Transcriptional and proteomic insights into phytotoxic activity of interspecific potato hybrids with low glycoalkaloid contents

Identification of CAP genes in finger lime (Citrus australasica) and their role in plant responses to abiotic and biotic stress

The study focuses on the in silico analysis of cysteine-rich secretory proteins and PR1-like (CAP) genes in finger lime (Citrus australasica), a citrus species known for its tolerance to Huanglongbing (HLB). We identified several PR1-like genes, all belonging to the CRISP family within the CAP superfamily. Of them, CaCAP2 transcript levels increased by over 300-fold in the finger lime compared to 'Valencia' sweet orange upon infection with 'Candidatus Liberibacter asiaticus' (CaLas). Localization studies using an EGFP fusion showed that the CAP2 protein is predominantly located in the nucleus, extracellular and plasma membrane. The study also examined CAP2 transcript levels in response to cold, drought stress, and salicylic acid application. Despite environmental stress causing apparent damage, CAP genes seem to play a significant role in managing both biotic and abiotic stresses. Analysis of CAP2 gene promoters from finger lime and sweet orange revealed 95.33% sequence identity, with variations in transcription factor-binding sites and cis-acting elements such as Stress Response Element (STRE: AGGGG), which might influence the differential expression of CAP2 between the two species. Additionally, expressing the finger lime-derived CaCAP2 gene in transgenic Nicotiana tabacum induced a strong defense response against Pseudomonas syringae pv. Tabaci., underscoring the CAP gene's crucial role in plant defense mechanisms against bacterial pathogens.

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

BACKGROUND

Derivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.

RESULTS

The results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism's specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points.

CONCLUSIONS

While we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

Glycoalkaloid Composition and Flavonoid Content as Driving Forces of Phytotoxicity in Diploid Potato

Despite their advantages, biotechnological and omic techniques have not been applied often to characterize phytotoxicity in depth. Here, we show the distribution of phytotoxicity and glycoalkaloid content in a diploid potato population and try to clarify the source of variability of phytotoxicity among plants whose leaf extracts have a high glycoalkaloid content against the test plant species, mustard. Six glycoalkaloids were recognized in the potato leaf extracts: solasonine, solamargine, α-solanine, α-chaconine, leptinine I, and leptine II. The glycoalkaloid profiles of the progeny of the group with high phytotoxicity differed from those of the progeny of the group with low phytotoxicity, which stimulated mustard growth. RNA sequencing analysis revealed that the upregulated flavonol synthase/flavonone 3-hydroxylase-like gene was expressed in the progeny of the low phytotoxicity group, stimulating plant growth. We concluded that the metabolic shift among potato progeny may be a source of different physiological responses in mustard. The composition of glycoalkaloids, rather than the total glycoalkaloid content itself, in potato leaf extracts, may be a driving force of phytotoxicity. We suggest that, in addition to glycoalkaloids, other metabolites may shape phytotoxicity, and we assume that these metabolites may be flavonoids.

Multiomic Analysis Reveals Core Regulatory Mechanisms underlying Steroidal Glycoalkaloid Metabolism in Potato Tubers

Steroidal glycoalkaloids (SGAs) present in germinated potato tubers are toxic; however, the mechanisms underlying SGA metabolism are poorly understood. Therefore, integrated transcriptome, metabolome, and hormone analyses were performed in this study to identify and characterize the key regulatory genes, metabolites, and phytohormones related to glycoalkaloid regulation. Based on transcriptome sequencing of bud eyes of germinated and dormant potato tubers, a total of 6260 differentially expressed genes were identified, which were mainly responsible for phytohormone signal transduction, carbohydrate metabolism, and secondary metabolite biosynthesis. Two TCP14 genes were identified as the core transcription factors that potentially regulate SGA synthesis. Metabolite analysis indicated that 149 significantly different metabolites were detected, and they were enriched in metabolic and biosynthetic pathways of secondary metabolites. In these pathways, the α-solanine content was increased and the expression of genes related to glycoalkaloid biosynthesis was upregulated. Levels of gibberellin and jasmonic acid were increased, whereas that of abscisic acid was decreased. This study lays a foundation for investigating the biosynthesis and regulation of SGAs and provides the reference for the production and consumption of potato tubers.