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

Ibotenic Acid Biosynthesis in the Fly Agaric Is Initiated by Glutamate Hydroxylation

The fly agaric, Amanita muscaria, is widely known for its content of the psychoactive metabolites ibotenic acid and muscimol. However, their biosynthetic pathway and the respective enzymes are entirely unknown. 50 years ago, the biosynthesis was hypothesized to start with 3-hydroxyglutamate. Here, we build on this hypothesis by the identification and recombinant production of a glutamate hydroxylase from A. muscaria. The hydroxylase gene is surrounded by six further biosynthetic genes, which we link to the production of ibotenic acid and muscimol using recent genomic and transcriptomic data. Our results pinpoint the genetic basis for ibotenic acid formation and thus provide new insights into a decades-old question concerning a centuries-old drug.

Evolution of a plant gene cluster in Solanaceae and emergence of metabolic diversity

Plants produce phylogenetically and spatially restricted, as well as structurally diverse specialized metabolites via multistep metabolic pathways. Hallmarks of specialized metabolic evolution include enzymatic promiscuity and recruitment of primary metabolic enzymes and examples of genomic clustering of pathway genes. Solanaceae glandular trichomes produce defensive acylsugars, with sidechains that vary in length across the family. We describe a tomato gene cluster on chromosome 7 involved in medium chain acylsugar accumulation due to trichome specific acyl-CoA synthetase and enoyl-CoA hydratase genes. This cluster co-localizes with a tomato steroidal alkaloid gene cluster and is syntenic to a chromosome 12 region containing another acylsugar pathway gene. We reconstructed the evolutionary events leading to this gene cluster and found that its phylogenetic distribution correlates with medium chain acylsugar accumulation across the Solanaceae. This work reveals insights into the dynamics behind gene cluster evolution and cell-type specific metabolite diversity.

Global profiling of plant nuclear membrane proteome in Arabidopsis

The nuclear envelope (NE) is structurally and functionally vital for eukaryotic cells, yet its protein constituents and their functions are poorly understood in plants. Here, we combined subtractive proteomics and proximity-labelling technology coupled with quantitative mass spectrometry to understand the landscape of NE membrane proteins in Arabidopsis. We identified ~200 potential candidates for plant NE transmembrane (PNET) proteins, which unravelled the compositional diversity and uniqueness of the plant NE. One of the candidates, named PNET1, is a homologue of human TMEM209, a critical driver for lung cancer. A functional investigation revealed that PNET1 is a bona fide nucleoporin in plants. It displays both physical and genetic interactions with the nuclear pore complex (NPC) and is essential for embryo development and reproduction in different NPC contexts. Our study substantially enlarges the plant NE proteome and sheds new light on the membrane composition and function of the NPC.

METACLUSTER-an R package for context-specific expression analysis of metabolic gene clusters

SUMMARY

Plants and microbes produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have yet to be elucidated. Some biosynthetic pathways are encoded by enzymes collocated in the chromosome. To facilitate a more comprehensive condition and tissue-specific expression analysis of metabolic gene clusters, we developed METACLUSTER, a probabilistic framework for characterizing metabolic gene clusters using context-specific gene expression information.

AVAILABILITY AND IMPLEMENTATION

METACLUSTER is freely available at https://github.com/mbanf/METACLUSTER.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation

INTRODUCTION

Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system.

AREAS COVERED

This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches.

EXPERT OPINION

Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.

Active and repressed biosynthetic gene clusters have spatially distinct chromosome states

While colocalization within a bacterial operon enables coexpression of the constituent genes, the mechanistic logic of clustering of nonhomologous monocistronic genes in eukaryotes is not immediately obvious. Biosynthetic gene clusters that encode pathways for specialized metabolites are an exception to the classical eukaryote rule of random gene location and provide paradigmatic exemplars with which to understand eukaryotic cluster dynamics and regulation. Here, using 3C, Hi-C, and Capture Hi-C (CHi-C) organ-specific chromosome conformation capture techniques along with high-resolution microscopy, we investigate how chromosome topology relates to transcriptional activity of clustered biosynthetic pathway genes in Arabidopsis thaliana Our analyses reveal that biosynthetic gene clusters are embedded in local hot spots of 3D contacts that segregate cluster regions from the surrounding chromosome environment. The spatial conformation of these cluster-associated domains differs between transcriptionally active and silenced clusters. We further show that silenced clusters associate with heterochromatic chromosomal domains toward the periphery of the nucleus, while transcriptionally active clusters relocate away from the nuclear periphery. Examination of chromosome structure at unrelated clusters in maize, rice, and tomato indicates that integration of clustered pathway genes into distinct topological domains is a common feature in plant genomes. Our results shed light on the potential mechanisms that constrain coexpression within clusters of nonhomologous eukaryotic genes and suggest that gene clustering in the one-dimensional chromosome is accompanied by compartmentalization of the 3D chromosome.

Computational approaches to unravel the pathways and evolution of specialized metabolism

Specialized metabolites serve as a chemical arsenal that protects plants from abiotic stress, pathogens, and herbivores, and they are an essential component of our nutrition and medicine. Despite their importance, we are still at the beginning of unravelling biosynthetic pathways that produce these compounds, which is needed to produce more resilient and nutritious crops, expand our inventory of useful biomolecules, and give valuable insights into plant evolution. This review focuses on the evolution of specialized metabolism in the plant kingdom and the state-of-the-art approaches used to identify the biosynthetic pathways of these useful compounds.

Parallel Metabolomic and Transcriptomic Analysis Reveals Key Factors for Quality Improvement of Tea Plants

As one of the most popular beverages globally, tea has enormous economic, cultural, and medicinal importance that necessitates a comprehensive metabolomics study of this species. In this study, a large-scale targeted metabolomics analysis on two types of leaf tissues of nine tea cultivars from five representative geographical origins within China was carried out using the liquid chromatography-mass spectrometry technique. RNA-seq-based transcriptomic analysis was in parallel conducted on the same samples, and gene expression and metabolic differentiation between tissues as well as between the multiple tea cultivars were investigated. The data obtained provide an accessible resource for further studies of naturally occurring metabolic variation of tea plants, which will aid in thoroughly interpreting the underlying genetic and molecular mechanisms of biosynthesis of specialized metabolites in this critical species. Candidate genes including a transcription factor (CsMYB5-like), which were highly correlated with both the content of flavonoids and the expression level of genes participating in the phenylpropanoid and flavonoid biosynthesis pathway, were identified as potential targets for quality improvement of tea.

Advances in biosynthesis, regulation, and metabolic engineering of plant specialized terpenoids

Plant specialized terpenoids are natural products that have no obvious role in growth and development, but play many important functional roles to improve the plant's overall fitness. Besides, plant specialized terpenoids have immense value to humans due to their applications in fragrance, flavor, cosmetic, and biofuel industries. Understanding the fundamental aspects involved in the biosynthesis and regulation of these high-value molecules in plants not only paves the path to enhance plant traits, but also facilitates homologous or heterologous engineering for overproduction of target molecules of importance. Recent developments in functional genomics and high-throughput analytical techniques have led to unraveling of several novel aspects involved in the biosynthesis and regulation of plant specialized terpenoids. The knowledge thus derived has been successfully utilized to produce target specialized terpenoids of plant origin in homologous or heterologous host systems by metabolic engineering and synthetic biology approaches. Here, we provide an overview and highlights on advances related to the biosynthetic steps, regulation, and metabolic engineering of plant specialized terpenoids.

The Evolutionary Forces Shaping Cis- and Trans-Regulation of Gene Expression within a Population of Outcrossing Plants

Understanding the persistence of genetic variation within populations has long been a goal of evolutionary biology. One promising route toward achieving this goal is using population genetic approaches to describe how selection acts on the loci associated with trait variation. Gene expression provides a model trait for addressing the challenge of the maintenance of variation because it can be measured genome-wide without information about how gene expression affects traits. Previous work has shown that loci affecting the expression of nearby genes (local or cis-eQTLs) are under negative selection, but we lack a clear understanding of the selective forces acting on variants that affect the expression of genes in trans. Here, we identify loci that affect gene expression in trans using genomic and transcriptomic data from one population of the obligately outcrossing plant, Capsella grandiflora. The allele frequencies of trans-eQTLs are consistent with stronger negative selection acting on trans-eQTLs than cis-eQTLs, and stronger negative selection acting on trans-eQTLs associated with the expression of multiple genes. However, despite this general pattern, we still observe the presence of a trans-eQTL at intermediate frequency that affects the expression of a large number of genes in the same coexpression module. Overall, our work highlights the different selective pressures shaping variation in cis- and trans-regulation.

Using networks to identify and interpret natural variation

Studies on natural variation and network biology inherently work to summarize vast amounts of information and data. The combination of these two areas of study while creating datasets of immense complexity is critical to their mutual progress. Networks are necessary as a way to work to reduce the dimensionality inherent in natural variation with 100 s to 1000 s of genotypes. Correspondingly natural variation is essential for testing how networks may or may not be shared across individuals or species. Advances in this area of cross-fertilization including using networks directly as phenotypes and the use of networks to help in prioritizing candidate gene validation efforts. Interesting new observations on frequent presence-absence variation in gene content and adaptation is beginning to highlight the potential for natural variation in network presence-absence. This review attempts to delve into these new insights.

Can metabolic tightening and expansion of co-expression network play a role in stress response and tolerance?

Plants respond and adapt to changes in their environment by employing a wide variety of genetic, molecular, and biochemical mechanisms. When so doing, they trigger large-scale rearrangements at the metabolic and transcriptional levels. The dynamics and patterns of these rearrangements and how they govern a stress response is not clear. In this opinion, we discuss a plant's response to stress from the perspective of the metabolic gene co-expression network and its rearrangement upon stress. As a case study, we use publicly available expression data of Arabidopsis thaliana plants exposed to heat and drought stress to evaluate and compare the co-expression networks of metabolic genes. The analysis highlights that stress conditions can lead to metabolic tightening and expansion of the co-expression network. We argue that this rearrangement could play a role in a plant's response to stress and thus may be an additional tool to assess and understand stress tolerance/sensitivity. Additional studies are needed to evaluate the metabolic network in response to multiple stresses at various intensities and across different genetic backgrounds (e.g., intra- and inter-species, sensitive and tolerant eco/genotypes).

The evolution of metabolism: How to test evolutionary hypotheses at the genomic level

The origin of primordial metabolism and its expansion to form the metabolic networks extant today represent excellent systems to study the impact of natural selection and the potential adaptive role of novel compounds. Here we present the current hypotheses made on the origin of life and ancestral metabolism and present the theories and mechanisms by which the large chemical diversity of plants might have emerged along evolution. In particular, we provide a survey of statistical methods that can be used to detect signatures of selection at the gene and population level, and discuss potential and limits of these methods for investigating patterns of molecular adaptation in plant metabolism.

The Acetate Pathway Supports Flavonoid and Lipid Biosynthesis in Arabidopsis

The phenylpropanoid pathway of flavonoid biosynthesis has been the subject of considerable research attention. By contrast, the proposed polyketide pathway, also known as the acetate pathway, which provides malonyl-CoA moieties for the C2 elongation reaction catalyzed by chalcone synthase, is less well studied. Here, we identified four genes as candidates for involvement in the supply of cytosolic malonyl-CoA from the catabolism of acyl-CoA, based on coexpression analysis with other flavonoid-related genes. Two of these genes, ACC and KAT5, have been previously characterized with respect to their involvement in lipid metabolism, but no information concerning their relationship to flavonoid biosynthesis is available. To assess the occurrence and importance of the acetate pathway, we characterized the metabolomes of two mutant or transgenic Arabidopsis lines for each of the four enzymes of this putative pathway using a hierarchical approach covering primary and secondary metabolites as well as lipids. Intriguingly, not only flavonoid content but also glucosinolate content was altered in lines deficient in the acetate pathway, as were levels of lipids and most primary metabolites. We discuss these data in the context of our current understanding of flavonoids and lipid metabolism as well as with regard to improving human nutrition.

Genomic Origin and Diversification of the Glucosinolate MAM Locus

Glucosinolates are a diverse group of plant metabolites that characterize the order Brassicales. The MAM locus is one of the most significant QTLs for glucosinolate diversity. However, most of what we understand about evolution at the locus is focused on only a few species and not within a phylogenetic context. In this study, we utilize a micro-synteny network and phylogenetic inference to investigate the origin and diversification of the MAM/IPMS gene family. We uncover unique MAM-like genes found at the orthologous locus in the Cleomaceae that shed light on the transition from IPMS to MAM. In the Brassicaceae, we identify six distinct MAM clades across Lineages I, II, and III. We characterize the evolutionary impact and consequences of local duplications, transpositions, whole genome duplications, and gene fusion events, generating several new hypothesizes on the function and diversity of the MAM locus.

Multiple genes recruited from hormone pathways partition maize diterpenoid defences

Duplication and divergence of primary pathway genes underlie the evolution of plant specialized metabolism; however, mechanisms partitioning parallel hormone and defence pathways are often speculative. For example, the primary pathway intermediate ent-kaurene is essential for gibberellin biosynthesis and is also a proposed precursor for maize antibiotics. By integrating transcriptional coregulation patterns, genome-wide association studies, combinatorial enzyme assays, proteomics and targeted mutant analyses, we show that maize kauralexin biosynthesis proceeds via the positional isomer ent-isokaurene formed by a diterpene synthase pair recruited from gibberellin metabolism. The oxygenation and subsequent desaturation of ent-isokaurene by three promiscuous cytochrome P450s and a new steroid 5α reductase indirectly yields predominant ent-kaurene-associated antibiotics required for Fusarium stalk rot resistance. The divergence and differential expression of pathway branches derived from multiple duplicated hormone-metabolic genes minimizes dysregulation of primary metabolism via the circuitous biosynthesis of ent-kaurene-related antibiotics without the production of growth hormone precursors during defence.

An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric

Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants.

Unearthing fungal chemodiversity and prospects for drug discovery

Natural products have drastically improved our lives by providing an excellent source of molecules to fight cancer, pathogens, and cardiovascular diseases that have revolutionized medicine. Fungi are prolific producers of diverse natural products and several recent advances in synthetic biology, genetics, bioinformatics, and natural product chemistry have greatly enhanced our ability to efficiently mine their genomes for the discovery of novel drugs. In this article, we provide an overview of improved heterologous expression platforms for targeted production of fungal secondary metabolites, of advances in chemical and bioinformatics dereplication, and of novel bioinformatic platforms to discover biosynthetic genes involved in the production of metabolites with specific bioactivities. These advances, coupled with the presence of vast numbers of biosynthetic gene clusters in fungal genomes whose natural products remain unknown, have revitalized efforts to mine the fungal treasure chest and renewed the promise of discovering new drugs.

Harnessing evolutionary diversification of primary metabolism for plant synthetic biology

Plants produce numerous natural products that are essential to both plant and human physiology. Recent identification of genes and enzymes involved in their biosynthesis now provides exciting opportunities to reconstruct plant natural product pathways in heterologous systems through synthetic biology. The use of plant chassis, although still in infancy, can take advantage of plant cells' inherent capacity to synthesize and store various phytochemicals. Also, large-scale plant biomass production systems, driven by photosynthetic energy production and carbon fixation, could be harnessed for industrial-scale production of natural products. However, little is known about which plants could serve as ideal hosts and how to optimize plant primary metabolism to efficiently provide precursors for the synthesis of desirable downstream natural products or specialized (secondary) metabolites. Although primary metabolism is generally assumed to be conserved, unlike the highly-diversified specialized metabolism, primary metabolic pathways and enzymes can differ between microbes and plants and also among different plants, especially at the interface between primary and specialized metabolisms. This review highlights examples of the diversity in plant primary metabolism and discusses how we can utilize these variations in plant synthetic biology. I propose that understanding the evolutionary, biochemical, genetic, and molecular bases of primary metabolic diversity could provide rational strategies for identifying suitable plant hosts and for further optimizing primary metabolism for sizable production of natural and bio-based products in plants.

Comparative co-expression network analysis extracts the SlHSP70 gene affecting to shoot elongation of tomato

Tomato is one of vegetables crops that has the highest value in the world. Thus, researchers are continually improving the agronomical traits of tomato fruits. Auxins and gibberellins regulate plant growth and development. Aux/indole-3-acetic acid 9 (SlIAA9) and the gene encoding the DELLA protein (SlDELLA) are well-known genes that regulate plant growth and development, including fruit set and enlargement by cell division and cell expansion. The absence of tomato SlIAA9 and SlDELLA results in abnormal shoot growth and leaf shape and giving rise to parthenocarpy. To investigate the key regulators that exist up- or downstream of SlIAA9 and SlDELLA signaling pathways for tomato growth and development, we performed gene co-expression network analysis by using publicly available microarray data to extract genes that are directly connected to the SlIAA9 and SlDELLA nodes, respectively. Consequently, we chose a gene in the group of heat-shock protein (HSP)70s that was connected with the SlIAA9 node and SlDELLA node in each co-expression network. To validate the extent of effect of SlHSP70-1 on tomato growth and development, overexpressing lines of the target gene were generated. We found that overexpression of the targeted SlHSP70-1 resulted in internode elongation, but the overexpressing lines did not show abnormal leaf shape, fruit set, or fruit size when compared with that of the wild type. Our study suggests that the targeted SlHSP70-1 is likely to function in shoot growth, like SlIAA9 and SlDELLA, but it does not contribute to parthenocarpy as well as fruit set. Our study also shows that only a single SlHSP70 out of 25 homologous genes could change the shoot length.

Co-expression networks for plant biology: why and how

Co-expression network analysis is one of the most powerful approaches for interpretation of large transcriptomic datasets. It enables characterization of modules of co-expressed genes that may share biological functional linkages. Such networks provide an initial way to explore functional associations from gene expression profiling and can be applied to various aspects of plant biology. This review presents the applications of co-expression network analysis in plant biology and addresses optimized strategies from the recent literature for performing co-expression analysis on plant biological systems. Additionally, we describe the combined interpretation of co-expression analysis with other genomic data to enhance the generation of biologically relevant information.

Plant Networks as Traits and Hypotheses: Moving Beyond Description

Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses.

The birth, evolution and death of metabolic gene clusters in fungi

Fungi contain a remarkable diversity of both primary and secondary metabolic pathways involved in ecologically specialized or accessory functions. Genes in these pathways are frequently physically linked on fungal chromosomes, forming metabolic gene clusters (MGCs). In this Review, we describe the diversity in the structure and content of fungal MGCs, their population-level and species-level variation, the evolutionary mechanisms that underlie their formation, maintenance and decay, and their ecological and evolutionary impact on fungal populations. We also discuss MGCs from other eukaryotes and the reasons for their preponderance in fungi. Improved knowledge of the evolutionary life cycle of MGCs will advance our understanding of the ecology of specialized metabolism and of the interplay between the lifestyle of an organism and genome architecture.

Gene Modules Co-regulated with Biosynthetic Gene Clusters for Allelopathy between Rice and Barnyardgrass

Allelopathy is a central process in crop–weed interactions and is mediated by the release of allelochemicals that result in adverse growth effects on one or the other plant in the interaction. The genomic mechanism for the biosynthesis of many critical allelochemicals is unknown but may involve the clustering of non-homologous biosynthetic genes involved in their formation and regulatory gene modules involved in controlling the coordinated expression within these gene clusters. In this study, we used the transcriptomes from mono- or co-cultured rice and barnyardgrass to investigate the nature of the gene clusters and their regulatory gene modules involved in the allelopathic interactions of these two plants. In addition to the already known biosynthetic gene clusters in barnyardgrass we identified three potential new clusters including one for quercetin biosynthesis and potentially involved in allelopathic interaction with rice. Based on the construction of gene networks, we identified one gene regulatory module containing hub transcription factors, significantly positively co-regulated with both the momilactone A and phytocassane clusters in rice. In barnyardgrass, gene modules and hub genes co-expressed with the gene clusters responsible for 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) biosynthesis were also identified. In addition, we found three genes in barnyardgrass encoding indole-3-glycerolphosphate synthase that regulate the expression of the DIMBOA cluster. Our findings offer new insights into the regulatory mechanisms of biosynthetic gene clusters involved in allelopathic interactions between rice and barnyardgrass, and have potential implications in controlling weeds for crop protection.

The Nutrient Response Transcriptional Regulome of Arabidopsis

Plants respond actively to changes in their environment. Variations in nutrient availability elicit substantial transcriptional reprogramming, and we aimed to systematically describe these adjustments and identify the regulators responsible. Using gene coexpression analysis based on 13 different nutrient availability anomalies, we defined and analyzed nutrient stress response signatures. We identified known regulators and could predict functions in nutrient responses for transcriptional regulators previously associated with other processes, thus linking development and environmental interaction. Three of the identified transcriptional regulators, PIF4, HY5, and NF-Y, known from their role in light signaling, targeted a substantial part of the network and may participate in remodeling the global Arabidopsis transcriptome in response to variations of nutrient availability. We present gene coexpression and transcriptional regulation networks, which can be used as tools to further explore regulatory events and dependencies even by users with basic informatics skills.

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Gene coexpression analysis is a powerful tool for elucidating nutrient stress

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Nutrient stress elicits unique signatures of modular transcriptional response

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Master transcriptional regulators coordinate plant growth and nutrient utilization

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Analysis suggests PIF4, HY5, and the NF-Y to be master regulators

MapMan4: A Refined Protein Classification and Annotation Framework Applicable to Multi-Omics Data Analysis

Genome sequences from over 200 plant species have already been published, with this number expected to increase rapidly due to advances in sequencing technologies. Once a new genome has been assembled and the genes identified, the functional annotation of their putative translational products, proteins, using ontologies is of key importance as it places the sequencing data in a biological context. Furthermore, to keep pace with rapid production of genome sequences, this functional annotation process must be fully automated. Here we present a redesigned and significantly enhanced MapMan4 framework, together with a revised version of the associated online Mercator annotation tool. Compared with the original MapMan, the new ontology has been expanded almost threefold and enforces stricter assignment rules. This framework was then incorporated into Mercator4, which has been upgraded to reflect current knowledge across the land plant group, providing protein annotations for all embryophytes with a comparably high quality. The annotation process has been optimized to allow a plant genome to be annotated in a matter of minutes. The output results continue to be compatible with the established MapMan desktop application.

Metabolome-Scale Genome-Wide Association Studies Reveal Chemical Diversity and Genetic Control of Maize Specialized Metabolites

Cultivated maize (Zea mays) has retained much of the genetic diversity of its wild ancestors. Here, we performed nontargeted liquid chromatography-mass spectrometry metabolomics to analyze the metabolomes of the 282 maize inbred lines in the Goodman Diversity Panel. This analysis identified a bimodal distribution of foliar metabolites. Although 15% of the detected mass features were present in >90% of the inbred lines, the majority were found in <50% of the samples. Whereas leaf bases and tips were differentiated by flavonoid abundance, maize varieties (stiff-stalk, nonstiff-stalk, tropical, sweet maize, and popcorn) showed differential accumulation of benzoxazinoid metabolites. Genome-wide association studies (GWAS), performed for 3,991 mass features from the leaf tips and leaf bases, showed that 90% have multiple significantly associated loci scattered across the genome. Several quantitative trait locus hotspots in the maize genome regulate the abundance of multiple, often structurally related mass features. The utility of maize metabolite GWAS was demonstrated by confirming known benzoxazinoid biosynthesis genes, as well as by mapping isomeric variation in the accumulation of phenylpropanoid hydroxycitric acid esters to a single linkage block in a citrate synthase-like gene. Similar to gene expression databases, this metabolomic GWAS data set constitutes an important public resource for linking maize metabolites with biosynthetic and regulatory genes.

Comparative Transcriptome Analysis Shows Conserved Metabolic Regulation during Production of Secondary Metabolites in Filamentous Fungi

Filamentous fungi possess great potential as sources of medicinal bioactive compounds, such as antibiotics, but efficient production is hampered by a limited understanding of how their metabolism is regulated. We investigated the metabolism of six secondary metabolite-producing fungi of the Penicillium genus during nutrient depletion in the stationary phase of batch fermentations and assessed conserved metabolic responses across species using genome-wide transcriptional profiling. A coexpression analysis revealed that expression of biosynthetic genes correlates with expression of genes associated with pathways responsible for the generation of precursor metabolites for secondary metabolism. Our results highlight the main metabolic routes for the supply of precursors for secondary metabolism and suggest that the regulation of fungal metabolism is tailored to meet the demands for secondary metabolite production. These findings can aid in identifying fungal species that are optimized for the production of specific secondary metabolites and in designing metabolic engineering strategies to develop high-yielding fungal cell factories for production of secondary metabolites. IMPORTANCE Secondary metabolites are a major source of pharmaceuticals, especially antibiotics. However, the development of efficient processes of production of secondary metabolites has proved troublesome due to a limited understanding of the metabolic regulations governing secondary metabolism. By analyzing the conservation in gene expression across secondary metabolite-producing fungal species, we identified a metabolic signature that links primary and secondary metabolism and that demonstrates that fungal metabolism is tailored for the efficient production of secondary metabolites. The insight that we provide can be used to develop high-yielding fungal cell factories that are optimized for the production of specific secondary metabolites of pharmaceutical interest.

A genetical metabolomics approach for bioprospecting plant biosynthetic gene clusters

OBJECTIVE

Plants produce a plethora of specialized metabolites to defend themselves against pathogens and insects, to attract pollinators and to communicate with other organisms. Many of these are also applied in the clinic and in agriculture. Genes encoding the enzymes that drive the biosynthesis of these metabolites are sometimes physically grouped on the chromosome, in regions called biosynthetic gene clusters (BGCs). Several algorithms have been developed to identify plant BGCs, but a large percentage of predicted gene clusters upon further inspection do not show coexpression or do not encode a single functional biosynthetic pathway. Hence, further prioritization is needed.

RESULTS

Here, we introduce a strategy to systematically evaluate potential functions of predicted BGCs by superimposing their locations on metabolite quantitative trait loci (mQTLs). We show the feasibility of such an approach by integrating automated BGC prediction with mQTL datasets originating from a recombinant inbred line (RIL) population of Oryza sativa and a genome-wide association study (GWAS) of Arabidopsis thaliana. In these data, we identified several links for which the enzyme content of the BGCs matches well with the chemical features observed in the metabolite structure, suggesting that this method can effectively guide bioprospecting of plant BGCs.

New lncRNA annotation reveals extensive functional divergence of the transcriptome in maize

Long non-coding RNAs (lncRNAs), whose sequences are approximately 200 bp or longer and unlikely to encode proteins, may play an important role in eukaryotic gene regulation. Although the latest maize (Zea mays L.) reference genome provides an essential genomic resource, genome-wide annotations of maize lncRNAs have not been updated. Here, we report on a large transcriptomic dataset collected from 749 RNA sequencing experiments across different tissues and stages of the maize reference inbred B73 line and 60 from its wild relative teosinte. We identified 18,165 high-confidence lncRNAs in maize, of which 6,873 are conserved between maize and teosinte. We uncovered distinct genomic characteristics of conserved lncRNAs, non-conserved lncRNAs, and protein-coding transcripts. Intriguingly, Shannon entropy analysis showed that conserved lncRNAs are likely to be expressed similarly to protein-coding transcripts. Co-expression network analysis revealed significant variation in the degree of co-expression. Furthermore, selection analysis indicated that conserved lncRNAs are more likely than non-conserved lncRNAs to be located in regions subject to recent selection, indicating evolutionary differentiation. Our results provide the latest genome-wide annotation and analysis of maize lncRNAs and uncover potential functional divergence between protein-coding, conserved lncRNA, and non-conserved lncRNA genes, demonstrating the high complexity of the maize transcriptome.

croFGD: Catharanthus roseus Functional Genomics Database

Catharanthus roseus is a medicinal plant, which can produce monoterpene indole alkaloid (MIA) metabolites with biological activity and is rich in vinblastine and vincristine. With release of the scaffolded genome sequence of C. roseus, it is necessary to annotate gene functions on the whole-genome level. Recently, 53 RNA-seq datasets are available in public with different tissues (flower, root, leaf, seedling, and shoot) and different treatments (MeJA, PnWB infection and yeast elicitor). We used in-house data process pipeline with the combination of PCC and MR algorithms to construct a co-expression network exploring multi-dimensional gene expression (global, tissue preferential, and treat response) through multi-layered approaches. In the meanwhile, we added miRNA-target pairs, predicted PPI pairs into the network and provided several tools such as gene set enrichment analysis, functional module enrichment analysis, and motif analysis for functional prediction of the co-expression genes. Finally, we have constructed an online croFGD database (http://bioinformatics.cau.edu.cn/croFGD/). We hope croFGD can help the communities to study the C. roseus functional genomics and make novel discoveries about key genes involved in some important biological processes.

Haplotype-phased genome and evolution of phytonutrient pathways of tetraploid blueberry

BACKGROUND

Highbush blueberry (Vaccinium corymbosum) has long been consumed for its unique flavor and composition of health-promoting phytonutrients. However, breeding efforts to improve fruit quality in blueberry have been greatly hampered by the lack of adequate genomic resources and a limited understanding of the underlying genetics encoding key traits. The genome of highbush blueberry has been particularly challenging to assemble due, in large part, to its polyploid nature and genome size.

FINDINGS

Here, we present a chromosome-scale and haplotype-phased genome assembly of the cultivar "Draper," which has the highest antioxidant levels among a diversity panel of 71 cultivars and 13 wild Vaccinium species. We leveraged this genome, combined with gene expression and metabolite data measured across fruit development, to identify candidate genes involved in the biosynthesis of important phytonutrients among other metabolites associated with superior fruit quality. Genome-wide analyses revealed that both polyploidy and tandem gene duplications modified various pathways involved in the biosynthesis of key phytonutrients. Furthermore, gene expression analyses hint at the presence of a spatial-temporal specific dominantly expressed subgenome including during fruit development.

CONCLUSIONS

These findings and the reference genome will serve as a valuable resource to guide future genome-enabled breeding of important agronomic traits in highbush blueberry.

Recent advances in gene function prediction using context-specific coexpression networks in plants

Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks-created by integrating multiple expression datasets-connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional "global" to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.

Robust predictions of specialized metabolism genes through machine learning

Specialized metabolites are critical for plant–environment interactions, e.g., attracting pollinators or defending against herbivores, and are important sources of plant-based pharmaceuticals. However, it is unclear what proportion of enzyme-encoding genes play a role in specialized metabolism (SM) as opposed to general metabolism (GM) in any plant species. This is because of the diversity of specialized metabolites and the considerable number of incompletely characterized pathways responsible for their production. In addition, SM gene ancestors frequently played roles in GM. We evaluate features distinguishing SM and GM genes and build a computational model that accurately predicts SM genes. Our predictions provide candidates for experimental studies, and our modeling approach can be applied to other species that produce medicinally or industrially useful compounds.

Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using Arabidopsis thaliana as a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of multiple sets of benchmark genes revealed that SM genes tend to be tandemly duplicated, coexpressed with their paralogs, narrowly expressed at lower levels, less conserved, and less well connected in gene networks relative to GM genes. Although the values of each of these features significantly differed between SM and GM genes, any single feature was ineffective at predicting SM from GM genes. Using machine learning methods to integrate all features, a prediction model was established with a true positive rate of 87% and a true negative rate of 71%. In addition, 86% of known SM genes not used to create the machine learning model were predicted. We also demonstrated that the model could be further improved when we distinguished between SM, GM, and junction genes responsible for reactions shared by SM and GM pathways, indicating that topological considerations may further improve the SM prediction model. Application of the prediction model led to the identification of 1,220 A. thaliana genes with previously unknown functions, each assigned a confidence measure called an SM score, providing a global estimate of SM gene content in a plant genome.

Gene Discovery in Gelsemium Highlights Conserved Gene Clusters in Monoterpene Indole Alkaloid Biosynthesis

Genome mining is a routine technique in microbes for discovering biosynthetic pathways. In plants, however, genomic information is not commonly used to identify novel biosynthesis genes. Here, we present the genome of the medicinal plant and oxindole monoterpene indole alkaloid (MIA) producer Gelsemium sempervirens (Gelsemiaceae). A gene cluster from Catharanthus roseus, which is utilized at least six enzymatic steps downstream from the last common intermediate shared between the two plant alkaloid types, is found in G. sempervirens, although the corresponding enzymes act on entirely different substrates. This study provides insights into the common genomic context of MIA pathways and is an important milestone in the further elucidation of the Gelsemium oxindole alkaloid pathway.

An efficient system composed of maize protoplast transfection and HPLC-MS for studying the biosynthesis and regulation of maize benzoxazinoids

BACKGROUND

Insect herbivory poses a major threat to maize. Benzoxazinoids are important anti-insect secondary metabolites in maize, whose biosynthetic pathway has been extensively studied. However, yet little is known about how benzoxazinoids are regulated in maize, partly due to lack of mutant resources and recalcitrance to genetic transformation. Transient systems based on mesophyll- or cultured cell-derived protoplasts have been exploited in several plant species and have become a powerful tool for rapid or high-throughput assays of gene functions. Nevertheless, these systems have not been exploited to study the regulation of secondary metabolites.

RESULTS

A protocol for isolation of protoplasts from etiolated maize seedlings and efficient transfection was optimized. Furthermore, a 10-min-run-time and highly sensitive HPLC-MS method was established to rapidly detect and quantify maize benzoxazinoids. Coupling maize protoplast transfection and HPLC-MS, we screened a few genes potentially regulating benzoxazinoid biosynthesis using overexpression or silencing by artificial microRNA technology.

CONCLUSIONS

Combining the power of maize protoplast transfection and HPLC-MS analysis, this method allows rapid screening for the regulatory and biosynthetic genes of maize benzoxazinoids in protoplasts, before the candidates are selected for in planta functional analyses. This method can also be applied to study the biosynthesis and regulation of other secondary metabolites in maize and secondary metabolites in other plant species, including those not amenable to transformation.

Cytokinin modulates context-dependent chromatin accessibility through the type-B response regulators

The phytohormone cytokinin regulates diverse aspects of plant growth and development, probably through context-dependent transcriptional regulation that relies on a dynamic interplay between regulatory proteins and chromatin. We employed the assay for transposase accessible chromatin with sequencing to profile changes in the chromatin landscape of Arabidopsis roots and shoots in response to cytokinin. Our results reveal differentially accessible chromatin regions indicative of dynamic regulation in response to cytokinin. These changes in chromatin occur preferentially upstream of cytokinin-regulated genes. The changes also largely overlap with binding sites for the type-B ARABIDOPSIS RESPONSE REGULATORS (ARRs), transcription factors that mediate the primary response to cytokinin. Furthermore, the type-B ARRs were found to be necessary for the changes in chromatin state in response to cytokinin. Last, we identified context-dependent responses by comparing root and shoot profiles. This study provides new insight into the dynamics between cytokinin and chromatin with regard to directing transcriptional programmes and how cytokinin mediates its pleiotropic effects.

Identification of Genes Encoding Enzymes Catalyzing the Early Steps of Carrot Polyacetylene Biosynthesis

Polyacetylenic lipids accumulate in various Apiaceae species after pathogen attack, suggesting that these compounds are naturally occurring pesticides and potentially valuable resources for crop improvement. These compounds also promote human health and slow tumor growth. Even though polyacetylenic lipids were discovered decades ago, the biosynthetic pathway underlying their production is largely unknown. To begin filling this gap and ultimately enable polyacetylene engineering, we studied polyacetylenes and their biosynthesis in the major Apiaceae crop carrot (Daucus carota subsp. sativus). Using gas chromatography and mass spectrometry, we identified three known polyacetylenes and assigned provisional structures to two novel polyacetylenes. We also quantified these compounds in carrot leaf, petiole, root xylem, root phloem, and root periderm extracts. Falcarindiol and falcarinol predominated and accumulated primarily in the root periderm. Since the multiple double and triple carbon-carbon bonds that distinguish polyacetylenes from ubiquitous fatty acids are often introduced by Δ12 oleic acid desaturase (FAD2)-type enzymes, we mined the carrot genome for FAD2 genes. We identified a FAD2 family with an unprecedented 24 members and analyzed public, tissue-specific carrot RNA-Seq data to identify coexpressed members with root periderm-enhanced expression. Six candidate genes were heterologously expressed individually and in combination in yeast and Arabidopsis (Arabidopsis thaliana), resulting in the identification of one canonical FAD2 that converts oleic to linoleic acid, three divergent FAD2-like acetylenases that convert linoleic into crepenynic acid, and two bifunctional FAD2s with Δ12 and Δ14 desaturase activity that convert crepenynic into the further desaturated dehydrocrepenynic acid, a polyacetylene pathway intermediate. These genes can now be used as a basis for discovering other steps of falcarin-type polyacetylene biosynthesis, to modulate polyacetylene levels in plants, and to test the in planta function of these molecules.

Integrating Networks, Phylogenomics, and Population Genomics for the Study of Polyploidy

Duplication events are regarded as sources of evolutionary novelty, but our understanding of general trends for the long-term trajectory of additional genomic material is still lacking. Organisms with a history of whole genome duplication (WGD) offer a unique opportunity to study potential trends in the context of gene retention and/or loss, gene and network dosage, and changes in gene expression. In this review, we discuss the prevalence of polyploidy across the tree of life, followed by an overview of studies investigating genome evolution and gene expression. We then provide an overview of methods in network biology, phylogenomics, and population genomics that are critical for advancing our understanding of evolution post-WGD, highlighting the need for models that can accommodate polyploids. Finally, we close with a brief note on the importance of random processes in the evolution of polyploids with respect to neutral versus selective forces, ancestral polymorphisms, and the formation of autopolyploids versus allopolyploids.

Natural genetic variation for expression of a SWEET transporter among wild species of Solanum lycopersicum (tomato) determines the hexose composition of ripening tomato fruit

The sugar content of Solanum lycopersicum (tomato) fruit is a primary determinant of taste and quality. Cultivated tomato fruit are characterized by near-equimolar levels of the hexoses glucose and fructose, derived from the hydrolysis of translocated sucrose. As fructose is perceived as approximately twice as sweet as glucose, increasing its concentration at the expense of glucose can improve tomato fruit taste. Introgressions of the Fgr^H allele from the wild species Solanum habrochaites (LA1777) into cultivated tomato increased the fructose-to-glucose ratio of the ripe fruit by reducing glucose levels and concomitantly increasing fructose levels. In order to identify the function of the Fgr gene, we combined a fine-mapping strategy with RNAseq differential expression analysis of near-isogenic tomato lines. The results indicated that a SWEET protein was strongly upregulated in the lines with a high fructose-to-glucose ratio. Overexpressing the SWEET protein in transgenic tomato plants dramatically reduced the glucose levels and increased the fructose : glucose ratio in the developing fruit, thereby proving the function of the protein. The SWEET protein was localized to the plasma membrane and expression of the SlFgr gene in a yeast line lacking native hexose transporters complemented growth with glucose, but not with fructose. These results indicate that the SlFgr gene encodes a plasma membrane-localized glucose efflux transporter of the SWEET family, the overexpression of which reduces glucose levels and may allow for increased fructose levels. This article identifies the function of the tomato Fgr gene as a SWEET transporter, the upregulation of which leads to a modified sugar accumulation pattern in the fleshy fruit. The results point to the potential of the inedible wild species to improve fruit sugar accumulation via sugar transport mechanisms.

Bioinformatics tools for the identification of gene clusters that biosynthesize specialized metabolites

Specialized metabolites (also called natural products or secondary metabolites) derived from bacteria, fungi, marine organisms and plants constitute an important source of antibiotics, anti-cancer agents, insecticides, immunosuppressants and herbicides. Many specialized metabolites in bacteria and fungi are biosynthesized via metabolic pathways whose enzymes are encoded by clustered genes on a chromosome. Metabolic gene clusters comprise a group of physically co-localized genes that together encode enzymes for the biosynthesis of a specific metabolite. Although metabolic gene clusters are generally not known to occur outside of microbes, several plant metabolic gene clusters have been discovered in recent years. The discovery of novel metabolic pathways is being enabled by the increasing availability of high-quality genome sequencing coupled with the development of powerful computational toolkits to identify metabolic gene clusters. To provide a comprehensive overview of various bioinformatics methods for detecting gene clusters, we compare and contrast key aspects of algorithmic logic behind several computational tools, including 'NP.searcher', 'ClustScan', 'CLUSEAN', 'antiSMASH', 'SMURF', 'MIDDAS-M', 'ClusterFinder', 'CASSIS/SMIPS' and 'C-Hunter' among others. We also review additional tools such as 'NRPSpredictor' and 'SBSPKS' that can infer substrate specificity for previously identified gene clusters. The continual development of bioinformatics methods to predict gene clusters will help shed light on how organisms assemble multi-step metabolic pathways for adaptation to various ecological niches.

Network Analyses Can Advance Above-Belowground Ecology

An understanding of above-belowground (AG-BG) ecology is important for evaluating how plant interactions with enemies, symbionts, and decomposers affect species diversity and will respond to global changes. However, research questions and experiments often focus on only a limited number of interactions, creating an incomplete picture of how entire communities may be involved in AG-BG community ecology. Therefore, a pressing challenge is to formulate hypotheses of AG-BG interactions when considering communities in their full complexity. Here we discuss how network analyses can be a powerful tool to progress AG-BG research, link across scales from individual to community and ecosystem, visualize community interactions between the two (AG and BG) subsystems, and develop testable hypotheses.

Plant Protection by Benzoxazinoids—Recent Insights into Biosynthesis and Function

Benzoxazinoids (BXs) are secondary metabolites present in many Poaceae including the major crops maize, wheat, and rye. In contrast to other potentially toxic secondary metabolites, BXs have not been targets of counter selection during breeding and the effect of BXs on insects, microbes, and neighbouring plants has been recognised. A broad knowledge about the mode of action and metabolisation in target organisms including herbivorous insects, aphids, and plants has been gathered in the last decades. BX biosynthesis has been elucidated on a molecular level in crop cereals. Recent advances, mainly made by investigations in maize, uncovered a significant diversity in the composition of BXs within one species. The pattern can be specific for single plant lines and dynamic changes triggered by biotic and abiotic stresses were observed. Single BXs might be toxic, repelling, attractive, and even growth-promoting for insects, depending on the particular species. BXs delivered into the soil influence plant and microbial communities. Furthermore, BXs can possibly be used as signalling molecules within the plant. In this review we intend to give an overview of the current data on the biosynthesis, structure, and function of BXs, beyond their characterisation as mere phytotoxins.

Metabolic engineering to increase crop yield: From concept to execution

Although the return on investment over the last 20 years for mass screening of individual plant genes to improve crop performance has been low, the investment in these activities was essential to establish the infrastructure and tools of modern plant genomics. Complex traits such as crop yield are likely multigenic, and the exhaustive screening of random gene combinations to achieve yield gains is not realistic. Clearly, smart approaches must be developed. In silico analyses of plant metabolism and gene networks can move a trait discovery program beyond trial-and-error approaches and towards rational design strategies. Metabolic models employing flux-balance analysis are useful to determine the contribution of individual genes to a trait, or to compare, optimize, or even design metabolic pathways. Regulatory association networks provide a transcriptome-based view of the plant and can lead to the identification of transcription factors that control expression of multiple genes affecting a trait. In this review, the use of these models from the perspective of an Ag innovation company's trait discovery and development program will be discussed. Important decisions that can have significant impacts on the cost and timeline to develop a commercial trait will also be presented.

Beyond Defense: Multiple Functions of Benzoxazinoids in Maize Metabolism

Benzoxazinoids are a class of indole-derived plant metabolites that function in defense against numerous pests and pathogens. Due to their abundance in maize (Zea mays) and other important cereal crops, benzoxazinoids have been the subject of extensive research for >50 years. Whereas benzoxazinoids can account for 1% or more of the dry weight in young seedlings constitutively, their accumulation in older plants is induced locally by pest and pathogen attack. Although the biosynthetic pathways for most maize benzoxazinoids have been identified, unanswered questions remain about the developmental and defense-induced regulation of benzoxazinoid metabolism. Recent research shows that, in addition to their central role in the maize chemical defense repertoire, benzoxazinoids may have important functions in regulating other defense responses, flowering time, auxin metabolism, iron uptake and perhaps aluminum tolerance. Investigation of natural variation in maize benzoxazinoid accumulation, which is greatly facilitated by recent genomics advances, will have a major impact in this research area by leading to the discovery of previously unknown genes and functions of benzoxazinoid metabolism.

Ranking genome-wide correlation measurements improves microarray and RNA-seq based global and targeted co-expression networks

Co-expression networks are essential tools to infer biological associations between gene products and predict gene annotation. Global networks can be analyzed at the transcriptome-wide scale or after querying them with a set of guide genes to capture the transcriptional landscape of a given pathway in a process named Pathway Level Coexpression (PLC). A critical step in network construction remains the definition of gene co-expression. In the present work, we compared how Pearson Correlation Coefficient (PCC), Spearman Correlation Coefficient (SCC), their respective ranked values (Highest Reciprocal Rank (HRR)), Mutual Information (MI) and Partial Correlations (PC) performed on global networks and PLCs. This evaluation was conducted on the model plant Arabidopsis thaliana using microarray and differently pre-processed RNA-seq datasets. We particularly evaluated how dataset × distance measurement combinations performed in 5 PLCs corresponding to 4 well described plant metabolic pathways (phenylpropanoid, carbohydrate, fatty acid and terpene metabolisms) and the cytokinin signaling pathway. Our present work highlights how PCC ranked with HRR is better suited for global network construction and PLC with microarray and RNA-seq data than other distance methods, especially to cluster genes in partitions similar to biological subpathways.