Component Parts of Bacteriophage Virions Accurately Defined by a Machine-Learning Approach Built on Evolutionary Features

Thung, Tze Young; White, Murray E.; Dai, Wei; Wilksch, Jonathan J.; Bamert, Rebecca S.; Rocker, Andrea; Stubenrauch, Christopher J.; Williams, D. F.; Schittelhelm, Ralf; Barr, Jeremy J.; Jameson, Eleanor; McGowan, Sheena; Zhang, Yanju; Wang, Jiawei; Dunstan, Rhys A.; Lithgow, Trevor

doi:10.1128/msystems.00242-21

Cited by 8 publications

(4 citation statements)

References 71 publications

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“…Machine learning methods have enabled fast identification of potential bacteriophages [190], and have been implemented to predict phage virion proteins with 83% accuracy, 82% sensitivity, and 89% specificity [191]. Furthermore, a machine learning tool was used for the targeted identification of phage depolymerase, which is likely to be a powerful weapon against antimicrobial-resistant bacteria [192].…”

Section: Drug Discoverymentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

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Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs’ kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

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Section: Drug Discoverymentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

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“…Gene calling and annotations were conducted using Prokka v1.14.6 ( 10 ) utilizing PHROGs ( 11 ) database. Putative tRNAs and virion structural proteins were predicted using ARAGORN v1.2.41 ( 12 ) and STEP3 ( 13 ). Default parameters were used for all software unless otherwise specified.…”

Section: Announcementmentioning

confidence: 99%

Isolation and genome sequencing of five lytic bacteriophages from hospital wastewater in the Philippines

Nada,

Ancla,

Yadao

et al. 2023

Microbiol Resour Announc

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Here, we report the genome sequences of five bacteriophages isolated from hospital wastewater, including two new species and two candidates for therapeutic application. No virulence, temperate marker and antibiotic resistance genes were found in the genomes of Escherichia phage vB_VIPECOOM03 and Klebsiella phage vB_VIPKPNUMC01, making them suitable candidate for therapy.

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“…High purity pKMKP103_1 was prepared using the cesium chloride gradient method (45). For TEM imaging, 5 μL of the phage solution was applied to a glow-discharged carbon-coated copper grid and stained with 1% uranyl acetate.…”

Section: Transmission Electron Microscopymentioning

confidence: 99%

Diversified repertoire of phage resistance inKlebsiella pneumoniaeand bidirectional steering effects impacting antibiotic susceptibility

Nang

Lü

et al. 2022

Preprint

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Phage therapy is a promising alternative treatment for multidrug-resistant bacterial infections. Unfortunately, phage resistance represents a major barrier hindering the clinical utility. Mechanistic insights into bacterial phage defense mechanisms are critical to optimize phages therapy. Here, we discovered a repertoire of phage resistance mechanisms in a model strainKlebsiella pneumoniaeMKP103, including the disruption of phage binding site (fhuA::Tn andtonB::Tn), extension of phage latent period (mnmE::Tn andrpoN::Tn) and increased mutation frequency (mutS::Tn andmutL::Tn). Different from the prevailing view that phage resistance re-sensitizes antibiotic-resistant bacteria, we demonstrated a bi-directional steering effect on antibiotic susceptibility. Specifically, it was uncovered that, whilerpoN::Tn became more susceptible to colistin,mutS::Tn andmutL::Tn caused increased resistance to rifampicin and the last-line colistin. Our findings highlight the diversified strategies utilized byK. pneumoniaeto overcome phage infection. Overall, mechanism-guided phage steering represents a vital strategy to maximize the success of phage therapy.

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Component Parts of Bacteriophage Virions Accurately Defined by a Machine-Learning Approach Built on Evolutionary Features

Abstract: In response to the global problem of antimicrobial resistance, there are moves to use bacteriophages (phages) as therapeutic agents. Selecting which phages will be effective therapeutics relies on interpreting features contributing to shelf-life and applicability to diagnosed infections.

Cited by 8 publications

References 71 publications

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Isolation and genome sequencing of five lytic bacteriophages from hospital wastewater in the Philippines

Diversified repertoire of phage resistance inKlebsiella pneumoniaeand bidirectional steering effects impacting antibiotic susceptibility

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