Genetic Diversity and Divergence between Southern Japonica and Northern Japonica Rice Varieties in China.

Given the notable ecological and breeding disparities between southern and northern rice regions, delving into the genetic diversity and divergence between southern and northern japonica rice contributes to enhancing the genetic pool for japonica rice breeding. In this study, we analyzed 90 southern and 51 northern japonica rice varieties, focusing on nucleotide diversity (Pi), agronomic trait variations, population structure, genetic divergence, and a neutral test. For genetic diversity, the results demonstrated higher Pi in northern japonica rice varieties (NJRVs) on Chr2, Chr5, Chr6, Chr8, and Chr10, whereas in southern japonica rice varieties (SJRVs) on Chr7 and Chr9. In addition, SJRVs exhibited higher grain width and thickness, whereas NJRVs featured a higher grain aspect ratio, filled grain number, and grain number per panicle. Regarding genetic divergence, geographic differentiation existed between NJRVs and SJRVs, with Chr5 exhibiting numerous higher genetic differentiation windows, including cloned grain shape-controlling genes RGA1 and SFD5, stemming from intensified selection pressure on SJRVs. In summary, SJRVs and NJRVs exhibited diversity differences and genetic differentiation. Hence, it was suggested to conduct genetic introgression between NJRVs and SJRVs to broaden the genetic basis of the local japonica rice germplasm. By exploiting their heterotic advantage, new japonica rice cultivars with superior comprehensive traits could be developed.

Discovery of numerous novel Helitron-like elements in eukaryote genomes using HELIANO.

Helitron-like elements (HLEs) are widespread eukaryotic DNA transposons employing a rolling-circle transposition mechanism. Despite their prevalence in fungi, animals, and plant genomes, identifying Helitrons remains a formidable challenge. We introduce HELIANO, a software for annotating and classifying autonomous and non-autonomous HLE sequences from whole genomes. HELIANO overcomes several limitations of existing tools in speed and accuracy, demonstrated through benchmarking and its application to the complex genomes of frogs (Xenopus tropicalis and Xenopus laevis) and rice (Oryza sativa), where it uncovered numerous previously unidentified HLEs. In an extensive analysis of 404 eukaryote genomes, we found HLEs widely distributed across phyla, with exceptions in specific taxa. HELIANO's application led to the discovery of numerous new HLEs in land plants and identified 20 protein domains captured by certain autonomous HLE families. A comprehensive phylogenetic analysis further classified HLEs into two primary clades, HLE1 and HLE2, and revealed nine subgroups, some of which are enriched within specific taxa. The future use of HELIANO promises to improve the global analysis of HLEs across genomes, significantly advancing our understanding of this fascinating transposon superfamily.

A novel method combining deep learning with the Kennard-Stone algorithm for training dataset selection for image-based rice seed variety identification.

BACKGROUND: Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial for both trading and planting. RESULTS: This study collected RGB images of 20 hybrid rice seed varieties. An enhanced deep super-resolution network (EDSR) was employed to enhance image resolution, and a variety classification model utilizing the high-resolution dataset demonstrated superior performance to that of the model using the low-resolution dataset. A novel training sample selection methodology was introduced integrating deep learning with the Kennard-Stone (KS) algorithm. Convolutional neural networks (CNN) and autoencoders served as supervised and unsupervised feature extractors, respectively. The extracted feature vectors were subsequently processed by the KS algorithm to select training samples. The proposed methodologies exhibited superior performance over the random selection approach in rice variety classification, with an approximately 10.08% improvement in overall classification accuracy. Furthermore, the impact of noise on the proposed methodology was investigated by introducing noise to the images, and the proposed methodologies maintained superior performance relative to the random selection approach on the noisy image dataset. CONCLUSION: The experimental results indicate that both supervised and unsupervised learning models performed effectively as feature extractors, and the deep learning framework significantly influenced the selection of training set samples. This study presents a novel approach for training sample selection in classification tasks and suggests the potential for extending the proposed method to image datasets and other types of datasets. Further exploration of this potential is warranted. © 2024 Society of Chemical Industry.

Can stable carbon isotope fingerprints be competent for geographic traceability of rice?

This study aimed to improve the traceability of rice-producing areas to address the increasing demand for accurate methods to confirm food quality and safety. Compound-specific δ13C of fatty acids, δ13C of starch and bulk of rice were measured. PCA, PLS-DA and VIP value analysis of the obtained data were performed to track the source of rice from the six regions. The PLS-DA model established with bulk δ13C, starch δ13C, and fatty acid δ13C, which clearly separated the rice from six regions. The VIP graph showed the value of starch, C18:0 and C18:2 δ13C values (VIP > 1) were important to distinguish the origin of rice. Also, according to loading plots the contribution of starch δ13C was the largest. The findings indicate that the introduction of starch δ13C improves the precision of rice traceability and provides an effective method for identifying rice origin.

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms.

The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 - 4,000 cm-1 (800-2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.

Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry.

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

Whole-Genome Sequencing and RNA-Seq Reveal Differences in Genetic Mechanism for Flowering Response between Weedy Rice and Cultivated Rice.

Flowering is a key agronomic trait that influences adaptation and productivity. Previous studies have indicated the genetic complexity associated with the flowering response in a photoinsensitive weedy rice accession PSRR-1 despite the presence of a photosensitive allele of a key flowering gene Hd1. In this study, we used whole-genome and RNA sequencing data from both cultivated and weedy rice to add further insights. The de novo assembly of unaligned sequences predicted 225 genes, in which 45 were specific to PSRR-1, including two genes associated with flowering. Comparison of the variants in PSRR-1 with the 3K rice genome (RG) dataset identified unique variants within the heading date QTLs. Analyses of the RNA-Seq result under both short-day (SD) and long-day (LD) conditions revealed that many differentially expressed genes (DEGs) colocalized with the flowering QTLs, and some DEGs such as Hd1, OsMADS56, Hd3a, and RFT1 had unique variants in PSRR-1. Ehd1, Hd1, OsMADS15, and OsMADS56 showed different alternate splicing (AS) events between genotypes and day length conditions. OsMADS56 was expressed in PSRR-1 but not in Cypress under both LD and SD conditions. Based on variations in both sequence and expression, the unique flowering response in PSRR-1 may be due to the high-impact variants of flowering genes, and OsMADS56 is proposed as a key regulator for its day-neutral flowering response.

Molecular Insights into Salinity Responsiveness in Contrasting Genotypes of Rice at the Seedling Stage.

Salinity is one of the most common unfavorable environmental conditions that limits plant growth and development, ultimately reducing crop productivity. To investigate the underlying molecular mechanism involved in the salinity response in rice, we initially screened 238 rice cultivars after salt treatment at the seedling stage and identified two highly salt-tolerant cultivars determined by the relative damage rate parameter. The majority of cultivars (94.1%) were ranked as salt-sensitive and highly salt-sensitive. Transcriptome profiling was completed in highly salt-tolerant, moderately salt-tolerant, and salt-sensitive under water and salinity treatments at the seedling stage. Principal component analysis displayed a clear distinction among the three cultivars under control and salinity stress conditions. Several starch and sucrose metabolism-related genes were induced after salt treatment in all genotypes at the seedling stage. The results from the present study enable the identification of the ascorbate glutathione pathway, potentially participating in the process of plant response to salinity in the early growth stage. Our findings also highlight the significance of high-affinity K+ uptake transporters (HAKs) and high-affinity K+ transporters (HKTs) during salt stress responses in rice seedlings. Collectively, the cultivar-specific stress-responsive genes and pathways identified in the present study act as a useful resource for researchers interested in plant responses to salinity at the seedling stage.

SiMYB19 from Foxtail Millet (Setaria italica) Confers Transgenic Rice Tolerance to High Salt Stress in the Field.

Salt stress is a major threat to crop quality and yield. Most experiments on salt stress-related genes have been conducted at the laboratory or greenhouse scale. Consequently, there is a lack of research demonstrating the merit of exploring these genes in field crops. Here, we found that the R2R3-MYB transcription factor SiMYB19 from foxtail millet is expressed mainly in the roots and is induced by various abiotic stressors such as salt, drought, low nitrogen, and abscisic acid. SiMYB19 is tentatively localized to the nucleus and activates transcription. It enhances salt tolerance in transgenic rice at the germination and seedling stages. SiMYB19 overexpression increased shoot height, grain yield, and salt tolerance in field- and salt pond-grown transgenic rice. SiMYB19 overexpression promotes abscisic acid (ABA) accumulation in transgenic rice and upregulates the ABA synthesis gene OsNCED3 and the ABA signal transduction pathway-related genes OsPK1 and OsABF2. Thus, SiMYB19 improves salt tolerance in transgenic rice by regulating ABA synthesis and signal transduction. Using rice heterologous expression analysis, the present study introduced a novel candidate gene for improving salt tolerance and increasing yield in crops grown in saline-alkali soil.

Genetic diversity and population structure of rice (Oryza sativa L.) landraces from Kerala, India analyzed through genotyping-by-sequencing.

Researchers stand at the vanguard of advancement and application of next-generation sequencing technology for developing dominant strategies for the sustainable management of genetically diverse crops. We attempt to fill the existing research lacuna in the molecular characterization of potent rice landraces in Kerala. Genotyping-by-sequencing (GBS) was performed on 96 Kerala rice accessions to identify single-nucleotide polymorphisms (SNPs), to examine the genetic diversity, population structure, and to delineate linkage disequilibrium (LD) pattern. GBS identified 5856 high-quality SNPs. The structure analysis indicated three subpopulations with the highest probability for population clustering with significant genetic differentiation, confirmed by principal component analysis. The genome-wide LD decay distance was 772 kb, at which the r2 dropped to half its maximum value. The analysis of genetic properties of the identified SNP panel with an average polymorphism information content (PIC) value of 0.22 and a minor allele frequency (MAF) > 0.1 unveiled their efficacy in genome-wide association studies (GWAS). High FST (0.266) and low Nm (0.692) portray a strong genetic differentiation among the rice landraces, complementing the genetic structuring observed in the studied population. Slow LD decay in the rice landraces reflects their self-pollinating behavior and the indirect selection of desired traits by domestication. Moreover, the high LD entails only a minimum number of SNP markers for detecting marker-trait association. The diverse germplasm utilized in this study can be further utilized to disclose genetic variants associated with phenotypic traits and define signatures of selection via GWAS and selective sweep, respectively.

Germin like protein genes exhibit modular expression during salt and drought stress in elite rice cultivars.

BACKGROUND: Germin-like proteins (GLPs) are ubiquitous plant proteins, which play significant role in plant responses against various abiotic stresses. However, the potential functions of GLPs in rice (Oryza sativa) against salt and drought stress are still unclear. METHODS AND RESULTS: In this study, transcriptional variation of eight OsGLP genes (OsGLP3-6, OsGLP4-1, OsGLP8-4, OsGLP8-7, OsGLP8-10, OsGLP8-11 and OsGLP8-12) was analyzed in leaves and roots of two economically important Indica rice cultivars, KS282 and Super Basmati, under salt and drought stress at early seedling stage. The relative expression analysis from qRT-PCR indicated the highest increase in expression of OsGLP3-6 in leaves and roots of both rice varieties with a significantly higher expression in KS282. Moreover, relative change in expression of OsGLP8-7, OsGLP8-10 and OsGLP8-11 under salt stress and OsGLP8-7 under drought stress was also commonly higher in leaves and roots of KS282 as compared to Super Basmati. Whereas, OsGLP3-7 and OsGLP8-12 after salt stress and OsGLP8-4 and OsGLP8-12 after drought stress were observed with higher relative expression in roots of Super Basmati than KS282. Importantly, the OsGLP3-6 and OsGLP4-1 from chromosome 3 and 4 respectively showed higher expression in leaves whereas most of the OsGLP genes from chromosome 8 exhibited higher expression in roots. CONCLUSION: Overall, as a result of this comparative analysis, OsGLP genes showed both general and specific expression profiles depending upon a specific rice variety, stress condition as well as tissue type. These results will increase our understanding of role of OsGLP genes in rice crop and provide useful information for the further in-depth research on their regulatory mechanisms in response to these stress conditions.

Use of microalgal biomass as functional ingredient for preparation of cereal based extrudates: impact of processing on amino acid concentrations and colour degradation kinetics

Abstract Suitability of developing Spirulina incorporated cereal based low cost nutritious extrudates was analysed against extrusion processing parameters. Most significant extrusion processing parameters considered for present study were feed moisture (20-25%), die temperature (100-120 °C) and screw speed (50-100 rpm). Different extrusion conditions were used to obtain most acceptable rice: Spirulina blend extrudates. In present study before extrusion processing different additives (citric acid and sodium bicarbonate) were added in rice: Spirulina blend and checked its effect on colour degradation kinetics at varied packaging and storage conditions. Higher screw speed (100 rpm) indicating less residence time of feed material inside the barrel resulted in higher colour retention of rice: Spirulina (97:03) blend extrudates. Kinetics for rice: Spirulina (97:03) blend extrudates indicates faster rate of colour degradation in terms of lightness (half-life of 4 days) when packed in metalized polyethylene at 50°C with 65% relative humidity. Increased concentration of Spirulina (1-3%) in raw formulations resulted in increase in concentration of all amino acids. Impact of extrusion processing has shown non-significant (p ≤ 0.05) effect on amino acid concentrations of rice: Spirulina blend extrudates. Also, all the spirulina added samples showed good consumer acceptability with the score of 6.7

Identification of climate induced optimal rice yield and vulnerable districts rankings of the Punjab, Pakistan.

The study attracted to insinuate the inhabitant anomalies of the crop yield in the districts of the Punjab where climate variation, inputs utilization, and district exponents are indispensable factors. Impact evaluation of sowing and harvesting dates for rice yield has been analyzed. Suitable sowing and harvesting dates and potential districts for the crop are proposed. Data consisting of 13,617 observations of more than 90 factors encompassing valuable dimensions of the growth of the crops collected through comprehensive surveys conducted by the Agriculture Department of Punjab are formulated to incorporate in this study. The results establish the significant negative repercussions of climate variability while the impacts vary in the districts. The crop yield deteriorates considerably by delaying the sowing and harvesting times. Districts climate-induced vulnerability ranking revealed Layyah, Jhelum, Mianwali, Khanewal and Chinniot, the most vulnerable while Kasur, Gujrat, Mandi Bhauddin, Nankana Sahib and Hafizabad, the least vulnerable districts. Spatial mapping explains the geographical pattern of vulnerabilities and yield/monetary losses. The study ranks districts using climate-induced yield and monetary loss (222.30 thousand metric tons of rice which are equal to 27.79 billion PKR climatic losses in single rice season) and recommends: the formation of district policy to abate the adverse climate impact, utilization of suitable climate variation by adhering proper sowing and harvesting times, setting the prioritized districts facing climate-induced losses for urgent attention and preferable districts for rice crop.

Growth and trend analysis of area, production and yield of rice: A scenario of rice security in Bangladesh.

Bangladesh positioned as third rice producing country in the world. In Bangladesh, regional growth and trend in rice production determinants, disparities and similarities of rice production environments are highly desirable. In this study, the secondary time series data of area, production, and yield of rice from 1969-70 to 2019-20 were used to investigate the growth and trend by periodic, regional, seasonal and total basis. Quality checking, trend fitting, and classification analysis were performed by the Durbin-Watson test, Exponential growth model, Cochrane-Orcutt iteration method and clustering method. The production contribution to the national rice production of Boro rice is increasing at 0.97% per year, where Aus and Aman season production contribution significantly decreased by 0.48% and 0.49% per year. Among the regions, Mymensingh, Rangpur, Bogura, Jashore, Rajshahi, and Chattogram contributed the most i.e., 13.9%, 9.8%, 8.6%, 8.6%, 8.2%, and 8.0%, respectively. Nationally, the area of Aus and Aman had a decreasing trend with a -3.63% and -0.16% per year, respectively. But, in the recent period (Period III) increasing trend was observed in the most regions. The Boro cultivation area is increasing with a rate of 3.57% per year during 1984-85 to 2019-20. High yielding variety adoption rate has increased over the period and in recent years it has found 72% for Aus, 73.5% for Aman, and 98.4% for Boro season. As a result, the yield of the Aus, Aman, and Boro seasons has been found increasing growth for most of the regions. We have identified different cluster regions in different seasons, indicating high dissimilarities among the rice production regions in Bangladesh. The region-wise actionable plan should be taken to rapidly adopt new varieties, management technologies and extension activities in lower contributor regions to improve productivity. Cluster-wise, policy strategies should be implemented for top and less contributor regions to ensure rice security of Bangladesh.

Comparative Phosphoproteomic Analysis Reveals the Response of Starch Metabolism to High-Temperature Stress in Rice Endosperm.

High-temperature stress severely affects rice grain quality. While extensive research has been conducted at the physiological, transcriptional, and protein levels, it is still unknown how protein phosphorylation regulates seed development in high-temperature environments. Here, we explore the impact of high-temperature stress on the phosphoproteome of developing grains from two indica rice varieties, 9311 and Guangluai4 (GLA4), with different starch qualities. A total of 9994 phosphosites from 3216 phosphoproteins were identified in all endosperm samples. We identified several consensus phosphorylation motifs ([sP], [LxRxxs], [Rxxs], [tP]) induced by high-temperature treatment and revealed a core set of protein kinases, splicing factors, and regulatory factors in response to high-temperature stress, especially those involved in starch metabolism. A detailed phosphorylation scenario in the regulation of starch biosynthesis (AGPase, GBSSI, SSIIa, SSIIIa, BEI, BEIIb, ISA1, PUL, PHO1, PTST) in rice endosperm was proposed. Furthermore, the dynamic changes in phosphorylated enzymes related to starch synthesis (SSIIIa-Ser94, BEI-Ser562, BEI-Ser620, BEI-Ser821, BEIIb-Ser685, BEIIb-Ser715) were confirmed by Western blot analysis, which revealed that phosphorylation might play specific roles in amylopectin biosynthesis in response to high-temperature stress. The link between phosphorylation-mediated regulation and starch metabolism will provide new insights into the mechanism underlying grain quality development in response to high-temperature stress.

The highly interactive BTB domain targeting other functional domains to diversify the function of BTB proteins in rice growth and development.

The BTB (broad-complex, tram track, and bric-abrac) proteins are involved in developmental processes, biotic, and abiotic stress responses in various plants, but the molecular basis of protein interactions is yet to be investiagted in rice. In this study, the identified BTB proteins were divided into BTB-TAZ, MATH-BTB, BTB-NPH, BTB-ANK, BTB-Skp, BTB-DUF, and BTB-TPR subfamilies based on the additional functional domains found together with the BTB domain at N- and C-terminal as well. This suggesting that the extension region at both terminal sites could play a vital role in the BTB gene family expansion in plants. The yeast two-hybrid system, firefly luciferase complementation imaging (LCI) assay and bimolecular fluorescence complementation (BiFC) assay further confirmed that BTB proteins interact with several other proteins to perform a certain developmental process in plants. The overexpression of BTB genes of each subfamily in Arabidopsis revealed that BTB genes including OsBTB4, OsBTB8, OsBTB64, OsBTB62, OsBTB138, and OsBTB147, containing certain additional functional domains, could play a potential role in the early flowering, branching, leaf, and silique development. Thus we concluded that the presence of other functional domains such as TAZ, SKP, DUF, ANK, NPH, BACK, PQQ, and MATH could be the factor driving the diverse functions of BTB proteins in plant biology.

On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo.

For various species, high quality sequences and complete genomes are nowadays available for many individuals. This makes data analysis challenging, as methods need not only to be accurate, but also time efficient given the tremendous amount of data to process. In this article, we introduce an efficient method to infer the evolutionary history of individuals under the multispecies coalescent model in networks (MSNC). Phylogenetic networks are an extension of phylogenetic trees that can contain reticulate nodes, which allow to model complex biological events such as horizontal gene transfer, hybridization and introgression. We present a novel way to compute the likelihood of biallelic markers sampled along genomes whose evolution involved such events. This likelihood computation is at the heart of a Bayesian network inference method called SnappNet, as it extends the Snapp method inferring evolutionary trees under the multispecies coalescent model, to networks. SnappNet is available as a package of the well-known beast 2 software. Recently, the MCMC_BiMarkers method, implemented in PhyloNet, also extended Snapp to networks. Both methods take biallelic markers as input, rely on the same model of evolution and sample networks in a Bayesian framework, though using different methods for computing priors. However, SnappNet relies on algorithms that are exponentially more time-efficient on non-trivial networks. Using simulations, we compare performances of SnappNet and MCMC_BiMarkers. We show that both methods enjoy similar abilities to recover simple networks, but SnappNet is more accurate than MCMC_BiMarkers on more complex network scenarios. Also, on complex networks, SnappNet is found to be extremely faster than MCMC_BiMarkers in terms of time required for the likelihood computation. We finally illustrate SnappNet performances on a rice data set. SnappNet infers a scenario that is consistent with previous results and provides additional understanding of rice evolution.

Functional mapping of tillering QTLs using the Wang-Lan-Ding model and a SSSL population.

Understanding dynamic changes in the genetic architecture of quantitative traits is crucial in developmental genetics. Functional mapping is an appropriate method that passes a mathematical equation to describe a biological developmental process with the genetic mapping framework. Appropriate genetic model and applicable mapping population are indispensable condition for functional mapping of important agronomic traits in plants. Based on the Wang-Lan-Ding model, we ever applied a DH population to carry out functional mapping QTLs underlying growth trajectory on tiller number. However, inconsistent genetic background among the DH lines might disturb the mapping results. With the advent of innovative research materials, single segment substitution lines, allows us to do more precise genetic analyses. Thus functional mapping was again conducted on tiller number using the Wang-Lan-Ding model and a single segment substitution line population with the genetic background of Huajingxian 74 so as to explore QTL dynamic mechanism to regulate developmental traits. We detected that all five single segment substitution lines harbored tillering QTLs with additives and/or dominances to influence the four functional parameters, the optimum tillering time (t0), the maximum tiller number (K), the tillering increased rate (r) and the tillering decreased rate (c), which were estimated from the Wang-Lan-Ding model and with some biological meaning. They mainly brought the inflexion point (t0) delay, the peak increase (K) and the degradation (c) acceleration, while the growth (r) slow down. Moreover, epistatic interactions among these QTLs were confirmed to be prevalent. A total of 39 significant epistatic effects were detected to associate with the four parameters, occupying 34.8% of 112 pairs of epistatic interactions investigated. Contrary to the QTL effects, these epistatic effects largely decreased t0, K and c, while increased r. Our results indicated that the five QTL effects and their epistatic effects significantly changed the shape and trajectory of tiller number via influence of the four functional parameters. Rational use of these QTLs is expected to improve tillering number of rice by molecular design breeding.

Genome-Wide Identification and Analysis of the Metallothionein Genes in Oryza Genus.

Metallothionein (MT) proteins are low molecular mass, cysteine-rich, and metal-binding proteins that play an important role in maintaining metal homeostasis and stress response. However, the evolutionary relationships and functional differentiation of MT in the Oryza genus remain unclear. Here we identified 53 MT genes from six Oryza genera, including O. sativa ssp. japonica, O. rufipogon, O. sativa ssp. indica, O. nivara, O. glumaepatula, and O. barthii. The MT genes were clustered into four groups based on phylogenetic analysis. MT genes are unevenly distributed on chromosomes; almost half of the MT genes were clustered on chromosome 12, which may result from a fragment duplication containing the MT genes on chromosome 12. Five pairs of segmental duplication events and ten pairs of tandem duplication events were found in the rice MT family. The Ka/Ks values of the fifteen duplicated MT genes indicated that the duplicated MT genes were under a strong negative selection during evolution. Next, combining the promoter activity assay with gene expression analysis revealed different expression patterns of MT genes. In addition, the expression of OsMT genes was induced under different stresses, including NaCl, CdCl2, ABA, and MeJ treatments. Additionally, we found that OsMT genes were mainly located in chloroplasts. These results imply that OsMT genes play different roles in response to these stresses. All results provide important insights into the evolution of the MT gene family in the Oryza genus, and will be helpful to further study the function of MT genes.

The identification of grain size genes by RapMap reveals directional selection during rice domestication.

Cloning quantitative trait locus (QTL) is time consuming and laborious, which hinders the understanding of natural variation and genetic diversity. Here, we introduce RapMap, a method for rapid multi-QTL mapping by employing F2 gradient populations (F2GPs) constructed by minor-phenotypic-difference accessions. The co-segregation standard of the single-locus genetic models ensures simultaneous integration of a three-in-one framework in RapMap i.e. detecting a real QTL, confirming its effect, and obtaining its near-isogenic line-like line (NIL-LL). We demonstrate the feasibility of RapMap by cloning eight rice grain-size genes using 15 F2GPs in three years. These genes explain a total of 75% of grain shape variation. Allele frequency analysis of these genes using a large germplasm collection reveals directional selection of the slender and long grains in indica rice domestication. In addition, major grain-size genes have been strongly selected during rice domestication. We think application of RapMap in crops will accelerate gene discovery and genomic breeding.