Silicon protects soybean plants against Phytophthora sojae by interfering with effector-receptor expression

Silicon protects soybean plants against Phytophthora sojae by interfering with effector-receptor... Background: Silicon (Si) is known to protect against biotrophic and hemibiotrophic plant pathogens; however, the mechanisms by which it exerts its prophylactic role remain unknown. In an attempt to obtain unique insights into the mode of action of Si, we conducted a full comparative transcriptomic analysis of soybean (Glycine max) plants and Phytophthora sojae, a hemibiotroph that relies heavily on effectors for its virulence. Results: Supplying Si to inoculated plants provided a strong protection against P. sojae over the course of the experiment (21 day). Our results showed that the response of Si-free (Si ) plants to inoculation was characterized early (4 dpi) by a high expression of defense-related genes, including plant receptors, which receded over time as the pathogen progressed into the roots. The infection was synchronized with a high expression of effectors by P. sojae, the nature of which changed over time. By contrast, the transcriptomic response of Si-fed (Si ) plants was remarkably unaffected by the presence of P. sojae, and the expression of effector-coding genes by the pathogen was significantly reduced. Conclusion: Given that the apoplast is a key site of interaction between effectors and plant defenses and receptors in the soybean-P. sojae complex, as well as the site of amorphous-Si accumulation, our results indicate that Si likely interferes with the signaling network between P. sojae and the plant, preventing or decreasing the release of effectors reaching plant receptors, thus creating a form of incompatible interaction. Keywords: Transcriptome, Glycine max, Plant receptors, Effectors, Defense genes Background where the pathogen can disseminate easily because of its Soybean (Glycine max L. Merr.) is economically and flagellated zoospores [45]. It is described as a hemibio- agriculturally the most important legume in the world, trophic pathogen and it secretes effector proteins (coded but its production is compromised by many biotic and by Avr genes) to manipulate and invade living host cells abiotic factors. Of primary importance, Phytophthora during the initial biotrophic stage of infection. In sojae Kaufm. and Gred. [27] can cause annual yield plant-pathogen interactions, effectors are recognized as losses as high as $200 million in the USA and $1–2 important virulence factors that are utilized by the billion worldwide [31]. Phytophthora sojae is a pathogen to suppress PAMPs (Pathogen-Associated soil-borne plant pathogen belonging to the oomycetes Molecular Patterns)-Triggered Immunity (PTI) and with a restricted host range, including soybean as its pri- Effector-Triggered Immunity (ETI) in plants or change mary host. It causes root and stem rot, and pre- and host metabolism so that it can easily colonize plant tis- post-emergence damping-off, particularly in flooded soils sues [12, 50]. In response, soybean can carry resistance genes to P. sojae (Rps), that encode, or are predicted to encode, nucleotide-binding leucine-rich repeat * Correspondence: richard.belanger@fsaa.ulaval.ca (NB-LRR)-type proteins [19, 26], which are able to Département de Phytologie, Faculté des Sciences de l’Agriculture et de recognize the Avr effector proteins of P. sojae and induce l’Alimentation, Université Laval, Québec City, Québec G1V 0A6, Canada Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 2 of 13 the appropriate defense response [10, 18]. The result of altered by silicon’s presence. Finally, Wang et al. [50], on this interaction between Rps genes and Avr genes will the basis of recent results with P. sojae, described the often determine compatible or incompatible interactions. apoplastic region as a major battle ground between Numerous studies have highlighted the prophylactic pathogen effectors and the host apoplastic surveillance effects of silicon (Si) fertilization [3, 16, 20] in the search system. for additional methods to prevent losses in the case of Since P. sojae is a hemibiotrophic pathogen that relies compatible interactions. Interestingly, Si appears to be heavily on effectors for its virulence, the P. sojae-soybean particularly efficient against biotrophic and hemibio- pathosystem was deemed well-suited to validate and trophic fungal/oomycete pathogens [5, 46]. In the case investigate the hypothesis that Si deposition altered the of soybean, Arsenault-Labrecque et al. [1] have shown release of virulence factors by P. sojae. In this context, that a Si treatment was effective against soybean rust two main objectives were defined: 1) to assess resistance caused by the biotrophic fungus Phakopsora pachyrizi. of soybean plants to P. sojae when fertilized with Si, and In addition, Deshmukh et al. [9] have identified Si trans- 2) to analyze the expression of salient genes involved in porters in soybean, thus confirming that the species is the virulence of P. sojae and the defense mechanisms of receptive to Si and can absorb the element. soybean in order to assess if a differential response could The mechanisms inherent to the prophylactic proper- be linked to the prophylactic role of Si. ties of Si have puzzled scientists for many years. Origin- ally, it was suggested that Si deposition along the cell Results walls created a physical barrier that halted fungal pene- Phenotypic responses tration into the plant [47]. However, additional studies Soybean plants were inoculated with zoospores of P. have linked the presence of Si with diverse plant-defense sojae in a recirculating hydroponic system fed with nu- reactions, thus suggesting that Si may play a role in the trient solution with and without 1.7 mM Si to compare induction of acquired resistance [6, 7, 16, 17, 53]. In a the phenotypic differences linked to Si. First symptoms recent study using Arabidopsis thaliana mutants defi- of root browning appeared as early as 4 days post inocu- cient in salicylic acid (SA) synthesis, Vivancos et al. [46] lation (dpi). Stunting and leaf discoloration followed showed that Si protected both mutant and wild-type within a few days, and first cases of mortality were plants against powdery mildew (Erisyphe cichora- recorded at 15 dpi in the Si treatment. The differences − + cearum). This led the authors to suggest that the depos- between Si and Si treatments increased with time, and ition of Si as amorphous gel in the apoplast may prevent by 21 dpi, plants in the Si treatment were clearly fungal effectors from reaching their targets, thereby healthier than non-treated plants (Fig. 1a). In terms of altering the development of the pathogen. This hypoth- dry weight, for non-inoculated plants there was no − + esis becomes particularly relevant in the context of the significant difference between Si (8.4 ± 0.5) and Si P. sojae-soybean interaction in light of recent results. plants (8.7 ± 0.4) plants. However, inoculation with P. Indeed, Ma et al. [34] recently showed that P. sojae sojae significantly reduced plant dry weight, but the employed an apoplastic decoy strategy with effectors to prophylactic effect of Si was quite apparent as plants attack soybean. Xin et al. [52] further proposed that an were significantly heavier in the Si (5.0 g ± 1.9) com- aqueous apoplast was required for pathogenicity rather pared with the Si (2.0 g ± 1.0) treatment. X-ray than immunosuppression, a condition that can be microanalysis mapping of soybean confirmed the a bc Fig. 1 Effect of silicon (Si) amendments on soybean plants 21 days after inoculation with Phytophthora sojae. a Plants in the Si treatment were clearly healthier than non-treated plants with more developed roots, stems and leaves. Comparative X-ray superimposed scanning electron micrographs of soybean root tips in plants treated (b) or not (c) with Si. At least, five plants per treatments were observed. A color scale of Si deposition was used, with blue indicating low Si and red high Si deposition. Black areas indicated no Si deposition Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 3 of 13 accumulation of Si throughout the roots in Si plants (Fig. 1b), while, in the absence of Si amendment, no clear evidence of Si deposition was observed (Fig. 1c). Dual RNA-seq analysis of the P. sojae-soybean interaction in the presence of Si A complete comparative transcriptomic analysis of soy- bean roots and P. sojae was carried out at 0, 4, 7 and 14 dpi to obtain a comprehensive gene-expression profile for both soybean and P. sojae in response to Si application. Soybean root transcriptome Mapping of the processed reads from roots to the soybean genome showed a very high percentage of mapped reads for non-inoculated samples (control) treated or not with Si. For control plants, 81 and 90% of reads mapped on − + soybean in Si and Si treatments, respectively. In inocu- lated plants at 4 dpi, 61 and 76% of reads mapped to soy- − + bean in Si and Si , respectively (Additional file 1:Table S1). Interestingly, the number of differentially expressed genes (DEGs) between control plants treated or not with Si was limited to 50 out of the potential 56,045 genes ana- Fig. 2 Heat map of differentially expressed genes in Phytophthora lyzed, and all were downregulated in the Si treatment sojae infecting soybean roots. In total, 3294 genes were differentially (Fold-change ≥4, FDR p-value ≤0.01). On the other hand, + − expressed as a result of P. sojae infection at 4 dpi (P Si ). Heat map − + plants responded to inoculation of P. sojae (Si P vs. shows gene expression pattern in soybean roots inoculated (P )or − − − + − not (P ) with P. sojae and treated (Si ) or not (Si ) with silicon. Each Si P ) with a differential expression of 3294 genes (Add- gene corresponds to a colored line indicating the normalized mean itional file 2: Table S2). Most of genes that were differen- (n = 5) of the differentially expressed transcripts (Fold-change ≥4, − + − − tially expressed as a result of the infection (Si P vs Si P ) FDR p-value ≤0.01) reverted to a pattern of expression closer to control plants + + + − +) in the Si treatment (Si P vs Si P as illustrated on the heat map (Fig. 2). expression at 4 dpi in the Si treatment (Additional file 3: 2+ Functional categorization of the DEGs in P. sojae-in- Figure S1c). Similarly, 33 Ca -dependent protein kinases fected plants showed that these genes belonged mainly (CDPKs) were highly expressed at 4 dpi (Additional file 3: to the following categories: defense-related genes, Figure S1d, Additional file 4: Table S3). secondary metabolism, hormone metabolism, primary NB-LRR proteins. Out of 80 differentially-expressed metabolism and no-ontology for which no function was NB-LRR genes over the experimental period annotated. (Fold-change ≥4, FDR p-value ≤0.01), 45 showed their highest expression at 4 dpi in Si plants. Heat map Defense-related genes results clearly showed a pattern of expression where Most known pattern recognition receptors (PRRs) that there was no expression of NB-LRR genes in can activate PTI in plants fall into one of two receptor non-inoculated plants (control) regardless of Si treat- classes: transmembrane receptor kinases and ment, followed by a sharp increase at 4 dpi in Si plants. receptor-like kinases (RLK; [10, 29, 32]). In our study, 46 While the expression was reduced at 7 and 14 dpi, it DEGs belonged to the receptor kinase family and 24 remained significantly higher in Si plants (Fig. 3, RLK showed higher expression at 4 dpi in the Si treat- Additional file 5: Table S4). ment (Additional file 3: Figure S1 a, b). After PRR acti- Pathogenesis-related proteins (PRs). Based on cluster ana- vation, the downstream signaling pathway transfers lysis, 11 PR genes were found to be differentially expressed signals from extracellular receptors to cellular responses in at least one timepoint (Fold-change ≥4, FDR p-value by mitogen-activated protein kinases (MAPKs) and cal- ≤0.01). Incidentally, heat-map results clearly showed that 4 2+ − + cium (Ca ). MAPKs are ubiquitous signal-transduction dpi was the critical timepoint differentiating Si and Si components, which have been implicated in both PTI plants in terms of PR-gene expression (Fig. 4, Additional file and ETI. Our results showed that out of nine 6: Table S5). The expression receded over time (7 and 14 differentially-expressed MAPKs, five had a higher dpi) to similar levels between the treatments. Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 4 of 13 Fig. 3 Expression profile of NB-LRRs genes. Heat map (a) and gene expression (b) show a higher expression of 45 receptor (NB-LRR) genes in − + Phytophthora sojae-inoculated soybean plants at 4 dpi under Si compared to Si treatment. b Graph shows the average relative (%) expression at each timepoint based on the highest level of expression for each gene as a measure to showcase the trend in expression. Bars represent standard error from the mean (n =5) Transcription Factors Associated with Defense plant protease inhibitors in at least one timepoint, 13 Expression. Out of the 67 differentially-expressed WRKY showed higher expression in the Si treatment at 4 dpi. transcription factors in at least one timepoint, most Similarly, out of the seven differentially-expressed poly- showed a higher expression in Si treatment, with 20 genes phenol oxidases in at least one timepoint, five showed − − showing a higher expression at 4 dpi in the Si treatment higher expression at 4 dpi in the Si treatment (Additional (Additional file 7: Figure S2, Additional file 8:Table S6). file 9: Figure S3, Additional file 10: Table S7). Miscellaneous defense responses. Other genes linked to defense responses showed a similar pattern of expression Secondary metabolism where they were quickly upregulated at 4 dpi in Si plants We observed 31 genes involved in flavonoid metabolism + − before receding to levels similar to those observed in Si with a higher expression at 4 dpi in the Si treatment. plants. For instance, of the 16 differentially-expressed We also observed nine genes involved in isoflavone ab Fig. 4 Expression profile of PR genes. Heat map (a) and gene expression (b) show a higher expression of 11 pathogenesis-related (PR) genes in − + Phytophthora sojae-inoculated soybean plants at 4 dpi under Si compared to Si treatment. b Graph shows the average relative (%) expression at each timepoint based on the highest level of expression for each gene as a measure to showcase the trend in expression dynamics. Bars represent standard error from the mean (n =5) Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 5 of 13 metabolism and 15 genes involved in isoprenoid metab- number of DEGs in P. sojae was higher under Si com- − + olism, all with higher expression at 4 dpi in the Si treat- pared to Si conditions (Fig. 6). The highest number of − + ment (Additional file 11: Figure S4, Additional file 12: DEGs per treatment (Si or Si ) was recorded at 4 dpi, Table S8). and this number kept receding over time. Hormone metabolism Annotation In our system, five genes involved in SA metabolism had When looking at the top 100 upregulated DEGs in P. sojae higher expression at 4 dpi in the Si treatment, in syn- over all timepoint and treatments, most were linked to chrony with the biotrophic phase of P. sojae, and 13 hypothetical proteins for which functional annotation was genes involved in JA metabolism were differentially not available (Additional file 16: Table S11). A notable ex- expressed with higher expression at 14 dpi in the same treat- ception was Avh1b-81, which ranked among the highest ment (Fig. 5, Additional file 13: Table S9), a period more expressed genes at 4 and 7 dpi in the Si treatment. in-line with the necrotrophic development of the pathogen. − + Primary metabolism Expression of effectors in P. sojae during Si and Si Based on functional categorization, primary metabolism in- treatments cluded the most categories that contained DEGs. Out of In oomycetes, many effectors are characterized by an 580 DEGs involved in primary metabolism as a result of P. RxLR motif [11]. Of the 348 RxLR effector genes sojae infection in the Si treatment, 70% were upregulated identified in P. sojae, 104 were found to be differen- and 30% were downregulated. In the Si treatment, the tially expressed in at least one timepoint (Fold-change number of DEGs was reduced to 420 genes with a similar ≥4, FDR p-value ≤0.01; Fig. 7). Time-series analysis proportion of up- and down-regulation. Upregulated tran- showed a higher number of upregulated RxLR effec- scripts were associated with processes involved in energy tors at 4 dpi in Si plants, in line with observations production, such as carbohydrate metabolism, TCA cycle, of first symptoms in soybean plants. On the other gluconeogenesis, mitochondrial electron transport, ATP hand, while the number was always higher in Si- biosynthesis and amino acids biosynthesis, as well as bio- plants, there were more RxLR effectors expressed in synthesis of lipid metabolism (Additional file 14:FigureS5). Si + plants at 7 dpi than at 4 dpi. Crinkler (CRN) effectors are another group of Phytophthora sojae transcriptome important secreted effectors by P. sojae. Of the 226 CRN More than 90% of the processed reads from the five bio- effector genes identified in P. sojae, 21 were found to be logical replications of P. sojae cultured in vitro mapped differentially expressed in at least one timepoint to the P. sojae genome (Additional file 15: Table S10). (Fold-change ≥4, FDR p-value ≤0.01; Fig. 7). Cluster To determine which genes were differentially expressed comparison showed a higher number of upregulated − + throughout the interaction with soybean, we compared CRN effectors in Si plants compared to Si plants and gene expression in planta with axenic samples. The particularly at 4 dpi. Interestingly, the recently reported ab Fig. 5 Expression profile of hormone-related genes. Gene expression shows a higher expression of five genes involved in SA metabolism (a), 13 − + genes involved in JA metabolism (b)in Phytophthora sojae-inoculated soybean plants at 4 dpi under Si compared to Si treatment. Graph shows the average relative (%) expression at each timepoint based on the highest level of expression for each gene as a measure to showcase the trend in expression dynamics. Bars represent standard error from the mean (n =5) Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 6 of 13 ab c Fig. 6 Venn diagram. Graphs show the number of differentially expressed genes (DEGs) in Phytophthora sojae infecting soybean in comparison − + with P. sojae in axenic culture. Number of DEGs (Fold-change ≥4, FDR p-value ≤0.01) observed in Si plants (a), Si plants (b) at 4 (blue), 7 (yellow) and 14 (green) dpi. and (c) comparative number of DEGs in P. sojae between Si + (blue) and Si- (yellow) plants at 4, 7, 14 dpi. The number of unique DEGs was consistently higher in Si- plants than in Si + plants and the highest at 4 dpi. The up and down arrows indicate the number of up- and down- regulated genes, respectively, for each timepoint apoplastic effector PsXEG1 and its decoy (PsXLP1;[34]) necrosis-inducing proteins, were highly expressed at had no expression under our experimental conditions. 7and/or14dpi (Fig. 8). Hierarchical cluster analysis was performed on P. sojae candidate secreted effector proteins (CSEPs) in Discussion order to identify genes with similar expression pro- A full comparative transcriptomic analysis of the effect files at different timepoints. Our results showed that of Si on P. sojae-infected soybean plants in this study some CSEPs were clearly more expressed at 4 dpi suggests that Si may protect plants by interfering with and particularly in the Si treatment (Fig. 8a). In the the dialogue between pathogen effectors and plant de- same manner, other CSEPs were preferentially fenses receptors, thus preventing a compatible inter- expressed at 7 dpi (Fig. 8b), while others were at the action. To date, the protective effect of Si against a later stage of infection (Fig. 8c), and the higher various range of plant pathogens has been well docu- expression was always associated with the Si treat- mented [3, 16, 46], but many questions regarding how it ment.Notably,these CSEPsshowed noexpressionin exacts its role on plant defenses remain unanswered. axenic culture. Interestingly, only CSEPs with an The mechanical barrier impeding fungal penetration was RxLR motif were highly expressed at 4 dpi, while the first explanation of the protective role of Si [47], but CRN effectors and members of other groups, such as this hypothesis has been slowly abandoned, namely based on results showing no sufficient increase in leaf toughness to retard fungal penetration following Si ap- plication [7, 54]. Cherif et al. [6, 8] were the first to re- port the induction of defense responses such as lignin, phenolic compounds and phytoalexins in association with root application of Si on cucumber plants infected by Pythium ultimum, an observation that has since been reproduced in other host-pathogen interactions [16, 17, 20, 40]. However, Vivancos et al. [46] recently reported that mutant Arabidopsis plants unable to mount defense reactions through the SA pathway were still protected against powdery mildew (Erisyphe cichoracearum) when fed with Si. This suggested that, unlike the mechanism proposed by Fawe et al. [17], the role of Si was not solely Fig. 7 Number of upregulated effector genes in Phytophthora sojae over time. Cluster comparison of P. sojae effectors in association with linked to the activation of defense responses, as sup- + − silicon-treated (Si )oruntreated(Si ) soybean plants shows the larger ported by results from this study. number of upregulated Crickler and RxLR effector genes in Si plants Our data have clearly established that the Si treat- compared to Si plants, particularly at 4 dpi ment offered a good protection of soybean plants Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 7 of 13 Fig. 8 Comparison of differentially expressed Phytophthora sojae CSEPs over time. Tables include normalized mean of the most differentially expressed CSEPs genes compared to axenic cultures specific to P. sojae at (a) 4 dpi, (b) 7 dpi and (c) 14 dpi (Fold-change ≥ 4, FDR p-value ≤ 0.01). Hierarchical cluster analysis at (a) 4 dpi, (b) 7 dpi and (c) 14 dpi shows a systematically higher level of expression in Si-soybean plants than in Si+ plants. Each graph shows the average relative (%) cluster expression at each timepoint based on the highest level of expression for each gene as a measure to showcase the trend in expression dynamics. Bars represent standard error from the mean (n =5) against P. sojae, confirming a previous report by Guérin insights into the mechanisms by which Si can protect et al. [21]. From a practical point of view, biotrophic plants. From the onset, it was interesting to observe that and hemibiotrophic (such as P. sojae)pathogens dom- the effect of Si amendment on control plants was negli- inate the list of pathogens being controlled by Si, and gible, where only 50 out of the ca. 56,000 soybean tran- the effect appears more durable and less transient than scripts were expressed differentially. Given that most what is reported with necrotrophic pathogens. Given were downregulated with no clear pathways being influ- that the former pathogens are usually more enced, this reinforces the notion that the beneficial species-specific in their host range, this led Vivancos et effects of Si are strictly protective or stress-related in al. [46] to suggest that Si may somehow interfere with nature. These results corroborate previous studies on host recognition. Arabidopsis-Erysiphe cichoracearum [15], wheat-Blu- The comparative transcriptomic analysis of the infec- meria graminis f. sp. tritici [5] and rice [51], where the tion process of P. sojae on soybean, as influenced by Si, effects of Si were nil or minimal in absence of a stress. has revealed an intricate pattern of gene expression by On the other hand, they contradict other studies both P. sojae and the plant along with unprecedented claiming that Si feeding alone can improve plant growth Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 8 of 13 [24, 33, 35, 48], even though this concept is being our results demonstrated the transcriptional induction challenged by many studies, including the present one. of multiple genes involved in secondary metabolism and Soybean plants infected with P. sojae displayed an signaling. The same observations were reported in soy- active transcriptome where over 3000 genes were differ- bean with the pathogens P. sojae [36], F. virguliforme entially expressed compared to control plants, especially [38], and F. oxysporum [29]. Our work further showed in the primary metabolism and stress-defense categories. that infection by P. sojae triggered SA metabolism These observations are in line with a recent study on during the early stages of infection while JA metabolism soybean- Fusarium oxysporum by Lanubile et al. [29], was more activated during the latter stages of infec- where they found the largest portion of DEGs assigned tion, especially in Si plants. This pattern of gene ex- to these two categories. While the role of primary me- pression has been reported previously in interactions tabolism as an energy provider is undeniable, its role on between plants and hemibiotrophic pathogens such as regulation of defense responses in plants has been well canola and Leptosphaeria maculans,and soybeanand documented in many studies [41]. High expression of P. sojae [21, 36, 42]. genes involved in primary metabolism can be associated Treatment of soybean plants with 1.7 mM Si provided with hemibiotrophic pathogens hijacking plant metabol- a strong protection against P. sojae, a result supported ism for their own benefits, thus requiring a greater ex- by the phenotypes and the heat map, whereby the overall pense of energy from the plant. The up-regulation of transcriptomic response of the infected plants treated primary metabolism is consistent with previous results with Si was somewhat similar to that of control plants. on wheat-Blumeria graminis f. sp. tritici in Si treatment These results corroborate those of Chain et al. [5], who [5], where the authors associated this up-regulation with showed that Si-treated plants were basically unrespon- the presence of the biotrophic pathogen, and in the case sive to the presence of E. cichoracearum. of the soybean-Fusarium oxysporum interaction [29], the To better understand this phenomenon, the transcrip- high activation of primary metabolism was associated tomic response of P. sojae on both control and Si plants with the increased demand for energy to induce was followed and compared over time. As reported in defense-related genes. many studies, hemibiotrophic pathogens will secrete The high expression of defense-related genes in in- CSEPs into the apoplastic region of plant cells, that can fected plants gives evidence of attempts by soybean either act directly in the apoplast or be translocated into plants to fend off P. sojae, especially during the early the cell, to neutralize plant defense reactions during the stage of infection (4 dpi), while the expression was re- biotrophic stage and induce necrosis during the duced at 7 and 14 dpi as the pathogen progressed within necrotrophic phase [49]. Cluster comparison of the roots. The same result has been observed in re- differentially-expressed P. sojae CSEPs at different time- sponse to a pathogenic isolate of Fusarium oxysporum, points showed a systematically higher number in Si where soybean accumulated more defense-response plants compared to Si plants associated with a higher transcripts in the first days following infection while expression of specific CSEPs at 4 dpi in the Si treat- these responses eventually receded [29]. This chain of ment, a result that corroborates the higher expression of events has been reported in previous studies including NB-LRRs at the same timepoint. For instance, Avh324, soybean-P. sojae interaction [36], Arabidopsis-E. cichora- previously reported as an effector involved in the bio- cearum [15], wheat-Blumeria graminis f. sp. tritici [5] trophic phase of P. sojae [49], was one of the most highly and soybean-Fusarium virguliforme [38], in which it was expressed CSEPs at 4 dpi in our study. Interestingly, our suggested that the first reaction of plants to pathogens results showed both a fewer number of RxLR effectors up- was the activation of an array of defense-related genes, regulated in Si + plants, and a delayed pattern from 4 to even in the case of compatible interactions. In the same 7 days in Si + plants compared to Si- plants. These results manner, at 4 dpi in Si plants, the higher activation of are perfectly congruent with a multitude of previous re- NB-LRR receptors, which are known as markers of acti- ports showing that Si will delay the onset of disease and vation of effector-triggered immune (ETI) responses, led reduce its magnitude [6, 7, 15, 20, 22]. Other effectors cat- nevertheless to a compatible interaction as opposed to egorized as necrosis-inducing effectors, such as Avh238 Si plants. This shows that expression of defense-related [49], were found to be highly expressed in the later stages genes does not necessarily translate into a resistance re- of infection in Si plants. The relative down-regulation of sponse. For instance, the higher activation of NB-LRRs CSEPs in incompatible interactions has been reported in has been also reported in the compatible soybean-Fusar- Arabidopsis-Blumeria graminis f. sp. hordei [25]. These re- ium virguliforme interaction [38]. The pattern of expres- sults are well in agreement with the phenotypic differ- − + sion for other pathways related to defense reactions was ences observed between Si and Si plants. similar where higher expression was consistently ob- Our results clearly demonstrate that feeding soybean served at 4 dpi in Si plants. In line with this pattern, plants with Si 7 days before inoculation with P. sojae Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 9 of 13 resulted in some protection against infection and a sys- P. sojae had a much lower diversity and intensity of ef- tematic deposition of Si in soybean roots (see Fig. 1). It fector transcripts on Si plants. These results support has been well demonstrated that Si will deposit in plants the hypothesis that Si interferes with the signaling in the form of amorphous silicon primarily in the apo- process between a plant and a biotrophic/hemibio- plastic region of plant cells [2, 55]. As mentioned previ- trophic pathogen to elicit an incompatible interaction. ously, the apoplast is a privileged site of initial release or activity for secreted CSEPs [37]. The apoplast plays a Methods crucial role in plant-pathogen interactions as it will dic- To evaluate the phenotypic responses of soybean plants tate the establishment or suppression of a pathogen to a Si treatment, we used the methodology developed based on the interaction between pathogen effectors and by Guérin et al. [21]. Plants were grown in hydroponic plant defenses and receptors [4, 50, 52]. Furthermore, systems with four different treatments: soybean plants filamentous pathogens/oomycetes will often release ef- inoculated with P. sojae and grown with and without Si fectors initially in the apoplast before they get translo- (pH 7.0) in the form of potassium silicate, and control cated inside the cell where they can interact with other plants (without P. sojae) grown with and without Si. In receptors such as NLRs [37]. Along these lines, recent all experiments, Si concentration was adjusted to reports have emphasized the particular importance of 1.7 mM as it represents the highest possible concentra- the apoplastic region for the development of P. sojae. tion of silicic acid in solution and it is the standard Most notably, Ma et al. [34] showed that P. sojae procedure used to test the prophylactic role of Si [46]. employed an apoplastic decoy strategy where Avr gene Each treatment included two separate troughs fed with a products elude recognition by receptors thus inhibiting nutrient solution. For each treatment and each trough, defense responses. Furthermore, Xin et al. [52] proposed five plants were used for a total of 10 plants per that water availability in the apoplast played a key role in treatment. the ensuing infection by P. sojae. Taken together, our re- sults showed that Si feeding led to a form of incompat- ible interaction between soybean and P. sojae and Plant growth conditions suggest that the presence of Si in the apoplast is possibly Soybean (Glycine max cv. Hikmok sorip obtained from linked to its prophylactic properties. As a first evidence, GRIN (PI 372415)) was selected based on the consistent polymerization of Si in the apoplast is known to reduce phenotypic characteristics observed over several genera- the abundance of free water [19], which would change tions and its ability to absorb Si and its lack of Rps genes the apoplastic environment and make it less conducive [1, 22]. The genotype used in present study was devel- for P. sojae development. Secondly, the presence of oped at University Laval. For the RNA-seq and disease amorphous Si along the plasma membrane could a) re- assay, seeds collected from plants raised at Laval Univer- strict the required signals from the plant toward the sity greenhouse were used after ensuring genetic purity. pathogen which act to induce cell differentiation and ex- Seeds were immersed in 5% sodium hypochlorite for press essential pathogenicity genes, b) interfere with the 1 min, followed by three subsequent washes with dis- signaling flow between P. sojae and the plant, preventing tilled water. Then, seeds were planted in Oasis cubes for or decreasing, for instance, the release of effectors reach- 4–5 days in the greenhouse. After emergence of the ing plant receptors, c) interfere with the translocation of hypocotyl and the roots, every Oasis cube containing effectors into the plant cell and d) confine the transition one plant was transferred to the hydroponic system. of nutrients from host toward hemibiotrophic pathogen, Plants received water for 2 days, and on the third day leading to a form of non-host resistance [28, 44]. Inci- solutions containing macro- and micronutrients were dentally, Vivancos et al. [46]. showed that even added to the trough (see below), Fe Na EDTA and Si in SA-deficient Arabidopsis mutants were resistant to pow- the form of potassium silicate (Kasil #6, 23.6% SiO ; dery mildew when fed with Si and suggested that inhib- National Silicates, Quebec, QC, Canada). After 7 days, ition of effector release explained that result. zoospores of P. sojae were added to the tanks. The mac- ronutrients (KNO , KCl, CaCl ,K HPO , MgSO ·7H O, 3 2 2 4 4 2 Conclusion MgCl 6H O) were prepared as a 30X solution; 2 2 Our work presents novel insights into the mechanistic micronutrients (H BO , MnSO ·H O, CuSO ·5H O, 3 3 4 2 4 2 role by which Si deposition influences the outcome of ZnSO ·7H O, NaMoO ·2H O, Co(NO ) ·6H O) as a 4 2 4 2 3 2 2 host-pathogen interactions. More specifically, results 5,000X solution; and FeNa-EDTA (13.2%) was separately showed that a Si treatment conferred a good protection prepared as a 3000X solution. A 50-L solution contain- of soybean plants against P. sojae. The transcriptome ing 2 l of macronutrients, 12 ml of micronutrients and analysis revealed that Si-treated plants had a surprisingly 19.8 ml of Fe-EDTA, and adjusted to pH 6.5, was added lower defense response than Si-deprived plants, and that to 60-L trough. Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 10 of 13 Phytophthora sojae inoculation process was repeated at each timepoint. In preliminary For P. sojae inoculation, we followed the procedure re- experiments, results showed that first symptoms [30] cently described by Lebreton et al. [30]. The isolate of P. and first P. sojae reads were consistently detectable after sojae was obtained from the bank collected and main- 4 days in the hydroponic system. Our sampling protocol tained by A. Xue at AAFC, Ottawa. Pieces from the was adjusted accordingly. Root tissues were placed in “long-term inoculum” of P. sojae isolate ont-42-1 (patho- the liquid nitrogen until transfer to the lab and stored at type 1a, 1c, 1d, 1 k and 7) were transferred into Petri − 80 C. Total RNA was extracted from five biological dishes containing V8 agar, sealed with parafilm and in- replicates of root samples that were collected at four cubated at 28 °C. After 6 days, 60 mycelial plugs (9 mm timepoints. [0 (pre- inoculation), 4, 7 and 14 days diameter) were immersed in a Petri dish (150 × 150 mm) post-inoculation] and five biological replicates of P. sojae in 60 ml of sterile tap water added to 15 ml of sterile cultivated in axenic culture, using TRIzol and RNeasy Agromix soil extract. Five plates were sealed with Paraf- mini kit (Qiagen) including DNAse treatment as per the ilm and gently shaken on an orbital shaker at room manufacturer’s instructions. RNA quality and concentra- temperature for 18–24 h. Zoospores were observed with tion were checked by agarose gel electrophoresis, spec- a microscope, and swimming and encysted zoospores trophotometry (Nanodrop ND-1000) and ultimately, by were counted. A 1-μl drop was placed on a glass slide an Agilent 2100 Bioanalyzer™ (Agilent Technologies). and observed under a 100X microscopic field. Ten ob- RNA-seq libraries were generated using the Lexogen® servations were used to determine the zoospore concen- RNA-Seq Sample Preparation kit according to the man- tration in suspension, and cultures ranging between 10 ufacturer’s protocol (Illumina Inc.). The Lexogen® is to 10 zoospores/ml were used for inoculation. Five designed to generate Illumina compatible libraries from plates of mycelial plugs provided ca. 350 ml of zoospore polyadenylated RNA and generate only one fragment suspension that were collected in a 500-ml bottle and per transcript. The efficiency of the cDNA library was added directly to the nutrient solution in 60-l tanks. The measured by an Agilent 2100 Bioanalyzer™ and sequencing spore suspension or an equal volume of water for con- was carried out on an Illumina HiSeq™ 2000 platform. trol plants was added to the tanks 7 days after transfer of seedlings to the hydroponic system. RNA-Seq data analysis The plants were monitored daily to record symptom Raw reads processing and high-quality reads alignment to development. After 21 days, plants were harvested and the reference genomes dried at 65 °C for 24 h to determine their dry weight. Poly-A, adaptor sequence contaminants and low-quality For axenic cultures of P. sojae, cellophane papers were bases (Q < 15) were trimmed from Illumina reads in placed on the top of V8 agar containing Petri dishes and FASTQ format using the RNA-seq analysis tool of CLC the pieces from the same “long-term inoculum” of P. Genomics Workbench V.9.5.4 (CLC Bio, Aarhus, sojae isolate ont-42-1 were transferred on the paper in Denmark) before further processing. All cleaned reads V8 agar culture, sealed with parafilm and incubated at >40 bp in length were aligned to the PLANAT ribosomal 28 °C. After 6 days, mycelium of P. sojae was removed RNA sequence to eliminate unexpected ribosomal RNA from the paper, lyophilized and ground for RNA from reads; the remaining unmapped reads were then extraction. aligned to the soybean reference genome (Glycine max Wm82.a2. v1) and then against the P. sojae reference Microscopic and X-ray analyses genomes (Phytophthora sojae V3). The criteria used X-ray microanalysis mapping was used to locate Si to map the unique sequence reads included: the mis- deposition in soybean roots fed with and without Si. At matchcostof2,insertcostof3, deletioncostof3, least five root samples per treatment were prepared as minimum length fraction of 0.9 and minimum simi- described by Guével et al. [23]. Briefly, roots were lyoph- larity fraction of 0.8. ilized and coated with gold and palladium to provide conductivity to the samples. Samples were analyzed Gene expression, differential gene expression analyses and using a CAMECA SX-100 Universal EPMA microscope clustering (Cameca instruments Inc., www.cameca.com) operating The normalized expression values were taken to esti- at a voltage of 15 kV and a current of 20 nA. mate the expression levels. The differentially expressed genes (DEGs) were identified using the EdgeR algorithm RNA extraction, library construction and sequencing implemented in CLC Genomics Workbench that utilizes Five separate soybean plants (biological replications) per the Exact Test developed by Robinson and Smyth [39]. treatment were grown in the hydroponic system. For The p-value threshold was determined by the false dis- each treatment, 3-cm samples of top roots were covery rate (FDR) to account for multiple tests of signifi- harvested from each of the treated plants and this cance. To judge the significance of the gene expression, Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 11 of 13 change during plant-pathogen interactions, a FDR expression at each timepoint based on the highest level of expression for threshold ≤0.01 was adopted. The absolute of log 2 (Fold each gene as a measure to showcase the trend in expression dynamics. Bars represent standard error from the mean (n = 5). (TIF 86 kb) change) > 4 relative to control conditions was used to se- Additional file 8: Table S6. Expression data of WRKY transcription factor lect up- and/or downregulated genes. To identify general genes. Normalized mean (n = 5), fold-change and FDR p-value of 20 WRKY trends during the interaction, a hierarchical clustering of genes in soybean plants treated (Si+) or not (Si-) with silicon (Si) at 0, 4, + − features was also generated for each condition (Si /Si ) 7, and 14 dpi with Phytophthora sojae. (XLSX 16 kb) using normalized expression values. Given that RNA-seq Additional file 9: Figure S3. Expression profile of a) protease inhibitors and b) polyphenol oxidase. Gene expression shows a higher expression analysis has been conducted on more than three repli- of 13 protease inhibitor genes (a) and five polyphenol oxidase genes (b) cates for each treatment, qPCR validation was deemed − in Phytophthora sojae-inoculated soybean plants at 4 dpi under Si unnecessary [14]. compared to Si treatment. Graph shows the average relative (%) expression at each timepoint based on the highest level of expression for each gene as a measure to showcase the trend in expression dynamics. Functional annotation and gene ontology Bars represent standard error from the mean (n = 5). (TIF 163 kb) The web-based Blast2GO and AgriGO tools [13] were Additional file 10: Table S7. Expression data of a) protease inhibitor genes and b) polyphenol oxidase genes. Normalized mean (n = 5), fold-change and used to obtain Gene Ontology (GO) annotations and to FDR p-value of 13 protease inhibitor genes (sheet 1) and five polyphenol oxi- perform singular enrichment analyses (FDR p-value dase genes (sheet 2) in soybean plants treated (Si+) or not (Si-) with silicon ≤0.05) of genes of soybean and P. sojae differentially (Si) at 0, 4, 7, and 14 dpi with Phytophthora sojae.(XLSX 37 kb) expressed during the interaction. Significant enrichment Additional file 11: Figure S4. Expression profile of secondary metabolism-related genes. Gene expression shows a higher expression of testing was performed for GO categories and Mercator 31 genes involved in flavonoid metabolism (a), nine genes involved in bins with Mapman [43] to visualize the pathways that isoflavone metabolism (b) and 15 genes involved in isoprenoid metabolism −- were activated during the time of infection. (c) in Phytophthora sojae-inoculated soybean plants at 4 dpi under Si compared to Si treatment. Graph shows the average relative (%) expression at each timepoint based on the highest level of expression for each gene as Additional files a measure to showcase the trend in expression dynamics. Bars represent standard error from the mean (n = 5). (TIF 261 kb) Additional file 1: Table S1. Summary of read numbers obtained from Additional file 12: Table S8. Expression data of secondary metabolism- related genes. Normalized mean (n = 5), fold-change and FDR p-value of soybean plants and Phytophthora sojae following inoculation experiments over time on plants treated or not with silicon (Si). Total read numbers 31 genes involved in flavonoid metabolism (sheet 1), nine genes involved and read numbers aligned onto soybean and P. sojae genomes are given in isoflavone metabolism (sheet 2) and 15 genes involved in isoprenoid in millions ± SE. (DOCX 14 kb) metabolism (sheet 3) in soybean plants treated (Si+) or not (Si-) with silicon (Si) at 0, 4, 7, and 14 dpi with Phytophthora sojae. (XLSX 48 kb) Additional file 2: Table S2. Normalized expression of differentially expressed genes in soybean roots infected with Phytophthora sojae. In Additional file 13: Table S9. Expression data of hormone-related genes. total, 3294 genes were differentially expressed as a result of P. sojae Normalized mean (n = 5), fold-change and FDR p-value of five genes in- + − volved in SA metabolism (sheet 1), and13 genes involved in JA metabol- infection at day 4 after inoculation (P Si ). The normalized mean (n =5) ism (sheet 2) in soybean plants treated (Si+) or not (Si-) with silicon (Si) at of the differentially expressed transcripts (Fold-change ≥4, FDR p-value + − ≤0.01) are presented in soybean roots inoculated (P ) or not (P ) with P. 0, 4, 7, and 14 dpi with Phytophthora sojae. (XLSX 36 kb) + − sojae and treated (Si ) or not (Si ) with silicon. (XLSB 215 kb) Additional file 14: Figure S5. Heat map of differentially expressed genes involved in primary metabolism. Heat map shows gene expression Additional file 3: Figure S1. Expression profile of signaling-related pattern of 580 DEGs involved in primary metabolism in soybean roots genes. Gene expression shows a higher expression of 46 receptor kinase + −- + −- family genes (a), 24 RLK genes (b), 5 MAPKs genes (c), and 33 CDPKs inoculated (P ) or not (P ) with P. sojae and treated (Si ) or not (Si ) genes (d) in Phytophthora sojae-inoculated soybean plants at 4 dpi under with silicon showing a notable higher expression of genes in P. sojae- − + infected plants. Each gene corresponds to a colored line indicating the Si compared to Si treatment. Graph shows the average relative (%) ex- pression at each timepoint based on the highest level of expression for normalized mean (n = 5) of the differentially expressed transcripts (Fold- each gene as a measure to showcase the trend in expression dynamics. change ≥ 4, FDR p-value ≤ 0.01). (TIF 205 kb) Bars represent standard error from the mean (n = 5). (TIF 371 kb) Additional file 15: Table S10. Summary of read numbers obtained from the five biological replications of Phytophthora sojae in axenic Additional file 4: Table S3. Expression data of signaling-related genes. Normalized mean (n = 5), fold-change and FDR p-value of 46 receptor culture. Total read numbers and read numbers aligned onto P. sojae kinase family genes (sheet 1), 24 RLK genes (sheet 2), 5 MAPKs genes genomes are given in millions. (DOCX 13 kb) (sheet 3), and 33 CDPKs genes (sheet 4) in soybean plants treated (Si+) or Additional file 16: Table S11. List of the top 100 upregulated not (Si-) with silicon (Si) at 0, 4, 7, and 14 dpi with Phytophthora sojae. Phytophthora sojae genes during the compatible interaction with (XLSX 65 kb) soybean plants at 4, 7 and 14 dpi. (DOCX 86 kb) Additional file 5: Table S4. Expression data of NB-LRRs genes. Normalized mean (n = 5), fold-change and FDR p-value of 45 receptor (NB-LRR) genes in soybean plants treated (Si+) or not (Si-) with silicon (Si) Abbreviations 2+ at 0, 4, 7, and 14 dpi with Phytophthora sojae. (XLSX 22 kb) Avr: Avirulence; CDPKs: Ca -Dependent Protein Kinases; CRN: Crinkler; CSEPs: Candidate Secreted Effector Proteins; DEGs: Number of Differentially Additional file 6: Table S5. Expression data of PR genes. Normalized Expressed Genes; dpi: Days Post Inoculation; ETI: Effector-Triggered Immunity; mean (n = 5), fold-change and FDR p-value of 11 pathogenesis-related FDR: False Discovery Rate; GO: Gene Ontology; JA: Jasmonic Acid; (PR) genes in soybean plants treated (Si+) or not (Si-) with silicon (Si) at 0, MAPKs: Mitogen-Activated Protein Kinases; NB-LRR: Nucleotide-Binding 4, 7, and 14 dpi with Phytophthora sojae. (XLSX 12 kb) Leucine-Rich Repeat; P. sojae: Phytophthora sojae; PAMPs: Pathogen- Additional file 7: Figure S2. Expression profile of WRKY transcription Associated Molecular Patterns; PRRs: Pattern Recognition Receptors; factor genes. Gene expression shows a higher expression of 20 WRKY − PRs: Pathogenesis-Related proteins; PTI: PAMPs -Triggered Immunity; genes in Phytophthora sojae-inoculated soybean plants at 4 dpi under Si + RLK: Receptor-Like Kinases; Rps: Resistance genes to P. sojae; SA: Salicylic Acid; compared to Si treatment. 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Silicon protects soybean plants against Phytophthora sojae by interfering with effector-receptor expression

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Abstract

Background: Silicon (Si) is known to protect against biotrophic and hemibiotrophic plant pathogens; however, the mechanisms by which it exerts its prophylactic role remain unknown. In an attempt to obtain unique insights into the mode of action of Si, we conducted a full comparative transcriptomic analysis of soybean (Glycine max) plants and Phytophthora sojae, a hemibiotroph that relies heavily on effectors for its virulence. Results: Supplying Si to inoculated plants provided a strong protection against P. sojae over the course of the experiment (21 day). Our results showed that the response of Si-free (Si ) plants to inoculation was characterized early (4 dpi) by a high expression of defense-related genes, including plant receptors, which receded over time as the pathogen progressed into the roots. The infection was synchronized with a high expression of effectors by P. sojae, the nature of which changed over time. By contrast, the transcriptomic response of Si-fed (Si ) plants was remarkably unaffected by the presence of P. sojae, and the expression of effector-coding genes by the pathogen was significantly reduced. Conclusion: Given that the apoplast is a key site of interaction between effectors and plant defenses and receptors in the soybean-P. sojae complex, as well as the site of amorphous-Si accumulation, our results indicate that Si likely interferes with the signaling network between P. sojae and the plant, preventing or decreasing the release of effectors reaching plant receptors, thus creating a form of incompatible interaction. Keywords: Transcriptome, Glycine max, Plant receptors, Effectors, Defense genes Background where the pathogen can disseminate easily because of its Soybean (Glycine max L. Merr.) is economically and flagellated zoospores [45]. It is described as a hemibio- agriculturally the most important legume in the world, trophic pathogen and it secretes effector proteins (coded but its production is compromised by many biotic and by Avr genes) to manipulate and invade living host cells abiotic factors. Of primary importance, Phytophthora during the initial biotrophic stage of infection. In sojae Kaufm. and Gred. [27] can cause annual yield plant-pathogen interactions, effectors are recognized as losses as high as $200 million in the USA and $1–2 important virulence factors that are utilized by the billion worldwide [31]. Phytophthora sojae is a pathogen to suppress PAMPs (Pathogen-Associated soil-borne plant pathogen belonging to the oomycetes Molecular Patterns)-Triggered Immunity (PTI) and with a restricted host range, including soybean as its pri- Effector-Triggered Immunity (ETI) in plants or change mary host. It causes root and stem rot, and pre- and host metabolism so that it can easily colonize plant tis- post-emergence damping-off, particularly in flooded soils sues [12, 50]. In response, soybean can carry resistance genes to P. sojae (Rps), that encode, or are predicted to encode, nucleotide-binding leucine-rich repeat * Correspondence: richard.belanger@fsaa.ulaval.ca (NB-LRR)-type proteins [19, 26], which are able to Département de Phytologie, Faculté des Sciences de l’Agriculture et de recognize the Avr effector proteins of P. sojae and induce l’Alimentation, Université Laval, Québec City, Québec G1V 0A6, Canada Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 2 of 13 the appropriate defense response [10, 18]. The result of altered by silicon’s presence. Finally, Wang et al. [50], on this interaction between Rps genes and Avr genes will the basis of recent results with P. sojae, described the often determine compatible or incompatible interactions. apoplastic region as a major battle ground between Numerous studies have highlighted the prophylactic pathogen effectors and the host apoplastic surveillance effects of silicon (Si) fertilization [3, 16, 20] in the search system. for additional methods to prevent losses in the case of Since P. sojae is a hemibiotrophic pathogen that relies compatible interactions. Interestingly, Si appears to be heavily on effectors for its virulence, the P. sojae-soybean particularly efficient against biotrophic and hemibio- pathosystem was deemed well-suited to validate and trophic fungal/oomycete pathogens [5, 46]. In the case investigate the hypothesis that Si deposition altered the of soybean, Arsenault-Labrecque et al. [1] have shown release of virulence factors by P. sojae. In this context, that a Si treatment was effective against soybean rust two main objectives were defined: 1) to assess resistance caused by the biotrophic fungus Phakopsora pachyrizi. of soybean plants to P. sojae when fertilized with Si, and In addition, Deshmukh et al. [9] have identified Si trans- 2) to analyze the expression of salient genes involved in porters in soybean, thus confirming that the species is the virulence of P. sojae and the defense mechanisms of receptive to Si and can absorb the element. soybean in order to assess if a differential response could The mechanisms inherent to the prophylactic proper- be linked to the prophylactic role of Si. ties of Si have puzzled scientists for many years. Origin- ally, it was suggested that Si deposition along the cell Results walls created a physical barrier that halted fungal pene- Phenotypic responses tration into the plant [47]. However, additional studies Soybean plants were inoculated with zoospores of P. have linked the presence of Si with diverse plant-defense sojae in a recirculating hydroponic system fed with nu- reactions, thus suggesting that Si may play a role in the trient solution with and without 1.7 mM Si to compare induction of acquired resistance [6, 7, 16, 17, 53]. In a the phenotypic differences linked to Si. First symptoms recent study using Arabidopsis thaliana mutants defi- of root browning appeared as early as 4 days post inocu- cient in salicylic acid (SA) synthesis, Vivancos et al. [46] lation (dpi). Stunting and leaf discoloration followed showed that Si protected both mutant and wild-type within a few days, and first cases of mortality were plants against powdery mildew (Erisyphe cichora- recorded at 15 dpi in the Si treatment. The differences − + cearum). This led the authors to suggest that the depos- between Si and Si treatments increased with time, and ition of Si as amorphous gel in the apoplast may prevent by 21 dpi, plants in the Si treatment were clearly fungal effectors from reaching their targets, thereby healthier than non-treated plants (Fig. 1a). In terms of altering the development of the pathogen. This hypoth- dry weight, for non-inoculated plants there was no − + esis becomes particularly relevant in the context of the significant difference between Si (8.4 ± 0.5) and Si P. sojae-soybean interaction in light of recent results. plants (8.7 ± 0.4) plants. However, inoculation with P. Indeed, Ma et al. [34] recently showed that P. sojae sojae significantly reduced plant dry weight, but the employed an apoplastic decoy strategy with effectors to prophylactic effect of Si was quite apparent as plants attack soybean. Xin et al. [52] further proposed that an were significantly heavier in the Si (5.0 g ± 1.9) com- aqueous apoplast was required for pathogenicity rather pared with the Si (2.0 g ± 1.0) treatment. X-ray than immunosuppression, a condition that can be microanalysis mapping of soybean confirmed the a bc Fig. 1 Effect of silicon (Si) amendments on soybean plants 21 days after inoculation with Phytophthora sojae. a Plants in the Si treatment were clearly healthier than non-treated plants with more developed roots, stems and leaves. Comparative X-ray superimposed scanning electron micrographs of soybean root tips in plants treated (b) or not (c) with Si. At least, five plants per treatments were observed. A color scale of Si deposition was used, with blue indicating low Si and red high Si deposition. Black areas indicated no Si deposition Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 3 of 13 accumulation of Si throughout the roots in Si plants (Fig. 1b), while, in the absence of Si amendment, no clear evidence of Si deposition was observed (Fig. 1c). Dual RNA-seq analysis of the P. sojae-soybean interaction in the presence of Si A complete comparative transcriptomic analysis of soy- bean roots and P. sojae was carried out at 0, 4, 7 and 14 dpi to obtain a comprehensive gene-expression profile for both soybean and P. sojae in response to Si application. Soybean root transcriptome Mapping of the processed reads from roots to the soybean genome showed a very high percentage of mapped reads for non-inoculated samples (control) treated or not with Si. For control plants, 81 and 90% of reads mapped on − + soybean in Si and Si treatments, respectively. In inocu- lated plants at 4 dpi, 61 and 76% of reads mapped to soy- − + bean in Si and Si , respectively (Additional file 1:Table S1). Interestingly, the number of differentially expressed genes (DEGs) between control plants treated or not with Si was limited to 50 out of the potential 56,045 genes ana- Fig. 2 Heat map of differentially expressed genes in Phytophthora lyzed, and all were downregulated in the Si treatment sojae infecting soybean roots. In total, 3294 genes were differentially (Fold-change ≥4, FDR p-value ≤0.01). On the other hand, + − expressed as a result of P. sojae infection at 4 dpi (P Si ). Heat map − + plants responded to inoculation of P. sojae (Si P vs. shows gene expression pattern in soybean roots inoculated (P )or − − − + − not (P ) with P. sojae and treated (Si ) or not (Si ) with silicon. Each Si P ) with a differential expression of 3294 genes (Add- gene corresponds to a colored line indicating the normalized mean itional file 2: Table S2). Most of genes that were differen- (n = 5) of the differentially expressed transcripts (Fold-change ≥4, − + − − tially expressed as a result of the infection (Si P vs Si P ) FDR p-value ≤0.01) reverted to a pattern of expression closer to control plants + + + − +) in the Si treatment (Si P vs Si P as illustrated on the heat map (Fig. 2). expression at 4 dpi in the Si treatment (Additional file 3: 2+ Functional categorization of the DEGs in P. sojae-in- Figure S1c). Similarly, 33 Ca -dependent protein kinases fected plants showed that these genes belonged mainly (CDPKs) were highly expressed at 4 dpi (Additional file 3: to the following categories: defense-related genes, Figure S1d, Additional file 4: Table S3). secondary metabolism, hormone metabolism, primary NB-LRR proteins. Out of 80 differentially-expressed metabolism and no-ontology for which no function was NB-LRR genes over the experimental period annotated. (Fold-change ≥4, FDR p-value ≤0.01), 45 showed their highest expression at 4 dpi in Si plants. Heat map Defense-related genes results clearly showed a pattern of expression where Most known pattern recognition receptors (PRRs) that there was no expression of NB-LRR genes in can activate PTI in plants fall into one of two receptor non-inoculated plants (control) regardless of Si treat- classes: transmembrane receptor kinases and ment, followed by a sharp increase at 4 dpi in Si plants. receptor-like kinases (RLK; [10, 29, 32]). In our study, 46 While the expression was reduced at 7 and 14 dpi, it DEGs belonged to the receptor kinase family and 24 remained significantly higher in Si plants (Fig. 3, RLK showed higher expression at 4 dpi in the Si treat- Additional file 5: Table S4). ment (Additional file 3: Figure S1 a, b). After PRR acti- Pathogenesis-related proteins (PRs). Based on cluster ana- vation, the downstream signaling pathway transfers lysis, 11 PR genes were found to be differentially expressed signals from extracellular receptors to cellular responses in at least one timepoint (Fold-change ≥4, FDR p-value by mitogen-activated protein kinases (MAPKs) and cal- ≤0.01). Incidentally, heat-map results clearly showed that 4 2+ − + cium (Ca ). MAPKs are ubiquitous signal-transduction dpi was the critical timepoint differentiating Si and Si components, which have been implicated in both PTI plants in terms of PR-gene expression (Fig. 4, Additional file and ETI. Our results showed that out of nine 6: Table S5). The expression receded over time (7 and 14 differentially-expressed MAPKs, five had a higher dpi) to similar levels between the treatments. Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 4 of 13 Fig. 3 Expression profile of NB-LRRs genes. Heat map (a) and gene expression (b) show a higher expression of 45 receptor (NB-LRR) genes in − + Phytophthora sojae-inoculated soybean plants at 4 dpi under Si compared to Si treatment. b Graph shows the average relative (%) expression at each timepoint based on the highest level of expression for each gene as a measure to showcase the trend in expression. Bars represent standard error from the mean (n =5) Transcription Factors Associated with Defense plant protease inhibitors in at least one timepoint, 13 Expression. Out of the 67 differentially-expressed WRKY showed higher expression in the Si treatment at 4 dpi. transcription factors in at least one timepoint, most Similarly, out of the seven differentially-expressed poly- showed a higher expression in Si treatment, with 20 genes phenol oxidases in at least one timepoint, five showed − − showing a higher expression at 4 dpi in the Si treatment higher expression at 4 dpi in the Si treatment (Additional (Additional file 7: Figure S2, Additional file 8:Table S6). file 9: Figure S3, Additional file 10: Table S7). Miscellaneous defense responses. Other genes linked to defense responses showed a similar pattern of expression Secondary metabolism where they were quickly upregulated at 4 dpi in Si plants We observed 31 genes involved in flavonoid metabolism + − before receding to levels similar to those observed in Si with a higher expression at 4 dpi in the Si treatment. plants. For instance, of the 16 differentially-expressed We also observed nine genes involved in isoflavone ab Fig. 4 Expression profile of PR genes. Heat map (a) and gene expression (b) show a higher expression of 11 pathogenesis-related (PR) genes in − + Phytophthora sojae-inoculated soybean plants at 4 dpi under Si compared to Si treatment. b Graph shows the average relative (%) expression at each timepoint based on the highest level of expression for each gene as a measure to showcase the trend in expression dynamics. Bars represent standard error from the mean (n =5) Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 5 of 13 metabolism and 15 genes involved in isoprenoid metab- number of DEGs in P. sojae was higher under Si com- − + olism, all with higher expression at 4 dpi in the Si treat- pared to Si conditions (Fig. 6). The highest number of − + ment (Additional file 11: Figure S4, Additional file 12: DEGs per treatment (Si or Si ) was recorded at 4 dpi, Table S8). and this number kept receding over time. Hormone metabolism Annotation In our system, five genes involved in SA metabolism had When looking at the top 100 upregulated DEGs in P. sojae higher expression at 4 dpi in the Si treatment, in syn- over all timepoint and treatments, most were linked to chrony with the biotrophic phase of P. sojae, and 13 hypothetical proteins for which functional annotation was genes involved in JA metabolism were differentially not available (Additional file 16: Table S11). A notable ex- expressed with higher expression at 14 dpi in the same treat- ception was Avh1b-81, which ranked among the highest ment (Fig. 5, Additional file 13: Table S9), a period more expressed genes at 4 and 7 dpi in the Si treatment. in-line with the necrotrophic development of the pathogen. − + Primary metabolism Expression of effectors in P. sojae during Si and Si Based on functional categorization, primary metabolism in- treatments cluded the most categories that contained DEGs. Out of In oomycetes, many effectors are characterized by an 580 DEGs involved in primary metabolism as a result of P. RxLR motif [11]. Of the 348 RxLR effector genes sojae infection in the Si treatment, 70% were upregulated identified in P. sojae, 104 were found to be differen- and 30% were downregulated. In the Si treatment, the tially expressed in at least one timepoint (Fold-change number of DEGs was reduced to 420 genes with a similar ≥4, FDR p-value ≤0.01; Fig. 7). Time-series analysis proportion of up- and down-regulation. Upregulated tran- showed a higher number of upregulated RxLR effec- scripts were associated with processes involved in energy tors at 4 dpi in Si plants, in line with observations production, such as carbohydrate metabolism, TCA cycle, of first symptoms in soybean plants. On the other gluconeogenesis, mitochondrial electron transport, ATP hand, while the number was always higher in Si- biosynthesis and amino acids biosynthesis, as well as bio- plants, there were more RxLR effectors expressed in synthesis of lipid metabolism (Additional file 14:FigureS5). Si + plants at 7 dpi than at 4 dpi. Crinkler (CRN) effectors are another group of Phytophthora sojae transcriptome important secreted effectors by P. sojae. Of the 226 CRN More than 90% of the processed reads from the five bio- effector genes identified in P. sojae, 21 were found to be logical replications of P. sojae cultured in vitro mapped differentially expressed in at least one timepoint to the P. sojae genome (Additional file 15: Table S10). (Fold-change ≥4, FDR p-value ≤0.01; Fig. 7). Cluster To determine which genes were differentially expressed comparison showed a higher number of upregulated − + throughout the interaction with soybean, we compared CRN effectors in Si plants compared to Si plants and gene expression in planta with axenic samples. The particularly at 4 dpi. Interestingly, the recently reported ab Fig. 5 Expression profile of hormone-related genes. Gene expression shows a higher expression of five genes involved in SA metabolism (a), 13 − + genes involved in JA metabolism (b)in Phytophthora sojae-inoculated soybean plants at 4 dpi under Si compared to Si treatment. Graph shows the average relative (%) expression at each timepoint based on the highest level of expression for each gene as a measure to showcase the trend in expression dynamics. Bars represent standard error from the mean (n =5) Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 6 of 13 ab c Fig. 6 Venn diagram. Graphs show the number of differentially expressed genes (DEGs) in Phytophthora sojae infecting soybean in comparison − + with P. sojae in axenic culture. Number of DEGs (Fold-change ≥4, FDR p-value ≤0.01) observed in Si plants (a), Si plants (b) at 4 (blue), 7 (yellow) and 14 (green) dpi. and (c) comparative number of DEGs in P. sojae between Si + (blue) and Si- (yellow) plants at 4, 7, 14 dpi. The number of unique DEGs was consistently higher in Si- plants than in Si + plants and the highest at 4 dpi. The up and down arrows indicate the number of up- and down- regulated genes, respectively, for each timepoint apoplastic effector PsXEG1 and its decoy (PsXLP1;[34]) necrosis-inducing proteins, were highly expressed at had no expression under our experimental conditions. 7and/or14dpi (Fig. 8). Hierarchical cluster analysis was performed on P. sojae candidate secreted effector proteins (CSEPs) in Discussion order to identify genes with similar expression pro- A full comparative transcriptomic analysis of the effect files at different timepoints. Our results showed that of Si on P. sojae-infected soybean plants in this study some CSEPs were clearly more expressed at 4 dpi suggests that Si may protect plants by interfering with and particularly in the Si treatment (Fig. 8a). In the the dialogue between pathogen effectors and plant de- same manner, other CSEPs were preferentially fenses receptors, thus preventing a compatible inter- expressed at 7 dpi (Fig. 8b), while others were at the action. To date, the protective effect of Si against a later stage of infection (Fig. 8c), and the higher various range of plant pathogens has been well docu- expression was always associated with the Si treat- mented [3, 16, 46], but many questions regarding how it ment.Notably,these CSEPsshowed noexpressionin exacts its role on plant defenses remain unanswered. axenic culture. Interestingly, only CSEPs with an The mechanical barrier impeding fungal penetration was RxLR motif were highly expressed at 4 dpi, while the first explanation of the protective role of Si [47], but CRN effectors and members of other groups, such as this hypothesis has been slowly abandoned, namely based on results showing no sufficient increase in leaf toughness to retard fungal penetration following Si ap- plication [7, 54]. Cherif et al. [6, 8] were the first to re- port the induction of defense responses such as lignin, phenolic compounds and phytoalexins in association with root application of Si on cucumber plants infected by Pythium ultimum, an observation that has since been reproduced in other host-pathogen interactions [16, 17, 20, 40]. However, Vivancos et al. [46] recently reported that mutant Arabidopsis plants unable to mount defense reactions through the SA pathway were still protected against powdery mildew (Erisyphe cichoracearum) when fed with Si. This suggested that, unlike the mechanism proposed by Fawe et al. [17], the role of Si was not solely Fig. 7 Number of upregulated effector genes in Phytophthora sojae over time. Cluster comparison of P. sojae effectors in association with linked to the activation of defense responses, as sup- + − silicon-treated (Si )oruntreated(Si ) soybean plants shows the larger ported by results from this study. number of upregulated Crickler and RxLR effector genes in Si plants Our data have clearly established that the Si treat- compared to Si plants, particularly at 4 dpi ment offered a good protection of soybean plants Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 7 of 13 Fig. 8 Comparison of differentially expressed Phytophthora sojae CSEPs over time. Tables include normalized mean of the most differentially expressed CSEPs genes compared to axenic cultures specific to P. sojae at (a) 4 dpi, (b) 7 dpi and (c) 14 dpi (Fold-change ≥ 4, FDR p-value ≤ 0.01). Hierarchical cluster analysis at (a) 4 dpi, (b) 7 dpi and (c) 14 dpi shows a systematically higher level of expression in Si-soybean plants than in Si+ plants. Each graph shows the average relative (%) cluster expression at each timepoint based on the highest level of expression for each gene as a measure to showcase the trend in expression dynamics. Bars represent standard error from the mean (n =5) against P. sojae, confirming a previous report by Guérin insights into the mechanisms by which Si can protect et al. [21]. From a practical point of view, biotrophic plants. From the onset, it was interesting to observe that and hemibiotrophic (such as P. sojae)pathogens dom- the effect of Si amendment on control plants was negli- inate the list of pathogens being controlled by Si, and gible, where only 50 out of the ca. 56,000 soybean tran- the effect appears more durable and less transient than scripts were expressed differentially. Given that most what is reported with necrotrophic pathogens. Given were downregulated with no clear pathways being influ- that the former pathogens are usually more enced, this reinforces the notion that the beneficial species-specific in their host range, this led Vivancos et effects of Si are strictly protective or stress-related in al. [46] to suggest that Si may somehow interfere with nature. These results corroborate previous studies on host recognition. Arabidopsis-Erysiphe cichoracearum [15], wheat-Blu- The comparative transcriptomic analysis of the infec- meria graminis f. sp. tritici [5] and rice [51], where the tion process of P. sojae on soybean, as influenced by Si, effects of Si were nil or minimal in absence of a stress. has revealed an intricate pattern of gene expression by On the other hand, they contradict other studies both P. sojae and the plant along with unprecedented claiming that Si feeding alone can improve plant growth Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 8 of 13 [24, 33, 35, 48], even though this concept is being our results demonstrated the transcriptional induction challenged by many studies, including the present one. of multiple genes involved in secondary metabolism and Soybean plants infected with P. sojae displayed an signaling. The same observations were reported in soy- active transcriptome where over 3000 genes were differ- bean with the pathogens P. sojae [36], F. virguliforme entially expressed compared to control plants, especially [38], and F. oxysporum [29]. Our work further showed in the primary metabolism and stress-defense categories. that infection by P. sojae triggered SA metabolism These observations are in line with a recent study on during the early stages of infection while JA metabolism soybean- Fusarium oxysporum by Lanubile et al. [29], was more activated during the latter stages of infec- where they found the largest portion of DEGs assigned tion, especially in Si plants. This pattern of gene ex- to these two categories. While the role of primary me- pression has been reported previously in interactions tabolism as an energy provider is undeniable, its role on between plants and hemibiotrophic pathogens such as regulation of defense responses in plants has been well canola and Leptosphaeria maculans,and soybeanand documented in many studies [41]. High expression of P. sojae [21, 36, 42]. genes involved in primary metabolism can be associated Treatment of soybean plants with 1.7 mM Si provided with hemibiotrophic pathogens hijacking plant metabol- a strong protection against P. sojae, a result supported ism for their own benefits, thus requiring a greater ex- by the phenotypes and the heat map, whereby the overall pense of energy from the plant. The up-regulation of transcriptomic response of the infected plants treated primary metabolism is consistent with previous results with Si was somewhat similar to that of control plants. on wheat-Blumeria graminis f. sp. tritici in Si treatment These results corroborate those of Chain et al. [5], who [5], where the authors associated this up-regulation with showed that Si-treated plants were basically unrespon- the presence of the biotrophic pathogen, and in the case sive to the presence of E. cichoracearum. of the soybean-Fusarium oxysporum interaction [29], the To better understand this phenomenon, the transcrip- high activation of primary metabolism was associated tomic response of P. sojae on both control and Si plants with the increased demand for energy to induce was followed and compared over time. As reported in defense-related genes. many studies, hemibiotrophic pathogens will secrete The high expression of defense-related genes in in- CSEPs into the apoplastic region of plant cells, that can fected plants gives evidence of attempts by soybean either act directly in the apoplast or be translocated into plants to fend off P. sojae, especially during the early the cell, to neutralize plant defense reactions during the stage of infection (4 dpi), while the expression was re- biotrophic stage and induce necrosis during the duced at 7 and 14 dpi as the pathogen progressed within necrotrophic phase [49]. Cluster comparison of the roots. The same result has been observed in re- differentially-expressed P. sojae CSEPs at different time- sponse to a pathogenic isolate of Fusarium oxysporum, points showed a systematically higher number in Si where soybean accumulated more defense-response plants compared to Si plants associated with a higher transcripts in the first days following infection while expression of specific CSEPs at 4 dpi in the Si treat- these responses eventually receded [29]. This chain of ment, a result that corroborates the higher expression of events has been reported in previous studies including NB-LRRs at the same timepoint. For instance, Avh324, soybean-P. sojae interaction [36], Arabidopsis-E. cichora- previously reported as an effector involved in the bio- cearum [15], wheat-Blumeria graminis f. sp. tritici [5] trophic phase of P. sojae [49], was one of the most highly and soybean-Fusarium virguliforme [38], in which it was expressed CSEPs at 4 dpi in our study. Interestingly, our suggested that the first reaction of plants to pathogens results showed both a fewer number of RxLR effectors up- was the activation of an array of defense-related genes, regulated in Si + plants, and a delayed pattern from 4 to even in the case of compatible interactions. In the same 7 days in Si + plants compared to Si- plants. These results manner, at 4 dpi in Si plants, the higher activation of are perfectly congruent with a multitude of previous re- NB-LRR receptors, which are known as markers of acti- ports showing that Si will delay the onset of disease and vation of effector-triggered immune (ETI) responses, led reduce its magnitude [6, 7, 15, 20, 22]. Other effectors cat- nevertheless to a compatible interaction as opposed to egorized as necrosis-inducing effectors, such as Avh238 Si plants. This shows that expression of defense-related [49], were found to be highly expressed in the later stages genes does not necessarily translate into a resistance re- of infection in Si plants. The relative down-regulation of sponse. For instance, the higher activation of NB-LRRs CSEPs in incompatible interactions has been reported in has been also reported in the compatible soybean-Fusar- Arabidopsis-Blumeria graminis f. sp. hordei [25]. These re- ium virguliforme interaction [38]. The pattern of expres- sults are well in agreement with the phenotypic differ- − + sion for other pathways related to defense reactions was ences observed between Si and Si plants. similar where higher expression was consistently ob- Our results clearly demonstrate that feeding soybean served at 4 dpi in Si plants. In line with this pattern, plants with Si 7 days before inoculation with P. sojae Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 9 of 13 resulted in some protection against infection and a sys- P. sojae had a much lower diversity and intensity of ef- tematic deposition of Si in soybean roots (see Fig. 1). It fector transcripts on Si plants. These results support has been well demonstrated that Si will deposit in plants the hypothesis that Si interferes with the signaling in the form of amorphous silicon primarily in the apo- process between a plant and a biotrophic/hemibio- plastic region of plant cells [2, 55]. As mentioned previ- trophic pathogen to elicit an incompatible interaction. ously, the apoplast is a privileged site of initial release or activity for secreted CSEPs [37]. The apoplast plays a Methods crucial role in plant-pathogen interactions as it will dic- To evaluate the phenotypic responses of soybean plants tate the establishment or suppression of a pathogen to a Si treatment, we used the methodology developed based on the interaction between pathogen effectors and by Guérin et al. [21]. Plants were grown in hydroponic plant defenses and receptors [4, 50, 52]. Furthermore, systems with four different treatments: soybean plants filamentous pathogens/oomycetes will often release ef- inoculated with P. sojae and grown with and without Si fectors initially in the apoplast before they get translo- (pH 7.0) in the form of potassium silicate, and control cated inside the cell where they can interact with other plants (without P. sojae) grown with and without Si. In receptors such as NLRs [37]. Along these lines, recent all experiments, Si concentration was adjusted to reports have emphasized the particular importance of 1.7 mM as it represents the highest possible concentra- the apoplastic region for the development of P. sojae. tion of silicic acid in solution and it is the standard Most notably, Ma et al. [34] showed that P. sojae procedure used to test the prophylactic role of Si [46]. employed an apoplastic decoy strategy where Avr gene Each treatment included two separate troughs fed with a products elude recognition by receptors thus inhibiting nutrient solution. For each treatment and each trough, defense responses. Furthermore, Xin et al. [52] proposed five plants were used for a total of 10 plants per that water availability in the apoplast played a key role in treatment. the ensuing infection by P. sojae. Taken together, our re- sults showed that Si feeding led to a form of incompat- ible interaction between soybean and P. sojae and Plant growth conditions suggest that the presence of Si in the apoplast is possibly Soybean (Glycine max cv. Hikmok sorip obtained from linked to its prophylactic properties. As a first evidence, GRIN (PI 372415)) was selected based on the consistent polymerization of Si in the apoplast is known to reduce phenotypic characteristics observed over several genera- the abundance of free water [19], which would change tions and its ability to absorb Si and its lack of Rps genes the apoplastic environment and make it less conducive [1, 22]. The genotype used in present study was devel- for P. sojae development. Secondly, the presence of oped at University Laval. For the RNA-seq and disease amorphous Si along the plasma membrane could a) re- assay, seeds collected from plants raised at Laval Univer- strict the required signals from the plant toward the sity greenhouse were used after ensuring genetic purity. pathogen which act to induce cell differentiation and ex- Seeds were immersed in 5% sodium hypochlorite for press essential pathogenicity genes, b) interfere with the 1 min, followed by three subsequent washes with dis- signaling flow between P. sojae and the plant, preventing tilled water. Then, seeds were planted in Oasis cubes for or decreasing, for instance, the release of effectors reach- 4–5 days in the greenhouse. After emergence of the ing plant receptors, c) interfere with the translocation of hypocotyl and the roots, every Oasis cube containing effectors into the plant cell and d) confine the transition one plant was transferred to the hydroponic system. of nutrients from host toward hemibiotrophic pathogen, Plants received water for 2 days, and on the third day leading to a form of non-host resistance [28, 44]. Inci- solutions containing macro- and micronutrients were dentally, Vivancos et al. [46]. showed that even added to the trough (see below), Fe Na EDTA and Si in SA-deficient Arabidopsis mutants were resistant to pow- the form of potassium silicate (Kasil #6, 23.6% SiO ; dery mildew when fed with Si and suggested that inhib- National Silicates, Quebec, QC, Canada). After 7 days, ition of effector release explained that result. zoospores of P. sojae were added to the tanks. The mac- ronutrients (KNO , KCl, CaCl ,K HPO , MgSO ·7H O, 3 2 2 4 4 2 Conclusion MgCl 6H O) were prepared as a 30X solution; 2 2 Our work presents novel insights into the mechanistic micronutrients (H BO , MnSO ·H O, CuSO ·5H O, 3 3 4 2 4 2 role by which Si deposition influences the outcome of ZnSO ·7H O, NaMoO ·2H O, Co(NO ) ·6H O) as a 4 2 4 2 3 2 2 host-pathogen interactions. More specifically, results 5,000X solution; and FeNa-EDTA (13.2%) was separately showed that a Si treatment conferred a good protection prepared as a 3000X solution. A 50-L solution contain- of soybean plants against P. sojae. The transcriptome ing 2 l of macronutrients, 12 ml of micronutrients and analysis revealed that Si-treated plants had a surprisingly 19.8 ml of Fe-EDTA, and adjusted to pH 6.5, was added lower defense response than Si-deprived plants, and that to 60-L trough. Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 10 of 13 Phytophthora sojae inoculation process was repeated at each timepoint. In preliminary For P. sojae inoculation, we followed the procedure re- experiments, results showed that first symptoms [30] cently described by Lebreton et al. [30]. The isolate of P. and first P. sojae reads were consistently detectable after sojae was obtained from the bank collected and main- 4 days in the hydroponic system. Our sampling protocol tained by A. Xue at AAFC, Ottawa. Pieces from the was adjusted accordingly. Root tissues were placed in “long-term inoculum” of P. sojae isolate ont-42-1 (patho- the liquid nitrogen until transfer to the lab and stored at type 1a, 1c, 1d, 1 k and 7) were transferred into Petri − 80 C. Total RNA was extracted from five biological dishes containing V8 agar, sealed with parafilm and in- replicates of root samples that were collected at four cubated at 28 °C. After 6 days, 60 mycelial plugs (9 mm timepoints. [0 (pre- inoculation), 4, 7 and 14 days diameter) were immersed in a Petri dish (150 × 150 mm) post-inoculation] and five biological replicates of P. sojae in 60 ml of sterile tap water added to 15 ml of sterile cultivated in axenic culture, using TRIzol and RNeasy Agromix soil extract. Five plates were sealed with Paraf- mini kit (Qiagen) including DNAse treatment as per the ilm and gently shaken on an orbital shaker at room manufacturer’s instructions. RNA quality and concentra- temperature for 18–24 h. Zoospores were observed with tion were checked by agarose gel electrophoresis, spec- a microscope, and swimming and encysted zoospores trophotometry (Nanodrop ND-1000) and ultimately, by were counted. A 1-μl drop was placed on a glass slide an Agilent 2100 Bioanalyzer™ (Agilent Technologies). and observed under a 100X microscopic field. Ten ob- RNA-seq libraries were generated using the Lexogen® servations were used to determine the zoospore concen- RNA-Seq Sample Preparation kit according to the man- tration in suspension, and cultures ranging between 10 ufacturer’s protocol (Illumina Inc.). The Lexogen® is to 10 zoospores/ml were used for inoculation. Five designed to generate Illumina compatible libraries from plates of mycelial plugs provided ca. 350 ml of zoospore polyadenylated RNA and generate only one fragment suspension that were collected in a 500-ml bottle and per transcript. The efficiency of the cDNA library was added directly to the nutrient solution in 60-l tanks. The measured by an Agilent 2100 Bioanalyzer™ and sequencing spore suspension or an equal volume of water for con- was carried out on an Illumina HiSeq™ 2000 platform. trol plants was added to the tanks 7 days after transfer of seedlings to the hydroponic system. RNA-Seq data analysis The plants were monitored daily to record symptom Raw reads processing and high-quality reads alignment to development. After 21 days, plants were harvested and the reference genomes dried at 65 °C for 24 h to determine their dry weight. Poly-A, adaptor sequence contaminants and low-quality For axenic cultures of P. sojae, cellophane papers were bases (Q < 15) were trimmed from Illumina reads in placed on the top of V8 agar containing Petri dishes and FASTQ format using the RNA-seq analysis tool of CLC the pieces from the same “long-term inoculum” of P. Genomics Workbench V.9.5.4 (CLC Bio, Aarhus, sojae isolate ont-42-1 were transferred on the paper in Denmark) before further processing. All cleaned reads V8 agar culture, sealed with parafilm and incubated at >40 bp in length were aligned to the PLANAT ribosomal 28 °C. After 6 days, mycelium of P. sojae was removed RNA sequence to eliminate unexpected ribosomal RNA from the paper, lyophilized and ground for RNA from reads; the remaining unmapped reads were then extraction. aligned to the soybean reference genome (Glycine max Wm82.a2. v1) and then against the P. sojae reference Microscopic and X-ray analyses genomes (Phytophthora sojae V3). The criteria used X-ray microanalysis mapping was used to locate Si to map the unique sequence reads included: the mis- deposition in soybean roots fed with and without Si. At matchcostof2,insertcostof3, deletioncostof3, least five root samples per treatment were prepared as minimum length fraction of 0.9 and minimum simi- described by Guével et al. [23]. Briefly, roots were lyoph- larity fraction of 0.8. ilized and coated with gold and palladium to provide conductivity to the samples. Samples were analyzed Gene expression, differential gene expression analyses and using a CAMECA SX-100 Universal EPMA microscope clustering (Cameca instruments Inc., www.cameca.com) operating The normalized expression values were taken to esti- at a voltage of 15 kV and a current of 20 nA. mate the expression levels. The differentially expressed genes (DEGs) were identified using the EdgeR algorithm RNA extraction, library construction and sequencing implemented in CLC Genomics Workbench that utilizes Five separate soybean plants (biological replications) per the Exact Test developed by Robinson and Smyth [39]. treatment were grown in the hydroponic system. For The p-value threshold was determined by the false dis- each treatment, 3-cm samples of top roots were covery rate (FDR) to account for multiple tests of signifi- harvested from each of the treated plants and this cance. To judge the significance of the gene expression, Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 11 of 13 change during plant-pathogen interactions, a FDR expression at each timepoint based on the highest level of expression for threshold ≤0.01 was adopted. The absolute of log 2 (Fold each gene as a measure to showcase the trend in expression dynamics. Bars represent standard error from the mean (n = 5). (TIF 86 kb) change) > 4 relative to control conditions was used to se- Additional file 8: Table S6. Expression data of WRKY transcription factor lect up- and/or downregulated genes. To identify general genes. Normalized mean (n = 5), fold-change and FDR p-value of 20 WRKY trends during the interaction, a hierarchical clustering of genes in soybean plants treated (Si+) or not (Si-) with silicon (Si) at 0, 4, + − features was also generated for each condition (Si /Si ) 7, and 14 dpi with Phytophthora sojae. (XLSX 16 kb) using normalized expression values. Given that RNA-seq Additional file 9: Figure S3. Expression profile of a) protease inhibitors and b) polyphenol oxidase. Gene expression shows a higher expression analysis has been conducted on more than three repli- of 13 protease inhibitor genes (a) and five polyphenol oxidase genes (b) cates for each treatment, qPCR validation was deemed − in Phytophthora sojae-inoculated soybean plants at 4 dpi under Si unnecessary [14]. compared to Si treatment. Graph shows the average relative (%) expression at each timepoint based on the highest level of expression for each gene as a measure to showcase the trend in expression dynamics. Functional annotation and gene ontology Bars represent standard error from the mean (n = 5). (TIF 163 kb) The web-based Blast2GO and AgriGO tools [13] were Additional file 10: Table S7. Expression data of a) protease inhibitor genes and b) polyphenol oxidase genes. Normalized mean (n = 5), fold-change and used to obtain Gene Ontology (GO) annotations and to FDR p-value of 13 protease inhibitor genes (sheet 1) and five polyphenol oxi- perform singular enrichment analyses (FDR p-value dase genes (sheet 2) in soybean plants treated (Si+) or not (Si-) with silicon ≤0.05) of genes of soybean and P. sojae differentially (Si) at 0, 4, 7, and 14 dpi with Phytophthora sojae.(XLSX 37 kb) expressed during the interaction. Significant enrichment Additional file 11: Figure S4. Expression profile of secondary metabolism-related genes. Gene expression shows a higher expression of testing was performed for GO categories and Mercator 31 genes involved in flavonoid metabolism (a), nine genes involved in bins with Mapman [43] to visualize the pathways that isoflavone metabolism (b) and 15 genes involved in isoprenoid metabolism −- were activated during the time of infection. (c) in Phytophthora sojae-inoculated soybean plants at 4 dpi under Si compared to Si treatment. Graph shows the average relative (%) expression at each timepoint based on the highest level of expression for each gene as Additional files a measure to showcase the trend in expression dynamics. Bars represent standard error from the mean (n = 5). (TIF 261 kb) Additional file 1: Table S1. Summary of read numbers obtained from Additional file 12: Table S8. Expression data of secondary metabolism- related genes. Normalized mean (n = 5), fold-change and FDR p-value of soybean plants and Phytophthora sojae following inoculation experiments over time on plants treated or not with silicon (Si). Total read numbers 31 genes involved in flavonoid metabolism (sheet 1), nine genes involved and read numbers aligned onto soybean and P. sojae genomes are given in isoflavone metabolism (sheet 2) and 15 genes involved in isoprenoid in millions ± SE. (DOCX 14 kb) metabolism (sheet 3) in soybean plants treated (Si+) or not (Si-) with silicon (Si) at 0, 4, 7, and 14 dpi with Phytophthora sojae. (XLSX 48 kb) Additional file 2: Table S2. Normalized expression of differentially expressed genes in soybean roots infected with Phytophthora sojae. In Additional file 13: Table S9. Expression data of hormone-related genes. total, 3294 genes were differentially expressed as a result of P. sojae Normalized mean (n = 5), fold-change and FDR p-value of five genes in- + − volved in SA metabolism (sheet 1), and13 genes involved in JA metabol- infection at day 4 after inoculation (P Si ). The normalized mean (n =5) ism (sheet 2) in soybean plants treated (Si+) or not (Si-) with silicon (Si) at of the differentially expressed transcripts (Fold-change ≥4, FDR p-value + − ≤0.01) are presented in soybean roots inoculated (P ) or not (P ) with P. 0, 4, 7, and 14 dpi with Phytophthora sojae. (XLSX 36 kb) + − sojae and treated (Si ) or not (Si ) with silicon. (XLSB 215 kb) Additional file 14: Figure S5. Heat map of differentially expressed genes involved in primary metabolism. Heat map shows gene expression Additional file 3: Figure S1. Expression profile of signaling-related pattern of 580 DEGs involved in primary metabolism in soybean roots genes. Gene expression shows a higher expression of 46 receptor kinase + −- + −- family genes (a), 24 RLK genes (b), 5 MAPKs genes (c), and 33 CDPKs inoculated (P ) or not (P ) with P. sojae and treated (Si ) or not (Si ) genes (d) in Phytophthora sojae-inoculated soybean plants at 4 dpi under with silicon showing a notable higher expression of genes in P. sojae- − + infected plants. Each gene corresponds to a colored line indicating the Si compared to Si treatment. Graph shows the average relative (%) ex- pression at each timepoint based on the highest level of expression for normalized mean (n = 5) of the differentially expressed transcripts (Fold- each gene as a measure to showcase the trend in expression dynamics. change ≥ 4, FDR p-value ≤ 0.01). (TIF 205 kb) Bars represent standard error from the mean (n = 5). (TIF 371 kb) Additional file 15: Table S10. Summary of read numbers obtained from the five biological replications of Phytophthora sojae in axenic Additional file 4: Table S3. Expression data of signaling-related genes. Normalized mean (n = 5), fold-change and FDR p-value of 46 receptor culture. Total read numbers and read numbers aligned onto P. sojae kinase family genes (sheet 1), 24 RLK genes (sheet 2), 5 MAPKs genes genomes are given in millions. (DOCX 13 kb) (sheet 3), and 33 CDPKs genes (sheet 4) in soybean plants treated (Si+) or Additional file 16: Table S11. List of the top 100 upregulated not (Si-) with silicon (Si) at 0, 4, 7, and 14 dpi with Phytophthora sojae. Phytophthora sojae genes during the compatible interaction with (XLSX 65 kb) soybean plants at 4, 7 and 14 dpi. (DOCX 86 kb) Additional file 5: Table S4. Expression data of NB-LRRs genes. Normalized mean (n = 5), fold-change and FDR p-value of 45 receptor (NB-LRR) genes in soybean plants treated (Si+) or not (Si-) with silicon (Si) Abbreviations 2+ at 0, 4, 7, and 14 dpi with Phytophthora sojae. (XLSX 22 kb) Avr: Avirulence; CDPKs: Ca -Dependent Protein Kinases; CRN: Crinkler; CSEPs: Candidate Secreted Effector Proteins; DEGs: Number of Differentially Additional file 6: Table S5. Expression data of PR genes. Normalized Expressed Genes; dpi: Days Post Inoculation; ETI: Effector-Triggered Immunity; mean (n = 5), fold-change and FDR p-value of 11 pathogenesis-related FDR: False Discovery Rate; GO: Gene Ontology; JA: Jasmonic Acid; (PR) genes in soybean plants treated (Si+) or not (Si-) with silicon (Si) at 0, MAPKs: Mitogen-Activated Protein Kinases; NB-LRR: Nucleotide-Binding 4, 7, and 14 dpi with Phytophthora sojae. (XLSX 12 kb) Leucine-Rich Repeat; P. sojae: Phytophthora sojae; PAMPs: Pathogen- Additional file 7: Figure S2. Expression profile of WRKY transcription Associated Molecular Patterns; PRRs: Pattern Recognition Receptors; factor genes. Gene expression shows a higher expression of 20 WRKY − PRs: Pathogenesis-Related proteins; PTI: PAMPs -Triggered Immunity; genes in Phytophthora sojae-inoculated soybean plants at 4 dpi under Si + RLK: Receptor-Like Kinases; Rps: Resistance genes to P. sojae; SA: Salicylic Acid; compared to Si treatment. Graph shows the average relative (%) Si: Silicon Rasoolizadeh et al. BMC Plant Biology (2018) 18:97 Page 12 of 13 Acknowledgements 8. Chérif M, Menzies JG, Benhamou N, Bélanger RR. Studies of silicon The authors would like to thank Dr. Devrim Coskun for critical review of the distribution in wounded and Pythium ultimum infected cucumber plants. manuscript. Physiol Mol Plant Pathol. 1992b;41:371–85. 9. Deshmukh RK, Vivancos J, Guérin V, Sonah H, Labbé C, Belzile F, Bélanger Funding RR. Identification and functional characterization of silicon transporters in The project was funded by a grant from the Natural Sciences and soybean using comparative genomics of major intrinsic proteins in Engineering Research Council of Canada (NSERC), the Fonds de recherche Arabidopsis and rice. Plant Mol Biol. 2013;83:303–15. du Québec – Nature et technologies (FRQNT) and the Canada Research 10. Dodds PN, Rathjen JP. Plant immunity: towards an integrated view of plant- Chairs, which supported totally or in part the salaries of the students and pathogen interactions. Nat Rev Genet. 2010;11:539–48. researchers involved in the project and the materials. The authors declare 11. Dong S, Yu D, Cui L, Quitob D, Tedman J, Kale SD, Tyler B, Wang Y, Gijzen that the research was conducted in the absence of any commercial or M, Yang CH. Sequence variants of the Phytophthora sojae RxLR effector financial relationships that could be construed as a potential conflict of Avr3a/5 are differentially recognized by Rps3a and Rps5 in soybean. PLoS interest. One. 2011;6:7. 12. Dou D, Kale SD, Wang X, Jiang RHY, Bruce NA, Arredondo FD, Zhang X, Availability of data and materials Tyler BM. RxLR-mediated entry of Phytophthora sojae effector Avr1b into The datasets generated and analyzed during the current study are available soybean cells does not require pathogen-encoded machinery. 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