Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology

Integrating Proteomics and Metabolomics Approaches to Elucidate the Mechanism of Responses to Combined Stress in the Bell Pepper (Capsicum annuum)

Bell pepper plants are sensitive to environmental changes and are significantly affected by abiotic factors such as UV-B radiation and cold, which reduce their yield and production. Various approaches, including omics data integration, have been employed to understand the mechanisms by which this crop copes with abiotic stress. This study aimed to find metabolic changes in bell pepper stems caused by UV-B radiation and cold by integrating omic data. Proteome and metabolome profiles were generated using liquid chromatography coupled with mass spectrometry, and data integration was performed in the plant metabolic pathway database. The combined stress of UV-B and cold induced the accumulation of proteins related to photosynthesis, mitochondrial electron transport, and a response to a stimulus. Further, the production of flavonoids and their glycosides, as well as affecting carbon metabolism, tetrapyrrole, and scopolamine pathways, were identified. We have made the first metabolic regulatory network map showing how bell pepper stems respond to cold and UV-B stress. We did this by looking at changes in proteins and metabolites that help with respiration, photosynthesis, and the buildup of photoprotective and antioxidant compounds.

Towards consolidated bioprocessing of biomass and plastic substrates for semi-synthetic production of bio-poly(ethylene furanoate) (PEF) polymer using omics-guided construction of artificial microbial consortia

Poly(ethylene furanoate) (PEF) plastic is a 100% renewable polyester that is currently being pursued for commercialization as the next-generation bio-based plastic. This is in line with growing demand for circular bioeconomy and new plastics economy that is aimed at minimizing plastic waste mismanagement and lowering carbon footprint of plastics. However, the current catalytic route for the synthesis of PEF is impeded with technical challenges including high cost of pretreatment and catalyst refurbishment. On the other hand, the semi-biosynthetic route of PEF plastic production is of increased biotechnological interest. In particular, the PEF monomers (Furan dicarboxylic acid and ethylene glycol) can be synthesized via microbial-based biorefinery and purified for subsequent catalyst-mediated polycondensation into PEF. Several bioengineering and bioprocessing issues such as efficient substrate utilization and pathway optimization need to be addressed prior to establishing industrial-scale production of the monomers. This review highlights current advances in semi-biosynthetic production of PEF monomers using consolidated waste biorefinery strategies, with an emphasis on the employment of omics-driven systems biology approaches in enzyme discovery and pathway construction. The roles of microbial protein transporters will be discussed, especially in terms of improving substrate uptake and utilization from lignocellulosic biomass, as well as from depolymerized plastic waste as potential bio-feedstock. The employment of artificial bioengineered microbial consortia will also be highlighted to provide streamlined systems and synthetic biology strategies for bio-based PEF monomer production using both plant biomass and plastic-derived substrates, which are important for circular and new plastics economy advances.

Genome assembly of Melilotus officinalis provides a new reference genome for functional genomics

BACKGROUND

Sweet yellow clover (Melilotus officinalis) is a diploid plant (2n = 16) that is native to Europe. It is an excellent legume forage. It can both fix nitrogen and serve as a medicine. A genome assembly of Melilotus officinalis that was collected from Best corporation in Beijing is available based on Nanopore sequencing. The genome of Melilotus officinalis was sequenced, assembled, and annotated.

RESULTS

The latest PacBio third generation HiFi assembly and sequencing strategies were used to produce a Melilotus officinalis genome assembly size of 1,066 Mbp, contig N50 = 5 Mbp, scaffold N50 = 130 Mbp, and complete benchmarking universal single-copy orthologs (BUSCOs) = 96.4%. This annotation produced 47,873 high-confidence gene models, which will substantially aid in our research on molecular breeding. A collinear analysis showed that Melilotus officinalis and Medicago truncatula shared conserved synteny. The expansion and contraction of gene families showed that Melilotus officinalis expanded by 565 gene families and shrank by 56 gene families. The contacted gene families were associated with response to stimulus, nucleotide binding, and small molecule binding. Thus, it is related to a family of genes associated with peptidase activity, which could lead to better stress tolerance in plants.

CONCLUSIONS

In this study, the latest PacBio technology was used to assemble and sequence the genome of the Melilotus officinalis and annotate its protein-coding genes. These results will expand the genomic resources available for Melilotus officinalis and should assist in subsequent research on sweet yellow clover plants.

Twenty Years of 1H NMR Plant Metabolomics: A Way Forward toward Assessment of Plant Metabolites for Constitutive and Inducible Defenses to Biotic Stress

Metabolomics has become an important tool in elucidating the complex relationship between a plant genotype and phenotype. For over 20 years, nuclear magnetic resonance (NMR) spectroscopy has been known for its robustness, quantitative capabilities, simplicity, and cost-efficiency. 1H NMR is the method of choice for analyzing a broad range of relatively abundant metabolites, which can be used for both capturing the plant chemical profile at one point in time and understanding the pathways that underpin plant defense. This systematic Review explores how 1H NMR-based plant metabolomics has contributed to understanding the role of various compounds in plant responses to biotic stress, focusing on both primary and secondary metabolites. It clarifies the challenges and advantages of using 1H NMR in plant metabolomics, interprets common trends observed, and suggests guidelines for method development and establishing standard procedures.

Integrating Targeted Metabolomics and Targeted Proteomics to Study the Responses of Wheat Plants to Engineered Nanomaterials

This manuscript presents a multiomics investigation into the metabolic and proteomic responses of wheat to molybdenum (Mo)- and copper (Cu)-based engineered nanomaterials (ENMs) exposure via root and leaf application methods. Wheat plants underwent a four-week growth period with a 16 h photoperiod (light intensity set at 150 μmol·m-2·s-1), at 22 °C and 60% humidity. Six distinct treatments were applied, including control conditions alongside exposure to Mo- and Cu-based ENMs through both root and leaf routes. The exposure dosage amounted to 6.25 mg of the respective element per plant. An additional treatment with a lower dose (0.6 mg Mo/plant) of Mo ENM exclusively through the root system was introduced upon the detection of phytotoxicity. Utilizing LC-MS/MS analysis, 82 metabolites across various classes and 24 proteins were assessed in different plant tissues (roots, stems, leaves) under diverse treatments. The investigation identified 58 responsive metabolites and 19 responsive proteins for Cu treatments, 71 responsive metabolites, and 24 responsive proteins for Mo treatments, mostly through leaf exposure for Cu and root exposure for Mo. Distinct tissue-specific preferences for metabolite accumulation were revealed, highlighting the prevalence of organic acids and fatty acids in stem or root tissues, while sugars and amino acids were abundant in leaves, mirroring their roles in energy storage and photosynthesis. Joint-pathway analysis was conducted and unveiled 23 perturbed pathways across treatments. Among these, Mo exposure via roots impacted all identified pathways, whereas exposure via leaf affected 15 pathways, underscoring the reliance on exposure route of metabolic and proteomic responses. The coordinated response observed in protein and metabolite concentrations, particularly in amino acids, highlighted a dynamic and interconnected proteomic-to-metabolic-to-proteomic relationship. Furthermore, the contrasting expression patterns observed in glutamate dehydrogenase (upregulation at 1.38 ≤ FC ≤ 1.63 with high Mo dose, and downregulation at 0.13 ≤ FC ≤ 0.54 with low Mo dose) and its consequential impact on glutamine expression (7.67 ≤ FC ≤ 39.60 with high Mo dose and 1.50 ≤ FC ≤ 1.95 with low Mo dose) following Mo root exposure highlighted dose-dependent regulatory trends influencing proteins and metabolites. These findings offer a multidimensional understanding of plant responses to ENMs exposure, guiding agricultural practices and environmental safety protocols while advancing knowledge on nanomaterial impacts on plant biology.

Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.

Holomics - a user-friendly R shiny application for multi-omics data integration and analysis

An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

The European Polysaccharide Network of Excellence (EPNOE) research roadmap 2040: Advanced strategies for exploiting the vast potential of polysaccharides as renewable bioresources

Polysaccharides are among the most abundant bioresources on earth and consequently need to play a pivotal role when addressing existential scientific challenges like climate change and the shift from fossil-based to sustainable biobased materials. The Research Roadmap 2040 of the European Polysaccharide Network of Excellence (EPNOE) provides an expert's view on how future research and development strategies need to evolve to fully exploit the vast potential of polysaccharides as renewable bioresources. It is addressed to academic researchers, companies, as well as policymakers and covers five strategic areas that are of great importance in the context of polysaccharide related research: (I) Materials & Engineering, (II) Food & Nutrition, (III) Biomedical Applications, (IV) Chemistry, Biology & Physics, and (V) Skills & Education. Each section summarizes the state of research, identifies challenges that are currently faced, project achievements and developments that are expected in the upcoming 20 years, and finally provides outlines on how future research activities need to evolve.

Genomic data integration tutorial, a plant case study

BACKGROUND

The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts.

RESULTS

To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration.

CONCLUSION

The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.

Polyploidization and genomic selection integration for grapevine breeding: a perspective

Grapevines are economically important woody perennial crops widely cultivated for their fruits that are used for making wine, grape juice, raisins, and table grapes. However, grapevine production is constantly facing challenges due to climate change and the prevalence of pests and diseases, causing yield reduction, lower fruit quality, and financial losses. To ease the burden, continuous crop improvement to develop superior grape genotypes with desirable traits is imperative. Polyploidization has emerged as a promising tool to generate genotypes with novel genetic combinations that can confer desirable traits such as enhanced organ size, improved fruit quality, and increased resistance to both biotic and abiotic stresses. While previous studies have shown high polyploid induction rates in Vitis spp., rigorous screening of genotypes among the produced polyploids to identify those exhibiting desired traits remains a major bottleneck. In this perspective, we propose the integration of the genomic selection approach with omics data to predict genotypes with desirable traits among the vast unique individuals generated through polyploidization. This integrated approach can be a powerful tool for accelerating the breeding of grapevines to develop novel and improved grapevine varieties.

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.

Unraveling the regulatory role of MYC2 on ASMT gene expression in wheat: Implications for melatonin biosynthesis and drought tolerance

Recognized for its multifaceted functions, melatonin is a hormone found in both animals and plants. In the plant kingdom, it plays diverse roles, regulating growth, development, and stress responses. Notably, melatonin demonstrates its significance by mitigating the effects of abiotic stresses like drought. However, understanding the precise regulatory mechanisms controlling melatonin biosynthesis genes, especially during monocots' response to stresses, requires further exploration. Seeking to understand the molecular basis of drought stress tolerance in wheat, we analyzed RNA-Seq libraries of wheat exposed to drought stress using bioinformatics methods. In light of our findings, we identified that the Myelocytomatosis oncogenes 2 (MYC2) transcription factor is a hub gene upstream of a main melatonin biosynthesis gene, N-acetylserotonin methyltransferase (ASMT), in the wheat drought response-gene network. Promoter analysis of the ASMT gene suggested that it might be a target gene of MYC2. We conducted a set of molecular and physiochemical assays along with robust machine learning approaches to elevate those findings further. MYC2 and ASMT were co-regulated under Jasmonate, drought, and a combination of them in the leaf tissues of wheat was detected. A meaningful correlation was observed among gene expression profiles, melatonin contents, photosynthetic activities, antioxidant enzyme activities, H2 O2 levels, and plasma membrane damage. The results indicated an evident relationship between jasmonic acid and the melatonin biosynthesis pathway. Moreover, it seems that the MYC2-ASMT module might contribute to wheat drought tolerance by regulating melatonin contents.

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.

Investigating Doxorubicin's mechanism of action in cervical cancer: a convergence of transcriptomic and metabolomic perspectives

Introduction: Cervical cancer remains a significant global health burden, and Doxorubicin is a crucial therapeutic agent against this disease. However, the precise molecular mechanisms responsible for its therapeutic effects are not fully understood. Methods: In this study, we employed a multi-omics approach that combined transcriptomic and metabolomic analyses with cellular and in vivo experiments. The goal was to comprehensively investigate the molecular landscape associated with Doxorubicin treatment in cervical cancer. Results: Our unbiased differential gene expression analysis revealed distinct alterations in gene expression patterns following Doxorubicin treatment. Notably, the ANKRD18B gene exhibited a prominent role in the response to Doxorubicin. Simultaneously, our metabolomic analysis demonstrated significant perturbations in metabolite profiles, with a particular focus on L-Ornithine. The correlation between ANKRD18B gene expression and L-Ornithine levels indicated a tightly controlled gene-metabolite network. These results were further confirmed through rigorous cellular and in vivo experiments, which showed reductions in subcutaneous tumor size and significant changes in ANKRD18B, L-Ornithine, and Doxorubicin concentration. Discussion: The findings of this study underscore the intricate interplay between transcriptomic and metabolomic changes in response to Doxorubicin treatment. These insights could have implications for the development of more effective therapeutic strategies for cervical cancer. The identification of ANKRD18B and L-Ornithine as key components in this process lays the groundwork for future research aiming to unravel the complex molecular networks that underlie Doxorubicin's therapeutic mechanism. While this study provides a solid foundation, it also highlights the necessity for further investigation to fully grasp these interactions and their potential implications for cervical cancer treatment.

Integrative time-scale and multi-omics analysis of host responses to viroid infection

Viroids are circular RNAs of minimal complexity compelled to subvert plant-regulatory networks to accomplish their infectious process. Studies focused on the response to viroid-infection have mostly addressed specific regulatory levels and considered specifics infection-times. Thus, much remains to be done to understand the temporal evolution and complex nature of viroid-host interactions. Here we present an integrative analysis of the temporal evolution of the genome-wide alterations in cucumber plants infected with hop stunt viroid (HSVd) by integrating differential host transcriptome, sRNAnome and methylome. Our results support that HSVd promotes the redesign of the cucumber regulatory-pathways predominantly affecting specific regulatory layers at different infection-phases. The initial response was characterised by a reconfiguration of the host-transcriptome by differential exon-usage, followed by a progressive transcriptional downregulation modulated by epigenetic changes. Regarding endogenous small RNAs, the alterations were limited and mainly occurred at the late stage. Significant host-alterations were predominantly related to the downregulation of transcripts involved in plant-defence mechanisms, the restriction of pathogen-movement and the systemic spreading of defence signals. We expect that these data constituting the first comprehensive temporal-map of the plant-regulatory alterations associated with HSVd infection could contribute to elucidate the molecular basis of the yet poorly known host-response to viroid-induced pathogenesis.

Osmoprotectants play a major role in the Portulaca oleracea resistance to high levels of salinity stress-insights from a metabolomics and proteomics integrated approach

Introduction

Purslane (Portulaca oleracea L.) is a non-conventional food plant used extensively in folk medicine and classified as a multipurpose plant species, serving as a source of features of direct importance to the agricultural and agri-industrial sectors. This species is considered a suitable model to study the mechanisms behind resistance to several abiotic stresses including salinity. The recently achieved technological developments in high-throughput biology opened a new window of opportunity to gain additional insights on purslane resistance to salinity stress-a complex, multigenic, and still not well-understood trait. Only a few reports on single-omics analysis (SOA) of purslane are available, and only one multi-omics integration (MOI) analysis exists so far integrating distinct omics platforms (transcriptomics and metabolomics) to characterize the response of purslane plants to salinity stress.

Methods

The present study is a second step in building a robust database on the morpho-physiological and molecular responses purslane to salinity stress and its subsequent use in attempting to decode the genetics behind its resistance to this abiotic stress. Here, the characterization of the morpho-physiological responses of adult purslane plants to salinity stress and a metabolomics and proteomics integrative approach to study the changes at the molecular level in their leaves and roots is presented.

Results and discussion

Adult plants of the B1 purslane accession lost approximately 50% of the fresh and dry weight (from shoots and roots) whensubmitted to very high salinity stress (2.0 g of NaCl/100 g of the substrate). The resistance to very high levels of salinity stress increases as the purslane plant matures, and most of the absorbed sodium remains in the roots, with only a part (~12%) reaching the shoots. Crystal-like structures, constituted mainly by Na⁺, Cl^-, and K⁺, were found in the leaf veins and intercellular space near the stoma, indicating that this species has a mechanism of salt exclusion operating on the leaves, which has its role in salt tolerance. The MOI approach showed that 41 metabolites were statistically significant on the leaves and 65 metabolites on the roots of adult purslane plants. The combination of the mummichog algorithm and metabolomics database comparison revealed that the glycine, serine, and threonine, amino sugar and nucleotide sugar, and glycolysis/gluconeogenesis pathways were the most significantly enriched pathways when considering the total number of occurrences in the leaves (with 14, 13, and 13, respectively) and roots (all with eight) of adult plants; and that purslane plants employ the adaptive mechanism of osmoprotection to mitigate the negative effect of very high levels of salinity stress; and that this mechanism is prevalent in the leaves. The multi-omics database built by our group underwent a screen for salt-responsive genes, which are now under further characterization for their potential to promote resistance to salinity stress when heterologously overexpressed in salt-sensitive plants.

Integrated transcriptomics and lipidomics investigation of the mechanism underlying the gastrointestinal mucosa damage of Loropetalum chinense (R.Br.) and its representative component

BACKGROUND

Loropetalum chinensis (R.Br) Oliv (Bhjm), a Chinese folk herbal medicine, was traditionally used in the treatment of wound bleeding and skin ulcers. A new drug named JIMUSAN granules used for gastrosia was developed by our group, and clinical trials have been approved. However, as the principal herb, the material basis and underlying mechanisms of Bhjm in attenuating gastrointestinal mucosa damage (GMD) remain to be systemically illuminated.

PURPOSE

An integrated strategy was used to explore the therapeutic effects and mechanisms of Bhjm and ellagic acid (EA) on GMD zebrafish, using network pharmacology, transcriptomics, lipidomics, and real-time quantitative PCR (RT-qPCR) verification.

METHODS

First, network pharmacological analysis was used to infer the major effective constituents and targets of Bhjm. Ultra high performance liquid chromatography-linear ion trap/orbitrap high resolution mass spectrometry (UHPLC-LTQ-Orbitrap HRMS) and ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) were employed to identify the chemical constituents and quantify the different types of constituents. Second, zebrafish model of GMD was established by using 2,4,6-trinitrobenzenesulfonic acid (TNBS) to evaluate the efficacy of Bhjm and EA. The potential mechanism was examined by integrated transcriptomics and lipidomics analysis. Finally, validation tests were implemented using RT-qPCR.

RESULTS

In this study, targets indentified by network pharmacology were related to inflammation and mucosal damage. Ten representative components that interacted with these targets were simultaneously determined by UHPLC-MS/MS. Sixty four compounds were identified or tentatively characterized, most of which were flavonoids and polyphenols. Bhjm and EA alleviated mucosal damage and reduced inflammation in a TNBS-induced zebrafish GMD model, indicating that EA was the main active compounds. Eight common differentially expressed genes were downregulated by Bhjm and EA, as determined by transcriptomics analysis. Lipidomics analysis confirmed 12 differential lipids, including phosphatidylcholine (PC) and triglyceride (TG). Further network enrichment analysis demonstrated that differential lipid metabolism was regulated by klf4 and hist1h2ba, and were validated by RT-qPCR.

CONCLUSION

In our study, the chemical profile of Bhjm was clarified. Moreover, the GMD repair effect and the mechanism of Bhjm and EA was comprehensively analyzed for the first time, involving inflammation and lipid metabolism. Collectively, these findings will be significantly helpful for deeply exploring the clinical application value of Bhjm.

Understanding the Proteomes of Plant Development and Stress Responses in Brassica Crops

Brassica crops have great economic value due to their rich nutritional content and are therefore grown worldwide as oilseeds, vegetables, and condiments. Deciphering the molecular mechanisms associated with the advantageous phenotype is the major objective of various Brassica improvement programs. As large technological advancements have been achieved in the past decade, the methods to understand molecular mechanisms underlying the traits of interest have also taken a sharp upturn in plant breeding practices. Proteomics has emerged as one of the preferred choices nowadays along with genomics and other molecular approaches, as proteins are the ultimate effector molecules responsible for phenotypic changes in living systems, and allow plants to resist variable environmental stresses. In the last two decades, rapid progress has been made in the field of proteomics research in Brassica crops, but a comprehensive review that collates the different studies is lacking. This review provides an inclusive summary of different proteomic studies undertaken in Brassica crops for cytoplasmic male sterility, oil content, and proteomics of floral organs and seeds, under different biotic and abiotic stresses including post-translational modifications of proteins. This comprehensive review will help in understanding the role of different proteins in controlling plant phenotypes, and provides information for initiating future studies on Brassica breeding and improvement programs.

Charting plant gene functions in the multi-omics and single-cell era

Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.

Multi-omics Analysis of Young Portulaca oleracea L. Plants' Responses to High NaCl Doses Reveals Insights into Pathways and Genes Responsive to Salinity Stress in this Halophyte Species

Soil salinity is among the abiotic stressors that threaten agriculture the most, and purslane (Portulaca oleracea L.) is a dicot species adapted to inland salt desert and saline habitats that hyper accumulates salt and has high phytoremediation potential. Many researchers consider purslane a suitable model species to study the mechanisms of plant tolerance to drought and salt stresses. Here, a robust salinity stress protocol was developed and used to characterize the morphophysiological responses of young purslane plants to salinity stress; then, leaf tissue underwent characterization by distinct omics platforms to gain further insights into its response to very high salinity stress. The salinity stress protocol did generate different levels of stress by gradients of electrical conductivity at field capacity and water potential in the saturation extract of the substrate, and the morphological parameters indicated three distinct stress levels. As expected from a halophyte species, these plants remained alive under very high levels of salinity stress, showing salt crystal-like structures constituted mainly by Na+, Cl-, and K+ on and around closed stomata. A comprehensive and large-scale metabolome and transcriptome single and integrated analyses were then employed using leaf samples. The multi-omics integration (MOI) system analysis led to a data-set of 51 metabolic pathways with at least one enzyme and one metabolite differentially expressed due to salinity stress. These data sets (of genes and metabolites) are valuable for future studies aimed to deepen our knowledge on the mechanisms behind the high tolerance of this species to salinity stress. In conclusion, besides showing that this species applies salt exclusion already in young plants to support very high levels of salinity stress, the initial analysis of metabolites and transcripts data sets already give some insights into other salt tolerance mechanisms used by this species to support high levels of salinity stress.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00061-2.

Effects of Poty-Potexvirus Synergism on Growth, Photosynthesis and Metabolite Status of Nicotiana benthamiana

Mixed virus infections threaten crop production because interactions between the host and the pathogen mix may lead to viral synergism. While individual infections by potato virus A (PVA), a potyvirus, and potato virus X (PVX), a potexvirus, can be mild, co-infection leads to synergistic enhancement of PVX and severe symptoms. We combined image-based phenotyping with metabolite analysis of single and mixed PVA and PVX infections and compared their effects on growth, photosynthesis, and metabolites in Nicotiana benthamiana. Viral synergism was evident in symptom severity and impaired growth in the plants. Indicative of stress, the co-infection increased leaf temperature and decreased photosynthetic parameters. In contrast, singly infected plants sustained photosynthetic activity. The host's metabolic response differed significantly between single and mixed infections. Over 200 metabolites were differentially regulated in the mixed infection: especially defense-related metabolites and aromatic and branched-chain amino acids increased compared to the control. Changes in the levels of methionine cycle intermediates and a low S-adenosylmethionine/S-adenosylhomocysteine ratio suggested a decline in the methylation potential in co-infected plants. The decreased ratio between reduced glutathione, an important scavenger of reactive oxygen species, and its oxidized form, indicated that severe oxidative stress developed during co-infection. Based on the results, infection-associated oxidative stress is successfully controlled in the single infections but not in the synergistic infection, where activated defense pathways are not sufficient to counter the impact of the infections on plant growth.

Exploitation of epigenetic variation of crop wild relatives for crop improvement and agrobiodiversity preservation

Crop wild relatives (CWRs) are recognized as the best potential source of traits for crop improvement. However, successful crop improvement using CWR relies on identifying variation in genes controlling desired traits in plant germplasms and subsequently incorporating them into cultivars. Epigenetic diversity may provide an additional layer of variation within CWR and can contribute novel epialleles for key traits for crop improvement. There is emerging evidence that epigenetic variants of functional and/or agronomic importance exist in CWR gene pools. This provides a rationale for the conservation of epigenotypes of interest, thus contributing to agrobiodiversity preservation through conservation and (epi)genetic monitoring. Concepts and techniques of classical and modern breeding should consider integrating recent progress in epigenetics, initially by identifying their association with phenotypic variations and then by assessing their heritability and stability in subsequent generations. New tools available for epigenomic analysis offer the opportunity to capture epigenetic variation and integrate it into advanced (epi)breeding programmes. Advances in -omics have provided new insights into the sources and inheritance of epigenetic variation and enabled the efficient introduction of epi-traits from CWR into crops using epigenetic molecular markers, such as epiQTLs.

Insights from a Multi-Omics Integration (MOI) Study in Oil Palm (Elaeis guineensis Jacq.) Response to Abiotic Stresses: Part Two-Drought

Drought and salinity are two of the most severe abiotic stresses affecting agriculture worldwide and bear some similarities regarding the responses of plants to them. The first is also known as osmotic stress and shows similarities mainly with the osmotic effect, the first phase of salinity stress. Multi-Omics Integration (MOI) offers a new opportunity for the non-trivial challenge of unraveling the mechanisms behind multigenic traits, such as drought and salinity resistance. The current study carried out a comprehensive, large-scale, single-omics analysis (SOA) and MOI studies on the leaves of young oil palm plants submitted to water deprivation. After performing SOA, 1955 DE enzymes from transcriptomics analysis, 131 DE enzymes from proteomics analysis, and 269 DE metabolites underwent MOI analysis, revealing several pathways affected by this stress, with at least one DE molecule in all three omics platforms used. Moreover, the similarities and dissimilarities in the molecular response of those plants to those two abiotic stresses underwent mapping. Cysteine and methionine metabolism (map00270) was the most affected pathway in all scenarios evaluated. The correlation analysis revealed that 91.55% of those enzymes expressed under both stresses had similar qualitative profiles, corroborating the already known fact that plant responses to drought and salinity show several similarities. At last, the results shed light on some candidate genes for engineering crop species resilient to both abiotic stresses.

Integration of multi-omics technologies for crop improvement: Status and prospects

With the rapid development of next-generation sequencing (NGS), multi-omics techniques have been emerging as effective approaches for crop improvement. Here, we focus mainly on addressing the current status and future perspectives toward omics-related technologies and bioinformatic resources with potential applications in crop breeding. Using a large amount of omics-level data from the functional genome, transcriptome, proteome, epigenome, metabolome, and microbiome, clarifying the interaction between gene and phenotype formation will become possible. The integration of multi-omics datasets with pan-omics platforms and systems biology could predict the complex traits of crops and elucidate the regulatory networks for genetic improvement. Different scales of trait predictions and decision-making models will facilitate crop breeding more intelligent. Potential challenges that integrate the multi-omics data with studies of gene function and their network to efficiently select desirable agronomic traits are discussed by proposing some cutting-edge breeding strategies for crop improvement. Multi-omics-integrated approaches together with other artificial intelligence techniques will contribute to broadening and deepening our knowledge of crop precision breeding, resulting in speeding up the breeding process.

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.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Insights from a Multi-Omics Integration (MOI) Study in Oil Palm (Elaeis guineensis Jacq.) Response to Abiotic Stresses: Part One—Salinity

Oil palm (Elaeis guineensis Jacq.) is the number one source of consumed vegetable oil nowadays. It is cultivated in areas of tropical rainforest, where it meets its natural condition of high rainfall throughout the year. The palm oil industry faces criticism due to a series of practices that was considered not environmentally sustainable, and it finds itself under pressure to adopt new and innovative procedures to reverse this negative public perception. Cultivating this oilseed crop outside the rainforest zone is only possible using artificial irrigation. Close to 30% of the world’s irrigated agricultural lands also face problems due to salinity stress. Consequently, the research community must consider drought and salinity together when studying to empower breeding programs in order to develop superior genotypes adapted to those potential new areas for oil palm cultivation. Multi-Omics Integration (MOI) offers a new window of opportunity for the non-trivial challenge of unraveling the mechanisms behind multigenic traits, such as drought and salinity tolerance. The current study carried out a comprehensive, large-scale, single-omics analysis (SOA), and MOI study on the leaves of young oil palm plants submitted to very high salinity stress. Taken together, a total of 1239 proteins were positively regulated, and 1660 were negatively regulated in transcriptomics and proteomics analyses. Meanwhile, the metabolomics analysis revealed 37 metabolites that were upregulated and 92 that were downregulated. After performing SOA, 436 differentially expressed (DE) full-length transcripts, 74 DE proteins, and 19 DE metabolites underwent MOI analysis, revealing several pathways affected by this stress, with at least one DE molecule in all three omics platforms used. The Cysteine and methionine metabolism (map00270) and Glycolysis/Gluconeogenesis (map00010) pathways were the most affected ones, each one with 20 DE molecules.

Advances in Metabolomics-Driven Diagnostic Breeding and Crop Improvement

Climate change continues to threaten global crop output by reducing annual productivity. As a result, global food security is now considered as one of the most important challenges facing humanity. To address this challenge, modern crop breeding approaches are required to create plants that can cope with increased abiotic/biotic stress. Metabolomics is rapidly gaining traction in plant breeding by predicting the metabolic marker for plant performance under a stressful environment and has emerged as a powerful tool for guiding crop improvement. The advent of more sensitive, automated, and high-throughput analytical tools combined with advanced bioinformatics and other omics techniques has laid the foundation to broadly characterize the genetic traits for crop improvement. Progress in metabolomics allows scientists to rapidly map specific metabolites to the genes that encode their metabolic pathways and offer plant scientists an excellent opportunity to fully explore and rationally harness the wealth of metabolites that plants biosynthesize. Here, we outline the current application of advanced metabolomics tools integrated with other OMICS techniques that can be used to: dissect the details of plant genotype–metabolite–phenotype interactions facilitating metabolomics-assisted plant breeding for probing the stress-responsive metabolic markers, explore the hidden metabolic networks associated with abiotic/biotic stress resistance, facilitate screening and selection of climate-smart crops at the metabolite level, and enable accurate risk-assessment and characterization of gene edited/transgenic plants to assist the regulatory process. The basic concept behind metabolic editing is to identify specific genes that govern the crucial metabolic pathways followed by the editing of one or more genes associated with those pathways. Thus, metabolomics provides a superb platform for not only rapid assessment and commercialization of future genome-edited crops, but also for accelerated metabolomics-assisted plant breeding. Furthermore, metabolomics can be a useful tool to expedite the crop research if integrated with speed breeding in future.

Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought

The possibility of introducing metabolic/biochemical phenotyping to complement genomics-based predictions in breeding pipelines has been considered for years. Here we examine to what extent and under what environmental conditions metabolic/biochemical traits can effectively contribute to understanding and predicting plant performance. In this study, multivariable statistical models based on flag leaf central metabolism and oxidative stress status were used to predict grain yield (GY) performance for 271 indica rice (Oryza sativa) accessions grown in the field under well-watered and reproductive stage drought conditions. The resulting models displayed significantly higher predictability than multivariable models based on genomic data for the prediction of GY under drought (Q2 = 0.54-0.56 versus 0.35) and for stress-induced GY loss (Q2 = 0.59-0.64 versus 0.03-0.06). Models based on the combined datasets showed predictabilities similar to metabolic/biochemical-based models alone. In contrast to genetic markers, models with enzyme activities and metabolite values also quantitatively integrated the effect of physiological differences such as plant height on GY. The models highlighted antioxidant enzymes of the ascorbate-glutathione cycle and a lipid oxidation stress marker as important predictors of rice GY stability under drought at the reproductive stage, and these stress-related variables were more predictive than leaf central metabolites. These findings provide evidence that metabolic/biochemical traits can integrate dynamic cellular and physiological responses to the environment and can help bridge the gap between the genome and the phenome of crops as predictors of GY performance under drought.

High-throughput plant phenotyping: a role for metabolomics?

High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.

Xanthone Biosynthetic Pathway in Plants: A Review

Xanthones are secondary metabolites rich in structural diversity and possess a broad array of pharmacological properties, such as antitumor, antidiabetic, and anti-microbes. These aromatic compounds are found in higher plants, such as Clusiaceae, Hypericaceae, and Gentianaceae, yet their biosynthetic pathways have not been comprehensively updated especially within the last decade (up to 2021). In this review, plant xanthone biosynthesis is detailed to illuminate their intricacies and differences between species. The pathway initially involves the shikimate pathway, either through L-phenylalanine-dependent or -independent pathway, that later forms an intermediate benzophenone, 2,3',4,6-tetrahydoxybenzophenone. This is followed by a regioselective intramolecular mediated oxidative coupling to form xanthone ring compounds, 1,3,5-trihydroxyxanthone (1,3,5-THX) or 1,3,7-THX, the core precursors for xanthones in most plants. Recent evidence has shed some lights onto the enzymes and reactions involved in this xanthone pathway. In particular, several biosynthetic enzymes have been characterized at both biochemical and molecular levels from various organisms including Hypericum spp., Centaurium erythraea and Garcinia mangostana. Proposed pathways for a plethora of other downstream xanthone derivatives including swertianolin and gambogic acid (derived from 1,3,5-THX) as well as gentisin, hyperixanthone A, α-mangostin, and mangiferin (derived from 1,3,7-THX) have also been thoroughly covered. This review reports one of the most complete xanthone pathways in plants. In the future, the information collected here will be a valuable resource for a more directed molecular works in xanthone-producing plants as well as in synthetic biology application.

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.

Capsicum Leaves under Stress: Using Multi-Omics Analysis to Detect Abiotic Stress Network of Secondary Metabolism in Two Species

The plant kingdom contains an enormous diversity of bioactive compounds which regulate plant growth and defends against biotic and abiotic stress. Some of these compounds, like flavonoids, have properties which are health supporting and relevant for industrial use. Many of these valuable compounds are synthesized in various pepper (Capsicum sp.) tissues. Further, a huge amount of biomass residual remains from pepper production after harvest, which provides an important opportunity to extract these metabolites and optimize the utilization of crops. Moreover, abiotic stresses induce the synthesis of such metabolites as a defense mechanism. Two different Capsicum species were therefore exposed to chilling temperature (24/18 ℃ vs. 18/12 ℃), to salinity (200 mM NaCl), or a combination thereof for 1, 7 and 14 days to investigate the effect of these stresses on the metabolome and transcriptome profiles of their leaves. Both profiles in both species responded to all stresses with an increase over time. All stresses resulted in repression of photosynthesis genes. Stress involving chilling temperature induced secondary metabolism whereas stresses involving salt repressed cell wall modification and solute transport. The metabolome analysis annotated putatively many health stimulating flavonoids (apigetrin, rutin, kaempferol, luteolin and quercetin) in the Capsicum biomass residuals, which were induced in response to salinity, chilling temperature or a combination thereof, and supported by related structural genes of the secondary metabolism in the network analysis.

Integration of metabolomics and transcriptomics data to further characterize Gliricidia sepium (Jacq.) Kunth under high salinity stress

Soil salinity is one abiotic stress that threatens agriculture in more than 100 countries. Gliricidia [Gliricidia sepium (Jacq.) Kunth] is a multipurpose tree known for its ability to adapt to a wide range of soils; however, its tolerance limits and responses to salt stress are not yet well understood. In this study, after characterizing the morphophysiological responses of young gliricidia plants to salinity stress, leaf metabolic and transcription profiles were generated and submitted to single and integrated analyses. RNA from leaf samples were subjected to RNA sequencing using an Illumina HiSeq platform and the paired-end strategy. Polar and lipidic fractions from leaf samples were extracted and analyzed on an ultra-high-performance liquid chromatography (UHPLC) coupled with electrospray ionization quadrupole time-of-flight high-resolution mass spectrometry (MS) system. Acquired data were analyzed using the OmicsBox, XCMS Online, MetaboAnalyst, and Omics Fusion platforms. The substrate salinization protocol used allowed the identification of two distinct responses to salt stress: tolerance and adaptation. Single analysis on transcriptome and metabolome data sets led to a group of 5,672 transcripts and 107 metabolites differentially expressed in gliricidia leaves under salt stress. The phenylpropanoid biosynthesis was the most affected pathway, with 15 metabolites and three genes differentially expressed. Results showed that the differentially expressed metabolites and genes from this pathway affect mainly short-term salt stress (STS). The single analysis of the transcriptome identified 12 genes coding for proteins that might play a role in gliricidia response at both STS and long-term salt stress (LTS). Further studies are needed to reveal the mechanisms behind the adaptation response.

An Epigenetic Alphabet of Crop Adaptation to Climate Change

Crop adaptation to climate change is in a part attributed to epigenetic mechanisms which are related to response to abiotic and biotic stresses. Although recent studies increased our knowledge on the nature of these mechanisms, epigenetics remains under-investigated and still poorly understood in many, especially non-model, plants, Epigenetic modifications are traditionally divided into two main groups, DNA methylation and histone modifications that lead to chromatin remodeling and the regulation of genome functioning. In this review, we outline the most recent and interesting findings on crop epigenetic responses to the environmental cues that are most relevant to climate change. In addition, we discuss a speculative point of view, in which we try to decipher the “epigenetic alphabet” that underlies crop adaptation mechanisms to climate change. The understanding of these mechanisms will pave the way to new strategies to design and implement the next generation of cultivars with a broad range of tolerance/resistance to stresses as well as balanced agronomic traits, with a limited loss of (epi)genetic variability.

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

Interpretation of network-based integration from multi-omics longitudinal data

Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.

Global Comparative Label-Free Yeast Proteome Analysis by LC-MS/MS After High-pH Reversed-Phase Peptide Fractionation Using Solid-Phase Extraction Cartridges

Discovery-driven comparative proteomics employing the bottom-up strategy with label-free quantification on high-resolution mass analyzers like an Orbitrap in a hybrid instrument has the capacity to reveal unique biological processes in the context of plant metabolic engineering. However, proteins are very heterogeneous in nature with a wide range of expression levels, and overall coverage may be suboptimal regarding both the number of protein identifications and sequence coverage of the identified proteins using conventional data-dependent acquisitions without sample fractionation before online nanoflow liquid chromatography-mass spectrometry (LC-MS) and tandem mass spectrometry (MS/MS). In this chapter, we detail a simple and robust method employing high-pH reversed-phase (HRP) peptide fractionation using solid-phase extraction cartridges for label-free proteomic analyses. Albeit HRP fractionation separates peptides according to their hydrophobicity like the subsequent nanoflow gradient reversed-phased LC relying on low pH mobile phase, the two methods are orthogonal. Presented here as a protocol with yeast (Saccharomyces cerevisiae) as a frequently used model organism and hydrogen peroxide to exert cellular stress and survey its impact compared to unstressed control as an example, the described workflow can be adapted to a wide range of proteome samples for applications to plant metabolic engineering research.

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

Potentials of single-cell genomics in deciphering cellular phenotypes

Single-cell genomics, particularly single-cell transcriptome profiling by RNA sequencing have transformed the possibilities to relate genes to functions, structures, and eventually phenotypes. We can now observe changes in each cell's transcriptome and among its neighborhoods, interrogate the sequence of transcriptional events, and assess their influence on subsequent events. This paradigm shift in biology enables us to infer causal relationships in these events with high accuracy. Here we review the latest single-cell studies in plants that uncover how cellular phenotypes emerge as a result of the transcriptome process such as waves of expression, trajectories of development and responses to the environment, and spatial information. With an eye on the advances made in animal and human studies, we further highlight some of the needed areas for future research and development, including computational methods.

Drought Stress Memory at the Plant Cycle Level: A Review

Plants are sessile organisms whose survival depends on their strategy to cope with dynamic, stressful conditions. It is urgent to improve the ability of crops to adapt to recurrent stresses in order to alleviate the negative impacts on their productivity. Although our knowledge of plant adaptation to drought has been extensively enhanced during the last decades, recent studies have tackled plant responses to recurrent stresses. The present review synthesizes the major findings from studies addressing plant responses to multiple drought events, and demonstrates the ability of plants to memorize drought stress. Stress memory is described as a priming effect allowing a different response to a reiterated stress when compared to a single stress event. Here, by specifically focusing on water stress memory at the plant cycle level, we describe the different underlying processes at the molecular, physiological and morphological levels in crops as well as in the model species Arabidopsis thaliana. Moreover, a conceptual analysis framework is proposed to study drought stress memory. Finally, the essential role of interactions between plants and soil microorganisms is emphasized during reiterated stresses because their plasticity can play a key role in supporting overall plant resilience.

Rescaling Biology: Increasing Integration Across Biological Scales and Subdisciplines to Enhance Understanding and Prediction

The organization of the living world covers a vast range of spatiotemporal scales, from molecules to the biosphere, seconds to centuries. Biologists working within specialized subdisciplines tend to focus on different ranges of scales. Therefore, developing frameworks that enable testing questions and predictions of scaling require sufficient understanding of complex processes across biological subdisciplines and spatiotemporal scales. Frameworks that enable scaling across subdisciplines would ideally allow us to test hypotheses about the degree to which explicit integration across spatiotemporal scales is needed for predicting the outcome of biological processes. For instance, how does genomic variation within populations allow us to explain community structure? How do the dynamics of cellular metabolism translate to our understanding of whole-ecosystem metabolism? Do patterns and processes operate seamlessly across biological scales, or are there fundamental laws of biological scaling that limit our ability to make predictions from one scale to another? Similarly, can sub-organismal structures and processes be sufficiently understood in isolation of potential feedbacks from the population, community, or ecosystem levels? And can we infer the sub-organismal processes from data on the population, community, or ecosystem scale? Concerted efforts to develop more cross-disciplinary frameworks will open doors to a more fully integrated field of biology. In this paper we discuss how we might integrate across scales, specifically by 1. Identifying scales and boundaries, 2. Determining analogous units and processes across scales, 3. Developing frameworks to unite multiple scales, and 4. Extending frameworks to new empirical systems.