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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Functional annotation of genomic data with metabolic inference.

R L Walzem¹, R A Baillie, M Wiest

¹Department of Poultry Science, Texas A&M University, College Station 77843, USA. rwalzem@poultry.tamu.edu

Poultry Science

|June 19, 2007

Summary

Lipid metabolomics revealed that increased abdominal adiposity in chickens is not universal, varying by genetic background. This systems biology approach can inform animal breeding programs by linking metabolic profiles to gene networks.

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Area of Science:

Systems biology
Metabolomics
Biochemistry

Background:

Biological systems are complex, with metabolites reflecting cellular responses to genetic or environmental changes.
Metabolites are the end products of cellular regulatory processes.
Lipids play a crucial role in metabolism.

Purpose of the Study:

To investigate the metabolic basis of increased abdominal adiposity in chickens using lipid metabolomics.
To understand how genetic background influences metabolic shifts related to adiposity.
To explore the application of metabolomics in reverse engineering selection programs.

Main Methods:

Utilized lipid metabolomics, leveraging well-developed analytical platforms.
Analyzed metabolic profiles in two strains of divergently selected chickens.
Associated metabolic descriptions with specific gene networks and transcriptional profiles.

Main Results:

Metabolic shifts leading to increased abdominal adiposity were found to be strain-specific.
Genetic background significantly influences the metabolic basis of adiposity.
Lipid metabolomics provided a detailed metabolic description of the selected chicken strains.

Conclusions:

Metabolomics offers a powerful tool for understanding complex biological systems and metabolic regulation.
The findings highlight the importance of genetic background in metabolic studies.
Metabolomics can guide future breeding strategies by revealing underlying metabolic mechanisms.