Directions for research and training in plant omics: Big Questions and Big Data

The role of priming and memory in rice environmental stress adaptation: Current knowledge and perspectives

Plant responses to abiotic stresses are dynamic, following the unpredictable changes of physical environmental parameters such as temperature, water and nutrients. Physiological and phenotypical responses to stress are intercalated by periods of recovery. An earlier stress can be remembered as 'stress memory' to mount a response within a generation or transgenerationally. The 'stress priming' phenomenon allows plants to respond quickly and more robustly to stressors to increase survival, and therefore has significant implications for agriculture. Although evidence for stress memory in various plant species is accumulating, understanding of the mechanisms implicated, especially for crops of agricultural interest, is in its infancy. Rice is a major food crop which is susceptible to abiotic stresses causing constraints on its cultivation and yield globally. Advancing the understanding of the stress response network will thus have a significant impact on rice sustainable production and global food security in the face of climate change. Therefore, this review highlights the effects of priming on rice abiotic stress tolerance and focuses on specific aspects of stress memory, its perpetuation and its regulation at epigenetic, transcriptional, metabolic as well as physiological levels. The open questions and future directions in this exciting research field are also laid out.

Arabidopsis as a model for translational research

Arabidopsis thaliana is currently the most-studied plant species on earth, with an unprecedented number of genetic, genomic, and molecular resources having been generated in this plant model. In the era of translating foundational discoveries to crops and beyond, we aimed to highlight the utility and challenges of using Arabidopsis as a reference for applied plant biology research, agricultural innovation, biotechnology, and medicine. We hope that this review will inspire the next generation of plant biologists to continue leveraging Arabidopsis as a robust and convenient experimental system to address fundamental and applied questions in biology. We aim to encourage lab and field scientists alike to take advantage of the vast Arabidopsis datasets, annotations, germplasm, constructs, methods, molecular and computational tools in our pursuit to advance understanding of plant biology and help feed the world's growing population. We envision that the power of Arabidopsis-inspired biotechnologies and foundational discoveries will continue to fuel the development of resilient, high-yielding, nutritious plants for the betterment of plant and animal health and greater environmental sustainability.

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

Using MetaboAnalyst to introduce undergraduates to lipidomic analysis and lipid remodeling in barley roots

Lipidomics is a discipline that focuses on the identification and quantification of lipids. Although a part of the larger omics field, lipidomics requires specific approaches for the analysis and biological interpretation of datasets. This article presents a series of activities for introducing undergraduate microbiology students to lipidomic analysis through tools from the web-based platform MetaboAnalyst. The students perform a complete lipidomic workflow, which includes experiment design, data processing, data normalization, and statistical analysis of molecular phospholipid species obtained from barley roots exposed to Fusarium macroconidia. The input data are provided by the teacher, but students also learn about the methods through which they were originally obtained (untargeted liquid chromatography coupled with mass spectrometry). The ultimate aim is for students to understand the biological significance of phosphatidylcholine acyl editing. The chosen methodology allows users who are not proficient in statistics to make a comprehensive analysis of quantitative lipidomic datasets. We strongly believe that virtual activities based on the analysis of such datasets should be incorporated more often into undergraduate courses, in order to improve students' data-handling skills for omics sciences.

Safety assessment of natural products in Malaysia: current practices, challenges, and new strategies

The belief that natural products are inherently safe is a primary reason for consumers to choose traditional medicines and herbal supplements for health maintenance and disease prevention. Unfortunately, some natural products on the market have been found to contain toxic compounds, such as heavy metals and microbes, as well as banned ingredients such as aristolochic acids. It shows that the existing regulatory system is inadequate and highlights the importance of thorough safety evaluations. In Malaysia, the National Pharmaceutical Regulatory Agency is responsible for the regulatory control of medicinal products and cosmetics, including natural products. For registration purpose, the safety of natural products is primarily determined through the review of documents, including monographs, research articles and scientific reports. One of the main factors hampering safety evaluations of natural products is the lack of toxicological data from animal studies. However, international regulatory agencies such as the European Food Safety Authority and the United States Food and Drug Administration are beginning to accept data obtained using alternative strategies such as non-animal predictive toxicological tools. Our paper discusses the use of state-of-the-art techniques, including chemometrics, in silico modelling and omics technologies and their applications to the safety assessments of natural products.

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

Untargeted Proteomics-Based Approach to Investigate Unintended Changes in Genetically Modified Maize for Environmental Risk Assessment Purpose

Profiling technologies, such as proteomics, allow the simultaneous measurement and comparison of thousands of plant components without prior knowledge of their identity. The combination of these non-targeted methods facilitates a more comprehensive approach than targeted methods and thus provides additional opportunities to identify genotypic changes resulting from genetic modification, including new allergens or toxins. The purpose of this study was to investigate unintended changes in GM Bt maize grown in South Africa. In the present study, we used bi-dimensional gel electrophoresis based on fluorescence staining, coupled with mass spectrometry in order to compare the proteome of the field-grown transgenic hybrid (MON810) and its near-isogenic counterpart. Proteomic data showed that energy metabolism and redox homeostasis were unequally modulated in GM Bt and non-GM maize variety samples. In addition, a potential allergenic protein—pathogenesis related protein −1 has been identified in our sample set. Our data shows that the GM variety is not substantially equivalent to its non-transgenic near-isogenic variety and further studies should be conducted in order to address the biological relevance and the potential risks of such changes. These finding highlight the suitability of unbiased profiling approaches to complement current GMO risk assessment practices worldwide.

Diversifying maize genomic selection models

Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.

GC usage of SARS-CoV-2 genes might adapt to the environment of human lung expressed genes

Understanding how SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) efficiently reproduces itself by taking resources from the human host could facilitate the development of drugs against the virus. SARS-CoV-2 translates its own proteins by using the host tRNAs, so that its GC or codon usage should fit that of the host cells. It is necessary to study both the virus and human genomes in the light of evolution and adaptation. The SARS-CoV-2 virus has significantly lower GC content and GC3 as compared to human. However, when we selected a set of human genes that have similar GC properties to SARS-CoV-2, we found that these genes were enriched in particular pathways. Moreover, these human genes have the codon composition perfectly correlated with the SARS-CoV-2, and were extraordinarily highly expressed in human lung tissues, demonstrating that the SARS-CoV-2 genes have similar GC usage as compared to the lung expressed human genes. RSCU (relative synonymous codon usage) and CAI (codon adaptation index) profiles further support the matching between SARS-CoV-2 and lungs. Our study indicates that SARS-CoV-2 might have adapted to the human lung environment by observing the high correlation between GC usage of SARS-CoV-2 and human lung genes, which suggests the GC content of SARS-CoV-2 is optimized to take advantage of human lung tissues.

Current status of the multinational Arabidopsis community

The multinational Arabidopsis research community is highly collaborative and over the past thirty years these activities have been documented by the Multinational Arabidopsis Steering Committee (MASC). Here, we (a) highlight recent research advances made with the reference plant Arabidopsis thaliana; (b) provide summaries from recent reports submitted by MASC subcommittees, projects and resources associated with MASC and from MASC country representatives; and (c) initiate a call for ideas and foci for the "fourth decadal roadmap," which will advise and coordinate the global activities of the Arabidopsis research community.

Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding

Crops are the major source of food supply and raw materials for the processing industry. A balance between crop production and food consumption is continually threatened by plant diseases and adverse environmental conditions. This leads to serious losses every year and results in food shortages, particularly in developing countries. Presently, cutting-edge technologies for genome sequencing and phenotyping of crops combined with progress in computational sciences are leading a revolution in plant breeding, boosting the identification of the genetic basis of traits at a precision never reached before. In this frame, machine learning (ML) plays a pivotal role in data-mining and analysis, providing relevant information for decision-making towards achieving breeding targets. To this end, we summarize the recent progress in next-generation sequencing and the role of phenotyping technologies in genomics-assisted breeding toward the exploitation of the natural variation and the identification of target genes. We also explore the application of ML in managing big data and predictive models, reporting a case study using microRNAs (miRNAs) to identify genes related to stress conditions.

Evidence That Ion-Based Signaling Initiating at the Cell Surface Can Potentially Influence Chromatin Dynamics and Chromatin-Bound Proteins in the Nucleus

We have developed tools and performed pilot experiments to test the hypothesis that an intracellular ion-based signaling pathway, provoked by an extracellular stimulus acting at the cell surface, can influence interphase chromosome dynamics and chromatin-bound proteins in the nucleus. The experimental system employs chromosome-specific fluorescent tags and the genome-encoded fluorescent pH sensor SEpHluorinA227D, which has been targeted to various intracellular membranes and soluble compartments in root cells of Arabidopsis thaliana. We are using this system and three-dimensional live cell imaging to visualize whether fluorescent-tagged interphase chromosome sites undergo changes in constrained motion concurrently with reductions in membrane-associated pH elicited by extracellular ATP, which is known to trigger a cascade of events in plant cells including changes in calcium ion concentrations, pH, and membrane potential. To examine possible effects of the proposed ion-based signaling pathway directly at the chromatin level, we generated a pH-sensitive fluorescent DNA-binding protein that allows pH changes to be monitored at specific genomic sites. Results obtained using these tools support the existence of a rapid, ion-based signaling pathway that initiates at the cell surface and reaches the nucleus to induce alterations in interphase chromatin mobility and the surrounding pH of chromatin-bound proteins. Such a pathway could conceivably act under natural circumstances to allow external stimuli to swiftly influence gene expression by affecting interphase chromosome movement and the structures and/or activities of chromatin-associated proteins.