Analyzing longitudinal microbial metabolomics data.
A longitudinal experimental design in combination with metabolomics and multiway data analysis is a powerful approach in the identification of metabolites whose correlation with bioproduct formation shows a shift in time. In this paper, a strategy is presented for the analysis of longitudinal microbial metabolomics data, which was performed in order to identify metabolites that are likely inducers of phenylalanine production by Escherichia coli. The variation in phenylalanine production as a function of differences in metabolism induced by the different environmental conditions in time was described by a validated multiway statistical model. Notably, most of the metabolites showing the strongest relations with phenylalanine production seemed to hardly change in time. Apparently, potential bottlenecks in phenylalanine seem to hardly change in the course of a batch fermentation. The approach described in this study is not limited to longitudinal microbial studies but can also be applied to other (biological) studies in which similar longitudinal data need to be analyzed.
Rubingh
Carina M
CM
TNO Quality of Life, Zeist, The Netherlands. carina.rubingh@tno.nl
Bijlsma
Sabina
S
Jellema
Renger H
RH
Overkamp
Karin M
KM
van der Werf
Mariët J
MJ
Smilde
Age K
AK
D000465:Algorithms; D004926:Escherichia coli / Q000378:metabolism*; D005285:Fermentation; D016018:Least-Squares Analysis; D055432:Metabolomics / Q000379:methods*; D008954:Models, Biological; D010649:Phenylalanine / Q000378:metabolism; D025341:Principal Component Analysis; D012044:Regression Analysis
D010649:Phenylalanine