A Global Coexpression Network Approach for Connecting Genes to Specialized Metabolic Pathways in Plants

Convergent gene clusters underpin hyperforin biosynthesis in St John's wort

The meroterpenoid hyperforin is responsible for the antidepressant activity of St John's wort extracts, but the genes controlling its biosynthesis are unknown. Using genome mining and biochemical work, we characterize two biosynthetic gene clusters (BGCs) that encode the first three steps in the biosynthesis of hyperforin precursors. The findings of syntenic and phylogenetic analyses reveal the parallel assembly of the two BGCs. The syntenous BGC in Mesua ferrea indicates that the first cluster was assembled before the divergence of the Hypericaceae and Calophyllaceae families. The assembly of the second cluster is the result of a coalescence of genomic fragments after a major duplication event. The differences between the two BGCs - in terms of gene expression, response to methyl jasmonate, substrate specificity and subcellular localization of key enzymes - suggest that the presence of the two clusters could serve to generate separate pools of precursors. The parallel assembly of two BGCs with similar compositions in a single plant species is uncommon, and our work provides insights into how and when these gene clusters form. Our discovery helps to advance our understanding of the evolution of plant specialized metabolism and its genomic organization. Additionally, our results offer a foundation from which hyperforin biosynthesis can be more fully understood, and which can be used in future metabolic engineering applications.

Using interdisciplinary, phylogeny-guided approaches to understand the evolution of plant metabolism

To cope with relentless environmental pressures, plants produce an arsenal of structurally diverse chemicals, often called specialized metabolites. These lineage-specific compounds are derived from the simple building blocks made by ubiquitous core metabolic pathways. Although the structures of many specialized metabolites are known, the underlying metabolic pathways and the evolutionary events that have shaped the plant chemical diversity landscape are only beginning to be understood. However, with the advent of multi-omics data sets and the relative ease of studying pathways in previously intractable non-model species, plant specialized metabolic pathways are now being systematically identified. These large datasets also provide a foundation for comparative, phylogeny-guided studies of plant metabolism. Comparisons of metabolic traits and features like chemical abundances, enzyme activities, or gene sequences from phylogenetically diverse plants provide insights into how metabolic pathways evolved. This review highlights the power of studying evolution through the lens of comparative biochemistry, particularly how placing metabolism into a phylogenetic context can help a researcher identify the metabolic innovations enabling the evolution of structurally diverse plant metabolites.

An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function

The evolutionary rates of functionally related genes often covary. We present a gene coevolution network inferred from examining nearly 3 million orthologous gene pairs from 332 budding yeast species spanning ~400 million years of evolution. Network modules provide insight into cellular and genomic structure and function. Examination of the phenotypic impact of network perturbation using deletion mutant data from the baker's yeast Saccharomyces cerevisiae, which were obtained from previously published studies, suggests that fitness in diverse environments is affected by orthologous gene neighborhood and connectivity. Mapping the network onto the chromosomes of S. cerevisiae and Candida albicans revealed that coevolving orthologous genes are not physically clustered in either species; rather, they are often located on different chromosomes or far apart on the same chromosome. The coevolution network captures the hierarchy of cellular structure and function, provides a roadmap for genotype-to-phenotype discovery, and portrays the genome as a linked ensemble of genes.

Gene regulatory networks shape developmental plasticity of root cell types under water extremes in rice

Understanding how roots modulate development under varied irrigation or rainfall is crucial for development of climate-resilient crops. We established a toolbox of tagged rice lines to profile translating mRNAs and chromatin accessibility within specific cell populations. We used these to study roots in a range of environments: plates in the lab, controlled greenhouse stress and recovery conditions, and outdoors in a paddy. Integration of chromatin and mRNA data resolves regulatory networks of the following: cycle genes in proliferating cells that attenuate DNA synthesis under submergence; genes involved in auxin signaling, the circadian clock, and small RNA regulation in ground tissue; and suberin biosynthesis, iron transporters, and nitrogen assimilation in endodermal/exodermal cells modulated with water availability. By applying a systems approach, we identify known and candidate driver transcription factors of water-deficit responses and xylem development plasticity. Collectively, this resource will facilitate genetic improvements in root systems for optimal climate resilience.

PlantNexus: A Gene Co-expression Network Database and Visualization Tool for Barley and Sorghum

Global gene co-expression networks (GCNs) are powerful tools for functional genomics whereby putative functions and regulatory mechanisms can be inferred by gene co-expression. Cereal crops, such as Hordeum vulgare (barley) and Sorghum bicolor (sorghum), are among the most important plants to civilization. However, co-expression network tools for these plants are lacking. Here, we have constructed global GCNs for barley and sorghum using existing RNA-seq data sets. Meta-information was manually curated and categorized by tissue type to also build tissue-specific GCNs. To enable GCN searching and visualization, we implemented a website and database named PlantNexus. PlantNexus is freely available at https://plantnexus.ohio.edu/.

Computational prediction of plant metabolic pathways

Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. Here, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.

Single-cell RNA-sequencing of Nicotiana attenuata corolla cells reveals the biosynthetic pathway of a floral scent

High-throughput single-cell RNA sequencing (scRNA-Seq) identifies distinct cell populations based on cell-to-cell heterogeneity in gene expression. By examining the distribution of the density of gene expression profiles, we can observe the metabolic features of each cell population. Here, we employ the scRNA-Seq technique to reveal the entire biosynthetic pathway of a flower volatile. The corolla of the wild tobacco Nicotiana attenuata emits a bouquet of scents that are composed mainly of benzylacetone (BA). Protoplasts from the N. attenuata corolla limbs and throat cups were isolated at three different time points, and the transcript levels of > 16 000 genes were analyzed in 3756 single cells. We performed unsupervised clustering analysis to determine which cell clusters were involved in BA biosynthesis. The biosynthetic pathway of BA was uncovered by analyzing gene co-expression in scRNA-Seq datasets and by silencing candidate genes in the corolla. In conclusion, the high-resolution spatiotemporal atlas of gene expression provided by scRNA-Seq reveals the molecular features underlying cell-type-specific metabolism in a plant.

The ease and complexity of identifying and using specialized metabolites for crop engineering

Plants produce a broad variety of specialized metabolites with distinct biological activities and potential applications. Despite this potential, most biosynthetic pathways governing specialized metabolite production remain largely unresolved across the plant kingdom. The rapid advancement of genetics and biochemical tools has enhanced our ability to identify plant specialized metabolic pathways. Further advancements in transgenic technology and synthetic biology approaches have extended this to a desire to design new pathways or move existing pathways into new systems to address long-running difficulties in crop systems. This includes improving abiotic and biotic stress resistance, boosting nutritional content, etc. In this review, we assess the potential and limitations for (1) identifying specialized metabolic pathways in plants with multi-omics tools and (2) using these enzymes in synthetic biology or crop engineering. The goal of these topics is to highlight areas of research that may need further investment to enhance the successful application of synthetic biology for exploiting the myriad of specialized metabolic pathways.

Unlocking plant metabolic diversity: A (pan)-genomic view

Plants produce a remarkable diversity of structurally and functionally diverse natural chemicals that serve as adaptive compounds throughout their life cycles. However, unlocking this metabolic diversity is significantly impeded by the size, complexity, and abundant repetitive elements of typical plant genomes. As genome sequencing becomes routine, we anticipate that links between metabolic diversity and genetic variation will be strengthened. In addition, an ever-increasing number of plant genomes have revealed that biosynthetic gene clusters are not only a hallmark of microbes and fungi; gene clusters for various classes of compounds have also been found in plants, and many are associated with important agronomic traits. We present recent examples of plant metabolic diversification that have been discovered through the exploration and exploitation of various genomic and pan-genomic data. We also draw attention to the fundamental genomic and pan-genomic basis of plant chemodiversity and discuss challenges and future perspectives for investigating metabolic diversity in the coming pan-genomics era.

The wheat dioxygenase BX6 is involved in the formation of benzoxazinoids in planta and contributes to plant defense against insect herbivores

Benzoxazinoids are plant specialized metabolites with defense properties, highly abundant in wheat (Triticum), one of the world's most important crops. The goal of our study was to characterize dioxygenase BX6 genes in tetraploid and hexaploid wheat genotypes and to elucidate their effects on defense against herbivores. Phylogenetic analysis revealed four BX6 genes in the hexaploid wheat T. aestivum, but only one ortholog was found in the tetraploid (T. turgidum) wild emmer wheat and the cultivated durum wheat. Transcriptome sequencing of durum wheat plants, damaged by either aphids or caterpillars, revealed that several BX genes, including TtBX6, were upregulated upon caterpillar feeding, relative to the undamaged control plants. A virus-induced gene silencing approach was used to reduce the expression of BX6 in T. aestivum plants, which exhibited both reduced transcript levels and reduced accumulation of different benzoxazinoids. To elucidate the effect of BX6 on plant defense, bioassays with different herbivores feeding on BX6-silenced leaves were conducted. The results showed that plants with silenced BX6 were more susceptible to aphids and the two-spotted spider mite than the control. Overall, our study indicates that wheat BX6 is involved in benzoxazinoid formation in planta and contributes to plant resistance against insect herbivores.

Integrative analysis of the shikonin metabolic network identifies new gene connections and reveals evolutionary insight into shikonin biosynthesis

Plant specialized 1,4-naphthoquinones present a remarkable case of convergent evolution. Species across multiple discrete orders of vascular plants produce diverse 1,4-naphthoquinones via one of several pathways using different metabolic precursors. Evolution of these pathways was preceded by events of metabolic innovation and many appear to share connections with biosynthesis of photosynthetic or respiratory quinones. Here, we sought to shed light on the metabolic connections linking shikonin biosynthesis with its precursor pathways and on the origins of shiknoin metabolic genes. Downregulation of Lithospermum erythrorhizon geranyl diphosphate synthase (LeGPPS), recently shown to have been recruited from a cytoplasmic farnesyl diphosphate synthase (FPPS), resulted in reduced shikonin production and a decrease in expression of mevalonic acid and phenylpropanoid pathway genes. Next, we used LeGPPS and other known shikonin pathway genes to build a coexpression network model for identifying new gene connections to shikonin metabolism. Integrative in silico analyses of network genes revealed candidates for biochemical steps in the shikonin pathway arising from Boraginales-specific gene family expansion. Multiple genes in the shikonin coexpression network were also discovered to have originated from duplication of ubiquinone pathway genes. Taken together, our study provides evidence for transcriptional crosstalk between shikonin biosynthesis and its precursor pathways, identifies several shikonin pathway gene candidates and their evolutionary histories, and establishes additional evolutionary links between shikonin and ubiquinone metabolism. Moreover, we demonstrate that global coexpression analysis using limited transcriptomic data obtained from targeted experiments is effective for identifying gene connections within a defined metabolic network.

Multi-Omics-Based Discovery of Plant Signaling Molecules

Plants produce numerous structurally and functionally diverse signaling metabolites, yet only relatively small fractions of which have been discovered. Multi-omics has greatly expedited the discovery as evidenced by increasing recent works reporting new plant signaling molecules and relevant functions via integrated multi-omics techniques. The effective application of multi-omics tools is the key to uncovering unknown plant signaling molecules. This review covers the features of multi-omics in the context of plant signaling metabolite discovery, highlighting how multi-omics addresses relevant aspects of the challenges as follows: (a) unknown functions of known metabolites; (b) unknown metabolites with known functions; (c) unknown metabolites and unknown functions. Based on the problem-oriented overview of the theoretical and application aspects of multi-omics, current limitations and future development of multi-omics in discovering plant signaling metabolites are also discussed.

Gene Co-expression Network Analysis

Gene co-expression analysis is a data analysis technique that helps identify groups of genes with similar expression patterns across several different conditions. By means of these techniques, different groups have been able to assign putative metabolic pathways and functions to understudied genes and to identify novel metabolic regulation networks for different metabolites. Some groups have even used network comparative studies to understand the evolution of these networks from green algae to land plants. In this chapter, we will go over the basic definitions required to understand network topology and gene module identification. Additionally, we offer the reader a walk-through a standard analysis pipeline as implemented in the package WGCNA that takes as input raw fastq files and obtains co-expressed gene clusters and representative gene expression patterns from each module for downstream applications.

Gene cluster from plant to microbes: Their role in genome architecture, organism's development, specialized metabolism and drug discovery

Plants and microbes fulfil our daily requirements through different high-value chemicals, e.g., nutraceuticals, pharmaceuticals, cosmetics, and through varieties of fruits, crops, vegetables, and many more. Utmost care would therefore be taken for growth, development and sustainability of these important crops and medicinal plants and microbes. Homeobox genes and HOX clusters and their recently characterized expanded family members, including newly discovered homeobox, WOX gene from medicinal herb, Panax ginseng, significantly contributes in the growth and development of these organisms. On the other hand, secondary metabolites produced through secondary metabolism of plants and microbes are used as organisms defense as well as drugs/drug-like molecules for humans. Both the developmental HOX cluster and the biosynthetic gene-cluster (BGC) for secondary metabolites are organised in organisms genome. Genome mining and genomewide analysis of these clusters will definitely identify and characterize many more important molecules from unexplored plants and microbes and underexplored human microbiota and the evolution studies of these clusters will indicate their source of origin. Although genomics revolution now continues at a pace, till date only few hundred plant genome sequences are available. However, next-generation sequencing (NGS) technology now in market and may be applied even for plants with recalcitrant genomes, eventually may discover genomic potential towards production of secondary metabolites of diverse plants and micro-organisms present in the environment and microbiota. Additionally, the development of tools for genome mining e.g., antiSMASH, plantiSMASH, and more and more computational approaches that predicts hundreds of secondary metabolite BGCs will be discussed.

Phylogenomic and Microsynteny Analysis Provides Evidence of Genome Arrangements of High-Affinity Nitrate Transporter Gene Families of Plants

Nitrate transporter 2 (NRT2) and NRT3 or nitrate-assimilation-related 2 (NAR2) proteins families form a two-component, high-affinity nitrate transport system, which is essential for the acquisition of nitrate from soils with low N availability. An extensive phylogenomic analysis across land plants for these families has not been performed. In this study, we performed a microsynteny and orthology analysis on the NRT2 and NRT3 genes families across 132 plants (Sensu lato) to decipher their evolutionary history. We identified significant differences in the number of sequences per taxonomic group and different genomic contexts within the NRT2 family that might have contributed to N acquisition by the plants. We hypothesized that the greater losses of NRT2 sequences correlate with specialized ecological adaptations, such as aquatic, epiphytic, and carnivory lifestyles. We also detected expansion on the NRT2 family in specific lineages that could be a source of key innovations for colonizing contrasting niches in N availability. Microsyntenic analysis on NRT3 family showed a deep conservation on land plants, suggesting a high evolutionary constraint to preserve their function. Our study provides novel information that could be used as guide for functional characterization of these gene families across plant lineages.

Comprehensive Analysis of SRO Gene Family in Sesamum indicum (L.) Reveals Its Association with Abiotic Stress Responses

SIMILAR TO RCD-ONEs (SROs) comprise a small plant-specific gene family which play important roles in regulating numerous growth and developmental processes and responses to environmental stresses. However, knowledge of SROs in sesame (Sesamum indicum L.) is limited. In this study, four SRO genes were identified in the sesame genome. Phylogenetic analysis showed that 64 SROs from 10 plant species were divided into two groups (Group I and II). Transcriptome data revealed different expression patterns of SiSROs over various tissues. Expression analysis showed that Group II SROs, especially SiSRO2b, exhibited a stronger response to various abiotic stresses and phytohormones than those in Group I, implying their crucial roles in response to environmental stimulus and hormone signals. In addition, the co-expression network and protein-protein interaction network indicated that SiSROs are associated with a wide range of stress responses. Moreover, transgenic yeast harboring SiSRO2b showed improved tolerance to salt, osmotic and oxidative stress, indicating SiSRO2b could confer multiple tolerances to transgenic yeast. Taken together, this study not only lays a foundation for further functional dissection of the SiSRO gene family, but also provides valuable gene candidates for genetic improvement of abiotic stress tolerance in sesame.

Beyond the Biosynthetic Gene Cluster Paradigm: Genome-Wide Coexpression Networks Connect Clustered and Unclustered Transcription Factors to Secondary Metabolic Pathways

Fungal secondary metabolites are widely used as therapeutics and are vital components of drug discovery programs. A major challenge hindering discovery of novel secondary metabolites is that the underlying pathways involved in their biosynthesis are transcriptionally silent under typical laboratory growth conditions, making it difficult to identify the transcriptional networks that they are embedded in. Furthermore, while the genes participating in secondary metabolic pathways are typically found in contiguous clusters on the genome, known as biosynthetic gene clusters (BGCs), this is not always the case, especially for global and pathway-specific regulators of pathways’ activities. To address these challenges, we used 283 genome-wide gene expression data sets of the ascomycete cell factory Aspergillus niger generated during growth under 155 different conditions to construct two gene coexpression networks based on Spearman’s correlation coefficients (SCCs) and on mutual rank-transformed Pearson’s correlation coefficients (MR-PCCs). By mining these networks, we predicted six transcription factors, named MjkA to MjkF, to regulate secondary metabolism in A. niger. Overexpression of each transcription factor using the Tet-On cassette modulated the production of multiple secondary metabolites. We found that the SCC and MR-PCC approaches complemented each other, enabling the delineation of putative global (SCC) and pathway-specific (MR-PCC) transcription factors. These results highlight the potential of coexpression network approaches to identify and activate fungal secondary metabolic pathways and their products. More broadly, we argue that drug discovery programs in fungi should move beyond the BGC paradigm and focus on understanding the global regulatory networks in which secondary metabolic pathways are embedded.

IMPORTANCE There is an urgent need for novel bioactive molecules in both agriculture and medicine. The genomes of fungi are thought to contain vast numbers of metabolic pathways involved in the biosynthesis of secondary metabolites with diverse bioactivities. Because these metabolites are biosynthesized only under specific conditions, the vast majority of the fungal pharmacopeia awaits discovery. To discover the genetic networks that regulate the activity of secondary metabolites, we examined the genome-wide profiles of gene activity of the cell factory Aspergillus niger across hundreds of conditions. By constructing global networks that link genes with similar activities across conditions, we identified six putative global and pathway-specific regulators of secondary metabolite biosynthesis. Our study shows that elucidating the behavior of the genetic networks of fungi under diverse conditions harbors enormous promise for understanding fungal secondary metabolism, which ultimately may lead to novel drug candidates.

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

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

FT-ICR-MS-based metabolomics: A deep dive into plant metabolism

Metabolomics involves the identification and quantification of metabolites to unravel the chemical footprints behind cellular regulatory processes and to decipher metabolic networks, opening new insights to understand the correlation between genes and metabolites. In plants, it is estimated the existence of hundreds of thousands of metabolites and the majority is still unknown. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a powerful analytical technique to tackle such challenges. The resolving power and sensitivity of this ultrahigh mass accuracy mass analyzer is such that a complex mixture, such as plant extracts, can be analyzed and thousands of metabolite signals can be detected simultaneously and distinguished based on the naturally abundant elemental isotopes. In this review, FT-ICR-MS-based plant metabolomics studies are described, emphasizing FT-ICR-MS increasing applications in plant science through targeted and untargeted approaches, allowing for a better understanding of plant development, responses to biotic and abiotic stresses, and the discovery of new natural nutraceutical compounds. Improved metabolite extraction protocols compatible with FT-ICR-MS, metabolite analysis methods and metabolite identification platforms are also explored as well as new in silico approaches. Most recent advances in MS imaging are also discussed.

Mining genomes to illuminate the specialized chemistry of life

All organisms produce specialized organic molecules, ranging from small volatile chemicals to large gene-encoded peptides, that have evolved to provide them with diverse cellular and ecological functions. As natural products, they are broadly applied in medicine, agriculture and nutrition. The rapid accumulation of genomic information has revealed that the metabolic capacity of virtually all organisms is vastly underappreciated. Pioneered mainly in bacteria and fungi, genome mining technologies are accelerating metabolite discovery. Recent efforts are now being expanded to all life forms, including protists, plants and animals, and new integrative omics technologies are enabling the increasingly effective mining of this molecular diversity.

Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Background

Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset.

Methods

We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher’s exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher’s exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.

Results

The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

Activation of Cryptic Secondary Metabolite Biosynthesis in Bamboo Suspension Cells by a Histone Deacetylase Inhibitor

Plants have evolved a diverse array of secondary metabolite biosynthetic pathways. Undifferentiated plant cells, however, tend to biosynthesize secondary metabolites to a lesser extent and sometimes not at all. This phenomenon in cultured cells is associated with the transcriptional suppression of biosynthetic genes due to epigenetic alterations, such as low histone acetylation levels and/or high DNA methylation levels. Here, using cultured cells of bamboo (Bambusa multiplex; Bm) as a model system, we investigated the effect of histone deacetylase (HDAC) inhibitors on the activation of cryptic secondary metabolite biosynthesis. The Bm suspension cells cultured in the presence of an HDAC inhibitor, suberoyl bis-hydroxamic acid (SBHA), exhibited strong biosynthesis of some compounds that are inherently present at very low levels in Bm cells. Two major compounds induced by SBHA were isolated and were identified as 3-O-p-coumaroylquinic acid (1) and 3-O-feruloylquinic acid (2). Their productivities depended on the type of basal culture medium, initial cell density, and culture period, as well as the SBHA concentration. The biosynthesis of these two compounds was also induced by another HDAC inhibitor, trichostatin A. These results demonstrate the usefulness of HDAC inhibitors to activate cryptic secondary metabolite biosynthesis in cultured plant cells.

Optimising the use of gene expression data to predict plant metabolic pathway memberships

Plant metabolites from diverse pathways are important for plant survival, human nutrition and medicine. The pathway memberships of most plant enzyme genes are unknown. While co-expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts. Utilising > 600 tomato (Solanum lycopersicum) expression data combinations, three strategies for predicting memberships in 85 pathways were explored. Optimal predictions for different pathways require distinct data combinations indicative of pathway functions. Naive prediction (i.e. identifying pathways with the most similarly expressed genes) is error prone. In 52 pathways, unsupervised learning performed better than supervised approaches, possibly due to limited training data availability. Using gene-to-pathway expression similarities led to prediction models that outperformed those based simply on expression levels. Using 36 experimental validated genes, the pathway-best model prediction accuracy is 58.3%, significantly better compared with that for predicting annotated genes without experimental evidence (37.0%) or random guess (1.2%), demonstrating the importance of data quality. Our study highlights the need to extensively explore expression-based features and prediction strategies to maximise the accuracy of metabolic pathway membership assignment. The prediction framework outlined here can be applied to other species and serves as a baseline model for future comparisons.

Systems biology for crop improvement

In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

Rice contains a biosynthetic gene cluster associated with production of the casbane-type diterpenoid phytoalexin ent-10-oxodepressin

Diterpenoids play important roles in rice microbial disease resistance as phytoalexins, as well as acting in allelopathy and abiotic stress responses. Recently, the casbane-type phytoalexin ent-10-oxodepressin was identified in rice, but its biosynthesis has not yet been elucidated. Here ent-10-oxodepressin biosynthesis was investigated via co-expression analysis and biochemical characterisation, with use of the CRISPR/Cas9 technology for genetic analysis. The results identified a biosynthetic gene cluster (BGC) on rice chromosome 7 (c7BGC), containing the relevant ent-casbene synthase (OsECBS), and four cytochrome P450 (CYP) genes from the CYP71Z subfamily. Three of these CYPs were shown to act on ent-casbene, with CYP71Z2 able to produce a keto group at carbon-5 (C5), while the closely related paralogues CYP71Z21 and CYP71Z22 both readily produce a keto group at C10. Together these C5 and C10 oxidases can elaborate ent-casbene to ent-10-oxodepressin (5,10-diketo-ent-casbene). OsECBS knockout lines no longer produce casbane-type diterpenoids and exhibit impaired resistance to the rice fungal blast pathogen Magnaporthe oryzae. Elucidation of ent-10-oxodepressin biosynthesis and the associated c7BGC provides not only a potential target for molecular breeding, but also, gives the intriguing parallels to the independently assembled BGCs for casbene-derived diterpenoids in the Euphorbiaceae, further insight into plant BGC evolution, as discussed here.

Getting back to the grass roots: harnessing specialized metabolites for improved crop stress resilience

Roots remain an understudied site of complex and important biological interactions mediating plant productivity. In grain and bioenergy crops, grass root specialized metabolites (GRSM) are central to key interactions, yet our basic knowledge of the chemical language remains fragmentary. Continued improvements in plant genome assembly and metabolomics are enabling large-scale advances in the discovery of specialized metabolic pathways as a means of regulating root-biotic interactions. Metabolomics, transcript coexpression analyses, forward genetic studies, gene synthesis and heterologous expression assays drive efficient pathway discoveries. Functional genetic variants identified through genome wide analyses, targeted CRISPR/Cas9 approaches, and both native and non-native overexpression studies critically inform novel strategies for bioengineering metabolic pathways to improve plant traits.

Co-expression networks in Chlamydomonas reveal significant rhythmicity in batch cultures and empower gene function discovery

The unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis and chloroplast metabolism, cilium assembly and function, lipid and starch metabolism, and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot, to easily assess co-expression and anti-correlation between genes. We extracted several hundred high-confidence genes at the intersection of multiple curated lists involved in cilia, cell division, and photosynthesis, illustrating the power of our method. Surprisingly, Chlamydomonas experiments retained a significant rhythmic component across the transcriptome, suggesting an underappreciated variable during sample collection, even in samples collected in constant light. Our results therefore document substantial residual synchronization in batch cultures, contrary to assumptions of asynchrony. We provide step-by-step protocols for the analysis of co-expression across transcriptome data sets from Chlamydomonas and other species to help foster gene function discovery.

Subfunctionalization of Paralog Transcription Factors Contributes to Regulation of Alkaloid Pathway Branch Choice in Catharanthus roseus

Catharanthus roseus produces a diverse range of specialized metabolites of the monoterpenoid indole alkaloid (MIA) class in a heavily branched pathway. Recent great progress in identification of MIA biosynthesis genes revealed that the different pathway branch genes are expressed in a highly cell type- and organ-specific and stress-dependent manner. This implies a complex control by specific transcription factors (TFs), only partly revealed today. We generated and mined a comprehensive compendium of publicly available C. roseus transcriptome data for MIA pathway branch-specific TFs. Functional analysis was performed through extensive comparative gene expression analysis and profiling of over 40 MIA metabolites in the C. roseus flower petal expression system. We identified additional members of the known BIS and ORCA regulators. Further detailed study of the ORCA TFs suggests subfunctionalization of ORCA paralogs in terms of target gene-specific regulation and synergistic activity with the central jasmonate response regulator MYC2. Moreover, we identified specific amino acid residues within the ORCA DNA-binding domains that contribute to the differential regulation of some MIA pathway branches. Our results advance our understanding of TF paralog specificity for which, despite the common occurrence of closely related paralogs in many species, comparative studies are scarce.

Multiomics-based characterization of specialized metabolites biosynthesis in Cornus Officinalis

Cornus officinalis, an important traditional medicinal plant, is used as major constituents of tonics, analgesics, and diuretics. While several studies have focused on its characteristic bioactive compounds, little is known on their biosynthesis. In this study, we performed LC-QTOF-MS-based metabolome and RNA-seq-based transcriptome profiling for seven tissues of C. officinalis. Untargeted metabolome analysis assigned chemical identities to 1,215 metabolites and showed tissue-specific accumulation for specialized metabolites with medicinal properties. De novo transcriptome assembly established for C. officinalis showed 96% of transcriptome completeness. Co-expression analysis identified candidate genes involved in the biosynthesis of iridoids, triterpenoids, and gallotannins, the major group of bioactive metabolites identified in C. officinalis. Integrative omics analysis identified 45 cytochrome P450s genes correlated with iridoids accumulation in C. officinalis. Network-based integration of genes assigned to iridoids biosynthesis pathways with these candidate CYPs further identified seven promising CYPs associated with iridoids’ metabolism. This study provides a valuable resource for further investigation of specialized metabolites’ biosynthesis in C. officinalis.

Mythimna separata herbivory primes maize resistance in systemic leaves

Biotic and abiotic cues can trigger priming in plants, which enables plants to respond to subsequent challenge with stronger and/or faster responses. It is well known that herbivory activates defense-related responses in systemic leaves. However, little is known about whether insect feeding activates priming in systemic leaves. To determine whether and how herbivory induces priming in maize systemic leaves, a combination of insect bioassays, phytohormone and defense metabolite quantification, and genetic and transcriptome analyses were performed. Actual and simulated Mythimna separata herbivory in maize local leaves primed the systemic leaves for enhanced accumulation of jasmonic acid and benzoxazinoids and increased resistance to M. separata. Activation of priming in maize systemic leaves depends on both the duration of simulated herbivory and perception of M. separata oral secretions in the local leaves, and genetic analysis indicated that jasmonic acid and benzoxazinoids mediate the primed defenses in systemic leaves. Consistently, in response to simulated herbivory, the primed systemic leaves exhibited a large number of genes that were uniquely regulated or showed further up- or down-regulation compared with the non-primed systemic leaves. This study provides new insight into the regulation and ecological function of priming in maize.

Interdependent evolution of biosynthetic gene clusters for momilactone production in rice

Plants can contain biosynthetic gene clusters (BGCs) that nominally resemble those found in microbes. However, while horizontal gene transmission is often observed in microbes, plants are limited to vertical gene transmission, implying that their BGCs may exhibit distinct inheritance patterns. Rice (Oryza sativa) contains two unlinked BGCs involved in diterpenoid phytoalexin metabolism, with one clearly required for momilactone biosynthesis, while the other is associated with production of phytocassanes. Here, in the process of elucidating momilactone biosynthesis, genetic evidence was found demonstrating a role for a cytochrome P450 (CYP) from the other "phytocassane" BGC. This CYP76M8 acts after the CYP99A2/3 from the "momilactone" BGC, producing a hemiacetal intermediate that is oxidized to the eponymous lactone by a short-chain alcohol dehydrogenase also from this BGC. Thus, the "momilactone" BGC is not only incomplete, but also fractured by the need for CYP76M8 to act in between steps catalyzed by enzymes from this BGC. Moreover, as supported by similar activity observed with orthologs from the momilactone-producing wild-rice species Oryza punctata, the presence of CYP76M8 in the other "phytocassane" BGC indicates interdependent evolution of these two BGCs, highlighting the distinct nature of BGC assembly in plants.

Indole-3-glycerolphosphate synthase, a branchpoint for the biosynthesis of tryptophan, indole, and benzoxazinoids in maize

The maize (Zea mays) genome encodes three indole-3-glycerolphosphate synthase enzymes (IGPS1, 2, and 3) catalyzing the conversion of 1-(2-carboxyphenylamino)-l-deoxyribulose-5-phosphate to indole-3-glycerolphosphate. Three further maize enzymes (BX1, benzoxazinoneless 1; TSA, tryptophan synthase alpha subunit; and IGL, indole glycerolphosphate lyase) convert indole-3-glycerolphosphate to indole, which is released as a volatile defense signaling compound and also serves as a precursor for the biosynthesis of tryptophan and defense-related benzoxazinoids. Phylogenetic analyses showed that IGPS2 is similar to enzymes found in both monocots and dicots, whereas maize IGPS1 and IGPS3 are in monocot-specific clades. Fusions of yellow fluorescent protein with maize IGPS enzymes and indole-3-glycerolphosphate lyases were all localized in chloroplasts. In bimolecular fluorescence complementation assays, IGPS1 interacted strongly with BX1 and IGL, IGPS2 interacted primarily with TSA, and IGPS3 interacted equally with all three indole-3-glycerolphosphate lyases. Whereas IGPS1 and IGPS3 expression was induced by insect feeding, IGPS2 expression was not. Transposon insertions in IGPS1 and IGPS3 reduced the abundance of both benzoxazinoids and free indole. Spodoptera exigua (beet armyworm) larvae show improved growth on igps1 mutant maize plants. Together, these results suggest that IGPS1 and IGPS3 function mainly in the biosynthesis of defensive metabolites, whereas IGPS2 may be involved in the biosynthesis of tryptophan. This metabolic channeling is similar to, though less exclusive than, that proposed for the three maize indole-3-glycerolphosphate lyases.

Conserved co-functional network between maize and Arabidopsis aid in the identification of seed defective genes in maize

BACKGROUND

The biological pathways related to Arabidopsis seed development have been well studied and functional genes involved in it have been discovered. However, functional studies about maize seed development were more limited compared to Arabidopsis.

OBJECTIVE

Therefore, transferring knowledge from Arabidopsis into maize would facilitate functional studies about maize seed development.

METHOD

In this study, public transcriptome data of the two species related to seed development were obtained. Co-expression network in each species was compared by integrating orthology information.

RESULTS

This conserved co-functional network contained 4510 maize and 4808 Arabidopsis genes, respectively. Most of these genes were expressed in throughout embryo, early or later endosperm/seed. These conserved co-functional genes were significantly enriched for members of PPR protein family, which was consistent with that PPR proteins play an important role in maize seed development. Spatial-temporally co-functional genes were discovered in the seed coat and embryo. Furthermore, 66 well-studied genes involved in Arabidopsis seed development were co-functional with 319 maize genes and one maize gene (GRMZM2G036050) was further confirmed using an EMS-induced seed defective mutant by bulked segregating RNA sequencing (BSR) analysis.

CONCLUSIONS

Altogether, these results showed the potential of this approach to support functional studies in maize seed development by transferring knowledge from Arabidopsis.

ADAP is a possible negative regulator of glucosinolate biosynthesis in Arabidopsis thaliana based on clustering and gene expression analyses

Glucosinolates (GSLs) are plant secondary metabolites consisting of sulfur and nitrogen, commonly found in Brassicaceae crops, such as Arabidopsis thaliana. These compounds are known for their roles in plant defense mechanisms against pests and pathogens. 'Guilt-by-association' (GBA) approach predicts genes encoding proteins with similar function tend to share gene expression pattern generated from high throughput sequencing data. Recent studies have successfully identified GSL genes using GBA approach, followed by targeted verification of gene expression and metabolite data. Therefore, a GSL co-expression network was constructed using known GSL genes obtained from our in-house database, SuCComBase. DPClusO was used to identify subnetworks of the GSL co-expression network followed by Fisher's exact test leading to the discovery of a potential gene that encodes the ARIA-interacting double AP2-domain protein (ADAP) transcription factor (TF). Further functional analysis was performed using an effective gene silencing system known as CRES-T. By applying CRES-T, ADAP TF gene was fused to a plant-specific EAR-motif repressor domain (SRDX), which suppresses the expression of ADAP target genes. In this study, ADAP was proposed as a negative regulator in aliphatic GSL biosynthesis due to the over-expression of downstream aliphatic GSL genes (UGT74C1 and IPMI1) in ADAP-SRDX line. The significant over-expression of ADAP gene in the ADAP-SRDX line also suggests the behavior of the TF that negatively affects the expression of UGT74C1 and IPMI1 via a feedback mechanism in A. thaliana.

Chromosome-level genome assembly of Ophiorrhiza pumila reveals the evolution of camptothecin biosynthesis

Plant genomes remain highly fragmented and are often characterized by hundreds to thousands of assembly gaps. Here, we report chromosome-level reference and phased genome assembly of Ophiorrhiza pumila, a camptothecin-producing medicinal plant, through an ordered multi-scaffolding and experimental validation approach. With 21 assembly gaps and a contig N50 of 18.49 Mb, Ophiorrhiza genome is one of the most complete plant genomes assembled to date. We also report 273 nitrogen-containing metabolites, including diverse monoterpene indole alkaloids (MIAs). A comparative genomics approach identifies strictosidine biogenesis as the origin of MIA evolution. The emergence of strictosidine biosynthesis-catalyzing enzymes precede downstream enzymes' evolution post γ whole-genome triplication, which occurred approximately 110 Mya in O. pumila, and before the whole-genome duplication in Camptotheca acuminata identified here. Combining comparative genome analysis, multi-omics analysis, and metabolic gene-cluster analysis, we propose a working model for MIA evolution, and a pangenome for MIA biosynthesis, which will help in establishing a sustainable supply of camptothecin.

Coexpressed subunits of dual genetic origin define a conserved supercomplex mediating essential protein import into chloroplasts

In photosynthetic eukaryotes, thousands of proteins are translated in the cytosol and imported into the chloroplast through the concerted action of two translocons-termed TOC and TIC-located in the outer and inner membranes of the chloroplast envelope, respectively. The degree to which the molecular composition of the TOC and TIC complexes is conserved over phylogenetic distances has remained controversial. Here, we combine transcriptomic, biochemical, and genetic tools in the green alga Chlamydomonas (Chlamydomonas reinhardtii) to demonstrate that, despite a lack of evident sequence conservation for some of its components, the algal TIC complex mirrors the molecular composition of a TIC complex from Arabidopsis thaliana. The Chlamydomonas TIC complex contains three nuclear-encoded subunits, Tic20, Tic56, and Tic100, and one chloroplast-encoded subunit, Tic214, and interacts with the TOC complex, as well as with several uncharacterized proteins to form a stable supercomplex (TIC-TOC), indicating that protein import across both envelope membranes is mechanistically coupled. Expression of the nuclear and chloroplast genes encoding both known and uncharacterized TIC-TOC components is highly coordinated, suggesting that a mechanism for regulating its biogenesis across compartmental boundaries must exist. Conditional repression of Tic214, the only chloroplast-encoded subunit in the TIC-TOC complex, impairs the import of chloroplast proteins with essential roles in chloroplast ribosome biogenesis and protein folding and induces a pleiotropic stress response, including several proteins involved in the chloroplast unfolded protein response. These findings underscore the functional importance of the TIC-TOC supercomplex in maintaining chloroplast proteostasis.

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.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

Fungi.guru: Comparative genomic and transcriptomic resource for the fungi kingdom

The fungi kingdom is composed of eukaryotic heterotrophs, which are responsible for balancing the ecosystem and play a major role as decomposers. They also produce a vast diversity of secondary metabolites, which have antibiotic or pharmacological properties. However, our lack of knowledge of gene function in fungi precludes us from tailoring them to our needs and tapping into their metabolic diversity. To help remedy this, we gathered genomic and gene expression data of 19 most widely-researched fungi to build an online tool, fungi.guru, which contains tools for cross-species identification of conserved pathways, functional gene modules, and gene families. We exemplify how our tool can elucidate the molecular function, biological process and cellular component of genes involved in various biological processes, by identifying a secondary metabolite pathway producing gliotoxin in Aspergillus fumigatus, the catabolic pathway of cellulose in Coprinopsis cinerea and the conserved DNA replication pathway in Fusarium graminearum and Pyricularia oryzae. The tool is available at www.fungi.guru.

MutRank: an R shiny web-application for exploratory targeted mutual rank-based coexpression analyses integrated with user-provided supporting information

The rapid assignment of genotypes to phenotypes has been a historically challenging process. The discovery of genes encoding biosynthetic pathway enzymes for defined plant specialized metabolites has been informed and accelerated by the detection of gene clusters. Unfortunately, biosynthetic pathway genes are commonly dispersed across chromosomes or reside in genes clusters that provide little predictive value. More reliably, transcript abundance of genes underlying biochemical pathways for plant specialized metabolites display significant coregulation. By rapidly identifying highly coexpressed transcripts, it is possible to efficiently narrow candidate genes encoding pathway enzymes and more easily predict both functions and functional associations. Mutual Rank (MR)-based coexpression analyses in plants accurately demonstrate functional associations for many specialized metabolic pathways; however, despite the clear predictive value of MR analyses, the application is uncommonly used to drive new pathway discoveries. Moreover, many coexpression databases aid in the prediction of both functional associations and gene functions, but lack customizability for refined hypothesis testing. To facilitate and speed flexible MR-based hypothesis testing, we developed MutRank, an R Shiny web-application for coexpression analyses. MutRank provides an intuitive graphical user interface with multiple customizable features that integrates user-provided data and supporting information suitable for personal computers. Tabular and graphical outputs facilitate the rapid analyses of both unbiased and user-defined coexpression results that accelerate gene function predictions. We highlight the recent utility of MR analyses for functional predictions and discoveries in defining two maize terpenoid antibiotic pathways. Beyond applications in biosynthetic pathway discovery, MutRank provides a simple, customizable and user-friendly interface to enable coexpression analyses relating to a breadth of plant biology inquiries. Data and code are available at GitHub: https://github.com/eporetsky/MutRank.

TagSeq for gene expression in non-model plants: A pilot study at the Santa Rita Experimental Range NEON core site

PREMISE

TagSeq is a cost-effective approach for gene expression studies requiring a large number of samples. To date, TagSeq studies in plants have been limited to those with a high-quality reference genome. We tested the suitability of reference transcriptomes for TagSeq in non-model plants, as part of a study of natural gene expression variation at the Santa Rita Experimental Range National Ecological Observatory Network (NEON) core site.

METHODS

Tissue for TagSeq was sampled from multiple individuals of four species (Bouteloua aristidoides and Eragrostis lehmanniana [Poaceae], Tidestromia lanuginosa [Amaranthaceae], and Parkinsonia florida [Fabaceae]) at two locations on three dates (56 samples total). One sample per species was used to create a reference transcriptome via standard RNA-seq. TagSeq performance was assessed by recovery of reference loci, specificity of tag alignments, and variation among samples.

RESULTS

A high fraction of tags aligned to each reference and mapped uniquely. Expression patterns were quantifiable for tens of thousands of loci, which revealed consistent spatial differentiation in expression for all species.

DISCUSSION

TagSeq using de novo reference transcriptomes was an effective approach to quantifying gene expression in this study. Tags were highly locus specific and generated biologically informative profiles for four non-model plant species.

HpeNet: Co-expression Network Database for de novo Transcriptome Assembly of Paeonia lactiflora Pall

The herbaceous peony (Paeonia lactiflora Pall.) is a well-known ornamental flowering and pharmaceutical plant found in China. Its high medicinal value has long been recognized by traditional Chinese medicine (as Radix paeoniae Alba and Radix paeoniae Rubra), and it has become economically valued for its oilseed in recent years; like other Paeonia species, it has been identified as a novel resource for the α-linolenic acid used in seed oil production. However, its genome has not yet been sequenced, and little transcriptome data on Paeonia lactiflora are available. To obtain a comprehensive transcriptome for Paeonia lactiflora, RNAs from 10 tissues of the Paeonia lactiflora Pall. cv Shaoyou17C were used for de novo assembly, and 416,062 unigenes were obtained. Using a homology search, it was found that 236,222 (approximately 57%) unigenes had at least one BLAST hit in one or more public data resources. The construction of co-expression networks is a feasible means for improving unigene annotation. Using in-house transcriptome data, we obtained a co-expression network covering 95.13% of the unigenes. Then we integrated co-expression network analyses and lipid-related pathway genes to study lipid metabolism in Paeonia lactiflora cultivars. Finally, we constructed the online database HpeNet (http://bioinformatics.cau.edu.cn/HpeNet) to integrate transcriptome data, gene information, the co-expression network, and so forth. The database can also be searched for gene details, gene functions, orthologous matches, and other data. Our online database may help the research community identify functional genes and perform research on Paeonia lactiflora more conveniently. We hope that de novo transcriptome assembly, combined with co-expression networks, can provide a feasible means to predict the gene function of species that do not have a reference genome.

EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways

Co-expressed genes tend to have regulatory relationships and participate in similar biological processes. Construction of gene correlation networks from microarray or RNA-seq expression data has been widely applied to study transcriptional regulatory mechanisms and metabolic pathways under specific conditions. Furthermore, since transcription factors (TFs) are critical regulators of gene expression, it is worth investigating TFs on the promoters of co-expressed genes. Although co-expressed genes and their related metabolic pathways can be easily identified from previous resources, such as EXPath and EXPath Tool, this information is not simultaneously available to identify their regulatory TFs. EXPath 2.0 is an updated database for the investigation of regulatory mechanisms in various plant metabolic pathways with 1,881 microarray and 978 RNA-seq samples. There are six significant improvements in EXPath 2.0: (i) the number of species has been extended from three to six to include Arabidopsis, rice, maize, Medicago, soybean and tomato; (ii) gene expression at various developmental stages have been added; (iii) construction of correlation networks according to a group of genes is available; (iv) hierarchical figures of the enriched Gene Ontology (GO) terms are accessible; (v) promoter analysis of genes in a metabolic pathway or correlation network is provided; and (vi) user's gene expression data can be uploaded and analyzed. Thus, EXPath 2.0 is an updated platform for investigating gene expression profiles and metabolic pathways under specific conditions. It facilitates users to access the regulatory mechanisms of plant biological processes. The new version is available at http://EXPath.itps.ncku.edu.tw.

Network-based approaches for understanding gene regulation and function in plants

Expression reprogramming directed by transcription factors is a primary gene regulation underlying most aspects of the biology of any organism. Our views of how gene regulation is coordinated are dramatically changing thanks to the advent and constant improvement of high-throughput profiling and transcriptional network inference methods: from activities of individual genes to functional interactions across genes. These technical and analytical advances can reveal the topology of transcriptional networks in which hundreds of genes are hierarchically regulated by multiple transcription factors at systems level. Here we review the state of the art of experimental and computational methods used in plant biology research to obtain large-scale datasets and model transcriptional networks. Examples of direct use of these network models and perspectives on their limitations and future directions are also discussed.

Fungal evolution: cellular, genomic and metabolic complexity

The question of how phenotypic and genomic complexity are inter-related and how they are shaped through evolution is a central question in biology that historically has been approached from the perspective of animals and plants. In recent years, however, fungi have emerged as a promising alternative system to address such questions. Key to their ecological success, fungi present a broad and diverse range of phenotypic traits. Fungal cells can adopt many different shapes, often within a single species, providing them with great adaptive potential. Fungal cellular organizations span from unicellular forms to complex, macroscopic multicellularity, with multiple transitions to higher or lower levels of cellular complexity occurring throughout the evolutionary history of fungi. Similarly, fungal genomes are very diverse in their architecture. Deep changes in genome organization can occur very quickly, and these phenomena are known to mediate rapid adaptations to environmental changes. Finally, the biochemical complexity of fungi is huge, particularly with regard to their secondary metabolites, chemical products that mediate many aspects of fungal biology, including ecological interactions. Herein, we explore how the interplay of these cellular, genomic and metabolic traits mediates the emergence of complex phenotypes, and how this complexity is shaped throughout the evolutionary history of Fungi.

Biased Gene Retention in the Face of Introgression Obscures Species Relationships

Phylogenomic analyses are recovering previously hidden histories of hybridization, revealing the genomic consequences of these events on the architecture of extant genomes. We applied phylogenomic techniques and several complementary statistical tests to show that introgressive hybridization appears to have occurred between close relatives of Arabidopsis, resulting in cytonuclear discordance and impacting our understanding of species relationships in the group. The composition of introgressed and retained genes indicates that selection against incompatible cytonuclear and nuclear-nuclear interactions likely acted during introgression, whereas linkage also contributed to genome composition through the retention of ancient haplotype blocks. We also applied divergence-based tests to determine the species branching order and distinguish donor from recipient lineages. Surprisingly, these analyses suggest that cytonuclear discordance arose via extensive nuclear, rather than cytoplasmic, introgression. If true, this would mean that most of the nuclear genome was displaced during introgression whereas only a small proportion of native alleles were retained.

Identification of Ear Morphology Genes in Maize (Zea mays L.) Using Selective Sweeps and Association Mapping

The performance of maize hybrids largely depend on two parental inbred lines. Improving inbred lines using artificial selection is a key task in breeding programs. However, it is important to elucidate the effects of this selection on inbred lines. Altogether, 208 inbred lines from two maize heterosis groups, named Shaan A and Shaan B, were sequenced by the genotype-by-sequencing to detect genomic changes under selection pressures. In addition, we completed genome-wide association analysis in 121 inbred lines to identify candidate genes for ear morphology related traits. In a genome-wide selection scan, the inbred lines from Shaan A and Shaan B groups showed obvious population divergences and different selective signals distributed in 337 regions harboring 772 genes. Meanwhile, functional enrichment analysis showed those selected genes are mainly involved in regulating cell development. Interestingly, some ear morphology related traits showed significant differentiation between the inbred lines from the two heterosis groups. The genome-wide association analysis of ear morphology related traits showed that four associated genes were co-localized in the selected regions with high linkage disequilibrium. Our spatiotemporal pattern and gene interaction network results for the four genes further contribute to our understanding of the mechanisms behind ear and fruit length development. This study provides a novel insight into digging a candidate gene for complex traits using breeding materials. Our findings in relation to ear morphology will help accelerate future maize improvement.

Within- and cross-species predictions of plant specialized metabolism genes using transfer learning

Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (F-measure = 0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure = 0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one.