Vitis vinifera L. Single-Nucleotide Polymorphism Detection with High

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Vitis vinifera L. single nucleotide polymorphism detection with High Resolution Melting analysis based on UDPglucose: flavonoid 3- O-glucosyltransferase gene Leonor Pereira, and Paula Martins-Lopes J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.5b03463 • Publication Date (Web): 01 Oct 2015 Downloaded from http://pubs.acs.org on October 3, 2015

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Journal of Agricultural and Food Chemistry

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Vitis vinifera L. single nucleotide polymorphism detection with High Resolution Melting analysis based on UDP-glucose: flavonoid 3- Oglucosyltransferase gene

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Leonor Pereira, Paula Martins-Lopes*

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University of Trás-os-Montes and Alto Douro, P.O. Box 1013, 5000-911 Vila Real,

8

Portugal

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University of Lisboa, Faculty of Sciences, BioISI – Biosystems & Integrative Sciences

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Institute, Campo Grande, Lisboa, Portugal

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ACS Paragon Plus Environment


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ABSTRACT

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Vitis vinifera L. is species with a large number of varieties, which differ in terms of

15

anthocyanin content. The genes involved on the anthocyanin biosynthesis pathway have

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a direct effect in the anthocyanin profile of each variety, being potentially interesting for

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varietal identification. The current study aimed at the design of an assay suitable for the

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discrimination of the largest number of grapevine varieties. Two genes of the

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anthocyanin pathway, chalcone isomerase (CHI) and UDP-glucose: flavonoid 3- O-

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glucosyltransferase (UFGT), were sequenced in 22 grapevine varieties. The CHI gene

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presented 5 SNPs within the sequence. A total of 58 SNPs and 1 INDEL were found

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among the UFGT gene, allowing the discrimination of 18 different genotypes within the

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22 grapevine varieties. A HRM assay designed for UFGT, containing 704 bp produced

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differentiated melting-curves for each of the 18 haplotypes. The developed HRM assay

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is efficient in the grapevine varietal discrimination.

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Keywords

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Vitis vinifera L., Anthocyanins, UFGT gene, SNP, HRM

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INTRODUCTION

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In the Vitaceae family, the Vitis genus is agronomically very important. Within this

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genus, the only European species, Vitis vinifera L., represents one of the oldest

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domesticated plants and is extremely relevant in the wine industry. The high

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adaptability of the V. vinifera species to different environmental conditions1 makes it

35

difficult to unequivocally identify the grapevine varieties. The traditional methods used

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in the identification and differentiation of grapevine varieties, based on ampelography

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and ampelometry, are dependent on the plants’ phenology that is influenced by

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environmental, phytosanitary and nutritional conditions. Nowadays there are several

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molecular marker based methods developed to guarantee the grapevine variety

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identification and they have been extended to must and wine samples, where

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morphological characterization is not applicable.2

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Among the main molecular marker systems, Simple Sequence Repeats (SSR) markers

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represent one of the most suitable genetic tools currently adopted by the international

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scientific community to define a grapevine variety.3 Recently, based on the whole

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genome sequencing of 12X V. vinifera PN40024/reference genome4, sequence-based

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molecular markers, as Single Nucleotide Polymorphism (SNP), have been generated.

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According to Cabezas and collaborators5 SNP markers present several advantages

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concerning varietal identification, namely: (1) mostly bi-allelic; (2) abundant

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throughout the genome; (3) relatively stable during evolution and; (4) low mutation rate.

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Furthermore, SNPs can be easily reproduced between laboratories and when using

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different detection methodologies, since the different alleles are not distinguished based

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on size but on the nucleotide presence at a given position. These features, in

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combination with their high availability, makes SNPs the most popular marker system

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for several genetic analyses.



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High-resolution melting (HRM) represents a method that enables the genotyping of

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SNPs in a large number of samples. The principle of HRM analysis is based on the

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generation of different melting-curve profiles due to the sequence variation present in

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the double-stranded DNA. Single-nucleotide changes represent the smallest genetic

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variation and are divided into four classes, distinguished by the different melting

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temperature (Tm) shifts they produce.6 SNP class 1 involves C/T and G/A, and SNP

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class 2 involves C/A and G/T, base exchanges that are easily genotyped by HRM due to

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Tm differences over 0.5 ºC.6 In contrast, in SNP class 3, only C/G base exchange occurs,

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and SNP class 4 is described by A/T base exchange, producing very small Tm

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differences (< 0.4 ºC for SNP class 3 and 3 bp). The

(Figure

1)


and

CHIfwd

(5’-

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PCR reactions were performed in a 20 µL volume, containing 20 ng of genomic DNA,

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1X PCR buffer, 25 mM MgCl2, 0.2 mM dNTPs, 0.2 µM of each primer and 0.3 U of

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Taq DNA polymerase (Roche). The reactions were incubated at 94 °C for 3 min,

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followed by 30 cycles of 94 °C/1 min, 58 °C/1 min, 72 °C/2 min and a final step of 72

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°C for 10 min.

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PCR amplicons resulting from the amplification of UFGT and CHI genes were directly

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sequenced (STAB VIDA; http://www.stabvida.com) and the genotypes of the twenty-

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two grapevine varieties were obtained. Sequence alignment was performed using the

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BioEdit program (http://www.mbio.ncsu.edu/BioEdit/BioEdit.html). The alignments of

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reverse and forward sequences were applied to produce consensus sequences. The

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sequences of each individual DNA fragments were aligned with original sequence to

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identify the SNP presence.

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High-resolution melting assay design.

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HRM analysis was performed in a specific 704 bp fragment of the UFGT gene using

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primers U_HRMfwd (5´- GCAATGTAATATCAAGTCC -3´) (Starting at 180 bp) and

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U_HRMrev (5´-TTTCTTTCTTTGAGCCATT-3´) (Ending at 884 bp) (Figure 1).

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PCR and HRM analysis was performed in a StepOne™ Real-Time PCR System

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(Applied Biosystems®, California, USA) in final volume of 20 µL containing the

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respective primer pair (5 pmol of each primer), 20 ng of gDNA and the MeltDoctor™

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HRM Master Mix (Applied Biosystems®, California, USA). PCR/HRM included an

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initial step of 10 min at 95 ºC and 40 cycles of 30 s at 95 ºC, 30 s at 58 ºC and 30 s at 72

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ºC. The melting curve was obtained in continuous, performed as follow: 30 s at 95 ºC, 1

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min at 65 ºC, 15 s at 95 ºC, rising 0.3 ºC/s and 15 s at 65 ºC. All reactions were

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performed in triplicate. A High Resolution Melt Software v3.0.1 (Applied



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Biosystems®, California, USA) was used to analyze the data. After normalization and

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determining the temperature shift, the different melting curves of the several plots were

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generated.

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In order to validate the reference HRM profile for each grapevine variety, DNA from

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the clones were analyzed using the developed HRM assay, using the previously

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described conditions.

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Phylogenetic analysis.

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Predicted amino acid sequences were used for phylogenetic analysis using COBALT

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multiple sequence alignment tool which takes into consideration the conserved domain

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database,

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va.ncbi.nlm.nih.gov/tools/cobalt/re_cobalt.cgi).26

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RESULTS AND DISCUSSION

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SNP identification.

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The CHI gene is located in a central position in the anthocyanin pathway. This gene was

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sequenced in twenty-two grapevine varieties (eighteen Portuguese and four

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international) revealing the existence of only five SNPs among all these varieties (data

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not shown). The low number of SNPs detected within this gene is in accordance with

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previous studies making it a non-interesting marker for varietal identification

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purposes.11, 27

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On the contrary, the UFGT gene revealed to be highly polymorphic presenting a high

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number of SNPs among the grapevine varieties studied. The UFGT gene length covered

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1500 bp and within this region a total of 58 SNPs and an insertion were detected (Table

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2). Eighteen SNPs and the insertion were found within the Exon 1 region (493 bp) with

protein

motif

database

and

sequence

similarity


(http://www.st-

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an average frequency of 1 SNP/25.9 nucleotides. Four SNPs were found in the Intron

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(74 bp) with an average frequency of 1 SNP/18.5 nucleotides. The remaining 36 SNPs

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were positioned within the Exon 2 region (881 bp) with an average frequency of 1

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SNP/24.5 nucleotides. These results are interesting since the number of SNPs detected

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within the coding-region is high, contrary to previous studies (sunflower28, cotton29, and

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grapevine30) where the majority of the SNPs were located in the non-coding region of

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the genes. The high SNP frequency found within this gene is particularly interesting

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since UFGT has been previously associated with several different types of

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anthocyanins11, which are directly involved in the skin berries color and the grapes

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organoleptic characteristics.

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High resolution melting-curve PCR analysis.

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CHI gene was not considered for the HRM approach since the number of SNPs found

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did not allow the discrimination of the varieties under study.

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The HRM assay was designed for UFGT gene considering a fragment size of 704 bp.

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The UFGT HRM fragment included 32 of the 58 SNPs present and the detected

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insertion, considering the twenty-two grapevine varieties under study, with an average

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frequency of 1 SNP/22 nucleotides. Considering the same fragment length Nicolè et

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al.31 reported a frequency of 1 SNP/31.69 nucleotides, revealing that this gene is highly

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polymorphic, and therefore interesting for varietal identification.

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Fragment size influences the sensitivity of subsequent HRM analysis, and it is usually

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advisable that they do not exceed 300 bp of length.32 In addition, long amplicons may

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contain several melting domains, resulting in complex melting profiles meaning that

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longer amplicons represent small differences in the melting curve caused by small

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sequence variation. In plants, the studies using HRM report a PCR amplicon range of 50



33-35

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to 260 bp.8,

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fragment size (704 bp). Therefore this method is quite promising as it broadens the

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genotyping potential using HRM analysis. The UFGT targeted sequence generated

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eighteen different melting curve profiles (Figure 2). The Tm values found within all the

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samples were similar (Tm1 - 82.7 - 83.0 ºC; Tm2 – 86.6 - 86.9 ºC), not distinguishing the

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different haplotypes. However, the shape of the melting curves was very informative,

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and even when the grapevine variety sequence only differed by a single SNP, this

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variation was detectable for: (a) CS, Sou and Vio and; (b) FP, TR and MF grapevine

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varieties (Table 2). Additionally, all these SNPs belong to Class 4, A/T, which is the

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most challenging genotype variation mainly due to the difficulty associated with the

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difference in Tm.7

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These results demonstrated the power, sensitivity and specificity of this particular HRM

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assay that allows the identification of several complex genotypes and subsequently the

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detection of different melting curves (Figure 2). The assay was successful in the

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definition of the several haplotypes once it was based on a combination of a high

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number of SNPs within the amplified fragment.

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Even though the assay is based on a long fragment, this HRM assay proved to be highly

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sensitive and was able to distinguish 18 haplotypes based on a combination of 33

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nucleotide differences in a unique assay. Previous studies reported the detection of

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several SNPs and INDELs but always considering simple events33,

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combination of nucleotide differences. Never, until now, has it been reported that by

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using a unique assay it was possible to detect such a high number of events in one

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reaction. The designed HRM assay proved to be powerful and specific.

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The shape of HRM curve for each grapevine variety needed to be further validated in

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relation to their varietal specification, once there are a high number of clones available

To our knowledge this is the first paper considering such a large


36-39

, and not a

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within each grapevine variety.40 Therefore, DNA samples from different clones of each

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grapevine variety were used and tested for reproducibility. The plants were sampled in

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established clonal field collections, maintained jointly in private and public institutions,

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including samples of different regions of the country. The melting-curve profiles

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obtained for each variety were coincident among clones of the same haplotype,

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indicating that such an assay can be used in the genotype identification of these

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particular grapevine varieties (Figure 3). The shape of the melting curve profiles of five

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red grapevine varieties used in the Douro region are presented, as an example in Figure

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3. Among them, Cabernet Sauvignon (CS) is used as an international grapevine

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reference variety. The clones of each grapevine variety present the same genetic profile,

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reinforcing the assay’s robustness.

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Four of the twenty-two grapevine varieties studied (Tinta Francisca, Alicante Bouschet,

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Côdega do Larinho and Tinta Amarela) could not be distinguished using this particular

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HRM assay (Figure 4), once they presented the same sequence for this particular region

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(Table 2). The sequence of these grapevine varieties only differ from Touriga Franca

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(TF), in a unique SNP at the 425 bp position (Table 2) which is discriminated using this

244

particular assay.

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The robustness of the HRM assay could also be confirmed by the profiles obtained in

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the clones of the undistinguishable grapevine varieties herein tested (Figure 4), once all

247

the samples tested presented the same shape HRM melting curve, as expected.

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Considering all the nucleotide sequences, in particularly the SNPs found in the 22

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grapevine varieties, the amino acid sequences were deduced and aligned. A total of 31

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amino acid residues variations were found within the grapevine varieties studied (Table

251

3).



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UFGT gene expression is correlated with the grapevine phenotype and its transcription

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is needed for berry pigmentation (no color/red color).41, 42 The anthocyanin profile and

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content can deeply influence the final wine quality.43 Considering the variation of the

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red grapevine varieties amino acid sequences studied, a Cobalt tree was constructed,

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giving rise to three major groups (Figure 5). Group A included CS, Sou, TR and Ruf;

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Group B incorporated M, TB, TBr and TN; Group C comprised TFi, AB, TA, TF, DT

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and TC. The Pinot Noir clones analyzed were completely distinguished from all the

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grapevine varieties, and therefore they generated an independent branch. The

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differences found between the amino acid sequence of Pinot Noir and the sequence of

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the NCBI database were expected, once the NCBI reference genome is a hybrid of Pinot

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Noir.4 Touriga Brasileira presented the same amino acid sequence as the reported in the

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NCBI database relatively to the UFGT locus.

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Although the UFGT gene expression is related with the anthocyanin profile of the

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grapes41 there is a lack of information of the specific profile of each grapevine variety.

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Some of the most widely used grapevine varieties have been characterized, among them

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Pinot Noir, Merlot and Cabernet Sauvignon.

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Pinot Noir grapes have a differentiated anthocyanin profile, with only monoglucosides

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anthocyanins, in contrast with other grapevine varieties (Merlot, Cabernet Sauvignon,

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Touriga Franca, Tinta Roriz, Touriga Nacional, Sousão, Rufete, Tinta Amarela, Tinta

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Barroca, Tinto Cão) that present all the three types of anthocyanin (monoglucosides,

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acetates and coumarates).44-47 This may justify the outstanding position of Pinot Noir in

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relation to the other grapevine varieties studied. However, the amino acid variation

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present within the UFGT sequence has a direct implication on the protein composition,

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which may or not influence the protein function.


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The results obtained through this work represent a good landmark in this particular area,

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and should be considered as a suitable platform for further studies. The deduced amino

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acid sequences sets the bases for a better understanding of the correlation between

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anthocyanin content and the amino acid profiles.

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The present work confirmed the low level of polymorphism of the CHI gene and

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revealed a high level of polymorphism present within the UFGT gene of grapevine. The

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UFGT gene allowed the discrimination of 18 different grapevine genotypes among 22

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grapevine varieties. From the 58 SNPs and 1 INDEL a total of 31 amino acid residual

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changes were predicted, enhancing the potential effect on the anthocyanin profile of

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each variety, which opens doors for further omic studies considering this particular

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gene.

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This study provides the first report on the use of a large fragment in HRM assays,

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thereby allowing the detection of multiple events (SNP and INDEL) in a unique assay,

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which can be adapted for large-scale genotyping and mapping in Vitis. This assay can

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be extended for grapevine varietal certification procedures, which are imperative

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throughout the entire wine-chain (plant nurseries to wine), once it allows varietal

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identification.

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Accession numbers

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Sequence data from this article can be found in the Gen-Bank database under the

296

accession numbers 1868281767; 1868281812; 1868281815; 1868281817; 1868281828; 1868281830;

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1868281834; 1868281836; 1868281838; 1868281843; 1868281845; 1868281852; 1868281855;

298

1868281862; 1868281865; 1868281867; 1868281870; 1868281872; 1868281874; 1868281876;

299

1868281878; 1868281881; 1868281883; 1868281885; 1868281887; 1868281892; 1868281894;

300

1868281897; 1868281899; 1868281901; 1868281903; 1868281905; 1868281907; 1868281909;

301

1868281911; 1868281913; 1868281915; 1868281917; 1868281919; 1868281921; 1868281924;



302

1868281926; 1868281928; 1868281930; 1868281933; 1868281935; 1868281937; 1868281939;

303

1868281941; 1868281943; 1868281945; 1868281947; 1868281949; 1868281951; 1868281953;

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1868281955; 1868281957; 1868281960; 1868281963.

305 306

Author information

307

Corresponding Author

308

*(P. M.-L.) Phone: ++351259350936 . Fax: + +351259350572

309

E-mail: [email protected]

310

Funding

311

This research was supported by the Portuguese Foundation for Science and Technology

312

(FCT) in the project Biosensor Development for Wine Traceability in the Douro Region

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– WineBioCode PTDC/AGR-ALI/117341/2010-FCOMP-01-0124-FEDER-019439 and

314

a PhD grant (SFRH/BD/44781/2008).

315 316

Acknowledgements

317

The authors are grateful to Prof. Ântero Martins, Doctor José Eiras-Dias (INIAV), Dr.

318

Paulo Costa (ADVID) for helping us in the clonal leaf collection, to the Sogrape Vinhos

319

S.A., Real Companhia Velha, Sociedade Borges S.A., Direção Regional de Agricultura e

320

Pescas do Norte for letting us collect the samples in their fields.

321 322

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Single nucleotide polymorphism and haplotype diversity of the gene NAC4 in

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grapevine. Ind. Crops Prod. 2013, 43, 718-724.

442 443

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region of the UFGT gene is a source of diagnostic SNP markers that allow single-locus

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DNA genotyping for the assessment of cultivar identity and ancestry in grapevine (Vitis

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vinifera L.). BMC Res. Notes 2013, 6, 502.

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Resolution Melt (HRM) analysis is an efficient tool to genotype EMS mutants in

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complex crop genomes. Plant Methods 2011, 7, 43.

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DNA Polymorphism Detection and Genotyping in Plants. J. Integr. Plant Biol. 2010,

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species. Biochem. Sys. Ecol. 2012, 44, 352-360.

473


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in grapevine. Mol. Breed. 2014, 33, 235-241.

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colour variation in table grape. Mol. Gen. Genet. 2006, 276, 427-435.

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Chromosome Replacement and Deletion Lead to Clonal Polymorphism of Berry Color

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511

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513 514 515 516 517 518

Figure captions

519 520

Figure 1. Schematic representation of the primers set used in the UFGT gene. The

521

empty space corresponds to the intron.

522


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523

Figure 2. HRM Difference plot of twenty-two grapevine varieties for UFGT fragment

524

showing eighteen different genotypes.

525 526

Figure 3 HRM different curves of the most representative grapevine varieties used in

527

wine production in Portugal and corresponding clones for UFGT fragment.

528 529

Figure 4. Difference plot of the grapevine varieties and corresponding clones presenting

530

the same genotype for UFGT fragment.

531 532

Figure 5. Phylogenetic tree based on deduced amino acid sequences variations of

533

UFGT from the red grapevine varieties using Cobalt multiple sequence alignment tool.

534 535 536 537 538 539 540 541 542

Figures



Figure 1.

Figure 2.


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Figure 3.


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Figure 4.



Figure 5.


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List of Tables

Table 1. List of 22 grapevine varieties used for SNP identification, corresponding code and berry color. Grapevine variety name Code Berry color Alicante Bouschet AB Red Cabernet Sauvignon CS Red Chardonnay Ch White Côdega do Larinho CL White Donzelinho Tinto DT Red Fernão Pires FP White Gouveio Gou White Merlot M Red Malvasia Fina MF White Moscatel Galego MG White Pinot Noir PN Red Rufete Ruf Red Sousão Sou Red Tinta Amarela TA Red Tinta Barroca TB Red Touriga Brasileira TBr Red Tinto Cão TC Red Touriga Franca TF Red Tinta Francisca TFi Red Touriga Nacional TN Red Tinta Roriz TR Red Viosinho Vio White

543



Page 30 of 33

Table 2. Single Nucleotide Polymorphisms identified in UFGT gene with information on the genotypes composition found across the 22 grapevine varieties (Vitis vinifera L.). Nucleotide position Sample

90

98

139

205

207

220

238

240

257

264

265

272

309

366

375

424

425

442

459

483

525

555

560

562

598

600

617

619

636

663

685

762

789

816

841

843

UFGT TBr Ch MG GOU CS Sou Vio FP TR MF Ruf M TB TN TF TFi AB TA CL TC DT PN

C . S S . S S S G G G G . . . . . . . . S . .

C . . . . . . . . . . . . . . Y Y Y Y Y Y Y Y

C . . . . . . . . . . . . . . S S S S S S S S

G . R R . R R R A A A R . . . . . . . . R . .

G . S S . S S S C C C S . . . . . . . . S . .

G . S S S S S S C C C C . . . S S S S S C C S

G . . . . . . . . . . R . . . R R R R R R R S

C . . . M . . . . . . . . . . . . . . . . M .

A . . . . . . . . . . . . . . R R R R R R R R

G . . . . . . . . . . . . . . K K K K K K K K

C . . . . . . . M M A M . . . . . . . . . . .

T . . Y . Y Y Y C C C C . . . . . . . . Y . .

G . . R . A A A A A A A W A R R R R R R R . .

T . . . . . . . . . . . W . . . . . . . . . .

T . Y Y Y C C C C C C C C C Y Y Y Y Y Y Y Y .

T T -

G . . . . . . . . . . . K K K K . . . . . . .

C . . . . . . . . . . M . . . . . . . . . . .

C . . Y . Y T Y T T T Y . . . . . . . . Y . Y

G . . . . S S S . . . . C C S S S S S S . . .

A . . W . W W W T T T W . . . . . . . . W . W

A . M M M C C C C C C C C C M C C C C C C C M

A . . . R . . . . . . . . . . R R R R R R G .

G . . . . . . . . . . . . . . R R R R R R R .

A . . . . . . . T W W W . . . . . . . . . . .

T . . . Y . . . . . . . . . . . . . . . . Y .

C . . . M . . . . . . . . . . M M M M M M A .

A . . . R . . . . . . . . . . R R R R R R G .

C . . . Y . . . . . . . . . . Y Y Y Y Y Y T .

C . . . Y . . . . . . . . . . Y Y Y Y Y Y T .

A . . . . . . . . . . . . . . . . . . . . . W

C . . . . . . . . . . . . . . Y Y Y Y Y Y Y .

C . . . . . . . . . . . . . . M M M M M M M .

T . . . . Y Y Y Y Y Y . . . . Y Y Y Y Y C Y .

A . R R R G G G G G G G G G R G G G G G G G S

A . . . . W W W . . . . T T W W W W W W . . W

Numbering starts with A of the start codon. Nucleotide Code: A (Adenine); C (Cytosine); T (Thymine); G (Guanine); M (A or C); R (A or G); W (A or T); S (C or G); Y ( C or T) and K (G or T).


Page 31 of 33


Nucleotide position Sample

850

943

955

UFGT TBr Ch MG GOU CS Sou Vio FP TR MF Ruf M TB TN TF TFi AB TA CL TC DT PN

A . . . . . . . . . . . . . . W W W W W W W .

C . . . . S S S S S S . . . . . . . . . S . .

G . . . . K K K K K K . . . . . . . . . K . .

1014 1054 1083 1122 1131 1134 1144 1146 1160 1165 1193 1232 1245 1273 1345 1404 1405 1409 1459 1481 G . K . . . . . . . . . . . . . . . . . . . .

T . . . . Y Y Y Y Y Y Y C C Y Y Y Y Y Y . . Y

G . . . . . . . . . . . . . . . . . . . . . R

A . . . . M M M M M M . . . . . . . . . M . .

T . . . . Y Y Y Y Y Y . . . . . . . . . Y . .

A . . . . T T T T T T T T T W T T T T T T W W

T . . . K . . . . . . . . . . . . . . . . K .

G . . . . . . . . . . . . . . R R R R R R R .

C . . . . . . . . . . . . . . S S S S S S S .

G . . . . . . . . . . K . . . . . . . . . . .

A . W W W T T T T T T T T T W T T T T T T T W

C . Y . . Y Y Y T T T T . . . Y Y Y Y Y T Y Y

A . . . . M M M M M . M . . . . . . . . M . .

G . R . . A A A A A A A A A R A A A A A A R R

G . R R R A A A A A A A A A R A A A A A A A R

T . . . . . . . . . . . . . . . . . . . . . Y

T . . . . . . . . W . . . . . . . . . . . . .

A . . . . W W W W W W . . . . . . . . . W . .

T . . . . . . . Y Y . Y . . . . . . . . . . .

A . . . . . . . . R . R . . . . . . . . . . .

Numbering starts with A of the start codon. Nucleotide Code: A (Adenine); C (Cytosine); T (Thymine); G (Guanine); M (A or C); R (A or G); W (A or T); S (C or G); Y ( C or T) and K (G or T).



Page 32 of 33

Table 3. Predicted amino acid sequence variations in UFGT, using the reference genome, based on the SNPs found across the twenty-two grapevine varieties (Vitis vinifera L.). Amino acid position Sample UFGT CS Sou Vio FP MF TR Ruf Ch MG TBr GOU M TB TN PN TFi AB CL TA TF DT TC

33 47 69 74 80 86 88 89 91 142 153 161 174 181 182 204 256 259 290 293 312 327 357 362 364 373 386 400 424 444 445

A Q V A A D E . . I P . . . . . I P . . . . . I P . . . . . I P . . . . . I P . . . . . . P . . . . . . P T . . . . . P . . . . . . P . . . . . . . . . . . . . P . . . . . . . . . . . . . . . . . . . . . . . . V E . P P G D V E . P T G D V E . P T G D V E . P T G D V E . P T G D V E . P T G D V E . P T G D V E I P T G D

L . . . M M M M . . . . . . . . . . . . . . .

M T T T T T T T . T . . . . . . . . . . . . T

A . . . . . . . . . . . S S S . . . . . S . .

T I I I I I I I . I . . . . . I . . . . . . I.

G A A A . . . . . . . . A A A . A A A A A . .

N . . . Y Y Y Y . . . . . . . . . . . . . . .

S K M I . . . V . . . V . . . V . . . V . . . V . . . V . . . V . . . . . . . . . . . . Y E . . . . V . . . V . . . . . . L L Y E . V Y E . V Y E . V Y E . V Y E . V Y E . V Y E . V

T . . . . . . . . . . . . . . . S S S S S S S

L A V S V S V S V S V S . S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V S

R . . . . . . . S . . . . . . . . . . . . . .

Y L A G H . . . H . . . H . . . H . . . H . . . H . . . H . . W . . . . . . . . . . . . . V . . H . . . H . . . H . . . H . . . H . G . H . G . H . G . H . G . H . G . . V G . . . G .

Y F F F F F F F . . . . F F . . F F F F F F F

A V V V V V V V V . . . . . . V V V V V V V V

E K K K K K K K . . . . K K . . K K K K K

G R R R R R R R . . . . R R . . R R R R R R K R

F . . . . . I . . . . . . . . . . . . . . . .

K I I I I I I . . . . . . . . . . . . . . . I

Amino acid code: A (Alanine); D (Aspartic Acid); E (Glutamic Acid); F (Phenylalanine); G (Glycine); H (Histidine); I (Isoleucine); K (Lysine); L (Leucine); M (Methionine); N (Asparagine); P (Proline); Q (Glutamine); R (Arginine); S (Serine); T (Threonine); V (Valine); W (Tryptophan); Y (Tyrosine).


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Graphical Abstract 320x320mm (96 x 96 DPI)


Vitis vinifera L. Single-Nucleotide Polymorphism Detection with High

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