Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients

Autores
Bosch, María Alejandra; Miñán, Alejandro; Vescina, Cecilia; Degrossi, José; Gatti, Blanca; Montanaro, Patricia; Messina, Matías; Franco, Mirta; Vay, Carlos; Schmitt, Juergen; Naumann, Dieter; Yantorno, Osvaldo Miguel
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The accurate and rapid identification of bacteria isolated from the respiratory tract of patients with cystic fibrosis (CF) is critical in epidemiological studies, during intrahospital outbreaks, for patient treatment, and for determination of therapeutic options. While the most common organisms isolated from sputum samples are Pseudomonas aeruginosa, Staphylococcus aureus, and Haemophilus influenzae, in recent decades an increasing fraction of CF patients has been colonized by other nonfermenting (NF) gram-negative rods, such as Burkholderia cepacia complex (BCC) bacteria, Stenotrophomonas maltophilia, Ralstonia pickettii, Acinetobacter spp., and Achromobacter spp. In the present study, we developed a novel strategy for the rapid identification of NF rods based on Fourier transform infrared spectroscopy (FTIR) in combination with artificial neural networks (ANNs). A total of 15 reference strains and 169 clinical isolates of NF gram-negative bacteria recovered from sputum samples from 150 CF patients were used in this study. The clinical isolates were identified according to the guidelines for clinical microbiology practices for respiratory tract specimens from CF patients; and particularly, BCC bacteria were further identified by recA-based PCR followed by restriction fragment length polymorphism analysis with HaeIII, and their identities were confirmed by recA species-specific PCR. In addition, some strains belonging to genera different from BCC were identified by 16S rRNA gene sequencing. A standardized experimental protocol was established, and an FTIR spectral database containing more than 2,000 infrared spectra was created. The ANN identification system consisted of two hierarchical levels. The top-level network allowed the identification of P. aeruginosa, S. maltophilia, Achromobacter xylosoxidans, Acinetobacter spp., R. pickettii, and BCC bacteria with an identification success rate of 98.1%. The second-level network was developed to differentiate the four most clinically relevant species of BCC, B. cepacia, B. multivorans, B. cenocepacia, and B. stabilis (genomovars I to IV, respectively), with a correct identification rate of 93.8%. Our results demonstrate the high degree of reliability and strong potential of ANN-based FTIR spectrum analysis for the rapid identification of NF rods suitable for use in routine clinical microbiology laboratories.
Centro de Investigación y Desarrollo en Fermentaciones Industriales
Facultad de Ciencias Exactas
Materia
Ciencias Exactas
Bacteria
Cystic Fibrosis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/83106

id SEDICI_38c5436e5496d3b9b607900442299f47
oai_identifier_str oai:sedici.unlp.edu.ar:10915/83106
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patientsBosch, María AlejandraMiñán, AlejandroVescina, CeciliaDegrossi, JoséGatti, BlancaMontanaro, PatriciaMessina, MatíasFranco, MirtaVay, CarlosSchmitt, JuergenNaumann, DieterYantorno, Osvaldo MiguelCiencias ExactasBacteriaCystic FibrosisThe accurate and rapid identification of bacteria isolated from the respiratory tract of patients with cystic fibrosis (CF) is critical in epidemiological studies, during intrahospital outbreaks, for patient treatment, and for determination of therapeutic options. While the most common organisms isolated from sputum samples are <i>Pseudomonas aeruginosa</i>, <i>Staphylococcus aureus</i>, and <i>Haemophilus influenzae</i>, in recent decades an increasing fraction of CF patients has been colonized by other nonfermenting (NF) gram-negative rods, such as <i>Burkholderia cepacia</i> complex (BCC) bacteria, <i>Stenotrophomonas maltophilia</i>, Ralstonia pickettii, <i>Acinetobacter</i> spp., and <i>Achromobacter</i> spp. In the present study, we developed a novel strategy for the rapid identification of NF rods based on Fourier transform infrared spectroscopy (FTIR) in combination with artificial neural networks (ANNs). A total of 15 reference strains and 169 clinical isolates of NF gram-negative bacteria recovered from sputum samples from 150 CF patients were used in this study. The clinical isolates were identified according to the guidelines for clinical microbiology practices for respiratory tract specimens from CF patients; and particularly, BCC bacteria were further identified by <i>recA</i>-based PCR followed by restriction fragment length polymorphism analysis with HaeIII, and their identities were confirmed by <i>recA</i> species-specific PCR. In addition, some strains belonging to genera different from BCC were identified by 16S rRNA gene sequencing. A standardized experimental protocol was established, and an FTIR spectral database containing more than 2,000 infrared spectra was created. The ANN identification system consisted of two hierarchical levels. The top-level network allowed the identification of <i>P. aeruginosa</i>, <i>S. maltophilia</i>, <i>Achromobacter xylosoxidans</i>, <i>Acinetobacter</i> spp., R. pickettii, and BCC bacteria with an identification success rate of 98.1%. The second-level network was developed to differentiate the four most clinically relevant species of BCC, <i>B. cepacia</i>, <i>B. multivorans</i>, <i>B. cenocepacia</i>, and <i>B. stabilis</i> (genomovars I to IV, respectively), with a correct identification rate of 93.8%. Our results demonstrate the high degree of reliability and strong potential of ANN-based FTIR spectrum analysis for the rapid identification of NF rods suitable for use in routine clinical microbiology laboratories.Centro de Investigación y Desarrollo en Fermentaciones IndustrialesFacultad de Ciencias Exactas2008info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf2535-2546http://sedici.unlp.edu.ar/handle/10915/83106enginfo:eu-repo/semantics/altIdentifier/issn/0095-1137info:eu-repo/semantics/altIdentifier/doi/10.1128/JCM.02267-07info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:07:37Zoai:sedici.unlp.edu.ar:10915/83106Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:07:37.509SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients
title Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients
spellingShingle Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients
Bosch, María Alejandra
Ciencias Exactas
Bacteria
Cystic Fibrosis
title_short Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients
title_full Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients
title_fullStr Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients
title_full_unstemmed Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients
title_sort Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients
dc.creator.none.fl_str_mv Bosch, María Alejandra
Miñán, Alejandro
Vescina, Cecilia
Degrossi, José
Gatti, Blanca
Montanaro, Patricia
Messina, Matías
Franco, Mirta
Vay, Carlos
Schmitt, Juergen
Naumann, Dieter
Yantorno, Osvaldo Miguel
author Bosch, María Alejandra
author_facet Bosch, María Alejandra
Miñán, Alejandro
Vescina, Cecilia
Degrossi, José
Gatti, Blanca
Montanaro, Patricia
Messina, Matías
Franco, Mirta
Vay, Carlos
Schmitt, Juergen
Naumann, Dieter
Yantorno, Osvaldo Miguel
author_role author
author2 Miñán, Alejandro
Vescina, Cecilia
Degrossi, José
Gatti, Blanca
Montanaro, Patricia
Messina, Matías
Franco, Mirta
Vay, Carlos
Schmitt, Juergen
Naumann, Dieter
Yantorno, Osvaldo Miguel
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Exactas
Bacteria
Cystic Fibrosis
topic Ciencias Exactas
Bacteria
Cystic Fibrosis
dc.description.none.fl_txt_mv The accurate and rapid identification of bacteria isolated from the respiratory tract of patients with cystic fibrosis (CF) is critical in epidemiological studies, during intrahospital outbreaks, for patient treatment, and for determination of therapeutic options. While the most common organisms isolated from sputum samples are <i>Pseudomonas aeruginosa</i>, <i>Staphylococcus aureus</i>, and <i>Haemophilus influenzae</i>, in recent decades an increasing fraction of CF patients has been colonized by other nonfermenting (NF) gram-negative rods, such as <i>Burkholderia cepacia</i> complex (BCC) bacteria, <i>Stenotrophomonas maltophilia</i>, Ralstonia pickettii, <i>Acinetobacter</i> spp., and <i>Achromobacter</i> spp. In the present study, we developed a novel strategy for the rapid identification of NF rods based on Fourier transform infrared spectroscopy (FTIR) in combination with artificial neural networks (ANNs). A total of 15 reference strains and 169 clinical isolates of NF gram-negative bacteria recovered from sputum samples from 150 CF patients were used in this study. The clinical isolates were identified according to the guidelines for clinical microbiology practices for respiratory tract specimens from CF patients; and particularly, BCC bacteria were further identified by <i>recA</i>-based PCR followed by restriction fragment length polymorphism analysis with HaeIII, and their identities were confirmed by <i>recA</i> species-specific PCR. In addition, some strains belonging to genera different from BCC were identified by 16S rRNA gene sequencing. A standardized experimental protocol was established, and an FTIR spectral database containing more than 2,000 infrared spectra was created. The ANN identification system consisted of two hierarchical levels. The top-level network allowed the identification of <i>P. aeruginosa</i>, <i>S. maltophilia</i>, <i>Achromobacter xylosoxidans</i>, <i>Acinetobacter</i> spp., R. pickettii, and BCC bacteria with an identification success rate of 98.1%. The second-level network was developed to differentiate the four most clinically relevant species of BCC, <i>B. cepacia</i>, <i>B. multivorans</i>, <i>B. cenocepacia</i>, and <i>B. stabilis</i> (genomovars I to IV, respectively), with a correct identification rate of 93.8%. Our results demonstrate the high degree of reliability and strong potential of ANN-based FTIR spectrum analysis for the rapid identification of NF rods suitable for use in routine clinical microbiology laboratories.
Centro de Investigación y Desarrollo en Fermentaciones Industriales
Facultad de Ciencias Exactas
description The accurate and rapid identification of bacteria isolated from the respiratory tract of patients with cystic fibrosis (CF) is critical in epidemiological studies, during intrahospital outbreaks, for patient treatment, and for determination of therapeutic options. While the most common organisms isolated from sputum samples are <i>Pseudomonas aeruginosa</i>, <i>Staphylococcus aureus</i>, and <i>Haemophilus influenzae</i>, in recent decades an increasing fraction of CF patients has been colonized by other nonfermenting (NF) gram-negative rods, such as <i>Burkholderia cepacia</i> complex (BCC) bacteria, <i>Stenotrophomonas maltophilia</i>, Ralstonia pickettii, <i>Acinetobacter</i> spp., and <i>Achromobacter</i> spp. In the present study, we developed a novel strategy for the rapid identification of NF rods based on Fourier transform infrared spectroscopy (FTIR) in combination with artificial neural networks (ANNs). A total of 15 reference strains and 169 clinical isolates of NF gram-negative bacteria recovered from sputum samples from 150 CF patients were used in this study. The clinical isolates were identified according to the guidelines for clinical microbiology practices for respiratory tract specimens from CF patients; and particularly, BCC bacteria were further identified by <i>recA</i>-based PCR followed by restriction fragment length polymorphism analysis with HaeIII, and their identities were confirmed by <i>recA</i> species-specific PCR. In addition, some strains belonging to genera different from BCC were identified by 16S rRNA gene sequencing. A standardized experimental protocol was established, and an FTIR spectral database containing more than 2,000 infrared spectra was created. The ANN identification system consisted of two hierarchical levels. The top-level network allowed the identification of <i>P. aeruginosa</i>, <i>S. maltophilia</i>, <i>Achromobacter xylosoxidans</i>, <i>Acinetobacter</i> spp., R. pickettii, and BCC bacteria with an identification success rate of 98.1%. The second-level network was developed to differentiate the four most clinically relevant species of BCC, <i>B. cepacia</i>, <i>B. multivorans</i>, <i>B. cenocepacia</i>, and <i>B. stabilis</i> (genomovars I to IV, respectively), with a correct identification rate of 93.8%. Our results demonstrate the high degree of reliability and strong potential of ANN-based FTIR spectrum analysis for the rapid identification of NF rods suitable for use in routine clinical microbiology laboratories.
publishDate 2008
dc.date.none.fl_str_mv 2008
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/83106
url http://sedici.unlp.edu.ar/handle/10915/83106
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/0095-1137
info:eu-repo/semantics/altIdentifier/doi/10.1128/JCM.02267-07
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
2535-2546
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1846064131838312448
score 13.22299