Rice Stress-Resistant SNP Database

Thousands of trait-specific KASP markers designed for diverse breeding applications in rice (Oryza sativa)

This study aimed to broaden applicability of KASP for Oryza sativa across diverse genotypes through incorporation of ambiguous (degenerate) bases into their primer designs and to validate 4,000 of them for genotyping applications. A bioinformatics pipeline was used to compare 129 rice genomes from 89 countries with the indica reference genome R498 and generate ∼1.6 million KASP designs for the more common variants between R498 and the other genomes. Of the designs, 98,238 were for predicted functional markers. Up to 5 KASP each for 1,024 breeder-selected loci were assayed in a panel of 178 diverse rice varieties, generating 3,366 validated KASP. The 84% success rate was within the normal range for KASP demonstrating that the ambiguous bases do not compromise efficacy. The 3,366-trait-specific marker panel was applied for population structure analysis in the diversity panel and resolved them into 4 expected groups. Target variations in 13 genomes used for designs were compared with the corresponding KASP genotypes in different accessions of the same 13 varieties in the diversity panel. There was agreement for 79% or more markers in 12 varieties; 10 having agreement >88%. One variety, a selection from a landrace, had only 46.5% marker agreement. Breeders can search for the validated KASP and more than a million so-far untested designs in three reference genomes (including Niponbare MSU7) with a search tool, that includes designs in proximity to previously published microsatellite markers, and retrieve target variations for 129 rice genomes plus their genomic locations with ±25 bp flanking sequences.

Confronting the data deluge: How artificial intelligence can be used in the study of plant stress

The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of plant stress resilience. The proliferation of experimental and analytical methods used in biology has resulted in a situation where plentiful data exists, but the volume and heterogeneity of this data has made analysis a significant challenge. Current advanced deep-learning models have displayed an unprecedented level of comprehension and problem-solving ability, and have been used to predict gene structure, function and expression based on DNA or protein sequence, and prominently also their use in high-throughput phenomics in agriculture. However, the application of deep-learning models to understand gene regulatory and signalling behaviour is still in its infancy. We discuss in this review the availability of data resources and bioinformatic tools, and several applications of these advanced ML/AI models in the context of plant stress response, and demonstrate the use of a publicly available LLM (ChatGPT) to derive a knowledge graph of various experimental and computational methods used in the study of plant stress. We hope this will stimulate further interest in collaboration between computer scientists, computational biologists and plant scientists to distil the deluge of genomic, transcriptomic, proteomic, metabolomic and phenomic data into meaningful knowledge that can be used for the benefit of humanity.

Haplotype-based breeding: A new insight in crop improvement

Haplotype-based breeding (HBB) is one of the cutting-edge technologies in the realm of crop improvement due to the increasing availability of Single Nucleotide Polymorphisms identified by Next Generation Sequencing technologies. The complexity of the data can be decreased with fewer statistical tests and a lower probability of spurious associations by combining thousands of SNPs into a few hundred haplotype blocks. The presence of strong genomic regions in breeding lines of most crop species facilitates the use of haplotypes to improve the efficiency of genomic and marker-assisted selection. Haplotype-based breeding as a Genomic Assisted Breeding (GAB) approach harnesses the genome sequence data to pinpoint the allelic variation used to hasten the breeding cycle and circumvent the challenges associated with linkage drag. This review article demonstrates ways to identify candidate genes, superior haplotype identification, haplo-pheno analysis, and haplotype-based marker-assisted selection. The crop improvement strategies that utilize superior haplotypes will hasten the breeding progress to safeguard global food security.

Identification, characterization of Apyrase (APY) gene family in rice (Oryza sativa) and analysis of the expression pattern under various stress conditions

Apyrase (APY) is a nucleoside triphosphate (NTP) diphosphohydrolase (NTPDase) which is a member of the superfamily of guanosine diphosphatase 1 (GDA1)-cluster of differentiation 39 (CD39) nucleoside phosphatase. Under various circumstances like stress, cell growth, the extracellular adenosine triphosphate (eATP) level increases, causing a detrimental influence on cells such as cell growth retardation, ROS production, NO burst, and apoptosis. Apyrase hydrolyses eATP accumulated in the extracellular membrane during stress, wounds, into adenosine diphosphate (ADP) and adenosine monophosphate (AMP) and regulates the stress-responsive pathway in plants. This study was designed for the identification, characterization, and for analysis of APY gene expression in Oryza sativa. This investigation discovered nine APYs in rice, including both endo- and ecto-apyrase. According to duplication event analysis, in the evolution of OsAPYs, a significant role is performed by segmental duplication. Their role in stress control, hormonal responsiveness, and the development of cells is supported by the corresponding cis-elements present in their promoter regions. According to expression profiling by RNA-seq data, the genes were expressed in various tissues. Upon exposure to a variety of biotic as well as abiotic stimuli, including anoxia, drought, submergence, alkali, heat, dehydration, salt, and cold, they showed a differential expression pattern. The expression analysis from the RT-qPCR data also showed expression under various abiotic stress conditions, comprising cold, salinity, cadmium, drought, submergence, and especially heat stress. This finding will pave the way for future in-vivo analysis, unveil the molecular mechanisms of APY genes in stress response, and contribute to the development of stress-tolerant rice varieties.

Global Pharmacopoeia Genome Database is an integrated and mineable genomic database for traditional medicines derived from eight international pharmacopoeias

Genomic data have demonstrated considerable traction in accelerating contemporary studies in traditional medicine. However, the lack of a uniform format and dispersed storage limits the full potential of herb genomic data. In this study, we developed a Global Pharmacopoeia Genome Database (GPGD). The database contains 34,346 records for 903 herb species from eight global pharmacopoeias (Brazilian, Egyptian, European, Indian, Japanese, Korean, the Pharmacopoeia of the People's Republic of China, and U.S. Pharmacopoeia's Herbal Medicines Compendium). In particular, the GPGD contains 21,872 DNA barcodes from 867 species, 2,203 organelle genomes from 674 species, 55 whole genomes from 49 species, 534 genomic sequencing datasets from 366 species, and 9,682 transcriptome datasets from 350 species. Among the organelle genomes, 534 genomes from 366 species were newly generated in this study. Whole genomes, organelle genomes, genomic fragments, transcriptomes, and DNA barcodes were uniformly formatted and arranged by species. The GPGD is publicly accessible at http://www.gpgenome.com and serves as an essential resource for species identification, decomposition of biosynthetic pathways, and molecular-assisted breeding analysis. Thus, the database is an invaluable resource for future studies on herbal medicine safety, drug discovery, and the protection and rational use of herbal resources.

Establishing a genomic database for the medicinal plants in the Brazilian Pharmacopoeia

BACKGROUND

Brazil is exceptionally abundant in medicinal plant resources and has a rich ethnopharmacological history. Brazilian Pharmacopoeia (BP) acts as a national standard that regulates drug quality and has six published editions. Recent genomic approaches have led to a resurgence of interest in herbal drugs. The genomic data of plants has been used for pharmaceutical applications, protecting natural resources, and efficiently regulating the market. However, there are few genomic databases specifically for medicinal plants, and the establishment of a database that focuses on the herbs contained in the BP is urgently required.

METHODS

The medicinal plant species included in each edition of the BP were analyzed to understand the evolution of the Brazilian herbal drugs. The data of 82 plants in the BP were collected and categorized into four sections: DNA barcodes, super-barcodes, genomes, and sequencing data. A typical web server architecture pattern was used to build the database and website. Furthermore, the cp-Gs of the Aloe genus in the database were analyzed as an illustration.

RESULTS

A new database, the Brazilian Pharmacopoeia Genomic Database (BPGD) was constructed and is now publicly accessible. A BLAST server for species identification and sequence searching with the internal transcribed spacer 2 (ITS2), the intergenic region (psbA-trnH), and the chloroplast genome (cp-G) of Brazilian medicinal plants was also embedded in the BPGD. The database has 753 ITS2 of 76 species, 553 psbA-trnH and 190 genomes (whole genome and chloroplast genome) of 57 species. In addition, it contains 37 genome sequence data sets of 24 species and 616 transcriptome sequence data sets of 34 species and also includes 187 cp-Gs representing 57 medicinal species in the BP. Analyses of the six cp-Gs of three Aloe species identified the variable regions in the cp-Gs. These can be used to identify species and understand the intraspecific relationships.

CONCLUSIONS

This study presents the first genomic database of medicinal plants listed in the latest BP. It serves as an efficient platform to obtain and analyze genomic data, accelerate studies regarding Brazilian medicinal plants and facilitate the rational development on their market regulation.

Understanding and Exploiting Post-Translational Modifications for Plant Disease Resistance

Plants are constantly threatened by pathogens, so have evolved complex defence signalling networks to overcome pathogen attacks. Post-translational modifications (PTMs) are fundamental to plant immunity, allowing rapid and dynamic responses at the appropriate time. PTM regulation is essential; pathogen effectors often disrupt PTMs in an attempt to evade immune responses. Here, we cover the mechanisms of disease resistance to pathogens, and how growth is balanced with defence, with a focus on the essential roles of PTMs. Alteration of defence-related PTMs has the potential to fine-tune molecular interactions to produce disease-resistant crops, without trade-offs in growth and fitness.