key: cord-1008643-czh6lfym authors: Chiodi, Claudia; Concheri, Giuseppe; Squartini, Andrea; Ravi, Samathmika; Broccanello, Chiara; Moro, Matteo; Stevanato, Piergiorgio title: Quantification of rhizomania virus by automated RNA isolation and PCR based methods in sugar beet date: 2021-03-19 journal: Virusdisease DOI: 10.1007/s13337-021-00674-7 sha: 783892097beda2d2a5aeb03d30afeac43f79eaa0 doc_id: 1008643 cord_uid: czh6lfym 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13337-021-00674-7. first source of resistance (Alba) was identified in Italy in 1978 [3] just by observing which plants were not showing the typical symptoms of infection (yellowing leaves, hairy roots, low sugar content, and processing quality). But the symptoms appear in quite a wide range, so visual inspection alone is not sufficient for a proper diagnosis [1] . Thus, between 1984 and 1985 , researchers started to utilize the enzyme-linked immunosorbent assay (ELISA) test for the detection of BNYVV [17, 23] . Both double and triple antibody sandwich (DAS-, TAS-) ELISA tests are suitable for its detection [2] . Since its introduction in the 1980s, this method has been the indisputable basis for genotype-phenotype association studies. Research shifted from the visual identification of resistant individuals to the identification of resistant genotypes using molecular markers associated with resistant phenotypes recognized through ELISA. This pipeline allowed several markers associated with resistant genotypes to be identified [5, 21, 27] . However, the ELISA test is unable to detect little infection levels [6] , and there have been no functional updates to the method since its introduction. There have been efforts to make the phenotyping of rhizomania more sensitive and precise using reverse transcriptase PCR, but these attempts did not streamline the workflow. Some studies proving how PCR-based workflows are more sensitive and specific than ELISA-based phenotyping are those from Henry et al. [14] , Morris et al. [20] , and Harju et al. [13] . However, the before-mentioned experiments were not able to work independently of the ELISA test. Yardımcı and Ç ulal [31] state that the reverse transcriptase PCR is preferable to ELISA, but RT-PCR with subsequent gel electrophoresis is still slower than qRT-PCR. In this paper, we provide improvements in rhizomania virus detection based on automated RNA extraction from seedling roots, followed by relative and absolute quantification by real-time PCR and digital PCR, respectively. Sugar beet pollinator lines L1 and L2 (resistant and susceptible to rhizomania, respectively), were provided by DAFNAE-University of Padova, Italy. Seeds were rinsed in ethanol 96% and then steeped in H 2 O 2 3% overnight to stimulate germination. They were then placed in the folded paper for germination in dark conditions at room temperature. After a couple of days, only germinated seeds were transplanted into rhizomania contaminated soil. This soil was a mixture of 50% contaminated soil (collected in Montagnana, Padova, Italy), 25% organic soil, and 25% sand. Seeds were grown in two transparent boxes with a depth-filtration system (Microbox TP5000-TPD5000, Micropoli, Italy) using a final volume of 1 l of soil, one box for L1 and one for L2. The two boxes contained a total of 8 plants each. At transplanting, 100 ml of purified water was added to the box. No more water was added during the growth. Plants were sampled after 4 weeks of growth. Whole roots (main root and lateral roots) were meticulously washed to remove any trace of soil that can interfere with the next PCR step [7] . Each root was homogenized, and only those weighing at least 0.22 g were kept (12 out of 16). A double sampling was done on the roots of each plant: 0.07 g of the root was taken for RNA extraction and qRT-PCR phenotyping, 0.15 g of the root was used for a backcheck ELISA test. Total nucleic acid extraction was conducted using a BioSprint 96 (QIAGEN, Germany) with the protocol optimized for the purification of total RNA from plant tissue. Collection microtubes (1 ml, 96 racked tubes, QIAGEN) with roots were filled with 200 ll RLT (Guanidine thiocyanate buffer under patent protection) and one 3 mm Ø tungsten bead. Tubes were loaded in a Tis-sueLyser II (QIAGEN) for cell lysis (5 min, 30 Hz) and centrifuged (20,000 g 9 5 min). 300 ll of lysate from each tube was transferred into the first 96-deep-well plate (S-Blocks, QIAGEN). Other 4 deep-wells plates were used for the extraction together with one 96-deep-well plate (96-Well Microplates MP, QIAGEN). Plates were filled as shown in Table 1 and loaded into the BioSprint 96 robotic station for nucleic acid extraction. Additional enzymatic treatment with DNase is recommended. In our case, DNase I (Thermo Fisher Scientific, US) was used following the manufacturer's instructions. A quantitative real-time PCR targeting the BNYVV was used to estimate the quantity of virus in each sample. The reaction kit (PCRBIOSYSTEMS, UK) was modified to run in 6 ll volumes on the QuantStudio 12 K-Flex (Thermo Fisher Scientific, USA) using 384-well plates. The mix was composed of 0.2 ll each of forward and reverse primer and 0.1 ll of TaqMan probe (Supplementary Material 1), 2.5 ll 2xqPCRBIO Probe 1-Step Go Mix, 0.5 ll 20xRTase Go, 1 ll PCR-grade water, and 1.5 ll template RNA. Cycling conditions: 95°C for 10 min, followed by 40 cycles of 95°C for 15 s, 57°C for 60 s, and 72°C for 15 s. All reactions were run in duplicate. As a reference check, we used an internal susceptible control (SC, Ct = 13.15). The qRT-PCR analysis highlighted a significant difference (P \ 0.01) of around 9 Ct between the two L1 and L2 lines (Supplementary Material 2) (Fig. 1) . Not all the cultivated genotypes present the same level of resistance to rhizomania: our results show a range of resistance, and they have been relativized to our internal SC. We also conducted an ELISA test on the same samples, using juice extracted from the roots, as a backcheck to establish a correlation between molecular (qRT-PCR) and serological (ELISA) results. Therefore, samples were analyzed by triple-antibody sandwich (TAS)-ELISA using a BNYVV kit supplied by Agdia EMEA (France) according to the manufacturer's instructions. Results recorded using a Jenway 640 S UV/Vis Spectrophotometer at 405 nm are shown in Supplementary Material 3. Due to the sampling method, the analysis of individual associations between the serological and molecular tests was possible only for 11 out of the 12 root samples. A significant correlation (r = -0.93; P \ 0.01) was found between molecular (qRT-PCR) and serological (ELISA) results (Fig. 2) . Digital PCR analysis was further carried out to support qRT-PCR with absolute quantification of the virus. dPCR quantification was conducted using the QuantStudio 3D Digital PCR System (Thermo Fisher Scientific). The dPCR mix was composed of 8 ll of QuantStudio 3D Digital PCR Master Mix (Thermo Fisher Scientific), 1.44 ll of both forward and reverse primers, 0.8 ll of the probe, and 2.82 ll of nuclease-free water. The primers and the probe used in the analysis were the same as those used for the qRT-PCR. The thermal profile was described by Stevanato and Biscarini [26] . Digital PCR data were analyzed with the QuantStudio 3D AnalysisSuite Cloud software (Thermo Fisher Scientific, USA). The absolute levels of the Fig. 3 . The whole experiment has been replicated three times anyway we have been using this method for 3 years. The research displays an alternative approach to the ELISA test. Differently from the foregoing protocols, ours not only makes the test more sensitive and specific, but it works independently of ELISA. Furthermore, we included the automated extraction of RNA and ran the qRT-PCR on 384-well plates, which jointly clearly speed up the protocol. This method enables the extraction of up to 96 RNA samples in about 30 min and to quantify the virus in up to 384 samples in two and a half hours. The qRT-PCR is remarkably sensitive: this allows not only to discriminate between susceptible and resistant samples, as ELISA does but also to select the most resistant samples among the resistant ones. This aspect is critical for breeders who already have resistant materials but are constantly seeking more resistant ones. The high-throughput of the analyses also makes it feasible to rapidly screen wide collections of plant materials. For this protocol, the one-step chemistry has been chosen, which offers some inherent advantages: joining the reverse transcriptase step with the PCR step reduces the time required for the analysis and possible pipetting mistakes. Besides, the qRT-PCR-based assay is supported by the dPCR: this detection method is well-known for its sensitivity, even higher than qRT-PCR [30] . The further value from the dPCR depends on several factors: (I) qRT-PCR, being a relative quantification, relies on an external reference, while dPCR does not [15] ; (II) dPCR is more tolerant to inhibitory substances than qRT-PCR [8] and this is useful given that RNA is extracted from the roots, and some traces of soil, rich in inhibitors, can remain on the samples despite the washing steps; (III) both qRT-PCR and dPCR work with similar fluorescence chemistry for nucleic acid detection, so it is not necessary to design two different assays for the two technologies; (IV) dPCR is extremely powerful in detecting minute traces of nucleic acids [15, 29] , allowing to discriminate between resistant plants and apparently resistant plant with minimal virus titers; Fig. 2 Correlation scatter plot between molecular (qRT-PCR) and serological (ELISA) results. The resistant samples L1 (blue dots) and the susceptible samples L2 (yellow triangles) were sampled twice to do the two types of analysis. The two different analyses present a significant correlation (r = -0.93) (V) dPCR has already been tested for the detection of human viruses, such as HIV but also on RNA viruses, and the measurement of low copy RNA targets has been satisfactory [25] . Many BNYVV mutations have already been identified and many others could occur. The qRT-PCR relies on specific primers and probes for the virus: in the case of new mutations or whenever discrimination among different strains of the virus is needed, the use of new or different probes would be sufficient to accurately detect the strain. It has also been challenging, in some cases, to distinguish between BNYVV and BSBMV (Beet soil-borne mosaic virus) applying the ELISA test, because the structure of the two viruses is similar [9] . This problem no longer exists with the qRT-PCR targeting specific nucleic acids. Interestingly, the detection method introduced for the BNYVV is analogous to the virus-detection method in the clinical field. For example, the detection protocol for COVID-19 on swabs is qRT-PCR-based and extremely similar [19] . Also, dPCR is used for the screening of human pathogens, such as viruses but also cancer. In fact, plant virology opened the way for medical virology: the first virus ever discovered was the Tobacco mosaic virus (TMV), which formed the basis for subsequent virologic studies [18] . It is thus not surprising that protocols used in plant biology can be easily reconverted for medical biology. Indeed, this is proof of the efficiency of such protocols. As proved from previous studies, the sugar beet community has been demanding for some time for an alternative high-throughput method to the ELISA test. Nevertheless, the best outcome was only achieved by reverse transcriptase PCR, with subsequent gel electrophoresis. With the arrival of one-step qPCR mixes, we gather it is required to introduce a new phenotyping method, better performing, precise, and also able to work in the presence of small quantities of virus. Anyway, we are not suggesting that the ELISA-based phenotyping is not good or sensitive enough but offering an alternative that can be useful also for high-throughput needs. Funding This project was funded by Veneto Region in the framework of the PSR 2014-2020 (Project: ''Implementation and validation of innovative plant protection methods to increase the environmental sustainability of organic and sugar beet production'') and by the University of Padova in the framework of the ''Progetto di Ateneo PRAT CPDA154841/15''. Conflict of interest The authors declare no conflict of interest. Human and animal rights statement This article does not contain any studies involving human participants and animals performed by any of the authors. Rhizomania-a review The inheritance of resistance to beet necrotic yellow vein virus (BNYVV) in B. vulgaris subsp. maritima, accession WB42: statistical comparisons with Holly-1-4 The origin of rhizomania resistance in sugar beet Occurrence of resistance-breaking strains of Beet necrotic yellow vein virus in sugar beet in northwestern Europe and identification of a new variant of the viral pathogenicity factor P25 A SNP mutation affects rhizomania-virus content of sugar beets grown on resistance-breaking soils Experiments and considerations on the detection of BNYVV in soil by means of bait plants Highthroughput isolation of nucleic acids from soil Tolerance of droplet-digital PCR vs real-time quantitative PCR to inhibitory substances Beet soil-borne mosaic virus: development of virus-specifc detection tools Comparative transcriptome analysis provides molecular insights into the interaction of Beet necrotic yellow vein virus and Beet soil-borne mosaic virus with their host sugar beet ICTV virus taxonomy profile: Benyviridae Long-distance movement of helical multipartite phytoviruses: keep connected or die? The use of real-time RT-PCR (TaqMan) and post-ELISA virus release for the detection of Beet necrotic yellow vein virus types containing RNA 5 and its comparison with conventional RT-PCR Detection of beet necrotic yellow vein virus using reverse transcription and polymerase chain reaction Digital PCR analysis of circulating nucleic acids Distribution of various types and P25 subtypes of Beet necrotic yellow vein virus in Germany and other European countries Beet necrotic yellow vein virus: Purification, preparation of antisera and detection by means of ELISA, and electro-blot immunoassay Découverte du premier virus, le virus de la mosaïque du tabac: 1892 ou 1898? Comptes Rendus de l'Academie des Sciences. Serie III Sciences de la vie Viral dynamics in mild and severe cases of COVID-19 Development of a highly sensitive nested RT-PCR method for Beet necrotic yellow vein virus detection Confirmation of some SCAR molecular markers linked to rhizomania resistance gene (Rz1) in sugar beet Molecular characterization of soilborne viruses infecting sugar beet in Europe and USA. Plant Biology-Master's Programme (Institutionen för växtbiologi Identification methods for beet necrotic yellow vein virus (BNYVV) and related viruses in beets Resistance in Beta vulgaris L. subsp. maritima (L.) Thell to the Rz1-breaking strain of rhizomania Evaluation of digital PCR for absolute RNA quantification Digital PCR as new approach to SNP genotyping in sugar beet Identification of SNP markers linked to the Rz1 gene in sugar beet RNA 3 deletion mutants of beet necrotic yellow vein virus do not cause rhizomania disease in sugar beets Methods for applying accurate digital PCR analysis on low copy DNA samples The development of a sensitive droplet digital PCR for quantitative detection of porcine reproductive and respiratory syndrome virus Identification of Beet necrotic yellow vein virus in Lakes District: a major beet growing area in Turkey p25 pathogenicity factor deletion mutants of beet necrotic yellow vein virus occurring in sugar beet fields in Turkey Investigation of rhizomania resistance through using a chimeric construct inducing RNA silencing against six BNYVVderived genes Acknowledgments We are grateful to Enrico Biancardi and Tetsuo Tamada for kindly revising the paper.