Open Access

High throughput sequencing of small RNAs reveals dynamic microRNAs expression of lipid metabolism during Camellia oleifera and C. meiocarpa seed natural drying

BMC Genomics201718:546

https://doi.org/10.1186/s12864-017-3923-z

Received: 7 December 2016

Accepted: 4 July 2017

Published: 20 July 2017

Abstract

Background

Camellia species are ancient oilseed plants with a history of cultivation over two thousand years. Prior to oil extraction, natural seed drying is often practiced, a process affecting fatty acid quality and quantity. MicroRNAs (miRNA) of lipid metabolism associated with camellia seed natural drying are unexplored. To obtain insight into the function of miRNAs in lipid metabolism during natural drying, Illumina sequencing of C. oleifera and C. meiocarpa small-RNA was conducted.

Results

A total of 274 candidate miRNAs were identified and 3733 target unigenes were annotated by performing a BLASTX. Through integrated GO and KEGG function annotation, 23 miRNA regulating 131 target genes were identified as lipid metabolism, regulating fatty acid biosynthesis, accumulation and catabolism. We observed one, two, and four miRNAs of lipid metabolism which were specially expressed in C. Meiocarpa, C. oleifera, and the two species collectively, respectively. At 30% moisture contents, C. meiocarpa and C. oleifer produced nine and eight significant differentially expressed miRNAs, respectively, with high fatty acid synthesis and accumulation activities. Across the two species, 12 significant differentially expressed miRNAs were identified at the 50% moisture content.

Conclusions

Sequencing of small-RNA revealed the presence of 23 miRNAs regulating lipid metabolism in camellia seed during natural drying and permitted comparative miRNA profiles between C. Meiocarpa and C. oleifera. Furthermore, this study successfully identified the best drying environment at which the quantity and quality of lipid in camellia seed are at its maximum.

Keywords

miRNAs Lipid metabolism Natural drying Camellia meiocarpa Camellia oleifera

Background

C. oleifera and C. meiocarpa belong to the camellia family, Theaceae, and are known for their high quality oilseed that is dubbed as the Eastern olive oil [1, 2]. Some of the camellia species are ancient oilseed plants with history of cultivation for over two thousand years [3]. Camellia oil is known for its edible and medicinal uses with an oleic acid content reaching above 80%, a high content of monounsaturated lipid, and the lowest level of saturated fats [4]. Camellia oil aids in cholesterol reduction, resistance to stress, oxidation reduction, reduced inflammation, and improved human immunity [5]. The drying management of camellia seed prior to oil extraction is a fundamental factor affecting its fatty acid quality and quantity [68].

MicroRNAs (miRNAs) are small regulatory molecules that have been shown to be involved in a wide range of biological pathways by modulating expression of specific mRNAs [9]. Sequencing small RNA libraries demonstrated the role of miRNAs in lipid metabolism in plants (safflower [10]; Camelina sativa [11]; soybean [12]; wheat [13]; Jatropha [14]; Arabidopsis [15]), animals [16, 17], and insects [18]). Different oil crops were differentially expressed with different seed oil content and composition. There were 28 miRNAs regulated lipid metabolism from soybean [12], 30 miRNAs from Camelina sativa [11], and 13 miRNAs differentially expressed from two safflower genotypes that have difference to regulate oleic acid accumulation [10]. So there may be different miRNA expression between C. Meiocarpa and C. oleifera.

Recent research uncovered miRNAs regulation of lipid metabolism, which related to diverse pathways, such as fatty acid synthesis, accumulation, and catabolism. For example, miR159b, miR5026, and miR2911 were identified to encode ∆12-desaturase (FAD2) [10, 15, 19, 20], miR408a and miR159b for Fatty acid elongase (FAE1) [11, 15, 44], which related to fatty acid synthesis. Additionally, miR319a, miR001 and miR007 were identified to encode 1-acylglycerol-3-phosphocholine acyltransferase (LPCAT) [11, 14], which involved in fatty acid accumulation and miR166a, miR2910, miR824, miR414, and miR5206 were identified to encode fatty acid oxidase [11, 13, 14, 21], which regulate fatty acid catabolism. As above, the same fatty acid protein can be regulated by different miRNA, and the same miRNA can encode different fatty acid protein. In C. Meiocarpa and C. oleifera, the presence of different miRNAs expression pattern at different environments and, in particular, in dry seed suggest that miRNAs may play critical roles in lipid metabolism during natural seed drying [2227].

In order to explore the potential role of miRNAs in lipid metabolism during C. Meiocarpa and C. oleifera natural drying, miRNA expression profiles of seed samples at different moisture content (10, 20, 30, 40, and 50%) were investigated using high throughput next generation small RNA sequencing technology, so the differentially expressed miRNAs are unraveled to help understand their involvement in lipid metabolism.

Methods

Plant material

In 2012, mature fruits of C. meiocarpa and C. oleifera were collected from the four cardinal directions of 3 superior trees’ crowns per species. The trees are growing at the Minhou Tongkou State Forest Farm (26°05′ N, 119°17′ E), Fujian Province, China. The collected fruits were placed in a ventilated room until they naturally cracked and seeds were extracted by manual shell cutting. The seed moisture content at the time of extraction was closed to 50%. Seeds were naturally dried and their moisture content was determined daily. Over time, seed samples were collected at moisture content of 50, 40, 30, 20, and 10% and were sequentially identified as S01 to S05 and S06 and S10 for C. meiocarpa and C. oleifera, respectively, and sampled three times, then were flash frozen in liquid nitrogen and stored at −80 °C until RNA extraction.

Oil content analysis

To obtain the camellia seed oil content, the Soxhlet method was used by a fatty acid instrument (SZF-06A, Shanghai Hongji Instrument Equipment company) [28]. Camellia seeds at different moisture content were cut into thin slices, dried by silica gel and liquid nitrogen, milled into a powder by a pulverizer (FW100, Tianjin Taisite Instrument company). About 2 g of powder were weighed out (M1), packed in a folded filter paper bag and bound with a skim cotton thread, placed into the Soxhlet extraction thimble with 15 mL of petroleum ether for 1 h. Soxhlet extraction bottle were weight (M2) into which extraction thimble was placed, extracted for 5 h using 70 ml petroleum ether (75 °C). After extraction, the solvent was evaporated in Soxhlet extraction bottle and dried at 75 °C drying oven. Then Soxhlet extraction bottle was weighed again (M3).
$$ \mathrm{Seed}\ \mathrm{oil}\ \mathrm{content}=\left(\mathrm{M}3-\mathrm{M}2\right)/\mathrm{M}1\times 100\% $$

All experiments were carried out at least in triplicate and data were analyzed using the SPSS statistics 17.0 software.

RNA isolation

Total RNA was extracted from the seed samples of the two camellia species using RNA kit (RNA simply total RNA kit, Tiangen, Beijing, China) according to the manufacturer’s instructions. The purity and quality of the RNA were determined by assessing the absorbance ratio OD260/280 using NanoDrop ND1000 Spectrophotometer (NanoDrop, Wilmington, DE). The RNA was quantified with a Qubit 2.0 Fluoremeter (Invitrogen Corporation, Carlsbad, CA, USA) in accordance with the manufacturer’s instructions. The integrity of the RNA samples was verified using an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA).

Small RNA library construction and sequencing

Equal amount of total RNA from the three samples at each moisture content of the two camellia species, was mixed to construct a transcriptome library using an Illumina TruSeq RNA Sample PrepKit following the manufacturer’s instructions. Small RNAs of 18–30 nt in length were separated and purified by denatureing polyacrylamide gel electrophoresis. After dephosphorylation and ligation of a pair of Solexa adaptors to their 5′ and 3′ ends, the products were reverse-transcribed and amplified by RT-PCR and gel purification. After the library was constructed, the Qubit 2.0 Fluorometer (Invitrogen Corporation, Carlsbad, CA, USA) were used to calculate the molar concentration and confirm the insert size. The cDNA libraries were sequenced using the Illumina HiSeq2500 Genome analyzer (Illumina Inc., San Diego, CA, USA).

miRNA-Seq data analysis

After sequencing, the raw reads (FASTQ files) were processed into clean reads, then the adaptor sequences, poy(A) tails and the inserted tag were removed, followed by filtering the low-quantity reads (ambiguous bases ‘N’ ≥ 10% and more than 20% with Quality Score < 30 bases), the clean 18–30 nt sRNAs were mapped to GenBank (http://www.ncbi.nlm.nih.gov/), Rfam (version 10.1) database (http://rfam.sanger.ac.uk), tRNAdb [29], SILVA rRNA [30] and Repbase (http://www.girinst.org/), and rRNA, tRNA, snRNA, and snoRNA were discarded from the sRNA reads using bowtie2 software with perfect matches (0 mismatches) used for further analysis [31].

The unannotated sequences were then analyzed by miRDeep-P software package to predict miRNAs, which was developed by modifying miRDeep2 [32]. All mature sequences, star sequences and precursor sequences cored by miRDeep2 were retained and regarded as putative miRNAs and used for further analysis to identify known and novel miRNAs [33, 34]. Known miRNAs were annotated by identifying their homologous imRNAs in miRBase database (http://www.mirbase.org/) using the following criteria: 1) seed region, nucleotides 2–7 must be identical; and 2) the remainder of the sequence alignment must contain no more than two mismatches [35, 36]. The putative miRNAs produced by miRDeep2 analysis were regarded as conservative miRNAs when it met the above criteria. Those miRNAs produced by miRDeep2 analysis that did not meet the above criteria were considered as novel miRNAs. In order to predict novel miRNAs with high confidence, only those with a miRDeep-P score higher than ≥0 were retained as true novel miRNAs [34, 37].

Screening of differentially expressed miRNAs

Differentially expressed miRNAs were identified using the TPM [38] and IDEG6 software [39]. TPM (Tags Per Million reads) is a standardized method for calculating miRNA expression levels. TPM values were calculated using the following equation: TPM = number of mapped miRNA reads/number of clean sample reads × 106. In order to calculate the levels of differential expressed miRNAs, normally the value was set as 0.01 by default when the sequencing read is 0 (no reads) [40]. We calibrated miRNA expression levels using multiple hypothesis tests with a false discovery rate (FDR) ≤0.01, performed generalized chi-square tests for differential miRNA expression using the IDEG6 software (http://telethon.bio.unipd.it/bioinfo/IDEG6/), and screened the miRNAs for those with P-values less than 0.01 or TPM ratios between samples that were greater than 1 (fold change ≥1) or FDR ≤0.01. The miRNAs that met these criteria were identified as being differentially expressed [41].

miRNA target prediction

The putative target sites of miRNAs were identified by aligning mature miRNA sequences with a draft genome sequence using TargetFinder, (http://carringtonlab.org/resources/targetfinder). miRNA targets were computationally predicted essentially as described [38, 42, 43]. Briefly, potential targets from FASTA searches were scored using a position-dependent, mispair penalty system. Penalties were assessed for mismatches, bulges, and gaps (+1 per position) and G:U pairs (+0.5 per position). Penalties were doubled if the mismatch, bulge, gap, or G:U pair occurred at positions 2 to 13 relative to the 59 end of the miRNAs. Only one single-nucleotide bulge or single-nucleotide gap was allowed, and targets with penalty scores of four or less were considered to be putative miRNA targets [42, 44].

Functional annotation of predicted target genes was assigned using Nr (non-redundant protein database, NCBI), Nt (non-redundant nucleotide database, NCBI), Swiss-Prot, GO (gene ontology, http://www.geneontology.org/) and COG (clusters of orthologous groups) databases. BLASTX was employed to identify related sequences in the protein databases based on an Evalue of less than 10−5 [45]. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis were performed with package GO stats (http://www.geneontology.org/) of P value <0.05 was set as the cut-off to select out significantly enriched terms [46].

Quantitative real-time PCR analysis of miRNAs

qRT-PCR was used to validate the miRNAs identified using deep sequencing and to analyze their expression patterns. Total RNA of C. meiocarpa and C. oleifera seeds samples, was extracted using TRUzol Universal Reagent according to the manufacturer’s protocol. They were then reverse-transcribed into cDNA using the microRNA cDNA First Strand cDNA Synthesis Kit (CWBIO, Beijing, China) according to the manufacturer’s instructions. The cDNA was quantified by microRNA Real-Time PCR Assay Kit (CWBIO, Beijing, China) using a 20 μL reaction mixture, which consisted of 1 μL of diluted cDNA, 0.25 μM forward and reverse primer, and 10 μL of 2 × SYBR Green PCR Master Mix (CWBIO, Beijing, China). All reactions were performed under the following conditions: 95 °C for 5 min, 40 cycles of 95 °C for 15 S, 62 °C for 45 S. Melting curve analysis was performed to verify specific amplification (from 72 to cycles at 60 °C for 15 S). Each sample was processed in triplicate, and 5.8S rRNA was used as an internal control [47, 48]. The equation ratio 2 -∆∆CT was applied to calculate the relative expression level of miRNAs. The qRT-PCR primers are listed in file (Additional file 1: Table S1) and Ct value of 5.8S (Additional file 1: Table S2).

Results

Oil content of two camellia species during natural seed drying

Camellia oil content were determined by Soxhlet extraction method during natural seed drying (Table 1). The two species showed increased in oil content at 50 to 30% moisture contents followed by a slow decline at 30 to 10% moisture content, with 30% moisture content producing the highest oil content. Oil content accumulation was largest when the seed moisture content dropped from 50 to 40% and 40 to 30% for C. meiocarpa and C. oleifera, respectively. At 30% moisture content, C. meiocarpa and C. oleifera showed increase in their seed oil content of 8.80 and 10.23%, respectively, indicating that the effect of appropriate seed natural drying on oil accumulation can be great. While the percentages of oil content increase seem different between the two species, the relative increase amounted to 4.40%, indicating that natural drying can promote oil accumulation in both camellia specie.
Table 1

Seed oil content during camellia seed natural drying

Moisture content (%)

Seed oil content (%)

C. meiocarpa

C. oleifera

50

33.41 ± 0.001d

37.66 ± 0.117e

40

39.91 ± 0.002b

38.72 ± 0.116d

30

42.21 ± 0.001a

47.89 ± 0.001a

20

40.35 ± 0.002b

44.25 ± 0.001b

10

37.83 ± 0.001c

42.09 ± 0.001c

different letters indicate significant at P < 0.05

Sequence analysis of small RNAs

To obtain a comprehensive profile of the sRNAs involved in natural drying, camellia seed from both species were subjected to Solexa high-throughput sequencing, with 5 libraries for each species corresponding to the sampled moisture contents. Average total of 30,641,435 and 31,012,304 reads were generated from C. meiocarpa and C. oleifera, respectively (Table 2). After filtering the low quality reads, such as 3′ insert null, poly(A), length < 18 nt or length > 30 nt, and other artifacts, the majority of the small RNAs were 21 to 24 nt in length. A total of 24,070,601 and 21,653,584 clean reads of 18–30 nt were obtained for C. meiocarpa and C. oleifera, respectively (Table 2). GC content of clean reads were more than 47.75 and 48.99 and the Q30 (meaning 1 error in 1000 reads) of clean reads were more than 85.29 and 85.25% for C. meiocarpa and C. oleifera, respectively (Table 2). Through quality control, each sample of clean data were greater than 19.40 M, indicating the high sRNA quality (Tables 2, 3 and 4).
Table 2

Statistics relating to C. meiocarpa and C. oleifera sRNA sequences produced by Solexa sequencing

Species

Samples

Raw reads

<18 nt reads

>30 nt reads

Clean reads

GC(%)

Q20(%)

CycleQ20(%)

Q30(%)

C. meiocarpa

S01

29,906,668

1,658,148

7,025,688

20,417,236

49.32

93.1

100

86.98

S02

32,844,767

1,179,743

3,844,931

27,795,553

47.75

91.9

100

86.17

S03

28,888,019

1,676,660

3,899,605

23,290,661

48.54

91.42

100

85.47

S04

28,599,822

1,902,807

3,697,488

2,2978,269

48.32

91.22

100

85.29

S05

32,967,901

2,333,282

4,738,442

25,871,287

48.67

91.24

100

85.36

Average

30,641,435

1,750,128

4,641,231

24,070,601

48.52

91.78

100

85.86

C. oleifera

S06

34,169,238

4,386,045

4,606,664

25,151,946

49.13

91.23

100

85.25

S07

31,865,014

1,751,974

9,877,436

19,405,898

49.28

93.21

100

87.02

S08

30,501,297

2,833,009

4,644,107

23,001,131

48.99

91.35

100

85.43

S09

31,087,840

3,519,595

5,720,360

21,016,629

49.7

93.05

100

86.74

S10

27,438,132

3,369,136

3,659,237

19,692,318

49.55

93.09

100

86.86

Average

31,012,304

3,171,952

5,701,561

21,653,584

49.33

92.39

100

86.26

Table 3

Distribution of small RNAs among different categories during natural drying in C. meiocarpa

Category

S01

S02

S03

S04

S05

Average

rRNA

2,538,153

2,904,882

3,126,948

3,295,085

4,259,745

3,224,963

snRNA

3532

6061

3311

5799

5000

4741

snoRNA

161

194

118

141

140

151

tRNA

337,182

247,988

383,889

368,857

485,157

364,615

Repbase

9927

9090

15,485

12,733

14,597

12,366

Unannotated

17,528,281

24,627,338

19,760,910

19,295,654

21,106,648

20,463,766

Total

20,417,236

27,795,553

23,290,661

22,978,269

25,871,287

24,070,601

Table 4

Distribution of small RNAs among different categories during natural drying in C. oleifera

Category

S06

S07

S08

S09

S10

Average

rRNA

4,061,785

3,880,373

4,384,408

5,417,932

4,269,148

4,402,729

snRNA

5381

4753

6280

6407

3761

5316

snoRNA

376

255

652

351

1224

572

tRNA

368,965

437,180

641,000

662,085

677,875

557,421

Repbase

18,113

11,547

11,476

14,753

13,396

13,857

Unannotated

20,697,326

15,071,790

17,957,315

14,915,101

14,726,914

16,673,689

Total

25,151,946

19,405,898

23,001,131

21,016,629

19,692,318

21,653,584

After searching against GenBank, Rfam, tRNAdb, Silva, and Repbase database for small RNA sequences by bowtie2 software, rRNA (3,224,963), snRNA (4741), snoRNA (151), tRNA (364,615), Repbase-associated sRNAs (12,366) were annotated and removed, and other unannotated RNAs (20,463,766) were obtained for C .meiocarpa on average (Table 3). The same process was conducted for C. oleifera and rRNA (4,402,729), snRNA (5316), snoRNA (572), tRNA (557,421), Repbase-associated sRNAs (13,857) were annotated and removed, and other unannotated RNAs (16,673,689) were obtained for C. oleifera on average (Table 4). The unannotated RNAs were subjected to further analyses for miRNA identification.

The majority of small RNAs were 21 to 24 nt in length, producing similar length distributions in both species (Fig. 1). The 24 nt small RNAs were the most abundant representing 35.07 and 27.85% of small RNAs in C. meiocarpa and C. oleifera, respectively, the second was 21 nt representing 17.72 and 18.85%, third was 22 nt representing 16.35 and 17.97% (Additional file 1: Tables S3 and S4). The 40% moisture level (sample S02) for C. meiocarpa, produced the highest 24-nt RNA peak while 10% moisture level (sample S05) produced the highest 21-nt and 22-nt RNA peaks among the studied moisture content levels (Additional file 1: Table S3), conversely, 50% moisture (sample S06) produced the highest 24-nt, 21-nt, and 22-nt RNA peaks in C. oleifera (Additional file 1: Table S4).
Fig. 1

Length distributions of small RNAs in two camellia species. Data of C. meiocarpa and C. oleifera were averaged across five level moisture content (50, 40, 30, 20 and 10%) for each species separately

Identification of miRNAs during natural drying

We used the software miRDeep2 to map the retained sequence reads to identify candidate miRNAs. Across the five moisture content levels, C. meiocarpa produced successful 2,355,539 reads (11.51%) that were mapped to the plant genomes, of which the 10 and 50% moisture content levels (S05 and S01) produced the most (2,724,598) and least (1,902,986) mapped reads, respectively. Similarly, on average, C. oleifera produced 2,396,805 (14.37%) successful mapped reads, of which 50 and 20% moisture content levels (S06 and S09) produced most (2,803,519) and least (2,121,984) mapped reads. In total, 2,288,508 (12.80%) mapped reads were successfully annotated (Table 5).
Table 5

Alignment information statistics with the plant genomes

Species

Sample

Unannotated

Mapped reads

Percent

C. meiocarpa

S01

17,528,281

1,902,986

10.86

S02

24,627,338

2,509,074

10.19

S03

19,760,910

2,288,508

11.58

S04

19,295,654

2,352,529

12.19

S05

21,106,648

2,724,598

12.91

Average

20,463,766

2,355,539

11.51

C. oleifera

S06

20,697,326

2,803,519

13.55

S07

15,071,790

2,295,301

15.23

S08

17,957,315

2,527,567

14.08

S09

14,915,101

2,121,984

14.23

S10

14,726,914

2,235,656

15.18

Average

16,673,689

2,396,805

14.37

Two species

 

18,568,728

2,376,172

12.80

A total of 274 candidate miRNAs, 248 and 246 unique sequences were assigned to C. meiocarpa and C. oleifera, respectively (Additional file 1: Table S5). Out of the identified candidate miRNA sequences, 110 were identified to 64 families, 57 families belonging to each of C. meiocarpa (99 out of 248 candidate miRNA) and C. oleifera (98 out of 246 candidate miRNA) (Additional file 1: Tables S5, S6 and S7). The diversity of miRNA families could be determined from their members’ number. As shown, MIR482 families were the largest with 10 members, followed by MIR159 and MIR535 with 5 members, and MIR160, MIR169_2 and MIR5272 with 4 members (Additional file 1: Table S6). Most of the conserved miRNA families had one member (65.63%) (Additional file 1: Table S6). The miRNAs sequences ranged in length from 18 to 25 nt, with a peak of 24 nt (Additional file 1: Figure S1). The miRNA first nucleotide preference distributions are show in Fig. 2a. miRNAs of 24 nt tended to start with 5′-A while the 21 nt tended with 5′-U (Fig. 2a). Tall miRNAs tended to start with 5′-U and not 5′-G (Fig. 2b). During the seed natural drying process, the number of miRNAs across moisture content levels showed a decreasing trend which was followed by increase with a peak at 40% moisture content (S02) for C. meiocarpa (Table 6). C. oleifera, on the other hand, showed a trend of reduction, followed by increase, followed by reduction in the number of miRNAs across the studied moisture content levels with a pronounced peak at 50% moisture content (S06) (Table 6).
Fig. 2

First nucleotide (a) and position nucleotide (b) biases of miRNA in two camellia species

Table 6

Sum of annotated miRNA during camellia seed natural drying

Species

Sample

All miRNA

miRNA with target

Target gene

C. meiocarpa

S01

169

169

3541

S02

196

196

5936

S03

190

190

3768

S04

183

183

3311

S05

173

173

3523

Total

248

248

6057

C. oleifera

S06

191

191

3738

S07

173

173

3397

S08

177

177

3616

S09

178

178

3451

S10

169

169

3545

Total

246

246

4532

Two species

 

274

274

6250

Next we conducted sequence homology search of these candidate miRNAs against known mature miRNAs in miRBase. Any miRNA that met the sequence homology criteria of Yang et al. [35] and Jain et al. [36] was considered a conserved miRNA gene. Through this analysis, we identified a total of 151 conserved miRNAs which belonging to 47 miRNA families in both camellia species (Additional file 1: Table S6). miRDeep2 score above 1.0 resulted in 98 pre-miRNAs (64.90%) of which 35 with a read count ranging between 10 and 100 (23.19%), 41 with read count above 100 (27.15%), and 75 with read count below 10 (49.67%) (Additional file 1: Table S6).

Those miRNA sequences, which met the threshold of miRDeep2 analysis but did not have any known homologous miRNA gene families in miRBase, were further analyzed to predict novel miRNAs in the two camellia species. The remaining miRNAs, which met the total score of >0, were considered to be true novel miRNAs. A total 123 novel mature miRNAs sequences were discovered and belonged to 36 miRNA families in both camellia species (Additional file 1: Table S7). miRDeep2 score above 1.0 had 87 pre-miRNAs (70.73%). The majority of pre-miRNA (69) read count ranged from 10 to 100 (56.10%), followed by 35 precursors in above 100 (28.45%) and 19 precursors below 10 (15.45%) (Additional file 1: Table S7).

Prediction and annotation of miRNAs target genes

To better understand the functions of the identified miRNAs, putative target genes were predicted using TargetFinder software [42]. A total of 6250 target genes were identified (Table 6). Only 3733 target unigenes (59.73%) were annotated by performing a BLASTX search against diverse protein databases, revealing that 1368 (21.89%), 2190 (35.04%), 901 (14.42%), 2160 (34.56%), 2730 (43.68%), 2735 (43.76%), and 3718 (59.49%) unigenes have significant matches with sequences in COG, GO, KEGG, KOG, Pfam, Swissprot, and nr protein databases respectively (Table 7). A total of 2743 (73.48%) annotated target genes had the length of ≥1000 (Table 7).
Table 7

Functional annotation of the two camellia species

Database

Annotated Number

300 ≤ length < 1000

length ≥ 1000

%Annotated target gene

COG_Annotation

1368

174 (12.72%)

1194 (87.28%)

21.89

GO_Annotation

2190

469 (21.42%)

1721 (78.58%)

35.04

KEGG_Annotation

901

198 (21.98%)

703 (78.02%)

14.42

KOG_Annotation

2160

430 (19.91%)

1730 (80.09%)

34.56

Pfam_Annotation

2730

424 (15.53%)

2306 (84.47%)

43.68

Swissprot_Annotation

2735

584 (21.35%)

2151 (78.65%)

43.76

nr_Annotation

3718

977 (26.28%)

2741 (73.72%)

59.49

All_Annotated

3733

990 (26.52%)

2743 (73.48%)

59.73

To evaluate the potential functions of these miRNA target genes, we next applied gene ontology (GO) and KEGG pathway analyses to categorize the miRNA targets. The miRNA target genes were categorized according to biological process, cellular component and molecular function by Go analysis (Fig. 3). A total of 1860 target genes were categorized into 19 biological process. Based on molecular function, 1722 target genes were classified to 14 categories. A total of 1212 target genes were categorized as cellular components. Target genes related to lipid metabolism were found among 51 GO terms, in which 18 GO terms are related to biological process and 33 related to molecular function. There were 31 miRNAs involved in lipid metabolism and targeted 148 unigenes (Additional file 1: Table S8). The KEGG enrichment analysis revealed 12 pathways related to lipid metabolism, involved in 15 miRNA targeted 93 unigenes. There were 19 target genes in Glycolysis/Gluconeogenesis pathway, 12 in Fatty acid biosynthesis pathway, and 7 in biosynthesis of unsaturated fatty acids pathway (Additional file 1: Table S9). Integrated GO and KEGG function annotation, identified 23 miRNA regulating 131 target genes that were annotated as lipid metabolism (Additional file 1: Table S10). These miRNAs regulated the changes of seed oil content at different moisture content levels. There were high correlations between transcript abundance with added value of oil content, for example MIR482 (Additional file 1: Table S11). Finally, miRNA of lipid metabolism only expressed were identified in C. Meiocarpa (Group1_Unigene_BMK.30485_635795) and C. oleifera (Group1_Unigene_BMK.37364_696840 and Group1_Unigene_BMK.38037_703962) (Table 8).
Fig. 3

GO categories and distribution of miRNA targets in two camellia species

Table 8

miRNA of lipid metabolism transcript abundance in the two camellia species during seed natural drying

Pre-miRNA

miRNA

C. meiocarpa

C. oleifera

S01

S02

S03

S04

S05

Average

S06

S07

S08

S09

S10

Average

CL22479Contig1_234494

Unkown

0

0

0

1

0

0.2

1

0

1

0

1

0.6

CL22738Contig1_61999

Unkown

0

0

1

0

0

0.2

0

0

0

0

1

0.2

Group1_Unigene_BMK.23434_588836

MIR5067

20

21

33

25

24

24.6

19

20

15

9

10

14.6

Group1_Unigene_BMK.30485_635795

Unkown

0

1

0

0

0

0.2

0

0

0

0

0

0.0

Group1_Unigene_BMK.37364_696840

Unkown

0

0

0

0

0

0.0

1

2

0

0

0

0.6

Group1_Unigene_BMK.37987_703484

MIR2118

302

563

747

807

602

604.2

650

280

434

325

359

409.6

Group1_Unigene_BMK.38037_703962

Unkown

0

0

0

0

0

0.0

0

0

0

0

1

0.2

Group2_Unigene_BMK.25259_1025465

MIR482

302

563

747

807

602

604.2

650

280

434

325

359

409.6

Group2_Unigene_BMK.25259_1025498

MIR482

302

563

747

807

602

604.2

650

280

434

325

359

409.6

Group2_Unigene_BMK.34335_1,093,229

MIR5067

20

21

33

25

24

24.6

19

20

15

9

10

14.6

Group2_Unigene_BMK.50808_1268477

Unkown

0

0

0

1

0

0.2

1

0

0

1

0

0.4

Group2_Unigene_BMK.9543_1378570

Unkown

15

22

11

26

21

19.0

17

10

15

11

7

12.0

CL19455Contig1_54014

Unkown

26

26

23

22

27

24.8

40

18

28

25

14

25.0

CL19777Contig1_314088

Unkown

23

34

30

29

26

28.4

35

18

31

26

20

26.0

CL2440Contig1_359627

MIR1861

2

6

13

5

4

6.0

5

1

5

3

4

3.6

CL4378Contig1_191450

Unkown

11

8

10

14

15

11.6

35

13

22

17

12

19.8

CL9644Contig1_380257

Unkown

26

24

21

21

18

22.0

20

12

18

10

24

16.8

Group1_Unigene_BMK.45675_802511

Unkown

4

4

8

1

10

5.4

9

4

4

5

3

5.0

Group2_Unigene_BMK.24252_1018543

Unkown

1

1

1

0

0

0.6

1

2

2

1

0

1.2

Group2_Unigene_BMK.38504_1137258

Unkown

35

31

29

40

33

33.6

45

25

24

13

25

26.4

Group2_Unigene_BMK.38504_1137263

Unkown

6

3

4

3

4

4.0

2

0

2

2

1

1.4

Group2_Unigene_BMK.63506_1315063

Unkown

58

47

89

49

53

59.2

96

49

34

50

20

49.8

Group2_Unigene_BMK.39605_1150110

Unkown

0

1

1

0

0

0.4

2

1

0

1

0

0.8

Differentially expressed miRNAs of lipid metabolism during natural drying

The differences between the miRNA profiles of the two camellia species are possibly related to differences in their responses to natural drying and were investigated using the IDEG6 software. For C. Meiocarpa, miRNA abundance pairwise analysis between the different moisture content libraries, indicated that 40, 46, 63, and 38 significantly differentially expressed miRNAs were identified between 40 and 50% (S01 vs. S02), 30–40% (S02 vs. S03), 20–30% (S03 vs. S04), and 10–20% (S04 vs. S05) moisture contents, respectively. The highest number of significantly differentially expressed miRNAs was observed between 20 and 30% (S03 vs. S04) moisture contents, with the 1owest up- and highest down-regulated numbers of 10 and 53, respectively (Table 9), with 9 significantly different miRNAs involved in lipid metabolism (Table 10 and Additional file 1: Table S12). C. oleifera produced similar results with 71, 54, 36, and 56 significantly differentially expressed miRNAs observed between 40 and 50% (S06 vs. S07), 30–40% (S07 vs. S08), 20–30% (S08 vs. S09), and 10–20% (S09 vs. S10) moisture contents, respectively. The highest number of significantly differentially expressed miRNAs was observed between 40 and 50% (S06 vs. S07) moisture contents, with the highest up- and lowest down-regulated numbers of 61 and 10, respectively (Table 9), with 8 significantly different miRNAs involved in lipid metabolism (Tables 11 and S13).
Table 9

Comparison of the number of differentially expressed miRNAs between samples with different moisture content within and across the two camellia species during seed natural drying

Type

Compared type

Number

Up-regulated

Down-regulated

C. meiocarpa

S01 vs. S02

40

21

19

S02 vs. S03

46

20

26

S03 vs. S04

63

10

53

S04 vs. S05

38

29

9

Average

47

20

27

C. oleifera

S06 vs. S07

71

61

10

S07 vs. S08

54

9

45

S08 vs. S09

36

19

17

S09 vs. S10

56

16

40

Average

54

26

28

Between species

S06. vs. S01

78

69

9

S07 vs. S02

51

22

29

S08 vs. S03

44

27

17

S09 vs. S04

58

19

39

S10 vs. S05

61

44

17

Average

58

36

22

Table 10

Significant differentially expressed miRNAs of lipid metabolism during C. meiocarpa seed natural drying across different moisture content levels (%)1

Pre-miRNA

50 vs. 40%

40 vs. 30%

30 vs. 20%

20 vs. 10%

Group2_Unigene_BMK.25259_1025498

1

1

1

0

Group1_Unigene_BMK.37987_703484

1

1

1

0

Group2_Unigene_BMK.34335_1,093,229

0

0

1

0

CL2440Contig1_359627

0

0

1

0

Group1_Unigene_BMK.23434_588836

0

0

1

0

CL19777Contig1_314088

0

0

1

0

Group2_Unigene_BMK.25259_1025465

1

1

1

0

Group1_Unigene_BMK.45675_802511

0

0

1

1

Group2_Unigene_BMK.63506_1315063

0

1

1

0

1 Represents significant DEG and 0 represents non-significant DEG

1significance at P < 0.05

Table 11

Significant differentially expressed miRNAs of lipid metabolism during C. oleifera seed natural drying across different moisture content levels (%)1

Pre-miRNA

50 vs. 40%

40 vs. 30%

30 vs. 20%

20 vs. 10%

Group2_Unigene_BMK.63506_1315063

1

1

0

1

CL19455Contig1_54014

0

0

0

1

Group1_Unigene_BMK.37987_703484

1

1

0

1

Group1_Unigene_BMK.23434_588836

1

1

0

0

Group2_Unigene_BMK.25259_1025498

1

1

0

1

Group2_Unigene_BMK.25259_1025465

1

1

0

1

Group2_Unigene_BMK.38504_1137258

1

1

0

0

Group2_Unigene_BMK.34335_1,093,229

1

1

0

0

1 Represents significant DEG and 0 represents non-significant DEG

1significance at P < 0.05

Comparing across the two species, the average number of miRNAs in C. oleifera seeds was higher than that of C. Meiocarpa (Table 9). Pairwise analysis of miRNA abundance between the two species for the same moisture level libraries indicated that there were 78, 51, 44, 58, and 61 significant differentially expressed miRNAs for the 50, 40, 30, 20, and 10% moisture contents, respectively. There were three differentially expressed miRNAs of lipid metabolism during the seed natural drying process of the studied two camellia species (Group1_Unigene_BMK.37987_703484, Group2_Unigene_BMK.25259_1025465, and Group2_Unigene_BMK.25259_1025498) (Tables 12 and S14). The highest up- (69) and lowest down-regulated number (9) of significant differentially expressed miRNAs were detected for 50% moisture contents (S06 vs. S01) (Table 9). This indicated that the greatest difference between the two species was observed at the 50% moisture content, with 12 significant differentially expressed miRNAs regulating lipid metabolism during seed natural drying (Tables 12 and S14).
Table 12

Significant differentially expressed miRNAs of lipid metabolism during the two camellia species seed natural drying across the same moisture content level (C. oleifera vs. C. meiocarpa MC%)1

Pre-miRNA

50 vs. 50%

40 vs. 40%

30 vs. 30%

20 vs. 20%

10 vs. 10%

CL19777Contig1_314088

1

0

0

0

0

CL19455Contig1_54014

1

0

0

0

0

Group2_Unigene_BMK.25259_1025465

1

1

1

1

1

Group2_Unigene_BMK.9543_1378570

1

0

0

0

0

CL9644Contig1_380257

1

0

0

0

0

Group2_Unigene_BMK.25259_1025498

1

1

1

1

1

Group2_Unigene_BMK.38504_1137258

1

0

0

0

0

Group2_Unigene_BMK.34335_1,093,229

1

0

1

0

0

Group2_Unigene_BMK.63506_1315063

1

0

1

1

1

Group2_Unigene_BMK.38504_1137263

1

0

0

0

0

Group1_Unigene_BMK.23434_588836

1

0

1

0

0

Group1_Unigene_BMK.37987_703484

1

1

1

1

1

1Represents significant DEG and 0represents non-significant DEG

1significance at P < 0.05

Validation of the expression patterns of differentially expressed miRNAs related lipid metabolism by RT-qPCR

To validate the data obtained from the high-throughput sequencing, four significantly differentially expressed miRNAs (Group1_Unigene_BMK.45675_802511, Group2_Unigene_BMK.63506_1315063, Group1_Unigene_BMK.37987_703484, and Group2_Unigene_BMK.38504_1137258) were predicted to target genes involved in lipid metabolism and their expression levels were quantified using stem-loop qRT-PCR (Fig. 4). The results were consistent with deep sequencing data (Table 8) and the majority of analyzed miRNAs showed moisture content- and species-specific expression. For C. meiocarpa, Group1_Unigene_BMK.45675_802511 and Group2_Unigene_BMK.63506_1315063 peaked at 10 and 30% moisture content while the other two miRNAs (Group1_Unigene_BMK.37987_703484, and Group2_Unigene_BMK.38504_1137258) peaked at 20% moisture content. Additionally, all four miRNAs had different lowest point (Fig. 4). C. oleifera showed four miRNAs peaked at 50% moisture content but had different lowest point at 10 (Group1_Unigene_BMK.45675_802511 and Group2_Unigene_BMK.63506_1315063), 20 (Group2_Unigene_BMK.38504_1137258), and 40% (Group1_Unigene_BMK.37987_703484) moisture content (Fig. 4). These expression patterns indicate that lipid metabolism of the two camellia species were regulated by miRNA during the seed natural drying process.
Fig. 4

qRT-PCR validation of miRNA in C. meiocarpa and C. oleifera. Relative expression of miRNA in 10, 20, 30, 40, and 50% moisture contents. Reference gene was 5.8 rRNA. Normalized miRNA in 50% moisture content of C. oleifera were arbitrarily set to 1. Error bars were calculated based on three replicates

Discussion

miRNAs related to the lipid metabolism of camellia species

Recently, sequencing of oil crops produced a large amount of miRNA associated with lipid metabolism-related genes (e.g., 97, 30, 10, and 4 miRNAs targeting 89, 133, 21, and 4 lipid biosynthesis genes were reported for soybean [12], Camelina sativa [11], castor bean [49], and peanut [19], respectively). In the present study, we detected 23 pre-miRNAs targeting 131 candidate genes regulating lipid metabolism functions during camellia seed natural drying (Additional file 1: Table S10). Of these pre-miRNAs, 5, 5, and 11 regulated fatty acid biosynthesis, accumulation, and catabolism, respectively, and additional 2 (Group1_Unigene_BMK.30485_635795 and Group1_Unigene_BMK.45675_802511) regulated not only fatty acid biosynthesis and accumulation, but also fatty acid catabolism (Additional file 1: Table S10). Group1_Unigene_BMK.30485_635795 and Group1_Unigene_BMK.45675_802511 encode 56 and 41 target genes involved in fatty acid biosynthesis, accumulation, and catabolism, respectively (Additional file 1: Table S10).

Concurring with the above, the most abundant miRNAs (osa-miR2118e: Group1_Unigene_BMK.37987_703484, Group2_Unigene_BMK.25259_1025465, and Group2_Unigene_BMK.25259_1025498 (Additional file 1: Table S10 and Table 8)) that target genes encoding a long-chain-alcohol oxidase involving fatty acid-oxidation [50]. The second abundant miRNAs (Group2_Unigene_BMK.63506_1315063) targeting diacylglycerol kinase, a lipid kinase converting diacylglycerol to phosphatidic acid, regulating balance of diacylglycerol and phosphatidic acid [51], and potentially involved in regulating lipid deposition [52]. The third, Group2_Unigene_BMK.38504_1137258 (Additional file 1: Table S10 and Table 8), targeting lipases/acylhydrolase, fatty acid hydroxylase, and carboxylesterase, which are hydrolytic enzymes involved in degradation of fatty acid [5355], collectively suggesting that these miRNAs may play important roles in decreasing the rate of lipid breakdown during camellia seed natural drying. This can be confirmed by the 4% increase in oil content in two camellia seed through seed nature drying (Table 1).

miRNAs regulate lipid metabolism during camellia seed natural drying

Several studies have shown that miRNAs are differentially regulated in response to stress [56] and that this differential regulation varied among different plant species [57]. For example, distinct responses to drought stresses were reported for miRNAs in Arabidopsis [58], switchgrass [59], Populus [60] and Caragana intermedia [61]. Especially, drought stress in switchgrass [59] and Populus [60] were regulated by miRNAs related to lipid metabolism; however, the linkage between drought responses and lipid metabolism miRNAs changes is not established.

In the present study, C. Meiocarpa produced 9 significant differentially expressed miRNAs regulating lipid metabolism with only 4 at 20–30% moisture contents. These pre-miRNAs belonged to Group2_Unigene_BMK.34335_1,093,229, Group1_Unigene_BMK.23434_588836, CL19777_Contig1_314088, and CL2440_Contig1_359627, with the former three showing more than 100 TPM (Table 10 and Additional file 1: Table S12) and targeting 3-ketoacyl-CoA synthase III, which catalyze the initial elongation step of fatty acid biosynthetic process [62] and glycerol-3-phosphate transporter, a precursor protein for phospholipid biosynthesis [63]. It is interesting to note that these three pre-miRNAs were down-regulated resulting in a reduction in the fatty acid biosynthesis, so seeds with 30% moisture content have high fatty acid synthesis and accumulation activities (Table 1).

Similarly, C. oleifera produced 8 significant differentially expressed miRNAs during seed natural drying, with up- and down-regulated for the 40–50 and 30–40% moisture contents, respectively (Tables 11 and S13). The largest fold changes were observed for Group1_Unigene_BMK.23434_588836 and Group2_Unigene_BMK.34335_1,093,229, which target 3-ketoacyl-CoA synthase III (Additional file 1: Table S10 and Table S13). These pre-miRNAs regulate fatty acid biosynthesis with seeds at 40–50% moisture content showing low fatty acid biosynthesis activities while those at 30–40% moisture content exhibiting high activities (Table S13). This can be confirmed by the observed 1.06 and 9.17% increase in oil content at 40–50 and 30–40% moisture contents, and reaching the highest point at 30% moisture content (Table 1). So seeds with 30% moisture content have also high fatty acid synthesis and accumulation activities.

Comparing the significant differentially expressed miRNAs of C. meiocarpa (9) with those of C. oleifera (8), indicated that the two species share 6 miRNAs involved in lipid metabolism, with unique miRNAs belonging to each species (CL19455Contig1_54014 and Group2_Unigene_BMK.38504_1137258 in C. oleifera, and CL2440Contig1_359627, CL19777Contig1_314088 and Group1_Unigene_BMK.45675_802511 in C. meiocarpa) (Tables 10 and 11). Group2_Unigene_BMK.38504_1137258 and CL2440Contig1_359627 regulated fatty acid catabolism, CL19455Contig1_54014 and CL19777Contig1_314088 regulated fatty acid accumulation, and Group1_Unigene_BMK.45675_802511 regulated fatty acid synthesis, accumulation, and catabolism (Additional file 1: Table S10). These indicate that oil content differences between the two camellia species are mainly due to their differential abilities miRNAs of fatty acid accumulation and catabolism.

Collectively, the studied two camellia species produced 12 significant differentially expressed miRNAs to regulate lipid metabolism during seed natural drying (Tables 12 and S14). These pre-miRNAs indicated that C. meiocarpa has higher activities to regulate the lipid metabolism and this can be confirmed by its lower oil content as compared to C. oleifera (Table 1). It should be stated that all these 12 differentially expressed miRNAs were significantly expressed in the 50% moisture content stage (Tables 12 and S14). Seeds with 50% moisture content had only significant differentially expressed pre-miRNAs (CL19777Contig1_314088, CL19455Contig1_54014, Group2_Unigene_BMK.9543_1378570, CL9644Contig1_380257, Group2_Unigene_BMK.38504_1137258 and Group2_Unigene_BMK.38504_1137263) (Tables 8 and S14). CL19777Contig1_314088 and CL19455Contig1_54014 target glycerol-3-phosphate transporter (Additional file 1: Table S10). Group2_Unigene_BMK.9543_1378570 target acetyl-CoA-carboxylase (Additional file 1: Table S10), which catalyze the ATP-dependent carboxylation of acetyl-CoA to form malonyl-CoA, the rate limiting and first committed reaction in fatty acid synthesis [64]. So these three pre-miRNA control different fatty acid synthesis and accumulation in the two camellia species. For all significant differentially expressed miRNAs, the largest fold change was observed for Group2_Unigene_BMK.38504_1137263 (Fatty acid hydroxylase), followed by CL9644Contig1_380257 (Carboxylesterase), and Group1_Unigene_BMK.23434_588836 (3-ketoacyl-CoA synthase III) and Group2_Unigene_BMK.34335_1,093,229 (3-ketoacyl-CoA synthase III) (Additional file 1: Table S10 and Table S14). So the oil content of C. oleifera is higher than C. meiocarpa and this is attributable to four pre-miRNAs, of which Group2_Unigene_BMK.38504_1137263 and CL9644Contig1_380257 regulating fatty acid catabolism and Group1_Unigene_BMK.23434_588836 and Group2_Unigene_BMK.34335_1,093,229 regulating fatty acid synthesis.

Conclusion

The present study identified 274 candidate miRNAs, with 248 and 246 unique sequences to C. meiocarpa and C. oleifera, respectively. Integrated GO and KEGG function annotation, produced 23 miRNAs regulating 131 target genes, all were annotated as lipid metabolism, regulating fatty acid biosynthesis, accumulation and catabolism during seed natural drying. Lipid metabolism primarily focused on fatty acid catabolism, with five miRNAs (Group1_Unigene_BMK.37987_703484, Group2_nigene_BMK.25259_1025465 Group2_Unigene_BMK.25259_1025498, Group2_Unigene_BMK.63506_1315063, and Group2_Unigene_BMK.38504_1137258) playing important roles in decreasing the rate of lipid breakdown and additional two miRNAs (Group2_Unigene_BMK.34335_1,093,229 and Group1_Unigene_BMK.23434_588836) regulating fatty acid synthesis. Across the two species, four pre-miRNAs were identified to regulate lipid metabolism, with Group2_Unigene_BMK.38504_1137263 and CL9644Contig1_380257, and Group1_Unigene_BMK.23434_588836 and Group2_Unigene_BMK.34335_1,093,229 regulating fatty acid catabolism and synthesis, respectively.

To our knowledge, this work provides the first small RNA expression analysis of lipid metabolism in camellia seed during natural drying as well as the first comparative miRNA profiling analysis between C. Meiocarpa and C. oleifera that exhibit significantly different fatty acid profiles.

Declarations

Acknowledgments

We thank Minhou Tongkou State Forest Farm of Fujian Province for providing access to research material. We also thank Professor Bruce C Larson, University of British Columbia, for helpful suggestions. This work was supported by grants received under the Doctoral Fund of the Ministry of Education of China (K4112020A) and Fujian Province Major Science and Technology Project (2013NZ0207).

Availability of data and materials

The data supporting this study are available in the Dryad Digital Repository, doi: 10.5061/dryad.1mj01

Authors’ contributions

J-LF, HC, YAK conceived and designed the experiment, J-LF, Z-JY performed the experiment, J-LF, Z-JY, S-P C, collected the data collection and prepared the figures, J-LF performed the data analysis, and J-LF, YAK wrote the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not required.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
College of Forestry, Fujian Agriculture and Forestry University
(2)
Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Forest Sciences Centre

References

  1. Lee CP, Yen GC. Antioxidant activity and bioactive compounds of tea seed (C.oleifera Abel.) oil. J Agric Food Chem. 2006;54(3):779–84.View ArticlePubMedGoogle Scholar
  2. Ma J, Ye H, Rui Y, Chen G, Zhang N. Fatty acid composition of C.oleifera oil. J Verbr Lebensm. 2011;6(1):9–12.View ArticleGoogle Scholar
  3. Yang C, Liu X, Chen Z, Lin Y, Wang S. Comparison of oil content and fatty acid profile of ten new C. oleifera cultivars. Journal of lipids. 2016. doi:10.1155/2016/3982486.
  4. Haiyan Z, Bedgood DR, Bishop AG, Prenzler PD, Robards K. Endogenous biophenol, fatty acid and volatile profiles of selected oils. Food Chem. 2007;100(4):1544–51.View ArticleGoogle Scholar
  5. Haro AD, Obregón S, Río Celestino MD, Mansilla P, Salinero MC. Variability in seed storage components (protein, oil and fatty acids) in a camellia germplasm collection. International Camellia Congress. 2014. http://hdl.handle.net/10261/126721.
  6. Tibaldi G, Emanuela F. Silvana Ni. Cultivation practices do not change the Salvia sclarea L. essential oil but drying process does. J Food Agric Environ. 2010;8:790–4.Google Scholar
  7. Hsieh C, Yang J, Chuang Y, Wang E, Lee Y. Effects of roasting prior to pressing on the camellia oil quality. J Taiwan Agric Res. 2013;62:249–58.Google Scholar
  8. Wang Y, Sun D, Chen H, Qian L, Xu P. Fatty acid composition and antioxidant activity of tea (Camellia sinensis L.) seed oil extracted by optimized supercritical carbon dioxide. Int J Mol Sci. 2011;12:7708–7719.Google Scholar
  9. Deiters A. Small molecule modifiers of the microRNA and RNA interference pathway. AAPS J. 2010;12(1):51–60.View ArticlePubMedGoogle Scholar
  10. Cao S, Zhu QH, Shen W, Jiao X, Zhao X, Wang MB, Liu L, Singh SP, Liu Q. Comparative profiling of microRNA expression in developing seeds of high linoleic and high oleic safflower (Carthamus tinctorius L.) plants. Front Plant Sci. 2013;4:489.PubMedPubMed CentralGoogle Scholar
  11. Poudel S, Aryal N, Lu C. Identification of MicroRNAs and transcript targets in Camelina sativa by deep sequencing and computational methods. PLoS One. 2015;10(3):e0121542.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Ye CY, Xu H, Shen E, Liu Y, Wang Y, Shen Y, Qiu J, Zhu Q, Fan L. Genome-wide identification of non-coding RNAs interacted with microRNAs in soybean. Front Plant Sci. 2014;5:743.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Han J, Kong ML, Xie H, Sun QP, Nan ZJ, Zhang QZ, Pan JB. Identification of microRNAs and their targets in wheat Triticum aestivum L. by EST analysis. Genet Mol Res. 2013;12:3793–805.View ArticlePubMedGoogle Scholar
  14. Galli V, Guzman F, de Oliveira LF, Loss-Morais G, Körbes AP, Silva SD, Margis-Pinheiro MMAN, Margis R. Identifying microRNAs and transcript targets in Jatropha seeds. PLoS One. 2014;9(2):e83727.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Belide S, Petrie JR, Shrestha P, Singh SP. Modification of seed oil composition in Arabidopsis by artificial microRNA-mediated gene silencing. Front Plant Sci. 2012;3:168.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Flowers E, Froelicher ES, Aouizerat BE. MicroRNA regulation of lipid metabolism. Metabolism. 2013;62(1):12–20.View ArticlePubMedGoogle Scholar
  17. Fernández-Hernando C, Suárez Y, Rayner KJ, Moore KJ. MicroRNAs in lipid metabolism. Curr Opin Lipidol. 2011;22(2):86–92.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Zhang X, Zheng Y, Cao X, Ren R, Yu XQ, Jiang H. Identification and profiling of Manduca sexta microRNAs and their possible roles in regulating specific transcripts in fat body, hemocytes, and midgut. Insect Biochem Mol Biol. 2015;62:11–22.View ArticlePubMedGoogle Scholar
  19. Chi X, Yang Q, Chen X, Wang J, Pan L, Chen M, Yang Z, He Y, Liang X, Yu S. Identification and characterization of microRNAs from peanut (Arachis hypogaea L.) by high-throughput sequencing. PLoS one. 2011;6(11):e27530.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Zhu Q, Luo Y. Identification of miRNAs and their targets in tea (Camellia sinensis). J Zhejiang Univ Sci B. 2013;14(10):916–23.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Trumbo JL, Zhang B, Stewart CN. Manipulating microRNAs for improved biomass and biofuels from plant feedstocks. Plant Biotechnol J. 2015;13(3):337–54.View ArticlePubMedGoogle Scholar
  22. Martin RC, Liu PP, Goloviznina NA, Nonogaki H. microRNA, seeds, and Darwin?: diverse function of microRNA in seed biology and plant responses to stress. J Exp Bot. 2010;61(9):2229–34.View ArticlePubMedGoogle Scholar
  23. Prabu GR, Mandal AKA. Computational identification of microRNAs and their target genes from expressed sequence tags of tea (Camellia sinensis). Genomics, Proteomics and Bioinformatics. 2010;8(2):113–21.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Li D, Wang L, Liu X, Cui D, Chen T, Zhang H, Jiang C, Xu C, Li P, Li S, et al. Deep sequencing of maize small RNAs reveals a diverse set of microRNA in dry and imbibed seeds. PLoS One. 2013;8(1):e55107.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Zhu QW, Luo YP. Identification of microRNAs and their targets in tea (Camellia sinensis). J Zhejiang Univ Sci B. 2013;I14(10):916–23.View ArticleGoogle Scholar
  26. Zhang Y, Zhu X, Chen X, Song C, Zou Z, Wang Y, Wang M, Fang W, Li X. Identification and characterization of cold-responsive microRNAs in tea plant (Camellia sinensis) and their targets using high-throughput sequencing and degradome analysis. BMC Plant Biol. 2014;14(1):271.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Jian H, Wang J, Wang T, Wei L, Li J, Liu L. Identification of rapeseed microRNAs involved in early stage seed germination under salt and drought stresses. Front Plant Sci. 2016;7:658.PubMedPubMed CentralGoogle Scholar
  28. Rajaei A, Barzegar M, Yamini Y. Supercritical fluid extraction of tea seed oil and its comparison with solvent extraction. Eur Food Res Technol. 2005;220(3):401–5.View ArticleGoogle Scholar
  29. Jühling F, Mōrl M, Hartmann RK, Sprinz M, Stadler PF, Pütz J. tRNAdb 2009: compilation of tRNA sequences and tRNA genes. Nucleic Acids Res. 2009;37:159–62.View ArticleGoogle Scholar
  30. Pruesse E, Quast C, Knitte K, Fuchs BM, Ludwig W, Peplines J, Glōckner FO. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Natural Methods. 2012;9:357–9.View ArticleGoogle Scholar
  32. Friedlander MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N. Discovering microRNAs from deep sequencing data using miRDeep. Natural Biotechnology. 2008;26:407–15.View ArticleGoogle Scholar
  33. Yang X, Li L. miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics. 2011;27:2614–5.View ArticlePubMedGoogle Scholar
  34. Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40(1):37–52.View ArticlePubMedGoogle Scholar
  35. Yang L, Zhang Z, He S. Bichir microRNA repertoire suggests a ray-finned fish affinity of Polypteriforme. Gene. 2015;566(2):242–7.View ArticlePubMedGoogle Scholar
  36. Jain M, Chevala VN, Garg R. Genome-wide discovery and differential regulation of conserved and novel microRNAs in chickpea via deep sequencing. J Exp Bot. 2014. doi:10.1093/jxb/eru333.
  37. Mobuchon L, Marthey S, Boussaha M, Le Guillou S, Leroux C, Le Provost F. Annotation of the goat genome using next generation sequencing of microRNA expressed by the lactating mammary gland: comparison of three approaches. BMC Genomics. 2015;16:285.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL, Carrington JC. High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One. 2007;2:e219.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Romualdi C, Bortoluzzi S, D'Alessi F, Danieli GA. IDEG6: a web tool for detection of differentially expressed genes in multiple tag sampling experiments. Physiol Genomics. 2003;12(2):159–62.View ArticlePubMedGoogle Scholar
  40. Qin QH, Wang ZL, Tian LQ, Gan HY, Zhang SW, Zeng ZJ. The integrative analysis of microRNA and mRNA expression in Apis mellifera following maze-based visual pattern learning. Insect Science. 2014;21(5):619–36.View ArticlePubMedGoogle Scholar
  41. Ma X, Xin Z, Wang Z, Yang Q, Guo S, Guo X, Cao L, Lin T. Identification and comparative analysis of differentially expressed microRNAs in leaves of two wheat (Triticum aestivum L.) genotypes during dehydration stress. BMC Plant Biology. 2015;15:21.Google Scholar
  42. Allen E, Xie Z. Gustafson AM. Carrington JC microRNA-directed phasing during trans-acting siRNA biogenesis in plants Cell. 2005;121:207–21.PubMedGoogle Scholar
  43. Fahlgren N, Carrington JC. microRNA target prediction in plants. Methods Mol Biol. 2010;592:51–7.View ArticlePubMedGoogle Scholar
  44. Hwang DG, Park JH, Lim JY, Kim D, Choi Y, Kim S, Choi Y, Kim S, Reeves G, Yeom SI, Lee JS, Park M, Kim S, Choi IY, Chol D, Shin C. The hot pepper (Capsicum annuum) microRNA transcriptome reveals novel and conserved targets: a foundation for understanding microRNA functional roles in hot pepper. PLoS One. 2013;8(5):e64238.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Xu Y, Chu L, Jin Q, Wang Y, Chen X, Zhao H, Xue Z. Transcriptome-wide identification of microRNAs and their targets from Typha angustifolia by RNA-Seq and their response to cadmium stress. PLoS One. 2015;10(4):e0125462.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012. doi:10.1093/nar/gkr988.
  47. Shi R, Chiang VL. Facile means for quantifying microRNA expression by real-time PCR. BioTechniques. 2005;39(4):519–25.View ArticlePubMedGoogle Scholar
  48. Li MY, Wang F, Xu ZS, Jiang Q, Ma J, Tan GF, Xiong AS. High throughput sequencing of two celery varieties small RNAs identifies microRNAs involved in temperature stress response. BMC Genomics. 2014;15:242.View ArticlePubMedPubMed CentralGoogle Scholar
  49. Xu W, Cui Q, Li F, Liu A. Transcriptome-wide identification and characterization of microRNAs from castor bean (Ricinus communis L.). PLoS one. 2013;8(7):e69995.Google Scholar
  50. Iwama R, Kobayashi S, Ohta A, Horiuchi H, Fukuda R. Alcohol dehydrogenases and an alcohol oxidase involved in the assimilation of exogenous fatty alcohols in Yarrowia lipolytica. FEMS Yeast Res. 2015;15(3):fov014.View ArticlePubMedGoogle Scholar
  51. Shirai Y, Saito N. Diacylglycerol kinase as a possible therapeutic target for neuronal diseases. J Biomed Sci. 2014;21:28.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Jiang LQ, de Castro BT, Massart J, Deshmukh AS, Löfgren L, Duque-Guimaraes DE, Ozilgen A, Osler ME, Chibalin AV, Zierath JR. Diacylglycerol kinase-δ regulates AMPK signaling, lipid metabolism, and skeletal muscle energetics. Am J Physiol-Endocrinol Metab. 2016;310(1):E51–60.PubMedGoogle Scholar
  53. Chepyshko H, Lai CP, Huang LM, Liu JH, Shaw JF. Multifunctionality and diversity of GDSL esterase/lipase gene family in rice (Oryza sativa L. japonica) genome: new insights from bioinformatics analysis. BMC Genomics. 2012;13:309.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Kim KR, Oh DK. Production of hydroxy fatty acids by microbial fatty acid-hydroxylation enzymes. Biotechnol Adv. 2013;31(8):1473–85.View ArticlePubMedGoogle Scholar
  55. Xiao D, Chen YT, Yang D, Yan B. Age-related inducibility of carboxylesterases by the antiepileptic agent phenobarbital and implications in drug metabolism and lipid accumulation. Biochem Pharmacol. 2012;84(2):232–9.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Bakhshi B, Fard EM, Nikpay N, Ebrahimi MA, Bihamta MR, Mardi M, Salekdeh GH. MicroRNA signatures of drought signaling in rice root. PLoS One. 2016;11(6):e0156814.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Lee WS, Gudimella R, Wong GR, Tammi MT, Khalid N, Harikrishna JA. Transcripts and microRNAs responding to salt stress in Musa acuminata Colla (AAA Group) cv. Berangan roots PLoS one. 2015;10(5):e0127526.View ArticlePubMedGoogle Scholar
  58. Ding Y, Tao Y, Zhu C. Emerging roles of microRNAs in the mediation of drought stress response in plants. J Exp Bot. 2013;64(11):3077–86.View ArticlePubMedGoogle Scholar
  59. Hivrale V, Zheng Y, Puli COR, Jagadeeswaran G, Gowdu K, Kakani VG, Barakat A, Sunkar R. Characterization of drought-and heat-responsive microRNAs in switchgrass. Plant Sci. 2016;242:214–23.View ArticlePubMedGoogle Scholar
  60. Shuai P, Liang D, Zhang Z, Yin W, Xia X. Identification of drought-responsive and novel Populus trichocarpa microRNAs by high-throughput sequencing and their targets using degradome analysis. BMC Genomics. 2013;14:233.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Wu BF, Li WF, Xu HY, Qi LW, Han SY. Role of cin-miR2118 in drought stress responses in Caragana intermedia and Tobacco. Gene. 2015;574(1):34–40.View ArticlePubMedGoogle Scholar
  62. Misra N, Patra MC, Panda PK, Sukla LB, Mishra BK. Homology modeling and docking studies of FabH (β-ketoacyl-ACP synthase III) enzyme involved in type II fatty acid biosynthesis of Chlorella variabilis: a potential algal feedstock for biofuel production. J Biomol Struct Dyn. 2013;31(3):241–57.View ArticlePubMedGoogle Scholar
  63. Lemieux MJ, Huang Y, Wang DN. Glycerol-3-phosphate transporter of Escherichia coli: structure, function and regulation. Res Microbiol. 2004;155(8):623–9.View ArticlePubMedGoogle Scholar
  64. Salie MJ, Thelen JJ. Regulation and structure of the heteromeric acetyl-CoA carboxylase. Biochimica et Biophysica Acta (BBA)-molecular and cell biology of. Lipids. 2016;1861(9):1207–13.Google Scholar

Copyright

© The Author(s). 2017

Advertisement