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.

Leaf samples at top layer of canopy from two S. curtisii 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 de novo 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 A. thaliana (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 A. thaliana flowering genes database (Bouché et al., 2016) to identify their homologs in S. curtisii. 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 < 0.05 were identified. Significantly enriched GO terms (P < 0.05) and KEGG pathways (FDR < 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.

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 Shorea curtisii 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 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 shows the raw read counts and annotations for each unigene in S. curtisii leaf transcriptome. The first column shows titles of data.

Table S4 shows a summary of flowering-related homologs in S. curtisii 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 S. curtisii leaf transcriptome. The first column shows titles of data.

Table_S6.xlsx is an Excel workbook which contains a summary of KEGG annotation in S. curtisii leaf transcriptome (Table S6A) and lists of enriched KEGG pathways in differentially expressed unigenes of S. curtisii for every pairwise time points comparison (Table S6B–S6D).

Table_S7 is an Excel workbook which contains a summary of GO annotation in S. curtisii leaf transcriptome (Table S7A) and lists of enriched GO terms in differentially expressed unigenes of S. curtisii for every pairwise time points comparison (Table S7B–S7D).