<table><caption>**TABLE 2** | Indices of genetic diversity among strains collected from non-agricultural habitats (environmental strains) and from crops (agricultural strains).</caption><thead><tr><th>Origin of strains</th><th>Number of strains genotyped</th><th>Number of <i>B. pseudocinerea</i> strains</th><th>Number of <i>B. cinerea</i> strains</th><th>Gene diversity (Hnb)</th><th>Mean number of alleles per locus</th><th>Number of distinct MLH</th><th>Haplotypic diversity</th></tr></thead><tbody><tr><td><strong>Total environment</strong></td><td>109</td><td>9</td><td>100</td><td>0.77 (0.16)</td><td>13.6</td><td>83</td><td>0.82</td></tr><tr><td>Precipitation</td><td>69</td><td>6</td><td>63</td><td>0.78 (0.14)</td><td>12.3</td><td>54</td><td>0.85</td></tr><tr><td>Snowpack</td><td>10</td><td>3</td><td>7</td><td>0.69 (0.18)</td><td>4</td><td>6</td><td>0.83</td></tr><tr><td>Fresh water</td><td>12</td><td>0</td><td>12</td><td>0.64 (0.29)</td><td>4.5</td><td>9</td><td>0.75</td></tr><tr><td>Epilithic biofilm</td><td>2</td><td>0</td><td>2</td><td>0.44 (0.33)</td><td>1.6</td><td>2</td><td>1</td></tr><tr><td>Litter</td><td>3</td><td>0</td><td>3</td><td>0.62 (0.26)</td><td>2.4</td><td>3</td><td>1</td></tr><tr><td>Plant debris</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1.0</td><td>1</td><td>nd</td></tr><tr><td>Wild plants</td><td>7</td><td>0</td><td>7</td><td>0.57 (0.27)</td><td>3.3</td><td>7</td><td>1</td></tr><tr><td>Bird feather</td><td>5</td><td>0</td><td>5</td><td>0.38 (0.27)</td><td>2.1</td><td>5</td><td>1</td></tr><tr><td><strong>Agricultural strains</strong></td><td>327</td><td>6</td><td>321</td><td>0.76 (0.18)</td><td>15.6</td><td>186</td><td>0.58</td></tr></tbody></table>

<table><caption>**TABLE 3** | Allelic richness (AR) and unbiased gene diversity (Hnb) of environmental and agricultural strains of *B. cinerea*, at each of the nine microsatellite loci (corrected for the smallest sample size: 100).</caption><thead><tr><th rowspan="2"></th><th colspan="2">BC1</th><th colspan="2">BC2</th><th colspan="2">BC3</th><th colspan="2">BC4</th><th colspan="2">BC5</th><th colspan="2">BC6</th><th colspan="2">BC7</th><th colspan="2">BC9</th><th colspan="2">BC10</th></tr><tr><th>AR</th><th>Hnb</th><th>AR</th><th>Hnb</th><th>AR</th><th>Hnb</th><th>AR</th><th>Hnb</th><th>AR</th><th>Hnb</th><th>AR</th><th>Hnb</th><th>AR</th><th>Hnb</th><th>AR</th><th>Hnb</th><th>AR</th><th>Hnb</th></tr></thead><tbody><tr><td>Environmental</td><td>21.0</td><td>0.91</td><td>15.0</td><td>0.87</td><td>11.0</td><td>0.84</td><td>11.0</td><td>0.84</td><td>12.0</td><td>0.87</td><td>25.0</td><td>0.83</td><td>11.0</td><td>0.81</td><td>10.0</td><td>0.55</td><td>14.0</td><td>0.86</td></tr><tr><td>Agricultural</td><td>21.2</td><td>0.87</td><td>15.8</td><td>0.89</td><td>15.2</td><td>0.84</td><td>15.2</td><td>0.84</td><td>12.4</td><td>0.81</td><td>21.4</td><td>0.84</td><td>11.0</td><td>0.85</td><td>9.6</td><td>0.47</td><td>16.8</td><td>0.86</td></tr></tbody></table>

## Environmental Strains Grow Faster and Sporulate Less Abundantly but Do Not Differ in Sclerotial Production Compared to Agricultural Strains

Among the 31 agricultural and 24 environmental strains tested, the mycelial growth between the first and the third day after the plating of mycelial plug varied widely among strains (Figure 3; ANOVA, *P* < 0.0001 for each set of strains). Four strains (BC83, 06C12, 06C163, and S381) had a mycelial growth rate significantly lower than the other strains. These four strains were all isolated from diseased plants (rose, grape and tomato). Globally, mycelial growth rate was significantly higher for environmental strains compared to agricultural strains (29.8 ± 0.4 vs. 25.7 ± 0.4; *P* < 0.0001). A significant correlation was detected between aggressiveness of strains on tomato plants and their mycelial growth on PDA medium (*r*² = 0.12, *P* = 0.009 for all the strains). However, this correlation is not significant for each set of strains taken independently (*r*² = 0.09, *P* = 0.17 for environmental strains and *r*² = 0.11, *P* = 0.08 for agricultural strains).

All strains of *B. cinerea* tested were able to sporulate on PDA medium but the number of spores produced varied widely among strains, both for environmental and for agricultural strains (Figure 4; ANOVA, *P* < 0.0001 for each population of strains). Globally, sporulation was significantly lower for environmental strains compared to agricultural strains (12.9 ± 1.1 vs. 17.4 ± 1.1 million spores/mL; *P* = 0.005). There was no significant correlation between aggressiveness of strains on tomato plants and sporulation (*r*² = 0.00001, *P* = 0.98 for all strains).

The number of sclerotia produced on PDA medium varied greatly among strains (Figure 5; ANOVA, *P* < 0.0001 for both environmental and agricultural strains) and the average number of sclerotia produced was not different between the two sets of strains (41.1 ± 5.5 and 65.5 ± 12.6 for environmental and agricultural strains, respectively, *P* = 0.11). Twelve strains of *B. cinerea* (7 from diseased plants and 5 from non-agricultural context) were not able to produce sclerotia under the conditions used here and one strain (BC96 isolated from hydrangea) produced a significantly higher number of sclerotia than all the other strains. We found a significant negative relationship for all strains between the aggressiveness and the number of sclerotia produced (*r*² = 0.09, *P* = 0.031). This correlation is highly significant for the 24 non-agricultural strains (*r*² = 0.47, *P* = 0.0002) while it was not significant for the 31 agricultural strains (*r*² = 0.03, *P* = 0.33), suggesting that the relationship between aggressiveness and sclerotial production differ among these two populations of strains.

## DISCUSSION

This study is the first systematic census of the fungal plant pathogen *B. cinerea* in diverse non-agricultural environments that could come into contact with agriculture such as snowpack, fresh water from river and lake, epilithic biofilms in mountain streams, leaf litter, plant debris and bird feathers. These results complement the prior detection of *B. cinerea* in the diverse substrates described in the introduction. It also contributes to information about sources of *B. cinerea* in precipitation (Monteil et al., 2014) and corroborates reports about its capacity to
<figure><img src="image_45.png" /><figcaption>**FIGURE 1** | Neighbor-joining tree showing the genetic distance between strains from non-agricultural habitats (red circle) and from crops, based on microsatellite markers.</figcaption></figure>
<figure><img src="image_46.png" /><figcaption>**FIGURE 2** | Aggressiveness of 128 environmental strains (A) and 156 agricultural strains (B) of *B. cinerea* on tomato plant in controlled condition (21°C, RH > 80%, photoperiod 14 h, 114 µmol m⁻² s⁻¹). Index of aggressiveness (in %) was calculated for each strain, relative to the reference strain BC1 (hatched). The horizontal red bar corresponds to an index of aggressiveness of 100% (value of BC1). Each value represents the mean of two or three independent repetitions. The vertical bar associated to each histogram correspond to SE.</figcaption></figure>

<table><caption>**TABLE 4** | Mean and range of aggressiveness of strains of *B. cinerea* collected on various substrates.</caption><thead><tr><th>Substrate</th><th>Number of strains</th><th>Mean Ia* ± SE</th><th>Minimum</th><th>Maximum</th><th>Median</th></tr></thead><tbody><tr><td>Total</td><td>284</td><td>54.2 ± 1.6</td><td>0.0</td><td>144.3</td><td>54.8</td></tr><tr><td>Environmental</td><td>128</td><td>58.9 ± 2.5<sup>a</sup></td><td>0.0</td><td>144.3</td><td>58.2</td></tr><tr><td>Tomato</td><td>101</td><td>59.5 ± 2.4<sup>a</sup></td><td>0.0</td><td>110.9</td><td>61.7</td></tr><tr><td>Grape</td><td>33</td><td>25.8 ± 2.5<sup>c</sup></td><td>3.1</td><td>72.5</td><td>25.7</td></tr><tr><td>Other plants</td><td>22</td><td>45.7 ± 4.7<sup>b</sup></td><td>11.8</td><td>97.6</td><td>43.4</td></tr><tr><td>ANOVA (p-value)</td><td></td><td>&lt;0.0001</td><td></td><td></td><td></td></tr></tbody></table>

colonize and survive internally in plants without causing any symptoms (Van Kan et al., 2014), traits that are essential for an endophytic phase. Our results illustrate that *B. cinerea* can survive in environmental reservoirs outside of agriculture *sensu stricto* and possibly in the absence of susceptible plants. This knowledge contributes to the growing notion that the life history of plant pathogens should be viewed from a wider angle than their direct interaction with plants in contexts that are favorable for disease. Furthermore, the relative ease with which we found strains of *B. cinerea* in environmental reservoirs highlights how defining this fungus as a plant pathogen skews research questions on its life history and ecology in spite of the numerous previous reports of its presence in a wide range of diverse habitats.

Our results also suggest that the environmental reservoirs are probable sources of inoculum. Previous reports of the genetic diversity of *B. cinerea* from wild plants (e.g., primrose, bramble, dandelion, *Hypochaeris radicata*, *Plantago lanceolata*, and *Sonchus asper*), illustrated the potential reservoirs of

inoculum for crops on wild plants and weeds in agricultural systems (Fournier and Giraud, 2008; Rajaguru and Shaw, 2010; Walker et al., 2015; Wessels et al., 2016), but there have been no studies on non-agricultural sources. Phylogenetic comparisons of strains from crop plants and from environmental substrates presented in this study revealed that strains from crops and from the environment could not be distinguished, thereby supporting the hypothesis that strains from these different sources mix. Furthermore, the genetic diversity of strains outside of agriculture was just as broad as within agriculture (Table 5; Bardin et al., 2014), suggesting that there are no strong selective pressures that remarkably distinguish these two categories of habitats. These results are consistent with those previously published for *B. cinerea* in different agricultural contexts, using the same genotyping tools developed by Fournier et al. (2002). In such studies, the haplotypic diversity (ratio of the number of different haplotypes over the total number of characterized strains) varied from 0.37 to 1.00. In the present study, this index was

<footnote>*Ia (index of aggressiveness) is expressed in % relative to the reference strain BC1. Values with the same uppercase letter are not significantly different according to Newman-Keuls multiple range test (P < 0.05).</footnote>
<figure><img src="image_47.png" /></figure>

**FIGURE 3** | Mycelial growth (mm/day) of 31 agricultural (black) and 24 environmental (white) strains of *B. cinerea* on PDA medium in controlled condition (21°C, photoperiod 14 h, 114 µmol m⁻² s⁻¹). Each value represents the mean of two independent repetitions. The vertical bar associated to each histogram corresponds to SE.

<figure><img src="image_48.png" /></figure>

**FIGURE 4** | Number of spores produced on PDA medium after 14 days of incubation in controlled condition (21°C, photoperiod 14 h, 114 µmol m⁻² s⁻¹) by 30 agricultural (black) and 24 environmental (white) strains of *B. cinerea*. Each value represents the mean of three replicates. The vertical bar associated to each histogram corresponds to SE.

high, with a value of 0.82, comparable to what is observed in populations of *B. cinerea* collected on diseased plant in open field situations. It reflects the scarcity of strains with identical genotypic profiles in the non-agricultural context. Although we have demonstrated that strains from a non-agricultural context cannot be genetically distinguished from strains from diseased

plants, various questions remain about the rate of flux of strains between non-agricultural environment and crops and on the contribution of environmental reservoirs to epidemics.

In tests to determine the severity of disease that strains could induce on tomato stems, the mean disease severity caused by strains from environmental substrates was statistically
<figure><img src="image_49.png" /><figcaption>**FIGURE 5** | Number of sclerotia produced by 31 agricultural (black) and 24 environmental (white) strains of *B. cinerea* on PDA medium after 5 weeks of incubation in controlled condition at 21°C (1 week with a photoperiod of 14 h at 114 µmol m⁻² s⁻¹ and 4 weeks in the dark) and 5 weeks of incubation at 4°C in the dark. Each value represents the mean of three replicates. The vertical bar associated to each histogram correspond to SE.</figcaption></figure>

<table><caption>**TABLE 5** | Genetic diversity reported for strains of *B. cinerea* collected from different origins and characterized using the same microsatellite markers developed by Fournier et al. (2002), as in this study.</caption><thead><tr><th>Strain origin<sup>a</sup></th><th>Localization</th><th>Number of isolates</th><th>Gene diversity</th><th>Mean number of alleles per locus</th><th>Haplotypic diversity<sup>b</sup></th><th>Reference</th></tr></thead><tbody><tr><td>Environmental</td><td>France</td><td>100</td><td>0.77</td><td>13.6</td><td>0.82</td><td>Present study</td></tr><tr><td>Pear orchard (OF)</td><td>South-Africa</td><td>181</td><td>0.69</td><td>5.49–7.76</td><td>0.50</td><td>Wessels et al., 2016</td></tr><tr><td>Grapevine (OF)</td><td>Italy</td><td>317</td><td>na<sup>c</sup></td><td>na</td><td>0.92</td><td>Campia et al., 2017</td></tr><tr><td>Grapevine (OF)</td><td>China</td><td>135</td><td>0.29</td><td>na</td><td>0.94</td><td>Zhang et al., 2018</td></tr><tr><td>Tomato, lettuce (G)</td><td>France</td><td>86</td><td>0.62–0.77</td><td>4.70–9.20</td><td>0.37–0.69</td><td>Leyronas et al., 2015b</td></tr><tr><td>Tomato (G)</td><td>France</td><td>170</td><td>0.76–0.77</td><td>11.6–11.3</td><td>0.55–0.65</td><td>Leyronas et al., 2015a</td></tr><tr><td>Tomato (G)</td><td>Algeria</td><td>170</td><td>0.57</td><td>8.22</td><td>0.63</td><td>Adjebli et al., 2015</td></tr><tr><td>Soil (G)</td><td>France</td><td>66</td><td>0.68</td><td>8.5</td><td>0.58</td><td>Leyronas et al., 2015c</td></tr><tr><td>Air (OF)</td><td>France</td><td>616</td><td>0.71</td><td>19.11</td><td>0.72</td><td>Leyronas et al., 2015d</td></tr></tbody></table>

The References presented here range from 2015 to 2018. For information on previous references, see Bardin et al. (2014). <sup>a</sup>Indication between brackets indicates the type of crop. OF, open field; G, greenhouse. <sup>b</sup>Haplotypic diversity index directly available or computed (on the basis of reported information) as the ratio of (number of distinct MLG – 1) over (sample size – 1). <sup>c</sup>na, data not available.

greater than the severity caused by strains from diseased crops (P = 0.009). It suggests that there are no selective pressures for strains of *B. cinerea* to lose pathogenicity while they are in contexts where they do not cause plant disease or are not even colonizing plants. On the other hand, the environment seems to select for more rapid mycelial growth whereas cropping contexts select for more abundant sporulation. This suggests that there are fitness or survival benefits for these traits in each of the respective environments. This hypothesis is reinforced by the fact that mycelial growth is positively correlated with aggressiveness. Therefore, it suggests that the non-agricultural environment could select strains for factors that would facilitate their pathogenicity on plants. It would be interesting to determine how much time is needed for mycelial growth and sporulation rates to change under the selection pressures of each environment

and if in fact these traits are fixed or due to epigenetic processes. This could give clues to the amount of time that strains from these habitats have been localized in one habitat or another. Interestingly, a trade-off between the aggressiveness and the number of sclerotia produced was observed for the environmental strains of *B. cinerea*. This suggests that the least aggressive environmental strains compensate this low level of aggressiveness by their high ability to survive in the environment in the absence of host plants.

Our results are consistent with those concerning the life history of the bacterial plant pathogen *P. syringae* (Morris et al., 2013) in that highly diverse populations of strains that maintain their pathogenic potential can persist outside of agriculture in association with substrates other than plants. These findings set the stage for new perspectives on factors that foster to
the evolution and emergence of pathogenic potential in microorganisms, on disease epidemiology and on the factors that contribute to the durability of disease control methods. They also beg for the development of systematic approaches to widen our understanding of the life history of plant pathogens in general beyond the context of agriculture.

## AUTHOR CONTRIBUTIONS

MB, CT, and CM achieved the sampling of environmental strains. MB and CT realized the phenotypic experiments and the associated data analysis. CL carried out microsatellite genotyping and the genetic data analysis. MB coordinated the writing of the manuscript. MB, CL, and CM wrote the manuscript. All authors reviewed the manuscript.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Bardin, Leyronas, Troulet and Morris. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Pathogenicity Traits Correlate With the Susceptible *Vitis vinifera* Leaf Physiology Transition in the Biotroph Fungus *Erysiphe necator*: An Adaptation to Plant Ontogenic Resistance

OPEN ACCESS

**Edited by:**
Omer Frenkel,
Agricultural Research Organization
(ARO), Israel

**Reviewed by:**
Andreia Figueiredo,
Universidade de Lisboa, Portugal
Courtney Coleman,
University of Missouri, United States

*Correspondence:
Agnes Calonnec
agnes.calonnec@inra.fr

†Present Address:
Sylvain Schnee,
Station de Recherche Agroscope
Changins-Wädenswil ACW, Nyon,
Switzerland

**Specialty section:**
This article was submitted to Plant Microbe Interactions, a section of the journal Frontiers in Plant Science

**Received:** 15 June 2018
**Accepted:** 20 November 2018
**Published:** 11 December 2018

**Citation:**
Calonnec A, Jolivet J, Vivin P and Schnee S (2018) Pathogenicity Traits Correlate With the Susceptible Vitis vinifera Leaf Physiology Transition in the Biotroph Fungus Erysiphe necator: An Adaptation to Plant Ontogenic Resistance. Front. Plant Sci. 9:1808. doi: 10.3389/fpls.2018.01808

Agnes Calonnec¹*, Jerome Jolivet¹, Philippe Vivin² and Sylvain Schnee¹†

¹UMR1065 SAVE Santé et Agroecologie du Vignoble, INRA, ISVV, Villenave d'Ormon, France, ²EGFV, Bordeaux Sciences Agro, INRA, University of Bordeaux, ISVV, Villenave d'Ormon, France

How and when the pathogen cycle is disrupted during plant development is crucial for harnessing ontogenic resistance in sustainable agriculture. Ontogenic resistance against powdery mildew (*Erysiphe necator*) was quantified on *Vitis vinifera*. Shoots were sampled in the vineyard at several dates during seasonal growth and processed in the laboratory under controlled conditions. Experiments were conducted on two susceptible *Vitis vinifera* Cabernet Sauvignon and Merlot. The process of leaf ontogenic resistance was investigated by measuring three quantitative traits of pathogenicity: the infection efficiency, sporulation and mycelium growth. Morphological and physiological plant indicators were used to identify leaf changes that resulted in ontogenic resistance and to predict pathogen variations that were linked to pathogenicity traits. The process of ontogenic resistance was established early in correspondence with the physiological transition of the leaf from sink to source status and was characterized by its increase in sugar content. The three traits of pathogenicity that we measured were affected, and their variation was strongly correlated with leaf age. Using leaf age, we were able to accurately predict the susceptibility of the leaf: a leaf aged, on average, 13.3 days had a very high probability (0.8) of being susceptible, while this probability decreased to 0.5 one week later. Sporulation was more closely correlated with variations in sugar and the infection efficiency in leaf water. The results for both cultivars were consistent. Ontogenic resistance on grapevine leaves is thus interpreted to be a strong, immutable physiological process that *E. necator* is able to circumvent by restricting its development to sink tissue. Future research should explore how this native plant resistance can be incorporated into grape management strategies to better control powdery mildew (PM) epidemics with reduced amounts of fungicides.

**Keywords:** powdery mildew, ontogenic resistance, sink-source transition, traits of pathogenicity, grapevine
# INTRODUCTION

Biotrophs totally depend on their hosts to complete their life cycles, deriving their nutrients from living host cells through the development of specialized infection structures (O'Connell and Panstruga, 2006). *Erysiphe necator* Schwein, the powdery mildew of grapevines, is very efficient and can adopt highly sophisticated mechanisms to invade living plant cells successfully, such as upregulating the expression of cell wall invertase and plant hexose transporter genes or downregulating vacuolar invertase genes (Hayes et al., 2010).

Powdery mildew of grapevine is a ubiquitous disease that affects common cultivars of *Vitis vinifera* L., and it is responsible for significant damage (Calonnec et al., 2004) if no protective measures are used. While fungicide-based strategies are generally effective in reducing disease (Delière et al., 2010; Deliere et al., 2015; Valdés-Gómez et al., 2017), finding alternative methods that consider practitioners and the environment and that are economically feasible remains a challenge for sustainable viticulture. One solution relies on the exploitation of resistant grape varieties, in which sources of resistance coming from other *Vitis* of American or Asian origin are introgressed (Calonnec et al., 2013b). The difficulty for breeders in associating traits of agronomical and oenological values and genes of pathogen resistance leads also to exploration of the native potential of plant defenses in cases of a susceptible *V. vinifera* host. Innovative strategies to counteract pathogen efficiency could rely on integration into the management of the disease of the time-course evolution of the plant (i.e., leaves and berries) resistance during the season (Burie et al., 2011; Valdes-Gomez et al., 2011; Gadoury et al., 2012; Calonnec et al., 2013a).

In many plant-pathogen interactions, a susceptible host can acquire resistance to a pathogen at a certain developmental stage (Develey-Rivière and Galiana, 2007). This age-related resistance, commonly called ontogenic resistance (when young tissues are susceptible) or receptivity (when old tissues are susceptible), could be defined as the dynamic modification of tissue receptivity during organ development, triggering resistance/tolerance to pathogenic micro-organisms. Ontogenic resistance has been described for many plant-pathogen systems [e.g., strawberry-powdery mildew (Carisse and Bouchard, 2010), cucurbit fruit—*Phytophthora capsici* (Ando et al., 2009), tobacco—*Phytophthora parasitica* (Hugot et al., 1999), apple-apple scab (Li and Xu, 2002), cocoa—*Phytophthora megakarya* (Takam Soh et al., 2012), pea-powdery mildew (Fondevilla et al., 2006), pea-aschochyta blight (Richard et al., 2012)]. They are therefore common traits but remain underexploited in disease management, principally because of a lack of understanding of the underlying mechanisms and of the potential variability linked to the physiological responses of the host to environmental factors. The phenomenon of ontogenic resistance or receptivity to the main pathogens of grapevines has been highlighted and assessed on bunches for *Plasmopara viticola* (Kennelly et al., 2005), *Guignardia bidwellii* (Molitor and Berkelmann-Loehnertz, 2011), and *Botrytis cinerea* (Deytieux-Belleau et al., 2009). Botrytis susceptibility is known to increase with fruit maturity. Although the mechanisms involved remain speculative, they might differ from those involved in

response to infection in the classical defense system (Develey-Rivière and Galiana, 2007; Gee et al., 2008). For powdery mildew, the process has been underscored for berries, on which infection stops when berry sugar content reaches 8% (Delp, 1954), and it is characterized by a low penetration rate of infectious structures following potential modification of the structure and chemical composition of the cuticle (Ficke et al., 2002). The level of irradiance received before inoculation can strengthen ontogenic resistance through modifications of berry physiology, such as decreases in pH and K concentration and increases in polyphenol and anthocyanin concentrations (Zahavi and Reuveni, 2012). Ontogenic resistance also exists in mature leaves, which suffer less infection (Doster and Schnathorst, 1985). Experiments on cuttings grown in a glasshouse under a controlled temperature showed that infection is maximal for leaves that have just ceased importing assimilates (Merry et al., 2013). Simulations have shown that, depending on its parameter value, ontogenic resistance could significantly reduce the severity of late epidemics (Calonnec et al., 2008, 2011). However, little information is available regarding the behavior of the pathogen at each step of its sequential development for a broad range of crop development, and no information is available for field-grown material.

In our study, leaf ontogenic resistance to powdery mildew in *V. vinifera* was investigated using the susceptible cultivars Cabernet Sauvignon and Merlot. They are the two most commonly grown cultivars in the world for wine grapes (OIV, 2018). Shoots were characterized and sampled in the field, and leaves were processed in the laboratory. Our questions were: When is ontogenic resistance established? How does it vary with variety, crop development and climatic environment? Which pathogenicity traits are the most affected, and how variable is the effect? We used leaf age, leaf surface, sugars and LWC to identify changes in leaf physiology linked to ontogenic resistance and to determine whether they could be used to predict variations in pathogenicity.

# MATERIALS AND METHODS

## Plants

*Vitis vinifera* Cabernet cv. Sauvignon and Merlot were grown on two INRA experimental vineyards near Bordeaux, France, at Cadaujac and Latresne, respectively. Plantation density was 5,050 vines/10,000 m²; the vinestocks were between 7 and 12 years old. The experimental plots were regularly protected with fungicides from natural infection by downy (“Remiltine or Dauphin” (cymoxanil, mancozeb) and powdery mildews (powder or wettable sulfur). Treatments were applied between 6 and 20 days before sampling. Plant phenological stages were monitored from bud break until ripening (Baillod and Baggiolini, 1993). Each new fully expanded primary leaf (width from 3 cm) was tagged by a set of plastic color markers to indicate the date of leaf appearance and subsequently to calculate leaf age. These color markers allowed leaf age (LA) and the sum of temperatures (above 10°C) accumulated by the leaf during its development (LDev) to be determined.
## Bioassays

Shoots were sampled at different dates during the vegetative growth of grapevines (May 12, May 26 and June 10 for Cabernet Sauvignon) in 2005; June 4, 2009, and May 27 and June 24, 2010, for Merlot). Selected shoots were cut early in the morning and immediately brought to the laboratory, with the base of the shoots kept in water. No more than one shoot per vine. Before leaf sampling, petioles were marked with a color code to specify their respective positions on the shoot and to maintain traceability throughout the whole experiment. An approximation of the leaf extension rate (LER) per day was calculated by measuring leaf dimensions 3 days (t-3) and the day before (t-1) sampling with. $LER = (LS_{t-3} - LS_{t-1})/2$ The maximum leaf width (W) and maximum leaf length (L) of each leaf were measured to calculate the leaf surface (LS) in accordance with Montero et al. (2000) ($LS = L \times W \times 0.6$). For each shoot, the leaves were detached, abundantly washed under permuted water and then dried on filter paper. Under axenic conditions, the leaves were disinfected in a 5% (w/v) aqueous calcium hypochlorite at 65% for 10 min, rinsed in sterile water and dried in sterile filter paper. Three leaf discs (Ø 22 mm) were cut into each leaf and distributed in three different Petri dishes prepared for the pathogenicity tests. The leaf discs were disposed abaxial face down on an agar medium ($20 g L^{-1}$) supplemented with benzimidazole ($30 mg L^{-1}$). Each Petri dish contained six discs from an identical leaf level (not necessarily the same LA, as shoots are not synchronous in their growth) from six different shoots (replicates). Petri dishes corresponding to the infection efficiency test and to the total production of spores per cm² were randomly placed in a settling tower and then artificially inoculated by blowing conidia from a leaf infected 14 days earlier (4-5 conidia on each conidiophore), in accordance with Cartolaro and Steva (1990). All leaf levels were inoculated together in one tower. Two towers were necessary to inoculate all of the replicates for each experiment. The average density of spores inoculated was 120, 388, and 610 spores/cm² for 2005, 2009, and 2010, respectively. The dishes were placed in a growth chamber at 22°C (12:12 h light:dark photoperiod) for pathogen development. One different monospore isolate was used each year. It was sampled from vinestock leaves from a vineyard in the Bordeaux area or in the greenhouse before each annual experiment and was bulked on Cabernet Sauvignon leaves from greenhouse-grown cuttings. Infection efficiency was assessed at 72 hpi (hours post-infection) by counting the conidia that reached the branching hyphae stage (stage 5-6 according to Leinhos et al., 1997) after removing tape from leaves and then applying a cotton blue staining procedure (Cartolaro and Steva, 1990). The total production of spores per infected disc was measured at 14-15 dpi. For colony growth and spores per colony tests, the leaf discs were inoculated by the deposit, with a needle, of a few conidia (from 1 to 10) in their centers. Colony growth was assessed at ~4, 7, 10, 14, and 18 dpi (days post-infection) and the production of spores per colony at 14 or 18 dpi. Sporulating disks were placed in a vial filled with 20 mL of isotone 2 and one drop of non-ionic dispersant (Nacconol 90F), shaken, and the spore production was assessed by counting the number of particles between 15 and 38 µm diameter on a sample of 500 µl using a particle counter (Coulter Counter® Multisizer™ 3—Beckman Coulter®, U.S). The disease variables

calculated were: Inf (% of infectious spores as defined above), Spo (number of spores per cm² of infected leaf disc), Spoc (number of spores per isolated colony), and Diam (final colony diameter in mm). A total of 12-20 shoots (120-220 leaves) were tested depending on the experiments. The experimental conditions and variables measured are summarized in Tables 1, 2, respectively and experimental design in Supplementary Figure S1.

## Physiological Indicators

For each sampled leaf, six discs (Ø 6 mm) were collected into a 2-ml Eppendorf tube, fresh weighed, dried in a hood (60-70°C for 72 h) and then weighed again. Leaf water content (LWC) was calculated from fresh (FW) and dry mass measurements (DW) ($LWC = (FW - DW)/FW$). The total non-structural carbohydrates were extracted and dosed as described by Gomez et al. (2007). Briefly, two metallic balls (Ø 5 mm) were added to each disc leaf samples, and the Eppendorf tube was submitted to intense shaking (3 min at 30 swings sec-1, Retsch Mixer Mill MM200). An ethanolic solution (80%, v/v) was added to the finely ground tissue, sonicated for 10 min at 60°C and centrifuged ($3,700 trs min^{-1}$ for 5 min). The pellet containing soluble starch (ST) was rinsed three times with nanopure water, autoclaved and then hydrolysed in the presence of 200 µl of amyloglucosidase at 55°C for 1 h. The supernatant representing soluble sugars (including glucose, fructose and sucrose) (SS) was dried and then hydrolysed by addition of 200 µl of invertase (Sigma 19253) at 55°C for 1 h. After drying, 800 µl of nanopure water were added to the two types of extracts and vigorously shaken by vortex agitation and sonication to ensure total carbohydrate solubilisation. Soluble sugars and starch solutions were prepared (kit LISA 200C, CETIM, France) and measured spectrophotometrically at 340 nm, with an ELx800UV automated micro-plate reader (Biotek Instruments Inc., Vermont, USA). Glucose (Sigma G8270) was used for the standard range, and all of the obtained data were expressed in glucose equivalents.

## Environmental Growing Conditions

The sum of degree-days above 10°C (growing degrees) was used to determine grapevine organogenesis (Schultz, 1992). Five variables were calculated to characterize the environmental conditions of vine growth: the sum of GD at the day of sampling, which is also an indicator of the time of the experiment; the sum of GD during the 10 days before sampling; the average evapotranspiration rate, according to the Penman formula for this period (ETPP in mm.day⁻¹); and the sum of rainfall for the 10 days before the experiments. The environmental conditions are summarized in Table 1.

## Data and Statistical Analyses

For infection efficiency (Inf), only data based on more than 30 counted spores were analyzed, and for all variables, only shoots with more than 3 validated samples were considered. To lessen the variation between shoots and to emphasize the variation between leaves of different ages, relative infection (RInf) and sporulation (RSpo) rates were also calculated by dividing the value obtained for a given leaf by the maximum value on its own shoot.
<table><caption>**TABLE 1** | Conditions of the experiments.</caption><thead><tr><th rowspan="2">Experiments</th><th rowspan="2">Variety</th><th rowspan="2">Date of sampling = D</th><th rowspan="2">Nb sampled shoots</th><th rowspan="2">mean Nb leaf/shoot ± σ</th><th colspan="6">Climatic conditions before the experiment</th></tr><tr><th colspan="3">During the 10 days before D</th><th colspan="3">at D-1</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th>Average T°C</th><th>GD</th><th>Sum Rain mm</th><th>Average ETPP</th><th>Average global radiance J.cm<sup>2</sup></th><th>GD</th><th>Average T°C</th></tr></thead><tbody><tr><td>Exp. 1</td><td>Cabernet Sauvignon</td><td>12-May</td><td>20</td><td>6.05 ± 0.74</td><td>15.2</td><td>51.9</td><td>1.5</td><td>3.82</td><td>2481</td><td>212.4</td><td>16.8</td></tr><tr><td>Exp. 2</td><td>Cabernet Sauvignon</td><td>26-May</td><td>20</td><td>9.5 ± 1.43</td><td>16.2</td><td>61.6</td><td>0.5</td><td>4.34</td><td>2868</td><td>295.7</td><td>20.7</td></tr><tr><td>Exp. 3</td><td>Cabernet Sauvignon</td><td>9-Jun</td><td>15</td><td>14.73 ± 1.34</td><td>18.3</td><td>83</td><td>0.5</td><td>4.9</td><td>3049</td><td>423.7</td><td>19.1</td></tr><tr><td>Exp. 4</td><td>Merlot</td><td>4-Jun</td><td>12</td><td>14.33 ± 1.37</td><td>18.7</td><td>86.9</td><td>11</td><td>3.78</td><td>4677</td><td>323.5</td><td>22.3</td></tr><tr><td>Exp. 5</td><td>Merlot</td><td>27-May</td><td>6</td><td>12.50 ± 0.96</td><td>18.1</td><td>84.2</td><td>5</td><td>4.03</td><td>2290</td><td>271</td><td>17.6</td></tr><tr><td>Exp. 6</td><td>Merlot</td><td>24-Jun</td><td>6</td><td>20.17 ± 1.10</td><td>15.9</td><td>58.9</td><td>48</td><td>3.09</td><td>4792</td><td>474.3</td><td>19.4</td></tr></tbody></table>

Nb, number; σ, standard deviation; ETPP, Evapotranspiration rate; GD, growing degrees, sum of degrees above 10°C.

<table><caption>**TABLE 2** | Measured variables for the experiments.</caption><thead><tr><th rowspan="2">Variables</th><th rowspan="2">Abbr.</th><th rowspan="2">Units</th><th colspan="6">Experiments</th></tr><tr><th>Exp. 1</th><th>Exp. 2</th><th>Exp. 3<sup>a</sup></th><th>Exp. 4</th><th>Exp. 5</th><th>Exp. 6</th></tr></thead><tbody><tr><td colspan="9"><strong>PLANT GROWTH VARIABLES</strong></td></tr><tr><td>Leaf age</td><td>LA</td><td>Day</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Leaf temperature of development</td><td>LAdev</td><td>Sum of °C days above 10</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Leaf surface</td><td>LS</td><td>cm<sup>2</sup></td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Leaf extension rate</td><td>LER</td><td>Day<sup>-1</sup></td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td colspan="9"><strong>PHYSIOLOGICAL INDICATORS</strong></td></tr><tr><td>Soluble sugars</td><td>SS</td><td>(glucose + fructose + saccharose) in % of dry weight</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Complex sugars</td><td>CS</td><td>Starch expressed in equivalent glucose in % of dry weight</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Leaf water content</td><td>LWC</td><td>–</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td colspan="9"><strong>DISEASE</strong></td></tr><tr><td>Infection efficiency</td><td>Inf</td><td>Rate of branched conidia at 72 hpi</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Relative infection</td><td>RInf</td><td>Relative rate</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Maximal colony diameter</td><td>Diam</td><td>mm</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Sporulation per colony</td><td>Spoc</td><td>Number of spores generated per colony</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Sporulation per leaf area</td><td>Spo</td><td>Number of spores generated per cm<sup>2</sup> of inoculated leaf area</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Relative sporulation</td><td>RSpo</td><td>Relative rate</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr></tbody></table>

Variance analyses (general linear modeling procedure in SAS) were performed on different variables, such as infection efficiency (Inf), sporulation (Spo), maximum diameter (Diam), and the number of spores produced per colony (Spoc), with leaf age as a factor. For Diam and Spoc, some of the age classes were grouped to allow for statistical comparison when the sample numbers were too low (<5) due to a failure of the infection process related to ontogenic resistance (only a

few spores are deposited) (Table 2). The effect of leaf age on the independent variables was measured by performing Tukey-Kramer's multiple comparisons test adapted for unbalanced designs. The mean number of spores produced per area of a colony (Spoc/[(Diam/2)<sup>2</sup>.π]) , a proxy of the sporulation capacity according to Lannou (2012), was compared by permutation testing (5000 permutations) for two classes of leaf age.

<footnote><sup>a</sup>No physiological indicators available.</footnote>
The disease data functions of leaf age were fitted to models using ordinary least squares methods. The choice of one specific model was based on Akaike's information criterion, which selects the model most likely to have generated the data. Goodness of fit was measured by $R^2$ and the adequacy of the model by replicate testing. A fitting procedure was performed using Prism software (version 5.04).

Relationships among experimental conditions, leaf growth characteristics, leaf physiological indicators and disease were studied by a partial least squares path model analysis (PLS-PM) (Tenenhaus et al., 2005). The PLS-path model was described by six unobservable or latent variables (LVs): “Variety,” “Climate,” “Leaf growth,” “Leaf physiology,” “Inoculum,” and “Disease.” This analysis provides how much variation of “Disease” can be predicted by the other latent variables. Each LV was constructed by a set of observable variables called manifest variables (MVs). “Variety” and “Inoculum” were constructed in a formative way by qualitative variables (CS and M for variety, 2005–2009–2010 for inoculum), corresponding either to the varieties Cabernet Sauvignon and Merlot or to the year using different isolates. “Climate” was described in a reflexive way by the average temperature over the 10 days before the experimentation (STJ-10) and the sum of rain (SRJ-10). “Leaf growth” was described in a reflexive way by leaf age (LA), the sum of GD temperature during leaf development (LAdev) and the leaf surface (LS). It is worth noting that, for one experiment, leaves sharing the same leaf age encountered the same climatic conditions, but they do not necessarily have the same plastochron index, which is a function of the rate of growth of the shoot, nor do they have the same LS, which is dependent on the variety, vigor and environmental conditions. An identical leaf age yields various LAdev depending on experiments. “Leaf physiology” was described by the leaf soluble carbohydrates (SS), starch (ST) concentrations and LWC. The “Disease” variable was described by the variables characterizing the two basic pathogen processes: the infection efficiency (Inf) and the sporulation per colony (Spoc). The standardized latent variables were estimated as linear combinations of their centered MVs. Collinearity between manifest variables was allowed. The PLS-path model was described by the measurement model, which related the different MVs to their own LVs, and the structural model, which linked the endogenous LV “Disease” to the other LVs: “Leaf growth,” “Leaf physiology,” and “Year.” The inner estimate was performed using a path weighting scheme algorithm. Confidence intervals on regression coefficients are estimated by bootstrap methods (500 random sampling of 280 individuals of the observed data set). Two criteria of the goodness of fit of the model (GoF) are provided to evaluate the external model (relationship between MVs and LVs) and the internal model (relationship between LVs). Altogether, 321 observations (leaves) were considered (49 from 2005_exp1, 64 from 2005_exp2, 117 from 2009_exp4, 50 from 2010_exp5, and 41 from 2010_exp6). Observations on leaves older than 31 days were not considered to avoid introducing bias between the early and late experiments. Missing values were estimated using NIPALS procedures. The analysis was performed using the XLstat PLS-PM module (version 2012.2.02).

Finally, logistic regression was performed on the whole data set, including old leaves. The Spo variable was transformed into binary form with a value of 1 for Spo >3000 (significant peak with the particle counter) and 0 for less. Then, a model was fitted to describe how the chance of the event of a “diseased” leaf occurring depends on a maximum of one or two covariables, among which are “LA,” “LAdev,” “LS,” “SS,” “ST,” and “1/LWC.” The regression assumes that $P(Y = 1/X = \exp(\beta_0 + \beta_1X_1 + \beta_2X_2)/\exp(\beta_0+\beta_1X_1 + \beta_2X_2))$ with Y the outcome and X the covariates. The model parameters $\beta$ are estimated by the method of maximum likelihood. The analysis was performed on 497 individuals from the 5 experiments [261 leaves classified as “healthy” = “non-sporulating”; and 236 classified as “diseased”; null hypothesis P(D) = 0.47]. Among these leaves, 30 were randomly sampled for model validation. The ability of the model to distinguish between low and high risk for a leaf to be diseased was tested by ROC (receiver operating curve) curve analysis. The ROC curve plots the sensitivity (proportion of truly “positive,” here “diseased,” observations classified as such by the model) function of the 1-specificity (the specificity is the probability of the model predicting the sample “negative,” given that the observation is “negative,” here “healthy”). The area under the curve yields the ability of the model to correctly distinguish between healthy and diseased leaves. When the AUC is high (>0.8), the model has high discrimination ability.

# RESULTS

## Environmental Growing Conditions

The first leaves were marked on April 29 for Cabernet Sauvignon (2005, exp. 1–2–3), corresponding to a sum of 138.7°C growing degrees (GDs) and on April 14 and April 19 for Merlot, corresponding to 52.1°C GD (2009 exp. 4), and 66.2°C (2010 exp. 5–6), respectively. For each experiment, there was a linear relationship between leaf age (LA) and the sum of GD for a leaf during its development (LAdev), with an increased difference for older leaves. The GD globally explained the number of leaves per shoot for each experiment, with ~21°C for each newly expanded leaf. For Cabernet Sauvignon, the leaves sampled for experiment 1 (early May 2005) were those developed at the coldest temperature: a 13-day-old leaf developed under a similar GD as a 10-day-old leaf from experiment 2 (Figure 1). In comparison, for Merlot, the leaves sampled for exp. 6 (late June) encountered the coldest period: 13-day-old leaves developed under a similar GD as 10-day-old leaves from exp. 5 (end of May).

## Infection Rate Function of Leaf Age

In 2005, for the first sampling date (exp. 1), no significant difference in infection efficiency was observed between the different leaf ages of Cabernet Sauvignon (Table 3), whereas for the second (exp. 2), and third experiments (exp. 3), which occurred later in the season, the youngest leaves up to 6 days old displayed significantly higher infection rates. For experiments 2 and 3, we could adjust the data of infectious efficiency to the same exponential decrease model (Figure 2A, Table 4-M1). In such a model, 10-day-old leaves had an infection efficiency that decreased to 0.25. When the data were standardized (data divided
<figure><img src="image_51.png" /><figcaption>**FIGURE 1** | Sum of growing days (Sum of daily temperature > 10°C) encountered by a leaf during its development depending on its age, for experiments on Cabernet Sauvignon (A) and Merlot (B). Experiments on Cabernet-Sauvignon were conducted on 12, 26, May and 9 June 2005 (exp. 1, 2, 3) and experiments on Merlot were conducted on 4 June 2009, 27 May and 24 June 2010 (exp. 4, 5, 6).</figcaption></figure>

by the maximum % of infection on the shoot), the whole set of data could be adjusted to a decreasing exponential model with an $R^2$ of 0.59 (Figure 2B, Table 4-M1). Thus, a 10-day-old leaf lost approximately half of its infectivity, compared to the most susceptible ones. For the Merlot cultivar, the rate of infection efficiency also showed an exponential decrease with leaf age. Very young leaves were excluded from the analysis since we could not exclude that the tape test was not disturbed on particularly hairy young leaves (Figure 5A, Table 5-M1). For experiment 4 (4 June 2009) and 5 (27 May 2010), with similar environmental conditions, the decrease with leaf age was quite similar, despite a higher rate of infection for very young leaves ($I_0$) in experiment 4. For experiment 6 (24 June 2010), leaves up to 16 days old that developed at cooler temperatures showed a high rate of infection, and then the rate decreased very quickly.

## Colony Growth Function of Leaf Age

The maximum diameter of a colony produced by single conidia was, on average, smaller on leaves older than 10 days for Cabernet

Sauvignon (Table 3). A model with a decreased diameter at the inflection point of 8.35 days (a50), a maximum diameter (M) on young leaves of 6.8 mm and a minimum diameter (m) of 4.6 mm for older leaves, fit the data with an $R^2$ = 0.41 (Table 4-M2, Figure 3A). On average, the sporulation of a colony was exponentially correlated with the maximal diameter attained by the mycelium network (Table 4-M4, Figure 3B), but at <5 mm, the level of sporulation was low and constant, indicating that a colony with only a two-millimeter difference in diameter (e.g., between 5 and 7 mm) will generate twice as many more spores. For Merlot, the trends and models were the same but with different parameters depending on the experimentation. In experiments 4 and 6, the maximum diameters were greater than in experiment 5. Similar to infection efficiency, slower growth appeared for older leaves in experiment 6 (a50 = 16.93 vs. 12.6 or 12.17 for exp. 5 and 4) (Table 5-M4, Figure 5B). Sporulation per colony was much higher for experiment 4 (Figure 5D).

## Sporulation Function of Leaf Age

The variable sporulation (Spo) measured on inoculated leaf discs results from three successive processes: infection efficiency, mycelium growth and sporulation. From this point of view, it could be considered an integrative variable of susceptibility. The Spo variable followed a clearly bell-shaped curve function of leaf age for experiments 1 and 2, with higher levels of spores produced on 3- to 6-day-old leaves and a decrease for 8-day-old leaves (Figure 4A, Tables 3, 4-M3). For experiment 3, on average, the youngest leaves had the highest level of sporulation. When the three experiments were pooled and standardized to homogenize the shoots, the same bell-shaped curve was obtained (Figure 4B, Table 4-M3) with a high level of sporulation for leaves aged between 3 and 8 days, corresponding to a plastochron index (LPI) between 1.3 and 1.7 (first to second leaf expended). Sporulation per colony (Spoc), which is independent of infection efficiency, also had this bell-shaped curve (Figure 4C, Table 4-M3). For experiment 2, for which the data were most numerous, the number of spores produced per area of spulating colony (the sporulation capacity) was significantly higher ($P = 1$) for very young, susceptible leaves (1-6 days), compared to older leaves ($\ge$10 days) (295 sp.mm$^2$ against 167 sp.mm$^2$). This finding is consistent with the hypothesis that, for very young leaves, the higher sporulation results from an increase rate in the hypha branching combined with an increased number of spores produced per conidiophore. For Merlot, the sporulation curve with leaf age was similar to that observed on Cabernet Sauvignon, but the stage of high sporulation (Spo and Spoc) lasted longer, with increased sporulation for 5- to 16-day-old leaves with a peak at 6, 9, or 14 days, depending on the experiment (Figure 5C, Table 5-M3). This outcome matched an LPI of 2.5 (exp. 6) to 3.3 (exp. 4).

## Leaf Physiological Evolution

Total non-structural carbohydrate (TNC) concentrations tended to follow sigmoid curves regarding leaf age. The soluble sugars concentration (SS) displayed a strong and rapid increase for young leaves, followed by a plateau for older ones. For Cabernet Sauvignon, this change appeared on 8- to 12-day-old leaves (LPI
<table><caption>TABLE 3 | Effect of leaf age of Cabernet Sauvignon on the disease variables tested by variance analyses and mean comparisons for each leaf age level.</caption><thead><tr><th rowspan="2">Leaf age</th><th colspan="4">Exp.1</th><th colspan="4">Exp.2</th><th colspan="4">Exp.3</th></tr><tr><th>Inf</th><th>Spo</th><th>Spoc</th><th>Diam</th><th>Inf</th><th>Spo</th><th>Spoc</th><th>Diam</th><th>Inf</th><th>Spo</th><th>Spoc</th><th>Diam</th></tr></thead><tbody><tr><td>1</td><td>0.373<sup>a</sup></td><td>20569.8<sup>ab</sup></td><td>8447.9<sup>a</sup></td><td>6.31<sup>ac</sup></td><td>0.534<sup>a</sup></td><td>16675.5<sup>b</sup></td><td>14935<sup>a</sup></td><td>7.15<sup>a</sup></td><td>0.442<sup>a</sup></td><td>34328.6<sup>a</sup></td><td>12920<sup>a</sup></td><td>8.06<sup>a</sup></td></tr><tr><td>3</td><td>0.476<sup>a</sup></td><td>24806.9<sup>ab</sup></td><td></td><td></td><td>0.467<sup>a</sup></td><td>45861.6<sup>a</sup></td><td></td><td></td><td>0.468<sup>ab</sup></td><td>25283.1<sup>a</sup></td><td></td><td></td></tr><tr><td>6</td><td>0.436<sup>a</sup></td><td>32483.6<sup>a</sup></td><td>8600.0<sup>a</sup></td><td>5.90<sup>ac</sup></td><td>0.434<sup>ac</sup></td><td>53627.0<sup>a</sup></td><td>12108<sup>ac</sup></td><td>7.8<sup>a</sup></td><td>0.387<sup>ab</sup></td><td>19720.7<sup>ab</sup></td><td>18280<sup>ab</sup></td><td>7.39<sup>ab</sup></td></tr><tr><td>8</td><td>0.474<sup>a</sup></td><td>21340.9<sup>b</sup></td><td>8431.9<sup>a</sup></td><td>6.50<sup>a</sup></td><td>0.292<sup>ab</sup></td><td>13138.9<sup>b</sup></td><td>6965<sup>bc</sup></td><td>6.58<sup>ac</sup></td><td>0.299<sup>abc</sup></td><td>18105.5<sup>abc</sup></td><td>12600<sup>abc</sup></td><td>6.96<sup>ab</sup></td></tr><tr><td>10</td><td>0.457<sup>a</sup></td><td>7438.0<sup>c</sup></td><td>4112.4<sup>b</sup></td><td>5.34<sup>bc</sup></td><td>0.214<sup>b</sup></td><td>8294.5<sup>b</sup></td><td>3673<sup>b</sup></td><td>4.41<sup>bc</sup></td><td>0.249<sup>bc</sup></td><td>8992.7<sup>bcd</sup></td><td>5655<sup>c</sup></td><td>4.63<sup>c</sup></td></tr><tr><td>13</td><td>0.349<sup>a</sup></td><td>1974.5<sup>c</sup></td><td>3915.6<sup>b</sup></td><td>5.25<sup>bc</sup></td><td>0.173<sup>b</sup></td><td>7739.5<sup>b</sup></td><td>4518<sup>b</sup></td><td>4.88<sup>bc</sup></td><td>0.170<sup>bcd</sup></td><td>4010.196<sup>d</sup></td><td>6664.3<sup>c</sup></td><td>4.78<sup>c</sup></td></tr><tr><td>15</td><td></td><td></td><td></td><td></td><td>0.090<sup>b</sup></td><td>8752.4<sup>b</sup></td><td></td><td></td><td>0.103<sup>cd</sup></td><td>1227.4<sup>d</sup></td><td></td><td></td></tr><tr><td>17</td><td></td><td></td><td></td><td></td><td>0.149<sup>b</sup></td><td>1864.4<sup>b</sup></td><td></td><td></td><td>0.143<sup>cd</sup></td><td>3362.370<sup>d</sup></td><td></td><td></td></tr><tr><td>20</td><td></td><td></td><td></td><td></td><td>0.238<sup>bc</sup></td><td>5820.6<sup>b</sup></td><td></td><td></td><td>0.105<sup>cd</sup></td><td>3518.1<sup>d</sup></td><td></td><td></td></tr><tr><td>22</td><td></td><td></td><td></td><td></td><td>0.113<sup>b</sup></td><td>8394.9<sup>b</sup></td><td></td><td></td><td>0.033<sup>d</sup></td><td>3324.0<sup>d</sup></td><td></td><td></td></tr><tr><td>24</td><td></td><td></td><td></td><td></td><td>0.176<sup>b</sup></td><td>4857.2<sup>b</sup></td><td>3510<sup>b</sup></td><td>4.62<sup>bc</sup></td><td>0.121<sup>cd</sup></td><td>4363.1<sup>bcd</sup></td><td>7590<sup>bc</sup></td><td>3.75<sup>c</sup></td></tr><tr><td>27</td><td></td><td></td><td></td><td></td><td>0.134<sup>b</sup></td><td>4857.2<sup>b</sup></td><td></td><td></td><td>0.003<sup>d</sup></td><td></td><td></td><td></td></tr><tr><td>29-39</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.048<sup>d</sup></td><td>4275.05<sup>cd</sup></td><td>7850<sup>bc</sup></td><td>5.75<sup>bc</sup></td></tr><tr><td>df model, error</td><td>5, 43</td><td>5, 88</td><td>5, 37</td><td>4, 44</td><td>11, 95</td><td>11, 114</td><td>5, 43</td><td>6, 56</td><td>12, 144</td><td>11, 76</td><td>6, 16</td><td>6, 36</td></tr><tr><td>F fisher</td><td>1.71</td><td>27.54</td><td>8.41</td><td>4.49</td><td>10.73</td><td>48.24</td><td>16.93</td><td>8.72</td><td>13.35</td><td>8.37</td><td>11.03</td><td>14.1</td></tr><tr><td>P-value</td><td>0.154</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>0.0039</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr></tbody></table>

Inf, percentage of infectious spores; Spo, sporulation of inoculated leaf disks; Spoc, sporulation of the colony; Diam, maximal colony diameter. Different letters near the average indicate significant difference at *P* < 0.05 according to the Tukey-Kramer multiple comparison test.

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /><figcaption>FIGURE 2 | Infection efficiency rate (Inf) (A) and relative infection efficiency rate (RInf) (B) as a function of leaf age for experiments on Cabernet Sauvignon. Bars indicate standard deviations. Plain lines correspond to the fitted model M1 in Table 4. Dashed lines correspond to the 95% confidence bands on the models.</figcaption></figure>

= 2.7 to 3.4), while for Merlot, it appeared later: 10- to 14-day-old leaves (exp. 4 and 5, LPI = 3.9) to 14-to 16-day-old leaves (exp. 6, LPI = 3.7) (Figures 6A,C). The level reached for old leaves depended on the experiments. The leaf starch concentration was also highly variable with leaf age, but no starch was detected within the youngest leaves (<10 days) (Figures 6B,D). Leaves showing an increase in SS reached ~50% of their final size (~150 and 270 cm² for, respectively, CS and M), consistent with the time for transition between sink-to-source tissue (Figures 7A,C). The increases in the leaf soluble sugar content and leaf age were associated with a decrease in leaf water content (Figures 7B,D).

Thus, the processes of infection efficiency, sporulation and colony growth associated with leaf age showed similarities between the experiments on Cabernet Sauvignon and Merlot:

a lower rate of mycelium growth and the end of the peak of a high level of sporulation matching an increase of sugar (SS); in comparison, the decrease in infection efficiency was more gradual with leaf age, as was the decrease in leaf water content. Infection efficiency is more closely correlated with leaf age (−0.68), leaf water content (−0.41), and starch (−0.32) than is sporulation (−0.54, −0.32, −0.30). Sporulation per colony was also significantly correlated with soluble sugar (−0.25), although it was more influenced by the experiments on Cabernet Sauvignon, for which we had more colonies for a wider range of leaf ages. For Cabernet Sauvignon, the proxy of the sporulation capacity (number of spores produced per unit area of sporulating colony) was significantly correlated with leaf age (−0.52) and with the amount of sugar (−0.64), indicating that colonies on
<table><caption>**TABLE 4** | Results of model fitting for disease variables for experiments on Cabernet Sauvignon.</caption><thead><tr><th rowspan="3">Model equation</th><th colspan="2">Y = I<sub>0</sub>exp-kX</th><th colspan="2">Y = m + <sup>M-m</sup>&frasl;<sub>1+10(X-450)</sub></th><th colspan="4">Y = B + Aexp[-((X-e)/D)]<sup>2</sup></th><th rowspan="3">Y = aexp<sup>bX</sup><br>with Y = Dependent variable<br>and X = leaf age variable</th></tr><tr><th colspan="2">with Y = Dependent variable<br>and X = leaf age variable</th><th colspan="2">with Y = Dependent variable<br>and X = leaf age variable</th><th colspan="4">with Y = Dependent variable and X = leaf<br>age variable</th></tr><tr><th>Inf</th><th>RInf</th><th>Diam</th><th>M2</th><th>Spo</th><th>Spo</th><th>Spo</th><th>Spo</th><th>Spoc</th></tr></thead><tbody><tr><td>Dependant variable</td><td></td><td></td><td></td><td></td><td>M3</td><td>M3</td><td>M3</td><td>M3</td><td>M4</td></tr><tr><td>Model identification</td><td>M1</td><td>M1</td><td></td><td></td><td>exp.1</td><td>exp.2</td><td>exp.3</td><td>exp.1-2</td><td>exp.2</td></tr><tr><td>Experimentation</td><td></td><td></td><td></td><td></td><td>56</td><td>91</td><td>66</td><td>64</td><td>41</td></tr><tr><td>df</td><td>209</td><td>231</td><td>120</td><td></td><td></td><td></td><td></td><td>182</td><td></td></tr><tr><td>Parameter value</td><td>0.606</td><td>1.181</td><td>4.64</td><td></td><td>A</td><td>34357</td><td>54676</td><td>32600</td><td>12673</td><td>a</td></tr><tr><td></td><td>k</td><td>0.086</td><td>M</td><td>6.86</td><td>B</td><td>2124</td><td>7498</td><td>3628</td><td>3029</td><td>b</td></tr><tr><td></td><td></td><td></td><td>a50</td><td>8.35</td><td>c</td><td>5.151</td><td>4.769</td><td>0.699</td><td>4.455</td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>D</td><td>3.479</td><td>2.957</td><td>6.666</td><td>3.566</td><td></td></tr><tr><td>Goodness of fit R<sup>2</sup></td><td>0.57</td><td>0.59</td><td>0.41</td><td></td><td></td><td>0.76</td><td>0.85</td><td>0.79</td><td>0.72</td><td>0.88</td><td>0.74</td></tr><tr><td>P-value for Replicates tests</td><td>0.7</td><td>0.1</td><td>0.71</td><td></td><td></td><td>0.7</td><td>0.6489</td><td>0.96</td><td>0.68</td><td>0.26</td><td>0.81</td></tr><tr><td>Evidence of inadequate model</td><td>No</td><td>No</td><td>No</td><td></td><td></td><td>No</td><td>No</td><td>No</td><td>No</td><td></td><td>No</td></tr><tr><td>Outliers</td><td>0</td><td>0</td><td>0</td><td></td><td></td><td>4</td><td>1</td><td>6</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>

Inf, rate of infectious spores (branched conidia at 72hpl); RInf, relative rate of infectious spores; Diam, colony diameter (mm); Spo, number of spores generated per cm² of inoculated leaf area; Spoc, number of spores generated per colony; RSpo, relative rate of spores generated per cm² of inoculated leaf.

<table><caption>**TABLE 5** | Results of model fitting for comparison of infection rate (Inf), colony diameter (Diam), and sporulation rate (Spo) for experiment on Merlot.</caption><thead><tr><th rowspan="3">Model equation</th><th colspan="2">Y = I<sub>0</sub>exp-kX</th><th colspan="2">Y = m + <sup>M-m</sup>&frasl;<sub>1+10(X-450)</sub></th><th colspan="4">Y = B + Aexp[-((X-e)/D)]<sup>2</sup></th><th rowspan="3">Y = aexp<sup>bX</sup><br>with Y = Dependent variable<br>and X = colony diameter</th></tr><tr><th colspan="2">With Y = Dependent variable<br>and X = leaf age</th><th colspan="2">With Y = Dependent variable and X = leaf age</th><th colspan="4">With Y = Dependent variable and X = leaf age</th></tr><tr><th>Inf</th><th>Inf</th><th>Diam</th><th>Diam</th><th>Spo</th><th>Spo</th><th>Spo</th><th>Spo</th><th>Spoc</th></tr></thead><tbody><tr><td>Dependant variable</td><td></td><td></td><td></td><td></td><td>M3</td><td>M3</td><td>M3</td><td>M3</td><td>M4</td></tr><tr><td>Model identification</td><td>M1</td><td>M1</td><td>M2</td><td>M2</td><td>exp.4</td><td>exp.5</td><td>exp.6</td><td>exp.4</td><td>exp.6</td></tr><tr><td>Experimentation</td><td></td><td></td><td></td><td></td><td>131</td><td>63</td><td>91</td><td>139</td><td>94</td></tr><tr><td>df</td><td>137</td><td>56</td><td>40</td><td>22</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Parameter value</td><td>0.63</td><td>0.38</td><td>4.91</td><td>5.15</td><td>A</td><td>73207</td><td>55842</td><td>62308</td><td>20134</td><td>a</td></tr><tr><td></td><td>k</td><td>0.095</td><td>M</td><td>9.93</td><td>B</td><td>2606</td><td>2312</td><td>449.5</td><td>807.6</td><td>b</td></tr><tr><td></td><td></td><td></td><td>a50</td><td>12.17</td><td>c</td><td>8.93</td><td>6.51</td><td>13.29</td><td>8.36</td><td>11.99</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>D</td><td>5.14</td><td>3.68</td><td>3.47</td><td>4.68</td><td>3.13</td></tr><tr><td>Goodness of fit R<sup>2</sup></td><td>0.74</td><td>0.54</td><td>0.75</td><td></td><td></td><td>0.81</td><td>0.72</td><td>0.89</td><td>0.6</td><td>0.67</td><td>0.54</td></tr><tr><td>P-value for Replicates</td><td>0.55</td><td>0.11</td><td>0.79</td><td></td><td></td><td>0.37</td><td>0.98</td><td>0.34</td><td>0.99</td><td>1</td><td>0.89</td></tr><tr><td>Evidence of inadequate model</td><td>No</td><td>No</td><td>No</td><td></td><td></td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>
<figure><img src="image_54.png" /><figcaption>**FIGURE 3** | Colony diameter in mm (Diam) as a function of leaf age after 14 days of colony growth (A) and number of spores produced per colony (Spoc) as a function of the colony diameter (B) for Cabernet-Sauvignon. (A) correspond to the average colony diameter from experiments 1–3 whereas Panel (B) to the spores per colony from experiment 2. Plain lines correspond to the fitted models M2 (A) and M4 (B) in **Table 4**. Dashed lines correspond to the 95% confidence bands on the models, and bars correspond to the standard deviation.</figcaption></figure>

young leaves with smaller amounts of sugar are significantly able to produce more spores per colony. When a period of cold is encountered, processes are delayed (increased sugar, leaf development, decreased leaf water content), and the duration of the sensitivity period is lengthened (exp. 6). Very early in the growing season (exp. 1), all of the leaves are as susceptible as each other to infection.

## Disease Severity Prediction

Partial least-squares path modeling allowed for highlighting the relative contributions of the studied composite variables ("Leaf growth" and "Leaf physiology") in the global explanation of the global "Disease" variable, depending on the experimental conditions ("Variety," "Climate," "Year"). The "Disease" variable was explained at $R^2 = 0.61$ by "Leaf growth," "Leaf physiology," and "Year," with relative contributions of 57.3, 19.5, and 23.2%, respectively (Figure 8). Variations in leaf physiology were explained at $R^2 = 0.50$, mainly by leaf growth (87%) (leaf age, leaf temperature for development and leaf surface). "Variety" and "Climate" only poorly contributed to leaf physiology level. When considering total effects (indirect plus direct effects) on the disease, "Leaf growth" had a higher effect on "Disease" (-0.68) compared with "Leaf Physiology" (-0.27), due to the correlation between variables. Leaf water content (1/LWC) and simple sugars (SS) made the greatest contributions to "Leaf physiology" (w = 0.62 and 0.38, respectively), indicating that, regardless of whether sugar (SS) increases or water content decreases, the "Disease" variable decreases. The "Year" effect certainly reflects the variation of isolate aggressiveness that was obvious when considering the level of sporulation per colony diameter. The relative goodness of fit of the model was high ($GoF_{rel} = 0.85$) with moderate absolute goodness of fit ($GoF_{abs} = 0.55$), indicating that the predictors used only partly explained disease variation.

Logistic regression showed that, with a maximum of one co-variable, leaf age was the best predictor ($Pr > LR < 0.0001, \beta_0 = 4.12, \beta_1 = -0.20$), with sensitivity of 86.02% (73.3% for the validation sample) and very high discrimination (AUC = 0.927) of "diseased" vs. "not diseased" leaves. The individuals not well assigned were those at the transition state or very young leaves

that were not sufficiently sporulating but that were considered susceptible by the model. If we analyzed the probability after model fitting, there was a probability for a leaf to be susceptible of 0.8 for an average leaf age of 13.3 days [11.4; 14.9] and a probability decrease at 0.5 for a leaf age of 20 days [18.7; 21.4]. With two co-variables, leaf age and starch content were the best predictors, but the model was only slightly improved (AUC = 0.936) with slightly higher specificity (prediction that the leaf is healthy given that it is observed to be healthy).

## DISCUSSION

Understanding and quantifying the process of ontogenic resistance of the grapevine leaf of the two susceptible, first widely cultivated wine cultivars Cabernet Sauvignon and Merlot to one of the major fungal pathogens, *E. necator* is a prerequisite to promote more sustainable pest management strategies in viticulture. Although ontogenic resistance on grapevine bunches has been well documented (Ficke et al., 2002; Gee et al., 2008), little evidence is available of similar processes for leaves, although leaves are the primary targets for the onset of epidemics. In this study, we attempted to analyse and quantify how the life traits of the pathogen were impacted by ontogenetic resistance and find predictors on the plant side associated with them. To obtain a generic view of the phenomenon, we used shoots coming from vinestocks grown in vineyards under different environmental conditions, and we used several predictable variables that were more or less easy to measure and relevant to analyse leaf state change.

Pathogen behavior was analyzed in relation to leaf age and was characterized by: (i) highly infectious colony growth and sporulation rates for young leaves (<10 days for Cabernet Sauvignon and <16 days for Merlot), with significantly higher sporulation on very young leaves; and (ii) lower colony growth, with a strong decrease in the sporulation rate and in the relative infection rate for older leaves (>10–16 days). Then, all of the chronologic infectious steps of the pathogen—infection efficiency, mycelium growth and sporulation—are affected by the leaf age. The sporulation capacity is even increased on very
<figure><img src="image_55.png" /><figcaption>**FIGURE 4** | Sporulation (number of spores produced per cm² of infected leaf disk) (Spo) (A), relative sporulation (RSpo) (B) and number of spores generated per colony (Spoc) (C) as a function of leaf age for experiments on Cabernet Sauvignon. Plain lines correspond to the fitted model M3 in **Table 4**. Dashed lines correspond to the 95% confidence bands on the models, and bars indicate standard deviation.</figcaption></figure>

young leaves, at least for Cabernet Sauvignon. This increase in sporulation was correlated to low sugar content, whereas the decrease in infection efficiency was more closely correlated with a decrease in leaf water content and an increase of starch. If we consider the leaf plastochron index, instead of leaf age, compared with other studies, the peak of sporulation was 1.4 for Cabernet Sauvignon and 2.7 for Merlot. These values of LPI varied by less than one point for the average temperature encountered during the experiments (15.9–18.7°C), indicating that, at each time during the growing season, very few leaves are potentially highly susceptible (~2 to 3 per shoot), and the

landscape of susceptibility should be higher for Merlot than for Cabernet Sauvignon. In a previous study (Merry et al., 2013), Cabernet Sauvignon leaves showing the maximum severity were those at LPI 3.7 at 18°C (constant temperature in the glasshouse), and the range of maximum severity was for leaf positions 2.1-4.8. At 25°C, for a higher rate of leaf emergence, the mean modal leaf position for maximum severity was 4.4. Comparatively, in our study with leaves coming from field shoots under natural irradiance and fluctuating temperatures, the maximum severity (corresponding more or less to Spo) was earlier (1.4), with the decrease of leaf susceptibility even sharper. In another study performed on cuttings in growth chambers, colony hyphal length and infection efficiency decreased on both resistant and susceptible cultivars as leaves matured (LPI 1 to LPI 8), with, however, high variations between cultivars (Doster and Schnathorst, 1985).

The high infection rate and increased colony growth on young leaves resulted in an increase in penetration sites and haustoria formation favoring pathogen nutrient uptake and, consequently, a higher sporulation rate. In our study, the very young, susceptible leaves were characterized by a small amount of glucose content, higher water content and a high leaf extension rate. These expanding leaves reached only ~1/3 of their final size, consistent with the status of sink tissues for grapevines (Lebon et al., 2008; Scott, 2008). Sink leaves depend on other leaves for their energy supply, and the carbohydrates that they receive from source plant parts are immediately used for respiration, growth and the synthesis of new cell components and are therefore not stored. Sink leaves correspond to leaf plastochron indices of 1–3, showing maximal photosynthetic activity (30% for LPI 3) (Zufferey et al., 2000) and higher day respiratory rates (Rd) over the whole growing period. Rd values in young leaves (LPI 3-5) on primary shoots were two times greater than those of mature leaves (Zufferey, 2016). The leaves in transition from sink to source showed a high increase in sugar levels and were able to export sugars when the supply in carbohydrate exceeded the needs for metabolism. In our study, this transition matched decreases in mycelium growth, sporulation and infection efficiency when the number of source leaves exceeded that of sink leaves. When it was not the case, early in the growing season (like for experiment 1), the infection efficiency was not affected by leaf age, and only sporulation decreased for the two basal leaves. The correspondence between the sink to source transition and susceptibility to powdery mildew were once identified on leaves from Cabernet Sauvignon cuttings by autoradiography after ¹⁴CO₂ labeling (Merry et al., 2013). Our study provided some clues about the ontogenic resistance process by decomposing the sequential development of the pathogen (infection rate, colony growth and sporulation), which converges toward a specific age-related transition in the receptivity of the leaf tissues. The complete life cycle of the fungus (up to sporulation) is restricted to sink tissue (small amount of sugar), while the first steps (infection, colony growth) can extend later. Based on these experiments for different environmental conditions and two varieties, we can accurately predict the susceptibility of the leaf with its age as a predictor: a leaf aged on average 13.3 days had a very high probability (0.8) of being susceptible, while this probability dropped to 0.5 one week later.
<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /></figure>

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

**FIGURE 5** | Infection efficiency rate (Inf) (A), colony diameter (Diam) (B), and sporulation (number of spores produced per cm² of infected leaf disc) (Spo) (C) as a function of leaf age and sporulation per colony (Spoc) as function of colony diameter (D) for Merlot experiments. Plain lines correspond to the fitted models M1 (A), M2 (B), M3 (C), and M4 (D) in **Table 5**. Dashed lines correspond to the 95% confidence bands on the models, and bars correspond to the standard deviation.

<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

**FIGURE 6** | Soluble sugars (SS) and complex sugar (ST) concentrations expressed as percentages of leaf disc dry weight and as a function of leaf age for the two first dates of sampling on Cabernet Sauvignon (A,B) and Merlot (C,D). Bars indicate standard deviations.
<figure><img src="image_64.png" /><figcaption>**FIGURE 7** | Evolution of leaf area (LA), leaf extension rate (LER) (dashed lines) and leaf water content (LWC) for Cabernet Sauvignon (A,B) and for Merlot (C,D). Bars indicate standard deviations.</figcaption></figure>

Decreases in incidence and severity of powdery mildew with leaf age or development stage were also identified on strawberry (Carisse and Bouchard, 2010) and were reported to be a robust and biologically consistent process with a response consistent across cultivars and isolates employed and between the various sites tested (Asalf et al., 2014). Regarding pea powdery mildew, a resistance gene involved in post-penetration cell death and a reduction in percentage penetration success in mature leaves confers complete resistance in the field under high temperatures or on mature leaves (Fondevilla et al., 2006). On *Eucalyptus grandis*, all of the steps of the development of the rust *Puccinia psidii*—germination, appressorium formation, penetration and sporulation—were altered with increased leaf age (Xavier et al., 2015). For other pathosystems, the susceptibility can increase with leaf age, as is the case for another rust, *Melampsora larici populina*, which is more aggressive on mature leaves of poplar (Maupetit et al., 2018). The authors observed an increase in uredia (colony) size and a higher sporulation rate with increased LPI and lower sporulation capacity. In this case, pathogen traits were not correlated with leaf sugar, but there was a negative correlation with phenolic content, which was higher in younger leaves. On pea, detached stipules were more receptive to *Mycosphaerella pinodes* as soon as visual senescence was observed (Richard et al., 2012).

## Which Factors Are Involved in the Variation of the Ontogenic Resistance Process?

Our results attest to the repeatability of ontogenic resistance on leaves during vegetative growth for two susceptible cultivars. However, our results only explained 61% of disease variation. These variations can be linked to sampling shoots with differences in the implementation age of the ontogenic resistance

process or between years/cultivars with variations in disease level. To explain variation between years, one parsimonious hypothesis is that it is based on differences in isolate aggressiveness. Indeed, a strong difference in sporulation for the same colony diameter was observed for different years. Variations between shoots and experiments depend on factors with effects on the sink-to-source status of the leaf, such as exposure to light before field sampling, crop load or even leaf damage. The transition to leaf resistance is, however, clearly related to the timing of the sink-to-source status rather than to the final level of glucose attained. Variations were also observed when leaves encountered a cold temperature, which slowed the physiological processes and delayed the timing of ontogenic resistance establishment (such as in experiment 6). We also observed that infection efficiency was not significantly different for the first leaves that emerged early in the season, which could emphasize the particular status of these first expanding leaves for which the source of assimilates used for growth first comes from retranslocation from the parent vine (Keller, 2015). Using ¹⁴C tracer, it has been shown that, at the 5-leaf stage shoot, only the first basal leaf is able to export assimilates to the youngest one, and at the 6-leaf stage, two to three basal leaves behave as sources (Yang and Hori, 1980). The experiments were conducted on a hybrid of Labrusca, but this outcome was consistent with what we observed. Indeed in our first experiment on Cabernet Sauvignon, only the two basal leaves being in the transition from sink to source showing significantly less sporulation. This should enhance infection success when epidemics start very early in the season and surely contributes to their particularly damaging effects.

Although infection efficiency is very low on old leaves, it is not always null. We do not know the impact of these rare infections considering that they were not sporulating at the time
<figure><img src="image_65.png" /><figcaption>**FIGURE 8** | Output of partial least squares path modeling for the two years of experiments describing the relationships between the endogenous latent variable “disease” to the other latent exogenous variables “Leaf growth,” “Leaf Physiology,” “Inoculum,” “Variety,” and “Climate/date.” Corr indicates the correlation coefficient between two latent variables, CR2 indicates the relative contribution of exogenous latent variables to endogenous one, Reg indicates the regression coefficient, R² indicates the regression coefficient between two latent variables, and w indicates the outer weights yielding the covariances between the manifest variable and its inner latent variable. Blue lines indicate negative correlations, and red lines indicate positive correlations.</figcaption></figure>

of assessment. In the pathosystems of appleapple scab, diffuse
colonies and mycelium that formed on old leaves continued to
grow (Li and Xu, 2002), and these authors wondered whether
these diffuse colonies could sporulate later in the season and
become a source of mycelium for the formation of pseudothecia.
On grapevines, we did not exclude that the translocation of
carbohydrates from old leaves to berries during grape ripening
would not modify leaf susceptibility or allow rare infected spores
to continue their development and affect late colonies. This
phenomenon has already been observed for other pathosystems,
as especially demonstrated in the case of hermaphrodite plants,
such as papaya (Carica papaya L.). Female fruit-bearing plants
are susceptible to *Oidium caricae* on old leaves and fruit, whereas
male plants are not attacked (Jarvis et al., 2002). These authors
have advanced hypotheses of the shunting of assimilates from leaf
to fruit to explain the differences. However, we could not observe

any modification of ontogenic resistance processes after bunch
closure (late July) on Merlot.

What Hypotheses Can Be Advanced to
Explain the Process of Ontogenic
Resistance Observed on Old Leaves?

Role of Sugar

The increase in carbohydrate concentration in old leaves
could be involved in the activation of secondary metabolism.
Soluble carbohydrates are known to control the expression
of various metabolic and plant defense-related genes (Koch,
1996; Rolland et al., 2006), and high sugar content can also
suppress fungus degradation enzymes, such as grape chitinase
(Saito et al., 2011). Powdery-mildew diseases have already been
classified as "high-sugar resistance" pathogens (Vanderplank,
1984), indicating reversal of susceptibility to high levels of sugars. The relationship between sugar amounts and osmotic pressure might also be involved (Koroleva et al., 2002). Increased osmotic pressure can challenge the pathogen during the first step of infection, slowing tissue colonization by limiting the process of penetration of secondary haustoria. This scenario is consistent with our observed reduction of colony diameter on old leaves. Increased osmotic pressure was hypothesized to explain the greater resistance of mature leaf epidermal cells to other powdery mildews on apple (Cimanowski and Millikan, 1970) and peach (Schnathorst and Weinhold, 1957; Weinhold and English, 1964).

## Activation of the PTI

Transcriptome and proteome analyses showed that the susceptible variety Cabernet Sauvignon is able to initiate basal defense mechanisms (Fung et al., 2008; Fekete et al., 2009; Marsh et al., 2010). Transcriptome analyses of leaves (Dufour et al., 2016) also revealed overexpression of defense genes (phenylalanine ammonialyase, stilbene synthase, anthocyanidin synthase, chitinase, lipase) on Cabernet Sauvignon leaves at ages of 15 and 20 days, compare to leaves at ages of 7 and 10 days, only after powdery mildew inoculation (unpublished data). For young expanding leaves, a high rate of cellular reactions of plant defense might be energetic and, for the carbohydrates, its consumption too costly (Bethany et al., 2014). MLO genes, known as "powdery mildew susceptibility genes," might be involved in this regulation. It was hypothesized that adapted powdery mildew species release effectors that, by combining with specific MLO proteins, could suppress the host PTI (plant triggered immunity) in the young leaves (Dry et al., 2010). One hypothesis explaining ontogenetic resistance is that changes occur in the old leaves in the expression or activity of the MLO genes with the consequence of restoring the PTI (Qiu et al., 2015).

## Constitutive Defenses

The transition in the trophic statute of the leaf bound up with development can also trigger the establishment of constitutive defenses to infection: cuticle thickness in *Lactuca sativa*, (Schnathorst, 1959) and *Prunus persica* (Weinhold and English, 1964); or the synthesis of antimicrobial compounds (Tattersall et al., 1997; Salzman et al., 1998). Precocious events of infection progress, such as germ tube initiation and appressorium differentiation, require physical or chemical cues (Muller and Riederer, 2005). Variations in cuticular waxes (quality and amount) during ontogenic development could be an inhibiting factor, consistent with the lower rate of conidium germination on old leaves that we observed. Constitutive defenses and PTI can be both active in source leaves.

## How Can Ontogenic Resistance Be Used in Epidemic Management?

Given the repeatability of the results, it is unlikely that the susceptibility of young leaves can be reduced without significant cost for the plant. Our results clearly demonstrated that powdery mildew takes advantage of the compromise that the plant must make between growth and defense (Bethany et al., 2014). Even if it is tempting to think that new genome editing techniques will allow for the easy knockout of genes, such as MLO, the effect of these knockouts on the physiology of the plant remain unknown (Acevedo-Garcia et al., 2014; Malnoy et al., 2016). The rate of both primary and secondary leaf appearance can, however, be slowed by the winegrower, hence reducing the proportion of available susceptible young tissues per unit of time. Indeed, agronomical practices, such as cover-cropping and the use of a vigor-controlling rootstock, can decrease the rate of leaf appearance and consequently disease level (Calonnec et al., 2009; Schnee et al., 2011; Valdes-Gomez et al., 2011). Simulations have shown that a reduction in grapevine vigor, through a decrease in both the numbers and the rates of secondary leaf appearance, can slow epidemics (Burie et al., 2011; Mammeri et al., 2014). Other practices, such as thinning of old leaves, will decrease the photosynthesis of younger leaves, thereby potentially delaying their ontogenic resistance, whereas shoot topping of younger leaves does not impact leaf photosynthesis per unit of leaf area (Petrie et al., 2003). Consequently, thinning might be less favorable to increasing leaf resistance than shoot topping, without considering the potentially positive effect of light on tissue receptivity (Zahavi and Reuveni, 2012). Other cultural practices, such as minimal pruning, which results in increased shoot numbers, reduce leaf and shoot size, and more synchronous leaves emergence (Poni et al., 2000), could allow the plant to escape disease by simultaneously causing all leaves to behave as a source. Such cultural practices could also be used to enhance the resistance of varieties with specific resistance genes. It would be interesting to revisit the theory of grapevine balance using tools of genetics and physiology in a general context of dieback and climate change, in which yields are sought, while maintaining control of pathogens that are very deleterious, such as mildews, is a prerequisite. Coupling functional structural grapevine physiology (Weinhold and English, 1964) and pathogen development models (Calonnec et al., 2008) would help to better understand crop susceptibility under various environmental conditions and complex canopies, while early protection of the foliage, which allows for desynchronizing of the disease cycle and the crop cycle toward a higher percentage of resistant leaves, should be preferred.

## CONCLUSIONS

Our results revealed a clear correlation between the appearance of grapevine leaf ontogenic resistance and the leaf transition state from sink to source. The soluble and complex sugars, leaf water content and leaves' extension rate constitute indicators of a change in the metabolic processes and are indirect markers of tissue age-related resistance. From a practical point of view, the additional effects of ontogenic resistance modification through crop management and resistant cultivars should be explored to obtain better control of powdery mildew epidemics in
sustainable agriculture (Calonnec et al., 2013a). Moreover, the integration of this developmental process into a grapevine disease management program requires reconsideration of fungicide spraying with regard to the formatting of organ susceptibility windows.

## AUTHOR CONTRIBUTIONS

AC: head of the research programme, data analyses, writer.
JJ: experimental technician. PV: collaborator for physiology data analysis and experimentation. SS: post-doctoral student, experimenter, writer.

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## FUNDING

This study was supported by a grant from the Agence Nationale de la Recherche program SYSTERRA (ANR-08-STRA-04) and by the Aquitaine Regional Council.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2018.01808/full#supplementary-material

**Supplementary Figure S1** | Scheme of the experimental design.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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# A Trade-Off Between Sporangia Size and Number Exists in the Potato Late Blight Pathogen *Phytophthora infestans*, and Is Not Altered by Biotic and Abiotic Factors

Nicolas Mariette, Alexander Kröner, Romain Mabon, Josselin Montarry, Bruno Marquer, Roselyne Corbière, Annabelle Androdias and Didier Andrivon*

INRA, UMR1349 Institute for Genetics, Environment and Plant Protection, Le Rheu, France

The negative relationship between offspring size and number is a classic example of trade-off between life-history traits, reported many times in animal and plant species. Here, we wanted to ascertain whether such a trade-off occurred in the oomycete *Phytophthora infestans*, and whether it was impacted by biotic and abiotic factors. We thus conducted three infection experiments under controlled conditions and measured the number and the size of sporangia (asexual propagules) produced on potato by different *P. infestans* isolates. In all experiments, we observed a negative relationship between sporangia size and number, demonstrating the existence of a trade-off. Moreover, although the potato host cultivar, temperature and host of origin (tomato or potato) all affected sporangia number, sporangia size or both, none of these biotic and abiotic factors did change the trade-off. Therefore, the trade-off between sporangia size and number could maintain the polyphenism for these traits in *P. infestans* populations, and favors the coexistence of distinct reproductive strategies within this species. Our results emphasize the relevance to focus on the relationship between offspring size and number in other fungal plant pathogens, as well as to study the impact of offspring size on fitness-linked traits (virulence and disease lesion development) in these organisms.

**Keywords:** fitness, life-history traits, plant pathogen, reproductive strategies, *Solanum tuberosum*

## OPEN ACCESS

**Edited by:**
Michele Perazzoli,
Fondazione Edmund Mach, Italy

**Reviewed by:**
Remco Stam,
Technische Universität München,
Germany
Daolong Dou,
Nanjing Agricultural University, China

*Correspondence:
Didier Andrivon
didier.andrivon@inra.fr

**Specialty section:**
This article was submitted to Plant Microbe Interactions, a section of the journal Frontiers in Plant Science

**Received:** 13 July 2018
**Accepted:** 27 November 2018

**Published:** 19 December 2018

**Citation:**
Mariette N, Kröner A, Mabon R, Montarry J, Marquer B, Corbière R, Androdias A and Andrivon D (2018) *A Trade-Off Between Sporangia Size and Number Exists in the Potato Late Blight Pathogen Phytophthora infestans, and Is Not Altered by Biotic and Abiotic Factors*. *Front. Plant Sci.* 9: 1841. doi: 10.3389/fpls.2018.01841

## INTRODUCTION

The widely accepted trade-off concept states that an organism cannot simultaneously maximize all traits involved in fitness, owing to dilemmas in resource allocation (Law, 1979). According to this theory, any beneficial change in one trait will unavoidably have a detrimental effect on another trait (Anderson and May, 1982; Stearns, 1989). The net result is a negative correlation between traits that mobilize the same resources. The trade-off concept has a prominent importance in evolutionary biology, as it can explain how the adaptation of populations to new environments can be constrained and how life-history strategies can evolve (Rausher, 1984; Roff, 1992). Trade-offs between fitness components are of central importance in plant pathogens, as they could limit their evolutionary potential (Lannou, 2012; Laine and Barres, 2013) and may thus be exploited to engineer durable control strategies (Quenouille et al., 2013; Brown, 2015).
Since they are key to determining fitness, trade-offs involving reproductive traits, especially the one between offspring size and number, have received the most attention in the scientific literature (Stearns, 1989; Roff, 2002; Begon et al., 2006). The trade-off between offspring size and offspring number is based on the hypothesis that, for a given total reproductive investment, if most resources are devoted to the production of many offspring individuals, each of them will have limited lower share of the reproductive investment (Begon et al., 2006). If this trade off exists (that is, if resources available for reproduction are constrained), reproductive strategies should range along a continuum, from the production of few large offsprings to the production of many small ones (Timi et al., 2005). This size-number trade-off was first advocated by Lack (1947) with experiments on clutch-size in birds, and has been subsequently observed in many animals (Charnov and Ernest, 2006; Walker et al., 2008) and plants (Werner and Platt, 1976; Jakobsson and Eriksson, 2000; Linkies et al., 2010).

The size-number trade-off could also be found in plant parasites, especially oomycetes and fungi. For such sporulating organisms (with spores as asexual propagules), producing large spores should indeed be more energetically costly than producing small spores, but may increase spore viability or infectious ability. Although trade-offs involving spore production on one hand and other life-history traits such as latent period (Pariaud et al., 2013), virulence (Thrall and Burdon, 2003; Montarry et al., 2010) or transmission success rate (Pasco et al., 2016) on the other hand, were observed in various plant pathogens, very few studies have investigated the existence of a trade-off between spore size and number (Delmotte et al., 2014). Fortunately, the advent of new techniques and equipments for counting and measuring thin particles like spores now make the exploration of such a trade-off possible.

As in all pathogenic interactions, the development of a disease caused by a plant pathogen is strongly influenced by both abiotic environment and host genotype (Laine, 2007; Wolinska and King, 2009; Clément et al., 2010; Delmotte et al., 2014). As a consequence, these factors can also have an impact on the shape and even the direction of the correlation between life-history traits in pathogens. Hence, fitness costs can differ among environments (e.g., temperature) as observed in the fungal pathogen *Podosphaera plantaginis* (Laine, 2008). Likewise, some studies reported shifts of life-history correlations in plant pathogens, depending on the cultivars tested (Huang et al., 2010; Susi and Laine, 2013). These data thus stress that it is crucial to take into account sufficient variation in both the host and the parasite, by measuring life-history traits in different environments, to draw conclusions on the status of a life-history trade-off in a plant pathogen.

Here, we aimed to investigate the existence of a trade-off between offspring number and size in the oomycete *Phytophthora infestans*, and the impact of some biotic and abiotic factors on this eventual trade-off. Infamous for having triggered the Irish Great Famine in the 1840s, *P. infestans* is the causing agent of late blight in solanaceous species and is responsible for significant losses in both potato and tomato crops (Fry et al., 2015). This pathogen is particularly suitable for exploring trade-offs, as it can be isolated

and maintained as axenic cultures on artificial media whereas miniature biotests performed in controlled conditions can be used for the assessment of life-history traits of the pathogen, particularly those linked to spore formation (Montarry et al., 2010). We thus conducted three experiments under controlled conditions in which we measured the size and number of sporangia (i.e., asexual spores) produced by *P. infestans* isolates after artificial inoculations on potato leaflets. In each experiment, we changed one factor [host resistance level, temperature, or the host of origin (tomato or potato)] which could impact the trade-off. We measured the effect of these factors on both sporangia size and number, analyzed the relationships between these two traits and compared them within each set of conditions.

## MATERIALS AND METHODS

We conducted three inoculation experiments of *P. infestans* isolates on detached potato leaflets in which we measured the size and the number of sporangia produced by the pathogen. Different biotic and abiotic parameters – host resistance level, temperature and host of origin (tomato or potato) – were modified in these experiments. The experimental protocols used for these experiments were mostly similar but contained some slight differences (Table 1).

### Isolate Collections

Single-lesion isolates were established and maintained as axenic cultures on pea agar medium as previously described (e.g., Montarry et al., 2010). Briefly, each single-lesion isolate was obtained by placing a fragment of infected leaf tissue on tuber slices of the susceptible potato cultivar Bintje. After incubation at 15°C in growth chambers for four to eight days, pure cultures were established by transferring the hyphal tips growing through the slices to sterile pea agar and subsequently maintained at 15°C in darkness by serial transfers on fresh pea agar.

In the first experiment, we used 119 isolates sampled in commercial potato fields of western France during late blight

<table><caption>TABLE 1 | Protocol specificities applied for each experiment lead during the study.</caption><thead><tr><th></th><th>Experiment 1</th><th>Experiment 2</th><th>Experiment 3</th></tr></thead><tbody><tr><td>Inoculum concentration</td><td>5 × 10<sup>4</sup> sporangia·mL<sup>-1</sup></td><td>5 × 10<sup>4</sup> sporangia·mL<sup>-1</sup></td><td>3 × 10<sup>4</sup> sporangia·mL<sup>-1</sup></td></tr><tr><td>Potato cultivars</td><td>● Bintje<br>● Robijn<br>● Möwe</td><td>Bintje</td><td>Bintje</td></tr><tr><td>Thermal regimes</td><td>15/18°C (day/night)</td><td>● 10°C<br>● 14°C<br>● 18°C</td><td>15/18°C (day/night)</td></tr><tr><td>Time of the sporangia collection</td><td>3 days after latent period</td><td>3 days after latent period</td><td>5 days after inoculation</td></tr><tr><td>No. independent replicates</td><td>1</td><td>2</td><td>2</td></tr></tbody></table>
epidemics of 2014 (Mariette, 2016). All isolates were tested on three potato cultivars: Bintje, Robijn, and Möwe. These cultivars do not bear race-specific resistance genes but differ in levels and components of partial resistance to late blight: Bintje is the reference susceptible cultivar to *P. infestans* commonly used in European laboratories, whereas the partially resistant Robijn and Möwe notably reduce spore production of the pathogen (Clément et al., 2010). In the second experiment, 16 western French isolates sampled in 2013 were tested on the potato cultivar Bintje under three thermal regimes: 10, 14, and 18°C. This temperature range was chosen because it covers the biological activity of *P. infestans* (Maziero et al., 2009). The third experiment included 21 isolates sampled either on potato (11) or tomato (10), in French and Algerian crops from 2013 to 2015, and were tested on the potato cv. Bintje.

## Plant Material

Three potato cultivars (Bintje, Robijn, and Möwe) and one tomato cultivar (Marmande) were used. Certified seed tubers of the potato cultivars were obtained from the INRA Biological Resource Center BrACySol (Ploudaniel, France), whereas commercial standard seed of the tomato cultivar was used.

Plants were grown in pots filled with 1:1:1 sand-peat-compost mixture placed in a glasshouse regulated at 15-20°C (night/day temperatures) with 16 h of photoperiod. Once a week, plants were watered with a nutrient solution (Hakaphos; NPK 15/10/15). For the inoculum preparation and life-history traits measurements, leaflets of similar size were picked from the median area of 6- to 8-week-old plants.

## Inoculum Preparation

Prior the assessment of their life-history traits and in order to restore pathogenicity possibly lost in axenic cultures (Jinks and Grindle, 1963), *P. infestans* isolates were first inoculated onto detached leaflets of the original host species: the potato cv. Bintje and the tomato cv. Marmande for potato and tomato isolates, respectively. For this purpose, droplets of sporangial suspensions, prepared from 3- to 4-week-old pea agar cultures by flooding with sterile water and scrapping the colony surface, were deposited on the lower (abaxial) side of detached leaflets kept on the empty lids of inverted Petri dishes containing 10 g L⁻¹ water agar and acting as humid chambers. After 7 days of incubation at 18/15°C (day/night temperature) and 16-h day length, newly formed sporangia were collected from infected leaflets by gently shaking in sterile water. Sporangia were then counted using a hemocytometer, adjusted to a final concentration of 3 × 10⁴ or 5 × 10⁴ sporangia mL⁻¹, depending on the experiment (Table 1), and then kept at 4°C for 2 h to promote zoospore release.

## Inoculation

Sporangial suspensions were inoculated onto a potato leaflet, except in the second experiment where three leaflets of potato cv. Bintje were inoculated due to the three thermal regimes tested (Table 1). To this end, leaflets were placed, abaxial face up, onto the empty lids of inverted Petri dishes containing 10 g L⁻¹ water agar. A 20-µL droplet of the prepared sporangial suspension

was then deposited on the center of the leaflet. The Petri dishes were kept in clear boxes and incubated in climate chambers regulated at thermal conditions desired (Table 1) with a 16 h photoperiod.

## Life-History Traits Measurements

Three days after the formation of the first sporangia – checked by daily observations under a magnifying glass – or 5 days after inoculation (depending on the experiment, Table 1), newly formed sporangia were washed from each leaflet in 10 mL Isoton II (saline buffer; Beckman Coulter, Villepinte, France). Suspensions were kept in glass tubes at –20°C until the counting of sporangia number (SN) produced using a Coulter Z2 counter (Beckman Coulter) equipped with a 100-µm aperture tube. Sporangia size (SS) was determined under a microscope fitted with a camera and using the image analysis software Histolab® v8.1.0 (Microvision Instruments, Evry, France). For this purpose, drops of the sporangial suspension were placed under the microscope and we used the camera measurement software to determine length and width of 100 randomly chosen sporangia. These measures allowed to calculate sporangia volumes, assuming a revolution ellipsoid shape for each sporangium (Philibert et al., 2011) by applying the standard equation $\frac{4\pi}{3} * \frac{length}{2} * \left(\frac{width}{2}\right)^2$.

## Statistical Analyses

All analyses were performed using the statistical software R v. 3.4.4 (R Core Team, 2017) and the significance threshold was fixed at $\alpha = 0.05$. We first compared SS and SN of *P. infestans* isolates tested on different potato cultivars (experiment 1) and at different temperatures (experiment 2) using analysis of variance (ANOVA) followed by Tukey's honestly significant difference *post hoc* comparisons. For the comparisons between isolates sampled on potato and tomato (experiment 3), we applied a Student *t*-test. When necessary, SS and SN were log-transformed to achieve assumptions of homoscedasticity and normality. For the three tested modalities (potato cultivar, temperature and host of origin), each *P. infestans* isolate was regarded as a biological repetition. Then, we investigated the relationship between SS and SN for each modality tested in the three experiments using Pearson correlations and linear regressions (where SS and SN were log-transformed). To test whether slopes of regression lines differed between modalities within each experiment, variation in SS was analyzed by mean of analysis of covariance (ANCOVA) with SN as the covariate and host cultivar, temperature or host of origin as the factor. In the first experiment implying 119 isolates, phenotypic measurements were made only once on each of them as the large number of tested individuals allowed a good assessment of significance. In the case of experiments 2 and 3, we compared data obtained from the two independent replicates and, as results showing no significant differences (i.e., the criterion was statistically fulfilled or rejected), data from both experiments were pooled, reanalyzed and represented graphically.
# RESULTS

## Impact of Biotic and Abiotic Factors on *P. infestans* Life-History Traits

In experiment 1, potato cultivar had a significant effect on SS of *P. infestans* isolates (ANOVA, $F_{2,352} = 3.58, P = 0.029$), with bigger sporangia produced on Bintje than on Robijn as revealed by Tukey post hoc tests (Table 2). Likewise, a significant effect of potato cultivar on SN was detected (ANOVA, $F_{2,352} = 158.48, P < 0.001$), with the highest number of sporangia produced on Bintje, then on Robijn, when the lowest number of sporangia was observed on Möwe (Table 2). In the second experiment, SS was significantly impacted by temperature (ANOVA, $F_{2,91} = 54.804, P < 0.001$): the lower the temperature, the bigger the sporangia (Table 2). In this experiment, temperature also had a significant effect on SN (ANOVA, $F_{2,91} = 106.31, P < 0.001$) with in this case, an increase of SN with increasing temperatures (Table 2). In experiment 3, no significant effect of isolate origin was detected on SS [$t(40) = 1.63, P = 0.111$; Table 2], nor on SN [$t(40) = 1.81, P = 0.079$; Table 2].

## Relationship Between Life-History Traits

In experiment 1, significant negative relationships between SS and SN of *P. infestans* isolates were detected in the tests conducted on potato cultivars Bintje ($r = -0.36, P < 0.001$; Figure 1), Robijn ($r = -0.49, P < 0.001$; Figure 1), and Möwe ($r = -0.48, P < 0.001$; Figure 1). ANCOVA revealed no significant interaction between host cultivar and SN (Table 3), indicating that the relationship between SS and SN did not significantly differ among potato cultivars. Likewise, in the second experiment, the relationship between SS and SN was significantly negative at 10°C ($r = -0.70, P < 0.001$; Figure 2), 14°C ($r = -0.62, P < 0.001$; Figure 2), and 18°C ($r = -0.68, P < 0.001$; Figure 2). Again, this relationship did not significantly differ among temperatures, since no significant interaction between temperature and SN was detected by ANCOVA (Table 3). Finally, in experiment 3, SS and SN were also negatively correlated and this relationship was significant for isolates sampled on both potato ($r = -0.57$,

<table><caption>**TABLE 2** | Mean values (SE) of sporangia size and sporangia number measured in the three experiments for each tested factor (potato cultivar, temperature, and host of origin).</caption><thead><tr><th></th><th></th><th></th><th></th><th>Sporangia size<br>(µm³)</th><th></th><th>Sporangia number<br>(No. sporangia/leaflet)</th><th></th></tr></thead><tbody><tr><td rowspan="3">Exp. 1</td><td>Bintje</td><td>8871</td><td>(138)</td><td>a</td><td>3.01 × 10<sup>5</sup></td><td>(10591)</td><td>a</td></tr><tr><td>Möwe</td><td>8814</td><td>(108)</td><td>ab</td><td>1.10 × 10<sup>5</sup></td><td>(4984)</td><td>c</td></tr><tr><td>Robijn</td><td>8477</td><td>(85)</td><td>b</td><td>1.64 × 10<sup>5</sup></td><td>(6753)</td><td>b</td></tr><tr><td rowspan="3">Exp. 2</td><td>10°C</td><td>7265</td><td>(204)</td><td>a</td><td>9.07 × 10<sup>4</sup></td><td>(6259)</td><td>c</td></tr><tr><td>14°C</td><td>6169</td><td>(143)</td><td>b</td><td>2.10 × 10<sup>5</sup></td><td>(12247)</td><td>b</td></tr><tr><td>18°C</td><td>4965</td><td>(91)</td><td>c</td><td>3.89 × 10<sup>5</sup></td><td>(8768)</td><td>a</td></tr><tr><td rowspan="2">Exp. 3</td><td>Potato</td><td>8650</td><td>(275)</td><td>a</td><td>1.32 × 10<sup>5</sup></td><td>(18493)</td><td>a</td></tr><tr><td>Tomato</td><td>8011</td><td>(430)</td><td>a</td><td>9.56 × 10<sup>4</sup></td><td>(17792)</td><td>a</td></tr></tbody></table>

Different letters in a column indicates significant differences between means within an experiment at a 0.05 threshold (Tukey HSD or Student-t-tests).

<figure><img src="image_67.png" /><figcaption>**FIGURE 1** | Relationship between size and number of sporangia produced by 119 *P. infestans* isolates after inoculations on potato cultivars Bintje, Möwe, and Robijn (experiment 1). All variables were LOG-transformed. For sporangia produced on Bintje, the linear regression is represented by a continuous gray line ($y = 10.89-0.14 \times, R^2 = 0.13, P < 0.001$), for sporangia produced on Möwe, the linear regression is represented by a continuous black line ($y = 10.58-0.13 \times, R^2 = 0.23, P < 0.001$), and for isolates inoculated on Robijn, the linear regression is represented by a dotted black line ($y = 10.22-0.10 \times, R^2 = 0.24, P < 0.001$).</figcaption></figure>

<table><caption>**TABLE 3** | Summary of analysis of covariance (ANCOVA) for testing the effects of biotic and abiotic factors on the relationship between size and number of sporangia produced by *P. infestans* isolates.</caption><thead><tr><th>Source of variation</th><th>df</th><th>Mean Sq</th><th>F</th></tr></thead><tbody><tr><td><strong>Experiment 1</strong></td><td></td><td></td><td></td></tr><tr><td>SN</td><td>1</td><td>1.19</td><td>77.730***</td></tr><tr><td>Cultivar</td><td>2</td><td>0.35</td><td>22.856***</td></tr><tr><td>SN × Cultivar</td><td>2</td><td>0.02</td><td>1.003</td></tr><tr><td>Error</td><td>351</td><td>0.02</td><td></td></tr><tr><td><strong>Experiment 2</strong></td><td></td><td></td><td></td></tr><tr><td>SN</td><td>1</td><td>0.72</td><td>72.610***</td></tr><tr><td>Temperature</td><td>2</td><td>0.05</td><td>4.854**</td></tr><tr><td>SN × Temperature</td><td>2</td><td>0.00</td><td>0.099</td></tr><tr><td>Error</td><td>90</td><td>0.01</td><td></td></tr><tr><td><strong>Experiment 3</strong></td><td></td><td></td><td></td></tr><tr><td>SN</td><td>1</td><td>0.43</td><td>19.036***</td></tr><tr><td>Origin</td><td>1</td><td>0.21</td><td>9.419**</td></tr><tr><td>SN × origin</td><td>1</td><td>0.00</td><td>0.044</td></tr><tr><td>Error</td><td>38</td><td>0.02</td><td></td></tr></tbody></table>

In the models, potato cultivar (experiment 1), temperature (experiment 2), and host of origin (experiment 3) were rated as factors, sporangia production (SP) as the covariate and sporangia size as the response variable. Statistical significances are indicated as follow (**P < 0.01 and ***P < 0.001). Sporangia size and sporangia number were LOG-transformed.

P = 0.006; Figure 3) and tomato ($r = -0.58, P = 0.007$; Figure 3). As in the other two experiments, no significant interaction was revealed by ANCOVA between ‘origin’ and SN (Table 3), indicating that the SS-SN relationship was not significantly different between tomato and potato isolates.
<figure><img src="image_68.png" /></figure>

**FIGURE 2** | Relationship between size and number of sporangia produced by 16 *P. infestans* isolates after inoculations on potato cultivar Bintje at 10, 14, and 18°C (experiment 2). All variables were LOG-transformed. For sporangia produced at 10°C, the linear regression is represented by a continuous gray line (y = 11.25–0.21x, R² = 0.49, P < 0.001), for sporangia produced at 14°C, the linear regression is represented by a dotted black line (y = 11.58–0.23x, R² = 0.39, P < 0.001) and for isolates inoculated at 18°C, the linear regression is represented by a continuous black line (y = 11.14–0.21x, R² = 0.46, P < 0.001).

<figure><img src="image_69.png" /></figure>

**FIGURE 3** | Relationship between size and number of sporangia produced by 21 *P. infestans* isolates sampled on potato or tomato after inoculations on potato cultivar Bintje (experiment 3). All variables were LOG-transformed. For sporangia produced by potato isolates, the linear regression is represented by a continuous black line (y = 10.23–0.10x, R² = 0.32, P = 0.006) and for sporangia produced by tomato isolates, the linear regression is represented by a continuous gray line (y = 10.20–0.11x, R² = 0.34, P = 0.007).

DISCUSSION

The trade-off between offspring number and offspring size has
been intensely studied and well characterized in animals and

plant species (Werner and Platt, 1976; Charnov and Ernest,
2006; Walker et al., 2008). Here, we report that it also applies
to a microorganism pathogenic to plants, potato late blight
oomycete pathogen *P. infestans*: higher offspring numbers (in this
case, asexual sporangia) correlates in this species with smaller
offspring size. This trade-off has been seldom investigated in
plant pathogens, even though a similar finding was reported
in another plant pathogenic oomycete, the grapevine downy
mildew pathogen *Plasmopara viticola* (Delmas et al., 2014). Such
a trade-off is thus probably present in other sporulating parasites.

Interestingly, we have found significantly negative
relationships between SS and number for all the modalities
tested in our study, and with similar slopes within each
experiment. Since host cultivar, temperature and isolate origin
(potato or tomato) are important factors for the fitness of an
almost obligate pathogen such as *P. infestans* (Andrivon et al.,
2013), we could have supposed that these biotic and abiotic
environmental variables could have altered the shape of the
trade-off. Indeed, previous studies focused on plant pathogens
reported for instance that host could modulate trade-offs,
like those implying infectivity and transmission (Susi and
Laine, 2013) or virulence and lesion size (Huang et al., 2010).
Therefore, the stability of the trade-off relationship between
SS and SN in *P. infestans* across a range of biotic and abiotic
environments suggest that this trade-off is constitutive in this
species, and might be exploited in control strategies of late blight
epidemics.

The stability of the trade-off relationship over the range
of environments tested is all the more remarkable that the
environmental factors investigated strongly impacted one or both
of the life history traits involved in the trade off. In fact, we
showed that sporangia production of *P. infestans* was strongly
host-dependent, confirming earlier observations made in this
species (Clément et al., 2010) and more generally in a range
of fungal and oomycete parasites (Hardham and Hyde, 1997;
Delmotte et al., 2014). Interestingly, we also showed a clear effect
of potato cultivars on SS, with smaller sporangia on Robijn –
especially compared to Bintje. A similar observation was reported
in *P. viticola* (Delmotte et al., 2014) and *Venturia inaequalis* (De
Gracia et al., 2015), suggesting a possible selection by host on this
trait among sporulating plant pathogens. Regarding temperature,
the thermal optimum for *P. infestans* development is around
18–20°C (Mizubuti and Fry, 1998; Mariette et al., 2016), and
spore production quickly decreases beyond this. In our second
experiment, sporangia were much less numerous at the lowest
temperatures, but they were bigger. This phenomenon could
be due to the longer latent period of the pathogen at low
temperatures (Maziero et al., 2009; Mariette et al., 2016), leading
to more time for sporangia formation. Finally, we observed that
isolates sampled on tomato produced less sporangia on the potato
cultivar Bintje than those sampled on potato, confirming the host
specialization previously highlighted in *P. infestans* (Oyarzun
et al., 1998; Kröner et al., 2017).

By preventing an organism to simultaneously maximize all
traits involved in fitness, trade-offs can equalize fitness across
individuals, genotypes, or species with different reproductive
strategies, leading to the maintenance of phenotypic diversity
(Roff, 1992). For instance, Héraudet et al. (2008) suggested that the trade-off between latent period and spore production in *H. arabidopsidis* may explain why the latent period remains variable in nature and did not achieve a uniformly minimal value. The constraint of total reproductive effort imposing a trade-off in the size and number of sporangia in *P. infestans*, as observed in our study, could therefore maintain variability for these life-history traits within populations of this pathogens. This tends to be confirmed with the large range of sporangia production values generally reported amongst isolates of single *P. infestans* populations (e.g., Montarry et al., 2008b, 2010; Mariette et al., 2016). Moreover, the polyphenism of life-history traits values imposed by a trade-off can lead to the emergence of various life-history strategies (Roff, 1992). This can be crucial in pathogens highly dependent on their environmental variations, as different life-history strategies can explain the coexistence of genetically differentiated clades, in complete or partial reproductive isolation, within single plant pathogen species (Montarry et al., 2008a).

Here, we report that a trade-off between number and size of asexual spores occurred in an important oomycete plant pathogen, *P. infestans*, and that its shape was not affected by different biotic and abiotic factors. It would be interesting to know the relationship between asexual spore number and size in other fungal plant pathogens as trade-offs (and their shape) can play a key role in evolutionary outcomes of these organisms (Kamo et al., 2007). Nevertheless, it is far from being the case at present, because if the production of the asexual propagules (spore or sporangia) is a trait extensively studied in fungal plant pathogens (Hardham and Hyde, 1997; Montarry et al., 2010), their size is still often ignored. Yet, the size of fungal propagules may be an important trait for different aspects of their fitness. The infection efficiency should be higher for large spores as they are supposed to have more resources available for hyphal growth (Bässler et al., 2015). Likewise, SS in oomycetes could also impact their indirect germination (formation of zoospores) as suggested by investigations on *P. viticola* showing that larger sporangia produced more zoospores (Delmas et al., 2014). Conversely, small spores might have higher aerial dispersal abilities and could therefore tend to deposit further to the source than larger ones (Norros et al., 2014). Nevertheless, owing to the lack of empirical evidences, the link between asexual spore size and such fitness aspects remain uncertain (De Gracia et al., 2015).

Further research efforts on this topic are thus needed and it would be particularly crucial to study the impact of offspring size on virulence or disease lesion development in fungal plant pathogens. Besides, a future way of research in plant pathogens could be to not only limit on the trade-off between number and size of offspring but, as recommended by Begon et al. (2006), to look for a trade-off between the number of offspring and their individual fitness.

## DATA AVAILABILITY STATEMENT

The datasets generated for this study will be found on data.inra.fr (doi: 10.15454/BB0WJN).

## AUTHOR CONTRIBUTIONS

RM, AA, BM, NM, and AK performed the experiments according to a protocol elaborated jointly by RC and DA. NM and AK analyzed the data. NM, JM, RC, and DA wrote the text and prepared the figures. All authors edited the paper and have approved the current version.

## ACKNOWLEDGMENTS

A part of this work presented in this paper first appeared in the NM thesis, which can be accessed online (see the reference list). We gratefully acknowledge the financial support by INRA ACCAF Metaprogramme (CLIF project) and ARIMnet (PoH-MED project; KBBE 219262/ANR). We also warmly thank Prof. Zouaoui Bouznad and Sihem Belkhiter (ENSA, Algiers) for providing Algerian isolates and Mathilde Bodiou for help with the sampling of *P. infestans* isolates. We express our thanks to E. Rolland and C. Piriou for their help with isolate maintenance and biological experiments. We are grateful to the INRA Genetic Resources Center BrACySol of Ploudaniel for providing the plant material used in the tests. NM was supported by a Ph.D. project cofunded by INRA and ACVNPT (Association des Créateurs de Variétés Nouvelles de Pomme de Terre) through a CIFRE grant (Conventions Industrielles de Formation par la Recherche). AK was supported by a post-doctoral grant funded under the ARIMnet PoH-MED project.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Mariette, Kröner, Mabon, Montarry, Marquer, Corbière, Androdias and Andrivon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Responses to Hydric Stress in the Seed-Borne Necrotrophic Fungus *Alternaria brassicicola*

Guillaume Quang N'Guyen<fnref n="1" />†, Roxane Raulo², Muriel Marchi<fnref n="1" />, Carlos Agusti-Brisach³, Beatrice Iacomi⁴, Sandra Pelletier<fnref n="1" />, Jean-Pierre Renou<fnref n="1" />, Nelly Bataillé-Simoneau<fnref n="1" />, Claire Campion<fnref n="1" />, Franck Bastide<fnref n="1" />, Bruno Hamon<fnref n="1" />, Chloé Mouchès<fnref n="1" />, Benoit Porcheron⁵, Remi Lemoine⁵, Anthony Kwasiborski<fnref n="1" />, Philippe Simoneau<fnref n="1" /> and Thomas Guillemette<fnref n="1" />*

OPEN ACCESS

**Edited by:**
Christophe Le May,
Agrocampus Ouest, France

**Reviewed by:**
Syama Chatterton,
Agriculture and Agri-Food Canada
(AAFC), Canada
Antoine Gravot,
University of Rennes 1, France

*Correspondence:
Thomas Guillemette
thomas.guillemette@univ-angers.fr

†Present address:
Guillaume Quang N'Guyen,
Département de Biologie, PROTEO,
Institut de Biologie Intégrative et des
Systèmes, Université Laval, Pavillon
Charles-Eugène-Marchand, Québec,
QC, Canada

**Specialty section:**
This article was submitted to
Plant Microbe Interactions,
a section of the journal
Frontiers in Microbiology

Received: 18 September 2018

Accepted: 09 August 2019

Published: 30 August 2019

**Citation:**
N'Guyen GQ, Raulo R, Marchi M, Agusti-Brisach C, Iacomi B, Pelletier S, Renou J-P, Bataillé-Simoneau N, Campion C, Bastide F, Hamon B, Mouchès C, Porcheron B, Lemoine R, Kwasiborski A, Simoneau P and Guillemette T (2019) Responses to Hydric Stress in the Seed-Borne Necrotrophic Fungus *Alternaria brassicicola*. *Front. Microbiol.* 10:1969. doi: 10.3389/fmicb.2019.01969

*Alternaria brassicicola* is a necrotrophic fungus causing black spot disease and is an economically important seed-borne pathogen of cultivated brassicas. Seed transmission is a crucial component of its parasitic cycle as it promotes long-term survival and dispersal. Recent studies, conducted with the *Arabidopsis thaliana*/*A. brassicicola* pathosystem, showed that the level of susceptibility of the fungus to water stress strongly influenced its seed transmission ability. In this study, we gained further insights into the mechanisms involved in the seed infection process by analyzing the transcriptomic and metabolomic responses of germinated spores of *A. brassicicola* exposed to water stress. Then, the repertoire of putative hydrophilins, a group of proteins that are assumed to be involved in cellular dehydration tolerance, was established in *A. brassicicola* based on the expression data and additional structural and biochemical criteria. Phenotyping of single deletion mutants deficient for fungal hydrophilin-like proteins showed that they were affected in their transmission to *A. thaliana* seeds, although their aggressiveness on host vegetative tissues remained intact.

**Keywords:** hydrophilins, plant pathogenic fungus, seed transmission, dehydration, *Alternaria brassicicola*

## INTRODUCTION

The fungus *Alternaria brassicicola* causes black spot disease and is an economically important seed-borne pathogen of Brassicaceae species. This necrotrophic fungus strongly depends on seed transmission process for its long-term survival and dispersal (van den Bosch et al., 2010). However, fungal and plant factors that impact seed transmission are still poorly described. Such knowledge is crucial to propose strategies for improving the seed health, which remains a major issue for seed companies. Recent studies conducted with the *Arabidopsis thaliana*/*A. brassicicola* pathosystem showed that the level of susceptibility of the fungus to water stress strongly influenced its seed transmission ability. For instance, two osmosensitive fungal mutants, defective for the class III Histidine kinase (HK) AbNik1 (Pochon et al., 2013) and the MAP kinase AbHog1 (unpublished result), respectively, were highly jeopardized in their ability to colonize

<footnote n="1">¹ Institut de Recherche en Horticulture et Semences - UMR 1345, INRA, Université d'Angers, Agrocampus-Ouest, SFR 4207 QUASAV, Angers, France, ² Université de Lille, INRA, ISA, Université d'Artois, Université du Littoral Côte d'Opale, EA 7394 - ICV - Institut Charles Viollette, Lille, France, ³ Departamento de Agronomía, ETSIAM, Universidad de Córdoba, Córdoba, Spain, ⁴ Department of Plant Sciences, University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania, ⁵ Equipe "Sucres & Echanges Végétaux-Environnement," UMR CNRS 7267 EBI Ecologie et Biologie des Interactions, Université de Poitiers, Poitiers, France</footnote>
seeds. Consistently, Iacomi-Vasilescu et al. (2008) had previously reported that *A. brassicicola* spontaneous phenylpyrrole resistant mutants with non-functional class III HK were found to be strongly impaired in their ability to infect radish seeds in field conditions, indicating that a functional high osmolarity pathway is required for efficient infection of seeds. Pochon et al. (2013) also showed that two dehydrin-like proteins were required for effective seed colonization by *A. brassicicola*. During colonization of maturing seeds, the fungi are exposed to severe water and osmotic constraints, and their ability to cope with such stresses is probably a key factor that determines their transmission to seeds.

The strategies to withstand low water availability have been mainly investigated in some organisms called anhydrobiotes belonging to kingdoms of bacteria, fungi and plants, which have the ability to resist desiccation, i.e., to survive to nearly total dehydration (Calahan et al., 2011; Dupont et al., 2014). Various mechanical, structural and oxidative constraints are induced during dehydration, which may lead to damage to membranes, proteins and DNA. Studies in the yeast *Saccharomyces cerevisiae* have shown that the protective system is based on a set of constitutive and inducible mechanisms. For instance, the synthesis of polyols occurs to maintain the osmotic balance. In yeast, the major polyol concerned with osmotic adjustment is glycerol and its synthesis, and hence its intracellular accumulation, is under control of the high osmolarity glycerol (HOG) pathway mediated by phosphorylation of the HOG1 MAP kinase. Some other molecules, such as heat shock proteins, hydrophilins or the non-reducing disaccharides trehalose and sucrose have been identified to be synthesized during desiccation and may be involved in preserving native protein structures or stabilizing membranes (Gadd et al., 1987; Garay-Arroyo et al., 2000; Sales et al., 2000; Tapia and Koshland, 2014).

Hydrophilins are a group of proteins that are present in organisms such as plants, fungi and bacteria, and that are biochemically defined as glycine-rich and highly hydrophilic disordered proteins. They are assumed to be involved in cellular dehydration tolerance, even if their specific role is still unclear. In *Sa. cerevisiae*, 12 genes, whose expression were induced in response to water deficit, encode proteins with specific hydrophilin characteristics (Garay-Arroyo et al., 2000). The deletion (or disruption) and overexpression of these genes showed that only some of them contributed to the desiccation tolerance of yeast (Dang and Hinha, 2011; López-Martínez et al., 2012; Rodríguez-Porrata et al., 2012). A first beneficial effect of hydrophilins on yeast viability during water deficit may be due to their antioxidant capacity which minimizes the accumulation of cellular ROS (López-Martínez et al., 2012). Another role of hydrophilins would be the protection of enzymes and the stabilization of membranes during drying (Sales et al., 2000; Goyal et al., 2005; Reyes et al., 2005). In virtue of their structural plasticity, which is due in part to the presence of disordered regions, hydrophilins may act as molecular shields or directly interact with various molecular partners (Tunnacliffe and Wise, 2007).

Except for the yeast hydrophilins, very little information is available on the presence and role of this class of proteins in filamentous fungi. To date, most studies have focused

on a particular class of fungal hydrophilins called dehydrins. Dehydrin-encoding genes were first identified in *Tuber borchii* during searches for genes controlling fruiting body maturation or conidial dormancy (Abba' et al., 2006). Then, Wong Sak Hoi et al. (2011, 2012) identified three dehydrin-encoding genes in *Aspergillus fumigatus*. Two of them were found to be involved in the protection against oxidative, osmotic and pH stress and the third dehydrin regulated freezing tolerance. A farnesol-induced dehydrin-like protein, that contributed to the high tolerance of resting conidia against oxidative and heat stress, was also identified in *Aspergillus nidulans* (Wartenberg et al., 2012). In *A. brassicicola*, three proteins harboring the typical asparagine-proline-arginine (DPR) signature pattern and sharing the characteristic features of fungal dehydrin-like proteins have been identified (Pochon et al., 2013). The expression of these genes was found to be regulated by the AbHog1 mitogen-activated protein kinase (MAPK) pathway. Phenotyping of single deletion mutants showed that dehydrin-like proteins have an impact mainly on oxidative stress tolerance and on conidial survival upon exposure to high and freezing temperatures. Moreover, a double deletion mutant was strongly affected with respect to conidiation and showed a highly compromised pathogenicity with a lower aggressiveness on *Brassica oleracea* leaves and a reduced capacity to be transmitted to *Arabidopsis* seeds via siliques. These results support the importance of dehydrin proteins and, more globally, of hydrophilins, with respect to the ability of *A. brassicicola* to efficiently accomplish key steps of its pathogen life cycle.

To identify new fungal molecular determinants that may be involved in the silique and seed infection process, the present study aimed at analyzing the transcriptomic and metabolomic responses of *A. brassicicola* exposed to water stress. Then, the repertoire of putative hydrophilins in this fungus was established based on expression data and structural and biochemical criteria. Finally, functional approaches were initiated to investigate the role of some of these putative hydrophilins.

RESULTS

Application of Water Stresses

Several preliminary tests were conducted to evaluate the level of stress for each treatment applied to fungal cells. Considering that the reference strain is naturally tolerant to water stress (Iacomi-Vasilescu et al., 2008), we additionally analyzed the effect of treatments on three mutant strains ($\Delta abnik1$, $\Delta abhog1$ and $\Delta absch9$) derived from the wild-type Abra43 strain, for which we hypothesized a higher sensitivity to water stress. As expected, the Abra43 reference strain exhibited high sorbitol tolerance with only 5% inhibition at a concentration of 1.2 M. At this concentration, a higher susceptibility was observed for the mutant strains (Table 1), confirming that the application of such a concentration indeed constitutes a stress for the fungal cells. In the same way, when a drastic decrease in relative humidity (RH) was applied to the germlings, no significant difference in mortality between the treated and the silica gel-free conditions was observed for the reference strain. On the contrary, the results
<table><caption>**TABLE 1** | Susceptibility of *Alternaria brassicicola* wild-type, ΔAbnik1, ΔAbhog1 and ΔAsch9 strains to sorbitol and desiccation stresses.</caption><thead><tr><th>Strains</th><th>% growth inhibition<br>Sorbitol (1.2 M)</th><th>Mortality rate after<br>4 days of desiccation</th></tr></thead><tbody><tr><td>WT</td><td>5 ± 2</td><td>5 ± 2</td></tr><tr><td>ΔAbnik1</td><td>29 ± 4</td><td>98 ± 2</td></tr><tr><td>ΔAbsch9</td><td>48 ± 5</td><td>35 ± 5</td></tr><tr><td>ΔAbhog1</td><td>39 ± 4</td><td>98 ± 1</td></tr></tbody></table>

Each condition was tested in triplicate and the experiments were repeated twice. Values are means of biological repetitions and represent the percentage growth inhibition under stress conditions compared with standard growth conditions.

showed a very high susceptibility of Δabnik1 and Δabhog1 to the desiccation stress, while the AbSch9 mutant exhibited an intermediate susceptibility (Table 1).

Based on these results, we chose to analyze the responses of the fungus when exposed to 1.2 M sorbitol for 0.5 and 2 h and when exposed to desiccation stress for 1 and 4 h. Regarding the latter stress, the chosen times implied that germlings exposed to silica gel beads were subjected to a RH value of 1% while control germlings were harvested at RH values of 70% and 90%, respectively.

In addition, we analyzed other phenotypic characteristics of these mutants: the susceptibility to oxidative stress (exposure to 10 mM H₂O₂ and 20 mM menadione), the ability to transmit to the *A. thaliana* seeds and the aggressiveness on cabbage leaves. Δabnik1 and Δabhog1 mutants were more susceptible to oxidative stress, and, consistently, had reduced ability to colonize *A. thaliana* seeds compared to the wild-type strain (Table 2). The Δabsch9 mutant was not susceptible to oxidative stress and its seed transmission capacity was not altered. Compared to seed transmission capacity, the ability of leaf infection was not or less dependent on the sensitivity of fungal genotypes to oxidative stress, since Δabnik1 and Δabsch9 strains did not have reduced aggressiveness on cabbage leaves.

## Modulation of Fungal Transcriptomes in Response to Water Stress

In order to identify potential effectors of the response of *A. brassicicola* to dehydration stress, we first focused on the analysis of the transcriptional response in germinating conidia exposed to previously defined water stress (i.e., exposure to sorbitol 1.2 M for 0.5 and 2 h and exposure to silica gel beads for 1 h and 4 h). Each treated sample was compared to control sample and, thus, we produced four transcriptome data sets (each set was obtained from three biological replicates). We used *A. brassicicola* Nimblegen microarrays bearing one probe for each of the 10 633 ORFs predicted in the *A. brassicicola* automatically annotated genome database (JGI Genome Portal<fnref n="1" />). Only probes with a P-values ≤0.05 and a log2 ratio ≥0.7 or ≤−0.7 were considered as differentially expressed. The numbers of genes that were induced or repressed under each condition, together with a summary of those regulated in more than one condition, are shown in Figure 1. To validate our microarray results, the regulation of

several genes by sorbitol or silica gel exposure was confirmed by quantitative PCR (Supplementary Figure S1).

Our data showed that, for all the treatments, with the exception of the 4 h-silica gel (4 h-desiccation) treatment, the number of up-regulated and down-regulated genes was less than 500 and 300, respectively. Most of these genes were found to be regulated in more than one condition, 37 were found to be induced in all 4 stress conditions while none were found to be repressed. The 4h-desiccation stress led to more profound transcriptomic modifications since 578 and 917 were induced or repressed compared to control condition, respectively. Moreover, this transcriptomic response seemed rather specific since more than 800 genes (268 up-regulated and 601 down-regulated genes) were specifically regulated after the fungus was exposed to this constraint. It should be noted that 157 genes, that were induced by the sorbitol exposure, were repressed following the silica gel treatment.

The GO enrichment analyses showed that the enriched GO terms were not generally shared within the gene sets representative of each type of treatment (sorbitol or desiccation) (Figure 2). One exception was the GO categories corresponding to the response to oxidative and osmotic stresses, which were enriched in all the lists of induced genes. Another configuration was that enriched categories emerging from the 4h-desiccation up-regulated genes were also enriched categories emerging from the down-regulated gene lists after sorbitol exposure. These results seem to point out that the two types of treatment did not cause the same effects at the cellular level and induced quite different transcriptomic responses.

Enriched categories emerging from genes regulated following silica gel exposure were mainly related to the translation process, mitochondrial functions, and carboxylic acid and amino acids metabolic processes. Within the up-regulated genes, the main enriched GO terms identified in the sorbitol data sets were predominantly associated with a response to oxidative stress and to sugar metabolic process. Within the sorbitol repressed gene list, enriched GO terms referred to a wider range of cellular processes such as gene expression (Figure 2).

## Metabolomic Responses to Water Stress

As several enriched GO categories emerging from transcriptomic data were linked to amino acid metabolism and catabolism, we investigated whether the response to water stress in *A. brassicicola* resulted in an alteration of the cellular content in amino acids. Regardless of the hydric stress applied, the total amino acid concentrations were significantly lower for the treatments conditions compared to the control conditions (Welch test P-value = 5.955E⁻⁰⁸, Supplementary Figure S2). Indeed, considering the 24 amino acids and derivatives that were quantified in this study, the mean ratio of amino acid concentration in the treated conditions compared to the respective controls decreased significantly for 17 and 12 amino acids (9 in common), regardless of time exposure for the sorbitol and the desiccation treatment, respectively (Figure 3A). Amino acid profiles that were differentially less accumulated compared to controls were quite similar after 0.5 and 2 h of sorbitol exposure with 9 shared amino acids (over 13). Glutamine,

<footnote n="1">¹https://genome.jgi.doe.gov/Altbr1/Altbr1.home.html</footnote>
<table><caption>**TABLE 2** | Other phenotypic characteristics of kinase mutants considered in this study.</caption><thead><tr><th rowspan="2">Fungal strains (origin)</th><th colspan="2">Susceptibility to oxidants</th><th rowspan="2">Seed transmission efficiency</th><th rowspan="2">Symptom development on leaves</th></tr><tr><th>H<sub>2</sub>O<sub>2</sub> (2.5 mM)</th><th>Menadione (5 mM)</th></tr></thead><tbody><tr><td>WT</td><td>22.2 ± 2</td><td>34.2 ± 3</td><td></td><td></td></tr><tr><td>ΔabNik1 (Dongo et al., 2009)</td><td>54.2 ± 5<sup>a</sup></td><td>64.5 ± 6<sup>a</sup></td><td>– (Pochon et al., 2012)</td><td>0 (Pochon et al., 2012)</td></tr><tr><td>ΔabHog1 (Joubert et al., 2011)</td><td>79.5 ± 6<sup>a</sup></td><td>84.5 ± 2<sup>a</sup></td><td>– (This study)</td><td>– (Joubert et al., 2011)</td></tr><tr><td>ΔabSch9 (this study)</td><td>11 ± 3<sup>a</sup></td><td>21 ± 1<sup>a</sup></td><td>0 (This study)</td><td>0 (This study)</td></tr></tbody></table>

Values were obtained in this study and are means of three biological repetitions and represent the percentage growth inhibition under stress conditions compared with standard growth conditions. <sup>a</sup>indicates a significant difference between the mutant and the Abra43 WT parental isolate (Student's t-test, *P* < 0.01). The abilities of fungal strains to transmit to Arabidopsis thaliana seeds and to develop necrosis on leaves were evaluated in this study or in indicated references, depending on the mutant; 0: unchanged comparing to the wild-type; –: decrease of the ability compared to the wild-type.

<figure><img src="image_71.png" /><figcaption>**FIGURE 1** | Global modulation of gene expression in *Alternaria brassicicola* in response to applied treatments. Sorbitol and desiccation treatments were compared to non-treated cultures at two time points 0.5 h, 2 h and 1 h, 4 h, respectively. Genes with a *P*-values ≤0.05 and a log2 ratio ≥0.7 or ≤-0.7 were considered as differentially expressed. Intersection with less than 10 genes were omitted. Colors displayed regulation compared to the control; red = up-regulated in all conditions, green = down-regulated in all conditions, orange = opposite regulation according to the considered condition.</figcaption></figure>

glutamate, alpha alanine, lysine and arginine concentration explained 70% [187.8/270.0 µmol.g⁻¹ DW (dry weight)] and 78% (156.3/199.8 µmol.g⁻¹ DW) of the amino acid variations for the sorbitol treatment at 0.5 and 2 h, respectively. For the 4 h desiccation condition, glutamine, GABA, alpha alanine and lysine explained 70% (134.7/194.6 µmol.g⁻¹ DW) of the amino acid variations. No significant concentration differences were observed for the 1 h desiccation treatment.

Then, we focused on sugar content, since their intracellular accumulation is a well-known protective mechanism to cope with the water constraints (Dupont et al., 2014). Eight saccharides and sugar alcohols were quantified in treated and control conditions (Figure 3B). The total concentration of those compounds was found to be significantly different only in the case of comparison

between sorbitol-treated and untreated conditions (Welch test *P*-value = 2.05E⁻⁵, **Supplementary Figure S2**) regardless of the time exposure. Unexpectedly, we observed a strong accumulation of some sugars (arabitol, trehalose, mannitol) in the control of the desiccation condition. This result could explain that no significant difference on the sugar cellular contents was observed between treated and untreated samples, except for an arabitol accumulation after a 4 h-treatment.

As expected, glycerol was accumulated in fungal cells exposed to sorbitol. Two other polyols, arabitol and dulcitol, were also strongly accumulated under these conditions. Conversely, the amounts of other polyols and the disaccharide trehalose were lower in cells treated with sorbitol than in untreated conditions.
<figure><img src="image_72.png" /><figcaption>**FIGURE 2** | Relevant Gene Ontology Biological Process (GO-BP) enrichments obtained from modulated gene sets after exposure to water stresses.</figcaption></figure>

<figure><img src="image_73.png" /><figcaption>**FIGURE 3** | Impact of sorbitol and desiccation treatments on amino acid (A), saccharides and sugar alcohols (B) contents of fungal cultures. The heat map background displays the mean control contents. Internal colored boxes display the mean difference between treatments and controls. Quantifications were performed from three separate samples. Asterisks indicate a significant difference between the mutant and the parental isolate (Welch test, \*p ≤ 0.05).</figcaption></figure>
## Hydrophilins Identification Through *in silico* Analysis

Hydrophilins could be defined as glycine-rich, highly hydrophilic disordered proteins. This criterion was used to search in *A. brassicicola* for putative hydrophilins among the whole protein set deduced from the recently published genome of *A. brassicicola* strain Abra43 (12,456 entries, Belmas et al., 2018). A total of 732 predicted proteins with an average hydrophilicity above one (GRAVY index lower than -1) were first selected and analyzed for their glycine content. The 210 predicted proteins with at least 8% glycine were retained and subjected to secondary structure prediction analysis. Almost three quarters of them (165) were predicted to adopt a coil configuration over more than 50% of their length. After removing mis-annotated and incomplete proteins and those that had an obvious match upon Blast analysis with proteins of known functions (except "stress response"), a final set of 107 candidate hydrophilins (c.a. 0.9% of the whole predicted proteome) was selected (Table 3). Interestingly, among proteins that had physicochemical features of hydrophilins but were discarded after Blast analysis, several displayed similarities with proteins involved in nucleic acids metabolic processes (Supplementary Table S1). Each of the 107 candidate hydrophilins was characterized by an index of significance, calculated based on the following formula: [-(gravy index - 1) X (% G - 8) X (% rc - 50)] X 100, whose value varied from 3 to 36.512. Six putative homologs of fungal hydrophilins were found within the whole set of *A. brassicicola* hydrophilin-like proteins: HSP12 (AB01868/Abra01064 and AB10131/Abra12207), STF2 (AB03803/Abra04061) from *S. cerevisiae*, CON10 (AB02056/Abra00844 and AB01782/Abra01163) and CON6 (Abra02927) from *N. neurospora crassa*, and the dehydrin-like proteins from *A. brassicicola* AbDhn1 (AB02513/Abra00274) and AbDhn3 (AB05365, AB05364/Abra08036). None of the other yeast hydrophilins had putative homolog in the *A. brassicicola* genome except NOP6 (AB08803/Abra04570) and WWM1 (AB05865/Abra11690) that were not selected due to a glycine content below 8% or a gravy index above -1, respectively. Similarly, due to an average hydrophilicity below the threshold value, the other *A. brassicicola* dehydrin-like protein AbDhn2 (AB08993/Abra04787) was not selected within the candidate hydrophilins set. This protein set also contains the putative homolog DNA-damage responsive DDR48 (AB03330/Abra05531) protein from yeast.

## Expression of Hydrophilins During Osmotic and Hydric Stress

Then, we explored the expression pattern of genes encoding putative hydrophilins from transcriptome data sets generated after exposing germinating conidia of *A. brassicicola* to 1.2 M sorbitol and to low relative humidity (silica gel beads). These data sets were obtained using microarrays designed based on the genome of the *A. brassicicola* ATCC 96836 strain available on the JGI Genome Portal (see footnote 1). This genomic sequence contained 10,514 predicted genes and the proportion of gaps between scaffolding boards in this sequence was relatively high (estimated to 5.2% by Dang et al., 2015). As described

above, the *in silico* analysis was performed from the recently published genome of *A. brassicicola* strain Abra43 (Belmas et al., 2018²,<fnref n="3" />), which is a higher quality sequence containing 12,456 predicted protein-coding genes. Considering these proteome differences, expression patterns were available only for 78 putative hydrophilins that matched with a predicted ORF from the ATCC 96836 strain proteome. Under these conditions, 17 of the 78 (21.8%) candidate hydrophilin genes were up-regulated by the sorbitol treatment (Figure 4A). For all of them, except AB03330 and AB00441, an induced expression was observed at 0.5 h (Figure 4B). Moreover, 13 candidate hydrophilin genes were up-regulated by the desiccation treatment, 10 of them being also up-regulated upon the sorbitol treatment. All of these proteins were named with the abbreviation "SIH" for "stress-induced hydrophilin-like" (Table 3).

To gain insight on the possible regulation of hydrophilin genes expression by different kinases known to regulate the osmostress response, the expression profiles of hydrophilin genes in the Δ*abhog1*, Δ*abnik1* and Δ*absch9* mutants were compared with those in the wild-type parental strain under both control condition and after exposure to sorbitol for 0.5 h (Figure 4). This analysis revealed that, among the sorbitol-inducible hydrophilin genes, 6 were dependent of at least two of the three kinases for their expression. Two were regulated by AbHog1 and AbNik1, two others by AbHog1- and AbSch9 and three others by AbHog1, AbNik1 and AbSch9. The gene encoding AbDhn2 was also induced by the treatment and was found Hog1-Nlk1-Sch9-dependent.

## Role of Selected Hydrophilins in *A. brassicicola*

Two putative hydrophilins (AbSih3/AB01868 and AbSih15/AB05148) were selected for further studies aiming to investigate their function in *A. brassicicola*. Knockout mutants deficient for AbSih3 and AbSih15 (called Δ*absih3* and Δ*absih15*), respectively, were constructed by replacing the respective ORFs with a hygromycin B resistance cassette (Supplementary Figure S3). For each targeted gene, two replacement mutants were selected using a PCR screen and further purified by two rounds of single-spore isolation. In all further experiments, the phenotypic characters for transformants of the same genotype were not found to be significantly different and the phenotyping values that we displayed were calculated by taking into account both transformants.

Although the expression of these genes was induced in germinating conidia exposed to previously defined water stress (i.e., exposure to 1.2 M sorbitol and to silica gel beads), the knock-out approach showed that only the *AbSih3* deletion had an impact on phenotype, mainly on oxidative stress tolerance. The corresponding deficient single mutants were indeed characterized by a slightly increased susceptibility toward oxidative stress generated by exposure to menadione and H₂O₂ compared to the wild-type (Figure 5). No significant difference in susceptibility to other tested stresses (exposure to PEG -0.7 MPa,

<footnote n="2">²https://www.ncbi.nlm.nih.gov/nuccore/PHFN00000000</footnote>

<footnote n="3">³https://bbric-pipelines.toulouse.inra.fr/myGenomeBrowser?public=1</footnote>
<table><caption>TABLE 3 | Set of candidate hydrophilins identified from the *Alternaria brassicicola* genome sequence through *in silico* analysis.</caption><thead><tr><th>Prot ID</th><th>Ab43 genome</th><th>ref genome</th><th>Gravy</th><th>Length</th><th>%G</th><th>%CC</th><th>Score</th><th>Blast</th><th>Stress induction</th><th>Proposed name</th></tr></thead><tbody><tr><td></td><td>Abra00077</td><td>AB02678</td><td>-1.11</td><td>100</td><td>11</td><td>51</td><td>34</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra00088</td><td>AB02665</td><td>-1.00</td><td>334</td><td>9.3</td><td>58</td><td>5</td><td></td><td>+</td><td>AbSih1</td></tr><tr><td></td><td>Abra00257</td><td>AB04427</td><td>-1.16</td><td>349</td><td>9.7</td><td>59</td><td>252</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra00274</td><td>AB02513</td><td>-1.07</td><td>408</td><td>9.1</td><td>72</td><td>172</td><td>DHN1</td><td>+</td><td>AbDhn1</td></tr><tr><td></td><td>Abra00844</td><td>AB02056</td><td>-1.07</td><td>114</td><td>15.8</td><td>69</td><td>1092</td><td>con10</td><td>+</td><td>AbSih2</td></tr><tr><td></td><td>Abra01053</td><td>AB01878</td><td>-1.02</td><td>77</td><td>15.6</td><td>62</td><td>213</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra01064</td><td>AB01868</td><td>-1.08</td><td>100</td><td>13</td><td>54</td><td>164</td><td>HSP12</td><td>+</td><td>AbSih3</td></tr><tr><td></td><td>Abra01163</td><td>AB01782</td><td>-1.17</td><td>75</td><td>17.3</td><td>54</td><td>625</td><td>con10</td><td></td><td></td></tr><tr><td></td><td>Abra01374</td><td>AB01197</td><td>-1.06</td><td>124</td><td>12.9</td><td>79</td><td>791</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra01387</td><td>AB01185</td><td>-1.14</td><td>255</td><td>27.1</td><td>76</td><td>7186</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra01450</td><td>AB01144</td><td>-1.16</td><td>551</td><td>8.5</td><td>62</td><td>98</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra01572</td><td>AB01031</td><td>-1.10</td><td>327</td><td>10.1</td><td>60</td><td>207</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra01616</td><td>AB00988</td><td>-1.10</td><td>236</td><td>12.2</td><td>64</td><td>598</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra01703</td><td>AB00914</td><td>-1.28</td><td>849</td><td>12</td><td>72</td><td>2426</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra01742</td><td>AB00884</td><td>-1.07</td><td>443</td><td>9.7</td><td>54</td><td>47</td><td></td><td>+</td><td>AbSih4</td></tr><tr><td></td><td>Abra01976</td><td>AB00692</td><td>-1.24</td><td>486</td><td>9.5</td><td>63</td><td>474</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra02025</td><td>AB00652</td><td>-1.38</td><td>196</td><td>10.1</td><td>61</td><td>884</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra02123</td><td>AB00572</td><td>-1.04</td><td>142</td><td>9.9</td><td>59</td><td>65</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra02179</td><td>AB00532</td><td>-1.78</td><td>62</td><td>16.1</td><td>75</td><td>15873</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra02286</td><td>AB00441</td><td>-1.20</td><td>84</td><td>10.7</td><td>71</td><td>1114</td><td></td><td>+</td><td>AbSih5</td></tr><tr><td></td><td>Abra02383</td><td>AB00362</td><td>-1.04</td><td>1020</td><td>8.9</td><td>67</td><td>59</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra02582</td><td>AB00195</td><td>-1.16</td><td>120</td><td>10</td><td>66</td><td>523</td><td></td><td>+</td><td>AbSih6</td></tr><tr><td></td><td>Abra02745</td><td>AB10671</td><td>-1.63</td><td>323</td><td>14.6</td><td>65</td><td>6262</td><td></td><td>+</td><td>AbSih7</td></tr><tr><td></td><td>Abra02936</td><td>AB09442</td><td>-1.01</td><td>139</td><td>12.2</td><td>63</td><td>35</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra03033</td><td>AB09525</td><td>-1.02</td><td>270</td><td>11.1</td><td>61</td><td>56</td><td></td><td>+</td><td>AbSih8</td></tr><tr><td></td><td>Abra03243</td><td>AB04503</td><td>-1.27</td><td>122</td><td>13.9</td><td>53</td><td>474</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra03342</td><td>AB04427</td><td>-1.12</td><td>309</td><td>9.7</td><td>67</td><td>343</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra03378</td><td>AB04393</td><td>-1.44</td><td>200</td><td>19</td><td>77</td><td>13068</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra03462</td><td>AB04328</td><td>-1.36</td><td>340</td><td>18.5</td><td>60</td><td>3805</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra03538</td><td>AB04263</td><td>-1.01</td><td>358</td><td>9.8</td><td>64</td><td>28</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra03729</td><td>AB04091</td><td>-1.04</td><td>112</td><td>14.3</td><td>60</td><td>225</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra03785</td><td>AB04037</td><td>-1.21</td><td>417</td><td>10.8</td><td>63</td><td>781</td><td>PAL1</td><td></td><td></td></tr><tr><td></td><td>Abra03984</td><td>AB03874</td><td>-1.33</td><td>494</td><td>10.1</td><td>71</td><td>1465</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra04061</td><td>AB03803</td><td>-1.13</td><td>155</td><td>9</td><td>62</td><td>153</td><td>STF2</td><td></td><td></td></tr><tr><td></td><td>Abra04301</td><td>AB10372</td><td>-1.49</td><td>396</td><td>8.1</td><td>53</td><td>15</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra04434</td><td>AB08681</td><td>-1.04</td><td>578</td><td>19.7</td><td>63</td><td>676</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra04604</td><td>AB08829</td><td>-1.15</td><td>344</td><td>9</td><td>58</td><td>118</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra04882</td><td>AB09668</td><td>-1.20</td><td>146</td><td>8.2</td><td>52</td><td>8</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra05347</td><td>AB01286</td><td>-1.02</td><td>664</td><td>9.8</td><td>66</td><td>59</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra05531</td><td>AB03330</td><td>-1.96</td><td>314</td><td>13.7</td><td>90</td><td>21943</td><td>DDR48</td><td>+</td><td>AbSih9</td></tr><tr><td></td><td>Abra05532</td><td>AB03329</td><td>-1.24</td><td>397</td><td>27.2</td><td>69</td><td>8592</td><td></td><td>+</td><td>AbSih10</td></tr><tr><td></td><td>Abra05581</td><td>AB03281</td><td>-1.24</td><td>210</td><td>15.7</td><td>79</td><td>5253</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra05783</td><td>AB03114</td><td>-1.49</td><td>89</td><td>10.1</td><td>60</td><td>1026</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra05849</td><td>AB03052</td><td>-1.11</td><td>310</td><td>13.2</td><td>71</td><td>1180</td><td></td><td>+</td><td>AbSih11</td></tr><tr><td></td><td>Abra06033</td><td>AB02899</td><td>-1.06</td><td>267</td><td>9</td><td>56</td><td>34</td><td></td><td>+</td><td>AbSih12</td></tr><tr><td></td><td>Abra06151</td><td>AB02794</td><td>-1.14</td><td>138</td><td>16.7</td><td>68</td><td>2145</td><td></td><td>+</td><td>AbSih13</td></tr><tr><td></td><td>Abra06575</td><td>AB07491</td><td>-1.22</td><td>587</td><td>10.7</td><td>56</td><td>357</td><td></td><td>+</td><td>AbSih14</td></tr><tr><td></td><td>Abra06733</td><td>AB07371</td><td>-1.04</td><td>79</td><td>13.9</td><td>72</td><td>509</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra06831</td><td>AB10289</td><td>-1.08</td><td>87</td><td>24.1</td><td>87</td><td>4519</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra07098</td><td>AB04617</td><td>-1.15</td><td>49</td><td>8.2</td><td>69</td><td>56</td><td></td><td></td><td></td></tr><tr><td></td><td>Abra07398</td><td>AB04822</td><td>-1.04</td><td>112</td><td>8.9</td><td>62</td><td>39</td><td></td><td></td><td></td></tr></tbody></table>

(Continued)