Metabolite-Centric Reporter Pathway and Tripartite Network Analysis of Arabidopsis Under Cold Stress

Comparative transcriptome analysis and meta-QTLs mapping reveal the regulatory mechanism of cold tolerance in rice at the budding stage

Rice (Oryza sativa L.) is one of the most extensively farmed food crops, but its development and productivity are significantly impacted by cold stress during the budding period. In this study, transcriptome sequencing was conducted on two types of rice: the cold-sensitive indica rice A117 and the substantially cold-tolerant japonica rice B106 under control and cold treatments. Differentially expressed genes between the two materials under cold conditions were analyzed using GO and KEGG enrichment analyses. The results revealed that processes such as the TCA cycle, glycolysis/glycogenesis, oxidative phosphorylation, and glutathione metabolism contribute to B106's cold tolerance. Additionally, an enrichment analysis of cold-induced genes in each material and shared genes identified significant enrichment in pathways such as glutathione metabolism, phenylpropanoid biosynthesis, and photosynthesis-antenna proteins. Initial cold tolerance QTLs at the rice bud stage were collected from published literature, and meta-QTL mapping identified 9 MQTLs. Gene expression profiling led to the identification of 75 potential DEGs within the 9 MQTLs region, from which four candidate genes (Os02g0194100, Os03g0802500, Os05g0129000, and Os07g0462000) were selected using qRT-PCR and gene annotation. These findings provide genetic resources for further research on the molecular mechanisms underlying rice's response to cold stress during the bud stage.

Multipartite network analysis to identify environmental and genetic associations of metabolic syndrome in the Korean population

Network analysis has become a crucial tool in genetic research, enabling the exploration of associations between genes and diseases. Its utility extends beyond genetics to include the assessment of environmental factors. Unipartite network analysis is commonly used in genomics to visualize initial insights and relationships among variables. Syndromic diseases, such as metabolic syndrome, are characterized by the simultaneous occurrence of various signs, symptoms, and clinicopathological features. Metabolic syndrome encompasses hypertension, diabetes, obesity, and dyslipidemia, and both genetic and environmental factors contribute to its development. Given that relevant data often consist of distinct sets of variables, a more intuitive visualization method is needed. This study applied multipartite network analysis as an effective method to understand the associations among genetic, environmental, and disease components in syndromic diseases. We considered three distinct variable sets: genetic factors, environmental factors, and disease components. The process involved projecting a tripartite network onto a two-mode bipartite network and then simplifying it into a one-mode network. This approach facilitated the visualization of relationships among factors across different sets and within individual sets. To transition from multipartite to unipartite networks, we suggest both sequential and concurrent projection methods. Data from the Korean Association Resource (KARE) project were utilized, including 352,228 SNPs from 8840 individuals, alongside information on environmental factors such as lifestyle, dietary, and socioeconomic factors. The single-SNP analysis step filtered SNPs, supplemented by reference SNPs reported in a genome-wide association study catalog. The resulting network patterns differed significantly by sex: demographic factors and fat intake were crucial for women, while alcohol consumption was central for men. Indirect relationships were identified through projected bipartite networks, revealing that SNPs such as rs4244457, rs2156552, and rs10899345 had lifestyle interactions on metabolic components. Our approach offers several advantages: it simplifies the visualization of complex relationships among different datasets, identifies environmental interactions, and provides insights into SNP clusters sharing common environmental factors and metabolic components. This framework provides a comprehensive approach to elucidate the mechanisms underlying complex diseases like metabolic syndrome.

Continuous Cropping Inhibits Photosynthesis of Polygonatum odoratum

Polygonatum odoratum (Mill.) Druce possesses widespread medicinal properties; however, the continuous cropping (CC) often leads to a severe consecutive monoculture problem (CMP), ultimately causing a decline in yield and quality. Photosynthesis is the fundamental process for plant growth development. Improving photosynthesis is one of the most promising approaches to increase plant yields. To better understand how P. odoratum leaves undergo photosynthesis in response to CC, this study analyzed the physiochemical indexes and RNA-seq. The physiochemical indexes, such as the content of chlorophyll (chlorophyll a, b, and total chlorophyll), light response curves (LRCs), and photosynthetic parameters (Fv/Fm, Fv/F0, Fm/F0, Piabs, ABS/RC, TRo/RC, ETo/RC, and DIo/RC) were all changed in P. odoratum under the CC system. Furthermore, 13,798 genes that exhibited differential expression genes (DEGs) were identified in the P. odoratum leaves of CC and first cropping (FC) plants. Among them, 7932 unigenes were upregulated, while 5860 unigenes were downregulated. Here, the DEGs encoding proteins associated with photosynthesis and carbon assimilation showed a significant decrease in expression under the CC system, such as the PSII protein complex, PSI protein complex, Cytochorome b6/f complex, the photosynthetic electron transport chain, light-harvesting chlorophyll protein complex, and Calvin cycle, etc., -related gene. This study demonstrates that CC can suppress photosynthesis and carbon mechanism in P. odoratum, pinpointing potential ways to enhance photosynthetic efficiency in the CC of plants.

Comparative Metabolomic Analysis Reveals the Role of OsHPL1 in the Cold-Induced Metabolic Changes in Rice

Cytochrome P450 (CYP74) family members participate in the generation of oxylipins and play essential roles in plant adaptation. However, the metabolic reprogramming mediated by CYP74s under cold stress remains largely unexplored. Herein, we report how cold-triggered OsHPL1, a member of the CYP74 family, modulates rice metabolism. Cold stress significantly induced the expression of OsHPL1 and the accumulation of OPDA (12-oxo-phytodienoic acid) and jasmonates in the wild-type (WT) plants. The absence of OsHPL1 attenuates OPDA accumulation to a low temperature. Then, we performed a widely targeted metabolomics study covering 597 structurally annotated compounds. In the WT and hpl1 plants, cold stress remodeled the metabolism of lipids and amino acids. Although the WT and hpl1 mutants shared over one hundred cold-affected differentially accumulated metabolites (DAMs), some displayed distinct cold-responding patterns. Furthermore, we identified 114 and 56 cold-responding DAMs, specifically in the WT and hpl1 mutants. In conclusion, our work characterized cold-triggered metabolic rewiring and the metabolic role of OsHPL1 in rice.

Cytokinin and abiotic stress tolerance -What has been accomplished and the way forward?

More than a half-century has passed since it was discovered that phytohormone cytokinin (CK) is essential to drive cytokinesis and proliferation in plant tissue culture. Thereafter, cytokinin has emerged as the primary regulator of the plant cell cycle and numerous developmental processes. Lately, a growing body of evidence suggests that cytokinin has a role in mitigating both abiotic and biotic stress. Cytokinin is essential to defend plants against excessive light exposure and a unique kind of abiotic stress generated by an altered photoperiod. Secondly, cytokinin also exhibits multi-stress resilience under changing environments. Furthermore, cytokinin homeostasis is also affected by several forms of stress. Therefore, the diverse roles of cytokinin in reaction to stress, as well as its interactions with other hormones, are discussed in detail. When it comes to agriculture, understanding the functioning processes of cytokinins under changing environmental conditions can assist in utilizing the phytohormone, to increase productivity. Through this review, we briefly describe the biological role of cytokinin in enhancing the performance of plants growth under abiotic challenges as well as the probable mechanisms underpinning cytokinin-induced stress tolerance. In addition, the article lays forth a strategy for using biotechnological tools to modify genes in the cytokinin pathway to engineer abiotic stress tolerance in plants. The information presented here will assist in better understanding the function of cytokinin in plants and their effective investigation in the cropping system.

Dissection of Paenibacillus polymyxa NSY50-Induced Defense in Cucumber Roots against Fusarium oxysporum f. sp. cucumerinum by Target Metabolite Profiling

Simple Summary

Plant growth-promoting rhizobacteria (PGPR) have significant potential to enhance the tolerance of biotic and abiotic stresses and the productivity of crops. However, the mechanism of PGPR in improving plant resistance to pathogens is unclear. Recently, the newly isolated Paenibacillus polymyxa strain NSY50 was shown to considerably suppress the Fusarium wilt of cucumber plants. This study was carried out to explore the underlying mechanism of NSY50 in improving plant resistance to pathogen invasion via target metabolite profiling, and the results indicated that strain NSY50 was able to alleviate Fusarium wilt stress by activating GSH metabolism and improving redox balance. Our research findings enable a deeper understanding of P. polymyxa NSY50-induced enhanced defense against F. oxysporum in cucumber.

Abstract

To gain insights into the roles of beneficial PGPR in controlling soil-borne disease, we adopted a metabolomics approach to investigate the beneficial impacts of P. polymyxa NSY50 on cucumber seedling roots under the pathogen of Fusarium oxysporum f. sp. cucumerinum (FOC). We found that NSY50 pretreatment (NSY50 + FOC) obviously reduced the production of reactive oxygen species (ROS). Untargeted metabolomic analysis revealed that 106 metabolites responded to NSY50 and/or FOC inoculation. Under FOC stress, the contents of root osmotic adjustment substances, such as proline and betaine were significantly increased, and dehydroascorbic acid and oxidized glutathione (GSH) considerably accumulated. Furthermore, the contents of free amino acids such as tryptophan, phenylalanine, and glutamic acid were also significantly accumulated under FOC stress. Similarly, FOC stress adversely affected glycolysis and the tricarboxylic acid cycles and transferred to the pentose phosphate pathway. Conversely, NSY50 + FOC better promoted the accumulation of α-ketoglutaric acid, ribulose-5-phosphate, and 7-phosphosodiheptanone compared to FOC alone. Furthermore, NSY50 + FOC activated GSH metabolism and increased GSH synthesis and metabolism-related enzyme activity and their encoding gene expressions, which may have improved redox homoeostasis, energy flow, and defense ability. Our results provide a novel perspective to understanding the function of P. polymyxa NSY50, accelerating the application of this beneficial PGPR in sustainable agricultural practices.

Genome-wide identification, phylogeny, and expression analysis of the bHLH gene family in tobacco (Nicotiana tabacum)

The basic helix-loop-helix (bHLH) is the second-largest TF family in plants that play important roles in plant growth, development, and stress responses. In this study, a total of 100 bHLHs were identified using Hidden Markov Model profiles in the Nicotiana tabacum genome, clustered into 15 major groups (I-XV) based on their conserved domains and phylogenetic relationships. Group VIII genes were found to be the most abundant, with 27 NtbHLH members. The expansion of NtbHLHs in the genome was due to segmental and tandem duplication. The purifying selection was found to have an important role in the evolution of NtHLHs. Subsequent qRT-PCR validation of five selected genes from transcriptome data revealed that NtbHLH3.1, NtbHLH3.2, NtbHLH24, NtbHLH50, and NtbHLH59.2 have higher expressions at 12 and 24 h in comparison to 0 h (control) of chilling stress. The validated results demonstrated that NtbHLH3.2 and NtbHLH24 genes have 3 and fivefold higher expression at 12 h and 2 and threefold higher expression at 24 h than control plant, shows high sensitivity towards chilling stress. Moreover, the co-expression of positively correlated genes of NtbHLH3.2 and NtbHLH24 confirmed their functional significance in chilling stress response. Therefore, suggesting the importance of NtbHLH3.2 and NtbHLH24 genes in exerting control over the chilling stress responses in tobacco.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-021-01042-x.

Biological networks across scales

Many biological systems across scales of size and complexity exhibit a time-varying complex network structure that emerges and self-organizes as a result of interactions with the environment. Network interactions optimize some intrinsic cost functions that are unknown and involve for example energy efficiency, robustness, resilience, and frailty. A wide range of networks exist in biology, from gene regulatory networks important for organismal development, protein interaction networks that govern physiology and metabolism, and neural networks that store and convey information to networks of microbes that form microbiomes within hosts, animal contact networks that underlie social systems, and networks of populations on the landscape connected by migration. Increasing availability of extensive (big) data is amplifying our ability to quantify biological networks. Similarly, theoretical methods that describe network structure and dynamics are being developed. Beyond static networks representing snapshots of biological systems, collections of longitudinal data series can help either at defining and characterizing network dynamics over time or analyzing the dynamics constrained to networked architectures. Moreover, due to interactions with the environment and other biological systems, a biological network may not be fully observable. Also, subnetworks may emerge and disappear as a result of the need for the biological system to cope with for example invaders or new information flows. The confluence of these developments renders tractable the question of how the structure of biological networks predicts and controls network dynamics. In particular, there may be structural features that result in homeostatic networks with specific higher-order statistics (e.g., multifractal spectrum), which maintain stability over time through robustness and/or resilience to perturbation. Alternative, plastic networks may respond to perturbation by (adaptive to catastrophic) shifts in structure. Here, we explore the opportunity for discovering universal laws connecting the structure of biological networks with their function, positioning them on the spectrum of time-evolving network structure, i.e. dynamics of networks, from highly stable to exquisitely sensitive to perturbation. If such general laws exist, they could transform our ability to predict the response of biological systems to perturbations-an increasingly urgent priority in the face of anthropogenic changes to the environment that affect life across the gamut of organizational scales.

Research Progress on the Roles of Cytokinin in Plant Response to Stress

Cytokinins promote plant growth and development under normal plant growth conditions and also play an important role in plant resistance to stress. Understanding the working mechanisms of cytokinins under adverse conditions will help to make full use of cytokinins in agriculture to increase production and efficiency of land use. In this article, we review the progress that has been made in cytokinin research in plant response to stress and propose its future application prospects.

Changes in photosynthesis and respiratory metabolism of maize seedlings growing under low temperature stress may be regulated by arbuscular mycorrhizal fungi

Arbuscular mycorrhizal fungi as an important soil microbe have been demonstrated to mitigate the harmful effects of stress on plants. However, little is known about the molecular mechanisms underlying the AM symbiotic response to low temperature. Here, differentially expressed genes (DEGs) in the maize seedlings were identified after inoculating AMF under low temperature conditions. A total of 10,400 DEGs were obtained among four treatments, including non-inoculated AMF under ambient temperature (NMA), inoculated AMF under ambient temperature (MA), non-inoculated with low temperature stress (NML), and inoculated with low temperature stress (ML). The relative expression of 858 genes increased and that of 497 genes decreased in AM plants under low temperature stress. 24 DEGs were identified related to photosynthesis and respiratory metabolism. Among these DEGs, 10 genes were upregulated, and 14 genes were downregulated. The results show that inoculating AMF might decrease the production and transmission of electrons under low temperature, and the cyclic electron flow process in chloroplasts was stimulated to protect plants against low temperature. The fungi also influenced transmission of electrons and production of phosphoric acid in mitochondria in response to low temperature. CO₂ assimilation capacity was affected and the tricarboxylic acid cycle was promoted by the adjustments in the glycolysis, pentose phosphate pathway, gamma-aminobutyric acid shunt pathway, and glyoxylic acid cycle to produce more adenosine triphosphate and raw materials for other metabolic pathways under low temperature. These findings provide new insight into low temperature tolerance induced by AMF, and help identify genes for further investigation and functional analyses.

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

Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.

Emergence of Hierarchical Modularity in Evolving Networks Uncovered by Phylogenomic Analysis

Networks describe how parts associate with each other to form integrated systems which often have modular and hierarchical structure. In biology, network growth involves two processes, one that unifies and the other that diversifies. Here, we propose a biphasic (bow-tie) theory of module emergence. In the first phase, parts are at first weakly linked and associate variously. As they diversify, they compete with each other and are often selected for performance. The emerging interactions constrain their structure and associations. This causes parts to self-organize into modules with tight linkage. In the second phase, variants of the modules diversify and become new parts for a new generative cycle of higher level organization. The paradigm predicts the rise of hierarchical modularity in evolving networks at different timescales and complexity levels. Remarkably, phylogenomic analyses uncover this emergence in the rewiring of metabolomic and transcriptome-informed metabolic networks, the nanosecond dynamics of proteins, and evolving networks of metabolism, elementary functionomes, and protein domain organization.

Reactive Oxygen Species and the Redox-Regulatory Network in Cold Stress Acclimation

Cold temperatures restrict plant growth, geographical extension of plant species, and agricultural practices. This review deals with cold stress above freezing temperatures often defined as chilling stress. It focuses on the redox regulatory network of the cell under cold temperature conditions. Reactive oxygen species (ROS) function as the final electron sink in this network which consists of redox input elements, transmitters, targets, and sensors. Following an introduction to the critical network components which include nicotinamide adenine dinucleotide phosphate (NADPH)-dependent thioredoxin reductases, thioredoxins, and peroxiredoxins, typical laboratory experiments for cold stress investigations will be described. Short term transcriptome and metabolome analyses allow for dissecting the early responses of network components and complement the vast data sets dealing with changes in the antioxidant system and ROS. This review gives examples of how such information may be integrated to advance our knowledge on the response and function of the redox regulatory network in cold stress acclimation. It will be exemplarily shown that targeting the redox network might be beneficial and supportive to improve cold stress acclimation and plant yield in cold climate.