Comparative metabolite profiling of salt sensitive Oryza sativa and the halophytic wild rice Oryza coarctata under salt stress.

To better understand the salt tolerance of the wild rice, Oryza coarctata, root tissue-specific untargeted comparative metabolomic profiling was performed against the salt-sensitive Oryza sativa. Under control, O. coarctata exhibited abundant levels of most metabolites, while salt caused their downregulation in contrast to metabolites in O. sativa. Under control conditions, itaconate, vanillic acid, threonic acid, eicosanoids, and a group of xanthin compounds were comparatively abundant in O. coarctata. Similarly, eight amino acids showed constitutive abundance in O. coarctata. In contrast, under control, glycerolipid abundances were lower in O. coarctata and salt stress further reduced their abundance. Most phospholipids also showed a distribution similar to the glycerolipids. Fatty acyls were however significantly induced in O. coarctata but organic acids were prominently induced in O. sativa. Changes in metabolite levels suggest that there was upregulation of the arachidonic acid metabolism in O. coarctata. In addition, the phenylpropanoid biosynthesis as well as cutin, suberin, and wax biosynthesis were also more enriched in O. coarctata, likely contributing to its anatomical traits responsible for salt tolerance. The comparative variation in the number of metabolites like gelsemine, allantoin, benzyl alcohol, specific phospholipids, and glycerolipids may play a role in maintaining the superior growth of O. coarctata in salt. Collectively, our results offer a comprehensive analysis of the metabolite profile in the roots of salt-tolerant O. coarctata and salt-sensitive O. sativa, which confirm potential targets for metabolic engineering to improve salt tolerance and resilience in commercial rice genotypes.

Novel QTLs for salinity tolerance revealed by genome-wide association studies of biomass, chlorophyll and tissue ion content in 176 rice landraces from Bangladesh.

Farmland is on the decline and worldwide food security is at risk. Rice is the staple of choice for over half the Earth's people. To sustain current demands and ascertain a food secure future, substandard farmland affected by abiotic stresses must be utilized. For rapid crop improvement, a broader understanding of polygenic traits like stress tolerance and crop yield is indispensable. To this end, the hidden diversity of resilient and neglected wild varieties must be traced back to their genetic roots. In this study, we separately assayed 11 phenotypes in a panel of 176 diverse accessions predominantly comprised of local landraces from Bangladesh. We compiled high resolution sequence data for these accessions. We collectively studied the ties between the observed phenotypic differences and the examined additive genetic effects underlying these variations. We applied a fixed effect model to associate phenotypes with genotypes on a genomic scale. Discovered QTLs were mapped to known genes. Our explorations yielded 13 QTLs related to various traits in multiple trait classes. 10 identified QTLs were equivalent to findings from previous studies. Integrative analysis assumes potential novel functionality for a number of candidate genes. These findings will usher novel avenues for the bioengineering of high yielding crops of the future fortified with genetic defenses against abiotic stressors.

Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses.

High-throughput sequencing technologies have greatly enabled the study of genomics, transcriptomics and metagenomics. Automated annotation and classification of the vast amounts of generated sequence data has become paramount for facilitating biological sciences. Genomes of viruses can be radically different from all life, both in terms of molecular structure and primary sequence. Alignment-based and profile-based searches are commonly employed for characterization of assembled viral contigs from high-throughput sequencing data. Recent attempts have highlighted the use of machine learning models for the task, but these models rely entirely on DNA genomes and owing to the intrinsic genomic complexity of viruses, RNA viruses have gone completely overlooked. Here, we present a novel short k-mer based sequence scoring method that generates robust sequence information for training machine learning classifiers. We trained 18 classifiers for the task of distinguishing viral RNA from human transcripts. We challenged our models with very stringent testing protocols across different species and evaluated performance against BLASTn, BLASTx and HMMER3 searches. For clean sequence data retrieved from curated databases, our models display near perfect accuracy, outperforming all similar attempts previously reported. On de novo assemblies of raw RNA-Seq data from cells subjected to Ebola virus, the area under the ROC curve varied from 0.6 to 0.86 depending on the software used for assembly. Our classifier was able to properly classify the majority of the false hits generated by BLAST and HMMER3 searches on the same data. The outstanding performance metrics of our model lays the groundwork for robust machine learning methods for the automated annotation of sequence data.