Fold induction of metabolites in sRNA mutants and controls upon
Abstract
Plant small RNAs (sRNAs) are pivotal regulators of gene expression, which are crucial in maintaining genome integrity and flexibility during development, abiotic and biotic stress responses. Current evidence suggests that sRNAs might be inherent to the sophisticated plant innate immune system battling bacteria. However, the role of sRNAs during antifungal plant defences is less clear. Therefore, this chapter investigates the sRNA‐mediated plant antifungal responses against the hemibiotrophic fungi Colletotrichum higginsianum and Colletotrichum graminicola in their respective compatible hosts Arabidopsis thaliana and Zea mays. A phenotypic and metabolomic analysis of A. thaliana sRNA mutants in response to C. higginsianum infection was performed, showing a hormonal and metabolic imbalance during fungal infection in these plants. To find whether fungal-induced sRNA could directly regulate defence genes in an agricultural important plant model, the expression of maize miRNAs in response to C. graminicola leaf and root infections was investigated. The results revealed the tissue‐specific local and systemic adaptation of the miRNA transcriptome, where only a few miRNAs were targeting defence pathways. The general picture presented here points towards a role of sRNAs as fine‐tuners of genetic and metabolomic defence response layers. This chapter also further discusses the potential of utilizing sRNA‐based fungal control strategies.
Keywords
- small RNA
- antifungal plant defence
- metabolomics
- deep sequencing
1. Introduction
Small RNAs (sRNA) are small noncoding RNA segments of 19–30 nucleotides in length [1]. They mediate gene silencing, a gene regulation mechanism acting on a transcriptional (transcriptional gene silencing (TGS)) and post‐transcriptional level (post‐transcriptional gene silencing (PTGS)). In general, sRNA molecules originate from the transcription of endogenous microRNA (miRNA genes), other genomic sRNA loci, aberrant RNA produced by transposons as well as invasive viral RNA [2]. Plants carry two main classes of sRNAs grouped according to their size, function and biogenesis, namely microRNAs (miRNA) and short‐interfering RNAs (siRNA) [3]. Such sRNAs are generated through various mechanisms; within the miRNA biogenesis pathway, miRNA precursors derived from MIR genes are processed in the nucleus by Dicer‐like protein 1 (DCL1) and exportin‐like protein (HYL1) into mature miRNA duplexes of 20–22 nucleotides in length. Mature miRNAs are then methylated at the 3′ terminus by HEN1 (small RNA methyltransferase) and exported to the cytoplasm. One strand of the duplex is incorporated into an argonaute protein (AGO) protein to form an RNA‐induced‐silencing complex (RISC) [4]. The siRNAs, however, originate from long dsRNA that can be derived from transgenes, viruses, transposons and natural sense‐antisense transcripts. Such long dsRNA is recognized and cleaved by a certain type of DCL proteins; thereby siRNA classes with different sizes are generated. Like miRNAs, siRNAs are loaded into an AGO protein‐containing RISC that controls gene expression patterns through the degradation of mRNA or the repression of translation of fully/partly complementary sequences of mRNAs, as well through epigenetic changes via mediation of DNA and histone methylation [5, 6].
Gene silencing is not only important for the maintenance of genome integrity by silencing transposons or by degrading the viral RNA but also important during host immune responses of both plants and animals [7–9]. The recognition of pathogens by plants leads to the activation of a multi‐layered immune system that comprises the establishment of a complex network of inducible defences including pathogen‐associated molecular pattern (PAMP)‐triggered immunity (PTI) and effector‐triggered immunity (ETI) [10, 11]. The entire signalling process involves the regulation of defence gene expression, the release of plant hormones and/or the induction of secondary metabolites [12]. Over the past few years, plant sRNA pathways were recognized as important players during PTI and ETI [13, 14]. In Arabidopsis, bacteria‐induced miRNAs were identified to orchestrate components of plant hormone signalling, including auxin, abscisic acid (ABA), jasmonic acid (JA) and salicylic acid (SA) [15, 16]. A canonical example of an miRNA regulating plant defence is miR393. It is up‐regulated upon treatment with a bacterial PAMP, and negatively regulates auxin signalling and therefore contributes to SA‐mediated PTI responses in Arabidopsis [17].
Although the important role of sRNAs in plant defence against viruses and bacteria is documented [8, 13], their function as components of the plants’ defence response against fungi is less clear. Advances in genome‐wide studies revealed a massive adaptation of host miRNA expression patterns after infection by fungal pathogens such as
In this study, we aim to elucidate the role of sRNAs in regulating susceptibility to
During the first step, a selection of sRNA mutants and two fully and intermediate fungal susceptible accessions of
This chapter provides a multi‐omics analysis of sRNA‐mediated antifungal plant reactions on a phenotypic, metabolomic as well as transcriptomic point of view. Altogether, our data propose a rather indirect defensive role of sRNAs in calibrating metabolomic and transcriptomic balances during antifungal responses against
2. Materials and methods
2.1. Plant material and growth conditions
2.2. Pathogen and pest cultivation and inoculation
2.3. Quantification of fungal growth
2.4. Hormone quantification
For hormone analysis, salicylic acid, jasmonic acid and abscisic acid were quantified simultaneously from leaf material using UHPLC‐MS/MS as described [32]. Hormone measurements were performed 4 days post
2.5. Metabolomic profiling
For metabolomic analysis, 4‐week‐old Arabidopsis plants were infected with
2.6. Gene expression analysis
Confirmation of down‐regulation of maize genes putatively targeted by miRNAs was conducted as described [32], using ZmGAPc as normalizing gene. Primer sequences are as follows: ZmATPS_fw: tcgtattaatgctggtgcaaac, ZmATPS_rev: ctctgtggggtggctcat; ZmSAT_fw: ttataaaaaccctgttcttctgctc, ZmSAT_rev: aggacaccttcctcaagaacc; ZmGAPc_fw: gcatcaggaaccctgaggaa, ZmGAPc_rev: catgggtgcatctttgcttg.
2.7. Deep sequencing and Northern blotting of maize sRNAs
For sRNA library preparation, six biological replicates were pooled and total RNA was isolated using Trizol (Invitrogen, www.invitrogen.com); 10 µg of total RNA was further processed using an Illumina‐Solexa deep‐sequencing approach at FASTERIS (http://www.fasteris.com). The expression of selected miRNAs was further analysed using sRNA Northern blotting techniques as described [33].
2.8. Identification and quantification of conserved miRNAs
To identify conserved maize miRNAs, sequences of 4677 mature plant miRNAs were downloaded from miRBase (release 18.0, November 2011). Identical miRNA sequences identified in different species or duplicated loci in a genome were collapsed, resulting in a non‐redundant list consisting of 2228 unique miRNAs. Sequences belonging to the same miRNA family were further analysed by multiple alignment using ClustalW (www.clustal.org) and classified in subgroups to distinguish bona fide mature miRNAs from misannotated miRNA* forms or sequences generated from different regions of the same precursor. This non‐redundant library was then applied to screen the small RNA libraries. All the small RNA reads in the range of 20–24 nt in size, and which are represented and represented by at least two reads in a library were aligned to the 1772 unique miRNAs derived from miRBase. For the screening, a maximum of three mismatches was allowed and up to 2 nt overhanging nucleotides at the 5’ and/or 3’ end. Alignments were performed using SeqMap [34]. The output was filtered and reformatted with custom PERL scripts, classifying the identified miRNAs according to miRBase.
2.9. Target prediction of maize miRNAs
Putative targets of maize miRNAs were identified using the psRNATarget web server (http://bioinfo3.noble.org/miRU2/) against
2.10. Statistical analysis
Variances of quantified levels of metabolites and fungal growth for multiple groups were analysed by a one‐way analysis of variance (ANOVA); a
3. Results
3.1. Arabidopsis sRNA mutants show different levels of susceptibility to C. higginsianum
To test if a functional silencing machinery is required for a proper antifungal‐defence response,
3.2. Arabidopsis sRNA mutants show an altered hormonal balance after C. higginsianum infection
Hormone signalling is a key process that regulates stress responses. To evaluate the implication of sRNA pathways in hormone‐mediated plant defence against
3.3. The metabolome of Arabidopsis sRNA mutants in responses to C. higginsianum infection
To compare the changes in the metabolomic profile of sRNA mutants and wild‐type plants induced by
Compound | Mass | Fragments (M‐H)‐ | Col‐0 FI | ||||
---|---|---|---|---|---|---|---|
Glucoberteroin | 434.0612 | 96.9603, 95.9523 | ‐ | ‐ | 0.4 | 0.9 | 1.9 |
Glucobrassicin | 447.0512 | 96.9601, 95.9523, 74.9914 | 0.8 | 1.7 | 2.0 | 0.6 | 2.4 |
Glucoerucin | 420.0457 | 96.9628, 95.9551, 74.9943 | 1.0 | 1.6 | 0.2 | 0.8 | 1.7 |
Glucoiberin | 422.0219 | 96.9619,95.9519, 74.9923 | 0.8 | 1.4 | 1.7 | 0.7 | 1.6 |
Glucoiberverin | 406.0301 | 96.9619, 95.9494, 74.9920 | 1.1 | 2.1 | 1.0 | 0.7 | 1.4 |
Glucolesquerellin | 448.0764 | 96.9590, 95.9513,74.9919 | 1.1 | 1.6 | 1.4 | 1.1 | 2.0 |
Gluconasturtiin | 422.0578 | 0.8 | 0.8 | 2.1 | |||
Glucoraphanin | 436.0406 | 372.0467, 178.0225 | 0.7 | 1.1 | 0.7 | 0.8 | 1.8 |
7‐Methylthioheptyl glucosinolate | 462.0958 | 95.9527, 74.9920 | 1.0 | 1.6 | 1.2 | 0.9 | 1.9 |
kaempferol 3‐O‐rhamnoside‐7‐O rhamnoside | 578.1552 | 431.0942, 285.0399, 283.0236 | 0.6 | 1.0 | 1.4 | 0.8 | 1.7 |
kaempferol 3‐rhamnoside‐7‐Glu | 593.1534 | 447.0905, 285.0410, 283.0240 | 0.6 | 0.9 | 0.8 | 0.7 | 1.4 |
Sinapoyl malate | 339.0745 | 223.0586, 164.0484, 149.0245 | 0.7 | 0.9 | 1.2 | 0.8 | 1.4 |
1‐O‐β‐D‐glucopyranosyl sinapate | 385.1147 | 265.0794, 190.0267, 175.0030 | 0.8 | 1.0 | 0.9 | 0.5 | 1.2 |
Camalexin | 199.0332 | 10.7 | 84.4 | 69.0 | 71.5 | 72.9 |
3.4. C. graminicola ‐infected maize sets up a tissue‐specific miRNA profile which is not directly linked to plant defence
Using annotated maize miRNAs (zma), known miRNAs were classified in the different maize sRNA libraries. In order to determine biostress‐specific miRNAs and to quantify their expression level in the treated samples, the fold change expression was determined by calculating the relative difference of sequence reads in treated samples compared to the control libraries. Selected miRNAs showing a fold change of >2 are summarized in Table 2. Comparing biotrophic and necrotrophic fungal infection stages to mock, zma‐miR479, zma‐miR1318 and zma‐miR1432 were found to be up‐regulated; however, their fold induction was higher during the necrotrophic stage. Other miRNAs such as zma‐miR393, zma‐miR1120 and zma‐miR2092 showed an altered expression level exclusively during the biotrophic stage. By contrast, the expression of zma‐miR168, zma‐miR2916 and zma‐miR5205 was altered only during the necrotrophic stage. Notably, zma‐miR1432 and zma‐miR2092 were also up‐regulated in infected roots, suggesting that some miRNAs are regulated organ independently. Notably, infected roots showed also a distinct expression profile with zma‐miR166, zma‐miR169 and zma‐miR395 that were down‐regulated, whereas zma‐miR909 and zma‐miR2863 were up‐regulated. A different situation was found in systemic leaves upon leaf infection. Compared to local infected tissues, less miRNAs showed an altered expression. For instance, zma‐miR397, zma‐miR916 and zma‐miR5169 were up‐regulated. In systemic leaves upon root infection, zma‐miR1877 and zma‐miR2592 were down‐ and up‐regulated, respectively. Interestingly, zma‐mi395 was down‐regulated, and zma‐miR479 showed elevated expression levels; zma‐miR479 was also found to be up‐regulated in local leaf infections, whereas the down‐regulation of zma‐miR395 was also observed in infected roots. In summary, although some miRNAs were commonly regulated in both locally infected leaves and roots, the miRNA transcriptome was specific for a given infection stage and in addition also organ‐specific (Table 2). To confirm the deep‐sequencing results, Northern blots of a selected miRNA were performed. Due to the relatively high expression level and the remarkable difference between control and treated samples, zma‐miR395 was selected (Figure 6).
Library | miRNA | FI | Putative target genes |
---|---|---|---|
Inf L 24h | miR393 | 2.23 | Calmodulin‐binding protein MPCBP; cyclin‐like F‐box |
miR479 | 3 | Unknown | |
miR1120 | −3 | Unknown | |
miR1432 | 2.3 | Para‐hydroxybenzoate‐polyprenyltransferase (LOC100282174) | |
miR2092 | 7 | Unknown | |
Inf L 96h | miR168 | 2.7 | Argonaute and Dicer protein; ubiquitin carboxyl‐terminal hydrolase— |
miR479 | 4 | Unknown | |
miR1432 | 18.3 | Para‐hydroxybenzoate‐polyprenyltransferase (LOC100282174) | |
miR2916 | 3.3 | Quinone reductase 2— | |
miR5205 | −3.25 | Unknown | |
Inf R 96h | miR166 | −6.5 | MFS14 protein precursor; basic‐leucine zipper (bZIP) transcription factor; lipid‐binding |
miR169 | −3.8 | RAPB protein— | |
miR395 | −15.5 | ATP sulphurylase (LOC541653), mRNA | |
miR909 | 5 | Inhibin, beta B subunit; vinculin; heavy metal transport/detoxification protein | |
miR1432 | 4.5 | Para‐hydroxybenzoate‐polyprenyltransferase (LOC100282174) | |
miR2092 | 2.6 | Unknown | |
miR2863 | 3.5 | Unknown | |
Inf L sys L | miR397 | 2.2 | Laccase; multicopper oxidase; |
miR916 | 3.3 | Zein protein‐body ER membrane protein | |
miR5169 | 2.2 | Unknown | |
Inf R sys L | miR395 | −2.7 | ATP sulphurylase (LOC541653), mRNA |
miR479 | 3 | Unknown | |
miR1877 | −3 | Putative protein binding protein | |
miR2592 | 3 | Unknown |
As expected, zma‐miR395 showed a reduced expression level upon fungal infections in roots. The signal intensity also corresponded to the sequence reads in the different libraries, with the highest number of reads (93) in control roots. To examine the putative role of zma‐miR395 during root infections, the maize genome was analysed for putative target genes. Five known target genes were identified: two genes (dienelactone hydrolase and FMR1‐interacting) exhibit two mismatch positions for zma‐miR395. The other genes, ATP sulphurylase (APS) on chromosomes 1 and 5, and a sulphate anion transporter, perfectly matched to the zma‐miR395 sequence. To confirm the genotype of a reduced expression level of zma‐miR395 in infected maize roots, the gene expression of two zma‐miR395 putative target genes (
4. Discussion
It has been documented that plant sRNAs can act as regulators of gene expression during plant‐defence responses as reviewed in Ref. [36]. However, the mechanisms of sRNA‐mediated immunity remain largely elusive, especially for host‐fungi interactions. In rice cultivars that are susceptible to
Notably, plant RNAi pathway components were shown in specific cases to be important for mounting proper antifungal‐defence responses. RDR6‐deficient plants were found to be more susceptible to
The present study widens the understanding of the putative role of sRNAs in fine‐tuning plant‐hormonal pathways during fungal infection. First of all, Arabidopsis sRNA pathway components were demonstrated to be required for antifungal responses against
To extend the view on antifungal responses possibly linked to sRNA pathways, the miRNA transcriptome of the agricultural important model crop
Although some sRNA pathways components were shown here to be required for battling
5. RNA silencing and plant defence: an outlook
Altogether, the strategic outline of Arabidopsis and maize antifungal defence against
Nevertheless, considering the fact that sRNA pathways are also involved in setting up proper abiotic stress responses, it might represent a multi‐valuable biotechnological approach to generate crops that are more efficient and variant in expressing their sRNA repertoire. Over the past years, a transgene‐based approach where pathogen‐targeting sRNAs are expressed in host species was repeatedly confirmed to efficiently control fungal diseases. This host‐induced gene‐silencing (HIGS) approach was successfully applied to a broader range of host‐pathogen systems, thus bearing a valuable industrial potential. Significant drawbacks with this technology are the restrictive acceptance of genetically modified crops, and the yet elusive question of how fast pathogens evolve tolerance or resistance. For instance,
Recent studies from two different research groups demonstrate fungal control by exogenous application of sRNAs to
Prospective investigations will help in further elucidating of the full potential of sRNA‐mediated antifungal defence. While the data presented here and in recent studies suggest that sRNAs are subtle players in the concert of mounted antifungal defence, and new approaches using exogenously applied sRNAs are promising, there remains challenging basic research to be completed first in order to truly understand sRNA trafficking and signalling in plant‐pathogen interactions.
Acknowledgments
We are grateful to Gaétan Glauser and Armelle Vallat‐Michel (Chemical Analytical Service of the University of Neuchâtel) for their technical assistance in metabolomics and hormone analysis. Financial support from the National Centre of Competence in Research ‘Plant Survival’ and grant number 31003A‐120197, both research programmes of the Swiss National Science Foundation, is gratefully acknowledged.
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