Evaluating gene effects on proteomes and the resulting indirect pleiotropic effects through the cell machinery on the chemical phenotype constitutes a formidable challenge to the analytical chemist. This paper demonstrates that near-infrared (NIR) spectroscopy and chemometrics on the level of the barley seed phenotype is able to differentiate between genetic and environmental effects in a PCA model involving normal barley lines and the gene regulator lys3a in different genetic backgrounds. The gene drastically changes the proteome quantitatively and qualitatively, as displayed in two- dimensional electrophoresis, resulting in a radically changed amino acid and chemical composition. A synergy interval partial least squares regression model (si-PLSR) is tested to select combinations of spectral segments which have a high correlation to defined chemical components indicative of the lys3a gene, such as direct effects of the changed proteome, for example, the amide content, or indirect effects due to changes in carbohydrate and fat composition. It is concluded that the redundancy of biological information on the DNA sequence level is also represented at the phenotypic level in the dataset read by the NIR spectroscopic sensor from the chemical physical fingerprint. The PLS algorithm chooses spectral intervals: which combine both direct and indirect proteome effects. This explains the robustness of NIR spectral predictions by PLSR for a wide range of chemical components. The new option of using spectroscopy, analytical chemistry and chemometrics in modeling the genetically based covariance of physical/chemical fingerprints of the intact phenotype in plant breeding and biotechnology is discussed.
|Journal||Analytica Chimica Acta|
|Publication status||Published - 2001|