C. Gerard van der Linden

Efficient targeting of plant disease resistance loci using NBS profiling

The conserved sequences in the nucleotide-binding sites of the nucleotide-binding site-leucine-rich repeat (NBS-LRR) class of disease resistance (R) genes have been used for PCR-based R-gene isolation and subsequent development of molecular markers. Here we present a PCR-based approach (NBS profiling) that efficiently targets R genes and R-gene analogs (RGAs) and, at the same time, produces polymorphic markers in these genes. In NBS profiling, genomic DNA is digested with a restriction enzyme, and an NBS-specific (degenerate) primer is used in a PCR reaction towards an adapter linked to the resulting DNA fragments. The NBS profiling protocol generates a reproducible polymorphic multilocus marker profile on a sequencing gel that is highly enriched for R genes and RGAs. NBS profiling was successfully used in potato with several restriction enzymes, and several primers targeted to different conserved motifs in the NBS. Across primers and enzymes, the NBS profiles contained 50–90% fragments that were significantly similar to known R-gene and RGA sequences. The protocol was similarly successful in other crops (including tomato, barley, and lettuce) without modifications. NBS profiling can thus be used to produce markers tightly linked to R genes and R-gene clusters for genomic mapping and positional cloning and to mine for new alleles and new sources of disease resistance in available germplasm.

QualitySNP: a pipeline for detecting single nucleotide polymorphisms and insertions/deletions in EST data from diploid and polyploid species

BackgroundSingle nucleotide polymorphisms (SNPs) are important tools in studying complex genetic traits and genome evolution. Computational strategies for SNP discovery make use of the large number of sequences present in public databases (in most cases as expressed sequence tags (ESTs)) and are considered to be faster and more cost-effective than experimental procedures. A major challenge in computational SNP discovery is distinguishing allelic variation from sequence variation between paralogous sequences, in addition to recognizing sequencing errors. For the majority of the public EST sequences, trace or quality files are lacking which makes detection of reliable SNPs even more difficult because it has to rely on sequence comparisons only.ResultsWe have developed a new algorithm to detect reliable SNPs and insertions/deletions (indels) in EST data, both with and without quality files. Implemented in a pipeline called QualitySNP, it uses three filters for the identification of reliable SNPs. Filter 1 screens for all potential SNPs and identifies variation between or within genotypes. Filter 2 is the core filter that uses a haplotype-based strategy to detect reliable SNPs. Clusters with potential paralogs as well as false SNPs caused by sequencing errors are identified. Filter 3 screens SNPs by calculating a confidence score, based upon sequence redundancy and quality. Non-synonymous SNPs are subsequently identified by detecting open reading frames of consensus sequences (contigs) with SNPs. The pipeline includes a data storage and retrieval system for haplotypes, SNPs and alignments. QualitySNPs versatility is demonstrated by the identification of SNPs in EST datasets from potato, chicken and humans.ConclusionQualitySNP is an efficient tool for SNP detection, storage and retrieval in diploid as well as polyploid species. It is available for running on Linux or UNIX systems. The program, test data, and user manual are available at http://www.bioinformatics.nl/tools/snpweb/ and as Additional files.

A pipeline for high throughput detection and mapping of SNPs from EST databases

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic variation that can be used as molecular markers. The SNPs that are hidden in sequence databases can be unlocked using bioinformatic tools. For efficient application of these SNPs, the sequence set should be error-free as much as possible, targeting single loci and suitable for the SNP scoring platform of choice. We have developed a pipeline to effectively mine SNPs from public EST databases with or without quality information using QualitySNP software, select reliable SNP and prepare the loci for analysis on the Illumina GoldenGate genotyping platform. The applicability of the pipeline was demonstrated using publicly available potato EST data, genotyping individuals from two diploid mapping populations and subsequently mapping the SNP markers (putative genes) in both populations. Over 7000 reliable SNPs were identified that met the criteria for genotyping on the GoldenGate platform. Of the 384 SNPs on the SNP array approximately 12% dropped out. For the two potato mapping populations 165 and 185 SNPs segregating SNP loci could be mapped on the respective genetic maps, illustrating the effectiveness of our pipeline for SNP selection and validation.

Large-scale identification of polymorphic microsatellites using an in silico approach

BackgroundSimple Sequence Repeat (SSR) or microsatellite markers are valuable for genetic research. Experimental methods to develop SSR markers are laborious, time consuming and expensive. In silico approaches have become a practicable and relatively inexpensive alternative during the last decade, although testing putative SSR markers still is time consuming and expensive. In many species only a relatively small percentage of SSR markers turn out to be polymorphic. This is particularly true for markers derived from expressed sequence tags (ESTs). In EST databases a large redundancy of sequences is present, which may contain information on length-polymorphisms in the SSR they contain, and whether they have been derived from heterozygotes or from different genotypes. Up to now, although a number of programs have been developed to identify SSRs in EST sequences, no software can detect putatively polymorphic SSRs.ResultsWe have developed PolySSR, a new pipeline to identify polymorphic SSRs rather than just SSRs. Sequence information is obtained from public EST databases derived from heterozygous individuals and/or at least two different genotypes. The pipeline includes PCR-primer design for the putatively polymorphic SSR markers, taking into account Single Nucleotide Polymorphisms (SNPs) in the flanking regions, thereby improving the success rate of the potential markers. A large number of polymorphic SSRs were identified using publicly available EST sequences of potato, tomato, rice, Arabidopsis, Brassica and chicken.The SSRs obtained were divided into long and short based on the number of times the motif was repeated. Surprisingly, the frequency of polymorphic SSRs was much higher in the short SSRs.ConclusionPolySSR is a very effective tool to identify polymorphic SSRs. Using PolySSR, several hundred putative markers were developed and stored in a searchable database. Validation experiments showed that almost all markers that were indicated as putatively polymorphic by polySSR were indeed polymorphic. This greatly improves the efficiency of marker development, especially in species where there are low levels of polymorphism, like tomato. When combined with the new sequencing technologies PolySSR will have a big impact on the development of polymorphic SSRs in any species.PolySSR and the polymorphic SSR marker database are available from http://www.bioinformatics.nl/tools/polyssr/.

Improving phosphorus use efficiency in agriculture: opportunities for breeding

Phosphorus (P) is often an important limiting factor for crop yields, but rock phosphate as fertilizer is a non-renewable resource and expected to become scarce in the future. High P input levels in agriculture have led to environmental problems. One of the ways to tackle these issues simultaneously is improving phosphorus use efficiency (PUE) of the crops through breeding. In this review, we describe plant architectural and physiological traits important for PUE. Subsequently, we discuss efficient methods of screening for PUE traits. We address targeted cultivation methods, including solid and hydroponic systems, as well as testing methods, such as image analysis systems, and biomass and photosynthesis measurements. Genetic variation for PUE traits has been assessed in many crops, and genetics of PUE has been studied by quantitative trait loci (QTL) analyses and genome-wide association study. A number of genes involved in the plant’s response to low P have been characterized. These genes include transcription factors, and genes involved in signal transduction, hormonal pathways, sugar signalling, P saving metabolic pathways, and in P scavenging, including transporters and metabolites and/or ATP-ases mobilizing P in the soil. In addition, the role of microorganisms promoting PUE of plants, particularly arbuscular mycorrhizal fungi is discussed. An overview is given of methods for selecting for optimal combinations of plant and fungal genotypes, and their genetics, incl. QTLs and genes involved. In conclusion, significant progress has been made in selecting for traits for PUE, developing systems for the difficult but highly relevant root phenotyping, and in identifying QTLs and genes involved.

Peroxidase profiling reveals genetic linkage between peroxidase gene clusters and basal host and non-host resistance to rusts and mildew in barley.

Background Higher plants possess a large multigene family encoding secreted class III peroxidase (Prx) proteins. Peroxidases appear to be associated with plant disease resistance based on observations of induction during disease challenge and the presence or absence of isozymes in resistant vs susceptible varieties. Despite these associations, there is no evidence that allelic variation of peroxidases directly determines levels of disease resistance. Methodology/Principal Findings The current study introduces a new strategy called Prx-Profiling. We showed that with this strategy a large number of peroxidase genes can be mapped on the barley genome. In order to obtain an estimate of the total number of Prx clusters we followed a re-sampling procedure, which indicated that the barley genome contains about 40 peroxidase gene clusters. We examined the association between the Prxs mapped and the QTLs for resistance of barley to homologous and heterologous rusts, and to the barley powdery mildew fungus. We report that 61% of the QTLs for partial resistance to P. hordei, 61% of the QTLs for resistance to B. graminis and 47% of the QTLs for non-host resistance to other Puccinia species co-localize with Prx based markers. Conclusions/Significance We conclude that Prx-Profiling was effective in finding the genetic location of Prx genes on the barley genome. The finding that QTLs for basal resistance to rusts and powdery mildew fungi tend to co-locate with Prx clusters provides a base for exploring the functional role of Prx-related genes in determining natural differences in levels of basal resistance.

HaploSNPer: a web-based allele and SNP detection tool

BackgroundSingle nucleotide polymorphisms (SNPs) and small insertions or deletions (indels) are the most common type of polymorphisms and are frequently used for molecular marker development. Such markers have become very popular for all kinds of genetic analysis, including haplotype reconstruction. Haplotypes can be reconstructed for whole chromosomes but also for specific genes, based on the SNPs present. Haplotypes in the latter context represent the different alleles of a gene. The computational approach to SNP mining is becoming increasingly popular because of the continuously increasing number of sequences deposited in databases, which allows a more accurate identification of SNPs. Several software packages have been developed for SNP mining from databases. From these, QualitySNP is the only tool that combines SNP detection with the reconstruction of alleles, which results in a lower number of false positive SNPs and also works much faster than other programs. We have build a web-based SNP discovery and allele detection tool (HaploSNPer) based on QualitySNP.ResultsHaploSNPer is a flexible web-based tool for detecting SNPs and alleles in user-specified input sequences from both diploid and polyploid species. It includes BLAST for finding homologous sequences in public EST databases, CAP3 or PHRAP for aligning them, and QualitySNP for discovering reliable allelic sequences and SNPs. All possible and reliable alleles are detected by a mathematical algorithm using potential SNP information. Reliable SNPs are then identified based on the reconstructed alleles and on sequence redundancy.ConclusionThorough testing of HaploSNPer (and the underlying QualitySNP algorithm) has shown that EST information alone is sufficient for the identification of alleles and that reliable SNPs can be found efficiently. Furthermore, HaploSNPer supplies a user friendly interface for visualization of SNP and alleles. HaploSNPer is available from http://www.bioinformatics.nl/tools/haplosnper/.

Responses to combined abiotic and biotic stress in tomato are governed by stress intensity and resistance mechanism

Highlight Salt stress severity differentially affected partial powdery mildew resistance. Stress combination uniquely resulted in accelerated senescence. mlo-based and R-gene-mediated resistance maintained robustness across salt treatments.

Novel applications of motif-directed profiling to identify disease resistance genes in plants

BackgroundMolecular profiling of gene families is a versatile tool to study diversity between individual genomes in sexual crosses and germplasm. Nucleotide binding site (NBS) profiling, in particular, targets conserved nucleotide binding site-encoding sequences of resistance gene analogs (RGAs), and is widely used to identify molecular markers for disease resistance (R) genes.ResultsIn this study, we used NBS profiling to identify genome-wide locations of RGA clusters in the genome of potato clone RH. Positions of RGAs in the potato RH and DM genomes that were generated using profiling and genome sequencing, respectively, were compared. Largely overlapping results, but also interesting discrepancies, were found. Due to the clustering of RGAs, several parts of the genome are overexposed while others remain underexposed using NBS profiling. It is shown how the profiling of other gene families, i.e. protein kinases and different protein domain-coding sequences (i.e., TIR), can be used to achieve a better marker distribution. The power of profiling techniques is further illustrated using RGA cluster-directed profiling in a population of Solanum berthaultii. Multiple different paralogous RGAs within the Rpi-ber cluster could be genetically distinguished. Finally, an adaptation of the profiling protocol was made that allowed the parallel sequencing of profiling fragments using next generation sequencing. The types of RGAs that were tagged in this next-generation profiling approach largely overlapped with classical gel-based profiling. As a potential application of next-generation profiling, we showed how the R gene family associated with late blight resistance in the SH*RH population could be identified using a bulked segregant approach.ConclusionsIn this study, we provide a comprehensive overview of previously described and novel profiling primers and their genomic targets in potato through genetic mapping and comparative genomics. Furthermore, it is shown how genome-wide or fine mapping can be pursued by choosing different sets of profiling primers. A protocol for next-generation profiling is provided and will form the basis for novel applications. Using the current overview of genomic targets, a rational choice can be made for profiling primers to be employed.

Systems genetics reveals key genetic elements of drought induced gene regulation in diploid potato.

In plants, tolerance to drought stress is a result of numerous minor effect loci in which transcriptional regulation contributes significantly to the observed phenotypes. Under severe drought conditions, a major expression quantitative trait loci hotspot was identified on chromosome five in potato. A putative Nuclear factor y subunit C4 was identified as key candidate in the regulatory cascade in response to drought. Further investigation of the eQTL hotspots suggests a role for a putative Homeobox leucine zipper protein 12 in relation to drought in potato. Genes strongly co-expressed with Homeobox leucine zipper protein 12 were plant growth regulators responsive to water deficit stress in Arabidopsis thaliana, implying a possible conserved mechanism. Integrative analysis of genetic, genomic, phenotypic and transcriptomic data provided insights in the downstream functional components of the drought response. The abscisic acid- and environmental stress-inducible protein TAS14 was highly induced by severe drought in potato and acts as a reliable biomarker for the level of stress perceived by the plant. The systems genetics approach supported a role for multiple genes responsive to severe drought stress of Solanum tuberosum. The combination of gene regulatory networks, expression quantitative trait loci mapping and phenotypic analysis proved useful for candidate gene selection.