Use of a Regression Model to Study Host-Genomic Determinants of Phage Susceptibility in MRSA

Henrike Zschach*, Mette Voldby Larsen, Henrik Hasman, Henrik Westh, Morten Nielsen, Ryszard Międzybrodzki, Ewa Jończyk-Matysiak, Beata Weber-Dąbrowska, Andrzej Górski

*Corresponding author for this work

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    Abstract

    Staphylococcus aureus is a major agent of nosocomial infections. Especially in methicillin-resistant strains, conventional treatment options are limited and expensive, which has fueled a growing interest in phage therapy approaches. We have tested the susceptibility of 207 clinical S. aureus strains to 12 (nine monovalent) different therapeutic phage preparations and subsequently employed linear regression models to estimate the influence of individual host gene families on resistance to phages. Specifically, we used a two-step regression model setup with a preselection step based on gene family enrichment. We show that our models are robust and capture the data’s underlying signal by comparing their performance to that of models build on randomized data. In doing so, we have identified 167 gene families that govern phage resistance in our strain set and performed functional analysis on them. This revealed genes of possible prophage or mobile genetic element origin, along with genes involved in restriction-modification and transcription regulators, though the majority were genes of unknown function. This study is a step in the direction of understanding the intricate host-phage relationship in this important pathogen with the outlook to targeted phage therapy applications.
    Original languageEnglish
    JournalThe Journal of Antibiotics
    Volume7
    Issue number1
    Number of pages16
    ISSN0021-8820
    DOIs
    Publication statusPublished - 2018

    Bibliographical note

    © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

    Keywords

    • Phage therapy
    • Bacterial phage resistance
    • Regression modeling
    • MRSA
    • RM1-950

    Cite this

    Zschach, Henrike ; Larsen, Mette Voldby ; Hasman, Henrik ; Westh, Henrik ; Nielsen, Morten ; Międzybrodzki, Ryszard ; Jończyk-Matysiak, Ewa ; Weber-Dąbrowska, Beata ; Górski, Andrzej. / Use of a Regression Model to Study Host-Genomic Determinants of Phage Susceptibility in MRSA. In: The Journal of Antibiotics. 2018 ; Vol. 7, No. 1.
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    abstract = "Staphylococcus aureus is a major agent of nosocomial infections. Especially in methicillin-resistant strains, conventional treatment options are limited and expensive, which has fueled a growing interest in phage therapy approaches. We have tested the susceptibility of 207 clinical S. aureus strains to 12 (nine monovalent) different therapeutic phage preparations and subsequently employed linear regression models to estimate the influence of individual host gene families on resistance to phages. Specifically, we used a two-step regression model setup with a preselection step based on gene family enrichment. We show that our models are robust and capture the data’s underlying signal by comparing their performance to that of models build on randomized data. In doing so, we have identified 167 gene families that govern phage resistance in our strain set and performed functional analysis on them. This revealed genes of possible prophage or mobile genetic element origin, along with genes involved in restriction-modification and transcription regulators, though the majority were genes of unknown function. This study is a step in the direction of understanding the intricate host-phage relationship in this important pathogen with the outlook to targeted phage therapy applications.",
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    author = "Henrike Zschach and Larsen, {Mette Voldby} and Henrik Hasman and Henrik Westh and Morten Nielsen and Ryszard Międzybrodzki and Ewa Jończyk-Matysiak and Beata Weber-Dąbrowska and Andrzej G{\'o}rski",
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    doi = "10.3390/antibiotics7010009",
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    Zschach, H, Larsen, MV, Hasman, H, Westh, H, Nielsen, M, Międzybrodzki, R, Jończyk-Matysiak, E, Weber-Dąbrowska, B & Górski, A 2018, 'Use of a Regression Model to Study Host-Genomic Determinants of Phage Susceptibility in MRSA', The Journal of Antibiotics, vol. 7, no. 1. https://doi.org/10.3390/antibiotics7010009

    Use of a Regression Model to Study Host-Genomic Determinants of Phage Susceptibility in MRSA. / Zschach, Henrike; Larsen, Mette Voldby; Hasman, Henrik; Westh, Henrik; Nielsen, Morten; Międzybrodzki, Ryszard; Jończyk-Matysiak, Ewa; Weber-Dąbrowska, Beata; Górski, Andrzej.

    In: The Journal of Antibiotics, Vol. 7, No. 1, 2018.

    Research output: Contribution to journalJournal articleResearchpeer-review

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    T1 - Use of a Regression Model to Study Host-Genomic Determinants of Phage Susceptibility in MRSA

    AU - Zschach, Henrike

    AU - Larsen, Mette Voldby

    AU - Hasman, Henrik

    AU - Westh, Henrik

    AU - Nielsen, Morten

    AU - Międzybrodzki, Ryszard

    AU - Jończyk-Matysiak, Ewa

    AU - Weber-Dąbrowska, Beata

    AU - Górski, Andrzej

    N1 - © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

    PY - 2018

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    N2 - Staphylococcus aureus is a major agent of nosocomial infections. Especially in methicillin-resistant strains, conventional treatment options are limited and expensive, which has fueled a growing interest in phage therapy approaches. We have tested the susceptibility of 207 clinical S. aureus strains to 12 (nine monovalent) different therapeutic phage preparations and subsequently employed linear regression models to estimate the influence of individual host gene families on resistance to phages. Specifically, we used a two-step regression model setup with a preselection step based on gene family enrichment. We show that our models are robust and capture the data’s underlying signal by comparing their performance to that of models build on randomized data. In doing so, we have identified 167 gene families that govern phage resistance in our strain set and performed functional analysis on them. This revealed genes of possible prophage or mobile genetic element origin, along with genes involved in restriction-modification and transcription regulators, though the majority were genes of unknown function. This study is a step in the direction of understanding the intricate host-phage relationship in this important pathogen with the outlook to targeted phage therapy applications.

    AB - Staphylococcus aureus is a major agent of nosocomial infections. Especially in methicillin-resistant strains, conventional treatment options are limited and expensive, which has fueled a growing interest in phage therapy approaches. We have tested the susceptibility of 207 clinical S. aureus strains to 12 (nine monovalent) different therapeutic phage preparations and subsequently employed linear regression models to estimate the influence of individual host gene families on resistance to phages. Specifically, we used a two-step regression model setup with a preselection step based on gene family enrichment. We show that our models are robust and capture the data’s underlying signal by comparing their performance to that of models build on randomized data. In doing so, we have identified 167 gene families that govern phage resistance in our strain set and performed functional analysis on them. This revealed genes of possible prophage or mobile genetic element origin, along with genes involved in restriction-modification and transcription regulators, though the majority were genes of unknown function. This study is a step in the direction of understanding the intricate host-phage relationship in this important pathogen with the outlook to targeted phage therapy applications.

    KW - Phage therapy

    KW - Bacterial phage resistance

    KW - Regression modeling

    KW - MRSA

    KW - RM1-950

    U2 - 10.3390/antibiotics7010009

    DO - 10.3390/antibiotics7010009

    M3 - Journal article

    VL - 7

    JO - The Journal of Antibiotics

    JF - The Journal of Antibiotics

    SN - 0021-8820

    IS - 1

    ER -