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Metagenomic Analysis of the Whole Gut Microbiota in Brazilian Termitidae Termites Cornitermes cumulans, Cyrilliotermes strictinasus, Syntermes dirus, Nasutitermes jaraguae, Nasutitermes aquilinus, Grigiotermes bequaerti, and Orthognathotermes mirim

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Abstract

Although some previous studies have described the microbial diversity of termite in Brazil, the lack of studies about this subject is still evident. In the present study, we described by whole genome sequencing, the gut microbiota of seven species of termites (Termitidae) with different feeding habits from four Brazilian locations. For the litter species, the most abundant bacterial phylum was Firmicutes, where Cornitermes cumulans and Syntermes dirus (Syntermitinae) were identified. For the humus species, the most abundant bacterial phylum was Proteobacteria where three species were studied: Cyrilliotermes strictinasus (Syntermitinae), Grigiotermes bequaerti (Apicotermitinae), and Orthognathotermes mirim (Termitinae). For the wood termites, Firmicutes and Spirochaetes were the most abundant phyla, respectively, where two species were identified: Nasutitermes aquilinus and Nasutitermes jaraguae (Nasutitermitinae). The gut microbiota of all four examined subfamilies shared a conserved functional and carbohydrate-active enzyme profile and specialized in cellulose and chitin degradation. Taken together, these results provide insight into the partnerships between termite and microbes that permit the use of refractory energy sources.

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References

  1. Cleveland LR (1923) Symbiosis between termites and their intestinal protozoa. Proc Natl Acad Sci USA 9:424–428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Donovan SE, Eggleton P, Bignell DE (2001) Gut content analysis and a new feeding group classification of termites. Ecol Entomol 26:356–366

    Article  Google Scholar 

  3. Eggleton P (2010) An introduction to termites: biology, taxonomy and functional morphology. In: Bignell DE, Roisin Y, Lo N (eds) Biology of termites: a modern synthesis. Springer, New York, pp 1–26

    Google Scholar 

  4. Brune A (2014) Symbiotic digestion of lignocellulose in termite guts. Nat Rev Microbiol 12:168–180. https://doi.org/10.1038/nrmicro3182

    Article  CAS  PubMed  Google Scholar 

  5. Bignell DE (1994) Soil-feeding and gut morphology in higher termites. In: Hunt JH, Nalepa CA Nourishment and evolution in insect societies. Westview Press, Boulder, pp 131–158

    Google Scholar 

  6. Brune A (1998) Termite guts: the world’s smallest bioreactors. Trends Biotechnol 16:16–21

    Article  CAS  Google Scholar 

  7. Tholen A, Brune A (1999) Localization and in situ activities of homoacetogenic bacteria in the highly compartmentalized hindgut of soil-feeding higher termites (Cubitermes spp.). Appl Environ Microbiol 65:4497–4505

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Watanabe H, Tokuda G (2010) Cellulolytic systems in insects. Annu Rev Entomol 55:609–632

    Article  CAS  PubMed  Google Scholar 

  9. Su L, Yang L, Huang S et al (2016) Comparative gut microbiomes of four species representing the higher and the lower termites. J Insect Sci 16:97. https://doi.org/10.1093/jisesa/iew081

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tai V, James ER, Nalepa CA et al (2015) The role of host phylogeny varies in shaping microbial diversity in the hindguts of lower termites. Appl Environ Microbiol 81:1059–1070

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Brune A, Ohkuma M (2010) Role of the termite gut microbiota in symbiotic digestion. In: Bignell DE, Roisin Y, Lo N (eds) Biology of termites: a modern synthesis. Springer, New York, pp 439–475

    Chapter  Google Scholar 

  12. Warnecke F, Luginbühl P, Ivanova N et al (2007) Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 450:560–565

    Article  CAS  PubMed  Google Scholar 

  13. Zhang S, Hu B, Wei W et al (2016) De novo analysis of Wolfiporia cocos transcriptome to reveal the differentially expressed carbohydrate-active enzymes (CAZymes) genes during the early stage of sclerotial growth. Front Microbiol 7(FEB):1–8. https://doi.org/10.3389/fmicb.2016.00083

    Article  Google Scholar 

  14. Andrade AC, Fróes A, Lopes FAC, Thompson FL et al (2017) Diversity of microbial carbohydrate- active enZYmes (CAZYmes) associated with freshwater and soil samples from Caatinga biome. Microb Ecol 74(1):89–105. https://doi.org/10.1007/s00248-016-0911-9

    Article  CAS  PubMed  Google Scholar 

  15. Ioannidis P, Lengelle J, Martin F (2013) Functional assays and metagenomic analyses reveals differences between the microbial communities inhabiting the soil horizons of a norway spruce plantation 8(2):e55929. https://doi.org/10.1371/journal.pone.0055929

  16. Munir RI, Schellenberg J, Henrissat B et al (2014) Comparative analysis of carbohydrate active enzymes in Clostridium termitidis CT1112 reveals complex carbohydrate degradation ability. PLoS ONE, 9(8). https://doi.org/10.1371/journal.pone.0104260

  17. Venter JC, Remington K, Heidelberg JF et al (2004) Environmental genome shotgun sequencing of the Sargasso Sea. Science 304:66–74. https://doi.org/10.1126/science.1093857

    Article  PubMed  Google Scholar 

  18. Scharf ME (2015) Omic research in termites: an overview and a roadmap. Front Genet 6:76

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Köhler T, Dietrich C, Scheffrahn RH, Brune A (2012) High-resolution analysis of gut environment and bacterial microbiota reveals functional compartmentation of the gut in wood-feeding higher termites (Nasutitermes spp.). Appl Environ Microbiol 78:4691–4701

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Dietrich C, Köhler T, Brune A (2014) The cockroach origin of the termite gut microbiota: patterns in bacterial community structure reflect major evolutionary events. Appl Environ Microbiol 80:2261–2269

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Butera G, Ferraro C, Alonzo G et al (2016) The gut microbiota of the wood-feeding termite Reticulitermes lucifugus (Isoptera; Rhinotermitidae). Ann Microbiol 66:253–260

    Article  CAS  Google Scholar 

  22. He S, Ivanova N, Kirton E et al (2013) Comparative metagenomic and metatranscriptomic analysis of hindgut paunch microbiota in wood-and dung-feeding higher termites. PLoS ONE 8:e61126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Abdul Rahman N, Parks DH, Willner DL et al (2015) A molecular survey of Australian and North American termite genera indicates that vertical inheritance is the primary force shaping termite gut microbiomes. Microbiome 3:5. https://doi.org/10.1186/s40168-015-0067-8

    Article  PubMed  PubMed Central  Google Scholar 

  24. Mikaelyan A, Köhler T, Lampert N et al (2015) Classifying the bacterial gut microbiota of termites and cockroaches: a curated phylogenetic reference database (DictDb). Syst Appl Microbiol 38:472–482. https://doi.org/10.1016/j.syapm.2015.07.004

    Article  CAS  PubMed  Google Scholar 

  25. Radek R, Meuser K, Strassert JF et al (2017) Exclusive gut flagellates of Serritermitidae suggest a major transfaunation event in lower termites: description of Heliconympha glossotermitis gen. nov. spec. nov. J Eukaryot Microbiol 65(1):77–92

    Article  CAS  PubMed  Google Scholar 

  26. Mikaelyan A, Dietrich C, Köhler T et al (2015) Diet is the primary determinant of bacterial community structure in the guts of higher termites. Mol Ecol 24:5284–5295. https://doi.org/10.1111/mec.13376

    Article  CAS  PubMed  Google Scholar 

  27. Mikaelyan A, Meuser K, Brune A (2016) Microenvironmental heterogeneity of gut compartments drives bacterial community structure in wood- and humus-feeding higher termites. FEMS Microbiol Ecol. https://doi.org/10.1093/femsec/fiw210

    Article  PubMed  Google Scholar 

  28. Duarte S, Duarte M, Borges PV, Nunes L (2016) Dietary-driven variation effects on the symbiotic flagellate protist communities of the subterranean termite Reticulitermes grassei Clément. J Appl Entomol. https://doi.org/10.1111/jen.12331

    Article  Google Scholar 

  29. Chevreux B, Pfisterer T, Drescher B et al (2004) Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res 14:1147–1159

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Schmieder R, Edwards R (2011) Quality control and preprocessing of metagenomic datasets. Bioinformatics 27:863–864. https://doi.org/10.1093/bioinformatics/btr026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Meyer F, Paarmann D, D’Souza M et al (2008) The metagenomics RAST server—a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform 9:386. https://doi.org/10.1186/1471-2105-9-386

    Article  CAS  Google Scholar 

  32. Overbeek R, Begley T, Butler RM et al (2005) The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 33:5691–5702. https://doi.org/10.1093/nar/gki866

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wilke A, Harrison T, Wilkening J et al (2012) The M5nr: a novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools. BMC Bioinform 13:141

    Article  CAS  Google Scholar 

  34. Lombard V, Golaconda Ramulu H, Drula E et al (2013) The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42:D490–D495

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Camacho C, Coulouris G, Avagyan V et al (2009) BLAST+: architecture and applications. BMC Bioinform 10:421

    Article  CAS  Google Scholar 

  36. Rho M, Tang H, Ye Y (2010) FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res 38:e191–e191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Rossmassler K, Dietrich C, Thompson C et al (2015) Metagenomic analysis of the microbiota in the highly compartmented hindguts of six wood-or soil-feeding higher termites. BioMed Central

  38. Parks DH, Tyson GW, Hugenholtz P, Beiko RG (2014) STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30:3123–3124. https://doi.org/10.1093/bioinformatics/btu494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  40. Oksanen J, Blanchet FG, Kindt R et al (2016) vegan: Community Ecology Package

  41. Hill MO (1973) Diversity and evenness: a unifying notation and its consequences. Ecology 54:427–432. https://doi.org/10.2307/1934352

    Article  Google Scholar 

  42. Hongoh Y, Deevong P, Hattori S et al (2006) Phylogenetic diversity, localization, and cell morphologies of members of the candidate phylum TG3 and a subphylum in the phylum Fibrobacteres, recently discovered bacterial groups dominant in termite guts. Appl Environ Microbiol 72:6780–6788

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Egert M, Wagner B, Lemke T et al (2003) Microbial community structure in midgut and hindgut of the humus-feeding larva of Pachnoda ephippiata (Coleoptera: Scarabaeidae). Appl Environ Microbiol 69:6659–6668

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Thongaram T, Hongoh Y, Kosono S et al (2005) Comparison of bacterial communities in the alkaline gut segment among various species of higher termites. Extremophiles 9:229–238. https://doi.org/10.1007/s00792-005-0440-9

    Article  PubMed  Google Scholar 

  45. Husseneder C, Ho H-Y, Blackwell M (2010) Comparison of the bacterial symbiont composition of the Formosan subterranean termite from its native and introduced range. Open Microbiol J 4:53

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Santana RH, Catão ECP, Lopes FAC et al (2015) The gut microbiota of workers of the litter-feeding termite syntermes wheeleri (Termitidae: Syntermitinae): archaeal, bacterial, and fungal communities. Microb Ecol 70:545–556. https://doi.org/10.1007/s00248-015-0581-z

    Article  PubMed  Google Scholar 

  47. Diouf M, Roy V, Mora P et al (2015) Profiling the succession of bacterial communities throughout the life stages of a higher termite Nasutitermes arborum (Termitidae, Nasutitermitinae) using 16S rRNA gene pyrosequencing. PLoS ONE 10:e0140014. https://doi.org/10.1371/journal.pone.0140014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Laffont ER, Torales GJ, Coronel JM et al (2004) Termite (Insecta, Isoptera) fauna from natural parks of the northeast region of Argentina. Sci Agric 61:665–670. https://doi.org/10.1590/S0103-90162004000600016

    Article  Google Scholar 

  49. Ben Guerrero E, Arneodo J, Bombarda Campanha R et al (2015) Prospection and evaluation of (Hemi) cellulolytic enzymes using untreated and pretreated biomasses in two Argentinean native termites. PLoS ONE 10:e0136573. https://doi.org/10.1371/journal.pone.0136573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Grieco MAB, Cavalcante JJ, Cardoso AM et al (2013) Microbial community diversity in the gut of the South American termite Cornitermes cumulans (Isoptera: Termitidae). Microb Ecol 65:197–204

    Article  PubMed  Google Scholar 

  51. Brauman A, Doré J, Eggleton P et al (2001) Molecular phylogenetic profiling of prokaryotic communities in guts of termites with different feeding habits. FEMS Microbiol Ecol 35:27–36. https://doi.org/10.1111/j.1574-6941.2001.tb00785.x

    Article  CAS  PubMed  Google Scholar 

  52. Lefebvre T, Miambi E, Pando A et al (2009) Gut-specific actinobacterial community structure and diversity associated with the wood-feeding termite species, Nasutitermes corniger (Motschulsky) described by nested PCR-DGGE analysis. Insect Soc 56:269–276. https://doi.org/10.1007/s00040-009-0020-6

    Article  Google Scholar 

  53. Hongoh Y, Deevong P, Inoue T et al (2005) Intra- and interspecific comparisons of bacterial diversity and community structure support coevolution of gut microbiota and termite host. Appl Environ Microbiol 71(11):6590–6599. https://doi.org/10.1128/AEM.71.11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ohkuma M, Brune A (2011) Diversity, structure, and evolution of the termite gut microbial community. In: Bignell ED, Roisin Y, Lo N (eds) Biology of termites: a modern synthesis. Springer, Dordrecht, pp 413–438

    Google Scholar 

  55. Brune A, Dietrich C (2015) The Gut microbiota of termites: digesting the diversity in the light of ecology and evolution. Annu Rev Microbiol 69:145–166. https://doi.org/10.1146/annurev-micro-092412-155715

    Article  CAS  PubMed  Google Scholar 

  56. Konig H, Li L, Fröhlich J (2013) The cellulolytic system of the termite gut. Appl Microbiol Biotechnol 97:7943–7962. https://doi.org/10.1007/s00253-013-5119-z

    Article  CAS  PubMed  Google Scholar 

  57. Schäfer A, Konrad R, Kuhnigk T, Kämpfer T et al (1996) Hemicellulose-degrading bacteria and yeasts from the termite gut. J Appl Microbiol 80:471–478

    Google Scholar 

  58. Peterson BF, Stewart HL, Scharf ME (2015) Quantification of symbiotic contributions to lower termite lignocellulose digestion using antimicrobial treatments. Insect Biochem Mol Biol 59:80–88. https://doi.org/10.1016/j.ibmb.2015.02.009

    Article  CAS  PubMed  Google Scholar 

  59. Todaka N, Inoue T, Saita K et al (2010) Phylogenetic analysis of cellulolytic enzyme genes from representative lineages of termites and a related cockroach. PloS One 5:e8636. https://doi.org/10.1371/journal.pone.0008636

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Sethi A, Kovaleva ES, Slack JM et al (2013) A GHF7 cellulase from the protist symbiont community of Reticulitermes flavipes enables more efficient lignocellulose processing by host enzymes. Arch Insect Biochem Physiol 84:175–193. https://doi.org/10.1002/arch.21135

    Article  CAS  PubMed  Google Scholar 

  61. Inoue J-I, Saita K, Kudo T et al (2007) Hydrogen production by termite gut protists: characterization of iron hydrogenases of parabasalian symbionts of the termite Coptotermes formosanus. Eukaryot Cell 6:1925–1932. https://doi.org/10.1128/EC.00251-07

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Sethi A, Xue Q-G, La Peyre JF et al (2011) Dual origin of gut proteases in Formosan subterranean termites (Coptotermes formosanus Shiraki) (Isoptera: Rhinotermitidae). Comp Biochem Physiol A Mol Integr Physiol 159:261–267. https://doi.org/10.1016/j.cbpa.2011.03.012

    Article  CAS  PubMed  Google Scholar 

  63. Ni J, Tokuda G (2013) Lignocellulose-degrading enzymes from termites and their symbiotic microbiota. Biotechnol Adv 31:838–850

    Article  CAS  PubMed  Google Scholar 

  64. Vasconcelos EA, Santana CG, Godoy CV et al (2011) A new chitinase-like xylanase inhibitor protein (XIP) from coffee (Coffea arabica) affects Soybean Asian rust (Phakopsora pachyrhizi) spore germination. BMC Biotechnol 11:14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Ebringerova A, Heinze T (2000) Xylan and xylan derivatives—biopolymers with valuable properties, 1. Naturally occurring xy9ans structures, isolation procedures and properties. Macromol Rapid Commun 21:542–556

    Article  CAS  Google Scholar 

  66. Miranda CS, Vasconcellos A, Bandeira AG (2004) Termites in sugar cane in Northeast Brazil: ecological aspects and pest status. Neotrop Entomol 33:237–241

    Article  Google Scholar 

  67. Constantino R (2002) The pest termites of South America: taxonomy, distribution and status. J Appl Entomol 126:355–365

    Article  Google Scholar 

  68. Roy V, Constantino R, Chassany V et al (2014) Species delimitation and phylogeny in the genus Nasutitermes (Termitidae: Nasutitermitinae) in French Guiana. Mol Ecol 23:902–920

    Article  CAS  PubMed  Google Scholar 

  69. Constantino R (1995) Revision of the neotropical termite genus Syntermes Holmgren (Isoptera: Termitidae). Univ Kans Sci Bull 55:455–518

    Google Scholar 

  70. Krishna K, Grimaldi DA, Krishna V, Engel MS (2013) Treatise on the Isoptera of the WORLD: volume 4 Termitidae (part one). Bull Am Mus Nat Hist 377:973–1495

    Article  Google Scholar 

  71. Oguma T, Tobe K, Kobayashi M (1994) Purification and properties of a novel enzyme from Bacillus spp. T-3040, which catalyses the conversion of dextran to cyclic isomaltooligosaccharides. FEBS Lett 345:135–138

    Article  CAS  PubMed  Google Scholar 

  72. Funane K, Kawabata Y, Suzuki R et al (2011) Deletion analysis of regions at the C-terminal part of cycloisomaltooligosaccharide glucanotransferase from Bacillus circulans T-3040. Biochim Biophys Acta BBA-Proteins Proteomics 1814:428–434

    Article  CAS  PubMed  Google Scholar 

  73. Inoue T, Kitade O, Yoshimura T, Yamaoka I (2000) Symbiotic associations with protists. In: Termites: evolution, sociality, symbioses, ecology. Springer, New York, pp 275–288

  74. Beckwith TD, Light SF (1927) The spirals within the termite gut for class use. Science 66:656–657. https://doi.org/10.1126/science.66.1722.656-b

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We are grateful for the support offered by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Grant No. ID0EUPAE6279), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) (Grant No. ID0ENSAE6280).

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Author notes

  1. Maria B. Grieco

    Present address: Departamento de Engenharia Bioquímica, Escola de Química, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil

  2. Maria B. Grieco and Fabyano A. C. Lopes contributed equally to this work.

Authors and Affiliations

  1. Divisão de Metrologia Aplicada às Ciências da Vida, Instituto Nacional de Metrologia Qualidade e Tecnologia (Inmetro), Rio de Janeiro, RJ, Brazil

    Maria B. Grieco, Luna C. Gonçalves & Wanderley de Souza

  2. Instituto de Biologia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil

    Fabyano A. C. Lopes, Louisi S. Oliveira, Diogo A. Tschoeke, Claudia C. Popov, Cristiane C. Thompson, Luna C. Gonçalves & Fabiano L. Thompson

  3. Instituto de Biologia, Universidade de Brasília (UnB), Brasília, DF, Brazil

    Fabyano A. C. Lopes, Reginaldo Constantino & Ricardo H. Kruger

  4. Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil

    Orlando B. Martins

  5. Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil

    Wanderley de Souza

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284_2019_1662_MOESM1_ESM.xlsx

Supplementary material 1 Online Resource 1 Relative abundance of the 10 most abundant Eukarya phyla and all protozoa in higher termite gut metagenomes. Sequences were classified using the MG-RAST server and M5NR database. All sequences assigned to Insecta were removed to avoid contamination (XLSX 14 KB)

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Supplementary material 2 Online Resource 2 Relative abundance of each domain, phylum, class, order, family, genus, and species in higher termite gut metagenomes. Sequences were classified using the MG-RAST server and M5NR database. Relative abundance was based on the normalized abundance for each metagenome using the read depth in the respective assembly (read depth of 1 for unassembled reads) (XLSX 7433 KB)

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Supplementary material 3 Online Resource 3 Number of reads for domains in each higher termite gut based on metagenome sequencing. Sequences were classified using the MG-RAST server and M5NR database (XLSX 11 KB)

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Supplementary material 4 Online Resource 4 Comparison of S. dirus gut microbiota (with those of other higher termites at SEED level 1) based on metagenome sequencing. Termite subfamilies were compared by t-test (p < 0.05) with the Bonferroni correction using STAMP software. Sequences were classified using the MG-RAST server and M5NR database (TIF 1269 KB)

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Supplementary material 5 Online Resource 5 Rarefaction curves of higher termite gut metagenomes. a Rarefaction curve based on taxonomic profile (species level). b Rarefaction curve based on functional profile (SEED level 3). Sequences were classified using the M5NR database for taxonomy and SEED database for functional profile with default sequence quality thresholds using the MG-RAST server (TIF 5260 KB)

284_2019_1662_MOESM6_ESM.xlsx

Supplementary material 6 Online Resource 6 The most representative CAZyme families in higher termite gut metagenomes. Each metagenome was annotated based on similarity to sequences in the CAZy database by BLASTp similarity search of codon regions predicted by FragGeneScan, using the default parameters and e-value of 0.01. Relative abundance was calculated separately for each metagenome (XLSX 22 KB)

284_2019_1662_MOESM7_ESM.xlsx

Supplementary material 7 Online Resource 7 Abundance of glycoside hydrolases (GH) CAZyme families in higher termite gut metagenomes. Each metagenome was annotated based on similarity to sequences in the CAZy database by BLASTp similarity search of the codon regions predicted by FragGeneScan, using the default parameters and e-value of 0.01. Relative abundance was calculated separately for each metagenome (XLSX 27 KB)

284_2019_1662_MOESM8_ESM.xlsx

Supplementary material 8 Online Resource 8 Relative abundance of genera that contributed to glycoside hydrolases (GHs) assignments in higher termite gut metagenomes. Each metagenome was annotated based on similarity to sequences in the CAZy database by BLASTp similarity search of the codon regions predicted by FragGeneScan, using the default parameters and e-value of 0.01. Relative abundance was based on the normalized abundance for each metagenome using the read depth in the respective assembly (read depth of 1 for unassembled reads). Relative abundance was calculated for each metagenome separately (XLSX 17922 KB)

284_2019_1662_MOESM9_ESM.xlsx

Supplementary material 9 Online Resource 9 Relative abundance of genera that contributed to glycosil transferases (GTs) assignments in higher termite gut metagenomes. Each metagenome was annotated based on similarity to sequences in the CAZy database by BLASTp similarity search of the codon regions predicted by FragGeneScan, using the default parameters and e-value of 0.01. Relative abundance was based on the normalized abundance for each metagenome using the read depth in the respective assembly (read depth of 1 for unassembled reads). Relative abundance was calculated for each metagenome separately (XLSX 15828 KB)

284_2019_1662_MOESM10_ESM.xlsx

Supplementary material 10 Online Resource 10 Relative abundance of genera that contributed to carbohydrate esterases (CEs) assignments in higher termite gut metagenomes. Each metagenome was annotated based on similarity to sequences in the CAZy database by BLASTp similarity search of the codon regions predicted by FragGeneScan, using the default parameters and e-value of 0.01. Relative abundance was based on the normalized abundance for each metagenome using the read depth in the respective assembly (read depth of 1 for unassembled reads). Relative abundance was calculated for each metagenome separately (XLSX 1971 KB)

284_2019_1662_MOESM11_ESM.xlsx

Supplementary material 11 Online Resource 11 Relative abundance of genera that contributed to polysaccharide lyases (PLs) assignments in higher termite gut metagenomes. Each metagenome was annotated based on similarity to sequences in the CAZy database by BLASTp similarity search of the codon regions predicted by FragGeneScan, using the default parameters and e-value of 0.01. Relative abundance was based on the normalized abundance for each metagenome using the read depth in the respective assembly (read depth of 1 for unassembled reads). Relative abundance was calculated for each metagenome separately (XLSX 745 KB)

284_2019_1662_MOESM12_ESM.xlsx

Supplementary material 12 Online Resource 12 Relative abundance of genera that contributed to enzymes with auxiliary activities (AAs) assignments in higher termite gut metagenomes. Each metagenome was annotated based on similarity to sequences in the CAZy database by BLASTp similarity search of the codon regions predicted by FragGeneScan, using the default parameters and e-value of 0.01. Relative abundance was based on the normalized abundance for each metagenome using the read depth in the respective assembly (read depth of 1 for unassembled reads). Relative abundance was calculated for each metagenome separately (XLSX 577 KB)

284_2019_1662_MOESM13_ESM.xlsx

Supplementary material 13 Online Resource 13 Relative abundance of glycoside hydrolase (GH) families from Adineta in higher termite gut metagenomes. Each metagenome was annotated based on similarity to sequences in the CAZy database by BLASTp similarity search of the codon regions predicted by FragGeneScan, using the default parameters and e-value of 0.01 (XLSX 9 KB)

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Supplementary material 14 Online Resource 14 Number of hits for glycoside hydrolase (GH) CAZyme family genes of each higher termite gut metagenome. The 10 most abundant genes for each metagenome are highlighted. Each metagenome was annotated based on similarity to sequences in the CAZy database by BLASTp similarity search of the codon regions predicted by FragGeneScan, using the default parameters and e-value of 0.01. Relative abundance was based on the normalized abundance for each metagenome using the read depth in the respective assembly (read depth of 1 for unassembled reads) (XLSX 2532 KB)

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Grieco, M.B., Lopes, F.A.C., Oliveira, L.S. et al. Metagenomic Analysis of the Whole Gut Microbiota in Brazilian Termitidae Termites Cornitermes cumulans, Cyrilliotermes strictinasus, Syntermes dirus, Nasutitermes jaraguae, Nasutitermes aquilinus, Grigiotermes bequaerti, and Orthognathotermes mirim. Curr Microbiol 76, 687–697 (2019). https://doi.org/10.1007/s00284-019-01662-3

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  • DOI: https://doi.org/10.1007/s00284-019-01662-3

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