Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA

Autores
Zschach, Henrike; Larsen, Mette V.; Hasman, Henrik; Westh, Henrik; Nielsen, Morten; Miedzybrodzki, Ryszard; Jónczyk Matysiak, Ewa; Weber-Dabrowska, Beata; Górski, Andrzej
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Staphylococcus aureus is a major agent of nosocomial infections. Especially in methicillinresistant 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.
Fil: Zschach, Henrike. Technical University of Denmark; Dinamarca
Fil: Larsen, Mette V.. Goseqit Aps; Dinamarca
Fil: Hasman, Henrik. Statens Serum Institut; Dinamarca
Fil: Westh, Henrik. Hvidovre Hospital; Dinamarca. Universidad de Copenhagen; Dinamarca
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Miedzybrodzki, Ryszard. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; Polonia. Medical University Of Warsaw; Polonia
Fil: Jónczyk Matysiak, Ewa. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; Polonia
Fil: Weber-Dabrowska, Beata. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; Polonia
Fil: Górski, Andrzej. Medical University Of Warsaw; Polonia. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; Polonia
Materia
BACTERIAL PHAGE RESISTANCE
MRSA
PHAGE THERAPY
REGRESSION MODELING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/96913
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/96913
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Use of a regression model to study host-genomic determinants of phage susceptibility in MRSAZschach, HenrikeLarsen, Mette V.Hasman, HenrikWesth, HenrikNielsen, MortenMiedzybrodzki, RyszardJónczyk Matysiak, EwaWeber-Dabrowska, BeataGórski, AndrzejBACTERIAL PHAGE RESISTANCEMRSAPHAGE THERAPYREGRESSION MODELINGhttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Staphylococcus aureus is a major agent of nosocomial infections. Especially in methicillinresistant 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.Fil: Zschach, Henrike. Technical University of Denmark; DinamarcaFil: Larsen, Mette V.. Goseqit Aps; DinamarcaFil: Hasman, Henrik. Statens Serum Institut; DinamarcaFil: Westh, Henrik. Hvidovre Hospital; Dinamarca. Universidad de Copenhagen; DinamarcaFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Miedzybrodzki, Ryszard. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; Polonia. Medical University Of Warsaw; PoloniaFil: Jónczyk Matysiak, Ewa. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; PoloniaFil: Weber-Dabrowska, Beata. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; PoloniaFil: Górski, Andrzej. Medical University Of Warsaw; Polonia. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; PoloniaMDPI2018-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/96913Zschach, Henrike; Larsen, Mette V.; Hasman, Henrik; Westh, Henrik; Nielsen, Morten; et al.; Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA; MDPI; Antibiotics; 7; 1; 3-2018; 1-162079-6382CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3390/antibiotics7010009info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2079-6382/7/1/9info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:03:29Zoai:ri.conicet.gov.ar:11336/96913instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:03:29.736CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA
title Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA
spellingShingle Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA
Zschach, Henrike
BACTERIAL PHAGE RESISTANCE
MRSA
PHAGE THERAPY
REGRESSION MODELING
title_short Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA
title_full Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA
title_fullStr Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA
title_full_unstemmed Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA
title_sort Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA
dc.creator.none.fl_str_mv Zschach, Henrike
Larsen, Mette V.
Hasman, Henrik
Westh, Henrik
Nielsen, Morten
Miedzybrodzki, Ryszard
Jónczyk Matysiak, Ewa
Weber-Dabrowska, Beata
Górski, Andrzej
author Zschach, Henrike
author_facet Zschach, Henrike
Larsen, Mette V.
Hasman, Henrik
Westh, Henrik
Nielsen, Morten
Miedzybrodzki, Ryszard
Jónczyk Matysiak, Ewa
Weber-Dabrowska, Beata
Górski, Andrzej
author_role author
author2 Larsen, Mette V.
Hasman, Henrik
Westh, Henrik
Nielsen, Morten
Miedzybrodzki, Ryszard
Jónczyk Matysiak, Ewa
Weber-Dabrowska, Beata
Górski, Andrzej
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv BACTERIAL PHAGE RESISTANCE
MRSA
PHAGE THERAPY
REGRESSION MODELING
topic BACTERIAL PHAGE RESISTANCE
MRSA
PHAGE THERAPY
REGRESSION MODELING
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Staphylococcus aureus is a major agent of nosocomial infections. Especially in methicillinresistant 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.
Fil: Zschach, Henrike. Technical University of Denmark; Dinamarca
Fil: Larsen, Mette V.. Goseqit Aps; Dinamarca
Fil: Hasman, Henrik. Statens Serum Institut; Dinamarca
Fil: Westh, Henrik. Hvidovre Hospital; Dinamarca. Universidad de Copenhagen; Dinamarca
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Miedzybrodzki, Ryszard. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; Polonia. Medical University Of Warsaw; Polonia
Fil: Jónczyk Matysiak, Ewa. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; Polonia
Fil: Weber-Dabrowska, Beata. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; Polonia
Fil: Górski, Andrzej. Medical University Of Warsaw; Polonia. Polish Academy Of Sciences. Ludwik Hirszfeld Institute of Immunology and Experimental Therapy; Polonia
description Staphylococcus aureus is a major agent of nosocomial infections. Especially in methicillinresistant 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.
publishDate 2018
dc.date.none.fl_str_mv 2018-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/96913
Zschach, Henrike; Larsen, Mette V.; Hasman, Henrik; Westh, Henrik; Nielsen, Morten; et al.; Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA; MDPI; Antibiotics; 7; 1; 3-2018; 1-16
2079-6382
CONICET Digital
CONICET
url http://hdl.handle.net/11336/96913
identifier_str_mv Zschach, Henrike; Larsen, Mette V.; Hasman, Henrik; Westh, Henrik; Nielsen, Morten; et al.; Use of a regression model to study host-genomic determinants of phage susceptibility in MRSA; MDPI; Antibiotics; 7; 1; 3-2018; 1-16
2079-6382
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/antibiotics7010009
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2079-6382/7/1/9
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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