Capturing flavors from Capsicum baccatum by introgression in sweet pepper.

KEY MESSAGE: Biochemical characterization in combination with genetic analyses in BC 2 S 1 plants and near-isogenic lines led to the detection and validation of C. baccatum loci affecting flavor, terpenoid content and Brix level. The species Capsicum baccatum includes the most common hot peppers of the Andean cuisine, known for their rich variation in flavors and aromas. So far the C. baccatum genetic variation remained merely concealed for Capsicum annuum breeding, due to post-fertilization genetic barriers encountered in interspecific hybridization. However, to exploit the potential flavor wealth of C. baccatum we combined interspecific crossing with embryo rescue, resulting in a multi-parent BC2S1 population. Volatile and non-volatile compounds plus some physical characters were measured in mature fruits, in combination with taste evaluation by a sensory panel. An enormous variation in biochemical composition and sensory attributes was found, with almost all traits showing transgression. A population-specific genetic linkage map was developed for QTL mapping. BC2S1 QTLs were validated in an experiment with near-isogenic lines, resulting in confirmed genetic effects for physical, biochemical and sensory traits. Three findings are described in more detail: (1) A small C. baccatum LG3 introgression caused an extraordinary effect on flavor, resulting in significantly higher scores for the attributes aroma, flowers, spices, celery and chives. In an attempt to identify the responsible biochemical compounds few consistently up- and down-regulated metabolites were detected. (2) Two introgressions (LG10.1 and LG1) had major effects on terpenoid content of mature fruits, affecting at least 15 different monoterpenes. (3) A second LG3 fragment resulted in a strong increase in Brix without negative effects on fruit size. The mapping strategy, the potential application of studied traits and perspectives for breeding are discussed.

Biosynthesis of antinutritional alkaloids in solanaceous crops is mediated by clustered genes.

Steroidal glycoalkaloids (SGAs) such as α-solanine found in solanaceous food plants--as, for example, potato--are antinutritional factors for humans. Comparative coexpression analysis between tomato and potato coupled with chemical profiling revealed an array of 10 genes that partake in SGA biosynthesis. We discovered that six of them exist as a cluster on chromosome 7, whereas an additional two are adjacent in a duplicated genomic region on chromosome 12. Following systematic functional analysis, we suggest a revised SGA biosynthetic pathway starting from cholesterol up to the tetrasaccharide moiety linked to the tomato SGA aglycone. Silencing GLYCOALKALOID METABOLISM 4 prevented accumulation of SGAs in potato tubers and tomato fruit. This may provide a means for removal of unsafe, antinutritional substances present in these widely used food crops.

MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data.

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.

A taste of sweet pepper: Volatile and non-volatile chemical composition of fresh sweet pepper (Capsicum annuum) in relation to sensory evaluation of taste.

In this study volatile and non-volatile compounds, as well as some breeding parameters, were measured in mature fruits of elite sweet pepper (Capsicum annuum) lines and hybrids from a commercial breeding program, several cultivated genotypes and one gene bank accession. In addition, all genotypes were evaluated for taste by a trained descriptive sensory expert panel. Metabolic contrasts between genotypes were caused by clusters of volatile and non-volatile compounds, which could be related to metabolic pathways and common biochemical precursors. Clusters of phenolic derivatives, higher alkanes, sesquiterpenes and lipid derived volatiles formed the major determinants of the genotypic differences. Flavour was described with the use of 14 taste attributes, of which the texture related attributes and the sweet-sour contrast were the most discriminatory factors. The attributes juiciness, toughness, crunchiness, stickiness, sweetness, aroma, sourness and fruity/apple taste could be significantly predicted with combined volatile and non-volatile data. Fructose and (E)-2-hexen-1-ol were highly correlated with aroma, fruity/apple taste and sweetness. New relations were found for fruity/apple taste and sweetness with the compounds p-menth-1-en-9-al, (E)-ß-ocimene, (Z)-2-penten-1-ol and (E)-geranylacetone. Based on the overall biochemical and sensory results, the perspectives for flavour improvement by breeding are discussed.

Recognition of multiple patterns in unaligned sets of sequences: comparison of kernel clustering method with other methods.

MOTIVATION: Transcription factor binding sites often differ significantly in their primary sequence and can hardly be aligned. Often one set of sites can contain several subsets of sequences that follow not just one but several different patterns. There is a need for sensitive methods to reveal multiple patterns in unaligned sets of sequences. RESULTS: We developed a novel method for analysis of unaligned sets of sequences based on kernel estimation. The method is able to reveal 'multiple local patterns'-a set of weight matrices. Every weight matrix characterizes a pattern that can be found in a significant subset of sequences under analysis. The method developed has been compared with several other methods of pattern discovery such as Gibbs sampling, MEME, CONSENSUS, MULTIPROFILER and PROJECTION. The kernel method showed the best performance in terms of how close the revealed weight matrices are to the original ones. We applied the kernel method to analyze three samples of promoters (cell-cycle, T-cells and muscle-specific). We compared the multiple patterns revealed with the TRANSFAC library of weight matrices and found a strong similarity to several weight matrices for transcription factors known to be involved in the mentioned specific gene regulation. AVAILABILITY: The program is available for on-line use at: http://www.biobase.de/cgi-bin/biobase/cbs2/bin/template.cgi?template=cbscall.html