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Unlocking basal and acquired thermotolerance potential in tropical sorghum

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  • Basal and acquired thermotolerance of 50 elite tropical sorghum genotypes was assessed in seedlings. Two sets of each assay were conducted following a split plot in a Completely Randomised Design replicated three times in two heat stress treatments in separate incubators. Coleoptile length was measured before and after heat treatments, and the differences were subjected to analysis of variance for heat treatments, genotypes and their interactions. Highly significant differences (p < 0.001) were observed between heat treatments, genotypes, and their interactions for both basal and acquired thermotolerance assays, signifying adverse effects of heat stress and the existence of genetic diversity in the thermotolerance of the assessed genotypes. Popular varieties 'Macia' and 'SV4' did not feature among the top performers for both forms of tolerance, indicating the risk subsistence farmers relying on them are to heat stress. Two genotypes were consistently amongst the top ten performers in terms of basal thermotolerance in the two sets, these are genotypes NPGRC1704, and IS24426. Genotypes NPGRC3093, and IS24272 consistently demonstrated superiority in acquired thermotolerance. Genotypes NPGRC1704, IS9567, NPGRC1197, NPGRC1868, and NPGRC1782 exhibited potential in both basal and acquired thermotolerance. The identified genotypes may be used as potential donors in crop improvement programs that seek to improve thermotolerance in sorghum.
  • Columnar cacti are plants of the Cactaceae family distributed across arid and semi-arid regions of America, with ecological, economic, and cultural value[1]. One trait that makes it possible for the columnar cactus to survive in the desert ecosystem is its thick epidermis covered by a hydrophobic cuticle, which limits water loss in dry conditions[1]. The cuticle is the external layer that covers the non-woody aerial organs of land plants. The careful control of cuticle biosynthesis could produce drought stress tolerance in relevant crop plants[2]. In fleshy fruits, the cuticle maintains adequate water content during fruit development on the plant and reduces water loss in fruit during postharvest[3]. Efforts to elucidate the molecular pathway of cuticle biosynthesis have been carried out for fleshy fruits such as tomato (Solanum lycopersicum)[4], apple (Malus domestica)[5], sweet cherry (Prunus avium)[6], mango (Mangifera indica)[7], and pear (Pyrus 'Yuluxiang')[8].

    The plant cuticle is formed by the two main layers cutin and cuticular waxes[3]. Cutin is composed mainly of oxygenated long-chain (LC) fatty acids (FA), which are synthesized by cytochrome p450 (CYP) enzymes. CYP family 86 subfamily A (CYP86A) enzymes carry out the terminal (ω) oxidation of LC-FA[9]. Then, CYP77A carries out the mid-chain oxidation to synthesize the main cutin monomers. In Arabidopsis, AtCYP77A4 and AtCYP77A6 carry out the synthesis of mid-chain epoxy and mid-chain dihydroxy LC-FA, respectively[10,11]. AtCYP77A6 is required for the cutin biosynthesis and the correct formation of floral surfaces[10]. The expression of CYP77A19 (KF410855) and CYP77A20 (KF410856) from potato (Solanum tuberosum) restored the petal cuticular impermeability in Arabidopsis null mutant cyp77a6-1, tentatively by the synthesis of cutin monomers[12]. In eggplant (Solanum torvum), the over-expression of StoCYP77A2 leads to resistance to Verticillium dahlia infection in tobacco plants[13]. Although the function of CYP77A2 in cutin biosynthesis has not yet been tested, gene expression analysis suggests that CaCYP77A2 (A0A1U8GYB0) could play a role in cutin biosynthesis during pepper fruit development[14].

    It has been hypothesized that the export of cuticle precursors is carried out by ATP binding cassette subfamily G (ABCG) transporters. ABCG11/WBC11, ABCG12, and ABCG13 are required for the load of cuticle lipids in Arabidopsis[1517], but ABCG13 function appears to be specific to the flower epidermis[18]. The overexpression of TsABCG11 (JQ389853) from Thellungiella salsugineum increases cuticle amounts and promotes tolerance to different abiotic stresses in Arabidopsis[19].

    Once exported, the cutin monomers are polymerized on the surface of epidermal cells. CD1 code for a Gly-Asp-Ser-Leu motif lipase/esterase (GDSL) from tomato required for the cutin formation through 2-mono(10,16-dihydroxyhexadecanoyl)glycerol esterification[20]. GDSL1 from tomato carries out the ester bond cross-links of cutin monomers located at the cuticle layers and is required for cuticle deposition in tomato fruits[21]. It has been shown that the transcription factor MIXTA-like reduces water loss in tomato fruits through the positive regulation of the expression of CYP77A2, ABCG11, and GDSL1[22]. Despite the relevant role of cuticles in maintaining cactus homeostasis in desert environments[1], the molecular mechanism of cuticle biosynthesis has yet to be described for cactus fruits.

    Stenocereus thurberi is a columnar cactus endemic from the Sonoran desert (Mexico), which produces an ovoid-globose fleshy fruit named sweet pitaya[23]. In its mature state, the pulp of sweet pitaya contains around 86% water with a high content of antioxidants and natural pigments such as betalains and phenolic compounds, which have nutraceutical and industrial relevance[23]. Due to the arid environment in which pitaya fruit grows, studying its molecular mechanism of cuticle biosynthesis can generate new insights into understanding species' adaptation mechanisms to arid environments. Nevertheless, sequences of transcripts from S. thurberi in public databases are scarce.

    RNA-sequencing technology (RNA-seq) allows the massive generation of almost all the transcripts from non-model plants, even if no complete assembled genome is available[24]. Recent advances in bioinformatic tools has improved our capacity to identify long non-coding RNA (lncRNA), which have been showed to play regulatory roles in relevant biological processes, such as the regulation of drought stress tolerance in plants[25], fruit development, and ripening[2629].

    In this study, RNA-seq data were obtained for the de novo assembly and characterization of the S. thurberi fruit peel transcriptome. As a first approach, three transcripts, StCYP77A, StABCG11, and StGDSL1, tentatively involved in cuticle biosynthesis, were identified and quantified during sweet pitaya fruit development. Due to no gene expression analysis having been carried out yet for S. thurberi, stably expressed constitutive genes were identified for the first time.

    Sweet pitaya fruits (S. thurberi) without physical damage were hand harvested from plants in a native conditions field located at Carbó, Sonora, México. They were collocated in a cooler containing dry ice and transported immediately to the laboratory. The superficial part of the peels (~1 mm deep) was removed carefully from the fruits using a scalpel. Peel samples from three fruits were pooled according to their tentative stage of development defined by their visual characteristics, frozen in liquid nitrogen, and pulverized to create a single biological replicate. Four samples belonging to four different plants were analyzed. All fruits harvested were close to the ripening stage. Samples named M1 and M2 were turning from green to ripe [~35−40 Days After Flowering (DAF)], whereas samples M3 and M4 were turning from ripe to overripe (~40−45 DAF).

    Total RNA was isolated from the peels through the Hot Borate method[30]. The concentration and purity of RNA were determined in a spectrophotometer Nanodrop 2000 (Thermo Fisher) by measuring the 260/280 and 260/230 absorbance ratios. RNA integrity was evaluated through electrophoresis in agarose gel 1% and a Bioanalyzer 2100 (Agilent). Pure RNA was sequenced in the paired-end mode in an Illumina NextSeq 500 platform at the University of Arizona Genetics Core Facility. Four RNA-seq libraries, each of them from each sample, were obtained, which include a total of 288,199,704 short reads with a length of 150 base pairs (bp). The resulting sequence data can be accessed at the Sequence Read Archive (SRA) repository of the NCBI through the BioProject ID PRJNA1030439. Libraries are named corresponding to the names of samples M1, M2, M3, and M4.

    FastQC software (www.bioinformatics.babraham.ac.uk/projects/fastqc) was used for short reads quality analysis. Short reads with poor quality were trimmed or eliminated by Trimmomatic (www.usadellab.org/cms/?page=trimmomatic) with a trailing and leading of 25, a sliding window of 4:25, and a minimum read length of 80 bp. A total of 243,194,888 reads with at least a 25 quality score on the Phred scale were used to carry out the de novo assembly by Trinity (https://github.com/trinityrnaseq/trinityrnaseq/wiki) with the following parameters: minimal k-mer coverage of 1, normalization of 50, and minimal transcript length of 200 bp.

    Removal of contaminating sequences and ribosomal RNA (rRNA) was carried out through SeqClean. To remove redundancy, transcripts with equal or more than 90% of identity were merged through CD-hit (www.bioinformatics.org/cd-hit/). Alignment and quantification in terms of transcripts per million (TPM) were carried out through Bowtie (https://bowtie-bio.sourceforge.net/index.shtml) and RSEM (https://github.com/deweylab/RSEM), respectively. Transcripts showing a low expression (TPM < 0.01) were discarded. Assembly quality was evaluated by calculating the parameters N50 value, mean transcript length, TransRate score, and completeness. The statistics of the transcriptome were determined by TrinityStats and TransRate (https://hibberdlab.com/transrate/). The transcriptome completeness was determined through a BLASTn alignment (E value < 1 × 10−3) by BUSCO (https://busco.ezlab.org/) against the database of conserved orthologous genes from Embryophyte.

    To predict the proteins tentatively coded in the S. thurberi transcriptome, the best homology match of the assembled transcripts was found by alignment to the Swiss-Prot, RefSeq, nr-NCBI, PlantTFDB, iTAK, TAIR, and ITAG databases using the BLAST algorithm with an E value threshold of 1 × 10−10 for the nr-NCBI database and of 1 × 10−5 for the others[3134]. An additional alignment was carried out to the protein databases of commercial fruits Persea americana, Prunus persica, Fragaria vesca, Citrus cinensis, and Vitis vinifera to proteins of the cactus Opuntia streptacantha, and the transcriptomes of the cactus Hylocereus polyrhizus, Pachycereus pringlei, and Selenicereus undatus. The list of all databases and the database websites of commercial fruits and cactus are provided in Supplementary Tables S1 & S2. The open reading frame (ORF) of the transcripts and the protein sequences tentative coded from the sweet pitaya transcriptome was predicted by TransDecoder (https://github.com/TransDecoder/TransDecoder/wiki), considering a minimal ORF length of 75 amino acids (aa). The search for protein domains was carried out by the InterPro database (www.ebi.ac.uk/interpro). Functional categorization was carried out by Blast2GO based on GO terms and KEGG metabolic pathways[35].

    LncRNA were identified based on the methods reported in previous studies[25,29,36]. Transcripts without homology to any protein from Swiss-Prot, RefSeq, nr-NCBI, PlantTFDB, iTAK, TAIR, ITAG, P. americana, P. persica, F. vesca, C. cinensis, V. vinifera, and O. streptacantha databases, without a predicted ORF longer than 75 aa, and without protein domains in the InterPro database were selected to identify tentative lncRNA.

    Transcripts coding for signal peptide or transmembrane helices were identified by SignalP (https://services.healthtech.dtu.dk/services/SignalP-6.0/) and TMHMM (https://services.healthtech.dtu.dk/services/TMHMM-2.0/), respectively, and discarded. Further, transcripts corresponding to other non-coding RNAs (ribosomal RNA and transfer RNA) were identified through Infernal by using the Rfam database[37] and discarded. The remaining transcripts were analyzed by CPC[38], and CPC2[39] to calculate their coding potential. Transcripts with a coding potential score lower than −1 for CPC and a coding probability lower than 0.1 for CPC2 were considered lncRNA. To characterize the identified lncRNA, the length and abundance of coding and lncRNA were calculated. Bowtie and RSEM were used to align and quantify raw counts, respectively. The edgeR package[40] was used to normalize raw count data in terms of counts per million (CPM) for both coding and lncRNA.

    To obtain the transcript's expression, the aligning of short reads and quantifying of transcripts were carried out through Bowtie and RSEM software, respectively. A differential expression analysis was carried out between the four libraries by edgeR package in R Studio. Only the transcripts with a count equal to or higher than 0.5 in at least one sample were retained for the analysis. Transcripts with log2 Fold Change (log2FC) between +1 and −1 and with a False Discovery Rate (FDR) lower than 0.05 were taken as not differentially expressed (NDE).

    For the identification of the tentative reference genes two strategies were carried out as described below: i) The NDE transcripts were aligned by BLASTn (E value < 1 × 10−5) to 43 constitutive genes previously reported in fruits from the cactus H. polyrhizus, S. monacanthus, and S. undatus[4143] to identify possible homologous constitutive genes in S. thurberi. Then, the homologous transcripts with the minimal coefficient of variation (CV) were selected; ii) For all the NDE transcripts, the percentile 95 value of the mean CPM and the percentile 5 value of the CV were used as filters to recover the most stably expressed transcripts, based on previous studies[44]. Finally, transcripts to be tested by quantitative reverse transcription polymerase chain reaction (qRT-PCR) were selected based on their homology and tentative biological function.

    The fruit harvesting was carried out as described above. Sweet pitaya fruit takes about 43 d to ripen, therefore, open flowers were tagged, and fruits with 10, 20, 30, 35, and 40 DAF were collected to cover the pitaya fruit development process (Supplementary Fig. S1). The superficial part of the peels (~1 mm deep) was removed carefully from the fruits using a scalpel. Peel samples from three fruits were pooled according to their stage of development defined by their DAF, frozen in liquid nitrogen, and pulverized to create a single biological replicate. One biological replicate consisted of peels from three fruits belonging to the same plant. Two to three biological replicates were evaluated for each developmental stage. Two technical replicates were analyzed for each biological replicate. RNA extraction, quantification, RNA purity, and RNA integrity analysis were carried out as described above.

    cDNA was synthesized from 100 ng of RNA by QuantiTect Reverse Transcription Kit (QIAGEN). Primers were designed using the PrimerQuest™, UNAFold, and OligoAnalyzer™ tools from Integrated DNA Technologies (www.idtdna.com/pages) and following the method proposed by Thornton & Basu[45]. Transcripts quantification was carried out in a QIAquant 96 5 plex according to the PowerUp™ SYBR™ Green Master Mix protocol (Applied Biosystems), with a first denaturation step for 2 min at 95 °C, followed by 40 cycles of denaturation step at 95 °C for 15 s, annealing and extension steps for 30 s at 60 °C.

    The Cycle threshold (Ct) values obtained from the qRT-PCR were analyzed through the algorithms BestKeeper, geNorm, NormFinder, and the delta Ct method[46]. RefFinder (www.ciidirsinaloa.com.mx/RefFinder-master/) was used to integrate the stability results and to find the most stable expressed transcripts in sweet pitaya fruit peel during development. The pairwise variation value (Vn/Vn + 1) was calculated through the geNorm algorithm in R Studio software[47].

    An alignment of 17 reported cuticle biosynthesis genes from model plants were carried out by BLASTx against the predicted proteins from sweet pitaya. Two additional alignments of 17 charaterized cuticle biosynthesis proteins from model plants against the transcripts and predicted proteins of sweet pitaya were carried out by tBLASTn and BLASTp, respectively. An E value threshold of 1 × 10−5 was used, and the unique best hits were recovered for all three alignments. The sequences of the 17 characterized cuticle biosynthesis genes and proteins from model plants are showed in Supplementary Table S3. The specific parameters and the unique best hits for all the alignments carried out are shown in Supplementary Tables S4S8.

    Cuticle biosynthesis-related transcripts tentatively coding for a cytochrome p450 family 77 subfamily A (CYP77A), a Gly-Asp-Ser-Leu motif lipase/esterase 1 (GDSL1), and an ATP binding cassette transporter subfamily G member 11 (ABCG11) were identified by best bi-directional hit according to the functional annotation described above. Protein-conserved domains, signal peptide, and transmembrane helix were predicted through InterProScan, SignalP 6.0, and TMHMM, respectively. Alignment of the protein sequences to tentative orthologous of other plant species was carried out by the MUSCLE algorithm[48]. A neighbor-joining (NJ) phylogenetic tree with a bootstrap of 1,000 replications was constructed by MEGA11[49].

    Fruit sampling, primer design, RNA extraction, cDNA synthesis, and transcript quantification were performed as described above. Relative expression was calculated according to the 2−ΔΔCᴛ method[50]. The sample corresponding to 10 DAF was used as the calibrator. The transcripts StEF1a, StTUA, StUBQ3, and StEF1a + StTUA were used as normalizer genes.

    Normality was assessed according to the Shapiro-Wilk test. Significant differences in the expression of the cuticle biosynthesis-related transcripts between fruit developmental stages were determined by one-way ANOVA based on a completely randomized sampling design and a Tukey honestly significant difference (HSD) test, considering a p-value < 0.05 as significant. Statistical analysis was carried out through the stats package in R Studio.

    RNA was extracted from the peels of ripe sweet pitaya fruits (S. thurberi) from plants located in the Sonoran Desert, Mexico. Four cDNA libraries were sequenced in an Illumina NextSeq 500 platform at the University of Arizona Genetics Core Facility. A total of 288,199,704 reads with 150 base pairs (bp) in length were sequenced in paired-end mode. After trimming, 243,194,888 (84.38%) cleaned short reads with at least 29 mean quality scores per read in the Phred scale and between 80 to 150 bp in length were obtained to carry out the assembly. After removing contaminating sequences, redundancy, and low-expressed transcripts, the assembly included 174,449 transcripts with an N50 value of 2,110 bp. Table 1 shows the different quality variables of the S. thurberi fruit peel transcriptome. BUSCO score showed that 85.4% are completed transcripts, although out of these, 37.2% were found to be duplicated. The resulting sequence data can be accessed at the SRA repository of the NCBI through the BioProject ID PRJNA1030439.

    Table 1.  Quality metrics of the Stenocereus thurberi fruit peel transcriptome.
    Metric Data
    Total transcripts 174,449
    N50 2,110
    Smallest transcript length (bp) 200
    Largest transcript length (bp) 19,114
    Mean transcript length (bp) 1,198.69
    GC (%) 41.33
    Total assembled bases 209,110,524
    TransRate score 0.05
    BUSCO score (%) C: 85.38 (S:48.22, D:37.16),
    F: 10.69, M: 3.93.
    Values were calculated through the TrinityStats function of Trinity and TransRate software. Completeness analysis was carried out through BUSCO by aligning the transcriptome to the Embryophyte database through BLAST with an E value threshold of 1 × 10−3. Complete (C), single (S), duplicated (D), fragmented (F), missing (M).
     | Show Table
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    A summary of the homology search in the main public protein database for the S. thurberi transcriptome is shown in Supplementary Table S1. From these databases, the higher homologous transcripts were found in RefSeq with 93,993 (53.87 %). Based on the E value distribution, for 41,685 (44%) and 68,853 (49%) of the hits, it was found a strong homology (E value lower than 1 × 10−50) to proteins in the Swiss-Prot and RefSeq databases, respectively (Supplementary Fig. S2a & b). On the other hand, 56,539 (52.34%) and 99,599 (71.11%) of the matches showed a percentage of identity higher than 60% in the Swiss-Prot and RefSeq databases, respectively (Supplementary Fig. S2c & d).

    Figure 1 shows the homology between transcripts from S. thurberi and proteins of commercial fruits, as well as proteins and transcripts of cacti. Transcripts from S. thurberi homologous to proteins from fruits of commercial interest avocado (P. americana), peach (P. persica), strawberry (F. vesca), orange (C. sinensis), and grapefruit (V. vinifera) ranged from 77,285 (44.30%) to 85,421 (48.96%), with 70,802 transcripts homologous to all the five fruit protein databases (Fig. 1a).

    Transcripts homologous to transcripts or proteins from the cactus dragon fruit (H. polyrhizus), prickly pear cactus (O. streptacantha), Mexican giant cardon (P. pringlei), and pitahaya (S. undatus) ranged from 76,238 (43.70%) to 114,933 (65.88%), with 64,009 transcripts homologous to all the four cactus databases (Fig. 1b). Further, out of the total of transcripts, 44,040 transcripts (25.25%) showed homology only to sequences from cactus, but not for model plants Arabidopsis, tomato, or the commercial fruits included in this study (Fig. 1c).

    Figure 1.  Venn diagram of the homology search results against model plants databases, commercial fruits, and cactus. The number in the diagram corresponds to the number of transcripts from S. thurberi homologous to sequences from that plant species. (a) Homologous to sequences from Fragaria vesca (Fa), Persea americana (Pa), Prunus persica (Pp), Vitis vinifera (Vv), and Citrus sinensis (Cs). (b) Homologous to sequences from Opuntia streptacantha (Of), Selenicereus undatus (Su), Hylocereus polyrhizus (Hp), and Pachycereus pringlei (Pap). (c) Homologous to sequences from Solanum lycopersicum (Sl), Arabidopsis thaliana (At), from the commercial fruits (Fa, Pa, Pp, Vv, and Cs), or the cactus included in this study (Of, Su, Hp, and Pap). Homologous searching was carried out by BLAST alignment (E value < 1 × 10−5). The Venn diagrams were drawn by ggVennDiagram in R Studio.

    A total of 45,970 (26.35%), 58,704 (33.65%), and 48,186 (27.65%) transcripts showed homology to transcription factors, transcriptional regulators, and protein kinases in the PlantTFDB, iTAK-TR, and iTAK-PK databases, respectively (Supplementary Tables S1, S9S11). For the PlantTFDB, the homologous transcripts belong to 57 transcriptional factors (TF) families (Fig. 2 & Supplementary Table S9), from which, the most frequent were the basic-helix-loop-helix (bHLH), myeloblastosis-related (MYB-related), NAM, ATAF, and CUC (NAC), ethylene responsive factor (ERF), and the WRKY domain families (WRKY) (Fig. 2).

    Figure 2.  Transcription factor (TF) families distribution of S. thurberi fruit peel transcriptome. The X-axis indicates the number of transcripts with hits to each TF family. Alignment to the PlantTFDB database by BLASTx was carried out with an E value threshold of 1 × 10−5. The bar graph was drawn by ggplot2 in R Studio.

    Based on the homology found and the functional domain searches, gene ontology terms (GO) were assigned to 68,559 transcripts (Supplementary Table S12). Figure 3 shows the top 20 GO terms assigned to the S. thurberi transcriptome, corresponding to the Biological Processes (BP) and Molecular Function (MF) categories. For BP, organic substance metabolic processes, primary metabolic processes, and cellular metabolic processes showed a higher number of transcripts (Supplementary Table S13). Further, for MF, organic cyclic compound binding, heterocyclic compound binding, and ion binding were the processes with the higher number of transcripts. S. thurberi transcripts were classified into 142 metabolic pathways from the KEGG database (Supplementary Table S14). The pathways with the higher number of transcripts recorded were pyruvate metabolism, glycerophospholipid metabolism, glycolysis/gluconeogenesis, and citrate cycle. Further, among the top 20 KEEG pathways, the cutin, suberin, and wax biosynthesis include more than 30 transcripts (Fig. 4).

    Figure 3.  Top 20 Gene Ontology (GO) terms assigned to the S. thurberi fruit peel transcriptome. Bars indicate the number of transcripts assigned to each GO term. Assignment of GO terms was carried out by Blast2GO with default parameters. BP and MF mean Biological Processes and Molecular Functions GO categories, respectively. The graph was drawn by ggplot2 in R Studio.
    Figure 4.  Top 20 KEGG metabolic pathways distribution in the S. thurberi fruit peel transcriptome. Bars indicate the number of transcripts assigned to each KEGG pathway. Assignment of KEGG pathways was carried out in the Blast2GO suite. The bar graph was drawn by ggplot2 in R Studio.

    Out of the total of transcripts, 43,391 (24.87%) were classified as lncRNA (Supplementary Tables S15 & S16). Figure 5 shows a comparison of the length (Fig. 5a) and expression (Fig. 5b) of lncRNA and coding RNA. Both length and expression values were higher in coding RNA than in lncRNA. In general, coding RNA ranged from 201 to 18,629 bp with a mean length of 1,507.18, whereas lncRNA ranged from 200 to 5,198 bp with a mean length of 481.51 (Fig. 5a). The higher expression values recorded from coding RNA and lncRNA were 12.83 and 9.45 log2(CPM), respectively (Fig. 5b).

    Figure 5.  Comparison of coding RNA and long non-coding RNA (lncRNA) from S. thurberi transcriptome. (a) Box plot of transcript length distribution. The Y-axis indicates the length of each transcript in base pairs. (b) Box plot of expression levels. The Y-axis indicates the log2 of the count per million of reads (log2(CPM)) recorded for each transcript. Expression levels were calculated by the edgeR package in R studio. (a), (b) The lines inside the boxes indicate the median. The higher and lower box limits represent the 75th and 25th percentiles, respectively. The box plots were drawn by ggplot2 in R Studio.

    To identify the transcripts without significant changes in expression between the four RNA-seq libraries, a differential expression analysis was carried out. Of the total of transcripts, 4,980 were not differentially expressed (NDE) at least in one paired comparison between the libraries (Supplementary Tables S17S20). Mean counts per million of reads (CPM) and coefficient of variation (CV)[44] were calculated for these NDE transcripts. Transcripts with a CV value lower than 0.113, corresponding with the percentile 5 of the CV, and a mean CPM higher than 1,138.06, corresponding with the percentile 95 of the mean CPM were used as filters to identify the most stably expressed transcripts (Supplementary Table S21). Based on its homology and its tentative biological function, five transcripts were selected to be tested as tentative reference genes. Besides, three NDE transcripts homologous to previously identified stable expressed reference genes in other species of cactus fruit[4143] were selected (Supplementary Table S22). Homology metrics for the eight tentative reference genes selected are shown in Supplementary Table S23. The primer sequences used to amplify the transcripts by qRT-PCR and their nucleotide sequence are shown in Supplementary Tables S24 & S25, respectively.

    The amplification specificity of the eight candidate reference genes determined by melting curves analysis is shown in Supplementary Fig. S3. For the eight tentative reference transcripts selected, the cycle threshold (Ct) values were recorded during sweet pitaya fruit development by qRT-PCR (Supplementary Table S26). The Ct values obtained ranged from 16.85 to 30.26 (Fig. 6a). Plastidic ATP/ADP-transporter (StTLC1) showed the highest Ct values with a mean of 27.34 (Supplementary Table S26). Polyubiquitin 3 (StUBQ3) showed the lowest Ct values in all five sweet pitaya fruit developmental stages (Fig. 6a).

    Figure 6.  Expression stability analysis of tentative reference genes. (a) Box plot of cycle threshold (Ct) distribution of candidate reference genes during sweet pitaya fruit development (10, 20, 30, 35, and 40 d after flowering). The black line inside the box indicates the median. The higher and lower box limits represent the 75th and 25th percentiles, respectively. (b) Bar chart of the geometric mean (geomean) of ranking values calculated by RefFinder for each tentative reference gene (X-axis). The lowest values indicate the best reference genes. (c) Bar chart of the pairwise variation analysis and determination of the optimal number of reference genes by the geNorm algorithm. A pairwise variation value lower than 0.15 indicates that the use of Vn/Vn + 1 reference genes is reliable for the accurate normalization of qRT-PCR data. The Ct data used in the analysis were calculated by qRT-PCR in a QIAquant 96 5 plex (QIAGEN) according to the manufacturer's protocol. The box plot and the bar graphs were drawn by ggplot2 and Excel programs, respectively. Abbreviations: Actin 7 (StACT7), alpha-tubulin (StTUA), elongation factor 1-alpha (StEF1a), COP1-interactive protein 1 (StCIP1), plasma membrane ATPase 4 (StPMA4), BEL1-like homeodomain protein 1 (StBLH1), polyubiquitin 3 (StUBQ3), and plastidic ATP/ADP-transporter (StTLC1).

    The best stability values calculated by NormFinder were 0.45, 0.51, 0.97, and 0.99, corresponding to the transcripts elongation factor 1-alpha (StEF1a), alpha-tubulin (StTUA), plastidic ATP/ADP-transporter (StTLC1), and actin 7 (StACT7), respectively (Supplementary Table S27). For BestKeeper, the most stable expressed transcripts were StUBQ3, StTUA, and StEF1a, with values of 0.72, 0.75, and 0.87, respectively. In the case of the delta Ct method[51], the transcripts StEF1a, StTUA, and StTLC1 showed the best stability.

    According to geNorm analysis, the most stable expressed transcripts were StTUA, StEF1a, StUBQ3, and StACT7, with values of 0.74, 0.74, 0.82, and 0.96, respectively. All the pairwise variation values (Vn/Vn + 1) were lower than 0.15, ranging from 0.019 for V2/V3 to 0.01 for V6/V7 (Fig. 6c). The V value of 0.019 obtained for V2/V3 indicates that the use of the best two reference genes StTUA and StEF1a is reliable enough for the accurate normalization of qRT-PCR data, therefore no third reference gene is required[47]. Except for BestKeeper analysis, StEF1a and StTUA were the most stable transcripts for all of the methods carried out in this study (Supplementary Table S27). The comprehensive ranking analysis indicates that StEF1a, followed by StTUA and StUBQ3, are the most stable expressed genes and are stable enough to be used as reference genes in qRT-PCR analysis during sweet pitaya fruit development (Fig. 6b).

    Three cuticle biosynthesis-related transcripts TRINITY_DN17030_c0_g1_i2, TRINITY_DN15394_c0_g1_i1, and TRINITY_DN23528_c1_g1_i1 tentatively coding for the enzymes cytochrome p450 family 77 subfamily A (CYP77A), Gly-Asp-Ser-Leu motif lipase/esterase 1 (GDSL1), and an ATP binding cassette transporter subfamily G member 11 (ABCG11/WBC11), respectively, were identified and quantified. The nucleotide sequence and predicted amino acid sequences of the three transcripts are shown in Supplementary File 1. The best homology match for StCYP77A (TRINITY_DN17030_c0_g1_i2) was for AtCYP77A4 (AT5G04660) from Arabidopsis and SmCYP77A2 (P37124) from eggplant (Solanum melongena) in the TAIR and Swiss-Prot databases, respectively (Supplementary Table S23).

    TransDecoder, InterPro, and TMHMM analysis showed that StCYP77A codes a polypeptide of 518 amino acids (aa) in length that comprises a cytochrome P450 E-class domain (IPR002401) and a transmembrane region (residues 10 to 32). The phylogenetic tree constructed showed that StCYP77A is grouped in a cluster with all the CYP77A2 proteins included in this analysis, being closer to CYP77A2 (XP_010694692) from B. vulgaris and Cgig2_012892 (KAJ8441854) from Carnegiea gigantean (Supplementary Fig. S4).

    StGDSL1 (TRINITY_DN15394_c0_g1_i1) alignment showed that it is homologous to a GDSL esterase/lipase from Arabidopsis (Q9LU14) and tomato (Solyc03g121180) (Supplementary Table S23). TransDecoder, InterPro, and SignalP analysis showed that StGDSL1 codes a polypeptide of 354 aa in length that comprises a GDSL lipase/esterase domain IPR001087 and a signal peptide with a cleavage site between position 25 and 26 (Supplementary Fig. S5).

    Supplementary Figure S6 shows the analysis carried out on the predicted amino acid sequence of StABCG11 (TRINITY_DN23528_c1_g1_i1). The phylogenetic tree constructed shows three clades corresponding to the ABCG13, ABCG12, and ABCG11 protein classes with bootstrap support ranging from 40% to 100% (Supplementary Fig. S6a). StABCG11 is grouped with all the ABCG11 transporters included in this study in a well-separated clade, being closely related to its tentative ortholog from C. gigantean Cgig2_004465 (KAJ8441854). InterPro and TMHMM results showed that the StABCG11 sequence contains an ABC-2 type transporter transmembrane domain (IPR013525; PF01061.27) with six transmembrane helices (Supplementary Fig. S6b).

    The predicted protein sequence of StABCG11 is 710 aa in length, holding the ATP binding domain (IPR003439; PF00005.30) and the P-loop containing nucleoside triphosphate hydrolase domain (IPR043926; PF19055.3) of the ABC transporters of the G family. Multiple sequence alignment shows that the Walker A and B motif sequence and the ABC signature[15] are conserved between the ABCG11 transporters from Arabidopsis, tomato, S. thurberi, and C. gigantean (Supplementary Fig. S6c).

    According to the results of the expression stability analysis (Fig. 6), four normalization strategies were tested to quantify the three cuticle biosynthesis-related transcripts during sweet pitaya fruit development. The four strategies consist of normalizing by StEF1a, StTUA, StUBQ3, or StEF1a+StTUA. Primer sequences used to quantify the transcripts StCYP77A (TRINITY_DN17030_c0_g1_i2), StGDSL1 (TRINITY_DN15394_c0_g1_i1), and StABCG11 (TRINITY_DN23528_c1_g1_i1) by qRT-PCR during sweet pitaya fruit development are shown in Supplementary Table S24.

    The three cuticle biosynthesis-related transcripts showed differences in expression during sweet pitaya fruit development (Supplementary Table S28). The same expression pattern was recorded for the three cuticle biosynthesis transcripts when normalization was carried out by StEF1a, StTUA, StUBQ3, or StEF1a + StTUA (Fig. 7). A higher expression of StCYP77A and StGDSL1 are shown at the 10 and 20 DAF, showing a decrease at 30, 35, and 40 DAF. StABCG11 showed a similar behavior, with a higher expression at 10 and 20 DAF and a reduction at 30 and 35 DAF. Nevertheless, unlike StCYP77A and StGDSL1, a significant increase at 40 DAF, reaching the same expression as compared with 10 DAF, is shown for StABCG11 (Fig. 7).

    Figure 7.  Expression analysis of cuticle biosynthesis-related transcripts StCYP77A, StGDSL1, and StABCG11 during sweet pitaya (Stenocereus thurberi) fruit development. Relative expression was calculated through the 2−ΔΔCᴛ method using elongation factor 1-alpha (StEF1a), alpha-tubulin (StTUA), polyubiquitin 3 (StUBQ3), or StEF1a + StTUA as normalizing genes at 10, 20, 30, 35, and 40 d after flowering (DAF). The Y-axis and error bars represent the mean of the relative expression ± standard error (n = 4−6) for each developmental stage in DAF. The Ct data for the analysis was recorded by qRT-PCR in a QIAquant 96 5 plex (QIAGEN) according to the manufacturer's protocol. The graph line was drawn by ggplot2 in R Studio. Abbreviations: cytochrome p450 family 77 subfamily A (StCYP77A), Gly-Asp-Ser-Leu motif lipase/esterase 1 (StGDSL1), and ATP binding cassette transporter subfamily G member 11 (StABCG11).

    Characteristics of a well-assembled transcriptome include an N50 value closer to 2,000 bp, a high percentage of conserved transcripts completely assembled (> 80%), and a high proportion of reads mapping back to the assembled transcripts[52]. In the present study, the first collection of 174,449 transcripts from S. thurberi fruit peel are reported. The generated transcriptome showed an N50 value of 2,110 bp, a TransRate score of 0.05, and a GC percentage of 41.33 (Table 1), similar to that reported for other de novo plant transcriptome assemblies[53]. According to BUSCO, 85.4% of the orthologous genes from the Embryophyta databases completely matched the S. thurberi transcriptome, and only 3.9% were missing (Table 1). These results show that the S. thurberi transcriptome generated is not fragmented, and it is helpful in predicting the sequence of almost all the transcripts expressed in sweet pitaya fruit peel[24].

    The percentage of transcripts homologous found, E values, and identity distribution (Supplementary Tables S1 & S2; Supplementary Fig. S2) were similar to that reported in the de novo transcriptome assembly for non-model plants and other cactus fruits[4143,54] and further suggests that the transcriptome assembled of S. thurberi peel is robust[52]. Of the total of transcripts, 70,802 were common to all the five commercial fruit protein databases included in this study, which is helpful for the search for conserved orthologous involved in fruit development and ripening (Fig. 2a). A total of 34,513 transcripts (20%) show homology only to sequences in the cactus's databases, but not in the others (Supplementary Tables S1 & S2; Fig. 1c). This could suggest that a significant conservation of sequences among plants of the Cactaceae family exists which most likely are to have a function in this species adaptation to desert ecosystems.

    To infer the biological functionality represented by the S. thurberi fruit peel transcriptome, gene ontology (GO) terms and KEGG pathways were assigned. Of the main metabolic pathways assigned, 'glycerolipid metabolism' and 'cutin, suberine, and wax biosynthesis' suggests an active cuticle biosynthesis in pitaya fruit peel (Fig. 4). In agreement with the above, the main GO terms assigned for the molecular function (MF) category were 'organic cyclic compound binding', 'transmembrane transporter activity', and 'lipid binding' (Fig. 3). For the biological processes (BP) category, the critical GO terms for the present research are 'cellular response to stimulus', 'response to stress', 'anatomical structure development', and 'transmembrane transport', which could suggest the active development of the fruit epidermis and cuticle biosynthesis for protection to stress.

    The most frequent transcription factors (TF) families found in S. thurberi transcriptome were NAC, WRKY, bHLH, ERF, and MYB-related (Fig. 2), which had been reported to play a function in the tolerance to abiotic stress in plants[55,56]. Although the role of NAC, WRKY, bHLH, ERF, and MYB TF in improving drought tolerance in relevant crop plants has been widely documented[57,58], their contribution to the adaptation of cactus to arid ecosystems has not yet been elucidated and further experimental pieces of evidence are needed.

    It has been reported that the heterologous expression of ERF TF from Medicago truncatula induces drought tolerance and cuticle wax biosynthesis in Arabidopsis leaf[59]. In tomato fruits, the gene SlMIXTA-like which encodes a MYB transcription factor avoids water loss through the positive regulation of genes related to the biosynthesis and transport of cuticle compounds[22]. Despite the relevant role of cuticles in maintaining cactus physiology in desert environments, experimental evidence showing the role of the different TF-inducing cuticle biosynthesis has yet to be reported for cactus fruits.

    Out of the transcripts, 43,391 were classified as lncRNA (Supplementary Tables S15 & S16). This is the first report of lncRNA identification for the species S. thurberi. In fruits, 3,679 lncRNA has been identified from tomato[26], 3,330 from peach (P. persica)[29], 3,857 from melon (Cucumis melo)[28], 2,505 from hot pepper (Capsicum annuum)[27], and 3,194 from pomegranate (Punica granatum)[36]. Despite the stringent criteria to classify the lncRNA of sweet pitaya fruit (S. thurberi), a higher number of lncRNAs are shown when compared with previous reports. This finding is most likely due to the higher level of redundancy found during the transcriptome analysis. To reduce this redundancy, further efforts to achieve the complete genome assembly of S. thurberi are needed.

    Previous studies showed that lncRNA is shorter and has lower expression levels than coding RNA[6062]. In agreement with those findings, both the length and expression values of lncRNA from S. thurberi were lower than coding RNA (Fig. 5). It has been suggested that lncRNA could be involved in the biosynthesis of cuticle components in cabbage[61] and pomegranate[36] and that they could be involved in the tolerance to water deficit through the regulation of cuticle biosynthesis in wild banana[60]. Nevertheless, the molecular mechanism by which lncRNA may regulate the cuticle biosynthesis in S. thurberi fruits has not yet been elucidated.

    A relatively constant level of expression characterizes housekeeping genes because they are involved in essential cellular functions. These genes are not induced under specific conditions such as biotic or abiotic stress. Because of this, they are very useful as internal reference genes for qRT-PCR data normalization[63]. Nevertheless, their expression could change depending on plant species, developmental stages, and experimental conditions[64]. Reliable reference genes for a specific experiment in a given species must be identified to carry out an accurate qRT-PCR data normalization[63]. An initial screening of the transcript expression pattern through RNA-seq improves the identification of stably expressed transcripts by qRT-PCR[44,64].

    Identification of stable expressed reference transcripts during fruit development has been carried out in blueberry (Vaccinium bracteatum)[65], kiwifruit (Actinidia chinensis)[66], peach (P. persica)[67], apple (Malus domestica)[68], and soursop (Annona muricata)[69]. These studies include the expression stability analysis through geNorm, NormFinder, and BestKeeper algorithms[68,69], some of which are supported in RNA-seq data[65,66]. Improvement of expression stability analysis by RNA-seq had led to the identification of non-previously reported reference genes with a more stable expression during fruit development than commonly known housekeeping genes in grapevine (V. vinifera)[44], pear (Pyrus pyrifolia and P. calleryana)[64], and pepper (C. annuum)[70].

    For fruits of the Cactaceae family, only a few studies identifying reliable reference genes have been reported[4143]. Mainly because gene expression analysis has not been carried out previously for sweet pitaya (S. thurberi), the RNA-seq data generated in this work along with geNorm, NormFinder, BestKeeper, and RefFinder algorithms were used to identify reliable reference genes. The comprehensive ranking analysis showed that out of the eight candidate genes tested, StEF1a followed by StTUA and StUBQ3 were the most stable (Fig. 6b). All the pairwise variation values (Vn/Vn + 1) were lower than 0.15 (Fig. 6c), which indicates that StEF1a, StTUA, and StUBQ3 alone or the use of StEF1a and StTUA together are reliable enough to normalize the gene expression data generated by qRT-PCR.

    The genes StEF1a, StTUA, and StUBQ3 are homologous to transcripts found in the cactus species known as dragonfruit (Hylocereus monacanthus and H. undatus)[41], which have been tested as tentative reference genes during fruit development. EF1a has been proposed as a reliable reference gene in the analysis of changes in gene expression of dragon fruit (H. monacanthus and H. undatus)[41], peach (P. persica)[67], apple (M. domestica)[68], and soursop (A. muricata)[69]. According to the expression stability analysis carried out in the present study (Fig. 6) four normalization strategies were designed. The same gene expression pattern was recorded for the three target transcripts evaluated when normalization was carried out by the genes StEF1a, StTUA, StUBQ3, or StEF1a + StTUA (Fig. 7). Further, these data indicates that these reference genes are reliable enough to be used in qRT-PCR experiments during fruit development of S. thurberi.

    The plant cuticle is formed by two main layers: the cutin, composed mainly of mid-chain oxygenated LC fatty acids, and the cuticular wax, composed mainly of very long-chain (VLC) fatty acids, and their derivates VLC alkanes, VLC primary alcohols, VLC ketones, VLC aldehydes, and VLC esters[3]. In Arabidopsis CYP77A4 and CYP77A6 catalyze the synthesis of midchain epoxy and hydroxy ω-OH long-chain fatty acids, respectively[10,11], which are the main components of fleshy fruit cuticle[3].

    The functional domain search carried out in the present study showed that StCYP77A comprises a cytochrome P450 E-class domain (IPR002401) and a membrane-spanning region from residues 10 to 32 (Supplementary Fig. S4). This membrane-spanning region has been previously characterized in CYP77A enzymes from A. thaliana and Brassica napus[11,71]. It suggests that the protein coded by StCYP77A could catalyze the oxidation of fatty acids embedded in the endoplasmic reticulum membrane of the epidermal cells of S. thurberi fruit. Phylogenetic analysis showed that StCYP77A was closer to proteins from its phylogenetic-related species B. vulgaris (BvCYP772; XP_010694692) and C. gigantea (Cgig2_012892) (Supplementary Fig. S4). StCYP77A, BvCYP77A2, and Cgig2_012892 were closer to SlCYP77A2 and SmCYP77A2 than to CYP77A4 and CYP77A6 proteins, suggesting that StCYP77A (TRINITY_DN17030_c0_g1_i2) could correspond to a CYP77A2 protein.

    Five CYP77A are present in the Arabidopsis genome, named CYP77A4, CYP77A5, CYP77A6, CYP77A7, and CYP77A9, but their role in cuticle biosynthesis has only been reported for CYP77A4 and CYP77A6[72]. It has been suggested that CYP77A2 from eggplant (S. torvum) could contribute to the defense against fungal phytopathogen infection by the synthesis of specific compounds[13]. In pepper fruit (C. annuum), the expression pattern of CYP77A2 (A0A1U8GYB0) and ABCG11 (LOC107862760) suggests a role of CYP77A2 and ABCG11 in cutin biosynthesis at the early stages of pepper fruit development[14].

    In the case of the protein encoded by StGDSL1 (354 aa), the length found in this work is similar to the length of its homologous from Arabidopsis (AT3G16370) and tomato (Solyc03g121180) (Supplementary Fig. S5). A GDSL1 protein named CD1 polymerizes midchain oxygenated ω-OH long-chain fatty acids to form the cutin polyester in the extracellular space of tomato fruit peel[20,21]. It has been suggested that the 25-amino acid N-signal peptide found in StGDSL1 (Supplementary Fig. S5), previously reported in GDSL1 from Arabidopsis, B. napus, and tomato, plays a role during the protein exportation to the extracellular space[21,73].

    A higher expression of StCYP77A, StGDSL1, and StABCG11 is shown at the 10 and 20 DAF of sweet pitaya fruit development (Fig. 7), suggesting the active cuticle biosynthesis at the early stages of sweet pitaya fruit development. In agreement with that, two genes coding for GDSL lipase/hydrolases from tomato named SGN-U583101 and SGN-U579520 are highly expressed in the early stages and during the expansion stages of tomato fruit development, but their expression decreases in later stages[74]. It has been shown that the expression of GDSL genes, like CD1 from tomato, is higher in growing fruit[20,21]. Like tomato, the increase in expression of StCYP77A and StGDSL1 shown in pitaya fruit development could be due to an increase in cuticle deposition during the expansion of the fruit epidermis[20].

    The phylogenetic analysis, the functional domains, and the six transmembrane helices found in the StABCG11 predicted protein (Supplementary Fig. S6), suggests that it is an ABCG plasma membrane transporter of sweet pitaya[15]. Indeed, an increased expression of StABCG11 at 40 DAF was recorded in the present study (Fig. 7). Further, this data strongly suggests that it could be playing a relevant role in the transport of cuticle components at the beginning and during sweet pitaya fruit ripening.

    In Arabidopsis, ABCG11 (WBC11) exports cuticular wax and cutin compounds from the plasma membrane[15,75]. It has been reported that a high expression of the ABC plasma membrane transporter from mango MiWBC11 correlates with a higher cuticle deposition during fruit development[7]. The expression pattern for StABCG11, StCYP77A, and StGDSL1 suggests a role of StABCG11 as a cutin compound transporter in the earlier stages of sweet pitaya fruit development (Fig. 7). Further, its increase at 40 DAF suggests that it could be transporting cuticle compounds other than oxygenated long-chain fatty acids, or long-chain fatty acids that are not synthesized by StCYP77A and StGDSL1 in the later stages of fruit development.

    Like sweet pitaya, during sweet cherry fruit (Prunus avium) development, the expression of PaWCB11, homologous to AtABCG11 (AT1G17840), increases at the earlier stages of fruit development decreases at the intermediate stages, and increases again at the later stages[76]. PaWCB11 expression correlated with cuticle membrane deposition at the earlier and intermediate stages of sweet cherry fruit development but not at the later[76]. The increased expression of StABCG11 found in the present study could be due to the increased transport of cuticular wax compounds, such as VLC fatty acids and their derivates, in the later stages of sweet pitaya development[15,75].

    Cuticular waxes make up the smallest amount of the fruit cuticle. Even so, they mainly contribute to the impermeability of the fruit's epidermis[3]. An increase in the transport of cuticular waxes at the beginning of the ripening stage carried out by ABCG transporters could be due to a greater need to avoid water loss and to maintain an adequate amount of water during the ripening of the sweet pitaya fruit. Nevertheless, further expression analysis of cuticular wax biosynthesis-related genes, complemented with chemical composition analysis of cuticles could contribute to elucidating the molecular mechanism of cuticle biosynthesis in cacti and their physiological contribution during fruit development.

    In this study, the transcriptome of the sweet pitaya (S. thurberi) fruit peel was assembled for the first time. The reference genes found here are a helpful tool for further gene expression analysis in sweet pitaya fruit. Transcripts tentatively involved in cuticle compound biosynthesis and transport are reported for the first time in sweet pitaya. The results suggest a relevant role of cuticle compound biosynthesis and transport at the early and later stages of fruit development. The information generated will help to improve the elucidation of the molecular mechanism of cuticle biosynthesis in S. thurberi and other cactus species in the future. Understanding the cuticle's physiological function in the adaptation of the Cactaceae family to harsh environmental conditions could help design strategies to increase the resistance of other species to face the increase in water scarcity for agricultural production predicted for the following years.

    The authors confirm contribution to the paper as follows: study conception and design: Tiznado-Hernández ME, Tafolla-Arellano JC, García-Coronado H, Hernández-Oñate MÁ; data collection: Tiznado-Hernández ME, Tafolla-Arellano JC, García-Coronado H, Hernández-Oñate MÁ; analysis and interpretation of results: Tiznado-Hernández ME, García-Coronado H, Hernández-Oñate MÁ, Burgara-Estrella AJ; draft manuscript preparation: Tiznado-Hernández ME, García-Coronado H. All authors reviewed the results and approved the final version of the manuscript.

    All data generated or analyzed during this study are included in this published article and its supplementary information files. The sequence data can be accessed at the Sequence Read Archive (SRA) repository of the NCBI through the BioProject ID PRJNA1030439.

    The authors wish to acknowledge the financial support of Consejo Nacional de Humanidades, Ciencias y Tecnologías de México (CONAHCYT) through project number 579: Elucidación del Mecanismo Molecular de Biosíntesis de Cutícula Utilizando como Modelo Frutas Tropicales. We appreciate the University of Arizona Genetics Core and Illumina for providing reagents and equipment for library sequencing. The author, Heriberto García-Coronado (CVU 490952), thanks the CONAHCYT (acronym in Spanish) for the Ph.D. scholarship assigned (749341). The author, Heriberto García-Coronado, thanks Dr. Edmundo Domínguez-Rosas for the technical support in bioinformatics for identifying long non-coding RNA.

  • The authors declare that they have no conflict of interest.

  • Supplemental Table S1 Profiles of the 50 selected African sorghum genotypes, their biological status and origins used in the study.
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    Ndlovu E, Maphosa M, Van Staden J. 2024. Unlocking basal and acquired thermotolerance potential in tropical sorghum. Technology in Agronomy 4: e026 doi: 10.48130/tia-0024-0023
    Ndlovu E, Maphosa M, Van Staden J. 2024. Unlocking basal and acquired thermotolerance potential in tropical sorghum. Technology in Agronomy 4: e026 doi: 10.48130/tia-0024-0023

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Unlocking basal and acquired thermotolerance potential in tropical sorghum

Technology in Agronomy  4 Article number: e026  (2024)  |  Cite this article

Abstract: Basal and acquired thermotolerance of 50 elite tropical sorghum genotypes was assessed in seedlings. Two sets of each assay were conducted following a split plot in a Completely Randomised Design replicated three times in two heat stress treatments in separate incubators. Coleoptile length was measured before and after heat treatments, and the differences were subjected to analysis of variance for heat treatments, genotypes and their interactions. Highly significant differences (p < 0.001) were observed between heat treatments, genotypes, and their interactions for both basal and acquired thermotolerance assays, signifying adverse effects of heat stress and the existence of genetic diversity in the thermotolerance of the assessed genotypes. Popular varieties 'Macia' and 'SV4' did not feature among the top performers for both forms of tolerance, indicating the risk subsistence farmers relying on them are to heat stress. Two genotypes were consistently amongst the top ten performers in terms of basal thermotolerance in the two sets, these are genotypes NPGRC1704, and IS24426. Genotypes NPGRC3093, and IS24272 consistently demonstrated superiority in acquired thermotolerance. Genotypes NPGRC1704, IS9567, NPGRC1197, NPGRC1868, and NPGRC1782 exhibited potential in both basal and acquired thermotolerance. The identified genotypes may be used as potential donors in crop improvement programs that seek to improve thermotolerance in sorghum.

    • The recent drastic and unforeseen changes in climatic patterns, especially recurrent heat waves in sub-Saharan regions, calls for a revolutionized focus on crop improvement. Temperature and rainfall are one of the two most important cardinal environmental factors which affect the growth and development of plants[1,2]. All plant species have specific temperature thresholds for different phases of growth and development[3,4]. An increase in temperature beyond critical threshold levels stimulates the production of reactive oxygen species (ROS) in plant cells[3]. The resultant imbalance in the level of production and removal of the ROS in plant cells results in oxidative stress, thus the emanation of heat stress[4]. Heat stress destabilizes biological molecules, structural stability, and enzymatic reactions which are key for the normal functioning of most biochemical and physiological reactions in plant cells[1]. The accumulation of ROS induced by extreme temperatures is more detrimental to delicate stages of growth and development of plants, particularly germination and seedling establishment[5].

      In the wake of reports that soil temperatures can reach 50 °C at midday in semi-arid and arid regions[4], it is becoming clear that crop establishment and growth in such areas are at risk. This will worsen the already dire situation for the resource poor farmers whose agricultural practices and means are climate dependent which of late has, however, proved variable and unreliable[6]. In field crops, the only way to reduce soil temperatures is through irrigation which is beyond reach for many smallholder farmers in marginal areas of semi-arid tropics of Africa. This calls for increased efforts in the exploration of resilient genotypes of known robust graniferous crops like sorghum so that their production can continue under sub-optimal conditions to meet the ever-increasing food demand.

      The widely accepted range of optimum temperatures for sorghum growth and development is between 20 and 32 °C[7]. In a study by Peacock[8], seedling emergence in sorghum failed at 45 °C. Similarly, in maize, coleoptile growth came to a halt at 45 °C[9], thus coleoptile elongation has been used as an indicator for heat stress tolerance[1012]. Even pearl millet, a known hardy crop that is a good candidate to complement and/or substitute sorghum, is equally affected by heat stress at germination and seedling establishment[6,12]. However, genotypic differences in inherent tolerance and natural acclimation to temperature extremes and fluctuations exist in sorghum[11], hence efforts can be directed to promising genotypes among the existing large populations. Thus the need for rapid and low-cost but effective techniques for evaluating large populations of genotypes for acquired and basal thermotolerance[12].

      Basal thermotolerance is the inherent ability of a plant to survive heat stress due to evolutionary adaptation[3]. While the ability to survive potentially lethal extreme temperatures through prior exposure to mild heat stress is termed acquired thermotolerance[13]. Acquired thermotolerance is measured in the laboratory by comparing the growth of germinants that have received prior exposure to supra-optimal temperatures to those that are directly exposed to high temperatures without acclimatization[14]. Controlled heat induction in the laboratory can be used to mimic field shocks or gradual increase in soil temperature[12]. Some genotypes possess higher basal than acquired thermotolerance[15]. Acquired themotolerance is controlled by genes that code for principal regulatory factors called heat shock proteins (HSPs)[10]. HSPs are molecular chaperones that are involved in the reduction of ROS, and repairing and reconfiguration of proteins which offers some level of protection to heat stress in plants[7,16,17]. Activation of such genes when plants are gradually exposed to heat stress results in increased synthesis of several metabolites, and proteins which confers some level of protection[3]. Accordingly, this study aimed at identifying sorghum genotypes with potential basal and acquired thermotolerance from a pool of genotypes that have never been tested obtained from the local and international gene banks with a view of using them as a tool to minimize sorghum losses due to heat stress.

    • Fifty diverse sorghum genotypes of sorghum were selected from a pool of 300 accessions obtained from the Genetic Resources and Biotechnology Institute and International Crop Research Institute of the Semi-Arid Tropics (ICRISAT) in Zimbabwe, multiplied and characterized through preliminary studies at Lupane State University experimental plots located in semi-arid part of Zimbabwe. The genotypes used in this study were selected based on yield potential and desirable morphological traits (Supplemental Table S1). Lupane State University plots are characterized by deep Kalahari sands with an annual rainfall average of 450−650 mm and temperature range of 10 to 35 °C. The temperatures at the study site ranged from 19.8 to 39.9 °C during the study period (Table 1).

      Table 1.  Rainfall (mm) and temperature (°C) data during the study period season (2022/23) at Lupane State University.

      Season Months
      Oct Nov Dec Jan
      2022/23 Mean minimum temperature 26.6 26.1 24.4 19.8
      Mean maximum temperature 39.9 37.4 31.9 28.1
      Total rainfall 9.6 164 75 161
      Source: Lupane State University weather station.
    • Twenty seeds for each genotype were surface sterilized for 5 min using 1% sodium hypochlorite, thoroughly rinsed three times in deionized water and germinated in Petri dishes lined with double Whatman No. 2 filter papers moistened with deionized water in a growth chamber at 30−35 °C. Petri dishes were placed in a completely randomized design with three replicates in an incubator and two sets of the experiment were done in tandem.

    • Five healthy two-day-old seedlings were randomly selected and exposed to heat shock treatment at 50 °C for 10 min in an incubator and then allowed to recover at 30−35 °C for 36 h in a growth chamber. Control treatments were not exposed to the heat shock treatment. The experiment was arranged following a split plot in Completely Randomised Design (CRD 50 × 2 × 3); where 50 genotypes were replicated three times in two separated heat stress treatments. Changes in coleoptile length after 36 h for the seedlings that were exposed to heat shock treatments as well as those not exposed to heat shock were measured using a digital Vernier caliper and differences were determined.

    • Five healthy two-day-old seedlings were exposed to heat stress treatment by gradually increasing the temperature in the growth chamber from 30 to 5 °C every hour until it reached 45 °C and was kept constant at that temperature for 1 h. Then, the seedlings were allowed to recover at 30−35 °C for up to four days. Seedlings were then exposed to a second heat shock at 50 °C for 10 min in a growth chamber and then allowed to recover again at 30−35 °C for up to four days. Control treatments were not acclimatized through gradual heat shock treatments but were exposed to a heat shock treatment at 50 °C. Similarly, the experiment was arranged following a split plot in Completely Randomised Design (CRD 50 × 2 × 3); where 50 genotypes were replicated three times in two separate heat stress treatments. Coleoptile length was measured before and after heat treatments using a digital Vernier caliper and the differences were determined.

    • In the assessment of basal and acquired thermotolerance, quantitative data on coleoptile length changes was subjected to a two-way analysis of variance (ANOVA) following a CRD in Genstat statistical package 14th edition, to determine significant differences in means for the two heat treatments, 50 genotypes and their interactions. Means were compared at 95% level of significance and separated using Bonferroni's test where significant differences were observed. Data was subjected to the tests of assumptions of ANOVA before being subjected to the F-test.

    • Assessment of 50 tropical sorghum accessions for basal and acquired thermotolerance using coleoptile elongation as an indicator revealed that heat shock of 50 °C, just for a short period of time suppressed coleoptile elongation in sorghum emergents while prior exposure of seedlings to gradually increasing temperatures up to 45 °C reduced the effects of heat shock as indicated by the extent of coleoptile elongation. Heat shocking of sorghum emergents significantly reduced coleoptile elongation (p < 0.001) as demonstrated by the F-test results for both sets of basal and acquired thermotolerance (Table 2). Significant variability in sorghum genotypes that were tested for basal and acquired thermotolerance at germination was also observed in the two sets of experiments for each test. The differential response of the assessed genotypes to heat shock and acclimatization treatments was demonstrated by the highly significant interaction of genotypic factors and the heat stress treatments in both sets of basal and acquired thermotolerance tests (Table 2). In both basal and acquired thermotolerance tests, temperature had the greatest effect as indicated by large mean of square errors (Table 2).

      Table 2.  Analysis of Variance for basal and acquired thermotolerance in diverse tropical sorghum accessions done in two sets.

      Source of variation DF Basal Acquired
      Mean squares Mean squares
      Set 1
      Temperature (T) 1 3,987.4*** 11,556.2***
      Genotype (G) 49 586.7*** 30.9***
      G × T 49 282.2*** 49.8***
      Error 1,400 119.2 6.5
      Set 2
      Temperature (T) 1 13,700.5*** 5,441.1***
      Genotype (G) 49 50.6*** 139.9***
      G × T 49 32.5*** 95.0***
      Error 1,400 6.7 16
      DF = degrees of freedom, *** significant at < 0.001.

      In both sets of the basal thermotolerance assay heat shocking of sorghum emergents significantly reduced coleoptile elongation by 13.7% and 31.9%, respectively (Table 3), when compared to their counterparts that were not exposed to heat shock. Acclimatization of sorghum emergents reduced the effects of heat shock, as demonstrated by significantly lower coleoptile elongation in non-acclimatized sorghum emergents in the two sets of the experiment by 62.1% and 52.5% respectively when compared to the acclimatized emergents (Table 3).

      Table 3.  Mean comparison of coleoptile changes for heat treatments of sorghum emergents indicating basal and thermotolerance in two sets of each experiment.

      Treatments Basal thermotolerance Acquired thermotolerance
      Set 1 Set 2 Set 1 Set 2
      Heat shocked 20.54b 12.91b 3.40b 3.45b
      Non-heat shocked 23.80a 18.96a 8.96a 7.26a
      LSD (5%) 1.11 0.26 0.26 0.41
      Non heat shock for acquired thermotolerance means genotypes were acclimatized through incremental temperature increase. Means with similar superscripted letters in the same column were significantly different at (p < 0.05) measured as coleoptile length in millimeters.
    • Following significant interactions of genotype and heat treatments for coleoptile elongation in sorghum emergents conferring differential expression of basal thermotolerance their means were separated using the Bonferroni's test. The top ten and five least performing genotypes in terms of coleoptile elongation were identified and are presented in Table 4 for the two sets of the experiment. Genotypes NPGRC1704 and IS24426 were consistently amongst the top ten performers in terms of basal thermotolerance in the two sets of the experiment (Table 4). 'Macia' a commercial check variety was amongst the top ten performers. In the first set only the two least performing genotypes namely IS30164 and NPGRC3127 were significantly different from the top two best performers in basal thermotolerance (Table 4).

      Table 4.  Mean comparison of coleoptile changes for 50 sorghum genotypes indicating seedlings basal thermotolerance in two sets.

      Set 1 Set 2
      Genotype Heat shocked Non-heat shocked Genotype Heat shocked Non heat shocked
      Top 10 Top 10
      NPGRC1695 30.80abcdef 36.06ab NPGRC1704 16.72g-x 22.5ab
      IS24426 30.56abcdefg 28.86abcdefg IS9567 16.57h-z 22.13abc
      NPGRC1478 29.24bcdefgh 33.28abcd IS24426 16.22i>-z 18.48b-n
      NPGRC1592 22.77bcdefghi 27.67bcdefghi NPGRC1222 15.03l-D 18.31c-n
      NPGRC3087 22.95bcdefghi 26.33bcdefghi NPGRC175*9 14.86m-D 18.22c-o
      "MACIA" 19.11bcdefghi 26.20bcdefghi NPGRC1628 14.2Oo-D 19.32b-j
      NPGRC1868 22.80bcdefghi 24.91bcdefghi NPGRC1197 14.08o-D 18.45b-n
      IS2867 20.81bcdefghi 24.75bcdefghi NPGRC1868 14.07o-D 21.0a-f
      IS26191 13.47fghi 24.67bcdfghi NPGRC1178 14.07o-D 20.24a-i
      NPGRC1704 24.57bcdefghi 25.31bcdefghi NPGRC1782 13.98p-D 14.66n-D
      Bottom 5 Bottom 5
      IS9548 14.52fghi 18.34cdefghi IS12391 11.56D 17.97c-q
      IS9303 14.11fghi 19.09bcdefghi NPGRC3087 11.33D 18.63b-n
      IS6944 13.01ghi 18.81bcdefghi NPGRC3127 11.20D 15.94j-C
      IS30164 10.92hi 17.97cdefghi NPGRC3092 11.13D 19.02b-m
      NPGRC3127 9.65i 20.42bcdefghi SV4 11.0D 18.66b-m
      Overall mean 22.17 15.94
      LSD 7.82 1.85
      Means with similar superscripted letter(s) in the same column were significantly different at p < 0.05. Genotypes appeared in the top performers or at least performers common in both sets and genotypes in bold mantained superiority in both sets.

      In set two all the five least performers were significantly different from the top three performers (Table 4). Only one genotype NPGRC3127 consistently appeared in the least five performers in both sets of basal thermotolerance tests. 'SV4' a commercial check variety had the lowest mean change in coleoptile length in set 2, indicating a lack of basal thermotolerance when compared to the tropical sorghum genotypes that were assessed in the current study.

    • Genotypes NPGRC3093, and IS24272 consistently demonstrated superiority in acquired thermotolerance in two sets of the test as confirmed by the separation of means using the Bonferroni's test (Table 5). No genotype showed consistency in the least performers for the two sets. Interestingly, several genotypes that were among the least performers in basal thermotolerance showed superiority in acquired thermotolerance. These genotypes are NPGRC1704, IS9567, NPGR3093, IS12391, and IS9548 (Tables 2 & 3). Genotype NPGRC 3127 which was the only genotype that consistently showed a lack of basal thermotolerance (Table 4) and also showed a lack of thermotolerance in one of the sets for the acquired thermotolerance experiment (Table 5).

      Table 5.  Mean comparison of seedlings coleoptile length changes for 50 sorghum genotypes acquired thermotolerance.

      Set 1 Set 2
      Genotype Acclimatized Non- acclimatized Genotype Acclimatized Non -acclimatized
      Top 10
      IS13837 13.56a 1.88B-D NPGRC3124 22.32a 4.64d-j
      NPGRC1704 12.5ab 3.24s-D IS12391 18.2ab 7.18c-j
      NPGRC1619 12.09a-c 1.63CD IS29925 18.18ab 2.98g-j
      IS9567 11.99a-d 2.8v-D IS24272 13.30bc 2.32g-j
      NPGRC1699 11.65a-d 2.9t-D NPGRC3093 10.72cd 4.12e-j
      NPGRC1476 11.48a-d 2.75w-D NPGRC1197 10.11c-f 2.25g-j
      NPGRC3093 11.24a-e 1.94B-D NPGRC1868 10.02c-f 3.31g-j
      IS24272 11.21a-e 2.64y-D NPGRC1478 8.71c-g 2.76g-j
      NPGRC3195 11.13a-e 4.42o-D NPGRC1782 8.48c-h 5.8d-j
      IS9548 11.12a-e 3.40s-D IS30164 8.42c-i 4.98d-j
      Bottom five
      NPGRC3105 6.69g-y 1.74CD NPGRC3195 4.22e-j 4.55d-j
      IS13813 6.43i-z 5.07m-C NPGRC1862 3.78e-j 2.86g-j
      NPGRC3127 5.94k-B 3.51s-D NPGRC3105 3.6f-j 1.55j
      NPGRC1593 5.08m-C 2.89t-D IS9548 2.67g-j 3.15g-j
      NPGRC1782 4.66n-D 3.41s-D IS6944 2.45g-j 3.8e-j
      Overall mean 6.18 5.35
      LSD 1.83 2.87
      Means with similar superscripted letters in the same column were significantly different at p < 0.05 and genotypes in bold represent top performers in both sets.
    • The study established the existence of basal and acquired thermotolerance in sorghum as previously observed in several other studies[1,11,12]. Coleoptile length was successfully evaluated as an indicator for both basal and acquired thermotolerance in heat-shocked and non-heat-shocked treatments, and between acclimatized and non-acclimatized treatments respectively. In a similar study by Arya et al.[18], germination and coleoptile elongation was reduced at temperatures above 35 °C in pearl millet which is a hardy crop that can match or surpass sorghum. High temperatures that were used to heat shock seedlings at 50 °C in this study mimic the soil temperatures in agronomic habitats of crop plants in semi-arid and arid tropics which may exceed 50 °C as postulated by Yadav et al.[19]. It is noteworthy that the effects of heat stress are organ and stage of growth specific[20]. Temperatures that exceed the threshold lead to heat stress which is associated with the accumulation of Reactive Oxygen Species (ROS) that manifests itself to oxidative stress[6,2123]. Reduced coleoptile elongation that was noted is a result of direct and indirect consequences of oxidative stress on cellular homeostasis which includes inhibition of protein synthesis, denaturation of proteins and other macromolecules, disintegration of membrane lipids, and loss of membrane integrity[24,25]. The prime indirect effect is obstruction of cell division and cell elongation which are critical physiological processes in the growth and development of all organisms[2,23,24].

      At the peak of the reported unfavorable effects of heat stress, sorghum has been proven to be among crops that tolerate and adapt to excessive temperatures above their threshold even at early stages of growth[1]. These observations in the present study, together with the existing assertions, cement the notion by Craufurd & Peacock[26] , that survival and crop establishment of these aforementioned thermophilic crops is mainly hinged on thermotolerance than drought stress tolerance and is associated with genetic variation. This study confirmed the existence of genetic variability within sorghum genotypes for basal and acquired thermotolerance, as previously shown in several studies on sorghum and other related hardy crops like pearl millet and finger millet[2730]. Genotypes like NPGRC1704, and IS24426 consistently exhibited inherent superiority in the current study showing relatively less inhibition of coleoptile elongation. Confirming the existence of basal thermotolerance in sorghum. Demonstrating the profound claims that some genotypes can emerge and continue to grow at temperatures above optimal[6].

      Basal thermotolerance is conceivably dependent on the intrinsic expression of transcripts for heat shock proteins, ROS scavenging enzymes like catalase, osmoprotectants, secondary metabolites, and antioxidant factors that offer protection against oxidative stress without prior exposure to lethal temperatures[11,17,31,32]. It is associated with swift response and protection against acute heat episodes that are becoming frequent in semi-arid tropics (SATs)[20].

      Some genotypes, i.e. NPGRC3093, and IS24272, consistently exhibited resilience to heat shock but after their prior exposure to gradual increases of temperatures up to suboptimal temperatures which is suggestive of the presence of acquired thermotolerance. Acquired thermotolerance is uniquely achieved through the stimulation of specific defense pathways in response to gradually increasing temperatures[17,33,34]. During the 'priming' period there is an accumulation of certain transcripts that code for molecular chaperones such as HSPs that offer protein folding, osmolytes like polyamines, proteins, secondary metabolites, including ROS-quenching enzymes like catalase (CAT) and ascorbate peroxidase (APX) that offer protection against oxidative stress[17,24]. The heat-shock based protection mechanism is complemented by a highly conserved sensory and signaling network that triggers heat shock regulation (HSR) pathways thereby enhancing acquired thermotolerance[24,35]. If we are to go by the mechanisms that confer acquired thermotolerance in crop plants, adaptation, and evolution are also more likely possibilities that render this kind of thermotolerance[36]. Considering the well-documented claim that sorghum evolved in SATs characterized by high temperatures hence their resilience to extreme temperatures[31].

      Interestingly, two genotypes that were consistent in acquired thermotolerance in the current study, are research materials originating from Tanzania and Chiredzi in Zimbabwe, respectively. Though the exact location of origin of the Tanzanian genotypes is not known, most of the country is semi-arid[32] , and Chiredzi in Zimbabwe is in the Lowveld region characterized by high temperatures with an average maximum temperature ranging between 28 and 32 °C[34]. Chiredzi is a sugar cane area which is a typical C4 plant like sorghum hence the convincing possibility of adaptation of these genotypes to high temperatures that rendered them the observed acquired thermotolerance. This could be a coevolutionary adaptation mechanism of germplasm to heat stress in these areas. In the current study, some genotypes displayed one type of resilience and not the other while some exhibited both. This all signifies that the differences in the type of thermotolerance displayed by crop plants is determined by the quality and quantity of the heat shock proteins that are produced prior to or during heat stress stimuli[6]. Whereas several genotypes in the present study exhibited traits of both basal and acquired thermotolerance, featuring in at least one of the sets of each assay. These were identified as NPGRC1704, IS9567, NPGRC1197, NPGRC1868, and NPGRC1782 (Tables 4 & 5). This is explained as thermotolerance diversity where certain subsets of HSR genes overlap rendering the genotype of both types of thermotolerance[34]. This is also shown in the expression of HSR genes across plant tissues, organs, and growth stages, thus hypocotyl, roots, and many more organs have been used as indicators of thermotolerance[3,17,33]. Significant positive correlation between acquired thermotolerance, expression of heat shock proteins and yield was established in wheat which is also a possibility in sorghum[36,37] .

      The significance of G × E implies that genotypes can be specifically deployed to areas prone to heat stress. Popular varieties like 'Macia' and 'SV4' may not perform in the near future given the increased severity and recurrence of drought. This is of great concern given their large-scale production by subsistence farmers in Southern Eastern and Central Africa. According to IPCC[38], global atmospheric temperature is likely to increase by 2.5 to 5.8 °C in the current century exacerbating the likelihood of heat stress. It was also interesting to note that differences in coleoptile elongation between the acclimatized and non-acclimatized sorghum emergents were greater than that observed between the heat-shocked and non-heat-shocked seedlings in the basal thermotolerance assay. This posits acclimation as a superior form of thermotolerance and an adaptive mechanism that enables crop plants to survive heat stress levels that would be otherwise lethal in their absence[20].

    • Sorghum genotypes showed a differential responses to heat shock in both basal and acquired thermotolerance indicating great diversity. Amongst the top ten performers for basal thermotolerance, genotypes NPGRC1704, and IS24426 emerged as the most consistent in the two sets of the assay while for acquired thermotolerance assay, genotypes NPGRC3093, and IS24272 consistently demonstrated superiority. Genotypes NPGRC1704, IS9567, NPGRC1197, NPGRC1868, and NPGRC1782 exhibited potential traits of both basal and acquired thermotolerance. Expression profiling will also be necessary in understanding the diversity of elicitated proteins while phenotyping and genotyping of the identified potential genotypes may be extended to other growth stages to enhance selection efficiency. Identified genotypes are potential donors in crop improvement programs that seek to improve thermotolerance in sorghum.

    • The authors confirm contribution to the paper as follows: conceptualization; data collection: Ndlovu E; acquisition of research materials, data analysis: Ndlovu E, Maphosa M; writing - original draft: Ndlovu E, Maphosa M; Van Staden J. All authors reviewed the results and approved the final version of the manuscript.

    • The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

    • The authors would like to thank the Genetic Resources and Biotechnology Institute in Zimbabwe for a generous donation of the landraces, International Crop Resources Institute for Semi-Arid Tropics, Bulawayo, Zimbabwe for a generous donation of germplasm.

      • The authors declare that they have no conflict of interest.

      • Supplemental Table S1 Profiles of the 50 selected African sorghum genotypes, their biological status and origins used in the study.
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
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    Cite this article
    Ndlovu E, Maphosa M, Van Staden J. 2024. Unlocking basal and acquired thermotolerance potential in tropical sorghum. Technology in Agronomy 4: e026 doi: 10.48130/tia-0024-0023
    Ndlovu E, Maphosa M, Van Staden J. 2024. Unlocking basal and acquired thermotolerance potential in tropical sorghum. Technology in Agronomy 4: e026 doi: 10.48130/tia-0024-0023

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