STSM Scientific Report COST STSM Reference Number: ECOST-STSM-FA1104-180116-070455-70455 Reference Code: COST-STSM-ECOST-STSM-FA1104-180116-070455

Sweet cherry genetic fingerprinting: methods and

techniques. Marker-assisted selection (MAS) approaches

for selection of sweet cherry varieties

Short term scientific mission (STSM) supported by and serving aims of: COST action FA1104: “Sustainable production of high-quality cherries for the European

market”

18.01 – 31.03.2016

Junior Researcher Yaroslav I. Ivanovych National Academy of Agrarian Sciences of Ukraine Institute of Horticulture Novosilky, 03027, Kyiv-27, Kyiv region, Ukraine

Dr José Quero-Garcia (Local Trainer and supervisor) Host Institution: INRA, Centre Bordeaux Aquitaine, A3C Villenave d'Ornon Cedex, 33882, France

1. Objective of the STSM

The main focus of the STSM was the study of the genetic diversity of Ukrainian sweet cherry varieties and landraces using SSR markers. The set of markers includes some which are linked with economically important traits (e.g.: fruit weight) and applicable for marker-assisted selection (MAS) strategies. The second important goal was to make molecular typing of the self-incompatibility (S) locus. Additionally, one of objectives was to provide an insight into the methods and techniques used at INRA A3C. In particular, it was planned an introduction into breeding activities (breeding objectives, breeding techniques such as hybridization and MAS etc.).

2. Materials and Methods

Total DNA of samples (Table 1) was previously extracted using homemade modified CTAB – buffer and DNeasy® Plant Mini Kit (Quiagen, Germany) following the manufacture’s protocol. The quality of DNA was tested and its concentration assessed after electrophoresis in 1% agarose. DNA samples were prepared by diluting to final concentration of 2 ng/µl. Samples from 79 to 94 were added as reference.

Table 1. The list of sweet cherry accessions included to the study

Variety Originator

1. AVXX1/Valerii Chkalov Melitopol Res. Stat. of Hort. 2. Melitopolska chorna Melitopol Res. Stat. of Hort. 3. Anons Melitopol Res. Stat. of Hort. 4. Talisman Melitopol Res. Stat. of Hort. 5. Krupnoplidna Melitopol Res. Stat. of Hort. 6. Kazka Melitopol Res. Stat. of Hort. 7. Melitopolska myrna Melitopol Res. Stat. of Hort. 8. Temporion Melitopol Res. Stat. of Hort. 9. Prostir Melitopol Res. Stat. of Hort.

10. Prestyzhna Melitopol Res. Stat. of Hort. 11. Anshlah Melitopol Res. Stat. of Hort. 12. Lasunia Melitopol Res. Stat. of Hort. 13. Mirazh Melitopol Res. Stat. of Hort. 14. Zodiak Melitopol Res. Stat. of Hort. 15. Shans Melitopol Res. Stat. of Hort. 16. Prysadybna Melitopol Res. Stat. of Hort. 17. Elektra Melitopol Res. Stat. of Hort. 18. Dachnytsia Melitopol Res. Stat. of Hort.

19. Proshchalna Taranenko Artemivsk Nurs. Res. Stat. 20. Vasylysa prekrasna Artemivsk Nurs. Res. Stat. 21. Donetska krasavytsia 1 Artemivsk Nurs. Res. Stat. 22. Donetska krasavytsia 2 Artemivsk Nurs. Res. Stat. 23. Otrada 1 Artemivsk Nurs. Res. Stat. 24. Otrada 2 Artemivsk Nurs. Res. Stat. 25. Annushka Artemivsk Nurs. Res. Stat. 26. Taina Artemivsk Nurs. Res. Stat. 27. Etyka Artemivsk Nurs. Res. Stat.

28. Yaroslavna 1 Artemivsk Nurs. Res. Stat. 29. Yaroslavna 2 Artemivsk Nurs. Res. Stat. 30. Donchanka Artemivsk Nurs. Res. Stat. 31. Dzherelo Artemivsk Nurs. Res. Stat. 32. Valeriia Artemivsk Nurs. Res. Stat. 33. Donetskyi uholok Artemivsk Nurs. Res. Stat. 34. Siestronka 1 Artemivsk Nurs. Res. Stat. 35. Siestronka 2 Artemivsk Nurs. Res. Stat. 36. Zheltochek Artemivsk Nurs. Res. Stat. 37. Rannia rozova Artemivsk Nurs. Res. Stat. 38. Amazonka Artemivsk Nurs. Res. Stat. 39. Studentka Artemivsk Nurs. Res. Stat. 40. Donetska rannia Artemivsk Nurs. Res. Stat. 41. Aelita Artemivsk Nurs. Res. Stat. 42. Ruksandra Artemivsk Nurs. Res. Stat. 43. Lesia Artemivsk Nurs. Res. Stat. 44. D 58-52 Artemivsk Nurs. Res. Stat. 45. D 44-15 Artemivsk Nurs. Res. Stat.

46. Nizhnist Institut of Horticulture 47. Liubava Institut of Horticulture 48. Kytaivska chorna Institut of Horticulture

49. Lehenda Mliieva Institut of Pomology 50. Dar Mliieva Institut of Pomology 51. Koralova Institut of Pomology 52. Zoriana Institut of Pomology 53. Biriuza Institut of Pomology 54. Naiada Institut of Pomology 55. Nektarna Institut of Pomology 56. Yuvileina mliivska Institut of Pomology 57. Chervneva Institut of Pomology 58. Mliivska chorna Institut of Pomology 59. Veselka Institut of Pomology 60. Aboryhenka Institut of Pomology 61. Pokazkova Institut of Pomology 62. Shchedrist Institut of Pomology 63. Yedyna Institut of Pomology

64. Oleh 1 Unknown 65. Oleh 2 Unknown 66. Heneralska Nikita Bot. Garden, Crimea 67. AVXX2/Drogan's Yellow Unknown 68. Plena Belgium

69. AVCV1 Chernivtsi region 70. AVCV2 Chernivtsi region 71. AVCV3 Chernivtsi region 72. AVCV4 Chernivtsi region 73. AVCV5 Chernivtsi region 74. AVCV6 Chernivtsi region 75. AVCV7 Chernivtsi region 76. AVCV8 Chernivtsi region 77. Mykhailivska chorna Lviv region 78. AVKV1 Kyiv region

79. Dönissens Gelbe Knorpelkirsche Germany 80. Margit Hungary 81. Folfer® France 82. Adriana Italy 83. Sato Nishiki Japan 84. Napoleon Germany 85. Xapata France 86. Jaboulay France 87. Scwecja A. Poland 88. Regina Germany 89. Burlat France 90. Garnet USA 91. Lapins Canada 92. Saint Georges France 93. Cristobalina Spain 94. Tunisian cv. / Bouargoub Tunisia

2.1. Microsatellite fingerprinting

A group of 19 microsatellite (SSR) markers (Table 2) with M13 tails was used for genotyping. Markers were grouped according to previously reported allele sizes in three classes (Small, Medium and Large). For the PCR reactions, M13 tails with different fluorescent labels were used for each class of markers.

The PCR reactions were performed in a final volume of 15 μl containing 2 ng genomic DNA, 1× PCR buffer, 2.5 mM MgCl2, 0.2 mM dNTPs, 0.2 μm each primer and 0.25 U Taq polymerase (Sigma, JumpStart™ Taq DNA Polymerase). ‘Touchdown’ PCR conditions were used: 94°C for 15 min followed by 15 cycles of 94 °C for 45 s, 56-58°C for 45 s, 72°C for 45 s and then 25 cycles of 94°C for 45 s, 53°C for 45 s, 72°C for 45 s with a final elongation step of 72 °C for 10 min.

Each PCR was carried out in separate plates. The presence and approximate concentration of PCR-products were assessed after amplification. PCR products were diluted and loaded in Beckman Coulter CEQ™ 8000 Genetic Analysis System. Preinstalled software was used for allele calling and sizing.

Table 2. Microsatellites used for genotyping

Loci name Label Linkage group (LG) Reference 1. EMPA002 D2 1 CLARKE AND TOBUTT (2003) 2. UDP98-022 D2 1 TESTOLIN et al. (2000) 3. BPPCT034 D4 2 DIRLEWANGER et al. (2002) 4. CPSCT034 D3 2 MNEJJA et al. (2004) 5. CPSCT038 D3 2 MNEJJA et al. (2004)

6. EMPaS02 D2 3 VAUGHAN AND RUSSELL (2004) 7. LG3_13.146 D3 3 STEGMEIR et al. (2015) 8. BPPCT040 D2 4 DIRLEWANGER et al. (2002) 9. EMPaS10 D4 4 VAUGHAN AND RUSSELL (2004)

10. pchgms55 D2 4 HOWAD et al. (2005)

11. BPPCT037 D2 5 DIRLEWANGER et al. (2002) 12. EMPaS14 D4 5 VAUGHAN AND RUSSELL (2004)

13. EPPB4230 D4 5 HOWAD et al. (2005) 14. EPPCU3090 D3 6 HOWAD et al. (2005) 15. M19A D3 6 YAMAMOTO et al. (2002)

16. CPSCT022 D4 7 MNEJJA et al. (2004) 17. EPDCU3392 D2 7 HOWAD et al. (2005) 18. EMPA026 D3 8 CLARKE AND TOBUTT (2003) 19. pchgms49 D4 8 HOWAD et al. (2005)

2.2. Molecular typing of the self-incompatibility locus

To date, the majority of the sweet cherry varieties of Ukrainian origin remain unstudied at the molecular level. There are only a few papers in which several of our accessions were studied. For that reason we looked for fast, reliable and reproducible means of identifying S haplotypes.

To achieve this aim we used fluorescently labelled S-RNase first intron consensus primers, second intron consensus primers and fluorescently labeled SFB intron consensus primers (Table 3). PCRs were carried out following the articles recommendations for PCR mix and cycles conditions.

Table 3. S-allele primers

Primers Sequence Reference

PaConsI-F PaConsI-R2

D3-(C/A)CT TGT TCT TG(C/G) TTT (T/C)GC TTT CTT C GCC ATT GTT GCA CAA ATT GA

SONNEVELD et al. (2006)

PaConsII-F PaConsII-R

G GCC AAG TAA TTA TTC AAA CC CA(T/A) AAC AAA (A/G)TA CCA CTT CAT GTA AC

SONNEVELD et al. (2003)

F-BOX5′A F-BOXintronR

D4-TTK SCH ATT RYC AAC CKC AAA AG CWG GTA GTC TTD SYA GGA TG

VAUGHAN et al. (2006)

Foreign sweet cherry varieties were used for the identification of S-alleles as

controls.

3. Results

3.1. Microsatellite fingerprinting

The microsatellite fingerprints allow us to conduct an identification of domestic sweet cherry varieties, true-to-type verification, evaluation of genetic diversity, elucidation of varieties relatedness and genetic tree construction.

The discriminatory possibilities of the marker systems were assessed applying GenAlEx and Cervus (Table 4). Allelic diversity was estimated for several parameters: Number of Alleles (Na), Number of Effective Alleles (Ne), Shannon's Information Index (I), Expected Heterozygosity (He), Observed Heterozygosity (Hо), Wright’s Fixation Index (F), Polymorphic Information Content (PIC), Average non-exclusion probability for identity of two unrelated individuals (Pid) and Average non-exclusion probability for identity of two siblings (Pid-sib).

Additionally, we evaluated the significance of deviation from Hardy-Weinberg equilibrium and null allele frequencies (data not shown). The EMPA026 locus showed a significant deviation at the 0.1% level. The same locus showed quite high (0.34) null allele frequency compared to the rest of loci.

Table 4. Indices of genetic variability and markers discriminatory possibilities

Locus Size§ Na Ne I He Hо F PIC Pid Pid-sib

CPSCT034 192-210 9 3.997 1.597 0.779 0.750 -0.039 0.713 0.099 0.400 pchgms49 160-204 9 1.828 0.983 0.467 0.453 -0.032 0.427 0.325 0.605 UDP98-022 102-118 7 2.899 1.371 0.559 0.655 0.147 0.620 0.154 0.461 M19A 189-192 2 1.022 0.059 0.022 0.021 -0.011 0.021 0.958 0.979 CPSCT022 265-279 8 2.351 1.116 0.578 0.575 -0.006 0.510 0.246 0.524 EPDCU3392 128-150 9 4.654 1.712 0.793 0.785 -0.011 0.755 0.076 0.377 EPPCU3090 190-206 8 2.876 1.292 0.553 0.652 0.152 0.596 0.177 0.468 EMPaS02 152-168 8 3.048 1.393 0.693 0.672 -0.032 0.632 0.148 0.451 EMPaS10 168-205 10 2.598 1.388 0.582 0.615 0.053 0.583 0.180 0.487 BPPCT040 138-166 11 4.151 1.708 0.798 0.759 -0.051 0.726 0.091 0.393 EMPA026*** 224-252 6 2.139 0.905 0.261 0.532 0.510 0.440 0.311 0.562 EMPaS14 218-232 3 2.099 0.799 0.648 0.524 -0.237 0.413 0.337 0.573 EPPB4230 276-314 16 3.294 1.728 0.677 0.696 0.027 0.673 0.115 0.431 EMPA002 124-150 5 2.058 0.815 0.598 0.514 -0.163 0.408 0.342 0.579 BPPCT037 144-180 10 5.715 1.855 0.840 0.825 -0.019 0.801 0.054 0.351 CPSCT038 202-246 8 1.865 0.941 0.453 0.464 0.022 0.430 0.321 0.598 BPPCT034 232-290 13 2.297 1.285 0.585 0.565 -0.036 0.536 0.218 0.522 pchgms55 144-204 9 3.791 1.567 0.602 0.736 0.182 0.695 0.111 0.410

Mean - 8.389 2.927 1.251 0.583 0.600 - 0.554 0.237 0.510

Total 151 6.705×

e-14 3.14×10-6

Notes: § – the size of bands is bigger (comparing with original) because of use the M13 tails; *** – HW significant at the 0.1% level.

The main allelic diversity data is represented on Figure 1.

Figure 1. Allelic Patterns across Population

It is highly interesting to study CPSCT038 and BPPCT034 microsatellite loci (ZHANG et al. 2009). Indeed, it was shown for these two microsatellites a very close localization to fruit weight QTL on LG2. Later, the gene PavCNR12, from family FW2.2/CNR, was discovered within the confidence interval of this QTL. Genes of this family have a significant influence on cell division, cell number and as a result, on the size of plant organs.

These markers have been directly used in MAS programs in the last years because of the economic importance of fruit weight in cherry cultivation. Previously, the haplotypes of CPSCT038 and BPPCT034, which are significantly associated with large, small and presumed small fruit size, were identified. Later, 3 alleles of PavCNR12 gene, with different influence on fruit weight, were described. A general good correspondence was found between haplotype variation at the two SSR and the PavCNR12 loci but a few exceptions were identified, such as the Ukrainian variety ‘Krupnoplidna’ (DE FRANCESCHI et al. 2013).

This is a promising direction for future studying of CPSCT038 and BPPCT034 haplotypes variation in a wide range of sweet cherry varieties. In our study we detected higher genetic variability in comparison to previous published results. Allelic frequencies and allele sizes at CPSCT038 and BPPCT034 loci areshown on Figure 2 and Figure 3.

8,389

3,389 2,927

1,251

8,389

0,600

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0,570

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Pop1

He

tero

zygo

sity

Me

an

Populations

Na

Na Freq. >= 5%

Ne

I

No. Private Alleles

He

Figure 2. Allele Frequency at CPSCT038 for Pop1 (n=86)

Figure 3. Allele Frequency at BPPCT034 for Pop1 (n=94)

0% 0%

71%

13%

1% 13%

1% 1%

202

204

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218

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246

232 1%

238 0%

240 13%

242 2%

244 0%

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250 13%

252 4%

254 0% 260

2%

262 1%

274 63%

290 1% 232

238

240

242

244

248

250

252

254

260

262

274

290

3.2. Molecular typing of the self-incompatibility locus

Gametophytic self-incompatibility (GSI) in sweet cherry is controlled by the multi-allelic S-locus, which prevents self-fertilization. The S-genotype is economically important because cultivars with the same S-genotype are cross incompatible. The stylar component of GSI was determined to be a ribonuclease (S-RNase) gene in the Rosaceae. In the past, several P. avium S-alleles were identified as a result of cross pollination experiments and progeny testing in sweet cherry and later correlated with stylar ribonucleases (SONNEVELD et al. 2003).

In sweet cherry varieties up to date 29 S-alleles are known. It was discovered that at least one of them, S34, is present in sweet and sour cherry accessions. However, only 13 of all sweet cherry S-alleles are frequent and their distribution depends on variety’s origin. The most frequent are: S1-S7, S9, S10, S12-S14, S16 and the rest are rare. The rare alleles usually spread among the landraces and varieties from countries close to the center of species origin (e.g.: Turkey)(SZIKRISZT et al. 2012).

Overall, within the 94 studied sweet cherry varieties, mainly of Ukrainian origin, we identified 188 S-alleles (Figure 4). In few varieties only one S-allele was detectedwhereas in two varieties three S-alleles were found. These two varieties are presumably triploids. Additionally it was found in all probability at least one new S-allele. The study of two more S-alleles will be continued to clarify whether they arenew or not.

Figure 4. Relative occurrence (%) of S-alleles in 94 sweet cherry cultivars

11,7

0,5

18,1

9,0

16,0 14,9

0,5

19,1

0,5 1,6 1,6 1,6 1,6

0,5 0,5 1,1 0,5 0,5

0,0

5,0

10,0

15,0

20,0

25,0

S1 S2 S3 S4 S5 S6 S7 S9 S10 S12 S13 S14 S17 S18 S29 SX1 SX2 SX3

Occ

ure

nce

(%

)

3.2. Genetic structure and relatedness of sweet cherry varieties

The software STRUCTURE was used for clarification of the genetic structure of sweet cherry varieties used in this study. STRUCTURE software is based on Bayes’ cauterization. When using this method, the variety amount is divided by grades K value of genetic clusters (genetic pools), bringing together the most related forms. In addition, each variety may be related to multiple clusters. The most significant K value is considered, which is characterized by the highest ln P(D) value. In our study the highest ln P(D) value was observed at K=10 (Figure 5). The running was carried out with the following parameters: 500 K of Length of Burn-in Period and 500K of Number of MCMC Reps after Burn-in in two repeats.

Figure 5. Genetic structure of Ukrainian sweet cherry varieties

Additionally, the average probability of assignment (Q) of P. avium to the nine gene pools identified by STRUCTURE analyses was calculated. For each cluster, Q was averaged over the 94 individuals genotyped in this study (Figure 6).

Figure 6. Overall proportion of membership of the 94 sweet cherry varieties in

each of the 10 clusters

The genetic relatedness between 94 sweet cherry varieties (Figure 7) was assessed with DARwin software using Weighted Neighbor-Joining (WN-J) method. The adequacy of tree pruning was estimated in the same DARwin software using 10000 bootstrap repeats.

10%

9% 8% 9%

7%

18%

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0%

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4%

6%

8%

10%

12%

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16%

18%

20%

1 2 3 4 5 6 7 8 9 10

Ava

rage

Q, %

Clusters

Figure 7. Neighbor-Joining tree showing genetic relatedness between 94 sweet cherry varieties based on 18 SSR loci and S-locus markers (only the bootstrap values above 50 are shown).

The colored varieties on the tree belong to the nine sweet cherry clusters previously described in CAMPOY et al. (2016).

4. Conclusions

The proposal of this scientific mission was successfully finished. The SSR fingerprinting of sweet cherry varieties was carried out. The potential of use of microsatellites associated with sweet cherry fruit size for domestic varieties will be assessed. S-haplotypes of the majority of accessions were successfully determined. The PCR-based detection of S-allele helps to promote and speed up traditional breeding activity. It is planned to introduce the use of sweet cherry S-alleles data in the Ukrainian breeding activity and in the design of new cherry plantations because of its direct influence on pollination and yield.

5. References

Campoy, J. A., E. Lerigoleur-Balsemin, H. Christmann, R. Beauvieux, N. Girollet et al., 2016 Genetic diversity, linkage disequilibrium, population structure and construction of a core collection of Prunus avium L. landraces and bred cultivars. BMC Plant Biology 16: 49.

Clarke, J. B., and K. R. Tobutt, 2003 Development and characterization of polymorphic microsatellites from Prunus avium‘Napoleon’. Molecular Ecology Notes 3: 578-580.

De Franceschi, P., T. Stegmeir, A. Cabrera, E. van der Knaap, U. R. Rosyara et al., 2013 Cell number regulator genes in provide candidate genes for the control of fruit size in sweet and sour cherry. Mol Breed 32: 311-326.

Dirlewanger, E., P. Cosson, M. Tavaud, J. Aranzana, C. Poizat et al., 2002 Development of microsatellite markers in peach [ Prunus persica (L.) Batsch] and their use in genetic diversity analysis in peach and sweet cherry ( Prunus avium L.). Theor Appl Genet 105: 127-138.

Howad, W., T. Yamamoto, E. Dirlewanger, R. Testolin, P. Cosson et al., 2005 Mapping With a Few Plants: Using Selective Mapping for Microsatellite Saturation of the Prunus Reference Map. Genetics 171: 1305-1309.

Mnejja, M., J. Garcia-Mas, W. Howad, M. L. Badenes and P. Arús, 2004 Simple-sequence repeat (SSR) markers of Japanese plum (Prunus salicina Lindl.) are highly polymorphic and transferable to peach and almond. Molecular Ecology Notes 4: 163-166.

Sonneveld, T., T. P. Robbins and K. R. Tobutt, 2006 Improved discrimination of self-incompatibility S-RNase alleles in cherry and high throughput genotyping by automated sizing of first intron polymerase chain reaction products. Plant Breeding 125: 305-307.

Sonneveld, T., K. R. Tobutt and T. P. Robbins, 2003 Allele-specific PCR detection of sweet cherry self-incompatibility (S) alleles S1 to S16 using consensus and allele-specific primers. Theoretical and Applied Genetics 107: 1059-1070.

Stegmeir, T., L. Cai, F. R. A. Basundari, A. M. Sebolt and A. F. Iezzoni, 2015 A DNA test for fruit flesh color in tetraploid sour cherry (Prunus cerasus L.). Molecular Breeding 35: 1-10.

Szikriszt, B., A. Doğan, S. Ercisli, M. E. Akcay, A. Hegedűs et al., 2012 Molecular typing of the self-incompatibility locus of Turkish sweet cherry genotypes reflects phylogenetic relationships among cherries and other Prunus species. Tree Genetics & Genomes 9: 155-165.

Testolin, R., T. Marrazzo, G. Cipriani, R. Quarta, I. Verde et al., 2000 Microsatellite DNA in peach (Prunus persica L. Batsch) and its use in fingerprinting and testing the genetic origin of cultivars. Genome 43: 512-520.

Vaughan, S. P., and K. Russell, 2004 Characterization of novel microsatellites and development of multiplex PCR for large-scale population studies in wild cherry, Prunus avium. Molecular Ecology Notes 4: 429-431.

Vaughan, S. P., K. Russell, D. J. Sargent and K. R. Tobutt, 2006 Isolation of S-locus F-box alleles in Prunus avium and their application in a novel method to determine self-incompatibility genotype. Theor Appl Genet 112: 856-866.

Yamamoto, T., K. Mochida, T. Imai, Y. Z. Shi, I. Ogiwara et al., 2002 Microsatellite markers in peach [Prunus persica (L.) Batsch] derived from an enriched genomic and cDNA libraries. Molecular Ecology Notes 2: 298-301.

Zhang, G., A. M. Sebolt, S. S. Sooriyapathirana, D. Wang, M. C. Bink et al., 2009 Fruit size QTL analysis of an F1 population derived from a cross between a domesticated sweet cherry cultivar and a wild forest sweet cherry. Tree Genetics & Genomes 6: 25-36.

Acknowledgements:

This scientific mission was supported by the COST action FA1104. Thanks to José Quero-Garcia and Francesco Marra for handling the application. I want to thank José Quero-Garcia for help in organizing the stay, for the training, and time. Thanks to Teresa Barreneche for interesting discussions on cherry diversity. A special thanks to Marie Brault for helping me with the lab work and the data analyses. Special thanks to the whole “Cherry-Team” for a warm welcome and the moments inside and outside the lab: José Antonio, Remi, Marie, Noemi, Hélène and Laurent.