Abstract
BackgroundThis study analyzes metabolomic data from a rice tillering (branching) developmental profile to define a set of biomarker metabolites that reliably captures the metabolite variance of this plant developmental event, and which has potential as a basis for rapid comparative screening of metabolite profiles in relation to change in development, environment, or genotype. Changes in metabolism, and in metabolite profile, occur as a part of, and in response to, developmental events. These changes are influenced by the developmental program, as well as external factors impinging on it. Many samples are needed, however, to characterize quantitative aspects of developmental variation. A biomarker metabolite set could benefit screening of quantitative plant developmental variation by providing some of the advantages of both comprehensive metabolomic studies and focused studies of particular metabolites or pathways.ResultsAn appropriate set of biomarker metabolites to represent the plant developmental period including the initiation and early growth of rice tillering (branching) was obtained by: (1) determining principal components of the comprehensive metabolomic profile, then (2) identifying clusters of metabolites representing variation in loading on the first three principal components, and finally (3) selecting individual metabolites from these clusters that were known to be common among diverse organisms. The resultant set of 21 biomarker metabolites was reliable (P = 0.001) in capturing 83% of the metabolite variation in development. Furthermore, a subset of the biomarker metabolites was successful (P = 0.05) in correctly predicting metabolite change in response to environment as determined in another rice metabolomics study.ConclusionThe ability to define a set of biomarker metabolites that reliably captures the metabolite variance of a plant developmental event was established. The biomarker metabolites are all commonly present in diverse organisms, so studies of their quantitative relationships can provide comparative information concerning metabolite profiles in relation to change in plant development, environment, or genotype.
Highlights
IntroductionThis study analyzes metabolomic data from a rice tillering (branching) developmental profile to define a set of biomarker metabolites that reliably captures the metabolite variance of this plant developmental event, and which has potential as a basis for rapid comparative screening of metabolite profiles in relation to change in development, environment, or genotype
This study analyzes metabolomic data from a rice tillering developmental profile to define a set of biomarker metabolites that reliably captures the metabolite variance of this plant developmental event, and which has potential as a basis for rapid comparative screening of metabolite profiles in relation to change in development, environment, or genotype
Reliability of the biomarker set to represent the pattern of metabolite variation observed among the tissues If the resultant set of biomarker metabolites captures much of the metabolite variance in development as obtained through the metabolomic profiling, we should be able to "flip" the analysis around and detect
Summary
This study analyzes metabolomic data from a rice tillering (branching) developmental profile to define a set of biomarker metabolites that reliably captures the metabolite variance of this plant developmental event, and which has potential as a basis for rapid comparative screening of metabolite profiles in relation to change in development, environment, or genotype. "An understanding of crop responses to environment will provide the fundamental basis for developing methods for achieving these increases in efficiency" (Hall,[2]). Plants interact with environment in both chemical and physical ways, but we have very little systematic understanding of how the plant responds chemically during development and in developmental response to environment [3,4]. This lack of knowledge of the broad changes in metabolite patterns during development limits our efficiency to manipulate the cellular or molecular aspects of plant development with intent to influence yield or sustainability of production. The time required to analyze large sample numbers can be lengthy for some studies
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