Common wild rice (Oryza rufipogon) plays an important role by contributing to modern rice breeding. In this paper, we report the sequence and analysis of a 172-kb genomic DNA region of wild rice around the RM5 locus, which is associated with the yield QTL yld1.1. Comparative sequence analysis between orthologous RM5 regions from Oryza sativa ssp. japonica, O. sativa ssp. indica and O. rufipogon revealed a high level of conserved synteny in the content, homology, structure, orientation, and physical distance of all 14 predicted genes. Twelve of the putative genes were supported by matches to proteins with known function, whereas two were predicted by homology to rice and other plant expressed sequence tags or complementary DNAs. The remarkably high level of conservation found in coding, intronic and intergenic regions may indicate high evolutionary selection on the RM5 region. Although our analysis has not defined which gene(s) determine the yld1.1 phenotype, allelic variation and the insertion of transposable elements, among other nucleotide changes, represent potential variation responsible for the yield QTL. However, as suggested previously, two putative receptor-like protein kinase genes remain the key suspects for yld1.1.
In plants, pathogen resistance is brought about by the binding of certain transcription factor (TF) proteins to the cis-elements of certain target genes. These cis-elements are present upstream in the motif of the promoters of each gene. This ensures the binding of a specific TF to a specific promoter, therefore regulating the expression of that gene. Therefore, the study of each promoter sequence of all the rice genes would help identify the target genes of a specific TF. Rice 1 kb upstream promoter sequences of 55,986 annotated genes were analyzed using the Perl program algorithm to detect WRKY13 binding motifs (bm). The resulting genes were grouped using Gene Ontology and gene set enrichment analysis. A gene with more than 4 TF bm in their promoter was selected. Ten genes reported to have a role in rice disease resistance were selected for further analysis. Cis-acting regulatory element analysis was carried out to find the cis-elements and confirm the presence of the corresponding motifs in the promoter sequences of these genes. The 3D structure of WRKY13 TF and the corresponding ten genes were built, and the interacting residues were determined. The binding capacity of WRKY13 to the promoter of these selected genes was analyzed using docking studies. WRKY13 was considered for docking analysis based on the prior reports of autoregulation. Molecular dynamic simulations provided more details regarding the interactions. Expression data revealed the expression of the genes that helped provide the mechanism of interaction. Further co-expression network helped to characterize the interaction of these selected disease resistance-related genes with the WRKY13 TF protein. This study suggests downstream target genes that are regulated by the WRKY13 TF. The molecular mechanism involving the gene network regulated by WRKY13 TF in disease resistance against rice fungal pathogens is explored.
By 2050, the global population is projected to exceed 9.5 billion, posing a formidable challenge to ensure food security worldwide. To address this pressing issue, mutation breeding in horticultural crops, utilizing physical or chemical methods, has emerged as a promising biotechnological strategy. However, the efficacy of these mutagens can be influenced by various factors, including biological and environmental variables, as well as targeted plant materials. This review highlights the global challenges related to food security and explores the potential of mutation breeding as an indispensable biotechnological tool in overcoming food insecurity. This review also covers the emergence of CRISPR-Cas9, a breakthrough technology offering precise genome editing for the development of high-yield, stress-tolerant crops. Together, mutation breeding and CRISPR can potentially address future food demands. This review focuses into these biotechnological advancements, emphasizing their combined potential to fortify global food security in the face of a booming population.
Cyanobacteria are oxygenic photosynthetic organisms which are found across many ecosystems, including freshwater and marine habitats. They are also found on natural and artificial surfaces. In this study, we cultured and characterise a novel cyanobacterium from the surfaces of foam microplastics of tropical coastal waters. We study the chemical ecology of this cyanobacterium, Sphaerothrix gracilis gen. et sp. nov., together with its potential to form harmful cyanobacterial blooms and bioremediation applications to combat plastic pollution. The genome of S. gracilis spanned 6.7 Mbp, with identification of antibiotic resistance, nitrogen-fixation, plastic-degrading and genes involved in harmful metabolite production. The transport of potentially harmful S. gracilis in coastal environments could have severe implications on human health and food security, especially in times of a cyanobacterial bloom.
In this paper, genomics and precision medicine have witnessed remarkable progress with the advent of high-throughput sequencing technologies and advances in data analytics. However, because of the data's great dimensionality and complexity, the processing and interpretation of large-scale genomic data present major challenges. In order to overcome these difficulties, this research suggests a novel Intelligent Mutation-Based Evolutionary Optimization Algorithm (IMBOA) created particularly for applications in genomics and precision medicine. In the proposed IMBOA, the mutation operator is guided by genome-based information, allowing for the introduction of variants in candidate solutions that are consistent with known biological processes. The algorithm's combination of Differential Evolution with this intelligent mutation mechanism enables effective exploration and exploitation of the solution space. Applying a domain-specific fitness function, the system evaluates potential solutions for each generation based on genomic correctness and fitness. The fitness function directs the search toward ideal solutions that achieve the problem's objectives, while the genome accuracy measure assures that the solutions have physiologically relevant genomic properties. This work demonstrates extensive tests on diverse genomics datasets, including genotype-phenotype association studies and predictive modeling tasks in precision medicine, to verify the accuracy of the proposed approach. The results demonstrate that, in terms of precision, convergence rate, mean error, standard deviation, prediction, and fitness cost of physiologically important genomic biomarkers, the IMBOA consistently outperforms other cutting-edge optimization methods.