Global small RNA analysis in fast-growing Arabidopsis thaliana with elevated concentrations of ATP and sugars
© Liang et al.; licensee BioMed Central Ltd. 2014
Received: 13 July 2013
Accepted: 4 February 2014
Published: 10 February 2014
In higher eukaryotes, small RNAs play a role in regulating gene expression. Overexpression (OE) lines of Arabidopsis thaliana purple acid phosphatase 2 (AtPAP2) were shown to grow faster and exhibit higher ATP and sugar contents. Leaf microarray studies showed that many genes involved in microRNAs (miRNAs) and trans-acting siRNAs (tasiRNAs) biogenesis were significantly changed in the fast-growing lines. In this study, the sRNA profiles of the leaf and the root of 20-day-old plants were sequenced and the impacts of high energy status on sRNA expression were analyzed.
9-13 million reads from each library were mapped to genome. miRNAs, tasiRNAs and natural antisense transcripts-generated small interfering RNAs (natsiRNAs) were identified and compared between libraries. In the leaf of OE lines, 15 known miRNAs increased in abundance and 9 miRNAs decreased in abundance, whereas in the root of OE lines, 2 known miRNAs increased in abundance and 9 miRNAs decreased in abundance. miRNAs with increased abundance in the leaf and root samples of both OE lines (miR158b and miR172a/b) were predicted to target mRNAs coding for Dof zinc finger protein and Apetala 2 (AP2) proteins, respectively. Furthermore, a significant change in the miR173-tasiRNAs-PPR/TPR network was observed in the leaves of both OE lines.
In this study, the impact of high energy content on the sRNA profiles of Arabidopsis is reported. While the abundance of many stress-induced miRNAs is unaltered, the abundance of some miRNAs related to plant growth and development (miR172 and miR319) is elevated in the fast-growing lines. An induction of miR173-tasiRNAs-PPR/TPR network was also observed in the OE lines. In contrast, only few cis- and trans-natsiRNAs are altered in the fast-growing lines.
The sources of dsRNAs that trigger siRNAs biogenesis could be exogenous (e.g., viral replication) or endogenous. Plant evolved several classes of endogenous siRNAs including tasiRNAs, natsiRNAs and cis-acting siRNAs (casiRNAs). In plants, tasiRNAs are generated by a pathway different from that of miRNAs (Figure 1). The genomic loci encoding tasiRNAs are known as TAS genes and are transcribed by polII. The generation of tasiRNA is initiated by miRNA-mediated cleavage of long non-coding transcripts of TAS genes. Eight TAS loci from four families (TAS1-4) are identified in Arabidopsis genome [2–4]. There are three loci in TAS1 family, TAS1A (At2g27400), TAS1B (At1g50055) and TAS1C (At2g39675). Both TAS1 and TAS2 (At2g39681) transcripts are cleaved by miR173 and associated with AGO1 to generate siRNAs, which mainly target pentatricopeptide repeat-containing (PPR) mRNAs [5–7]. There are three TAS3 loci in Arabidopsis thaliana, TAS3A (At3g17185), TAS3B (At5g49615) and TAS3C (At5g57735). miR390 guides cleavage of these transcripts with AGO7 to generate siRNAs which target mRNAs of auxin responsive factors (ARF) family (e.g. ARF2, ARF3 and ARF4) [5, 8, 9]. TAS4 transcript is initiated by miR828 in association with AGO1 to generate tasiRNAs and their targets are MYB transcription factors . The cleaved RNAs from the eight loci are bounded by suppressor of gene silencing 3(SGS3) and copied into dsRNAs by RNA-dependent RNA polymerase 6 (RDR6). The dsRNAs are cleaved in multiple rounds by DCL4 from the end defined by miRNA-mediated cleavage such that the tasiRNAs are in 21-nucleotide (nt) register from the cleavage site. The tasiRNAs are loaded into AGO1 complex to initiate tasiRNA guided mRNA degradation [4, 11]. Another class of siRNAs is nat-siRNAs, which could be derived from RNAs transcribed from opposite strands of the same loci (cis-nat-siRNAs)  or by transcripts from different loci (trans-nat-siRNAs). There are 1,739 and 4,828 potential cis- and trans- natural antisense transcripts (NATs), respectively in Arabidopsis. The production of nat-siRNAs are is dependent on RDR6 and DCL2 (24-nt) or DCL1 (21-nt).
Overexpression of AtPAP2 in A. thaliana speeds up plant growth. The OE lines flower early and grow faster than the wild type (WT) plants. The seed yield and silique numbers of OE lines are also more than the control lines . AtPAP2 was shown to be dually targeted to chloroplasts and mitochondria . Metabolomics studies showed that some sugars (sucrose, glucose, fructose and myo-inositol), TCA metabolites (citrate, fumarate, malate and succinate) and certain amino acids (alanine, glycine, glutamate, proline, serine and valine) are significantly higher in the OE lines than in the WT . The concentrations of ATP and ADP are also higher in the OE lines . All of these phenotypes pointed to a dramatic shift of metabolism in the overexpression lines . Besides, overexpression of AtPAP2 in another member of Brassicaceae (Camelina sativa) can also generate fast growing and higher seed-yield transgenic plants .
Microarray data of genes involved in miRNA and siRNA biogenesis in WT, pap2 , OE7 and OE21
Primary gene symbol
Argonaute 1 (AGO1)
Argonaute 5 (AGO5)*
Argonaute 7 (AGO7)*
Dicer-like 1 (DCL1)*
Dicer-like 3 (DCL3)*
Hyponastic leaves 1 (HYL1)*
RNA-dependent rna polymerase 1 (RDR1)*
Suppressor of gene silencing 3 (SGS3)*
Small RNA degrading nuclease 1 (SDN1)*
Small RNAs length distribution and annotation
Eight small RNA libraries were generated from the leaves and the roots of 20-day-old Arabidopsis. After removal of adaptors and low quality tags, the number of cleans reads were calculated and mapped to the A. thaliana genome. In total, 13 to 19 million raw reads were generated from each library, and approximately 95% of the reads remained for further research after clearing the adaptors sequences. Approximately 9 to 13 million sequence reads (approximate 73 - 87% of the clean reads) corresponding to 0.5 to 1.3 million unique sequence signatures could be mapped onto the genome (Additional file 1).
The length of the clean reads ranged from 18 to 30 nucleotides. The size distribution of small RNA sequences in various leaf (a) and root (b) samples were reported in Additional file 2. Similar distributions were observed in OE, pap2 and wild type leaf libraries. Consistent with the earlier reports , two major peaks appeared at 21 and 24 nucleotides in length in the total sequence reads (Additional file 2a). In the root libraries, an additional peak at 19 nucleotides was observed. These 19-nucletide sequences are mainly originated from tRNAs, and they are cleaved from the 5′ end of Gly-tRNATCC which represented over 80% of tRNA-derived small RNAs in the roots . Interestingly, the total reads of these 19-nucletide sequences are relatively more (approximate 6 million) in both OE lines than in WT (3.8 million) and pap2 (3.4 million) (Additional file 2b). Clean reads from the eight libraries were mapped and classified according to the annotation of the genome. A significant higher proportion of small RNAs were mapped to tRNA genes in the root libraries (21.28 - 47.30% of the clean reads) than in the leaf libraries (3.60 - 6.96%). For the repeat small RNAs, both unique and total reads are less in OE leaf and root samples. We observed that the total reads of sRNA antisense to exons (Exon antisense) are higher in leaves of the OE lines, while there are smaller number of unique reads (Additional file 3); these indicate that the sRNA antisense to exons are more concentrated to their targets in the leaves of OE lines.
Identification of known miRNAs
Known miRNAs with significant changes between both OE lines and WT
Target gene family
Increase in abundance in OE vs WT
Apetala 2-like transcription factors
Apetala 2-like transcription factors
TAS1A, TAS1B, TAS1C, TAS2
TCP Transcription factor
LYR family of Fe/S cluster biogenesis protein
Decrease in abundance in OE vs WT
Squamosa-promoter binding protein-like
HAP2-like transcription factors
HAP2-like transcription factors
Growth Regulating Factor
Cysteine/Histidine-rich C1 domain family protein
F-box family protein
Analysis of novel miRNAs candidates and their targets
Novel miRNAs and their target genes were predicted by the Mireap software. Target genes are predicted based on the rules suggested by Allen et al.  and Schwab . Secondary structures were predicted and analyzed for stable stem-loop hairpins (Additional file 5). The detail of novel MIR genes including location, minus free energy (MFE), sequence and structures are listed in Additional file 6. According to the criteria described in methods, seven novel miRNAs with counts > 100 were identified in leaf libraries, of which two were also identified in root libraries (Additional file 7). For leaf/root_miRNA0001RNA_5p, it was predicted to target to chromomethylase1 (CMT1). And for leaf_miRNA0002_5p and root_miRNA0002_3p, only found in WT/pap2, were predicted to target retrotransposon ORF-1 protein. Leaf_miRNA0003_3p and leaf_miRNA008_5p, which had relatively high reads, were only found in pap2 (3281 reads) and WT (2001 reads), respectively and their targets were six F-box family proteins. Leaf_miRNA0005_5p and leaf_miRNA0006_3p were predicted to target pentatricopeptide repeat (PPR) family proteins (AT4G28010 and AT1G62260), both are proteins located in mitochondria. These two miRNAs were either not found (leaf_miRNA0006_3p) or the counts were too low (leaf_miRNA0005_5p) in the leaves of WT and pap2 line and therefore not detected in previous studies.
Trans-acting small interference sRNAs analysis
Natural-antisense-transcript derived small interference RNAs
Out of 1,739 potential cis-NATs predicted from Arabidopsis TAIR 10 database, we could only map our sRNAs to 1665 cis-NATs; only 44 (2.6%) and 36 (2.1%) pairs were significantly altered in the leaves and roots of both OE lines (Additional file 11). Only 12 cis-NATs increased in abundance in the leaves of the OE lines, some have significant fold changes. The genes in the gene pairs include 3 miRNAs (miR780, miR836, miR841), TAS1C, one PPR gene, two genes involved in auxin response, a histone H2A gene and a phosphoribosylanthranilate isomerase gene. The sRNA counts for the latter two gene pairs increased by 10- and 60-fold in leaves, respectively. Some cis-NATs strongly decreased in abundance in the leaves of OE lines. The genes in the gene pairs include genes of Lhcb1.5, Lhcb6, a protein kinase APk2b, 2 F-box proteins and a UDP-3-O-[3-hydroxymyristoyl] N-acetylglucosamine deacetylase. The sRNA mapped to the latter gene pair dropped ~100-fold in both leaves and roots.
Out of 4,828 possible trans-NATs pairs, only 253 (5.2%) and 256 (5.3%) pairs were significantly altered in the leaves and roots of both OE lines, respectively (Additional file 12). Most of the genes involved are transponsable elements (264 and 302 genes in the leaf and root datasets), pseudogenes (38 and 10 genes in leaf and root datasets) and genes for unknown proteins (87 and 86 genes in leaf and root datasets).
Validation of siRNAs and candidate genes by qRT-PCR
To validate the sRNA sequencing data, the abundance of selected miRNAs (miR173, miR390 and miR391) and 21-nt tasi-RNAs mapped to CDS of TASIB (551-571), TAS1C (378 – 398), TAS2 (629 – 649) and TAS4-siR81(-) in different lines were compared by qRT-PCR. All the results of selected small RNAs were consistent between qRT-PCR and sequencing. The microarray data has been validated by qRT-PCR in our previous study  and the transcript abundance of selected genes, including SGS3, At1g12620 (PPR), were also well validated by qRT-PCR in this study (Additional file 13).
sRNA biogenesis is one of the regulatory mechanisms in organism. In this study, the sRNA profiles of high energy plants were compared with that of WT plants under soil-grown condition. The size and growth stages of the plants were identical so that variations due to developmental stages and morphology were minimized. Many miRNAs and siRNAs were activated by various biotic and abiotic stresses and subsequently modulated the mRNA stability of their target genes, so that the plants can cope with the stresses accordingly . Our data shows that very few miRNAs were significantly altered in the OE lines under the experimental conditions. Out of 243 known miRNAs in Arabidopsis, only 15 and 9 miRNAs significantly increased or decreased in abundance in the leaves of OE lines. Very few miRNAs were altered in the roots, only 2 and 9 miRNAs significantly increased or decreased in abundance, respectively.
Some miRNAs were shown to control plant development . Overexpression of miR156 and miR159 resulted in late-flowering [21, 24], and overexpression of miR160 resulted in increased lateral rooting . However, the abundance of these miRNAs was unaltered in the OE lines. Only the abundance of miR172 and miR319 were significantly higher in the leaves of the OE lines (Table 2). The targets of miR172 and miR319 are AP2 and TCP, transcription factors, respectively. Both are involved in leaf development, floral organ identity and flowering time [21, 26–28]. Overexpression of miR172 in Arabidopsis and potato can promote flowering development and tuberization respectively . The higher expression of miR172 in the leaves of OE lines correlates with the earlier bolting and flowering phenotypes of the OE lines . For miRNAs that are induced by nutritional stresses, such as phosphate deficiency (miR399) copper deficiency (miR398), and sulfate deficiency (miR395), their abundance were low in all lines and were not significantly changed in our OE lines [22, 30–32]. This is reasonable as the plants were grown in soil with adequate supply of nutrients. Similarly, miRNAs responsive to bacterial (miR160, miR167, miR393, miR396, miR398 and miR825) and viral infections (miR156 and miR164) were not altered in the OE lines [33–35]. The abundance of some miRNA (miR172 and miR397) induced by cold stresses increased in the OE lines but many other miRNAs (miR166, miR393, miR396 and miR408) induced by cold were unaltered . Accumulation of sucrose in the cytosol is a key protection mechanism for cold tolerance. Since the OE lines contain higher sucrose contents , miR172 and miR397 might be induced indirectly by cold-induced sucrose accumulation.
Microarray data of PPR genes that had significant changes between both two OE lines and wild-type
Transacting siRNA generating PPR gene
Transacting siRNA generating PPR gene
Transacting siRNA generating PPR gene
Anthocyanin production can be induced in Arabidopsis by stresses such as phosphate deficiency  and sugar treatments . Phosphate deficiency induces MYB transcription factors (MYB75/90/113), which subsequently activate the expression of multiple genes involved in anthocyanin synthesis. MYB75 also stimulates the expression of miR828, which initiates the production of TAS4-SiR81(-) from the TAS4 locus. TAS4-SiR81(-) targets mRNAs of MYB transcription factors for cleavage and serves as a negative feedback loop of anthocyanin production . In this study, the counts of miR828 are zero in all the four lines, whereas the basal abundance of TAS4-SiR81(-) is lower in the OE lines (1,728 and 3,698 reads) than the WT (13,783 reads). Microarray studies showed that the basal abundance of many genes of enzymes involved in anthocyanin synthesis decreased in the leaves of the OE lines, while the transcript abundance of MYB75/90/119 was indifferent . These lower basal levels are physiologically relevant, as high sucrose (6%) treatment induced less anthocyanin production in the OE lines than in the WT . Phosphate deficiency induced all the three MYB factors (MYB75/90/113) , but only exogenous sucrose treatment induced MYB75 . In the lines, high endogenous sugars suppress the basal transcript abundance of enzymes, which are independent of the transcript abundance of the MYB transcription factors . These data indicates that the induction of anthocyanin production by exogenous sucrose and phosphate deficiency are overlapped but distinguishable. In addition, since the count of miR828 is zero in all our samples, the cleavage of TAS4 RNA maybe initiated by an uncharacterized sRNA, adding complexity to the regulatory pathway of anthocyanin production.
Energy is the driving force of growth; various biological processes are regulated by the availability of energy. The AtPAP2 overexpression lines provide a unique opportunity to study the impact of high energy status on the regulatory mechanisms of plants. Our previous works showed that there are massive changes in the gene transcription profiles in the OE lines . Small RNA (sRNA)-mediated cleavage of mRNA is the key mechanism to induce mRNA degradation. The present study is the first to report the sRNA expression profile of Arabidopsis when ample supply of energy is available. Significant changes in certain miRNAs and the miR173-tasiRNAs-PPR/TPR network were observed in the fast-growing lines. Further investigation is required to delineate the roles of the miR173-tasiRNAs network in plant growth and energy metabolism.
Arabidopsis thaliana ecotypes Columbia (Col-0) (wild type: WT), AtPAP2 mutant (pap2) in Col-0 background, AtPAP2 overexpressors (OE7 and OE21) in Col-0 background were used in this study . Arabidopsis seeds of WT, pap2 and OE lines were surface sterilized with 20% (v/v) bleach for 15 ~ 20 minutes and washed with sterilized water for 4 ~ 5 times. The seeds were plated on Murashige and Skoog medium supplemented with 2% (w/v) sucrose for 10 days and the seedling were transferred to soil under 16 h light (22°C)/8 h dark (18°C) period. 20-day-old Arabidopsis leaf and root without bolting were frozen immediately in liquid nitrogen for RNA extraction. For every plant line, individual leaf/root was mixed as one sample.
Total RNA isolation
The total RNA of leaf and root were extracted with DNase I digestion according to the manufacturer’s instructions (RNeasy Plant Mini Kit, Qiagen, Hong Kong). The preliminarily quality was detected by running 1% (w/v) agarose gel stained by GelRed (Biotium, USA). The quality of the RNA samples was tested with Agilent 2100 Bioanalyzer RNA Nanochip. At least 10 μg of total RNA at a concentration of ≥ 400 ng/μl, OD 260/280 = 1.8 ~ 2.2 were used for cDNA library preparation.
Small RNA sequencing and sequencing process
The total RNA was sent to Beijing Genomics Institute (BGI) (Shenzhen, Guangdong province, China), small RNAs were sequenced using Illumina HiSeq™ 2000 high throughput sequencing technology. sRNAs with the length of 18-30 nt were first separated from the total RNA by running on 15% (w/v) TBE urea polyacrylamide gel. The excised gel with sRNAs from 18 to 30 nt in length were submerged in 600 μL of 0.3 M sodium chloride overnight at 4°C. The sRNA fragments were dissolved in diethylpyrocarbonate treated water (Ambion, Austin, TX, USA). The sRNAs were then added 5′ adaptor by T4 RNA ligase (Takara, Dalian, China) in the presence of RNase OUT Ribonuclease Inhibitor (Invitrogen, China) overnight at 4°C according to the manual. After obtaining the 5′ ligation products, a 3′ adaptor was added to the selecting 5′ adaptor sRNAs following the same procedures as the 5′ adaptor ligation. Finally, both 3′ and 5′ adaptors ligated sRNAs were run on a 10% (w/v) TBE urea polyacrylamide gel, then the 44 nt RNAs were excised. The sRNA was reversely transcribed to cDNA with the RT primer by Superscript II reverse transcriptase (Invitrogen, China). The cDNA was further quantified by Agilent 2100 and sequenced by the Illumina 1G Genome Analyzer.
Small RNAs annotations in NCBI and Rfam databases
Raw reads were produced by the Illumina 1G Genome Analyzer at BGI-Shenzhen, China and processed into clean full length reads by the BGI small RNA pipeline procedure . During this procedure, all low quality reads including 3′ adapter reads and 5′ adapter contaminants were removed. Adapter sequences were trimmed off from the remaining high quality sequences and sequences lager than 30 nt and smaller than 18 nt were discarded. All high quality sequences were used for further analysis. Unique small RNA tags were first mapped to Arabidopsis thaliana genome (http://www.arabidopsis.org/, TAIR version 10) using SOAP software to analyze the expression and distribution on the genome . Annotated small RNAs derived from rRNA, scRNA, snoRNA, snRNA and tRNA deposited at the Rfam 9.1 and NCBI GenBank databases (http://www.ncbi.nlm.nih.gov) were identified by NCBI blast. Besides, sRNA tags were also aligned to exons, introns, repeats and small interference RNAs. A final small RNA annotation was obtained by following a priority rule: Genbank > Rfam > known miRNA > repeat > exon > intron > siRNA. All the raw data and processed data were deposited in NCBI GEO (http://www.ncbi.nlm.nih.gov/geo/) with accession number GSE48309.
Known miRNAs and novel miRNAs identification and targets prediction
Where N1 is the total clean reads without normalization in control sample, N2 is the total reads in treatment, x is the number of a certain sRNA in control library and y is the number of the same sRNA in over-expressed library. A two-tailed p-value was calculated as p = 2q, where q is the accumulated probability . If q is larger than 0.5, p = 2*(1-q).
MIREAP (http://sourceforge.net/projects/mireap/) was used to identify potential novel miRNAs by folding the flaking genome sequence of unique small RNAs. Target genes are predicted based on the rules suggested by Allen et al  and Schwab . All the novel miRNA candidates and their targets were identified according to the following criteria: (1) there should be no more than four mismatches between the sRNAs and their targets, (2) in positions 2-12 of the miRNA/target duplex, there should be no adjacent mismatches, (3) no more than two adjacent mismatches in the miRNA/target duplex, (4) in positions 10-11 of miRNA/target duplex, there should be no mismatches, (5) in 5′ end of miRNAs, the mismatches in position 1-12 should no more than 2.5 basepairs with their targets (G-U bases count as 0.5 mismatches), and (6) the Minimum free energy (MFE) value of miRNA/target duplex should be no less than 75% of the value of the same miRNA matched to its perfect target.
All the 21-nt sRNA clean reads (5′-3′direction) from each library were mapped to the eight TAS cDNA sequences (5′-3′direction) using SOAP3 software  with perfect match. The reads were normalized and the fold change (log2-ratio) and the p-value were calculated between OE and WT lines. For phasing register analysis, we plotted 21nt-phasing register for each TAS cDNA based on the count and position information. The network in Figure 3 was generated by using Cytoscape software. miR173, all 21-nt small RNAs that are perfected mapped to TAS loci, all potential PPR, TPR targets, TAS1A, 1B, 1C, TAS2 and MIR173 genes are imported as nodes. The 21-nt small RNAs and their potential targets were connected with edges, as well as the mapped TAS genes. miR173 was connected to MIR173 gene and TAS1A, 1B, 1C and TAS2, respectively. The network is clustered by using the Cytoscape cluster plugin. The sRNA data were fit into the miR173-tasiRNAs-PPR network using Cytoscape 3.0 .
Databases of cis- and tran-NATs pairs (TAIR version 10, http://www.arabidopsis.org) were provided by Prof. Weixiong Zhang (Washington University in Saint Louis). The mapping procedure was listed in Additional file 15. Based on gene-pair names and genomic locations, we were able to align clean reads to Arabidopsis genome and crosscheck the database to find the natsiRNAs with significant differences in abundance between the lines. For each gene-pair, the start and the end for overlapping region (OL) and the whole region (WL) of two OL genes were used for mapping by SOAP3 software. Both strands were used in the mapping. If the sequence of a read is identical to part of either strand with the same length from 5′ to 3′ direction, then the read is perfectly matched. If the read is transcribed from positive strand, then its sequence is identical to the negative strand; therefore, its mapping orientation is denoted as negative (-), otherwise denoted as positive (+). For cis-NATs mapping, all clean reads from the 8 libraries from both leaf and root were mapped to Arabidopsis cis-NATs pairs regions with no mismatch allowed and all mapping positions were kept for each read. Finally we counted the number of reads aligned within each OL and WL region for all lines, and computed fold change and p-value between WT and OE lines.
Quantitative reverse transcriptase PCR (qRT-PCR) analysis was carried out using cDNA samples transcribed from 20-day-old tissues of Arabidopsis. Primer premier 5.0 (http://www.premierbiosoft.com/primerdesign/) was used to design the qRT-PCR primers. The PCR reactions were performed in a 10 μL volume containing a 2 × SYBR Green Master Mix (ABI systems). The amplification parameters were 95°C for 1 min; followed by 40 cycles of 95°C, 15 s and 60°C 1 min. ACTIN 2 was used as the internal control. For every transcript, each cDNA sample was analyzed in triplicate, and relative transcript abundance was calculated by normalizing to the maximum amount of concentration. The assessment of expression comparing different targets was determined by the ddCt comparative threshold (ΔΔCt) method. p-values were determined by a two-tailed paired Student’s t test. sRNAs were transcribed using the miRNA First-Strand Synthesis Kit (Clontech) and qRT-PCR of sRNA was carried out at the same condition of the qRT-PCR of mRNA. All the primers used in this study were listed in Additional file 16.
This project was supported by the Seed Funding Program for Basic Research (201011159050) and the Initiatives for Clean Energy and Environment (ICEE) of the University of Hong Kong, the General Research Fund (HKU772710M) and the Innovation and Technology Fund (Funding Support to Partner State Key Laboratories in Hong Kong) of the HKSAR, China.
- Reinhart BJ, Weinstein EG, Rhoades MW, Bartel B, Bartel DP: MicroRNAs in plants. Genes Dev. 2002, 16 (13): 1616-1626. 10.1101/gad.1004402.PubMed CentralPubMedView ArticleGoogle Scholar
- Howell MD, Fahlgren N, Chapman EJ, Cumbie JS, Sullivan CM, Givan SA, Kasschau KD, Carrington JC: Genome-wide analysis of the RNA-DEPENDENT RNA POLYMERASE6/DICER-LIKE4 pathway in Arabidopsis reveals dependency on miRNA- and tasiRNA-directed targeting. Plant Cell. 2007, 19 (3): 926-942. 10.1105/tpc.107.050062.PubMed CentralPubMedView ArticleGoogle Scholar
- Rajagopalan R, Vaucheret H, Trejo J, Bartel DP: A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev. 2006, 20 (24): 3407-3425. 10.1101/gad.1476406.PubMed CentralPubMedView ArticleGoogle Scholar
- Allen E, Howell MD: miRNAs in the biogenesis of trans-acting siRNAs in higher plants. Semin Cell Dev Biol. 2010, 21 (8): 798-804. 10.1016/j.semcdb.2010.03.008.PubMedView ArticleGoogle Scholar
- Allen E, Xie Z, Gustafson AM, Carrington JC: microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell. 2005, 121 (2): 207-221. 10.1016/j.cell.2005.04.004.PubMedView ArticleGoogle Scholar
- Montgomery TA, Yoo SJ, Fahlgren N, Gilbert SD, Howell MD, Sullivan CM, Alexander A, Nguyen G, Allen E, Ahn JH, et al: AGO1-miR173 complex initiates phased siRNA formation in plants. Proc Natl Acad Sci USA. 2008, 105 (51): 20055-20062. 10.1073/pnas.0810241105.PubMed CentralPubMedView ArticleGoogle Scholar
- Felippes FF, Weigel D: Triggering the formation of tasiRNAs in Arabidopsis thaliana: the role of microRNA miR173. Embo Rep. 2009, 10 (3): 264-270. 10.1038/embor.2008.247.PubMed CentralPubMedView ArticleGoogle Scholar
- Yoon EK, Yang JH, Lim J, Kim SH, Kim SK, Lee WS: Auxin regulation of the microRNA390-dependent transacting small interfering RNA pathway in Arabidopsis lateral root development. Nucleic Acids Res. 2010, 38 (4): 1382-1391. 10.1093/nar/gkp1128.PubMed CentralPubMedView ArticleGoogle Scholar
- Marin E, Jouannet V, Herz A, Lokerse AS, Weijers D, Vaucheret H, Nussaume L, Crespi MD, Maizel A: miR390, Arabidopsis TAS3 tasiRNAs, and their Auxin response factor targets define an autoregulatory network quantitatively regulating lateral root growth. Plant Cell. 2010, 22 (4): 1104-1117. 10.1105/tpc.109.072553.PubMed CentralPubMedView ArticleGoogle Scholar
- Luo QJ, Mittal A, Jia F, Rock CD: An autoregulatory feedback loop involving PAP1 and TAS4 in response to sugars in Arabidopsis. Plant Mol Biol. 2012, 80 (1): 117-129. 10.1007/s11103-011-9778-9.PubMed CentralPubMedView ArticleGoogle Scholar
- Baumberger N, Baulcombe DC: Arabidopsis ARGONAUTE1 is an RNA Slicer that selectively recruits microRNAs and short interfering RNAs. Proc Natl Acad Sci USA. 2005, 102 (33): 11928-11933. 10.1073/pnas.0505461102.PubMed CentralPubMedView ArticleGoogle Scholar
- Jin H, Vacic V, Girke T, Lonardi S, Zhu JK: Small RNAs and the regulation of cis-natural antisense transcripts in Arabidopsis. BMC Mol Biol. 2008, 9: 6-10.1186/1471-2199-9-6.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang X, Xia J, Lii YE, Barrera-Figueroa BE, Zhou X, Gao S, Lu L, Niu D, Chen Z, Leung C, et al: Genome-wide analysis of plant nat-siRNAs reveals insights into their distribution, biogenesis and function. Genome Biol. 2012, 13 (3): R20-10.1186/gb-2012-13-3-r20.PubMed CentralPubMedView ArticleGoogle Scholar
- Sun F, Suen PK, Zhang Y, Liang C, Carrie C, Whelan J, Ward JL, Hawkins ND, Jiang L, Lim BL: A dual-targeted purple acid phosphatase in Arabidopsis thaliana moderates carbon metabolism and its overexpression leads to faster plant growth and higher seed yield. The New phytologist. 2012, 194 (1): 206-219. 10.1111/j.1469-8137.2011.04026.x.PubMedView ArticleGoogle Scholar
- Sun F, Carrie C, Law S, Murcha MW, Zhang R, Law YS, Suen PK, Whelan J, Lim BL: AtPAP2 is a tail-anchored protein in the outer membrane of chloroplasts and mitochondria. Plant signaling & behavior. 2012, 7 (8): 927-932. 10.4161/psb.20769.View ArticleGoogle Scholar
- Sun F, Liang C, Whelan J, Yang J, Zhang P, Lim BL: Global transcriptome analysis of AtPAP2 - overexpressing Arabidopsis thaliana with elevated ATP. BMC Genomics. 2013, 14 (1): 752-10.1186/1471-2164-14-752.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang Y, Yu L, Yung KF, Leung DY, Sun F, Lim BL: Over-expression of AtPAP2 in Camelina sativa leads to faster plant growth and higher seed yield. Biotechnol Biofuels. 2012, 5: 19-10.1186/1754-6834-5-19.PubMed CentralPubMedView ArticleGoogle Scholar
- Hsieh LC, Lin SI, Shih AC, Chen JW, Lin WY, Tseng CY, Li WH, Chiou TJ: Uncovering small RNA-mediated responses to phosphate deficiency in Arabidopsis by deep sequencing. Plant physiology. 2009, 151 (4): 2120-2132. 10.1104/pp.109.147280.PubMed CentralPubMedView ArticleGoogle Scholar
- Qiu D, Pan X, Wilson IW, Li F, Liu M, Teng W, Zhang B: High throughput sequencing technology reveals that the taxoid elicitor methyl jasmonate regulates microRNA expression in Chinese yew (Taxus chinensis). Gene. 2009, 436 (1–2): 37-44.PubMedView ArticleGoogle Scholar
- Sunkar R, Jagadeeswaran G: In silico identification of conserved microRNAs in large number of diverse plant species. BMC Plant Biol. 2008, 8: 37-10.1186/1471-2229-8-37.PubMed CentralPubMedView ArticleGoogle Scholar
- Schwab R, Palatnik JF, Riester M, Schommer C, Schmid M, Weigel D: Specific effects of microRNAs on the plant transcriptome. Dev cell. 2005, 8 (4): 517-527. 10.1016/j.devcel.2005.01.018.PubMedView ArticleGoogle Scholar
- Khraiwesh B, Zhu JK, Zhu J: Role of miRNAs and siRNAs in biotic and abiotic stress responses of plants. Biochimica et biophysica acta. 2012, 1819 (2): 137-148. 10.1016/j.bbagrm.2011.05.001.PubMed CentralPubMedView ArticleGoogle Scholar
- Meng Y, Ma X, Chen D, Wu P, Chen M: MicroRNA-mediated signaling involved in plant root development. Biochem Biophys Res Commun. 2010, 393 (3): 345-349. 10.1016/j.bbrc.2010.01.129.PubMedView ArticleGoogle Scholar
- Achard P, Herr A, Baulcombe DC, Harberd NP: Modulation of floral development by a gibberellin-regulated microRNA. Development. 2004, 131 (14): 3357-3365. 10.1242/dev.01206.PubMedView ArticleGoogle Scholar
- Wang JW, Wang LJ, Mao YB, Cai WJ, Xue HW, Chen XY: Control of root cap formation by MicroRNA-targeted auxin response factors in Arabidopsis. Plant Cell. 2005, 17 (8): 2204-2216. 10.1105/tpc.105.033076.PubMed CentralPubMedView ArticleGoogle Scholar
- Chen XM: A microRNA as a translational repressor of APETALA2 in Arabidopsis flower development. Science. 2004, 303 (5666): 2022-2025. 10.1126/science.1088060.PubMedView ArticleGoogle Scholar
- Nag A, King S, Jack T: miR319a targeting of TCP4 is critical for petal growth and development in Arabidopsis. Proc Natl Acad Sci USA. 2009, 106 (52): 22534-22539. 10.1073/pnas.0908718106.PubMed CentralPubMedView ArticleGoogle Scholar
- Palatnik JF, Allen E, Wu XL, Schommer C, Schwab R, Carrington JC, Weigel D: Control of leaf morphogenesis by microRNAs. Nature. 2003, 425 (6955): 257-263. 10.1038/nature01958.PubMedView ArticleGoogle Scholar
- Martin A, Adam H, Diaz-Mendoza M, Zurczak M, Gonzalez-Schain ND, Suarez-Lopez P: Graft-transmissible induction of potato tuberization by the microRNA miR172. Development. 2009, 136 (17): 2873-2881. 10.1242/dev.031658.PubMedView ArticleGoogle Scholar
- Fujii H, Chiou TJ, Lin SI, Aung K, Zhu JK: A miRNA involved in phosphate-starvation response in Arabidopsis. Curr Biol. 2005, 15 (22): 2038-2043. 10.1016/j.cub.2005.10.016.PubMedView ArticleGoogle Scholar
- Sunkar R, Kapoor A, Zhu JK: Posttranscriptional induction of two Cu/Zn superoxide dismutase genes in Arabidopsis is mediated by downregulation of miR398 and important for oxidative stress tolerance. Plant Cell. 2006, 18 (8): 2051-2065. 10.1105/tpc.106.041673.PubMed CentralPubMedView ArticleGoogle Scholar
- Sunkar R, Zhu JK: Novel and stress-regulated microRNAs and other small RNAs from Arabidopsis. Plant Cell. 2004, 16 (8): 2001-2019. 10.1105/tpc.104.022830.PubMed CentralPubMedView ArticleGoogle Scholar
- Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL, et al: High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One. 2007, 2 (2): e219-10.1371/journal.pone.0000219.PubMed CentralPubMedView ArticleGoogle Scholar
- Jagadeeswaran G, Saini A, Sunkar R: Biotic and abiotic stress down-regulate miR398 expression in Arabidopsis. Planta. 2009, 229 (4): 1009-1014. 10.1007/s00425-009-0889-3.PubMedView ArticleGoogle Scholar
- Kasschau KD, Xie ZX, Allen E, Llave C, Chapman EJ, Krizan KA, Carrington JC: P1/HC-Pro, a viral suppressor of RNA silencing, interferes with Arabidopsis development and miRNA function. Dev cell. 2003, 4 (2): 205-217. 10.1016/S1534-5807(03)00025-X.PubMedView ArticleGoogle Scholar
- Liu HH, Tian X, Li YJ, Wu CA, Zheng CC: Microarray-based analysis of stress-regulated microRNAs in Arabidopsis thaliana. Rna. 2008, 14 (5): 836-843. 10.1261/rna.895308.PubMed CentralPubMedView ArticleGoogle Scholar
- Chen HM, Li YH, Wu SH: Bioinformatic prediction and experimental validation of a microRNA-directed tandem trans-acting siRNA cascade in Arabidopsis. Proc Natl Acad Sci USA. 2007, 104 (9): 3318-3323. 10.1073/pnas.0611119104.PubMed CentralPubMedView ArticleGoogle Scholar
- Lurin C, Andres C, Aubourg S, Bellaoui M, Bitton F, Bruyere C, Caboche M, Debast C, Gualberto J, Hoffmann B, et al: Genome-wide analysis of Arabidopsis pentatricopeptide repeat proteins reveals their essential role in organelle biogenesis. Plant Cell. 2004, 16 (8): 2089-2103. 10.1105/tpc.104.022236.PubMed CentralPubMedView ArticleGoogle Scholar
- Small ID, Peeters N: The PPR motif - a TPR-related motif prevalent in plant organellar proteins. Trends Biochem Sci. 2000, 25 (2): 46-47.PubMedView ArticleGoogle Scholar
- Takenaka M, Zehrmann A, Verbitskiy D, Kugelmann M, Hartel B, Brennicke A: Multiple organellar RNA editing factor (MORF) family proteins are required for RNA editing in mitochondria and plastids of plants. Proc Natl Acad Sci USA. 2012, 109 (13): 5104-5109. 10.1073/pnas.1202452109.PubMed CentralPubMedView ArticleGoogle Scholar
- Solfanelli C, Poggi A, Loreti E, Alpi A, Perata P: Sucrose-specific induction of the anthocyanin biosynthetic pathway in Arabidopsis. Plant physiology. 2006, 140 (2): 637-646. 10.1104/pp.105.072579.PubMed CentralPubMedView ArticleGoogle Scholar
- Hafner M, Landgraf P, Ludwig J, Rice A, Ojo T, Lin C, Holoch D, Lim C, Tuschl T: Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing. Methods. 2008, 44 (1): 3-12. 10.1016/j.ymeth.2007.09.009.PubMed CentralPubMedView ArticleGoogle Scholar
- Li R, Li Y, Kristiansen K, Wang J: SOAP: short oligonucleotide alignment program. Bioinformatics. 2008, 24 (5): 713-714. 10.1093/bioinformatics/btn025.PubMedView ArticleGoogle Scholar
- Ruby JG, Jan C, Player C, Axtell MJ, Lee W, Nusbaum C, Ge H, Bartel DP: Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans. Cell. 2006, 127 (6): 1193-1207. 10.1016/j.cell.2006.10.040.PubMedView ArticleGoogle Scholar
- Barrera-Figueroa BE, Gao L, Diop NN, Wu Z, Ehlers JD, Roberts PA, Close TJ, Zhu JK, Liu R: Identification and comparative analysis of drought-associated microRNAs in two cowpea genotypes. BMC Plant Biol. 2011, 11: 127-10.1186/1471-2229-11-127.PubMed CentralPubMedView ArticleGoogle Scholar
- Liu CM, Wong T, Wu E, Luo R, Yiu SM, Li Y, Wang B, Yu C, Chu X, Zhao K, et al: SOAP3: ultra-fast GPU-based parallel alignment tool for short reads. Bioinformatics. 2012, 28 (6): 878-879. 10.1093/bioinformatics/bts061.PubMedView ArticleGoogle Scholar
- Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, Pico AR, Bader GD, Ideker T: A travel guide to cytoscape plugins. Nat Methods. 2012, 9 (11): 1069-1076. 10.1038/nmeth.2212.PubMed CentralPubMedView ArticleGoogle Scholar
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