Inheritance of resistance to Ralstonia pseudosolanacerum in tomato genotypes Yoshimatsu and Hawaii 7996

doi:10.21203/rs.3.rs-4373009/v1

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Inheritance of resistance to Ralstonia pseudosolanacerum in tomato genotypes Yoshimatsu and Hawaii 7996

https://doi.org/10.21203/rs.3.rs-4373009/v1

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Among the various diseases that affect tomato crops, bacterial wilt stands out due to its high level of damage during the cycle and the difficulty of controlling it. Among the control strategies is the use of resistant cultivars. However, in order to obtain these cultivars, resistance inheritance studies are an essential step. With this in mind, the aim of this work was to study the inheritance of resistance to Ralstonia pseudosolanacearum in the tomato genotypes Yoshimatsu and Hawaii 7996 and to determine whether the resistance loci that govern the trait in these materials are the same. The Yoshimatsu and Hawaii 7996 genotypes and the F₁, F₂, BC₁₁ and BC₂₁ generations were evaluated in one experiment and 60 F_2:3 progenies in a second experiment. The severity of bacterial wilt was assessed using a descriptive scale of scores at 20 days after inoculation. In the Yoshimatsu genotype, the inheritance of resistance to Ralstonia pseudosolanacearum is governed by two major effect genes in recessive homozygosity. In the Hawaii 7996 genotype, the inheritance of Ralstonia psedosolanacearum resistance is governed by a gene with partial dominance action. The greatest contribution of gene effects was due to additive variance. For Ralstonia psedosolanacearum it is recommended to select 20 days after inoculation between and within F_2:3 progenies.

Genetic control

Bacterial wilt

Bacteriosis

Solanum lycopersicum L

The tomato plant (Solanum lycopersicum L.) is one of the most widely grown vegetables in the world, and its fruit is widely used in the cuisine of various countries (Rodrigues et al. 2020). Belonging to the Solanaceae family, it is a herbaceous plant that grows from one to three meters in height and can have a determinate or indeterminate growth habit (Kumar 2018). It is considered one of the most lucrative species to produce, as it serves different markets (Maciel et al. 2016).

Worldwide, tomato production was estimated at 186 million tons in 2019, on approximately five million hectares (FAO 2020). Commercially, 166 countries grow tomatoes and supply the world market, of which China is the largest producer with a cultivated area of more than one million hectares and an annual production of more than 56 million tons. Brazil ranks ninth with a production of 3,809,986 tons in an area of 54,502 hectares, with an average productivity of 69.905 tons per hectare (FAO 2022). In Brazil, the state of Goiás is the largest producer, and the state of Pernambuco ranks 11th (IBGE 2022).

During its production cycle, the tomato crop is affected by various pathogens, especially the bacteria of the Ralstonia solanacearum Species Complex (RSCs), which consists of three species, R. solanacearum, R. pseudosolanacearum and R. sygzii (Sharma et al. 2022). According to Mamphogoro et al. (2020) this is the most important soil-borne bacteriosis in tomato cultivation, as it is considered the most infectious and destructive worldwide. In Brazil, the RSC species responsible for causing bacterial wilt in tomatoes are R. solanacearum and R. pseudosolanacearum, which inhabit the soil of all Brazilian states, mainly in the North and Northeast regions of the country, due to the high temperature and humidity that favors the development of bacteria (Santiago 2020).

Infected tomato plants can show symptoms on the second or third day after infection and at any stage of plant development (Manda et al. 2020). The most typical symptom of tomato bacterial wilt is wilting of the leaves from top to bottom without changing their green color, which is the result of the partial or total interruption of the flow of water from the roots to the aerial part of the plant (Ahmed et al. 2022), as a result of bacterial multiplication that causes the deposition of exopolysaccharides in the xylem, which can be observed from the darkening seen by longitudinal cutting in the stem of infected plants. This symptom is considered to be the bacterium's main virulence factor (Tsuzuki et al. 2023). In some cases, the formation of adventitious roots can be seen in the lower part of the stem, which is a reaction of the plant to the lack of sufficient water to keep it turgid (Lopes 2022). In the field, it is common to see bacterial wilt symptoms in clumps located in the lower, moist parts of planted areas (Lopes 2009).

Bacterial wilt is a disease that causes up to 100% economic damage and is very difficult to control. Among the control methods, the chemical one is very offensive to the environment and human health, as well as causing the pathogen to become resistant and ineffective because it does not act in the deep layers, since the pathogen can reach up to one meter deep (Rani et al. 2022). In the current global situation of so many environmental problems, it is of great importance to think about more sustainable methods of disease control. Therefore, obtaining cultivars that are resistant to bacterial wilt is considered to be the best approach to controlling this devastating disease (Lee et al. 2020).

There are records in the literature of some tomato genotypes resistant to bacterial wilt, such as Caraíba, CRA-66, Drica, Hawaii 7996, Hawaii 7997, Hawaii 7998, Saturn, Venus and Yoshimatsu (Costa et al. 2019). Some sources of resistance have already been mapped, but there are still no resistant commercial cultivars on the market (Nick and Silva 2016). This is because some alleles promoting resistance to bacterial wilt are negatively associated with desirable agronomic characteristics in a fruit, which makes it unviable for both producers and consumers to accept it (Yuliar et al. 2015).

For breeding programs, knowledge of the inheritance of resistance to bacterial wilt is essential for the development of superior cultivars. Knowledge of genetic control helps breeders to determine population size, breeding methods, selection methods and other factors that make improving the species more efficient. In most studies, inheritance is considered to be complex, with a great deal of influence from the environment, the virulence of the local isolates, the resistant genitor used, and finally the interaction between all these factors (Costa et al. 2018; Costa et al. 2019). However, in the literature there are several contrasting results regarding the inheritance of resistance, some authors report it as monogenic (Tharkur et al. 2004), oligogenic (Sharma and Sharma 2015) and polygenic (Lima Neto 2002).

Among the tomato genotypes characterized as sources of resistance are Yoshimatsu, developed by INPA, which shows high resistance to bacterial wilt (Nick and Silva 2016) and the Hawaii 7996 cultivar indicated as an international reference for resistance (Cardoso et al. 2006). However, few studies in the literature address the genetic control involved in the resistance presented by these materials, highlighting the need for studies that explore the genetic relationship between these cultivars for this characteristic, since in the face of results that show divergence between the genetic inheritance of these sources of resistance, a range of possibilities arises for breeding programs, such as the development of new strategies for obtaining new cultivars. This work is very important because it is necessary to know whether or not the inheritance of resistance to Ralstonia pseudosolanacearum is the same for each of the sources of resistance.

In view of the above, the aim of this work was to study the inheritance of resistance to Ralstonia pseudosolanacearum in the tomato genotypes Yoshimatsu and Hawaii 7996 and to determine whether the resistance loci that govern the trait in these materials are the same.

The experiments were carried out in a greenhouse located in the Department of Agronomy at the Federal Rural University of Pernambuco (DEPA/UFRPE) (8°01'02"S and 34°56'41"O). The first stage consisted of an evaluation of the generations (P₁, P₂, F₁, F₂, BC₁₁ and BC₂₁) from September 2017 to november 2017, when the average temperature relative humidity were 70.31% and 26.39°C, respectively. The second stage consisted of an evaluation of the F_2:3 progenies from july 2018 to september 2018, with average temperature and relative humidity of 27.85°C and 73.55%, respectively. Meteorologica data were obtained from Thermo-hygrometer inside the greenhouse.

With the plants of the bacterial wilt resistant parents (Yoshimatsu and Hawaii 7996), in hydroponic cultivation in the UFRPE greenhouse, crosses were made to obtain the F₁, F₂, BC₁₁, BC₂₁ and F_2:3 generations. The techniques and procedures described by Costa (2017) were used to carry out this stage. According to Costa et al. (2023) Yoshimatsu and Hawaii 7996 are reported as genotypes resistant to bacterial wilt. Yoshimatsu was developed by the Instituto Nacional de Pesquisas da Amazônia - INPA and Hawaii 7996 is considered the international standard for resistance to bacterial wilt, both genotypes are not commercial cultivars, but are reported in the literature as sources of resistance (Nick and Silva 2016).

The generations (P₁, P₂, F_1, F_2, BC₁₁, BC₂₁ and the cultivar IPA-7) and subsequently the F_2:3 progenies were evaluated for their resistance to R. pseudosolanacearum using the CRMRS116 isolate belonging to the Crop Collection of the Laboratory of Plant Bacteriology at UFRPE. This isolate, characterized as biovar 3, phylotype I, and sequevar I/18, was collected from scarlet tomato in the municipality of Chã Grande, Agreste mesoregion of Pernambuco State, Brazil.

In the first stage, six treatments were evaluated in 99 plants from each parent (Yoshimatsu and Hawaii 7996) and from generation F₁; 180 plants form each backcross (BC₁₁ and BC₂₁); and 402 plants from generation F₂. In addition to these treatments, 99 plants of the IPA-7 cultivar were added, just as a control for the pathogenicity of the isolate, since according to Costa et al. (2023) it is a cultivar susceptible to bacterial wilt. In the second stage, 62 treatments were evaluated, consisting of 60 F_2:3 progenies together with parents Yoshimatsu and Hawaii 7996. Each progeny was represented by 15 plants. Both experiments used a randomized block design with three replications.

Seeds were sown in expanded 128-cell polystyrene trays containing the commercial substrate Basaplant®. Each cell had the approximate volume of 40 mL. Irrigation and fertigation were applied as required for the formation of high-quality seedlings. At 21 days after sowing (DAS), plants were transplanted into 500-mL plastic pots containing substrate based on a soil:humus mixture at the 3:1 ratio.

To prepare the bacterial suspension, isolates were retrieved from preservation in water and grown in TTC (triphenyltetrazolium tetrachloride) medium (Kelman 1954) for 48 h at 30 ± 2 C. The bacterial suspension was adjusted in a photocolorimeter (Analyser 500 M, Brazil) to A₅₇₀ = 0.54, which corresponds to 1x10⁸ CFU mL^− 1. At 30 DAS, plants were inoculated by the method of root sectioning followed inoculum deposition, using a scalpel to make a semi-circular section in the soil mixture near the neck of the plant into which 15 mL of the bacterial suspension were deposited (Felix et al. 2012). After inoculation, irrigation was performed by the subirrigation method, which consists of depositing water into plastic containers located under the 500-mL pots to prevent inoculant leaching and maintain the soil moist at all times.

Evaluations took place at 20th days after inoculation. The disease severity was measured using a descriptive scale with scores ranging from 1 to 5, adapted from Nielsen and Haynes (1960), where 1 = lack of symptoms; 2 = plants with up to 1/3 of the leaves wilted; 3 = plants with up to 2/3 of the leaves wilted; 4 = plants fully wilted; and 5 = plants dead. The classification of susceptible and resistant plants from all the generations (P₁, P₂, F₁, F₂, BC₁₁, and BC₂₁) was established by a truncation point (TP).

Statistical analyses were carried out separately for each experiment. With the data from the first experiment, frequency distribution graphs of bacterial wilt scores were generated for each genitor and generation evaluated. The means and variances of the six generations were used to obtain estimates of environmental variance (V_E), genetic variance (V_G), additive variance (V_A), dominance variance (V_D), broad-sense heritability (h²_a) and narrow-sense heritability (h²_r) (Ramalho et al. 1993). The average additive [a] and non-additive [d] effects of the gene (s) controlling the wilt score character were estimated from the generation averages using the weighted least squares method (Mather and Jinks 1984). The GMD ([d] / [a]) was then obtained using this method.

In the second experiment, analyses of variance were carried out in randomized blocks with information within plots, to estimate the genetic and phenotypic parameters, and then the Skoot Knoot grouping test was applied at 5% probability. In both experiments, score 2 was established as the screening point (PT), considering the score above which the greatest number of susceptible plants were found and below which the greatest number of resistant plants were found.

With the observed frequency of resistant and susceptible plants in the F_2:3 progenies obtained in the grouping test, hypothesis tests were carried out considering expected frequencies for one, two and three genes involved in resistance to Ralstonia pseudosolanacearum. To test these hypotheses, the non-parametric chi-square test (χ_c²) at 5% probability was used, as carried out by Costa et al. (2018) and Costa et al. (2019).

The GraphPad Prism 9 (2023) computer application was used to obtain the frequency distribution graphs. The other analyses were carried out using the Genes computer application (Cruz 2013).

Figure 1 shows the frequency distributions at 20 days after inoculation for scores of bacterial wilt caused by Ralstonia pseudosolanacearum in the parents, in the F₁, F₂, BC₁₁, BC₂₁ generations and in the control. The genitors Yoshimatsu and Hawaii 7996 showed a frequency of 100% of plants resistant to bacterial wilt, with a score of 1, characterized by the absence of symptoms. These results corroborate Costa et al. (2018), who found the same percentage of plants of the Yoshimatsu cultivar resistant to wilt. Mendes et al. (2018) working with Yoshimatsu and Hawaii 7996 observed a low incidence of bacterial wilt, with only 2.77% of plants showing symptoms. It is worth noting that the studies by Costa et al. (2018) and Mendes et al. (2018) were also carried out with inoculation of Ralstonia pseudosolanacearum in the city of Recife, Pernambuco, Brazil. Therefore, the Yoshimatsu and Hawaii 7996 genotypes can be considered as resistance standards for this study.

The control IPA-7 confirmed the high incidence and severity of the CRMRs 116 isolate as it showed a high susceptibility pattern, with 100% of the plants showing symptoms of bacterial wilt. IPA-7 showed 78 plants with a score of 5, i.e. dead plants, and 21 plants with a score of 4, showing completely wilted plants. Similar results were also observed by Costa et al. (2018) in relation to the susceptibility pattern of IPA-7.

Based on the bacterial wilt data on the resistant genitors and the susceptible control, grade 2 was established as a truncation point, i.e. the point that separates resistant plants from susceptible plants according to the highest frequency of plants below and above it. Resistant plants are those with score 1 with no symptoms, or score 2 with symptoms up to 1/3 of the wilted plant; susceptible plants are those with scores 3, 4 and 5.

The F₁ generation had 66% of plants with a score of 1 and 31% with a score of 2, i.e. 97% resistant plants. In the F₂ generation, there were plants with scores in all ranges, with resistant plants with scores 1 and 2 (34%), but more frequently with scores 3 and 4 (64%). The scores outside the upper limit of the genitors in the segregating population demonstrate the occurrence of transgressive segregation towards susceptibility, indicating that the expression of resistance is probably governed by more than one gene, thus being oligogenic or polygenic in nature, and also suggesting that different loci are involved in the genetic control of the resistance trait in each genitor. A similar result was found by Silva Lobo et al. (2005), who observed transgressive segregation in F₂ populations obtained from tomato materials with different levels of resistance to bacterial spot, which is also a disease that significantly affects the crop.

The BC₁₁ generation (F₁ x Yoshimatsu) showed a pattern of bacterial wilt tending towards resistance, with 94% resistant plants and only 3% dead plants. For the other backcross BC₁₂ (F₁ x Hawaii 7996), there was also a pattern of bacterial wilt tending towards resistance, as a total of 92% resistant plants were found for only 5% dead plants. In backcrossing, each generation the proportion of heterozygous loci is reduced by 50% compared to the previous one, so the re-establishment of resistance observed indicates a strong influence of the homozygous loci. This behavior was also observed by Costa et al. (2018) in plants obtained from backcrossing using the Yoshimatsu genitor.

In general, there is an approximation to a continuous distribution, which can be seen in the F₂ generation, in which the plants are distributed in all the grades established for the scale, indicating that possibly the inheritance of resistance to R. pseudosolanacearum in the Yoshimatsu cultivar is different from the inheritance of resistance governed by Hawaii 7996 and reinforcing the idea that the inheritance of resistance is governed by more than one gene, probably oligogenic or polygenic.

Table 1 shows the mean and variance components at 20 days after inoculation obtained from the scores of bacterial wilt caused by Ralstonia pseudosolanacearum in the genitors, in the F₁, F₂, BC₁₁, BC₂₁ generations. As for the average, it can be seen that the values of the genitors were the same (1.00). The mean of the F₁ generation (1.11) is close to the mean of the resistant genitors, which indicates that the resistance character is greatly influenced by dominant alleles, since in the F₁ generation the dominant alleles overlap with the recessive ones. This hypothesis is reinforced by looking at the average of the other generations, in which F₂ and the two backcrosses tended towards resistance scores. Dominant alleles linked to resistance to bacterial wilt were also reported by Sharma and Sharma (2015).

As for gene effects, the contribution of non-additive gene effects [d] predominated over the contribution of additive gene effects [a], which shows that the deviation from the mean is mainly due to heterozygous loci. The average degree of dominance (GMD) found was greater than 1, and with a negative value, it is characteristic of an overdominant allelic interaction in the direction of resistance, reinforcing the strong influence of non-additive gene effects on the deviation from the mean.

Table 1

Averages and variances in plants of Yoshimatsu (P₁), Hawaii 7996 (P₂), generations F₁, F₂, BC₁₁, and BC₂₁; Average components; χ_c² value (p = 0.05) for test of the dominant additive model; components of variance and heritability in the broad and narrow sense in relation to resistance to bacterial wilt caused by *R. pseudosolanacearum* at 20th days after inoculation. Recife-PE, 2019
Average Components	Grades
P₁	1.00
P₂	1.00
F₁	1.11
F₂	2.65
BC₁₁	1.03
BC₂₁	1.09
m	1.1919
[a]	-0,0122
[d]	0.3030
ADD	-24.8213
χ_c²	0.0028^ns
Variance Components	Variance
V_P1	0.0000
V_P2	0.0000
V_F1	0.5464
V_F2	1.1875
V_RC11	0.5777
V_RC21	0.9156
V_E	0.1888
V_G	0.9987
V_A	0.8817
V_D	0.1169
h²_a	84.10
h²_r	74.25

m = estimated average of the parents; [a] = additive gene effect; [d] = non-additive gene effect; ADD = mean degree of dominance; χ_c² = Chi square for the test of the dominant additive model; V_E = Enviromental variance; V_G = Genetic variance; V_A = variance due to additive effects; V_D = variance due to non-additive effects; h²_a = heritability in the broad sense (%) e h²_r = heritability in the narrow sense (%).

These mean component results are consistent since the dominant additive model is efficient for explaining the inheritance of resistance to Ralstonia pseudosolanacearum in the tomato genotypes Yoshimatsu and Hawaii 7996, since its validity is proven by the fact that there are no significant differences using the chi-square test (χ_c²) at 5% probability. According to Ramalho et al. (2012) the absence of epistasis facilitates the selection process in plant breeding programs.

As for the variances, it can be seen that both the Yoshimatsu and Hawaii 7996 genitors showed zero variances, due to the full resistance reaction, in which no plants showed symptoms. In the segregating generations, variance values were higher than those of the parents, even though the crosses were between similar individuals for the resistance character, which can be explained by the recombination of genes characteristic of the generations and by the distribution of plants in all grades of the evaluation scale. Null variance for the resistant genotype (Yoshimatsu) was also found in the studies conducted by Costa et al. (2018) and Costa et al. (2019) when conducting inheritance studies for tomato resistance to Ralstonia pseudosolanacearum and Ralstonia solanacearum, respectively.

Regarding the variance components, there was a low contribution from the environmental variance (0.1888), which indicates that the variation is mostly due to genetic factors (0.9987). From the genetic variance, it can be inferred that the additive variance (0.8817) makes a greater contribution than the variance due to non-additive effects (0.1169). In this sense, it can be seen that homozygous loci make a greater contribution to the genetic variability observed in the population.

Both the broad-sense heritability (84.10%) and the narrow-sense heritability (74.25%) were considered high. Heritability is a value obtained from the selection of plants at any time during the evaluation period, and it tells us how reliably the phenotypic value is linked to the genotypic value (Vencovsky 1987). Characteristics with high heritability are very favorable for the efficient selection of superior individuals in breeding programs, since they have low environmental influence, thus reducing the chances of good genotypes being masked or bad genotypes being mistakenly chosen, and high heritability provides greater certainty that the resistance characteristic will be maintained throughout the generations. It is worth noting that the high heritability in the restricted sense confirms the greater contribution of additivity to the resistance trait and corroborates Costa et al. (2018) and Costa et al. (2019) who observed a greater increase in additive gene effects when studying the inheritance of resistance to bacterial wilt in the Yoshimatsu and IPA-7 cultivars.

Table 2 shows the summary values of the analysis of variance, genetic and phenotypic parameters of the 60 F_2:3 tomato progenies at 20 days after inoculation, obtained from the scores for bacterial wilt caused by Ralstonia pseudosolanacearum. Using the F-test at 1% probability, it was possible to identify significant differences between the F_2:3 progenies. The significance of this test indicates the existence of variability among the progenies, a very favorable factor for the development of plant breeding programs. Based on this preliminary analysis, it is important to identify how much of the phenotypic variability found among the progenies is genetic or environmental in nature. To this end, the genetic parameters were estimated using analysis of variance with information on the plot, as done by Costa et al. (2018) in segregating tomato progenies. It is worth noting that Costa et al. (2018) also found variability between progenies using this methodology.

Table 2

Summary of the analyses of variance, genetic and phenotypic parameters of 60 F_2:3 tomato progenies and their respective parents in relation to scores for bacterial wilt caused by Ralstonia pseudosolanacearum at 20 days after inoculation. Recife-PE, 2019
Source of variation	Grades / Parameters
Blocks	1.8178
Progenies	29.9728**
Between progenies	0.5296
Within progenies	0.3744
Average	3.3088
V_F total	2.3726
V_F between progenies	0.3744
V_G within progenies	1.9628
V_G between de progenies	0.3271
V_E environmental	0.0310
h² progeny average (%)	98.23
h² within progenies (%)	87.37
h² individual block (%)	96.69
h² individual experiment (%)	96.52
CV_e% experimental (CV1)	9.83
CV_ee% experimental between (CV2)	5.32
CV_ge% genetic between (CV3)	42.34
CV_gd% genetic within (CV4)	17.28
e between progenies	7.95
e within progenies	3.25

**: Significant at 1% probability by the F test. e between families: CVge/CVee. e within families: CVgd/CVee.

Considering the principle that phenotypic variance is the result of the sum of genetic factors, environmental factors and their interaction, the analysis of variance shows that the variability in the F_2:3 progenies is mainly due to genetic factors. The total phenotypic variance was 2.3726 and the genetic variance between the progenies was 1.9628, indicating high genetic variability and showing that around 83% of the variance observed is due to genetic factors, a fact that is further reinforced by the smaller contribution of the environmental variance of 0.0310.

The phenotypic variance within progenies was 0.3744 and, as between progenies, a greater genetic contribution to phenotypic expression can be seen when observing the genetic variance within progenies of 0.3271, i.e. around 89% of the phenotypic variance is inherent to genetic factors. There is a greater contribution from the genetic part between progenies than within progenies, a fact that was also observed by Costa et al. (2018). Genetic variance is the most important portion of phenotypic variance for the success of the selection process in breeding programs, so knowledge of genetic parameters in bacterial wilt resistance experiments is fundamental (Borém et al. 2021).

The estimates of the coefficients of heritability at progeny mean level (98.23%) and at plant level within blocks and within experiment (96.52%) exceed those within progenies (87.37%). It is therefore clear that selection based on progenies and at plant level within blocks and within experiments is more efficient than selection based on plants within progenies, although the latter type of selection should not be ruled out, as the heritability values, regardless of the method, were high. According to Vencovsky (1987) and Borém et al. (2021), the estimate of the heritability coefficient tells us how reliably the phenotypic value represents the genotypic value, in other words, how accurate it is. Nevertheless, considering the high heritabilities found, it is clear that there is a high probability of efficiency in selecting plants resistant to Ralstonia pseudosolanacearum, as well as predicting high genetic gains. The total experimental coefficient of variation (CV1) and the experimental coefficient of variation between progenies (CV2) showed values of 9.83% and 5.32, respectively, which are low and, according to Ferreira criteria (2018), indicate good experimental precision (Table 4).

The e between progenies (CV3/CV2) represents the ratio between the genetic coefficient of variation between progenies divided by the experimental coefficient of variation between progenies. O e within progenies (CV4/CV2) represents the ratio between the genetic coefficient of variation within progenies divided by the experimental coefficient of variation between progenies. According to Vencovsky (1987), both parameters indicate the feasibility of practicing selection that allows for good genetic gains. The two indices showed values of 7.95 and 3.25, respectively, and these ratios, which are well above 1.0, reinforce what was discussed earlier, in terms of the genetic contribution being significantly higher than the environmental influence, as well as indicating high genetic variability, especially between progenies, showing that there is sufficient variability to allow for improvement and selection between and within progenies in relation to bacterial wilt caused by Ralstonia pseudosolanacearum.

Table 3 shows the Scott-Knott grouping test of 60 F_2:3 tomato progenies and their respective parents, Hawaii 7996 and Yoshimatsu, at 20 days after inoculation based on the scores for bacterial wilt caused by Ralstonia pseudosolanacearum. It was possible to classify the F_2:3 progenies and genitors into two groups, separating them into phenotypic classes of resistance and susceptibility. The first group consisted of the cultivars Yoshimatsu, Hawaii 7996 and 18 progenies that did not differ significantly from each other and were therefore considered resistant to bacterial wilt caused by Ralstonia pseudosolanacearum: 2, 5, 7, 8, 11, 12, 13, 17, 20, 21, 33, 40, 45, 47, 50, 55, 59 and 60. The second group corresponded to 42 families that differed statistically from the resistant cultivars and were therefore classified as susceptible to Ralstonia pseudosolanacearum: 1, 3, 4, 6, 9, 10, 14, 15, 16, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 46, 48, 49, 51, 52, 53, 54, 56, 57 and 58.

Table 3

Scott-Knott grouping of 60 F_2:3 tomato progenies and their respective parents, Hawaii 7996 and Yoshimatsu, at 20 days after inoculation based on the scores for bacterial wilt caused by Ralstonia pseudosolanacearum. Recife-PE, 2019
Progenies	Grades	Progenies	Grades
Hawaii 7996	1.00 a	Progeny F_2:3 # 30	4.06 b
Yoshimatsu	1.00 a	Progeny F_2:3 # 31	4.20 b
Progeny F_2:3 # 1	4.33 b	Progeny F_2:3 # 32	4.13 b
Progeny F_2:3 # 2	1.00 a	Progeny F_2:3 # 33	1.00 a
Progeny F_2:3 # 3	4.26 b	Progeny F_2:3 # 34	4.20 b
Progeny F_2:3 # 4	4.33 b	Progeny F_2:3 # 35	4.06 b
Progeny F_2:3 # 5	1.00 a	Progeny F_2:3 # 36	3.93 b
Progeny F_2:3 # 6	4.20 b	Progeny F_2:3 # 37	4.26 b
Progeny F_2:3 # 7	1.00 a	Progeny F_2:3 # 38	4.13 b
Progeny F_2:3 # 8	1.00 a	Progeny F_2:3 # 39	3.93 b
Progeny F_2:3 # 9	4.53 b	Progeny F_2:3 # 40	1.00 a
Progeny F_2:3 # 10	4.20 b	Progeny F_2:3 # 41	4.53 b
Progeny F_2:3 # 11	1.00 a	Progeny F_2:3 # 42	3.80 b
Progeny F_2:3 # 12	1.00 a	Progeny F_2:3 # 43	4.26 b
Progeny F_2:3 # 13	1.00 a	Progeny F_2:3 # 44	4.00 b
Progeny F_2:3 # 14	4.06 b	Progeny F_2:3 # 45	1.00 a
Progeny F_2:3 # 15	4.40 b	Progeny F_2:3 # 46	4.06 b
Progeny F_2:3 # 16	4.13 b	Progeny F_2:3 # 47	1.00 a
Progeny F_2:3 # 17	1.00 a	Progeny F_2:3 # 48	4.13 b
Progeny F_2:3 # 18	4.13 b	Progeny F_2:3 # 49	4.26 b
Progeny F_2:3 # 19	4.06 b	Progeny F_2:3 # 50	1.00 a
Progeny F_2:3 # 20	1.00 a	Progeny F_2:3 # 51	4.20 b
Progeny F_2:3 # 21	1.00 a	Progeny F_2:3 # 52	4.20 b
Progeny F_2:3 # 22	4.26 b	Progeny F_2:3 # 53	4.13 b
Progeny F_2:3 # 23	4.00 b	Progeny F_2:3 # 54	4.13 b
Progeny F_2:3 # 24	3.80 b	Progeny F_2:3 # 55	1.00 a
Progeny F_2:3 # 25	4.26 b	Progeny F_2:3 # 56	4.06 b
Progeny F_2:3 # 26	3.73 b	Progeny F_2:3 # 57	3.66 b
Progeny F_2:3 # 27	4.26 b	Progeny F_2:3 # 58	4.00 b
Progeny F_2:3 # 28	4.33 b	Progeny F_2:3 # 59	1.00 a
Progeny F_2:3 # 29	4.33 b	Progeny F_2:3 # 60	1.00 a

Averages followed by the same letter in the column do not differ significantly by the Scott-Knott grouping test at 5% probability.

Based on the phenotypic distribution of resistance and susceptibility of the 60 F_2:3 progenies and considering that the loci involved in resistance are different between the cultivars, according to the results discussed, Table 4 shows the hypotheses for the genetic control of resistance. The chi-square test was used by Carvalho Filho et al. (2011), Batista et al. (2013) Costa et al. (2018) and Costa et al. (2019) to identify and test hypotheses about the number of genes linked to the inheritance of resistance. The first hypothesis tested was that of a gene, in which resistance would be promoted in recessive homozygosity arising from the genetic inheritance of the Yoshimatsu cultivar, or dominant homozygosity due to the contribution of the Hawaii 7996 cultivar, in which a 2:2 phenotypic segregation would be expected. The test for this hypothesis showed significant differences using the chi-squared test at 5% probability, indicating that resistance is governed by more than one gene.

Table 4

Chi square tests (χ_c²) (p = 0.05) for genetic control hypotheses of resistance to R. pseudosolanacearum in Yoshimatsu and Hawaii 7996 tomato cultivars at 20th days after inoculation in 60 F2:3 progenies. Recife-PE, 2019
Fenotypes	FO	FE	Genotypes
One gene
Resistant	18	30*	AA; aa
Susceptible	42	30*	Aa
Two gene
Resistant	18	26*	AABB; aabb; Aabb; AAbb; AABb
Susceptible	42	34*	AaBB; aaBb; aaBB; AaBb; AaBB
Three gene
Resistant	18	17.8 ^ns	AABBDD; aabbdd; Aabbdd; Aabbdd; AAbbDd; AAbbDD; AABbdd; AABbDd; AABbDd; AABBdd; AABBDd;
Susceptible	42	42.2^ns	AaBBDD; aabbDd; aabbDD; aaBbdd; aaBbDd; aaBbDD; aaBBdd; aaBBDd; aaBBDD; AabbDd; AabbDD; AaBbdd; AaBbDd; AaBbDD; AaBBdd; AaBBDd; AaBBDD

Expected and observed frequency for 1, 2 and 3 genes with partial dominance and/or recessive dominance. *= Significant at 5% probability. ns = Not significant at 5% probability. χ_c² at 5% probability: 3.84.

The second hypothesis tested was the existence of two genes, in which, similar to the previous hypothesis, resistance would be caused by the expression of the locus in recessive homozygosity due to the genetic inheritance of the Yoshimatsu cultivar and/or dominant homozygosity due to the contribution of the Hawaii 7996 cultivar, in which the expected phenotypic ratio would be 13:17. In this case, significant differences were also obtained using the chi-square test at 5% probability, indicating that more than two genes were involved.

Next, the three-gene hypothesis was tested, considering that resistance would be governed by the expression of two genes in recessive homozygosity arising from the genetic inheritance of the Yoshimatsu cultivar and/or dominant homozygosity due to the contribution of the Hawaii 7996 cultivar, in which the expected phenotypic segregation would be 9:21. This hypothesis was confirmed, as the chi-squared test showed no significant differences at 5% probability.

Given these results, it can be inferred that resistance to R. pseudosolanacearum in the progenies evaluated has an oligogenic character, resulting from the action of three genes, two of which are the contribution of the Yoshimatsu cultivar and conditioned by recessive alleles, and one characterizing the contribution of the Hawaii 7996 cultivar, conditioned by dominant alleles with a partial dominance effect. These results corroborate those observed by Costa et al. (2018) who, when evaluating the resistance of the Yoshimatsu cultivar to R. pseudosolanacearum, pointed to the action of two genes of greater effect in recessive homozygosis as being responsible for the resistance characteristic. Grimault et al. (1995) in inheritance studies with the Hawaii 7996 cultivar in relation to resistance to bacterial wilt also indicate dominant monogenic genetic control, although they did not observe a partial effect of dominance.

It is worth noting that the confirmation of the hypothesis that the genetic inheritance of resistance to R. pseudosolanacearum is the result of different loci in the Yoshimatsu and Hawaii 7996 cultivars provides important contributions to breeding programs, offering information that can be exploited to develop new sources of resistance that include the loci of both cultivars, opening up possibilities for the development of resistant cultivars with lower chances of resistance being broken by the pathogen.

In the Yoshimatsu genotype, the inheritance of resistance to Ralstonia pseudosolanacearum is governed by two major effect genes in recessive homozygosity. In the Hawaii 7996 genotype, the inheritance of Ralstonia psedosolanacearum resistance is governed by a gene with partial dominance action. The greatest contribution of gene effects was due to additive variance. For Ralstonia psedosolanacearum it is recommended to select 20 days after inoculation between and within F2:3 progenies.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author Contribution

Author ContributionsAll the authors contributed to the conception and design of the study. Material preparation, data collection and analysis were carried out by Djayran Sobral Costa, Ester da Silva Costa and Kleyton Danilo da Silva Costa. Professors Elineide Barbosa de Souza, Rejane Rodrigues da Costa e Carvalho, Adriano Márcio Freire e Silva and José Luiz Sandes de Carvalho Filho were responsible for providing guidance on genetic improvement and phytobacteriology, as well as contributing to the writing of the article. The first draft of the manuscript was written by Djayran Sobral Costa and all the authors commented on the previous versions of the manuscript. All the authors have read and approved the final manuscript.

Acknowledgement

Acknowledgements To the Laboratory of Phytobacteriology of the Federal Rural University of Pernambuco for the support in the stages of inoculation and to the National Council for Scientific and Technological Development for the doctoral scholarship.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Inheritance of resistance to Ralstonia pseudosolanacerum in tomato genotypes Yoshimatsu and Hawaii 7996

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