Page 1 of 16 Title Transcriptome profiling data of Botrytis cinerea infection on whole plant Solanum lycopersicum Authors Dhruv Aditya Srivastava 1, Gulab Chand Arya 1, Eswari PJ Pandaranayaka 1, Ekaterina Manasherova 1, Dov Prusky 2, Yigal Elad 3, Omer Frenkel 3, and Arye Harel*1 1 Department of Vegetable Research, Institute of Plant Sciences; 2 Department of Postharvest and Food Sciences, and 3 Department of Plant Pathology and Weed Research, Volcani Center, 68 HaMaccabim Road, P.O. Box 15159, Rishon LeZion 7505101, Israel * Correspondence: aryeharel@volcani.agri.gov.il; Tel.: +972-3-968-3644 Page 2 of 16 Abstract Botrytis cinerea is a foliar necrotrophic fungal-pathogen capable of infecting >580 genera of plants, is often used as model organism for studying fungal-host interactions. We used RNAseq to study transcriptome of B. cinerea infection on a major (worldwide) vegetable crop, tomato (Solanum lycopersicum). Value to the scientific community. Most previous works explored only few infection stages, using RNA extracted from entire leaf-organ diluting the expression of studied infected region. Many studied B. cinerea infection, on detached organs assuming that similar defense/physiological reactions occurs in the intact plant. We analyzed transcriptome of the pathogen and host in 5 infection stages of whole-plant leaves at the infection site. We supply high quality, pathogen-enriched gene count that facilitates future research of the molecular processes regulating the infection process. Keyword – Botrytis cinerea, Solanum lycopersicum, Transcriptome, Plant response to pathogen, Fungus-Plant interaction Transcriptome Announcement Botrytis cinerea, is an important pathogen which is commonly used as a model organism of necrotrophic fungal phytopathogen (Williamson et al., 2007; Dean et al., 2012). B. cinerea, the causal agent of the gray mold disease, is capable of infecting over 580 genera including abundantly used crops such as tomatoes, grapevines and berries (Dean et al., 2012; Elad et al., 2016). The infection affects foliar organs of the plant causing severe symptoms, such as soft rot, lesions, and fruit drop, ultimately leading to host death. Tomato, Solanum lycopersicum, is the second major crops in the world, (http://www.fao.org/land-water/databases-and-software/crop-information/tomato/en/). The gray mold disease is abundant in greenhouse tomato due to optimal conditions of high humidity and foliar density (Stall, 1991) and to susceptibility to stem infection that often follows the pruning of leaves and side shoots. Page 3 of 16 The importance of obtaining the transcription profiling of B. cinerea infection in S. lycopersicum (cv. M82). A) Transcriptomics was shown to be a major research tool used to understand dynamics of underline molecular changes supporting biotic interactions during the infection process. However many interesting studies of B. cinerea infection process were directed only to analysis of plant-host transcriptome in Arabidopsis (Windram et al., 2012), tomato (Vega et al., 2015), tomato fruit (BlancoUlate et al., 2013) and strawberry fruits (Haile et al., 2019). Other studies were focused on transcript dynamics involved in the plants showing limited analysis of B. cinerea transcriptome. The transcriptome of B. cinerea infection was previously studied in various hosts such as: i) lettuce (De Cremer et al., 2013) with less than 80K reads mapped to each B. cinerea sample of early infection (12, and 24 hpi); ii) wild tomatoes (Smith et al., 2014) detached leaves with less than 10K reads mapped to B. cinerea in each of the infection stages; iii) grapevine fruits (Kelloniemi et al., 2015; Haile et al., 2020); and iv) cucumber late infection stage (96 hpi) (Kong et al., 2015). Few exceptions that also used high read counts to study the B. cinerea transcriptome were performed in fruits of grape vine as opposed to leaf tissue in our work (Haile et al., 2020), and in late infection stage (96 hpi) of cucumber infection which is part of Cucurbitaceae (Kong et al., 2015) as opposed to 4 infection stages in Solanaceae representative in our study. Other interesting studies have used high depth sequencing from detached leaves tissue to explore genetic variability in transcriptomes of 96 B. cinerea isolates at 16 hours post infection of the Arabidopsis wild-type (Col-0) (Soltis et al 2020) and two mutants with jasmonate or salicylic acid compromised immunities (Zhang et al. 2019). The authors acknowledge that although sequence depth is critical to obtain measurements on numbers of genes, at a certain depth there is a rapid loss of new gene information per unit read, and it is better to use more samples to obtain better statistical power, as done in this work by using 3 samples per treatment. B) Previous study of B. cinerea infection on tomato has illustrated that inhibition of host photosynthetic electron transport was restricted to the direct vicinity of the infection site (Berger et al., 2004). This work illustrates the importance of measuring transcription at the infections site, as opposed to extraction of RNA from entire leave tissue which would dilute expression of interest related to infection by non infected background. However, distal host responses to B. cinerea Page 4 of 16 infection (e.g., in the infected organ or in systemic acquired resistance) could play an important role in plant fungal interactions. Therefore, for studies which are aimed to gain more holistic understanding of plant defense mechanism it is important to explore transcriptome in remote regions. C) The danger posed by fungal pathogens is enhanced by accelerated pathogens evolution mainly due to continues fungicides utilization in monoculture practice, human or climate dependent dispersal, and their "genomic plasticity" enabling formation of genetic variations in these phytopathogens that allows them to adapt quickly to human systems (Raffaele and Kamoun, 2012; Giraldo and Valent, 2013; Niño-Sánchez et al., 2015; Srivastava et al., 2018). In B. cinerea this process is reflected by the emergence of isolates which are resistant to several classes of fungicides in multiple geographical locations (Hahn, 2014; Rupp et al., 2017), and even in by the emergence of multiple fungicide resistance strains (Elad et al., 2016; Rupp et al., 2017). Improving our understanding of the infection process using advanced genomics and transcriptomics technologies (e.g., (Pandaranayaka et al., 2019)), should facilitate future development of new and existing strategies that will enable to control plant pathogens. In this study we supply high quality data of 5 infection stages of B. cinerea strain B05.10 (Van Kan et al., 1997) on S. lycopersicum (cv. M82), in each stage 3 biological replicates were used. B05.10 strain used throughout this study was routinely cultured on Potato dextrose agar (PDA, Difco; adjusted to 2% agar; with 0.25 w/v chloramphenicol, Sigma) at 18 ºC in dark, unless otherwise specified. Tomato seedlings were routinely grown in green house at 25°C for 4-5 weeks (under 16/8 hours fluorescent based light/dark regimes in Green quality soil mix, Tuff soil, Merom Golan, Israel) before inoculation of whole plants. Plants were transferred to 2 L plastic pots in growth chamber controlled at 21°C with 16/8 hours of fluorescent based light/dark regimes, acclimatized for one week before inoculation. Adaxial side of tomato plant leaves were inoculated with 1000 spores of B. cinerea (six inoculation sites per leaf, four leaves per plant), suspended in 5 µl of 1/4 PDB (Potato dextrose broth, Difco) to facilitate better synchronization of spore germination and to achieve infection of 100% of the inoculated sites (Elad et al., 1994; Srivastava et al., 2018). Disease progression was monitored at 0, 16, 23, 40 and 47 hours post infection (hpi), 23 and 47 hpi measures were taken under red Page 5 of 16 light one hour before light was turned on. Minor necrotic patches started to appear only at 23 hpi, and establishment of necrotic growth was observed at 40 hpi. Note that the plant responds to the red light via the phytochrome system, while B. cinerea genome contains receptors which respond to both red and green light (the latter is often used for sampling of plants in the dark) (Schumacher, 2017). However, red light was shown to represses conidiation while green light inhibits conidial germination and mycelial growth which are pivotal for the infection process of B. cinerea (Schumacher, 2017; Zhu et al., 2013). Therefore, to obtain minimal effect on fungal virulence, we have used red light in the sampling procedure. Each biological sample consisted pool of three independent plants (i.e., separate plants were used for each time point), four leaves per plant (i.e., 12 leaves, inoculated altogether with 72 spots). For 0 hpi, RNA was extracted from whole leaf with mock infection (infection medium) immediately taken for extraction and mix with 20% (by weight) RNA extracted from spores of B. cinerea. RNA extraction was done following manufacturer's instructions (Norgen, Canada), followed by DNase treatment (Qiagen, Hilden, Germany). Total RNAs extractions were tested for quality using nanodrop and subsequently with Bioanalyzer (Agilent technologies). RNA sample having RIN number >4.1, 260/280 >1.8 and 260/230 >1.9 were used to prepare libraries. Libraries were prepared at the Crown Genomics institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science (G-INCPM). 500 ng of total RNA for each sample was processed using the in house poly A based RNAseq protocol. Libraries were evaluated by Qubit and TapeStation. Samples were sequenced on 4 lanes of Illumina NextSeq machine, using the Single-Read 75 protocol. The output was on average 32.5 million reads per sample. Altogether, transcriptome of 15 biological samples was studied, i.e., 3 biological replicates for each infection stage. Analysis of fungal expression in the course of infection could be hampered by high enrichment of plant host RNA, which is even higher in early stages of the infection that hardly contain visible hyphae. Thus, to allow the analysis of fungal expression, earlier infection stages (up to 16 and 23 hpi), were sequenced with high depth (approximately 46 million reads/sample), and later infection stages with lower, but still Page 6 of 16 high depth (approximately 23 million/reads per sample). Reads were trimmed using cutadapt (Martin, 2011) and mapped to B. cinerea B05.10 genome (Ensembl id ASM83294v1 (Van Kan et al., 2017)) and S. lycopersicum genomes assemblies (ITAG3.1 Solanaceae Genomics Network, SGN, http://solgenomics.net/), using STAR (Dobin et al., 2013) v2.4.2a (using End To End option, and out Filter Mismatch NoverL max set to 0.04). Approximately 97% of the uniquely mapped reads were counted using Htseq-count (Anders et al., 2015) (intersection-strict or union mode for fungi or tomato, respectively). Approximately 23 million reads per sample were mapped to tomato, and 6.7 million reads per sample for fungi (average for all 15 samples in each organism). Minimum reads that were mapped per sample within each genome were: 7,741,620, and 582,037 for the tomato and the fungi, respectively. Hierarchical clustering based on RPKM values enabled separating all treatments of tomato and fungi. Multidimensional scaling (MDS, using limma, R package (Ritchie et al., 2015)), were distances on the plot are in terms of fold-changes, enabled the separation of treatments of plant and fungal transcriptomes. Illustrating expression for of more than 63% of fungal CDSs in early infection stages (Table 1), this study was able to answer an important challenge of B. cinerea transcriptome during infection, showing a major increase compared to most other studies (except e.g., to Haile et al., (2020) that worked on grape vine fruits, and Kong et al. (2015) which worked on single late infection stage (96 hpi) of cucumber). This study will assist to shed light on both pathogen infection and defense processes. The presented fungal transcriptome will help plant pathologist to design experiments aimed to study fungal genes required for infection. The transcriptome sequence data in this study was deposited in NCBI sequence read archive with accession numbers: SRR11624722, SRR11624720, SRR11624721, SRR11624402, SRR11611206, SRR11639198, SRR11639193, SRR11639196, SRR11639194, SRR11639197, SRR11639195, SRR11625188, SRR11625189, SRR11625190, and SRR11624505. The Real-time RTPCR validation, and MDS of samples are available in Supplementary Fig. S1-S3, and Supplementary information file. Page 7 of 16 Literature Cited Anders, S., Pyl, P. T., and Huber, W. (2015). HTSeq-A Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169. doi:10.1093/bioinformatics/btu638. Berger, S., Papadopoulos, M., Schreiber, U., Kaiser, W., and Roitsch, T. (2004). Complex regulation of gene expression, photosynthesis and sugar levels by pathogen infection in tomato. Physiol. Plant. 122, 419–428. doi:10.1111/j.13993054.2004.00433.x. Blanco-Ulate, B., Vincenti, E., Powell, A. L. T., and Cantu, D. (2013). Tomato transcriptome and mutant analyses suggest a role for plant stress hormones in the interaction between fruit and Botrytis cinerea. Front. Plant Sci. 4, 142. doi:10.3389/fpls.2013.00142. De Cremer, K., Mathys, J., Vos, C., Froenicke, L., Michelmore, R. W., Cammue, B. P. A., et al. (2013). RNAseq-based transcriptome analysis of Lactuca sativa infected by the fungal necrotroph Botrytis cinerea. Plant, Cell Environ. 36, 1992–2007. doi:10.1111/pce.12106. Dean, R., Van Kan, J. A. L., Pretorius, Z. A., Hammond-Kosack, K. E., Di Pietro, A., Spanu, P. D., et al. (2012). The Top 10 fungal pathogens in molecular plant pathology. Mol. Plant Pathol. 13, 414–430. doi:10.1111/j.1364-3703.2011.00783.x. Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., et al. (2013). STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21. doi:10.1093/bioinformatics/bts635. Elad, Y., Köhl, J., and Fokkema, N. J. (1994). Control of infection and sporulation of Botrytis cinerea on bean and tomato by saprophytic bacteria and fungi. Eur. J. Plant Pathol. 100, 315–336. doi:10.1007/BF01876443. Elad, Y., Vivier, M., and Fillinger, S. (2016). “Botrytis, the Good, the Bad and the Ugly,” in Botrytis – the Fungus, the Pathogen and its Management in Agricultural Systems (Cham: Springer International Publishing), 1–15. doi:10.1007/978-3-319-233710_1. Giraldo, M. C., and Valent, B. (2013). Filamentous plant pathogen effectors in action. Nat. Rev. Microbiol. 11, 800–814. doi:10.1038/nrmicro3119. Hahn, M. (2014). The rising threat of fungicide resistance in plant pathogenic fungi: Botrytis as a case study. J. Chem. Biol. 7, 133–41. doi:10.1007/s12154-014-0113-1. Haile, Z. M., Malacarne, G., Pilati, S., Sonego, P., Moretto, M., Masuero, D., et al. (2020). Dual transcriptome and metabolic analysis of Vitis vinifera cv. Pinot Noir berry and Botrytis cinerea during quiescence and egressed infection. Front. Plant Sci. 10, 1704. doi:10.3389/fpls.2019.01704. Haile, Z. M., Nagpala-De Guzman, E. G., Moretto, M., Sonego, P., Engelen, K., Zoli, L., et al. (2019). Transcriptome profiles of strawberry (Fragaria vesca) fruit interacting with Botrytis cinerea at different ripening stages. Front. Plant Sci. 10. doi:10.3389/fpls.2019.01131. Kelloniemi, J., Trouvelot, S., Héloir, M.-C., Simon, A., Dalmais, B., Frettinger, P., et al. (2015). Analysis of the molecular dialogue between gray mold ( Botrytis cinerea ) and grapevine ( Vitis vinifera ) reveals a clear shift in defense mechanisms during berry ripening. Mol. Plant-Microbe Interact. 28, MPMI-02-15-0039. Page 8 of 16 doi:10.1094/MPMI-02-15-0039-R. Kong, W., Chen, N., Liu, T., Zhu, J., Wang, J., He, X., et al. (2015). Large-Scale Transcriptome analysis of cucumber and Botrytis cinerea during infection. PLoS One 10, e0142221. doi:10.1371/journal.pone.0142221. Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10. doi:10.14806/ej.17.1.200. Niño-Sánchez, J., Tello, V., Castillo, V. C. del, Thon, M. R., Benito, E. P., and DíazMínguez, J. M. (2015). Gene expression patterns and dynamics of the colonization of common bean (Phaseolus vulgaris L.) by highly virulent and weakly virulent strains of Fusarium oxysporum. Front. Microbiol. 6. doi:10.3389/fmicb.2015.00234. Pandaranayaka, E. P. J., Frenkel, O., Elad, Y., Prusky, D., and Harel, A. (2019). Network analysis exposes core functions in major lifestyles of fungal and oomycete plant pathogens. BMC Genomics 20, 1–15. doi:10.1186/s12864-019-6409-3. Pontier, D., Miao, Z.-H., and Lam, E. (2001). Trans-dominant suppression of plant TGA factors reveals their negative and positive roles in plant defense responses. Plant J. 27, 529–538. doi:10.1046/j.1365-313X.2001.01086.x. Raffaele, S., and Kamoun, S. (2012). Genome evolution in filamentous plant pathogens: Why bigger can be better. Nat. Rev. Microbiol. 10, 417–430. doi:10.1038/nrmicro2790. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47. https://doi.org/10.1093/nar/gkv007 Rupp, S., Weber, R. W. S., Rieger, D., Detzel, P., and Hahn, M. (2017). Spread of Botrytis cinerea strains with multiple fungicide resistance in German horticulture. Front. Microbiol. 7. doi:10.3389/fmicb.2016.02075. Schumacher, J. (2017). How light affects the life of Botrytis. Fungal Genetics and Biology, 106, 26–41. https://doi.org/10.1016/J.FGB.2017.06.002 Smith, J. E., Mengesha, B., Tang, H., Mengiste, T., and Bluhm, B. H. (2014). Resistance to Botrytis cinerea in Solanum lycopersicoides involves widespread transcriptional reprogramming. BMC Genomics 15, 334. doi:10.1186/1471-2164-15-334. Soltis, N. E., Caseys, C., Zhang, W., Corwin, J. A., Atwell, S., & Kliebenstein, D. J. (2020). Pathogen genetic control of transcriptome variation in the Arabidopsis thaliana - Botrytis cinerea pathosystem . Genetics, 215(1), genetics.303070.2020. https://doi.org/10.1534/genetics.120.303070 Srivastava, D. A., Yakubov, M., Feldbaum, R., Tish, N., Shoyhet, H., Manasherova, E., et al. (2018). Multiparametric analysis of diversity in Botrytis cinerea isolates from Israel. Phytoparasitica, 1–13. doi:10.1007/s12600-018-0684-4. Stall, R. . (1991). Gray mold in compendium of tomato diseases. APS Press. J.B. Jones, 16–17. Van Kan, J. A. L., Stassen, J. H. M., Mosbach, A., Van Der Lee, T. A. J., Faino, L., Farmer, A. D., et al. (2017). A gapless genome sequence of the fungus Botrytis cinerea. Mol. Plant Pathol. 18, 75–89. doi:10.1111/mpp.12384. Van Kan, J. A., Van’t Klooster, J. W., Wagemakers, C. A., Dees, D. C., and Van der Vlugt-Bergmans, C. J. (1997). Cutinase A of Botrytis cinerea is expressed, but not essential, during penetration of gerbera and tomato. Mol. Plant. Microbe. Interact. Page 9 of 16 10, 30–38. doi:10.1094/MPMI.1997.10.1.30. Vega, A., Canessa, P., Hoppe, G., Retamal, I., Moyano, T. C., Canales, J., et al. (2015). Transcriptome analysis reveals regulatory networks underlying differential susceptibility to Botrytis cinerea in response to nitrogen availability in Solanum lycopersicum. Front. Plant Sci. 6, 911. doi:10.3389/fpls.2015.00911. Williamson, B., Tudzynski, B., Tudzynski, P., and Van Kan, J. A. L. (2007). Botrytis cinerea: The cause of grey mould disease. Mol. Plant Pathol. 8, 561–580. doi:10.1111/j.1364-3703.2007.00417.x. Windram, O., Madhou, P., McHattie, S., Hill, C., Hickman, R., Cooke, E., et al. (2012). Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis. Plant Cell 24, 3530–57. doi:10.1105/tpc.112.102046. Zhang, W., Corwin, J. A., Copeland, D. H., Feusier, J., Eshbaugh, R., Cook, D. E., Atwell, S., & Kliebenstein, D. J. (2019). Plant–necrotroph co-transcriptome networks illuminate a metabolic battlefield. ELife, 8. https://doi.org/10.7554/eLife.44279 Zhu, P., Zhang, C., Xiao, H., Wang, Y., Toyoda, H., & Xu, L. (2013). Exploitable regulatory effects of light on growth and development of Botrytis cinerea. Journal of Plant Pathology (Vol. 95, pp. 509–517). Springer. https://doi.org/10.2307/23721571 Page 10 of 16 eXtra figure titles Supplementary Figure S1 - Multidimensional scaling (generated by plotMDS, limma package, R) of all fifteen samples for Botrytis cinerea (B05.10) and Solanum lycopersicum (cv. M82) (Red 0 hpi , Blue 16 hpi, Green 23hpi, Violet 40 hpi, Orange 47 hpi). Distances represent leading log2-fold changes between samples. Supplementary Figure S2 - Infection and sampling point (asterisks indicate sampling under red light). Supplementary Figure S3 - Pearson correlation of qPCR of fold change compared with normalized RPKM. Supplementary Information – Method used to perform qPCR analysis. Page 11 of 16 Table 1 - Transcriptome statistics of Botrytis cinerea (B05.10) and Tomato (Solanum lycopersicum cv. M82) during infection Total read * Mapped reads *a Mapped CDS b Ref CDS (%) c d High Count CDS Ref high CDS (%) mapped reads a Mapped CDS b Ref CDS (%) c d High Count CDS Ref high CDS (%) 0 hpi 16 hpi 23 hpi 40 hpi 47 hpi 71,437,565 135,776,539 146,040,697 73,059,701 62,568,364 Botrytis cinerea (B05.10) 2,404,324 6,265,170 29,849,404 35,571,601 23,589,882 (582,037 minimal sample) 10,496 9,870 10,844 11,096 10,916 87.39% 82.18% 90.29% 92.39% 90.89% 9,647 80.32% 9,352 77.87% 8,174 7,671 9,288 68.06% 63.87% 77.34% Solanum lycopersicum (cv. M82) 49,344,680 (7,741,620 minimal sample) 93,327,812 77,880,059 24,234,150 22,679,766 22,797 24,665 24,259 22,543 22,213 65.36% 70.72% 69.55% 64.63% 63.69% 18,753 53.77% 21,435 61.46% 20,670 59.26% 17,840 51.15% 17,225 49.39% * Results represent sum of 3 replicates unless otherwise specified. a Mapped reads - reads which were finally mapped to fungi or tomato. b Mapped CDS - number of CDS’s having non-zero count for at least one biological replicate. c Percent of CDS's in reference genomes of B. cinerea and S. lycopersicum. d High count CDS- CDS’s mapping based only for genes having average count of 10 or more for at least one biological replicate. Page 12 of 16 Figure S1 Supplementary Figure S1 - Multidimensional scaling (generated by plotMDS, limma package, R) of all fifteen samples for Botrytis cinerea (B05.10) and Solanum lycopersicum (cv. M82) (Red 0 hpi , Blue 16 hpi, Green 23hpi, Violet 40 hpi, Orange 47 hpi). Distances represent leading log2-fold changes between samples. Page 13 of 16 Figure S2 Supplementary Figure S2 - Infection and sampling point (asterisks indicate sampling under red light). Page 14 of 16 Figure S3 Supplementary Figure S3 - Pearson correlation of qPCR of fold change compared with normalized RPKM. Page 15 of 16 Title Transcriptome profiling data of Botrytis cinerea infection on whole plant Solanum lycopersicum Authors Dhruv Aditya Srivastava 1, Gulab Chand Arya 1, Eswari PJ Pandaranayaka 1, Ekaterina Manasherova 1, Dov Prusky 2, Yigal Elad 3, Omer Frenkel 3, and Arye Harel*1 1 Department of Vegetable Research, Institute of Plant Sciences; 2 Department of Postharvest and Food Sciences, and 3 Department of Plant Pathology and Weed Research, Volcani Center, 68 HaMaccabim Road, P.O. Box 15159, Rishon LeZion 7505101, Israel * Correspondence: aryeharel@volcani.agri.gov.il; Tel.: +972-3-968-3644 Keywords: Botrytis cinerea, virulence, pathogenicity, defense mechanisms, Solanum lycopersicum Transciptome data was validated for selected B. cinerea genes using Real-time RT-PCR (qPCR) reactions (based on 3 biological repeats). The qPCR cDNAs were made using Thermo Scientific Maxima First strand kit (cat- K1671) from 500ng total RNA following manufacturer instructions. Primer for selected genes were designed based on B. cinerea genome sequence (Van Kan et al., 2017), and subsequently tested (utilizing BLASTN in the National Center for Biotechnology Information, NCBI, database) on B. cinerea and S. lycopersicum DNA/cDNA to make sure that the primers specifically amplify cDNA of the fungal tissue. These PCR products were sequenced by Sanger sequencing, which was carried out by Hy Laboratories Ltd. (Rehovot, Israel) on an ABI PRISM 3730xl DNA Analyzer. Real-time RT-PCR (qPCR) reactions were performed using Dream Taq master mix (Thermo, Massachusetts, United States) on rotor gene 6000 (Corbett, Thermos Fisher, Massachusetts, United States). B. cinerea housekeeping gene Page 16 of 16 encoding for ubiquitin-conjugating enzyme (UCE) was used as control (Ren et al., 2017). The Real-time RT-PCR was used to analyze 4 genes (Table S1), for each gene 3 biological repeats (from different RNA extractions) were tested. Project overview NCBI link (BioProject id: PRJNA628162): https://dataview.ncbi.nlm.nih.gov/object/PRJNA628162?reviewer=cpub8ag2dppf443hl705npck5 7 Table S1. Primers used in the study D_Primer1 D_Primer2 D_Primer3 D_Primer4 D_Primer5 D_Primer6 D_Primer7 D_Primer8 D_Primer9 D_Primer10 Bcin09g02630 Bcin09g02630 Bcin01g04550 Bcin01g04550 Bcin02g01590 Bcin02g01590 Bcin12g01950 Bcin12g01950 Bcin02g04920 - UCE (control) Bcin02g04920 - UCE (control) TACAGCCTCGGAAGGTACGA ACGGACTTCGACCAGTCAAC GGGCTCTCTCCAAGCAAGTT ATCTTTACCTGTTGGGGCGG TTGGCCGGTAGTTGTAGGTC AAAGTTGGCTGGGTCTCTGG TGTGGGAGAAGTTGGGGGTA TATTGCTGAGTGGAGTGGCG ATCACCCAAACATCAACT CATAGAGCAGATGGACAA Literature Cited Ren, H., Wu, X., Lyu, Y., Zhou, H., Xie, X., Zhang, X., & Yang, H. (2017). Selection of reliable reference genes for gene expression studies in Botrytis cinerea. Journal of Microbiological Methods, 142, 71–75. https://doi.org/10.1016/J.MIMET.2017.09.006 Van Kan, J. A. L., Stassen, J. H. M., Mosbach, A., Van Der Lee, T. A. J., Faino, L., Farmer, A. D., Scalliet, G. (2017). A gapless genome sequence of the fungus Botrytis cinerea. Molecular Plant Pathology, 18(1), 75–89. https://doi.org/10.1111/mpp.12384