Development and evaluation of robust molecular markers linked to disease resistance in tomato for distinctness, uniformity and stability testing

Molecular markers linked to phenotypically important traits are of great interest especially when traits are difficult and/or costly to be observed. In tomato where a strong focus on resistance breeding has led to the introgression of several resistance genes, resistance traits have become important characteristics in distinctness, uniformity and stability (DUS) testing for Plant Breeders Rights (PBR) applications. Evaluation of disease traits in biological assays is not always straightforward because assays are often influenced by environmental factors, and difficulties in scoring exist. In this study, we describe the development and/or evaluation of molecular marker assays for the Verticillium genes Ve1 and Ve2, the tomato mosaic virusTm1 (linked marker), the tomato mosaic virus Tm2 and Tm22 genes, the Meloidogyne incognita Mi1-2 gene, the Fusarium I (linked marker) and I2 loci, which are obligatory traits in PBR testing. The marker assays were evaluated for their robustness in a ring test and then evaluated in a set of varieties. Although in general, results between biological assays and marker assays gave highly correlated results, marker assays showed an advantage over biological tests in that the results were clearer, i.e., homozygote/heterozygote presence of the resistance gene can be detected and heterogeneity in seed lots can be identified readily. Within the UPOV framework for granting of PBR, the markers have the potential to fulfil the requirements needed for implementation in DUS testing of candidate varieties and could complement or may be an alternative to the pathogenesis tests that are carried out at present

Variance of allele balance calculated from low coverage sequencing data infers departure from a diploid state

BackgroundPolyploidy and heterokaryosis are common and consequential genetic phenomena that increase the number of haplotypes in an organism and complicate whole-genome sequence analysis. Allele balance has been used to infer polyploidy and heterokaryosis in diverse organisms using read sets sequenced to greater than 50× whole-genome coverage. However, sequencing to adequate depth is costly if applied to multiple individuals or large genomes.ResultsWe developed VCFvariance.pl to utilize the variance of allele balance to infer polyploidy and/or heterokaryosis at low sequence coverage. This analysis requires as little as 10× whole-genome coverage and reduces the allele balance profile down to a single value, which can be used to determine if an individual has two or more haplotypes. This approach was validated using simulated, synthetic, and authentic read sets from the oomycete species Bremia lactucae and Phytophthora infestans, the fungal species Saccharomyces cerevisiae, and the plant species Arabidopsis arenosa. This approach was deployed to determine that nine of 21 genotyped European race-type isolates of Bremia lactucae were inconsistent with diploidy and therefore likely heterokaryotic.ConclusionsVariance of allele balance is a reliable metric to detect departures from a diploid state, including polyploidy, heterokaryosis, a mixed sample, or chromosomal copy number variation. Deploying this strategy is computationally inexpensive, can reduce the cost of sequencing by up to 80%, and used to test any organism

RNA Biomarkers as a Response Measure for Survival in Patients with Metastatic Castration-Resistant Prostate Cancer

Treatment evaluation in metastatic castration-resistant prostate cancer is challenging. There is an urgent need for biomarkers to discriminate short-term survivors from long-term survivors, shortly after treatment initiation. Thereto, the added value of early RNA biomarkers on predicting progression-free survival (PFS) and overall survival (OS) were explored. The RNA biomarkers: KLK3 mRNA, miR-375, miR-3687, and NAALADL2-AS2 were measured in 93 patients with mCRPC, before and 1 month after start of first-line abiraterone acetate or enzalutamide treatment, in two prospective clinical trials. The added value of the biomarkers to standard clinical parameters in predicting PFS and OS was tested by Harell’s C-index. To test whether the biomarkers were independent markers of PFS and OS, multivariate Cox regression was used. The best prediction model for PFS and OS was formed by adding miR-375 and KLK3 (at baseline and 1 month) to standard clinical parameters. Baseline miR-375 and detectable KLK3 after 1 month of therapy were independently related to shorter PFS, which was not observed for OS. In conclusion, the addition of KLK3 and miR-375 (at baseline and 1 month) to standard clinical parameters resulted in the best prediction model for survival assessment