Genetic architecture, bulk segregant analysis, DNA extraction and sequencing

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Impact of heat stress on plant immunity

Heat stress is one of the major abiotic stresses and a main component of climate warming that can affect plant growth and development. A temperature increase is considered as a heat stress when temperature shifts above the optimal growth temperature of plants and causes damages (Zaidi et al., 2014; Liu et al., 2015). A heat stress is also defined according to the applied temperature range while depending on the species. For instance, in the case of Arabidopsis thaliana, temperatures are considered warm between 22°C and 27°C, high between 27°C and 30°C and extremely high between 37°C and 42°C (Liu et al., 2015).
Numerous studies have assessed plant response to heat stress at the phenotypic, physiological and molecular levels (Saidi et al., 2011; Bita and Gerats, 2013; Hasanuzzaman et al., 2013; Hatfield and Prueger, 2015; Gray and Brady, 2016; Nievola et al., 2017). Importantly, all the major forms of immunity described in the previous section can be drastically affected by heat stress. Regardless of the plant pathosystem considered, numerous studies reported that an increase in temperature inhibits several major defense mechanisms induced following pathogen attack, notably by suppressing ETI-HR related phenotypes (de Jong et al., 2002; Xiao et al., 2003; Yang and Hua, 2004; Wang et al., 2009; Cheng et al., 2013; Menna et al., 2015; Aoun et al., 2017).
However, most of these studies have focused on the impact of more than 5°C of temperature increase, which does not corroborate with the expected increase of global surface temperature means in the climate changes scenarios. Thus, what is the impact of a lower increase of temperature on plant resistance? It is therefore highly important and urgent to find efficient mechanisms of resistance maintained under realistic heat stress.
The main findings on the impact of heat stress on plants, pathogens and their interactions are summarized in the following review co-authored with Henri Desaint, another PhD student from the team.

Round three: Impact of temperature elevation on plant-pathogen interactions

In agreement with the observed effects of global warming and the prediction of its impact on living organisms and ecosystems, the number of studies reporting an alteration of plant disease resistances under TpE largely increased in recent years. Therefore, we decided to review current available knowledge to assess the impact of a TpE on either model plants or crops/pathogenic microorganism’s interactions. Using “high temperature, temperature elevation, pathogens, plants, resistance, immune response and combined stresses” keywords, we performed our bibliographic searches on the Web of Science, google scholar and PubMed-NCBI websites. We selected 43 studies in which the effect of TpE on 123 resistances to pathogens were described (Tables 1 and 2). First, these studies involve 25 pathosystems corresponding to a combination of ten plant species (including nine crop species) with 25 pathogen species (including eight fungi, seven viruses, four oomycetes, three bacteria and three nematodes). Second, of all the temperature elevation applied, only five correspond to variations less than or equal to 5°C above the initial temperature. Third, depending on the method of application of temperature elevation and pathogen attack, we classified the studies as follow: i) a continuous or intermittent and repeated application of the first stress, several days before the second stress, is considered as an acclimatization, ii) an application of the first stress just before (one day or hours) and/or after the second stress is defined as sequential, and iii) an application of both stresses at the same time is qualified as simultaneous. Among the 43 studies, 28 and 9 studies reported a sequential or simultaneous (with a permanent TpE applied) application of stresses, respectively. Two studies correspond to acclimatization (Wang et al., 2009; Menna et al., 2015) while the remaining studies do not specify the mode of application of stresses. There is only one study in which the thermosensitivity of soybean resistance to Phytophtora sojae was assessed using two methods of TpE (Gijzen et al., 1996). Fourth, most of the resistances were evaluated in controlled conditions (41 studies). Only two resistances related to Mi-1 and Xa-7 genes were assessed in both controlled (Jablonska et al., 2007; Cohen et al., 2017) and field conditions over several years (Dropkin, 1969; Webb et al., 2010). Additionally, two other resistances were also evaluated in both controlled and field conditions, in the same study (Uauy et al., 2005a; Plotnikova and Stubei, 2013a). Carrying out experiments under controlled conditions has the advantage of supervising precisely specific abiotic and biotic factors. On the other hand, while field experiments (especially when conducted over several years) increase the level of complexity, they allow the evaluation of robustness and transferability of resistances into more ecologically realistic conditions.
Alarmingly, we found that TpE resulted in an increased plant susceptibility or an inhibition of plant defenses for 60% of the studied resistances (Table 1). All the remaining studies reported no significant effect of TpE on the level of plant resistance (Table 2) and none reported a positive effect of TpE on plant resistance. The alteration of plant resistance is not dependent on plant species, pathogens species or the nutrition mode of pathogens. A primary explanation for this high frequency of resistance alteration in presence of TpE may rely on the adaptation of most pathogens to temperatures above the optimal growth temperature of plant species. Accordingly, all the resistances observed in wheat in presence of TpE, were observed for pathogens that have an optimal growth temperature below the TpE applied (Table 2). Noteworthy, the effect of TpE on plant resistance is highly dependent on the mode of stress application. For instance, while Cheng et al. (2013) applied TpE and promoted AvrRpt2 expression simultaneously in A. thaliana for 3 to 6 h, Menna et al. (2015) primed A. thaliana plants 24h at elevated temperature prior to inoculation with Pst strains expressing either HopZ1a or AvRpt2 avirulent factors and harvested samples 4 days after infection. In both cases, plant resistance was altered. Cheng et al. (2013) clearly showed that elevated temperature inhibits AvrRpt2 related ETI response. However, in Menna et al. (2015), HR was suppressed but the bacterial multiplication remained repressed at 28°C, suggesting the resistance is still active even if bacteria multiplication repression appears to be less effective than at 22°C. Thus, TpE treatment before or after inoculation could influence the output of an interaction. Acclimatization and priming effects following a chronic and intermittent abiotic stress exposure are also known to help plants resist against biotic stresses (Hilker et al., 2015). When wheat was exposed at 15°C or 25°C until the booting stage before inoculation with Blumeria graminis f. sp. Tritici, plants were resistant and the level of expression of Pm4a and Pm4b resistance genes was correlated to the inoculation temperature applied before the inoculation (Ge et al., 1998a). Wang et al. (2001) also speculate that the expression of some race specific R genes such as Pib rice-blast resistance genes could be primed by specific environmental conditions.

Understanding mechanisms involved in resistance alteration under elevated temperatures

Genetic sources of resistance are often the most effective and environment-friendly mean of controlling plant diseases. One of the first current challenges is to investigate the robustness and the spectrum of new resistance mechanisms identified under TpE, either in controlled or in field conditions. Secondly, dedicated understanding of the physiological, metabolic, molecular, genetic and epigenetic basis of plant defense response modulation under TpE is essential to propose alternative solutions (i.e. use of natural allelic variants or editing methods) to maintain efficiency of already identified resistances. So far, these mechanisms have been mostly investigated in few plant species. Furthermore, during combined stresses, the response of plants depends not only on their ability to adapt to TpE but also on the effect that TpE might have on pathogens and plant-pathogen interactions. It is therefore important to study at the pathogen level the mechanisms underlying their perception and regulation of response to TpE conditions. Although this domain is well studied in mammalian’s and human’s pathogens (Shapiro and Cowen, 2012) (Figure 1A), it remains poorly explored for plant pathogens. Third, the genetic diversity of both interacting partners should be better considered to help i) in the evaluation of the immune response robustness and ii) in the study of mechanisms that could promote it. Indeed, the majority of studies listed in this review were mostly carried-out on a limited number of genotypes, both within plant and pathogen species. This observation is not limited to the impact of TpE on plant-pathogen interactions. A bibliometric analysis realized by Gimenez et al. (2018) on papers published in the field of plant pathology between 1979 and 2016 revealed that a majority of the studies was realized on a single (or few) genotype(s) of A. thaliana and on a limited number of pathogens, mainly the bacterium P. syringae. However, since 2017, this situation seems to be reversed, with a decrease of studies on A. thaliana and an increase of studies on crop species, in particular tomato and wheat.

Identification and study of uncovered robust resistance mechanisms

GWA mapping approaches, developed on either model plants or crops proved their efficiency in identifying already known resistance mechanisms as well as the genetic bases of many new QDRs mechanisms to bacteria, fungi and oomycetes (French et al., 2016; Bartoli and Roux, 2017; Bruessow et al., 2019). Therefore, such strategy could be developed to uncover robust resistance mechanisms under TpE and more broadly considering the changing abiotic environment. For instance, in this project, we successfully identified and functionally validated several defense response mechanisms that remain efficient under TpE (Aoun et al., 2017; Aoun et al., submitted). Furthermore, the development of a new statistical method allowing simultaneous GWA mapping on two interacting species makes it possible to map a phenotypic trait on a pair of genomes (Wang et al., 2018c). Its application, taking into account the genetic diversity of both the plant and the pathogen for a given pathosystem, should facilitate the identification of molecular actors ruling the interaction under TpE conditions. Implementing such strategies directly on model crop species in field conditions over several years and monitoring the climatic parameters is another major challenge that could be addressed.

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Consider the natural complexity of interactions

Although they proved their accuracy in the characterization of the mechanisms involved in plant immunity, studies of plant-pathogen interactions are still generally performed on simple pathosystems composed of a single host plant interacting with a single pathogen. However, in nature, plants are often co-infected by several pathogens (i.e. Pathobiota) (Bartoli et al., 2018). Therefore, in order to predict and optimize plant responses to pathogens under abiotic stresses, it is crucial to investigate how the plant manage its interactions with all the living micro-organisms in its environment. Recent studies on plant-multi-pathogenic systems showed that interactions between pathogens could be based on coexistence, cooperation or competition, resulting in very different outcomes for the hosts (Abdullah et al., 2017). They also reported the importance of microbiota to help plants cope with biotic or abiotic stresses. Indeed, plant’s « beneficial » microbiota can improve and even contribute in extending the defense response to diseases by i) the direct modulation of plant immunity (IMM) or ii) the competition between members of the microbiota that can indirectly influence the host (DCM) (Vannier et al., 2019). For example, in the case of our R. solanacearum study model, the transplantation of microbiota from the rhizosphere of the Hawaii tomato variety, tolerant to R. solanacearum, to a susceptible tomato variety contributes to the suppression of disease symptoms in susceptible plants. Interestingly a flavobacterium, more abundant in the plant resistant rhizosphere, was demonstrated to inhibit the R. solanacearum development (Kwak et al., 2018).

Bacterial strain, plant material and growth conditions

The wild type R. solanacearum GMI1000 strain used in all inoculation experiments was grown in complete BG medium as described by Plener et al. (2010). A collection of 176 A. thaliana worldwide accessions was used in this study (Table S1). Five to ten seeds of each accession were directly sown on Jiffy pots (Jiffy Products International AS, Norway) and stratified for 48h at 4°C in order to release seed dormancy. Accessions were then grown in growth chamber under controlled conditions for four weeks (22°C, 70% relative humidity (RH), 9h of light) prior to phenotyping experiments. The 38 T-DNA insertion mutants (Ws-0 and Col-0 background) corresponding to 22 genes included in a 80 kb genomic region underlying the QTL of early plant defense response to R. solanacearum were identified using the online Arabidopsis gene mapping tool T-DNA express (http://signal.salk.edu/cgi-bin/tdnaexpress) (Table S2) and ordered from the Nottingham Arabidopsis Stock center (http://arabidopsis.info/).
Corresponding seeds were sown and stratified as described above and grown in greenhouse conditions (26.5°C +/- 1.5°C, 16h light). Progenies of genotyped homozygous of each mutant were harvested and grown for four weeks as described above before inoculation. The Col-0 accession (N60000), susceptible to the R. solanacearum GMI1000 strain, was used as a control in all experiments.

Plant inoculation and phenotyping

Four-week-old plants were used in all experiments. Plant response to R. solanacearum GMI1000 strain was assessed at 27°C and 30°C using two inoculation conditions: (i) the UNCUT condition previously described (Lohou et al., 2014), where the roots were not wounded thereby mimicking natural infection, and (ii) the CUT condition (Deslandes et al., 1998) where the roots where sectioned with scissors, approximately 1 cm from the bottom of the pot, giving the bacteria a direct access to the xylem vessels. Plants were soaked for 15 min in 2 L per tray of a bacterial suspension at 1.107 bacteria/mL and 1.108 bacteria/mL, for the CUT and the UNCUT conditions, respectively. Inoculated plants were then transferred in growth chambers with controlled conditions at 27°C or at 30°C (75% HR, 12h light, 100μmol m–2s–1). The wilting symptoms were scored on an established 0 to 4 disease index scale (Deslandes et al., 1998) with the score 0 and 4 corresponding to healthy and dead plants, respectively. Symptoms were monitored from 3 to 13 days after inoculation (dai), and from 3 dai to 10 dai for plants incubated at 27°C and 30°C, respectively.

Gene ontology and biological pathways enrichment tests

To determine the biological processes involved in response to R. solanacearum GM1000 strain at 30°C and perform comparisons between the two inoculation methods used, we first tested for each ‘CUT/UNCUT condition x time point of phenotyping’ to determine whether SNPs in the 0.1% upper tail of the –log10 p-value distribution were over-represented in each of 736 Gene Ontology Biological Processes from the GOslim set (Consortium, 2008). A total of 10,000 permutations were run to assess significance using the same methodology as described in Hancock et al. (2011). For each significant enriched biological process at a P < 0.05, we then retrieved the identity of all the genes containing SNPs in the 0.1% upper tail of the –log10 p-value distribution. Finally, each list of genes, corresponding to each phenotyping time point, was used after removal of duplicates, to identify biological pathways significantly over-represented (P < 0.01) with the classification superviewer tool on the university of Toronto website (http://bar.utoronto.ca/ntools/cgi-bin/ntools_classification_superviewer.cgi) using the MAPMAN classification (Provart and Zhu, 2003).

T-DNA insertion mutants’ validation, plant assays and statistical analyses

For each of the 38 T-DNA insertion mutants located in a QTL region of early A. thaliana defense responses to R. solanacearum (see Results section), 12 seedlings were genotyped to check the presence of the T-DNA insertion and to identify homozygous plants. For each seedling, one leaf was collected and used for genomic DNA extraction was adapted from QIAGEN DNeasy kit®as described in Mayjonade (Mayjonade et al., 2016). For genotyping, primer pairs were designed using the T-DNA primer design online tool (http://signal.salk.edu/tdnaprimers.2.html). All the primer sequences and corresponding PCR fragment sizes are listed in Table S3. For one PCR reaction, 2 µL of genomic DNA (10 ng/µL) were added to a PCR master mix composed of: 1 µL (10 pM) of each primer composing the LP+RP or RP+BP primer pairs (see Table S2), 0.5 μl (10 mM) dNTPs, 0.2 μl (10 u/μl) of GoTaq® DNA polymerase (Promega, Madison, WI, USA), 5 µL of 5X GoTaq buffer and 16.2 µL of sterilized water. The PCR cycling conditions were as follow: 95°C for 2 min; 10 cycles at 95°C for 30 sec, 62°C to 52°C for 30 sec (touch-down, 1°C decrease at each cycle) and 72°C for 1 min; 30 cycles at 95°C for 30 sec, 52°C for 30 sec and 72°C for 1 min; 72°C for 2 min. T-DNA insertion mutants were inoculated using the CUT inoculation method with a bacterial suspension of 1.107 bacteria/mL and transferred at 30°C. Three to six independent experiments were made for each T-DNA insertion mutant. In all experiments, plants were organized according to a RCBD. To test whether the disease index was statistically different between the wild type Col-0 and each T-DNA mutants, we used a Kruskal-Wallis analysis under the R environment version 3.3.2 (R_Development_Core_Team, 2013).. The dynamics of T-DNA mutant lines response to the R. solanacearum GMI1000 strain was drawn using ggplot2 package (http://ggplot2.org/) showing the confidence interval.

Table of contents :

Chapter I. General introduction
A. General overview
B. Physical barriers and immune signaling responses as defense mechanisms against microbial pathogens
C. Impact of heat stress on plant immunity
Review article: Fight hard or die trying: plants versus pathogens under heat stress
D. The lofty goal of identifying uncovered resistance mechanisms under heat stress
1. The pathosystem Arabidopsis thaliana – Ralstonia solanacearum
a. Arabidopsis thaliana
b. Ralstonia solanacearum
c. Arabidopsis thaliana and Ralstonia solanacearum interactions
2. Exploring natural diversity of plants to identify the genetic basis of resistance
a. Traditional QTL mapping
b. Genome wide association mapping (GWA)
E. Thesis project objectives
Chapter II. Identification of the genetic basis of natural variation of plant response to Ralstonia solanacearum under elevated temperature in a worldwide population of Arabidopsis
A. Introduction
B. Genetic basis of early resistance to Ralstonia solanacearum under elevated temperature
Article: Quantitative Disease Resistance Under Elevated Temperature: Genetic Basis of New Resistance
Mechanisms to Ralstonia solanacearum
Supporting information
C. Naturally resilient: the genetic basis of total resistance to Ralstonia solanacearum remaining efficient at elevated temperature
1. Introduction
2. Material and methods
Bacterial strains, plant materials and growth conditions
Plant inoculation and phenotyping
Genetic architecture, bulk segregant analysis, DNA extraction and sequencing
SNP mapping analysis
RPS4/RRS1-REden-1 locus cloning and analysis of polymorphisms
Hypersensitive Response assays
3. Results
EDEN-1: robust resistance to GMI1000 at 30°C
Genetic architecture of Eden-1 total resistance to GMI1000 at 30°C
Heat-stable resistance of Eden-1 is dependent on perception of PopP2
Eden-1 genome contains a specific allelic version of RPS4-RRS1-R
Heat-stable resistance of Eden-1 is conferred by RPS4/RRS1-REden-1
4. Discussion
D. Conclusion
Chapter III. Identification of the genetic basis of natural variation of plant response to Ralstonia solanacearum under elevated temperature in a local population of Arabidopsis
A. Introduction
B. Article: Natural variation of Arabidopsis thaliana quantitative disease response to Ralstonia solanacearum is controlled by a complex genetic architecture involving additive and epistatic QTLs
C. Supporting information
D. Discussion and conclusion
Chapter IV. Discussion and Perspectives
Discussion
Perspectives
References .

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