingxia Jiang
    • 3
  • Jinli Tang
    • 3
  • Nan Li
    • 3
    Email author
  • Chengjian Jiang
    • 1
    • 2
    Email author
  1. 1.State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and TechnologyGuangxi UniversityGuangxiP. R. China
  2. 2.Guangxi Key Laboratory of Mangrove Conservation and Utilization, Guangxi Mangrove Research CenterGuangxi Academy of SciencesGuangxiP. R. China
  3. 3.Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Guangxi Teachers Education University)Ministry of EducationGuangxiP. R. China

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