Genetical genomics of quality related traits in potato tubers using proteomics.

BACKGROUND: Recent advances in ~omics technologies such as transcriptomics, metabolomics and proteomics along with genotypic profiling have permitted the genetic dissection of complex traits such as quality traits in non-model species. To get more insight into the genetic factors underlying variation in quality traits related to carbohydrate and starch metabolism and cold sweetening, we determined the protein content and composition in potato tubers using 2D-gel electrophoresis in a diploid potato mapping population. Upon analyzing we made sure that the proteins from the patatin family were excluded to ensure a better representation of the other proteins. RESULTS: We subsequently performed pQTL analyses for all other proteins with a sufficient representation in the population and established a relationship between proteins and 26 potato tuber quality traits (e.g. flesh colour, enzymatic discoloration) by co-localization on the genetic map and a direct correlation study of protein abundances and phenotypic traits. Over 1643 unique protein spots were detected in total over the two harvests. We were able to map pQTLs for over 300 different protein spots some of which co-localized with traits such as starch content and cold sweetening. pQTLs were observed on every chromosome although not evenly distributed over the chromosomes. The largest number of pQTLs was found for chromosome 8 and the lowest for chromosome number 10. For some 20 protein spots multiple QTLs were observed. CONCLUSIONS: From this analysis, hotspot areas for protein QTLs were identified on chromosomes three, five, eight and nine. The hotspot on chromosome 3 coincided with a QTL previously identified for total protein content and had more than 23 pQTLs in the region from 70 to 80 cM. Some of the co-localizing protein spots associated with some of the most interesting tuber quality traits were identified, albeit far less than we had anticipated at the onset of the experiments.

MetaBase--the wiki-database of biological databases.

Biology is generating more data than ever. As a result, there is an ever increasing number of publicly available databases that analyse, integrate and summarize the available data, providing an invaluable resource for the biological community. As this trend continues, there is a pressing need to organize, catalogue and rate these resources, so that the information they contain can be most effectively exploited. MetaBase (MB) (http://MetaDatabase.Org) is a community-curated database containing more than 2000 commonly used biological databases. Each entry is structured using templates and can carry various user comments and annotations. Entries can be searched, listed, browsed or queried. The database was created using the same MediaWiki technology that powers Wikipedia, allowing users to contribute on many different levels. The initial release of MB was derived from the content of the 2007 Nucleic Acids Research (NAR) Database Issue. Since then, approximately 100 databases have been manually collected from the literature, and users have added information for over 240 databases. MB is synchronized annually with the static Molecular Biology Database Collection provided by NAR. To date, there have been 19 significant contributors to the project; each one is listed as an author here to highlight the community aspect of the project.

Marker2sequence, mine your QTL regions for candidate genes.

UNLABELLED: Marker2sequence (M2S) aims at mining quantitative trait loci (QTLs) for candidate genes. For each gene, within the QTL region, M2S uses data integration technology to integrate putative gene function with associated gene ontology terms, proteins, pathways and literature. As a typical QTL region easily contains several hundreds of genes, this gene list can then be further filtered using a keyword-based query on the aggregated annotations. M2S will help breeders to identify potential candidate genes for their traits of interest. AVAILABILITY: Marker2sequence is freely accessible at http://www.plantbreeding.wur.nl/BreeDB/marker2seq/. The source code can be obtained at https://github.com/PBR/Marker2Sequence. CONTACT: richard.finkers@wur.nl

Organ specificity and transcriptional control of metabolic routes revealed by expression QTL profiling of source--sink tissues in a segregating potato population.

BACKGROUND: With the completion of genome sequences belonging to some of the major crop plants, new challenges arise to utilize this data for crop improvement and increased food security. The field of genetical genomics has the potential to identify genes displaying heritable differential expression associated to important phenotypic traits. Here we describe the identification of expression QTLs (eQTLs) in two different potato tissues of a segregating potato population and query the potato genome sequence to differentiate between cis- and trans-acting eQTLs in relation to gene subfunctionalization. RESULTS: Leaf and tuber samples were analysed and screened for the presence of conserved and tissue dependent eQTLs. Expression QTLs present in both tissues are predominantly cis-acting whilst for tissue specific QTLs, the percentage of trans-acting QTLs increases. Tissue dependent eQTLs were assigned to functional classes and visualized in metabolic pathways. We identified a potential regulatory network on chromosome 10 involving genes crucial for maintaining circadian rhythms and controlling clock output genes. In addition, we show that the type of genetic material screened and sampling strategy applied, can have a high impact on the output of genetical genomics studies. CONCLUSIONS: Identification of tissue dependent regulatory networks based on mapped differential expression not only gives us insight in tissue dependent gene subfunctionalization but brings new insights into key biological processes and delivers targets for future haplotyping and genetic marker development.

Genetic analysis of metabolites in apple fruits indicates an mQTL hotspot for phenolic compounds on linkage group 16.

Apple (Malus×domestica Borkh) is among the main sources of phenolic compounds in the human diet. The genetic basis of the quantitative variations of these potentially beneficial phenolic compounds was investigated. A segregating F1 population was used to map metabolite quantitative trait loci (mQTLs). Untargeted metabolic profiling of peel and flesh tissues of ripe fruits was performed using liquid chromatography-mass spectrometry (LC-MS), resulting in the detection of 418 metabolites in peel and 254 in flesh. In mQTL mapping using MetaNetwork, 669 significant mQTLs were detected: 488 in the peel and 181 in the flesh. Four linkage groups (LGs), LG1, LG8, LG13, and LG16, were found to contain mQTL hotspots, mainly regulating metabolites that belong to the phenylpropanoid pathway. The genetics of annotated metabolites was studied in more detail using MapQTL®. A number of quercetin conjugates had mQTLs on LG1 or LG13. The most important mQTL hotspot with the largest number of metabolites was detected on LG16: mQTLs for 33 peel-related and 17 flesh-related phenolic compounds. Structural genes involved in the phenylpropanoid biosynthetic pathway were located, using the apple genome sequence. The structural gene leucoanthocyanidin reductase (LAR1) was in the mQTL hotspot on LG16, as were seven transcription factor genes. The authors believe that this is the first time that a QTL analysis was performed on such a high number of metabolites in an outbreeding plant species.