DOI: 10.5061/dryad.69p8cz94f
title: Characterization of leaf transcriptome in a tropical tree species, Shorea curtisii, over a flowering season

description: General flowering (GF) is a synchronous flowering event in the Southeast
 Asian tropical rainforests that occurs at irregular intervals of multiple
 years. The unpredictable intervals of GF raise conservation concerns for
 these under-researched forests with rich economically and ecologically
 important species. In this study, the leaf transcriptome of a GF species,
 Shorea curtisii obtained from three time points – before and after floral
 initiation, and post flowering stage – was sequenced. We assembled
 243,759,478 sequencing reads into 39,943 non-redundant unigenes including
 677 putative homologs of Arabidopsis thaliana flowering-related genes.
 Differential expression analysis conducted on pairwise comparisons of the
 time points identified 930 differentially expressed unigenes, which
 includes 17 flowering-related homologs. The differential expression of
 unigenes with significant enrichments of functions related to drought
 corroborated the involvement of drought as an environmental cue for GF.
 The outcomes of this study offer an insight into the conservation of
 floral regulatory genes and pathways in Shorea and could be used as a
 model to better understand the floral initiation cues and regulation of GF
 trees.
description: Leaf samples at top layer of
 canopy from two <em>S. curtisii </em>individuals, C1 and C2
 were collected from Semangkok Forest Reserve (2°58’N, 102°18’E). We
 selected three time points (TPs) corresponding to before and after floral
 initiation, and abortion/fruiting stage. Samples were collected at each TP
 around midday, soaked in RNAlater reagent (Ambion, USA), and stored at
 –80°C. Total RNA was
 extracted from leaf samples using the method described by Kobayashi et al.
 (2013). RNA-seq was performed using Illumina HiSeq 4000 Sequencer
 (Illumina, USA). After removal of low quality bases by Trimmomatic v0.36
 (Bolger et al., 2014), the sequence reads from each sample were combined
 and <em>de</em> <em>novo</em> assembled using
 Trinity v2.8.5 (Grabherr et al., 2011). Only non-redundant transcripts
 with complete open reading frame were retained by using TransDecoder
 v5.5.0 (http://transdecoder.github.io) and CD-HIT v4.8.1 (Fu et al., 2012). The quality of the assembly was assessed with BUSCO v5.2.1 (Simao et al., 2015). The unigenes were queried against the proteome of <em>A. thaliana</em> (Cheng et al., 2017) using BLASTx v2.10 (Camacho et al., 2009) with E-value cut-off: 1E–10. The annotated unigenes were searched against <em>A. thaliana</em> flowering genes database (Bouché et al., 2016) to identify their homologs in <em>S. curtisii</em>. Translated unigenes were queried against other public protein databases using BLASTp v2.10 (Camacho et al., 2009) and InterProScan v5.36 (Jones et al., 2014). We also searched the unigenes against Gene Ontology (GO; Ashburner et al., 2000) and KEGG pathways (Kanehisa & Goto, 2000) to characterize the unigenes. GO classification is divided into cellular component (CC), molecular function (MF), and biological process (BP) categories. The unigenes were quantified by Salmon v1.5.1 (Patro et al., 2016) and subjected to differential expression analysis using DESeq2 v1.32.0 (Love et al., 2014). The samples were compared in a pairwise manner and differentially expressed unigenes (DEUs) with absolute log2 fold change ≥ 1 and FDR &lt; 0.05 were identified. Significantly enriched GO terms (P &lt; 0.05) and KEGG pathways (FDR &lt; 0.05) in the DEUs were also identified using topGO package v2.40.0 (Alexa & Rahnenfuhrer, 2020) and KOBAS v3.0 (Bu et al., 2021), respectively.
description: Fig_S1_3_Table_S1_2.pdf: A pdf file which contains Supplementary
 Figure S1–S3 and Table S1–S2.  Fig S1 is a flow chart of research
 strategy employed to study the leaf transcriptome of <em>Shorea
 curtisii</em> during a flowering season. Details of the methodology
 are described in the legend in details. Fig S2 is a histogram which shows
 the distribution of the contig lengths of <em>S. curtisii</em>
 transcriptome. Fig
 S3 is a bar chart which depicts the classification of <em>S.
 curtisii </em>unigenes into three main Gene Ontology
 categories. Table
 S1 is a table which contains details of meteorological status,
 specifically daily minimum, maximum, and mean temperature, as well as
 rainfall records during sampling days. The records were obtained from the
 nearest meteorological and hydrological stations to the sampling site. The
 first line shows titles of data which include information on when the
 sampling was done i.e. time point, date, developmental stage, and sampling
 time for individual trees, as well as meteorological status i.e. daily
 temperature and rainfall records. Table S2 is a table with the
 summary of BUSCO analysis which reflects the completeness of <em>S.
 curtisii </em>transcriptome assembly. The first column shows the
 description for each category of BUSCO genes and the following columns
 show the number of genes recovered and percentage for each
 category. Table_S3_4_5.xlsx is an Excel workbook which
 contains the following sheets: Table S3 shows the raw read counts
 and annotations for each unigene in <em>S. curtisii</em> leaf
 transcriptome. The first column shows titles of data. Table S4 shows a summary of
 flowering-related homologs in <em>S. curtisii</em> leaf
 transcriptome. The table contains information on flowering-related
 homologs in the transcriptome including number of homologs that were
 differentially expressed in the study period. Table S5 shows normalized read
 counts, annotations, and pairwise comparisons of differentially expressed
 unigenes in <em>S. curtisii</em> leaf transcriptome. The first
 column shows titles of data. Table_S6.xlsx is an Excel
 workbook which contains a summary of KEGG annotation in <em>S.
 curtisii</em> leaf transcriptome (Table S6A) and lists of enriched
 KEGG pathways in differentially expressed unigenes of <em>S.
 curtisii</em> for every pairwise time points comparison (Table
 S6B–S6D). Table_S7 is an Excel workbook which contains
 a summary of GO annotation in <em>S. curtisii</em> leaf
 transcriptome (Table S7A) and lists of enriched GO terms in differentially
 expressed unigenes of <em>S. curtisii</em> for every pairwise
 time points comparison (Table S7B–S7D).
description: # Characterization of leaf transcriptome in a tropical tree species,
 Shorea curtisii, over a flowering season
 [https://doi.org/10.5061/dryad.69p8cz94f](https://doi.org/10.5061/dryad.69p8cz94f) ### Description of the data and file structure Fig\_S1\_3\_Table\_S1\_2.pdf: A pdf file which contains Supplementary Figure S1-3 and Table S1-2. * Fig S1 is a flow chart of research strategy employed to study the leaf transcriptome of Shorea curtisii during a flowering season. Details of the methodology is described in the legend in details. * Fig S2 is a histogram which shows the distribution of the contig lengths of S. curtisii transcriptome. * Fig S3 is a bar chart which depicts the classification of S. curtisii unigenes into three main Gene Ontology categories. Table S1 is a table which contains details of meteorological status, specifically daily minimum, maximum, and mean temperature, as well as rainfall records during sampling days. The records were obtained from the nearest meteorological and hydrological stations to the sampling site. The first line shows titles of data which include information on when the sampling was done i.e. time point, date, developmental stage, and sampling time for individual trees, as well as meteorological status i.e. daily temperature and rainfall records. Table S2 is a table with the summary of BUSCO analysis which reflects the completeness of S. curtisii transcriptome assembly. The first column shows the description for each category of BUSCO genes and the following columns show the number of genes recovered and percentage for each category. Table\_S3\_4\_5.xlsx is an Excel workbook which contains the following sheets: * Table S3. Raw read counts and annotations of unigenes in Shorea curtisii leaf transcriptome. C1: Individual 1; C2: Individual 2; TAIR\_ID: TAIR (Arabidopsis) protein database; UniProt\_ID: UniProtKB/Swiss-Prot database; Pfam\_ID: Pfam database; PANTHER\_ID: PANTHER database. * Table S4. Summary of flowering-related homologs in Shorea curtisii leaf transcriptome. TAIR\_ID: Annotation in TAIR (Arabidopsis) protein database. * Table S5. Normalized read counts, annotations and pairwise time points comparisons of differentially expressed unigenes in Shorea curtisii leaf transcriptome. C1: Individual 1; C2: Individual 2; TAIR\_ID: TAIR (Arabidopsis) protein database; UniProt\_ID: UniProtKB/Swiss-Prot database; Pfam\_ID: Pfam database; PANTHER\_ID: PANTHER database; PC: Pairwise time points comparison. Table\_S6 is an Excel workbook which contains S7A&7D: * Table S6A. Summary of GO annotations of Shorea curtisii leaf transcriptome. * Table S6B. Enriched GO terms in differentially expressed unigenes of Shorea curtisii leaf transcriptome between TP-A and TP-B. &gt;Table S6C. Enriched GO terms in differentially expressed unigenes of Shorea curtisii leaf transcriptome between TP-B and TP-C. &gt;Table S6D. Enriched GO terms in differentially expressed unigenes of Shorea curtisii leaf transcriptome between TP-A and TP-C. CC: Cellular Component; MF: Molecular Function; BP: Biological Process. Table\_S7.xlsx is an Excel workbook which contains S6A亡6D: * Table S7A. Summary of KEGG annotations of Shorea curtisii leaf transcriptome. The table shows each KEGG pathway and ID and the number of members identified in the transcriptome. * Table S7B. Enriched KEGG pathways in differentially expressed unigenes of Shorea curtisii leaf transcriptome between TP-A and TP-B. * Table S7C. Enriched KEGG pathways in differentially expressed unigenes of Shorea curtisii leaf transcriptome between TP-B and TP-C. * Table S7D. Enriched KEGG pathways in differentially expressed unigenes of Shorea curtisii leaf transcriptome between TP-A and TP-C. Reference(s): Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology. 2010;11(3). doi: ARTN R2510.1186/gb-2010-11-3-r25. PubMed PMID: WOS:000277309100013. <br>