Segregation of Soft Rot Enterobacteria (SRE) tolerance in Solanum Chacoense offspring

Transcription

1 Segregation of Soft Rot Enterobacteria (SRE) tolerance in Solanum Chacoense offspring Jeanine Janssen MPS specialisation Plant Breeding and Genetic Resources MSc major Thesis PBR Student number: th of April 2017, Wageningen Supervisor: Jack Vossen Second examiner: Richard Visser 0

2 CONTENTS Abstract... 3 Introduction... 3 Agro-economic importance... 3 Resistance versus tolerance... 4 Pathogenicity of Pectobacterium spp. and Dikeya spp Plant immune response to SRE... 6 Current knowledge on the genetic basis of SRE tolerance... 7 Materials and methods... 9 Handling of bacterial strains and inoculation procedures... 9 Plant materials Petioles Tubers Experiments Petiole maceration tests Tuber maceration tests Handling of data Results Petiole maceration tolerance Results of tests over different years combined General characteristics of obtained petiole maceration data Comparison of parents and grandparents Comparison of Ds and Pw Reproducibility of tests Heritabillity of petiole maceration tolerance Petiole maceration resistance in extended population Tuber maceration resistance Tuber maceration pilots Tuber test for maceration tolerance of aa Tuber test for maceration tolerance of aa Conclusion and Discussion Petiole maceration resistance Tuber tests Recommendations

3 Acknowledgements References Appendix Supplementary figures Petiole tests aa Petiole tests aa Petiole test extended population Tuber tests

4 ABSTRACT Soft-Rot Enterobacteriaceae (SRE) of the genera Pectobacterium and Dickeya cause damage in potato crops worldwide by macerating plant and tuber tissue. Resistant potato cultivars are necessary in minimizing the damage of Pectobacterium spp. and Dickeya spp., but are not available. Resistance to SRE is a quantitative and polygenic trait which makes breeding challenging. In previous screens, wild potato relative Solanum chacoense accession was found to be tolerant against petiole maceration by Pectobacterium spp. and Dickeya spp. and could be a first source of resistance for use in breeding soft-rot resistant potato cultivars. To obtain more information about the genetic basis of the soft-rot tolerance of Solanum chacoense 470-3, segregating populations generated by crosses between and several susceptible species, were subjected to petiole and tuber maceration tolerance tests. Petiole maceration tolerance was found to be high compared to reference cultivars and heritability of this trial was relatively high (in most tests around 0.5). However, there is no clear cut-off between susceptible and tolerant genotypes and variation between repetitions of the test at different times of the year was high. For tuber maceration, there was no clear cut-off between susceptible and tolerant genotypes either and heritability was low. No significant correlation was found between tolerance to tuber maceration and petiole maceration, indicating that these are two different aspects of SRE tolerance with a different genetic basis. From segregation patterns in tested members of the populations, it can be concluded that tolerance against maceration by Pectobacterium spp. and Dickeya spp.is a quantitative and polygenic trait. INTRODUCTION AGRO-ECONOMIC IMPORTANCE Tuber soft rot, stem rot, wilt and blackleg: these are names for symptoms which occur in potato plants infected by Soft-Rot Enterobacteriaceae (SRE). In the Netherlands, the species Pectobacterium wasabiae, Pectobacterium carotovorum and Dickeya solani are the most common SRE species that cause problems in potato. The genera Pectobacterium and Dickeya are both coliform plantpathogenic bacteria and are part of the Soft-Rot Enterobacteriaceae (SRE) family. They were previously classified as part of the genus Erwinia (Hauben et al., 1998, Samson et al., 2005). In general, Pectobacterium spp. and Dickeya spp. have a broad host range (Ma et al., 2007) and can be found worldwide in water, moist soil and on plant surfaces (Perombelon and Kelman, 1980). These genera contain many species, strains and isolates diverse in host range, geographic distribution and lifestyle. These plant-pathogenic bacteria cause disease and loss of harvests in many crops worldwide (Perombelon and Kelman, 1980). Potato (Solanum tuberosum) is a staple crop in many parts of the world and is strongly affected by disease caused by Pectobacterium spp. and Dickeya spp., which causes great losses of harvest during potato production and during storage of harvested tubers (Pérombelon, 2002). Pectobacterium wasabiae and Dickeya solani mainly cause losses during production while Pectobacterium carotovorum is the most frequent cause of losses during tuber storage (Rietman et al., 2014). Pectobacterium and Dickeya species can be transmitted via contaminated plant 3

5 material, soil, water and contaminated machinery used during planting or harvesting and can be transmitted by insect vectors as well (Pérombelon, 1992). Another important source of contamination are infected seed tubers, from which the pathogens can move through the stem, soil or stolons and enter neighbouring tubers, the plant and progeny tubers (Czajkowski et al., 2010). Several environmental factors influence pathogenicity on tubers and living plants. For soft rot of potato plants or tubers, the presence of free water is essential (Pérombelon and Lowe, 1975) as a water film will create anaerobic conditions which impairs plant defence systems that depend on oxygen (Pérombelon, 2002). It also makes penetration of tuber tissue and uptake of nutrients easier, as lenticels open and cell membrane permeability increases under anaerobic conditions (Maher and Kelman, 1983, Pérombelon, 2002). Blackleg, which is rot of the stem base, generally results from soft rot of the mother tuber, from which the bacteria are transported passively through the xylem. Aerial stem rot is mostly caused by wounding of the stem in the presence of the bacteria and will usually occur under wet conditions by transmission of soft-rot Enterobacteria via contaminated water or mechanical damage like insect feeding or wind damage (Pérombelon, 2002). Chemical treatments and other agricultural practices are only partly effective in providing protection against soft-rot Enterobacteria (SRE) (Czajkowski et al., 2011, Pasco et al., 2006). Growing resistant cultivars would be the best solution to minimize these problems. However, commercial potato cultivars with a sufficient resistance to Pectobacterium spp. and Dickeya spp. are not readily available (Lebecka and Zimnoch-Guzowska, 2004, Rietman et al., 2014). Gaining knowledge about the genetic basis of soft-rot resistance could contribute to the development of resistant potato cultivars. RESISTANCE VERSUS TOLERANCE In literature on SRE resistance in potato, the term resistance is commonly used, although the term tolerance is used as well, and the distinction between the two seems unclear. Resistance is defined as the capacity of a plant to reduce or stop the growth, development and reproduction of the attacker after the establishment of intimate contact (Niks et al., 2011). This may or may not lead to a decrease in (severity of) disease. Tolerance is defined as the capacity of the host plant to restrict the symptoms or harmful effects per unit of pathogen other than by restricting the amount of infection (Niks et al., 2011). To distinguish between tolerance and resistance, information about the bacterial titres inside the inoculated plants is needed. In the research of Rietman et al. in 2014 and in the experiments presented in this report, no information was obtained on bacterial titres, only on the severity of symptoms. The severity of symptoms could be indicative of the bacterial titres present on plant tissue, but this is not entirely reliable. Therefore, despite the fact that the objective of this project is to obtain knowledge about the genetic basis of SRE resistance and obtain resistant genotypes, the term tolerant instead of resistant will be used to indicate genotypes that show decrease of maceration by SRE. The same will be done with information from literature where no information on bacterial titres is provided or measured to ensure clarity of the difference between these two terms in this report. 4

6 PATHOGENICITY OF PECTOBACTERIUM SPP. AND DIKEYA SPP. Pectobacterium wasabiae (Pw) has been identified as a soft-rot pathogen on potato all over the world. It mainly causes blackleg and other disease symptoms in the stem, but can also cause disease of the tuber (Pasanen et al., 2013, Pérombelon, 2002). Dickeya solani (Ds) is a relatively new species of SRE that has started causing problems in potato production in Europe since 2004 (Toth et al., 2011) and becoming an increasingly prevalent pathogen. In a screening for soft rot tolerance (Rietman et al., 2014, Czajkowski et al., 2011) which used Pw, Pcc and Ds, Ds was found to be the most aggressive species of the three, causing high maceration levels at lower densities compared to Pw and Pcc. In the same screening, Pectobacterium carotovorum subsp. carotovorum (Pcc) was found to be less aggressive than Pw and Ds. The different strains of Pcc have a wide host range (Pérombelon, 2002). On potato, Pcc causes pre-emergence rot and soft rot of stored tubers (Pasanen et al., 2013). In some cases Pcc was found cause potato blackleg. However, in general it is thought to mainly cause tuber soft rot resulting in halting or delaying of shoot emergence (Pasanen et al., 2013, de Haan et al., 2008). In a study by Pasanen et al. in 2013, Pectobacterium strains isolated from diseased potato tissue were grouped according to their putative phylogeny. The different Pcc strains identified in this analysis were divided over two phylogenetic groups. One group mostly contained strains isolated from rotting tubers and produced auto inducers both in late and early infection stages. The other group was mainly comprised of strains from rotten stems which produced auto inducers only in early growth phases. This could indicate that different isolates of Pcc cause soft rot symptoms on different tissues. For the genera Pectobacterium and Dickeya in general there are many isolates for each different species, and each isolate seems to be better adapted to a certain host species or plant tissue (Charkowski, 2012). Pectobacterium spp. and Dickeya spp. and obtain nutrients from dead plant cells and are frequently described as necrothrophs (Kraepiel and Barny, 2016). However, as highlighted in an opinion paper (Kraepiel and Barny, 2016) their lifestyle might best be described as hemibiotrophicas these bacteria can be present on host plants in asymptomatic biotrophic infections. This asymptomatic biothrophic phase can last up to several months until a switch from the asymptomatic biothrophic phase to a symptomatic necrotrophic phase occurs. The exact function of the biothrophic phase and what causes the switch to the necrothrophic phase remains uncertain. It is thought that the biotrophic phase and the switch are related to the timing and fine-tuning of PCWDE production and other virulence determinants, which were found to be regulated by quorum sensing in Pectobacterium atrosepticum and Pectobacterium carotovorum (Liu et al., 2008, Jones et al., 1993). Quorum sensing (QS) is a cell density-dependent signalling process in which bacteria release diffusible pheromones called autoinducers. When the cell density is high enough, autoinducers produced by these bacterial cells will accumulate until the concentration exceeds a certain threshold. This will trigger changes in the regulation of genes controlled by this pheromone, which results in a response from the bacteria (Fuqua et al., 1994, Mäe et al., 2001). In several studies on the role of quorum sensing in pathogenicity of SRE (Mäe et al., 2001, Liu et al., 2008, Fray et al., 1999), results suggest that QS is essential for timing of PCWDE production and regulation of many genes involved in infection of the host plant. Starting infection of the host plant too early is undesirable as this will trigger activation of the plant s immune system. If this occurs while the bacterial cell density is too 5

7 low to overpower plant defences the infection will likely be aborted (Mäe et al., 2001). SRE are capable of degrading plant material very effectively with the many different plant cell wall degrading enzymes (PCWDE s) they secrete mainly through Type 2 secretion system (T2SS) during pathogenesis (Toth et al., 2003), such as pectinases, cellulases and hemicellulases (Pérombelon, 2002, Collmer and Keen, 1986). These PCWDE s and the T2SS are essential for symptomatic virulence (Kotoujansky, 1987, Hugouvieux-Cotte-Pattat et al., 1996, Toth et al., 2003). Additionally, the genomes of several SRE encode a Type 3 secretion system (T3SS), which translocates effector proteins into host plant cells (Lahaye and Bonas, 2001). While T2SS-secreted PCWDE s are often seen as a brute force effectors, the T3SS mainly secretes compounds seen as stealth effectors which enter plant cells and supress plant defences (Toth and Birch, 2005). There are species and strains of Pectobacterium that lack a T3SS and Pectobacterium wasabiae completely lacks a T3SS (Nykyri et al., 2012, Kim et al., 2009), but this does not have a large effect on symptomatic virulence on potato compared to strains that do have a T3SS (Kim et al., 2009). Compared to other hemibiotrophic phytopathogenic bacteria, SRE have few type 3-secreted proteins which includes DspE, the only one known effector in Pectobacterium spp. and Dickeya spp. DspE was found to be required for Pectobacterium caorotovrum to cause plant cell death on leaves of Nicotiana benthamiana, Solanum tuberosum (cultivated potato) and several wild potato relatives (Kim et al., 2011). The DspE effector does not suppress plant defence but seems to promote virulence by eliciting cell death in leaf tissue. From this dead leaf tissue disease can be initiated and spread (Kim et al., 2011). PLANT IMMUNE RESPONSE TO SRE Plant hormones play an essential role in regulation of plant defence. Salicylic acid (SA) mediated responses are usually most effective against biothrophs, while jasmonic acid (JA) iand ethylene (ET) mediate plant defence effective against necrothrophs (Verhage et al., 2010, Glazebrook, 2005). Both defences induced by JA/ET or SA can be effective against Pectobacterium. The SA mediated responses could be most effective during the early latent biothopic phases of infection, while JA/ET responses are effective during the late necrothropic stage of infection (Davidsson et al., 2013). There are two types of pathogen-derived molecules that can activate these inducible plant innate immunity responses upon recognition. The first type consist of the Pathogen-associated molecular patterns (PAMPS), which are usually pathogen-derived molecules that are conserved among microorganisms, and the Damage-associated molecular patterns (DAMPS), which are molecular patterns that are the result of infection or damage to the plant itself. Recognition of these DAMPS and PAMPS by pattern recognition receptors results in Patterntriggered immunity (PTI)(Jones and Dangl, 2006). The second type pathogen-derived molecules to induce plant innate immunity responses are the effectors. Effectors are secreted via the T3SS by many plant pathogenic microorganisms. The function of effectors is to suppress the plant immune response triggered by PTI. Effectors are very variable proteins across species and strains, which can be recognized by specific corresponding plant resistance proteins (R proteins) and this recognition results in effector triggered 6

8 immunity (ETI). One of the results of ETI is localized cell death, also called the hypersensitive response (HR) at the location of the infection (Davidsson et al., 2013) (Jones and Dangl, 2006). The observations that Pectobacterium spp. and Dickeya spp. only have one known effector, that the loss of T3SS only has relatively small consequences for symptomatic virulence compared to the T2SS indicate that the main plant immunity type of importance in SRE resistance is PTI (Davidsson et al., 2013, Kraepiel and Barny, 2016). If this is found to be true, it is likely that resistance that can be obtained against maceration by SRE will be effective against multiple different species or isolates of Pectobacterium and Dickeya. The molecular patterns associated with these microbes or the damage they cause are similar in Pectobacterium and Dickeya so different species should trigger PTI in similar ways. CURRENT KNOWLEDGE ON THE GENETIC BASIS OF SRE TOLERANCE Several wild relatives of the cultivated potato were found to be suitable sources of SRE tolerance (and potentially resistance) (Zimnoch-Guzowska et al., 2000, Rietman et al., 2014, Capo et al., 2002) and some cultivars show various levels of tolerance (Pasco et al., 2006, Whitworth et al., 2016). Resistance to soft-rot Enterobacteria is polygenic, additive, partial and non-specific (Pasco et al., 2006, Lebecka and Zimnoch-Guzowska, 2004) which makes breeding for this trait rather challenging. Solanum chacoense is a wild relative and can be crossed with diploid Solanum tuberosum. Diploid interspecific hybrid clones with Solanum chacoense in their pedrigree were found to show relatively high levels of tolerance to Pectobacterium atrosepticum (Lebecka and Zimnoch-Guzowska, 2004, Zimnoch-Guzowska et al., 1999). In a QTL mapping study (Zimnoch-Guzowska et al., 2000) for resistance to Pectobacterium atrosepticum, such tolerant hybrids were crossed with a susceptible genotype to obtain a mapping population. Leaf tolerance and tuber tolerance were found to have a low correlation, both phenotypically and genetically. Twelve putative QTLs were found for tuber soft rot tolerance. For leaf tolerance 15 putative QTLs were found. Correlation between leaf and tuber tolerance was observed to be low. The two types of tolerance were frequently not observed in the same plants and QTLs linked to tuber tolerance were linked to different markers than the QTLs for leaf resistance. This corresponds with earlier observations (Allefs et al., 1995) where incidence of blackleg was observed to be more related to stem than to tuber tolerance. Additionally, environmental factors have a strong influence on SRE tolerance as well, especially on leaf tolerance (Zimnoch-Guzowska et al., 2000). Despite of the polygenic manner inheritance, the broad- and narrow- sense heritability for tuber soft-rot tolerance were found to be quite high, 0.92 and 0.88 (Lebecka and Zimnoch-Guzowska, 2004) respectively. This, combined with the finding that there is no significant correlation between soft-rot tolerance and certain agronomic traits, indicate that it could be possible to breed for a highly tolerant (and potentially resistant) cultivar which also has other characteristics desired by growers (Zimnoch- Guzowska et al., 2000). In spite of the large number of studies devoted to soft rot resistance, no major QTLs or genes have been reported yet. In 2014 Rietman et al. screened 532 genotypes from most of the known potato species for tolerance to Ds, Pw and Pcc using a petiole maceration test. In this stringent maceration test Solanum chacoense accession was identified as 7

9 the most tolerant against Pectobacterium wasabiae and Pectobacterium carotovorum as it had the lowest average percentage of macerated petiole tissue (below 20%). Solanum chacoense accession is thought to be a potential source of SRE resistance in potato. Crosses were made between 470-3, the most tolerant genotype and potential source of SRE resistance, and several very susceptible diploid potato relatives to obtain segregating populations (figure 1). Offspring of these crosses was tested for petiole maceration tolerance, selected for tuber production and resistance or susceptibility for petiole maceration (figure 1). These contrasting genotypes were crossed again to obtain segregating populations aa33 and aa36 (figure 1). Figure 1. Pedigree of used populations. Codes consist of a population number ( i.e. 1574) and a genotype number (i.e indicates genotype 10 from population 1574). S=susceptible, R= resistant(referred to in text as tolerant), k=small/little tuber formation K=large tuber formation. Populations aa33 and aa36 will be tested for soft-rot tolerance in this study, along with the (grand-)parents of these populations. The main objective of this project was to obtain more information about the genetic basis of resistance to tuber maceration and petiole maceration, caused by Pectobacterium wasabiae, Pectobacterium carotovorum and Dickeya solani. This was done by studying segregation of SRE tolerance in Solanum chacoense offspring. In this thesis project, part of the population aa33 and aa36 was subjected to several screens for tuber tolerance to maceration by Ds, Pcc and Pw and petiole tolerance by Ds and Pw. From these screens phenotype data was obtained on the segregation of SRE tolerance in these populations. The consistency and heritability of these phenotypes was studied to show whether these tests would be a suitable basis for future QTL analyses. The correlation between the tuber tolerance and petiole tolerance was assessed to determine whether they have the same genetic basis and whether one can serve as a predictor for the other during selection. 8

10 MATERIALS AND METHODS Handling of bacteria and plant material and the execution of the petiole test was all done according to the protocol described by Rietman et. al, HANDLING OF BACTERIAL STRAINS AND INOCULATION PROCEDURES Table 1. Bacterial strains used in the experiments. Strains were obtained from the Plant Research International collection. Strain indicates the reference number in the collection s catalogue. Strain Scientific name Abbreviation IPO1948 Pectobacterium carotovorum spp. Pcc carotovorum IPO1957 Pectobacterium wasabiae Pw IPO2222 Dickeya solani Ds Bacteria were plated on 1/10 strength tryptic soya agar (TSA) directly from the Plant Research International collection which was stored at -80 C. Plated bacterial strains were stored at 18 C. Bacterial strains were maintained by picking a single colony and transferring it to a new 1/10 TSA plate every week for about four weeks until the first experiments. After the first inoculation, for each strain a sample from the whole surface of the plate was taken and suspended in gelrite with hollow beads. These samples were stored at -80 C. For every subsequent experiment a sample of these freezer-stored bacteria was plated, grown at 18 C for approximately 4 days and transferred at least once by taking a sample with an inoculation loop from across the whole surface of the plate. Approximately 24 hours before use in experiments, a sample consisting of multiple colonies was taken by swiping a disposable inoculation loop across the whole plate surface. This sample was transferred to a fresh plate and grown at 25 C until use. For inoculation, a sample taken from the whole plate surface was suspended in Ringer s Buffer. Optical cell density of suspensions was measured at 600 nm using the Ultrospec 10 device (Amersham Biosciences). If necessary the suspension densities were adjusted by diluting with Ringer s buffer. The resulting bacterial suspension in ringer s buffer was used to inoculate both the petiole and the tuber maceration resistance tests. To determine the true number of bacteria in the inoculums used in each test, the bacterial suspensions used for inoculation were diluted up to 10 7 times with ringer s buffer. Of dilution 10 4 to µl was plated on Petri dishes with TSA. Plates were kept for 5 days at 24 C. After this the number of colonies was counted. From this colony count, the number of Colony forming Units (CFU) in the undiluted inoculum was determined for each experiment ( table 2 and table 3). During the petiole test of aa36 in January 2017 and part 2 of the tuber test of aa36, the ringer s buffer used to dilute the bacteria became contaminated. However, as the concentration of the unknown contaminant was approximately 1*10 2 cfu per ml in the tuber test and only 10 cfu per ml in the petiole test, 9

11 much lower compared to the concentration of the bacteria applied in the test (which was at least 1*10 5, Table 2 and 3). This means that the contaminant bacteria would have been severely outnumbered and would be outperformed by their competitors. In controls that were mock-inoculated with contaminated buffer, a few genotypes showed low levels of maceration in a few cases, but this was not corrected for as in the test inoculations contaminant bacteria would have been outcompeted. Table 2. Bacterial concentrations in cfu per ml applied in petiole maceration tests as calculated from colony counts on plated dilution series of inoculum. Petiole test Bacterium Bacterial concentration inoculum (cfu per ml) aa33 June 2015 * Pw Approximately 1*10 7 aa33 June 2015 * Ds Approximately 1*10 5 aa36 June 2015 * Pw Approximately 1*10 7 aa36 June 2015 * Ds Approximately 1*10 5 aa33 November 2016 Pw 1.35*10 7 aa33 November 2016 Ds 1.89*10 6 aa36 November 2016 Pw 1.31*10 7 aa36 November 2016 Ds 0.76*10 6 aa33 January 2017 Pw 1.63*10 7 aa33 January 2017 Ds 1.26*10 6 aa36 January 2017 Pw Approximately 1*10 7, no count available due to buffer contamination aa36 January 2017 Ds Approximately 1*10 6, no count available due to buffer contamination aa33 and aa36 new Pw 0.58*10 7 genotypes 2017 aa33 and aa36 new genotypes 2017 Ds 0.643*10 6 Table 3. Bacterial concentrations in cfu per ml applied in tuber maceration tests as calculated from colony counts on plated dilution series of inoculum. Tuber test Bacterium Bacterial concentration inoculum (cfu per ml) aa36 pilot 2 Pw 1*10 8 aa36 pilot 2 Pcc 1*10 8 aa36 pilot 2 Ds 1.94*10 8 aa36 pilot 3 Pw Approximately 1*10 8, no count available aa36 pilot 3 Ds Approximately 1*10 8, no count available aa36 Test part 1 Pw 2,05*10 8 aa36 Test part 2 Pw Approximately 1*10 8, no count available due to buffer contamination 10

12 PLANT MATERIALS PETIOLES All plant material was obtained from the Wageningen UR Plant Breeding collection. For the petiole tests on populations aa33 and aa36 number 1 to 32, plants were obtained from the Plant Breeding in-vitro maintenance culture. Each genotype was micropropagated by taking single-node cuttings which were placed on MS 20 (Murashige and Skoog, 1962) with vitamins (Duchefa Biochemie), supplemented with 20 g L -1 sacharose and 8 g L -1 of Microagar. The ph was adjusted to 5.8 using potassium hydroxide. Cuttings were placed in climate chambers with an 16/8 day/ night regime at 24 C. After 2 to 3 weeks, the cuttings were transferred to round 5 L pots with common potting mix. In each pot, three shoots were planted. Per genotype a 3 to 5 pots were prepared in this way. Shoots were left to grow in a greenhouse with a 16 hour day/8hour night regime, with night minimum night temperatures of 12 C and minimum day temperatures of 22 C for approximately 5 weeks. For the petiole tests on aa33 and aa36, two batches of 150 botanical seeds per population were sown on MS 20(described above). Seeds were surface sterilized by rinsing them in 80% ethanol for approximately 30 seconds and washing them in a 1.5% chlorine solution for approximately 15 minutes. After 15 minutes seeds were rinsed with sterile water and divided over growing medium. Seeds were kept in climate chambers with a 16/8 day/ night regime at 24 C. After germination, seedlings were treated in the same way as the plants obtained from in-vitro maintenance culture. TUBERS In September 2016, tubers had been harvested from in vitro micro propagated plants grown outside in protective gauze cages by WUR Plant Breeding. Represented plants included the following genotypes: aa36 number 1 to 32 and aa33 number 9, 14, 13, and 27. Tubers were sorted and the number and size class of the tubers was recorded. For each genotype, 6 tubers of 2 to 4 cm were selected for use in a tuber maceration resistance test. The 6 largest tubers of each genotype were selected for use as seed potatoes for the next year and 10 tubers of 1 to 2 cm were selected for use in a blackleg resistance test. All tubers were stored in a dark cold room in Radix clima in cooling cell #9 at 5 C until further use. EXPERIMENTS PETIOLE MACERATION TESTS The petiole maceration test was performed according to a previously developed protocol (Rietman et al., 2014) that was used for the petiole maceration test of aa33 and 36 performed in 2015 by Iris Tinnenbroek. This protocol describes the following procedure: Plastic jars (Greigner Bio-One, No ) were filled with 15 ml 4.3 g L -1 Murashige and Skoog basal salt mixture ( Duchefa Biochemie, No. M0221) supplemented with 2.13 g L -1 MES (Duchefa Biochemie, No. M1503) and adjusted to ph 5.8 using potassium hydroxide. Petioles were cut from plants 11

13 in the greenhouse, sorted per genotype and placed in plastic trays lined with wet filter paper and packed in plastic bags. Petioles selected for this experiment were numbers 3 to 6 (on a scale where petiole number 1 is the youngest petiole near the apical bud of the plant and the number increases going further down the stem). In the lab, leaves were removed and the petioles were cut from the bottom end to a length of approximately 8 cm. Per genotype/ inoculum combination, 8 petioles were prepared which were divided over 2 jars. The bottom of the petioles was placed in small piece of florists foam, and this was placed in the plastic jars with medium. Each jar contained 4 petioles of the same plant genotype. The inoculum was prepared as described in handling of bacterial strains and inoculation procedures Bacterial suspension of Ds was diluted to an optical density of 0.1 and Pw was diluted to an optical density of µl of these suspensions was added to the 15 ml MS+ MES medium in the plastic jars with the petioles, resulting in a concentration of approximately 10 6 CFU for Ds and approximately 10 7 CFU for Pw. Jars were incubated at 25 C with a 16h/8h day/night cycle. Maceration of each petiole in centimetres was scored at 2 and 3 days post inoculation. This data was converted into a 0-100% scale afterwards. TUBER MACERATION TESTS Six tubers of 2 to 4 centimetres in length were selected for this experiment. The protocol used here is based on that described by Pasco et al. in 2006 with some adjustments. Tubers were washed with tap water and surface disinfected by submerging in 80% ethanol for approximately 2 minutes. Each tuber was sliced in half by cutting through the basal and apical end. In each tuber half, a well of 5 mm wide and 5 mm deep was cut out using a metal tube. The well was cut in the vascular ring between the peel and the medulla tissue of the tuber at the edge of the tuber half. During the pilot tests for half of the tubers of each genotype a well was cut into the middle (medulla) of the tuber half. The tuber halves were placed in open petri dishes or tube caps matching the tuber s size to ensure that the cut side would stay up and the half tubers would not tumble over. If necessary tuber halves were stabilized with drawing pins to prevent tumbling. Bacterial suspensions with an optical density of 0.1 (equivalent to approximately 10 8 CFU) were used. Tuber halves were inoculated by depositing 50 µl of bacterial suspension in each well. Controls were inoculated with sterile Ringer s Buffer. Inoculated half tubers and their containers were placed in large plastic trays lined with wet filter paper which was packed in a translucent plastic bag to create a moist environment. Petri dishes of different genotypes were randomized over different trays/locations in the trays. Trays with inoculated tubers were placed in a climate chamber at 25 C in the dark. After approximately 48 hours, tuber halves were checked for soft rot. Tuber halves with soft-rotted tissue were weighed and the weight in grams of these tuber halves was noted. Next, the soft rotten tissue was removed using a spatula and tap water. After removal of rotten tissue each tuber half was weighed again. Using this change in weight the percentage of macerated tissue for each tuber half was calculated. HANDLING OF DATA Raw data and the corresponding maceration percentages were stored in a separate file per experiment. For the tuber maceration, the half tubers that were scored were the experimental units to which were subjected to statistical analysis. For the petiole test, the mean maceration percentage per jar was the 12

14 experimental unit and the subject of statistical analysis. Microsoft Excel was used to store data and calculate averages and standard deviations. Statistical analyses were performed using SPSS (IBM corporation, version 22). For all data obtained from both tuber maceration tests and petiole maceration tests, assumptions required for parametric tests were not fulfilled. Qantile- Qantile plots revealed that the assumption of normally distributed data was violated, as the high number of observations with a maceration percentage of 0 or 100, caused the data to be heavy tailed compared to the expected normal distribution. Levene s test for homogeneity of variances was significant for all obtained data, which indicated that the data was heteroscedastic and therefore the assumption of equal variances was violated as well. Therefore only nonparametric tests were used for hypothesis testing on obtained data for all experiments. The only exception to this is the use of the analysis of variance table to obtain estimates for broad-sense heritability (H 2 ) of maceration resistance. RESULTS PETIOLE MACERATION TOLERANCE RESULTS OF TESTS OVER DIFFERENT YEARS COMBINED Crosses of 470-3, which was previously identified as tolerant (Rietman et al., 2014) with susceptible diploid potato relatives were made to obtain populations segregating for petiole maceration resistance. Of two populations resulting from these crosses, aa33 and aa36, the first 32 genotypes were tested in summer 2015 for petiole maceration resistance during infection by Dickeya solani and Pectobacterium wasabiae by Iris Tinnenbroek (Wageningen University and Research Plant breeding chair group). To confirm these results and assess the consistency of the observed phenotypes, this petiole test was repeated twice for each population: in fall 2016 and winter In the results reported below, the tests performed as part of this MSc thesis project will be referred to as 2017 and These will be compared to the results obtained by Iris Tinnenbroek which will be referred to as the 2015 test. Petiole maceration was measured approximately 48 hours after inoculation (2 dpi) and 72 hours after inoculation (3 dpi). GENERAL CHARACTERISTICS OF OBTAINED PETIOLE MACERATION DATA For both population aa33 and aa36,the petiole maceration resistance test results show that there are many different phenotypes on a continuous scale, and that it is difficult to assign a meaningful cut-off value to distinguish tolerant and susceptible phenotypes. In general, most of the Solanum chacoense offspring are more tolerant to maceration compared to the susceptible cultivars, which were included as a reference (indicated as the grey bars in figure 2 to 5). The clearest differences in petiole maceration percentages between genotypes can be observed on 3 dpi for Pw and on 2 Dpi for Ds. Therefore, the data obtained for these time points were the focus of the data analysis. However, for petiole test 2017 of population aa33 the maceration percentages for both bacteria were much higher compared to the results from 2015 and 2016 (Appendix, supplementary figures 8-11, page 30-33). Maceration percentages of 13

15 the 2017 test at 2 dpi were in the same range or higher compared to those of 2015 and 2016 at 3 dpi. Therefore the 3 dpi data for 2015 and 2016 was combined with the 2 dpi 2017 data for further analysis. An independent samples two-sided Kruskall-Wallis H test reported no significant difference between the maceration percentages averaged over all test years of aa33 and aa36 inoculated with Ds. For inoculation with Pw there were significant differences between three genotypes. For aa33 treated with Pw (figure 2), and aa33-05, the most tolerant genotypes, were not significantly different from each other but both significantly different from Annabelle, the second-most susceptible genotype. For aa36 treated with pw, aa36-17 and aa36-11 were significantly different from Compass, Annabelle and aa36-23 (figure 4). The low number of significant differences is most likely caused by the high variances and standard errors of the maceration data and because of the continuous nature of the trait with no clear cut-off value for susceptible, intermediate and tolerant genotypes. COMPARISON OF PARENTS AND GRANDPARENTS Compared to the tolerant grandparent Solanum chacoense the tolerant parents and their offspring in both populations have a higher maceration percentage. This indicates that part of the maceration resistance/tolerance was lost during the first crossing of Solanum chacoense with the susceptible grandparents. The phenotype of was, as expected, one of the most tolerant genotypes in every test. Phenotypes of susceptible grandparents (aa33), (aa36) and RH (both populations) showed the expected phenotype as well. They were among the most susceptible genotypes in each test. However, the parental genotypes did not react as expected. The susceptible parent (both populations) was not among the most susceptible genotypes but had intermediate maceration percentages. Tolerant parents (aa33) and (aa36) were less tolerant compared to tolerant and rather showed intermediate maceration tolerance. Even, when inoculated with Ds the rankings of the susceptible parent and the tolerant parent were reversed. The average maceration percentage of the tolerant parent was higher compared to the susceptible parent (figure 3 and 5). In both populations, the offspring shows a lower or in many cases a higher maceration percentage compared to the parents. This could indicate that both parents contain different genes or alleles, inherited from or alleles that contribute to maceration tolerance. COMPARISON OF DS AND PW Of the two bacteria used in these petiole tests, Ds is more aggressive and yields higher maceration percentages in a shorter time compared to Pw. Inoculation with Ds generally results in higher maceration percentages for all genotypes when compared to Pw (compare figure 2 and 3 and figure 4 and 5). On 2 dpi, maceration percentages of petioles inoculated with Ds are similar to those of Pw inoculated petioles on 3 dpi. On 3 dpi the maceration percentages of tested genotypes are generally very high with most genotypes scoring 50% or higher. 14

16 Figure 2. Maceration percentages of aa33 for Pw, 2015 and dpi combined with dpi data. Bars indicate average of all petiole tests repetitions for each genotype. Error bars indicate 1 standard deviation, which was calculated as the standard deviation between genotype means for each repetition. Grey: susceptible control cultivars, Dark blue: population grandparents, Red: population parents. N=6 for all genotypes except for the following: and N=5, N=4, Ivory Russet N=4, aa33-02 and aa33-03 N =4, N=2, RH N=1. Genotypes with different letters are significantly different from each other in (2- tailed p<0.05) in a Kruskall-Wallis H test. x-axis: maceration percentage, y-axis: genotype codes. Figure 3. Maceration percentages of aa33 for Pw, 2015 and dpi combined with dpi data. Bars indicate average of all petiole tests repetitions for each genotype. Error bars indicate 1 standard deviation, which was calculated as the standard deviation between genotype means for each repetition. Grey: susceptible control cultivars, Dark blue: population grandparents, Red: population parents. N=6 for all genotypes except for the following: and N=5, N=4, Ivory Russet N=4, aa33-02 and aa33-03 N =4, N=2, RH N=1. Genotypes with different letters are significantly different from each other in (2- tailed p<0.05) in a Kruskall-Wallis H test. x-axis: maceration percentage, y-axis: genotype codes. 15

17 Figure 4. Petiole maceration percentages of aa36 for Pw 3 dpi data. Bars indicate average of all petiole tests repetitions for each genotype. Error bars indicate 1 standard deviation, which was calculated as the standard deviation between genotype means for each repetition. Grey: susceptible control cultivars, Dark blue: population grandparents, Red: population parents. N=6 for all genotypes except for the following: and N=5, N=4, Ivory Russet N=4, RH N=2, N=1. Genotypes with different letters are significantly different (2- tailed p<0.05) in a Kruskall-Wallis H test. x-axis: maceration percentage, y-axis: genotype codes. Figure 5. Petiole maceration percentages of aa36 for Ds 3 dpi data. Bars indicate average of all petiole tests repetitions for each genotype. Error bars indicate 1 standard deviation, which was calculated as the standard deviation between genotype means for each repetition. Grey: susceptible control cultivars, Dark blue: population grandparents, Red: population parents. N=6 for all genotypes except for the following: and N=5, N=4, Ivory Russet N=4, RH N=2, N=1. x-axis: maceration percentage, y-axis: genotype codes. 16

18 REPRODUCIBILITY OF TESTS The graphs of the petiole test results for both aa33 and aa36 show that there was variation between the jar means of each genotype within each test repetition (Appendix, supplementary figures 8-15, page 30-37). as well as between the different test repetitions in 2015, 2016 and 2017 (Appendix, supplementary figures 8-15, page 30-37). In figures 2 to 5, the error bars which indicate the standard deviations between the repetitions in different years are relatively large. Additionally, the differences between the average maceration percentages of different genotypes are often not very pronounced. To quantify the consistency of maceration scores for each genotype of population aa33 and aa36 between repetitions of the same experiment performed in the three different years, Spearman s correlation coefficients of the scores in different years were generated (table 4). The correlations for test repetitions are low to moderate for both population aa33 and aa36. Strongest Spearman s correlations are observed between the repetitions in 2015 and The weakest correlations are between the 2015 and 2017 repetitions (Table 4). The three repetitions of the petiole maceration tests reveal that there is strong variation between different repetitions of the same test at different moments in time, although there is some consistency in the maceration percentages of each genotype. Because each repetition was conducted at a different time of the year, these differences are likely effects of the day length and light intensity on plant growth and development of the plants during the approximately five weeks of growing in the greenhouse. Table 4. Spearman s correlation between petiole maceration tests performed in 2015,2016 and 2017 Spearman s correlation between maceration percentages observed in petiole tests performed in 2015,2016 and 2017 for the first 32 genotypes of population aa33(left) and aa36 (right). Data used were jar averages per genotype. An * indicates that correlation is significant (2-tailed p<0.05) 17

19 HERITABILLITY OF PETIOLE MACERATION TOLERANCE To examine to what extent the phenotypic variation in petiole maceration in the populations could be attributed to differences in genotype, broad sense heritability was estimated for each test repetition and for the results of three repetitions pooled for population aa33 (table 5) and population aa36 (table 6). In general, the H 2 estimates are quite high for the test repetitions in 2015 and 2016 and in many cases more than half of the phenotypic variation for each individual experiment could be attributed to genetic differences. In both populations, the 2017 test for Pw has a much lower H 2 compared to the other repetitions. What stands out for both populations is that H 2 of the pooled test repetitions is much lower compared to the H 2 for the separate repetitions of the same experiment. For aa dpi Pw the difference with the other test years is the most extreme (table 5) of the H 2 estimates for aa36 and aa33 and the correlation with the 2015 test is the lowest (-0.016, table 4). Therefore the heritability of just test year 2015 and 2016 combined was calculated as well. For the Pw data the grouped H 2 is much higher when the 2017 test is left out. This is related to the low correlation between maceration percentages in different test repetitions, which are likely caused by seasonal effects on petiole maceration. Table 5. Broad-sense Heritability (H 2 ) estimates for population aa33 Broad-sense Heritability (H 2) estimates calculated from ANOVA tables for population aa33, based on jar averages (number of jar averages indicated by n) per genotype. 18

20 Table 6. Broad-sense Heritability (H 2 ) estimates for population aa36 Broad-sense Heritability (H 2) estimates calculated from ANOVA tables for population aa36, based on jar averages (number of jar averages indicated by n) per genotype. PETIOLE MACERATION RESISTANCE IN EXTENDED POPULATION Because only a limited number of offspring plants was available that could be grouped as tolerant or susceptible, population aa33 and aa36 were extended by growing additional seeds form the same crossing. In January genotypes of aa36 and 22 genotypes from aa33 were subjected to the previously used petiole maceration test using Pw and Ds as inoculum. Again, cultivars Annabelle and Compass had been included as susceptible controls. For the newly added aa33 and aa36 genotypes, it was observed that, like in the other petiole tests, Ds causes maceration of petioles faster and more severe compared to Pw (Appendix, supplementary figures 16 and 18, page 38-39). The results are also similar in that the scores for Ds show the strongest differences on 2 dpi while for Pw the differences between genotypes are strongest on 3 dpi (Appendix, supplementary figures 16 and 17, page 38-39). Broad-sense heritability in these new genotypes (table 7) is lower compared to the other tested subsets of aa33 (table 5) and aa 36 (table 6), but this could be because this test was performed in January In the tests of the first 32 genotypes of 19

21 aa33 and aa36 Broad-sense heritability was much lower for the tests performed in January 2017 compared to those performed in fall 2016 and summer Table 7. Broad-sense Heritability (H 2 ) estimates for population aa36 Broad-sense Heritability (H 2) estimates for population and aa33 aa36, based on jar averages (number of jar averages indicated by n) per genotype. TUBER MACERATION RESISTANCE To investigate the segregation of tuber maceration resistance in population aa36 and aa33, tuber maceration tests were performed. Plants of the first 32 genotypes of both populations were used for tuber production. In aa36 most of the genotypes produced tubers, as opposed to just the four genotypes 9, 14, 13, and 27 in aa33. Some genotypes yielded just a few tubers or only tubers that were very small (< 2 cm). TUBER MACERATION PILOTS It was the first time tubers of aa36 were tested for tolerance to maceration by SRE. To ensure making the best use of the limited number of available tubers, the effectiveness of the planned experimental approach was assessed in small pilot experiments with the 6 genotypes that had yielded the largest number of tubers. As there was a limited number of tubers it was important use them in an experiment that produced consistent results. In this pilot tubers were tested for resistance to maceration caused by Ds, Pcc and Pw. Besides testing what species of bacterium gives the most consistent results, it was also tested which of two inoculation sites on a tuber half would yield the most consistent results. The tested inoculation sites were the medulla in the middle of the tuber, and the vascular ring at the edge of the tuber halves, between the medulla and the skin. Of these two inoculation sites, the vascular ring inoculation yielded slightly more 20

22 consistent results compared to inoculation in the medulla, with the lowest variation between the experiments. Inoculation with Ds yielded the least consistent results. Pcc and Pw were quite similar, although in combination with the vascular ring inoculation Pw yielded the most consistent results (figure 6). Additionally, the use pf Pw was favoured because this bacterium was also used in the petiole tests. The use of the same bacterium in test on both tissues was the only way in which the results of these tests could validly be compared. TUBER TEST FOR MACERATION TOLERANCE OF AA36 Each of the first 32 genotypes of aa36, tubers were cut in half and inoculated in the vascular ring with bacterial suspension of Pw. Maceration percentage was measured as the percentage of rotten tissue of each half-tuber s total fresh weight. The tuber maceration test results (Figure 7) seem to show a clearer difference between scores of different genotypes when compared to the petiole test results for aa36 inoculated with Pw (figure 4). In these results the standard deviation is relatively high (figure 7) as differences between observations are large within some genotypes. The heritability (H 2 ) estimate for tuber maceration resistance is 0.34 To determine whether tuber maceration percentage and petiole maceration percentage of the tested aa36 genotypes are correlated, Spearman s correlation coefficient of the genotype means of both tests was generated. There was no significant correlation detected (N=35, correlation coefficient = , two-tailed p = 0,762). This corresponds with the seemingly random spread of data points when tuber and petiole maceration ranks are plotted against each other (Appendix, supplementary figures 20, page 41). For aa36 there is no correlation between tuber maceration resistance 21

23 Figure 6. Boxplot of Tuber maceration test pilots performed on a small subset of. Black lines inside boxes indicate the median. Results of the first tuber maceration pilot are indicated in blue, results for the second pilot test are indicated in green. The results shown for each genotype-bacteriuminoculation site combination is are based on three half tubers for both pilot 1 and pilot 2. 22

24 Figure 7. Tuber maceration percentages at 2dpi of aa36 inoculated with Pw in the vascular bundle. Bars indicate average of observed maceration percentage from 12 half-tubers for each genotype (N = 12), error bars indicate 1 standard deviation. x-axis: maceration percentage, y-axis: genotype codes. TUBER TEST FOR MACERATION TOLERANCE OF AA33 The same test applied to the first 32 genotypes of aa36 was also performed with the four genotypes of aa33 that had produced tubers. Table 8. Mean tuber maceration percentage and mean petiole maceration percentage for genotypes of aa33 included in tuber maceration test. Genotype Mean tuber maceration percentage (n=12) Mean petiole maceration percentage (n=6) Aa % 9.65% Aa % 24.40% Aa % 24.18% Aa % 38.93% Aa33-09 is the most susceptible genotype in the tuber maceration test, while in the petiole test it is one of the more tolerant genotype. Aa33-14 and aa33-13 are both intermediate in the petiole maceration test and relatively susceptible in the tuber maceration test. Aa33-27 is the second-most susceptible genotype in the tuber maceration test, and is also susceptible in the petiole test (table 8). For these four genotypes of aa33 tested for tuber maceration, there does not seem to be a clear correlation between maceration percentages of petioles and tubers either. 23

25 CONCLUSION AND DISCUSSION PETIOLE MACERATION RESISTANCE In general, the results of the petiole maceration tests for both aa33 and aa36 reveal that the Solanum chacoense offspring is more tolerant to petiole maceration by SRE as compared to the susceptible grandparents and the control cultivars Annabelle, Compass and Ivory Russet. The susceptible grandparents were among the most susceptible genotypes in the tests and the tolerant grandparent was among the most tolerant genotypes. This confirms earlier results obtained for these genotypes (Rietman et al., 2014). However, the phenotypes of the parents of the populations were not as extreme as expected. In previous tests (Unpublished results from WUR plant Breeding) population parent was classified as relatively susceptible and population parent and were classified as tolerant. In the current tests the differences between the tolerant and susceptible parents were relatively small, and the tolerant and the susceptible parent genotypes were found to be more intermediate or intermediately tolerant. For both tests with Ds the ranks of the tolerant and the susceptible parents were reversed. Susceptible parent was quite tolerant compared to a large part of the population, so at least in the context of population aa33 and aa36 this genotype should be called intermediate instead of susceptible. Several offspring genotypes were more tolerant than their tolerant parent and many genotypes were more susceptible compared to their parents. It can be concluded that there are at least two different genes that contribute to petiole maceration tolerance in a quantitative way. This suggests that both the tolerant and the susceptible parents have at least one allelic variant of two or more different genes involved in petiole maceration resistance. In previous research on the genetic basis of soft rot tolerance it was also concluded that SRE tolerance is a polygenic quantitative trait (Lebecka and Zimnoch-Guzowska, 2004, Pasco et al., 2006), and in mapping studies on populations with Solanum chacoense in their pedigree it was proposed that tolerance against petiole maceration involved at least 15 putative QTLs (Zimnoch-Guzowska et al., 2000). Petiole maceration tolerance, observed in the experiments presented here, is heritable, as indicated by the mostly high heritability for each separate experiment, and therefore it should be possible to breed for resistant Solanum tuberosum cultivars with this trait. High phenotypic variation in between genotypes in the populations and high heritability indicates that these populations are a promising subject for QTL analyses, as there could be many different genes and/or loci related to this trait that can be mapped. Phenotypes of the data obtained in this test are in general consistent, although there is quite some variation as well: the correlation between results of different test repetitions was generally around 0,5, while ideally it should be closer to 1. Petiole resistance data was collected during three separate repetitions of aa33 24

26 and aa36. These test repetitions took place in different seasons: repetition 2015 was performed in spring/summer, the 2016 repetition in the autumn and the 2017 repetition in winter. The used plants have to be grown in a greenhouse for approximately six weeks. During this time, the difference of daylength and light intensity between the seasons could have affected plant growth and physiology which again could have affected maceration tolerance. Despite the fact that repetition 2015 was performed by a someone else than in the test s repetitions in 2016 and 2017, the strongest correlation between tests was between 2015 and This indicates that the observed maceration percentages are influenced by seasonal changes, which leads to inconsistencies in results. Effects of environmental factors on leaf and petiole resistance was observed in other studies as well (Zimnoch-Guzowska et al., 2000, Rietman et al., 2014). In future tests, these seasonal differences and other environmental effects will have to be minimized in order to obtain solid phenotype data suitable for use in QTL mapping. This could be done by restricting one type of test to a certain season or period with similar day lengths or intentionally dividing test repetitions over different seasons to correct for these effects. TUBER TESTS In the tuber maceration test of population aa36 with Pw, parent had a susceptible phenotype, and parent could be classified as an intermediate phenotype. Only one genotype had a higher maceration percentage than parent , while there were several genotypes that had a lower maceration percentage than parent Regarding the genetic basis of tuber maceration tolerance, this could indicate that, like in the petiole test, there are at least two genes involved in this trait that contribute to tuber maceration tolerance. This corresponds with earlier findings where it was proposed that tuber maceration tolerance is under the influence of at least 12 putative QTLs (Zimnoch-Guzowska et al., 2000). However, broad-sense heritability for tuber maceration tolerance obtained in this research project was 0.34 which is rather low and could indicate that genetic effects are less important for maceration tolerance compared to other factors. From the genotypes of aa36 tested here, it could be concluded that only has the resistance conferring allele of one or more minor genes, while has the resistance conferring allele for one or more different genes involved in tuber maceration resistance. The genotypes that have a higher resistance than have inherited all the different resistance conferring alleles. The gene from has a very small contribution to resistance on its own, but it could have a complementary effect with one of the resistance genes form In population aa36, no correlation was found between petiole maceration resistance and tuber maceration resistance by Pectobacterium wasabiae (Pw). For the four genotypes of aa33 the phenotypes do not show any correlation, although the number of genotypes was too low to perform statistical correlation tests. This corresponds with findings in of similar experiments (Zimnoch- Guzowska et al., 2000) where correlation between mean overall leaf maceration and tuber maceration was found to be slightly positive but not significant. These results imply that petiole maceration resistance and tuber maceration resistance 25

27 are two separate aspects of resistance to soft rot caused by SRE. Selection for resistance in the tubers and the petioles will have to be done separately and simultaneously, and the results have to be combined to obtain genotypes with both types of resistance in both tissues. For mapping genes related to tuber maceration tolerance, population aa36 could be useful. However, compared to the cultivars included as reference, the aa36 population does not show higher resistance. Tubers of Ivory Russet and Annabelle seemed to be quite tolerant to maceration by Pw as well. This indicates that for the sole purpose of obtaining tuber maceration resistance, aa36 might not be very useful. This will depend on whether the lower tuber maceration percentages are due to true resistance instead of tolerance and whether this resistance proves to hold in practice. RECOMMENDATIONS Besides tuber soft rot and areial stem rot which were tested here, blackleg is an important disease caused by Dickeya spp. and Pectobacterium spp. It would be interesing to test genotypes of population aa33 and aa36 for tolerance to blackeg, which is rot of the plant shoots caused by infected seed tubers. It is expected that there will be a stronger correlation between petiole maceration tolerance and blackleg tolerance than between tuber maceration tolerance and black leg tolerance (Allefs et al., 1995). To confirm that Solanum chacoense and populations resulting from crosses with these genotypes are truly resistant, data on the bacterial titres should be collected. It is essential to distinguish between resistant and tolerant genotypes. In tolerant genotypes, high bacterial densities can build up without showing symptoms, which could cause problems in practice. Those that have the capacity to decrease the number of bacteria and show a decrease in symptoms as a result of this are therefore truly resistant (Niks et al., 2011). Additionally, it will have to be investigated wether the resistance observed in the lab hold in practice as well. In the experiments described here, the concentration of bacteria applied to tubers and petioles was high and above the approximate QS threshold for the switch to a necrotrophic life phase of Pectobacterium (Jan van der Wolf, Biointeractions and Plant Health, Wageningen University and Research, personal communication, 2016). This means that possible resistance observed in this research project was most likely the result of JA and ET mediated plant defence that is effective against necrotrophs and thus is likely most effective at the necrotrophic infection phase of Pectobacterium wasabiae (Verhage et al., 2010, Glazebrook, 2005). SA mediated defence, which is usually associated with effectivity against biotrophs, was also shown to be effective against Pectobacterium (Davidsson et al., 2013). SA defence is probably effective during the latent biotrophic infection phase, where the bacteria cause no symptoms yet and the number of bacteria is still lower. Therefore it could be interesting to test petiole and tuber maceration tolerance at with lower inoculums concentrations to see whether the possible resistance occurs during infections of both infection phases. 26

28 A QTL analysis is one of the next steps needed in identifying genes and genetic loci related to a quantitative trait such as tolerance to maceration of petioles or tubers by SRE. Up until now only a small subset of aa33 and aa36 has been phenotyped, and the number of extremely tolerant or susceptible offspring in the population is too small to make a clear distinction between tolerant and susceptible genotypes. This distinction is necessary to and make groups or socalled bulks of the most tolerant and susceptible genotypes for a Bulked Segregant Analysis (BSA). The populations have been extended to approximately 150 genotypes, which should provide enough variation in phenotypes to construct bulks of a sufficient number of genotypes with extreme resistance or susceptibility. The next step in unravelling the genetic basis of soft rot resistance is to phenotype these additional genotypes and use these to perform a BSA. However, the observation that the parents of population aa33 and aa36 are intermediate could mean that they share a number of genes related to maceration tolerance. This would limit the number of maceration tolerance related genes that can be identified, as the principle of a BSA is to find genetic differences between the bulks (Quarrie et al., 1999). However, it is still expected that there will be differences between the susceptible and the tolerant bulks and traces back to their corresponding (grand) parents which will allow QTL discovery for resistance in potato to Dikeya spp.and Pectobacterium spp. In summary, it will be possible to obtain consistent phenotypes of population aa33 and aa36 if environmental effects are taken into account in experimental design and planning. These phenotypes can be used for QTL analysis to identify genes involved in SRE resistance. By checking whether the observed tolerant phenotypes are truly tolerant or resistant, and by testing the populations for blackleg resistance, it can be evaluated whether the tolerance that has been observed could be suitable application in practice. This will result in a more complete comprehensionof the practical potential and the genetic and molecular basis of SRE resistance obtained from Solanum chacoense ACKNOWLEDGEMENTS I would like to thank the following people from the plant breeding department: Jack Vossen for supervising this MSc thesis, Rischard Visser for being the second examiner, Iris Tinnenbroek, Marjan Bergervoet and Isolde Bertram for their help and advice in the cell culture lab and during the petiole tests, Dirk- Jan Huigen for arranging greenhouse space and other practical things and Charlotte Prodhomme for advice on heritability calculations. From the department of Biointeractions and Plant Health I would like to thank Patricia van der Zouwen and Pieter Kastelein for their help and advice in handling the bacteria and Jan van der Wolf for his valuable advice. Finally I would like to thank Joao Caldas Paulo from Biometris for her advice on the statistical analysis. 27

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31 APPENDIX SUPPLEMENTARY FIGURES PETIOLE TESTS AA33 Figure 8. Mean maceration percentage per jar, population Aa33, Pw 2 dpi indicates experiment executed by Iris Tinnenbroek in summer Each jar data point is the average of the 4 petioles inside the jar. y-axis indicates genotype code (please note that should be ). Error bars indicate 1 standard deviation. 30

32 Figure 9. Mean maceration percentage per jar, population Aa33 Pw 3 dpi indicates experiment executed by Iris Tinnenbroek in summer Each jar data point is the average of the 4 petioles inside the jar. y-axis indicates genotype code (please note that should be ). Error bars indicate 1 standard deviation. 31

33 Figure 10. Mean maceration percentage per jar, population Aa33 Ds 2 dpi indicates experiment executed by Iris Tinnenbroek in summer Each jar data point is the average of the 4 petioles inside the jar. y-axis indicates genotype code Error bars indicate 1 standard deviation. 32

34 Figure 11. Mean maceration percentage per jar, population Aa33 Ds 3 dpi indicates experiment executed by Iris Tinnenbroek in summer Each jar data point is the average of the 4 petioles inside the jar. y-axis indicates genotype code Error bars indicate 1 standard deviation. 33

35 PETIOLE TESTS AA36 Figure 12. Mean maceration percentage per jar, population Aa36 Ds 2 dpi indicates experiment executed by Iris Tinnenbroek in summer Each jar data point is the average of the 4 petioles inside the jar. y-axis indicates genotype code (please note that should be ). Error bars indicate 1 standard deviation. 34

36 Figure 13. Mean maceration percentage per jar, population Aa36 Ds 3 dpi indicates experiment executed by Iris Tinnenbroek in summer Each jar data point is the average of the 4 petioles inside the jar. y-axis indicates genotype code (please note that should be ). Error bars indicate 1 standard deviation. 35

37 Figure 14. Mean maceration percentage per jar, population Aa36 Pw 2 dpi indicates experiment executed by Iris Tinnenbroek in summer Each jar data point is the average of the 4 petioles inside the jar. y-axis indicates genotype code. Error bars indicate 1 standard deviation. ). Error bars indicate 1 standard deviation. 36

38 Figure 15. Mean maceration percentage per jar, population Aa36 Pw 3 dpi indicates experiment executed by Iris Tinnenbroek in summer Each jar data point is the average of the 4 petioles inside the jar. y-axis indicates genotype code. Error bars indicate 1 standard deviation. ). Error bars indicate 1 standard deviation. 37

39 PETIOLE TEST EXTENDED POPULATION Figure 16. Bar chart of mean maceration percentage per jar per genotype of petiole test for aa36 extended population. Tested with Pw (left) and Ds (right), error bars indicate 1 standard deviation. y- axis indicates genotype codes: number indicates aa36-34, etc. 38

40 Figure 17. Bar chart of mean maceration percentage per jar per genotype for petiole test for aa33 extended population. Tested with Pw, error bars indicate 1 standard deviation. y- axis indicates genotype codes: number indicates aa33-34, etc. Figure 18. Bar chart of mean maceration percentage per jar per genotype for petiole test for aa33 extended population. Tested with Ds, error bars indicate 1 standard deviation. y- axis indicates genotype codes: number indicates aa33-34, etc. 39

41 TUBER TESTS Figure 19. Bar chart of mean tuber maceration percentage per test (see legend) per genotype for petiole test for aa33 extended population. Tested with Pw, error bars indicate 1 standard deviation. y- axis indicates genotype codes: number indicates aa33-34, etc. 40

42 Figure 20. Scatterplot depicting correlation between tuber and petiole maceration in population aa36: y-axis depicts rankings of average tuber maceration percentage per genotype (N=12), x- axis depicts rankings of average petiole maceration percentage per genotype, with averaged data from tests in 2015, 2016 and 2017 (N=6). 41