Integration of multi-omics techniques and physiological phenotyping within a holistic phenomics approach to study senescence in model and crop plants

Soybean2035: A decadal vision for soybean functional genomics and breeding

Soybean, the fourth most important crop in the world, uniquely serves as a source of both plant oil and plant protein for the world's food and animal feed. Although soybean production has increased approximately 13-fold over the past 60 years, the continually growing global population necessitates further increases in soybean production. In the past, especially in the last decade, significant progress has been made in both functional genomics and molecular breeding. However, many more challenges should be overcome to meet the anticipated future demand. Here, we summarize past achievements in the areas of soybean omics, functional genomics, and molecular breeding. Furthermore, we analyze trends in these areas, including shortages and challenges, and propose new directions, potential approaches, and possible outputs toward 2035. Our views and perspectives provide insight into accelerating the development of elite soybean varieties to meet the increasing demands of soybean production.

Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review

Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience.

A study on waterlogging tolerance in sugarcane: a comprehensive review

Sugarcane (Saccharum officinarum) is an important crop, native to tropical and subtropical regions and it is a major source of sugar and Bioenergy in the world. Abiotic stress is defined as environmental conditions that reduce growth and yield below the optimum level. To tolerate these abiotic stresses, plants initiate several molecular, cellular, and physiological changes. These responses to abiotic stresses are dynamic and complex; they may be reversible or irreversible. Waterlogging is an abiotic stress phenomenon that drastically reduces the growth and survival of sugarcane, which leads to a 15-45% reduction in cane's yield. The extent of damage due to waterlogging depends on genotypes, environmental conditions, stage of development and duration of stress. An improved understanding of the physiological, biochemical, and molecular responses of sugarcane to waterlogging stress could help to develop new breeding strategies to sustain high yields against this situation. The present review offers a summary of recent findings on the adaptation of sugarcane to waterlogging stress in terms of growth and development, yield and quality, as well as biochemical and adaptive-molecular processes that may contribute to flooding tolerance.

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.

Tulip transcription factor TgWRKY75 activates salicylic acid and abscisic acid biosynthesis to synergistically promote petal senescence

WRKY transcription factors play a central role in controlling plant organ senescence; however, it is unclear whether and how they regulate petal senescence in the widely grown ornamental plant tulip (Tulipa gesneriana). In this study, we report that TgWRKY75 promotes petal senescence by enhancing the synthesis of both abscisic acid (ABA) and salicylic acid (SA) in tulip and in transgenic Arabidopsis. The expression level of TgWRKY75 was up-regulated in senescent petals, and exogenous ABA or SA treatment induced its expression. The endogenous contents of ABA and SA significantly increased during petal senescence and in response to TgWRKY75 overexpression. Two SA synthesis-related genes, TgICS1 and TgPAL1, were identified as direct targets of TgWRKY75, which binds to their promoters. In parallel, TgWRKY75 activated the expression of the ABA biosynthesis-related gene TgNCED3 via directly binding to its promoter region. Site mutation of the W-box core motif located in the promoters of TgICS1, TgPAL1, and TgNCED3 eliminated their interactions with TgWRKY75. In summary, our study demonstrates a dual regulation of ABA and SA biosynthesis by TgWRKY75, revealing a synergistic process of tulip petal senescence through feedback regulation between TgWRKY75 and the accumulation of ABA and SA.

The role of microbial partners in heavy metal metabolism in plants: a review

Heavy metal pollution threatens plant growth and development as well as ecological stability. Here, we synthesize current research on the interplay between plants and their microbial symbionts under heavy metal stress, highlighting the mechanisms employed by microbes to enhance plant tolerance and resilience. Several key strategies such as bioavailability alteration, chelation, detoxification, induced systemic tolerance, horizontal gene transfer, and methylation and demethylation, are examined, alongside the genetic and molecular basis governing these plant-microbe interactions. However, the complexity of plant-microbe interactions, coupled with our limited understanding of the associated mechanisms, presents challenges in their practical application. Thus, this review underscores the necessity of a more detailed understanding of how plants and microbes interact and the importance of using a combined approach from different scientific fields to maximize the benefits of these microbial processes. By advancing our knowledge of plant-microbe synergies in the metabolism of heavy metals, we can develop more effective bioremediation strategies to combat the contamination of soil by heavy metals.

Improved Transformer for Time Series Senescence Root Recognition

The root is an important organ for plants to obtain nutrients and water, and its phenotypic characteristics are closely related to its functions. Deep-learning-based high-throughput in situ root senescence feature extraction has not yet been published. In light of this, this paper suggests a technique based on the transformer neural network for retrieving cotton's in situ root senescence properties. High-resolution in situ root pictures with various levels of senescence are the main subject of the investigation. By comparing the semantic segmentation of the root system by general convolutional neural networks and transformer neural networks, SegFormer-UN (large) achieves the optimal evaluation metrics with mIoU, mRecall, mPrecision, and mF1 metric values of 81.52%, 86.87%, 90.98%, and 88.81%, respectively. The segmentation results indicate more accurate predictions at the connections of root systems in the segmented images. In contrast to 2 algorithms for cotton root senescence extraction based on deep learning and image processing, the in situ root senescence recognition algorithm using the SegFormer-UN model has a parameter count of 5.81 million and operates at a fast speed, approximately 4 min per image. It can accurately identify senescence roots in the image. We propose that the SegFormer-UN model can rapidly and nondestructively identify senescence root in in situ root images, providing important methodological support for efficient crop senescence research.

Metabolomics-driven investigation of plant defense response against pest and pathogen attack

The advancement of metabolomics has assisted in the identification of various bewildering characteristics of the biological system. Metabolomics is a standard approach, facilitating crucial aspects of system biology with absolute quantification of metabolites using minimum samples, based on liquid/gas chromatography, mass spectrometry and nuclear magnetic resonance. The metabolome profiling has narrowed the wide gaps of missing information and has enhanced the understanding of a wide spectrum of plant-environment interactions by highlighting the complex pathways regulating biochemical reactions and cellular physiology under a particular set of conditions. This high throughput technique also plays a prominent role in combined analyses of plant metabolomics and other omics datasets. Plant metabolomics has opened a wide paradigm of opportunities for developing stress-tolerant plants, ensuring better food quality and quantity. However, despite advantageous methods and databases, the technique has a few limitations, such as ineffective 3D capturing of metabolites, low comprehensiveness, and lack of cell-based sampling. In the future, an expansion of plant-pathogen and plant-pest response towards the metabolite architecture is necessary to understand the intricacies of plant defence against invaders, elucidation of metabolic pathway operational during defence and developing a direct correlation between metabolites and biotic stresses. Our aim is to provide an overview of metabolomics and its utilities for the identification of biomarkers or key metabolites associated with biotic stress, devising improved diagnostic methods to efficiently assess pest and pathogen attack and generating improved crop varieties with the help of combined application of analytical and molecular tools.

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.

Omics-driven exploration and mining of key functional genes for the improvement of food and fiber crops

The deployment of omics technologies has obtained an incredible boost over the past few decades with the advances in next-generation sequencing (NGS) technologies, innovative bioinformatics tools, and the deluge of available biological information. The major omics technologies in the limelight are genomics, transcriptomics, proteomics, metabolomics, and phenomics. These biotechnological advances have modernized crop breeding and opened new horizons for developing crop varieties with improved traits. The genomes of several crop species are sequenced, and a huge number of genes associated with crucial economic traits have been identified. These identified genes not only provide insights into the understanding of regulatory mechanisms of crop traits but also decipher practical grounds to assist in the molecular breeding of crops. This review discusses the potential of omics technologies for the acquisition of biological information and mining of the genes associated with important agronomic traits in important food and fiber crops, such as wheat, rice, maize, potato, tomato, cassava, and cotton. Different functional genomics approaches for the validation of these important genes are also highlighted. Furthermore, a list of genes discovered by employing omics approaches is being represented as potential targets for genetic modifications by the latest genome engineering methods for the development of climate-resilient crops that would in turn provide great impetus to secure global food security.

Ameliorating the effects of multiple stresses on agronomic traits in crops: modern biotechnological and omics approaches

While global climate change poses a significant environmental threat to agriculture, the increasing population is another big challenge to food security. To address this, developing crop varieties with increased productivity and tolerance to biotic and abiotic stresses is crucial. Breeders must identify traits to ensure higher and consistent yields under inconsistent environmental challenges, possess resilience against emerging biotic and abiotic stresses and satisfy customer demands for safer and more nutritious meals. With the advent of omics-based technologies, molecular tools are now integrated with breeding to understand the molecular genetics of genotype-based traits and develop better climate-smart crops. The rapid development of omics technologies offers an opportunity to generate novel datasets for crop species. Identifying genes and pathways responsible for significant agronomic traits has been made possible by integrating omics data with genetic and phenotypic information. This paper discusses the importance and use of omics-based strategies, including genomics, transcriptomics, proteomics and phenomics, for agricultural and horticultural crop improvement, which aligns with developing better adaptability in these crop species to the changing climate conditions.

Response and inversion of skewness parameters to meteorological factors based on RGB model of leaf color digital image

In the natural environment, complex and changeable meteorological factors can influence changes in the internal physiology and phenotype of crops. It is important to learn how to convert complex meteorological factor stimuli into plant perception phenotypes when analyzing the biological data obtained under the natural field condition. We restored the true gradation distribution of leaf color, which is also known as the skewed distribution of color scale, and obtained 20 multi-dimensional color gradation skewness-distribution (CGSD) parameters based on the leaf color skewness parameter system. Furthermore, we analyzed the correlation between the five corresponding meteorological factors and canopy CGSD parameters of peppers growing in a greenhouse and cabbages growing in an open air environment, built response model and inversion mode of leaf color to meteorological factors. Based on the analysis, we find a new method for correlating complex environmental problems with multi-dimensional parameters. This study provides a new idea for building a correlation model that uses leaf color as a bridge between meteorological factors and plants internal physiological state.

A Relationship Prediction Method for Magnaporthe oryzae-Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder

Magnaporthe oryzae Oryzae (MoO) pathotype is a devastating fungal pathogen of rice; however, its pathogenic mechanism remains poorly understood. The current research is primarily focused on single-omics data, which is insufficient to capture the complex cross-kingdom regulatory interactions between MoO and rice. To address this limitation, we proposed a novel method called Weighted Gene Autoencoder Multi-Omics Relationship Prediction (WGAEMRP), which combines weighted gene co-expression network analysis (WGCNA) and graph autoencoder to predict the relationship between MoO-rice multi-omics data. We applied WGAEMRP to construct a MoO-rice multi-omics heterogeneous interaction network, which identified 18 MoO small RNAs (sRNAs), 17 rice genes, 26 rice mRNAs, and 28 rice proteins among the key biomolecules. Most of the mined functional modules and enriched pathways were related to gene expression, protein composition, transportation, and metabolic processes, reflecting the infection mechanism of MoO. Compared to previous studies, WGAEMRP significantly improves the efficiency and accuracy of multi-omics data integration and analysis. This approach lays out a solid data foundation for studying the biological process of MoO infecting rice, refining the regulatory network of pathogenic markers, and providing new insights for developing disease-resistant rice varieties.

A critical review of machine-learning for "multi-omics" marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.

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.

How far can omics go in unveiling the mechanisms of floral senescence?

Floral senescence is of fundamental interest in understanding plant developmental regulation, it is of ecological and agricultural interest in relation to seed production, and is of key importance to the production of cut flowers. The biochemical changes occurring are well-studied and involve macromolecular breakdown and remobilisation of nutrients to developing seeds or other young organs in the plant. However, the initiation and regulation of the process and inter-organ communication remain to be fully elucidated. Although ethylene emission, which becomes autocatalytic, is a key regulator in some species, in other species it appears not to be as important. Other plant growth regulators such as cytokinins, however, seem to be important in floral senescence across both ethylene sensitive and insensitive species. Other plant growth regulators are also likely involved. Omics approaches have provided a wealth of data especially in ornamental species where genome data is lacking. Two families of transcription factors: NAC and WRKY emerge as major regulators, and omics information has been critical in understanding their functions. Future progress would greatly benefit from a single model species for understanding floral senescence; however, this is challenging due to the diversity of regulatory mechanisms. Combining omics data sets can be powerful in understanding different layers of regulation, but in vitro biochemical and or genetic analysis through transgenics or mutants is still needed to fully verify mechanisms and interactions between regulators.

Salicylic acid metabolism and signalling coordinate senescence initiation in aspen in nature

Deciduous trees exhibit a spectacular phenomenon of autumn senescence driven by the seasonality of their growth environment, yet there is no consensus which external or internal cues trigger it. Senescence starts at different times in European aspen (Populus tremula L.) genotypes grown in same location. By integrating omics studies, we demonstrate that aspen genotypes utilize similar transcriptional cascades and metabolic cues to initiate senescence, but at different times during autumn. The timing of autumn senescence initiation appeared to be controlled by two consecutive "switches"; 1) first the environmental variation induced the rewiring of the transcriptional network, stress signalling pathways and metabolic perturbations and 2) the start of senescence process was defined by the ability of the genotype to activate and sustain stress tolerance mechanisms mediated by salicylic acid. We propose that salicylic acid represses the onset of leaf senescence in stressful natural conditions, rather than promoting it as often observed in annual plants.

Omics Approaches in Invasion Biology: Understanding Mechanisms and Impacts on Ecological Health

Invasive species and rapid climate change are affecting the control of new plant diseases and epidemics. To effectively manage these diseases under changing environmental conditions, a better understanding of pathophysiology with holistic approach is needed. Multiomics approaches can help us to understand the relationship between plants and microbes and construct predictive models for how they respond to environmental stresses. The application of omics methods enables the simultaneous analysis of plant hosts, soil, and microbiota, providing insights into their intricate relationships and the mechanisms underlying plant-microbe interactions. This can help in the development of novel strategies for enhancing plant health and improving soil ecosystem functions. The review proposes the use of omics methods to study the relationship between plant hosts, soil, and microbiota, with the aim of developing a new technique to regulate soil health. This approach can provide a comprehensive understanding of the mechanisms underlying plant-microbe interactions and contribute to the development of effective strategies for managing plant diseases and improving soil ecosystem functions. In conclusion, omics technologies offer an innovative and holistic approach to understanding plant-microbe interactions and their response to changing environmental conditions.

Multi-omics revolution to promote plant breeding efficiency

Crop production is the primary goal of agricultural activities, which is always taken into consideration. However, global agricultural systems are coming under increasing pressure from the rising food demand of the rapidly growing world population and changing climate. To address these issues, improving high-yield and climate-resilient related-traits in crop breeding is an effective strategy. In recent years, advances in omics techniques, including genomics, transcriptomics, proteomics, and metabolomics, paved the way for accelerating plant/crop breeding to cope with the changing climate and enhance food production. Optimized omics and phenotypic plasticity platform integration, exploited by evolving machine learning algorithms will aid in the development of biological interpretations for complex crop traits. The precise and progressive assembly of desire alleles using precise genome editing approaches and enhanced breeding strategies would enable future crops to excel in combating the changing climates. Furthermore, plant breeding and genetic engineering ensures an exclusive approach to developing nutrient sufficient and climate-resilient crops, the productivity of which can sustainably and adequately meet the world's food, nutrition, and energy needs. This review provides an overview of how the integration of omics approaches could be exploited to select crop varieties with desired traits.

Tapping into Plant-Microbiome Interactions through the Lens of Multi-Omics Techniques

This review highlights the pivotal role of root exudates in the rhizosphere, especially the interactions between plants and microbes and between plants and plants. Root exudates determine soil nutrient mobilization, plant nutritional status, and the communication of plant roots with microbes. Root exudates contain diverse specialized signaling metabolites (primary and secondary). The spatial behavior of these metabolites around the root zone strongly influences rhizosphere microorganisms through an intimate compatible interaction, thereby regulating complex biological and ecological mechanisms. In this context, we reviewed the current understanding of the biological phenomenon of allelopathy, which is mediated by phytotoxic compounds (called allelochemicals) released by plants into the soil that affect the growth, survival, development, ecological infestation, and intensification of other plant species and microbes in natural communities or agricultural systems. Advances in next-generation sequencing (NGS), such as metagenomics and metatranscriptomics, have opened the possibility of better understanding the effects of secreted metabolites on the composition and activity of root-associated microbial communities. Nevertheless, understanding the role of secretory metabolites in microbiome manipulation can assist in designing next-generation microbial inoculants for targeted disease mitigation and improved plant growth using the synthetic microbial communities (SynComs) tool. Besides a discussion on different approaches, we highlighted the advantages of conjugation of metabolomic approaches with genetic design (metabolite-based genome-wide association studies) in dissecting metabolome diversity and understanding the genetic components of metabolite accumulation. Recent advances in the field of metabolomics have expedited comprehensive and rapid profiling and discovery of novel bioactive compounds in root exudates. In this context, we discussed the expanding array of metabolomics platforms for metabolome profiling and their integration with multivariate data analysis, which is crucial to explore the biosynthesis pathway, as well as the regulation of associated pathways at the gene, transcript, and protein levels, and finally their role in determining and shaping the rhizomicrobiome.

The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization

The OMICAS alliance is part of the Colombian government's Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program's kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS' R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field.

Enzyme activity profiling for physiological phenotyping within functional phenomics: plant growth and stress responses

High-throughput profiling of key enzyme activities of carbon, nitrogen, and antioxidant metabolism is emerging as a valuable approach to integrate cell physiological phenotyping into a holistic functional phenomics approach. However, the analyses of the large datasets generated by this method represent a bottleneck, often keeping researchers from exploiting the full potential of their studies. We address these limitations through the exemplary application of a set of data evaluation and visualization tools within a case study. This includes the introduction of multivariate statistical analyses that can easily be implemented in similar studies, allowing researchers to extract more valuable information to identify enzymatic biosignatures. Through a literature meta-analysis, we demonstrate how enzyme activity profiling has already provided functional information on the mechanisms regulating plant development and response mechanisms to abiotic stress and pathogen attack. The high robustness of the distinct enzymatic biosignatures observed during developmental processes and under stress conditions underpins the enormous potential of enzyme activity profiling for future applications in both basic and applied research. Enzyme activity profiling will complement molecular -omics approaches to contribute to the mechanistic understanding required to narrow the genotype-to-phenotype knowledge gap and to identify predictive biomarkers for plant breeding to develop climate-resilient crops.

Functional phenomics for improved climate resilience in Nordic agriculture

The five Nordic countries span the most northern region for field cultivation in the world. This presents challenges per se, with short growing seasons, long days, and a need for frost tolerance. Climate change has additionally increased risks for micro-droughts and water logging, as well as pathogens and pests expanding northwards. Thus, Nordic agriculture demands crops that are adapted to the specific Nordic growth conditions and future climate scenarios. A focus on crop varieties and traits important to Nordic agriculture, including the unique resource of nutritious wild crops, can meet these needs. In fact, with a future longer growing season due to climate change, the region could contribute proportionally more to global agricultural production. This also applies to other northern regions, including the Arctic. To address current growth conditions, mitigate impacts of climate change, and meet market demands, the adaptive capacity of crops that both perform well in northern latitudes and are more climate resilient has to be increased, and better crop management systems need to be built. This requires functional phenomics approaches that integrate versatile high-throughput phenotyping, physiology, and bioinformatics. This review stresses key target traits, the opportunities of latitudinal studies, and infrastructure needs for phenotyping to support Nordic agriculture.

Microalgae's prospects in attaining sustainable economic and environmental development

Sustainable Development Goals (SDGs) have been part of much worldwide cooperation in engineering design, nutrients production that contributes towards a better and more sustainable future. This review intends to uncover a potential renewable source that could significantly contribute to various goals under the SDGs. The prospects of algae tackling the socio-ecological, economic, and environmental issues faced globally are discussed, along with approaches of algae that can be utilized to achieve many of the SDGs are reviewed and discussed. Moreover, the recent trends in terms of engineering application that co-relate to novel algae-based technology has also been included. Apart from that, algae have high oil content which is suitable for producing affordable and clean energy, which can be used for biofuels or electricity generation. The promising characteristics of algae will lead to its global acceptance and utilization for sustainability to help create a better world.

Metabolic control of arginine and ornithine levels paces the progression of leaf senescence

Leaf senescence can be induced by stress or aging, sometimes in a synergistic manner. It is generally acknowledged that the ability to withstand senescence-inducing conditions can provide plants with stress resilience. Although the signaling and transcriptional networks responsible for a delayed senescence phenotype, often referred to as a functional stay-green trait, have been actively investigated, very little is known about the subsequent metabolic adjustments conferring this aptitude to survival. First, using the individually darkened leaf (IDL) experimental setup, we compared IDLs of wild-type (WT) Arabidopsis (Arabidopsis thaliana) to several stay-green contexts, that is IDLs of two functional stay-green mutant lines, oresara1-2 (ore1-2) and an allele of phytochrome-interacting factor 5 (pif5), as well as to leaves from a WT plant entirely darkened (DP). We provide compelling evidence that arginine and ornithine, which accumulate in all stay-green contexts-likely due to the lack of induction of amino acids (AAs) transport-can delay the progression of senescence by fueling the Krebs cycle or the production of polyamines (PAs). Secondly, we show that the conversion of putrescine to spermidine (SPD) is controlled in an age-dependent manner. Thirdly, we demonstrate that SPD represses senescence via interference with ethylene signaling by stabilizing the ETHYLENE BINDING FACTOR1 and 2 (EBF1/2) complex. Taken together, our results identify arginine and ornithine as central metabolites influencing the stress- and age-dependent progression of leaf senescence. We propose that the regulatory loop between the pace of the AA export and the progression of leaf senescence provides the plant with a mechanism to fine-tune the induction of cell death in leaves, which, if triggered unnecessarily, can impede nutrient remobilization and thus plant growth and survival.

Response of Population Canopy Color Gradation Skewed Distribution Parameters of the RGB Model to Micrometeorology Environment in Begonia Fimbristipula Hance

The high quality and efficient production of greenhouse vegetation depend on micrometeorology environmental adjusting such as system warming and illumination supplement. In order to improve the quantity, quality, and efficiency of greenhouse vegetation, it is necessary to figure out the relationship between the crop growth conditions and environmental meteorological factors, which could give constructive suggestions for precise control of the greenhouse environment and reduce the running costs. The parameters from the color information of the plant canopy reflect the internal physiological conditions, thus, the RGB model has been widely used in the color analysis of digital pictures of leaves. We take photographs of Begonia Fimbristipula Hance (BFH) growing in the greenhouse at a fixed time every day and measure the meteorological factors. The results showed that the color scale for the single leaf, single plant, and the populated canopy of the BFH photographs all have skewed cumulative distribution histograms. The color gradation skewness-distribution (CGSD) parameters of the RGB model were increased from 4 to 20 after the skewness analysis, which greatly expanded the canopy leaf color information and could simultaneously describe the depth and distribution characteristics of the canopy color. The 20 CGSD parameters were sensitive to the micrometeorology factors, especially to the radiation and temperature accumulation. The multiple regression models of mean, median, mode, and kurtosis parameters to microclimate factors were established, and the spatial models of skewness parameters were optimized. The models can well explain the response of canopy color to microclimate factors and can be used to monitor the variation of plant canopy color under different micrometeorology.

A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms

Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.

Multi-omics approaches and genetic engineering of metabolism for improved biorefinery and wastewater treatment in microalgae

Microalgae, a group of photosynthetic microorganisms rich in diverse and novel bioactive metabolites, have been explored for the production of biofuels, high value-added compounds as food and feeds, and pharmaceutical chemicals as agents with therapeutic benefits. This article reviews the development of omics resources and genetic engineering techniques including gene transformation methodologies, mutagenesis, and genome-editing tools in microalgae biorefinery and wastewater treatment. The introduction of these enlisted techniques has simplified the understanding of complex metabolic pathways undergoing microalgal cells. The multiomics approach of the integrated omics datasets, big data analysis, and machine learning for the discovery of objective traits and genes responsible for metabolic pathways was reviewed. Recent advances and limitations of multiomics analysis and genetic bioengineering technology to facilitate the improvement of microalgae as the dual role of wastewater treatment and biorefinery feedstock production are discussed. This article is protected by copyright. All rights reserved.

The cytokinin-producing plant beneficial bacterium Pseudomonas fluorescens G20-18 primes tomato (Solanum lycopersicum) for enhanced drought stress responses

Plant growth-promoting rhizobacteria (PGPR) are known for exerting beneficial effects on plant growth and tolerance to plant pathogens. However, their specific role in mediating protection against abiotic stress remains underexplored. The aim of this study was to characterise the ability of the cytokinin-producing beneficial bacterium Pseudomonas fluorescens G20-18 to enhance tomato growth and boost tolerance to drought stress. Tomato seedlings were root inoculated and their growth and physiological and molecular responses assessed under well-watered conditions and also in response to progressive drought stress and a subsequent recovery period. Root inoculation with G20-18 had a significant positive impact on tomato growth. Furthermore, G20-18 inoculated and drought-stressed plants showed higher leaf chlorophyll and abscisic acid (ABA) content and stomatal closure than non-inoculated controls. Root inoculation also increased the activity of different carbohydrate metabolism enzymes, which are important for root and leaf growth and development in drought stressed plants. A significant increase in the activity of different antioxidant enzymes and total antioxidant capacity correlated with elevated levels of relevant secondary metabolites, such as phenolics, anthocyanins and flavonoids. RNA sequencing revealed distinct qualitative and quantitative differences in gene regulation in response to G20-18. Notably, the number of genes differentially regulated in response to G20-18 was approximately sevenfold higher during drought stress, indicating that root inoculation with the bacteria primed the plants for a much stronger transcriptionally regulated systemic drought stress response. The regulated genes are related to phenylalanine metabolism and other key processes linked to plant growth, development and drought stress resilience. A role of the ability of G20-18 to produce the plant hormone cytokinin for interaction with tomato was established by the cytokinin-deficient biosynthesis mutants CNT1 and CNT2. In comparison with G20-18, the inoculation of plants with CNT1 resulted in a reduced number of differentially regulated genes. The relative change was most prominent under well-watered conditions with a 85 % reduction, corresponding to 462 genes. However, under drought conditions the absolute number of differentially regulated genes was reduced by even 2219 in response to the CNT1 mutant. The relevance of the ability of G20-18 to produce cytokinins for interaction with plants was also evident from differences in growth and specific cell and ecophysiological parameters in response to CNT1 and CNT2. These findings provide novel insights about G20-18's ability to improve drought stress responses and the role of interkingdom signalling by bacterial-derived cytokinins, and contribute to enhance the robustness of the practical application of these microorganisms to improve crop resilience in agricultural production.

Multi-Omics Landscape of DNA Methylation Regulates Browning in “Fuji” Apple

Browning seriously affects the quality of fresh-cut fruits, and its mechanism was thought to be polyphenol oxidase (PPO) in the past. A way of non-different PPO browning was found in our previous studies. However, the landscape of this browning way is still unclear in “Fuji” apples. Multi-omics (methylomics, transcriptomics, and proteomics) methods were performed to the global profiles of DNA methylation and gene and protein expression. We employed two natural bud mutation varieties of apple as materials and found a positive correlation between browning index (BI) and methylation (5mC%, MdCMT3, and MdCMT3c) and a negative correlation between BI and demethylation (MdROS1 and MdDME). DNA methylation inhibitor 5-azacytidine can delay apple browning. Further analysis showed that methylated-NCA1 and OMT1 increased significantly in apple browning. Methylated-NCA1 might inhibit NCA1 gene expression and resulted in the decline of catalase activity, thereafter significantly increased apple browning. These findings insight into a new pathway and landscape that DNA hypermethylation significantly accelerated the browning in “Fuji” apple.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

Omics Path to Increasing Productivity in Less-Studied Crops Under Changing Climate-Lentil a Case Study

Conventional breeding techniques for crop improvement have reached their full potential, and hence, alternative routes are required to ensure a sustained genetic gain in lentils. Although high-throughput omics technologies have been effectively employed in major crops, less-studied crops such as lentils have primarily relied on conventional breeding. Application of genomics and transcriptomics in lentils has resulted in linkage maps and identification of QTLs and candidate genes related to agronomically relevant traits and biotic and abiotic stress tolerance. Next-generation sequencing (NGS) complemented with high-throughput phenotyping (HTP) technologies is shown to provide new opportunities to identify genomic regions and marker-trait associations to increase lentil breeding efficiency. Recent introduction of image-based phenotyping has facilitated to discern lentil responses undergoing biotic and abiotic stresses. In lentil, proteomics has been performed using conventional methods such as 2-D gel electrophoresis, leading to the identification of seed-specific proteome. Metabolomic studies have led to identifying key metabolites that help differentiate genotypic responses to drought and salinity stresses. Independent analysis of differentially expressed genes from publicly available transcriptomic studies in lentils identified 329 common transcripts between heat and biotic stresses. Similarly, 19 metabolites were common across legumes, while 31 were common in genotypes exposed to drought and salinity stress. These common but differentially expressed genes/proteins/metabolites provide the starting point for developing high-yielding multi-stress-tolerant lentils. Finally, the review summarizes the current findings from omic studies in lentils and provides directions for integrating these findings into a systems approach to increase lentil productivity and enhance resilience to biotic and abiotic stresses under changing climate.

Opportunities and limits of controlled-environment plant phenotyping for climate response traits

Rising temperatures and changing precipitation patterns will affect agricultural production substantially, exposing crops to extended and more intense periods of stress. Therefore, breeding of varieties adapted to the constantly changing conditions is pivotal to enable a quantitatively and qualitatively adequate crop production despite the negative effects of climate change. As it is not yet possible to select for adaptation to future climate scenarios in the field, simulations of future conditions in controlled-environment (CE) phenotyping facilities contribute to the understanding of the plant response to special stress conditions and help breeders to select ideal genotypes which cope with future conditions. CE phenotyping facilities enable the collection of traits that are not easy to measure under field conditions and the assessment of a plant's phenotype under repeatable, clearly defined environmental conditions using automated, non-invasive, high-throughput methods. However, extrapolation and translation of results obtained under controlled environments to field environments is ambiguous. This review outlines the opportunities and challenges of phenotyping approaches under controlled environments complementary to conventional field trials. It gives an overview on general principles and introduces existing phenotyping facilities that take up the challenge of obtaining reliable and robust phenotypic data on climate response traits to support breeding of climate-adapted crops.

Construction of sRNA Regulatory Network for Magnaporthe oryzae Infecting Rice Based on Multi-Omics Data

Studies have shown that fungi cause plant diseases through cross-species RNA interference mechanism (RNAi) and secreted protein infection mechanism. The small RNAs (sRNAs) of Magnaporthe oryzae use the RNAi mechanism of rice to realize the infection process, and different effector proteins can increase the autotoxicity by inhibiting pathogen-associated molecular patterns triggered immunity (PTI) to achieve the purpose of infection. However, the coordination of sRNAs and proteins in the process of M. oryzae infecting rice is still poorly understood. Therefore, the combination of transcriptomics and proteomics to study the mechanism of M. oryzae infecting rice has important theoretical significance and practical value for controlling rice diseases and improving rice yields. In this paper, we used the high-throughput data of various omics before and after the M. oryzae infecting rice to screen differentially expressed genes and sRNAs and predict protein interaction pairs based on the interolog and the domain-domain methods. We were then used to construct a prediction model of the M. oryzae-rice interaction proteins according to the obtained proteins in the proteomic network. Finally, for the differentially expressed genes, differentially expressed sRNAs, the corresponding mRNAs of rice and M. oryzae, and the interacting protein molecules, the M. oryzae-rice sRNA regulatory network was built and analyzed, the core nodes were selected. The functional enrichment analysis was conducted to explore the potential effect pathways and the critical infection factors of M. oryzae sRNAs and proteins were mined and analyzed. The results showed that 22 sRNAs of M. oryzae, 77 secretory proteins of M. oryzae were used as effect factors to participate in the infection process of M. oryzae. And many significantly enriched GO modules were discovered, which were related to the infection mechanism of M. oryzae.

Differentiation of the High Night Temperature Response in Leaf Segments of Rice Cultivars with Contrasting Tolerance

High night temperatures (HNT) affect rice yield in the field and induce chlorosis symptoms in leaves in controlled chamber experiments. However, little is known about molecular changes in leaf segments under these conditions. Transcript and metabolite profiling were performed for leaf segments of six rice cultivars with different HNT sensitivity. The metabolite profile of the sheath revealed a lower metabolite abundance compared to segments of the leaf blade. Furthermore, pre-adaptation to stress under control conditions was detected in the sheath, whereas this segment was only slightly affected by HNT. No unique significant transcriptomic changes were observed in the leaf base, including the basal growth zone at HNT conditions. Instead, selected metabolites showed correlations with HNT sensitivity in the base. The middle part and the tip were most highly affected by HNT in sensitive cultivars on the transcriptomic level with higher expression of jasmonic acid signaling related genes, genes encoding enzymes involved in flavonoid metabolism and a gene encoding galactinol synthase. In addition, gene expression of expansins known to improve stress tolerance increased in tolerant and sensitive cultivars. The investigation of the different leaf segments indicated highly segment specific responses to HNT. Molecular key players for HNT sensitivity were identified.

Next-Generation Breeding Strategies for Climate-Ready Crops

Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.

Ionomic Approaches for Discovery of Novel Stress-Resilient Genes in Plants

Plants, being sessile, face an array of biotic and abiotic stresses in their lifespan that endanger their survival. Hence, optimized uptake of mineral nutrients creates potential new routes for enhancing plant health and stress resilience. Recently, minerals (both essential and non-essential) have been identified as key players in plant stress biology, owing to their multifaceted functions. However, a realistic understanding of the relationship between different ions and stresses is lacking. In this context, ionomics will provide new platforms for not only understanding the function of the plant ionome during stresses but also identifying the genes and regulatory pathways related to mineral accumulation, transportation, and involvement in different molecular mechanisms under normal or stress conditions. This article provides a general overview of ionomics and the integration of high-throughput ionomic approaches with other "omics" tools. Integrated omics analysis is highly suitable for identification of the genes for various traits that confer biotic and abiotic stress tolerance. Moreover, ionomics advances being used to identify loci using qualitative trait loci and genome-wide association analysis of element uptake and transport within plant tissues, as well as genetic variation within species, are discussed. Furthermore, recent developments in ionomics for the discovery of stress-tolerant genes in plants have also been addressed; these can be used to produce more robust crops with a high nutritional value for sustainable agriculture.

Genetic Improvement in Sunflower Breeding—Integrated Omics Approach

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.

Algae to angiosperms: Autofluorescence for rapid visualization of plant anatomy among diverse taxa

PREMISE

Fluorescence microscopy is an effective tool for viewing plant internal anatomy. However, using fluorescent antibodies or labels hinders throughput. We present a minimal protocol that takes advantage of inherent autofluorescence and aldehyde-induced fluorescence in plant cellular and subcellular structures to markedly increase throughput in cellular and ultrastructural visualization.

METHODS AND RESULTS

Twelve species distributed across the plant phylogeny were each subjected to five fixative treatments: 1% paraformaldehyde and 2% glutaraldehyde, 2% paraformaldehyde, 2% glutaraldehyde, formalin-acid-alcohol (FAA), and 70% ethanol. Samples were prepared by embedding and mechanically sectioning or via whole mount. A confocal laser scanning system was used to collect micrographs. We evaluated and compared fixative influence on sample structural preservation and tissue autofluorescence.

CONCLUSIONS

Formaldehyde fixation of Viridiplantae taxa samples generates useful structural data while requiring no additional histological staining or clearing. In addition, a fluorescence-capable microscope is the only specialized equipment required for image acquisition. The minimal protocol developed in this experiment enables high-throughput sample processing by eliminating the need for multi-day preparations.

Mass Spectrometry-Based Metabolomics to Investigate the Effect of Mechanical Shaking on Sauvignon Blanc Berry Metabolism

Previous commercial studies carried out in New Zealand showed that mechanical shaking significantly reduced the incidence of Botrytis cinerea infection in wine grapes. However, the reasons behind this reduction are not well understood. Here, we employed a metabolomics approach to gain insights into the biochemical changes that occur in grape berries due to mechanical shaking. Berry samples were analyzed using three different analytical approaches including gas chromatography and mass spectrometry (MS), liquid chromatography and MS, and imaging mass spectrometry (IMS). Combined data provided a comprehensive overview of metabolic changes in grape berry, indicating the initiation of different stress mitigation strategies to overcome the effect of mechanical shaking. Berry primary metabolism was distinctly altered in the green berries in response to mechanical shaking, while secondary metabolism significantly changed in berries collected after veraison. Pathway analysis showed upregulation of metabolites related to nitrogen and lipid metabolism in the berries from shaken vines when compared with controls. From IMS data, we observed an accumulation of different groups of metabolites including phenolic compounds and amino and fatty acids in the areas near to the skin of berries from shaken vines. This observation suggests that mechanical shaking caused an accumulation of these metabolites, which may be associated with the formation of a protective barrier, leading to the reduction in B. cinerea infection in berries from mechanically shaken vines.

Field multi-omics analysis reveals a close association between bacterial communities and mineral properties in the soybean rhizosphere

The plant root-associated environments such as the rhizosphere, rhizoplane, and endosphere are different from the outer soil region (bulk soil). They establish characteristic conditions including microbiota, metabolites, and minerals, and they can directly affect plant growth and development. However, comprehensive insights into those characteristic environments, especially the rhizosphere, and molecular mechanisms of their formation are not well understood. In the present study, we investigated the spatiotemporal dynamics of the root-associated environment in actual field conditions by multi-omics analyses (mineral, microbiome, and transcriptome) of soybean plants. Mineral and microbiome analyses demonstrated a characteristic rhizosphere environment in which most of the minerals were highly accumulated and bacterial communities were distinct from those in the bulk soil. Mantel's test and co-abundance network analysis revealed that characteristic community structures and dominant bacterial taxa in the rhizosphere significantly interact with mineral contents in the rhizosphere, but not in the bulk soil. Our field multi-omics analysis suggests a rhizosphere-specific close association between the microbiota and mineral environment.

Multi-omics approaches to improve malaria therapy

Malaria contributes to the most widespread infectious diseases worldwide. Even though current drugs are commercially available, the ever-increasing drug resistance problem by malaria parasites poses new challenges in malaria therapy. Hence, searching for efficient therapeutic strategies is of high priority in malaria control. In recent years, multi-omics technologies have been extensively applied to provide a more holistic view of functional principles and dynamics of biological mechanisms. We briefly review multi-omics technologies and focus on recent malaria progress conducted with the help of various omics methods. Then, we present up-to-date advances for multi-omics approaches in malaria. Next, we describe resistance phenomena to established antimalarial drugs and underlying mechanisms. Finally, we provide insight into novel multi-omics approaches, new drugs and vaccine developments and analyze current gaps in multi-omics research. Although multi-omics approaches have been successfully used in malaria studies, they are still limited. Many gaps need to be filled to bridge the gap between basic research and treatment of malaria patients. Multi-omics approaches will foster a better understanding of the molecular mechanisms of Plasmodium that are essential for the development of novel drugs and vaccines to fight this disastrous disease.

Proteometabolomic characterization of apical bud maturation in Pinus pinaster

Bud maturation is a physiological process that implies a set of morphophysiological changes that lead to the transition of growth patterns from young to mature. This transition defines tree growth and architecture, and in consequence traits such as biomass production and wood quality. In Pinus pinaster Aiton, a conifer of great timber value, bud maturation is closely related to polycyclism (multiple growth periods per year). This process causes a lack of apical dominance, and consequently increased branching that reduces its timber quality and value. However, despite its importance, little is known about bud maturation. In this work, proteomics and metabolomics were employed to study apical and basal sections of young and mature buds in P. pinaster. Proteins and metabolites in samples were described and quantified using (n)UPLC-LTQ-Orbitrap. The datasets were analyzed employing an integrative statistical approach, which allowed the determination of the interactions between proteins and metabolites and the different bud sections and ages. Specific dynamics of proteins and metabolites such as histones H3 and H4, ribosomal proteins L15 and L12, chaperonin TCP1, 14-3-3 protein gamma, gibberellins A1, A3 and A8, strigolactones and abscisic acid, involved in epigenetic regulation, proteome remodeling, hormonal signaling and abiotic stress pathways showed their potential role during bud maturation. Candidates and pathways were validated employing interaction databases and targeted transcriptomics. These results increase our understanding of the molecular processes behind bud maturation, a key step towards improving timber production and natural pine forests management in a future scenario of climate change. However, further studies are necessary using different P. pinaster populations that show contrasting wood quality and stress tolerance in order to generalize the results.

Abiotic Stress and Reactive Oxygen Species: Generation, Signaling, and Defense Mechanisms

Climate change is an invisible, silent killer with calamitous effects on living organisms. As the sessile organism, plants experience a diverse array of abiotic stresses during ontogenesis. The relentless climatic changes amplify the intensity and duration of stresses, making plants dwindle to survive. Plants convert 1-2% of consumed oxygen into reactive oxygen species (ROS), in particular, singlet oxygen (1O2), superoxide radical (O2•-), hydrogen peroxide (H2O2), hydroxyl radical (•OH), etc. as a byproduct of aerobic metabolism in different cell organelles such as chloroplast, mitochondria, etc. The regulatory network comprising enzymatic and non-enzymatic antioxidant systems tends to keep the magnitude of ROS within plant cells to a non-damaging level. However, under stress conditions, the production rate of ROS increases exponentially, exceeding the potential of antioxidant scavengers instigating oxidative burst, which affects biomolecules and disturbs cellular redox homeostasis. ROS are similar to a double-edged sword; and, when present below the threshold level, mediate redox signaling pathways that actuate plant growth, development, and acclimatization against stresses. The production of ROS in plant cells displays both detrimental and beneficial effects. However, exact pathways of ROS mediated stress alleviation are yet to be fully elucidated. Therefore, the review deposits information about the status of known sites of production, signaling mechanisms/pathways, effects, and management of ROS within plant cells under stress. In addition, the role played by advancement in modern techniques such as molecular priming, systems biology, phenomics, and crop modeling in preventing oxidative stress, as well as diverting ROS into signaling pathways has been canvassed.

Applications of Multi-Omics Technologies for Crop Improvement

Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the "phenotype to genotype" and "genotype to phenotype" concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

Senescence-Specific Expression of RAmy1A Accelerates Non-structural Carbohydrate Remobilization and Grain Filling in Rice (Oryza sativa L.)

Remobilization of pre-anthesis NSCs (non-structural carbohydrates) is significant for effective grain filling in rice (Oryza sativa L.). However, abundant starch particles as an important component of NSCs are still present in the leaf sheath and stem at the late stage of grain filling. There are no studies on how bioengineering techniques can be used to improve the efficiency of NSC remobilization. In this study, RAmy1A was expressed under the senescence-specific promoter of SAG12, which was designed to degrade starch in the leaf sheath and stem during grain filling. RAmy1A mRNA successfully accumulated in the leaf, stem, and sheath of transgenic plants after anthesis. At the same time, the starch and total soluble sugar content in the leaf, stem, and leaf sheath were obviously decreased during the grain-filling period. The photosynthetic rate of transgenic lines was higher than that of the wild types by an average of 4.0 and 9.9%, at 5 and 10 days after flowering, respectively. In addition, the grain-filling rate of transgenic lines was faster than that of the wild types by an average of 26.09%. These results indicate an enhanced transport efficiency of NSCs from source tissues in transgenic rice. Transgenic rice also displayed accelerated leaf senescence, which was hypothesized to contribute to decreased grain weight.

Identification of Root-Associated Bacteria That Influence Plant Physiology, Increase Seed Germination, or Promote Growth of the Christmas Tree Species Abies nordmanniana

Abies nordmanniana is used for Christmas tree production but poor seed germination and slow growth represent challenges for the growers. We addressed the plant growth promoting potential of root-associated bacteria isolated from A. nordmanniana. Laboratory screenings of a bacterial strain collection yielded several Bacillus and Paenibacillus strains that improved seed germination and produced indole-3-acetic acid. The impact of three of these strains on seed germination, plant growth and growth-related physiological parameters was then determined in greenhouse and field trials after seed inoculation, and their persistence was assessed by 16S rRNA gene-targeted bacterial community analysis. Two strains showed distinct and significant effects. Bacillus sp. s50 enhanced seed germination in the greenhouse but did not promote shoot or root growth. In accordance, this strain did not increase the level of soluble hexoses needed for plant growth but increased the level of storage carbohydrates. Moreover, strain s50 increased glutathione reductase and glutathione-S-transferase activities in the plant, which may indicate induction of systemic resistance during the early phase of plant development, as the strain showed poor persistence in the root samples (rhizosphere soil plus root tissue). Paenibacillus sp. s37 increased plant root growth, especially by inducing secondary root formation, under in greenhouse conditions, where it showed high persistence in the root samples. Under these conditions, it further it increased the level of soluble carbohydrates in shoots, and the levels of starch and non-structural carbohydrates in roots, stem and shoots. Moreover, it increased the chlorophyll level in the field trial. These findings indicate that this strain improves plant growth and vigor through effects on photosynthesis and plant carbohydrate reservoirs. The current results show that the two strains s37 and s50 could be considered for growth promotion programs of A. nordmanniana in greenhouse nurseries, and even under field conditions.

A WRKY transcription factor, TaWRKY40-D, promotes leaf senescence associated with jasmonic acid and abscisic acid pathways in wheat

Leaf senescence is a complex and precise regulatory process that is correlated with numerous internal and environmental factors. Leaf senescence is tightly related to the redistribution of nutrients, which significantly affects productivity and quality, especially in crops. Evidence shows that the mediation of transcriptional regulation by WRKY transcription factors is vital for the fine-tuning of leaf senescence. However, the underlying mechanisms of the involvement of WRKY in leaf senescence are still unclear in wheat. Using RNA sequencing data, we isolated a novel WRKY transcription factor, TaWRKY40-D, which localizes in the nucleus and is basically induced by the progression of leaf senescence. TaWRKY40-D is a promoter of natural and dark-induced leaf senescence in transgenic Arabidopsis thaliana and wheat. We also demonstrated a positive response of TaWRKY40-D in wheat upon jasmonic acid (JA) and abscisic acid (ABA) treatment. Consistent with this, the detached leaves of TaWRKY40-D VIGS (virus-induced gene silencing) wheat plants showed a stay-green phenotype, while TaWRKY40-D overexpressing Arabidopsis plants showed premature leaf senescence after JA and ABA treatment. Moreover, our results revealed that TaWRKY40-D positively regulates leaf senescence, possibly by altering the biosynthesis and signalling of JA and ABA pathway genes. Together, our results suggest a new regulator of JA- and ABA-related leaf senescence, as well as a new candidate gene that can be used for molecular breeding in wheat.