Article pubs.acs.org/JAFC
Comparative Transcriptomic Analysis in Paddy Rice under Storage and Identification of Differentially Regulated Genes in Response to High Temperature and Humidity Chanjuan Zhao, Junqi Xie, Li Li, and Chongjiang Cao* Collaborative Innovation Center for Modern Grain Circulation and Safety, College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, People’s Republic of China ABSTRACT: The transcriptomes of paddy rice in response to high temperature and humidity were studied using a highthroughput RNA sequencing approach. Effects of high temperature and humidity on the sucrose and starch contents and α/β-amylase activity were also investigated. Results showed that 6876 differentially expressed genes (DEGs) were identified in paddy rice under high temperature and humidity storage. Importantly, 12 DEGs that were downregulated fell into the “starch and sucrose pathway”. The quantitative real-time polymerase chain reaction assays indicated that expression of these 12 DEGs was significantly decreased, which was in parallel with the reduced level of enzyme activities and the contents of sucrose and starch in paddy rice stored at high temperature and humidity conditions compared to the control group. Taken together, high temperature and humidity influence the quality of paddy rice at least partially by downregulating the expression of genes encoding sucrose transferases and hydrolases, which might result in the decrease of starch and sucrose contents. KEYWORDS: paddy rice, high temperature and humidity, RNA-Seq, sucrose and starch, metabolic pathway
■
INTRODUCTION Paddy rice (Oryza sativa L.) is an important stable food for people in many countries in Asia, which provides not only essential nutrients but also carbohydrates for energy. In China, rice can be cultivated 1−2 times per year, depending upon water resource, climate, and precipitation.1 To satisfy the demands of the consumers, the storage of harvested rice is needed.2 It is wellknown that temperature and humidity are the most important environmental factors affecting the quality of rice during storage.3 The current effective methods of rice storage include low-temperature and controlled atmosphere strategies,4 for the reason that low temperature and moisture content can protect the rice from fungi and insects, and proper aeration is capable of preventing spontaneous heating. However, in the summer, the temperature generally reaches 38−41 °C or above with 85−98% relative humidity (RH) in some producing areas in southern China. Moreover, the high physiological activity of new harvested paddy rice can lead to the elevated temperature of grain bulk, with about 10 °C higher than the granary temperature. Therefore, studying the effects of a high temperature on the paddy rice is essential to the development of effective and safe practices for rice storage. Rice is extremely sensitive to high temperature. Once the storage temperatures exceed the optimum ranges (20−28 °C) during the storage period, the aging of rice significantly accelerates, mainly showing off-flavor probably as a result of the changes of dry matter production and grain chemical components.5−7 It was reported that most physicochemical properties of rice, such as pasting properties, texture characteristics, nutrition, and flavor, have been dramatically impaired under high-temperature storage.8,9 Meanwhile, a high temperature may reduce the transcriptional activity of glutelin and/or prolamin family genes and rice prolamin box binding factor, thereby resulting in deterioration of rice.10 Recently, a comprehensive © XXXX American Chemical Society
atlas of metabolome and transcriptome revealed that high temperatures downregulated the sucrose import/degradation and starch biosynthesis but also upregulated the starch degradation.6 In addition to a high temperature, environmental humidity also affects the quality of stored rice. High moisture conditions can cause a rapid decline in germination, color, baking quality, malting quality, and fatty acid composition. Rice is naturally contaminated with microorganisms during production, harvesting, and processing.11 High humidity storage conditions can lead to proliferation of microorganisms and spoilage on rice, resulting in reduction in quality.12 Studies reported that the number of yeasts and molds on rice generally increased at 85% related humidity, regardless of the degree of storage temperature and milling.13 Starch is the main component of rice, at up to 90%.14 The content and structure of starch correlated with stickiness and hardness of rice, and it is considered as the key determinant of rice quality. Starch properties are also quite different among different temperatures and humidities.15 Hence, starch is an important parameter to characterize the changes of rice quality. Sucrose as a sweetener not only affects the taste quality of rice but also changes the structure and stickiness of cooking rice.16 The presence of sucrose can also contribute to the storage of rice. Because sucrose, starch, and other important metabolites comprise the quality of paddy rice,17−19 the influences of high temperature and humidity on the metabolisms of starch, sucrose, and other metabolites in rice during storage need to be investigated further. High-throughput RNA sequencing (RNA-Seq) technology was usually applied in the research of rice cultivation and breeding. This method generates a large collection of transcriptomes, Received: August 21, 2017 Accepted: August 28, 2017 Published: August 28, 2017 A
DOI: 10.1021/acs.jafc.7b03901 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry
The amylase activities of the samples were determined using the amylase assay kit, provided by the Nanjing Jiancheng Bioengineering Institute. Each sample used three biological repeats. Determination of Pasting Properties. The pasting properties of rice starch were determined using a rapid visco analyzer (RVA, Perten 4500, Australia).22 Starch samples (3 g) were mixed with 25 mL of deionized water. The heating process was obtained from 50 to 95 °C with a rate of 13 °C/min under a constant speed of 160 rpm and maintained at 95 °C for 3 min. Then, the cooling process was reduced to 50 °C at a rate of 13 °C/min and maintained at 50 °C for 4 min. Changes in peak viscocity, through viscocity, final viscocity, breakdown, setback, and pasting temperature were analyzed during the RVA test. Three biological replicates were used in each sample. RNA Extraction and cDNA Cloning. Frozen samples in triplicate were ground in liquid nitrogen using a mortar and pestle. The total RNA of each sample was extracted using the Pure Plant Total RNA extraction kit (Waryong Company). The RNA concentration and purity were determined using a Biospec-nano spectrophotometer (Shimadzu, Kyoto, Japan). Only RNA samples with 260/280 ratio between 1.9 and 2.1 and 260/230 ratio higher than 2.0 were used for subsequent analyses. The integrity of the RNA samples was also assessed on 2.0% agarose/formaldehyde gel electrophoresis. A total of 2 μg of total RNA was reverse-transcribed using the PrimeScript RT reagent kit (Takara) according to the instructions of the manufacturer. Library Construction and Transcriptome Sequencing. RNA from control and treatment were from three biological repeats. Sequencing libraries were constructed using NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, U.S.A.) according to the directions of the manufacturer, and index codes were added to attribute sequences to each sample. Briefly, sequencing projects included the purification of mRNA and the synthesis of cDNA. Purification of the synthesis of the double cDNA and fragment selection were by the AMPure XP system (Beckman Coulter, Beverly, MA, U.S.A.). cDNA fragments were preferentially selected 150−200 bp in length. At last, the libraries were constructed, and library quality was assessed on the Agilent Bioanalyzer 2100 system. The Illumina Genome Analyzer system was used to analyze the transcriptome sequencing resulted from RNA isolated from samples D1 and D2 comparatively. Sequencing Assessment and Assembly of Raw Data. The original sequencing sequence, which contains low-quality and adapter reads, was obtained. To ensure the quality of information analysis, raw reads must be filtered to obtain reads clean. Clean reads were a foundation for the next analysis. The steps of data processing mainly included the following aspects. Removing reads containing adapter and the uncertain reads were based on read information greater than 10%. At last, the low-quality reads were removed, with Qphred of ≤20 and length of ≥50% of the reads containing ploy-N in the complete reads. TopHat, version 2.0.12, was selected as the mapping tool to map the clean reads according to reference genome and reference gene sequences for generating a database of splice junctions. Reference genome and gene model annotation files were downloaded from the genome website directly (ftp://ftp.ensemblgenomes.org/pub/plants/ release-36/fasta/oryza_sativa/dna/). Quantification of the Gene Expression Level and Differential Expression Analysis. The direct embodiment of the gene expression level was the abundance of the transcript. The higher the abundance of the transcript, the higher the gene expression level. Considering the effects of the depth and length of the gene sequencing accounting for fragments, the number of fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM) was currently the most common method to estimate the level of gene expression.23 The FPKM of each gene was calculated according to the length of the gene using HTSeq software to analyze gene expression levels of various samples. Differential expression analysis was used to identify the differentially expressed genes (DEGs) between D1 and D2 using the DESeq R package (1.18.0).24 Before the probability of hypothesis test was calculated according to the model, the read count was standardized. Subsequently, multiple hypothesis testing was performed. DESeq analyzed gene expression data using the negative binomial distribution. The p value was adjusted, and the false discovery rate was controlled using the
which is suitable for identifying the transcriptional network of a certain biological event rapidly. To understand the effects of high temperature and humidity on the changes of paddy rice at the molecular level, the differentially expressed genes in response to high temperature and high humidity were investigated using RNA-Seq technology. Also, genes involved in the starch and sucrose metabolism pathway were further studied. Our works provide novel insights into the transcriptional program of paddy rice under high temperature and humidity storage, which might lay the foundation for optimizing the rice storage conditions.
■
MATERIALS AND METHODS
Material Preparation and Treatment. Newly harvested paddy rice (crop year 2015, cultivar Nanjing 9108) grown in Nanjing (Jiangsu, China) was obtained from Jiangsu Academy of Agricultural Sciences (JAAS). The newly harvested paddy rice was pretreated with sun drying in the open air for 2 days to remove the moisture content. Then, one part of them was stored at normal temperature (25 °C/50% RH) as a control group, marked D1, and the rest was stored at 37 °C and RH of 70%, marked D2. After 30 days of storage, samples were immediately frozen in liquid nitrogen and then stored at −80 °C for the RNA extraction and RNA-Seq experiments. According to our previous findings, paddy rice stored at 37 °C/70% RH for 30 days showed significant changes in the quality.20 Each sample used three biological replicates. Determination of the Sucrose Content, Starch Content, and Amylase Activity. 3,5-Dinitrosalicylic acid (DNS) colorimetry was used to determine the contents of sucrose and starch in rice.21 The standard curve was accurately prepared with 1 mg/mL glucose solution and different ratios of glucose. Then, the solution was measured at 540 nm, and the standard curve was drawn. About 3 g (M) of rice powder in triplicate was dissolved in a beaker with 50 mL of distilled water, heating at 50 °C water baths for 20 min to leach the reducing sugar. The solution centrifuged for 5 min at 4000 revolutions/min to obtain the supernatant. The supernatant was added to a 100 mL volumetric flask and diluted with distilled water to 100 mL (V1) to wait for measuring the content of reducing sugar. A total of 0.5 mL of supernatant was added to 1.5 mL of distilled water and 1.5 mL of DNS, mixed, and diluted with distilled water to 10 mL. The solution was taken at 1 mL (V2) and used to determine the soluble sucrose content. The absorbance (A1) of the solution was measured at 540 nm, and the sucrose content (%) was calculated by eq 1 sucrose content (%) =
xV1V2 × 100 M
(1)
where x is the weight of glucose calculated from the glucose standard curve, V1 is the total volume of the reaction system, V2 is the volume of the sample solution under test, colorimetric light path, and M is the sample weight. The 1 g rice powder (m) in triplicate was dissolved in 15 mL of distilled water and 10 mL of 6 M HCl, and then samples were reacted for 30 min at 100 °C. Starch was hydrolyzed into sugar through the water bath heating. After solution cooling, 1 drop of phenolphthalein indicator and neutralized acid with 6 M NaOH were added and diluted with distilled water to 100 mL. The solution was taken after a constant volume, filtered, and diluted 10 times as the total sugar solution under test. A total of 1 mL of total sugar solution was mixed with 1 mL of distilled water and 1.5 mL of DNS and diluted with distilled water to 10 mL. The content of starch (%) was expressed by starch hydrolyzed to sucrose and calculated from the absorbance at 540 nm of the samples using eq 2
starch content (%) =
100x × 0.9 × 100% m
(2)
where 100 is the solution dilution times, x is the weight of glucose calculated from the glucose standard curve, m is the sample weight, and 0.9 is the conversion coefficient between the sugar content and starch content. B
DOI: 10.1021/acs.jafc.7b03901 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry Table 1. Primers Used for Real-Time PCR gene name
F-primer sequence (5′ → 3′)
R-primer sequence (5′ → 3′)
OS09G0553200 OS01G0952600 OS03G0712700 OS07G0420700 OS02G0528300 OS06G0683300 OS02G0661100 OS09G0298200 OS06G0320200 OS07G0656200 OS06G0229800 OS02G0141300
CGATTGATGAAACGGTTGG TGCCAAGTAGGAATCCGTAA CAACCTTGGAGGCGATAA GGGTTTGGGTGCTTTATTTC CCCGAAACGACTACTTGG ATGGAAACCCGCCCGTAG CTGTCGCCTATTGTGGATGA ACAAAGAAGAGGGCGAAGC AGGGAGTTTGGTGACAGG CCAAATGGGACTGGGATG GCTGGACAGGATCTGGAAGTGAAA GCCCTATGCCTGCTTTCC
GCAGGAGGTCATGCCATTTAG CCCAGAAGGGTTCAAGTGTAG CACCCTGATGCTTGGAGAC CCCTTTGGCAACTTATGTGA AAACTTTAGGGATGAGGTGCT TCGCACCGTGTAGCCTGA CACCTTATTGCGGGACCTT GTAGTCCATACGGTACAGGTGAT GGTGCTAATATGCCGTTG GAGATGGTGGGCAGCAAG GCATGGAACGTGCCAAGGAA CCTCCGTACCATCTCAACG
Figure 1. Effect of different storage conditions on (B) sucrose content, (C) starch content, (D) α-amylase activity, and (E) β-amylase activity in rice. The sucrose and starch contents were calculated according to the (A) glucose standard curve. (∗) p < 0.05 indicated statistically significant differences versus D1. Benjamini−Hochberg approach. The genes with p value of 0 indicated upregulated genes, whereas those of log2 fold change of 0 were downregulated genes. As shown in Figure 2, 3553 DEGs were downregulated, while 3323 DEGs were upregulated in rice stored at high temperature and humidity conditions. Classification of GO Categories and Pathways of 6876 DEGs. The GO enrichment analysis (Figure 3) showed the distribution of genetic differences, such as biological processes, cellular components, and molecular functions.30 The 6876 DEGs in these three groups were classified into 30 GO categories. In the biological process group, a great majority of DEGs fell into the nitrogen compound metabolic process,31 followed by the cellular macromolecule biosynthetic process. Figure 3 showed that those DEGs were mainly enriched in the cellular component group. Those DEGs were mainly involved in functions of the protein complex, intracellular macromolecular complex. In this type of enriched GO term, those DEGs were mainly associated with functions of the intracellular part, cell part, macromolecular complex, and intracellular membrane bond. These GO mapping
Figure 2. Scatter plots of DEGs at a probability of p < 0.05 in the paddy rice of the control group (D1) and high temperature and humidity storage group (D2). Significantly differentially expressed genes were indicated in red and green, and no significant difference was marked in blue. The abscissa represents the fold change in gene expression in different samples, and the ordinate represents the statistical significance of the difference in gene expression. E
DOI: 10.1021/acs.jafc.7b03901 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry
Figure 3. GO enrichment analysis.
Figure 4. Vertical axis represents the name of the pathway, while the horizontal axis represents the rich factor. The greater the rich factor, the greater the degree of enrichment. The size of the point indicates the number of DEGs in this pathway, and the color of the points corresponds to different Q value ranges. The values of Q were between 0 and 1. The closer the Q value to zero, the more significant the enrichment. F
DOI: 10.1021/acs.jafc.7b03901 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry Table 4. Differential Gene KEGG Enrichment List term
gene ID
gene name
functional description
starch and sucrose metabolism
OS09G0553200 OS07G0420700 OS02G0528300 OS06G0683300 OS02G0661100 OS09G0298200 OS06G0320200 OS07G0656200 OS06G0229800 OS02G0141300 OS01G0952600 OS03G0712700
UDP−glucose pyrophosphorylase glycosyl hydrolase hypothetical gene β-glucosidase homologue trehalose-6-phosphate phosphatase ADP−glucose pyrophosphorylase small subunit 1 β-glucosidase 24 β-glucosidase 26 alkali digestion arabinokinase-like protein β-galactosidase phosphoglucomutase
catalytic/oxidoreductase activity carbohydrate derivative binding hydrolase activity hydrolase activity, acting on glycosyl bonds catalytic/transferase activity carbon−carbon lyase activity protein binding hydrolase activity, acting on glycosyl bonds hydrolase activity/catalytic activity transferase activity/molecular function β-galactosidase activity/molecular function carbohydrate binding/molecular function
galactose metabolism
Figure 5. Expression analysis of 12 DEG mRNAs in the samples D1 and D2. Total RNA was isolated from fresh paddy rice and paddy rice stored at high temperature and humidity. UBQ5 gene (ubiquitin 5) was used as an internal reference. Expression levels of each gene are shown as a ratio relative to the treatment time (0 day) that was set to 1. Each value represents the means of three biological replicates from three different samples, with the standard error (SE) indicated by vertical bars. (∗) p < 0.05 indicated statistically significant differences versus D1.
Figure 5 shows that the expression of 12 DEGs was decreased to 0.2−0.7 times in response to high temperature and humidity compared that of the control (p < 0.05). In general, transcript
levels of all mRNAs were decreased after high temperature and humidity treatment for 30 days; particularly, OS09G0553200, OS06G0229800, and OS02G0141300 showed a fast decreased G
DOI: 10.1021/acs.jafc.7b03901 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry pattern. OS09G0553200, OS06G0229800, and OS02G0141300 encoded transferase, hydrolase, and catalyze that were inhibited in the starch and sucrose metabolic pathways. It has been reported that adverse conditions can affect the process of sugar metabolism in growing plants.32 On the other hand, starch phosphorylase and α-amylase were major starch hydrolyzing enzymes in plants.33 The energy for the growth of grain was provided from α-amylase, which produced D-glucose and oligosaccharide units. The decrease of OS09G0553200, OS06G0229800, and OS02G0141300 accumulation suggests a reduction of the hydrolase activity under high temperature and moisture. In contrast, the expression level of OS03G0712700 and OS06G0683300 declined relatively slowly. The genes in sucrose metabolic pathway, including OS01G0952600, OS02G0661100, and OS02G0528300, were significantly decreased. It is noted that sucrose catalyzes and transferases were involved in sucrose breakdown and integrated in energy metabolism, relating to the metabolic function of storage cells.34 Our results, together with some studies, have shown that high temperature reduced transferase activity. In summary, we used RNA-Seq to obtain comprehensive sequences from fresh paddy rice and paddy rice stored at high temperature and humidity. Our investigation represents the first report on the systematic study about the storage of paddy rice with a high temperature and humidity environment using RNA-Seq and qRT-PCR. GO enrichment analysis and KEGG pathway enrichment analysis revealed that high temperature and humidity affected the aspects of molecular function and metabolism pathways. From the comparison of transcriptomes from two storage stages, we identified several possible pathways associated with starch and sucrose metabolism, in which most genes were downregulated. Our results suggest that high temperature might inhibit the activity of sucrose transferase and hydrolase, which, in turn, might lead to the decreased content of starch and sugar, thereafter resulting in the poor quality of the paddy rice. Further validation of highly enriched DEGs and pathways will be the key emphasis of our future research.
■
quantitative real-time polymerase chain reaction; KEGG, Kyoto Encyclopedia of Genes and Genomes
■
REFERENCES
(1) Liao, X.-Y.; Chen, T.-B.; Xie, H.; Liu, Y.-R. Soil As contamination and its risk assessment in areas near the industrial districts of Chenzhou City, Southern China. Environ. Int. 2005, 31, 791−798. (2) Choi, S.; Jun, H.; Bang, J.; Chung, S. H.; Kim, Y.; Kim, B. S.; Kim, H.; Beuchat, L. R.; Ryu, J. H. Behaviour of Aspergillus flavus and Fusarium graminearum on rice as affected by degree of milling, temperature, and relative humidity during storage. Food Microbiol. 2015, 46, 307−313. (3) Cao, Z.; Zhao, Q.; Pan, G.; Wei, K.; Zhou, L.; Cheng, F. Comprehensive expression of various genes involved in storage protein synthesis in filling rice grain as affected by high temperature. Plant Growth Regul. 2017, 81, 477−488. (4) Ahmad, U.; Alfaro, L.; Yeboah-Awudzi, M.; Kyereh, E.; Dzandu, B.; Bonilla, F.; Chouljenko, A.; Sathivel, S. Influence of milling intensity and storage temperature on the quality of Catahoula rice (Oryza sativa L.). LWTFood Sci. Technol. 2017, 75, 386−392. (5) Krishnan, P.; Ramakrishnan, B.; Reddy, K. R.; Reddy, V. R. HighTemperature Effects on Rice Growth, Yield, and Grain Quality. Adv. Agron. 2011, 111, 87−206. (6) Jagadish, S. V.; Murty, M. V.; Quick, W. P. Rice responses to rising temperatures–challenges, perspectives and future directions. Plant, Cell Environ. 2015, 38, 1686−1698. (7) Geigenberger, P. Regulation of starch biosynthesis in response to a fluctuating environment. Plant Physiol. 2011, 155, 1566−1577. (8) Zhou, Z.; Robards, K.; Helliwell, S.; Blanchard, C. Effect of rice storage on pasting properties of rice flour. Food Res. Int. 2003, 36, 625− 634. (9) Zhou, Z.; Robards, K.; Helliwell, S.; Blanchard, C. Ageing of Stored Rice: Changes in Chemical and Physical Attributes. J. Cereal Sci. 2002, 35, 65−78. (10) Kawakatsu, T.; Yamamoto, M. P.; Touno, S. M.; Yasuda, H.; Takaiwa, F. Compensation and interaction between RISBZ1 and RPBF during grain filling in rice. Plant J. 2009, 59, 908−920. (11) Laca, A.; Mousia, Z.; Díaz, M.; Webb, C.; Pandiella, S. S. Distribution of microbial contamination within cereal grains. J. Food Eng. 2006, 72, 332−338. (12) Alvo, P.; Raghavan, G. S. V. Cereal GrainsMycotoxins, Fungi and Quality in Drying and Storage. Drying Technol. 1993, 11, 411−412. (13) Choi, S.; Kim, H.; Kim, Y.; Kim, B. S.; Beuchat, L. R.; Ryu, J. H. Fate of Bacillus cereus and naturally occurring microbiota on milled rice as affected by temperature and relative humidity. Food Microbiol. 2014, 38, 122−127. (14) Zhang, C.; Zhou, L.; Zhu, Z.; Lu, H.; Zhou, X.; Qian, Y.; Li, Q.; Lu, Y.; Gu, M.; Liu, Q. Characterization of Grain Quality and Starch Fine Structure of Two Japonica Rice (Oryza sativa) Cultivars with Good Sensory Properties at Different Temperatures during the Filling Stage. J. Agric. Food Chem. 2016, 64, 4048−4057. (15) Hu, X.; Lu, L.; Fang, C.; Duan, B.; Zhu, Z. Determination of Apparent Amylose Content in Rice by Using Paper-Based Microfluidic Chips. J. Agric. Food Chem. 2015, 63, 9863−9868. (16) Torres, M. D.; Raymundo, A.; Sousa, I. Effect of sucrose, stevia and xylitol on rheological properties of gels from blends of chestnut and rice flours. Carbohydr. Polym. 2013, 98, 249−256. (17) Xu, S. B.; Li, T.; Deng, Z. Y.; Chong, K.; Xue, Y.; Wang, T. Dynamic proteomic analysis reveals a switch between central carbon metabolism and alcoholic fermentation in rice filling grains. Plant Physiol. 2008, 148, 908−925. (18) Price, J.; Laxmi, A.; St. Martin, S. K.; Jang, J. C. Global transcription profiling reveals multiple sugar signal transduction mechanisms in Arabidopsis. Plant Cell 2004, 16, 2128−2150. (19) Gibson, S. I. Plant sugar-response pathways. Part of a complex regulatory web. Plant Physiol. 2000, 124, 1532−1539. (20) Li, L.; Zhao, C.; Zhang, Y.; Yao, J.; Yang, W.; Hu, Q.; Wang, C.; Cao, C. Effect of stable antimicrobial nano-silver packaging on inhibiting mildew and in storage of rice. Food Chem. 2017, 215, 477−482.
AUTHOR INFORMATION
Corresponding Author
*Telephone/Fax: 13770625999. E-mail:
[email protected]. ORCID
Chongjiang Cao: 0000-0001-6160-1166 Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS This research was supported by the National Natural Science Foundation of China (Grant 31571901), the National Key Research and Development Program of China (2016YFD0400901), the Key Research and Development Program of Jiangsu Province (BE2016386), the Agricultural Independent Innovation Fund of Jiangsu Province [CX (16)1062], the Natural Science Foundation of Jiangsu Province (BK20141486), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
■
ABBREVIATIONS USED RNA-Seq, high-throughput RNA sequencing; DEG, differentially expressed gene; GO, gene ontology; FPKM, number of fragments per kilobase of transcript sequence per million base pairs sequenced; RVA, rapid visco analyzer; qRT-PCR, H
DOI: 10.1021/acs.jafc.7b03901 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry (21) Li, F.-h.; Hu, H.-j.; Yao, R.-s.; Wang, H.; Li, M.-m. Structure and Saccharification of Rice Straw Pretreated with Microwave-Assisted Dilute Lye. Ind. Eng. Chem. Res. 2012, 51, 6270−6274. (22) Chun, A.; Lee, H. J.; Hamaker, B. R.; Janaswamy, S. Effects of ripening temperature on starch structure and gelatinization, pasting, and cooking properties in rice (Oryza sativa). J. Agric. Food Chem. 2015, 63, 3085−3093. (23) Trapnell, C.; Pachter, L.; Salzberg, S. L. TopHat: Discovering splice junctions with RNA-Seq. Bioinformatics 2009, 25, 1105−1111. (24) Anders, S.; Huber, W. Differential expression analysis for sequence count data. Genome Biol. 2010, 11, R106. (25) Wang, L.; Feng, Z.; Wang, X.; Wang, X.; Zhang, X. DEGseq: An R package for identifying differentially expressed genes from RNA-Seq data. Bioinformatics 2010, 26, 136−138. (26) Young, M. D.; Wakefield, M. J.; Smyth, G. K.; Oshlack, A. Gene ontology analysis for RNA-Seq: Accounting for selection bias. Genome Biol. 2010, 11, R14. (27) Kanehisa, M.; Araki, M.; Goto, S.; Hattori, M.; Hirakawa, M.; Itoh, M.; Katayama, T.; Kawashima, S.; Okuda, S.; Tokimatsu, T.; Yamanishi, Y. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2007, 36, D480−D484. (28) Jain, M.; Nijhawan, A.; Tyagi, A. K.; Khurana, J. P. Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem. Biophys. Res. Commun. 2006, 345, 646−651. (29) Livak, K. J.; Schmittgen, T. D. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method. Methods 2001, 25, 402−408. (30) Cai, H.; Xie, W.; Lian, X. Comparative Analysis of Differentially Expressed Genes in Rice Under Nitrogen and Phosphorus Starvation Stress Conditions. Plant Mol. Biol. Rep. 2013, 31, 160−173. (31) Zhu, Y.; Li, Y.; Xin, D.; Chen, W.; Shao, X.; Wang, Y.; Guo, W. RNA-Seq-based transcriptome analysis of dormant flower buds of Chinese cherry (Prunus pseudocerasus). Gene 2015, 555, 362−376. (32) Zhou, R.; Sicher, R. C.; Quebedeaux, B. Apple leaf sucrosephosphate synthase is inhibited by sorbitol-6-phosphate. Funct. Plant Biol. 2002, 29, 569−574. (33) Yang, J.; Zhang, J.; Wang, Z.; Zhu, Q. Activities of starch hydrolytic enzymes and sucrose-phosphate synthase in the stems of rice subjected to water stress during grain filling. J. Exp. Bot. 2001, 52, 2169− 2179. (34) Ranwala, A. P.; Miller, W. B. Sucrose-cleaving enzymes and carbohydrate pools in Lilium longiflorum floral organs. Physiol. Plant. 1998, 103, 541−550.
I
DOI: 10.1021/acs.jafc.7b03901 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
