Metabolomics as a Tool for Functional Genomics

Dear Colleagues,

A key objective in biology is understanding how genes, proteins, and metabolites interact in a network to control a variety of cellular processes. Transcriptomics, proteomics, and metabolomics, together with integrated bioinformatics, are functional genomics technologies that allow the large-scale interrogation of mRNA, proteins, and metabolites, respectively. Compared to either the transcriptome or proteome of an organism, the metabolome is the most direct readout of the current status of an organism, and the fastest to register a measured response. Moreover, metabolomics provides a new and interesting layer of knowledge when modeling cellular functions from a mechanistic point of view; however, more studies are needed in order to achieve a level of knowledge that will allow us to accurately predict complex cellular responses. Metabolomics is defined as the total complement of metabolite changes according to the developmental, physiological, or pathological state of an organism, specific tissue, or cell. As a multidisciplinary science, metabolomics comprises areas of chemistry, bioinformatics, statistics, genetics, ecology, biotechnology, nutrition, and cytology. Targeted and untargeted mass spectrometry-based approaches for metabolite profiling have been applied to diverse scientific domains and opened up new ways to assess animal and human physiology in health and disease, including in clinical settings (“from bench to bedside”), environmental stress responses in plants, or pathogenicity and drug resistance in microbial strains.

This Special Issue welcomes but is not limited to contributions dealing with:

• Animal and human metabolomics, including metabolic studies of disease, aging or nutrition;
• Plant metabolomics for crop improvement, genotype classification, characterization of the response to environmental perturbations, and development of plant-derived functional foods and nutraceuticals;
• Microbial metabolomics to analyze the effect of genetic variation on strain-specific adaptive capacity and vulnerability, as well as the function and performance of transgenic and fermentation microorganisms;
• Yeast metabolomics to analyze genetic determinants relevant to bioengineering strategies;
• Integrative modeling of metabolomics and other omics, such as transcriptomics, proteomics, or genomics.

Prof. Dr. Sara Rinalducci
Dr. María Peña-Chilet
Dr. Camilla Hill
Guest Editors

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