Identification of reference genes for quantitative real-time PCR in different developmental stages and under refrigeration conditions in soursop fruits (Annona muricata L.)

Scientia Horticulturae 260 (2020) 108893

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Short communication

Identification of reference genes for quantitative real-time PCR in different developmental stages and under refrigeration conditions in soursop fruits (Annona muricata L.)

T

Guillermo Berumen-Varelaa, Yolotzin A. Palomino-Hermosilloa, Pedro U. Bautista-Rosalesa, ⁎ Gabriela R. Peña-Sandovalb,c, Graciela G. López-Gúzmanb, Rosendo Balois-Moralesa, a Unidad de Tecnología de Alimentos-Secretaría de Investigación y Posgrado. Universidad Autónoma de Nayarit, Ciudad de la Cultura SN. C.P. 63000, Tepic, Nayarit; México b Unidad Académica de Agricultura. Universidad Autónoma de Nayarit. Carretera Tepic-Compostela, Km. 9. C.P. 63780, Xalisco, Nayarit; México c Centro Nayarita de Innovación y Transferencia de Tecnología. Laboratorio de Agrobiotecnología, Av. Emilio M. González SN. Ciudad del Conocimiento. C.P. 63173. Tepic, Nayarit; México

A R T I C LE I N FO

A B S T R A C T

Keywords: Cold storage Fruit ripening Gene expression Transcript

The quantitative real-time polymerase chain reaction (qRT-PCR) is a technique to quantify the gene expression. However, data normalization needs suitable references genes that show a constant expression under different conditions. Up to date, no reference genes have been evaluated in soursop fruits (Annona muricata L.). The objective of this work was to analyze the transcript stability of some genes in different developmental stages and under refrigeration conditions in soursop fruits. We created a database from a public transcriptome data of soursop leaf, identify homologous gene in soursop and designed primers to amplify the reference genes: Ubiquitin carrier-like protein (UBCc), Ubiquitin-conjugating enzyme E2 2 (UBCg), Elongation factor 1α (EF1α), β-tubulin (TUB) and 18 S ribosomal RNA (18S). Total RNA was extracted from soursop mesocarp at 0, 3 and 6 days from fruits stored at 25+1 °C and 0, 3, 6 and 9 days from fruits stored at 15+1 °C. cDNA was synthesized through SuperScript III kit following the manufacturer instructions. The gene expression was analyzed by qRT-PCR using the Rotor-Gene Q equip, the geNorm and RefFinder web-tool were used to classify the most stable transcript for each condition. The results indicated that the UBCc was the most stable transcript during the fruit development at 25+1 °C. On the other hand, EF1α showed the highest stability in soursop fruits stored at 15+1 °C. Global analysis of both conditions demonstrated that UBCc and EF1α were the most stable transcripts and can be used as reference genes for the normalization of the data in soursop during fruit development and under refrigeration.

1. Introduction Quantitative real-time PCR (qRT-PCR) is the most used method to measure gene expression patterns and validate the information from RNA-seq analysis (Takamori et al., 2017). However, it is necessary an appropriate normalization of the target gene mRNA levels with a reference gene, to correct the variation of the gene expression. The normalization depends on the reference genes selected in the expression analysis, due to no universal control gene exits. Hence, the selection and validation need to be carried out in each organism because of the expression of the endogenous gene change from an organism and under different experimental conditions. The expression stability of the reference genes requires to be validated to select the appropriate gene (Andersen et al., 2004; Ye et al., 2015). A reference gene can be defined ⁎

as a gene sequence which is expected to be unaffected in the given condition (Joseph et al., 2018) and stably expressed at a continuous level under diverse experimental treatments or tissues, organs, etc. (Løvdal and Lillo, 2009). The most common reference genes used for data normalization in plants includes Actin (Act), Ubiquitin (UBC), βtubulin (Tub), 18 S ribosomal RNA (18S), Elongation factor 1α (EF1α), Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) among others. On the other hand, the best algorithms to categorize the reference genes among a set of candidates are geNorm (Vandesompele et al., 2002), NormFinder (Andersen et al., 2004), BestKeeper (Pfaffl et al., 2004) and ΔCt method (Silver et al., 2006). These are the most used software to calculate the stability of a reference gene in certain cases. Nonetheless, in the last years, RefFinder (Xie et al., 2012) have been also used to rank the best reference genes. RefFinder is a web-tool that incorporates the

Corresponding author. E-mail address: [email protected] (R. Balois-Morales).

https://doi.org/10.1016/j.scienta.2019.108893 Received 19 December 2018; Received in revised form 20 August 2019; Accepted 26 September 2019 Available online 07 October 2019 0304-4238/ © 2019 Elsevier B.V. All rights reserved.

Scientia Horticulturae 260 (2020) 108893

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Table 1 List and description of the genes used in this study. F and R means forward and reverse primers, respectively. R2 indicates the correlation coefficients. Gene name

Accesion number

Sequence (5′–3′)

Amplicon size (bp)

Primer efficiency (%)

R2

Ubiquitin carrier-like protein (UBCc)

FJ664263

128

98.36

0.93

Ubiquitin-conjugating enzyme E2 2 (UBCg)

XM_021765528

109

112.05

0.90

Elongation factor 1 alpha (EF1α)

X14449

101

92.36

0.93

18 S ribosomal RNA (18 s)

AF206850

137

107.78

0.93

β-tubulin (Tub)

DQ205342

F: AACCTCTATCCAGTCTCTCCTC R: TGAGATAGTGGAGCAGAGCT F: CTCTGGAACGCTGTCATCTT R: CAACAAACCTCCAACCGTTC F: AAGTATGCGTGGGTGCTTG R: AGACCACCAAATATTACTGCAC F: CCTTCGGGATCGGAGTAATG R: AGATACCGTCCTAGTCTCAACC F: CAGCTGGTGGAAAATGCAGATG R: CCCTCAAGCTAACTACTCCTAGT

93

———

———

2.2. RNA extraction and first cDNA synthesis

algorithms previously mentioned and create a comprehensive rank from a group of candidate genes (Takamori et al., 2017). Soursop (Annona muricata L.) is a climacteric fruit, which possesses an elevated level of deterioration due to its high respiration rate and ethylene production (Berumen-Varela et al., 2019; Jiménez-Zurita et al., 2017a; Pareek et al., 2011). Because of this, the commercialization of this crop is limited and therefore, it is necessary to find technologies to delay fruit ripening. In this sense, Jiménez-Zurita et al. (2017b) showed that refrigeration temperatures at 15 °C, prolong the shelf life of soursop without affecting its quality. Taking this into account, most of the efforts have been made to prolong the postharvest shelf life of the soursop fruits and genomics resources regarding this fruit are scarce. Up to date, no transcriptome of the soursop fruit has been published. Matasci et al. (2014) generated the first transcriptomic data of this crop from soursop leaves, however, sequence annotation was not performed. In our research group, we are currently performing gene expression studies and transcriptome analysis of soursop mesocarp. With this information, we might understand the postharvest physiological changes of soursop fruit, improves the postharvest shelf life and increase the commercialization of this fruit to national and international markets. Nevertheless, the validation of a transcriptome needs the correct data normalization of the transcripts, and to the best of our knowledge, there is no report related with the identification of stably expressed transcripts in soursop during fruit development and under refrigeration conditions. Therefore, the objective of the current study was to analyze the transcript stability of four putative reference genes in different developmental stages and under refrigeration to use them as an internal control for future molecular studies using qRT-PCR.

Total RNA was isolated from 75 mg of soursop mesocarp tissue (pulverized with liquid nitrogen) using the Spectrum Plant Total RNA kit (Sigma) at 0, 3, 6 and 9 days of postharvest storage. The RNA quality and quantity were determined in a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). The integrity of the RNA was evaluated by electrophoresis on a 1.0% agarose gel. RNA with a 260/280 ratio between 1.8 and 2.1 were used for the next experiments. For each condition, 400 ng of total RNA was used to synthesize first-strand cDNAs using the SuperScript III kit, according to the manufacturer's protocol. cDNA was stored at -20 °C until further use. 2.3. Selection of reference genes and primer design Bioinformatics analysis was conducted in order to identify putative reference genes in soursop. We download the available transcriptome data of soursop leaves from the online public database http://www. onekp.com/public_data.html and built a BLAST database with these sequences using –makeblastdb command. Moreover, we performed a BLASTn search against the soursop database created using the sequence nucleotides of reference genes from soursop, cherimoya, tomato, and mango as a query due to the close and evolutionary relationship between the species as well as the genomics resources available. The genes with 80% or more similarity were then selected to primer design. Primer sequences for candidate reference genes: UBCc, UBCg, EF1α, βtubulin, 18S, were designed using Primer Quest Tool (Integrated DNA Technologies, IA, USA) with the following parameters: primer length of 18–25 nucleotides, expected amplicon length between 90–140 bp; melting temperature (Tm) between 55–60 °C and GC% of 45–55. The characteristics of the primers used in this experiment are in Table 1. The specificity of the primers was tested by the Primer-Blast tool (http://www.ncbi.nlm.nih.gov/tools/primer-blast/). PCR products were sequenced to confirm their identity.

2. Methods 2.1. Plant material Soursop fruits of ecotype ‘Lisa’ were hand-harvested from 10 ungrafted trees in a 23 years-old orchard located in Las Varas, Nayarit, Mexico. Five fruits per tree (50 fruits in total) were selected at physiological maturity (stage of development when the fruit will continue ontogeny even if harvest) according to fruit shape, peel color, size and around 150–160 days after anthesis. Fruits with no mechanical, physical and pathogenic damage were selected, disinfected with 2.0% NaClO, washed with distilled water and then stored at 25+1 °C and 15+1 °C. Mesocarp from five fruits was taken in three stages of development: physiological maturity (0 days), maturity of consumption (3 days) and initiation of senescence (6 days) at 25+1 °C. Likewise, due to the postharvest shelf life of soursop was increased up to 9 days (initiation of senescence) after storage at 15+1 °C, fruit mesocarp was taken at 0, 3, 6 and 9 days at 15+1 °C. All the samples were immediately placed in RNAlater (Sigma) solution, frozen in liquid nitrogen and stored at −80 °C until further analysis.

2.4. PCR All primer pairs were tested by conventional PCR using the RedTaq Ready Mix (Sigma) according to the manufacturer instructions. PCR was performed in a T-100 thermal cycler (Bio-Rad Laboratories, Inc) with the following conditions: initial denaturation at 94 °C for 5 min, followed by 40 cycles of 94 °C for 45 s, 56 °C for 60 s and 72 °C for 60 s and a final extension of 72 °C for 10 min. Amplicons were analyzed by 1.0% agarose gel electrophoresis and visualized in a PhotoDoc-It Imaging System (Ultra-Violet Products, Ltd). 2.5. Standard curve and qRT-PCR The qRT-PCR was carried out in a Rotor-Gene Q-5 plex real-time cycler (Qiagen, Valencia, CA, USA) using a Maxima SYBR Green/ROX qPCR Master Mix kit (Thermo Fisher Scientific, USA) with a final 2

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reaction volume of 20 μL in each tube, containing 6 μL SYBR Green Master mix, 5 μL cDNA, 1 μL (10 μM) of each forward and reverse primer and 7 μL PCR-grade water. Standard curves were generated using 3-fold serial dilutions of cDNA in triplicate for each condition. Amplification efficiency (E) of each primer was calculated according to the formula E = (10(−1/slope) − 1) × 100 (Kong et al., 2014). Data of the standard curves for the qRT-PCR are listed in Table 1. The qRT-PCR reactions were run in three technical replicates with two biological replicates from 20 ng of cDNA of each condition. The qRT-PCR assay consisted of a two-step protocol with an initial polymerase activation step of 95 °C for 5 min, followed by 40 cycles of 95 °C for 45 s and 56 °C for 60 s. Acquisition of fluoresce was performed at melting temperature during each cycle. Samples superior of cycle 36 was not considered for the analysis. Melting curve analysis (55 °C to 95 °C) was performed at the end of each qRT-PCR analysis to verify primer specificity.

3.3. Transcript stability of candidate reference genes Ct values were subject to evaluation of the transcript stability to identify the best reference genes for normalizing gene expression under the experimental conditions evaluated. In this regard, we used the geNorm algorithm (Vandesompele et al., 2002) and RefFinder online tool (Xie et al., 2012) to calculate and perform a stability ranking of candidate reference genes for all conditions tested as shown in Table 2. The geNorm was used to calculate M and then the V value to evaluate the most stably expressed transcript and the optimal number of reference genes, respectively. Vandesompele et al. (2002) recommended a 0.15 as a cut-off value of Vn/n+1, indicating that V value below 0.15 will not improve the normalization of qPCR-analysis. 3.3.1. Developmental stages According to the geNorm method, the UBCc / EF1α was the most stable transcripts, while the UBCg was the least. In addition, the ΔCt method and Normfinder showed similar results, which revealed the UBCc to be the most stable transcript and UBCg the least during different developmental stages. However, BestKeeper ranked first EF1α and second UBCc. The comprehensive ranking calculated by RefFinder demonstrated that the most stable to least stable transcripts during different developmental stages were: UBCc > EF1α > UBCg > 18 s as shown in Table 2. In Fig. 2, we show the V analysis by geNorm. The values of V2/3 and V3/4 were all above the proposed cut-off value of 0.15. Subsequently, the use of two reference genes, UBCc and EF1α are required for the accurate normalization of qRT-PCR analysis across different developmental stages.

2.6. Transcript stability analysis The average stability value (M) and pairwise variation analysis (V) was calculated with the geNorm algorithm using the R-based NormqPCR package (Perkins et al., 2012). Furthermore, the RefFinder web tool, which incorporates the algorithms of geNorm, NormFinder, BestKeeper, and the comparative ΔCt method was used to evaluate the qRT-PCR data to estimate a comprehensive ranking of transcript stability. Besides, we performed a Venn diagram in Rstudio using the venneuler package to summarizes the best reference genes selected by all algorithms.

3.3.2. Refrigeration In refrigeration conditions, geNorm ranked EF1α and UBCc as the most stable transcripts. Normfinder, ΔCt, and BestKeeper methods ranked also EF1α as the most stable transcript and 18 s as the least stable (Table 2). Consistent with the other algorithms, the results generated by RefFinder showed that EF1α was the most stable transcript, exhibiting the following ranking (most to the least stable) in refrigeration: EF1α > UBCc > UBCg > 18 s (Table 2). As well as across different developmental stages, the values of V2/3 and V3/4 in refrigeration conditions were below the suggested cut-off value of 0.15 (Fig. 2), indicating that two reference genes (EF1α and UBCc) were necessary to normalize the gene expression levels in refrigeration. Therefore, these results strongly suggest that the EF1α might be used as a reference gene for refrigeration treatments.

3. Results 3.1. Selection of candidate reference genes and amplification specificity RNA was properly isolated and cDNA synthesized from all conditions. The cDNA was used to amplify the candidate genes using specific primers for each gene by conventional PCR. Single PCR products were amplified for 4 out of the 5 primer pairs (UBCc, UBCg, EF1α, and 18 s genes). β-tubulin gene was excluded since no amplification with the primers used in this experiment was achieved. The specificity of the primers was identified by gel electrophoresis, showing a single band with the expected size. Further, genes with a positive amplification result were subjected to qRT-PCR analysis. In this regard, the amplification efficiencies for the 4 pairs of primers were ranged from 92.36 to 112.05, and correlation coefficients ranged from 0.90 to 0.93 (Table 1). Moreover, the accuracy of the qRT-PCR was carried out by melting curve analysis and gel electrophoresis. The melting curve analysis demonstrated the presence of a single peak and agarose gel showed a single band with no primer-dimers. Therefore, these primers were used in the qRT-PCR analysis.

3.3.3. Global analysis For the entire data, geNorm method, M values ranged from 0.035 to 0.077, being the UBCc / EF1α the most stable transcripts and the UBCg the least. ΔCt method and Normfinder ranked UBCc as the most stable transcript. Nevertheless, BestKeeper ranked EF1α as the most stable followed by UBCc. UBCg was detected as the least stable transcript for all algorithms (Table 2). For all datasets, the stability ranking by RefFinder (most stable to least stable transcript) was: UBCc > EF1α > UBCg > 18 s. The global analysis showed values of V2/3 and V3/4 that were also below the cut-off value of 0.15 (Fig. 2), demonstrating the use of the same two reference genes previously mentioned to normalize the gene expression under all conditions. Considering all algorithms, UBCc and EF1α are the most stable transcripts under the different experimental conditions analyzed as shown in Fig. 3. Specifically, UBCc from developmental stages and global analysis, while the EF1α for refrigeration (Fig. 3).

3.2. Expression levels of candidate reference genes The threshold cycle (Ct) values were analyzed using qRT-PCR in all conditions. The Ct means values of the reference genes ranged from 28.81 (UBCc) to 34.4 (18 s) in soursop fruits at different developmental stages, 28.19 (UBCc) to 33.52 (18 s) for refrigeration and 28.5 (UBCc) to 33.96 (18 s) for all conditions (Fig. 1). As can be seen, UBCc and 18 s transcripts showed the highest and lowest expression levels in all conditions tested, respectively. On the other hand, the mean expression level for developmental stages of UBCg and EF1α transcripts was 32.51 and 31.13, respectively, 30.82 (UBCg) and 30.39 (EF1α) for refrigeration (Fig. 1). Furthermore, Ct means values of 31.67 (UBCg) and 30.76 (EF1α) were found for all conditions (Fig. 1). According to the data previously mentioned, similar values and behavior were observed to the primers used in all conditions tested.

4. Discussion Up to now, the data related to the transcriptomics resources of soursop are practically null, and the few sequences available are not 3

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Fig. 1. Average of Ct values of the candidate reference genes. A) Developmental stages, B) Refrigeration and C) Global analysis. Each box indicates the 25th percentile and 75th percentile, whiskers represent the maximum and minimum Ct values, the line across the box signifies the median values. Table 2 Transcript stability values of candidate reference genes calculated by geNorm (Average expression stability M), NormFinder (Stability value), ΔCt method (Mean SD), BestKeeper (SD [ ± CP] crossing point values) and the final comprehensive ranking calculated by RefFinder. S and R represent the stability values and rank of each method, respectively. Approach

Developmental stages

Refrigeration

Global analysis

Gene name

UBCc UBCg EF1α 18 s UBCc UBCg EF1α 18 s UBCc UBCg EF1α 18 s

geNorm

ΔCt method

Normfinder

BestKeeper

RefFinder

S

R

S

R

S

R

S

R

S

R

0.032 0.090 0.032 0.069 0.040 0.050 0.040 0.064 0.035 0.077 0.035 0.066

1 4 1 3 1 3 1 4 1 4 1 3

0.415 2.178 1.506 1.44 0.961 0.776 0.22 1.611 0.734 1.634 1.027 1.448

1 4 3 2 3 2 1 4 1 4 2 3

1.638 2.485 1.935 2.106 1.312 1.314 1.152 1.754 1.475 1.971 1.561 1.875

1 4 2 3 2 3 1 4 1 4 2 3

0.397 2.335 0.375 1.803 0.561 0.736 0.183 1.14 0.438 1.683 0.372 1.545

2 4 1 3 2 3 1 4 2 4 1 3

1.189 1.565 2.711 4 1.861 2.711 1 4 1.189 4 1.414 3

1 3 2 4 2 3 1 4 1 4 2 3

Fig. 2. Determination of the optimal number of reference genes by pairwise variation analysis using geNorm. Pairwise variation (Vn/Vn+1) was analyzed between the normalization factors NFn and NFn+1, in all conditions tested. 4

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Fig. 3. Venn diagram showing the most stable genes identified by all algorithms tested in each condition evaluated.

ubiquitin carrier-like protein was used as a reference gene to normalize the expression of citric and malic acid genes during A. cherimola fruit growth (González-Agüero et al., 2016). In our study, we found that the UBCc gene using the primers reported the González-Agüero et al. (2011) was one of the most stable transcript. On refrigeration conditions, EF1α was considered the most stable transcript according to all algorithms. The function of this gene is to catalyze the binding of the aminoacyl-tRNA to the A-site of the ribosome by a GTP-dependent mechanism (Browning, 1996). Takamori et al. (2017) found the EF1α as the most stable reference gene in Urochloa brizantha under cold and heat stress. For apple flesh, EF1α was selected as the most stable gene during fruit development (Zhu et al., 2019). Furthermore, this gene has been found as one of the most accepted references gene in several plant species for abiotic stress such as potato (Nicot et al., 2005) and tobacco (Schmidt and Delaney, 2010)). In another study of cherimola, Actin gene was used as a reference gene to normalize the data of gene expression (Liu et al., 2016b). In sugar apple (Annona squamosa L.), the Actin gene was also employed as a reference gene in fruits, flowers and leaves (Gupta et al., 2015; Liu et al., 2017, 2016a). However, no studies of qRT-PCR data normalization using candidate reference gene were used in the experiments previously mentioned. Lastly, the rankings of the reference genes analyzed were similar under developmental stages and refrigeration according to most of the algorithms used. Taking all into account, UBCc and EF1α are reliable reference genes to normalize gene expression in soursop.

fully annotated (Berumen-Varela et al., 2019). In plant molecular studies, qRT-PCR is an important tool for understanding gene expression under different conditions, and the use of an appropriate reference gene is essential (Liu et al., 2016c). In recent years, even when the soursop reports have augmented, no studies related to the gene expression patterns in soursop fruits can be found. Thus, it is necessary to identify in advance reference genes with the goal to obtain reliable results and boost the understanding of this tropical fruit. In this study, we designed primers using bioinformatics tools and evaluated candidate reference genes using geNorm and RefFinder web-tool which comprises the four main algorithms (geNorm, Normfinder, BestKeeper, and the comparative ΔCT method). geNorm calculates the M value, which is directly proportional to the stable expression of the gene. In this sense, higher M values represent more stable expression for the gene, and vice versa (Zhu et al., 2019). Further, with this algorithm, we can calculate the V value to determine the optimal number of reference genes. NormFinder is based on the variance estimation to compute the Normalization Factor (Andersen et al., 2004), the ΔCt method is used to directly analyze the expression stability of endogenous genes (Silver et al., 2006) and the RefFinder is an online tool integrates the results of these four algorithms to obtain a comprehensive ranking of candidate reference genes (Xie et al., 2012). The rankings of the references genes selected in this experiment by these algorithms share much in common (Table 2). On different developmental stages and under refrigeration conditions, all the methods tested selected UBCc as the most stable transcript, with the exception of the BestKeeper software that selected EF1α instead. Moreover, the current study shows that UBCg and 18 s are not suitable reference genes for soursop. BestKeeper classifies as the most stable reference gene the ones demonstrating the lowest coefficient of variance and standard deviation (Pfaffl et al., 2004), however, does not calculate the exactitude of each reference gene, contrary of the geNorm algorithm (Mallona et al., 2010). Moreover, the BestKeeper evaluate the stability of reference genes independently, whereas the other three algorithms consider the pairwise variation between two reference genes (Silver et al., 2006). González-Agüero et al. (2011) characterized several genes in cherimoya (Annona cherimola Mill.) after chilling injury, founding an ubiquitin carrier-like protein, which is part of the proteasome system that is present in eukaryotic cells. Later, the

5. Conclusion Based on the combined analysis of all algorithms, the UBCc gene was considered as the most stable transcript in fruit mesocarp during different developmental stages and for the all conditions analyzed, whereas the EF1α was selected the most stable under refrigeration. Here we show that any of the UBCc and EF1α genes are suitable reference genes to normalize the gene expression in soursop fruits under different conditions. These genes could be useful for further studies in soursop gene expression analysis.

5

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Declaration of Competing Interest

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The authors declare that they have no conflict of interest Acknowledgments The authors thanks Fondo Sectorial de Investigación para la Educación for the financial support by the grant: “Caracterización morfológica, bioquímica y genética de guanábana (Annona muricata L.)” number 242718 and CONACyT (Consejo Nacional de Ciencia y Tecnología) for the fellowship granted to Guillermo Berumen Varela, PhD by the grant “Apoyos para la Incorporación de Investigadores Vinculada a la Consolidación Institucional de Grupos de Investigación y/o Fortalecimiento del Posgrado Nacional”. References Andersen, C.L., Jensen, J.L., Ørntoft, T.F., 2004. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 64, 5245–5250. Berumen-Varela, G., Hernández-Oñate, M.-A., Tiznado-Hernández, M.-E., 2019. Utilization of biotechnological tools in soursop (Annona muricata L.). Sci. Hortic. 245, 269–273. https://doi.org/10.1016/j.scienta.2018.10.028. Browning, K.S., 1996. The plant translational apparatus. Post-Transcriptional Control of Gene Expression in Plants. Springer, pp. 107–144. González-Agüero, M., Cifuentes-Esquivel, N., Ibanez-Carrasco, F., Gudenschwager, O., Campos-Vargas, R., Defilippi, B.G., 2011. Identification and characterization of genes differentially expressed in cherimoya (Annona cherimola Mill) after exposure to chilling injury conditions. J. Agric. Food Chem. 59, 13295–13299. González-Agüero, M., Tejerina Pardo, L., Zamudio, M.S., Contreras, C., Undurraga, P., Defilippi, B.G., 2016. The unusual acid-accumulating behavior during ripening of cherimoya (Annona cherimola Mill.) is linked to changes in transcription and enzyme activity related to citric and malic acid metabolism. Molecules 21, 398. Gupta, Y., Pathak, A.K., Singh, K., Mantri, S.S., Singh, S.P., Tuli, R., 2015. De novo assembly and characterization of transcriptomes of early-stage fruit from two genotypes of Annona squamosa L. with contrast in seed number. BMC Genomics 16, 86. Jiménez-Zurita, J.O., Balois-Morales, R., Alia-Tejacal, I., Juárez-López, P., Jiménez-Ruíz, E.I., Sumaya-Martínez, M.T., Bello-Lara, J.E., 2017a. Tópicos del manejo poscosecha del fruto de guanábana (Annona muricata L.). Revista mexicana de ciencias agrícolas 8, 1155–1167. Jiménez-Zurita, J.O., Balois-Morales, R., Alia-Tejacal, I., Sánchez Herrera, L.M., JiménezRuiz, E.I., Bello-Lara, J.E., García-Paredes, J.D., Juárez-López, P., 2017b. Cold storage of two selections of soursop (Annona muricata L.) in Nayarit, Mexico. J. Food Qual. 2017. Joseph, J.T., Poolakkalody, N.J., Shah, J.M., 2018. Plant reference genes for development and stress response studies. J. Biosci. 1, 173–187. Kong, Q., Yuan, J., Gao, L., Zhao, S., Jiang, W., Huang, Y., Bie, Z., 2014. Identification of suitable reference genes for gene expression normalization in qRT-PCR analysis in

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