Metabolic Pathway Assignment of Plant Genes based on Phylogenetic Profiling-A Feasibility Study

Genome architecture and genetic diversity of allopolyploid okra (Abelmoschus esculentus)

The allopolyploid okra (Abelmoschus esculentus) unveiled telomeric repeats flanking distal gene-rich regions and short interstitial TTTAGGG telomeric repeats, possibly representing hallmarks of chromosomal speciation. Ribosomal RNA (rRNA) genes organize into 5S clusters, distinct from the 18S-5.8S-28S units, indicating an S-type rRNA gene arrangement. The assembly, in line with cytogenetic and cytometry observations, identifies 65 chromosomes and a 1.45 Gb genome size estimate in a haploid sibling. The lack of aberrant meiotic configurations implies limited to no recombination among sub-genomes. k-mer distribution analysis reveals 75% has a diploid nature and 15% heterozygosity. The configurations of Benchmarking Universal Single-Copy Ortholog (BUSCO), k-mer, and repeat clustering point to the presence of at least two sub-genomes one with 30 and the other with 35 chromosomes, indicating the allopolyploid nature of the okra genome. Over 130 000 putative genes, derived from mapped IsoSeq data and transcriptome data from public okra accessions, exhibit a low genetic diversity of one single nucleotide polymorphisms per 2.1 kbp. The genes are predominantly located at the distal chromosome ends, declining toward central scaffold domains. Long terminal repeat retrotransposons prevail in central domains, consistent with the observed pericentromeric heterochromatin and distal euchromatin. Disparities in paralogous gene counts suggest potential sub-genome differentiation implying possible sub-genome dominance. Amino acid query sequences of putative genes facilitated phenol biosynthesis pathway annotation. Comparison with manually curated reference KEGG pathways from related Malvaceae species reveals the genetic basis for putative enzyme coding genes that likely enable metabolic reactions involved in the biosynthesis of dietary and therapeutic compounds in okra.

Enhancement of nitrogen use efficiency through agronomic and molecular based approaches in cotton

Cotton is a major fiber crop grown worldwide. Nitrogen (N) is an essential nutrient for cotton production and supports efficient crop production. It is a crucial nutrient that is required more than any other. Nitrogen management is a daunting task for plants; thus, various strategies, individually and collectively, have been adopted to improve its efficacy. The negative environmental impacts of excessive N application on cotton production have become harmful to consumers and growers. The 4R's of nutrient stewardship (right product, right rate, right time, and right place) is a newly developed agronomic practice that provides a solid foundation for achieving nitrogen use efficiency (NUE) in cotton production. Cropping systems are equally crucial for increasing production, profitability, environmental growth protection, and sustainability. This concept incorporates the right fertilizer source at the right rate, time, and place. In addition to agronomic practices, molecular approaches are equally important for improving cotton NUE. This could be achieved by increasing the efficacy of metabolic pathways at the cellular, organ, and structural levels and NUE-regulating enzymes and genes. This is a potential method to improve the role of N transporters in plants, resulting in better utilization and remobilization of N in cotton plants. Therefore, we suggest effective methods for accelerating NUE in cotton. This review aims to provide a detailed overview of agronomic and molecular approaches for improving NUE in cotton production, which benefits both the environment and growers.

Optimising the use of gene expression data to predict plant metabolic pathway memberships

Plant metabolites from diverse pathways are important for plant survival, human nutrition and medicine. The pathway memberships of most plant enzyme genes are unknown. While co-expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts. Utilising > 600 tomato (Solanum lycopersicum) expression data combinations, three strategies for predicting memberships in 85 pathways were explored. Optimal predictions for different pathways require distinct data combinations indicative of pathway functions. Naive prediction (i.e. identifying pathways with the most similarly expressed genes) is error prone. In 52 pathways, unsupervised learning performed better than supervised approaches, possibly due to limited training data availability. Using gene-to-pathway expression similarities led to prediction models that outperformed those based simply on expression levels. Using 36 experimental validated genes, the pathway-best model prediction accuracy is 58.3%, significantly better compared with that for predicting annotated genes without experimental evidence (37.0%) or random guess (1.2%), demonstrating the importance of data quality. Our study highlights the need to extensively explore expression-based features and prediction strategies to maximise the accuracy of metabolic pathway membership assignment. The prediction framework outlined here can be applied to other species and serves as a baseline model for future comparisons.

Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology

Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.