Skip to main content

A new in vitro monitoring system reveals a specific influence of Arabidopsis nitrogen nutrition on its susceptibility to Alternaria brassicicola at the seedling stage

Abstract

Background

Seedling growth is an early phase of plant development highly susceptible to environmental factors such as soil nitrogen (N) availability or presence of seed-borne pathogens. Whereas N plays a central role in plant-pathogen interactions, its role has never been studied during this early phase for the interaction between Arabidopsis thaliana and Alternaria brassicicola, a seed-transmitted necrotrophic fungus. The aim of the present work was to develop an in vitro monitoring system allowing to study the impact of the fungus on A. thaliana seedling growth, while modulating N nutrition.

Results

The developed system consists of square plates placed vertically and filled with nutrient agar medium allowing modulation of N conditions. Seeds are inoculated after sowing by depositing a droplet of conidial suspension. A specific semi-automated image analysis pipeline based on the Ilastik software was developed to quantify the impact of the fungus on seedling aerial development, calculating an index accounting for every aspect of fungal impact, namely seedling death, necrosis and developmental delay. The system also permits to monitor root elongation. The interest of the system was then confirmed by characterising how N media composition [0.1 and 5 mM of nitrate (NO3), 5 mM of ammonium (NH4+)] affects the impact of the fungus on three A. thaliana ecotypes. Seedling development was strongly and negatively affected by the fungus. However, seedlings grown with 5 mM NO3 were less susceptible than those grown with NH4+ or 0.1 mM NO3, which differed from what was observed with adult plants (rosette stage).

Conclusions

The developed monitoring system allows accurate determination of seedling growth characteristics (both on aerial and root parts) and symptoms. Altogether, this system could be used to study the impact of plant nutrition on susceptibility of various genotypes to fungi at the seedling stage.

Background

Rapid and homogeneous seedling growth is crucial for successful crop establishment and yield. However, this early stage of plant development is highly susceptible to environmental conditions including variations of nitrogen (N) availability in the soil [1, 2], as well as development of diseases generally caused by seed- and soil-borne plant pathogens [3, 4].

Nitrogen plays a crucial role driving plant development, growth and yield. Nitrate (NO3), the main form and source of N absorbed by seedlings and young plants in temperate regions, also acts as a major signal molecule modulating plant metabolism, growth and plant defence responses [5]. Nitrogen uptake and metabolism are also central to plant defence mechanisms as they provide the material for defence compounds and allow N mobilisation away from pathogens [6]. For instance, the phytoalexin camalexin, which has been shown to have an effect in the Arabidopsis thalianaAlternaria brassicicola interaction [7], is derived from the tryptophan amino acid [8]. Amino acids, including methionine, phenylalanine and tryptophan, are also the basis of phytoanticipins such as glucosinolates in plants of the Brassicaceae family [9] which, once broken down into isothiocyanates and nitriles, may be toxic to fungi, bacteria and insects [10]. Another important molecule involved in plant defence mechanisms and derived from NO3, is nitric oxide (NO). NO is rapidly accumulated during biotic stress conditions and plays a role in phytoalexin accumulation [11] and salicylic acid biosynthesis [12], a major pathway for plant resistance to infection leading for instance to hypersensitive response [13]. Nitrogen is not only vital for the plants, but also for the fungal pathogens, the latter being entirely dependent on plant N sources, such as NO3, ammonium (NH4+) and free amino acids, for their growth [6]. This makes the host N metabolism a direct factor affecting fungal growth, by modulating the N sources available for the invader.

The role of N on pathogenicity is unclear. Indeed, a large supply of exogenous N improves plant nutrition and defences but can also promote disease development, by increasing the availability of N compounds assimilable by the pathogen [14, 15]. It has been shown that high N fertilisation could promote the disease [16, 17] or increase host resistance [18], which would suggest that the role of N in the plant-pathogen interaction is complex and pathosystem specific. In addition to the overall availability of N, its form also plays an important role in plant-pathogen interaction. Studies have shown that NH4+ nutrition could increase disease severity compared to NO3 [19, 20], while the contrary was observed in other pathosystems [21], notably A. thaliana—A. brassicicola [22].

Until now, inorganic N fertilisers and conventional agrochemicals, such as fungicides, have been widely used for many years to protect seeds and seedlings against various stresses in the seedbed and to increase seedling establishment. However, the agro-ecological transition requires a substantial reduction of N input and seed treatment with fungicides. These practices would however promote the presence of seed-borne pathogens such as necrotrophic fungi on both seeds and seedlings. Moreover, N fertilisers do not only affect seedling growth but can also have different effects on disease development, shifting the balance in favour of the host or of the pathogen as reported at other stages of plant development [6, 16]. In this context and surprisingly, the impact of N fertilisation on the interaction between plants and pathogens has been little studied at the seedling stage and requires more investigation.

In order to study the impact of N nutrition on plant-pathogen interaction during seedling development, one would need a protocol allowing symptom severity, fungus growth and seedling development (i.e. both aerial and root parts) to be monitored, in a nutrient controlled sterile environment. Studies on early plant stage interaction with pathogens commonly involve an inoculation on seven to eighteen days old seedlings growing in soil [17, 23, 24] or in a Petri dish [25], neither permitting the study of the interaction during germination, early seedling growth nor root development. In order to investigate the interaction as early as possible, inoculation has to take place directly on the seeds, by placing them in contact with dry spores or by immersing them in an inoculum suspension [26,27,28]. The best option to achieve a sterile and controlled environment allowing to monitor both aerial and root growth seems to be the use of agar media [29]. A protocol combining a seed inoculation with a modular sterile agar medium could therefore be a good solution to study the role of N on the plant-pathogen interaction during seedling development.

In the present work, we developed a monitoring system focusing on the interaction between the model plant for the Brassicacae family A. thaliana and the necrotrophic fungus A. brassicicola. Arabidopsis thaliana is a practical and already well studied model for host–pathogen interactions [30], and the interaction with A. brassicicola, in particular, has been abundantly used as a model for diseases caused by fungal necrotrophs [31]. This fungus is responsible for the black spot disease on leaves of Brassicaceae plants [32], leading to important worldwide economic losses [33]. Infection with A. brassicicola is however not limited to the leaves, but this fungus can affect all aerial parts of the plant, such as siliques and seeds in particular. Such a wide infection spectrum constitutes an important mode of conservation and transmission of the fungal inoculum and therefore makes of A. brassicicola a major seed-borne disease leading to seedling damping-off [34,35,36]. The monitoring system, allowing both homogenised seed contamination and following seedling aerial and root development by image analysis, was firstly developed on one A. thaliana ecotype grown in one N nutrition medium, 5 mM NO3. To validate its interest for screening the impact of the fungus on seedling growth, the protocol was then used to characterise the response to the fungus of three ecotypes with contrasting susceptibilities, on three N media varying in forms and concentration (5 mM NH4+, 0.1 mM NO3 and 5 mM NO3). Finally, the results obtained at seedling stage with this developed monitoring system were compared with behaviours at adult stage (i.e. rosette).

Results

The developed monitoring system allows studying how the fungus affects seedling growth

A new monitoring system, allowing an extensive study of the effects of the fungus on seedling growth, was developed (Fig. 1). Seeds of the A. thaliana Columbia (Col-0) ecotype, either treated with sterile water (H2O) or inoculated with the Abra43 fungus, were sown in vertical square plates on agar Murashige and Skoog [37] modified medium supplemented with 5 mM NO3 (see the Methods section; Fig. 1a). Using this system, seedlings can develop at least to the four-leaf stage, and the impact of the fungus on both seedling aerial and root system development can be monitored, as shown in the Fig. 1b and c with four-leaf stage seedlings treated with H2O and inoculated seedlings affected by the fungus, respectively.

Fig. 1
figure 1

Pictures of 14 days old Col-0 seedlings in vertical square plates filled with MS modified agar medium supplemented with 5 mM KNO3 (scale bars = 1 cm). a Overview of a square plate. b Seedlings treated with sterile water. c Seedlings inoculated with A. brassicicola

Choice of seed inoculation method

Two methods were tested for seed inoculation with A. brassicicola: (i) seed immersion in a conidial suspension (103, 104 or 105 conidia per mL) and (ii) deposition of a droplet of conidial suspension (103 or 104 conidia per mL) on each individual seed. Results obtained at 3 days after inoculation (DAI) showed that the droplet method of inoculation allowed 100% of the seeds to be contaminated using a suspension of 104 conidia per mL contrary to the immersion method which allowed a lower and heterogeneous contamination rate, even with 105 conidia per mL (Fig. 2). In addition, even though the contamination rate is close to 100% with the immersion method of inoculation at 105 conidia per mL, observations of 50 seeds by scanning electron microscopy revealed that conidia were not homogeneously distributed on the seed surface, with three categories of conidia distribution (Fig. 3), seeds exhibiting either no conidia (Fig. 3b), a few conidia (not illustrated) or a cluster of conidia (Fig. 3c and d) on their surface. It is also important to note that symptoms on seedlings were too severe with 105 conidia per mL preventing the observation of seedlings at 14 DAI (data not shown), which could be explained by the conidial clusters, as illustrated on Figs. 3c and d. On the contrary, none of the 210 observed seeds inoculated with the droplet method with 104 conidia per mL presented any cluster of conidia on their surface, but only a few scattered conidia (Fig. 3e and f). The droplet method of inoculation with a suspension of 104 conidia per mL was thus chosen as it allowed a more homogeneous seed contamination, as well as a compromise between disease severity and seedling development.

Fig. 2
figure 2

Seed contamination percentage observed 3 DAI for the immersion and droplet methods at different A. brassicicola conidial suspension concentrations (103, 104 and 105 conidia per mL). Values are means of 20 seeds per biological replicate, 8 biological replicates. Error bars indicate standard error of the mean (SEM)

Fig. 3
figure 3

Scanning electron microscopy observations showing the heterogeneity of A. thaliana seed contamination with the immersion method compared to the droplet method, white circles highlighting A. brassicicola conidia. The white scale bars show 50 μm. a Seed treated with sterile water. b Seed inoculated with the immersion method (105 conidia. mL−1) showing no A. brassicicola conidia on its surface. c Seed inoculated with the immersion method (105 conidia. mL−1) presenting a cluster of conidia on its surface, d zoom on the conidia cluster. e Seed inoculated with the droplet method (104 conidia. mL−1) presenting 2 conidia on its surface, f zoom on the conidia

Development of an image analysis method to monitor the impact of the fungus on seedling aerial development

In order to evaluate the impact of the N supply condition, of the fungus and of their interaction on seedling aerial part growth, a method based on a semi-automated image analysis pipeline was developed associating the Ilastik software [38] with a Python script. The first stage of the process consisted in training a classification random forest model with Ilastik. Twenty-five representative images were selected and annotated by associating pixels to a class. Three classes were defined corresponding to healthy tissues (green), necrotic tissues (brown) and all other objects present in the image that do not correspond to aerial parts of seedlings (Fig. 4a). Once the model trained, it was applied to the other images to identify the healthy (green) and necrotic (brown) pixels of aerial parts of the seedling (leaves, cotyledons and hypocotyl) per square plate (Fig. 4b). Classification results were then subjected to the Python script to evaluate the areas of healthy and necrotic tissues and generate colour-coded final images. The model was validated on a technical standpoint by comparing the spatial overlap between 30 representative seedlings annotated by the Ilastik model or by an expert observer using the ImageJ software, as reported in the Methods section.

Fig. 4
figure 4

Overview of the semi-automated pipeline used to detect and calculate the green and necrotic areas per square plate. The pipeline is divided in two steps: a a model training phase on a sample of images and b the analysis of every image and computation of the surfaces. In the training phase, blue pixels correspond to green areas, red pixels to necrotic areas and yellow pixels to the background

To express the results, a healthy area index (HAI) representing the impact of the fungus on seedling aerial growth was developed. It depicts the percentage of healthy area variation between seedlings treated with H2O (\({\text{HA}}_{{{\text{H}}_{2} {\text{O}}}}\)) and Abra43 (HAAbra43) and integrates both direct and indirect fungal effects (area deficit due to necrotic tissues and seedling growth deficit, respectively).

$${\text{HAI}}\,{ = }\,{{(({\text{HA}}_{{{\text{H}}_{2} {\text{O}}}} - {\text{HA}}_{{{\text{Abra43}}}} } \mathord{\left/ {\vphantom {{(({\text{HA}}_{{{\text{H}}_{2} {\text{O}}}} - {\text{HA}}_{{{\text{Abra43}}}} } {{\text{HA}}_{{{\text{H}}_{2} {\text{O}}}} )*100}}} \right. \kern-\nulldelimiterspace} {{\text{HA}}_{{{\text{H}}_{2} {\text{O}}}} )*100}}$$

The impact on the fungus was also measured using a visual rating scale (VRS) to quantify the symptom severity on the aerial parts of seedlings. The VRS integrated both colonisation by the mycelium and necrotic area, evaluated on seedlings, and was based on a score ranging from 0 (no symptom) to 5 (100% of surface area colonised and dead seedlings) (Table 1) assigned by an expert observer.

Table 1 Illustration and description of the visual rating scale (VRS) used to evaluate the symptoms observed on the aerial parts of the seedlings

In order to find out if the HAI and the VRS produced similar results regarding the fungal effect on seedlings, the correlation between the two methods was investigated (Fig. 5a). If the results of two methods shared a similar tendency, they did not correlate very well (R2 = 0.67, exponential function, y = 1.9709e0.0046), with a very uneven distribution. Another way to evaluate the fungal impact on seedlings, closer to what is measured with the VRS, would be the necrosis area index (NAI) obtained by dividing the necrosis area by the total area (necrosis + healthy area) of a squared plate. This index correlated better with the VRS (R2 = 0.76, power function, y = 3.4141x0.1764; Fig. 5b) and presented a very balanced distribution, indicating that it seemed possible to infer the results of VRS from the measures evaluated with the Ilastik model.

Fig. 5
figure 5

Correlation between the Ilastik-based indexes and the visual rating scale (VRS). Each point corresponds to the mean of three plates of 12 seedlings for a set of conditions. a VRS as a function of healthy area index, supplemented with the estimated correlation curve and R2 coefficient. b VRS as a function of necrosis area index, supplemented with the estimated correlation curve and R2 coefficient

It is to note that both HAI and NAI appeared to assess the fungal impact in a more satisfactory way than the VRS. Firstly, VRS being based on a rating given by an observer makes it more qualitative and less objective than methods based on pixel quantification by a software. Secondly, the comparison of the two most correlated methods showed that NAI distribution of values evenly covered the full range of possible values (from 0 to 1) while VRS was only restricted to the 1.5–4 range, the two extremes of the scale being totally unused and not allowing for an adequate representation and discrimination of the fungal impact high variability. Finally, HAI was deemed superior to NAI because it was a more complete representation of the fungal effect on seedlings. Indeed, NAI measured solely the necrosis areas while HAI considered not only the necrosis areas, which are deduced of the measured healthy areas, but also the healthy area deficit caused by the fungal presence, dead seedlings and non-developed organs mostly which are very important aspects of the fungal impact on seedlings. HAI was thus chosen to express the impact of the fungus on seedling growth.

The new monitoring system reveals different and specific seedling susceptibility to the fungus depending on nitrogen conditions at the seedling stage

The monitoring system was developed using the Col-0 ecotype on a 5 mM of NO3 agar medium with seeds treated with either sterilised water or conidia suspension. To confirm whether it enables discriminating susceptibility to the fungus between genotypes and N growth conditions, three ecotypes presenting different susceptibility to the pathogen at adult stage [39] were tested on different media varying in concentration and form of N. The selected ecotypes were Col-0 (resistant), Wassilewskija (Ws; susceptible) and Landsberg erecta (Ler; susceptible) which were grown on media in presence of 5 mM of NO3, 0.1 mM of NO3 or 5 mM of NH4+.

Seedling aerial growth is affected by the fungus depending on genotype and nitrogen conditions

The developed image analysis pipeline (Fig. 4) has allowed quantifying the aerial growth of seedlings from seeds treated with H2O by measuring their green area (\({\text{HA}}_{{{\text{H}}_{{2}} {\text{O}}}}\); Fig. 6a; Additional file 1: Table S1a). In absence of the fungus, seedling aerial green area was higher under 5 mM NO3 nutrition, with an average of 48.7 mm2 per square plate, compared to under the two others N media (18.8 and 15.7 mm2 in average for 5 mM NH4+ and 0.1 mM NO3, respectively) (Fig. 6a; Additional file 1: Table S1a). Significant differences were also found between genotypes, with Ler showing lower green leaf area than Col-0 and Ws under both 5 mM NH4+ and 0.1 mM NO3 (Additional file 1: Table S1a). Then, using the calculated HAI allowed revealing the effect of N media on susceptibility to the fungus (Fig. 6b; Additional file 1: Table S1b). Seedlings grown under 5 mM NH4+ or 0.1 mM NO3 were dramatically affected, with a fungal impact amounting for more than 80% of green area deficit for both N conditions and for all genotypes, compared to the H2O treated seedlings (Fig. 6b; Additional file 1: Table S1b). On the contrary, seedlings grown under 5 mM NO3 were less susceptible to the fungus (green area reduced by 21% on average). It is also important to note the significant interaction between genotype and N nutrition for HAI (P < 0.0001; Additional file 1: Table S1b), notably explained by the different response of genotypes under 5 mM NO3, with both Col-0 and Ws seemingly unaffected in their growth (− 4% and + 5% of fungal impact on green area, respectively), and by contrast, Ler that was highly impacted (67% reduction of green area). In addition to the different susceptibilities to the fungus revealed depending on N media and genotypes, infected seedlings grown under 5 mM NO3 appeared particularly healthy as they were at the same time more developed in control conditions and less affected by the fungus. NAI data corroborate the global conclusions obtained with the HAI (seedlings less susceptible under 5 mM NO3 nutrition). They are presented in Additional file 2: Table S2.

Fig. 6
figure 6

Aerial part development for Col-0, Ler or Ws seedlings grown on different nutritive media (5 mM NH4+, 0.1 mM NO3 or 5 mM NO3) at 14 DAI. (a) Green area per square plate in the absence of the fungus (seeds inoculated with sterile H2O). (b) Green area ratio between seedlings treated with H2O and seedlings inoculated with Abra43 (HAI). Twelve seedlings per square plate, 3 square plates per independent experiment, 3 independent experiments. Error bars indicate SEM. Letters denote significant differences at the 0.05 level between N conditions, inside each genotype

The presence of fungus also impacts primary root elongation depending on genotype and nitrogen conditions

Primary root length was measured at both 8 and 14 DAI by image analysis with the Image J software (Additional file 3: Table S3a and b; Fig. 7). At 8 DAI, root length highly differed between genotypes (G effect), N media (N effect) as well as inoculation modalities (I effect). All interactions between these factors were also significant, with the G × N effect the most significant (Additional file 3: Table S3a). Ler presented the shortest roots compared to Col-0 and Ws. Regarding N effect, roots were the shortest under 5 mM NH4+ (7 mm in average) and the longest under 5 mM NO3 (13.8 mm in average). Finally, when considering all the data, the presence of the fungus negatively affected root elongation (− 12.8% in average). This was mainly due to the NH4+ condition where the reduction was the strongest for all the genotypes (− 38.2% in average). For the NO3 conditions, various genotype behaviours were observed in accordance with the significant interactions revealed (Additional file 3: Table S3a), and this is more visible when results were expressed as an index of response of root length to the presence vs. the absence of the fungus (Fig. 7). Indeed, root length was highly affected under both nitrate conditions for Ler, but not affected for Ws. Regarding Col-0, root length decreased in the presence of the fungus under 5 mM but increased under 0.1 mM NO3. This result suggested that, in some nutritive conditions, the presence of the fungus could have no effect or even enhance root elongation early after inoculation (Fig. 7).

Fig. 7
figure 7

Primary root length (PRL) variation between H2O and Abra43 seedlings at 8 and 14 DAI, expressed as a percentage and calculated as follows: 100 * [(PRLAbra43−PRLH2O)/PRLH2O]. Red lines correspond to 5 mM NH4+, green lines to 0.1 mM NO3 and blue lines to 5 mM NO3. Solid lines correspond to Col-0, dashed lines to Ler and dotted lines to Ws. Error bars indicate SEM

After 14 days, the same main effects were found (G, N and I effects; Additional file 3: Table S3b). Ler was still the genotype presenting the shortest roots (16.2 mm compared to Col-0 and Ws, 22.1 and 24.4, respectively). In addition, roots were still globally the shortest under 5 mM NH4+ (10.5 mm) and the longest under 5 mM NO3 (32.4 mm), with the differences between media being amplified compared to 8 DAI (+ 50% growth for 5 mM NH4+, + 65% for 0.1 mM NO3 and + 135% for 5 mM NO3). Results also indicated that root elongation was highly affected by the fungus between 8 and 14 DAI (− 38.3% at 14 DAI on average for all genotypes and N conditions compared to − 12.8% after 8 DAI). This was more or less marked depending on the N medium (Fig. 7), the impact being stronger for seedlings grown on NH4+ (− 62%), than for those grown on NO3 (− 38% and − 26% for 5 mM and 0.1 mM, respectively). Considering the combined effects, only the G × N and N × I interactions were still significant, while the G × I and G × N × I interactions were not significant anymore, which could be explained by the amplified main effect flattening the subtler differences.

The new monitoring system reveals a stage specific effect of nitrogen condition on susceptibility to the fungus on seedlings, compared to adult plants

To go further, findings using the developed monitoring system at the seedling stage were compared to those observed on adult plants (rosette stage) grown on a hydroponic system with the three studied N conditions (5 mM NH4+, 0.1 mM NO3 or 5 mM NO3). Leaf necrosis area was determined at 7 DAI. Significant differences between genotypes and between N conditions were revealed (Fig. 8; Additional file 4: Table S4). The genotype Col-0 was on average significantly less susceptible than Ler and Ws genotypes (mean necrosis area, 0.76 mm2, 1.51 mm2 and 1.43 mm2, respectively) under almost all N conditions (except for the comparison with Ws under 5 mM NO3). This difference of susceptibility to the pathogen between Col-0 and Ler was also found at the seedling stage, whereas there was no difference at the seedling stage between Col-0 and Ws.

Fig. 8
figure 8

Fungal lesions for Col-0, Ler or Ws adult plants grown on different nutritive media (5 mM NH4+, 0.1 mM NO3 or 5 mM NO3) in a hydroponic system, at 7 DAI. Four leaves per plant, 6 plants per independent experiment, 3 independent experiments. Error bars indicate SEM. Letters denote significant differences at the 0.05 level between N conditions, inside each genotype

When comparing N conditions, plants grown on NH4+ were significantly less susceptible than those grown on NO3 (mean necrosis area, 0.71 mm2, 1.51 mm2, and 1.42 mm2 for 5 mM NH4+, 0.1 mM NO3 and 5 mM NO3, respectively). This difference was also found when considering the genotypes separately, except for Ws under 5 mM NO3 condition. This result contrasted very strongly with that obtained at the seedling stage, where seedlings grown under 5 mM NH4+ and 0.1 mM NO3 were much more susceptible than those grown under 5 mM NO3.

Discussion

A new monitoring system to study plant x pathogen interaction at the seedling stage

A new system allowing the monitoring of seedling growth under modulating N supply as well as the impact of the necrotrophic fungus A. brassicicola was developed. It permitted to follow the kinetics of both aerial and root part growth of the seedling, from germination until at least the four-leaf stage, enabling the survey of seedlings at different points in time. It also allowed for easy sampling, the seedlings being well separated and in a clean environment (no soil on the roots for example). This environment, a nutrient controlled agar medium, is another advantage of the system enabling the study of varying nutrition regimes, N forms and concentrations in this work’s case. The fully sterile system suited perfectly the study of biotic interactions at the early stages of plant development, such as the plant-fungal pathogen interaction between A. thaliana and A. brassicicola featured in the present work and could be adapted for the study of other pathosystems. Inoculation of seedlings is in most studies carried out several days after sowing [17, 23,24,25], preventing the study of the earlier stages of the interaction. Seed immersion in a conidial suspension is often used to solve this problem [27, 28] but, in our developed monitoring system, the droplet inoculation method implemented allowed for a more homogeneous contamination (Fig. 2). The impact of A. brassicicola on seedling growth was determined for both primary root elongation and seedling aerial development from image analyses. For the aerial development, a specific semi-automated pipeline was developed, based on image analyses with the Ilastik software [38] and a python script (Fig. 4). An index (HAI) was successfully applied to quantify the impact of the fungus on seedling development as it allowed accounting for every aspect of fungal impact, namely seedling death, necrosis and developmental delay. HAI was deemed the best in our study but the pipeline for image analysis is flexible enough to be used for different ways of assessing the fungal effect on plants, depending on the pathosystem or the aim of the research (e.g. necrosis alone with NAI index). The use of such a method to evaluate the fungal impact on seedlings appears more integrative than what is usually done. Indeed, studies directly inoculating the seeds commonly evaluated the infection percentage [25, 26] or the germination rate [27], and not the subsequent seedling growth, while studies inoculating already grown seedlings were restricted to latter stages and evaluated the fungal growth and sporulation [17], often with a visual qualitative scale [23, 24].

Influence of nitrogen nutrition on fungal infection at the seedling stage

Firstly, N affected aerial development in the absence of the fungus, with seedlings grown under 5 mM NO3 being larger than those grown under 0.1 mM NO3 and 5 mM NH4+ (Fig. 6a). This was expected, because it is well known that NO3 is a more advantageous N source than NH4+. Exclusive NH4+ nutrition can indeed result in toxicity symptoms in plants, which can lead to important yield losses [40], notably on A. thaliana [41]. However, the concentration used in our study was not stressful enough to cause leaf chlorosis, a common symptom of severe NH4+ toxicity [42]. Furthermore, given the importance of N in plant growth and development, reduced seedling growth under NO3 deprivation conditions (0.1 mM) were also expected. Some differences between genotypes were also highlighted for seedlings grown under 0.1 mM NO3 and 5 mM NH4+, with Ler seedlings smaller than those of Col-0 and Ws. Regarding primary root growth, the global impact of N nutrition was similar to that found on the aerial part, with the longest primary roots belonging to seedlings grown on 5 mM NO3 and the shortest to seedlings grown on 5 mM NH4+ (Fig. 7). This is consistent with the NH4+-induced repression of A. thaliana primary root growth reported in literature [43]. On the other hand, NO3 is known for stimulating primary root growth of A. thaliana seedlings [44], while lower NO3 concentrations led to a reduction of fresh root weight [45]. Differences between genotypes were found again with primary roots of Col-0 and Ws generally longer than those of Ler, especially in the two NO3 conditions (Additional file 3: Table S3; Fig. 7).

While the effect of N on plant-pathogen interactions has been extensively studied at the adult stage [46], studies at the seedling stage are very uncommon and most appear to be on months to years-old tree seedlings [47,48,49,50], which makes our results at this stage a novelty. The closest study was on the effect of N supply on barley seedlings inoculated with Erysiphe graminis f. sp. hordei [17], but the latter inoculation date (12 days after sowing) makes it very different from the present work. Generally, the role of N on pathogenicity can depend on both N concentration and N form [14,15,16]. In our study, the developed monitoring system highlighted that seedlings grown under 0.1 mM NO3 and 5 mM NH4+ conditions were more affected than those under 5 mM NO3 14 days after seed inoculation (Fig. 6b). Higher NO3 concentration was thus linked with less susceptibility, as it is the case of Botrytis cinerea on tomato [18], while NH4+ nutrition was linked with an increase of disease severity compared to NO3, something that was also observed in the Solanum lycopersicum L.—Fusarium oxysporum and Nicotiana tabacumPseudomonas syringae interactions [19, 20]. It is important to keep in mind that the effects of N concentration and form are pathosystem specific and can have an opposite effect on plant susceptibility [17, 21, 46]. High N supply commonly enhances susceptibility to biotrophs and hemibiotrophs while reducing that to necrotrophs [21, 51, 52] which is consistent with our results.

The influence of N nutrition on fungal infection was also evaluated at the rosette stage with the same ecotypes and nitrogen media as those studied at the seedling stage. We showed that plants grown on NH4+ were significantly less susceptible than those grown on NO3 (Fig. 8), which was consistent with a previous study we conducted with one ecotype and two N media [22]. The results obtained at the rosette stage thus contrasted strongly with those obtained at the seedling stage. The monitoring system developed to study specifically seedling growth thus allowed to demonstrate that N nutrition modulates the susceptibility of A. thaliana to A. brassicicola according to the developmental stage of the plant. Susceptibility to pathogens often vary with age, generally in the sense that younger plants are more susceptible, as demonstrated in numerous studies [53,54,55,56]. This phenomenon can be linked to leaf maturity [57] and leaf rank, [55]. For example, epidermal cuticle thickness as a physical barrier reduced the incidence of disease [58], and have been shown to increase with leaf age [59] and A. thaliana developmental stage [60]. It should also be noted that early seedling growth relies on both exogenous N supply and endogenous N resources from seed reserve mobilisation. This specificity may also contribute to explain the difference highlighted between the two developmental stages studied. Finally, in rosettes, the genotype Col-0 was on average significantly less susceptible than Ler and Ws, which is consistent with the literature [39]. This difference between Col-0 and Ler was also present at the seedling stage but, in contrast, there was no difference at the seedling stage between Col-0 and Ws, showing that genotype responses to A. brassicicola seem dependent on developmental stage.

Conclusions

A new system allowing the monitoring of A. thaliana seedling growth under modulating N supply, as well as the impact of the necrotrophic fungus A. brassicicola, was developed. It enables the survey of seedlings at different points in time, in varying nutrition regimes, N forms and concentrations. A specific semi-automated image analysis pipeline was developed to quantify the impact of the fungus on seedling development. It has allowed calculating an index (HAI) accounting for every aspect of fungal impact, namely seedling death, necrosis and developmental delay, as thus highlighting the influence of N nutrition on fungal infection at the seedling stage. Seedlings subjected to NH4+ and lower NO3 conditions were more susceptible to the fungus than those subjected to higher NO3 conditions. The assessment of fungal impact on adult plants revealed that they were significantly less susceptible when grown on NH4+, compared to NO3, which dramatically contrasted with findings at the seedling stage. Thanks to the monitoring system developed, it has therefore been possible to highlight that N nutrition modulates A. thaliana susceptibility to A. brassicicola according to the plant developmental stage. Because primary and secondary metabolism are of critical importance to the effect of N in the plant-pathogen interaction, elucidation of the metabolic bases associated with seedling susceptibility regulated by N supply, as well as the role of endogenous N nutrients from mobilisation of seed reserves, should provide insight into the specific findings observed at the seedling stage.

Methods

Biological material

Three ecotypes of A. thaliana (Col-0, Ler and Ws) were studied. Seeds of each genotype were collected from plants simultaneously grown for 13 weeks under long day conditions (16 h of light at 21 °C, 8 h of darkness at 19 °C) in a growth chamber (IRHS, Angers, France). The A. brassicicola wild-type strain Abra43 was initially isolated from R. sativus seeds [7], sequenced recently [61], and was routinely grown and maintained on potato dextrose agar at 24 °C.

Experimental monitoring system developed for Arabidopsis seedling growth

To follow seedling development and fungus-induced symptoms, A. thaliana seedlings were grown vertically on agar nutrient medium in square plates (12 × 12 × 1.3 cm) filled with 1.2% agar modified MS medium (0.5 mM CaCl2, 0.5 mM MgSO4, 1 mM KH2PO4, 50 µM iron-EDTA and 0.5 mL/L micro-elements buffered with 0.5 g/L MES (2-(N-morpholino)ethanesulfonic acid), pH 5.7) [37], either supplemented with 5 mM NO3 (provided as KNO3), 0.1 mM NO3 or 5 mM NH4+ (provided as NH4Cl). Twelve seeds per plate were sown on a plane surface obtained by cutting the medium 3 cm from the top to make easier seed sowing and inoculation but also to allow the aerial part of seedlings to develop normally. Before sowing, seeds were stratified for 3 days (in order to break their residual dormancy) and then sterilised by successive immersions, 5 min in ethanol 70°, 15 min in sodium hypochlorite (2.6% active chlorine) and three times 5 min in sterile water. Once sown, seeds were individually inoculated by deposit of a 1 µL droplet of conidia suspension (104 conidia per mL; Abra43 condition) or of sterile milli-Q water (H2O condition). The plates were then placed for 18 days in a growth chamber under long day conditions (16 h of light at 21 °C, 8 h of darkness at 19 °C). Three independent experiments were carried out, containing each 3 plates of 12 seedlings per set of conditions.

Seed observation by scanning electron microscopy

Seeds inoculated either by immersion (1 h in a 105 conidia per mL suspension or sterile milli-Q H2O) or by deposit of a 1 µL droplet (104 conidia per mL suspension or sterile milli-Q H2O) were dried and observed directly by scanning electron microscopy (Phenom™ G2 Pro) in collaboration with the IMAC platform (IMAgerie Cellulaire; SFR 4207 Quasav, Angers, France).

Image analysis

Pictures of square plates containing seedlings were taken using a Nikon D5000 digital camera, in the same conditions of lighting and distance. The classification of images parts for the seedling aerial part analyses was performed with the Ilastik software (version 1.3.3), as presented in the global pipeline (Fig. 4). The training dataset was composed of 19 pictures of square plates (3216 × 2136 px) and 6 pictures of selected seedlings (from 176 × 328 to 1230 × 332 px). The training dataset was chosen to best represent all the variations in our data: pictures of seedlings from the three genotypes grown in different N conditions and inoculated with the fungus or not, with either homogeneity or heterogeneity in the picture regarding levels of disease severity or seedling size. These images were annotated by associating pixels to a class. Three classes were defined corresponding to healthy tissues (green), necrotic tissues (brown) and all other objects present in the image that do not correspond to aerial parts of seedlings. These annotations were then used to train Ilastik random forest model classifier. The selected features were the colour/intensity, the edge and the texture and the spatial scales for the convolution 0.70, 1.00, 1.60, and 3.50 for every feature, plus 0.30 for the colour/intensity feature. Once the model trained, it was applied to the other images to identify the healthy (green) and necrotic (brown) pixels of aerial parts of the seedling (leaves, cotyledons and hypocotyl) per square plate. Classification results were then subjected to a Python 3.7 script to evaluate the areas of healthy and necrotic tissues and generate the colour-coded final images. This script was based mainly on the numpy [62], scikit-image [63] and matplotlib [64] libraries.

The pipeline was tested on 18 pictures of square plates (3216 × 2136 px) and 5 pictures of selected seedlings (from 176 × 328 to 1230 × 332 px) to assess the quality of the classification. The evaluation of the quality of the model was based on the comparison of the areas of 30 representative seedlings measured by our image analysis pipeline and a ground truth corresponding to annotations performed manually by an expert observer with the ImageJ software (version 1.53 k) [65]. The metric selected to measure the spatial overlap between those two sets of classified pixels was the classical Dice Sørensen coefficient (DSC) = 2*|A ∩ B| / |A| +|B|. The DSC then equals twice the number of elements common to both sets (|A ∩ B|) divided by the sum of the number of elements in each set (|A| +|B|). The DSC for our data was high (0.98), showing that there was no real difference between the model and the manual annotation.

To assess the impact of variability of annotation by the expert, the random forest model was trained with the same image dataset with new annotations. The variability found was 1.6% of surface on the test data set. This was found to be negligible by comparison with the biological variation inside the conditions.

Regarding the root part, ImageJ (version 1.53 k) [65] was used to determine the length of primary roots.

Experiments at the rosette stage

Experiments at the rosette stage were carried out according to Barrit et al. [22]. Seeds of three A. thaliana ecotypes (Col-0, Ler and Ws) were sown in pots filled with soil and have been grown in a growth chamber (8 h light / 16 h darkness, 21 °C). After 4 weeks, plants were transferred individually in a hydroponic system containing modified MS medium supplemented with 5 mM KNO3 for 2 weeks. Afterwards, plants were placed in a new hydroponic nutrient solution containing either 5 mM NH4+ (provided as NH4Cl) or 0.1 mM or 5 mM NO3 (provided as KNO3), as sole N source for 2 weeks. Nutritive solution was replaced every week. For plant infection assays, 5 μL drops of A. brassicicola (Abra43) conidia suspension (105 conidia per mL) were deposited on leaves from eight weeks-old plants. For control plants, 5 µL drops of H2O were used. Plants were then maintained under saturating humidity (100% relative humidity) for 2 days. Foliar symptoms were quantified at 7 DAI by measuring manually the lesion area surface.

Statistical analyses

Statistical analyses were carried out with the R software (version 4.1.2). Parametric tests (ANOVA followed by Tukey’s HSD test in the case of multiple comparisons, Student’s t test in the case of additional side-by-side comparisons) were performed when the conditions of normal distribution and homogeneity of variances were met. Otherwise, nonparametric tests (ANOVA using permutation tests for multiple comparisons, or Wilcoxon-Mann–Whitney test for additional side by side comparisons) were used to test the significance of the results. Statistically significant differences were denoted by P < 0.05.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information file. Script source code used during the current study for image analysis is available from the corresponding author on reasonable request.

Abbreviations

DAI:

Day after inoculation

DSC:

Dice Sørensen coefficient

GABA:

Gamma-aminobutyric acid

HAI:

Healthy area index

N:

Nitrogen

NAI:

Necrosis area index

PRL:

Primary root length

SEM:

Standard error of the mean

VRS:

Visual rating scale

References

  1. Paul MJ, Stitt M. Effects of nitrogen and phosphorus deficiencies on levels of carbohydrates, respiratory enzymes and metabolites in seedlings of tobacco and their response to exogenous sucrose. Plant Cell Env. 1993;16(9):1047–57.

    Article  CAS  Google Scholar 

  2. Martin T, Oswald O, Graham IA. Arabidopsis seedling growth, storage lipid mobilization, and photosynthetic gene expression are regulated by carbon: nitrogen availability. Plant Physiol. 2002;128(2):472–81.

    Article  CAS  Google Scholar 

  3. Humpherson-Jones FM, Maude RB. Studies on the epidemiology of Alternaria brassicicola in Brassica oleracea seed production crops. Ann Appl Biol. 1982;100:61–71.

    Article  Google Scholar 

  4. Neergaard P. Economic significance of seed-borne diseases. In: Seed pathology. London: Palgrave; 1977. p. 3–39.

  5. Richard-Molard C, Wuilleme S, Scheel C, Gresshoff PM, Morot-Gaudry JF, Limami AM. Nitrogen-induced changes in morphological development and bacterial susceptibility of belgian endive (Cichorium intybus L.) are genotype-dependent. Planta. 1999;209:389–98.

    Article  CAS  Google Scholar 

  6. Mur LAJ, Simpson C, Kumari A, Gupta AK, Gupta KJ. Moving nitrogen to the centre of plant defence against pathogens. Ann Bot. 2016;119:703–9.

    Google Scholar 

  7. Sellam A, Dongo A, Guillemette T, Hudhomme P, Simoneau P. Transcriptional responses to exposure to the brassicaceous defence metabolites camalexin and allyl-isothiocyanate in the necrotrophic fungus Alternaria brassicicola. Mol Plant Pathol. 2007;8:195–208.

    Article  CAS  Google Scholar 

  8. Glawischnig E, Hansen BG, Olsen CE, Halkier BA. Camalexin is synthesized from indole-3-acetaldoxime, a key branching point between primary and secondary metabolism in Arabidopsis. Proc Natl Acad Sci USA. 2004;101(21):8245–50.

    Article  CAS  Google Scholar 

  9. Wittstock U, Halkier BA. Glucosinolate research in the Arabidopsis era. Trends Plant Sci. 2002;7(6):263–70.

    Article  CAS  Google Scholar 

  10. Kliebenstein DJ. Secondary metabolites and plant/environment interactions: a view through Arabidopsis thaliana tinged glasses. Plant Cell Environ. 2004;27(6):675–84.

    Article  CAS  Google Scholar 

  11. Noritake T, Kawakita K, Doke N. Nitric oxide induces phytoalexin accumulation in potato tuber tissues. Plant Cell Physiol. 1996;37(1):113–6.

    Article  CAS  Google Scholar 

  12. Durner J, Wendehenne D, Klessig DF. Defense gene induction in tobacco by nitric oxide, cyclic GMP, and cyclic ADP-ribose. Proc Natl Acad Sci USA. 1998;95(17):10328–33.

    Article  CAS  Google Scholar 

  13. Mur LA, Prats E, Pierre S, Hall MA, Hebelstrup KH. Integrating nitric oxide into salicylic acid and jasmonic acid/ethylene plant defense pathways. Front Plant Sci. 2013;4:215.

    Article  Google Scholar 

  14. Huber DM, Watson RD. Nitrogen form and plant disease. Annu Rev Phytopathol. 1974;12(1):139–65.

    Article  CAS  Google Scholar 

  15. Tavernier V, Cadiou S, Pageau K, Laugé R, Reisdorf-Cren M, Langin T, Masclaux-Daubresse C. The plant nitrogen mobilization promoted by Colletotrichum lindemuthianum in Phaseolus leaves depends on fungus pathogenicity. J Exp Bot. 2007;58(12):3351–60.

    Article  CAS  Google Scholar 

  16. Solomon PS, Tan KC, Oliver RP. The nutrient supply of pathogenic fungi; a fertile field of study. Mol Plant Pathol. 2003;4:203–10.

    Article  Google Scholar 

  17. Jensen B, Munk L. Nitrogen-induced changes in colony density and spore production of Erysiphe graminis f. sp. hordei on seedlings of six spring barley cultivars. Plant Pathol. 1997;46(2):191–202.

    Article  Google Scholar 

  18. Hoffland E, Jeger MJ, van Beusichem ML. Effect of nitrogen supply rate on disease resistance in tomato depends on the pathogen. Plant Soil. 2000;218(1):239–47.

    Article  CAS  Google Scholar 

  19. Borrero C, Trillas MI, Delgado A, Avilés M. Effect of ammonium/nitrate ratio in nutrient solution on control of Fusarium wilt of tomato by Trichoderma asperellum T34. Plant Pathol. 2012;61(1):132–9.

    Article  CAS  Google Scholar 

  20. Gupta KJ, Brotman Y, Segu S, Zeier T, Zeier J, Persijn ST, Cristescu SM, Harren FJ, Bauwe H, Fernie AR, Kaiser WM. The form of nitrogen nutrition affects resistance against Pseudomonas syringae pv. phaseolicola in tobacco. J Exp Bot. 2013;64(2):553–68.

    Article  CAS  Google Scholar 

  21. Dordas C. Role of nutrients in controlling plant diseases in sustainable agriculture. A review. Agron Sustain Dev. 2008;28(1):33–46.

    Article  CAS  Google Scholar 

  22. Barrit T, Porcher A, Cukier C, Satour P, Guillemette T, Limami AM, Teulat B, Campion C, Planchet E. Nitrogen nutrition modifies the susceptibility of Arabidopsis thaliana to the necrotrophic fungus Alternaria brassicicola. Physiol Plant. 2022;6:e13621.

    Google Scholar 

  23. Jacobs T. Postulation of virulence groups and resistance factors in the quinoa/downy mildew pathosystem using material from Ecuador. Plant Pathol. 1999;48(3):425–30.

    Article  Google Scholar 

  24. Ziadi S, Barbedette S, Godard JF, Monot C, Le Corre D, Silue D. Production of pathogenesis-related proteins in the cauliflower (Brassica oleracea var. botrytis)-downy mildew (Peronospora parasitica) pathosystem treated with acibenzolar-S-methyl. Plant Pathol. 2001;50(5):579–86.

    Article  CAS  Google Scholar 

  25. Ouborg NJ, Biere A, Mudde CL. Inbreeding effects on resistance and transmission-related traits in the Silene-Microbotryum pathosystem. Ecology. 2000;81(2):520–31.

    Google Scholar 

  26. Meyer SE, Nelson DL, Clement S. Evidence for resistance polymorphism in the Bromus tectorum-Ustilago bullata pathosystem: implications for biocontrol. Can J Plant Pathol. 2001;23(1):19–27.

    Article  CAS  Google Scholar 

  27. Finch-Boekweg H, Allen P, Meyer S. Exposure to low water potentials and seed dormancy favour the fungus in the Pyrenophora semeniperda-Bromus tectorum pathosystem. Plant Protect Sci. 2013;49:S15-21.

    Article  Google Scholar 

  28. Bang J, Kim H, Kim H, Beuchat LR, Ryu JH. Inactivation of Escherichia coli O157: H7 on radish seeds by sequential treatments with chlorine dioxide, drying, and dry heat without loss of seed viability. Appl Environ Microbiol. 2011;77(18):6680–6.

    Article  CAS  Google Scholar 

  29. Vahabi K, Reichelt M, Scholz SS, Furch AC, Matsuo M, Johnson JM, Sherameti I, Gershenzon J, Oelmüller R. Alternaria brassicae induces systemic jasmonate responses in Arabidopsis which travel to neighboring plants via a Piriformsopora indica hyphal network and activate abscisic acid responses. Front Plant Sci. 2018;9:626.

    Article  Google Scholar 

  30. Schlaich NL. Arabidopsis thaliana-the model plant to study host-pathogen interactions. Curr Drug Targets. 2011;12(7):955–66.

    Article  CAS  Google Scholar 

  31. Lawrence CB, Mitchell TK, Craven KD, Cho YR, Cramer RA, Kim KH. At death’s door: Alternaria pathogenicity mechanisms. Plant Pathol J. 2008;24(2):101–11.

    Article  CAS  Google Scholar 

  32. Köhl J, Van Tongere CAM, Groenenboom-de Haas BH, Van Hoof RA, Driessen R, Van Der Heijden L. Epidemiology of dark leaf spot caused by Alternaria brassicicola and A. brassicae in organic seed production of cauliflower. Plant Pathol. 2010;59:358–67.

    Article  Google Scholar 

  33. Rotem J. The genus Alternaria: biology, epidemiology and pathogenicity. St Paul: APS; 1994. p. 1–326.

    Google Scholar 

  34. Rimmer SR, Buchwaldt H. Diseases. In: Kimber DS, McGregor DI, editors. Brassica oil seeds-production and utilization. Wallingford: CAB International; 1995. p. 111–40.

    Google Scholar 

  35. Kubota M, Abiko K, Yanagisawa Y, Nishi K. Frequency of Alternaria brassicicola in commercial cabbage seeds in Japan. J Gen Plant Pathol. 2006;72(4):197–204.

    Article  Google Scholar 

  36. Maude RB, Humpherson-Jones FM. Studies on the seed-borne phases of dark leaf spot Alternaria brassicicola and grey leaf spot Alternaria brassicae of brassicas. Ann Appl Biol. 1980;95(3):311–9.

    Article  Google Scholar 

  37. Murashige T, Skoog F. A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol Plant. 1962;15(3):473–97.

    Article  CAS  Google Scholar 

  38. Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, Schiegg M, Ales J, Beier T, Rudy M, Eren K, Cervantes JI, Xu B, Beuttenmueller F, Wolny A, Zhang C, Koethe U, Hamprecht FA, Kreshuk A. ilastik: interactive machine learning for (bio)image analysis. Nat Methods. 2019;16:1226–32.

    Article  CAS  Google Scholar 

  39. Mukherjee AK, Lev S, Gepstein S, Horwitz BA. A compatible interaction of Alternaria brassicicola with Arabidopsis thaliana ecotype DiG: evidence for a specific transcriptional signature. BMC Plant Biol. 2009;9:31.

    Article  Google Scholar 

  40. Britto DT, Kronzucker HJ. NH4+ toxicity in higher plants: a critical review. J Plant Physiol. 2002;159(6):567–84.

    Article  CAS  Google Scholar 

  41. Sarasketa A, González-Moro MB, González-Murua C, Marino D. Exploring ammonium tolerance in a large panel of Arabidopsis thaliana natural accessions. J Exp Bot. 2014;65(20):6023–33.

    Article  CAS  Google Scholar 

  42. Gerendás J, Zhu Z, Bendixen R, Ratcliffe RG, Sattelmacher B. Physiological and biochemical processes related to ammonium toxicity in higher plants. Z Pflanz Bodenkunde. 1997;160(2):239–51.

    Article  Google Scholar 

  43. Gao K, Zhou T, Hua Y, Guan C, Zhang Z. Transcription factor WRKY23 is involved in ammonium-induced repression of Arabidopsis primary root growth under ammonium toxicity. Plant Physiol Biochem. 2020;150:90–8.

    Article  CAS  Google Scholar 

  44. Walch-Liu P, Forde BG. Nitrate signalling mediated by the NRT1.1 nitrate transporter antagonises L-glutamate-induced changes in root architecture. Plant J. 2008;54(5):820–8.

    Article  CAS  Google Scholar 

  45. North KA, Ehlting B, Koprivova A, Rennenberg H, Kopriva S. Natural variation in Arabidopsis adaptation to growth at low nitrogen conditions. Plant Physiol Biochem. 2009;47(10):912–8.

    Article  CAS  Google Scholar 

  46. Sun Y, Wang M, Mur LA, Shen Q, Guo S. Unravelling the roles of nitrogen nutrition in plant disease defences. Int J Mol Sci. 2020;21(2):572.

    Article  CAS  Google Scholar 

  47. Solel Z, Bruck RI. Effect of nitrogen fertilization and growth suppression on pitch canker development on loblolly pine seedlings. J Phytopathol. 1989;125(4):327–35.

    Article  Google Scholar 

  48. Entry JA, Cromack K Jr, Hansen E, Waring R. Response of western coniferous seedlings to infection by Armillaria ostoyae under limited light and nitrogen. Phytopathology. 1991;81(1):89–94.

    Article  Google Scholar 

  49. Fleischmann F, Raidl S, Osswald WF. Changes in susceptibility of beech (Fagus sylvatica) seedlings towards Phytophthora citricola under the influence of elevated atmospheric CO2 and nitrogen fertilization. Environ Pollut. 2010;158(4):1051–60.

    Article  CAS  Google Scholar 

  50. Brown A, Heckman RW. Light alters the impacts of nitrogen and foliar pathogens on the performance of early successional tree seedlings. PeerJ. 2021;9:e11587.

    Article  Google Scholar 

  51. Ballini E, Nguyen TT, Morel J. Diversity and genetics of nitrogen-induced susceptibility to the blast fungus in rice and wheat. Rice. 2013;6:32.

    Article  Google Scholar 

  52. Snoeijers SS, Pérez-García A, Joosten MH, De Wit PJ. The effect of nitrogen on disease development and gene expression in bacterial and fungal plant pathogens. Eur J Plant Pathol. 2000;106:493–506.

    Article  CAS  Google Scholar 

  53. Panter S, Jones D. Age-related resistance to plant pathogens. Adv Bot Res. 2002;19:251.

    Article  Google Scholar 

  54. Mazzola M, Leach JE, Nelson R, White FF. Analysis of the interaction between Xanthomonas oryzae pv. oryzae and the rice cultivars IR24 and IRBB21. Phytopathology. 1994;84(4):392–7.

    Article  CAS  Google Scholar 

  55. Kus JV, Zaton K, Sarkar R, Cameron RK. Age-related resistance in Arabidopsis is a developmentally regulated defense response to Pseudomonas syringae. Plant Cell. 2002;14(2):479–90.

    Article  CAS  Google Scholar 

  56. Rusterucci C, Zhao Z, Haines K, Mellersh D, Neumann M, Cameron RK. Age-related resistance to Pseudomonas syringae pv. tomato is associated with the transition to flowering in Arabidopsis and is effective against Peronospora parasitica. Physiol Mol Plant Pathol. 2005;66(6):222–31.

    Article  CAS  Google Scholar 

  57. Li B, Xu X. Infection and development of apple scab (Venturia inaequalis) on old leaves. J Phytopathol. 2002;150(11–12):687–91.

    Article  Google Scholar 

  58. Kumar A, Yogendra KN, Karre S, Kushalappa AC, Dion Y, Choo TM. WAX INDUCER1 (HvWIN1) transcription factor regulates free fatty acid biosynthetic genes to reinforce cuticle to resist Fusarium head blight in barley spikelets. J Exp Bot. 2016;67(14):4127–39.

    Article  CAS  Google Scholar 

  59. Pantin F, Simonneau T, Muller B. Coming of leaf age: control of growth by hydraulics and metabolics during leaf ontogeny. New Phytol. 2012;196(2):349–66.

    Article  Google Scholar 

  60. Shumborski SJ, Samuels AL, Bird DA. Fine structure of the Arabidopsis stem cuticle: effects of fixation and changes over development. Planta. 2016;244(4):843–51.

    Article  CAS  Google Scholar 

  61. Belmas E, Briand M, Kwasiborski A, Colou J, N’Guyen G, Iacomi B, Grappin P, Campion C, Simoneau P, Barret M, Guillemette T. Genome sequence of the necrotrophic plant pathogen Alternaria brassicicola Abra43. Genome Announc. 2018;6(6):e01559-e1617.

    Article  Google Scholar 

  62. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R, Picus M, Hoyer S, van Kerkwijk MH, Brett M, Haldane A, Del Río JF, Wiebe M, Peterson P, Gérard-Marchant P, Sheppard K, Reddy T, Weckesser W, Abbasi H, Gohlke C, Oliphant TE. Array programming with NumPy. Nature. 2020;585:357–62.

    Article  CAS  Google Scholar 

  63. van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T. Scikit-image: image processing in Python. Peer J. 2014;2:e453.

    Article  Google Scholar 

  64. Hunter JD. Matplotlib: a 2D graphics. Comp Sci Eng. 2007;9(3):90–5.

    Article  Google Scholar 

  65. Schneider CA, Rasband WS, Eliceiri KW. NIH image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671–5.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Fabienne Simonneau and Aurélia Rolland of the IMAC technical platform of SFR Quasav (Angers, France) for supporting microscopy experiments as well to Alexia Crézé for taking some MEB pictures. The authors are also thankful to Daniel Sochard for seed production.

Funding

This research was conducted in the framework of the French regional council program ‘Objectif Végétal, Research, Education and Innovation in Pays de la Loire’ supported by the French Region Pays de la Loire, Angers Loire Métropole (FuSIoN project). The PhD grant of TB was awarded by the National Agronomy School “Institut Agro—Agrocampus Ouest”, cofinanced by French Ministry of Agriculture and Food and the regional Council of Pays de la Loire as part of a Contrat de Plan Etat-Région (CPER). This work was also supported by the Agence Nationale de la Recherche, programme Investissements d’avenir, project PHENOME, ANR-11-INBS-0012.

Author information

Authors and Affiliations

Authors

Contributions

TB, CC, EP and BT designed this work. TB and SA performed the experiments. TB, JB and DR developed the semi-automated image analysis pipeline. TB, CC, EP and BT wrote the manuscript. TB, CC, JB, DR, EP and BT interpreted data and revised the manuscript. CC, EP and BT supervised the work. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Béatrice Teulat.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

: Table S1. (a) Average values of green area, in square millimetres (per square plate), for Col-0, Ler or Ws seedlings grown on different nutritive media (5 mM NH4+, 0.1 mM NO3- or 5 mM NO3-), 14 DAI with sterile H2O. Twelve seedlings per square plate, 3 square plates per independent experiment, 3 independent experiments. Lowercase letters indicate a statistical difference between genotypes, inside a N condition. Uppercase letters indicate a statistical difference between N conditions, inside a genotype, for each N condition separately and for their mean. No letter indicates the absence of statistical difference for the comparison. The different factor effects and their interaction are presented at the bottom of the table, with the P-value. Significance of P: 0 < *** < 0.001. (b) Average values of green area ratio between seedlings treated with H2O and seedlings inoculated with Abra43 (HAI), for Col-0, Ler or Ws seedlings grown on different nutritive media (5 mM NH4+, 0.1 mM NO3- or 5 mM NO3-), 14 DAI. Twelve seedlings per square plate, 3 square plates per independent experiment, 3 independent experiments. Lowercase letters indicate a statistical difference between genotypes, inside a N condition. Uppercase letters indicate a statistical difference between N conditions, inside a genotype, for each N condition separately and for their mean. No letter indicates the absence of statistical difference for the comparison. The different factor effects and their interaction are presented at the bottom of the table, with the P-value. Significance of P: 0 < *** < 0.001 < ** < 0.01.

Additional file 2

: Table S2. Average values of the necrosis area index (NAI) obtained by dividing the necrosis area by the total aerial area, for Col-0, Ler or Ws seedlings inoculated with Abra43 and grown on different nutritive media (5 mM NH4+, 0.1 mM NO3- or 5 mM NO3-), at 14 DAI. Twelve seedlings per square plate, 3 square plates per independent experiment, 3 independent experiments. Lowercase letters indicate a statistical difference between genotypes, inside a N condition. Uppercase letters indicate a statistical difference between N conditions, inside a genotype, for each N condition separately and for their mean. No letter indicates the absence of statistical difference for the comparison. The different factor effects and their interaction are presented at the bottom of the table, with the P-value. Significance of P: 0 < *** < 0.001.

Additional file 3

: Table S3. (a) Average values of primary root length 8 DAI, in centimetres, for Col-0, Ler or Ws seedlings after H2O treatment or Abra43 inoculation, grown on different nutritive media (5 mM NH4+, 0.1 mM NO3- or 5 mM NO3-). Twelve seedlings per square plate, 3 square plates per independent experiment, 3 independent experiments. Lowercase letters indicate a statistical difference between genotypes, inside a condition of N and inoculation, for each genotype separately and for their mean. Uppercase letters indicate a statistical difference between N conditions, for a genotype and an inoculation condition, for each N condition separately and for their mean. Greek letters indicate a statistical difference between inoculation conditions, for a genotype and a N condition, for each inoculation condition separately and for their mean. No letter indicates the absence of statistical difference for the comparison. The different factor effects and their interaction are presented at the bottom of the table, with the P-value. Significance of P: 0 < *** < 0.001 < ** < 0.01 < * < 0.05. (b) Average values of primary root length 14 DAI, in centimetres, for Col-0, Ler or Ws seedlings after H2O treatment or Abra43 inoculation, grown on different nutritive media (5 mM NH4+, 0.1 mM NO3- or 5 mM NO3-). Twelve seedlings per square plate, 3 square plates per independent experiment, 3 independent experiments. Lowercase letters indicate a statistical difference between genotypes, inside a condition of N and inoculation, for each genotype separately and for their mean. Uppercase letters indicate a statistical difference between N conditions, for a genotype and an inoculation condition, for each N condition separately and for their mean. Greek letters indicate a statistical difference between inoculation conditions, for a genotype and a N condition, for each inoculation condition separately and for their mean. No letter indicates the absence of statistical difference for the comparison. The different factor effects and their interaction are presented at the bottom of the table, with the P-value. Significance of P: 0 < *** < 0.001.

Additional file 4

: Table S4. Average values of fungal lesions (lesion area) for Col-0, Ler or Ws adult plants grown on different nutritive media (5 mM NH4+, 0.1 mM NO3- or 5 mM NO3-) in a hydroponic system, 7 DAI. Four leaves per plant, 6 plants per independent experiment, 3 independent experiments. Lowercase letters indicate a statistical difference between genotypes, inside a condition of N, for each genotype separately and for their mean. Uppercase letters indicate a statistical difference between N conditions, inside a genotype, for each N condition separately and for their mean. No letter indicates the absence of statistical difference for the comparison. The different factor effects and their interaction are presented at the bottom of the table, with the P-value. Significance of P: 0 < *** < 0.001.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Barrit, T., Campion, C., Aligon, S. et al. A new in vitro monitoring system reveals a specific influence of Arabidopsis nitrogen nutrition on its susceptibility to Alternaria brassicicola at the seedling stage. Plant Methods 18, 131 (2022). https://doi.org/10.1186/s13007-022-00962-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13007-022-00962-3

Keywords

  • Ammonium
  • Image analysis
  • Necrosis
  • Necrotrophic fungus
  • Nitrate
  • Root
  • Seedling
  • Symptom