Introduction
Soil salinity is one of the major environmental factors causing crop loss worldwide.
Currently, 33% of the global arable land is affected by salinity, hampering crop production
in these fields (Flowers and Colmer, 2008). As the global population continues to
rise, crop production is facing increasing demands (Flowers and Muscolo, 2015). With
the combined pressures to sustain or even increase the world's food supply, salt tolerance
is becoming an important agronomic trait to support crop plant growth and production
in marginal and high saline soils.
Salt tolerance is a genetically complex trait that has evolved independently by different
mechanisms in numerous lineages (Bromham, 2015). Efforts to improve salt tolerance
in crops through selective breeding have proven difficult (Ruan et al., 2010) due
to a lack of genetic resources and limited salt tolerance associated with known molecular
markers (Deinlein et al., 2014).
We conducted a transcriptome analysis of a wild relative of the salt-sensitive sweet
potato (Ipomoea batatas): the beach morning glory (Ipomoea imperati). Beach morning
glory is a halophyte that thrives in beach ecosystems of high salt content. Our objective
was to better understand the genetic basis for salt tolerance in I. imperati, so that
future studies might transfer the salt tolerance genes into sweet potatoes.
Value of the data
Beach Morning is closely related to sweet potatoes, but commonly grows in high salt
conditions. This creates a potential genetic source for adding much-needed salt tolerance
into future sweet potato breeding strategies.
To date, there is no well-characterized transcriptome for either sweet potato or morning
glory and no source for gene annotation when exposed to high levels of salt. This
dataset of biological triplicates can help in the further understanding of the plant
pathways involved under varying salt levels.
These data will help identify relevant genes that are significant differentially expressed
under salt stress as well as identify genes that are detectable under normal growing
conditions in both root and leaf tissue. Gene expression can be compared between the
2 tissue types to identify how different tissues respond within the plant to salt
exposure.
Data
Experimental design, materials, and methods
Plant materials
Total RNA extraction and quality control, library preparation, and RNA-seq
Seeds from I. imperati were collected from St. George Island, Florida as seed and
grown in the lab. At 2 weeks of growth 600 mM NaCl solution (treatment) or water (control)
was applied to the soil, daily, for 7 days. Three biological replicates for each treatment
and species were harvested at 0, 3, 24 h, and 7 days. Total RNA was extracted from
the roots and leaves using the Qiagen protocol and treated with DNAse (Qiagen). Quantity
and integrity of the extracted total RNA were determined using an Agilent 2100 bioanalyzer
(Agilent), respectively, to be RIN >9. A total of 12 RNA-Seq libraries, including
three biological replicates, were prepared using Illumina TruSeqRNA sample Preparation
Kit (Illumina). Twelve normalized cDNA libraries were constructed and sequenced using
the Illumina Hiseq2500 platform (North Carolina State University) to generate 100
bp paired-end raw reads.
Raw reads were deposited into the Short Read Archive (SRA database, http://www.ncbi.nlm.nih.gov/sra)
with the following accession information:
Bioproject ID = PRJNA322032
Biosample accession
Roots = SAMN05007696, SAMN05271550, SAMN05271551
Leafs = SAMN05271552, SAMN05271553, SAMN05271554
SRA Root tissue experiment = SRX1771615, SRX1858743, SRX1858745
SRA Leaf tissue experiment = SRX1858747, SRX1858786, SRX1858810
Root and leaf tissue experiments contain sequence reads of triplicate runs for both
salt treated and control.
Transcriptome De novo assembly
Sequence reads were filtered using the Fastx-toolkit (Gordon and Hannon, 2010) for
quality and adapter removal using the fastq_quality_trimmer tool with the following
parameters: -Q33 -v -t 20. Paired ends were corrected and repaired using Perl script,
PE_FIX_POSTQC.pl (all scripts described herein are available at https://github.com/bioinformagical/SweetPotatoRNA-Seq).
Paired reads were validated using validateHiseqPairs.pl.
Reads were combined across all conditions and de novo assembled via Trinity (Grabherr
et al., 2011; version r2013-02-25) using default settings in order to build a suitable
set of reference contigs (column 4 of Table 1). These contigs are used for the purposes
of determining differential gene expression and pathway level analysis (paper in preparation).
Assembly is publicly available on Figshare at: https://figshare.com/articles/Morning_Glory_Transcriptome_assembly/3498239.
Table 1
Summary of assembly, from sequencing reads produced to final unigenes assembled.
Pre assembly
Number of reads
Post assembly
Number of sequences
Raw reads
Leaf + Root
252,166,154
Trinity
94,728
Filtered reads
Leaf + Root
201,357,272
CD-Hit “cluster sequences”
67,911
Salt treated
Leaf x3
29,577,252
Transdecoder
50,668
condition
Root x3
23,409,894
proteins
Control
Leaf x3
17,581,336
Matches to
39,902
condition
Root x3
27,194,620
NCBI NR
There is roughly 10X coverage for the transcriptome assembly.
Trinity contigs of high similarity were clustered into groups with CD-HIT-EST (version
v4.6.1-2012-08-27) and a single representative from each cluster was used as a reference
sequence for read alignment. Clustered contigs are publicly available on Figshare
at: https://figshare.com/articles/CDHIT_Cluster_of_assemblies/3498263.
Predicted proteins and initial annotation
Sequences from each CD-HIT cluster were transdecoded into predicted proteins using
Transdecoder (http://transdecoder.github.io), a software tool that identifies the
most likely protein sequence by finding the longest open reading frame and comparing
the translated protein sequence to known proteins in the PFAM domain (Haas et al.,
2013). Only proteins greater than 100 amino acids in length were retained for further
annotating. After protein translation, 50,688 predicted proteins were found (column
4 of Table 1). Sequences are available on Figshare at: https://figshare.com/articles/Morning_Glory_Predicted_proteins/3498308.
Sequences were initially annotated by blasting nucleotide sequences against the NCBI
NR database (BLASTX, -evalue 1e-10 -soft_masking true -max_target_seqs 1) where 39,902
sequences had a match. The most commonly occurring matches were to genes from Solanum
lycopersium. We mapped the CD-HIT clusters onto the records from the GO database and
retrieved 21,418 GO annotations. BLAST2GO assigned 18,034 with terms of “Biological
process,” 13,322 with terms of “cellular component,” and 17,134 with terms of “molecular
functions.”
Author contributions
BS: conceived the idea and acquired funding; YL, BS, SY, TM: collected seeds and conducted
the experiment; RR: performed analysis on the data; RR, YL, BS, SY, TM: wrote the
manuscript.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.