Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes

Recent advances in triterpenoid pathway elucidation and engineering

Triterpenoids are among the most assorted class of specialized metabolites found in all the taxa of living organisms. Triterpenoids are the leading active ingredients sourced from plant species and are utilized in pharmaceutical and cosmetic industries. The triterpenoid precursor 2,3-oxidosqualene, which is biosynthesized via the mevalonate (MVA) pathway is structurally diversified by the oxidosqualene cyclases (OSCs) and other scaffold-decorating enzymes such as cytochrome P450 monooxygenases (P450s), UDP-glycosyltransferases (UGTs) and acyltransferases (ATs). A majority of the bioactive triterpenoids are harvested from the native hosts using the traditional methods of extraction and occasionally semi-synthesized. These methods of supply are time-consuming and do not often align with sustainability goals. Recent advancements in metabolic engineering and synthetic biology have shown prospects for the green routes of triterpenoid pathway reconstruction in heterologous hosts such as Escherichia coli, Saccharomyces cerevisiae and Nicotiana benthamiana, which appear to be quite promising and might lead to the development of alternative source of triterpenoids. The present review describes the biotechnological strategies used to elucidate complex biosynthetic pathways and to understand their regulation and also discusses how the advances in triterpenoid pathway engineering might aid in the scale-up of triterpenoid production in engineered hosts.

Analysis of Arabidopsis non-reference accessions reveals high diversity of metabolic gene clusters and discovers new candidate cluster members

Metabolic gene clusters (MGCs) are groups of genes involved in a common biosynthetic pathway. They are frequently formed in dynamic chromosomal regions, which may lead to intraspecies variation and cause phenotypic diversity. We examined copy number variations (CNVs) in four Arabidopsis thaliana MGCs in over one thousand accessions with experimental and bioinformatic approaches. Tirucalladienol and marneral gene clusters showed little variation, and the latter was fixed in the population. Thalianol and especially arabidiol/baruol gene clusters displayed substantial diversity. The compact version of the thalianol gene cluster was predominant and more conserved than the noncontiguous version. In the arabidiol/baruol cluster, we found a large genomic insertion containing divergent duplicates of the CYP705A2 and BARS1 genes. The BARS1 paralog, which we named BARS2, encoded a novel oxidosqualene synthase. The expression of the entire arabidiol/baruol gene cluster was altered in the accessions with the duplication. Moreover, they presented different root growth dynamics and were associated with warmer climates compared to the reference-like accessions. In the entire genome, paired genes encoding terpene synthases and cytochrome P450 oxidases were more variable than their nonpaired counterparts. Our study highlights the role of dynamically evolving MGCs in plant adaptation and phenotypic diversity.

Determinants of correlated expression of transcription factors and their target genes

While transcription factors (TFs) are known to regulate the expression of their target genes (TGs), only a weak correlation of expression between TFs and their TGs has generally been observed. As lack of correlation could be caused by additional layers of regulation, the overall correlation distribution may hide the presence of a subset of regulatory TF-TG pairs with tight expression coupling. Using reported regulatory pairs in the plant Arabidopsis thaliana along with comprehensive gene expression information and testing a wide array of molecular features, we aimed to discern the molecular determinants of high expression correlation of TFs and their TGs. TF-family assignment, stress-response process involvement, short genomic distances of the TF-binding sites to the transcription start site of their TGs, few required protein-protein-interaction connections to establish physical interactions between the TF and polymerase-II, unambiguous TF-binding motifs, increased numbers of miRNA target-sites in TF-mRNAs, and a young evolutionary age of TGs were found particularly indicative of high TF-TG correlation. The modulating roles of post-transcriptional, post-translational processes, and epigenetic factors have been characterized as well. Our study reveals that regulatory pairs with high expression coupling are associated with specific molecular determinants.

Subnuclear gene positioning through lamina association affects copper tolerance

The nuclear lamina plays an important role in the regulation of chromatin organization and gene positioning in animals. CROWDED NUCLEI (CRWN) is a strong candidate for the plant nuclear lamina protein in Arabidopsis thaliana but its biological function was largely unknown. Here, we show that CRWNs localize at the nuclear lamina and build the meshwork structure. Fluorescence in situ hybridization and RNA-seq analyses revealed that CRWNs regulate chromatin distribution and gene expression. More than 2000 differentially expressed genes were identified in the crwn1crwn4 double mutant. Copper-associated (CA) genes that form a gene cluster on chromosome 5 were among the downregulated genes in the double mutant exhibiting low tolerance to excess copper. Our analyses showed this low tolerance to copper was associated with the suppression of CA gene expression and that CRWN1 interacts with the CA gene locus, enabling the locus to localize at the nuclear lamina under excess copper conditions.

The nuclear lamina regulates chromatin organization and gene positioning. Here the authors show that CROWDED NUCLEI proteins contribute to the meshwork lamina structure in Arabidopsis nuclei and regulate copper tolerance by promoting lamina association and expression of copper response genes.

The algal selenoproteomes

Background

Selenium is an essential trace element, and selenocysteine (Sec, U) is its predominant form in vivo. Proteins that contain Sec are selenoproteins, whose special structural features include not only the TGA codon encoding Sec but also the SECIS element in mRNA and the conservation of the Sec-flanking region. These unique features have led to the development of a series of bioinformatics methods to predict and research selenoprotein genes. There have been some studies and reports on the evolution and distribution of selenoprotein genes in prokaryotes and multicellular eukaryotes, but the systematic analysis of single-cell eukaryotes, especially algae, has been very limited.

Results

In this study, we predicted selenoprotein genes in 137 species of algae by using a program we previously developed. More than 1000 selenoprotein genes were obtained. A database website was built to record these algae selenoprotein genes (www.selenoprotein.com). These genes belong to 42 selenoprotein families, including three novel selenoprotein gene families.

Conclusions

This study reveals the primordial state of the eukaryotic selenoproteome. It is an important clue to explore the significance of selenium for primordial eukaryotes and to determine the complete evolutionary spectrum of selenoproteins in all life forms.

Ab initio GO-based mining for non-tandem-duplicated functional clusters in three model plant diploid genomes

A functional Non-Tandem Duplicated Cluster (FNTDC) is a group of non-tandem-duplicated genes that are located closer than expected by mere chance and have a role in the same biological function. The identification of secondary-compounds–related FNTDC has gained increased interest in recent years, but little ab-initio attempts aiming to the identification of FNTDCs covering all biological functions, including primary metabolism compounds, have been carried out. We report an extensive FNTDC dataset accompanied by a detailed assessment on parameters used for genome scanning and their impact on FNTDC detection. We propose 70% identity and 70% alignment coverage as intermediate settings to exclude tandem duplicated genes and a dynamic scanning window of 24 genes. These settings were applied to rice, arabidopsis and grapevine genomes to call for FNTDCs. Besides the best-known secondary metabolism clusters, we identified many FNTDCs associated to primary metabolism ranging from macromolecules synthesis/editing, TOR signalling, ubiquitination, proton and electron transfer complexes. Using the intermediate FNTDC setting parameters (at P-value 1e^-6), 130, 70 and 140 candidate FNTDCs were called in rice, arabidopsis and grapevine, respectively, and 20 to 30% of GO tags associated to called FNTDC were common among the 3 genomes. The datasets developed along with this work provide a rich framework for pinpointing candidate FNTDCs reflecting all GO-BP tags covering both primary and secondary metabolism with large macromolecular complexes/metabolons as the most represented FNTDCs. Noteworthy, several FNTDCs are tagged with GOs referring to organelle-targeted multi-enzyme complex, a finding that suggest the migration of endosymbiont gene chunks towards nuclei could be at the basis of these class of candidate FNTDCs. Most FNTDC appear to have evolved prior of genome duplication events. More than one-third of genes interspersed/adjacent to called FNTDCs lacked any functional annotation; however, their co-localization may provide hints towards a candidate biological role.

Co-Regulation of Clustered and Neo-Functionalized Genes in Plant-Specialized Metabolism

Current findings of neighboring genes involved in plant specialized metabolism provide the genomic signatures of metabolic evolution. Two such genomic features, namely, (i) metabolic gene cluster and (ii) neo-functionalization of tandem gene duplications, represent key factors corresponding to the creation of metabolic diversity of plant specialized metabolism. So far, several terpenoid and alkaloid biosynthetic genes have been characterized with gene clusters in some plants. On the other hand, some modification genes involved in flavonoid and glucosinolate biosynthesis were found to arise via gene neo-functionalization. Although the occurrence of both types of metabolic evolution are different, the neighboring genes are generally regulated by the same or related regulation factors. Therefore, the translation-based approaches associated with genomics, and transcriptomics are able to be employed for functional genomics focusing on plant secondary metabolism. Here, we present a survey of the current understanding of neighboring genes involved in plant secondary metabolism. Additionally, a genomic overview of neighboring genes of four model plants and transcriptional co-expression network neighboring genes to detect metabolic gene clusters in Arabidopsis is provided. Finally, the insights functional genomics have provided concerning the evolution and mechanistic regulation of both the formation and operation of metabolic neighboring clusters is discussed.

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.

Cytokinin-overinduced transcription factors and thalianol cluster genes in CARBOXYL-TERMINAL DOMAIN PHOSPHATASE-LIKE 4-silenced Arabidopsis roots during de novo shoot organogenesis

Cytokinin (CK) is one of key phytohormones for de-differentiation and de novo organogenesis in plants. During the CK-mediated organogenesis not only genes in CK homeostasis, perception and signal transduction, but also factors regulating basic transcription, splicing and chromatin remodeling contribute to coordinate a sequence of events leading to formation of new organs. We have found that silencing of RNA polymerase II CTD-phosohatase-like 4 (CPL4_RNAi) in Arabidopsis induces CK-oversensitive de novo shoot organogenesis (DNSO) from roots, partly by early activation of transcription factors such as WUSCHEL and SHOOT MERISTEMLESS during pre-incubation on callus induction media. Here we show that a cluster of thalianol-biogenesis genes is highly expressed in the CPL4_RNAi during DNSO, implying involvement of CPL4 in transcriptional regulation of the thalianol pathway in DNSO.

Global gene expression reveals stress-responsive genes in Aspergillus fumigatus mycelia

Background

Aspergillus fumigatus is a human fungal pathogen that causes aspergillosis in immunocompromised hosts. A. fumigatus is believed to be exposed to diverse environmental stresses in the host cells. The adaptation mechanisms are critical for infections in human bodies. Transcriptional networks in response to diverse environmental challenges remain to be elucidated. To gain insights into the adaptation to environmental stresses in A. fumigatus mycelia, we conducted time series transcriptome analyses.

Results

With the aid of RNA-seq, we explored the global gene expression profiles of mycelia in A. fumigatus upon exposure to diverse environmental changes, including heat, superoxide, and osmotic stresses. From the perspective of global transcriptomes, transient responses to superoxide and osmotic stresses were observed while responses to heat stresses were gradual. We identified the stress-responsive genes for particular stresses, and the 266 genes whose expression levels drastically fluctuated upon exposure to all tested stresses. Among these, the 77 environmental stress response genes are conserved in S. cerevisiae, suggesting that these genes might be more general prerequisites for adaptation to environmental stresses. Finally, we revealed the strong correlations among expression profiles of genes related to ‘rRNA processing’.

Conclusions

The time series transcriptome analysis revealed the stress-responsive genes underlying the adaptation mechanisms in A. fumigatus mycelia. These results will shed light on the regulatory networks underpinning the adaptation of the filamentous fungi.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4316-z) contains supplementary material, which is available to authorized users.

The PhytoClust tool for metabolic gene clusters discovery in plant genomes

The existence of Metabolic Gene Clusters (MGCs) in plant genomes has recently raised increased interest. Thus far, MGCs were commonly identified for pathways of specialized metabolism, mostly those associated with terpene type products. For efficient identification of novel MGCs, computational approaches are essential. Here, we present PhytoClust; a tool for the detection of candidate MGCs in plant genomes. The algorithm employs a collection of enzyme families related to plant specialized metabolism, translated into hidden Markov models, to mine given genome sequences for physically co-localized metabolic enzymes. Our tool accurately identifies previously characterized plant MGCs. An exhaustive search of 31 plant genomes detected 1232 and 5531 putative gene cluster types and candidates, respectively. Clustering analysis of putative MGCs types by species reflected plant taxonomy. Furthermore, enrichment analysis revealed taxa- and species-specific enrichment of certain enzyme families in MGCs. When operating through our web-interface, PhytoClust users can mine a genome either based on a list of known cluster types or by defining new cluster rules. Moreover, for selected plant species, the output can be complemented by co-expression analysis. Altogether, we envisage PhytoClust to enhance novel MGCs discovery which will in turn impact the exploration of plant metabolism.

Genome-wide identification of physically clustered genes suggests chromatin-level co-regulation in male reproductive development in Arabidopsis thaliana

Co-expression of physically linked genes occurs surprisingly frequently in eukaryotes. Such chromosomal clustering may confer a selective advantage as it enables coordinated gene regulation at the chromatin level. We studied the chromosomal organization of genes involved in male reproductive development in Arabidopsis thaliana. We developed an in-silico tool to identify physical clusters of co-regulated genes from gene expression data. We identified 17 clusters (96 genes) involved in stamen development and acting downstream of the transcriptional activator MS1 (MALE STERILITY 1), which contains a PHD domain associated with chromatin re-organization. The clusters exhibited little gene homology or promoter element similarity, and largely overlapped with reported repressive histone marks. Experiments on a subset of the clusters suggested a link between expression activation and chromatin conformation: qRT-PCR and mRNA in situ hybridization showed that the clustered genes were up-regulated within 48 h after MS1 induction; out of 14 chromatin-remodeling mutants studied, expression of clustered genes was consistently down-regulated only in hta9/hta11, previously associated with metabolic cluster activation; DNA fluorescence in situ hybridization confirmed that transcriptional activation of the clustered genes was correlated with open chromatin conformation. Stamen development thus appears to involve transcriptional activation of physically clustered genes through chromatin de-condensation.

The biosynthetic pathway of the nonsugar, high-intensity sweetener mogroside V from Siraitia grosvenorii

The consumption of sweeteners, natural as well as synthetic sugars, is implicated in an array of modern-day health problems. Therefore, natural nonsugar sweeteners are of increasing interest. We identify here the biosynthetic pathway of the sweet triterpenoid glycoside mogroside V, which has a sweetening strength of 250 times that of sucrose and is derived from mature fruit of luo-han-guo (Siraitia grosvenorii, monk fruit). A whole-genome sequencing of Siraitia, leading to a preliminary draft of the genome, was combined with an extensive transcriptomic analysis of developing fruit. A functional expression survey of nearly 200 candidate genes identified the members of the five enzyme families responsible for the synthesis of mogroside V: squalene epoxidases, triterpenoid synthases, epoxide hydrolases, cytochrome P450s, and UDP-glucosyltransferases. Protein modeling and docking studies corroborated the experimentally proven functional enzyme activities and indicated the order of the metabolic steps in the pathway. A comparison of the genomic organization and expression patterns of these Siraitia genes with the orthologs of other Cucurbitaceae implicates a strikingly coordinated expression of the pathway in the evolution of this species-specific and valuable metabolic pathway. The genomic organization of the pathway genes, syntenously preserved among the Cucurbitaceae, indicates, on the other hand, that gene clustering cannot account for this novel secondary metabolic pathway.

Renaissance in phytomedicines: promising implications of NGS technologies

Medicinal plant research is growing significantly in faith to discover new and more biologically compatible phytomedicines. Deposition of huge genome/trancriptome sequence data assisted by NGS technologies has revealed the new possibilities for producing upgraded bioactive molecules in medicinal plants. Growing interest of investors and consumers in the herbal drugs raises the need for extensive research to open the facts and details of every inch of life canvas of medicinal plants to produce improved quality of phytomedicines. As in agriculture crops, knowledge emergence from medicinal plant's genome/transcriptome, can be used to assure their amended quality and these improved varieties are then transported to the fields for cultivation. Genome studies generate huge sequence data which can be exploited further for obtaining information regarding genes/gene clusters involved in biosynthesis as well as regulation. This can be achieved rapidly at a very large scale with NGS platforms. Identification of new RNA molecules has become possible, which can lead to the discovery of novel compounds. Sequence information can be combined with advanced phytochemical and bioinformatics tools to discover functional herbal drugs. Qualitative and quantitative analysis of small RNA species put a light on the regulatory aspect of biosynthetic pathways for phytomedicines. Inter or intra genomic as well as transcriptomic interactive processes for biosynthetic pathways can be elucidated in depth. Quality management of herbal material will also become rapid and high throughput. Enrichment of sequence information will be used to engineer the plants to get more efficient phytopharmaceuticals. The present review comprises of role of NGS technologies to boost genomic studies of pharmaceutically important plants and further, applications of sequence information aiming to produce enriched phytomedicines. Emerging knowledge from the medicinal plants genome/transcriptome can give birth to deep understanding of the processes responsible for biosynthesis of medicinally important compounds.

Arabidopsis seed mucilage secretory cells: regulation and dynamics

Seeds from various angiosperm species produce polysaccharide mucilage facilitating germination and, therefore, conferring major evolutionary advantages. The seed epidermal mucilage secretory cells (MSCs) undergo numerous tightly controlled changes of their extracellular matrixes (ECMs) throughout seed development. Recently, major progress based on the model species Arabidopsis thaliana was published, including the identification of 54 genes necessary for mucilage synthesis and release. Here, we review these genes that constitute the so-called 'MSC toolbox', within which transcription factors and proteins related to polysaccharide production, secretion, modification, and stabilization are the most abundant and belong to complex regulatory networks. We also discuss how seed coat 'omics data-mining, comparative genomics, and operon-like gene cluster studies will provide means to identify new members of the MSC toolbox.

Targeted Integration of RNA-Seq and Metabolite Data to Elucidate Curcuminoid Biosynthesis in Four Curcuma Species

Curcuminoids, namely curcumin and its analogs, are secondary metabolites that act as the primary active constituents of turmeric (Curcuma longa). The contents of these curcuminoids vary among species in the genus Curcuma. For this reason, we compared two wild strains and two cultivars to understand the differences in the synthesis of curcuminoids. Because the fluxes of metabolic reactions depend on the amounts of their substrate and the activity of the catalysts, we analyzed the metabolite concentrations and gene expression of related enzymes. We developed a method based on RNA sequencing (RNA-Seq) analysis that focuses on a specific set of genes to detect expression differences between species in detail. We developed a 'selection-first' method for RNA-Seq analysis in which short reads are mapped to selected enzymes in the target biosynthetic pathways in order to reduce the effect of mapping errors. Using this method, we found that the difference in the contents of curcuminoids among the species, as measured by gas chromatography-mass spectrometry, could be explained by the changes in the expression of genes encoding diketide-CoA synthase, and curcumin synthase at the branching point of the curcuminoid biosynthesis pathway.

Production of bioactive diterpenoids in the euphorbiaceae depends on evolutionarily conserved gene clusters

The Euphorbiaceae produce a diverse range of diterpenoids, many of which have pharmacological activities. These diterpenoids include ingenol mebutate, which is licensed for the treatment of a precancerous skin condition (actinic keratosis), and phorbol derivatives such as resiniferatoxin and prostratin, which are undergoing investigation for the treatment of severe pain and HIV, respectively. Despite the interest in these diterpenoids, their biosynthesis is poorly understood at present, with the only characterized step being the conversion of geranylgeranyl pyrophosphate into casbene. Here, we report a physical cluster of diterpenoid biosynthetic genes from castor (Ricinus communis), including casbene synthases and cytochrome P450s from the CYP726A subfamily. CYP726A14, CYP726A17, and CYP726A18 were able to catalyze 5-oxidation of casbene, a conserved oxidation step in the biosynthesis of this family of medicinally important diterpenoids. CYP726A16 catalyzed 7,8-epoxidation of 5-keto-casbene and CYP726A15 catalyzed 5-oxidation of neocembrene. Evidence of similar gene clustering was also found in two other Euphorbiaceae, including Euphorbia peplus, the source organism of ingenol mebutate. These results demonstrate conservation of gene clusters at the higher taxonomic level of the plant family and that this phenomenon could prove useful in further elucidating diterpenoid biosynthetic pathways.

The rise of operon-like gene clusters in plants

Gene clusters are common features of prokaryotic genomes also present in eukaryotes. Most clustered genes known are involved in the biosynthesis of secondary metabolites. Although horizontal gene transfer is a primary source of prokaryotic gene cluster (operon) formation and has been reported to occur in eukaryotes, the predominant source of cluster formation in eukaryotes appears to arise de novo or through gene duplication followed by neo- and sub-functionalization or translocation. Here we aim to provide an overview of the current knowledge and open questions related to plant gene cluster functioning, assembly, and regulation. We also present potential research approaches and point out the benefits of a better understanding of gene clusters in plants for both fundamental and applied plant science.

A predictor for predicting Escherichia coli transcriptome and the effects of gene perturbations

Background

A means to predict the effects of gene over-expression, knockouts, and environmental stimuli in silico is useful for system biologists to develop and test hypotheses. Several studies had predicted the expression of all Escherichia coli genes from sequences and reported a correlation of 0.301 between predicted and actual expression. However, these do not allow biologists to study the effects of gene perturbations on the native transcriptome.

Results

We developed a predictor to predict transcriptome-scale gene expression from a small number (n = 59) of known gene expressions using gene co-expression network, which can be used to predict the effects of over-expressions and knockdowns on E. coli transcriptome. In terms of transcriptome prediction, our results show that the correlation between predicted and actual expression value is 0.467, which is similar to the microarray intra-array variation (p-value = 0.348), suggesting that intra-array variation accounts for a substantial portion of the transcriptome prediction error. In terms of predicting the effects of gene perturbation(s), our results suggest that the expression of 83% of the genes affected by perturbation can be predicted within 40% of error and the correlation between predicted and actual expression values among the affected genes to be 0.698. With the ability to predict the effects of gene perturbations, we demonstrated that our predictor has the potential to estimate the effects of varying gene expression level on the native transcriptome.

Conclusion

We present a potential means to predict an entire transcriptome and a tool to estimate the effects of gene perturbations for E. coli, which will aid biologists in hypothesis development. This study forms the baseline for future work in using gene co-expression network for gene expression prediction.

Current challenges and future potential of tomato breeding using omics approaches

As tomatoes are one of the most important vegetables in the world, improvements in the quality and yield of tomato are strongly required. For this purpose, omics approaches such as metabolomics and transcriptomics are used not only for basic research to understand relationships between important traits and metabolism but also for the development of next generation breeding strategies of tomato plants, because an increase in the knowledge improves the taste and quality, stress resistance and/or potentially health-beneficial metabolites and is connected to improvements in the biochemical composition of tomatoes. Such omics data can be applied to network analyses to potentially reveal unknown cellular regulatory networks in tomato plants. The high-quality tomato genome that was sequenced in 2012 will likely accelerate the application of omics strategies, including next generation sequencing for tomato breeding. In this review, we highlight the current studies of omics network analyses of tomatoes and other plant species, in particular, a gene coexpression network. Key applications of omics approaches are also presented as case examples to improve economically important traits for tomato breeding.