A Comprehensive Self-Resistance Gene Database for Natural-Product Discovery with an Application to Marine Bacterial Genome Mining

Overview

Journal Int J Mol Sci

Publisher MDPI

Specialties Biochemistry
Chemistry
Molecular Biology

Date 2023 Aug 12

PMID 37569821

Authors

Hua Dong

Dengming Ming

Affiliations

Soon will be listed here.

Abstract

In the world of microorganisms, the biosynthesis of natural products in secondary metabolism and the self-resistance of the host always occur together and complement each other. Identifying resistance genes from biosynthetic gene clusters (BGCs) helps us understand the self-defense mechanism and predict the biological activity of natural products synthesized by microorganisms. However, a comprehensive database of resistance genes is still lacking, which hinders natural product annotation studies in large-scale genome mining. In this study, we compiled a resistance gene database (RGDB) by scanning the four available databases: CARD, MIBiG, NCBIAMR, and UniProt. Every resistance gene in the database was annotated with resistance mechanisms and possibly involved chemical compounds, using manual annotation and transformation from the resource databases. The RGDB was applied to analyze resistance genes in 7432 BGCs in 1390 genomes from a marine microbiome project. Our calculation showed that the RGDB successfully identified resistance genes for more than half of the BGCs, suggesting that the database helps prioritize BGCs that produce biologically active natural products.

References

Rokas A, Mead M, Steenwyk J, Raja H, Oberlies N . Biosynthetic gene clusters and the evolution of fungal chemodiversity. Nat Prod Rep. 2020; 37(7):868-878. PMC: 7332410. DOI: 10.1039/c9np00045c. View

Gupta S, Padmanabhan B, Diene S, Lopez-Rojas R, Kempf M, Landraud L . ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother. 2013; 58(1):212-20. PMC: 3910750. DOI: 10.1128/AAC.01310-13. View

Newman D, Cragg G . Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019. J Nat Prod. 2020; 83(3):770-803. DOI: 10.1021/acs.jnatprod.9b01285. View

Arango-Argoty G, Garner E, Pruden A, Heath L, Vikesland P, Zhang L . DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome. 2018; 6(1):23. PMC: 5796597. DOI: 10.1186/s40168-018-0401-z. View

Ramaswamy S, Musser J . Molecular genetic basis of antimicrobial agent resistance in Mycobacterium tuberculosis: 1998 update. Tuber Lung Dis. 2000; 79(1):3-29. DOI: 10.1054/tuld.1998.0002. View

Willmott C, Maxwell A . A single point mutation in the DNA gyrase A protein greatly reduces binding of fluoroquinolones to the gyrase-DNA complex. Antimicrob Agents Chemother. 1993; 37(1):126-7. PMC: 187618. DOI: 10.1128/AAC.37.1.126. View

Waschulin V, Borsetto C, James R, Newsham K, Donadio S, Corre C . Biosynthetic potential of uncultured Antarctic soil bacteria revealed through long-read metagenomic sequencing. ISME J. 2021; 16(1):101-111. PMC: 8692599. DOI: 10.1038/s41396-021-01052-3. View

Liu M, Li Y, Li H . Deep Learning to Predict the Biosynthetic Gene Clusters in Bacterial Genomes. J Mol Biol. 2022; 434(15):167597. DOI: 10.1016/j.jmb.2022.167597. View

Ju K, Gao J, Doroghazi J, Wang K, Thibodeaux C, Li S . Discovery of phosphonic acid natural products by mining the genomes of 10,000 actinomycetes. Proc Natl Acad Sci U S A. 2015; 112(39):12175-80. PMC: 4593130. DOI: 10.1073/pnas.1500873112. View

10.

Wright G . Something old, something new: revisiting natural products in antibiotic drug discovery. Can J Microbiol. 2014; 60(3):147-54. DOI: 10.1139/cjm-2014-0063. View

11.

Newman D, Cragg G . Natural Products as Sources of New Drugs from 1981 to 2014. J Nat Prod. 2016; 79(3):629-61. DOI: 10.1021/acs.jnatprod.5b01055. View

12.

Alcock B, Huynh W, Chalil R, Smith K, Raphenya A, Wlodarski M . CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res. 2022; 51(D1):D690-D699. PMC: 9825576. DOI: 10.1093/nar/gkac920. View

13.

Doroghazi J, Albright J, Goering A, Ju K, Haines R, Tchalukov K . A roadmap for natural product discovery based on large-scale genomics and metabolomics. Nat Chem Biol. 2014; 10(11):963-8. PMC: 4201863. DOI: 10.1038/nchembio.1659. View

14.

Shrestha A, Bajracharya A, Subedi H, Turha R, Kafle S, Sharma S . Multi-drug resistance and extended spectrum beta lactamase producing Gram negative bacteria from chicken meat in Bharatpur Metropolitan, Nepal. BMC Res Notes. 2017; 10(1):574. PMC: 5678746. DOI: 10.1186/s13104-017-2917-x. View

15.

Kautsar S, Blin K, Shaw S, Navarro-Munoz J, Terlouw B, van der Hooft J . MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Res. 2019; 48(D1):D454-D458. PMC: 7145714. DOI: 10.1093/nar/gkz882. View

16.

Lambert P . Bacterial resistance to antibiotics: modified target sites. Adv Drug Deliv Rev. 2005; 57(10):1471-85. DOI: 10.1016/j.addr.2005.04.003. View

17.

Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee G . Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021; 373(6557):871-876. PMC: 7612213. DOI: 10.1126/science.abj8754. View

18.

Lobanovska M, Pilla G . Penicillin's Discovery and Antibiotic Resistance: Lessons for the Future?. Yale J Biol Med. 2017; 90(1):135-145. PMC: 5369031. View

19.

Walker A, Clardy J . A Machine Learning Bioinformatics Method to Predict Biological Activity from Biosynthetic Gene Clusters. J Chem Inf Model. 2021; 61(6):2560-2571. PMC: 8243324. DOI: 10.1021/acs.jcim.0c01304. View

20.

Wilson B, Thornburg C, Henrich C, Grkovic T, OKeefe B . Creating and screening natural product libraries. Nat Prod Rep. 2020; 37(7):893-918. PMC: 8494140. DOI: 10.1039/c9np00068b. View