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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

  • Maria B. Grieco
  • Fabyano A. C. Lopes
  • Louisi S. Oliveira
  • Diogo A. Tschoeke
  • Claudia C. Popov
  • Cristiane C. Thompson
  • Luna C. Gonçalves
  • Reginaldo Constantino
  • Orlando B. Martins
  • Ricardo H. Kruger
  • Wanderley de Souza
  • Fabiano L. ThompsonEmail author
Article

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.

Notes

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).

Compliance with Ethical Standards

Conflict of interest

All the authors declare no conflict of interest regarding this manuscript.

Supplementary material

284_2019_1662_MOESM1_ESM.xlsx (14 kb)
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)
284_2019_1662_MOESM2_ESM.xlsx (7.3 mb)
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)
284_2019_1662_MOESM3_ESM.xlsx (12 kb)
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)
284_2019_1662_MOESM4_ESM.tif (1.2 mb)
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)
284_2019_1662_MOESM5_ESM.tif (5.1 mb)
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 (22 kb)
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 (27 kb)
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 (17.5 mb)
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 (15.5 mb)
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 (1.9 mb)
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 (745 kb)
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 (578 kb)
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 (9 kb)
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)
284_2019_1662_MOESM14_ESM.xlsx (2.5 mb)
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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Maria B. Grieco
    • 1
    • 6
  • Fabyano A. C. Lopes
    • 2
    • 3
  • Louisi S. Oliveira
    • 2
  • Diogo A. Tschoeke
    • 2
  • Claudia C. Popov
    • 2
  • Cristiane C. Thompson
    • 2
  • Luna C. Gonçalves
    • 1
    • 2
  • Reginaldo Constantino
    • 3
  • Orlando B. Martins
    • 4
  • Ricardo H. Kruger
    • 3
  • Wanderley de Souza
    • 1
    • 5
  • Fabiano L. Thompson
    • 2
    Email author
  1. 1.Divisão de Metrologia Aplicada às Ciências da VidaInstituto Nacional de Metrologia Qualidade e Tecnologia (Inmetro)Rio de JaneiroBrazil
  2. 2.Instituto de BiologiaUniversidade Federal do Rio de Janeiro (UFRJ)Rio de JaneiroBrazil
  3. 3.Instituto de BiologiaUniversidade de Brasília (UnB)BrasíliaBrazil
  4. 4.Instituto de Bioquímica Médica Leopoldo de MeisUniversidade Federal do Rio de Janeiro (UFRJ)Rio de JaneiroBrazil
  5. 5.Instituto de Biofísica Carlos Chagas FilhoUniversidade Federal do Rio de Janeiro (UFRJ)Rio de JaneiroBrazil
  6. 6.Departamento de Engenharia Bioquímica, Escola de QuímicaUniversidade Federal do Rio de Janeiro (UFRJ)Rio de JaneiroBrazil

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