Sunday, 3 July 2016

What are Turmeric & Curcumin Good for?

Turmeric is a spice derived from the rhizome and root of the Curcuma longa plant, which is native to India and Indonesia. People have consumed turmeric for its flavor and health-promoting properties for 4,000 years. Curcumin is the major active ingredient responsible for turmeric’s medicinal properties. Using turmeric as a spice is a healthful practice, but taking its derivative curcumin as an herbal supplement will produce a more profound medicinal effect. As with all herbal supplements, talk to your health-care practitioner before supplementing with curcumin.

Curcumin, the active ingredient in turmeric, is a powerful antioxidant. It helps to reduce and neutralize free radicals, which damage and destroy your cells and DNA. Curcumin also reduces two inflammation-promoting enzymes in your body and is therefore an effective anti-inflammatory agent. Due to curcumin's ability to reduce inflammation, the University of Maryland Medical Center, or UMMC, states that curcumin supplements may help to relieve symptoms of osteoarthritis, a condition characterized by inflammation, pain and stiffness in the joints.

Curcumin stimulates the gallbladder to produce bile, and consuming the spice turmeric regularly, or supplementing with its active ingredient curcumin, may help to improve digestion, reduce bloating and gas and treat digestive disorders. In addition, curcumin supplements combined with conventional medical treatment may help treat bowel disorders such as ulcerative colitis.

Curcumin is purported to help prevent cardiovascular disease, and may be particularly beneficial for the prevention of atherosclerosis, a condition characterized by a hardening and narrowing of the arteries. UMMC states that curcumin has been found to lower total cholesterol and LDL cholesterol levels and to prevent the blood from clotting. The research on curcumin supplements for cardiovascular disease is preliminary, however, and has not yet been conducted on humans.

Consuming turmeric regularly or taking a curcumin supplement may help to prevent and treat cancer. Curcumin appears to block the blood supply to cancerous tumors, and consequently to suppress the growth and replication of tumor cells. According to "Principles & Practice of Pediatric Oncology," extensive research has indicated that curcumin can prevent cancer in animals. In addition, high intakes of turmeric have reduced the rate of colorectal, lung and prostate cancer in humans.

Taking a curcumin supplement may help prevent and treat bacterial and viral infections. Curcumin supplements are also used to treat liver problems and skin disease, lower blood sugar levels in diabetics and reduce kidney stones. In addition, curcumin may help to treat an inflammatory eye condition known as uveitis. More research needs to be done confirm the efficacy of curcumin supplements for these purposes, however.

Turmeric is safe for consumption, and curcumin supplements are generally considered safe. However, prolonged intake of high-dose curcumin extracts may cause stomach upset and, in extreme cases, ulcers. Curcumin supplements may also interact with certain medications and should not be taken by individuals on blood-thinning drugs or by diabetics without doctor supervision.

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Turmeric, Ginger and Cancer

Ginger, a pungent aromatic rhizome of the plant Zingiber officinale, is native to China, where it has been used as a medicinal plant for thousands of years. There is evidence about the chemopreventive effect of ginger; however, more human trials are needed to strengthen this evidence. Turmeric, a bright, yellow spice derived from the rhizomes of the plant Curcuma longa, is grown in tropical Asia and Africa. It contains various polyphenolic compounds, with curcumin the most potent. Curcumin gives turmeric most of its medicinal benefits, including cancer prevention. Do not take ginger, turmeric and any other herbal supplements without first consulting your doctor.

Pungent constituents of ginger have a myriad of biological effects, including anti-inflammatory and anti-cancer, based on a study published in the May 2007 issue of the journal “Food and Chemical Toxicology.” Y. Shukla and M. Singh, ginger researchers at the Industrial Toxicology Research Centre in India, have identified a dozen of compounds with anticancer activities in ginger. These include vallinoids, gingerols, paradols, shogaols and zingerone.

Scientists at the University of Michigan Comprehensive Cancer Center examined the chemosensitizing effect of ginger in ovarian cancer cells. Ginger selectively kills the ovarian cancer cells that have acquired resistance to standard therapy, according to Dr. J. Rebecca Liu, an assistant professor of obstetrics and gynecology at the University of Michigan Medical School. It destroys cancer cells through two distinct cell death pathways called apoptosis and autophagy.

Turmeric may have a number of tumor-suppressor functions in human carcinogenesis, including cell-cycle arrest and apoptosis, according to Jane Higdon, a cancer researcher at Oregon State University. It inhibits cell cycle in cells exceeding a critical threshold of DNA damage, thus eliminating the risk of becoming cancerous. Turmeric also protects your body, inducing apoptosis — a normal physiological process in which your body removes genetically damaged cells and unwanted cells.

Researchers at the University of California-San Francisco have examined the possible mechanism through which turmeric confers protection against prostate cancer. These findings suggest that turmeric regulates cytoprotective genes in normal prostate cancer cells that help prevent cellular oxidative stress and inflammation, factors that can affect the risk of developing prostate cancer. Turmeric may also exert preferential cytotoxic effects against malignant prostate cells, according to Jeffery Herman, an investigator in this study.

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How to Get a Six Pack Fast for Girls

Six-pack abs are sought after by most teenage girls as well as adult women. Although it requires effort, girls can achieve six-pack abs by sticking to healthy eating plans and exercise regimens. Body-fat percentage must be low to see toned abdominals which may otherwise be hiding under layers of fat. Be on your way to bare abs with a combination of cardio, core strengthening and diet.

Cut caloric intake to lose excess fat hiding abdominal muscles. Weight-loss occurs when you ingest less calories than you burn off. To lose a pound of fat a week, you will need to burn off roughly 3,500 calories or 500 calories per week. Decrease caloric intake by snacking on fruit instead of cookies, drinking water versus soda and eating green vegetables with lean protein for meals. A nutrient-rich diet should consist of veggies, fruits, protein and healthy fats in moderation while reducing or eliminating sugar and polyunsaturated fats.

Tone up abdominals with planking exercises. Front planks target the rectus abdominis and deep transversus abdominis muscles which flatten your belly. Do a front plank by lying on the floor face-down. Push yourself up to rest on your toes and forearms with elbows underneath your shoulders. Contract your abdominal muscles to avoid sagging in the middle as you maintain a straight body from head to toe. Hold for 30 to 60 seconds and repeat three times five days per week.

Target the obliques, the muscles on the sides of the trunk, with side planks. Toned obliques will whittle your middle and give your stomach a sculpted look. Do a side-plank by lying on your right side on the floor, legs stacked on top of each other, and body forming a straight line from head to toe. Lift yourself up so that you are resting on the side of your right foot and right forearm, elbow underneath your shoulder. Keep your hips lifted to avoid sagging in the middle while contracting the abdominal muscles. Hold for 30 to 60 seconds and repeat three times on each side of the body. Do side planks five days per week to maximize six-pack potential.

Run, bike, swim, hike and sweat your way to lean, ripped abs. Cardio exercise raises the heart rate and metabolism to burn off calories for fat-loss all over the body including the abs. The less fat you have, the more visible abs muscles will be to the eye. Aim to do five cardio sessions per week of 45 to 60 minutes duration at a moderate-pace. Two of those sessions should include high-intensity intervals done at a vigorous pace, to increase caloric burn both during and hours after exercise. A sample interval session would be to cycle hard for one minute followed by two minutes of easier recovery cycling. Repeat 10 times and include a five- to 10-minute warm-up and cool-down.

Consult with a medical professional prior to initiating any dietary or exercise regimens that could adversely affect your health.

Use proper form for each exercise to maximize its effectiveness and avoid injury.
Eat protein first to fill up on good calories and wait five minutes to let the food settle before searching for an extra snack.

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Six-Pack Abs Diet for Women

The Abs Diet, and its sequel, The Abs Diet for Women, was created by Men's Health magazine editor-in-chief David Zinczenko, who claims his diets can help people achieve the coveted six-pack abs. While this six-week diet incorporates a number of healthy principles and has the potential to lead to at least some weight loss, no scientific studies are associated with this diet, and it probably won't give most women the extremely toned abdominal muscles typically called a "six pack." Check with your doctor before starting any weight-loss diet or exercise program to make sure it is safe for you.

Women following the Abs Diet are advised to eat three meals a day as well as snacks two hours before both lunch and dinner and a snack two hours after dinner. Eating regularly spaced-out meals and snacks throughout the day can help maintain steadier blood sugar levels, thus limiting cravings and hunger in between meals. A study published in the American Journal of Epidemiology in 2003 found that eating at least four times per day was associated with a lower risk of obesity than eating three or fewer times.

In this diet, you don't need to count calories or carbs; you just need to eat at least two of the Abs Diet power foods at each meal and one at each snack. These foods, which are are nutritious and filling, include nuts, peanut butter, legumes, green leafy vegetables, dairy, plain instant oatmeal, eggs, whole grains, olive oil, berries, whey protein powder, and turkey and other lean meats; they can easily fit into a healthy weight-loss diet. You're also supposed to drink smoothies made from a mix of these power foods, such as yogurt, whey, berries and peanut butter, and include these foods in some of your snacks throughout the week.

Yogurt, vegetables, fruits, nuts and whole grains are all associated with weight loss, according to the results of a study published in The New England Journal of Medicine in 2011. Nuts, peanut butter, lean meat, eggs, whey protein powder, legumes and dairy are all good sources of protein, which can help improve satiety and weight loss. A study published in Nutrition, Metabolism and Cardiovascular Diseases in 2009 found that high-protein diets resulted in greater weight loss than standard protein diets. Both nuts and olive oil provide monounsaturated fat, which can help to limit deposits of fat in the abdominal area, according to a study published in Diabetes Care in 2007. Eating at least three servings per day of whole grains, such as oatmeal, are associated with less abdominal fat and a lower body mass index, notes a review article published in Public Health Nutrition in 2008.

You don't want to just add the power foods to your diet; you'll also need to avoid or limit certain other foods. For example, you're advised to avoid alcohol and drink no more than two glasses of diet soda per day. Instead, drink green tea, low-fat milk or water. Substances in green tea called catechins may help increase abdominal fat loss from exercise, according to a study published in The Journal of Nutrition in 2008. Avoid foods with trans fats, saturated fat, refined carbohydrates or high-fructose corn syrup. The exception to these rules is that one meal per week you can have a "cheat meal" and eat whatever you want so you won't be craving it the rest of the week.

The Abs Diet for Women requires exercise, including at least three strength-training sessions and two abs workouts each week. Cardio is included in the warm-ups for the strength-training workouts, but Zinczenco recommends light cardio workouts on days in between strengh-training workouts as well as at least one interval-training workout per week. Getting regular cardiovascular exercise may help more with weight loss than stretching, including decreases in both abdominal and total fat, according to a study published in The Journal of the American Medical Association in 2003. A combination of strength training and cardio is even more helpful for decreasing abdominal fat than cardio alone, according to another study published in the Journal of Physiological Anthropology and Applied Human Science in 2003. To get the full details on the diet and exercise program, you'll need to either buy the book or pay to use the online version of the diet.

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Muscle Supplements for a Six Pack

Developing six pack abs means decreasing your body fat percentage. Everyone has abdominal muscles underneath their skin, says "The Abs Diet" author David Zinczenko. In order to show them the light of day, you need to focus on diet. There are powerful natural supplements than can help you lose body fat faster to define your midsection and boost muscle building at the same time. Add these supplements to your diet to increase fat burning and see your six pack abs.

Considered a conditionally essential amino acid, glutamine can boost fat burning in several ways to help you reveal your hidden six pack. Glutamine play a direct role in fat burning and helps the body store carbohydrates as muscle glycogen. By boosting muscle recovery and sparing muscle tissue from catabolism, glutamine also promotes lean muscle retention, says "Good News on Glutamine" by Matthew Kadey. Take 5 to 10g, up to three times daily.

The branched-chain amino acids are L-leucine, L-valine and L-isoleucine; they get the name from their branched molecular structure, which makes them unique among amino acids. According to "Natural Anabolics" author Jerry Brainum, BCAAs fuel muscles directly, bypassing the liver when they absorb into the body. This prevents muscle breakdown, triggers protein synthesis and makes sure that your body uses fat for energy, rather than your own hard-earned muscle. Take 5g before, during and/or after workouts.

The combination of whey and casein proteins represents one of the most powerful supplement strategies for gaining muscle and burning fat. Nature's preeminent growth food, milk, contains both whey and casein protein. Whey digests quickly, while casein has a much slower rate of digestion. "Xtreme Lean" author Jonathan Lawson attributes these combined traits to staying full longer, burning more fat and experiencing less muscle loss while dieting. Use a 50/50 whey casein blend between meals or after your workout.

According to "Supplements 101" by Jim Stoppani, forskolin has many fat burning effects that can help you define your abs. By boosting a critical enzyme involved in testosterone production, forskolin helps the body produce more of the fat burning and muscle building hormone. In addition, forskolin may boost thyroid function, though more research is needed on this point. Regardless, forskolin decreased body fat levels in exercising men, says Brainum. Take 25 to 50 mg of forskolin extract three times daily.

L-Arginine acts as a nitric oxide of NO precursor in the body. NO dilates blood vessels, decreasing blood pressure and allowing for more blood flow to working muscles. During your training sessions, this means better muscle pumps and more energy. More importantly, however, higher NO means that your body burns fat faster and more efficiently, says "Everything You Need to NO" by Jordana Brown. Take up to 3g, three times daily.

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Meal Plans for the ABS Diet for Women

"The Abs Diet for Women" is a diet plan book written by David Zinczenko, the editor-in-chief of Men's Health magazine. The diet is an offshoot of "The Abs Diet," a similar book and program designed for both men and women. In the diet, Zinczenko recommends eating a variety of healthy fats and exercising regularly in order to get a flat, toned stomach. Talk to your doctor before trying the diet program, especially if you have health conditions or injuries.

While following the Abs Diet for Women, eat a breakfast filled with lean protein, fruits and vegetables. Egg whites will help build lean muscle tissue instead of fat in the body. One breakfast sample meal may include an egg white omelet with a small amount of low-fat cheese, bell peppers and tomatoes inside. Eat the omelet along with a slice of whole-grain bread with a small amount of butter on top, if desired.

Stick with lean protein for lunch, like turkey, chicken or fish. Make a salad with spinach or romaine lettuce, sliced avocado, apples and sliced turkey or chicken. Sprinkle a small amount of olive oil and lemon juice on top instead of dressing. Stay away from foods that are high in saturated fat, like full-fat dairy products and red meat. Opt instead for nonfat and low-fat dairy, lean protein and vegetable protein sources like black beans and chickpeas.

Don't deprive yourself at dinner. Enjoy a filling plate of whole-wheat pasta with a small amount of tomato sauce and turkey meatballs for dinner. Mix a small amount of olive oil into the sauce to help add omega-3 fatty acids to the meal, if desired -- olive oil is a source of monounsaturated fat that is one of the 12 Power Foods listed in the diet.

Zinczenko recommends eating three small, protein-filled snacks in between meals. These snacks may include hard-boiled eggs, apples with peanut butter, yogurt with nuts and fruit sprinkled in, or whole-grain crackers with slices of avocado. Eat one snack between breakfast and lunch, one snack between lunch and dinner and one snack after dinner. You may be less apt to crave dessert if you eat one nutritious snack after dinner instead of a sweet treat.

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Transcriptome analysis reveals key differentially expressed genes involved in wheat grain development

Published Date

April 2016, Vol.4(2):92–106, doi:10.1016/j.cj.2016.01.006

Open Access, Creative Commons license, Funding information

Title

Transcriptome analysis reveals key differentially expressed genes involved in wheat grain development

Author

Yonglong Yu^a,1

Dong Zhu^a,1

Chaoying Ma^a,1

Hui Cao^a,1

Yaping Wang^a

Yanhao Xu^b

Wenying Zhang^b

Yueming Yan^a,b,^,

aCollege of Life Sciences, Capital Normal University, Beijing 100048, China
bHubei Collaborative Innovation Center for Grain Industry/Yangtze University, Jingzhou 434025, China

Received 18 December 2015. Revised 8 January 2016. Accepted 2 February 2016. Available online 13 February 2016.

Abstract

Wheat seed development is an important physiological process of seed maturation and directly affects wheat yield and quality. In this study, we performed dynamic transcriptome microarray analysis of an elite Chinese bread wheat cultivar (Jimai 20) during grain development using the GeneChip Wheat Genome Array. Grain morphology and scanning electron microscope observations showed that the period of 11–15 days post-anthesis (DPA) was a key stage for the synthesis and accumulation of seed starch. Genome-wide transcriptional profiling and significance analysis of microarrays revealed that the period from 11 to 15 DPA was more important than the 15–20 DPA stage for the synthesis and accumulation of nutritive reserves. Series test of cluster analysis of differential genes revealed five statistically significant gene expression profiles. Gene ontology annotation and enrichment analysis gave further information about differentially expressed genes, and MapMan analysis revealed expression changes within functional groups during seed development. Metabolic pathway network analysis showed that major and minor metabolic pathways regulate one another to ensure regular seed development and nutritive reserve accumulation. We performed gene co-expression network analysis to identify genes that play vital roles in seed development and identified several key genes involved in important metabolic pathways. The transcriptional expression of eight key genes involved in starch and protein synthesis and stress defense was further validated by qRT-PCR. Our results provide new insight into the molecular mechanisms of wheat seed development and the determinants of yield and quality.

Keywords

Wheat

Transcriptome microarray

Differentially expressed genes

Grain development

1 Introduction

Wheat (Triticum aestivum L., 2n = 6x = 42, AABBDD), an allohexaploid species, is the principal food crop used for humans and livestock globally. Wheat is counted among the “big three” cereal crops and is unrivaled in its range of cultivation owing to its extensive agronomic adaptability, high yield potential, and nutritional profile (including essential amino acids, minerals, vitamins, beneficial phytochemicals, and dietary fiber) [1] and [2].

Cereal seeds consist of two main tissue fractions: starchy endosperm/aleurone and embryo/scutellum. The starchy endosperm of mature wheat seeds is a major source of nutrition, as well as the primary site for the storage of starch and proteins important for grain yield and flour quality [3] and [4]. The aleurone is a single cell thick and has two biological roles: (1) digesting the starchy endosperm to release nutrients and amino acids to the germinating embryo and (2) protecting the endosperm from stress and pathogens [5]. In addition, the wheat seed aleurone layer is the most concentrated source of vitamins and minerals and is also rich in proteins and lipids [6].

Starch and protein are the principal storage reserves in the wheat seed. Wheat seed development includes five main phases: fertilization (0 days post-anthesis [DPA]), “coenocytic” endosperm (1–5 DPA), cellularization, and early grain-filling (6–13 DPA), maximum grain filling (14–24 DPA), and desiccation (25–38 DPA). In general, the grain-filling period is considered to comprise cellularization and the early and maximum grain-filling phases [7].

Seed development is a very important stage in the cereal crop seed life cycle. The nutritive reserves of mature wheat seeds provide not only human food and livestock feed but also the energy for seed germination. Initial primary studies on seed development focused mainly on seed physiology and biochemistry [8], providing us with a basic understanding of the seed development process. In recent years, proteomics has been used to study the biochemical mechanisms of plant seed development, including barley [9], Cunninghamia lanceolata [10], Medicago truncatula [11] and [12], Lotus japonicus [13] and wheat [14]. However, the number of proteins identified by the proteomics approach is limited and does not permit a complete genome-wide comparison. Dry mature seeds of crops contain a vast number of mRNA species, which were first identified in cotton [15]. Since the 1990s, stored RNA in the mature dry seeds of plant species has been shown to be universal [16], [17] and [18], and gene expression patterns can be detected in stored seed mRNA. Affymetrix arrays can provide a comprehensive, real-time picture of changes at the whole-transcriptome level during seed development, and this tool has been used to investigate the biological processes of wheat grain development [6], [19] and [20]. Although transcriptome analysis during wheat grain development by RNA-Seq has been performed [21], studies of the comprehensive dynamic transcriptional characterization of grain filling stages are still limited.

Modern allohexaploid wheat has a huge and complex genome (up to 17,000 Mb) composed of the ancestral A, B, and D genomes. Recently, there has been much progress in wheat genome sequencing [22]. The A^u genome in Triticum urartu and D^tgenome in Aegilops tauschii are the progenitors of the A and B genomes, respectively, of hexaploid wheat. Studies of the A^u and D^t genomes have recently been completed [23] and [24] and will facilitate further proteomics and transcriptomics research on wheat seed development. In the present study, we used an elite Chinese bread wheat cultivar (Jimai 20) with high yield, wide adaptability, and superior quality [25] to perform a dynamic transcriptome microarray analysis during grain-filling stages using the GeneChip Wheat Genome Array (Affymetrix, Santa Clara, CA, USA). We identified key differentially expressed genes involved in grain development. Our results shed new light on the molecular mechanisms of the accumulation of nutritive reserves in wheat, as well as the determinants of yield and quality.

2 Materials and methods

2.1 Plant material and field experiment

The wheat cultivar Jimai 20 was planted at the experimental station of Chinese Agricultural University, Beijing, in the 2013 to 2014 growing season. Experiments were performed in three biological replicates (each plot with 50 m²). Cultivation and management followed local field production conditions. Grain samples were harvested at 11, 15, and 20 days post-anthesis (DPA). The collected samples were immediately placed in liquid nitrogen and stored at − 80 °C until use.

2.2 Grain ultrastructure observation by scanning electron microscope (SEM)

Grain samples harvested at 11, 15, and 20 DPA were placed in fixative (5 mL 38% formalin, 5 mL glacial acetic, 90 mL 70% ethyl alcohol) for more than 12 h. Sequentially, the samples were dehydrated in 70% ethanol solution (20 min), 80% ethanol solution (20 min), 90% ethanol solution (overnight), and 100% ethanol solution (20 min). The samples were then treated stepwise for 20 min each in mixtures of ethanol and isoamyl acetate with ratios 3:1, 1:1, and 1:3 before soaking in isoamyl acetate. Finally, critical-point drying was performed for SEM observation. Observation of grain endosperm ultrastructures was performed with a SEM S-4800 FESEM instrument (Hitachi, Japan).

2.3 RNA isolation, microarray hybridization, and data treatments

Total RNAs isolation and microarray hybridization were performed as described previously [2]. All microarray data from the three biological replicates obtained in this study were deposited in the NCBI GEO database and are accessible under GEO Series accession number GSE75561 (http://www.ncbi.nlm.nih.gov/geo/query/acc. cgi?acc = GSE75561).

2.4 Filter of multi-class differential genes

As the random variance model (RVM) F-test can raise degrees of freedom effectively in the cases of small samples, it was applied to filter the differentially expressed genes for the control and experiment group. The differentially expressed genes were selected based on P-value threshold after significance analysis and false discovery rate (FDR) analysis [26], [27] and [28].

2.5 Series test of cluster (STC) and gene ontology (GO) annotation analysis of differential genes

According to the random variance model (RVM) corrective ANOVA, the series test of cluster (STC) was performed by selecting differential expression genes. In accordance with the different signal density change tendency of genes under different situations, we identified a set of unique model expression tendencies. The raw expression values were converted into log₂ ratios. Some unique profiles were further defined using a strategy for clustering short time-series gene expression data. The expression model profiles are involved in the actual or the expected number of genes assigned to each model profile. Fisher's exact test and multiple-comparison tests were used to determine whether the significant profiles had higher probability than expected [29] and [30]. Gene ontology (GO) analysis of genes showing certain specific tendencies was performed following Dong et al. [31] to identify the main functions of genes with the same expression trend [32] and [33].

2.6 MapMan analysis

In the MapMan analysis [2], input files were created by calculating the natural log ratio of the three control samples to the mean detection in the treatment samples. Genes were considered as not expressed under the respective experimental condition if they were absent in two of the three replicates. Final analyses were performed with MapMan version 1.6.1 [34], including the automatic application of the Wilcoxon rank sum test. Entire data sets from NascArrays and from Nakabayashi et al. [35] were downloaded to allow comparison with public Affymetrix ATH1 data sets.

2.7 Metabolic pathway analysis

To identify significant pathways of differentially expressed genes, pathway network analysis was conducted using KEGG, Biocarta, and Reatome [2]. First, significant pathways were selected using Fisher's exact test and χ² test, in which P-value (< 0.05) and FDR (< 0.05) were used to define the threshold of significance. The enrichment Re was calculated according to previous reports [36], [37] and [38]. The Path–Net, the interaction net of the significant pathways of the differentially expressed genes, was constructed through the interaction among pathways of the KEGG database, which was used to directly and systemically identify interactions between significant pathways. The Path–Net summarizes the pathway interaction of genes differentially expressed during disease and explains why certain pathways were activated [37].

2.8 Dynamic gene net analysis

Gene dynamic co-expression networks were built based on functional gene associations to identify gene interactions in biological processes, in which a net was constructed from significantly correlated gene pairs according to the calculation of the Pearson correlation for each pair [39]. In the net, cycle nodes and edges between the two nodes represent genes and interactions between genes, respectively. The network analysis was performed to locate the core regulatory factors (genes) showing the highest k-core values or degree values and showing connection with most adjacent genes. A k-core of the network represents a subnetwork in which all nodes are connected to at least k other genes. As a consequence, the rank of the k-core value shows the complexity of the gene association relationships, with the maximum core order in the graph considered the primary or highest k-core [40]. Cycles with identical colors are parts of the same subgraph [41] and [42]. We further analyzed the various properties of networks, in which the degree centrality, an important measure of gene centrality within a network, was constructed to estimate the relative importance.

2.9 Total mRNA extraction and qRT-PCR analysis

Total mRNA was extracted using TRIzol Reagent (Invitrogen) based on the manufacturer's instructions. A PrimeScriptRT reagent Kit with gDNA Eraser (TaKaRa) was used to remove genomic DNA and to synthesize the cDNA. Gene-specific primers were designed with the online tool Primer3Plus (http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi). The primers were further checked by blasting primer sequences against the NCBI database (http://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome). ADP-ribosylation factor (ADP) was selected as the reference gene. Transcription levels of genes were quantified with a CFX96 Real-Time PCR Detection System (Bio-Rad) based on the 2(− Delta Delta C(T)) method [43]. The intercalating dye SYBR Green was used, and the experiment was performed in a 20 μL volume reaction system containing 10 μL 2 × SYBR Premix ExTaq (TaKaRa), 2 μL 10-fold diluted cDNA, 0.15 μL of each gene-specific primer, and 7.7 μL ddH₂O. The PCR running protocol was as follows: 95 °C for 3 min, 40 cycles at 95 °C for 20 s, 60 °C for 15 s, and 72 °C for 20 s. Three biological replicates and triplicates for each PCR reaction were performed for each gene. The qRT-PCR efficiency was detected by five serial 10-fold dilutions of cDNA. In addition, the PCR amplification efficiency (E) of 95–105% and correlation coefficient (R²) of 0.994–1.000 were controlled.

3 Results

3.1 Grain morphology and ultrastructure changes during seed development

The accumulation of nutritive reserves (such as starch, sucrose, storage proteins, and lipids) begins in early grain development and continues until the seed matures. We harvested grain samples based on thermal times (the cumulative average daily temperature after anthesis, °C) corresponding to DPA: 252 °C (11 DPA), 353 °C (15 DPA), and 461 °C (20 DPA) in order to analyze gene expression patterns during grain development. In general, grain size and weight gradually increased from flowering to maturity. Morphology and SEM analysis showed that seed size gradually increased during grain development (Fig. 1-A), as did weight and accumulation of starch granules (Fig. 1-B and C). Wheat endosperm contains three kinds of starch granule: A-type (diameter > 10 μm), B-type (diameter 5–10 μm), and C-type (diameter < 5 μm), which accumulate during grain development and supply energy for seed germination. Previous observations have indicated that A-type starch granules appear at 6 DPA, while the B-type appears at 11 DPA [44] and [45]. Our results showed that A-type granule size and grain weight increased more rapidly from 11 to 15 DPA than during the subsequent phases, showing that the period 11–15 DPA is a key stage for the synthesis and accumulation of seed starch (Fig. 1-C).

Fig. 1. Grain morphological and ultrastructural changes during seed development. A. Changes in grain morphology. B. Changes in grain weight during the development process. C. Ultrastructural changes in wheat endosperm based on SEM observation. A- and B-granules are indicated.

3.2 Transcriptome expression profiles and differential expression gene analysis during seed development

Using transcriptome expression analysis using GeneChip Wheat Genome Arrays, we identified a total of 61,703 probe sets (the sum of 61,115 actual probe sets and the other 588 repeated probe sets), which could be classified into 35 bins representing 51,411 transcripts (Table S1, Table S2). Of these, 19,782 probe sets with different functions were classified with automatic annotations of metabolic pathways and large enzyme families. The other 41,921 probe sets were classified as “unknown” or “not assigned.”

Genome-wide transcriptional profiling showed that extensive gene expression occurred during grain development. In total, 1904 differentially expressed probe sets corresponding to 1736 transcripts were discovered during three seed development phases (Table S3). To compare the numbers of up- or downregulated differential genes between two successive time points, we used significance analysis of microarrays (SAM) with a stringent 5% FDR (Table S4, Table S5). SAM plotsheet analysis (Fig. 2) showed that 8656 significantly differentially expressed genes were detected in the comparison of 11 to 15 DPA, of which 3105 genes were upregulated and the remainder downregulated. However, between 15 and 20 DPA, there were only 2101 significant differentially expressed genes, of which 1316 were upregulated and 785 downregulated. Apparently, there were 4-fold more differentially expressed genes between 11 and 15 DPA than between 15 and 20 DPA, suggesting that the former period is more important during reserve synthesis and accumulation.

Fig. 2. The number of differentially expressed genes. A. SAM Plot analysis displays significant difference genes in the periods 11–15 DPA and 15–20 DPA. B. Venn diagram analysis of the differentially expressed genes.

3.3 STC and GO annotation analysis of differentially expressed genes

STC analysis of differentially expressed genes revealed 16 gene expression profiles that can be classified into four groups (Fig. 3). Among these 16 expression profiles, we identified 5 significant profiles, all belonging to groups I and II, based on their P values (Fig. S1, Fig. 3). As shown in Table S6, the upregulated genes in group I and downregulated genes in group II accounted for 67.6% of the total differentially expressed genes. The genes assigned to group III showed increased expression in the period 11–15 DPA, but were sharply downregulated in the next period. The genes assigned to group IV were downregulated between 11 and 15 DPA and upregulated after 15 DPA (Fig. 3). Among the five significant expression profiles, two major significant patterns (profile 2 in group I and profile 7 in group II) had the largest numbers of differentially expressed genes (294 and 169, respectively) (Fig. S1, Table S6).

Fig. 3. Gene expression patterns analyzed by model profile and two significant patterns (profiles 2 and 7). A. Each box represents a model expression profile. The upper number in the profile box is the model profile number and the lower number is the P value used to summarize the different gene expression patterns. In total, five expression patterns of genes showed significant P-values (P < 0.05) and are labeled with asterisk. The same color represents the same group. The model profiles marked with red (group I) represent upregulated expression patterns, whereas the green profile boxes (group II) represent downregulated expression patterns. Profiles 5, 12, and 16 (group III) belong to the pattern of genes that were upregulated first and then downregulated, whereas the remaining profile boxes (group IV) belong to the opposite pattern to that of group III. B. Profile 2 increased in expression and profile 7 decreased in expression during seed germination. The horizontal axis represents development phase and the vertical axis the time series of gene expression levels after log₂-normalized transformation.

To obtain an overview of differentially expressed genes involved in the five statistically significant differential expression patterns during seed development, we performed GO annotation and enrichment analysis. The GO annotation results are listed in Table S7. The distribution bar charts of the five profiles (profiles 2, 1, and 11 belonging to upregulated group I and profiles 7 and 4 belonging to downregulated group II) are shown in Fig. 4 according to the P-values of their annotations (P < 0.05). In profile 2, “response to water” (GO: 0009415, P = 3.23E-10) was the most significantly enriched term. “Chitin catabolic process” (GO: 0006032, P = 3.72E-05) and “cell wall macromolecule catabolic process” (GO: 0016998, P = 0.000124) were also significantly enriched (Fig. 4-A). In profile 1, “dicarboxylic acid transport” (GO: 0006835, P = 0.000426), “proteolysis” (GO: 0006508, P = 0.000699), and “response to oxidative stress” (GO: 0006979, P = 0.008485) were the top three significantly overrepresented terms (Fig. 4-B). In profile 11, the top two significantly enriched terms were “chitin catabolic process” (GO: 0,006,032, P = 0.000217) and “cell wall macromolecule catabolic process” (GO: 0016998, P = 0.000534). “Positive regulation of cell division” (GO: 0051781, P = 0.009934) and “cell wall modification” (GO: 0042545, P = 0.011779) were also significant (Fig. 4-C). In profile 7, “metabolic process” (GO: 0008152, P = 5.90E-05) and “polysaccharide catabolic process” (GO: 0000272, P = 0.006663) were the top two terms (Fig. 4-D). In profile 4, “response to oxidative stress” (GO: 0006979, P = 0.004654), “oxidation reduction” (GO: 0055114, P = 0.005996), “lignin biosynthetic process” (GO: 0009809, P = 0.011846), and “peptidoglycan biosynthetic process” (GO: 0009252, P = 0.017743) were the top four significantly enriched terms (Fig. 4-E).

Fig. 4. GO annotation analysis of five significant expression profiles and all the differentially expressed genes. A, B, C, D, and E show the GO analyses of five significant expression profiles, whereas the last, F, is the GO analysis of all the differentially expressed genes. Horizontal axis is –lg P (P is the abbreviate of P-value), vertical axis represents the different functional groups.

“Response to water” (GO: 0009415, P = 3.23E-10) and “metabolic process” (GO: 0008152, P = 5.90E-05) in profiles 2 and 7, respectively, were the two most significantly enriched terms. This suggests that genes related to water stress are upregulated and genes involved in metabolic process are downregulated during the period of grain-filling before desiccation (Fig. 4-A and D). Functional annotations associated with stress response and cell wall metabolism were detected frequently among the five profiles, suggesting that both functional groups are important during seed development.

We also performed GO analysis for all the differentially expressed genes, as listed in Table S7 and shown in Fig. 4-F (P < 0.05). We found that “proteolysis” (GO: 0006508, P = 3.20E-08), “response to water” (GO: 0009415, P = 1.88E-05), “chitin catabolic process” (GO: 0006032, P = 2.45E-06), “chromatin assembly or disassembly” (GO: 0006333, P = 1.55E-05), and “cell wall macromolecule catabolic process” (GO: 0016998, P = 2.01E-05) were most significantly overrepresented.

3.4 Reserve accumulation and abiotic stress responses during seed development as revealed by MapMan analysis

We used MapMan analysis to map transcriptome data, define functional categories, and identify significantly overrepresented functional groups, as well as to display important functional groups of genes activated in different stages of grain development (Fig. 5, Table S1, Table S2). Reserve accumulation in the endosperm, which can provide energy for seed germination, post-germination and growth, is initiated in early grain development and ends when the seed matures. The seed development stages (11–20 DPA) in this study involved mainly grain filling (reserve accumulation), which corresponds to the early grain-filling and maximum grain-filling stages [7].

Fig. 5. MapMan metabolism overview maps showing differences in transcript levels (11 vs. 15 and 15 vs. 20 DPA) during seed development. A. 11 vs. 15 HAI. B. 15 vs. 20 DPA. log₂ ratios for average transcript abundance were based on three replicates of Affymetrix GeneChip Wheat Genome Array. The resulting file was loaded into the MapMan Image Annotator module to generate the metabolism overview map. On the logarithmic color scale, blue represents downregulated transcripts and red represents upregulated transcripts.

Energy provision via the activation of glycolysis, the tricarboxylic acid (TCA) cycle, and mitochondrial electron transportation are essential for grain development [14]and [46]. In our gene expression data, most genes involved in sucrose hydrolysis and glycolysis were upregulated, especially from 15 to 20 DPA (Fig. 5). Key enzymes involved in sucrose hydrolysis and glycolysis include sucrose invertase, hexokinase, phosphofructokinase (PPFK), and pyruvate kinase (PK). From 11 to 20 DPA, the expression values of genes CD454795 (encoding sucrose invertase) and BQ162543 (encoding hexokinase) increased 8-fold and 3-fold, respectively. The genes CA661266 (related to PPFK) and CA640211 (involved in PK) were strongly activated, while CA661266 and CA640211 showed distinct upregulation at 11–15 DPA and 15–20 DPA, respectively.

A large number of genes involved in starch synthesis were detected in our results. Starch synthesis genes are gradually activated and upregulated from 11 to 20 DPA, and sucrose synthesis-related genes followed a similar expression pattern (Fig. 5). As one of the key enzymes in the process of starch synthesis, sucrose synthase (SUSY) breaks down sucrose into fructose and glucose that are then converted into uridine diphosphate (UDPG) with catalysis by UDPG pyrophosphorylase (UGPase). UDPG is the glucose donor in the synthesis process for glycosides, oligosaccharides, polysaccharides, and related molecules. We found that the genes encoding SUSY (BJ277098 and CD453631) and UGPase (CA617830) had significantly upregulated expression during grain development. Previous studies have suggested that UGPase also couples with ADPG pyrophosphorylase (AGPase), another key rate-limiting enzyme in starch synthesis, to produce ADPG for starch synthesis [47] and [48]. In our data, we found an important gene (CA619571) related to AGPase that was upregulated from 11 to 20 DPA. The upregulation of these related genes contributes to the gradual accumulation of starch.

Protein synthesis depends on amino acid synthesis and accumulation. We found that genes related to the synthesis of common amino acids were activated from 11 to 15 DPA, and their expression levels increased significantly from 15 to 20 DPA (Fig. 5). In particular, the genes for methionine (Met) and aspartic acid (Asp) synthesis, two important amino acids for protein synthesis, were strongly upregulated, indicating an acceleration in protein synthesis during the 15–20 DPA period.

The MapMan cellular response overview visualization tool revealed cell stress responses during seed development (Fig. 6). The majority of heat transcripts associated with abiotic stress were upregulated from 11 to 15 DPA, and their expression continued to increase until 20 DPA. The upregulation of cold, drought/salt, and touch/wounding transcripts was more pronounced at 15–20 DPA than at 11–15 DPA. Heme serves as a key enzyme for clearing H₂O₂ in antioxidant pathways and was active mainly from 11 to 15 DPA. After 15 DPA, the genes associated with heme were downregulated (Fig. 6).

Fig. 6. MapMan metabolic overview maps showing differences in transcript levels (11 versus 15 and 15 versus 20 DPA) of cellular response during seed development. For further details, see legend to Fig. 5.

3.5 Metabolic pathway network analysis

We established a network for metabolic pathways (Fig. 7). According to the degree value (Table S8), glycolysis/gluconeogenesis, pyruvate metabolism, and the TCA cycle were the top three metabolic pathways. Starch synthesis during grain-filling was the top contributor to nutritive reserve accumulation, followed by protein synthesis. One important pathway is starch and sucrose metabolism (colored orange in Fig. 7). The source and target pathways for starch and sucrose metabolism both suggest that glycolysis/gluconeogenesis and pentose and glucuronate interconversion play direct roles in starch and sucrose metabolism. Other pathways (such as the pentose phosphate pathway, pyruvate metabolism, and the TCA cycle) may play indirect roles in starch and sucrose metabolism. The pathways directly associated with protein synthesis include glycine, serine, and threonine metabolism (Gly, Ser, and Thr), which involve the main enzymes of protein metabolism (cysteine protease, serine protease, and threonine protease). The other essential amino acids are likely to participate indirectly in protein synthesis and degradation by affecting the metabolism of glycine, serine, and threonine. According to our results, lysine (Lys) and valine (Val) biosynthesis, as well as leucine and isoleucine (Leu and Ile) biosynthesis, are the source, as well the targets, of glycine, serine, and threonine metabolism (Gly, Ser, and Thr); thus, these amino acids play key roles in protein metabolism. Tyrosine (Tyr), phenylalanine (Phe), and arginine and proline (Arg and Pro) metabolism affect the pathways of Gly, Ser, and Thr metabolism through their roles in the TCA cycle, pyruvate metabolism, and glyoxylate and dicarboxylate metabolism. All of the major and minor metabolic pathways regulate one another to ensure regular seed development and nutritive reserve accumulation.

Fig. 7. Metabolic pathway net established with significant pathways. Cycle nodes represent different pathways. Arrows represent the interaction between one pathway and another pathway by degree value. The source and points of arrows show the sources and targets of pathways in the network. More arrows at a node mean more pathways interacting with it, corresponding to greater importance of the pathway in the pathway net.

3.6 Gene co-expression network with k-core algorithm

To identify functional genes that play vital roles in wheat seed development, we created a gene coexpression network with a k-core algorithm using the genes involved in important metabolic pathways (Fig. 8). The most central genes have the highest degree values within the network. The interactions and their degrees, as well as k-core values, are listed in Table S9. Most genes were involved in stress response, major CHO metabolism, protein metabolism, hormone metabolism, cell wall metabolism, lipid metabolism, DNA/RNA processing, signaling, or development. Four of the most important core genes were located at key positions in the network BQ172037, encoding the thaumatin family and involved in abiotic stress (k-cores of 8 and 29 degrees); BJ294280, encoding TBC (Tre2/Bub2/Cdc16) and involved in the secretory pathway (k-cores of 9 and 28 degrees); CD490538, encoding an ara54-like RING finger protein and involved in abscisic acid metabolism (k-cores of 9 and 28 degrees); and CA717577, associated with biotic stress (k-cores of 8 and 28 degrees). These four genes directly regulated 112 neighboring genes (Table S9). BQ172037 was downregulated, whereas the other three genes were upregulated (Table S1, Table S2). BQ172037 and BJ294280 expressions were negatively correlated, suggesting that down/upregulation of abiotic stress genes leads to up/downregulation of secretory pathway genes and vice versa.

Fig. 8. Gene co-expression network during seed development with k-core algorithm. Cycle nodes represent genes in the net. The sizes of nodes represent the strength of the interrelation among the nodes, and the edges between two nodes represent interactions between genes. The more edges on a gene, the more genes connect to it, and the more central the role it plays in the network. In the network, dashed lines represent negative and solid lines positive regulation.

Protein metabolism, stress response, and major CHO metabolism during wheat seed development are important metabolic pathways. Our results show that BJ294280 (related to the secretory pathway and located at the center of the network) is a pivotal gene. According to the k-cores of BJ294280, 28 neighboring genes involved in stress response, cell wall, major CHO metabolism, development, RNA transcription regulation and hormone metabolism were regulated by BJ294280. Some of these regulated genes were downregulated, such as genes associated with abiotic stress, cell walls, and major CHO metabolism, whereas other genes were upregulated by BJ294280. The k-cores of all 28 co-expressed genes were greater than 6 (Table S9), indicating that these genes play key roles in seed development.

3.7 Validation of key differentially expressed genes by qRT-PCR

To validate the microarray data, the transcriptional expressions of eight key differentially expressed genes involved in starch and protein synthesis and stress defense were further analyzed by qRT-PCR (Fig. 9). The gene-specific primers used in this study are listed in Table S10 and the double standard curves and melting temperature curves as optimal performance parameters are shown in Fig. S4. The expression trends of the eight genes by qRT-PCR were highly consistent with those from cDNA microarray analysis (Table S1).

Fig. 9. The dynamic expression patterns of eight important differentially expressed genes by qRT-PCR. The gene expression levels at 11 DPA were defined as 1.0. ^⁎P < 0.05, ^⁎⁎P < 0.01.

4 Discussion

The economic and nutritional importance of wheat grain depends on the accumulation of nutritive reserves during wheat seed development. Reserves include principally starch (55–75% of total dry grain weight), storage protein (10–15%), and lesser lipids. Thus, wheat grain development relies upon the synthesis and accumulation of starch and storage proteins. The activation and expression of stress-associated genes can protect plants from various biotic and abiotic stresses during grain development. Recently [21], transcriptome analysis during wheat grain development by RNA-Seq has been performed. It focused on cell type- and stage-dependent expression profiles and expression differences between different genomes. In this study, we used the GeneChip Wheat Genome Array for dynamic transcriptome profiling during grain development, focusing particularly on important differentially expressed genes involved in starch and protein synthesis and stress defense. The main metabolic pathways involved in wheat grain development are discussed in the following section.

4.1 Activation of starch synthesis genes

Carbohydrates are stored mainly in the form of starch in the wheat seed endosperm. Given that starch is the primary source of nutrition, seed germination requires adequate starch reserves. A previous study [20] using cDNA microarrays showed that the onset of wheat grain-filling occurred at 7–14 DPA, when genes encoding enzymes related to starch synthesis and storage protein synthesis were expressed at the highest levels. In the present study, we found that genes involved in sucrose and starch synthesis were activated at around 11 DPA, and that a large number of related genes were upregulated during starch accumulation (Fig. 5). Genes encoding enzymes involved in starch and storage proteins synthesis were expressed at their highest levels at 15–20 DPA.

The 14-3-3 proteins, a family of ubiquitous regulatory molecules that affect the activity of a broad range of targets by direct protein–protein interaction, are present in every eukaryotic organism and tissue [49] and [50]. This family regulates nitrogen and carbon assimilation, as well as various metabolic processes during plant development, including signal transduction, ATP production, peroxide detoxification, checkpoint control, apoptosis, and nutrient-sensing pathways [49] and [51]. In the present study, 14-3-3 proteins were revealed to be involved mainly in signaling-associated metabolic pathways, including signal reception, MAPK cascade reaction, and calcium regulation, with the cooperation of G proteins (Fig. S2). Previous research has shown that 14-3-3b transcript accumulation is inversely related to starch accumulation and may inhibit starch biosynthesis [20], [51] and [52]. We found that the expression of the 14-3-3 gene was downregulated during grain-filling (Fig. 9, Fig. S2), indicating that 14-3-3 transcript accumulation and starch accumulation are negatively correlated, in agreement with the inhibition of starch biosynthesis by 14-3-3b transcript accumulation [20], [51] and [52]. In contrast, G-protein genes were gradually upregulated during starch accumulation, especially at 15–20 DPA. This observation suggests that G-protein transcript accumulation and starch accumulation are positively correlated, and that the upregulation of G protein genes could promote starch accumulation. Additionally, receptor kinase genes were largely upregulated from 11 to 20 DPA (Fig. 9, Fig. S2), suggesting that various signaling pathways are involved in starch gene activation and starch biosynthesis during grain development.

4.2 Activation of protein synthesis genes

Synthesis and accumulation are the major metabolic activities during the grain-filling period of seed development, and storage proteins are an important nutritive reserve during this stage. Prolamins, glutenins and gliadins are the main storage proteins and the major components of the gluten polymer [53]. The accumulation of these storage proteins affects wheat quality formation. All of the protein enzymes in wheat seed development are produced by the activation of protein synthesis genes.

Previous research has revealed that several enzymes involved in carbohydrate and protein metabolism (such as orthophosphate dikinase, aspartate aminotransferase, aspartic protease, and alanine aminotransferase) are most highly expressed at the transcript level at 14 DPA [54]. In the present study, we also identified several important enzyme families, such as cytochrome P450, peroxidases, UDP glycosyltransferases, O-methyltransferases, glutathione-S-transferases, and beta-1,3-glucan hydrolases (Fig. S3). However, the expression of these enzymes tends be at its highest at around 20 DPA, several days later than the previously reported enzymes. Cytochrome P450 is a mixed-function oxidase with many important activities, including biosynthesis, detoxification, and stress resistance. Peroxidases play many roles in plant seeds, such as removing noxious H₂O₂, facilitating seed maturation by inhibiting IAA activity, and promoting ethylene synthesis. UDP glycosyltransferases are enzymes that can regulate the activity of donor molecules by transferring glycosyl from the activated donor molecule to the receptor. O-methyltransferases are responsible for amino transfer, which plays an important role in amino acid synthesis and protein synthesis. Glutathione-S-transferases are vital enzymes in seed development and have important detoxification functions. Beta-1,3-glucan hydrolases affect carbohydrate synthesis and degradation by hydrolyzing various glycosidic bonds. The appropriate activation and upregulation of all of these enzymes are critical for successful seed development.

4.3 Activation of stress defense genes

Seed development and maturation activate a series of mechanisms in response to various biotic and abiotic stresses, accompanied by an increased tolerance to desiccation [55]. In our results, genes involved in biotic and abiotic stresses were all upregulated during the accumulation of nutritive reserves (Fig. 6), particularly heat and drought/salt stress-associated genes. Drought, low temperatures, and salinity have dehydrating effects upon developmental stages, such as pollen and seeds [56]and [57], and late embryogenesis abundant (LEA) proteins are produced in response to such stimuli [58]. Interestingly, we found three important genes (CD913555, CD905348, and AY148491.1) encoding LEA proteins that belong to the functional group related to development (Table S1, Table S2). At the early grain-filling stage, upregulated genes comprised half of all the expressed genes in the group involved in development. After the grain-filling stages, most genes involved in development were upregulated gradually (Fig. 6). Thus, upregulation of development-related genes not only occurs in response to dehydrating stimuli, but also promotes the accumulation of storage reserves.

γ-Aminobutyric acid (GABA) is widely present in plants and plays important roles in plant growth and development. Studies on plants, such as Arabidopsis [59], rice [60]and sunflower [61], have shown that GABA has two highly important functions: the GABA shunt influences the TCA cycle and amino acid metabolism and GABA also provides protection from oxidative stress [60]. One previous study suggested that the accumulation of GABA in response to high temperatures corresponded to changes in glutamate decarboxylase and GABA transaminase activity [60]. Glutamate decarboxylase is synchronously upregulated with GABA and catalyzes GABA synthesis, while GABA transaminase induces GABA degradation. In the present study, we identified several genes encoding glutamate decarboxylase and GABA transaminase (Fig. 5, Table S1, Table S2). The genes encoding glutamate decarboxylase were marginally upregulated (Fig. 9), while GABA transaminase was downregulated. This finding suggests that GABA may be synthesized largely during the grain-filling stage and in response to oxidative stress. Low levels of O₂ have been found to induce GABA production by activating glutamate decarboxylase [62] and [63]. A similar result was reported in Arabidopsis mutants: GABA inhibited the accumulation of reactive oxygen species (ROS) [59]. This inhibition occurs likely because the GABA shunt can weaken the respiration of mitochondria in order to provide reaction substrates for the TCA cycle, such as NADH and succinate. In our results, photorespiration was repressed by the accumulation of GABA (Fig. 5): most genes involved in photorespiration were downregulated at 11–20 DPA.

Acknowledgments

This research was financially supported by grants from the National Natural Science Foundation of China (31471485), Natural Science Foundation of Beijing City and the Key Developmental Project of Science and Technology from Beijing Municipal Commission of Education (KZ201410028031).

Supplementary material

The following are the supplementary data related to this article.

DOCX (559K)

Fig. S1. The trends of 16 expression profiles.

DOCX (809K)

Fig. S2. MapMan metabolic overview maps showing differences in transcript levels (11 versus 15 and 15 versus 20 DPA) of regulation during seed development. For further details, see legend to Fig. 5.

DOCX (598K)

Fig. S3. MapMan metabolic overview maps showing differences in transcript levels (11 versus 15 and 15 versus 20 DPA) of large enzyme families during seed development. For further details, see legend to Fig. 5.

DOCX (470K)

Fig. S4. The qRT-PCR optimal performance parameters of standard curves and melting temperature curves. The red standard curves represent the reference gene (ADP), the blue standard curves represent the target genes. The dissolution curves of different genes are indicated.

XLSX (4.4M)

Table S1. Complete list of normalized expression values obtained from three experimental series based on independently grown plant material during seed development.

XLSX (12K)

Table S2. Overview of MapMan functional classes (BINs and subBINs) of the Affymetrix GeneChip Wheat Genome Array.

XLS (732K)

Table S3. Complete list of differentially expressed genes obtained from the whole list of normalized expression values based on P-value and FDR during seed development.

XLS (8.0M)

Table S4. SAM Plot analysis displaying positive and negative genes for 11 vs. 15 DPA.

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Table S5. SAM Plot analysis displaying positive and negative genes at 15 vs. 20 DPA.

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Table S6. Sixteen expression profiles and their assigned genes.

XLSX (147K)

Table S7. Complete list of co-expression genes. A. the interaction between genes, the value of interaction represents the degree of co-expression; B. the attribute of each gene, the value of degree and k-core represents the significance of each gene.

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Table S8. Source and target pathways in the pathway net during seed development.

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Table S9. Complete list of co-expressed genes. A. Interaction between genes, with the value of the interaction representing the degree of co-expression; B. attributes of each gene, with the values of degree and k-core representing the significance of the gene.

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Table 10. The gene-specific primer sequences used for qRT-PCR analysis. ADP-ribosylation factor(ADP) gene was selected as a reference gene.

Supplementary data for this article can be found online at http://dx.doi.org/10.1016/j.cj.2016.01.006.

References

- [1]
- P.R. Shewry
- Wheat
- J. Exp. Bot., Volume 60, 2009, pp. 1537–1553
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (232)
- [2]
- Y.L. Yu, G.F. Guo, D.W. Lv, Y.K. Hu, J.R. Li, X.H. Li, Y.M. Yan
- Transcriptome analysis during seed germination of elite Chinese bread wheat cultivar Jimai 20
- BMC Plant Biol., Volume 14, 2014, p. 20, doi:10.1186/1471-2229-14-20
- Full Text via CrossRef
- [3]
- P. Aquino, F. Carron, R. Calvo
- Selected wheat statistics
- CIMMYT 1998–1999 World Wheat Facts and Trends, Global Wheat Research in a Changing World: Challenges and Achievements, P.L. Pingali, 1999, CIMMYT, D.F. Mexico, pp. 33–45
- [4]
- E. Triboi, A.M. Triboi-Blondel
- Productivity and grain orseed composition: a new approach to an old problem–invited paper
- Eur. J. Agron., Volume 16, 2002, pp. 163–186
- Article
  |
  PDF (635 K)
  |
  View Record in Scopus
  
  Citing articles (143)
- [5]
- A. Jerkovic, A.M. Kriegel, J.R. Bradner, B.J. Atwell, T.H. Roberts, R.D. Willows
- Strategic distribution of protective proteins within bran layers of wheat protects the nutrient-rich endosperm
- Plant Physiol., Volume 152, 2010, pp. 1459–1470
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (36)
- [6]
- S.A. Gillies, A. Futardo, R.J. Henry
- Gene expression in the developing aleuroneand starchyendosperm of wheat
- Plant Biotechnol. J., Volume 10, 2012, pp. 668–679
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (28)
- [7]
- D. Simmonds, T. O'Brien
- Morphological and biochemical development of the wheat endosperm
- Adv. Cereal Sci. Technol., Volume 4, 1981, pp. 5–70
- [8]
- A.A. Khan
- The physiology and biochemistry of seed development, dormancy and germination
- The Physiology and Biochemistry of Seed Development, Dormancy and Germination, A.A. Khan, 1982, Elsevier Biomedical, Amsterdam, Netherlands
- [9]
- C. Finnie, S. Melchior, P. Roepstorff, B. Svensson
- Proteome analysis of grain filling and seed maturation in barley
- Plant Physiol., Volume 129, 2002, pp. 1308–1319
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (166)
- [10]
- J.S. Shi, Y. Zhen, R.H. Zheng
- Proteome profiling of early seed development in Cunninghamia lanceolata(Lamb.) Hook
- J. Exp. Bot., Volume 61, 2010, pp. 2367–2381
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (27)
- [11]
- K. Gallardo, C.L. Signor, J. Vandekerckhove, R.D. Thompson, J. Burstin
- Proteomics of Medicago truncatula seed development establishes the time frame of diverse metabolic processes related to reserve accumulation
- Plant Physiol., Volume 133, 2003, pp. 664–682
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (169)
- [12]
- J.S. Greenwood, J.D. Bewley
- Seed development in Ricinus communis (Castor Bean): I. Descriptive morphology
- Can. J. Bot., Volume 60, 2011, pp. 1751–1760
- [13]
- S. Dam, B.S. Laursen, J.H. Ørnfelt, B. Jochimsen, H.H. Stærfeldt, C. Friis, K. Nielsen, N. Goffard, S. Besenbacher, L. Krusell, S. Sato, S. Tabata, I.B. Thøgersen, J.J. Enghild, J. Stougaard
- The proteome of seed development in the model legumelotus japonicas
- Plant Physiol., Volume 149, 2009, pp. 1325–1340
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (49)
- [14]
- G.F. Guo, D.W. Lv, X. Yan, S. Subburaj, P. Ge, X.H. Li, Y.K. Hu, Y.M. Yan
- Proteome characterization of developing grains in bread wheat cultivars (Triticum aestivum L.)
- BMC Plant Biol., Volume 12, 2012, p. 147, doi:10.1186/1471–2229-12-147
- Full Text via CrossRef
- [15]
- L. Dure, L. Waters
- Long-lived messenger RNA: evidence from cotton seed germination
- Science, Volume 147, 1965, pp. 410–412
- View Record in Scopus
  
  Citing articles (57)
- [16]
- C. Almoguera, J. Jordano
- Development and environment concurrent expression of sunflower dry-seed-stored low-molecular-weight heat-shock protein and Lea mRNAs
- Plant Mol. Biol., Volume 19, 1992, pp. 781–792
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (106)
- [17]
- N. Ishibashi, D. Yamauchi, T. Minamikawa
- Stored mRNA in cotyledons of Vigna unguiculata seeds: nucleotide sequence of cloned cDNA for a stored mRNA and induction of its synthesis by precocious germination
- Plant Mol. Biol., Volume 15, 1990, pp. 59–64
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (34)
- [18]
- J. Kuligowski, M. Ferrand, E. Chenou
- Stored mRNA in early embryos of a fern marsileavestita: a paternal and maternal origin
- Mol. Reprod. Dev., Volume 30, 1991, pp. 27–33
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (18)
- [19]
- Y.F. Wan, R.L. Poole, A.K. Huttly, C. Toscano-Underwood, K. Feeney, S. Welham, M.J. Gooding, C. Mills, K.J. Edwards, P.R. Shewry, R.A.C. Mitchell
- Transcriptome analysis of grain development in hexaploid wheat
- BMC Genomics, Volume 9, 2008, p. 121, doi:10.1186/1471-2164-9-121
- Full Text via CrossRef
- [20]
- D.L. Laudencia-Chingcuanco, B.S. Stamova, F.M. You, G.R. Lazo, D.M. Beckles, O.D. Anderson
- Transcriptional profiling of wheat caryopsis development using cDNA microarrays
- Plant Mol. Biol., Volume 63, 2007, pp. 651–668
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (50)
- [21]
- M. Pfeifer, K.G. Kugler, S.R. Sandve, B.J. Zhan, H. Rudi, T.R. Hvidsten
- International wheat genome sequencing consortium
- Genome Interplay in the Grain Transcriptome of Hexaploid Bread Wheat, K.F.X. Mayer, O.A. Olsen, Science, Volume 345, 2014, p. 1250091, doi:10.1126/science.1250091
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (26)
- [22]
- R. Brenchley, M. Spannagl, M. Pfeifer, G.L.A. Barker, R. D'Amore, A.M. Allen, N. McKenzie, M. Kramer, A. Kerhornou, D. Bolser, S. Kay, D. Waite, M. Trick, I. Bancroft, Y. Gu, N. Huo, M.C. Luo, S. Sehgal, B. Gill, S. Kianian, O. Anderson, P. Kersey, J. Dvorak, W.R. McCombie, A. Hall, K.F.X. Mayer, K.J. Edwards, M.W. Bevan, N. Hall
- Analysis of the bread wheat genome using whole-genome shotgun sequencing
- Nature, Volume 491, 2012, pp. 705–710
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (358)
- [23]
- H.Q. Ling, J. Wang, S.C. Zhao, D.C. Liu, J.Y. Wang, H. Sun, C. Zhang, H.J. Fan, D. Li, L.L. Dong, Y. Tao, C. Gao, H.L. Wu, Y.W. Li, Y. Cui, X.S. Guo, S.S. Zheng, B. Wang, K. Yu, Q.S. Liang, W.L. Yang, X.Y. Lou, J. Chen, M.J. Feng, J.B. Jian, X.F. Zhang, G.B. Luo, Y. Jiang, J.J. Liu, Z.B. Wang, Y.H. Sha, B.R. Zhang, H.J. Wu, D.Z. Tang, Q.H. Shen, P.Y. Xue, S.H. Zou, X.J. Wang, X. Liu, F.M. Wang, Y.P. Yang, X.L. An, Z.Y. Dong, K.P. Zhang, X.Q. Zhang, M.C. Luo, J. Dvorak, Y.P. Tong, J. Wang, H.M. Yang, Z.S. Li, D.W. Wang, A.M. Zhang
- Draft genome of the wheat A-genome progenitor Triticum urartu
- Nature, Volume 496, 2013, pp. 87–90
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (223)
- [24]
- J.Z. Jia, S.C. Zhao, X.Y. Kong, Y.R. Li, G.Y. Zhao, W.M. He, R. Appels, M. Pfeifer, Y. Tao, X.Y. Zhang, R.L. Jing, C. Zhang, Y.Z. Ma, L.F. Gao, C. Gao, M. Spannagl, K.F.X. Mayer, D. Li, S.K. Pan, F.Y. Zheng, Q. Hu, X.C. Xia, J.W. Li, Q.S. Liang, J. Chen, T. Wicker, C.Y. Gou, H.H. Kuang, G.Y. He, Y.D. Luo, B. Keller, Q.J. Xia, P. Lu, J.Y. Wang, H.F. Zou, R.Z. Zhang, J.Y. Xu, J.L. Gao, C. Middleton, Z.W. Quan, G.M. Liu, J. Wang, H.M. Yang, X. Liu, Z.H. He, L. Mao, J. Wang
- Aegilops tauschii draft genome sequence reveals a gene repertoire for wheat adaptation
- Nature, Volume 496, 2013, pp. 91–95
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (246)
- [25]
- J.C. Luo, J.G. Jiao, D.C. Ren, J.J. Liu
- The new wheat cultivar Jimai 20 with high yield and good quality
- J. Triticeae Crops, Volume 26, Issue 1, 2006, p. 159 (in Chinese)
- [26]
- G.W. Wright, R.M. Simon
- A random variance model for detection of differential gene expression in small microarray experiments
- Bioinformatics, Volume 19, 2003, pp. 2448–2455
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (422)
- [27]
- H.Y. Yang, N. Crawford, L. Lukes, R. Finney, M. Lancaster, K.W. Hunter
- Metastasis predictive signature profiles pre-exist in normal tissues
- Clin. Exp. Metastas, Volume 22, 2005, pp. 593–603
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (60)
- [28]
- R. Clarke, H.W. Ressom, A. Wang, J. Xuan, M.C. Liu, E.A. Gehan, Y. Wang
- The properties of high-dimensional data spaces: implications for exploring gene and protein expression data
- Nat. Rev. Cancer, Volume 8, 2008, pp. 37–49
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (188)
- [29]
- M.F. Ramoni, P. Sebastiani, S. KohaneI
- Cluster analysis of gene expression dynamics
- Proc. Natl. Acad. Sci. U. S. A., Volume 99, 2002, pp. 9121–9126
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (266)
- [30]
- L.D. Miller, P.M. Long, L. Wong, S. Mukherjee, L.M. McShane, E.T. Liu
- Optimal gene expression analysis by microarrays
- Cancer Cell, Volume 2, 2002, pp. 353–361
- Article
  |
  PDF (430 K)
  |
  View Record in Scopus
  
  Citing articles (101)
- [31]
- H.B. Dong, S.Q. Wang, Y.Y. Jia, Y.D. Ni, Y.S. Zhang, S. Zhuang, X.Z. Shen, R.Q. Zhao
- Long-term effects of subacute ruminal acidosis (SARA) on milk quality and hepatic gene expression in lactating goats fed a high-concentrate diet
- PLoS One, Volume 8, 2013, e82850, doi:10.1371/journal.pone.0082850
- Full Text via CrossRef
- [32]
- Gene Ontology Consortium
- The Gene Ontology (GO) project in 2006
- Nucleic Acids Res., Volume 34, 2006, pp. D322–D326
- Full Text via CrossRef
- [33]
- The Gene Ontology Consortium, M. Ashburner, C.A. Ball, J.A. Blake, D. Botstein, H. Butler, J.M. Cherry, A.P. Davis, K. Dolinski, S.S. Dwight, J.T. Eppig, M.A. Harris, D.P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J.C. Matese, J.E. Richardson, M. Ringwald, G.M. Rubin, G. Sherlock
- Gene ontology: tool for the unification of biology
- Nat. Genet., Volume 25, 2000, pp. 25–29
- [34]
- B. Usadel, A. Nagel, O. Thimm, H. Redestig, O.E. Blaesing, N. Palacios-Rojas, J. Selbig, J. Hannemann, M.C. Piques, D. Steinhauser, W.R. Scheible, Y. Gibon, R. Morcuende, D. Weicht, S. Meyer, M. Stitt
- Extension of the visualization tool MapMan to allow statistical analysis of arrays, display of corresponding genes, and comparison with known responses
- Plant Physiol., Volume 138, 2005, pp. 1195–1204
- View Record in Scopus
  
  Citing articles (337)
- [35]
- K. Nakabayashi, M. Okamoto, T. Koshiba, Y. Kamiya, E. Nambara
- Genome-wide profiling of stored mRNA in Arabidopsis thaliana seed germination: epigenetic and genetic regulation of transcription in seed
- Plant J., Volume 41, 2005, pp. 697–709
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (256)
- [36]
- M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno, M. Hattori
- The KEGG resource for deciphering the genome
- Nucleic Acids Res., Volume 32, 2004, pp. D277–D280
- View Record in Scopus
  
  Citing articles (1625)
- [37]
- M. Yi, J.D. Horton, J.C. Cohen, H.H. Hobbs, R.M. Stephens
- Whole pathway scope: a comprehensive pathway-based analysis tool for high-throughput data
- BMC Bioinformatics, Volume 7, 2006, p. 30, doi:10.1186/1471-2105-7-30
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (3)
- [38]
- S. Draghici, P. Khatri, A.L. Tarca, K. Amin, A. Done, C. Voichita, C. Georgescu, R. Romero
- A systems biology approach for pathway level analysis
- Genome Res., Volume 17, 2007, pp. 1537–1545
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (420)
- [39]
- V. Vermeirssen, M.I. Barrasa, C.A. Hidalgo, J.A.B. Babon, R. Sequerra, L. Doucette-Stamm, A.L. Barabási, A.J.M. Walhout
- Transcription factor modularity in a gene-centered C. elegans core neuronal protein-DNA interaction network
- Genome Res., Volume 17, 2007, pp. 1061–1071
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (61)
- [40]
- W. Huber, V.J. Carey, L. Long, S. Falcon, R. Gentleman
- Graphs in molecular biology
- BMC Bioinf., Volume 8, Issue Suppl. 6, 2007, p. S8, doi:10.1186/1471–2105-8-S6-S8
- Full Text via CrossRef
- [41]
- E. Ravasz, A.L. Somera, D.A. Mongru, Z.N. Oltvai, A.L. Barabási
- Hierarchical organization of modularity in metabolic networks
- Science, Volume 297, 2002, p. 1551
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (1984)
- [42]
- A.L. Barabási, Z.N. Oltvai
- Network biology: understanding the cell's functional organization
- Nat. Rev. Genet., Volume 5, 2004, pp. 101–113
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (3511)
- [43]
- K.J. Livak, T.D. Schmittgen
- Analysis of relative gene expression data using real-time quantitative PCR and the 2^{− ΔΔCT} method
- Methods, Volume 25, 2001, pp. 402–408
- Article
  |
  PDF (700 K)
  |
  View Record in Scopus
  
  Citing articles (44130)
- [44]
- D.B. Bechtel, J.D. Wilson
- Amyloplast formation and starch granule development in hard red winter wheat
- Cereal Chem., Volume 80, 2003, pp. 175–183
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (54)
- [45]
- J.D. Wilson, D.B. Bechtel, T.C. Todd, P.A. Seib
- Measurement of wheat starch granule size distribution using image analysis and laser diffraction technology
- Cereal Chem., Volume 83, 2006, pp. 259–268
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (52)
- [46]
- A.R. Fernie, F. Carrari, L.J. Sweetlove
- Respiratory metabolism: glycolysis, the TCA cycle and mitochondrial electron transport
- Curr. Opin. Plant Biol., Volume 7, 2004, pp. 254–261
- Article
  |
  PDF (482 K)
  |
  View Record in Scopus
  
  Citing articles (292)
- [47]
- K. Eimerta, P. Villanda, A. Kilianc, L.A. Kleczkowski
- Cloning and characterization of several cDNAs for UDP-glucose pyrophosphorylase from barley (Hordeum vulgare) tissues
- Gene, Volume 170, 1996, pp. 227–232
- [48]
- T. Rees, M. Leja, F.D. Macdonald, J.H. Green
- Nucleotide sugars and starch synthesis in spadix of Arum maculatum and suspension cultures of Glycine max
- Phytochemistry, Volume 23, 1984, pp. 2463–2468
- [49]
- H. Fulgosi, J. Soll, F.S. Maraschin, H.A.A.J. Korthout, M. Wang, C. Testerink
- 14-3-3 proteins and plant development
- Plant Mol. Biol., Volume 50, 2002, pp. 1019–1029
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (34)
- [50]
- M. Lancien, T.C. McCabe, C. Chang, M.R. Roberts
- Roles for plant 14-3-3 proteins in signaling and development
- Comp. Biochem. Physiol. Part A, Volume 141, 2005, pp. S258–S259
- [51]
- R.J. Ferl
- 14-3-3 proteins and signal transduction
- Annu. Rev. Plant Physiol. Plant Mol. Biol., Volume 47, 1996, pp. 49–73
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (121)
- [52]
- P.C. Sehnke, H.J. Chung, K. Wu, R.J. Ferl
- Regulation of starch accumulation by granule associated plant 14-3-3 proteins
- Proc. Natl. Acad. Sci. U. S. A., Volume 98, 2001, pp. 765–770
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (90)
- [53]
- P.R. Shewry, N.G. Halford
- Cereal seed storage proteins: structures, properties and role in grain utilization
- J. Exp. Bot., Volume 53, 2002, pp. 947–958
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (524)
- [54]
- I.J. Tetlow, M.K. Morell, M.J. Emes
- Recent developments in understanding the regulation of starch metabolism in higher plants
- J. Exp. Bot., Volume 55, 2004, pp. 2131–2145
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (182)
- [55]
- C. Finnie, S. Melchior, P. Roepstorff, B. Svensson
- Proteome analysis of grain filling and seed maturation in barley
- Plant Physiol., Volume 129, 2002, pp. 1308–1319
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (166)
- [56]
- J. Danyluk, A. Perron, M. Houde, A. Limin, B. Fowler, N. Benhamou, F. Sarhan
- Accumulation of an acidic dehydrin in the vicinity of the plasma membrane during cold acclimation of wheat
- Plant Cell, Volume 10, 1998, pp. 623–638
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (268)
- [57]
- S.A. Campbell, T.J. Close
- Dehydrins: genes, proteins, and associations with phenotypic traits
- New Phytol., Volume 137, 1997, pp. 61–74
- View Record in Scopus
  
  Citing articles (177)
- [58]
- J.M. Farrant, C. Bailly, J. Leymarie, B. Hamman, D. Come, F. Corbineau
- Wheat seedlings as a model to understand desiccation tolerance and sensitivity
- Plant Physiol., Volume 120, 2004, pp. 563–574
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (44)
- [59]
- A. Fait, A. Yellin, H. Fromm
- GABA shunt deficiencies and accumulation of reactive oxygen intermediate, insight from Arabidopsis mutants
- FEBS Lett., Volume 579, 2005, pp. 415–420
- Article
  |
  PDF (231 K)
  |
  View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (63)
- [60]
- H. Yamakawa, M. Hakata
- Atlas of rice grain filling-related metabolism under high temperature: joint analysis of metabolome and transcriptome demonstrated inhibition of starch accumulation and induction of amino acid accumulation
- Plant Cell Physiol., Volume 51, 2010, pp. 795–809
- View Record in Scopus
  |
  Full Text via CrossRef
  
  Citing articles (69)
- [61]
- H. Rolletschek, L. Borisjuk, A. Sánchez-García, C. Gotor, L.C. Romero, J.M. Martínez-Rivas, M. Mancha
- Temperature-dependent endogenous oxygen concentration regulates microsomal oleatedesaturase in developing sunflower seeds
- J. Exp. Bot., Volume 58, 2007, pp. 3171–3181
- View article
- [62]
- N. Aurisano, A. Bertani, R. Reggiani
- Involvement of calciumand calmodulin in protein and amino acid metabolism in rice rootsunder anoxia
- Plant Cell Physiol., Volume 36, 1995, pp. 1525–1529
- View article
- [63]
- R. Reggiani, C.A. Cantu, I. Brambilla, A. Bertani
- Accumulation and interconversion of amino acids in rice rootsunder anoxia
- Plant Cell Physiol., Volume 29, 1988, pp. 981–987
- View article

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These authors contributed equally to this work.

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Title

Transcriptome analysis reveals key differentially expressed genes involved in wheat grain development

1 Introduction

2 Materials and methods

2.1 Plant material and field experiment

2.2 Grain ultrastructure observation by scanning electron microscope (SEM)

2.3 RNA isolation, microarray hybridization, and data treatments

2.4 Filter of multi-class differential genes

2.5 Series test of cluster (STC) and gene ontology (GO) annotation analysis of differential genes

2.6 MapMan analysis

2.7 Metabolic pathway analysis

2.8 Dynamic gene net analysis

2.9 Total mRNA extraction and qRT-PCR analysis

3 Results

3.1 Grain morphology and ultrastructure changes during seed development

3.2 Transcriptome expression profiles and differential expression gene analysis during seed development

3.3 STC and GO annotation analysis of differentially expressed genes

3.4 Reserve accumulation and abiotic stress responses during seed development as revealed by MapMan analysis

3.5 Metabolic pathway network analysis

3.6 Gene co-expression network with k-core algorithm

3.7 Validation of key differentially expressed genes by qRT-PCR

4 Discussion

4.1 Activation of starch synthesis genes

4.2 Activation of protein synthesis genes

4.3 Activation of stress defense genes

Acknowledgments

Supplementary material

References