Abstract
An in-depth exploration of the headspace content of Aspergillus niger cultures was performed upon different growth conditions, using a methodology based on advanced multidimensional gas chromatography. This volatile fraction comprises 428 putatively identified compounds distributed over several chemical families, being the major ones hydrocarbons, alcohols, esters, ketones and aldehydes. These metabolites may be related with different metabolic pathways, such as amino acid metabolism, biosynthesis and metabolism of fatty acids, degradation of aromatic compounds, mono and sesquiterpenoid synthesis and carotenoid cleavage. The A. niger molecular biomarkers pattern was established, comprising the 44 metabolites present in all studied conditions. This pattern was successfully used to distinguish A. niger from other fungi (Candida albicans and Penicillium chrysogenum) with 3 days of growth by using Partial Least Squares-Discriminant Analysis (PLS-DA). In addition, PLS-DA-Variable Importance in Projection was applied to highlight the metabolites playing major roles in fungi distinction; decreasing the initial dataset to only 16 metabolites. 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 A. niger metabolome construction and A. niger future detection may be proposed based on this molecular biomarkers pattern.
Highlights
Information on etiological agent, which impairs their treatment
Microbial metabolomics has been breaking new ground as a 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
An in-depth study of the headspace content of A. niger cultures was performed upon different growth conditions, using a methodology based on headspace-solid phase microextraction combined with GC ×GC with time of flight mass spectrometry detection (HS-SPME/GC ×GC-ToFMS), an advanced gas chromatographic based methodology with high resolution and high throughput potentialities
Summary
Information on etiological agent, which impairs their treatment. the development and implementation of faster, accurate and cost-effective detection tests are sorely needed[22,23]. Microbial metabolomics has been breaking new ground as a 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[23,24]. Partial Least Squares-Discriminant Analysis (PLS-DA) and cross validation were performed to assess both the predictive power and classification models robustness, going a step further on exploring the potential of this methodology towards A. niger future detection using different fungi species and testing its reliability for genera distinction, based on the molecular biomarkers pattern achieved
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
