Fourier Transform Infrared Spectroscopy for Rapid Identification of Nonfermenting Gram-Negative Bacteria Isolated from Sputum Samples from Cystic Fibrosis Patients

Bosch, Alejandra; Miñán, Alejandro; Vescina, Cecilia; Degrossi, José; Gatti, Blanca; Montanaro, Patricia; Messina, Matías T; Franco, Mirta; Vay, Carlos; Schmitt, J. H. M. M.; Naumann, Dieter; Yantorno, Osvaldo

doi:10.1128/jcm.01820-08

Cited by 25 publications

(30 citation statements)

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“…Thus, taking into account that the monocolonized, antibiotic-treated and conventional mouse models that we used represent extreme cases, we concluded that diet alters spectra to a higher extent than bacterial composition. It has been shown that the excess of certain nutrients, including lipids, can result in the formation of storage granules High-fat diet alters gut microbiota physiology H Daniel et al or vesicles in the microbial cells (Bauchart et al, 1990), which can have pronounced effects on FT-IR fingerprints (Naumann, 2000) and may even mask differences in microbial composition (Bosch et al, 2008). Still, it is very likely that part of the spectral differences that we observed were not of microbial origin, that is, due to dietary constituents remaining in the sample or adhering to bacteria.…”

Section: Discussionmentioning

confidence: 99%

High-fat diet alters gut microbiota physiology in mice

Daniel¹,

Gholami²,

Berry³

et al. 2013

The ISME Journal

588

386

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The intestinal microbiota is known to regulate host energy homeostasis and can be influenced by highcalorie diets. However, changes affecting the ecosystem at the functional level are still not well characterized. We measured shifts in cecal bacterial communities in mice fed a carbohydrate or high-fat (HF) diet for 12 weeks at the level of the following: (i) diversity and taxa distribution by high-throughput 16S ribosomal RNA gene sequencing; (ii) bulk and single-cell chemical composition by Fourier-transform infrared-(FT-IR) and Raman micro-spectroscopy and (iii) metaproteome and metabolome via highresolution mass spectrometry. High-fat diet caused shifts in the diversity of dominant gut bacteria and altered the proportion of Ruminococcaceae (decrease) and Rikenellaceae (increase). FT-IR spectroscopy revealed that the impact of the diet on cecal chemical fingerprints is greater than the impact of microbiota composition. Diet-driven changes in biochemical fingerprints of members of the Bacteroidales and Lachnospiraceae were also observed at the level of single cells, indicating that there were distinct differences in cellular composition of dominant phylotypes under different diets. Metaproteome and metabolome analyses based on the occurrence of 1760 bacterial proteins and 86 annotated metabolites revealed distinct HF diet-specific profiles. Alteration of hormonal and anti-microbial networks, bile acid and bilirubin metabolism and shifts towards amino acid and simple sugars metabolism were observed. We conclude that a HF diet markedly affects the gut bacterial ecosystem at the functional level.

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Section: Discussionmentioning

confidence: 99%

High-fat diet alters gut microbiota physiology in mice

Daniel¹,

Gholami²,

Berry³

et al. 2013

The ISME Journal

588

386

View full text Add to dashboard Cite

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“…Confocal micro-Raman spectroscopy has been recently used to detect hospital-acquired Staphylococcus aureus-associated infections (77), pathogenic endospores (70), clean-room-relevant microbiological contamination (65), and Candida species (48). As a complementary counterpart, infrared spectroscopy, especially FT-IR spectroscopy, has been widely used to identify and differentiate various types of bacteria (3,34,36,37,49,59,60). Mouwen and colleagues applied FT-IR spectroscopy coupled with HCA (55) or an artificial neural network (54) to detect and differentiate C. jejuni and C. coli.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, they provide the unique advantage of differentiating taxonomic entities at the species or subspecies level on the basis of variations in the spectral features of bacterial cells (41). Since the two groundbreaking publications in Nature about the use of infrared spectroscopy (57) and Raman spectroscopy (61) to study microorganisms, these two techniques have been extensively employed to detect and discriminate different microorganisms and have been shown to be useful as real-time typing methods in bacterial epidemiology (3,7,33,34,36,37,48,49,59,60,65,77). Fourier-transformed infrared (FT-IR) spectroscopy, in combination with multivariate analyses, has been used to identify and discriminate C. jejuni and C. coli (54,55).…”

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confidence: 99%

Comprehensive Detection and Discrimination of Campylobacter Species by Use of Confocal Micro-Raman Spectroscopy and Multilocus Sequence Typing

Huang

Miller

et al. 2012

J Clin Microbiol

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A novel strategy for the rapid detection and identification of traditional and emerging Campylobacter strains based upon Raman spectroscopy (532 nm) is presented here. A total of 200 reference strains and clinical isolates of 11 different Campylobacter species recovered from infected animals and humans from China and North America were used to establish a global Raman spectroscopy-based dendrogram model for Campylobacter identification to the species level and cross validated for its feasibility to predict Campylobacter -associated food-borne outbreaks. Bayesian probability coupled with Monte Carlo estimation was employed to validate the established Raman classification model on the basis of the selected principal components, mainly protein secondary structures, on the Campylobacter cell membrane. This Raman spectroscopy-based typing technique correlates well with multilocus sequence typing and has an average recognition rate of 97.21%. Discriminatory power for the Raman classification model had a Simpson index of diversity of 0.968. Intra- and interlaboratory reproducibility with different instrumentation yielded differentiation index values of 4.79 to 6.03 for wave numbers between 1,800 and 650 cm −1 and demonstrated the feasibility of using this spectroscopic method at different laboratories. Our Raman spectroscopy-based partial least-squares regression model could precisely discriminate and quantify the actual concentration of a specific Campylobacter strain in a bacterial mixture (regression coefficient, >0.98; residual prediction deviation, >7.88). A standard protocol for sample preparation, spectral collection, model validation, and data analyses was established for the Raman spectroscopic technique. Raman spectroscopy may have advantages over traditional genotyping methods for bacterial epidemiology, such as detection speed and accuracy of identification to the species level.

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“…The prevalence increased over the last decade from 0.2 to 3.6%, depending on the medical center. In early 2004, an outbreak involving main Argentine CF care centers occurred, and the proportion of patients from whom B. cepacia complex species were recovered ranged from 19 to 36% (2)(3)(4). Currently, through strict infection control procedures, the prevalence of Burkholderia spp.…”

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confidence: 99%

Genetic Diversity of Burkholderia contaminans Isolates from Cystic Fibrosis Patients in Argentina

et al. 2013

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A total of 120 Burkholderia cepacia complex isolates collected during 2004 -2010 from 66 patients in two cystic fibrosis reference centers in Argentina were analyzed. Burkholderia contaminans was the species most frequently recovered (57.6%), followed by Burkholderia cenocepacia (15%), a species distribution not reported so far. The recA-PCR-based techniques applied to the B. contaminans isolates revealed that 85% of the population carried the recA-ST-71 allele. Our results showed the utility of BOX-PCR genotyping in analyzing B. contaminans diversity. This approach allowed us to address clonal transmission during an outbreak and the genetic changes occurring in infecting bacteria over the course of chronic infection. Burkholderia cepacia complex species are capable of causing chronic and often severe respiratory tract infections in cystic fibrosis (CF) patients and other types of infections in immunocompromised patients. Although many CF patients remain infected by these bacteria and yet stay relatively healthy for prolonged periods, others either have a severe decline in their pulmonary status or die shortly after the initial colonization (1). In Argentina, the first reports of B. cepacia complex bacteria infecting CF patients started around 1990. At that time, Burkholderia spp. were recovered sporadically and with a very low prevalence (Ͻ0.1%). The prevalence increased over the last decade from 0.2 to 3.6%, depending on the medical center. In early 2004, an outbreak involving main Argentine CF care centers occurred, and the proportion of patients from whom B. cepacia complex species were recovered ranged from 19 to 36% (2-4). Currently, through strict infection control procedures, the prevalence of Burkholderia spp. in local patients has decreased to approximately 10%. Recent worldwide surveillance studies concerning the distribution of B. cepacia complex species in CF patients have cited B. multivorans and B. cenocepacia as the most frequently recovered species. These species account for approximately 80% of the infected patients, and the prevalence of one or the other is geographically and temporally dependent (5-7). Unlike this worldwide situation, in Argentina a relatively high prevalence of B. contaminans followed by B. cenocepacia has been observed (3, 4). To our knowledge, no other geographical region has been reported in the literature to have such a high occurrence of B. contaminans infecting CF patients. In view of this particular scenario, coupled with the lack of biodiversity information available for B. contaminans species, we decided to study this local population by recA-PCR-based techniques and by repetitive element sequence-based PCR (rep-PCR) approaches. These methods allowed us to address the genetic diversity of B. contaminans isolates recovered during that outbreak along with the genetic changes occurring in the infecting bacteria over the course of a chronic infection.

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Fourier Transform Infrared Spectroscopy for Rapid Identification of Nonfermenting Gram-Negative Bacteria Isolated from Sputum Samples from Cystic Fibrosis Patients

Cited by 25 publications

References 0 publications

High-fat diet alters gut microbiota physiology in mice

High-fat diet alters gut microbiota physiology in mice

Comprehensive Detection and Discrimination of Campylobacter Species by Use of Confocal Micro-Raman Spectroscopy and Multilocus Sequence Typing

Genetic Diversity of Burkholderia contaminans Isolates from Cystic Fibrosis Patients in Argentina

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