Comprehensive profiling of antibiotic resistance genes in diverse environments and novel function discovery

Yuguo Zha; Cheng Chen; Qihong Jiao; Xiaomei Zeng; Xuefeng Cui; Kang Ning

doi:10.59717/j.xinn-life.2024.100054

The Innovation Life > 2024 Vol. 2 > No. 1 > 100054

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Comprehensive profiling of antibiotic resistance genes in diverse environments and novel function discovery

1.
Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2.
School of Computer Science and Technology, Shandong University, Qingdao 266237, China

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Corresponding authors: xmzeng@hust.edu.cn (X.Z.); xfcui@email.sdu.edu.cn (X.C.); ningkang@hust.edu.cn (K.N.)
Received Date: November 05, 2023

Accepted Date: January 23, 2024

Published Online: February 28, 2024

The Innovation Life 2(1), https://doi.org/10.59717/j.xinn-life.2024.100054 | Cite this article

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1. ONN4ARG is an ontology-aware neural network model for antibiotic resistance gene (ARG) prediction.
  
  ONN4ARG detects remotely homologous ARGs and generates a comprehensive set of ARGs with high fidelity.
  
  ONN4ARG enables comprehensive enrichment analysis of ARGs among diverse hosts and environments.
  
  Computational work and wet-lab experimental validation together validated a novel streptomycin ARG.

ABSTRACT

ABSTRACT

Antibiotic resistance genes (ARGs) have emerged in pathogens and are arousing worldwide concern, and accurately identifying unknown ARGs is a formidable challenge in studying the generation and spread of antibiotic resistance in diverse environments. Current methods can identify known ARGs but have limited utility for the discovery of novel ARGs, thus rendering the profiling of ARGs incomprehensive. Here, we developed ONN4ARG, an ontology-aware deep learning approach for comprehensive ARG discovery. Systematic evaluation revealed that ONN4ARG outperforms previous methods in terms of efficiency, accuracy, and comprehensiveness. Experiments using 200 million microbial genes collected from 815 metagenomic samples from diverse environments or hosts have resulted in 120,726 candidate ARGs, of which more than 20% are not yet present in public databases. The comprehensive set of ARGs revealed environment-specific and host-specific patterns. The wet-lab functional validation together with structural investigation have validated a novel streptomycin resistance gene from oral microbiome samples, confirming ONN4ARG’s ability to discover novel functions. In summary, ONN4ARG enables comprehensive ARG discovery toward a grand view of ARGs worldwide.
- antibiotic resistance gene /
- ontology-aware neural network /
- deep learning /
- novel ARG /
- microbiome

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Zha Y., Chen C., Jiao Q., et al., (2024). Comprehensive profiling of antibiotic resistance genes in diverse environments and novel function discovery. The Innovation Life 2(1): 100054. https://doi.org/10.59717/j.xinn-life.2024.100054

Zha Y., Chen C., Jiao Q., et al., (2024). Comprehensive profiling of antibiotic resistance genes in diverse environments and novel function discovery. The Innovation Life 2(1): 100054. https://doi.org/10.59717/j.xinn-life.2024.100054

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Comprehensive profiling of antibiotic resistance genes in diverse environments and novel function discovery

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