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Leveraging non-targeted metabolite profiling via statistical genomics.

Shen M, Broeckling CD, Chu EY, Ziegler G, Baxter IR, Prenni JE, Hoekenga OA - PLoS ONE (2013)

Bottom Line: Nineteen modules returned significant results, illustrating the genetic control of biochemical networks within the maize kernel.Our approach leverages the correlations between the genome and metabolome to mutually enhance their annotation and thus enable biological interpretation.This method is applicable to any organism with sufficient bioinformatic resources.

View Article: PubMed Central - PubMed

Affiliation: Boyce Thompson Institute for Plant Research, Ithaca, New York, United States of America.

ABSTRACT
One of the challenges of systems biology is to integrate multiple sources of data in order to build a cohesive view of the system of study. Here we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Using a network analysis, we can include 97.5% of the 8,710 features detected from 210 varieties into a single framework. More conservatively, 47.1% of compounds detected can be organized into a network with 48 distinct modules. Eigenvalues were calculated for each module and then used as inputs for genome-wide association studies. Nineteen modules returned significant results, illustrating the genetic control of biochemical networks within the maize kernel. Our approach leverages the correlations between the genome and metabolome to mutually enhance their annotation and thus enable biological interpretation. This method is applicable to any organism with sufficient bioinformatic resources.

Show MeSH
Genomics-assisted chemistry & chemistry-assisted genomics.This flow chart describes the process by which statistical genetics and genomics can enable metabolite profiling to have greater power and impact.
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pone-0057667-g001: Genomics-assisted chemistry & chemistry-assisted genomics.This flow chart describes the process by which statistical genetics and genomics can enable metabolite profiling to have greater power and impact.

Mentions: Our long-term goal is to characterize phenotypic variation in maize grain quality and to identify the genetic and environmental factors that influence the metabolomic composition of this important staple food and model plant. This effort will provide information to better describe the existing food supply and also project what new grain quality traits may be achievable in the future using conventional plant breeding. Towards this goal, we chose to use mass spectrometry based non-targeted metabolite profiling of maize meal prepared from cooked, whole kernels (Figure 1). While it may be counterintuitive to treat samples in this way, our dataset represents a genetically diverse sample of a foodstuff that could be consumed by either humans or animals. This choice helps to define the range of normal and acceptable variation within a highly diverse crop plant [17]. More than 8,710 metabolomic features were detected from the whole kernel methanolic extracts (Table S1). Principal component analysis (PCA) gave an initial characterization of the profiling results. PCA explains about 22% of the variance with 2 PC’s (Figure S1). The performance of PCA for this dataset is typical as the composition varies very widely across genetically distant accessions.


Leveraging non-targeted metabolite profiling via statistical genomics.

Shen M, Broeckling CD, Chu EY, Ziegler G, Baxter IR, Prenni JE, Hoekenga OA - PLoS ONE (2013)

Genomics-assisted chemistry & chemistry-assisted genomics.This flow chart describes the process by which statistical genetics and genomics can enable metabolite profiling to have greater power and impact.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585405&req=5

pone-0057667-g001: Genomics-assisted chemistry & chemistry-assisted genomics.This flow chart describes the process by which statistical genetics and genomics can enable metabolite profiling to have greater power and impact.
Mentions: Our long-term goal is to characterize phenotypic variation in maize grain quality and to identify the genetic and environmental factors that influence the metabolomic composition of this important staple food and model plant. This effort will provide information to better describe the existing food supply and also project what new grain quality traits may be achievable in the future using conventional plant breeding. Towards this goal, we chose to use mass spectrometry based non-targeted metabolite profiling of maize meal prepared from cooked, whole kernels (Figure 1). While it may be counterintuitive to treat samples in this way, our dataset represents a genetically diverse sample of a foodstuff that could be consumed by either humans or animals. This choice helps to define the range of normal and acceptable variation within a highly diverse crop plant [17]. More than 8,710 metabolomic features were detected from the whole kernel methanolic extracts (Table S1). Principal component analysis (PCA) gave an initial characterization of the profiling results. PCA explains about 22% of the variance with 2 PC’s (Figure S1). The performance of PCA for this dataset is typical as the composition varies very widely across genetically distant accessions.

Bottom Line: Nineteen modules returned significant results, illustrating the genetic control of biochemical networks within the maize kernel.Our approach leverages the correlations between the genome and metabolome to mutually enhance their annotation and thus enable biological interpretation.This method is applicable to any organism with sufficient bioinformatic resources.

View Article: PubMed Central - PubMed

Affiliation: Boyce Thompson Institute for Plant Research, Ithaca, New York, United States of America.

ABSTRACT
One of the challenges of systems biology is to integrate multiple sources of data in order to build a cohesive view of the system of study. Here we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Using a network analysis, we can include 97.5% of the 8,710 features detected from 210 varieties into a single framework. More conservatively, 47.1% of compounds detected can be organized into a network with 48 distinct modules. Eigenvalues were calculated for each module and then used as inputs for genome-wide association studies. Nineteen modules returned significant results, illustrating the genetic control of biochemical networks within the maize kernel. Our approach leverages the correlations between the genome and metabolome to mutually enhance their annotation and thus enable biological interpretation. This method is applicable to any organism with sufficient bioinformatic resources.

Show MeSH