Disclosing the molecular basis of salinity priming in olive trees using proteogenomic model discovery

Application of proteomics in investigating the responses of plant to abiotic stresses

MAIN CONCLUSION

This review summarizes the application of proteomic techniques in investigating the responses of plant to abiotic stresses. In the natural environment, the plants are exposed to a diverse range of adverse abiotic factors that significantly impact their growth and development. The plants have evolved intricate stress response mechanisms at the genetic, protein, metabolic, and phenotypic levels to mitigate damage caused by unfavorable conditions. Proteomics serves as an effective tool for studying protein changes in plants and provides valuable insights into the physiological mechanisms underlying plant stress resistance. Several proteins involved in abiotic stress responses have been identified in plants, including transcription factors, protein kinases, ATP synthases, heat shock proteins, redox proteins, and enzymes in secondary metabolite pathways. Medicinal plants are a unique category of crops capable of synthesizing secondary metabolites, which play a crucial role in resisting abiotic stress and exhibit changes in content under stress conditions. In this review, we present an overview of proteomic tools employed for investigating the responses of plants to abiotic stresses and summarize alterations observed at the protein level under various abiotic stresses such as signal transduction, oxidative damage, carbohydrate and energy metabolism, protein and amino acid metabolism, cellular homeostasis, and enzyme involvement in secondary metabolism. This work aims to facilitate the application of proteomics techniques in plants research while enhancing our understanding of the response mechanisms exhibited by these plants towards abiotic stresses.

Multi-Omic Advances in Olive Tree (Olea europaea subsp. europaea L.) Under Salinity: Stepping Towards 'Smart Oliviculture'

Soil salinisation is threatening crop sustainability worldwide, mainly due to anthropogenic climate change. Molecular mechanisms developed to counteract salinity have been intensely studied in model plants. Nevertheless, the economically relevant olive tree (Olea europaea subsp. europaea L.), being highly exposed to soil salinisation, deserves a specific review to extract the recent genomic advances that support the known morphological and biochemical mechanisms that make it a relative salt-tolerant crop. A comprehensive list of 98 olive cultivars classified by salt tolerance is provided, together with the list of available olive tree genomes and genes known to be involved in salt response. Na+ and Cl- exclusion in leaves and retention in roots seem to be the most prominent adaptations, but cell wall thickening and antioxidant changes are also required for a tolerant response. Several post-translational modifications of proteins are emerging as key factors, together with microbiota amendments, making treatments with biostimulants and chemical compounds a promising approach to enable cultivation in already salinised soils. Low and high-throughput transcriptomics and metagenomics results obtained from salt-sensitive and -tolerant cultivars, and the future advantages of engineering specific metacaspases involved in programmed cell death and autophagy pathways to rapidly raise salt-tolerant cultivars or rootstocks are also discussed. The overview of bioinformatic tools focused on olive tree, combined with machine learning approaches for studying plant stress from a multi-omics perspective, indicates that the development of salt-tolerant cultivars or rootstocks adapted to soil salinisation is progressing. This could pave the way for 'smart oliviculture', promoting more productive and sustainable practices under salt stress.

Autoregressive exogenous neural structures for synthetic datasets of olive disease control model with fractional Grünwald-Letnikov solver

A fundamental element of the Mediterranean diet, olive oil is abundant in heart-healthy monounsaturated fats and antioxidants, lowering the risk of cardiovascular diseases. However, the olive oil industry confronts hurdles arising from olive tree diseases, despite the numerous health advantages associated with its consumption. In pursuit of research goals, this study endeavors to employ cutting-edge intelligent computing paradigms, specifically nonlinear autoregressive exogenous neural networks utilizing the Levenberg-Marquardt scheme (NNLMS), to comprehensively analyze the complex dynamic interactions of the fractional-order olive disease control (FO-ODC) model. In the realm of nonlinear fractional differential modeling, this study explores a system governed by four distinct populations: the branches and leaves of healthy olive trees, olive trees affected by a detrimental fungus, a pathogenic filamentous fungus causing infection and damage to olive leaves, and branches, and the microbial organisms residing in the phyllosphere. The research aims to scrutinize the transmission patterns of olive disease within this complex ecological framework. Employing the fractional Grünwald-Letnikov backward finite difference method, this study undertakes the generation of a synthetic dataset that accurately illustrates variations in several key parameters, including the rate of healthy leaf production, natural mortality rate, growth rate of beneficial fungi, nutrient acquisition rate by pathogens from infected leaves, the scaling factor governing food acquisition in their mutualistic relationship, and the rate at which leaves are adversely affected or degrade due to the influence of harmful fungi. In each iteration of the NNLMS application, the synthetic dataset is arbitrarily segmented into training, testing, and validation samples, facilitating the computation of an approximate solution for the dynamics embedded in the nonlinear FO-ODC model. The viability of the design approach is evaluated/assessed by consistently matching outcomes with reference solutions through numerous variations of the FO-ODC model. The reliability and efficiency of the design approach are measured using various measures, such as regression analysis, absolute errors, mean errors, autocorrelations and error histograms.

Identification of Proteoforms Related to Nelumbo nucifera Flower Petaloid Through Proteogenomic Strategy

Nelumbo nucifera is an aquatic plant with a high ornamental value due to its flower. Despite the release of several versions of the lotus genome, its annotation remains inefficient, which makes it difficult to obtain a more comprehensive knowledge when -omic studies are applied to understand the different biological processes. Focusing on the petaloid of the lotus flower, we conducted a comparative proteomic analysis among five major floral organs. The proteogenomic strategy was applied to analyze the mass spectrometry data in order to dig out novel proteoforms that are involved in the petaloids of the lotus flower. The results revealed that a total of 4863 proteins corresponding to novel genes were identified, with 227 containing single amino acid variants (SAAVs), and 72 originating from alternative splicing (AS) genes. In addition, a range of post-translational modifications (PTMs) events were also identified in lotus. Through functional annotation and homology analysis with 24 closely related plant species, we identified five candidate proteins associated with floral organ development, which were not identified by ordinary proteomic analysis. This study not only provides new insights into understanding the mechanism of petaloids in lotus but is also helpful in identifying new proteoforms to improve the annotation of the lotus genome.

Proteogenomics-based functional genome research: approaches, applications, and perspectives in plants

Proteogenomics (PG) integrates the proteome with the genome and transcriptome to refine gene models and annotation. Coupled with single-cell (SC) assays, PG effectively distinguishes heterogeneity among cell groups. Affiliating spatial information to PG reveals the high-resolution circuitry within SC atlases. Additionally, PG can investigate dynamic changes in protein-coding genes in plants across growth and development as well as stress and external stimulation, significantly contributing to the functional genome. Here we summarize existing PG research in plants and introduce the technical features of various methods. Combining PG with other omics, such as metabolomics and peptidomics, can offer even deeper insights into gene functions. We argue that the application of PG will represent an important font of foundational knowledge for plants.

Single Nucleotide Polymorphisms' Causal Structure Robustness within Coronary Artery Disease Patients

An ever-growing amount of accumulated data has materialized in several scientific fields, due to recent technological progress. New challenges emerge in exploiting these data and utilizing the valuable available information. Causal models are a powerful tool that can be employed towards this aim, by unveiling the structure of causal relationships between different variables. The causal structure may avail experts to better understand relationships, or even uncover new knowledge. Based on 963 patients with coronary artery disease, the robustness of the causal structure of single nucleotide polymorphisms was assessed, taking into account the value of the Syntax Score, an index that evaluates the complexity of the disease. The causal structure was investigated, both locally and globally, under different levels of intervention, reflected in the number of patients that were randomly excluded from the original datasets corresponding to two categories of the Syntax Score, zero and positive. It is shown that the causal structure of single nucleotide polymorphisms was more robust under milder interventions, whereas in the case of stronger interventions, the impact increased. The local causal structure around the Syntax Score was studied in the case of a positive Syntax Score, and it was found to be resilient, even when the intervention was strong. Consequently, employing causal models in this context may increase the understanding of the biological aspects of coronary artery disease.

Integration of mRNA and miRNA Analysis Reveals the Post-Transcriptional Regulation of Salt Stress Response in Hemerocallis fulva

MicroRNAs (miRNAs) belong to non-coding small RNAs which have been shown to take a regulatory function at the posttranscriptional level in plant growth development and response to abiotic stress. Hemerocallis fulva is an herbaceous perennial plant with fleshy roots, wide distribution, and strong adaptability. However, salt stress is one of the most serious abiotic stresses to limit the growth and production of Hemerocallis fulva. To identify the miRNAs and their targets involved in the salt stress resistance, the salt-tolerant H. fulva with and without NaCl treatment were used as materials, and the expression differences of miRNAs-mRNAs related to salt-tolerance were explored and the cleavage sites between miRNAs and targets were also identified by using degradome sequencing technology. In this study, twenty and three significantly differential expression miRNAs (p-value < 0.05) were identified in the roots and leaves of H. fulva separately. Additionally, 12,691 and 1538 differentially expressed genes (DEGs) were also obtained, respectively, in roots and leaves. Moreover, 222 target genes of 61 family miRNAs were validated by degradome sequencing. Among the DE miRNAs, 29 pairs of miRNA targets displayed negatively correlated expression profiles. The qRT-PCR results also showed that the trends of miRNA and DEG expression were consistent with those of RNA-seq. A gene ontology (GO) enrichment analysis of these targets revealed that the calcium ion pathway, oxidative defense response, microtubule cytoskeleton organization, and DNA binding transcription factor responded to NaCl stress. Five miRNAs, miR156, miR160, miR393, miR166, and miR396, and several hub genes, squamosa promoter-binding-like protein (SPL), auxin response factor 12 (ARF), transport inhibitor response 1-like protein (TIR1), calmodulin-like proteins (CML), and growth-regulating factor 4 (GRF4), might play central roles in the regulation of NaCl-responsive genes. These results indicate that non-coding small RNAs and their target genes that are related to phytohormone signaling, Ca²⁺ signaling, and oxidative defense signaling pathways are involved in H. fulva's response to NaCl stress.

A wide foodomics approach coupled with metagenomics elucidates the environmental signature of potatoes

The term "terroir" has been widely employed to link differential geographic phenotypes with sensorial signatures of agricultural food products, influenced by agricultural practices, soil type, and climate. Nowadays, the geographical indications labeling has been developed to safeguard the quality of plant-derived food that is linked to a certain terroir and is generally considered as an indication of superior organoleptic properties. As the dynamics of agroecosystems are highly intricate, consisting of tangled networks of interactions between plants, microorganisms, and the surrounding environment, the recognition of the key molecular components of terroir fingerprinting remains a great challenge to protect both the origin and the safety of food commodities. Furthermore, the contribution of microbiome as a potential driver of the terroir signature has been underestimated. Herein, we present a first comprehensive view of the multi-omic landscape related to transcriptome, proteome, epigenome, and metagenome of the popular Protected Geographical Indication potatoes of Naxos.