Global analysis of miRNA sequencing
In this study, miRNAs of two peanut cultivars with or without S. rolfsii infection were sequenced. Stem samples were collected from pre-inoculation (JT_0 and Rzh_0), 36 hours after inoculation (JT_A and Rzh_A, 36 HAI), 3 days after inoculation (JT_B and Rzh_B, 3 DAI) and 6 days after inoculation (JT_C and Rzh_C, 6 DAI) of two peanut cultivars (Figure S1c). Based on the sequencing results from 3 replicates, 22.79 M clean reads were measured for each sample on average, and the mapping rate was above 87.67% on average (Fig. 1a and b, Table S1). Interestingly, the mapping rate in the JT group displayed a significant decrease at the early stage of S. rolfsii infection (90% at the pre-inoculation, 74% at the 36 hours of inoculation) and gradually increased to a similar level to control at a later stage (Fig. 1b). The length of miRNAs from all sequenced samples showed the highest abundance at 24 nt, then followed by 21 nt (Fig. 1c). In plants, 24-nt small interfering RNAs (siRNAs) are known to be related to RNA-directed DNA methylation (RdDM) (Matzke and Mosher, 2014). The ratio of 24nt/21nt in both susceptible and tolerant peanuts might reflect the degree of DNA methylation during S. rolfsii infection. Notably, the ratio in the JT group increased along with infection time, however, Rzh, the resistance group showed a stable ratio during the infection period (Fig. 1d). This result might indicate that the RdDM mechanism is involved in responding to S. rolfsii infection in peanuts since the susceptible cultivar showed high RdDM activity during infection.
To identify miRNAs in each sample, total sRNAs were classified into 13 groups (Figure S2). Results showed that sRNAs mapping to the intergenic region constituted the majority (49–66%); introns and exons made up 7–12%, and the miRNAs (mature) made up 3–8%. The rest part of the sRNAs including rRNAs, tRNAs, snRNAs, snoRNAs, unmapped and repeats, etc. filled the remaining classification. After alignment against miRBase, a total of 203 including 29 known and 174 novel miRNAs were detected from all samples (Table S2). To further identify miRNAs in samples, the average reads of miRNAs from 3 replicates were calculated, and those with less than 20 reads in all samples were removed. Finally, 18 known and 142 novel miRNAs were identified (Table S2).
Identification And Analysis Of Differentially Expressed Mirnas (Dems)
To understand how miRNAs respond to S. rolfsii infection, the fold changes in the expression of miRNAs under treatment were calculated both within and between JT and Rzh groups. Also, to understand the difference between JT and Rzh cultivar, the fold changes of miRNAs expression in JT and Rzh control groups (pre-inoculation, JT_0, and Rzh_0) were compared. According to the result of differentially expressed miRNA (|log2(-fold changes) |>1.5) between the JT_0 and Rzh_0 (JT_0 was used as control), a total of 61 miRNAs (1 upregulated and 60 downregulated) were detected (Fig. 2a). Since the background of the JT and Rzh variety is different, the DEMs contained both common and Rzh unique fungi infection responsive miRNAs, however, the DEMs between Rzh and JT might uncover the secret of resistance to S. rolfsii infection in the Rzh variety. To further identify the miRNAs responding to S. rolfsii infection, the DEMs within the JT or the Rzh group were investigated as well. The result showed that the DEM was 36 (6 upregulated, 30 downregulated), 35 (5 upregulated, 30 downregulated), and 56 (8 upregulated, 48 downregulated) in 36 HAI, 3 DAI, and 6 DAI samples respectively (Fig. 2a). Interestingly, when we investigate the DEMs in the Rzh group, more upregulated miRNAs were detected while the downregulated miRNAs were the majority in the JT group. In the Rzh group, 23 (20 upregulated, 3 downregulated), 28 (21 upregulated, 7 downregulated), and 34 (19 upregulated, 15 downregulated) DEMs were found in 36 HAI, 3 DAI, and 6 DAI samples respectively (Fig. 2a).
Although DEMs in individual samples reflected the involvement of miRNAs in responding S. rolfsii infection in peanuts, differentially expressed miRNAs in both peanut varieties might have a higher correlation with defense response. Based on this hypothesis, we examined DEMs in the JT and the Rzh groups with different combinations to dig out S. rolfsii infection defense responding miRNAs. There were 29 and 12 miRNAs that showed overlaps during infection in the JT_A/B/C and the Rzh_A/B/C DEM respectively (Fig. 2b, 2c). Then we investigated miRNAs from JT_A/B/C vs JT_0, Rzh_0 vs JT_0, and Rzh_A/B/C vs Rzh_0. Subsequently, the control group DEM (Rzh_0 vs JT_0) was overlapped with JT and Rzh to identify predicted Rzh unique expressed miRNAs. Among the 61 DEMs, 14 DEMs were considered Rzh unique (Fig. 2d). 27 miRNAs that were differentially expressed in both JT and Rzh groups were considered S. rolfsii infection responsive (Fig. 2d).
Identification of miRNAs involved in S. rolfsii infection resistance
Since the Rzh cultivar is resistant to S. rolfsii infection (Figure S1c), the DEMs between the JT and the Rzh cultivars might be the miRNAs involved in establishing S. rolfsii resistance in the Rzh peanut variety. Here we analyzed those miRNAs differentially expressed in Rzh_0 compared to JT_0 samples. The expression data of 61 DEM in all samples were plotted, and an expression heatmap was shown (Fig. 3a). According to the expression pattern, the heatmap was classified into four types (type I-IV). The type I (in red box) miRNAs showed low expression levels, the type II (in black box) contained the miRNAs with high expression levels in all samples, the type III (in gray box) was miRNAs with moderate-low expression and the type IV (in blue box) miRNAs have a moderate-high expression (Fig. 3a). Further analysis focused on identifying the miRNAs involved in S. rolfsii infection resistance. 14 Rzh unique DEMs were removed since they might be only related to Rzh background. However, 18 DEMs that were identified from the cluster containing both Rzh_0 vs JT_0 and JT groups were considered S. rolfsii infection responsive and responsible for enhanced resistance in the Rzh variety. Notably, 12 out of 18 miRNA families showed low abundance (type I and III) in Rzh variety while they showed significant fold changes (Fig. 3b). According to the expression data (Table S2), these miRNAs were repressed by S. rolfsii infection. In addition, they were less expressed in the Rzh variety, therefore these miRNAs might regulate the genes that are involved in pathogen resistance establishment.
Identification of S. rolfsii infection responsive miRNAs
Besides the predicted resistance miRNAs, DEMs from both peanut varieties during infection was highly likely responsible for infection response. To identify these miRNAs, we investigated the DEMs in both JT and Rzh samples. Along the infection time, a total of 36, 35, and 56, and 23, 28, and 34 DEMs were identified at 36 hours, 3 days and 6 days infected samples in JT and Rzh varieties respectively. The number of DEMs including known and novel miRNAs displayed an increasing trend (Fig. 4a). Interestingly, the JT, susceptible variety, has more affected miRNAs while the Rzh has only 2/3 of that in JT. This result might be due to the protection mechanism in the resistant variety. Among the DEMs, we identified 10 known miRNAs, and the expression fold changes of these miRNAs were shown (Fig. 4b). Most of the known miRNAs showed upregulated expression levels in the Rzh group even though they were downregulated in the JT group (Fig. 4b). Moreover, the maximum expression difference during the infection course was detected earlier in the Rzh variety than in the JT variety, indicating that the defense response activation in the Rzh variety is faster than in the JT variety, resulting in enhanced resistance to S. rolfsii infection.
Kegg Pathway Enrichment And Mirna Target Gene Prediction
According to the study of miRNA functions, miRNAs are considered post-transcriptional regulators of their messenger RNA (mRNA) targets via mRNA degradation and/or translational repression (Catalanotto et al., 2016). The expression change of miRNAs directly affects the target gene expression or function. To understand the possible biological function or mechanistic pathways of miRNAs that are responsible for S. rolfsii infection response in peanuts, the DEMs from JT and Rzh were used to predict the target genes. To get a comprehensive understanding of pathways involved in S. rolfsii infection response, KEGG pathway terms with predicted candidate gene numbers larger than 100 were selected and plotted (Fig. 5). The result showed that the immune response, MAPK signal, and phytohormone signal transduction were the main driving force to counter S. rolfsii infection in peanuts. Together with the previously identified S. rolfsii infection responsive miRNAs, target genes were further predicted with the online software psRNA target (https://www.zhaolab.org/psRNATarget/analysis). A prediction was done to discover all 82 miRNA target genes (Table S3). Consistent with the KEGG pathway prediction, most miRNA target genes were related to stress sensing, defense activating, immune response, and phytohormone signal transduction (Table 1). In the Rzh control group, the expression of known miRNAs (ahy-miR167-3p, ahy-miR398, and ahy-miR408-3p) in peanut were found significantly repressed compared to that of in JT control, and they were identified target on the auxin response factor, las1-like family protein and blue copper protein-like respectively. Notably, in the Rzh group, novel-ahy-miR5-3p was highly repressed, though the expression was more stable in the JT group during infection. The target gene prediction of this miRNA went to chlorophyll synthase. This might result in higher chlorophyll content in the Rzh genotype. In addition, the novel-ahy-miR101-5p was highly repressed in Rzh groups compared to JT groups and the prediction showed it was responsible for regulating glycerophosphoryl diester phosphodiesterase (GDGP) which is related to plant cell wall organization. Together with the predicted higher chlorophyll content, these results indicated that the Rzh genotype has better resistance to fungal infection due to higher chloroplast and cell wall activities.
Table 1
Main miRNAs and their predicted target gene families were discovered in peanuts responding to Sclerotium rolfsii infection.
miRNA
|
Target gene
|
Predicted function
|
Ahy-miR398
|
Superoxide dismutase (SOD)
|
Protein folding protection
|
Ahy-miR408-3p
|
Blue copper protein-like (BCP)
|
Novel-ahy-miR60-5p
|
Protein disulfide isomerase (PDI)
|
Novel-ahy-miR101-5p
|
Glycerophosphoryl diester phosphodiesterase (GDGP)
|
Cell wall organization
|
Novel-ahy-miR163-5p
|
Asparagine synthetase (AS)
|
Novel-ahy-miR5-3p
|
Chlorophyll synthase (ChlG)
|
Stress associated
|
Ahy-miR3511-5p
|
Repressor of silencing 1 (ROS1)
|
RdDM regulation
|
Ahy-miR3516
|
Novel-ahy-miR128-5p
|
MATE efflux family (MATE)
|
Metabolites transport
|
Novel-ahy-miR241-3p
|
Aquaporin (AQP)
|
Novel-ahy-miR31-3p
|
Cationic amino acid transporter (CAT)
|
Novel-ahy-miR34-5p
|
Disease resistance protein (DRP)
|
Immune response
|
Novel-ahy-miR190-5p
|
RPM1 interaction protein (RIN)
|
Novel-ahy-miR83-5p
|
Receptor-like kinase (RLK)
|
Signal transduction
|
Novel-ahy-miR47-3p
|
Novel-ahy-miR130-5p
|
Novel-ahy-miR77-5p
|
Ahy-miR156b-3p
|
Squamosa promoter-binding-like protein (SPL)
|
Transcription factors
|
Novel-ahy-miR9-3p
|
Ethylene responsive transcription factor (ERF)
|
Ahy-miR160-5p
|
Auxin response factor (ARF)
|
Ahy-miR167-5p
|
Novel-ahy-miR31-5p
|
Novel-ahy-miR74-5p
|
Novel-ahy-miR19-3p
|
Scarecrow like (SCL)
|
Novel-ahy-miR61-3p
|
Besides the miRNAs or candidate target genes that are responsible for establishing fungal resistance in the Rzh peanut, a few miRNAs and their predicted target genes that are considered to respond to fungal infection were also identified from JT genotype groups. For example, the novel-ahy-miR190-5p, novel-ahy-miR60-5p, novel-ahy-miR128-5p, novel-ahy-miR69-5p, and novel-ahy-miR113-5p that are targeted on RPM1-interacting protein, protein disulfide isomerase and MATE efflux family protein (Table 1) were identified from DEMs respectively. Moreover, candidate target genes that are involved in phytohormone transduction pathways were also identified. The transcription factors such as auxin response factor (ARF) and scarecrow-like protein (SCL) were predicted from novel-ahy-miR31-5p and novel-ahy-miR19-3p/miR61-3p respectively (Table 1). According to the gene prediction, the fungal infection response is a systemic reaction that includes pathogen sensing, metabolites transport, protein aggregation prevention, phytohormone transduction, and immune activation in peanuts.