Dataset Information

Fluorescent Sensor Arrays Can Predict and Quantify the Composition of Multicomponent Bacterial Samples.

ABSTRACT: Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition methods and data from pure bacterial species, we demonstrate that this approach can be used to quantify the composition of mixtures, as well as to predict their components with the accuracy of ~80% without the need to acquire additional reference data. The described approach significantly expands the functionality of sensor arrays and provides important insights into data processing for the analysis of other complex samples.

SUBMITTER: Svechkarev D

PROVIDER: S-EPMC6974461 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Fluorescent Sensor Arrays Can Predict and Quantify the Composition of Multicomponent Bacterial Samples.

Svechkarev Denis D Sadykov Marat R MR Houser Lucas J LJ Bayles Kenneth W KW Mohs Aaron M AM

Frontiers in chemistry 20200115

Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition ...[more]

PMID: 32010667

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Project description:BackgroundPrevious studies have demonstrated the importance of DNA extraction methods for molecular detection of Staphylococcus, an important bacterial group in cystic fibrosis (CF). We sought to evaluate the effect of enzymatic digestion (EnzD) prior to DNA extraction on bacterial communities identified in sputum and oropharyngeal swab (OP) samples from patients with CF.MethodsDNA from 81 samples (39 sputum and 42 OP) collected from 63 patients with CF was extracted in duplicate with and without EnzD. Bacterial communities were determined by rRNA gene sequencing, and measures of alpha and beta diversity were calculated. Principal Coordinate Analysis (PCoA) was used to assess differences at the community level and Wilcoxon Signed Rank tests were used to compare relative abundance (RA) of individual genera for paired samples with and without EnzD.ResultsShannon Diversity Index (alpha-diversity) decreased in sputum and OP samples with the use of EnzD. Larger shifts in community composition were observed for OP samples (beta-diversity, measured by Morisita-Horn), whereas less change in communities was observed for sputum samples. The use of EnzD with OP swabs resulted in significant increase in RA for the genera Gemella (p < 0.01), Streptococcus (p < 0.01), and Rothia (p < 0.01). Staphylococcus (p < 0.01) was the only genus with a significant increase in RA from sputum, whereas the following genera decreased in RA with EnzD: Veillonella (p < 0.01), Granulicatella (p < 0.01), Prevotella (p < 0.01), and Gemella (p = 0.02). In OP samples, higher RA of Gram-positive taxa was associated with larger changes in microbial community composition.DiscussionWe show that the application of EnzD to CF airway samples, particularly OP swabs, results in differences in microbial communities detected by sequencing. Use of EnzD can result in large changes in bacterial community composition, and is particularly useful for detection of Staphylococcus in CF OP samples. The enhanced identification of Staphylococcus aureus is a strong indication to utilize EnzD in studies that use OP swabs to monitor CF airway communities.

Dataset Information

Fluorescent Sensor Arrays Can Predict and Quantify the Composition of Multicomponent Bacterial Samples.

Publications

Fluorescent Sensor Arrays Can Predict and Quantify the Composition of Multicomponent Bacterial Samples.

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