Plant Systems Biology Research Articles

Prioritizing Maize Metabolic Gene Regulators through Multi-Omic Network Integration.

Elucidating gene regulatory networks (GRNs) is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. In this study, we integrated publicly available co-expression networks derived from more than 6,000 RNA-seq samples, 283 protein-DNA interaction assays, and 16 million of SNPs used to identify expression quantitative loci (eQTL), to construct TF-target networks. In total, we analyzed ~4.6M interactions to generate four distinct types of TF-target networks: co-expression, protein-DNA interaction (PDI), trans-expression quantitative loci (trans-eQTL), and cis-eQTL combined with PDIs. To improve the functional annotation of TFs based on its target genes, we implemented three different strategies to integrate these four types of networks. We subsequently evaluated the effectiveness of our method through loss-of function mutant and random networks. The multi-network integration allowed us to identify transcriptional regulators of hormone-, metabolic- and development-related processes. Finally, using the topological properties of the fully integrated network, we identified potentially functional redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

Establishment and development of the Center of Plant Systems Biology and Biotechnology in Plovdiv, Bulgaria

The Bulgarian research landscape, presented mainly by the research institutes that are part of the Bulgarian Academy of Sciences and the Agricultural Academy, needs diversification to match the research and innovation potential of the other European Union (EU) countries. This article describes the establishment of the Center of Plant Systems Biology and Biotechnology (CPSBB), a new innovative type of independent research organization that is changing the research landscape in Bulgaria. Supported by the EU Commission, Bulgarian Government, and Plovdiv Municipality, CPSBB has quickly become the leading plant science institute in Bulgaria, creating knowledge in diverse fields such as bioinformatics, biotechnology, genetics and genomics, metabolomics, and systems biology. We outline the organizational structure of CPSBB, the development of its infrastructure, and its scientific productivity. Finally, we compare CPSBB with other similar research establishments in Europe and we conclude that such new types of institutes have a bright future in Bulgaria due to their operational flexibility, productivity, and connections with academia and industry.

The landscape of transcription factor promoter activity during vegetative development in Marchantia.

Transcription factors (TFs) are essential for the regulation of gene expression and cell fate determination. Characterizing the transcriptional activity of TF genes in space and time is a critical step toward understanding complex biological systems. The vegetative gametophyte meristems of bryophytes share some characteristics with the shoot apical meristems of flowering plants. However, the identity and expression profiles of TFs associated with gametophyte organization are largely unknown. With only ∼450 putative TF genes, Marchantia (Marchantia polymorpha) is an outstanding model system for plant systems biology. We have generated a near-complete collection of promoter elements derived from Marchantia TF genes. We experimentally tested reporter fusions for all the TF promoters in the collection and systematically analyzed expression patterns in Marchantia gemmae. This allowed us to build a map of expression domains in early vegetative development and identify a set of TF-derived promoters that are active in the stem-cell zone. The cell markers provide additional tools and insight into the dynamic regulation of the gametophytic meristem and its evolution. In addition, we provide an online database of expression patterns for all promoters in the collection. We expect that these promoter elements will be useful for cell-type-specific expression, synthetic biology applications, and functional genomics.

Establishment and development of the Center of Plant Systems Biology and Biotechnology in Plovdiv, Bulgaria.

The Bulgarian research landscape, presented mainly by the research institutes that are part of the Bulgarian Academy of Sciences and the Agricultural Academy, needs diversification to match the research and innovation potential of the other European Union (EU) countries. This article describes the establishment of the Center of Plant Systems Biology and Biotechnology (CPSBB), a new innovative type of independent research organization that is changing the research landscape in Bulgaria. Supported by the EU Commission, Bulgarian Government, and Plovdiv Municipality, CPSBB has quickly become the leading plant science institute in Bulgaria, creating knowledge in diverse fields such as bioinformatics, biotechnology, genetics and genomics, metabolomics, and systems biology. We outline the organizational structure of CPSBB, the development of its infrastructure, and its scientific productivity. Finally, we compare CPSBB with other similar research establishments in Europe and we conclude that such new types of institutes have a bright future in Bulgaria due to their operational flexibility, productivity, and connections with academia and industry.

A comprehensive overview of omics-based approaches to enhance biotic and abiotic stress tolerance in sweet potato.

Biotic and abiotic stresses negatively affect the yield and overall plant developmental process, thus causing substantial losses in global sweet potato production. To cope with stresses, sweet potato has evolved numerous strategies to tackle ever-changing surroundings and biological and environmental conditions. The invention of modern sequencing technology and the latest data processing and analysis instruments has paved the way to integrate biological information from different approaches and helps to understand plant system biology more precisely. The advancement in omics technologies has accumulated and provided a great source of information at all levels (genome, transcript, protein, and metabolite) under stressful conditions. These latest molecular tools facilitate us to understand better the plant's responses to stress signaling and help to process/integrate the biological information encoded within the biological system of plants. This review briefly addresses utilizing the latest omics strategies for deciphering the adaptive mechanisms for sweet potatoes' biotic and abiotic stress tolerance via functional genomics, transcriptomics, proteomics, and metabolomics. This information also provides a powerful reference to understand the complex, well-coordinated stress signaling genetic regulatory networks and better comprehend the plant phenotypic responses at the cellular/molecular level under various environmental stimuli, thus accelerating the design of stress-resilient sweet potato via the latest genetic engineering approaches.

Advances in the Application of Single-Cell Transcriptomics in Plant Systems and Synthetic Biology.

Plants are complex systems hierarchically organized and composed of various cell types. To understand the molecular underpinnings of complex plant systems, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for revealing high resolution of gene expression patterns at the cellular level and investigating the cell-type heterogeneity. Furthermore, scRNA-seq analysis of plant biosystems has great potential for generating new knowledge to inform plant biosystems design and synthetic biology, which aims to modify plants genetically/epigenetically through genome editing, engineering, or re-writing based on rational design for increasing crop yield and quality, promoting the bioeconomy and enhancing environmental sustainability. In particular, data from scRNA-seq studies can be utilized to facilitate the development of high-precision Build-Design-Test-Learn capabilities for maximizing the targeted performance of engineered plant biosystems while minimizing unintended side effects. To date, scRNA-seq has been demonstrated in a limited number of plant species, including model plants (e.g., Arabidopsis thaliana), agricultural crops (e.g., Oryza sativa), and bioenergy crops (e.g., Populus spp.). It is expected that future technical advancements will reduce the cost of scRNA-seq and consequently accelerate the application of this emerging technology in plants. In this review, we summarize current technical advancements in plant scRNA-seq, including sample preparation, sequencing, and data analysis, to provide guidance on how to choose the appropriate scRNA-seq methods for different types of plant samples. We then highlight various applications of scRNA-seq in both plant systems biology and plant synthetic biology research. Finally, we discuss the challenges and opportunities for the application of scRNA-seq in plants.

Physiotyping of Plants and Modeling the Soil Plant Atmospheric Continuum (SPAC).

This chapter presents a holistic and quantitative approach to the carbon cycle in plant systems biology. It includes (rapid) phenotyping and monitoring of physiological key interactions of plants with its respective soil and atmospheric environment (soil plant atmospheric continuum-SPAC). The approach aims at qualifying and quantifying key components of this microhabitat as influenced by a single plant or a local group of plants in order to contribute to a flux-based modelling approach. The toolset consists of plant biometry, gas exchange, metabolomics, ionomics, root exudate characterization as well as soil biological and physical-chemical characterization. The results are presented as a basic interaction and input-output model aka conceptual system model employing H. T. Odum-style plots based on empirical data.

Development and applications of metabolic models in plant multi-omics research.

Plant growth and development are characterized by systematic and continuous processes, each involving intricate metabolic coordination mechanisms. Mathematical models are essential tools for investigating plant growth and development, metabolic regulation networks, and growth patterns across different stages. These models offer insights into secondary metabolism patterns in plants and the roles of metabolites. The proliferation of data related to plant genomics, transcriptomics, proteomics, and metabolomics in the last decade has underscored the growing importance of mathematical modeling in this field. This review aims to elucidate the principles and types of metabolic models employed in studying plant secondary metabolism, their strengths, and limitations. Furthermore, the application of mathematical models in various plant systems biology subfields will be discussed. Lastly, the review will outline how mathematical models can be harnessed to address research questions in this context.

Plant Metabolomics: Current Initiatives and Future Prospects.

Plant metabolomics is a rapidly advancing field of plant sciences and systems biology. It involves comprehensive analyses of small molecules (metabolites) in plant tissues and cells. These metabolites include a wide range of compounds, such as sugars, amino acids, organic acids, secondary metabolites (e.g., alkaloids and flavonoids), lipids, and more. Metabolomics allows an understanding of the functional roles of specific metabolites in plants' physiology, development, and responses to biotic and abiotic stresses. It can lead to the identification of metabolites linked with specific traits or functions. Plant metabolic networks and pathways can be better understood with the help of metabolomics. Researchers can determine how plants react to environmental cues or genetic modifications by examining how metabolite profiles change under various crop stages. Metabolomics plays a major role in crop improvement and biotechnology. Integrating metabolomics data with other omics data (genomics, transcriptomics, and proteomics) provides a more comprehensive perspective of plant biology. This systems biology approach enables researchers to understand the complex interactions within organisms.

Establishment and development of the Center of Plant Systems Biology and Biotechnology in Plovdiv, Bulgaria

The Bulgarian research landscape, presented mainly by the research institutes that are part of the Bulgarian Academy of Sciences and the Agricultural Academy, needs diversification to match the research and innovation potential of the other European Union (EU) countries. This article describes the establishment of the Center of Plant Systems Biology and Biotechnology (CPSBB), a new innovative type of independent research organization that is changing the research landscape in Bulgaria. Supported by the EU Commission, Bulgarian Government, and Plovdiv Municipality, CPSBB has quickly become the leading plant science institute in Bulgaria, creating knowledge in diverse fields such as bioinformatics, biotechnology, genetics and genomics, metabolomics, and systems biology. We outline the organizational structure of CPSBB, the development of its infrastructure, and its scientific productivity. Finally, we compare CPSBB with other similar research establishments in Europe and we conclude that such new types of institutes have a bright future in Bulgaria due to their operational flexibility, productivity, and connections with academia and industry.

Role of F-box E3-ubiquitin ligases in plant development and stress responses.

F-box E3-ubiquitin ligases regulate critical biological processes in plant development and stress responses. Future research could elucidate why and how plants have acquired a large number of F-box genes. The ubiquitin-proteasome system (UPS) is a predominant regulatory mechanism employed by plants to maintain the protein turnover in the cells and involves the interplay of three classes of enzymes, E1 (ubiquitin-activating), E2 (ubiquitin-conjugating), and E3 ligases. The diverse and most prominent protein family among eukaryotes, F-box proteins, are a vital component of the multi-subunit SCF (Skp1-Cullin 1-F-box) complex among E3 ligases. Several F-box proteins with multifarious functions in different plant systems have evolved rapidly over time within closely related species, but only a small part has been characterized. We need to advance our understanding of substrate-recognition regulation and the involvement of F-box proteins in biological processes and environmental adaptation. This review presents a background of E3 ligases with particular emphasis on the F-box proteins, their structural assembly, and their mechanism of action during substrate recognition. We discuss how the F-box proteins regulate and participate in the signaling mechanisms of plant development and environmental responses. We highlight an urgent need for research on the molecular basis of the F-box E3-ubiquitin ligases in plant physiology, systems biology, and biotechnology. Further, the developments and outlooks of the potential technologies targeting the E3-ubiquitin ligases for developing crop improvement strategies have been discussed.

Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves

BackgroundHigh-throughput metabolomics analytical methodology is needed for population-scale studies of bioenergy-relevant feedstocks such as poplar (Populus sp.). Here, the authors report the relative abundance of extractable aromatic metabolites in Populus trichocarpa leaves rapidly estimated using pyrolysis-molecular beam mass spectrometry (py-MBMS). Poplar leaves were analyzed in conjunction with and validated by GC/MS analysis of extracts to determine key spectral features used to build PLS models to predict the relative composition of extractable aromatic metabolites in whole poplar leaves.ResultsThe Pearson correlation coefficient for the relative abundance of extractable aromatic metabolites based on ranking between GC/MS analysis and py-MBMS analysis of the Boardman leaf set was 0.86 with R2 = 0.76 using a simplified prediction approach from select ions in MBMS spectra. Metabolites most influential to py-MBMS spectral features in the Clatskanie set included the following compounds: catechol, salicortin, salicyloyl-coumaroyl-glucoside conjugates, α-salicyloylsalicin, tremulacin, as well as other salicylates, trichocarpin, salicylic acid, and various tremuloidin conjugates. Ions in py-MBMS spectra with the highest correlation to the abundance of extractable aromatic metabolites as determined by GC/MS analysis of extracts, included m/z 68, 71, 77, 91, 94, 105, 107, 108, and 122, and were used to develop the simplified prediction approach without PLS models or a priori measurements.ConclusionsThe simplified py-MBMS method is capable of rapidly screening leaf tissue for relative abundance of extractable aromatic secondary metabolites to enable prioritization of samples in large populations requiring comprehensive metabolomics that will ultimately inform plant systems biology models and advance the development of optimized biomass feedstocks for renewable fuels and chemicals.

Antioxidant Green Factories: Toward Sustainable Production of Vitamin E in Plant In Vitro Cultures.

Vitamin E is a dietary supplement synthesized only by photosynthetic organisms and, hence, is an essential vitamin for human well-being. Because of the ever-increasing demand for natural vitamin E and limitations in existing synthesis modes, attempts to improve its yield using plant in vitro cultures have gained traction in recent years. With inflating industrial production costs, integrative approaches to conventional bioprocess optimization is the need of the hour for multifold vitamin E productivity enhancement. In this review, we briefly discuss the structure, isomers, and important metabolic routes of biosynthesis for vitamin E in plants. We then emphasize its vital role in human health and its industrial applications and highlight the market demand and supply. We illustrate the advantages of in vitro plant cell/tissue culture cultivation as an alternative to current commercial production platforms for natural vitamin E. We touch upon the conventional vitamin E metabolic pathway engineering strategies, such as single/multigene overexpression and chloroplast engineering. We highlight the recent progress in plant systems biology to rationally identify metabolic bottlenecks and knockout targets in the vitamin E biosynthetic pathway. We then discuss bioprocess optimization strategies for sustainable vitamin E production, including media/process optimization, precursor/elicitor addition, and scale-up to bioreactors. We culminate the review with a short discussion on kinetic modeling to predict vitamin E production in plant cell cultures and suggestions on sustainable green extraction methods of vitamin E for reduced environmental impact. This review will be of interest to a wider research fraternity, including those from industry and academia working in the field of plant cell biology, plant biotechnology, and bioprocess engineering for phytochemical enhancement.

Using PlaPPISite to Predict and Analyze Plant Protein-Protein Interaction Sites.

Proteome-wide characterization of protein-protein interactions (PPIs) is crucial to understand the functional roles of protein machinery within cells systematically. With the accumulation of PPI data in different plants, the interaction details of binary PPIs, such as the three-dimensional (3D) structural contexts of interaction sites/interfaces, are urgently demanded. To meet this requirement, we have developed a comprehensive and easy-to-use database called PlaPPISite ( http://zzdlab.com/plappisite/index.php ) to present interaction details for 13 plant interactomes. Here, we provide a clear guide on how to search and view protein interaction details through the PlaPPISite database. Firstly, the running environment of our database is introduced. Secondly, the input file format is briefly introduced. Moreover, we discussed which information related to interaction sites can be achieved through several examples. In addition, some notes about PlaPPISite are also provided. More importantly, we would like to emphasize the importance of interaction site information in plant systems biology through this user guide of PlaPPISite. In particular, the easily accessible 3D structures of PPIs in the coming post-AlphaFold2 era will definitely boost the application of plant interactome to decipher the molecular mechanisms of many fundamental biological issues.

Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology.

Advances in high-throughput omics technologies are leading plant biology research into the era of big data. Machine learning (ML) performs an important role in plant systems biology because of its excellent performance and wide application in the analysis of big data. However, to achieve ideal performance, supervised ML algorithms require large numbers of labeled samples as training data. In some cases, it is impossible or prohibitively expensive to obtain enough labeled training data; here, the paradigms of unsupervised learning (UL) and semi-supervised learning (SSL) play an indispensable role. In this review, we first introduce the basic concepts of ML techniques, as well as some representative UL and SSL algorithms, including clustering, dimensionality reduction, self-supervised learning (self-SL), positive-unlabeled (PU) learning and transfer learning. We then review recent advances and applications of UL and SSL paradigms in both plant systems biology and plant phenotyping research. Finally, we discuss the limitations and highlight the significance and challenges of UL and SSL strategies in plant systems biology.

Improving Crop Nitrogen Use Efficiency Toward Sustainable Green Revolution.

The Green Revolution of the 1960s improved crop yields in part through the widespread cultivation of semidwarf plant varieties, which resist lodging but require a high-nitrogen (N) fertilizer input. Because environmentally degrading synthetic fertilizer use underlies current worldwide cereal yields, future agricultural sustainability demands enhanced N use efficiency (NUE). Here, we summarize the current understanding of how plants sense, uptake, and respond to N availability in the model plants that can be used to improve sustainable productivity in agriculture. Recent progress in unlocking the genetic basis of NUE within the broader context of plant systems biology has provided insights into the coordination of plant growth and nutrient assimilation and inspired the implementation of a new breeding strategy to cut fertilizer use in high-yield cereal crops. We conclude that identifying fresh targets for N sensing and response in crops would simultaneously enable improved grain productivity and NUE to launch a new Green Revolution and promote future food security.

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.

Revisiting Julius Sachs’s “Physiological Notes: II. Contributions to the Theory of the Cell. a) Energids and Cells” (1892)

Julius Sachs (1832–1897), who has been quite rightly called “the father of plant physiology,” was a German physiologist of international standing, whose research interests contributed to virtually every branch of the plant sciences, and whose work presaged plant molecular biology and systems biology. Here, we focus on one of his last publications, from 1892, wherein he argued that the term “cell” (Zelle) is misleading and should be replaced by “energid” (Energide), which he defined as “a nucleus together with the corresponding protoplasm that is governed by it,” based on his observations of coenocytic algae such as Caulerpa whose nuclei “can only control” so much cytoplasm. Although most of his colleagues did not accept this novel terminology for the description of the “basic, minimal living unit” (Elementarorganismus) of animals, plants, and microbes, we argue that the energid concept prefigured the subsequent discovery of mRNA. We also argue that the resistance to the energid concept revolved around a deep-seated philosophical debate between those adhering to cell theory versus organismal theory. The first English translation of the seminal work by Sachs, “Physiologische Notizen. II. Beiträge zur Zellentheorie. a) Energiden und Zellen,” originally published in Flora (75: 57–67, 1892), is provided as a separate article in this volume as part of the journal’s “Classics in Biological Theory” collection (https://doi.org/10.1007/s13752-022-00399-w); the original German version is available here as supplementary material in the online version of this article.

Plant Systems Biology: Lessons from Teaching.

Systems biology is the study of biological interactions. These interactions exist between biological entities at every scale, from genes to population, and create incredibly complex networks of feedbacks responsible for emerging behaviors. To study these behaviors, biologists can use models based on mathematical and computational formalisms grounded on vast existing corpus of theoretical work. This chapter develops an overview of this process of plant systems biology study from the point of view of a teaching course, and introduces the methods and studies presented in this second edition of the "Plant Systems Biology" book series.

Future Challenges in Plant Systems Biology.

Plant systems biology is currently facing several important challenges, whose nature depend on the considered frame of reference and associated scale. This review covers some of the issues associated respectively with the molecular, tissue, and whole-plant scales, as well as discusses the potential for latest advances in synthetic biology and machine-learning methods to be of use in the future of plant systems biology.