Next Article in Journal
Erythrocytes Functionality in SARS-CoV-2 Infection: Potential Link with Alzheimer’s Disease
Previous Article in Journal
The Role of the Insular Cortex in Pain
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 6
10.3390/ijms24065737
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

MicroRNA miR171b Positively Regulates Resistance to Huanglongbing of Citrus

by
Yuanda Lv
1,2,
Yun Zhong
1,2,*,
Bo Jiang
1,3,
Huaxue Yan
1,2,*,
Shuang Ren
1 and
Chunzhen Cheng
1,†
1
Institute of Fruit Tree Research, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
2
Key Laboratory of South Subtropical Fruit Biology and Genetic Resource Utilization, Ministry of Agriculture and Rural Affairs, Guangzhou 510640, China
3
Guangdong Provincial Key Laboratory of Tropical and Subtropical Fruit Tree Research, Guangzhou 510640, China
*
Authors to whom correspondence should be addressed.
†
Current Address: College of Horticulture, Shanxi Agricultural University, Jinzhong 030801, China.
Int. J. Mol. Sci. 2023, 24(6), 5737; https://doi.org/10.3390/ijms24065737
Submission received: 13 February 2023 / Revised: 12 March 2023 / Accepted: 13 March 2023 / Published: 17 March 2023
(This article belongs to the Section Molecular Plant Sciences)
Browse Figures
Versions Notes

Abstract

:
Huanglongbing (HLB) is one of the most severe citrus diseases in the world, causing huge economic losses. However, efficient methods of protecting citrus from HLB have not yet been developed. microRNA (miRNA)-mediated regulation of gene expression is a useful tool to control plant diseases, but the miRNAs involved in regulating resistance to HLB have not yet been identified. In this study, we found that miR171b positively regulated resistance to HLB in citrus. Upon infection with HLB bacteria, the bacteria were detected in the second month in the control plants. However, in the miR171b-overexpressing transgenic citrus plants, the bacteria could not be detected until the 24th month. RNA-seq data indicated that multiple pathways, such as photosynthesis, plant–pathogen interaction, the MAPK signaling pathway, etc., might be involved in improving the resistance to HLB in miR171b-overexpressing plants compared with the control. Finally, we determined that miR171b could target SCARECROW-like (SCL) genes to downregulate their expression, which then led to promoted resistance to HLB stress. Collectively, our results demonstrate that miR171b plays a positive regulatory role in resistance to citrus HLB, and provides a new insight into the role of miRNAs in the adaptation of citrus to HLB stress.

1. Introduction

Citrus Huanglongbing (HLB) disease has spread globally to the major citrus-producing areas, posing a serious threat to the sustainability of the world citrus industry [1]. The disease is caused by Candidatus Liberibacter asiaticus (CLas), Candidatus Liberibacter africanus (CLaf), and Candidatus Liberibacter americanus (CLam), which are members of the α-subclass of prokaryotic Proteobacteria [2]. CLas is the most widespread and severe one [3]. These bacteria have highly simplified genomes that lack the key genes for autotrophy [4], and therefore exploit host cells to survive. Their natural hosts include citrus (subfamily Aurantioidae) and citrus relatives. To disperse, the bacteria must be vectored by insects, either the Asian citrus psyllid Diaphorina citri (ACP), which transmits CLas, or the African psyllid Trioza erytreae, which transmits CLaf [5]. Currently, the widely used strategies in the control of HLB include pathogen-free propagation materials, the timely eradication of diseased trees to reduce field inoculum, and the aggressive suppression of ACP populations [6,7]. It is very difficult to wipe out ACP from an area where the insect has established a population, and satisfactory control is not always achieved, even when using very aggressive control programs.
Breeding for disease resistance is the most cost-efficient and environmentally friendly way to curb the spread of diseases [8]. The conventional method of breeding for resistance is the incorporation of a disease-resistance gene into a plant genome through a cross followed by multiple back-crosses [9]. This strategy is largely dependent on the availability of usable resistance genes. Unfortunately, no HLB resistance gene has been identified in citrus or its close relatives Fotunella and Poncirus so far [10]; therefore, little progress has been made in breeding for HLB resistance. Nevertheless, some intergeneric hybrids between Citrus and Poncirus have shown some degrees of HLB tolerance and have been released as rootstocks [11,12,13,14]. Evidently, different and novel approaches should be tested, such as the use of host susceptibility genes (S-genes), which are essential to the survival of the invading pathogens [15]. Theoretically, HLB resistance can be achieved if dysfunctional S-genes are additively pyramided, if one is not enough.
There are several ways to interrupt gene function, including T-DNA insertion, CRISPR/Cas-mediated gene editing, and mRNA targeting, among others [16]. MicroRNAs (miRNA), a class of small (21–23 nt), single-stranded, non-coding RNA molecules, could bind target mRNA in a complementary base-pairing manner to induce the degradation of the target mRNA [17,18].
miRNAs are key players in regulating abiotic stress responses in plants. Yang et al. found that the expression of most miRNAs exhibits a negative correlation with the expression of their targets under salt stress in the comparative analysis of two sweet potato (Ipomoea batatas (L.) Lam.) cultivars with different salt sensitivity [19,20]. miR156, miR166, and miR171 regulate transcription factor genes to adapt maize (Zea mays L.) to low phosphorus stress [21]. miR166, miR171, miR390, miR156, and miR168 respond to Cd stress in rice (Oryza sativa) [22,23]. Plant biotic stress responses also involve miRNAs. For example, miR1507 regulates rhizobial symbiosis in legume species [24], and miR393, miR160, and miR167 were found to be upregulated in tomato leaves infected with Pseudomonas syringae [25]. Some cassava (Manihot esculenta) miRNAs, miR160, miR167, miR390, and miR393, respond to Xanthomonas infection, similar to those in Arabidopsis [26]. miRNAs have been evidenced to improve plant resistance but have not been used in citrus HLB.
A miRNA can target one or several species of mRNAs and vice versa [27]. This is exemplified by miR171. miR171 can target multiple members of the SCARECROW-like (SCL) transcription factor gene family [28,29]. The Scarecrow-like 6 (SCL6) gene family is primarily involved in the response of tomato to GA/Auxin signals [30,31]. miR171 can thus regulate axillary bud growth, embryogenesis induction, and leaf and root activities that heavily depend on the regulation of plant growth hormones, as shown by its involvement in embryo genes in lily (Lilium brownii var. viridulum) [32]. Additionally, miR171 is associated with associated with biotic and abiotic stresses. For example, Arabidopsis and rice miR171 are involved, through interaction with miR169, in the salt stress response [29]. Soybean (Glycine max (L.) Merr.) miR171 enhances the colonization of arbuscular mycorrhizae in roots [33], while citrus miR171b, by joining with miR167h, participates in the symbiosis of arbuscular mycorrhizae in roots [34]. In ORS571 rhizobium infected wheat, the expression level of miR171 in buds was higher than that in roots to improve the resistance [35]. In citrus, miR171 and miR156 were involved in the stress of Citrus psorosis virus by down-regulating the target genes [36].
In our previous study, we analyzed the changes in miRNA expression in citrus roots infected with HLB. The results demonstrate that the differential expression of miRNAs mainly targeted genes involved in stress response, metabolism, transcription, growth and development. In addition, one of the most striking findings was that miR171b expression is significantly induced by HLB infection [37]. In this work, finding the mechanism of miR171b improved resistance to HLB, which is of great theoretical interest and has potential applications in breeding for disease resistance. We set out to determine whether the miR171b plays a role in the defense of citrus against CLas.

2. Results

2.1. Generation of Transgenic Plants Overexpressing pre-ctr-miR171b

Sweet orange pre-ctr-miR171b was successfully cloned (Figure 1A) into a TA-cloning vector using a pair of 171f/171r primers. The insert was excised from the TA vector using double enzyme digestion and inserted into the pFGC5941 over-expression vector. Transgenic citrus plants were obtained using Agrobacterium tumefaciens-mediated transformation (Figure 1B). A total of 16 Basta-resistant transgenic buds were grafted onto rootstocks. DNA was extracted from the leaves of the transgenic lines when 5–8 true leaves were present, and the pre-ctr-miR171b transgene was detected via PCR. The transgenic plant was eventually identified and designated as 171-5 (Figure 1C).
Transgene expression in 171-5 was monitored via qPCR for five years. It was found that the expression of the gene was stable, varying from 1.6- to 2.5-fold, and averaged 2.16-fold compared with those in non-transgenic plants (Figure 1D).

2.2. The 171-5 Asexual Progenies Show HLB-Tolerance

Three 171-5 grafted progenies and three non-transgenic controls were inoculated with HLB in November 2016, as described in the Materials and Methods. CLas was detected in leaves one month post-inoculation in the non-transgenic control plants, but was not detected in the transgenic plants until the 24th month (Figure 2). All three non-transgenic plants died less than two years after inoculation with HLB. In contrast, all three of the transgenic plants have survived to date, despite CLas being detectable since the 24th month (Figure 3). Two of the three transgenic plants began to fruit in the fifth year (2021); however, most of the fruit were abscised and only two were harvested in December.

2.3. Transcriptome Analysis via Illumina-Based RNA Sequencing

To understand the effect of overexpressing miR171b in relation to HLB resistance, both transgenic and non-transgenic plants were subjected to RNA-seq analysis. The mapped reads and uniquely mapped reads were more than 91.30% and 88.67%, respectively (Supplemental Table S3).
Differentially expressed genes (DEGs) among the treatments were identified using a p value ≤ 0.05 and |log2Ratio| ≥ 1 as the threshold. For the non-transgenic plants, 1770 and 1306 genes were up- and downregulated by overexpressing miRNA 171b, respectively, as shown in Supplemental Figure S1.

2.4. Gene Expression Validation Using qRT-PCR

The genes were randomly selected from the results of the RNA-Seq for the real-time fluorescence quantitative PCR detection, and the detection results were consistent with the RNA-Seq results, which indicates that the data of RNA-Seq were reliable (Supplemental Figure S2).

2.5. GO and KEGG Analysis

Based on gene ontology (GO) annotations, all of the DEGs were grouped into three major categories: biological processes, cellular components, and molecular functions. In the group of biological processes, most DEGs were involved in cellular processes, metabolic processes, single-organism processes, biological regulation, response to stimulus, cellular component organization or biogenesis, localization, multicellular organismal processes, developmental processes, signaling, reproductive processes, multi-organism processes, reproduction, immune system processes, growth, rhythmic processes, detoxification, and locomotion (Figure 4). In the functional group of cell components, most DEGs were associated with the cell, cell part, membrane, membrane part, organelle, organelle part, macromolecular complex, extracellular region, membrane-enclosed lumen, cell junction, symplast, extracellular region part, other organism, other organism part, supramolecular complex, and nucleoid. Lastly, in the group of molecular function, most DEGs were involved in binding, catalytic activity, transporter activity, nucleic acid binding transcription factor activity, structural molecule activity, electron carrier activity, antioxidant activity, molecular transducer activity, signal transducer activity, transcription factor activity, protein binding, nutrient reservoir activity, and molecular function regulation (Figure 4).
GO enrichment analysis of the DEGs revealed that GO terms were the most enriched in photosynthesis-, stress- and growth-related terms (Supplemental Table S4).
Based on the comparison of the different groups, with the p value ≤ 0.05 as the standard, the enriched KEGG pathways are shown in Figure 5 and Supplemental Table S5. Most of the identified KEGG pathways were found to mainly be involved in photosynthesis, photosynthetic antenna proteins, plant–pathogen interactions, the MAPK signaling pathway, ABC transporters, and so on (Figure 5).

2.6. Analysis of the DEGs in the Photosynthesis Pathway

The photosynthesis pathway was significantly changed in the non-transgenic plants compared to the transgenics; the DEGs related to photosystem I and photosystem II were all upregulated, except PsbB (Ciclev10003146m) and PetF (Ciclev10002793m, Ciclev10003356m, and Ciclev10029499m) (Figure 6). The PsbB associated with photosystem II was downregulated 1.24 times, and the FPKM of the non-transgenics was 10.05 and that of the transgenics was 3.66, with a slightly low expression level. The PetF genes were up- and down-regulated. The FPKM of the Ciclev10002793m ranged from 372.04 to 1530.69 and that of the Ciclev10003356m increased from 11.46 to 68.23, while only the FPKM of the Ciclev10029499m decreased from 343.86 to 125.27. The FPKM of some DEGs with respect to photosynthesis was higher in the transgenics than in the non-transgenics (Figure 7). The differences in the changes between the different samples seems to indicate that the transgenics were more able to store energy through photosynthesis in preparation for a response to HLB stress than non-transgenics.

2.7. Analysis of the DEGs in the Plant–Pathogen Interaction Pathway

The plant–pathogen interaction pathway exhibited a difference in the compared group. Comparing non-transgenic plants with transgenic plants, the RboH (Ciclev10018741m, Ciclev10030649m), which were associated with a hypersensitive response [38,39], were downregulated by 1.62 and 1.94 times, respectively, in the transgenic plants. The decrease in gene expression indicates that there was no HR response in transgenic plants in the absence of pathogen stress. In the transgenic plants, expression of the CNGC protein (Ciclev10004901m, Ciclev10007168m) in the Ca2+ pathway was up-regulated, with the FPKM increasing from 7.57 and 1.18 to 23.36 and 8.32, and the expressions of the CALM genes (Ciclev10006076m, Ciclev10012752m, Ciclev10024475m, Ciclev10002693m and Ciclev10005842m) were changed similarly, as shown in Figure 8, indicating that the CALMs were affected. The expression of the NOS gene (Ciclev10014661m) was downregulated, with FPKM falling from 5.01 to 1.70, although the level of gene expression was not significant.
Flagellin is a pathogen-associated molecular pattern that triggers the innate immune response in plants, in which FLS2 (FLAGELLIN SENSITIVE2) is essential. In our results, for the transgenic plants, FLS2 and BAK1, which are sensitive to pathogen infection, were activated, and the genes were up- and down-regulated, respectively. The FLS2 genes (Ciclev10004229m, Ciclev10004247m and Ciclev10013594m) were upregulated, indicating that the immune response of the transgenic plant was activated.
The DEGs associated with disease resistance demonstrated significant changes in the transgenic plants. Among the genes, RIN4 (Ciclev10006483m and Ciclev10006802m), which is an important susceptibility gene, was up-regulated 1.78 and 1.64 times, respectively. The FPKMs of the RIN4 genes are shown in Figure 8, and the FPKM of Ciclev10006802m was much higher in the transgenic plants. At the same time, the RPM1 gene, which interacts with the RIN4 gene, was up-regulated, and both the RPM1 and the RIN4 genes were up-regulated simultaneously, suggesting that the RIN4 network was activated in preparation for HLB stress. The RPS2 disease-resistance genes (Ciclev10012055m and Ciclev10017001m) were also altered. The FPKM of RPS2 is shown in Figure 8. The two genes of RPS2 were both up-regulated. In addition to the RIN4 gene, other genes involved in disease resistance, such as RPS4 and EDS1, were also altered. The EDS1 (Enhanced Disease Susceptibility 1) gene, as a core regulatory factor of plant immunity, regulates basic resistance by limiting pathogen invasion [40]. In our work, the EDS1 genes included Ciclev10000326m, Ciclev10000629m, and Ciclev10001029m, which were up-regulated by 2.91, 1.86, and 2.94 times, respectively. Similarly, the FPKM of EDS1 were identical to that of the RIN4 gene, which was higher in the transgenic plants. EDS1 was found to be an integral part of RPS4 (Ciclev10018612m; Citrus_clementina_newGene_4148) signal transduction. The expression of RPS4 was down-regulated, and the variation was consistent with EDS1. In addition, pathogen-related transcriptional factors such as RPS1 (Ciclev10032192m) and UPA20 (Ciclev10001768m, Ciclev10015437m) were all up-regulated, which was directly reflected in the FPKM values. In the plant–pathogen interaction pathway, the relevant genes were significantly altered in the majority of cases, suggesting that the plants overexpressing miR171b could enhance disease resistance for the plants at the base.

2.8. Analysis of the DEGs in the MAPK Pathway

The MAPK (mitogen-activated protein kinase) pathway plays an important role in transducing signals from upstream to downstream in the plant defense responses. A MAPK cascade usually contains a MAP kinase kinase kinase (MAPKKK), a MAP kinase kinase, and a MAPK [41,42]. From the results comparing non-transgenic plants with transgenic plants, it is evident that the MAPK pathway was significantly altered. The MEKK1 and MKK4/5 proteins of the MAPK cascade were both up-regulated in the transgenic plants. Compared with the non-transgenic plants, the MEKK1 (Ciclev10002831m) increased by 1.32 times and the FPKM value increased from 2.71 to 6.78 in the transgenic plants. The expression of MKK4/5 (Ciclev10016755m) was increased by 1.60 times, and the FPKM of Ciclev10016755m increased from 20.95 to 61.24. Other MAPK proteins, including MKK9, MAPKKK17/18, and MPK8, were all up-regulated.
In plants, the MAPK cascades were also involved in a variety of physiological, developmental, and hormonal processes. As shown in Figure 9, in the ethylene pathway of the transgenic plants, the ETR (Ciclev10004385m, Ciclev10018972m), which encodes an ethylene receptor and acted upstream of CTR1, was down-regulated by 2.26 and 3.16 times, and the ethylene-related genes, ERF (Ciclev10005820m, Ciclev10021622m) and EBF1/2 (Ciclev10007708m), were up-regulated and down-regulated, respectively. The DEGs in this pathway, such as ChiB (Ciclev10026057m, Ciclev10028964m, Ciclev10028959m and Ciclev10028831m) and RAN1 (Ciclev10014141m), were all down-regulated. In the jasmonic acid pathway, the wounding-inducible expression of the VSP2 (Ciclev10009222m, Ciclev10026223m) gene was up-regulated in the transgenic plants, and the stress-induced gene MYC2 (Ciclev10007937m) was down-regulated. The FPKM of the MYC2 gene ranged from 9 to 3, with low values. In the abscisic acid pathway, the related DEGs were all activated, which suggests that the hormones in the MAPK cascades were activated to enhance plant resistance.
In the MAPK pathway, WRKY22 (Ciclev10021624m) and WRKY33 (Ciclev10009761m, Ciclev10019383m), which belong to the WRKY superfamily of transcription factors involved in pathogen defense, were up-regulated in the transgenic plants (Figure 10). The WRKY proteins were involved in the biotic stress of plants, and several studies have shown that WRKY transcription factors play a regulatory role in the defense response of plants to pathogen infections [43]. The WRKYs in this pathway were all up-regulated. These results demonstrate that overexpression of the miR171b could activate the MAPK signal and enhance the resistance of transgenic plants through this pathway in the absence of pathogens.

2.9. Analysis of the DEGs in the Plant Hormone Signal Transduction Pathway

Several studies have shown that plant hormones are associated with citrus HLB. When the plants detect the pathogens, a network of plant hormones coordinate to carry out a defense response [44]. In this work, the plant hormone signal transduction pathway was not significantly altered. However, there were also 128 DEGs in this pathway, which may have enhanced our interpretation of these findings.
Auxin plays an indispensable role in the growth and development of plants. The AUX/IAA protein is an auxin response inhibition factor that was activated in the samples, and the FPKM of this gene is shown in Figure 11. The downstream gene ARF (Ciclev10027901m) demonstrated the same trend as the AUX/IAA genes (Ciclev10008935m and Ciclev10021493m), which were all down-regulated in the transgenic plants, and the auxin-induced target genes GH3 (Ciclev10014670m) and SAUR (Ciclev10022560m, Ciclev10022581m) were both up-regulated. Cytokinins play an important role in plant growth and development. In the transgenic plants, CRE1 (Ciclev10014137m), which acts as a sensor for cytokinin, was down-regulated by 2.17 times. The cytokinin signal regulators B-ARR (Ciclev10001292m, Ciclev10007648m and Ciclev10015266m) and A-ARR (Ciclev10021937m) were both up-regulated at the same time. Brassinosteroids (BR) are a new class of plant steroid hormones, and increasing numbers of studies have shown that BR can improve resistance for plants. In our results, BRI1 (brassinosteroid-insensitive 1) (Ciclev10004321m, Ciclev10018110m, Ciclev10018319m and Ciclev10030632m) and BAK1 (BRI1-ASSOCIATED KINASE 1) (Ciclev10010482m and Ciclev10016255m) were changed at the same time, and the FPKM of these genes shows that the BR pathway was activated. However, the BSK (Ciclev10011574m) and BR signaling pathways downstream of BRI1 was down-regulated by 1.24 times in the transgenic plants.
In this pathway, changes in the gibberellin signal pathway were noticeable. In the transgenic plants, the gibberellin receptor GID1 (Ciclev10012167m; Ciclev10026049m) was up-regulated, the fold changes in GID1 were 3.84 and 1.24 times, and the FPKMs increased from 9.89 and 5.40 to 144.09 and 12.23, respectively. The DELLA protein (Ciclev10010825m, Ciclev10011141m, Ciclev10011367m and Ciclev10011458m), which has a negative regulation effect on gibberellin [45], was obviously down-regulated; meanwhile, the FPKM indicated that the genes had been altered, which implies that gibberellin responds to HLB preparation in transgenic plants.
Salicylic acid (SA) is an important endogenous signal molecule in the activation of plant defense responses. In the SA signaling pathway, NPR1 (Nonexpressor of pathogenesis-related genes 1) is a key regulator that plays a very important role [46]. In our results, NPR1 (Ciclev10017873m) was up-regulated 1.70 times. The FPKM of the NPR1 gene ranged from 23.52 to 72.77. The TGA protein (Ciclev10002163m), which interacted with the NPR1, was also up-regulated in the transgenic plants.
The pathways above were changed, which means that transgenic plants potentially provide a basis for growth through the photosynthesis-related pathways, and transform the plant–pathogen interaction, MAPK, plant hormones, and other pathways to enhance the tolerance of the transgenic plants and maintain the plant under HLB infection.

2.10. Predicted Targets of miRNA171b

The candidate genes were predicted to be the targets of miR171b (Table 1). Using rigorous screening parameters, the four candidate target genes (Cs5g08980.1, Orange1.1t00199.1, Orange1.1t00200.1, and Orange1.1t00200.2) were identified. They were all found to belong to SCL6 gene homologs.

2.11. Expression of the Genes Targeted by miR171b

qPCR was used to compare the expression of the assumed target genes of miR171b in the transgenic and non-transgenic plants. Two of the four predicted targets were significantly downregulated (Figure 12). More specifically, orange1.1t00199.1 expression was reduced by 1.6-fold and Cs5g08980.1 expression was reduced by approximately 2-fold in the transgenic plants. The expressions of the other two predicted target genes did not differ significantly. The Cs5g08980.1 and the orange1.1t00199.1 genes were all the target genes of miR171b.

3. Discussion

miRNAs play significant roles not only in plant growth and development [19,25], but also in plant defense against abiotic and biotic stresses [47]. It is therefore not surprising to find that significant changes occurred in the expression of some miRNAs in citrus following infection with CLas, as shown in our previous study [37]. Although not all of these HLB-modulated miRNAs are necessarily involved in the responses of citrus to HLB, one class, miR171, caught our eye, for its homologs are involved in plant–microbe interactions in other plants. For example, miR171h in Medicago truncatula controls fungal colonization by downregulating the GRAS transcription factor NSP2, a member of the super SCL gene family [48]. In wheat (Triticum aestivum L.), miR171 demonstrated differential expression patterns in response to powdery mildew fungal infection [49]. In soybean, miR171o and miR171q affect the expression of SCL6 and NSP2 during the rhizobi–soybean nodulation process [50]. Recently, miR171 was found to be involved in Citrus psorosis virus infection in citrus [36]. Therefore, we investigated the possible role of miR171b in the response of citrus to HLB. The precursor gene of miR171b was cloned from orange and transgenic plants overexpressing the gene were generated. The clonal progenies of the transgenic plants were challenged with CLas bacteria in 2016. It was a surprise to us that no detectable CLas was found by PCR until the 24th month of infection, and that the plants have survived to date. Contrastingly, all non-transgenic plants collapsed from CLas within two years. These results clearly indicate that miR171b positively regulated the resistance to HLB of citrus.
The results from RNA-seq sequencing, by comparing the enrichment of DEG pathways in the transgenic plants versus the non-transgenics, indicate that the pathways are similar in the two biological groups, such as the photosynthesis and photosynthetic antenna protein pathways. Among the photosynthesis pathway, the DEGs were most involved in photosystem I and photosystem II. The DEGs in the photosynthetic antenna pathway were found to be involved in chlorophyll a-b binding protein. A change in photosynthetic machinery is common amongst the various pathogenic responses [51]. The inhibition of photosynthesis has already been found during the pathogenesis of Botrytis cinerea in plants such as Arabidopsis, tomato, and lettuce [52,53]. Thus, DEGs in the photosynthetic pathway were active in both transgenic plants and control plants for the same purpose of providing energy for plant survival, but the changes in the genes related to chlorophyll metabolism seem to explain why the leaves mottled after being infected with HLB bacteria.
Pathways related to disease resistance, such as plant–pathogen interactions, the MAPK signaling pathway, and ABC transporters, were all activated in transgenic plants. In the plant–pathogen interaction pathway, 234 DEGs were up/down-regulated, including the disease-related genes, such as RIN4, EDS1, WRKYs, and others. A total of 106 DEGs were involved in the MAPK signaling pathway, and MAPK has been shown to be involved in plant disease resistance. Li et al. found that flg22-MEKK1-MKK4/5-MPK3/6-WRKY22/29 could promote the expression of resistance genes by using the transient expression system of Arabidopsis protoplasts [54]. It was also found that flg22-induced MPK4 activity was significantly reduced in Arabidopsis mekk1 mutants, while the activity of MPK3/6 activity was not affected. These studies suggest that the MEKK1-MKK4/5-MPK3/6-WRKY22/29/33 cascade pathway is involved in the resistance response to Arabidopsis thaliana. In this work, the related genes MEKK1-MKK4/5-WRKY22 were up-regulated, but MPK3/6 was unchanged. The above may be one of the reasons for the promotion of resistance to HLB in the transgenic plants.
Smirnova et al. found that genes were expressed differently in pathogen invasion [55]. Furthermore, gene expression patterns changed the metabolism pathways and pathogen defense systems [56]. Although there was no significant difference in plant hormone signal transduction, a total of 128 DEGs were associated with this pathway. The DELLA gene, a key gene for inhibiting GA response, was altered in the GA pathway. The GID1, GID2, and DELLA genes were all altered in comparison with the non-transgenic plants versus the transgenic plants, which seems to indicate that the GA pathway was prepared for stress in the transgenic plants. Ma et al. found that SCL27, which was the target gene of miR171, interacted with DELLA, suggesting that miR171-SCL plays an important role in GA signaling [57]. Ma et al. discovered that the addition of gibberellin alleviated the stress of HLB by upregulating the genes encoding the ROS-producing NADPH oxidase and down-regulated antioxidant enzyme genes [58]. In this study, the DELLA gene was altered in transgenic plants; it may be related to the regulation of the target gene by miR171b, and also indicates that miR171b may be involved in the regulation of the GA signaling pathway.
Salicylic acid (SA) is an important phytohormone that regulates plant growth, environmental stress, and pathogen stress. Within a few years after SA was first discovered to play a role in heat production, SA was also found to be used as a signaling molecule during pathogen infection [59,60]. In apple (Malus domestica), exogenous SA could enhance the resistance to Glomerella leaf spot (GLS) [61]. The NPR1 gene is a key regulator in the SA signal transduction pathway. The overexpression of AtNPR1 resulted in citrus trees with normal phenotypes exhibiting enhanced resistance to HLB [62]. According to our results, the NPR1 gene and TGA gene expression were both upregulated in the SA pathway, which indicates that the salicylic acid pathway was activated for the overexpression of miR171b.
To our knowledge, miRNAs function through silencing complementary mRNA to regulate the expression of target genes. The miR171 family primarily targets the GRAS transcription factor gene family and regulates genes at the post-transcriptional level [63,64]. SCL (Scarecrow-like, including SCL, NSP2, NORK, etc.) is a core member of the GRAS family. In the model plant Arabidopsis thaliana, miR171 was found to slice the AtSCL6, AtSCL22, and AtSCL27 genes [28,63]. In recent years, GRAS genes have also been connected with plant disease resistance and responses to abiotic stress [28,40,65,66]. For example, GRAS genes play a role in pathogen resistance in tomato (Solanum lycopersicum) [63]. The expression of S1GRAS4 and S1GRAS6 was induced by the fungal elicitor EIX in tomato [63]. Similarly, the application of H2O2 to tobacco induced the expression of GRAS genes, demonstrating their involvement in plant protection against pathogens [67,68]. In chickpeas infected by Pseudomonas putida, miR171 down-regulated the target gene NPS2, which affected other rhizosphere-beneficial bacteria and promoted the growth of chickpea roots under drought stress [29]. In this study, bioinformatics was used to identify the target genes of miR171b in citrus. We observed a decrease in the expression of the target genes Cs5g08980.1 and orange1.1t00199.1, likely due to these genes being the target genes of miR171b, but verification of this is the next step. These results suggest that miR171b may increase resistance to citrus HLB through the target genes as well. However, the mechanism of how miR171b interacts with the SCL6 gene to improve resistance to citrus HLB remains unknown, and will be the target of future research.
In the current study, we provide evidence that miR171b plays a positive regulatory role in increasing citrus resistance to HLB through the downregulation of its target genes and multiple metabolic and biological pathways. This study is the first to report the regulation of HLB resistance at the level of microRNAs, and may represent a new molecular mechanism for the regulation of HLB stress adaption.

4. Materials and Methods

4.1. Cloning of pre-ctr-miR171b

Genomic DNA was extracted from leaves of trifoliate orange (Citrus trifoliata) using a DNA extraction kit (Dong sheng Biotechnology, Guangzhou, China). PCR primers are listed in Supplemental Table S1. The ctr-miR171b precursor (pre-ctr-miR171b) was PCR amplified using the 171f/171r primer pair and KOD FX DNA polymerase (Toyobo, Shanghai, China). The precursor sequence was downloaded from miRbase (http://www.mirbase.org/cgi-bin/browse.pl) (accessed on 3 August 2015). The amplified product was purified, ligated into the pEASY-T1 vector (TransGen Biotechnology Co., Ltd., Beijing, China), and cloned into Escherichia coli strain Trans1-T1. Positive clones were used to extract plasmids using a Vazyme plasmid purification kit (Vazyme Biotech Co., Ltd., Nanjing, China), and the correct inserts were verified by sequencing several plasmids with M13F/R primers.

4.2. Construction of Expression Vectors

A plasmid containing the correct ctr-miR171b precursor insert was digested with SacI and BamHI to release the insert. The digested materials were separated via electrophoresis, and the insert was recovered from the gel using a TaKaRa MiniBEST Agarose Gel DNA Extraction Kit (Takara, Dalian, China). The precursor was then ligated using T4 ligase into the linearized vector (pFGC5941) and prepared via digestion with SacI and BamHI. The ligated product was transformed into E. coli competent cells purchased from TransGen (Beijing, China) using the freeze–thaw method. Positive clones were extracted using a Vazyme plasmid purification kit (Nanjing, China) and verified using PCR.

4.3. Genetic Transformation and Identification of Transgenic Plants

Citrus transformation was performed according to the method described by Cheng et al. [69] using Citrus reticulata Blanco “tangerine” epicotyls as explants. Agrobacterium infection solution was prepared and the OD600 was adjusted to 0.5. The epicotyls of citrus were cultured in the dark, and the epicotyls were diagonally cut into sections of 1~2 cm, which were placed in Agrobacterium infection solution (containing 100 µM Acetosyringone) and infected for 30 min. The explants were removed and incubated in MS medium at 25 °C for 3 days. The explants were transferred to screening medium containing 10 mg/L Basta and cultured at 28 °C for 30 to 45 days. When the resistant regeneration buds had grown to 2 cm, the whole regeneration buds, together with the epicotyls, were grafted onto the stocks. Genomic DNA was isolated from fully expanded leaves of the regenerated plants and examined for the presence of the transgene via PCR using the pFGC5941 vector specific primer pair P1/P2 flanking the expression cascade. Further verification was carried out using PCR with the upstream 35S-F primer and the downstream 171r primer. The PCR-positive transgenic plants were propagated by grafting onto red tangerine rootstocks. The graft-propagated transgenic plants were re-examined using 35S-F/35S-R and Bar-F/Bar-R primers. Non-transgenic plant buds were simultaneously graft-propagated and used as controls.

4.4. Analysis of miR171b Expression Levels

Small RNA (sRNA) was isolated from the leaves of transgenic plants using the RNAiso for Small RNA kit (Takara, Dalian, China) according to the manufacturer’s instructions, and complementary DNA (cDNA) synthesis was performed using the miRcute Plus miRNA First-Strand cDNA kit (TianGen Biotechnology Co., Ltd., Beijing, China). miR171b was quantified using quantitative real-time PCR (qPCR) with the SYBR Green Premix for miRNA (TianGen Biotechnology Co., Ltd., Beijing, China) on the QuantStudio5 Real-Time PCR System (Thermo Fisher Scientific).
The transcription levels of miR171b were expressed as relative ratios between qPCR values of miR171b and the reference small RNA U6 using the 2−ΔΔCT method [70], and P-miR171b and P-U6 were used as primers. The thermo cycler was programmed as follows: pre-incubation at 95 °C for 30 s followed by 45 cycles of denaturation at 95 °C for 5 s and annealing at 60 °C for 20 s. Three technical replicates were used in this study. The differences in expression between transgenic and non-transgenic plants were considered significant when p < 0.05 and very significant when p < 0.01.

4.5. Determining the Ct Values of HLB Bacteria

Transgenic plants and non-transgenic controls were infected with CLas by grafting with HLB-infected buds. Three biological replicates were used. qPCR with the TaqMan probe method was used to quantify HLB DNA in the transgenic plants at an interval of 30 d in the first year, 90 d in the following three years, and 180 d in the remaining years. The positive and negative controls (HLB diseased and healthy trees, respectively) were confirmed based on the symptoms and by using PCR with HLB-specific primers. DNA was extracted from the leaves of the samples using a plant genomic DNA Rapid Extraction Kit (Dongsheng, Guangzhou, China). The DNA samples were diluted to uniform concentrations and used as the template to titer CLas bacteria in the samples. A Ct value of >36 was used as the cutoff for HLB-negative samples because noisy signals from non-specific amplifications occurred frequently above this value [71,72].

4.6. Plant Materials, RNA Isolation, and Quantification

Five-year-old transgenic trees expressing miRNA 171b precursor and non-transgenic trees were used as the material for RNA-seq, with three biological replicates for each. All trees were planted in the greenhouse at the Institute of Fruit Research, Guangdong Academy of Agricultural Sciences. Mature leaves from the plants of the two compared groups with the three replicates were used to extract total RNA by using an RNA extraction kit (RP3202, polysaccharide and polyphenol total RNA isolation kit, BioTeke, Beijing, China). RNA quality control and inspection used agarose gel (1.5%) electrophoresis and spectrophotometer measurements (NanoDrop 2000, Thermo Fisher Scientific, Lafayette, CO, USA). Three biological samples were used. Then, the libraries were constructed using the VAHTS Universal V6 RNA-seq Library Prep Kit according to the manufacturer’s instructions. The transcriptome sequencing and analysis were conducted by Biomarker Technologies Co. (Beijing, China).

4.7. RNA-Seq Experiment

Total RNA was enriched for mRNA with Oligo(dT) beads, while the mRNA was enriched by removing rRNA using the Ribo-ZeroTM Magnetic Kit (Epicentre, Madison, WI, USA). Then, the enriched mRNA was fragmented into short fragments using fragmentation buffer and reverse transcribed into cDNA with random primers. Second-strand cDNA was synthesized using DNA polymerase I, RNase H, dNTP, and buffer. Then, the cDNA fragments were purified with the QiaQuick PCR extraction kit (Qiagen, Venlo, The Netherlands), end repaired, poly(A) added, and ligated to Illumina sequencing adapters. The ligation products were selected using agarose gel electrophoresis, PCR amplified, and sequenced using Illumina HiSeq2500 by Biomarker Technologies Co. (Beijing, China) and 150 bp paired-end reads were generated. The raw reads for each sample were generated. Raw reads of fastq format were firstly processed using fastp, and the low-quality reads were removed to obtain the clean reads. Then, the clean reads for each sample were retained for subsequent analyses. The clean reads were mapped to the reference genome using HISAT2. Fragments Per Kilobase per Million (FPKM) of each gene was calculated and the read counts of each gene were obtained by HTSeq-count.
Differential expression analysis was performed using the DESeq2. Hierarchical cluster analysis of DEGs was performed using R (v 3.2.0) to demonstrate the expression pattern of genes in different groups and samples. Based on the hypergeometric distribution, GO and KEGG pathway enrichment analysis of DEGs was performed to screen the significantly enriched terms using R (v 3.2.0). R (v 3.2.0) was used to draw the column diagram and bubble diagram of the significant enrichment term.

4.8. Validation of DEGs in a qRT-PCR Assay

The relative expression levels of genes in citrus were detected via qRT-PCR using a previously described method. The thermo cycler was programmed as follows: pre-incubation at 95 °C for 10 min followed by 40 cycles of denaturation at 95 °C for 5 s, 60 °C for 20 s, 95 °C for 15 s and 60 °C for 1 min. All of the qRT-PCR experiments were performed with three technical replicates each. PCR primers and the genes info are listed in Supplemental Table S2.

4.9. Bioinformatics Analysis of miR171b Target Genes

The website https://www.zhaolab.org/psRNATarget/home, accessed on 15 October 2020 was used for the analysis of the miRNA target genes. Targets were identified by restricting mismatches to <3 nt and gaps to <1 nt. More rigorous screening parameters were used to narrow down the target genes: (1) less than or equal to three mismatches between mature miRNA and its target sites; (2) less than or equal to one mismatch on the first 1–9 nt between the target and the mature miRNA; (3) no mismatches at the 10–11 nt between the target and the mature miRNA; and (4) no mismatches at the base-pairing region between the target and the miRNA.

4.10. Data Analysis

Statistical analysis was performed using SPSS (version 17.0; https://www.ibm.com/analytics/data-science/predictive-analytics/spss-statistical-software). (accessed on 9 August 2020). Between-group comparisons were made using one-way analysis of variance (ANOVA) followed by a Tukey’s multiple range test. The mean ± SD of the three replicates were calculated. p < 0.05 and p < 0.01 were considered statistically significant or very statistically significant, respectively.

5. Conclusions

In general, this study demonstrates that overexpressing miR171b enhanced resistance to HLB through relevant pathways and related target genes. Moreover, this study provides novel insights into the molecular breeding of citrus against HLB.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24065737/s1.

Author Contributions

Y.Z., H.Y. and C.C. conceived and designed the research. Y.L. conducted the experiments. B.J. and S.R. contributed reagents and analytical tools. Y.L. and Y.Z. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the National Natural Science Foundation of China [32002016]; the Special Fund for Scientific Innovation Strategy, Construction of High Level, Academy of Agriculture Science [R2019PY-QY004]; the Guangdong Provincial Rural Revitalization Strategy Project [2021KJ269]; the open competition program of the top ten critical priorities of Agricultural Science and Technology Innovation for the 14th Five-year Plan of Guangdong Province [2022SDZG06] and the Guangdong Provincial science and technology special project in 2021 [210907114532092].

Institutional Review Board Statement

Not applicable.

Data Availability Statement

RNA-seq data have been submitted to the NCBI Sequence Read Archive (SRA). https://www.ncbi.nlm.nih.gov/sra/PRJNA906724 (accessed on 30 November 2022).

Conflicts of Interest

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

References

  1. Donkersley, P.; Silva, F.W.; Carvalho, C.M.; Al-Sadi, A.M.; Elliot, S.L. Biological, environmental and socioeconomic threats to citrus lime production. J. Plant Dis. Prot. 2018, 125, 339–356. [Google Scholar] [CrossRef] [Green Version]
  2. Morris, J.; Shiller, J.; Mann, R.; Smith, G.; Yen, A.; Rodoni, B. Novel ‘Candidatus Liberibacter’species identified in the Australian eggplant psyllid, Acizzia solanicola. Microb. Biotechnol. 2017, 10, 833–844. [Google Scholar] [CrossRef] [PubMed]
  3. Zhong, Y.; Cheng, C.; Jiang, B.; Jiang, J.; Zhang, Y.; Hu, M.; Zhong, G. Digital Gene Expression Analysis of Ponkan Mandarin (Citrus reticulata Blanco) in Response to Asia Citrus Psyllid-Vectored Huanglongbing Infection. Int. J. Mol. Sci. 2016, 17, 1063. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Duan, Y.; Zhou, L.; Hall, D.G.; Li, W.; Doddapaneni, H.; Lin, H.; Gottwald, T. Complete genome sequence of citrus huanglongbing bacterium,‘Candidatus Liberibacter asiaticus’ obtained through metagenomics. Mol. Plant-Microbe Interact. 2009, 22, 1011–1020. [Google Scholar] [CrossRef] [Green Version]
  5. Narouei-Khandan, H.A.; Halbert, S.E.; Worner, S.P.; van Bruggen, A.H. Global climate suitability of citrus huanglongbing and its vector, the Asian citrus psyllid, using two correlative species distribution modeling approaches, with emphasis on the USA. Eur. J. Plant Pathol. 2016, 144, 655–670. [Google Scholar] [CrossRef]
  6. McRoberts, N.; Figuera, S.G.; Olkowski, S.; McGuire, B.; Luo, W.; Posny, D.; Gottwald, T. Using models to provide rapid programme support for California’s efforts to suppress Huanglongbing disease of citrus. Philos. Trans. R. Soc. B 2019, 374, 20180281. [Google Scholar] [CrossRef] [Green Version]
  7. Graham, J.; Gottwald, T.; Setamou, M. Status of huanglongbing (HLB) outbreaks in Florida, California and Texas. Trop. Plant Pathol. 2020, 45, 265–278. [Google Scholar] [CrossRef]
  8. Sillero, J.C.; Villegas-Fernández, A.M.; Thomas, J.; Rojas-Molina, M.M.; Emeran, A.A.; Fernández-Aparicio, M.; Rubiales, D. Faba bean breeding for disease resistance. Field Crops Res. 2010, 115, 297–307. [Google Scholar] [CrossRef] [Green Version]
  9. Georges, F.; Ray, H. Genome editing of crops: A renewed opportunity for food security. GM Crops Food 2017, 8, 1–12. [Google Scholar] [CrossRef] [Green Version]
  10. Caruso, M.; Smith, M.W.; Froelicher, Y.; Russo, G.; Gmitter, F.G., Jr. Traditional breeding. In The Genus Citrus; Woodhead Publishing: Sawston, UK, 2020; pp. 129–148. [Google Scholar]
  11. Albrecht, U.; Bowman, K.D. Tolerance of the trifoliate citrus hybrid US-897 (Citrus reticulata Blanco x Poncirus trifoliate L.Raf.) to Huanglongbing. Hortscience 2011, 46, 16–22. [Google Scholar] [CrossRef] [Green Version]
  12. Albrecht, U.; Bowman, K.D. Tolerance of trifoliate citrus rootstock hybrids to Candidatus Liberibacter asiaticus. Sci. Hortic. 2012, 147, 71–80. [Google Scholar] [CrossRef]
  13. Ramadugu, C.; Keremane, M.L.; Halbert, S.E.; Duan, Y.P.; Roose, M.L.; Stover, E.; Lee, R.F. Long-term field evaluation reveals Huanglongbing resistance in Citrus relatives. Plant Dis. 2016, 100, 1858–1869. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Huang, M.; Roose, M.L.; Yu, Q.; Du, D.; Yu, Y.; Zhang, Y.; Gmitter, F.G., Jr. Construction of high-density genetic maps and detection of QTLs associated with Huanglongbing tolerance in citrus. Front. Plant Sci. 2018, 9, 1694. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Moniruzzaman, M.; Zhong, Y.; Yan, H.; Yuanda, L.; Jiang, B.; Zhong, G. Exploration of susceptible genes with clustered regularly interspaced short palindromic repeats–tissue-specific knockout (CRISPR-TSKO) to enhance host resistance. Crit. Rev. Plant Sci. 2020, 39, 387–417. [Google Scholar] [CrossRef]
  16. Van Eck, J. Applying gene editing to tailor precise genetic modifications in plants. J. Biol. Chem. 2020, 295, 13267–13276. [Google Scholar] [CrossRef] [PubMed]
  17. Li, Z.; Rana, T.M. Therapeutic targeting of microRNAs: Current status and future challenges. Nat. Rev. Drug Discov. 2014, 13, 622–638. [Google Scholar] [CrossRef] [PubMed]
  18. Budak, H.; Akpinar, B.A. Plant miRNAs: Biogenesis, organization and origins. Funct. Integr. Genom. 2015, 15, 523–531. [Google Scholar]
  19. Yang, Z.; Zhu, P.; Kang, H.; Liu, L.; Cao, Q.; Sun, J.; Xu, T. High-throughput deep sequencing reveals the important role that microRNAs play in the salt response in sweet potato (Ipomoea batatas L.). BMC Genom. 2020, 21, 164. [Google Scholar] [CrossRef] [Green Version]
  20. Yang, Z.; Dong, T.; Dai, X.; Wei, Y.; Fang, Y.; Zhang, L.; Xu, T. Comparative analysis of salt responsive microRNAs in two sweetpotato [Ipomoea batatas (L.) Lam.] cultivars with different salt stress resistance. Front. Plant Sci. 2022, 13, 879819. [Google Scholar]
  21. Li, Z.; Zhang, X.; Liu, X.; Zhao, Y.; Wang, B.; Zhang, J. miRNA alterations are important mechanism in maize adaptations to low-phosphate environments. Plant Sci. 2016, 252, 103–117. [Google Scholar] [CrossRef]
  22. Ding, Y.; Chen, Z.; Zhu, C. Microarray-based analysis of cadmium-responsive microRNAs in rice (Oryza sativa). J. Exp. Bot. 2011, 62, 3563–3573. [Google Scholar] [CrossRef]
  23. Fang, X.; Zhao, Y.; Ma, Q.; Huang, Y.; Wang, P.; Zhang, J.; Yang, C. Identification and comparative analysis of cadmium tolerance-associated miRNAs and their targets in two soybean genotypes. PLoS ONE 2013, 8, e81471. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Subramanian, S.; Fu, Y.; Sunkar, R.; Barbazuk, W.B.; Zhu, J.K.; Yu, O. Novel and nodulation-regulated microRNAs in soybean roots. BMC Genom. 2008, 9, 160. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Sunkar, R.; Li, Y.F.; Jagadeeswaran, G. Functions of microRNAs in plant stress responses. Trends Plant Sci. 2012, 17, 196–203. [Google Scholar] [CrossRef] [PubMed]
  26. Pérez-Quintero, Á.L.; Quintero, A.; Urrego, O.; Vanegas, P.; López, C. Bioinformatic identification of cassava miRNAs differentially expressed in response to infection by Xanthomonas axonopodis pv. manihotis. BMC Plant Biol. 2012, 12, 29. [Google Scholar] [CrossRef] [Green Version]
  27. Ergun, S.; Oztuzcu, S. Oncocers: ceRNA-mediated cross-talk by sponging miRNAs in oncogenic pathways. Tumor Biol. 2015, 36, 3129–3136. [Google Scholar] [CrossRef]
  28. Huang, W.; Xian, Z.Q.; Kang, X.; Tang, N.; Li, Z.G. Genome-wideidentification, phylogeny and expression analysis of GRAS gene family intomato. BMC Plant Biol. 2015, 15, 209. [Google Scholar] [CrossRef] [Green Version]
  29. Jatan, R.; Chauhan, P.S.; Lata, C. Pseudomonas putida modulates the expression of miRNAs and their target genes in response to drought and salt stresses in chickpea (Cicer arietinum L.). Genomics 2019, 111, 509–519. [Google Scholar] [CrossRef]
  30. Huang, W.; Peng, S.; Xian, Z.; Lin, D.; Hu, G.; Yang, L.; Li, Z. Overexpression of a tomato miR171 target gene SlGRAS 24 impacts multiple agronomical traits via regulating gibberellin and auxin homeostasis. Plant Biotechnol. J. 2017, 15, 472–488. [Google Scholar] [CrossRef]
  31. Habib, S.; Waseem, M.; Li, N.; Yang, L.; Li, Z. Overexpression of SlGRAS7 affects multiple behaviors leading to confer abiotic stresses tolerance and impacts gibberellin and auxin signaling in tomato. Int. J. Genom. 2019, 2019, 4051981. [Google Scholar] [CrossRef] [Green Version]
  32. Li, H.; Zhang, J.; Yang, Y.; Jia, N.; Wang, C.; Sun, H. miR171 and its target gene SCL6 contribute to embryogenic callus induction and torpedo-shaped embryo formation during somatic embryogenesis in two lily species. Plant Cell Tissue Organ Cult. 2017, 130, 591–600. [Google Scholar] [CrossRef]
  33. Couzigou, J.M.; Lauressergues, D.; André, O.; Gutjahr, C.; Guillotin, B.; Bécard, G.; Combier, J.P. Positive gene regulation by a natural protective miRNA enables arbuscular mycorrhizal symbiosis. Cell Host Microbe 2017, 21, 106–112. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Song, F.; He, C.; Yan, X.; Bai, F.; Pan, Z.; Deng, X.; Xiao, S. Small RNA profiling reveals involvement of microRNA-mediated gene regulation in response to mycorrhizal symbiosis in Poncirus trifoliata L. Raf. Tree Genet. Genomes 2018, 14, 42. [Google Scholar] [CrossRef]
  35. Li, Q.; Zhang, J.; Chen, Y.; Lin, X.; Sun, C.; Zhang, B. Migration of endophytic diazotroph Azorhizobium caulinodans ORS571 inside wheat (Triticum aestivum L) and its effect on microRNAs. Funct. Integr. Genom. 2017, 17, 311–319. [Google Scholar]
  36. Reyes, C.A.; Ocolotobiche, E.E.; Marmisollé, F.E.; Robles Luna, G.; Borniego, M.B.; Bazzini, A.A.; García, M.L. Citrus psorosis virus 24 K protein interacts with citrus miRNA precursors, affects their processing and subsequent miRNA accumulation and target expression. Mol. Plant Pathol. 2016, 17, 317–329. [Google Scholar] [CrossRef]
  37. Zhong, Y.; Cheng, C.; Moniruzzaman, M.; Jiang, B.; Jiang, N.; Zhong, G. Expression of miRNAs and their target genes in roots of ‘Sanhu’tangerine (Citrus reticulata blanco cv. ‘Sanhu’) in response to Candidatus Liberibacter asiaticus infection. J. Plant Dis. Prot. 2021, 128, 407–420. [Google Scholar]
  38. Terrón-Camero, L.C.; Molina-Moya, E.; Sanz-Fernández, M.; Sandalio, L.M.; Romero-Puertas, M.C. Detection of reactive oxygen and nitrogen species (ROS/RNS) during hypersensitive cell death. Plant Program. Cell Death Methods Protocols 2018, 97–105. [Google Scholar]
  39. Zurbriggen, M.D.; Carrillo, N.; Hajirezaei, M.R. ROS signaling in the hypersensitive response: When, where and what for? Plant Signal. Behav. 2010, 5, 393–396. [Google Scholar] [CrossRef] [Green Version]
  40. Cui, H.; Kong, D.; Liu, X.; Hao, Y. SCARECROW, SCR-LIKE 23 and SHORT-ROOT control bundle sheath cell fate and function in Arabidopsis thaliana. Plant J. 2014, 78, 319–327. [Google Scholar] [CrossRef]
  41. Pitzschke, A.; Schikora, A.; Hirt, H. MAPK cascade signalling networks in plant defence. Curr. Opin. Plant Biol. 2009, 12, 421–426. [Google Scholar] [CrossRef]
  42. Boller, T.; Felix, G. A renaissance of elicitors: Perception of microbe-associated molecular patterns and danger signals by pattern-recognition receptors. Annu. Rev. Plant Biol. 2009, 60, 379–406. [Google Scholar] [CrossRef]
  43. Hwang, S.H.; Kwon, S.; Jang, J.Y.; Fang, I.L.; Lee, H.; Choi, C.; Park, S.; Ahn, I.; Bae, S.; Hwang, D.J. OsWRKY51, a rice transcription factor, functions as a positive regulator in defense response against Xanthomonas oryzae pv. oryzae. Plant Cell Rep. 2016, 35, 1975–1985. [Google Scholar] [CrossRef] [PubMed]
  44. Verma, V.; Ravindran, P.; Kumar, P.P. Plant hormone-mediated regulation of stress responses. BMC Plant Biol. 2016, 16, 86. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Hu, Y.; Zhou, L.; Yang, Y.; Zhang, W.; Chen, Z.; Li, X.; Hou, X. The gibberellin signaling negative regulator RGA-LIKE3 promotes seed storage protein accumulation. Plant Physiology. 2021, 185, 1697–1707. [Google Scholar] [CrossRef] [PubMed]
  46. Saleem, M.; Fariduddin, Q.; Castroverde, C.D.M. Salicylic acid: A key regulator of redox signalling and plant immunity. Plant Physiology and Biochemistry. 2021, 168, 381–397. [Google Scholar] [CrossRef]
  47. Jatan, R.; Lata, C.H.A.R.U. Role of microRNAs in abiotic and biotic stress resistance in plants. Proc. Indian Natl. Sci. Acad. 2019, 13, 553–567. [Google Scholar]
  48. Lauressergues, D.; Delaux, P.M.; Formey, D.; Lelandais-Brie`re, C.; Fort, S.; Cottaz, S.; Be’card, G.; Niebel, A.; Roux, C.; Combier, J.P. The microRNA miR171h modulates arbuscular mycorrhizal colonization of Medicago truncatula by targeting NSP2. Plant J. 2012, 72, 512–522. [Google Scholar] [CrossRef]
  49. Xin, M.; Wang, Y.; Yao, Y.; Xie, C.; Peng, H.; Ni, Z.; Sun, Q. Diverse set of microRNAs are responsive to powdery mildew infection and heat stress in wheat (Triticum aestivum L.). BMC Plant Biol. 2010, 10, 123. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Hossain, M.S.; Hoang, N.T.; Yan, Z.; Tóth, K.; Meyers, B.C.; Stacey, G. Characterization of the spatial and temporal expression of two soybean miRNAs identifies SCL6 as a novel regulator of soybean nodulation. Front. Plant Sci. 2019, 10, 475. [Google Scholar] [CrossRef] [Green Version]
  51. Berger, S.; Sinha, A.K.; Roitsch, T. Plant physiology meets phytopathology: Plant primary metabolism and plant pathogen interactions. J. Exp. Bot. 2007, 58, 4019–4026. [Google Scholar] [CrossRef]
  52. Kanwar, P.; Jha, G. Alterations in plant sugar metabolism: Signatory of pathogen attack. Planta 2019, 249, 305–318. [Google Scholar] [CrossRef]
  53. Smith, J.E.; Mengesha, B.; Tang, H.; Mengiste, T.; Bluhm, B.H. Resistance to Botrytis cinerea in Solanum lycopersicoides involves widespread transcriptional reprogramming. BMC Genom. 2014, 15, 334. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Li, Q.; Chen, Y.; Wang, J.; Zou, F.; Jia, Y.; Shen, D.; Zhang, M. A Phytophthora capsici virulence effector associates with NPR1 and suppresses plant immune responses. Phytopathology Research. 2019, 1, 1–11. [Google Scholar] [CrossRef] [Green Version]
  55. Smirnova, O.G.; Kochetov, A.V. Promoters of plant genes responsive to pathogen invasion. Russ. J. Genet. Appl. Res. 2015, 5, 254–261. [Google Scholar] [CrossRef]
  56. Yang, C.; Liang, Y.; Qiu, D.; Zeng, H.; Yuan, J.; Yang, X. Lignin metabolism involves Botrytis cinerea BcGs1-induced defense response in tomato. BMC Plant Biol. 2018, 18, 103. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Ma, H.S.; Liang, D.; Shuai, P.; Xia, X.L.; Yin, W.L. The salt- and drought-inducible poplar GRAS protein SCL7 confers salt and drought tolerance in Arabidopsis thaliana. J. Exp. Bot. 2010, 61, 4011–4019. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Ma, W.; Pang, Z.; Huang, X.; Xu, J.; Pandey, S.S.; Li, J.; Wang, N. Citrus Huanglongbing is a pathogen-triggered immune disease that can be mitigated with antioxidants and gibberellin. Nat. Commun. 2022, 13, 529. [Google Scholar] [CrossRef]
  59. Klessig, D.F.; Choi, H.W.; Dempsey, D.M.A. Systemic acquired resistance and salicylic acid: Past, present, and future. Mol. Plant-Microbe Interact. 2018, 31, 871–888. [Google Scholar] [CrossRef] [Green Version]
  60. Kunkel, B.N.; Brooks, D.M. Cross talk between signaling pathways in pathogen defense. Curr. Opin. Plant Biol. 2002, 5, 325–331. [Google Scholar] [CrossRef]
  61. Zhang, Y.; Shi, X.; Li, B.; Zhang, Q.; Liang, W.; Wang, C. Salicylic acid confers enhanced resistance to Glomerella leaf spot in apple. Plant Physiology and Biochemistry. 2016, 106, 64–72. [Google Scholar] [CrossRef]
  62. Dutt, M.; Barthe, G.; Irey, M.; Grosser, J. Transgenic citrus expressing an Arabidopsis NPR1 gene exhibit enhanced resistance against Huanglongbing (HLB; Citrus Greening). PLoS ONE 2015, 10, e0137134. [Google Scholar] [CrossRef]
  63. Wang, L.; Mai, Y.X.; Zhang, Y.C.; Luo, Q.; Yang, H.Q. MicroRNA171c-targeted SCL6-II, SCL6-III, and SCL6-IV genes regulate shoot branching in Arabidopsis. Mol. Plant 2010, 3, 794–806. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Branscheid, A.; Devers, E.A.; May, P.; Krajinski, F. Distribution pattern of small RNA and degradome reads provides information on miRNA gene structure and regulation. Plant Signal. Behav. 2011, 6, 1609–1611. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Zeng, X.; Ling, H.; Chen, X.; Guo, S. Genome-wide identification, phylogeny and function analysis of GRAS gene family in Dendrobium catenatum (Orchidaceae). Gene. 2019, 705, 5–15. [Google Scholar] [CrossRef] [PubMed]
  66. Grimplet, J.; Agudelo-Romero, P.; Teixeira, R.T.; Martinez-Zapater, J.M.; Fortes, A.M. Structural and functional analysis of the GRAS gene family in grapevine indicates a role of GRAS proteins in the control of development and stress responses. Front. Plant Sci. 2016, 7, 353. [Google Scholar] [CrossRef] [Green Version]
  67. Navarro, L.; Bari, R.; Achard, P.; Lisón, P.; Nemri, A.; Harberd, N.P.; Jones, J.D. DELLAs control plant immune responses by modulating the balance of jasmonic acid and salicylic acid signaling. Curr. Biol. 2008, 18, 650–655. [Google Scholar] [CrossRef]
  68. Khan, Y.; Xiong, Z.; Zhang, H.; Liu, S.; Yaseen, T.; Hui, T. Expression and roles of GRAS gene family in plant growth, signal transduction, biotic and abiotic stress resistance and symbiosis formation-a review. Plant Biol. 2021, 24, 404–416. [Google Scholar] [CrossRef]
  69. Cheng, C.Z.; Yang, J.W.; Yan, H.B.; Bei, X.J.; Zhang, Y.Y.; Lu, Z.M.; Zhong, G.Y. Expressing p20 hairpin RNA of Citrus tristeza virus confers Citrus aurantium with tolerance/resistance against stem pitting and seedling yellow CTV strains. J. Integr. Agric. 2015, 14, 1767–1777. [Google Scholar] [CrossRef]
  70. Tang, F.; Chu, L.; Shu, W.; He, X.; Wang, L.; Lu, M. Selection and validation of reference genes for quantitative expression analysis of miRNAs and mRNAs in Poplar. Plant Methods. 2019, 15, 1–15. [Google Scholar] [CrossRef]
  71. Li, W.; Hartung, J.S.; Levy, L. Evaluation of DNA amplification methods for improved detection of “Candidatus Liberibacter species” associated with citrus huanglongbing. Plant Dis. 2007, 91, 51–58. [Google Scholar] [CrossRef] [Green Version]
  72. Morgan, J.K.; Zhou, L.; Li, W.; Shatters, R.G.; Keremane, M.; Duan, Y.P. Improved real-time PCR detection of ‘Candidatus Liberibacter asiaticus’ from citrus and psyllid hosts by targeting the intragenic tandem-repeats of its prophage genes. Mol. Cell. Probes 2012, 26, 90–98. [Google Scholar] [CrossRef]
Ijms 24 05737 g001 550
Figure 1. Generation of transgenic plant overexpression of pre-ctr-miR171b. (A). pre-ctr-miR171b cloning and the transgenic plant identification with 35SF + 171r primers. (B). Confirm the 171-5 is the transgenic plant with 35S and Basta. (C). Transgenic plants at two months post-grafting. (D). Quantitative real-time PCR results of miR171b in transgenic line 171-5. Experiments were repeated twice with similar results. ** indicate p values of <0.01, compared with the non-transgenic plants.
Figure 1. Generation of transgenic plant overexpression of pre-ctr-miR171b. (A). pre-ctr-miR171b cloning and the transgenic plant identification with 35SF + 171r primers. (B). Confirm the 171-5 is the transgenic plant with 35S and Basta. (C). Transgenic plants at two months post-grafting. (D). Quantitative real-time PCR results of miR171b in transgenic line 171-5. Experiments were repeated twice with similar results. ** indicate p values of <0.01, compared with the non-transgenic plants.
Ijms 24 05737 g001
Ijms 24 05737 g002 550
Figure 2. Quantitative real-time PCR detection of HLB in transgenic plants overexpressing miR171b. The horizontal axis represents detection time points following the inoculation of HLB. The vertical axis represents Ct values of the HLB-specific amplicon. Experiments were repeated twice with similar results. ** indicates p values of <0.01 compared with the non-transgenic plants. The M on the X-axis means the month.
Figure 2. Quantitative real-time PCR detection of HLB in transgenic plants overexpressing miR171b. The horizontal axis represents detection time points following the inoculation of HLB. The vertical axis represents Ct values of the HLB-specific amplicon. Experiments were repeated twice with similar results. ** indicates p values of <0.01 compared with the non-transgenic plants. The M on the X-axis means the month.
Ijms 24 05737 g002
Ijms 24 05737 g003 550
Figure 3. Phenotypes of transgenic plants after four years. (A). transgenic miR171b plants without HLB. (B). Transgenic miR171b plants with HLB. Non-transgenic plants with HLB died prior to four years after inoculation.
Figure 3. Phenotypes of transgenic plants after four years. (A). transgenic miR171b plants without HLB. (B). Transgenic miR171b plants with HLB. Non-transgenic plants with HLB died prior to four years after inoculation.
Ijms 24 05737 g003
Ijms 24 05737 g004 550
Figure 4. Summary of GO enrichment analysis. Biological processes, cellular components, and molecular functions are shown.
Figure 4. Summary of GO enrichment analysis. Biological processes, cellular components, and molecular functions are shown.
Ijms 24 05737 g004
Ijms 24 05737 g005 550
Figure 5. KEGG pathway enrichment analysis in the non-transgenic plants versus the transgenic plants.
Figure 5. KEGG pathway enrichment analysis in the non-transgenic plants versus the transgenic plants.
Ijms 24 05737 g005
Ijms 24 05737 g006 550
Figure 6. The KEGG map of the photosynthesis pathway in the non-transgenic plants versus the transgenic plants. The red means the DEGs were up-regulated, the green means the DEGs were down-regulated, and the blue means both up- and down- regulated.
Figure 6. The KEGG map of the photosynthesis pathway in the non-transgenic plants versus the transgenic plants. The red means the DEGs were up-regulated, the green means the DEGs were down-regulated, and the blue means both up- and down- regulated.
Ijms 24 05737 g006
Ijms 24 05737 g007 550
Figure 7. The FPKM of the DEGs in the photosynthesis pathway in the non-transgenic plants versus the transgenic plants. The N−CK−1−3 means the non-transgenic plants, the N−OE−1−3 means the transgenic plants. Other figures were the same.
Figure 7. The FPKM of the DEGs in the photosynthesis pathway in the non-transgenic plants versus the transgenic plants. The N−CK−1−3 means the non-transgenic plants, the N−OE−1−3 means the transgenic plants. Other figures were the same.
Ijms 24 05737 g007
Ijms 24 05737 g008 550
Figure 8. The FPKM of the DEGs in the plant–pathogen interaction pathway in the non-transgenic plants versus the transgenic plants.
Figure 8. The FPKM of the DEGs in the plant–pathogen interaction pathway in the non-transgenic plants versus the transgenic plants.
Ijms 24 05737 g008
Ijms 24 05737 g009 550
Figure 9. The FPKMs of the DEGs in the MAPK pathway in the non-transgenic plants versus the transgenic plants.
Figure 9. The FPKMs of the DEGs in the MAPK pathway in the non-transgenic plants versus the transgenic plants.
Ijms 24 05737 g009
Ijms 24 05737 g010 550
Figure 10. The FPKM of the WRKYs in the non-transgenic plants versus the transgenic plants.
Figure 10. The FPKM of the WRKYs in the non-transgenic plants versus the transgenic plants.
Ijms 24 05737 g010
Ijms 24 05737 g011 550
Figure 11. The FPKM of the DEGs in the plant hormone signal transduction pathway in the non-transgenic plants versus the transgenic plants.
Figure 11. The FPKM of the DEGs in the plant hormone signal transduction pathway in the non-transgenic plants versus the transgenic plants.
Ijms 24 05737 g011
Ijms 24 05737 g012 550
Figure 12. The expression level of the predicted target genes for miR71b. Experiments were repeated twice with similar results. ** indicates p values of <0.01 compared with the non-transgenic plants.
Figure 12. The expression level of the predicted target genes for miR71b. Experiments were repeated twice with similar results. ** indicates p values of <0.01 compared with the non-transgenic plants.
Ijms 24 05737 g012
Table
Table 1. The predicted target genes of miR171b.
Table 1. The predicted target genes of miR171b.
Gene IDGene Info
orange1.1t00200.1Scarecrow transcription factor family protein; Scarecrow-like protein 6; DELLA protein RGA
orange1.1t00200.2GRAS family transcription factor containing protein; Scarecrow-like protein 6; DELLA protein RGA
Cs5g08980.1GRAS family transcription factor containing protein; Scarecrow-like protein 6
orange1.1t00199.1GRAS family transcription factor containing protein; Scarecrow-like protein 6
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lv, Y.; Zhong, Y.; Jiang, B.; Yan, H.; Ren, S.; Cheng, C. MicroRNA miR171b Positively Regulates Resistance to Huanglongbing of Citrus. Int. J. Mol. Sci. 2023, 24, 5737. https://doi.org/10.3390/ijms24065737

AMA Style

Lv Y, Zhong Y, Jiang B, Yan H, Ren S, Cheng C. MicroRNA miR171b Positively Regulates Resistance to Huanglongbing of Citrus. International Journal of Molecular Sciences. 2023; 24(6):5737. https://doi.org/10.3390/ijms24065737

Chicago/Turabian Style

Lv, Yuanda, Yun Zhong, Bo Jiang, Huaxue Yan, Shuang Ren, and Chunzhen Cheng. 2023. "MicroRNA miR171b Positively Regulates Resistance to Huanglongbing of Citrus" International Journal of Molecular Sciences 24, no. 6: 5737. https://doi.org/10.3390/ijms24065737

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop