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Identification of novel drought-responsive miRNA regulatory network of drought stress response in common vetch (Vicia sativa)

  • Yongqun Zhu , Qiuxu Liu , Wenzhi Xu , Li Yao , Xie Wang , Hong Wang , Yalin Xu , Linxiang Li , Chunhua Duan , Zhixin Yi and Chaowen Lin EMAIL logo
Published/Copyright: October 11, 2021
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Open Life Sciences
From the journal Open Life Sciences Volume 16 Issue 1

Abstract

Drought is among the most important natural disasters with severe effects on animals and plants. MicroRNAs are a class of noncoding RNAs that play a crucial role in plant growth, development, and response to stress factors, including drought. However, the microRNAs in drought responses in common vetch (Vicia sativa), an annual herbaceous leguminous plant commonly used for forage by including it in mixed seeding during winter and spring, have not been characterized. To explore the microRNAs’ response to drought in common vetch, we sequenced 10 small RNA (sRNA) libraries by the next-generation sequencing technology. We obtained 379 known miRNAs belonging to 38 families and 47 novel miRNAs. The two groups had varying numbers of differentially expressed miRNAs: 85 in the comparison group D5 vs C5 and 38 in the comparison group D3 vs C3. Combined analysis of mRNA and miRNA in the same samples under drought treatment identified 318 different target genes of 123 miRNAs. Functional annotation of the target genes revealed that the miRNAs regulate drought-responsive genes, such as leucine-rich repeat receptor-like kinase-encoding genes (LRR-RLKs), ABC transporter G family member 1 (ABCG1), and MAG2-interacting protein 2 (MIP2). The genes were involved in various pathways, including cell wall biosynthesis, reactive oxygen removal, and protein transport. The findings in this study provide new insights into the miRNA-mediated regulatory networks of drought stress response in common vetch.

Keywords: common vetch; Vicia sativa ; microRNA; drought stress; high-throughput sequencing; target gene

1 Introduction

Common vetch is an annual high-yielding leguminous forage with high crude protein content and good palatability and is mainly used for hay, silage, and green manure [1,2]. It is an irreplaceable component in mixed cropping because of its high yields, forage quality, and capacity to increase soil nitrogen and promote the build up of organic matter content [3,4]. These attributes, in turn, promote soil fertility, which reduces fertilizer application, thereby reducing environmental pollution and production costs [5]. Besides, common vetch is low in fat, has a high starch content, and its protein content is about twice that of many grains, [6] and hence, suitable as a low-cost substitute for animal feed [6]. Compared to other forages, common vetch has the best usage potential index when used singly as a ruminant feed [7] and improves the nutritional quality of mixed fodder. Under cultivation, common vetch is more tolerant to extreme weather conditions such as winter and drought than other cultivated food legumes [8] and perennial forage legumes [9]. For example, common vetch can withstand up to 24 days of drought, and its biological functions are fully restored upon watering [10].

Common vetch, being a drought-tolerant forage, can be grown in marginal zones, which presents great potential as a sustainable forage source [11]. Drought is a common environmental stress that negatively affects plant growth leading to excessive production of reactive oxygen species (ROS) [12]. To attenuate the effects of ROS, the antioxidant levels in plants are increased as a plant tolerance mechanism against drought and other abiotic stresses. For example, manganese superoxide dismutase (Mn-SOD) is expressed in rice [13], and there is overexpression of ascorbic peroxidase (APX) in tobacco chloroplasts under drought stress [14]. Similarly, there is overexpression of monodehydroascorbate reductase (MDAR) in transgenic tobacco under salt stress [14], while the overexpression of dehydroascorbic acid reductase (DHAR) enhances plant tolerance against various abiotic stresses [15]. This suggests that genes encoding antioxidant enzymes improve plant abiotic stress tolerance, including drought tolerance.

Drought stress inevitably damages the plant cell membranes [16]. The membrane system of plants creates a barrier between cells and the external environment, which is crucial for maintaining the microenvironment and normal metabolism of the cells. Membrane phospholipids are sensitive to ROS damage, which causes peroxidation of membrane lipids [17], leading to Malondialdehyde (MDA). The accumulation of MDA causes electrolytes to leak out of the cells, resulting in increased conductivity. Thus, increased electrical conductivity (EC) can be used as a marker of cell membrane damage and drought resistance in plants [18].

MicroRNAs (miRNAs) are small noncoding RNAs of 18–24nt in length. They negatively regulate the expression of downstream target genes by degrading or inhibiting translation [19,20,21], which plays important regulatory roles at various stages of plant growth [22]. They play critical regulatory roles in enhancing plant adaptability to stress conditions by modulating plant response to drought stress [23,24,25,26,27,28]. For example, miR156 expression can promote flowering under drought stress, which mitigates the damaging effects of drought stress [29]. Similarly, the expression level of miR160 in transgenic tobacco is downregulated under drought stress, thereby enhancing its drought tolerance [30]. Moreover, the expression levels of some conserved miRNAs, such as miR159, miR167, miR169, and miR397, are up- or downregulated under drought or high salt stress conditions [25,30,31].

Therefore, the objective of this study was to identify the novel and known miRNAs expressed in common vetch under drought stress and analyze miRNA-mediated regulatory networks in drought stress response. The findings in this study will enhance the understanding of molecular coordination mechanisms in response to drought stress and promote the genetic improvement of drought tolerance in common vetch.

2 Materials and methods

2.1 Treatments and sampling

The common vetch cultivar “Chuanbei,” a breeding variety well adapted to the southwest region of China [32], was used in the present study. Common vetch seeds were sterilized using 75% ethanol for 5 min and then rinsed with sterile distilled water. The seeds (10 g per pot) were planted in sterilized quartz in plastic pots (20 cm in length, 15 cm width, and 8 cm deep). The pots were kept in a controlled growth chamber at the Sichuan Agricultural University in Chengdu. The chamber had a 12 h photoperiod, 19°C/15°C day/night temperature, and 75% relative humidity. Thirty-five days after planting, the plants were incubated with 25% (w/v) polyethylene glycol (PEG) 6000 dissolved in Hoagland’s solution for five days to induce drought stress. Control plants were treated with Hoagland’s solution without PEG. The aboveground parts of the control plants were sampled at 0 days (C0), day 3 (C3), and day 5 (C5), and at day 3 (D3) and day 5 (D5) in drought-stressed plants and immediately stored in liquid nitrogen. One plant per treatment was sampled. There were two biological replicates per treatment.

2.2 RNA isolation, library construction, and sequencing

Total RNA was extracted from the aboveground part of plant samples using the Trizol reagent (TransGen, China) following the manufacturer’s instructions. sRNA libraries were prepared using 3 μg of the total RNA as the input material. Sequencing libraries were generated using the NEBNext® Multiplex Small RNA Library Prep Set for Illumina® (NEB, USA) following the manufacturer’s instructions. Subsequent clustering of the index-coded samples was then performed on a cBot Cluster Generation System using a TruSeq SR Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions.

2.3 Reads mapping, alignment of known miRNAs, and prediction of novel miRNAs

The sRNA tags without mismatches were mapped to the reference sequences using Bowtie (http://bowtie.cbcb.umd.edu) [33] to analyze their expression and distribution. Mapped sRNA tags were then used to identify known miRNA. miRBase20.0 was used as the reference database, while the modified mirdeep2 software [34] and srna-tools-cli were used to determine the potential miRNAs and draw their secondary structures. Custom scripts were used to obtain the miRNA counts and base bias on the first position of the identified miRNA with various lengths and on each position of all identified miRNAs. Hairpin structure characteristics of miRNA precursors were used to predict novel miRNA. MiREvo [35] and mirdeep2 [34] software were employed to predict the novel miRNAs.

2.4 Differentially expressed miRNAs in response to drought stress

Differential expression analysis for two conditions was performed using the DESeq R package (version 1.8.3). The gene ontology (GO) function was used for enrichment analysis of the candidate target gene of differentially expressed miRNAs. After that, the KOBAS software was used to identify the significantly enriched candidate target gene in the KEGG pathways [36].

2.5 RT-qPCR validation of miRNA sequencing data

RT-qPCR was used to validate the accuracy of the miRNA sequencing data. Total RNA was isolated from the drought-stressed and control plants using Trizol reagent (TransGen, China). The first-strand cDNA for RT-qPCR analysis was synthesized from 1,000 ng of total RNA using HiScript™ Reverse Transcriptase (Vazyme, Nanjing, China) according to the manufacturer’s instructions. Six miRNAs were selected for validation. The primers were designed using Prime5 software (Table S1). The RT-qPCR reaction was performed using Bio-Rad CFX96 following the manufacturer’s instructions with Taq Pro Universal SYBR qPCR Master Mix(#Q712) (Vazyme Biotech Co., Ltd). The PCR reaction was carried out in a total volume of 20 μL containing 1 μL of cDNA, 0.5 μL of forward primers, 0.5 μL of reverse primers, 10 μL Taq Master Mix solution, and 8 μL of RNase-free water. All the RT-qPCR reactions were conducted in three replicates. The relative expression levels were quantified using the 2−ΔΔt method [37].

3 Results

3.1 Overview of transcriptome dynamics and sRNA sequencing

Ten sRNA libraries were constructed to explore the molecular mechanisms of drought response in common vetch. A total of 134,222,486 raw reads were generated. The datasets generated were deposited in the National Center for Biotechnology Information database [BioProject PRJNA681445]. Subsequently, the reads were filtered and cleaned to remove the low-quality reads, those containing ploy-N, 5′ adapter contaminants, ploy A, T, G, and C, and those without 3′ adapter or the insert tag, obtaining 129,577,792 clean reads (Table 1).

Table 1

Summary of sRNA libraries of common vetch under drought stress

Library Raw reads N% > 10% Low-quality reads 5′ Adapter contamine 3′ dapter null Ploy A/T/G/C Clean reads
C0-1 15,399,906 59 (0.00%) 65,251 (0.42%) 6,661 (0.04%) 1,755,349 (11.40%) 6,255 (0.04%) 13,566,331 (88.09%)
C0-2 12,703,957 16 (0.00%) 13,199 (0.10%) 7,217 (0.06%) 176,218 (1.39%) 31,978 (0.25%) 12,475,329 (98.20%)
C3-1 16,623,813 15 (0.00%) 16,677 (0.10%) 11,214 (0.07%) 199,285 (1.20%) 27,765 (0.17%) 16,368,857 (98.47%)
C3-2 12,510,514 9 (0.00%) 11,121 (0.09%) 42,384 (0.34%) 327,115 (2.61%) 20,770 (0.17%) 12,109,115 (96.79%)
C5-1 13,292,233 343 (0.00%) 40,572 (0.31%) 6,785 (0.05%) 237,217 (1.78%) 33,776 (0.25%) 12,973,540 (97.60%)
C5-2 11,802,347 315 (0.00%) 36,975 (0.31%) 5,657 (0.05%) 211,620 (1.79%) 32,381 (0.27%) 11,515,399 (97.57%)
D3-1 13,164,735 328 (0.00%) 36,593 (0.28%) 13,411 (0.10%) 333,750 (2.54%) 19,667 (0.15%) 12,760,986 (96.93%)
D3-2 12,213,823 318 (0.00%) 34,747 (0.28%) 7,334 (0.06%) 216,747 (1.77%) 20,372 (0.17%) 11,934,305 (97.71%)
D5-1 12,870,298 324 (0.00%) 37,365 (0.29%) 5,316 (0.04%) 229,422 (1.78%) 20,103 (0.16%) 12,577,768 (97.73%)
D5-2 13,640,860 322 (0.00%) 39,353 (0.29%) 5,912 (0.04%) 278,851 (2.04%) 20,260 (0.15%) 13,296,162 (97.47%)
Total 134,222,486 129,577,792 (96.54%)

Note: (1) N: Undetermined base information (2) N% > 10%: The proportion of filtered reads with undetermined base information in the total number of raw reads exceeding 10%. (3) 5′ Adapter Contamine: The proportion of filtered reads with 5′ adapter contamine in the total raw reads. (4) 3′ Adapter Null: The proportion of filtered reads without 3′ joints in the total raw reads. (5) PloyA/T/G/C: The proportion of filtered reads due to the presence of ployA/T/G/C in the total raw reads.

Clean reads of 24nt (26.01%) in length accounted for the largest proportion of clean reads, while reads of 21–24nt collectively accounted for 55.60% of all the reads (Figure 1). The 24nt sRNAs represent small interfering RNAs (siRNAs) directly involved in RNA-directed DNA methylation, while the 24nt/21nt ratio indicates the degree of DNA methylation [38,39]. The results revealed that the methylation levels of both the control and drought-stressed plants increased over time (Figure 1).

Figure 1 Small RNAs length distribution in common vetch. The X-axis shows the length of sRNAs, while the Y-axis shows the number of sRNAs identified. Different colors denote the ten libraries.
Figure 1

Small RNAs length distribution in common vetch. The X-axis shows the length of sRNAs, while the Y-axis shows the number of sRNAs identified. Different colors denote the ten libraries.

The unique sequences were compared with known plant miRNA sequences in the miRBase database to identify the conserved known miRNAs. The sRNAs were assigned into eight categories based on a BLASTN search and further sequence analyses: known miRNAs, rRNA, tRNA, snRNA, snoRNA, novel miRNA, TAS, and unannotated sRNAs. Each library had an average of 1,174 known miRNAs and 398 novel miRNAs (Table 2). Moreover, a significant proportion of unique sequences (>90% in all treatments) had not previously been identified and annotated, suggesting that common vetch has many novel miRNAs.

Table 2

Distribution of small RNAs among the different categories in common vetch

Library Total Known miRNA rRNA tRNA snRNA snoRNA Novel miRNA TAS Unannotated
C0-1 633,999 637 39,376 2 1,087 1,263 111 183 591,340
100% 0.10% 6.21% 0.00% 0.17% 0.20% 0.02% 0.03% 93.27%
C0-2 1,021,425 1,324 34,727 2 1,095 1,347 497 1,599 980,834
100.00% 0.13% 3.40% 0.00% 0.11% 0.13% 0.05% 0.16% 96.03%
C3-1 1,088,582 1,281 45,534 1 1,943 1,926 480 1,197 1,036,220
100.00% 0.12% 4.18% 0.00% 0.18% 0.18% 0.04% 0.11% 95.19%
C3-2 880,605 1,335 43,458 1 1,737 1,935 469 1,268 830,402
100.00% 0.15% 4.94% 0.00% 0.20% 0.22% 0.05% 0.14% 94.30%
C5-1 1,159,086 1,348 37,018 4 1,489 1,694 567 1,530 1,115,436
100.00% 0.12% 3.19% 0.00% 0.13% 0.15% 0.05% 0.13% 96.23%
C5-2 1,069,208 1,326 34,267 1 1,303 1,560 494 1,466 1,028,791
100.00% 0.12% 3.20% 0.00% 0.12% 0.15% 0.05% 0.14% 96.22%
D3-1 848,088 1,092 32,569 1 1,774 2,298 317 1,061 808,976
100.00% 0.13% 3.84% 0.00% 0.21% 0.27% 0.04% 0.13% 95.39%
D3-2 843,771 1,114 33,452 1 1,560 1,576 332 1,213 804,523
100.00% 0.13% 3.96% 0.00% 0.18% 0.19% 0.04% 0.14% 95.35%
D5-1 963,398 1,130 38,485 3 1,696 1,899 350 1,291 918,544
100.00% 0.12% 3.99% 0.00% 0.18% 0.20% 0.04% 0.13% 95.34%
D5-2 949,441 1,152 37,309 1 1,783 1,870 365 1,530 905,431
100.00% 0.12% 3.93% 0.00% 0.19% 0.20% 0.04% 0.16% 95.36%

Note: (1) rRNA/tRNA/snRNA/snoRNA: Refers to the proportion of rRNA/tRNA/snRNA/snoRNA in each library. (2) Novel miRNA: Indicates the proportion of new miRNAs identified in each library. (3) TAS: Refers to the proportion of TAS gene in each library. (4) Unannotated: Indicates proportion of sRNAs in each library that were not successfully annotated.

3.2 Known and novel miRNAs in drought-stressed common vetch

A total of 379 unique sequences belonging to 78 known miRNA families were identified in all libraries (Figure 2 and Table S2). The largest miRNA family was miR156 with 36 members, followed by miR172 and miR396, respectively.

Figure 2 Distribution of known miRNA families in common vetch. Y-axis represents the number of members in each miRNA family. The X-axis represents the known miRNA families.
Figure 2

Distribution of known miRNA families in common vetch. Y-axis represents the number of members in each miRNA family. The X-axis represents the known miRNA families.

There were significant differences in abundance among the miRNA families, as shown in Table S2. The average transcripts per million reads (TPM) for each miRNA in ten libraries were calculated to compare their expression levels [40,41,42]. After TPM standardization (Table S3), the highest relative expression level obtained was mtr-miR5213-5p (243058.37), followed by ath-miR159a (94704.81) and gma-miR319d (74525.87), respectively.

Prediction of novel miRNAs identified 47 novel miRNAs (Table S4), all of which were 20nt to 25nt in length. The majority of the novel miRNAs were 21nt (53.19%) in length. Expression analysis further revealed that the expression levels of the 47 miRNAs were below 10 read counts except for novel_2 *, novel_119*, novel_17*, novel_131*, novel_60*, and novel_54* (Table S5). Base distribution results for each position in the miRNAs revealed that uracil (U) occupied the first position in the novel miRNA present in the 10 libraries (Figure A1).

3.3 Differentially expressed miRNAs in drought-stressed common vetch

The DESeq was used for differential expression analysis of miRNAs in each sample. In total, 85 differentially expressed miRNAs (37 upregulated and 48 downregulated) in D5 vs C5 and 28 differentially expressed miRNAs (9 upregulated and 19 downregulated) in D3 vs C3 (Figure 3a) were identified. The Venn diagram of (D5 vs C5) vs (D3 vs C3) further revealed 11 common genes in the two comparisons, including ahy-miR398, gma-miR159d, osa-miR166h-5p, among others. These results indicate that the expression of these 11 genes constantly changes during drought stress. Further analysis of the relative expression levels of the 11 genes revealed that the expression of vvi-miR3630-3p, ptc-miR167f-3p, and gma-miR168b increased under drought stress, while the other genes were downregulated (Figure 3b). To confirm the miRNA-Seq data, selected miRNAs that showed differential expression of miRNA under stress were chosen. The RT-qPCR validation revealed that the differentially expressed miRNAs had a similar expression pattern to the sequencing data (Figure 4). This implies that the data produced in this study are reliable.

Figure 3 Differential expression analysis of miRNAs in common vetch under drought stress. (a) Venn diagram of (D5 vs C5) vs (D3 vs C3). (b) The relative expression levels of the 11 common genes in the two comparison groups.
Figure 3

Differential expression analysis of miRNAs in common vetch under drought stress. (a) Venn diagram of (D5 vs C5) vs (D3 vs C3). (b) The relative expression levels of the 11 common genes in the two comparison groups.

Figure 4 RT-qPCR validation of differentially expressed miRNAs under drought stress in common vetch.
Figure 4

RT-qPCR validation of differentially expressed miRNAs under drought stress in common vetch.

3.4 Prediction and annotation of target genes for drought-responsive miRNAs

GO and KEGG pathway analysis of the target genes of differentially expressed miRNAs was done to explain further the change of miRNA in common vetch under drought stress. A total of 283 differentially expressed target genes were predicted in 85 differentially expressed miRNAs in the comparison group D5 vs C5. Gene ontology enrichment analysis revealed that most of the differentially expressed target genes were related to metal ion binding, cation binding, and transition metal ion binding. The KEGG enrichment analysis revealed that the genes were significantly enriched in the nitrogen metabolism pathway. The different target genes corresponding to different-miRNA were classified based on their differential expression profiles (up-miRNA-down-mRNA and down-miRNA-up-mRNA). Two genes annotated as abscisic acid 8′-hydroxylase (played a key role in abscisic acid biosynthesis) in the carotenoid biosynthesis were found to be significantly enriched in the down-miRNA-up-mRNA group (Figure 5a). In the up-miRNA-down-mRNA group, clusters 8152.47925 and 8152.47927 in porphyrin and chlorophyll metabolism were significantly enriched (Figure 5b). The genes were thus annotated as divinyl chlorophyllide, an 8-vinyl-reductase that regulates the synthesis of protochlorophyllide. In addition, ath-miR156a-5p was significantly upregulated. However, its predicted target genes cluster 8152.61131 and cluster 8152.8716 were downregulated. Cluster 8152.61131 was annotated as PCX1: a member of the LRR-RLK family, which regulates cell wall construction [43]. MicroRNA ath-miR156a was significantly upregulated and its predicted target gene cluster 8152.40191 was involved in anthocyanin synthesis. Cluster 8152.79559 was annotated as a driver protein involved in cell wall synthesis. Besides, cluster 8152.84378 was involved in protein ubiquitination, which strongly indicated it would respond to drought stress from multiple vantage points.

Figure 5 KEGG enrichment analysis of differentially expressed target genes. (a), (b) and (c) respectively represented the comparison group D5 vs C5：down-miRNA-up-mRNA，D5 vs C5：down-mRNA-up-miRNA and D3 vs C3：down-miRNA-up-mRNA.
Figure 5

KEGG enrichment analysis of differentially expressed target genes. (a), (b) and (c) respectively represented the comparison group D5 vs C5：down-miRNA-up-mRNA，D5 vs C5：down-mRNA-up-miRNA and D3 vs C3：down-miRNA-up-mRNA.

In the comparison group D3 vs C3, 35 differentially expressed target genes were predicted in the 28 differentially expressed miRNAs. Only 16 pairs of differentially expressed target genes and miRNAs were present in the down-miRNA-up-mRNA group. KEGG enrichment analysis revealed that cluster 8152.56666 was the most significantly enriched and was annotated as phospholipase C. Meanwhile, cluster 8152.56666 was enriched in ether fat metabolism, inositol phosphate metabolism, and glycerophospholipid metabolism pathways (Figure 5c). Further analysis revealed that novel_68* had varying degrees of downregulated expression in the two comparison groups (FCD5 vs C5 = −2.3147 and FCD3 vs C3 = −1.9144). Its target gene was cluster 8152.40257, annotated as ABCG1 and related to peroxidase and ATP synthesis. ABCG1 plays a vital role in the detoxification of plant ROS [44].

4 Discussion

4.1 miRNA profiling of drought stress in common vetch

MicroRNAs are a class of small noncoding RNAs that play a crucial role in gene regulation. Numerous studies postulate that miRNAs widely respond to environmental stress by regulating gene expression [45,46,47,48,49,50,51]. For example, during drought stress, different miRNAs are expressed in Arabidopsis thaliana [23], rice [52], maize [53], and wheat [54]. In A. thaliana, several miRNAs such as miR396, miR168, and miR398 are upregulated under drought conditions [23]. Similarly, several miRNAs, such as miR159, miR1871, and miR397, are regulated in rice under drought stress [52]. A similar phenomenon has been observed in maize subjected to drought conditions where some miRNAs (miR156, miR159, miR160, and miR398) are upregulated while others, such as miR319, miR396, and miR399, are downregulated [53]. In wheat, miR156, miR159, and miR168 are upregulated, while miR171, miR395, and miR916 are downregulated under drought stress [44]. Most of the miRNAs that regulate plant response to drought stress are evolutionarily conserved across different plant species. Herein, many sRNA sequencing data were obtained in common vetch seedlings under drought stress. For example, miR396 was upregulated under drought stress in common vetch and Arabidopsis thaliana [55], but was downregulated by 37% in pitaya fruit under drought stress [56]. This difference may be due to the different expression levels of miR396 at different time scales in response to drought stress in the plant. While in pitaya, the downregulation of miR396 reached a minimum level (−37%) after 20 h [56], in common vetch, miR396 reached its highest level after 5 days, which was an upregulation by about two times. miR396 is a growth-regulating factor (GRF) [57,58], which causes different response degrees when plants are stressed at different growth stages.

Moreover, some drought-responsive miRNAs in common vetch regulate other biological and abiotic stresses. For example, miRNA169 and miRNA319 play key roles in response to abscisic acid, salt stress, and drought stress [48,59,60]. Similarly, miRNA398 is considered a bridge connecting plant responses to oxidative stress and other stresses (water deficit, salt stress, ABA, and biological stress) [61]. This is because these miRNAs partially regulate a gene that responds to different environmental stresses. Nonetheless, the specific molecular mechanism of drought-responsive miRNAs identified herein should be verified in future studies.

4.2 miRNA-mediated regulatory network of drought stress

MicroRNAs regulate gene expression by up- or downregulating target genes. Numerous studies have revealed the expression patterns of miRNAs and their target genes under plant drought stress through high-throughput sequencing [52,62]. Herein, a large data set of miRNAs was obtained. The data set was jointly analyzed with mRNA data to identify specific drought-responsive genes. Based on those results, a miRNA-based model for regulating plant drought response was proposed (Figure 6).

Figure 6 The proposed miRNA-based model for regulating plant drought response in common vetch.
Figure 6

The proposed miRNA-based model for regulating plant drought response in common vetch.

5 Conclusion

This study identified and characterized 379 known miRNAs belonging to 38 families and 47 novel miRNAs using sRNA sequencing and bioinformatics approaches. In total, 85 miRNAs were differentially expressed in the comparison group D5 vs C5 and 38 in D3 vs C3 under drought stress. Furthermore, functional annotation of the target genes provided further evidence for their possible involvement in drought stress. The findings in this study contribute valuable information for future studies on the function of miRNAs in common vetch and other plants during drought stress.

These authors contributed equally to this work.

Acknowledgments

The authors appreciate the Provincial Key Laboratory of Water-Saving Agriculture in Hill Areas of Southern China for their assistance in data analysis.

  1. Funding information: This research was supported by the Outstanding Papers Promotion Project for Financial Innovation in Sichuan province (2018LWJJ-013), Sichuan Financial Innovation Capacity Enhancement Project (2016TSCY-005), Sichuan Forage Innovation Team Project of the Industrial System Construction of Modern Agriculture of China (sccxtd-2020-16), and Sichuan Forage grass breeding (2016NYZ0039).

  2. Conflict of interest: The authors state no conflict of interest.

  3. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Appendix

Figure A1
Figure A1

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Received: 2021-05-28
Revised: 2021-08-06
Accepted: 2021-08-19
Published Online: 2021-10-11

© 2021 Yongqun Zhu et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/biol-2021-0109
Keywords for this article
common vetch; Vicia sativa; microRNA; drought stress; high-throughput sequencing; target gene
Creative Commons
BY 4.0

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  10. Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats
  11. ABCB1 polymorphism in clopidogrel-treated Montenegrin patients
  12. Metabolic profiling of fatty acids in Tripterygium wilfordii multiglucoside- and triptolide-induced liver-injured rats
  13. miR-338-3p inhibits cell growth, invasion, and EMT process in neuroblastoma through targeting MMP-2
  14. Verification of neuroprotective effects of alpha-lipoic acid on chronic neuropathic pain in a chronic constriction injury rat model
  15. Circ_WWC3 overexpression decelerates the progression of osteosarcoma by regulating miR-421/PDE7B axis
  16. Knockdown of TUG1 rescues cardiomyocyte hypertrophy through targeting the miR-497/MEF2C axis
  17. MiR-146b-3p protects against AR42J cell injury in cerulein-induced acute pancreatitis model through targeting Anxa2
  18. miR-299-3p suppresses cell progression and induces apoptosis by downregulating PAX3 in gastric cancer
  19. Diabetes and COVID-19
  20. Discovery of novel potential KIT inhibitors for the treatment of gastrointestinal stromal tumor
  21. TEAD4 is a novel independent predictor of prognosis in LGG patients with IDH mutation
  22. circTLK1 facilitates the proliferation and metastasis of renal cell carcinoma by regulating miR-495-3p/CBL axis
  23. microRNA-9-5p protects liver sinusoidal endothelial cell against oxygen glucose deprivation/reperfusion injury
  24. Long noncoding RNA TUG1 regulates degradation of chondrocyte extracellular matrix via miR-320c/MMP-13 axis in osteoarthritis
  25. Duodenal adenocarcinoma with skin metastasis as initial manifestation: A case report
  26. Effects of Loofah cylindrica extract on learning and memory ability, brain tissue morphology, and immune function of aging mice
  27. Recombinant Bacteroides fragilis enterotoxin-1 (rBFT-1) promotes proliferation of colorectal cancer via CCL3-related molecular pathways
  28. Blocking circ_UBR4 suppressed proliferation, migration, and cell cycle progression of human vascular smooth muscle cells in atherosclerosis
  29. Gene therapy in PIDs, hemoglobin, ocular, neurodegenerative, and hemophilia B disorders
  30. Downregulation of circ_0037655 impedes glioma formation and metastasis via the regulation of miR-1229-3p/ITGB8 axis
  31. Vitamin D deficiency and cardiovascular risk in type 2 diabetes population
  32. Circ_0013359 facilitates the tumorigenicity of melanoma by regulating miR-136-5p/RAB9A axis
  33. Mechanisms of circular RNA circ_0066147 on pancreatic cancer progression
  34. lncRNA myocardial infarction-associated transcript (MIAT) knockdown alleviates LPS-induced chondrocytes inflammatory injury via regulating miR-488-3p/sex determining region Y-related HMG-box 11 (SOX11) axis
  35. Identification of circRNA circ-CSPP1 as a potent driver of colorectal cancer by directly targeting the miR-431/LASP1 axis
  36. Hyperhomocysteinemia exacerbates ischemia-reperfusion injury-induced acute kidney injury by mediating oxidative stress, DNA damage, JNK pathway, and apoptosis
  37. Potential prognostic markers and significant lncRNA–mRNA co-expression pairs in laryngeal squamous cell carcinoma
  38. Gamma irradiation-mediated inactivation of enveloped viruses with conservation of genome integrity: Potential application for SARS-CoV-2 inactivated vaccine development
  39. ADHFE1 is a correlative factor of patient survival in cancer
  40. The association of transcription factor Prox1 with the proliferation, migration, and invasion of lung cancer
  41. Is there a relationship between the prevalence of autoimmune thyroid disease and diabetic kidney disease?
  42. Immunoregulatory function of Dictyophora echinovolvata spore polysaccharides in immunocompromised mice induced by cyclophosphamide
  43. T cell epitopes of SARS-CoV-2 spike protein and conserved surface protein of Plasmodium malariae share sequence homology
  44. Anti-obesity effect and mechanism of mesenchymal stem cells influence on obese mice
  45. Long noncoding RNA HULC contributes to paclitaxel resistance in ovarian cancer via miR-137/ITGB8 axis
  46. Glucocorticoids protect HEI-OC1 cells from tunicamycin-induced cell damage via inhibiting endoplasmic reticulum stress
  47. Prognostic value of the neutrophil-to-lymphocyte ratio in acute organophosphorus pesticide poisoning
  48. Gastroprotective effects of diosgenin against HCl/ethanol-induced gastric mucosal injury through suppression of NF-κβ and myeloperoxidase activities
  49. Silencing of LINC00707 suppresses cell proliferation, migration, and invasion of osteosarcoma cells by modulating miR-338-3p/AHSA1 axis
  50. Successful extracorporeal membrane oxygenation resuscitation of patient with cardiogenic shock induced by phaeochromocytoma crisis mimicking hyperthyroidism: A case report
  51. Effects of miR-185-5p on replication of hepatitis C virus
  52. Lidocaine has antitumor effect on hepatocellular carcinoma via the circ_DYNC1H1/miR-520a-3p/USP14 axis
  53. Primary localized cutaneous nodular amyloidosis presenting as lymphatic malformation: A case report
  54. Multimodal magnetic resonance imaging analysis in the characteristics of Wilson’s disease: A case report and literature review
  55. Therapeutic potential of anticoagulant therapy in association with cytokine storm inhibition in severe cases of COVID-19: A case report
  56. Neoadjuvant immunotherapy combined with chemotherapy for locally advanced squamous cell lung carcinoma: A case report and literature review
  57. Rufinamide (RUF) suppresses inflammation and maintains the integrity of the blood–brain barrier during kainic acid-induced brain damage
  58. Inhibition of ADAM10 ameliorates doxorubicin-induced cardiac remodeling by suppressing N-cadherin cleavage
  59. Invasive ductal carcinoma and small lymphocytic lymphoma/chronic lymphocytic leukemia manifesting as a collision breast tumor: A case report and literature review
  60. Clonal diversity of the B cell receptor repertoire in patients with coronary in-stent restenosis and type 2 diabetes
  61. CTLA-4 promotes lymphoma progression through tumor stem cell enrichment and immunosuppression
  62. WDR74 promotes proliferation and metastasis in colorectal cancer cells through regulating the Wnt/β-catenin signaling pathway
  63. Down-regulation of IGHG1 enhances Protoporphyrin IX accumulation and inhibits hemin biosynthesis in colorectal cancer by suppressing the MEK-FECH axis
  64. Curcumin suppresses the progression of gastric cancer by regulating circ_0056618/miR-194-5p axis
  65. Scutellarin-induced A549 cell apoptosis depends on activation of the transforming growth factor-β1/smad2/ROS/caspase-3 pathway
  66. lncRNA NEAT1 regulates CYP1A2 and influences steroid-induced necrosis
  67. A two-microRNA signature predicts the progression of male thyroid cancer
  68. Isolation of microglia from retinas of chronic ocular hypertensive rats
  69. Changes of immune cells in patients with hepatocellular carcinoma treated by radiofrequency ablation and hepatectomy, a pilot study
  70. Calcineurin Aβ gene knockdown inhibits transient outward potassium current ion channel remodeling in hypertrophic ventricular myocyte
  71. Aberrant expression of PI3K/AKT signaling is involved in apoptosis resistance of hepatocellular carcinoma
  72. Clinical significance of activated Wnt/β-catenin signaling in apoptosis inhibition of oral cancer
  73. circ_CHFR regulates ox-LDL-mediated cell proliferation, apoptosis, and EndoMT by miR-15a-5p/EGFR axis in human brain microvessel endothelial cells
  74. Resveratrol pretreatment mitigates LPS-induced acute lung injury by regulating conventional dendritic cells’ maturation and function
  75. Ubiquitin-conjugating enzyme E2T promotes tumor stem cell characteristics and migration of cervical cancer cells by regulating the GRP78/FAK pathway
  76. Carriage of HLA-DRB1*11 and 1*12 alleles and risk factors in patients with breast cancer in Burkina Faso
  77. Protective effect of Lactobacillus-containing probiotics on intestinal mucosa of rats experiencing traumatic hemorrhagic shock
  78. Glucocorticoids induce osteonecrosis of the femoral head through the Hippo signaling pathway
  79. Endothelial cell-derived SSAO can increase MLC20 phosphorylation in VSMCs
  80. Downregulation of STOX1 is a novel prognostic biomarker for glioma patients
  81. miR-378a-3p regulates glioma cell chemosensitivity to cisplatin through IGF1R
  82. The molecular mechanisms underlying arecoline-induced cardiac fibrosis in rats
  83. TGF-β1-overexpressing mesenchymal stem cells reciprocally regulate Th17/Treg cells by regulating the expression of IFN-γ
  84. The influence of MTHFR genetic polymorphisms on methotrexate therapy in pediatric acute lymphoblastic leukemia
  85. Red blood cell distribution width-standard deviation but not red blood cell distribution width-coefficient of variation as a potential index for the diagnosis of iron-deficiency anemia in mid-pregnancy women
  86. Small cell neuroendocrine carcinoma expressing alpha fetoprotein in the endometrium
  87. Superoxide dismutase and the sigma1 receptor as key elements of the antioxidant system in human gastrointestinal tract cancers
  88. Molecular characterization and phylogenetic studies of Echinococcus granulosus and Taenia multiceps coenurus cysts in slaughtered sheep in Saudi Arabia
  89. ITGB5 mutation discovered in a Chinese family with blepharophimosis-ptosis-epicanthus inversus syndrome
  90. ACTB and GAPDH appear at multiple SDS-PAGE positions, thus not suitable as reference genes for determining protein loading in techniques like Western blotting
  91. Facilitation of mouse skin-derived precursor growth and yield by optimizing plating density
  92. 3,4-Dihydroxyphenylethanol ameliorates lipopolysaccharide-induced septic cardiac injury in a murine model
  93. Downregulation of PITX2 inhibits the proliferation and migration of liver cancer cells and induces cell apoptosis
  94. Expression of CDK9 in endometrial cancer tissues and its effect on the proliferation of HEC-1B
  95. Novel predictor of the occurrence of DKA in T1DM patients without infection: A combination of neutrophil/lymphocyte ratio and white blood cells
  96. Investigation of molecular regulation mechanism under the pathophysiology of subarachnoid hemorrhage
  97. miR-25-3p protects renal tubular epithelial cells from apoptosis induced by renal IRI by targeting DKK3
  98. Bioengineering and Biotechnology
  99. Green fabrication of Co and Co3O4 nanoparticles and their biomedical applications: A review
  100. Agriculture
  101. Effects of inorganic and organic selenium sources on the growth performance of broilers in China: A meta-analysis
  102. Crop-livestock integration practices, knowledge, and attitudes among smallholder farmers: Hedging against climate change-induced shocks in semi-arid Zimbabwe
  103. Food Science and Nutrition
  104. Effect of food processing on the antioxidant activity of flavones from Polygonatum odoratum (Mill.) Druce
  105. Vitamin D and iodine status was associated with the risk and complication of type 2 diabetes mellitus in China
  106. Diversity of microbiota in Slovak summer ewes’ cheese “Bryndza”
  107. Comparison between voltammetric detection methods for abalone-flavoring liquid
  108. Composition of low-molecular-weight glutenin subunits in common wheat (Triticum aestivum L.) and their effects on the rheological properties of dough
  109. Application of culture, PCR, and PacBio sequencing for determination of microbial composition of milk from subclinical mastitis dairy cows of smallholder farms
  110. Investigating microplastics and potentially toxic elements contamination in canned Tuna, Salmon, and Sardine fishes from Taif markets, KSA
  111. From bench to bar side: Evaluating the red wine storage lesion
  112. Establishment of an iodine model for prevention of iodine-excess-induced thyroid dysfunction in pregnant women
  113. Plant Sciences
  114. Characterization of GMPP from Dendrobium huoshanense yielding GDP-D-mannose
  115. Comparative analysis of the SPL gene family in five Rosaceae species: Fragaria vesca, Malus domestica, Prunus persica, Rubus occidentalis, and Pyrus pyrifolia
  116. Identification of leaf rust resistance genes Lr34 and Lr46 in common wheat (Triticum aestivum L. ssp. aestivum) lines of different origin using multiplex PCR
  117. Investigation of bioactivities of Taxus chinensis, Taxus cuspidata, and Taxus × media by gas chromatography-mass spectrometry
  118. Morphological structures and histochemistry of roots and shoots in Myricaria laxiflora (Tamaricaceae)
  119. Transcriptome analysis of resistance mechanism to potato wart disease
  120. In silico analysis of glycosyltransferase 2 family genes in duckweed (Spirodela polyrhiza) and its role in salt stress tolerance
  121. Comparative study on growth traits and ions regulation of zoysiagrasses under varied salinity treatments
  122. Role of MS1 homolog Ntms1 gene of tobacco infertility
  123. Biological characteristics and fungicide sensitivity of Pyricularia variabilis
  124. In silico/computational analysis of mevalonate pyrophosphate decarboxylase gene families in Campanulids
  125. Identification of novel drought-responsive miRNA regulatory network of drought stress response in common vetch (Vicia sativa)
  126. How photoautotrophy, photomixotrophy, and ventilation affect the stomata and fluorescence emission of pistachios rootstock?
  127. Apoplastic histochemical features of plant root walls that may facilitate ion uptake and retention
  128. Ecology and Environmental Sciences
  129. The impact of sewage sludge on the fungal communities in the rhizosphere and roots of barley and on barley yield
  130. Domestication of wild animals may provide a springboard for rapid variation of coronavirus
  131. Response of benthic invertebrate assemblages to seasonal and habitat condition in the Wewe River, Ashanti region (Ghana)
  132. Molecular record for the first authentication of Isaria cicadae from Vietnam
  133. Twig biomass allocation of Betula platyphylla in different habitats in Wudalianchi Volcano, northeast China
  134. Animal Sciences
  135. Supplementation of probiotics in water beneficial growth performance, carcass traits, immune function, and antioxidant capacity in broiler chickens
  136. Predators of the giant pine scale, Marchalina hellenica (Gennadius 1883; Hemiptera: Marchalinidae), out of its natural range in Turkey
  137. Honey in wound healing: An updated review
  138. NONMMUT140591.1 may serve as a ceRNA to regulate Gata5 in UT-B knockout-induced cardiac conduction block
  139. Radiotherapy for the treatment of pulmonary hydatidosis in sheep
  140. Retraction
  141. Retraction of “Long non-coding RNA TUG1 knockdown hinders the tumorigenesis of multiple myeloma by regulating microRNA-34a-5p/NOTCH1 signaling pathway”
  142. Special Issue on Reuse of Agro-Industrial By-Products
  143. An effect of positional isomerism of benzoic acid derivatives on antibacterial activity against Escherichia coli
  144. Special Issue on Computing and Artificial Techniques for Life Science Applications - Part II
  145. Relationship of Gensini score with retinal vessel diameter and arteriovenous ratio in senile CHD
  146. Effects of different enantiomers of amlodipine on lipid profiles and vasomotor factors in atherosclerotic rabbits
  147. Establishment of the New Zealand white rabbit animal model of fatty keratopathy associated with corneal neovascularization
  148. lncRNA MALAT1/miR-143 axis is a potential biomarker for in-stent restenosis and is involved in the multiplication of vascular smooth muscle cells
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