Co-expression networks for plant biology: why and how

Unlocking saponin biosynthesis in soapwort

Soapwort (Saponaria officinalis) is a flowering plant from the Caryophyllaceae family with a long history of human use as a traditional source of soap. Its detergent properties are because of the production of polar compounds (saponins), of which the oleanane-based triterpenoid saponins, saponariosides A and B, are the major components. Soapwort saponins have anticancer properties and are also of interest as endosomal escape enhancers for targeted tumor therapies. Intriguingly, these saponins share common structural features with the vaccine adjuvant QS-21 and, thus, represent a potential alternative supply of saponin adjuvant precursors. Here, we sequence the S. officinalis genome and, through genome mining and combinatorial expression, identify 14 enzymes that complete the biosynthetic pathway to saponarioside B. These enzymes include a noncanonical cytosolic GH1 (glycoside hydrolase family 1) transglycosidase required for the addition of D-quinovose. Our results open avenues for accessing and engineering natural and new-to-nature pharmaceuticals, drug delivery agents and potential immunostimulants.

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

Deciphering the regulatory networks involved in mild and severe salt stress responses in the roots of wild grapevine Vitis vinifera spp. sylvestris

Transcriptional regulatory networks are pivotal components of plant's response to salt stress. However, plant adaptation strategies varied as a function of stress intensity, which is mainly modulated by climate change. Here, we determined the gene regulatory networks based on transcription factor (TF) TF_gene co-expression, using two transcriptomic data sets generated from the salt-tolerant "Tebaba" roots either treated with 50 mM NaCl (mild stress) or 150 mM NaCl (severe stress). The analysis of these regulatory networks identified specific TFs as key regulatory hubs as evidenced by their multiple interactions with different target genes related to stress response. Indeed, under mild stress, NAC and bHLH TFs were identified as central hubs regulating nitrogen storage process. Moreover, HSF TFs were revealed as a regulatory hub regulating various aspects of cellular metabolism including flavonoid biosynthesis, protein processing, phenylpropanoid metabolism, galactose metabolism, and heat shock proteins. These processes are essentially linked to short-term acclimatization under mild salt stress. This was further consolidated by the protein-protein interaction (PPI) network analysis showing structural and plant growth adjustment. Conversely, under severe salt stress, dramatic metabolic changes were observed leading to novel TF members including MYB family as regulatory hubs controlling isoflavonoid biosynthesis, oxidative stress response, abscisic acid signaling pathway, and proteolysis. The PPI network analysis also revealed deeper stress defense changes aiming to restore plant metabolic homeostasis when facing severe salt stress. Overall, both the gene co-expression and PPI network provided valuable insights on key transcription factor hubs that can be employed as candidates for future genetic crop engineering programs.

PEO: Plant Expression Omnibus - a comparative transcriptomic database for 103 Archaeplastida

The Plant Expression Omnibus (PEO) is a web application that provides biologists with access to gene expression insights across over 100 plant species, ~60 000 manually annotated RNA-seq samples, and more than 4 million genes. The tool allows users to explore the expression patterns of genes across different organs, identify organ-specific genes, and discover top co-expressed genes for any gene of interest. PEO also provides functional annotations for each gene, allowing for the identification of genetic modules and pathways. PEO is designed to facilitate comparative kingdom-wide gene expression analysis and provide a valuable resource for plant biology research. We provide two case studies to demonstrate the utility of PEO in identifying candidate genes in pollen coat biosynthesis in Arabidopsis and investigating the biosynthetic pathway components of capsaicin in Capsicum annuum. The database is freely available at https://expression.plant.tools/.

RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci

BACKGROUND

As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.

RESULTS

We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.

CONCLUSIONS

RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis

Tropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits.

Genetic mapping and transcriptome profiling of a chickpea (Cicer arietinum L.) mutant identifies a novel locus (CaEl) regulating organ size and early vigor

Chickpea is among the top three legumes produced and consumed worldwide. Early plant vigor, characterized by good germination and rapid seedling growth, is an important agronomic trait in many crops including chickpea, and shows a positive correlation with seed size. In this study, we report a gamma-ray-induced chickpea mutant with a larger organ and seed size. The mutant (elm) exhibits increased early vigor and contains higher proline that contributes to a better tolerance under salt stress at germination, seedling, and early vegetative phase. The trait is governed as monogenic recessive, with wild-type allele being incompletely dominant over the mutant. Genetic mapping of this locus (CaEl) identified it as a previously uncharacterized gene (101503252) in chromosome 1 of the chickpea genome. There is a deletion of this gene in the mutant with a complete loss of expression. In silico analysis suggests that the gene is present as a single copy in chickpea and related legumes of the galegoid clade. In the mutant, cell division and expansion are affected. Transcriptome profiling identified differentially regulated transcripts related to cell division, expansion, cell wall organization, and metabolism in the mutant. The mutant can be exploited in chickpea breeding programs for increasing plant vigor and seed size.

Integrated Systems Biology Pipeline to Compare Co-Expression Networks in Plants and Elucidate Differential Regulators

To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression network analysis can provide deeper insights into gene regulation when combined with transcriptomics. The coordination and integration of all these complex networks to deduce gene regulation are major challenges for plant biologists. Python and R have emerged as major tools for managing complex scientific data over the past decade. In this study, we describe a reproducible protocol POTFUL (pant co-expression transcription factor regulators), implemented in Python 3, for integrating co-expression and transcription factor target protein networks to infer gene regulation.

Linking genetic and environmental factors through marker effect networks to understand trait plasticity

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome.

Integrated Transcriptome Analysis Identified Key Expansin Genes Associated with Wheat Cell Wall, Grain Weight and Yield

This research elucidates the dynamic expression of expansin genes during the wheat grain (Triticum aestivum L.) development process using comprehensive meta-analysis and experimental validation. We leveraged RNA-seq data from multiple public databases, applying stringent criteria for selection, and identified 60,852 differentially expressed genes across developmental stages. From this pool, 28,558 DEGs were found to exhibit significant temporal regulation in at least two different datasets and were enriched for processes integral to grain development such as carbohydrate metabolism and cell wall organization. Notably, 30% of the 241 known expansin genes showed differential expression during grain growth. Hierarchical clustering and expression level analysis revealed temporal regulation and distinct contributions of expansin subfamilies during the early stages of grain development. Further analysis using co-expression networks underscored the significance of expansin genes, revealing their substantial co-expression with genes involved in cell wall modification. Finally, qPCR validation and grain morphological analysis under field conditions indicated a significant negative correlation between the expression of select expansin genes, and grain size and weight. This study illuminates the potential role of expansin genes in wheat grain development and provides new avenues for targeted genetic improvements in wheat.

Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance

Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~40% of protein-coding genes in the model plant Arabidopsis thaliana L. are still not categorized in the Gene Ontology (GO) Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus L.), the number of BP term annotations is far fewer, ~22%. In the current study, we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust, high-quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTLs or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. All in all, we have implemented a valuable strategy to better describe genes within specific biological processes.

Molecular network of the oil palm root response to aluminum stress

BACKGROUND

The solubilization of aluminum ions (Al³⁺) that results from soil acidity (pH < 5.5) is a limiting factor in oil palm yield. Al can be uptaken by the plant roots affecting DNA replication and cell division and triggering root morphological alterations, nutrient and water deprivation. In different oil palm-producing countries, oil palm is planted in acidic soils, representing a challenge for achieving high productivity. Several studies have reported the morphological, physiological, and biochemical oil palm mechanisms in response to Al-stress. However, the molecular mechanisms are just partially understood.

RESULTS

Differential gene expression and network analysis of four contrasting oil palm genotypes (IRHO 7001, CTR 3-0-12, CR 10-0-2, and CD 19 - 12) exposed to Al-stress helped to identify a set of genes and modules involved in oil palm early response to the metal. Networks including the ABA-independent transcription factors DREB1F and NAC and the calcium sensor Calmodulin-like (CML) that could induce the expression of internal detoxifying enzymes GRXC1, PER15, ROMT, ZSS1, BBI, and HS1 against Al-stress were identified. Also, some gene networks pinpoint the role of secondary metabolites like polyphenols, sesquiterpenoids, and antimicrobial components in reducing oxidative stress in oil palm seedlings. STOP1 expression could be the first step of the induction of common Al-response genes as an external detoxification mechanism mediated by ABA-dependent pathways.

CONCLUSIONS

Twelve hub genes were validated in this study, supporting the reliability of the experimental design and network analysis. Differential expression analysis and systems biology approaches provide a better understanding of the molecular network mechanisms of the response to aluminum stress in oil palm roots. These findings settled a basis for further functional characterization of candidate genes associated with Al-stress in oil palm.

Design, execution, and interpretation of plant RNA-seq analyses

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.

Seiðr: Efficient calculation of robust ensemble gene networks

Gene regulatory and gene co-expression networks are powerful research tools for identifying biological signal within high-dimensional gene expression data. In recent years, research has focused on addressing shortcomings of these techniques with regard to the low signal-to-noise ratio, non-linear interactions and dataset dependent biases of published methods. Furthermore, it has been shown that aggregating networks from multiple methods provides improved results. Despite this, few useable and scalable software tools have been implemented to perform such best-practice analyses. Here, we present Seidr (stylized Seiðr), a software toolkit designed to assist scientists in gene regulatory and gene co-expression network inference. Seidr creates community networks to reduce algorithmic bias and utilizes noise corrected network backboning to prune noisy edges in the networks. Using benchmarks in real-world conditions across three eukaryotic model organisms, Saccharomyces cerevisiae, Drosophila melanogaster, and Arabidopsis thaliana, we show that individual algorithms are biased toward functional evidence for certain gene-gene interactions. We further demonstrate that the community network is less biased, providing robust performance across different standards and comparisons for the model organisms. Finally, we apply Seidr to a network of drought stress in Norway spruce (Picea abies (L.) H. Krast) as an example application in a non-model species. We demonstrate the use of a network inferred using Seidr for identifying key components, communities and suggesting gene function for non-annotated genes.

WheatCENet: A Database for Comparative Co-expression Networks Analysis of Allohexaploid Wheat and Its Progenitors

Genetic and epigenetic changes after polyploidization events could result in variable gene expression and modified regulatory networks. Here, using large-scale transcriptome data, we constructed co-expression networks for diploid, tetraploid, and hexaploid wheat species, and built a platform for comparing co-expression networks of allohexaploid wheat and its progenitors, named WheatCENet. WheatCENet is a platform for searching and comparing specific functional co-expression networks, as well as identifying the related functions of the genes clustered therein. Functional annotations like pathways, gene families, protein-protein interactions, microRNAs (miRNAs), and several lines of epigenome data are integrated into this platform, and Gene Ontology (GO) annotation, gene set enrichment analysis (GSEA), motif identification, and other useful tools are also included. Using WheatCENet, we found that the network of WHEAT ABERRANT PANICLE ORGANIZATION 1 (WAPO1) has more co-expressed genes related to spike development in hexaploid wheat than its progenitors. We also found a novel motif of CCWWWWWWGG (CArG) specifically in the promoter region of WAPO-A1, suggesting that neofunctionalization of the WAPO-A1 gene affects spikelet development in hexaploid wheat. WheatCENet is useful for investigating co-expression networks and conducting other analyses, and thus facilitates comparative and functional genomic studies in wheat. WheatCENet is freely available at http://bioinformatics.cpolar.cn/WheatCENet and http://bioinformatics.cau.edu.cn/WheatCENet.

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.

Tandemly duplicated CYP82Ds catalyze 14-hydroxylation in triptolide biosynthesis and precursor production in Saccharomyces cerevisiae

Triptolide is a valuable multipotent antitumor diterpenoid in Tripterygium wilfordii, and its C-14 hydroxyl group is often selected for modification to enhance both the bioavailability and antitumor efficacy. However, the mechanism for 14-hydroxylation formation remains unknown. Here, we discover 133 kb of tandem duplicated CYP82Ds encoding 11 genes on chromosome 12 and characterize CYP82D274 and CYP82D263 as 14-hydroxylases that catalyze the metabolic grid in triptolide biosynthesis. The two CYP82Ds catalyze the aromatization of miltiradiene, which has been repeatedly reported to be a spontaneous process. In vivo assays and evaluations of the kinetic parameters of CYP82Ds indicate the most significant affinity to dehydroabietic acid among multiple intermediates. The precursor 14-hydroxy-dehydroabietic acid is successfully produced by engineered Saccharomyces cerevisiae. Our study provides genetic elements for further elucidation of the downstream biosynthetic pathways and heterologous production of triptolide and of the currently intractable biosynthesis of other 14-hydroxyl labdane-type secondary metabolites.

Cytochrome P450s in plant terpenoid biosynthesis: discovery, characterization and metabolic engineering

As the largest family of natural products, terpenoids play valuable roles in medicine, agriculture, cosmetics and food. However, the traditional methods that rely on direct extraction from the original plants not only produce low yields, but also result in waste of resources, and are not applicable at all to endangered species. Modern heterologous biosynthesis is considered a promising, efficient, and sustainable production method, but it relies on the premise of a complete analysis of the biosynthetic pathway of terpenoids, especially the functionalization processes involving downstream cytochrome P450s. In this review, we systematically introduce the biotech approaches used to discover and characterize plant terpenoid-related P450s in recent years. In addition, we propose corresponding metabolic engineering approaches to increase the effective expression of P450 and improve the yield of terpenoids, and also elaborate on metabolic engineering strategies and examples of heterologous biosynthesis of terpenoids in Saccharomyces cerevisiae and plant hosts. Finally, we provide perspectives for the biotech approaches to be developed for future research on terpenoid-related P450.

Toward kingdom-wide analyses of gene expression

Gene expression data for Archaeplastida are accumulating exponentially, with more than 300 000 RNA-sequencing (RNA-seq) experiments available for hundreds of species. The gene expression data stem from thousands of experiments that capture gene expression in various organs, tissues, cell types, (a)biotic perturbations, and genotypes. Advances in software tools make it possible to process all these data in a matter of weeks on modern office computers, giving us the possibility to study gene expression in a kingdom-wide manner for the first time. We discuss how the expression data can be accessed and processed and outline analyses that take advantage of cross-species analyses, allowing us to generate powerful and robust hypotheses about gene function and evolution.

Overexpression of PKMYT1 associated with poor prognosis and immune infiltration may serve as a target in triple-negative breast cancer

Breast cancer (BC) is one of the most common malignancies among women worldwide. It is necessary to search for improvement in diagnosis and treatment methods to improve the prognosis. Protein kinase, membrane associated tyrosine/threonine 1 (PKMYT1), a member of the Wee family of protein kinases, has been studied in some tumors except BC. This study has explored that PKMYT1 functional role by bioinformatics methods combined with local clinical samples and experiments. Comprehensive analysis showed that PKMYT1 expression was higher in BC tissues, especially in advanced patients than that in normal breast tissues. The expression of PKMYT1 was an independent determinant for BC patients' prognosis when combined with the clinical features. In addition, based on multi-omics analysis, we found that the PKMYT1 expression was closely relevant to several oncogenic or tumor suppressor gene variants. The analysis of single-cell sequencing indicated that PKMYT1 expression was upregulated in triple-negative breast cancer (TNBC), consistent with the results of bulk RNA-sequencing. High PKMYT1 expression was correlated with a poor prognosis. Functional enrichment analysis revealed that PKMYT1 expression was associated with cell cycle-related, DNA replication-related, and cancer-related pathways. Further research revealed that PKMYT1 expression was linked to immune cell infiltration in the tumor microenvironment. Additionally, loss-of-function experiments in vitro were performed to investigate the role of PKMYT1. TNBC cell lines' proliferation, migration, and invasion were inhibited when PKMYT1 expression was knock-down. Besides, the down-regulation of PKMYT1 induced apoptosis in vitro. As a result, PKMYT1 might be a biomarker for prognosis and a therapeutic target for TNBC.

From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology

The selection of plant genotypes with improved productivity and tolerance to environmental constraints has always been a major concern in plant breeding. Classical approaches based on the generation of variability and selection of better phenotypes from large variant collections have improved their efficacy and processivity due to the implementation of molecular biology techniques, particularly genomics, Next Generation Sequencing and other omics such as proteomics and metabolomics. In this regard, the identification of interesting variants before they develop the phenotype trait of interest with molecular markers has advanced the breeding process of new varieties. Moreover, the correlation of phenotype or biochemical traits with gene expression or protein abundance has boosted the identification of potential new regulators of the traits of interest, using a relatively low number of variants. These important breakthrough technologies, built on top of classical approaches, will be improved in the future by including the spatial variable, allowing the identification of gene(s) involved in key processes at the tissue and cell levels.

Integrated analysis of lncRNAs, mRNAs, and TFs to identify network modules underlying diterpenoid biosynthesis in Salvia miltiorrhiza

Long non-coding RNAs (lncRNAs) are transcripts of more than 200 nucleotides (nt) in length, with minimal or no protein-coding capacity. Increasing evidence indicates that lncRNAs play important roles in the regulation of gene expression including in the biosynthesis of secondary metabolites. Salvia miltiorrhiza Bunge is an important medicinal plant in China. Diterpenoid tanshinones are one of the main active components of S. miltiorrhiza. To better understand the role of lncRNAs in regulating diterpenoid biosynthesis in S. miltiorrhiza, we integrated analysis of lncRNAs, mRNAs, and transcription factors (TFs) to identify network modules underlying diterpenoid biosynthesis based on transcriptomic data. In transcriptomic data, we obtained 6,651 candidate lncRNAs, 46 diterpenoid biosynthetic pathway genes, and 11 TFs involved in diterpenoid biosynthesis. Combining the co-expression and genomic location analysis, we obtained 23 candidate lncRNA-mRNA/TF pairs that were both co-expressed and co-located. To further observe the expression patterns of these 23 candidate gene pairs, we analyzed the time-series expression of S. miltiorrhiza induced by methyl jasmonate (MeJA). The results showed that 19 genes were differentially expressed at least a time-point, and four lncRNAs, two mRNAs, and two TFs formed three lncRNA-mRNA and/or TF network modules. This study revealed the relationship among lncRNAs, mRNAs, and TFs and provided new insight into the regulation of the biosynthetic pathway of S. miltiorrhiza diterpenoids.

Introducing Protein Homeostasis and Glycogen Synthesis as Two Targets of Blue Light Radiation in Lentinula edodes

Introduction: There are documents about the biological effects of blue light radiation on different organisms. An understanding of the molecular mechanism of radiation effects on biological samples is an important event which has attracted researchers' attention. Determining the critical dysregulated proteins of Lentinula edodes following blue light radiation is the aim of this study. Methods: 22 differentially expressed proteins of L. edodes in response to 300 lux of blue light were extracted from the related literature. Experimental, text mining and co-expression connections between the queried proteins were assessed via the STRING database. The maps were compared and the critical proteins were identified. Results: Among the 21 queried proteins, six individuals including heat shock HSP70 protein, 20S proteasome subunit, 26S proteasome subunit P45, Aspartate aminotransferase, phosphopyruvate hydratase, and phosphoglucomutase were highlighted as the critical proteins in response to blue light radiation. Conclusion: The finding indicates that protein homeostasis and glycogen synthesis are affected by blue light radiation. Due to the critical roles of proteins as enzymes and structural elements in life maintenance and involvement of glycogen synthesis in energy consumption, blue light radiation can be considered as a life promotional agent in future investigations.

Integrative omics approaches for biosynthetic pathway discovery in plants

Covering: up to 2022With the emergence of large amounts of omics data, computational approaches for the identification of plant natural product biosynthetic pathways and their genetic regulation have become increasingly important. While genomes provide clues regarding functional associations between genes based on gene clustering, metabolome mining provides a foundational technology to chart natural product structural diversity in plants, and transcriptomics has been successfully used to identify new members of their biosynthetic pathways based on coexpression. Thus far, most approaches utilizing transcriptomics and metabolomics have been targeted towards specific pathways and use one type of omics data at a time. Recent technological advances now provide new opportunities for integration of multiple omics types and untargeted pathway discovery. Here, we review advances in plant biosynthetic pathway discovery using genomics, transcriptomics, and metabolomics, as well as recent efforts towards omics integration. We highlight how transcriptomics and metabolomics provide complementary information to link genes to metabolites, by associating temporal and spatial gene expression levels with metabolite abundance levels across samples, and by matching mass-spectral features to enzyme families. Furthermore, we suggest that elucidation of gene regulatory networks using time-series data may prove useful for efforts to unwire the complexities of biosynthetic pathway components based on regulatory interactions and events.

Exploiting plant transcriptomic databases: Resources, tools, and approaches

There are now more than 300 000 RNA sequencing samples available, stemming from thousands of experiments capturing gene expression in organs, tissues, developmental stages, and experimental treatments for hundreds of plant species. The expression data have great value, as they can be re-analyzed by others to ask and answer questions that go beyond the aims of the study that generated the data. Because gene expression provides essential clues to where and when a gene is active, the data provide powerful tools for predicting gene function, and comparative analyses allow us to study plant evolution from a new perspective. This review describes how we can gain new knowledge from gene expression profiles, expression specificities, co-expression networks, differential gene expression, and experiment correlation. We also introduce and demonstrate databases that provide user-friendly access to these tools.

Gene Co-Expression Network Tools and Databases for Crop Improvement

Transcriptomics has significantly grown as a functional genomics tool for understanding the expression of biological systems. The generated transcriptomics data can be utilised to produce a gene co-expression network that is one of the essential downstream omics data analyses. To date, several gene co-expression network databases that store correlation values, expression profiles, gene names and gene descriptions have been developed. Although these resources remain scattered across the Internet, such databases complement each other and support efficient growth in the functional genomics area. This review presents the features and the most recent gene co-expression network databases in crops and summarises the present status of the tools that are widely used for constructing the gene co-expression network. The highlights of gene co-expression network databases and the tools presented here will pave the way for a robust interpretation of biologically relevant information. With this effort, the researcher would be able to explore and utilise gene co-expression network databases for crops improvement.

ATTED-II v11: A Plant Gene Coexpression Database Using a Sample Balancing Technique by Subagging of Principal Components

ATTED-II (https://atted.jp) is a gene coexpression database for nine plant species based on publicly available RNAseq and microarray data. One of the challenges in constructing condition-independent coexpression data based on publicly available gene expression data is managing the inherent sampling bias. Here, we report ATTED-II version 11, wherein we adopted a coexpression calculation methodology to balance the samples using principal component analysis and ensemble calculation. This approach has two advantages. First, omitting principal components with low contribution rates reduces the main contributors of noise. Second, balancing large differences in contribution rates enables considering various sample conditions entirely. In addition, based on RNAseq- and microarray-based coexpression data, we provide species-representative, integrated coexpression information to enhance the efficiency of interspecies comparison of the coexpression data. These coexpression data are provided as a standardized z-score to facilitate integrated analysis with different data sources. We believe that with these improvements, ATTED-II is more valuable and powerful for supporting interspecies comparative studies and integrated analyses using heterogeneous data.

Use of a graph neural network to the weighted gene co-expression network analysis of Korean native cattle

In the general framework of the weighted gene co-expression network analysis (WGCNA), a hierarchical clustering algorithm is commonly used to module definition. However, hierarchical clustering depends strongly on the topological overlap measure. In other words, this algorithm may assign two genes with low topological overlap to different modules even though their expression patterns are similar. Here, a novel gene module clustering algorithm for WGCNA is proposed. We develop a gene module clustering network (gmcNet), which simultaneously addresses single-level expression and topological overlap measure. The proposed gmcNet includes a “co-expression pattern recognizer” (CEPR) and “module classifier”. The CEPR incorporates expression features of single genes into the topological features of co-expressed ones. Given this CEPR-embedded feature, the module classifier computes module assignment probabilities. We validated gmcNet performance using 4,976 genes from 20 native Korean cattle. We observed that the CEPR generates more robust features than single-level expression or topological overlap measure. Given the CEPR-embedded feature, gmcNet achieved the best performance in terms of modularity (0.261) and the differentially expressed signal (27.739) compared with other clustering methods tested. Furthermore, gmcNet detected some interesting biological functionalities for carcass weight, backfat thickness, intramuscular fat, and beef tenderness of Korean native cattle. Therefore, gmcNet is a useful framework for WGCNA module clustering.

Combining QTL mapping and gene co-expression network analysis for prediction of candidate genes and molecular network related to yield in wheat

BACKGROUND

Wheat (Triticum aestivum L.) is an important cereal crop. Increasing grain yield for wheat is always a priority. Due to the complex genome of hexaploid wheat with 21 chromosomes, it is difficult to identify underlying genes by traditional genetic approach. The combination of genetics and omics analysis has displayed the powerful capability to identify candidate genes for major quantitative trait loci (QTLs), but such studies have rarely been carried out in wheat. In this study, candidate genes related to yield were predicted by a combined use of linkage mapping and weighted gene co-expression network analysis (WGCNA) in a recombinant inbred line population.

RESULTS

QTL mapping was performed for plant height (PH), spike length (SL) and seed traits. A total of 68 QTLs were identified for them, among which, 12 QTLs were stably identified across different environments. Using RNA sequencing, we scanned the 99,168 genes expression patterns of the whole spike for the recombinant inbred line population. By the combined use of QTL mapping and WGCNA, 29, 47, 20, 26, 54, 46 and 22 candidate genes were predicted for PH, SL, kernel length (KL), kernel width, thousand kernel weight, seed dormancy, and seed vigor, respectively. Candidate genes for different traits had distinct preferences. The known PH regulation genes Rht-B and Rht-D, and the known seed dormancy regulation genes TaMFT can be selected as candidate gene. Moreover, further experiment revealed that there was a SL regulatory QTL located in an interval of about 7 Mbp on chromosome 7A, named TaSL1, which also involved in the regulation of KL.

CONCLUSIONS

A combination of QTL mapping and WGCNA was applied to predicted wheat candidate genes for PH, SL and seed traits. This strategy will facilitate the identification of candidate genes for related QTLs in wheat. In addition, the QTL TaSL1 that had multi-effect regulation of KL and SL was identified, which can be used for wheat improvement. These results provided valuable molecular marker and gene information for fine mapping and cloning of the yield-related trait loci in the future.

Systems biology-based analysis indicates that PHO1;H10 positively modulates high light-induced anthocyanin biosynthesis in Arabidopsis leaves

Arabidopsis PHO1;H10 is a member of the PHO1 gene family with SPX and EXS domains, and its functions remain largely unknown. As shown in PCSD database, the upstream region of PHO1;H10 gene is in the active chromatin states, with high DHS accessibility and binding sites of multiple transcription factors, especially ABI5, SPCH and HY5. Co-expression network and data-mining analyses showed PHO1;H10 and co-expression genes were with activation under high light stress. We did wet-lab experiments, and found that the detached leaves of PHO1;H10 overexpression lines accumulated more anthocyanin than those of WT and mutant under high light treatment. RNA-seq results showed overexpression of PHO1;H10 up-regulated many anthocyanin biosynthetic genes. The GSEA analysis result showed that the functional module related to anthocyanin pathway was significantly enriched. In summary, we conducted systems biology approach, combining dry- and wet-lab analyses, and discovered that PHO1;H10 might play an essential role during modulating high light-induced anthocyanin accumulation in the Arabidopsis detached leaves.

Presence of a Mitovirus Is Associated with Alteration of the Mitochondrial Proteome, as Revealed by Protein–Protein Interaction (PPI) and Co-Expression Network Models in Chenopodium quinoa Plants

Simple Summary

Plants often harbor persistent plant virus infection transmitted only vertically through seeds, resulting in no obvious symptoms (cryptic infections). Several studies have shown that such cryptic infections provide resilience against abiotic (and biotic) stress. We have recently discovered a new group of cryptic plant viruses infecting mitochondria (plant mitovirus). Mitochondria are cellular organelles displaying a pivotal role in protecting cells from the stress of nature . Here, we look at the proteomic alterations caused by the mitovirus cryptic infection of Chenopodium quinoa by Systems Biology approaches allowing one to evaluate data at holistic level. Quinoa is a domesticated plant species with many exciting features of abiotic stress resistance, and it is distinguished by its exceptional nutritional characteristics, such as the content and quality of proteins, minerals, lipids, and tocopherols. These features determined the growing interest for the quinoa crop by the scientific community and international organizations since they provide opportunities to produce high-value grains in arid, high-salt and high-UV agroecological environments. We discovered that quinoa lines hosting mitovirus activate some metabolic processes that might help them face drought. These findings present a new perspective for breeding crop plants through the augmented genome provided by accessory cryptic viruses to be investigated in the future.

Abstract

Plant mitoviruses belong to Mitoviridae family and consist of positive single-stranded RNA genomes replicating exclusively in host mitochondria. We previously reported the biological characterization of a replicating plant mitovirus, designated Chenopodium quinoa mitovirus 1 (CqMV1), in some Chenopodium quinoa accessions. In this study, we analyzed the mitochondrial proteome from leaves of quinoa, infected and not infected by CqMV1. Furthermore, by protein–protein interaction and co-expression network models, we provided a system perspective of how CqMV1 affects mitochondrial functionality. We found that CqMV1 is associated with changes in mitochondrial protein expression in a mild but well-defined way. In quinoa-infected plants, we observed up-regulation of functional modules involved in amino acid catabolism, mitochondrial respiratory chain, proteolysis, folding/stress response and redox homeostasis. In this context, some proteins, including BCE2 (lipoamide acyltransferase component of branched-chain alpha-keto acid dehydrogenase complex), DELTA-OAT (ornithine aminotransferase) and GR-RBP2 (glycine-rich RNA-binding protein 2) were interesting because all up-regulated and network hubs in infected plants; together with other hubs, including CAT (catalase) and APX3 (L-ascorbate peroxidase 3), they play a role in stress response and redox homeostasis. These proteins could be related to the higher tolerance degree to drought we observed in CqMV1-infected plants. Although a specific causative link could not be established by our experimental approach at this stage, the results suggest a new mechanistic hypothesis that demands further in-depth functional studies.

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.

Arabidopsis bioinformatics: tools and strategies

The sequencing of the Arabidopsis thaliana genome 21 years ago ushered in the genomics era for plant research. Since then, an incredible variety of bioinformatic tools permit easy access to large repositories of genomic, transcriptomic, proteomic, epigenomic and other '-omic' data. In this review, we cover some more recent tools (and highlight the 'classics') for exploring such data in order to help formulate quality, testable hypotheses, often without having to generate new experimental data. We cover tools for examining gene expression and co-expression patterns, undertaking promoter analyses and gene set enrichment analyses, and exploring protein-protein and protein-DNA interactions. We will touch on tools that integrate different data sets at the end of the article.

Bacteria.guru: Comparative Transcriptomics and Co-Expression Database for Bacterial Pathogens

While bacteria can be beneficial to our health, their deadly pathogenic potential has been an ever-present concern exacerbated by the emergence of drug-resistant strains. As such, there is a pressing urgency for an enhanced understanding of their gene function and regulation, which could mediate the development of novel antimicrobials. Transcriptomic analyses have been established as insightful and indispensable to the functional characterization of genes and identification of new biological pathways, but in the context of bacterial studies, they remain limited to species-specific datasets. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and researched bacterial pathogens, creating bacteria.guru, an interactive database that can identify, visualize, and compare gene expression profiles, coexpression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that bacteria.guru could potentially aid in discovering multi-faceted antibiotic targets and, overall, facilitate future bacterial research. AVAILABILITY: The database and coexpression networks are freely available from https://bacteria.guru/. Sample annotations can be found in the supplemental data.

From gene to biomolecular networks: a review of evidences for understanding complex biological function in plants

Although at the infancy stage, biomolecular network biology is a comprehensive approach to understand complex biological function in plants. Recent advancements in the accumulation of multi-omics data coupled with computational approach have accelerated our current understanding of the complexities of gene function at the system level. Biomolecular networks such as protein-protein interaction, co-expression and gene regulatory networks have extensively been used to decipher the intricacies of transcriptional reprogramming of hundreds of genes and their regulatory interaction in response to various environmental perturbations mainly in the model plant Arabidopsis. This review describes recent applications of network-based approaches to understand the biological functions in plants and focuses on the challenges and opportunities to harness the full potential of the approach.

Multi-omics network-based functional annotation of unknown Arabidopsis genes

Unraveling gene function is pivotal to understanding the signaling cascades that control plant development and stress responses. As experimental profiling is costly and labor intensive, there is a clear need for high-confidence computational annotation. In contrast to detailed gene-specific functional information, transcriptomics data are widely available for both model and crop species. Here, we describe a novel automated function prediction method, which leverages complementary information from multiple expression datasets by analyzing study-specific gene co-expression networks. First, we benchmarked the prediction performance on recently characterized Arabidopsis thaliana genes, and showed that our method outperforms state-of-the-art expression-based approaches. Next, we predicted biological process annotations for known (n = 15 790) and unknown (n = 11 865) genes in A. thaliana and validated our predictions using experimental protein-DNA and protein-protein interaction data (covering >220 000 interactions in total), obtaining a set of high-confidence functional annotations. Our method assigned at least one validated annotation to 5054 (42.6%) unknown genes, and at least one novel validated function to 3408 (53.0%) genes with computational annotations only. These omics-supported functional annotations shed light on a variety of developmental processes and molecular responses, such as flower and root development, defense responses to fungi and bacteria, and phytohormone signaling, and help fill the information gap on biological process annotations in Arabidopsis. An in-depth analysis of two context-specific networks, modeling seed development and response to water deprivation, shows how previously uncharacterized genes function within the respective networks. Moreover, our automated function prediction approach can be applied in future studies to facilitate gene discovery for crop improvement.

Gene-Metabolite Network Analysis Revealed Tissue-Specific Accumulation of Therapeutic Metabolites in Mallotus japonicus

Mallotus japonicus is a valuable traditional medicinal plant in East Asia for applications as a gastrointestinal drug. However, the molecular components involved in the biosynthesis of bioactive metabolites have not yet been explored, primarily due to a lack of omics resources. In this study, we established metabolome and transcriptome resources for M. japonicus to capture the diverse metabolite constituents and active transcripts involved in its biosynthesis and regulation. A combination of untargeted metabolite profiling with data-dependent metabolite fragmentation and metabolite annotation through manual curation and feature-based molecular networking established an overall metabospace of M. japonicus represented by 2129 metabolite features. M. japonicus de novo transcriptome assembly showed 96.9% transcriptome completeness, representing 226,250 active transcripts across seven tissues. We identified specialized metabolites biosynthesis in a tissue-specific manner, with a strong correlation between transcripts expression and metabolite accumulations in M. japonicus. The correlation- and network-based integration of metabolome and transcriptome datasets identified candidate genes involved in the biosynthesis of key specialized metabolites of M. japonicus. We further used phylogenetic analysis to identify 13 C-glycosyltransferases and 11 methyltransferases coding candidate genes involved in the biosynthesis of medicinally important bergenin. This study provides comprehensive, high-quality multi-omics resources to further investigate biological properties of specialized metabolites biosynthesis in M. japonicus.

Transcription Factor-Based Genetic Engineering in Microalgae

Sequence-specific DNA-binding transcription factors (TFs) are key components of gene regulatory networks. Advances in high-throughput sequencing have facilitated the rapid acquisition of whole genome assembly and TF repertoires in microalgal species. In this review, we summarize recent advances in gene discovery and functional analyses, especially for transcription factors in microalgal species. Specifically, we provide examples of the genome-scale identification of transcription factors in genome-sequenced microalgal species and showcase their application in the discovery of regulators involved in various cellular functions. Herein, we highlight TF-based genetic engineering as a promising framework for designing microalgal strains for microalgal-based bioproduction.

Network biology to uncover functional and structural properties of the plant immune system

In the last two decades, advances in network science have facilitated the discovery of important systems' entities in diverse biological networks. This graph-based technique has revealed numerous emergent properties of a system that enable us to understand several complex biological processes including plant immune systems. With the accumulation of multiomics data sets, the comprehensive understanding of plant-pathogen interactions can be achieved through the analyses and efficacious integration of multidimensional qualitative and quantitative relationships among the components of hosts and their microbes. This review highlights comparative network topology analyses in plant-pathogen co-expression networks and interactomes, outlines dynamic network modeling for cell-specific immune regulatory networks, and discusses the new frontiers of single-cell sequencing as well as multiomics data integration that are necessary for unraveling the intricacies of plant immune systems.

Comparative transcriptomic analysis reveals conserved programmes underpinning organogenesis and reproduction in land plants

The appearance of plant organs mediated the explosive radiation of land plants, which shaped the biosphere and allowed the establishment of terrestrial animal life. The evolution of organs and immobile gametes required the coordinated acquisition of novel gene functions, the co-option of existing genes and the development of novel regulatory programmes. However, no large-scale analyses of genomic and transcriptomic data have been performed for land plants. To remedy this, we generated gene expression atlases for various organs and gametes of ten plant species comprising bryophytes, vascular plants, gymnosperms and flowering plants. A comparative analysis of the atlases identified hundreds of organ- and gamete-specific orthogroups and revealed that most of the specific transcriptomes are significantly conserved. Interestingly, our results suggest that co-option of existing genes is the main mechanism for evolving new organs. In contrast to female gametes, male gametes showed a high number and conservation of specific genes, which indicates that male reproduction is highly specialized. The expression atlas capturing pollen development revealed numerous transcription factors and kinases essential for pollen biogenesis and function.

Comparative transcriptomic analysis reveals conserved programmes underpinning organogenesis and reproduction in land plants

The appearance of plant organs mediated the explosive radiation of land plants, which shaped the biosphere and allowed the establishment of terrestrial animal life. The evolution of organs and immobile gametes required the coordinated acquisition of novel gene functions, the co-option of existing genes and the development of novel regulatory programmes. However, no large-scale analyses of genomic and transcriptomic data have been performed for land plants. To remedy this, we generated gene expression atlases for various organs and gametes of ten plant species comprising bryophytes, vascular plants, gymnosperms and flowering plants. A comparative analysis of the atlases identified hundreds of organ- and gamete-specific orthogroups and revealed that most of the specific transcriptomes are significantly conserved. Interestingly, our results suggest that co-option of existing genes is the main mechanism for evolving new organs. In contrast to female gametes, male gametes showed a high number and conservation of specific genes, which indicates that male reproduction is highly specialized. The expression atlas capturing pollen development revealed numerous transcription factors and kinases essential for pollen biogenesis and function.

Transcriptome profiling reveal key hub genes in co-expression networks involved in Iridoid glycosides biosynthetic machinery in Picrorhiza kurroa

Picrorhiza kurroa is a medicinal herb rich in hepatoprotective iridoid glycosides, picroside-I (P-I) and picroside-II (P-II). The biosynthetic machinery of picrosides is poorly understood, therefore, 'no-direction' gene co-expression networks were used to extract linked/closed and separated interactions in terpenoid glycosides-specific sub-networks. Transcriptomes generated from different organs, varying for P-I and P-II contents such as shoots grown at 15 and 25 °C and nursery-grown shoots, stolons, and roots resulted in 47,726, 44,958, 40,117, 66,979, and 55,578 annotated transcripts, respectively. Occurrence of 2810 ± 136 nodes and 15,626 ± 696 edges in these networks indicated intense, co-expressed, closed loop interactions. Either deregulation/inhibition of abscisic acid (ABA) biosynthesis/signaling or constitutive degradation of ABA resulted in organ-specific accumulation of P-I and P-II. Biosynthesis, condensation and glucosylation of isoprene units may occur in shoots, roots or stolons; but addition of phenylpropanoid moiety and further modification/s of the iridoid backbone occurs mainly inside vacuoles in roots.

Tissue-type specific accumulation of the plastoglobular proteome, transcriptional networks, and plastoglobular functions

Plastoglobules are dynamic protein-lipid microcompartments in plastids enriched for isoprenoid-derived metabolites. Chloroplast plastoglobules support formation, remodeling, and controlled dismantling of thylakoids during developmental transitions and environmental responses. However, the specific molecular functions of most plastoglobule proteins are still poorly understood. This review harnesses recent co-mRNA expression data from combined microarray and RNA-seq information in ATTED-II on an updated inventory of 34 PG proteins, as well as proteomics data across 30 Arabidopsis tissue types from ATHENA. Hierarchical clustering based on relative abundance for the plastoglobule proteins across non-photosynthetic and photosynthetic tissue types showed their coordinated protein accumulation across Arabidopsis parts, tissue types, development, and senescence. Evaluation of mRNA-based forced networks at different coefficient thresholds identified a central hub with seven plastoglobule proteins and four peripheral modules. Enrichment of specific nuclear transcription factors (e.g. Golden2-like) and support for crosstalk between plastoglobules and the plastid gene expression was observed, and specific ABC1 kinases appear part of a light signaling network. Examples of other specific findings are that FBN7b is involved with upstream steps of tetrapyrrole biosynthesis and that ABC1K9 is involved in starch metabolism. This review provides new insights into the functions of plastoglobule proteins and an improved framework for experimental studies.

Genome-Wide Mining of MYB Transcription Factors in the Anthocyanin Biosynthesis Pathway of Gossypium Hirsutum

The MYB family, one of the largest transcription factor (TF) families, plays an important role in plant growth, development, and stress response. Although genome-wide analysis of the MYB family has been performed in many species based on sequence similarity, predicting the potential functions of the MYB genes and classifying the regulators into specific metabolic pathways remains difficult. In this study, using a hidden Markov model search and co-expression regulatory network analysis, we demonstrated a process to screen and identify potential MYB TFs in the anthocyanin biosynthesis pathway of Gossypium hirsutum. As a result, we identified 617 and 784 MYB genes (812 in total) from the previously reported and recently released genomes, respectively. Using 126 structural genes involved in the anthocyanin biosynthesis pathway as targets for several co-expression network analyses, we sorted out 31 R2R3-MYB genes, which are potential regulators in the specific pathway. Phylogenetic and collinearity analyses indicated that 83.9% of the 31 MYB genes originated from whole genome duplication or polyploidization. In addition, we revealed relatively specific regulatory relationships between the MYB TFs and their target structural genes. Approximately, 71% of the MYBs could regulate only a single anthocyanin-related structural gene. Moreover, we found that the A- and D- subgenome homoeologs of MYB TFs in G. hirsutum rarely co-regulate the same target gene. The current study not only demonstrated an easy method to rapidly predict potential TFs in a specific metabolic pathway, but also enhanced our understanding of the evolution, gene characteristics, expression, and regulatory pattern of MYB TFs in G. hirsutum.

Using Gene Expression to Study Specialized Metabolism-A Practical Guide

Plants produce a vast array of chemical compounds that we use as medicines and flavors, but these compounds' biosynthetic pathways are still poorly understood. This paucity precludes us from modifying, improving, and mass-producing these specialized metabolites in suitable bioreactors. Many of the specialized metabolites are expressed in a narrow range of organs, tissues, and cell types, suggesting a tight regulation of the responsible biosynthetic pathways. Fortunately, with unprecedented ease of generating gene expression data and with >200,000 publicly available RNA sequencing samples, we are now able to study the expression of genes from hundreds of plant species. This review demonstrates how gene expression can elucidate the biosynthetic pathways by mining organ-specific genes, gene expression clusters, and applying various types of co-expression analyses. To empower biologists to perform these analyses, we showcase these analyses using recently published, user-friendly tools. Finally, we analyze the performance of co-expression networks and show that they are a valuable addition to elucidating multiple the biosynthetic pathways of specialized metabolism.

Response of phytohormone mediated plant homeodomain (PHD) family to abiotic stress in upland cotton (Gossypium hirsutum spp.)

BACKGROUND

The sequencing and annotations of cotton genomes provide powerful theoretical support to unravel more physiological and functional information. Plant homeodomain (PHD) protein family has been reported to be involved in regulating various biological processes in plants. However, their functional studies have not yet been carried out in cotton.

RESULTS

In this study, 108, 55, and 52 PHD genes were identified in G. hirsutum, G. raimondii, and G. arboreum, respectively. A total of 297 PHD genes from three cotton species, Arabidopsis, and rice were divided into five groups. We performed chromosomal location, phylogenetic relationship, gene structure, and conserved domain analysis for GhPHD genes. GhPHD genes were unevenly distributed on each chromosome. However, more GhPHD genes were distributed on At_05, Dt_05, and At_07 chromosomes. GhPHD proteins depicted conserved domains, and GhPHD genes exhibiting similar gene structure were clustered together. Further, whole genome duplication (WGD) analysis indicated that purification selection greatly contributed to the functional maintenance of GhPHD gene family. Expression pattern analysis based on RNA-seq data showed that most GhPHD genes showed clear tissue-specific spatiotemporal expression patterns elucidating the multiple functions of GhPHDs in plant growth and development. Moreover, analysis of cis-acting elements revealed that GhPHDs may respond to a variety of abiotic and phytohormonal stresses. In this regard, some GhPHD genes showed good response against abiotic and phytohormonal stresses. Additionally, co-expression network analysis indicated that GhPHDs are essential for plant growth and development, while GhPHD genes response against abiotic and phytohormonal stresses may help to improve plant tolerance in adverse environmental conditions.

CONCLUSION

This study will provide useful information to facilitate further research related to the vital roles of GhPHD gene family in plant growth and development.

Network Topological Analysis for the Identification of Novel Hubs in Plant Nutrition

Network analysis is a systems biology-oriented approach based on graph theory that has been recently adopted in various fields of life sciences. Starting from mitochondrial proteomes purified from roots of Cucumis sativus plants grown under single or combined iron (Fe) and molybdenum (Mo) starvation, we reconstructed and analyzed at the topological level the protein-protein interaction (PPI) and co-expression networks. Besides formate dehydrogenase (FDH), already known to be involved in Fe and Mo nutrition, other potential mitochondrial hubs of Fe and Mo homeostasis could be identified, such as the voltage-dependent anion channel VDAC4, the beta-cyanoalanine synthase/cysteine synthase CYSC1, the aldehyde dehydrogenase ALDH2B7, and the fumaryl acetoacetate hydrolase. Network topological analysis, applied to plant proteomes profiled in different single or combined nutritional conditions, can therefore assist in identifying novel players involved in multiple homeostatic interactions.

Co-expression Networks From Gene Expression Variability Between Genetically Identical Seedlings Can Reveal Novel Regulatory Relationships

Co-expression networks are a powerful tool to understand gene regulation. They have been used to identify new regulation and function of genes involved in plant development and their response to the environment. Up to now, co-expression networks have been inferred using transcriptomes generated on plants experiencing genetic or environmental perturbation, or from expression time series. We propose a new approach by showing that co-expression networks can be constructed in the absence of genetic and environmental perturbation, for plants at the same developmental stage. For this, we used transcriptomes that were generated from genetically identical individual plants that were grown under the same conditions and for the same amount of time. Twelve time points were used to cover the 24-h light/dark cycle. We used variability in gene expression between individual plants of the same time point to infer a co-expression network. We show that this network is biologically relevant and use it to suggest new gene functions and to identify new targets for the transcriptional regulators GI, PIF4, and PRR5. Moreover, we find different co-regulation in this network based on changes in expression between individual plants, compared to the usual approach requiring environmental perturbation. Our work shows that gene co-expression networks can be identified using variability in gene expression between individual plants, without the need for genetic or environmental perturbations. It will allow further exploration of gene regulation in contexts with subtle differences between plants, which could be closer to what individual plants in a population might face in the wild.

Genomics of Evolutionary Novelty in Hybrids and Polyploids

It has long been recognized that hybridization and polyploidy are prominent processes in plant evolution. Although classically recognized as significant in speciation and adaptation, recognition of the importance of interspecific gene flow has dramatically increased during the genomics era, concomitant with an unending flood of empirical examples, with or without genome doubling. Interspecific gene flow is thus increasingly thought to lead to evolutionary innovation and diversification, via adaptive introgression, homoploid hybrid speciation and allopolyploid speciation. Less well understood, however, are the suite of genetic and genomic mechanisms set in motion by the merger of differentiated genomes, and the temporal scale over which recombinational complexity mediated by gene flow might be expressed and exposed to natural selection. We focus on these issues here, considering the types of molecular genetic and genomic processes that might be set in motion by the saltational event of genome merger between two diverged species, either with or without genome doubling, and how these various processes can contribute to novel phenotypes. Genetic mechanisms include the infusion of new alleles and the genesis of novel structural variation including translocations and inversions, homoeologous exchanges, transposable element mobilization and novel insertional effects, presence-absence variation and copy number variation. Polyploidy generates massive transcriptomic and regulatory alteration, presumably set in motion by disrupted stoichiometries of regulatory factors, small RNAs and other genome interactions that cascade from single-gene expression change up through entire networks of transformed regulatory modules. We highlight both these novel combinatorial possibilities and the range of temporal scales over which such complexity might be generated, and thus exposed to natural selection and drift.

Host-Pathogen Responses to Pandemic Influenza H1N1pdm09 in a Human Respiratory Airway Model

The respiratory Influenza A Viruses (IAVs) and emerging zoonotic viruses such as Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) pose a significant threat to human health. To accelerate our understanding of the host–pathogen response to respiratory viruses, the use of more complex in vitro systems such as normal human bronchial epithelial (NHBE) cell culture models has gained prominence as an alternative to animal models. NHBE cells were differentiated under air-liquid interface (ALI) conditions to form an in vitro pseudostratified epithelium. The responses of well-differentiated (wd) NHBE cells were examined following infection with the 2009 pandemic Influenza A/H1N1pdm09 strain or following challenge with the dsRNA mimic, poly(I:C). At 30 h postinfection with H1N1pdm09, the integrity of the airway epithelium was severely impaired and apical junction complex damage was exhibited by the disassembly of zona occludens-1 (ZO-1) from the cell cytoskeleton. wdNHBE cells produced an innate immune response to IAV-infection with increased transcription of pro- and anti-inflammatory cytokines and chemokines and the antiviral viperin but reduced expression of the mucin-encoding MUC5B, which may impair mucociliary clearance. Poly(I:C) produced similar responses to IAV, with the exception of MUC5B expression which was more than 3-fold higher than for control cells. This study demonstrates that wdNHBE cells are an appropriate ex-vivo model system to investigate the pathogenesis of respiratory viruses.