View source for IC4R008-RNA-Seq-2016-26752408

==Project Title==
'''Transcriptome Analysis of Salt Stress Responsiveness in the Seedlings of Dongxiang Wild Rice (Oryza rufipogon Griff.)'''
==The Background of This Project==
* Salt stress is a vital problem for plant growth and agricultural productivity. Rice (Oryza sativa L.) is one of the most important food crops in the world and also a model for genomic research in monocots [1]. However, salinity is one of the most devastating abiotic stresses in rice, and the salt-affected soils currently account for about 20% of the total paddy rice planting area. More seriously, the area of salt-affected irrigated land is expanding and spreading in China.[[File:sg7-RNA-Seq-2016-26752408-1.png|thumb|center|800px|]]
* Dongxiang wild rice (Oryza rufipogon Griff.) is the progenitor of cultivated rice (Oryza sativa L.), and is well known for its superior level of tolerance against cold, drought and diseases. To date, however, little is known about the salt-tolerant character of Dongxiang wild rice. '''In this study, researchers used the Illumina sequencing to perform deep transcriptome sequencing to compare the genome-wide differential expression between salt-treatment and normal condition DXWR at the seedling stage to elucidate the molecular genetic mechanisms of salt-stress tolerance in Dongxiang wild rice.'''

==Plant Culture & Treatment==
* Seeds of Dongxiang wild rice (Oryza rufipogon Griff.; Dongxiang County, Jiangxi Province) and rice Xieqingzao B (O. sativa L. ssp. indica) were immersed in distilled water in the dark, and the uniformly germinated seeds were sown in 96-well plates supported by a plastic container. For RNA-Seq analysis, 14-day-old seedlings of Dongxiang wild rice were grown with or without 200 mM NaCl treatment for 3 days and then the leaves (penu- ltimate leaves) and total roots (separated from the culture solution and washed carefully) of these seedlings were collected and immediately frozen in liquid nitrogen, respectively.
* Total RNA was extracted for the three biological replicates from the sampled leaf or root tissues collected from the ten seedlings for each biological replicate using the TRIzol kit following the manufacturer’s instructions (Invitrogen). Total RNA was then purified and concentrated using the RNeasy MinElute cleanup kit (Qiagen).
[[File:sg7-RNA-Seq-2016-26752408-3.png|thumb|center|800px|]]

==Illumina RNA-Sequencing==
* cDNA is synthesized using the mRNA fragments as templates. Short fragments were purified and resolved with EB buffer for end repara- tion and poly (A) addition. After that, the short fragments were connected with adapters. After agarose gel electrophoresis, the suitable fragments (200 bp) were selected for the PCR amplification as templates. The library was sequenced using the Illumina HiSeq − 2000 platform.
[[File:sg7-RNA-Seq-2016-26752408-4.png|thumb|center|800px|]]
* Before assembly, adaptor sequences, empty reads, low quality sequences with ‘N’ percentage over 10% and those containing more than 50% bases with a Q-value < 20 were removed using the Perl program written according to the custom method of Program editing. The retained high-quality reads were mapped to the Nipponbare reference genome [51] by Tophat and then the resulting aligned reads were used to create a RABT (Reference Annotation Based Transcript) assembly using Cufflinks. Expression levels for each gene were calculated by quantifying the reads according to the RPKM (reads per kilobase per million reads) method.
==Research Findings==
[[File:sg7-RNA-Seq-2016-26752408-2.png|thumb|right|397px|]]
* A total of 46.8 million, 48.2 million, 43.6 million and 46.7 million high-quality 100-bp paired-end reads were obtained from the leaf and root transcriptome libraries of DXWR seedlings under the normal condition (control) and salt treatment.
* Putative DEGs from the treatment vs. control (LS vs. LCK and RS vs. RCK) were identified and a total of 2,216 transcripts showed up-regulation (S1 Table) and 4,651 transcripts showed down-regulation (S2 Table) in LS vs. LCK, whereas 3,105 transcripts showed up-regulation (S3 Table) and 1,883 transcripts showed down-regulation (S4 Table) in RS vs. RCK (Fig 2). Among the DEGs, 892 transcripts were up-regulated and 743 transcripts were down-regulated in both the LS vs. LCK and RS vs. RCK.
* Web Gene Ontology Annotation Plot (WEGO) software [100] was used to perform the GO classifications and to draw the GO tree to classify the up- and down-regulated transcripts into putative functional groups for the LS vs. LCK and RS vs. RCK. A total of 15,636 and 17,937 transcripts were assigned GO terms for the DEGs in the LS vs. LCK and RS vs. RCK, respectively. Among the 15,636 transcripts from the LS vs. LCK (Fig 3A), there were 5,633 transcripts at the cellular level, 5,068 transcripts at the molecular level and 4,935 transcripts at the biological level. Among the 17,937 transcripts from the RS vs. RCK (Fig 3B), there were 6,302 transcripts at the cellular level, 5,907 transcripts at the molecular level and 5,728 transcripts at the biological level.
* To identify metabolic pathways in which DEGs were involved and enriched, pathway-based analysis was performed using the KEGG pathway database [107]. As a result, 4,131 of 6,876 in the LS vs. LCK and 3,069of 4,988 in the RS vs. RCK were classified into 20 functional categories. Then they were classified into 125 and 126 subcategories, respectively. We further identified over-represented KEGG Orthology (KO) terms (Q-value < 0.05), and classified them into 10 categories, respectively (Fig 4). As shown in Fig 4, these transcripts belonged mainly to the following KEGG pathways both in the LS vs. LCK and RS vs. RCK: Biosynthesis of other secondary metabolites, carbohydrate metabolism, global map, metabolism of terpenoids and polyketides, translation, and transport and catabolism.
[[File:sg7-RNA-Seq-2016-26752408-5.png|left|thumb|500px|'''KEGG pathway assignments in the LS vs. LCK (A)''']]
[[File:sg7-RNA-Seq-2016-26752408-6.png|center|thumb|500px|'''KEGG pathway assignments in the RS vs. RCK (B)''']]<br>

== Labs working on this Project ==
* College of Life Sciences, Jiangxi Normal University, Nanchang, China.
* Institute for Advanced Study, Jiangxi Normal University, Nanchang, China.