Integration of miRNA dynamics and drought tolerant QTLs in rice reveals the role of miR2919 in drought stress response.

To combat drought stress in rice, a major threat to global food security, three major quantitative trait loci for 'yield under drought stress' (qDTYs) were successfully exploited in the last decade. However, their molecular basis still remains unknown. To understand the role of secondary regulation by miRNA in drought stress response and their relation, if any, with the three qDTYs, the miRNA dynamics under drought stress was studied at booting stage in two drought tolerant (Sahbaghi Dhan and Vandana) and one drought sensitive (IR 20) cultivars. In total, 53 known and 40 novel differentially expressed (DE) miRNAs were identified. The primary drought responsive miRNAs were Osa-MIR2919, Osa-MIR3979, Osa-MIR159f, Osa-MIR156k, Osa-MIR528, Osa-MIR530, Osa-MIR2091, Osa-MIR531a, Osa-MIR531b as well as three novel ones. Sixty-one target genes that corresponded to 11 known and 4 novel DE miRNAs were found to be co-localized with the three qDTYs, out of the 1746 target genes identified. We could validate miRNA-mRNA expression under drought for nine known and three novel miRNAs in eight different rice genotypes showing varying degree of tolerance. From our study, Osa-MIR2919, Osa-MIR3979, Osa-MIR528, Osa-MIR2091-5p and Chr01_11911S14Astr and their target genes LOC_Os01g72000, LOC_Os01g66890, LOC_Os01g57990, LOC_Os01g56780, LOC_Os01g72834, LOC_Os01g61880 and LOC_Os01g72780 were identified as the most promising candidates for drought tolerance at booting stage. Of these, Osa-MIR2919 with 19 target genes in the qDTYs is being reported for the first time. It acts as a negative regulator of drought stress tolerance by modulating the cytokinin and brassinosteroid signalling pathway.

Regulatory Networks of lncRNAs, miRNAs, and mRNAs in Response to Heat Stress in Wheat (Triticum Aestivum L.): An Integrated Analysis.

Climate change has become a major source of concern, particularly in agriculture, because it has a significant impact on the production of economically important crops such as wheat, rice, and maize. In the present study, an attempt has been made to identify differentially expressed heat stress-responsive long non-coding RNAs (lncRNAs) in the wheat genome using publicly available wheat transcriptome data (24 SRAs) representing two conditions, namely, control and heat-stressed. A total of 10,965 lncRNAs have been identified and, among them, 153, 143, and 211 differentially expressed transcripts have been found under 0 DAT, 1 DAT, and 4 DAT heat-stress conditions, respectively. Target prediction analysis revealed that 4098 lncRNAs were targeted by 119 different miRNA responses to a plethora of environmental stresses, including heat stress. A total of 171 hub genes had 204 SSRs (simple sequence repeats), and a set of target sequences had SNP potential as well. Furthermore, gene ontology analysis revealed that the majority of the discovered lncRNAs are engaged in a variety of cellular and biological processes related to heat stress responses. Furthermore, the modeled three-dimensional (3D) structures of hub genes encoding proteins, which had an appropriate range of similarity with solved structures, provided information on their structural roles. The current study reveals many elements of gene expression regulation in wheat under heat stress, paving the way for the development of improved climate-resilient wheat cultivars.

Genetic diversity, population structure, and genome-wide association study for the flowering trait in a diverse panel of 428 moth bean (Vigna aconitifolia) accessions using genotyping by sequencing.

BACKGROUND: Moth bean (Vigna aconitifolia) is an underutilized, protein-rich legume that is grown in arid and semi-arid areas of south Asia and is highly resistant to abiotic stresses such as heat and drought. Despite its economic importance, the crop remains unexplored at the genomic level for genetic diversity and trait mapping studies. To date, there is no report of SNP marker discovery and association mapping of any trait in this crop. Therefore, this study aimed to dissect the genetic diversity, population structure and marker-trait association for the flowering trait in a diversity panel of 428 moth bean accessions using genotyping by sequencing (GBS) approach. RESULTS: A total of 9078 high-quality single nucleotide polymorphisms (SNPs) were discovered by genotyping of 428 moth bean accessions. Model-based structure analysis and PCA grouped the moth bean accessions into two subpopulations. Cluster analysis revealed accessions belonging to the Northwestern region of India had higher variability than accessions from the other regions suggesting that this region represents its center of diversity. AMOVA revealed more variations within individuals (74%) and among the individuals (24%) than among the populations (2%). Marker-trait association analysis using seven multi-locus models including mrMLM, FASTmrEMMA FASTmrEMMA, ISIS EM-BLASSO, MLMM, BLINK and FarmCPU revealed 29 potential genomic regions for the trait days to 50% flowering, which were consistently detected in three or more models. Analysis of the allelic effect of the major genomic regions explaining phenotypic variance of more than 10% and those detected in at least 2 environments showed 4 genomic regions with significant phenotypic effect on this trait. Further, we also analyzed genetic relationships among the Vigna species using SNP markers. The genomic localization of moth bean SNPs on genomes of closely related Vigna species demonstrated that maximum numbers of SNPs were getting localized on Vigna mungo. This suggested that the moth bean is most closely related to V. mungo. CONCLUSION: Our study shows that the north-western regions of India represent the center of diversity of the moth bean. Further, the study revealed flowering-related genomic regions/candidate genes which can be potentially exploited in breeding programs to develop early-maturity moth bean varieties.

Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.

Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.

Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach.

Rice is an important staple food grain consumed by most of the population around the world. With climate and environmental changes, rice has undergone a tremendous stress state which has impacted crop production and productivity. Plant growth hormones are essential component that controls the overall outcome of the growth and development of the plant. Cytokinin is a hormone that plays an important role in plant immunity and defense systems. Trans-zeatin is an active form of cytokinin that can affect plant growth which is mediated by a multi-step two-component phosphorelay system that has different roles in various developmental stages. Systems biology is an approach for pathway analysis to trans-zeatin treated rice that could provide a deep understanding of different molecules associated with them. In this study, we have used a weighted gene co-expression network analysis method to identify the functional modules and hub genes involved in the cytokinin pathway. We have identified nine functional modules comprising of different hub genes which contribute to the cytokinin signaling route. The biological significance of these identified hub genes has been tested by applying well-proven statistical techniques to establish the association with the experimentally validated QTLs and annotated by the DAVID server. The establishment of key genes in different pathways has been confirmed. These results will be useful to design new stress-resistant cultivars which can provide sustainable yield in stress-specific conditions.

Identification and characterization of trait-specific SNPs using ddRAD sequencing in water buffalo.

Single Nucleotide Polymorphism (SNP) is one of the important molecular markers widely used in animal breeding program for improvement of any desirable genetic traits. Considering this, the present study was carried out to identify, annotate and analyze the SNPs related to four important traits of buffalo viz. milk volume, age at first calving, post-partum cyclicity and feed conversion efficiency. We identified 246,495, 168,202, 74,136 and 194,747 genome-wide SNPs related to mentioned traits, respectively using ddRAD sequencing technique based on 85 samples of Murrah Buffaloes. Distribution of these SNPs were highest (61.69%) and lowest (1.78%) in intron and exon regions, respectively. Under coding regions, the SNPs for the four traits were further classified as synonymous (4697) and non-synonymous (3827). Moreover, Gene Ontology (GO) terms of identified genes assigned to various traits. These characterized SNPs will enhance the knowledge of cellular mechanism for enhancing productivity of water buffalo through molecular breeding.

MicroRNA-related markers associated with corpus luteum tropism in buffalo (Bubalus bubalis).

The study was undertaken to decipher the microRNA (miRNA) related markers associated with corpus luteum (CL) tropism in buffalo. The data obtained from deep sequencing of CL tissue from different physiological stages was mined in silico for the identification of miRNA-related markers (SSR & SNP). From the present study, 5 annotated and 176 unannotated miRNA were deduced while comparing with Bos taurus genome. In addition, 4 SSRs and 9 SNPs were deduced from the miRNA sequences. These SSRs were on the genes viz. Eukaryotic translation initiation factor 1-like, myocyte enhancer factor 2A, beta casein, T cell receptor gamma cluster 1. The SNP positions on genes viz. PYGO1 (Pygopus family PHD finger 1), LOC100337244 (Multidrug resistance-associated protein 4), FTH1 (Ferritin heavy chain 1), LOC788634 (BOLA class I histocompatibility antigen), PLXND1 (Plexin D1) and UBC (Ubiquitin C) show that these genes play critical role in CL tropism during estrous cycle in buffalo.

RiceMetaSysB: a database of blast and bacterial blight responsive genes in rice and its utilization in identifying key blast-resistant WRKY genes.

Nearly two decades of revolution in the area of genomics serves as the basis of present-day molecular breeding in major food crops such as rice. Here we report an open source database on two major biotic stresses of rice, named RiceMetaSysB, which provides detailed information about rice blast and bacterial blight (BB) responsive genes (RGs). Meta-analysis of microarray data from different blast- and BB-related experiments across 241 and 186 samples identified 15135 unique genes for blast and 7475 for BB. A total of 9365 and 5375 simple sequence repeats (SSRs) in blast and BB RGs were identified for marker development. Retrieval of candidate genes using different search options like genotypes, tissue, developmental stage of the host, strain, hours/days post-inoculation, physical position and SSR marker information is facilitated in the database. Search options like 'common genes among varieties' and 'strains' have been enabled to identify robust candidate genes. A 2D representation of the data can be used to compare expression profiles across genes, genotypes and strains. To demonstrate the utility of this database, we queried for blast-responsive WRKY genes (fold change ≥5) using their gene IDs. The structural variations in the 12 WRKY genes so identified and their promoter regions were explored in two rice genotypes contrasting for their reaction to blast infection. Expression analysis of these genes in panicle tissue infected with a virulent and an avirulent strain of Magnaporthe oryzae could identify WRKY7, WRKY58, WRKY62, WRKY64 and WRKY76 as potential candidate genes for resistance to panicle blast, as they showed higher expression only in the resistant genotype against the virulent strain. Thus, we demonstrated that RiceMetaSysB can play an important role in providing robust candidate genes for rice blast and BB.

An efficient algorithm for protein structure comparison using elastic shape analysis.

BACKGROUND: Protein structure comparison play important role in in silico functional prediction of a new protein. It is also used for understanding the evolutionary relationships among proteins. A variety of methods have been proposed in literature for comparing protein structures but they have their own limitations in terms of accuracy and complexity with respect to computational time and space. There is a need to improve the computational complexity in comparison/alignment of proteins through incorporation of important biological and structural properties in the existing techniques. RESULTS: An efficient algorithm has been developed for comparing protein structures using elastic shape analysis in which the sequence of 3D coordinates atoms of protein structures supplemented by additional auxiliary information from side-chain properties are incorporated. The protein structure is represented by a special function called square-root velocity function. Furthermore, singular value decomposition and dynamic programming have been employed for optimal rotation and optimal matching of the proteins, respectively. Also, geodesic distance has been calculated and used as the dissimilarity score between two protein structures. The performance of the developed algorithm is tested and found to be more efficient, i.e., running time reduced by 80-90 % without compromising accuracy of comparison when compared with the existing methods. Source codes for different functions have been developed in R. Also, user friendly web-based application called ProtSComp has been developed using above algorithm for comparing protein 3D structures and is accessible free. CONCLUSIONS: The methodology and algorithm developed in this study is taking considerably less computational time without loss of accuracy (Table 2). The proposed algorithm is considering different criteria of representing protein structures using 3D coordinates of atoms and inclusion of residue wise molecular properties as auxiliary information.