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Cunninghamia lanceolata

ABSTRACT: Identification and characterization of small non-coding RNAs from Chinese fir by high throughput sequencing

PROVIDER: PRJNA132921 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR066638.fastq.gz	Fastqsanger.gz

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Project description:Small non-coding RNAs (sRNAs) play key roles in plant development, growth and responses to biotic and abiotic stresses. At least four classes of sRNAs have been well characterized in plants, including repeat-associated siRNAs (rasiRNAs), microRNAs (miRNAs), trans-acting siRNAs (tasiRNAs) and natural antisense transcript-derived siRNAs. Chinese fir (Cunninghamia lanceolata) is one of the most important coniferous evergreen tree species in China. No sRNA from Chinese fir has been described to date. To obtain sRNAs in Chinese fir, we sequenced a sRNA library generated from seeds, seedlings, leaves, stems and calli, using Illumina high throughput sequencing technology. A comprehensive set of sRNAs were acquired, including conserved and novel miRNAs, rasiRNAs and tasiRNAs. With BLASTN and MIREAP we identified a total of 115 conserved miRNAs comprising 40 miRNA families and one novel miRNA with precursor sequence. The expressions of 16 conserved and one novel miRNAs and one tasiRNA were detected by RT-PCR. Utilizing real time RT-PCR, we revealed that four conserved and one novel miRNAs displayed developmental stage-specific expression patterns in Chinese fir. In addition, 209 unigenes were predicted to be targets of 30 Chinese fir miRNA families, of which five target genes were experimentally verified by 5' RACE, including a squamosa promoter-binding protein gene, a pentatricopeptide (PPR) repeat-containing protein gene, a BolA-like family protein gene, AGO1 and a gene of unknown function. We also demonstrated that the DCL3-dependent rasiRNA biogenesis pathway, which had been considered absent in conifers, existed in Chinese fir. Furthermore, the miR390-TAS3-ARF regulatory pathway was elucidated. We unveiled a complex population of sRNAs in Chinese fir through high throughput sequencing. This provides an insight into the composition and function of sRNAs in Chinese fir and sheds new light on land plant sRNA evolution. The aim of this study is to investigate the small RNA transcriptome in Cunninghamia lanceolata. Total RNAs of seeds and calli were extracted using RNAiso-mate for plant tissue and RNAiso plus (Takara, Dalian, Liaoning, China), whereas total RNAs of seedlings, adult leaves and stems were isolated with the Concert Plant RNA Reagent (Invitrogen, Carlsbad, CA, USA), and were then treated with RNase-free DNase I (Promega, Madison, WI, USA). Equal amount of total RNAs from the 5 different samples were mixed to form a single RNA pool. Twenty micrograms of total RNAs from the pool were used and 16 to 30-nt sRNAs were purified using Novex 15% TBE-¬Urea gel (Invitrogen). Two adaptors were sequentially ligated to the 5' and 3' ends of purified sRNAs. The ligation products were further purified from Novex 10% TBE-Urea gel. Reverse transcriptase SuperScript II (Invitrogen) and high-fidelity DNA polymerase Phusion (New England Biolabs, Ipswich, MA, USA) were used in the following RT-PCR reaction. The amplification products were cut from Novex 6% TBE-Urea gel. The purified DNA fragments were used for sequencing on an Illumina 1G Genome Analyzer at the Beijing Genomics Institute, Shenzhen, China.

Project description:Chinese fir (Cunninghamia lanceolata) is one of southern China's most important native tree species, which has experienced noticeable climate-induced changes. Published papers (1978-2020) on tree growth of Chinese fir forests in China were collected and critically reviewed. After that, a comprehensive growth data set was developed from 482 sites, which are distributed between 102.19° and 130.07°E in longitude, between 21.87° and 37.24°N in latitude and between 5 and 2260 m in altitude. The dataset consists of 2265 entries, including mean DBH (cm), mean H (m), volume (m3), biomass (dry weight) (kg) (stem (over bark) biomass, branches biomass, leaves biomass, bark biomass, aboveground biomass, roots biomass, total trees biomass) and related information, i.e. geographical location (Country, province, study site, longitude, latitude, altitude, slope, and aspect), climate (mean annual precipitation-MAP and mean annual temperature-MAT), stand description (origin, age, canopy density and stand density), and sample regime (plot size, number and investigation year). Our results showed that (1) the best prediction of height was obtained using nonlinear composite model Height = [Formula: see text], (R2 = 0.8715, p < 0.05), (2) the equation Volume = DBH2/(387.8 + 19,190/Height) (R2 = 0.9833, p < 0.05) was observed to be the most suitable model for volume estimation. Meanwhile, when the measurements of the variables are difficult to carry out, the volume model Volume = 0.03957 - 0.01215*DBH + 0.00118*DBH2 (R2 = 0.9573, p < 0.05) is determined from DBH only has a practical advantage, (3) the regression equations of component biomass against DBH explained more significant than 86% variability in almost all biomass data of woody tissues, which were ranked as total trees (97.25%) > aboveground (96.55%) > stems (with bark) (96.17%) > barks (88.95%) > roots (86.71%), and explained greater than 64% variability in branch biomass. The foliage biomass equation was the poorest among biomass components (R2 = 0.6122). The estimation equations derived in this study are particularly suitable for the Chinese fir forests in China. This dataset can provide a theoretical basis for predicting and assessing the potential of carbon sequestration and afforestation activities of Chinese fir forests on a national scale.

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Cunninghamia lanceolata

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