FHB): Resistance Components, QTL Identiﬁcation, and Phenotyping—A Review

plants

Review Updating the Breeding Philosophy of Wheat to Fusarium Head Blight (FHB): Resistance Components, QTL Identiﬁcation, and Phenotyping—A Review

Akos Mesterhazy

Cereal Research Non-Proﬁt Ltd., 6726 Szeged, Hungary; [email protected]

Received: 21 October 2020; Accepted: 30 November 2020; Published: 3 December 2020

Abstract: Fusarium head blight has posed continuous risks to wheat production worldwide due to its effects on yield, and the fungus provides additional risks with production of toxins. Plant resistance is thought to be the most powerful method. The host plant resistance is complex, Types I–V were reported. From the time of spraying inoculation (Type I), all resistance types can be identified and used to determine the total resistance. Type II resistance (at point inoculation) describes the spread of head blight from the ovary to the other parts of the head. Therefore, it cannot solve the resistance problem alone. Type II QTL (quantitative trait locus) Fhb1 on 3BS from Sumai 3 descendant CM82036 secures about the same resistance level as Type I QTL does on 5AS and 5ASc in terms of visual symptoms, FDK (Fusarium damaged kernel), and deoxynivalenol response. Recently, increasing evidence supports the association of deoxynivalenol (DON) content and low kernel infection with FHB (Fusarium head blight) resistance (Types III and IV), as QTL for individual resistance types has been identified. In plant breeding practice, the role of visual selection remains vital, but the higher correlations for FDK/DON make it possible to select low-DON genotypes via FDK value. For phenotyping, the use of more independent inocula (isolates or mixtures) makes resistance evaluation more reliable. The large heterogeneity of the mapping populations is a serious source of underestimating genetic effects. Therefore, the increasing of homogeneity is a necessity. As no wheat varieties exist with full resistance to FHB, crops must be supported by proper agronomy and fungicide use.

Keywords: toxin overproduction; disease index; resistance to DON; resistance to FDK; Fusarium damaged kernel; deoxynivalenol; diﬀering QTL functions; upgrading phenotyping of QTLs; resistance types; breeding for complex FHB resistance

1. Introduction Fusarium head blight (FHB) is one of the main diseases of wheat, and the toxins produced by the fungus make it a feared problem. FHB is a very complex disease of wheat; many genetic and non genetic eﬀectors modify disease response [1–4]. In the review, we concentrate on three important points that were not in the centrum of the research, therefore they were rather neglected in the past decades (problems in resistance evaluation, the resistance types, and the phenotyping problems in the quantitative trait locus (QTL) analyses and recognition of their role in forming genetic responses). However, they are important for breeding and Fusarium research in wheat and other small grains. This treatise is not intended to evaluate the molecular biology methodologies as QTL (quantitative trait locus) analysis, genome wide association mapping, and other methods. All depend on the quality and reliability of phenotyping. As we have a long experience with working with four or more isolates in a test [5,6], and in all tests, we evaluated visual symptoms as disease index (DI), FDK (Fusarium

Plants 2020, 9, 1702; doi:10.3390/plants9121702 www.mdpi.com/journal/plants Plants 2020, 9, 1702 2 of 33 damaged kernel), and DON (deoxynivalenol), we come to conclusions that can be useful also in other research groups. In the phenotyping (resistance studies, genetic analyses, host-pathogen relation studies, and all tests that need artificial inoculation), normally one inoculum is used. This can be a conidium suspension from a single isolate, can be a mixture of different isolates, can be mycelium or a mixture of conidia and mycelium, and also ascospores can be used. Many authors use mixtures of different isolates [5] or a single isolate [6]. The authors cited in [5,6] used very variable conidium concentrations, without any reference to aggressiveness. The resistance tests were conducted with single isolates or mixtures by spraying or point inoculation, and in several experiments, both were applied [7–20]. Gadaleta et al. [21] used spraying inoculation and visual symptoms were recorded. In the papers, incidence (% of the infected spikes), severity (the extent of infection within spike), and disease index (DI) were used. They evaluated the extent of the infection of the spike, e.g., severity, and this was addressed as Type II resistance. However, Type II resistance can be identified only by point inoculation. Spreading of the infection is a general phenomenon in diseases, and during epidemics, steadily growing numbers are recorded. Type I resistance has this spreading function, which causes issues. Ma et al. [22] underlines this problem and stresses the problem of identification of Type I resistance, which was also mentioned by Dill-Macky [23]. The lack of testing for FDK and DON is another problem. Lemmens et al. [24] say that the final goal of breeding is to decrease the amount of fungal toxins. However, they added that mycotoxin analysis is normally not conducted as it is costly, the same for FDK analysis, and only at the end of the breeding process will a toxin test be performed, if at all. Our problem is that QTL identifications are done for genetic work, and except for several QTL, we never can be sure that a visual symptom reduction is followed by toxin reduction. As QTLs were identified with different effects on visual symptoms, FDK, and DON, there is a chance to observe fewer symptoms but no reduction in DON [25] or even an increase in DON [26] (toxin overproduction). Schroeder and Christensen [27] described Type II resistance (resistance to spreading) as a response to the point or single floret inoculation. However, the spreading following spray inoculation cannot be described as Type II resistance as done in [21]. The spreading function is also found in Type I resistance (area under disease progress curve) [28]. For this and other reasons, we have to rethink the problem of the resistance types/components. Liu et al. [29] used corn grains infected by a mixture of 20 F. graminearum strains with chemotypes 3- and 15-ADON (acetyl DON) for spawn inoculation. As heading date was variable (no information on the extent), the early genotypes had much longer irrigation times than the later ones, which can cause a stronger infection in the earlier genotypes. Such an analysis has not been conducted. FDK and DON were not tested. This is a problem as farmers want to have low DON. Mendes et al. [30] tested FDK and DON against F. graminearum and F. meridionale, which is a nivalenol (NIV) producer. It is important that severity (meaning here disease index) against F. graminearum was between 99.2% and 100% without significant difference, and the less pathogenic F. meridionale had between 64.6% and 97.1% severity, but the FDK was very diverse with values between 21% and 97%. DON values were between 45 and 691 mg/kg, and NIV values were between 4.3–355 mg/kg. The data are not supported by LSD values or LSD values were not calculated. Their data show clearly that the three traits are not synonyms. In cultivar Quartzo, 691 mg/kg DON was measured at 99.8% severity with a very strong DON overproduction. In CD105, 45 mg/kg DON was found, but NIV had 355 mg/kg, which seems to have been also an overproduction. The results of [30] seem to support the toxin overproduction proved earlier [26]. This aspect was not discussed in the paper [30]. It is also important that the FHB resistance cannot be characterized only by the visual symptoms. The data show large variations, such as that in FDK, the difference between 97.1% and 40.6% was not statistically significant. This shows that a much higher amount of data is required, but we can suppose methodical problems were also a factor. Amarashinghe [31] is another exception; she had seven isolates in a fungicide test with highly susceptible and moderately resistant wheat varieties. The results of the 3-ADON and 15-ADON isolates Plants 2020, 9, 1702 3 of 33 were pooled, and an analysis to measure possible differences between isolates has not been conducted. The cultivar/isolate interaction was significant in 2009, identifying different responses of the cultivars to the different isolates, but in 2010, no significant interaction was found. It is important that both FDK and DON were analyzed. Of course, the possibility was there to view the individual isolates separately, but this analysis was not conducted. As the publication did not contain such data, further analysis was impossible. Chen et al. [32] worked with three isolates separately, including three replicates and two plants, by point inoculation. Only visual symptoms were recorded and were pooled, so the data according to isolates could not be analyzed. Many QTL and resistance analyses were conducted using a single isolate at very different conidium concentrations [8,14,16,17,19,20,33], or by mixtures [5]. For these reasons, the objectives of the paper are:

1. Summarize the experimental methods and their uses for phenotyping supplemented with FDK and DON measurements and symptom evaluation. 2. Evaluation of new developments in determining Type I and Type II resistance and their significance for breeding and research. 3. The role of Type III (FDK) and Type IV (DON) in the resistance of FHB in wheat. 4. The role of QTL in the resistance to different traits (DI, FDK, DON) and influence of flowering variation on the mapping QTLs and its influence on the QTL performance. 5. How the breeding of more resistant cultivars can be enhanced by the new insights.

2. Advances in Phenotyping As shown before [5,6], the general praxis is that the artificial inoculations are made nearly exclusively with one inoculum from a single isolate or a mixture of different isolates. Therefore, the first issue is determining if the response against one inoculum can properly describe the amount of resistance and the differences in resistance. In about 70% of the studies, only visual rating was done. The question is, how well can the visual rating describe FHB resistance? The data show that the very variable conidium concentrations applied led to very different disease severities. However, papers applying the same conidium concentration cannot secure a similar level of infection. So, an arbitrary chosen conidium concentration could not regulate the aggressiveness level in the tests, but the amount of inoculum inoculated indeed affect severity of the disease. This is not surprising as there are genetic differences in aggressiveness between isolates, but the resistance level is also different. As no aggressiveness control was made, the regulation of the conidium concentration had no experimental basis. Experimental data prove that the dilution influences the aggressiveness of the inoculum differently for different isolates and its influence cannot be predicted [6]. The mixing of isolates is a similar situation. The result of the mixing cannot be predicted well, and the aggressiveness of the mixture never reached the aggressiveness of the most aggressive component [5], but could balance to some extent the low-aggressiveness partner in the mixture. Mesterhazy et al. [34] conducted tests with eight isolates of F. graminearum and F. culmorum. The results were not evaluated in this respect, so we concentrated on the means. The analysis has further lessons. Figure1 shows the disease index data for the eight isolates. Among the genotypes, several represented highly resistant ones from Sumai 3 (Sum3) and Nobeoka Bozu (NB) backgrounds (FHB breeding program, Szeged). The more resistant genotypes showed lower variation below five, but 78.1.04 had a variance of 262. As in the published data, only one inoculum was used, and the figure clearly shows that the genotypic differences had a large variation for the different isolates. There is only a low reliability of the identification of medium or lower effective QTLs. The large genotype differences can be identified with a large probability when aggressiveness is high. There is an exception; GK Zombor had a variance of only 56 compared to 262 of line 78.0.04. This can explain the artifacts in the QTL analyses and the false resistance or susceptibility classification of the given genotype. The means of isolates shows the level of resistance better than any of the single isolates. The number of Plants 2019, 8, x FOR PEER REVIEW 4 of 33

70.0 Plants 2020, 9, 1702 4 of 33 60.0 Fg12377 Fg207 50.0 correlations between cultivar reactions to the eight isolates was significant in 112 casesFc12375 of the three 40.0 Fc12551 hundred,Plants 2019, 37%.8, x FOR This PEER shows REVIEW well the differing responses of the genotypes. 4 of 33 30.0 Fc89.4 Fg40 70.020.0 Disease Index % Fg44 60.010.0 Fc223Fg12377 MeanFg207 50.00.0 S S A R R S S S P P H 8 B R K S K S J G 7 Ő Z Z C Fc12375 g g r S S io K u 2 4 D 1 e e i z e g b ó 8 r o u s v v in t z u 8 m 1 3 8 .6 n c n ő n v j5 b .1 s m g a 40.0 -N -N a -M tá x 0 3 1 7 4 0 c h cs k d -G 0 é . é b o b Fc12551 B r l 9 ^ 8 1 .4 -N e s ő e e T 04 g o ly a B M a 0 2 .8 .8 2 le . r /M /M /N n - 9 8 B r .. M d 8 /K M B 1. ő Fc89.4 30.0 -S -S 6 u u 1 m m 3 3 Fg40 20.0 a b Disease Index % Fg44 10.0 Fc223 Figure 1. Resistance of winter wheat lines to F. graminearum and F. culmorum isolates 1994–1996.Mean 0.0 Disease index, [34]. Larger variations in surveying of the susceptible cultivars could suggest that the S S A R R S S S P P H 8 B R K S K S J G 7 Ő Z Z C g g r S S io K u 2 4 D 1 e e i z e g b ó 8 r o u s disease developmentv v in t is morez u variable8 m 1 in 3susceptible8 .6 n cultivarsc n ő duen tov a jmixture5 b .1 ofs factorsm g includinga -N -N a -M tá x 0 3 1 7 4 0 c h cs k d -G 0 é . é b o b B r l 9 ^ 8 1 .4 -N e s ő e e T 04 g o ly a B M a 0 2 .8 .8 2 le . r the genotypes./ MThe/M most /resistantN n ones- have9 high8 stabilityB andr low variation... M d 8 /K M B 1. ő -S -S 6 u u 1 m m 3 The FDK data (Figurea 3b 2) show a similar picture with the difference that even among susceptible cultivars there can be reactions close to zero. The variation in response to different isolates is larger than that seen in the disease index. The variation is also higher. The two most resistantFigure lines 1.1. Resistance Resistancehad their ofvariance of winter winter belowwheat wheat 10lines lines (0.4 to to and F.F. graminearum 8.9), and the andand highest F. culmorum was found isolates in GK1994–1996. Zugoly, 472.Disease The mean index, of [[34].34the]. eightLarger isolates variations give in a surveying much more of the reliable susceptible resistance cultivars ranking could suggestthan any that of the the isolatesdisease separately developmentdevelopment (n = 8). is is more Inmore FDK, variable variable 85 correlations in in susceptible susceptible were cultivars cultivars significant due due to from to a mixture a themixture 300, of factors28%.of factors including including the genotypes.theThe genotypes. DON Theresponse The most most resistant of theresistant cultivars ones ones have tohave high the high eight stability stability isolates and and low (Figure low variation. variation. 3) presents a similar picture, as shown in Figures 1 and 2. As susceptibility grows, the variation showed an increasingly wider The FDK data (Figure2) show a similar picture with the di fference that even among susceptible stripe.The Five FDK genotypes data (Figure had lower 2) varianceshow a thansimilar five picturedid, two withhave valuethe difference between 5 andthat 10,even and amongthe cultivarssusceptiblehighest therenumber cultivars can was be there reactions393. canThe close behigher reactions to zero.the resistance Theclose variation to zero.is, the inThe responselower variation the to variance. diinff erentresponse isolatesSeventy to ‐different isnine larger thanisolatescorrelations that is seen larger (26%) in the than of disease thethat possible seen index. in The300 the variationshowed disease significantindex. is also higher.The correlation variation The two atis most palso = 0.05 resistanthigher. or higher. The lines two had This most their varianceresistantmeans that belowlines the had 10 high (0.4 their andresistance variance 8.9), and belowgives the highest a10 much(0.4 wasand better found8.9), chance and in GKthe to Zugoly,highest achieve 472.was lower Thefound meanor inlow GK of DON the Zugoly, eight isolates472.contamination The give mean a much ofin theall more epidemiceight reliable isolates situations. resistance give aFor rankingmuch the moremost than susceptible anyreliable of the resistance isolates genotypes, separately ranking any valuethan (n = 8).anycan In beof FDK, the 85isolatesfound correlations between separately were 5 and (n significant =40 8). or In 9 andFDK, from 59 85 mg/kg. the correlations 300, This 28%. confirms were significant earlier data from [3,34]. the 300, 28%. The DON response of the cultivars to the eight isolates (Figure 3) presents a similar picture, as shown 90in Figures 1 and 2. As susceptibility grows, the variation showed an increasingly wider stripe. 80Five genotypes had lower variance than five did, two have value between 5 and 10, and the 207FgD highest 70number was 393. The higher the resistance is, the lower the variance. Seventy‐nine 12375FcH correlations (26%) of the possible 300 showed significant correlation at p = 0.05 or higher. This 60 12377FgH means that the high resistance gives a much better chance to achieve lower or low DON % 50 12551FcH contamination in all epidemic situations. For the most susceptible genotypes, any value can be 40FgA found FDK between40 5 and 40 or 9 and 59 mg/kg. This confirms earlier data [3,34]. 89.4FcF 30 44FgA 9020 223FcD 10 80 Mean 207FgD 700 Sgv-NB.Sgv-NB252Ar RSt-MM/NBRSz Sum3^8 SK8 Sgv-GT...Be P HD KincSz Ken Sio CsabaRe P GóbéZo 7 Jbj-5oŐ Zugoly 12375FcH 1 4 2 8 r in .6 n 371 ő c 118 m .1 s 60 tá r 0 0 ce 8 k d ux hsl . é a - 9 4. s e e bor 0 g 12377FgH N 0 . 4 ő la .8 4 2-8 B/Kő 88 2 n e .. d r 9 % 50 1 12551FcH .6 0 40FgA FDK FDK 40 89.4FcF 30 Figure 2. Resistance of winter wheat lines F. graminearum and F. culmorum isolates 1994–1996. Fusarium44FgA 20 damaged kernels (FDK), percentage, [34]. Larger variations in surveying of the susceptible cultivars223FcD 10 could suggest that the disease development is more variable in susceptible cultivars due to a mixtureMean of factors0 including the genotypes. The most resistant ones have high stability and low variation. Sgv-NB.Sgv-NB252Ar RSt-MM/NBRSz Sum3^8 SK8 Sgv-GT...Be P HD KincSz Ken Sio CsabaRe P GóbéZo 7 Jbj-5oŐ Zugoly 1 4 2 8 r in .6 n 371 ő c 118 m .1 s tá r 0 0 ce 8 k d ux hsl . é a - 9 4. s e e bor 0 g N 0 . 4 ő la .8 4 2-8 B/Kő 88 2 n e .. d r 9 1 .6 0

Plants 2020, 9, 1702 5 of 33

The DON response of the cultivars to the eight isolates (Figure3) presents a similar picture, as shown in Figures1 and2. As susceptibility grows, the variation showed an increasingly wider stripe.Plants 2019 Five, 8, x genotypes FOR PEER REVIEW had lower variance than five did, two have value between 5 and 10,5 and of 33 the highest number was 393. The higher the resistance is, the lower the variance. Seventy-nine correlations Figure 2. Resistance of winter wheat lines F. graminearum and F. culmorum isolates 1994–1996. p (26%)Fusarium of the possible damaged 300 kernels showed (FDK), significant percentage, correlation [34]. atLarger= 0.05 variations or higher. in Thissurveying means of that the the high resistancesusceptible gives cultivars a much could better suggest chance that to achievethe disease lower development or low DON is more contamination variable in susceptible in all epidemic situations.cultivars For due the to mosta mixture susceptible of factors genotypes, including the any genotypes. value can The be most found resistant between ones 5 have and 40high or 9 and 59 mgstability/kg. This and confirms low variation. earlier data [3,34].

80.0

70.0 89.4FcF 12375FcH 60.0 12377FgH 50.0 207FgD 40.0 12551FcH 30.0 44FgA DONmg/kg 20.0 40FgA 223FcD 10.0 Mean 0.0 S A S R R H 8 P S S P S R S K K B S 7 G J C Z Ő Z g r g i S D 1 4 K io 2 u e g i e e z 8 ó b s u r o v in v n t 8 .6 3 8 1 m c v n n n ő .1 b j- a g s m - a - g - 4 0 7 0 u 1 h - c d c k . é 5 b o é N N ó M . - 1 9 x 8 3 s G s e e e 0 o a l g b B B S M 4 N .8 0 la .8 ^ l T ő 4 y o * * 2 B 8 n 9 2 e . r M M ta /N / d -8 r .. M M r B K 1 ő .6 -S -S 0 u u m m 3 3

Figure 3. Resistance of of winter winter wheat wheat lines lines and and cultivars cultivars to to F. F.graminearum graminearum andand F. culmorumF. culmorum isolates,isolates, 1994–1996.1994–1996. Deoxynivalenol Deoxynivalenol (DON) (DON) contamination, contamination, mg/kg mg /[34].kg [ 34Larger]. Larger variations variations in surveying in surveying of the of thesusceptible susceptible cultivars cultivars could could suggest suggest that that the thedisease disease development development is more is more variable variable in insusceptible susceptible cultivars duedue to to a a mixture mixture of of factors factors including including the the genotypes. genotypes. The The most most resistant resistant ones ones have have high stabilityhigh andstability low and variation. low variation.

The correlations between between traits traits for for each each isolate isolate were were counted counted for for the the 25 25genotypes genotypes (Table (Table 1).1 ). TheThe DI/FDK DI/FDK correlations correlations varied varied between between r = r 0,= 640, and 64 andr = 0.86, r = the0.86, DI/DON the DI correlation/DON correlation ranked from ranked fromr = 0.74 r =to 0.74r = 0.87, to r and= 0.87, the FDK/DON and the FDK correlations/DON correlations varied between varied r = 0.69 between and r r = =0.89.0.69 The and patterns r = 0.89. Theseem patterns to be different, seem to beand di whenfferent, considering and when consideringthe correlations the correlationsbetween experimental between experimental means, DI/FDK means, DIgives/FDK r = gives 0.84, rthe= 0.84, DI/FDK the DIpresents/FDK presents r = 0.89, r and= 0.89, FDK/DON and FDK shows/DON r shows= 0.81. r Similar= 0.81. patterns Similar patternswere werefound found [35], but [35], significantly but significantly higher higher correlations correlations were found were found for FDK/DON for FDK/ DONthan for than DI/DON. for DI/DON. In Inother other tests, tests, different different results results were were found; found; the the DI/DON DI/DON had had much much lower lower values values than than the the FDK/DON FDK/DON correlationscorrelations [[6,26,36–38].6,26,36–38]. The three traits shared several common features. The most resistant genotypes had low variation, and theTable medium 1. Isolate and specific highly correlations susceptible between genotypes traits of showed the second increasing resistance deviations tests with eight in responses isolates to the of F. graminearum and F. culmorum, 1994–1996. No. of genotypes = 25 [34]. different isolates. The genotype differences between isolates showed higher differentiation in the most aggressive isolatesIsolate and in the lower-aggressiveCorrelations isolates, the diBetweenfferentiation was much lower and the genotype differences were less expressed.DI/FDK The genotypeDI/DON ranking between FDK/DON genotype reactions to the eight isolatesFc12375 was diff erent, and the highest0.6420 rate of significant0.8756 correlations 0.6963 of the 300 possible correlations betweenFc12551 the genotype responses0.7449 to the eight isolates0.8315 was found for DI0.7316 (37%), while for FDK and DON, it was lowerFc223 (26 and 28%). This means0.8037 that the resistance0.8695 ranking of0.7826 the genotypes to the eight isolates showedFc89.4 a high variability for the0.8435 isolates and so the0.7615 resistance ranking0.7854 to the individual isolates was not reliableFg12377 enough to use it as0.7997 a reliable phenotyping0.7949 result. When0.8942 there was only one inoculum, it couldFg207 be any of the eight types.0.7142 When they were0.7545 analyzed in separate0.8101 QTL analyses, we could see the diffFg40erences. We will come back0.7458 to this problem0.8399 later. It is also0.7256 clear that the mean of Fg44 0.8571 0.7388 0.7483 Mean 0.8357 0.8855 0.8088 Gen. Mean 0.7689 0.8083 0.77176 All are significant at p = 0.001 Plants 2020, 9, 1702 6 of 33 the eight isolates reflects the amount of the resistance more precisely than any of the isolates separately.

ThisPlants may2019, 8 cause, x FOR a PEER significant REVIEW error in the phenotyping and can influence the QTLs to be identified.6 of 33

Table 1. IsolateFg: speciﬁcF. graminearum correlations, Fc between: F. culmorum traits of the second resistance tests with eight isolates of F. graminearum and F. culmorum, 1994–1996. No. of genotypes = 25 [34].

The three traits Isolateshared several common Correlationsfeatures. The Between most resistant genotypes had low variation, and the medium and highly susceptible genotypes showed increasing deviations in DI/FDK DI/DON FDK/DON responses to the different isolates. The genotype differences between isolates showed higher differentiation in theFc12375 most aggressive 0.6420 isolates and 0.8756 in the lower 0.6963‐aggressive isolates, the Fc12551 0.7449 0.8315 0.7316 differentiation was muchFc223 lower and the 0.8037 genotype differences 0.8695 were less 0.7826 expressed. The genotype ranking between genotypeFc89.4 reactions to 0.8435 the eight isolates 0.7615 was different, 0.7854 and the highest rate of significant correlationsFg12377 of the 300 possible 0.7997 correlations between 0.7949 the genotype 0.8942 responses to the eight isolates was found forFg207 DI (37%), while 0.7142 for FDK and DON, 0.7545 it was lower 0.8101 (26 and 28%). This means that the resistance rankingFg40 of the genotypes 0.7458 to the eight 0.8399 isolates showed 0.7256 a high variability for the Fg44 0.8571 0.7388 0.7483 isolates and so the resistance ranking to the individual isolates was not reliable enough to use it as a reliable phenotyping result.Mean When there 0.8357 was only one inoculum, 0.8855 it could 0.8088 be any of the eight types. When they were analyzedGen. Mean in separate 0.7689 QTL analyses, we 0.8083 could see the 0.77176 differences. We will come back to this problemAll later. are signiﬁcant It is also atclearp = 0.001that the mean of the eight isolates reflects the amount of Fg: F. graminearum, Fc: F. culmorum the resistance more precisely than any of the isolates separately. This may cause a significant error in the phenotyping and can influence the QTLs to be identified. Based on the results, the level of thethe resistanceresistance shouldshould alsoalso bebe evaluated.evaluated. When the genotypes are ranked at a low or medium aggressiveness level, even the most infected DI at 20% (Figure 11)) cancan be considered to be considerably resistant on a scale of 0–100% DI or FDK, or highly susceptible, depending which isolate was used. Snijders and van Eeuwijk [39] [39] made a similar test with 17 winter wheat genotypes and four independent isolatesisolates of ofF. culmorumF. culmorum. They. They presented presented only visualonly datavisual as incidence,data as incidence, e.g., the percentage e.g., the percentageof diseased of heads diseased (Figure heads4). The (Figure lessons 4). The are the lessons same are as presentedthe same as in presented Figure1. in Figure 1.

40 '39-.01 329-01 30 348-01

20 436-01 Incidence index % index Incidence 10

0 Arina Saiga Nautica 73012-1-2-3 72003-4-2-4 SVP 77076-4SVP 77076-1SVP 72005-20-3-1 SVP 75059-46SVP 72017-17-5-10 SVP 75059-32SVP SVP 77079-15SVP 75059-28SVP SVP 77076-38SVP 77078-30SVP SVP 73016-2-4SVP SVP 73030-8-1-ISVP SVP SVP SVP SVP

Figure 4.4. IncidenceIncidence of of 17 17 winter winter wheat wheat genotypes genotypes against against four four isolates isolates of F. of culmorum F. culmorumacross across three years,three years,IPO Wageningen, IPO Wageningen, Snijders Snijders and van and Eeuwijk van Eeuwijk [39]. [39].

The reactionsreactions of of the the genotypes genotypes can can also also be grouped be grouped according according to the to visual the recordsvisual records of the di ffoferent the differentisolates [ 26isolates]. As [26]. the ranking As the ranking of the isolates of the isolates in aggressiveness in aggressiveness is normally is normally different different between between years, years, the influence of the aggressiveness level can be clearly evaluated. In this case, the lowest DI from the different isolates came from the low‐aggressiveness group which includes 2–3 different isolates. The medium group contained the second largest data, and the same was applied to the high and very high‐aggressiveness groups. The DON response of the genotypes is plotted in Figure 5. The DI and FDK data are shown in Figures S1 and S2. The highest aggressiveness showed the Plants 2020, 9, 1702 7 of 33 the influence of the aggressiveness level can be clearly evaluated. In this case, the lowest DI from the different isolates came from the low-aggressiveness group which includes 2–3 different isolates. The medium group contained the second largest data, and the same was applied to the high and very high-aggressiveness groups. The DON response of the genotypes is plotted in Figure5. The DI andPlants FDK 2019, 8 data, x FOR are PEER shown REVIEW in Figures S1 and S2. The highest aggressiveness showed the7 of 33 greatest expressed genotype differences in each case, but with different distances between aggressiveness levels. greatest expressed genotype differences in each case, but with different distances between Lower genotype differences were seen with the high level of aggressiveness. The scientific value aggressiveness levels. Lower genotype differences were seen with the high level of aggressiveness. of the two lower aggressiveness levels was more moderate and the low level was close to useless. The scientific value of the two lower aggressiveness levels was more moderate and the low level Thewas common close to useless. feature The between common Figures feature3 and between5 is that Figures the mean 3 and data 5 is showthat the the mean most data useful show data the series andmost the useful amount data of series resistance and the seems amount to be of the resistance closest toseems reality. to be Several the closest genotypes to reality. were Several identified at lowgenotypes aggressiveness were identified that had at unexpectedlylow aggressiveness high infectionthat had orunexpectedly DON rates. high These infection data are or useful DON when estimatingrates. These risks data asare low useful epidemic when estimating levels occur risks more as low frequently. epidemic levels occur more frequently.

35 Low 30 Medium High 25 Very high Mean 20

10 DON mg/kg

0 MIDAS Rst/Nba Rst/NBb RAINER GK ÉLET SzD 5501 SzD 6401 SzD GK FÉNY 6626 SzD 5205 SzD 6701 SzD 5417 SzD 5275 SzD 6515 SzD 7257 SzD SzD 5319 SzD 4511 SzD 5066 SzD 5377 SzD GALLUS BALATON GK SZALA GK BÉKÉS GK PETUR Zu//Ré/Nba GK KAPOS Zu//Ré/NBb SzD 0016 F SzD SzD 0016 S SzD GK HOLLÓ GK GK PIACOS GK GK KALÁSZ Ttj/RC103/3/… GK HATTYU SzD 18364 SzD GK CSILLAG Attila/3/Sgv/N… GK HUNYAD GK Be/SK 48.21/.. Zu//Ré/NB/5/Kö

Figure 5. Deoxynivalenol (DON) data (mg/kg) in the methodical test for inoculation methods across yearsFigure and 5. Deoxynivalenol methods according (DON) to data aggressiveness (mg/kg) in the level, methodical 2009–2012 test for [26 inoculation] LSD 5% methods for genotypes: across 2.14, betweenyears and any methods points according in the chart to (exceptaggressiveness genotype level, means) 2009–2012 4.28. [26] LSD 5% for genotypes: 2.14, between any points in the chart (except genotype means) 4.28. For inoculation methods, we use the spraying inoculation combined with 48 h polyethylene bag coverageFor inoculation following sprayingmethods, inwe Table use the 9 in spraying [26], which inoculation gave the combined best diff witherentiation 48 h polyethylene of the genotypes andbag verifiedcoverage the following inoculation spraying praxis in done Table until 9 in now. [26], Spraying which gave inoculation the best combined differentiation by mist of irrigationthe wasgenotypes less eff andective verified (reduction the inoculation of DI of about praxis 50%, done reduction until now. in Spraying FDK of 63%,inoculation and reduction combined in by DON of 70%).mist irrigation This indicates was less the practicaleffective (reduction usefulness of of DI the of spraying about 50%, and reduction polyethylene in FDK (PE) of coverage 63%, and method, comparedreduction in to DON the spraying of 70%). method This indicates with misting, the practical and theusefulness spawn of method. the spraying The traits and polyethylene reacted differently; in(PE) DI, coverage the three method, methods compared gave very to close the spraying results. However,method with the misting, FDK severity and the decreased spawn method. by 74% The using the traits reacted differently; in DI, the three methods gave very close results. However, the FDK spawn method, and the DON showed 85% reduction compared to the Spray + PE method, i.e., the least severity decreased by 74% using the spawn method, and the DON showed 85% reduction effective differentiation of the genotypes was performed here. compared to the Spray+PE method, i.e., the least effective differentiation of the genotypes was performedMost breeding here. programs use spraying inoculation combined with some type of mist irrigation for massMost selection; breeding therefore,programs use the spraying usefulness inoculation of this screening combined shouldwith some be tested,type of evenmist irrigation if for scientific purposesfor mass selection; the point therefore, inoculation the methodusefulness is usedof this most screening often. should This aspect be tested, will even be discussed if for scientific in the next chapterpurposes on the resistance point inoculation types. Of method the traits is used tested, most theoften. DON This was aspect the will most be useful,discussed and in inthe most next cases, thischapter correlated on resistance better types. with FDKOf the than traits with tested, the the visual DON symptoms. was the most In useful, this and and other in most tests, cases, we used fourthis correlated or eight isolates better with without FDK than mixing with [ 35the,40 visual,41] andsymptoms. an isolate In this/year and interaction other tests, waswe used demonstrated four inor each eight experiment; isolates without therefore, mixing we[35,40,41] cannot and speak an isolate/year about stability interaction in aggressiveness. was demonstrated This in agrees each with theexperiment; need of thetherefore, growers we and cannot breeders speak about to receive stability information in aggressiveness. about the This risk agrees of DON with contamination.the need Theof the diff growerserentiation and of breeders the genotypes to receive is much information better atabout high the aggressiveness risk of DON levels, contamination. therefore The the use of differentiation of the genotypes is much better at high aggressiveness levels, therefore the use of more, in our case four or eight isolates, in research can balance the individually differing isolate data and can provide a higher accuracy (Figure 4) [26]. As a mean product of several isolates, the amount of the resistance and the ranking of genotypes can be demonstrated at higher reliability. Plants 2020, 9, 1702 8 of 33 more, in our case four or eight isolates, in research can balance the individually differing isolate data and can provide a higher accuracy (Figure4)[ 26]. As a mean product of several isolates, the amount of the resistance and the ranking of genotypes can be demonstrated at higher reliability. The conclusions are that (1) an aggressiveness test is useful to select the best inocula for infection. The research supported the view that previous experimental data are useful in the selection of the best inocula for experimentation. (2) The previous aggressiveness test of isolates for use proved to be a good practice [5,6,23,39,42,43]. (3) As tests of aggressiveness at the seedling stage show a significant correlation with FHB symptoms [5,6], tests to select more aggressive isolates for field usage can be performed. The same is true to screen inocula for field inoculations. It is important to identify the resistance differences for the identification of resistance level in parental lines or cultivars and for variety registration. For this work, four isolates were needed. The comparison of resistance within ripening groups was not a problem, because in each group, control cultivars or lines helped in the classification in field experiments. (4) For phenotyping in QTL, this method was suitable, but additional problems coming from the mostly wide flowering period and other problems should be resolved in future research; see the QTL chapter. (5) For mass selection, one aggressive isolate or mixture following an aggressiveness test can secure the disease pressure to perform an effective negative selection.

3. Type I and Type II Resistance to Infection and Spreading It is known that Type II resistance is only a part of the total resistance. However, Type I is normally neglected as people do not see clearly what is it and how it should be worked with. This can lead to susceptibility. The parents of Sumai 3 (Funo from Italy and Taiwanxiaomai from China) were moderate resistant genotypes [44], therefore Fhb1 alone cannot secure high resistance) as supposed in many early papers having 3BS (fhb1) QTL [9]. Chrpova et al. [11] reported no significant difference between 3BS and no QTL plants (this could be genotype dependent), but the 3BS+5AS plants showed much better resistance. The famous paper written by Schroeder and Christensen [27] reported two resistance types, where Type I was the component for initial infection and response to spray inoculation, and Type II was the component designed for spreading following point inoculation. In the text, the terms resistance to infection and resistance to initial infection were used as synonyms. However, the spray and point inoculation were evaluated by using the same method, and according to the data, no difference in visual symptoms was found between Type I and Type II. The only difference was that the Type II resistance had much lower ratio in Fusarium damaged kernels (FDK). In both components, we can determine the time from inoculation to the appearance of first visual infection measured by the number of infected spikelets. For this, the inoculation time must be known exactly. This was the reason Schroeder and Christensen [27] applied artificial inoculation methods for both types; otherwise, the determination of time between inoculation and the appearance of the first (initial) symptoms could not be resolved. This meant that they [27] did not differentiate between incidence and severity; their numbers refer to disease index. The timing of the exact inoculation date is a problem of the natural epidemics, and the spawn method has a similar difficulty, as discussed by Dill-Macky [23]. Therefore, differentiation in this case was hardly possible. The role of FDK was later forgotten despite the emphasis on this issue. Ma et al. [45] characterize the situation “The inherent relationships or differences of distinct resistance types are far from clear”. I agree. Therefore, we need to develop more useful hypotheses to understand what is happening in the resistant and susceptible plants. In view of the evaluated text [27], we see that the first visually infected spikelet was observed and this was the initial infection. This information was obtained from the given data. This means that all other explanations can be considered as speculation. One could hypothesize that a physiological trait inhibits the first infection following germination of the Fusarium spores. Theoretically, this could be possible, but in the text, nothing was said to insinuate this solution. However, the same symptom was also observed for the point inoculation where a droplet of suspension was placed over the ovary in the wheat spikelet (in the middle of the head) at the lower floret [46]. In both resistance types, Plants 2020, 9, 1702 9 of 33 several evaluations were made and area under the disease progress curve (AUDPC) was counted. Later, only one reading was made on the 21st day after inoculation [47]. Working with a point inoculation (single floret inoculation), excellent correlation was found between AUDPC and this evaluation. Therefore, clarification should be made on the definition of Type I and II resistances, e.g., the difference between the effect of spraying and point inoculation. Researchers were not aware of Type I resistance [22,23], therefore, Type II was prioritized as it was easier to work with. Type II had clear definition and could be analyzed under controlled conditions that were hardly possible in field inoculation where large materials had to be handled. However, as Dill-Macky [23] (2003) remarked, Type II resistance and other types of resistances are hard to identify in field nurseries. This is mainly due to the fact that the secondary infections cannot be separated from the primary infection. Therefore, the inoculation date cannot be omitted. It should be added also that even without multiple infections, the problem is that the time of the first inoculation is also unknown. Without this, no AUDPC evaluation is possible. So, without artificial inoculation, the case cannot be solved. To this contributed the fact that Chinese research [46] started to apply point inoculation exclusively, and it seemed to be the right solution for the breeding problems [23]. We have to consider that Frontana was classified as Type II resistance [27], while in later research, it was characterized as Type I resistance [28] on chromosomes 3A, 1B, 2A, 2B, 4B, 5A, and 6B, but only a small effect of Type II QTL was also found. They characterized all others as resistance to penetration (Type I). In effect, this was resistance to initial infection in senso, Schroeder and Christensen [27]. Mardi et al. [48] came to the same conclusion identifying QTL in 1BL, 3AL, and 7AS explaining 7.9%, 7.7%, and 7.6% of the variation by spraying inoculation, so the original classification of Frontana could not be confirmed. It is also true that Schroeder and Christensen [27] tested only seven genotypes and one inoculum was used, and this is not a significant number to find consequent behavior schemes [5,6]. From their Table1[ 27], we can see that three inoculation dates were applied; interestingly, the latest inoculation date at soft dough stage gave the highest numbers in five of the seven genotypes, even normally the inoculation at flowering produces the highest infection [36]. There is no clear explanation for this in the text. The aggressiveness was not measured [27], but high conidium production was searched for in isolates. A range of 3–7 106 conidia was used from F. graminearum; this is much higher than used by × the present praxis. Therefore, a number of questions remained unanswered. Cassini [49] mentioned the ongoing breeding program in France and Canada, but no information was given for resistance types. Cook [50] concluded that as the Fusaria infect many other plants than Gramineae, "there is little prospect for good or even moderate genetic resistance to these pathogens in wheat, barley or oats". Cook [51] reported briefly about the Chinese problems, but no useful information was given on resistance. However, Sumai 3 was registered at the beginning of the 1970s [44], so the negative opinions were not supported. It is also important to know the requirements. Some authors preferred incidence (percent of the heads showing visual symptoms) in mapping QTLs, some others severity (the rate of the spikelets infected in one ear is expressed in the number of spikelets or percentage), but only for the heads that showed infection [9]. In several cases, both were evaluated and the disease index, a multiplication of incidence, and severity were used. In older literature, severity often meant disease index [52], so the meaning of the terms also changed. However, the text shows in most cases the meaning of the term used. The trait that corresponds to the description of Schroeder and Christensen [27] is the disease index. The first review article for wheat FHB QTLs [9] summarized the findings of 52 papers from the first decade. In their Table1, 59 QTLs were identified for spreading (Type II), 59 for severity, and 13 for incidence (Type I). No data were shown for disease index, but in several populations, both severity and incidence were mapped [28]. In five cases, DON was mentioned and kernel resistance was mapped in six cases. In several cases, an overlap was shown, as the same QTL was identified by more authors. The number of validated QTLs is much smaller (independently proven in more mapping populations), 3BS, 6B, and 5A QTL have multiple proof by different authors [9]. In 3BS, there is multiple data Plants 2020, 9, 1702 10 of 33 on kernel infection, spread, and DON. In 5A, severity and DON were mapped together. As in the paperPlants 2019 [9],, 8 the, x FOR percentage PEER REVIEW of the explained variation is given, even the data from the same source10 of of 33 resistance can be different, indicating unknown background effects. Additionally, near isogenic or DHAdditionally, lines can diminishnear isogenic background or DH elinesffect. can It is diminish worth discussion, background whether effect. thisIt is is worth advantageous discussion, or disadvantageous.whether this is advantageous In this review, or no disadvantageous. papers were found In tothis map review, spreading no papers or severity were with found DON to map and kernelspreading infection or severity together. with DON and kernel infection together. MesterhazyMesterhazy etet al.al. [[49,53]49,53] testedtested thethe CM82036CM82036/Remus/Remus mappingmapping populationpopulation developeddeveloped byby BuerstmayrBuerstmayr etet al.al. [[50]50] toto mapmap TypeType II andand Type Type II II resistance. resistance. SpraySpray inoculationinoculation waswas usedused inin bothboth experimentalexperimental sites,sites, Szeged Szeged (Hungary) (Hungary) and and Tulln Tulln (Austria), (Austria), in 2002 in/2003. 2002/2003. For the test,For 24-24the test, genotypes 24‐24 weregenotypes selected were without selected QTL, without 3BS, 5A, QTL, and 3BS, 3BS +5A,5A and together. 3BS+5A Two-two together. isolates Two‐ oftwoF. isolates graminearum of F., F.graminearum culmorum, and, F. F.culmorum avenaceum, andwere F. used,avenaceum accompanied were used, by a accompaniedF. sporotrichioides byand a F.Gerlachia sporotrichioides nivalis (Syn. and MicrodochiumGerlachia nivalis nivale (Syn., F. Microdochium nivale). Using nivale the, spraying F. nivale). inoculation, Using the spraying it was possible inoculation, to know it was the possible genetic backgroundto know the of genetic the QTLs background and to see of their the rate QTLs within and the to totalsee resistancestheir rate within suggested the bytotal Ruckenbauer resistances andsuggested Buerstmayr, by Ruckenbauer who published and Buerstmayr, the presence who of the published two QTLs the in presence this mapping of the population two QTLs [in54 ,this55]. Themapping disease population severity index [54,55]. presented The disease clear di ffseverityerences betweenindex presented genotype groups.clear differences 5AS (AUDPC between 153) (Typegenotype I) is groups. stronger 5AS than (AUDPC 3BS (AUDPC 153) (Type 192) (Type I) is II),stronger their combinationthan 3BS (AUDPC gives the192) least (Type severe II), datatheir (AUDPCcombination= 116), gives and the the least No severe QTL group data (AUDPC showed the = 116), highest and severity the No (AUDPCQTL group= 245).showed The the reduction highest comparedseverity (AUDPC to the No = QTL245). groupThe reduction was 22% compared for the 3BS, to 37% the forNo the QTL 5A, group and 53% was for 22% the for combined the 3BS, QTL37% groupfor the (Figure 5A, and6). 53% for the combined QTL group (Figure 6).

450

400

350

300 3BS/5A 250 3BS 200 5A 0

Severity AUDPC Severity 150

100

0 123456789101112131415161718192021222324

FigureFigure 6. 6.Area Area under under disease disease progress progress curve (AUDPC) curve (AUDPC) values for diseasevalues severityfor disease in the CM82036severity /Remusin the populationCM82036/Remus across years,population isolates, across and location,years, isolates, 2002–2003. and Limit location, of signiﬁcant 2002–2003. diﬀ erenceLimit of (LSD) significant 5% for genotypesdifference 32.54,(LSD) for5% quantitativefor genotypes trait 32.54, locus for (QTL) quantitative groups trait 6.62 [locus53]. (QTL) groups 6.62 [53].

ItIt seemsseems thatthat thethe variabilityvariability withinwithin thethe groupgroup waswas significant,significant, soso wewe cancan assumeassume thethe presencepresence ofof otherother QTLsQTLs in in the the population. population. It It is suggestedis suggested to estimateto estimate the realthe real genetic genetic value value of the of QTLs the QTLs is the meanis the productmean product of the four of mostthe four susceptible most lines,susceptible as counted lines, in as the counted breeding in work, the andbreeding the probability work, and to have the otherprobability unidentified to have QTLs other was unidentified much lower here.QTLs For was FDK, much the 5Alower was here. also stronger For FDK, than the the 5A 3BS, was but also the distrongerfference than was smallerthe 3BS, and but not the significant difference (Figure was smaller7). However, and not the significant combined e(Figureffect of 7). the However, two QTLs the on FDKcombined was significantly effect of the more two eQTLsffective on than FDK was was found significantly for the visual more symptoms,effective than and was the found distance for was the alsovisual larger symptoms, between and the Nothe QTL distance group was and also the otherlarger variants. between The the mean No QTL reduction group was and 41% the for other 5A, 34%variants. for 3BS, The and mean 76% reduction for the combined was 41% QTL for 5A, group. 34% The for ANOVA 3BS, and of 76% the for FDK the data combined (Table2) QTL shows group. that theThe main ANOVA effect of of the genotype FDK data significantly (Table 2) shows differed that from the the main otherwise effect of significant genotype significantly two-way interaction differed containingfrom the otherwise a genotype significant component, two and‐way this interaction was valid containing for the three-way a genotype interaction, component, where and only this one was of thevalid three for wasthe significant.three‐way interaction, It seems that where the genotype only one e ffofect the is three dominant was andsignificant. the resistance It seems ranking that the is rathergenotype similar effect under is dominant different conditions.and the resistance The variability ranking within is rather groups similar was under the same different for visual conditions. scores. The variability within groups was the same for visual scores.

Table 2. ANOVA of the FDK data of the CM82036/Remus population.

Source of Variance SS df MS F F AxBxC LSD 5% Plants 2019, 8, x FOR PEER REVIEW 11 of 33

Genotype A 516,296 95 5434.7 54.02 *** 27.21 *** 3.27 Isolate B 987,904 8 123,488 1227.51 *** 618.36 *** Year C 73,138 1 73,138 727.02 *** 366.24 *** Location D 320,301 1 320,301 3183.90 *** 1603.91 *** AxB 257,093 760 338.3 3.36 *** 1.69 *** AxC 74,391 95 783.1 7.78 *** 3.92 *** AxD 125,158 95 1317.4 13.09 *** 6.59 *** BxC 257,743 8 32,217.9 320.25 *** 161.33 *** BxD 102,493 8 12,811.7 127.35 *** 64.15 *** CxD 27,953 1 27,952.7 277.85 *** 139.97 *** AxBxC 133,930 760 176.2 1.75 *** 0.88 ns AxBxD 140,742 760 185.2 1.84 *** 0.92 ns AxCxD 75,562 95 795.4 7.90 *** 3.98 *** BxCxD 101,247 8 12,655.8 125.80 *** 63.37 *** AxBxCxD 151,776 760 199.7 1.98 *** Within 347,666 3456 100.6 Plants 2020, 9, 1702Total 3,693,392 6911 11 of 33 *** p = 0.001, ns = non significant. Red font color: interactions with Genotype A [53].

Figure 7. FDKFDK va valueslues (percentage) (percentage) in inthe the CM82036/Remus CM82036/Remus population population across across years, years, locations, locations, and isolates,and isolates, 2002 2002–2003.–2003. LSD LSD5% for 5% genotypes for genotypes 3.37, 3.37,for QTL for QTLgroups groups 0.67 0.67[53]. [53].

For DON, theTable situation 2. ANOVA is similar of the FDK (Figure data of8). the Only CM82036 the / fourRemus DON population.-producing isolates were considered, and the reduction compared to the No QTL group was 23% for 5A, 22% for 3BS, and Source of Variance SS df MS F F AxBxC LSD 5% 65% for the 3BS+5A together. As 3BS was significantly less effective in controlling visual symptoms than 5A,Genotype in FDK, Athey were 516,296much closer to 95 each other 5434.7 with no 54.02significant *** differ 27.21ence *** between 3.27 them. Isolate B 987,904 8 123,488 1227.51 *** 618.36 *** The mean Yearfor DON C in 3BS plants 73,138 became 1 slightly 73,138better (without 727.02 significant *** 366.24 difference) *** and in 11 cases of theLocation 24, the D 3BS plants 320,301 were slightly 1 lower in 320,301 DON contamination. 3183.90 *** The 1603.91 improvement *** can be explained byAxB the enzyme glycosyltransferase 257,093 760 mediating 338.3 the detoxification 3.36 *** of1.69 DON *** to the non-toxic DON-3-glucosideAxC [56]. 74,391 95 783.1 7.78 *** 3.92 *** AxD 125,158 95 1317.4 13.09 *** 6.59 *** BxC 257,743 8 32,217.9 320.25 *** 161.33 *** BxD 102,493 8 12,811.7 127.35 *** 64.15 *** CxD 27,953 1 27,952.7 277.85 *** 139.97 *** AxBxC 133,930 760 176.2 1.75 *** 0.88 ns AxBxD 140,742 760 185.2 1.84 *** 0.92 ns AxCxD 75,562 95 795.4 7.90 *** 3.98 *** BxCxD 101,247 8 12,655.8 125.80 *** 63.37 *** AxBxCxD 151,776 760 199.7 1.98 *** Within 347,666 3456 100.6 Total 3,693,392 6911 *** p = 0.001, ns = non signiﬁcant. Red font color: interactions with Genotype A [53].

For DON, the situation is similar (Figure8). Only the four DON-producing isolates were considered, and the reduction compared to the No QTL group was 23% for 5A, 22% for 3BS, and 65% for the 3BS+5A together. As 3BS was significantly less effective in controlling visual symptoms than 5A, in FDK, they were much closer to each other with no significant difference between them. The mean for DON in 3BS plants became slightly better (without significant difference) and in 11 cases of the 24, the 3BS plants were slightly lower in DON contamination. The improvement can be explained by the enzyme glycosyltransferase mediating the detoxification of DON to the non-toxic DON-3-glucoside [56]. Plants 2020, 9, 1702 12 of 33 Plants 2019, 8, x FOR PEER REVIEW 12 of 33

Figure 8. 8. DONDON content content (mg/kg) (mg/ kg)in the in CM82036/Remus the CM82036/Remus population population across across years, years,locations, locations, and F. andgraminearumF. graminearum and F. culmorumand F. culmorum isolates, isolates,2002–2003. 2002–2003. LSD 5% for LSD genotypes 5% for 3.37, genotypes for QTL 3.37, groups for QTL2.16 groups[53]. 2.16 [53].

This means two things: the the effects effects of Type II resistance can be detecteddetected without any problems from thethe spraying spraying inoculation, inoculation, and and the the 5A 5A is reallyis really an eanffective effective QTL QTL for FHB, for FHB, especially especially when when we see we its DONsee its performance DON performance as confirmed as confirmed by Steiner by et Steiner al. [57]. et In al. all [57]. traits, In there all traits, is a transgressive there is a transgressive segregation ofsegregation the two e ffofective the QTLstwo workeffective as described QTLs work by Liu as and described Wang [ 44by] forLiu visual and symptoms.Wang [44] for visual symptoms.In all countries, several Fusarium species are responsible for the FHB. In Hungary, F. graminearum is theIn ruling all species,countries, but severalF. culmorum Fusarium, F. avenaceum species, F. are sporotrichioides responsible, and forF. poaethe canFHB. occur In inHungary, given years F. andgraminearum areas [58 ].is In the Belgium, ruling species,F. graminearum but F. andculmorumF. culmorum, F. avenaceumdominate,, F.F. sporotrichioides avenaceum and, andF. poae F. canpoae have can importanceoccur in given [59]. years Poland and has areasF. graminearum [58]. In Belgium,and F. culmorum F. graminearumas main and pathogens F. culmorum and F. dominate, crookwellense F. occursavenaceum also and at higher F. poae density can have [60 importance]. In Germany, [59]F.. Poland poae, F. has avenaveum F. graminearum, F. culmorum and ,F. and culmorumF. graminearum as main havepathogens higher and significance F. crookwellense [61]. From occurs Canada, also atF. higher poae, F. density graminearum [60]., InF. culmorumGermany,, and F. poaeF. avenaceum, F. avenaveumwere, identifiedF. culmorum as main, and pathogens F. graminearum [62]. So, thehave multiple higher exposition significance of the [61] wheat. From to di fferentCanada,Fusarium F. poaespp., F. is agraminearum general fact., F. In culmorum this experiment, and F. avenaceum [53], eight were isolates identified from five as main important pathogensFusarium [62]. spp So,. the were multiple tested. Figureexposition9 shows of the the wheat QTL e toff ectdifferent on the Fusarium infection byspp. the is eight a general isolates. fact. The In 3BSthis andexperiment 5A QTL [53], have eight very similarisolates e fffromect to five all of important them; therefore, Fusarium the QTLsspp. were inherit tested. a complex Figure resistance 9 shows to the the di ffQTLerent effectFusarium on sppthe. Theinfection results by are the very eight similar isolates. to what The 3BS we had and in 5A a testQTL with have genotypes very similar of di effectfferent to origin all of them; against therefore, different Fusariumthe QTLs sppinherit. Other a complex papers [resistance3,35,63] supported to the different the non-specificity Fusarium spp also.. The Here, results we are got very the molecularsimilar to geneticwhat we explanation, had in a too.test Therefore,with genotypes we can speakof different about a common, origin against species-non-specific different Fusarium resistance inspp wheat. Other for thepapers Fusarium [3,35,63] spp. supported and the two the QTLs non tested.‐specificity As a conclusion, also. Here, this we work got should the molecular be extended genetic including explanation, more QTLs andtoo. theTherefore, not tested we Fusarium can speak spp. about The a common, Fusarium species population‐non‐specific can change resistance sharply in between wheat for years the and Fusarium in different spp. regions,and the two other QTLs Fusarium tested. spp. As area conclusion, dominant. Therefore,this work should the breeder be extended needs information including whethermore QTLs his problemand the cannot betested solved Fusarium against spp. F. langsethii The Fusarium or other population species that can are change important sharply in his between area where years he and/she in has different F. graminearum regions, resistanceother Fusarium for sure. spp. It are is a dominant. fascinating Therefore, theoretical the challenge, breeder butneeds also information a practical whether breeding his problem. problem How can our be resistantsolved against cultivars F. langsethii will behave or toother the dispeciesfferent that Fusarium are important species structurein his area in where regions he/she where has not F. thesegraminearum two now knownresistance QTL for are sure. used. It The is a experimental fascinating hypothesistheoretical ischallenge, that probably but yes.also Thisa practical is supported breeding by problem. the observations How our thatresistant breeding cultivars material will behave originated to the fromdifferent other Fusarium continents species not structure having these in regions two QTLswhere arenot mostlythese two useful now inknown the recipientQTL are countries.used. The Nevertheless,experimental hypothesis we did not is havethat probably experimental yes. This proof is for supported it. The only by onethe paperobservations that compares that breeding resistance material from originated genetically from very other diff erentcontinents winter not wheat having cultivars these two show QTLs highly are similaritymostly useful in resistance in the recipient behavior countries. to different Nevertheless,Fusarium graminearum we did not andhave its experimental stricto senso proof species for and it. alsoThe only in F. culmorumone paperisolates that compares from large resistance geographical from genetically distances [64 very]. different winter wheat cultivars show highly similarity in resistance behavior to different Fusarium graminearum and its stricto senso species and also in F culmorum isolates from large geographical distances [64].

Plants 2020, 9, 1702 13 of 33 Plants 2019, 8, x FOR PEER REVIEW 13 of 33

40 % 30 FDK FDK 20 No QTL 3BS 10 5AS 3BS/5AS 0 Fg44 Fspor Fc89.4 Fav-38 Fc12375 Fg12377 FavIFA60 Mni v 3/ 98

Figure 9. ResistanceResistance protection protection of of QTLs QTLs 3BS 3BS and and 5AS against eight isolates of Fusarium on the CM82036CM82036/Remus/Remus population in wheat, Szeged-Tulln,Szeged‐Tulln, 2002–2003. Fav: F.F. avenaceum ,, Fc: F.F. culmorum ,, Fg: F.F. graminearum graminearum, Fspor:, Fspor:F. F. sporotrichioides sporotrichioidesand and Mniv: Mniv:Microdochium Microdochium nivale nivale, F. nivale, F. nivale, Gerlachia, Gerlachia nivalis nivalis[48]. [48]. The experimental results showed further that the spraying inoculation does not show clear Type I resistance,The experimental but both Type results I and showed Type II further together. that Bai the and spraying Sharen [inoculation65] and Zwart does et al.not [ 66show] showed clear similarType I resistance, results. Their but test both gave Type further I and information. Type II together. The resistance Bai and Sharen of 5AS [65] and andfhb1 Zwartplants et (in al. sensu [66] Buerstmayrshowed similar et al. results. [9] was Their similar test to gave the further difference information. mentioned The earlier. resistance This QTL of 5AS (fhb1 and/QFhs.ndsu-3BSi fhb1 plants (in) wassensu not Buerstmayr found in the Europeanet al. [9] winter was wheatsimilar materials to the tested. difference However, mentioned the plants earlier. having This both QTLsQTL had(fhb1/QFhs.ndsu only half of‐3BS DI,i) FDK, was not or DON, found confirming in the European the transgressive winter wheat segregation materials tested. described However, for Sumai the 3plants [44]. having Arruda both et al. QTLs [67] worked had only with half spawn of DI, inoculation FDK, or that DON, was confirming a natural close the varianttransgressive of the spraysegregation inoculation, described and for the Sumai effect on3 [44]. the Arruda 3B chromosome et al. [67] ( Fhb1workedQTL) with with spawn its Type inoculation II resistance that couldwas a benatural verified, close supporting variant of the the spray overall inoculation, view of resistance and the effect of the on spraying the 3B chromosome inoculation. (Fhb1 As fhb1 QTL)is with only responsibleits Type II resistance for half of could the resistance be verified, in the supporting plant, it is the not overall an accident view thatof resistance the breeding of the focusing spraying on theinoculation.fhb1 QTL As could fhb1 notis only produce responsible a large for amount half of of the resistant resistance plants. in the In my plant, opinion, it is not using an Sumai-3accident andthat workingthe breeding with focusing point inoculation, on the fhb1 the QTL 5AS could QTL not could produce not be a large found, amount and when of resistant it was thereplants. and In helpedmy opinion, to secure using high Sumai resistance,‐3 and working it was thought with point to be inoculation, a function ofthe the 5ASfhb1 QTLQTL. could Steiner not be et al.found, [57], workingand when with it was the samethere populationand helped we to didsecure (CM82036 high resistance,/Remus), foundit was another thought QTL to beQfhs.ifa a function-5Ac in of 5AS, the whichfhb1 QTL. is responsible Steiner et foral. resistance[57], working to FHB with and the anther same extrusion population that we could did be (CM82036/Remus), interesting for the future.found Sprayinganother QTL inoculation Qfhs.ifa‐5Ac was in used, 5AS, and which visual is responsible head symptoms for resistance were rated. to FHB However, and anther no influence extrusion on DONthat could contamination be interesting was tested.for the Afuture. new development Spraying inoculation in China iswas testing used, the and combination visual head of symptoms the Type I andwere Type rated. II resistanceHowever, by no spraying influence inoculation on DON [ 68contamination]. was tested. A new development in ChinaIt is should testing also the be combination considered of that the Type Type 1 I oughtand Type to be II characterizedresistance by spraying because itinoculation does not contain[68]. the spreadingIt should also function. be considered For this that reason, Type incidence 1 ought (rateto be ofcharacterized infected plants because without it does looking not contain at the severitythe spreading of the infection)function. wasFor evaluated.this reason, In incidence addition, severity(rate of wasinfected evaluated, plants indicatingwithout looking the rate at of the infectedseverity spikeletsof the infection) of the plants was evaluated. showing infection In addition, (incidence) severity [9 was]. Most evaluated, authors indicating made several the ratingsrate of tothe follow infected disease spikelets development. of the plants Usually, showing an AUDPC infection or (incidence) mean of ratings [9]. Most was authors calculated. made However, several thisratings automatically to follow containeddisease development. the spreading Usually, function. an This AUDPC is true or also mean for the of evaluations, ratings was made calculated. on the 21stHowever, day after this inoculation automatically [47 ],contained as the symptoms the spreading summarized function. the This disease is true development also for the until evaluations, this date andmade the on spreading the 21st day function after inoculation is clearly behind [47], as the the numbers symptoms evaluated. summarized Reminiscing the disease on pastdevelopment decades, thisuntil is this not date a surprise, and the as spreading this is also function true for the is clearly disease behind index that the hasnumbers the same evaluated. background, Reminiscing as this also on containspast decades, the multiplication this is not a of surprise, the incidence as this and is severity.also true This for explainsthe disease why index the spraying that has inoculation the same coversbackground, both Type as this I and also Type contains II resistances the multiplication and the original of the Type incidence I cannot and be severity. presented This purely explains from thewhy response the spraying to spraying inoculation inoculation. covers Typeboth IIType has I a and clear Type definition II resistances and this and term the should original be kept Type for I thecannot future, be presented as we have purely no reason from the to change response it. to The spraying real di ffinoculation.erence between Type the II has types a clear is that definition Type II onlyand this considers term should the disease be kept progress for the from future, the inoculatedas we have ovarium no reason (point to change inoculation) it. The toreal both difference ends of between the types is that Type II only considers the disease progress from the inoculated ovarium (point inoculation) to both ends of the head. It is clear that the infection starts in this case from the Plants 2020, 9, 1702 14 of 33 the head. It is clear that the infection starts in this case from the ovary and neighboring area. It does not consider other traits influencing infection from the inoculation site on the outer surface of the head, its complicated way to the ovary, the possible role of the lemma, palea and head axis, the role of anthers (anther extrusion or anther retention, etc.), are not considered, see more details at [69,70]. Of course, this is also a time function, it takes time for a conidium landing on the palea, germinate, and grow into the inner area of the floret. As many parts are unknown, the research is tasked to better understand the process. There are many other phenomena we now know also influence disease development, together or independently from the spreading function. There are antifungal proteins, secondary metabolic products that may influence disease development, morphology traits, histology, and physiology of the host and pathogen [71]. As both components have the spreading function, it is not an accident that corresponding FDK and DON values accompany both. This corresponds to the finding of Schroeder and Christensen [27]. For this reason, we have to think about the meaning of the data we classified as Type I resistance data for the last decades. All these can be summarized in terms of the total resistance in Type I. This can be dissected to Type II, III, IV, and V and others like another extrusion etc. could be added when justified. In each genotype, there is a different mix. They can be evaluated by the spraying inoculation and its different components can be identified by a further evaluation of the harvested grains (FDK, DON, etc.), or other specific inoculation methods that could be used. For Type II, a separate test will be needed. When we see their biological role, we return to the resistance component term we suggested earlier [34]. This describes the real situation better than the resistance type that indicates their fully independent presence in the plant. As a result, the scientific logic would also be kept, the most important comes of which comes to the forefront. I have chosen "total" as Liu et al. [72] used the “overall” word for resistance being active in the seedling and adult stages at the same time. We have found, among plants selected for seedling resistance, genotypes with good FHB resistance, but this was only a tendency, not a general rule we could use in a general breeding program [52]. In several cases [28], both spray and point inoculation were used, but it is difficult to say how much percentage of the total resistance is explained by Type I and II. When we have only spraying or other natural close inoculation (spawn), it is unknown, in agreement with Dill-Macky [23], how much of the identified resistance is caused by the not tested Type II QTLs. We know now that QTL fhb1 (Type II) determines alone maximally a medium or a somewhat higher resistance securing about 50% of the total resistance in CM82036. The same is true for 5AS QTL [44]. However, their very effective cooperation can secure a high resistance level. As they also secure low FDK and low DON at a much higher level than the two QTLs alone, it is not an accident that they are popular in breeding programs. During QTL analyses, genotypes were found with nearly pure Type I QTLs like in Frontana [28], in CM82036 the rate can be 50% or in others even higher. Most of the breeding firms use some form of spraying inoculation or natural close spawn inoculation for total resistance. Scientific research mostly uses Type II resistance. Additionally, in most cases, only visual symptoms are mapped. However, these define only about 50% of the resistance of Sumai 3 (or its derivative CM82036) and this can be much higher and or lower in others. From Figure8, it is clear that these two QTLs similarly protect against four Fusarium spp. and M. nivalis [53], e.g., the resistance seems to be also non-species-specific. In other genotypes, the rate of the two resistance types is mostly unknown. Here, I would like to cite Schroeder and Christensen [27] again who insisted that the Type I and Type II resistances could be differentiated only by their reaction to FDK. This is not obtainable presently. Based on current research, it is not certain that the FDK has such a differentiation power between the two resistance components. What is really happening is that at the point inoculation, we have a resistance-dependent slower or more rapid extension of the infection differing in time, and the top and the base of the head the grain are normally later and less infected and may remain healthy. In the spraying and natural inoculation, the multiple infections can be profound within a head; therefore, the rate of infected scabby heads comes mostly from the more or less uniform infection of the spraying infection. The DON contamination mirrors these processes Plants 2020, 9, 1702 15 of 33 well. For this reason, I think that Schroeder and Christensen [27] did not recognize the real role of the FDK, but they were the first to stress its role in resistance to FHB, which was not considered seriously by the later generations. It seems that a reliable rate of the resistance components can be identified correctly when a mapping population plants is selected that has only Component I and Component II plants. Then, following a spraying inoculation, we can see the weight of the components in the given genotype, as it was done in the CM82036/Remus population [53]. In the chapter “Breeding aspects”, we will come back to this problem from the practical point of view of the breeders, and in the chapter "QTL parts", there are also important new insights that might influence further developments. As a conclusion, without Type I resistance component, the securing of high level resistance is questionable.

4. Resistance to DON (Component/Type III) and FDK (Component/Type IV) Lemmens et al. [24] reported that the ultimate goal of breeding is to decrease mycotoxin contamination, but generally, toxin contamination is not measured, because it is costly. Therefore, breeding concentrates on the visual symptoms, on ears, and much less on FDK, and only the advanced lines will be monitored for mycotoxin contamination, if at all. The result is that we do not know how the basic relations work in the populations, which are the sources of resistance that decrease not only visual symptoms, but also mycotoxins. The resistance to DON is a complex trait with several background mechanisms [3,34]. It can be caused by a toxin degradation mechanism reported by Miller and Arnison [73]; the increased resistance can cause less disease, so this does not require a separate resistance trait. However, we have cases where toxin contamination is consequently less than forecasted based on symptoms over the years. This can be a result of detoxification of DON to DON-3 glycoside [24,56,74]. The overproduction of DON is a fact [26], but the genetic background is unknown. Mesterhazy [3] described three further resistance types. For five years (1983–1988), four isolates were used independently in three replicates; altogether, 60 replicates were involved. This secures the reliable data. He [3] reported the resistance to kernel infection (FDK), describing the lower FDK level in one genotype compared to another genotype not differing significantly from the other in visual symptom severity (DI), termed Type III resistance (Figure 10). Here, seven genotypes were toxin overproducers, six had kernel resistance, and nine responded regularly. Similar findings were found in [34,35,40]. However, it was realized [26], that later on not only can an FDK decrease identified, but extra FDK overproduction also has significance for breeding, and resistance to DON was also mentioned and confirmed [34,35]. The statistical method [3] was modified. It was argued earlier that we can prove resistance to kernel infection when the two genotypes do not differ significantly in resistance (e.g., disease index), but show significant differences in FDK. The newer evaluations [26] are stricter. The distance for all data points was calculated between them and their corresponding point on the regression line. We have the LSD 5% value for the FDK. When the difference between this point on the line and the data point is larger than the LSD 5% value, we can speak about proven relative resistance to kernel infection or a significant overproduction of the scabby grains. This applies also to DON. Additionally, this identifies overproduction or relative resistance, when no genotype within significant distance exists on the x-axis. This is also an advantage. In the 1990–1993 tests, six genotypes (30%) showed FDK overproduction and six (30%) relative FDK resistance in tests with four isolates when compared to DI data, and eight responded proportionally for the traits (40%). The DI/DON comparison resulted in four DON overproducers (20%) and 7 genotypes (35%), with relative DON resistance of the 20 genotypes tested. When FDK and DON were compared, four DON overproducers (20%) and only 3 relative DON resistant genotypes could be identified (15%) and 13 (65%) responded proportionally with FDK. As correlation was between DI/FDK r = 0.74 (p = 0.001), for DI/FDK r = 0.57 (p = 0.01), and for FDK/DON r = 0.73 (p = 0.001), it is clear that the FDK is a better trait than DI to estimate DON response. Plants 2019, 8, x FOR PEER REVIEW 15 of 33 the resistance components can be identified correctly when a mapping population plants is selected that has only Component I and Component II plants. Then, following a spraying inoculation, we can see the weight of the components in the given genotype, as it was done in the CM82036/Remus population [53]. In the chapter “Breeding aspects”, we will come back to this problem from the practical point of view of the breeders, and in the chapter ʺQTL partsʺ, there are also important new insights that might influence further developments. As a conclusion, without Type I resistance component, the securing of high level resistance is questionable.

80 y = 27.616x + 11.608 70 R2 = 0.4649, r=0.6813, P = 0.001 60 p = 0.001 50

40 FDK % 30

0 00.511.52 FHB disease index %, scale 0-4

Figure 10.10. FDKFDK overproduction overproduction and and resistance resistance in FHB in FHB resistance resistance tests intests winter in winter wheat genotypeswheat genotypes against twoagainstF. graminearum two F. graminearumand two F. and culmorum two strains,F. culmorum 1983–1988. strains, Red 1983–1988. data points: Red FDK data overproduction, points: FDK dataoverproduction, from the regression data from line arethe farer regression than the line LSD are 5% farer 4.49 to than the largerthe LSD DON 5% concentrations; 4.49 to the larger green DON data: resistanceconcentrations; to kernel green infection, data: resistance data from to the kernel regression infection, line data are farer from than the regression the LSD 5% line 4.49 are to farer the lower than DONthe LSD concentrations. 5% 4.49 to the LSD lower 5% DON for FDK concentrations. is 4.49 [3]. LSD 5% for FDK is 4.49 [3].

In another test, 40 genotypes were tested [26]. In this test, we had 96 replicates behind each data point; it became possible to prove the overproduction and DON resistance occurred at a high probability. From data with several replicates, the LSD 5% values are normally too high to use them for such an analysis. We found a correlation between DI and FDK data, r = 0.54, p = 0.001 (Figure 11). Ten genotypes (25%) showed higher FDK severity than the LSD 5% difference of 1.66 between genotypes, e.g., an increased susceptibility to FDK, and 12 (30%) produced less FDK than forecasted by the proportional response. These were the relative FDK resistant genotypes and 45% produced DON proportionally with the FDK data. The correlation between DI and DON was r = 0.5425, p = 0.001 at n = 40. For the DI/DON relation 9 (22.5%), DON overproducers and seven (17.5%) relative DON resistant genotypes were identified. Twenty-four (60%) genotypes belonged to the group that did not differ significantly from their data points on the regression line (Figure 12). The DI/FDK correlation was only r = 0.5110 (p = 0.01). The FDK/DON regression was much closer, r = 0.8132, p = 0.001 (Figure 12). We had six proven overproducers and only two genotypes with relative DON resistance. Compared to the DI/DON relations, this seems to be a more solid situation (Figure 13). Of the 40 genotypes, nine had for DON overproduction, but in food safety, they can be critical and 12 presented relative DON resistance, so 21 were off types, which is considerable. Plants 2019, 8, x FOR PEER REVIEW 17 of 33 Plants 2020, 9, 1702 17 of 33 Plants 2019, 8, x FOR PEER REVIEW 17 of 33

21 y = 0.6004x + 4.1906 R2 = 0.2973, r = 0.5425, p = 0.001 2118 y = 0.6004x + 4.1906 R2 = 0.2973, r = 0.5425, p = 0.001 1815 1512 12 9 FDK % FDK 9 FDK % FDK 6 6 3 3 0 0 0 5 10 15 20 0 5 10FHB % 15 20 FHB %

FigureFigure 11. 11.DI DI and and FDK FDK regression regression in FHB FHB resistance resistance tests tests in inwinter winter wheat wheat genotypes genotypes against against two F. two F. graminearumFiguregraminearum 11. DI and and two FDK F. regression culmorum in strains,strains, FHB resistance 2009–2012. 2009–2012. tests Red Red in data:winter data: FDK FDKwheat overproduction, overproduction, genotypes against data data two from from F. the the regressiongraminearumregression line line and are aretwo farer farerF. than culmorum than the LSDthe strains, LSD 5% 1.66 2009–2012.5% to 1.66 the to larger Red the data: DONlarger FDK concentrations; DON overproduction, concentrations; green data data: fromgreen resistance the data: toregressionresistance kernel infection, toline kernel are data farer infection, from than the datathe regression LSD from 5% the 1.66 lineregression to are the farer largerline than are DON thefarer LSDconcentrations; than 5% the 1.66 LSD to green5% the 1.66 lower data: to the DON concentrations.resistancelower DON to concentrations.kernel LSD 5%:infection, for FDKLSD data 5%: is from 1.66 for [theFDK26]. regression is 1.66 [26]. line are farer than the LSD 5% 1.66 to the lower DON concentrations. LSD 5%: for FDK is 1.66 [26].

16 16 y = 0.4047x + 3.1193 14 R2 = 0.2612,y = 0.4047x r=0.5110, + 3.1193 p = 0.001 14 R2 = 0.2612, r=0.5110, p = 0.001 12 12 10 10 8 8 6 6

DON mg/kg 4

DON mg/kg 4 22 00 00 5 5 10 10 15 15 20 20

DiseaseDisease index index % %

FigureFigureFigure 12. 12.12.DI DIDI andand and DON DON regression regressionregression in in in FHB FHB FHB resistance resistance resistance tests tests tests in inwinter inwinter winter wheat wheat wheat genotypes genotypes genotypes against against againsttwo two F. F. two F. graminearumgraminearum andand two two F. F. culmorum culmorum strains, strains,strains, 2009–2012. 2009–2012. 2009–2012. Red Red Red data: data: data: FDK FDK FDK overproduction, overproduction, overproduction, data data from data from the from the the regressionregressionregression line lineline are areare farer farerfarer than thanthan the the LSDthe LSD LSD 5% 2.145% 5% 2.14 to 2.14 the to to largerthe the larger DONlarger DON concentrations; DON concentrations; concentrations; green green data: green data: resistance data: to kernel infection, data from the regression line are farer than the LSD 5% 2.14 to the lower DON concentrations. LSD 5%: for DON is 2.14 [26]. Plants 2019, 8, x FOR PEER REVIEW 18 of 33

Plantsresistance2020, 9, 1702 to kernel infection, data from the regression line are farer than the LSD 5% 2.14 to the18 of 33 lower DON concentrations. LSD 5%: for DON is 2.14 [26].

16 y = 0.5849x + 1.1789 14 R2 = 0.6614, r=0.8132, p=0.001 12 10 8 6 4 LSD 5%>2.14 2

DON mg/kg 0 0 5 10 15 20 25 Fusarium damaged kernels (FDK) %

Figure 13. FDK and DON regression in FHB resistance tests in winter wheat genotypes against two F. graminearumgraminearum andand two twoF. F. culmorum culmorumstrains, strains, 2009–2012. 2009–2012. Red Red data: data: FDK FDK overproduction, overproduction, data data from from the theregression regression line areline farer are thanfarer thethan LSD the 5% LSD 2.14 5% to the2.14 larger to the DON larger concentrations; DON concentrations; green data: green resistance data: resistanceto kernel infection,to kernel datainfection, from thedata regression from the lineregression are farer line than are the farer LSD than 5% the 2.14 LSD to the 5% lower 2.14 to DON the lowerconcentrations. DON concentrations. LSD 5%: for LSD DON 5%: is 2.14for DON [26]. is 2.14 [26].

As we did not find ﬁnd it in the literature data, we tested large-scalelarge‐scale breeding material to see what thesethese correlations looklook like.like. TheThe testtest waswas mademade in in two two replicates replicates and and with with four four isolates isolates [75 [75].]. In In Table Table3, 3,we we show show the the ﬁve five years years of of correlations correlations (2011–2015). (2011–2015).

Table 3.3. Correlations between FHB traits disease index, FDK, and DON in advanced breeding lines and cultivars, Szeged, 2011–2015 [75]. [75].

BetweenBetween 20112011 20122012 20132013 20142014 20152015 MeanMean nn 129129 8989 111111 151151 9494 574574 FHB/FDKFHB/FDK 0.6280.628 *** *** 0.5780.578 *** *** 0.4670.467 *** *** 0.8020.802 *** *** 0.6540.654 *** *** 0.6260.626 FHB/DONFHB/DON 0.5660.566 *** *** 0.5590.559 *** *** 0.3410.341 *** *** 0.4410.441 *** *** 0.3410.341 *** *** 0.4500.450 FDK/DONFDK/DON 0.7470.747 *** *** 0.8530.853 *** *** 0.6300.630 *** *** 0.7310.731 *** *** 0.3910.391 *** *** 0.6700.670 ****** p = 0.001.

ItIt seems clear that the disease index provides, in most cases, a signiﬁcantlysignificantly looser correlation with DONDON than than FDK. FDK. It isIt also is clearalso thatclear the that disease the disease index and index DON and showed DON a moderateshowed a relationship. moderate relationship.The data proves The that,data inproves FDK, that, special in FDK, additional special genetic additional factors genetic are active factors to modifyare active the to FDK modify and theDON FDK response and DON in an response overproduction in an overproduction or to a relative or resistance. to a relative These resistance. latter twoThese were latter described two were as describedFDK and DONas FDK underproduction and DON underproduction or relative resistance or relative components resistance components [3]. The occurrence [3]. The ofoccurrence the toxin ofoverproduction the toxin overproduction or higher FDK or higher severity FDK can severity take 10–15% can take of the 10–15% population. of the Thepopulation. same is validThe same for the is validrelative for resistance, the relative e.g., resistance, resistance e.g., to FDK resistance and resistance to FDK and to DON. resistance In some to experiments,DON. In some the experiments, correlations thebetween correlations DI and between DON can DI be and on theDON same can level, be on but the this same is level, not regular. but this is not regular. The data gave further support to the idea that the disease index is significantly signiﬁcantly less suitable to forecastforecast DONDON response response than than the the FDK FDK ratio. ratio. The pattern The pattern is similar is tosimilar that in to the that previous in the experiments. previous experiments.On the other On hand, the other Arruda hand, et al. Arruda [76] compared et al. [76] compared the usefulness the usefulness of the trait of incidence,the trait incidence, severity, severity,disease index, disease FDK, index, and DONFDK, usingand DON the spawn using method the spawn of inoculation method of and inoculation came to the and conclusion came to thatthe the best traits for resistance were FDK and DON, which agrees well with our experience. However, Plants 2020, 9, 1702 19 of 33

Arruda et al. [67] concluded that most traits had very low frequency and they hoped to select better combinations with favorable alleles. He et al. [77,78] found useful correlations between DI and DON, but FDK was not tested, so we cannot compare it with the FDK response. It is important that before the genotypes were included in crosses. They were tested for three years for FHB resistance. This is highly important information supporting the line of research we follow. The analysis showed clearly that the resistance components are well characterized. This is valid also for the FDK and DON overproduction. This is not new, but the real new development is finding their role in the genetic and breeding system. Beside this, the detection of the DON overproduction in 10–20% of the genotypes tested is also an important step to identify new food safety risks beyond those we knew earlier. Therefore, the cultivars must be checked for this trait. This is true also for the FDK, the same ratio of genotypes may show agreeable DI, but bad FDK data. In our praxis, about 50% of the lines having low to moderate DI have high or very high FDK level. The visual rating finishes 21–24 days after inoculation. Thereafter, disease development can be continued in the remaining time of 2–3 weeks of harvest. In a dry season, from flowering to 3–4 weeks, no rain is measured and the morning dew is rare, the natural infection is low or absent, and correlations between traits are closer when artificial inoculation is made. Following inoculation, larger rains come (above 5 mm per day) in 2–3 weeks, an additional disease development will start, but will be realized only later than 21–26 days after inoculation when we stop reading, as the ripening heads cannot be evaluated precisely. In this case, the additional disease progress will increase FDK, but differently at the genotypes. We discovered from Jones and Mirocha [79] that the scabby grains have the highest DON contamination. There is another phenomenon, to have DON (normally not a high amount) on grains without visual head infection. DON can be translocated from stem base infection [80]. DON translocation is possible to cells in head not infected by F. culmorum [81]. The chaff can also be a source of DON translocated to otherwise healthy grains in the susceptible genotype, but in resistant genotype, the DON translocation was inhibited at little fungal colonization. Further, DON starts to be produced 2–3 days after inoculation [82] in susceptible genotype, but in Sumai 3, did not respond positively. Savard et al. [83] found four days after inoculation 1200 mg/kg in the inoculation point and 500–600 mg/kg in the spikelet below the inoculation point. As in susceptible plants, 6–12 days are necessary to see symptoms, a significant amount of DON can be present at the appearance of the symptoms. It is important that DON translocation seems to be resistance dependent. It was observed at FDK screening that among grains, for example 50 grains, showed characteristic scabby symptoms, however, 10–20% looked normal, only the grain surface showed a light powdery impression. DON was calculated for both the scabby and the scabby+powdery (total) FDK data and the correlation was stronger by 0.1–0.15 with the FDK, including also the powdery grains. This means that the not characteristic scabby grains may also contain DON (Mesterhazy unpublished).

5. Tolerance to FHB (Component/Type V) The tolerance measures the yield response to FHB infection [3,34]. It is no longer in use, as it is very laborious to detect. We see now that it can be a useful trait, but in normal breeding praxis, it has moderate significance. We remark that yield loss comes from three different sources. The first is the decrease in grain number/head, which can be up to 70–80% in extremely susceptible genotypes. The second source is the rate of light scabby grains that are blown out and fall to the soil. In experiments with hand harvesting and careful threshing, this is not a problem. The third reason is the bleaching; there are dead spikelets that are indirectly infected, but the infection of the head axis stops the metabolism and the grains become smaller with normal color. They are responsible for the shriveled and normal colored grains at the top of the head (Figure 14). Multiple infections in an ear are often the case in susceptible cultivars; they may originate from different dates that can make evaluation harder, as described by Dill-Macky [23]. Using a more resistant cultivar, the assimilate transport can function at a lower rate, but the consequences on the grain shriveling is significantly smaller (Figure 15). These three phenomena explain the tolerance or differing yield responses. We cannot exclude the Plants 2020, 9, 1702 20 of 33 PlantsPlants 2019 2019, ,8 8, ,x x FORFOR PEER PEER REVIEW REVIEW 2020 of of 33 33 presencedifferingdiffering of yieldyield further responses.responses. agents like WeWe fertilization, cannotcannot excludeexclude other thethe diseases, presencepresence draught, ofof furtherfurther water agentsagents stress, likelike etc. fertilization,fertilization, [71,84]; therefore, otherother researchdiseases,diseases, in draught,draught, this field waterwater would stress,stress, be useful. etc.etc. [71,84];[71,84]; therefore,therefore, researchresearch inin thisthis fieldfield wouldwould bebe useful.useful.

FigureFigure 14. 14.14. Ear EarEar of ofof a highlyaa highlyhighly susceptible susceptiblesusceptible genotype. genotype.genotype. At theAtAt bottom,thethe bottom,bottom, normal normalnormal green-colored, greengreen‐‐colored,colored, full sized fullfull grain, sizedsized evengrain,grain, the eveneven head thethe axis headhead is axis damagedaxis isis damageddamaged to some toto extent. somesome extent.extent. One spikelet OneOne spikeletspikelet higher, higher,higher, the grain thethe startsgraingrain starts tostarts show toto show earlyshow ripeningearlyearly ripeningripening discoloration, discoloration,discoloration, but it is butbut still itit full isis still size.still fullfull The size.size. next The spikeletThe nextnext is spikeletspikelet infected, isis the infected,infected, green color thethe green coveredgreen colorcolor by mycelium,coveredcovered byby and mycelium,mycelium, shriveled. andand The shriveled. nextshriveled. spikelet TheThe is lightnextnext brownspikeletspikelet close isis light tolight waxy brownbrown ripening. closeclose Thereafter, toto waxywaxy ripening. thereripening. are fourThereafter,Thereafter, strongly therethere infected areare four spikeletsfour stronglystrongly with infected massiveinfected myceliumspikeletsspikelets withwith coverage, massivemassive then, myceliummycelium two infected coverage,coverage, grains then, similarthen, twotwo to theinfectedinfected second grainsgrains one from similarsimilar the toto bottom, thethe secondsecond and at oneone the fromfrom top three thethe bottom,bottom, strongly andand shriveled, atat thethe buttoptop bleachedthreethree stronglystrongly normal shriveled,shriveled, colored, notbutbut directly bleachedbleached infected, normalnormal grains. colored,colored, notnot directlydirectly infected,infected, grains.grains.

Figure 15. A medium resistant genotype with one spikelet infected, and above the infection site, Figure 15.15. AA medium medium resistant resistant genotype genotype with with one one spikelet spikelet infected, infected, and above and above the infection the infection site, normal site, normal size grains sit in the spikelet without shriveling (except the spikelet above the infected one) sizenormal grains size sit grains in the sit spikelet in the withoutspikelet shrivelingwithout shriveling (except the (except spikelet the above spikelet the above infected the one) infected and onlyone) and only the light brown discoloration shows the accelerated ripening process. From this head, we theand light only brown the light discoloration brown discoloration shows the shows accelerated the accelerated ripening process. ripening From process. this From head, this we willhead, have we will have a yield close to the full healthy heads. awill yield have close a yield to the close full to healthy the full heads. healthy heads.

InInIn conclusion,conclusion,conclusion, thethethe resistanceresistanceresistance componentscomponentscomponents areareare valid,valid,valid, well-definedwellwell‐‐defineddefined traits.traits.traits. In In a a largerlarger partpart ofof thethethe population,population,population, the thethe responses responsesresponses to toto DI, DI,DI, FDK, FDK,FDK, and andand DON DONDON agree agreeagree well; well;well; however howeverhowever in a inin smaller, aa smaller,smaller, but significant butbut significantsignificant part ofpartpart the ofof population, thethe population,population, the FDK, thethe DON, FDK,FDK, andDON,DON, DI and reactionsand DIDI reactionsreactions can be very cancan di bebeff erent.veryvery different.different. Therefore, Therefore,Therefore, more attention moremore isattentionattention needed is tois neededneeded manage toto a wellmanagemanage working aa wellwell selection workingworking system selectionselection than systemsystem only a thanthan screening onlyonly a fora screeningscreening DI. Of course, forfor DI.DI. it OfOf is assumedcourse,course, itit that isis assumedassumed the diverging thatthat thethe responses divergingdiverging are responsesresponses genetically areare regulated. geneticallygenetically Therefore, regulated.regulated. the Therefore,Therefore, genetic background thethe geneticgenetic shouldbackgroundbackground also be shouldshould better alsoalso understood. bebe betterbetter understood.understood.

6.6. QTLsQTLs andand TheirTheir ValueValue Here,Here, referencesreferences werewere collectedcollected thatthat mappedmapped moremore traitstraits onon thethe givengiven mappingmapping population.population. However,However, first firstfirst an anan important importantimportant methodical methodicalmethodical problem problemproblem should shouldshould be bebe discussed. discussed.discussed. InIn thethe mappingmapping populations,populations, usuallyusually wide widewide flowering floweringflowering time timetime di ff differencesdifferenceserences exist existexist and andand the artificial thethe artificialartificial inoculations inoculationsinoculations should shouldshould adapt to adaptadapt it (11–18 toto itit days, (11–(11– 4–51818 days, inoculationsdays, 4–54–5 inoculationsinoculations in a season in in fieldaa seasonseason tests). inin This fieldfield might tests).tests). cause ThisThis problems mightmight cause ascause the weatherproblemsproblems normally asas thethe weather changesweather normallynormally changeschanges overover suchsuch aa longlong period.period. InIn severalseveral papers,papers, wewe seesee thatthat QTLsQTLs forfor floweringflowering datedate cancan influenceinfluence FHBFHB datadata [9,85].[9,85]. TableTable 44 showsshows thethe DIDI andand FDKFDK meansmeans forfor thethe differentdifferent inoculationinoculation Plants 2020, 9, 1702 21 of 33 over such a long period. In several papers, we see that QTLs for flowering date can influence FHB data [9,85]. Table4 shows the DI and FDK means for the di fferent inoculation days in 2002 and 2004 of the Frontana/Remus population [86]. In 2002, the earliest inoculation gave the highest infection, both for AUDPC and FDK. Thereafter, a slow decrease was seen in DI, and the FDK reduced to half at the end of the readings. The same population gave perfectly different numbers in 2004: the AUDPC increased from 151 to 832, but the FDK remained stable. When there are two years with weather conditions similar to each other, like in 2004, and only AUDPC, there is a decisive effect of flowering time. It could be the opposite. When high infection starts at the beginning because of an early warm and wet period, and at the end (during cooler weather), much lower data are yielded. In these cases, we observed significant flowering time effects, but in opposite directions, depending on the weather. This caused deviations that cannot be corrected. Additionally, a significant part will not have genotype-related phenotyping and QTLs from this data set will be high percentage artifacts. For FDK, there was a definite decrease in 2002 at the later inoculations, but a rather stable performance in 2004. Considering that, the resistance level in each flowering group was nearly the same as there was no significant difference between two inoculations and group means and the minimum and maximum values were close to each other, the data can be pooled into one group. In this case, according to FDK, the whole population can be treated as a pooled material that is impossible based on DI data. As this is not considered in the analyses, QTLs for resistance may be artifact in most cases when a QTL will be identified for flowering time. In reality, this is the influence of disease response caused by environmental traits and not a resistance trait itself. This means that flowering period can significantly influence resistance ranking, and therefore, it can cause a serious error in the QTL analyses following the incomplete phenotyping. This means that the inhomogeneity of the population is a real risk factor.

Table 4. FHB resistance test in wheat, AUDPC and FDK values in the Frontana/Remus mapping populations according to the inoculation dates, 2002 and 2004 [71].

Year 2002 2004 Inoculation DI AUDPC FDK % Inoculation DI AUDPC FDK % May Mean Mean May/June Mean Mean 10 368.44 34.91 19 151.14 37.42 13 248.83 31.39 21 172.62 40.27 15 303.21 25.80 24 169.25 31.39 21 315.18 19.38 26 440.06 43.68 - - - 7 831.74 35.32

Steiner et al. [28] (2004) worked with the same Frontana/Remus population that Szabo-Hever et al. [86] (2012) worked with and did not map FDK, and no other authors working with different Frontana populations have such data [48,87], therefore, a comparison for FDK was not possible. Burlakoti et al. [88] (2010) tested Frontana together with Alsen in AUDPC for disease severity and they were found to be equal. In FDK, Frontana was higher, but the difference was not significant. DON in Alsen was 50% less than Frontana, 7.90 versus 18.15 mg/kg, respectively. This is important as very similar ear blight data may result in very different DON contamination. This means that visual symptoms alone were not enough to choose a resistance donor or select against DON contamination. They tested 3BS and 6A (Type II) and 5a (Type I) from the cross Frontana/W9207//Alsen, but from the data, it is not clear how much the two QTL types contributed to the resistance. Alsen was the more resistant parent, but no line differed significantly from it with somewhat better resistance except line 3545 for AUDPC. On the other side, the more susceptible crossing partners did not inhibit to select lines at the resistance level of Alsen. In the Frontana/Remus population, six experiments were conducted over four years. In two years, 1-1 isolates were used and in another two years, 2-2 isolates were used [86]. FDK was also mapped beside the disease index. Not all QTL were significant in each year and each experiment. Plants 2020, 9, 1702 22 of 33

In the 6 experiments, 10 markers were found, therefore, 60 possible significant cases could be identified. The marker Xgwm120-Xs12m19_9 was significant only in the first test for DI and this was also the case for FDK, but not for the others. Xgwm526 was significant only in one test of six for 3A, and one of six for FDK, both in Exp. 5. Altogether 12 significant cases of 60 were given for DI and 18 cases for FDK. For some reason(s), FDK seems to be a better trait. When we see the mean performance, we see that F. culmorum had only six significant cases for LOD values. For F. graminearum, we had only two significant cases. As shown in the paper, the two F. graminearum experiments had low DI and FDK data; this can be the reason for the poor QTL result, as differentiation of the genotypes was low. The other finding was that a QTL could have different functions. Comparing the Xgwm526 has not been effective for DI on 2B, but was significant for FDK. The 3A QTL was effective only for DI, but ineffective against FDK. The 3D was ineffective against DI, but had a significant influence on FDK. In 4A, we had a significant effect on DI, but no influence on FDK. 4B was significant for both traits. 5A was specific only for FDK. 6B outlined a significant effect only in FDK, and 7B was effective for both traits. In summary, of the ten QTL 3 that were effective for both DI and FDK. Three were specific to DI and four influenced only FDK. Several QTLs overlapped with the results of Steiner et al. [28], but there is no good explanation for the QTL on 7B that was strong in our tests, but could not be identified in Tulln. From the genetic variance, 61.6% could be explained for DI by eight QTL and for FDK, 57.7% was explained by seven QTL. The lesson for us was that we should increase the number of isolates used, and we should shorten the flowering period of the population. For this reason, a new mapping population, Mini Mano/Frontana was developed [25], with a far higher DH number than 200. The too early and too late genotypes were discarded and several of the very tall plants were omitted. In the test, 168 genotypes were used. Six isolates were used yearly, so 12 experiments served as outgoing materials. The FHB data were checked for normal distribution and five tests were discarded, so seven remained. There was no data transformation. The heading date did not show any significant correlation with DI, so the decrease in inhomogeneity for this trait was successful. The FDK and DON showed significant, but not very close, correlations with heading date (r = 0.5 and r = 0.38). The taller plants tended to have higher lodging values (r = 0.68, p = 0.001). Therefore, it seems that beyond flowering time, the omission of the very tall and dwarf plants could reduce the inhomogeneity of the population and switch off traits that strongly influence FHB development. The DI/DON correlation was r = 0.319, the FDK/DON r = 0.54, both being significant at p = 0.001 but the FDK correlated closer with DON data than DI did. The mapping results across the seven tests yielded significant improvement compared with other tests [48,87] (Table5). Altogether, 15 QTLs were identified for FHB. Therefore, in the seven tests, we had 105 possible LOD values. Among them, 73 were significant. Six QTLs were significant in all seven experiments, four QTLs had five, four had five QTL, two showed four and 1-1 had 2, and 1 had zero. In FDK, 62 positive cases were found. One QTL was significant in all (7) tests, six had six significant LOD values, one had five LODs, four had three, two had one, and one had zero significant LOD numbers. Compared with the Frontana/Remus population, these numbers are much higher. The size of the significant LOD values was between 2.14 and 6.86 for DI as well as 2.01 and 5.42 for FDK. This refers for the individual experiments, their means in Table5 shows somewhat higher numbers. The number of identified QTLs in the Mini Mano/Frontana population doubled [25]. Of them, 15 QTL gave positive responses for some of the traits. Two QTLs were active only for DI. Seven acted for DI and FDK, but these nine QTL did not have any influence on the DON contamination. The QTL 3A is not listed here, as it had no measurable response for DI and FDK, but it gave a positive answer for DON. We detected only six of the 15 QTLs influencing DON contamination. Of them, only four had a reducing capacity for all three traits. This shows clearly that the effect of the QTLs is strongly structured, and without knowing the influence of the given QTL for FDK and DON, the breeding value of the given QTL cannot be evaluated. We should mention that for DI, two QTLs explained more than 20% of the variation and sic counted values between 10 and 19. For FDK, one gave more than Plants 2020, 9, 1702 23 of 33

20%, and eight gave numbers between 10 and 20. For DON only six QTLs were found and three were between 10 and 20%.

Table 5. Mapping of FHB QTLs in the Mini Mano/Frontana population (168 DH lines including parents) for DI, FDK, and DON, means for seven experiments 2008 and 2009 [25].

DI FDK DON Active for Marker Map Interval Chromosome LOD VE LOD VE LOD VE No wPt-800509 - wPt-2780 * 4A ** 5.73 14.6 1.71 4.4 no DI *** wPt-5334 - wPt-4243 4B 3.60 9.1 1.31 3.4 no no DI Xgwm205 - Xgwm156 5A 4.73 12.2 4.71 12.7 no no DI/FDK wPt-734078 - wPt-731843 1A 3.08 8.0 2.10 5.6 no no DI/FDK wPt-7204 - wPt-744786 6A 3.97 10.5 5.79 14.2 no no DI/FDK wPt-6039 - Xgwm88 6B 8.14 20.5 5.31 13.3 no no DI/FDK wPt-9925 - wPt-5922 7B 3.52 10.8 5.67 13.9 no no DI/FDK Xgwm44 - wPt-744219 NA1 3.33 8.5 2.16 5.6 no no DI/FDK wPt-666593 - wPt-664682 NA2 2.21 7.6 3.18 11.1 no no DI/FDK wPt-3812 - wPt-732411 2D 0.77 2.0 4.73 11.8 2.49 6.4 FDK/DON wPt-0934 - wPt-743601 7D 0.65 1.7 3.12 7.9 3.41 8.7 FDK/DON wPt-5347 - wPt-2315 1B 5.06 17.0 5.02 13.0 2.78 7.1 DI/FDK/DON wPt-732882 - wPt-667765 2D 6.61 23.0 6.34 20.6 4.12 10.3 DI/FDK/DON Xgwm533 - wPt-3921 3B 3.97 10.0 2.54 6.6 4.01 10.1 DI/FDK/DON wPt-741134 - wPt-5896 5B 2.54 8.7 5.34 15.0 4.53 15.2 DI/FDK/DON * Bold text: The most eﬀective QTL in the given group. ** Bold: LOD signiﬁcant at p = 0.001. *** no: no response at all.

When comparing the LOD values for Frontana for the identical QTLs in [25,28,86], we saw rather large differences. We do not think that the QTLs were here 2–3 fold stronger in Mini Mano/Frontana. The variance explained (VE) above and near 20% means only that the background noise was smaller, and this allowed the detection of double the amount of QTLs than were previously found. The other feature is that the number of identified QTL is about double than that mentioned in other papers [28,48,87]. This means that the traditional mapping work sees about 50% of the QTLs, only those that could be detected. When we see the correlations between plant height, lodging, and Fusarium traits, further improvement in the increasing reliability of the QTL analysis is possible when the tall and mostly lodging-susceptible genotypes do not influence the FHB response to different traits. Besides these results, other authors detected structured QTLs with different functions. Lemes da Silva [89] identified eight QTLs; the Qksu.fhbE-7AL influenced only FDK in one year. The Qksu.fhbE-6BS determined AUDPC only for one year, the Qksu.fhbE-5AS, the FDK and DON, and the Qksu.fhbE-4BL determined only visual symptoms. The Qksu.fhbE-3DS determined DON and 1000 kernel mass, the Qksu.fhb-2AS, AUDPC, and FDK, the Qksu.fhbE-1BS determined the visual symptom development, DON, FDK, thousand corm weight (TKW), and the Qksu.fhbC-1AS influenced visual scores, DON, and FDK. The combinations found by Szabó-Hever et al. [25] were quite similar. Most QTLs explained less than 10% of the variability, but in eight cases, the values were higher with the maximum of 20% for TKW. Liu et al. [90] found on 4BS a QTL determining FDK, severity, incidence, FDK, and DON. The 3BL coded severity, FDK, and DON, and on 4DS incidence, severity, FDK, and DON, but only incidence was positive for DI. Petersen et al. [91], also identified QTLs with differing influence on the Fusarium resistance traits like visual evaluation, FDK, and DON. The estimation of FDK is critical, and when the shriveled but healthy-looking grains are included, very high numbers can be achieved [78]. These originate from bleached spikelets having no direct infection (see Figure 13 in this paper), but as their tracheas were destroyed and assimilate transfer was stopped by the fungus, the delivery of assimilates and water became impossible. Besides this, FDK and DON data were investigated only for 2012, so the repeatability of the results cannot be checked. Arruda et al. [67] mapped all the important FHB traits. They found also that QTLs had different functions, so DON was determined only by two QTL (1D, 3B). Interestingly, incidence was influenced Plants 2020, 9, 1702 24 of 33 by five different chromosomes (7D, 6A, 4D, 4A, 7A), but disease index was regulated only by 3B. On the other hand, the spawn method has the disadvantage that the inoculation time is not so easy to identify; this can cause instability in the system. The epidemic severity was high, so the differentiation of the genotypes was acceptable. Tessmann et al. [92,93] used spraying inoculation and identified QTLs influencing FDK and DON on chromosomes 4A, 5B, and 6B, and they reported additional FDK and DON specific QTL in several genotypes. Among the breeding lines, they identified highly FHB resistant genotypes with low DON contamination. He et al. [94] found two QTLs for DON; one was independent of FHB resistance, and the other was located as a minor QTL on 3BL chromosome. Wu et al. [95] found that QTLs Type II controlled both DI and DON. This means that we have convincing genetic evidence for the different role of the QTLs. It is also clear that the DON and DI or FDK regulations are not synonymous expressions. Therefore, the skepticism of Ma et al. [22] about the resistance to kernel infection or DON resistance is not supported by novel data. However, we are currently at the beginning of a process of understanding and that should be continued. The majority of the QTL work concentrates on the visual symptoms [21,53,57,96–101] as shown and many others. They applied spraying or point inoculation and analyzed only the visual symptoms. Haile et al. [102] in their review of durum wheat FHB resistance listed a number of QTL analyses, but no papers were cited where the reaction of DON could be mapped. Low DON contamination in durum wheat is top priority. Most of the early QTL research was the same [9]. QTL f hb1 was one of the few exceptions [9], and the reducing influence on DI, FDK, and DON is well documented. As in recent years, the QTLs had a much more complex and diverse effect, influencing one or more traits. In our opinion, the QTLs identified for DI, severity, or incidence should be extended to FDK and DON in order to have a higher chance of decreasing DON contamination. For practical breeding, the spraying inoculation is more important, as this signals the commencement of both Type I and Type II resistance, and resistance components for FDK and DON can be evaluated from the data. The best resistance until now was the combination of Type I and II in Sumai 3. It appears that a combination of the QTLs with different functions is the way forward for future research. Liu et al. [29] worked with the spawn method, but this is less reliable as inoculation data cannot be secured exactly. Therefore, more epidemic waves can merge. The disease pressure was rather uniform and high over the different years analyzed. Prediction of the FHB resistance depends mostly on the number of markers used, but no further improvement was seen above 1000. QTL mapping has been done in two main directions. Populations and genotypes were tested in most cases for Type I and Type II resistance. As Type I is often mapped as incidence or severity, it would be better to use the disease index in the future. I think that research should be to compare visual data with FDK and DON data. As mentioned, most of the past and present literature considers only visual symptoms [5,6,9]. This means that in most cases, we do not have an idea about what the influence will be of these QTLs on DON contamination will be. The possible functions of QTLs are presented in Table6. As most QTLs are characterized by DI (or separately by incidence and severity), their breeding value cannot be determined as the effect against FDK and DON is not known. For this reason, they are not suitable for MAS because the decreasing effect of DON is unknown and may be absent with a large quantity of the QTLs. For the reasons presented, the marker assisted selection is not yet a general tool, it can be applied on several well characterized sources, but the vast majority of the genotypes having unknown resistance to FHB should yet be detected first, then characterized and later used them. The critical point is the validation, e.g., to check the effect of the donor QTLs in different crosses. The other problem is that most of the QTLs are of small effect [103]. It seems that authors overestimate the significance of Type II resistance for the breeding of resistance and underestimate the problems arising from the use of partially characterized small and medium strength QTLs. Plants 2020, 9, 1702 25 of 33

Table 6. Possible QTL functions with activity to diﬀerent FHB traits in wheat [86].

Inoculation DI FDK DON Spraying DI X FDK X DON X DI/FDK X X DI/DON X X FDK/DON X X Inoculation DI/FDK/DON X X X Point/one ﬂoret DI X FDK X DON X DI/FDK X X DI/DON X X FDK/DON X X DI/FDK/DON X X X X: positive response of the given trait, Bold: the most useful combinations.

Of course, the measurement of these traits and discovering their genetic background has two very important fields. One is the characterization of possible crossing partners and resistance sources, and the qualification of the product of the breeding process. The variety should not only have DI resistance, but should also secure low DON contamination. For these tasks, improved methodology is necessary; otherwise, we might produce artifacts that do not benefit the research.

7. Breeding Aspects The reality is that the QTLs have different functions. As QTL analyses were made in spraying inoculations and point inoculations, their results cannot be compared. What seems to be certain is that the traditional Type I resistance is significantly more effective, as anticipated, and the response to spraying inoculation contains the effect of the Type II resistance. This means that the spraying inoculation is more important for breeding than generally estimated. Actually, this is what most breeders are doing; they use some version of this method. The spawn method is popular in the US [67,76]. It can be considered as a variant of the spraying inoculation being more natural but less reliable. For this reason, Type II resistance will lose in significant resistance breeding, but its significance remains when the resistance background should be characterized. The US experience in the year 2000 and thereafter showed that the fhb1 resistance did not solve the problem and the Chinese progress was slower than expected. For this reason, the combination of Type II resistance, which is part of the total resistance and the Type I resistance measured by the response to spraying inoculation, provides a more successful breeding philosophy. Mostly incidence, severity, or their combination disease index are measured, and they may be differently regulated. Additionally, the visual symptoms are not enough to characterize the effect of the resistance, their relationship with the FDK and DON contamination is also of high importance. Spraying inoculation has been successful [104] and highly resistant genotypes were created with this philosophy, so it is a good option for practical breeding. For us, those QTLs are important that, additionally to DI, reduce both FDK and DON. In the Mini Mano/Frontana mapping population, only six of the 15 QTLs had influence on DON. Many papers published similar results indicating that the Frontana story was not an exception. For this reason, genetic research should use the best methodology, and the selection and control of crossing partners as well as the registration trials need similar accuracy. In the practical breeding, the case is simpler. He et al. [77] used spraying inoculation at CIMMYT, evaluated disease index, and DON control was conducted on selected elite lines in the 2nd and 3rd year. In an unknown material, as most winter wheat nurseries are not known for QTLs, a screening of the nursery is important. After having considerable information, the breeding program can be Plants 2020, 9, 1702 26 of 33 modified to cross only partners with moderate or higher resistance to FHB. Then, several heads can be inoculated by spray inoculation by one aggressive inoculum and a negative selection can be done by also considering other traits. In F2 and F3 generations, no artificial inoculation was necessary when parents were checked; otherwise, the one row from the ear-to-rows of a combination can be inoculated by FHB. The best homogeneous lines from ear-to-row lines coming to the B lines in 5 m2 will be tested without replicates for yield and other traits, and one row 1.5 m long will be exposed to artificial mass inoculation using the same method as mentioned earlier [75]. A negative selection follows again and the supposed plus variants should be checked for FDK. The selected plus variants (C lines, n = about 250–300) tested in four replicated trials should be checked by more isolates for resistance. We used four, (three should be the minimum) in one replicate in two row plots as described earlier with spraying inoculation and humidity secured by polyethylene (PE) bags. This was the first DON measurement. Ten percent of the samples from the yield of the four isolates was separated and then pooled. After fine grinding, only one analysis was made. Of course, it was only for those whose other traits were OK and may be suitable for registration. In the multi-location tests (about 48 lines), two plot replicates were used. For the most important lines, all isolates and replicates were worth testing, while for the others, the two pooled samples of the four inocula was enough, and others were not measured at all. When yield was not top, but DON and FDK were very low, the line was identified as valuable crossing partner and used in crosses. Irrespective of the high complexity of genetic background, we should select for low DI, FDK, and DON. Marker-assisted selection (MAS) is a very popular tool suggested to solve the issues in breeding cultivars with higher resistance. The prerequisite of this work is the correct knowledge of the donor genotype to all traits; otherwise, the work may be unsuccessful. With the use of well-characterized QTLs like fhb1 and several others, it can be successful. However, MAS with partially characterized QTLs is risky. As Lemmens et al. [24] stated, if DON reduction is the ultimate goal, only QTLs that are known in this respect ought to be used. This is valid also for the genomic selection [105]. On the other side, many of the existing QTLs are not identified yet and most of the known ones are not validated in crosses. For this reason, careful screening helps to detect new resistance sources in our own material [75], where the effects of the unknown QTLs are also included. As knowledge increases, the breeding program will be more successful. DON control is further important as the toxin overproducers can be discarded by this method. On the other hand, the knowledge of toxin overproduction is an important scientific problem and its genetic background should be known. The variety candidates, control cultivars, and important competitor varieties are also included in this test, n = about 100, differing each year. It was found [65] that about 50% of the plus variants in the first test year will keep their resistance in the next year. Highly resistant spring wheat sources were used in winter wheat breeding, and now we have such plants. The variety registration for FHB remains a central task for the future. Without this, we cannot achieve significant improvement in the wheat fields. At least, medium resistant cultivars can be protected effectively with the most updated fungicides and fungicide technology [37,38]. We should remark that breeding is ahead of the science. Dong et al. [106], He et al. [77], and Murphy et al. [107] screened variety trials for visual symptoms, FDK, and DON, and consequently allowed a more precise and successful identification of low DON content cultivars. It is important that the complexity of the FHB resistance in wheat needs a better understanding as stressed by Ma et al. [45]. I think this is a challenge for science. On the other hand, the consequent screening for all traits can identify new genotypes for mapping and detect new QTL that can be useful for breeding to create new cultivars with higher resistance to disease and toxins.

8. Conclusions In conclusion, we demonstrated that a better phenotyping (including aggressiveness tests, secure high aggressiveness level, use more isolates, to ensure a higher reliability of experimental Plants 2020, 9, 1702 27 of 33 data both for resistance tests and for genetic analysis. The visual evaluation is highly important, without correct data, the FDK and DON cannot be properly evaluated. However, in later developmental stages, other genetic factor can influence disease and toxin development, therefore we cannot consider the resistance components as fully independent traits. We speak about a total resistance that contains all possible resistance components (types) and the corresponding inoculation method is spraying inoculation. As low DON should be secured, we need, besides low visual scores, low FDK and low DON values. As they can be differently regulated, care should be given to test them. As QTLs have different functions, only QTLs are suggested which can reduce FDK and DON contamination in addition to visual symptoms. The selection of the crossing partners is therefore highly important work. In QTL analyses, the heterogeneity of the population for flowering time, plant height is highly significant, it is suggested to reduce the variance, like 1 week difference between the most early and late genotype and in plant height 20–30 cm larger difference is not allowed. For this reason, it is reasonable to use parents that are closer in agronomic traits and differ mostly in FHB resistance. The highly susceptible cultivars should be withdrawn from commercial production. The newly released cultivars should have low risk to secure food safety standards and easy and profitable growing. Fungicides can protect them easier and they resist more to agrotechnology mistakes and high inoculum concentration in the debris.

Supplementary Materials: The following are available online at http://www.mdpi.com/2223-7747/9/12/1702/s1, Figure S1. Disease index data (%) in the methodical test for inoculation methods across years and methods according to epidemic severities, 2009–2012. Figure S2. FDK data (%) in the methodical test for inoculation methods across years and methods according to epidemic severities, 2009–2012. Author Contributions: The author planned the paper; he made the necessary additional statistical evaluations and created the Figures and Tables as well the writing of the text. The author has read and agreed to the published version of the manuscript. Funding: The authors are thankful for the financial support of the FUCOMYR FP5 QLRT-2000-02044, MycoRed EU FP7 (KBBE-2007-2-5-05), the GOP-1.1.1-11-2012-0159 and projects of Ministry for Innovation and Technology TUDFO/51757/2019-ITM and TKP2020-NKA-21. For the older papers, our sincere gratitude goes to the Cereal Research Institute for financing the research. Acknowledgments: The author expresses his appreciation to Beata Toth and Csaba Lantos for reading through the manuscript. He appreciates the spiritual help of Antonio Logrieco, CNR, Bari, Italy, head of the MycoKey project (Grant Agreement No. 678781). Conflicts of Interest: The author declares no conflict of interest.

Abbreviations

AUDPC Area Under Disease Progress Curve cvs cultivars DI disease index Fav F. avenaceum Fc F. culmorum FDK Fusarium damaged or scabby kernels Fg F. graminearum FHB Fusarium head blight Fspor F. sporotrichioides LOD limit of odds in QTL analysis LSD limit of signiﬁcant diﬀerence MAS marker aided selection Mniv Microdochium nivale, Gerlachia nivalis NIV nivalenol PE polyethylene QTL Quantitative trait locus. Plants 2020, 9, 1702 28 of 33

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