Protein-Protein Interaction Network Analysis Reveals Distinct Patterns of Antibiotic Resistance Genes

Moumi, Nazifa Ahmed; Brown, Connor; Vikesland, Peter J.; Pruden, Amy; Zhang, Liqing

doi:10.1109/bibm55620.2022.9995224

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…Our research builds on our previous work that utilized PPIN analysis in Escherichia coli and Acinetobacter baumannii (39). We have now developed a pipeline that extends this approach to differentiate ARGs from nonARGs, providing insights into ARG mechanisms and mobility patterns across all ESKAPE pathogens.…”

Section: Discussionmentioning

confidence: 99%

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Resistance genes are distinct in protein-protein interaction networks according to drug class and gene mobility

Moumi,

Brown,

Ahmed

et al. 2024

Preprint

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With growing calls for increased surveillance of antibiotic resistance as an escalating global health threat, improved bioinformatic tools are needed for tracking antibiotic resistance genes (ARGs) across One Health domains. Most studies to date profile ARGs using sequence homology, but such approaches provide limited information about the broader context or function of the ARG in bacterial genomes. Here we introduce a new pipeline for identifying ARGs in genomic data that employs machine learning analysis of Protein-Protein Interaction Networks (PPINs) as a means to improve predictions of ARGs while also providing vital information about the context, such as gene mobility. A random forest model was trained to effectively differentiate between ARGs and nonARGs and was validated using the PPINs of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumonia, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae), which represent urgent threats to human health because they tend to be multiantibiotic resistant. The pipeline exhibited robustness in discriminating ARGs from nonARGs, achieving an average area under the precision-recall curve of 88%. We further identified that the neighbors of ARGs, i.e., genes connected to ARGs by only one edge, were disproportionately associated with mobile genetic elements, which is consistent with the understanding that ARGs tend to be mobile compared to randomly sampled genes in the PPINs. This pipeline showcases the utility of PPINs in discerning distinctive characteristics of ARGs within a broader genomic context and in differentiating ARGs from nonARGs through network-based attributes and interaction patterns.

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Section: Discussionmentioning

confidence: 99%

“…Our research builds on our previous work that utilized PPIN analysis in Escherichia coli and Acinetobacter baumannii (39).…”

Section: Discussionmentioning

confidence: 99%

Resistance genes are distinct in protein-protein interaction networks according to drug class and gene mobility

Moumi,

Brown,

Ahmed

et al. 2024

Preprint

Self Cite

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“…In this section, our primary emphasis is on the binary classification performance of DeepMRG, specifically in distinguishing positive samples (MRGs) from negative samples. To create negative samples, we collected 27,036 bacterial housekeeping genes from UniProt [23], using 184 different Gene Ontology (GO) terms obtained from [24]. The associated GO terms and biological functions for these genes can be found in S9 File.…”

Section: Validation Of Deepmrg Through An In Silico Spike-in Experimentsmentioning

confidence: 99%

DeepMRG: a multi-label deep learning classifier for predicting bacterial metal resistance genes

Emon,

Zhang

2023

Preprint

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The widespread misuse of antibiotics has escalated antibiotic resistance into a critical global public health concern. Beyond antibiotics, metals function as antibacterial agents. Metal resistance genes (MRGs) enable bacteria to tolerate metal-based antibacterials and may also foster antibiotic resistance within bacterial communities through co-selection. Thus, predicting bacterial MRGs is vital for elucidating their involvement in antibiotic resistance and metal tolerance mechanisms. Currently, the “best hit” approaches are utilized to identify and annotate MRGs. This method is sensitive to cutoff values and produces a high false negative rate. To address such limitations, we introduce DeepMRG, a deep learning-based multi-label classifier, to predict bacterial MRGs. Because a bacterial MRG can confer resistance to multiple metals, DeepMRG is designed as a multi-label classifier capable of predicting multiple metal labels associated with an MRG. It leverages bit score-based similarity distribution of sequences with experimentally verified MRGs. To ensure unbiased model evaluation, we employed a clustering method to partition our dataset into six subsets, five for cross-validation and one for testing, with non-homologous sequences, mitigating the impact of sequence homology. DeepMRG consistently achieved high overall F1-scores and significantly reduced false negative rates across a wide range of datasets. It can be used to predict bacterial MRGs in metagenomic or isolate assemblies. The web server of DeepMRG can be accessed athttps://deepmrg.cs.vt.edu/deepmrgand the source code is available athttps://github.com/muhit-emon/DeepMRGunder the MIT license.

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Protein-Protein Interaction Network Analysis Reveals Distinct Patterns of Antibiotic Resistance Genes

Cited by 2 publications

References 17 publications

Resistance genes are distinct in protein-protein interaction networks according to drug class and gene mobility

Resistance genes are distinct in protein-protein interaction networks according to drug class and gene mobility

DeepMRG: a multi-label deep learning classifier for predicting bacterial metal resistance genes

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