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Cited by 10Genomics and Phylogenetic StudiesMicrobial Community Ecology and PhysiologyGut microbiota and health

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Learning from the unknown: exploring the range of bacterial functionality

Open access
Mahlich, Yannick, Zhu, Chengsheng, Chung, Henri, Velaga, Pavan K, De Paolis Kaluza, M Clara, Radivojac, Predrag, Friedberg, Iddo, Bromberg, Yana·2023·Nucleic Acids Research
DOI 10.1093/nar/gkad757PMC PMC10602916

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Abstract

Determining the repertoire of a microbe's molecular functions is a central question in microbial biology. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA. Here, we describe a novel approach to exploring bacterial functional repertoires without reference databases. Our Fusion scheme establishes functional relationships between bacteria and assigns organisms to Fusion-taxa that differ from otherwise defined taxonomic clades. Three key findings of our work stand out. First, bacterial functional comparisons outperform marker genes in assigning taxonomic clades. Fusion profiles are also better for this task than other functional annotation schemes. Second, Fusion-taxa are robust to addition of novel organisms and are, arguably, able to capture the environment-driven bacterial diversity. Finally, our alignment-free nucleic acid-based Siamese Neural Network model, created using Fusion functions, enables finding shared functionality of very distant, possibly structurally different, microbial homologs. Our work can thus help annotate functional repertoires of bacterial organisms and further guide our understanding of microbial communities.

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Research works printed but not linked: 83

  1. Blaser, 2016, Toward a predictive understanding of Earth's microbiomes to address 21st century challenges, Mbio, 7, e00714-16, 10.1128/mBio.00714-16 · DOI
  2. Falkowski, 2008, The microbial engines that drive Earth's biogeochemical cycles, Science, 320, 1034, 10.1126/science.1153213 · DOI · Paper page
  3. Jousset, 2017, Where less may be more: how the rare biosphere pulls ecosystems strings, ISME J., 11, 853, 10.1038/ismej.2016.174 · DOI
  4. Russell, 2014, Nature's microbiome: introduction, Mol. Ecol., 23, 1225, 10.1111/mec.12676 · DOI
  5. Bromberg, 2022, Quantifying structural relationships of metal-binding sites suggests origins of biological electron transfer, Sci. Adv., 8, eabj3984, 10.1126/sciadv.abj3984 · DOI
  6. Shade, 2018, Understanding microbiome stability in a changing world, Msystems, 3, e00157-17, 10.1128/mSystems.00157-17 · DOI
  7. Franzosa, 2018, Species-level functional profiling of metagenomes and metatranscriptomes, Nat. Methods, 15, 962, 10.1038/s41592-018-0176-y · DOI · Paper page
  8. Kaminski, 2015, High-specificity targeted functional profiling in microbial communities with ShortBRED, PLoS Comput. Biol., 11, e1004557, 10.1371/journal.pcbi.1004557 · DOI
  9. Bolyen, 2019, Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2, Nat. Biotechnol., 37, 852, 10.1038/s41587-019-0209-9 · DOI · Paper page
  10. Stackebrandt, 1994, Taxonomic note: a place for DNA–DNA reassociation and 16S rRNA sequence analysis in the present species definition in bacteriology, Int. J. Syst. Evol. Microbiol., 44, 846, 10.1099/00207713-44-4-846 · DOI
  11. Brenner, 1969, Polynucleotide sequence relationships among members of Enterobacteriaceae, J. Bacteriol., 98, 637, 10.1128/jb.98.2.637-650.1969 · DOI
  12. Goris, 2007, DNA–DNA hybridization values and their relationship to whole-genome sequence similarities, Int. J. Syst. Evol. Microbiol., 57, 81, 10.1099/ijs.0.64483-0 · DOI
  13. Boone, 2001, Bergey's Manual® of Systematic Bacteriology: Volume One The Archaea and the Deeply Branching and Phototrophic Bacteria, 10.1007/978-0-387-21609-6 · DOI
  14. Woese, 1990, Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya, Proc. Natl. Acad. Sci. U.S.A., 87, 4576, 10.1073/pnas.87.12.4576 · DOI · Paper page
  15. Johnson, 2019, Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis, Nat. Commun., 10, 5029, 10.1038/s41467-019-13036-1 · DOI
  16. Konstantinidis, 2006, Toward a more robust assessment of intraspecies diversity, using fewer genetic markers, Appl. Environ. Microbiol., 72, 7286, 10.1128/AEM.01398-06 · DOI
  17. Konstantinidis, 2007, Prokaryotic taxonomy and phylogeny in the genomic era: advancements and challenges ahead, Curr. Opin. Microbiol., 10, 504, 10.1016/j.mib.2007.08.006 · DOI
  18. Gevers, 2005, Re-evaluating prokaryotic species, Nat. Rev. Microbiol., 3, 733, 10.1038/nrmicro1236 · DOI
  19. Rosselló-Mora, 2005, Updating Prokaryotic Taxonomy, J. Bacteriol., 187, 6255, 10.1128/JB.187.18.6255-6257.2005 · DOI
  20. Gevers, 2006, Stepping stones towards a new prokaryotic taxonomy, Philos. Trans. Roy. Soc. B: Biol. Sci., 361, 1911, 10.1098/rstb.2006.1915 · DOI
  21. Babić, 2008, Direct Visualization of Horizontal Gene Transfer, Science, 319, 1533, 10.1126/science.1153498 · DOI
  22. Goldenfeld, 2007, Biology's next revolution, Nature, 445, 369, 10.1038/445369a · DOI
  23. Price, 2008, Horizontal gene transfer and the evolution of transcriptional regulation in Escherichia coli, Genome Biol., 9, R4, 10.1186/gb-2008-9-1-r4 · DOI
  24. He, 2020, Adaptive convergence at the genomic level—prevalent, uncommon or very rare?, Natl. Sci. Rev., 7, 947, 10.1093/nsr/nwaa076 · DOI
  25. Farhat, 2013, Genomic analysis identifies targets of convergent positive selection in drug-resistant Mycobacterium tuberculosis, Nat. Genet., 45, 1183, 10.1038/ng.2747 · DOI
  26. Zhu, 2015, Functional basis of microorganism classification, PLoS Comput. Biol., 11, e1004472, 10.1371/journal.pcbi.1004472 · DOI
  27. Rastogi, 2011, Microbes and Microbial Technology: Agricultural and Environmental Applications, 29, 10.1007/978-1-4419-7931-5_2 · DOI
  28. Langille, 2013, Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences, Nat. Biotechnol., 31, 814, 10.1038/nbt.2676 · DOI · Paper page
  29. Schleifer, 2009, Classification of Bacteria and Archaea: past, present and future, Syst. Appl. Microbiol., 32, 533, 10.1016/j.syapm.2009.09.002 · DOI
  30. Young, 2001, Implications of alternative classifications and horizontal gene transfer for bacterial taxonomy, Int. J. Syst. Evol. Microbiol., 51, 945, 10.1099/00207713-51-3-945 · DOI
  31. Zhu, 2018, Fusion DB: assessing microbial diversity and environmental preferences via functional similarity networks, Nucleic Acids Res., 46, D535, 10.1093/nar/gkx1060 · DOI
  32. Koehler Leman, 2023, Sequence-structure-function relationships in the microbial protein universe, Nat. Commun., 14, 2351, 10.1038/s41467-023-37896-w · DOI
  33. Nissen, 2021, Improved metagenome binning and assembly using deep variational autoencoders, Nat. Biotechnol., 39, 555, 10.1038/s41587-020-00777-4 · DOI
  34. Pan, 2022, A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments, Nat. Commun., 13, 2326, 10.1038/s41467-022-29843-y · DOI
  35. Kang, 2019, MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies, PeerJ, 7, e7359, 10.7717/peerj.7359 · DOI
  36. Wu, 2015, MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets, Bioinformatics, 32, 605, 10.1093/bioinformatics/btv638 · DOI
  37. Benson, 2013, GenBank, Nucleic Acids Res., 41, D36, 10.1093/nar/gks1195 · DOI · Paper page
  38. Sayers, 2019, GenBank, Nucleic Acids Res., 47, D94, 10.1093/nar/gky989 · DOI
  39. Li, 2006, Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences, Bioinformatics, 22, 1658, 10.1093/bioinformatics/btl158 · DOI
  40. Fu, 2012, CD-HIT: accelerated for clustering the next-generation sequencing data, Bioinformatics, 28, 3150, 10.1093/bioinformatics/bts565 · DOI · Paper page
  41. Mahlich, 2018, HFSP: high speed homology-driven function annotation of proteins, Bioinformatics, 34, i304, 10.1093/bioinformatics/bty262 · DOI
  42. Steinegger, 2017, MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets, Nat. Biotechnol., 35, 1026, 10.1038/nbt.3988 · DOI · Paper page
  43. Azad, 2018, HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks, Nucleic Acids Res., 46, e33, 10.1093/nar/gkx1313 · DOI
  44. Van Dongen, 2000, Graph Clustering by Flow Simulation
  45. Enright, 2002, An efficient algorithm for large-scale detection of protein families, Nucleic Acids Res., 30, 1575, 10.1093/nar/30.7.1575 · DOI
  46. Bairoch, 2000, The ENZYME database in 2000, Nucleic Acids Res., 28, 304, 10.1093/nar/28.1.304 · DOI
  47. Bairoch, 1997, The SWISS-PROT protein sequence database: its relevance to human molecular medical research, J Mol Med (Berl), 75, 312
  48. Consortium, 2020, UniProt: the universal protein knowledgebase in 2021, Nucleic Acids Res., 49, D480, 10.1093/nar/gkaa1100 · DOI · Paper page
  49. El-Gebali, 2018, The Pfam protein families database in 2019, Nucleic Acids Res., 47, D427, 10.1093/nar/gky995 · DOI · Paper page
  50. Madeira, 2019, The EMBL-EBI search and sequence analysis tools APIs in 2019, Nucleic Acids Res., 47, W636, 10.1093/nar/gkz268 · DOI · Paper page
  51. Eddy, 2011, Accelerated Profile HMM Searches, PLoS Comput. Biol., 7, e1002195, 10.1371/journal.pcbi.1002195 · DOI · Paper page
  52. 2021, The Gene Ontology resource: enriching a GOld mine, Nucleic Acids Res., 49, D325, 10.1093/nar/gkaa1113 · DOI · Paper page
  53. Ashburner, 2000, Gene ontology: tool for the unification of biology. The Gene Ontology Consortium, Nat. Genet., 25, 25, 10.1038/75556 · DOI · Paper page
  54. Rosenberg, 2007, Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), 410
  55. Pedregosa, 2011, Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825
  56. Schoch, 2020, NCBI Taxonomy: a comprehensive update on curation, resources and tools, Database (Oxford), 2020, baaa062, 10.1093/database/baaa062 · DOI · Paper page
  57. Parks, 2020, A complete domain-to-species taxonomy for Bacteria and Archaea, Nat. Biotechnol., 38, 1079, 10.1038/s41587-020-0501-8 · DOI
  58. Sukumaran, 2010, DendroPy: a Python library for phylogenetic computing, Bioinformatics, 26, 1569, 10.1093/bioinformatics/btq228 · DOI
  59. Menardo, 2018, Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity, BMC Bioinf., 19, 164, 10.1186/s12859-018-2164-8 · DOI
  60. Blondel, 2008, Fast unfolding of communities in large networks, J. Stat. Mech. Theory Exp., 2008, P10008, 10.1088/1742-5468/2008/10/P10008 · DOI · Paper page
  61. Cole, 2014, Ribosomal Database Project: data and tools for high throughput rRNA analysis, Nucleic Acids Res., 42, D633, 10.1093/nar/gkt1244 · DOI
  62. Nawrocki, 2013, Infernal 1.1: 100-fold faster RNA homology searches, Bioinformatics, 29, 2933, 10.1093/bioinformatics/btt509 · DOI
  63. Ondov, 2016, Mash: fast genome and metagenome distance estimation using MinHash, Genome Biol., 17, 132, 10.1186/s13059-016-0997-x · DOI · Paper page
  64. Hoarfrost, 2020, Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter, Nat. Commun., 13, 2606, 10.1038/s41467-022-30070-8 · DOI
  65. Berman, 2000, The Protein Data Bank, Nucleic Acids Res., 28, 235, 10.1093/nar/28.1.235 · DOI · Paper page
  66. Zhang, 2004, Scoring function for automated assessment of protein structure template quality, Proteins Struct. Funct. Bioinf., 57, 702, 10.1002/prot.20264 · DOI · Paper page
  67. Zhang, 2005, TM-align: a protein structure alignment algorithm based on the TM-score, Nucleic Acids Res., 33, 2302, 10.1093/nar/gki524 · DOI · Paper page
  68. Mistry, 2020, Pfam: the protein families database in 2021, Nucleic Acids Res., 49, D412, 10.1093/nar/gkaa913 · DOI · Paper page
  69. Barrera, 2014, Analysis of the protein domain and domain architecture content in fungi and its application in the search of new antifungal targets, PLoS Comput. Biol., 10, e1003733, 10.1371/journal.pcbi.1003733 · DOI
  70. Koonin, 2002, The structure of the protein universe and genome evolution, Nature, 420, 218, 10.1038/nature01256 · DOI
  71. Itoh, 2007, Evolutionary history and functional implications of protein domains and their combinations in eukaryotes, Genome Biol., 8, R121, 10.1186/gb-2007-8-6-r121 · DOI
  72. Peisajovich, 2010, Rapid diversification of cell signaling phenotypes by modular domain recombination, Science, 328, 368, 10.1126/science.1182376 · DOI
  73. Radivojac, 2022, Advancing remote homology detection: a step toward understanding and accurately predicting protein function, Cell Syst., 13, 435, 10.1016/j.cels.2022.05.006 · DOI
  74. Rosselló-Mora, 2001, The species concept for prokaryotes, FEMS Microbiol. Rev., 25, 39, 10.1016/S0168-6445(00)00040-1 · DOI
  75. Větrovský, 2013, The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses, PLoS One, 8, e57923, 10.1371/journal.pone.0057923 · DOI
  76. Zhu, 2017, Functional sequencing read annotation for high precision microbiome analysis, Nucleic Acids Res., 46, e23, 10.1093/nar/gkx1209 · DOI
  77. Hernández-Salmerón, 2022, FastANI, Mash and Dashing equally differentiate between Klebsiella species, PeerJ, 10, e13784, 10.7717/peerj.13784 · DOI
  78. Jain, 2018, High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries, Nat. Commun., 9, 5114, 10.1038/s41467-018-07641-9 · DOI · Paper page
  79. Baker, 2019, Dashing: fast and accurate genomic distances with HyperLogLog, Genome Biol., 20, 265, 10.1186/s13059-019-1875-0 · DOI
  80. Parks, 2018, A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life, Nat. Biotechnol., 36, 996, 10.1038/nbt.4229 · DOI · Paper page
  81. Chicco, 2021, Artificial Neural Networks, 73, 10.1007/978-1-0716-0826-5_3 · DOI
  82. Krissinel, 2007, On the relationship between sequence and structure similarities in proteomics, Bioinformatics, 23, 717, 10.1093/bioinformatics/btm006 · DOI
  83. Rost, 1999, Twilight zone of protein sequence alignments, Protein Eng. Des. Selection, 12, 85, 10.1093/protein/12.2.85 · DOI · Paper page

Metadata

Authors
Mahlich, Y., Zhu, C., Chung, H., Velaga, P. K., De Paolis Kaluza, M. C., Radivojac, P., Friedberg, I., & Bromberg, Y.
Affiliations
  • Rutgers, The State University of New Jersey (US)
  • Iowa State University (US)
  • Khoury College of Computer Sciences, Northeastern University , 177 Huntington Avenue, Boston , MA  02115, USA
Corresponding author
  • Mahlich, Yannick
  • Bromberg, Yana
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2023-09-22
Volume
51
Issue
19
Pages
10162-10175
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkad757
PMC
PMC10602916
OpenAlex
W4386947433
Cited by
10
References
9
Citations by year
2026: 3 · 2025: 4 · 2024: 3
FWCI
0.89
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by-nc)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation (1553289)
  • NIH (R01 GM115486)
  • NAI (11 80NSSC18M0093)
  • Iowa State University's Translational Artificial Intelligence Center
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
Keywords
marker genes, 16S rRNA, alignment-free sequence comparison, Siamese network, microbial communities
MeSH terms
Bacteria, Phylogeny, RNA, Ribosomal, 16S, Databases, Factual, Bacterial Physiological Phenomena, Microbiota

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