Subcellular localization and tissue specific expression of amidase 1 from Arabidopsis thaliana

Amidase 1 (AMI1) from Arabidopsis thaliana converts indole-3-acetamide (IAM), into indole-3-acetic acid (IAA). AMI1 is part of a small isogene family comprising seven members in A. thaliana encoding proteins which share a conserved glycine- and serine-rich amidase-signature. One member of this family has been characterized as an N-acylethanolamine-cleaving fatty acid amidohydrolase (FAAH) and two other members are part of the preprotein translocon of the outer envelope of chloroplasts (Toc complex) or mitochondria (Tom complex) and presumably lack enzymatic activity. Among the hitherto characterized proteins of this family, AMI1 is the only member with indole-3-acetamide hydrolase activity, and IAM is the preferred substrate while N-acylethanolamines and oleamide are not hydrolyzed significantly, thus suggesting a role of AMI1 in auxin biosynthesis. Whereas the enzymatic function of AMI1 has been determined in vitro, the subcellular localization of the enzyme remained unclear. By using different GFP-fusion constructs and an A. thaliana transient expression system, we show a cytoplasmic localization of AMI1. In addition, RT-PCR and anti-amidase antisera were used to examine tissue specific expression of AMI1 at the transcriptional and translational level, respectively. AMI1-expression is strongest in places of highest IAA content in the plant. Thus, it is concluded that AMI1 may be involved in de novo IAA synthesis in A. thaliana.

Molecular characterization of Glu-B3 locus in wheat cultivars and segregating populations

Bread wheat quality constitutes a key trait for the demands of the baking industry as well as the broad consumer preferences. The role of the low molecular weight glutenin subunits (LMW-GS) with regard to bread quality is so far not well understood owing to their genetic complexity and to the use of different nomenclatures and standards for the LMW-GS assignment by different research groups, which has made difficult the undertaking of association studies between genotypes and bread quality. The development of molecular markers to carry out genetic characterization and allele determination is demanding. Nowadays, the most promising LMW gene marker system is based on PCR and high resolution capillary electrophoresis for the simultaneous analysis of the complete multigene family. The molecular analysis of the bread wheat Glu-B3 locus in F2 and F4:6 populations expressed the expected one-locus Mendelian segregation pattern, thus validating the suitability of this marker system for the characterization of LMW-GS genes in segregating populations, allowing for the successful undertaking of studies related to bread-making quality. Moreover, the Glu-B3 allele characterization of standard cultivars with the molecular marker system has revealed its potential as a complementary tool for the allelic determination of this complex multigene family.

Detection and quantification of peanut traces in wheat flour by near infrared hyperspectral imaging spectroscopy using principal-component analysis

The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging (HSI) is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. The obtained loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination (r2) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.