Allelic diversity of S‑RNase alleles in diploid potato species

S-ribonucleases (S-RNases) control the pistil specificity of the self-incompatibility (SI) response in the genus Solanum and several other members of the Solanaceae. The nucleotide sequences of S-RNases corresponding to a large number of S-alleles or S-haplotypes have been characterised. However, surprisingly few S-RNase sequences are available for potato species. The identification of new S-alleles in diploid potato species is desirable as these stocks are important sources of traits such as biotic and abiotic resistance. S-RNase sequences are reported here from three distinct diploid types of potato: cultivated Solanum tuberosum Group Phureja, S. tuberosum Group Stenotomum, and the wild species Solanum okadae. Partial S-RNase sequences were obtained from pistil RNA by RT-PCR or 3’RACE (Rapid Amplification of cDNA Ends) using a degenerate primer. Full length sequences were obtained for two alleles by 5’RACE. Database searches with these sequences, identified sixteen S-RNases in total, all of which are novel. The sequence analysis revealed all the expected features of functional S-RNases. Phylogenetic analysis with selected published S-RNase and S-like-RNase sequences from the Solanaceae revealed extensive trans-generic evolution of the S-RNases and a clear distinction from S-like-RNases. Pollination tests were used to confirm the self-incompatibility status and cross-compatibility relationships of the S. okadae accessions. All the S. okadae accessions were found to be self-incompatible as expected with crosses amongst them exhibiting both cross-compatibility and semi-compatibility consistent with the S-genotypes determined from the S-RNase sequence data. The progeny analysis of four semi-compatible crosses examined by allele-specific PCR provided further confirmation that these are functional S-RNases

Tuber shape and eye depth variation in a diploid family of Andean potatoes.

BACKGROUND: Tuber appearance is highly variable in the Andean cultivated potato germplasm. The diploid backcross mapping population ‘DMDD’ derived from the recently sequenced genome ‘DM’ represents a sample of the allelic variation for tuber shape and eye depth present in the Andean landraces. Here we evaluate the utility of morphological descriptors for tuber shape for identification of genetic loci responsible for the shape and eye depth variation. RESULTS: Subjective morphological descriptors and objective tuber length and width measurements were used for assessment of variation in tuber shape and eye depth. Phenotypic data obtained from three trials and male–female based genetic maps were used for quantitative trait locus (QTL) identification. Seven morphological tuber shapes were identified within the population. A continuous distribution of phenotypes was found using the ratio of tuber length to tuber width and a QTL was identified in the paternal map on chromosome 10. Using toPt-437059, the marker at the peak of this QTL, the seven tuber shapes were classified into two groups: cylindrical and non-cylindrical. In the first group, shapes classified as ‘compressed’, ‘round’, ‘oblong’, and ‘long-oblong’ mainly carried a marker allele originating from the male parent. The tubers in this group had deeper eyes, for which a strong QTL was found at the same location on chromosome 10 of the paternal map. The non-cylindrical tubers classified as ‘obovoid’, ‘elliptic’, and ‘elongated’ were in the second group, mostly lacking the marker allele originating from the male parent. The main QTL for shape and eye depth were located in the same genomic region as the previously mapped dominant genes for round tuber shape and eye depth. A number of candidate genes underlying the significant QTL markers for tuber shape and eye depth were identified. CONCLUSIONS: Utilization of a molecular marker at the shape and eye depth QTL enabled the reclassification of the variation in general tuber shape to two main groups. Quantitative measurement of the length and width at different parts of the tuber is recommended to accompany the morphological descriptor classification to correctly capture the shape variation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-015-0213-0) contains supplementary material, which is available to authorized users

Competition between Phytophthora infestans Effectors Leads to Increased Aggressiveness on Plants Containing Broad-Spectrum Late Blight Resistance

BACKGROUND: The destructive plant disease potato late blight is caused by the oomycete pathogen Phytophthora infestans (Mont.) de Bary. This disease has remained particularly problematic despite intensive breeding efforts to integrate resistance into cultivated potato, largely because of the pathogen's ability to quickly evolve to overcome major resistance genes. The RB gene, identified in the wild potato species S. bulbocastanum, encodes a protein that confers broad-spectrum resistance to most P. infestans isolates through its recognition of highly conserved members of the corresponding pathogen effector family IPI-O. IpiO is a multigene family of effectors and while the majority of IPI-O proteins are recognized by RB to elicit host resistance, some variants exist that are able to elude detection (e.g. IPI-O4). METHODS AND FINDINGS: In the present study, analysis of ipiO variants among 40 different P. infestans isolates collected from Guatemala, Thailand, and the United States revealed a high degree of complexity within this gene family. Isolate aggressiveness was correlated with increased ipiO diversity and especially the presence of the ipiO4 variant. Furthermore, isolates expressing IPI-O4 overcame RB-mediated resistance in transgenic potato plants even when the resistance-eliciting IPI-O1 variant was present. In support of this finding, we observed that expression of IPI-O4 via Agrobacterium blocked recognition of IPI-O1, leading to inactivation of RB-mediated programmed cell death in Nicotiana benthamiana. CONCLUSIONS: In this study we definitively demonstrate and provide the first evidence that P. infestans can defeat an R protein through inhibition of recognition of the corresponding effector protein

Benchmarking Ontologies: Bigger or Better?

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them

A New Method for Species Identification via Protein-Coding and Non-Coding DNA Barcodes by Combining Machine Learning with Bioinformatic Methods

Species identification via DNA barcodes is contributing greatly to current bioinventory efforts. The initial, and widely accepted, proposal was to use the protein-coding cytochrome c oxidase subunit I (COI) region as the standard barcode for animals, but recently non-coding internal transcribed spacer (ITS) genes have been proposed as candidate barcodes for both animals and plants. However, achieving a robust alignment for non-coding regions can be problematic. Here we propose two new methods (DV-RBF and FJ-RBF) to address this issue for species assignment by both coding and non-coding sequences that take advantage of the power of machine learning and bioinformatics. We demonstrate the value of the new methods with four empirical datasets, two representing typical protein-coding COI barcode datasets (neotropical bats and marine fish) and two representing non-coding ITS barcodes (rust fungi and brown algae). Using two random sub-sampling approaches, we demonstrate that the new methods significantly outperformed existing Neighbor-joining (NJ) and Maximum likelihood (ML) methods for both coding and non-coding barcodes when there was complete species coverage in the reference dataset. The new methods also out-performed NJ and ML methods for non-coding sequences in circumstances of potentially incomplete species coverage, although then the NJ and ML methods performed slightly better than the new methods for protein-coding barcodes. A 100% success rate of species identification was achieved with the two new methods for 4,122 bat queries and 5,134 fish queries using COI barcodes, with 95% confidence intervals (CI) of 99.75–100%. The new methods also obtained a 96.29% success rate (95%CI: 91.62–98.40%) for 484 rust fungi queries and a 98.50% success rate (95%CI: 96.60–99.37%) for 1094 brown algae queries, both using ITS barcodes