Rhizomania is a grave disease affecting sugar beet (Beta vulgaris L.). It is caused by the Beet Necrotic Yellow Vein Virus (BNYVV), an RNA virus transmitted by the plasmodiophorid vector Polymyxa betae. Genetic resistance to the virus has been accomplished mostly using phenotype-genotype association studies. As yet, the most convenient method to ascertain plant resistance has been the quantification of viral titer in roots through the ELISA test. This method is particularly time-consuming and clashes with the necessities of modern plant breeding. Here, we propose an alternative and successful phenotyping method based on the automatic extraction of the viral RNA from sugar beet roots and its relative and absolute quantification by quantitative real-time PCR (qRT-PCR) and digital PCR (dPCR), respectively. Such a method enables an improved standardization of the study, as well as an accurate quantification of the virus also in those samples presenting low virus titer, with respect to the ELISA test.

The identification of efficient molecular markers related to low bolting tendency is a priority in sugar beet (Beta vulgaris L.) breeding. This study aimed to identify SNP markers associated with low bolting tendency by establishing a genome-wide association study. An elaborate 3-year field trial comprising 13 sugar beet lines identified L14 as the one exhibiting the lowest bolting tendency along with an increased survival rate after autumnal sowing. For SNP discovery following phenotyping, contrasting phenotypes of 24 non-bolting and 15 bolting plants of the L14 line were sequenced by restriction site-associated DNA sequencing (RAD-seq). An association model was established with a set of 10,924 RAD-based single nucleotide polymorphism (SNP) markers. The allelic status of the most significantly associated SNPs ranked based on their differential allelic status between contrasting phenotypes (p < 0.01) was confirmed on three different validation datasets comprising diverse sugar beet lines and varieties adopting a range of SNP detection technologies. This study has led to the identification of SNP_36780842 and SNP_48607347 linked to low bolting tendency and can be used for marker-assisted breeding and selection in sugar beet.

DNA-based technologies have become widespread tools for soil microbiological analyses in recent years. DNA extraction from the soil is a key step for these approaches: it is a challenge for researchers as it is still both expensive and time-consuming when large surveys are planned. The aim of this study was to develop a high-throughput automated protocol for DNA extraction and purification from soil. The protocol was based on the BioSprint 96 platform and compared for validation with another automated procedure and two commercial column-based kits. To evaluate the performances of the protocols, we considered quality, quantity, and amplifiability of the isolated DNA. The material isolated by means of the four protocols showed appropriate yield and quality and positive amplification. The isolation protocol presented here provided similar results to those of the commercial kits but with two essential differences: cost and time for DNA extraction were drastically reduced. This rapid and efficient protocol is envisaged as ideal to standardize soil studies and treat large numbers of samples, representing a workable alternative to low-throughput and expensive manual extraction methods.