Journal of Chromatography & Separation Techniques
Open Access
ISSN: 2157-7064
+44 1300 500008
ISSN: 2157-7064
+44 1300 500008
SÃ?Âlvia M Rocha
Universidade de Aveiro, Portugal
Posters & Accepted Abstracts: J Chromatogr Sep Tech
Opportunistic infections by Aspergillus niger have increased in the last years, either in paediatric patients as adults, presenting a high mortality rate, therefore strongly suggesting the need for prevention or earlier diagnosis and treatment.Microbial metabolomics has been breaking new ground as very useful tool in several areas, including those related to microbial detection, since microorganisms produce several volatile metabolites that can be used as unique chemical fingerprints of each species, and possibly of strains. This richness of information holds the promise for diagnosing infections in situ (e.g. from body fluids, food products, environmental samples, among others), circumventing the laborious recovering of microbes or their genetic material. Microbial metabolomics studies have been mainly focused on the study of the volatile fraction by using 1D-GC. Nevertheless, the use of comprehensive two-dimensional gas chromatography (GCÃ?Â?GC) has revealed that sensitivity and limits of detection are improved compared to 1D-GC. Several challenges should be overcome, since microbial culturing in representative conditions, alongside the technical difficulties to identify and/or quantify trace metabolites within complex matrixes, as well as the inherent problems related to data processing are partially responsible for the paucity of information on the full volatile metabolome of common microbial pathogens. Thus, this talk aims to discuss new developments towards the establishment of a comprehensive platform for A. niger detection management, contributing to in-depth explore its exometabolome, which was studied upon different growth conditions, using a methodology based on headspace-solid phase microextraction combined with GCÃ?Â?GC-ToFMS, an advanced gas chromatographic based methodology with high resolution and high throughput potentialities. Partial Least Squares-Discriminant Analysis (PLS-DA) and cross validation were performed to assess both the predictive power and classification models robustness.In addition, PLS-DA-Variable Importance in Projection was applied to highlight the metabolites playing major roles in species distinction; decreasing the initial dataset to only 16 metabolites (A. niger Biomarker pattern). The data pre-processing time was substantially reduced, and an improvement of quality-of-fit value was achieved. This study goes a step further on exploring the potentialities of metabolomics for constructing A. niger omics pipeline that can be proposed as a high throughput tool towards its future detection based on a molecular biomarkers pattern.
Email: smrocha@ua.pt