Do Metabolomics and Taxonomic Barcode Markers Tell the Same Story about the Evolution of Saccharomyces sensu stricto Complex in Fermentative Environments?
Luca RosciniAngela ContiDebora Casagrande PierantoniVincent RobertLaura CorteGianluigi CardinaliPublished in: Microorganisms (2020)
Yeast taxonomy was introduced based on the idea that physiological properties would help discriminate species, thus assuming a strong link between physiology and taxonomy. However, the instability of physiological characteristics within species configured them as not ideal markers for species delimitation, shading the importance of physiology and paving the way to the DNA-based taxonomy. The hypothesis of reconnecting taxonomy with specific traits from phylogenies has been successfully explored for Bacteria and Archaea, suggesting that a similar route can be traveled for yeasts. In this framework, thirteen single copy loci were used to investigate the predictability of complex Fourier Transform InfaRed spectroscopy (FTIR) and High-performance Liquid Chromatography-Mass Spectrometry (LC-MS) profiles of the four historical species of the Saccharomyces sensu stricto group, both on resting cells and under short-term ethanol stress. Our data show a significant connection between the taxonomy and physiology of these strains. Eight markers out of the thirteen tested displayed high correlation values with LC-MS profiles of cells in resting condition, confirming the low efficacy of FTIR in the identification of strains of closely related species. Conversely, most genetic markers displayed increasing trends of correlation with FTIR profiles as the ethanol concentration increased, according to their role in the cellular response to different type of stress.
Keyphrases
- mass spectrometry
- high performance liquid chromatography
- induced apoptosis
- genome wide
- escherichia coli
- cell cycle arrest
- heart rate
- genetic diversity
- high resolution
- tandem mass spectrometry
- single molecule
- cell death
- gene expression
- simultaneous determination
- liquid chromatography
- copy number
- gas chromatography
- dna methylation
- artificial intelligence
- machine learning
- ms ms
- deep learning
- signaling pathway
- capillary electrophoresis