Untargeted Metabolomics to Explore the Bacteria Exo-Metabolome Related to Plant Biostimulants

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

The control and development of plant growth promoters is a key factor for the agro-nomy industry in its economic performance. Different genera of bacteria are widely used as natural biostimulants with the aim of enhancing nutrition efficiency, abiotic stress tolerance and/or crop quality traits, regardless of their nutrients content. However, the complete exo-metabolome of the bacteria responsible for the biostimulant effect is still unknown and needs to be investigated. Three bacteria with different biostimulant effects were studied by untargeted metabolomics in order to describe the metabolites responsible for this effect. The pentose phosphate pathway, tryptophan metabolism, zeatin biosynthesis, vitamin B6 metabolism and amino acid metabolism were the highlighted pathways related to bacteria biostimulant activity. These results are related to the plant hormones biosynthesis pathway for auxins and zeatins biosynthesis. Fourteen metabolites were identified as biomarkers of the biostimulant activity. The results suggest a greater relevance of auxins than cytokinin pathways due the importance of the precursors identified. The results show a clear trend of using indole-3-pyruvate and 3-Indoleglycolaldehyde pathways to produce auxins by bacteria. The results demonstrate for the first time that 4-Pyridoxic acid, the fructosamines N-(1-Deoxy-1-fructosyl)phenylalanine and N-(1-Deoxy-1-fructosyl)isoleucine and the tripeptides diprotin A and B are metabolites related to biostimulant capabilities. This study shows how untargeted metabolomic approaches can be useful tools to investigate the bacteria exo-metabolomes related to biostimulant effects.

Related collections

Most cited references 50

Record: found
Abstract: found
Article: not found

Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI).

Lloyd W. Sumner, Alexander Amberg, Dave Barrett … (2008)

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.

0 comments Cited 881 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

Seaweed Extracts as Biostimulants of Plant Growth and Development

Wajahatullah Khan, Usha Rayirath, Sowmyalakshmi Subramanian … (2009)

0 comments Cited 329 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Centering, scaling, and transformations: improving the biological information content of metabolomics data

Robert A van den Berg, Huub Hoefsloot, Johan A. Westerhuis … (2006)

Background Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. Results Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. Conclusion Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis). In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.