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Bacterial genome reconstruction and community profiling in Neotropical Drosophila

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Abstract

Drosophila species serve as key models for microbiota research due to their relatively simple microbial communities. However, microbial diversity and dynamics in Neotropical Andean Drosophila remain underexplored. Here we applied shotgun metagenomics to characterize the microbiota of 24 Neotropical Drosophila species from Ecuador, reconstructing 64 high-quality bacterial genomes predominantly from Acetobacteraceae and Enterobacterales. Microbial communities were consistently dominated by yeasts, lactic acid bacteria, acetic acid bacteria, and Wolbachia. Comparative analyses revealed no strong correlation between host phylogeny and microbial community composition, suggesting environmental factors and microbial interactions shape these communities. Notably, shifts in relative abundances indicate dynamic ecological succession and metabolic cooperation among microbes. These findings expand genomic resources for Drosophila-associated bacteria and highlight the complex ecological processes influencing host–microbiota relationships in natural populations.

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Data availability

The *Drosophila* -filtered metagenomic libraries and high-quality MAGs derived from this study have been deposited in the European Nucleotide Archive (ENA) under Project ID PRJEB70495.

References

  1. Douglas, A. E. Simple animal models for microbiome research. Nat. Rev. Microbiol. 17, 764–775 (2019).

    Google Scholar 

  2. Erkosar, B., Storelli, G., Defaye, A. & Leulier, F. Host-intestinal microbiota mutualism: “Learning on the fly”. Cell Host Microbe 13, 8–14 (2013).

    Google Scholar 

  3. Ludington, W. B. & Ja, W. W. Drosophila as a model for the gut microbiome. PLoS Pathog. 16, e1008398 (2020).

    Google Scholar 

  4. Chaston, J. M., Dobson, A. J., Newell, P. D. & Douglas, A. E. Host genetic control of the microbiota mediates the Drosophila nutritional phenotype. Appl. Environ. Microbiol. 82, 671–679 (2016).

    Google Scholar 

  5. Delbare, S. Y. N., Ahmed-Braimah, Y. H., Wolfner, M. F. & Clark, A. G. Interactions between the microbiome and mating influence the female’s transcriptional profile in Drosophila melanogaster. Sci. Rep. 10, 18168 (2020).

    Google Scholar 

  6. McMullen, J. G. II., Peters-Schulze, G., Cai, J., Patterson, A. D. & Douglas, A. E. How gut microbiome interactions affect nutritional traits of Drosophila melanogaster. J. Exp. Biol. 223, jeb227843 (2020).

    Google Scholar 

  7. Leitão-Gonçalves, R. et al. Commensal bacteria and essential amino acids control food choice behavior and reproduction. PLoS Biol. 15, e2000862 (2017).

    Google Scholar 

  8. Sommer, A. J. & Newell, P. D. Metabolic basis for mutualism between gut bacteria and its impact on the Drosophila melanogaster host. Appl. Environ. Microbiol. 85, e01882-e1918 (2019).

    Google Scholar 

  9. Storelli, G. et al. Lactobacillus plantarum promotes Drosophila systemic growth by modulating hormonal signals through TOR-Dependent nutrient sensing. Cell Metab. 14, 403–414 (2011).

    Google Scholar 

  10. Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).

    Google Scholar 

  11. Zheng, D., Liwinski, T. & Elinav, E. Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492–506 (2020).

    Google Scholar 

  12. Valles-Colomer, M. et al. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat. Microbiol. 4, 623–632 (2019).

    Google Scholar 

  13. Jia, Y. et al. Gut microbiome modulates Drosophila aggression through octopamine signaling. Nat. Commun. 12, 2698 (2021).

    Google Scholar 

  14. Chandler, J. A., Lang, J. M., Bhatnagar, S., Eisen, J. A. & Kopp, A. Bacterial communities of diverse Drosophila species: Ecological context of a host-microbe model system. PLoS Genet. 7, e1002272 (2011).

    Google Scholar 

  15. Combe, B. E. et al. Drosophila microbiota modulates host metabolic gene expression via IMD/NF-κB signaling. PLoS ONE 9, e94729 (2014).

    Google Scholar 

  16. Chandler, J. A., Eisen, J. A. & Kopp, A. Yeast communities of diverse Drosophila species: comparison of two symbiont groups in the same hosts. Appl. Environ. Microbiol. 78, 7327–7336 (2012).

    Google Scholar 

  17. Brown, J. J. et al. Microbiome structure of a wild Drosophila community along tropical elevational gradients and comparison to laboratory lines. Appl. Environ. Microbiol. 89, e00099-e123 (2023).

    Google Scholar 

  18. Staubach, F., Baines, J. F., Künzel, S., Bik, E. M. & Petrov, D. A. Host species and environmental effects on bacterial communities associated with Drosophila in the laboratory and in the natural environment. PLoS ONE 8, e70749 (2013).

    Google Scholar 

  19. Mazzucco, R. & Schlötterer, C. Long-term gut microbiome dynamics in Drosophila melanogaster reveal environment-specific associations between bacterial taxa at the family level. Proc. R. Soc. B Biol. Sci. 288, 20212193 (2021).

    Google Scholar 

  20. Simhadri, R. K. et al. The gut commensal microbiome of Drosophila melanogaster is modified by the endosymbiont wolbachia. mSphere https://doi.org/10.1128/msphere.00287-17 (2017).

    Google Scholar 

  21. Qin, M., Jiang, L., Qiao, G. & Chen, J. Phylosymbiosis: The eco-evolutionary pattern of insect-symbiont interactions. Int. J. Mol. Sci. 24, 15836 (2023).

    Google Scholar 

  22. Ravigné, V. et al. Fruit fly phylogeny imprints bacterial gut microbiota. Evol. Appl. 15, 1621–1638 (2022).

    Google Scholar 

  23. van Opstal, E. J. & Bordenstein, S. R. Phylosymbiosis impacts adaptive traits in nasonia wasps. MBio https://doi.org/10.1128/mbio.00887-19 (2019).

    Google Scholar 

  24. Tinker, K. A. & Ottesen, E. A. Phylosymbiosis across deeply diverging lineages of omnivorous cockroaches (Order Blattodea). Appl. Environ. Microbiol. 86, e02513-e2519 (2020).

    Google Scholar 

  25. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).

    Google Scholar 

  26. Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: Estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, e104 (2017).

    Google Scholar 

  27. Strunov, A., Schmidt, K., Kapun, M. & Miller, W. J. Restriction of wolbachia bacteria in early embryogenesis of Neotropical Drosophila species via endoplasmic reticulum-mediated autophagy. MBio 13, e03863-e3921 (2022).

    Google Scholar 

  28. Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).

    Google Scholar 

  29. Shannon, C. E. A mathematical theory of communication. Bell. Syst. Tech. J. 27, 379–423 (1948).

    Google Scholar 

  30. Chao, A. Nonparametric estimation of the number of classes in a population. Scand. J. Stat. 11, 265–270 (1984).

    Google Scholar 

  31. Rodriguez-R, L. M. et al. The Microbial genomes atlas (MiGA) webserver: taxonomic and gene diversity analysis of archaea and bacteria at the whole genome level. Nucleic Acids Res 46, W282–W288 (2018).

    Google Scholar 

  32. Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: A toolkit to classify genomes with the genome taxonomy database. Bioinformatics 36, 1925–1927 (2020).

    Google Scholar 

  33. Briand, S., Dessimoz, C., El-Mabrouk, N., Lafond, M. & Lobinska, G. A generalized Robinson-foulds distance for labeled trees. BMC Genomics 21, 779 (2020).

    Google Scholar 

  34. Broderick, N. A. & Lemaitre, B. Gut-associated microbes of Drosophila melanogaster. Gut Microbes 3, 307–321 (2012).

    Google Scholar 

  35. Martinson, V. G., Douglas, A. E. & Jaenike, J. Community structure of the gut microbiota in sympatric species of wild Drosophila. Ecol. Lett. 20, 629–639 (2017).

    Google Scholar 

  36. Ponomarova, O. et al. Yeast creates a Niche for symbiotic lactic acid bacteria through nitrogen overflow. Cell Syst. 5, 345-357.e6 (2017).

    Google Scholar 

  37. Consuegra, J. et al. Metabolic cooperation among commensal bacteria supports Drosophila juvenile growth under nutritional stress. iScience 23, 101232 (2020).

    Google Scholar 

  38. Elhalis, H., Cox, J. & Zhao, J. Coffee fermentation: Expedition from traditional to controlled process and perspectives for industrialization. Appl. Food Res. 3, 100253 (2023).

    Google Scholar 

  39. Xia, M. et al. Interaction of acetic acid bacteria and lactic acid bacteria in multispecies solid-state fermentation of traditional Chinese cereal vinegar. Front. Microbiol. 13, 964855 (2022).

    Google Scholar 

  40. Adler, P. et al. The key to acetate: Metabolic fluxes of acetic acid bacteria under cocoa pulp fermentation-simulating conditions. Appl. Environ. Microbiol. 80, 4702–4716 (2014).

    Google Scholar 

  41. Tran, T. et al. Microbial dynamics between yeasts and acetic acid bacteria in Kombucha: Impacts on the chemical composition of the beverage. Foods 9, 963 (2020).

    Google Scholar 

  42. Pacheco-Montealegre, M. E., Dávila-Mora, L. L., Botero-Rute, L. M., Reyes, A. & Caro-Quintero, A. Fine resolution analysis of microbial communities provides insights into the variability of cocoa bean fermentation. Front. Microbiol. 11, 650 (2020).

    Google Scholar 

  43. Dodge, R. et al. A symbiotic physical niche in Drosophila melanogaster regulates stable association of a multi-species gut microbiota. Nat. Commun. 14, 1557 (2023).

    Google Scholar 

  44. Rafael, V. & Arcos, G. Drosophila guayllabambae n. sp., un nuevo miembro del grupo repleta, subgrupo hydei (Diptera, Drosophilidae). Evol. Biol. 2, 167–176 (1988).

    Google Scholar 

  45. Piñol, J., Francino, O., Fontdevila, A. & Cabré, O. Rapid isolation of Drosophila high molecular weight DNA to obtain genomic libraries. Nucleic Acids Res. Refeecs 16, 2736–2736 (1988).

    Google Scholar 

  46. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Google Scholar 

  47. Andrew, S. FastQC: A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).

  48. BMTagger: Best Match Tagger for Removing Human Reads from Metagenomics Datasets. NIH.

  49. Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinf. 10, 421 (2009).

    Google Scholar 

  50. Lu, J. et al. Metagenome analysis using the Kraken software suite. Nat. Protoc. 17, 2815–2839 (2022).

    Google Scholar 

  51. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Google Scholar 

  52. Oksanen, J. et al. Vegan: Community Ecology Package. (2022).

  53. Vavrek, M. J. Fossil: Palaeoecological and palaeogeographical analysis tools. Palaeontol. Electron. 14, 1T (2011).

    Google Scholar 

  54. Gower, J. C. Principal Coordinates Analysis. In Wiley StatsRef: Statistics Reference Online 1–7 (John Wiley & Sons, Ltd, 2015). https://doi.org/10.1002/9781118445112.stat05670.pub2.

  55. R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2022).

  56. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).

    Google Scholar 

  57. Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    Google Scholar 

  58. Meziti, A. et al. The reliability of metagenome-assembled genomes (MAGs) in representing natural populations: Insights from comparing MAGs against isolate genomes derived from the same fecal sample. Appl. Environ. Microbiol. 87, e02593-e2620 (2021).

    Google Scholar 

  59. Wu, Y.-W., Tang, Y.-H., Tringe, S. G., Simmons, B. A. & Singer, S. W. MaxBin: An automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome 2, 26 (2014).

    Google Scholar 

  60. Kang, D. D. et al. MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).

    Google Scholar 

  61. Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).

    Google Scholar 

  62. Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol 3, 836–843 (2018).

    Google Scholar 

  63. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    Google Scholar 

  64. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Google Scholar 

  65. Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10, goab008 (2021).

    Google Scholar 

  66. Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).

    Google Scholar 

  67. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).

    Google Scholar 

  68. Rodriguez-R, L. & Konstantinidis, K. The Enveomics Collection: A Toolbox For Specialized Analyses of Microbial Genomes and Metagenomes. (2016) https://doi.org/10.7287/peerj.preprints.1900v1.

  69. Edgar, R. C. MUSCLE: A multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 5, 113 (2004).

    Google Scholar 

  70. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: Computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).

    Google Scholar 

  71. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).

    Google Scholar 

  72. Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).

    Google Scholar 

  73. Schliep, K. P. phangorn: Phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).

    Google Scholar 

  74. Smith, M. R. TreeDist: distances between phylogenetic trees. Zenodo https://doi.org/10.5281/zenodo.3528124 (2019).

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Acknowledgements

We thank the LMI-BIO-INCA (International Mixed Laboratory on Biodiversity and Sustainable Agriculture in the Tropical Andes) funded by the French Research Institute for Development for partially funding travel opportunities to create the network that led to the current study. We thank Pilar Garcia Guerreiro and the Universitat Autónoma de Barcelona for their support for DNA extraction. We also thank the Ministerio del Ambiente del Ecuador (MAE) for the permission granted (MAE-DNB-CM-2015-0030) to develop this research. We thank the IT Services Department and the ExaCore-IT Core Facility, Vice Presidency for Research and Creation, Universidad de Los Andes, for providing high-performance computing support.

Funding

This work was supported by the research grant QINV0196-IINV529010100 from the Pontificia Universidad Católica del Ecuador (PUCE).

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Author notes

  1. Maria Alejandra Ulloa and Angela Viviana Serrano contributed equally to this work.

Authors and Affiliations

  1. Group in Computational Biology and Microbial Ecology, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia

    Maria Alejandra Ulloa, Angela Viviana Serrano, Laura Carolina Camelo & Alejandro Reyes Muñoz

  2. Institut de Recherche Pour Le Développement (IRD), UMR DIADE, CIRAD, Université de Montpellier, 34394, Montpellier, France

    Romain Guyot

  3. Facultad de Ciencias Exactas, Naturales y Ambientales, Laboratorio de Genética Evolutiva, Pontificia Universidad Católica de Ecuador, Quito, Ecuador

    Doris Vela

  4. Wellcome Sanger Institute, Cambridge, UK

    Alejandro Reyes Muñoz

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  1. Maria Alejandra Ulloa
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  2. Angela Viviana Serrano
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  3. Laura Carolina Camelo
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Contributions

M.A.U. and A.V.S. performed data processing, interpretation, data analysis, visualization and wrote the initial manuscript draft. L.C.C. contributed to data processing. R.G. assisted with data acquisition and bioinformatic analyse. D.V. contributed to study design field sample collection and provided ecological context for data interpretation. A.R.M. supervised the study, secured funding, and contributed to the conceptualization and final editing of the manuscript. All authors reviewed and approved the final version of the manuscript.

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Alejandro Reyes Muñoz.

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Ulloa, M.A., Serrano, A.V., Camelo, L.C. et al. Bacterial genome reconstruction and community profiling in Neotropical Drosophila.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-36282-y

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  • DOI: https://doi.org/10.1038/s41598-026-36282-y

Keywords

  • Neotropical Drosophila spp
  • Metagenome-assembled genomes (MAGs)
  • Microbiota
  • Gut bacteria
  • Phylogenetic analysis

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