WikiPathways for plants: a community pathway curation portal and a case study in rice and arabidopsis seed development networks

Functionally coherent transcriptional responses of Jatropha curcas and Pseudomonas fragi for rhizosphere mediated degradation of pyrene

Pyrene is an extremely hazardous, carcinogenic polycyclic aromatic hydrocarbon (PAH). The plant-microbe interaction between Pseudomonas fragi DBC and Jatropha curcas was employed for biodegradation of pyrene and their transcriptional responses were compared. The genome of P. fragi DBC had genes for PAH degrading enzymes i.e. dioxygenases and dehydrogenases, along with root colonization (trpD, trpG, trpE and trpF), chemotaxis (flhF and flgD), stress adaptation (gshA, nuoHBEKNMG), and detoxification (algU and yfc). The transcriptional expression of catA and yfc that respectively code for catabolic enzyme (catechol-1, 2-dioxygnase) and glutathione-s-transferase for detoxification functions were quantitatively measured by qPCR. The catA was expressed in presence of artificial root exudate with or without pyrene, and glucose confirming the non-selective approach of bacteria, as desired. Pyrene induced 100-fold increase of yfc expression than catA, while there was no expression of yfc in absence of pyrene. The transcriptome of plant roots, in presence of pyrene, with or without P. fragi DBC inoculation was analysed. The P. fragi DBC could upregulate the genes for plant growth, induced the systemic acquired resistance and also ameliorated the stress response in Jatropha roots.

Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects

Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is popular for addressing the challenges of hidden population stratification and polygenic effects. However, most of these methods including LMM are sensitive to phenotypic outliers that may lead the misleading results. To overcome this problem, in this paper, we proposed a way to robustify the LMM approach for reducing the influence of outlying observations using the β-divergence method. The performance of the proposed method was investigated using both synthetic and real data analysis. Simulation results showed that the proposed method performs better than both linear regression model (LRM) and LMM approaches in terms of powers and false discovery rates in presence of phenotypic outliers. On the other hand, the proposed method performed almost similar to LMM approach but much better than LRM approach in absence of outliers. In the case of real data analysis, our proposed method identified 11 SNPs that are significantly associated with the rice flowering time. Among the identified candidate SNPs, some were involved in seed development and flowering time pathways, and some were connected with flower and other developmental processes. These identified candidate SNPs could assist rice breeding programs effectively. Thus, our findings highlighted the importance of robust GWAS in identifying candidate genes.

WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research

WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.

Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect

Flowering time is an important agronomic trait, attributed by multiple genes, gene-gene interactions and environmental factors. Population stratification and polygenic effects might confound genetic effects of the causal loci underlying this complex trait. We proposed a two-step approach for detecting epistasis interactions underlying rice flowering time by accounting population structure and polygenic effects. Simulation studies showed that the approach used in this study performs better than classical and PC-linear approaches in terms of powers and false discovery rates in the case of population stratification and polygenic effects. Whole genome epistasis analyses identified 589 putative genetic interactions for flowering time. Eighteen of these interactions are located within 10 kilobases of regions of known protein-protein interactions. Thirty-seven SNPs near to twenty-five genes involve in rice or/and Arabidopsis (orthologue) flowering pathway. Bioinformatics analysis showed that 66.55% pairwise genes of the identified interactions (392 out of the 589 interactions) have similarity in various genomic features. Moreover, significant numbers of detected epistatic genes have high expression in different floral tissues. Our findings highlight the importance of epistasis analysis by controlling population stratification and polygenic effect and provided novel insights into the genetic architecture of rice flowering which could assist breeding programmes.

Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials

A paradigm shift is taking place in risk assessment to replace animal models, reduce the number of economic resources, and refine the methodologies to test the growing number of chemicals and nanomaterials. Therefore, approaches such as transcriptomics, proteomics, and metabolomics have become valuable tools in toxicological research, and are finding their way into regulatory toxicity. One promising framework to bridge the gap between the molecular-level measurements and risk assessment is the concept of adverse outcome pathways (AOPs). These pathways comprise mechanistic knowledge and connect biological events from a molecular level toward an adverse effect outcome after exposure to a chemical. However, the implementation of omics-based approaches in the AOPs and their acceptance by the risk assessment community is still a challenge. Because the existing modules in the main repository for AOPs, the AOP Knowledge Base (AOP-KB), do not currently allow the integration of omics technologies, additional tools are required for omics-based data analysis and visualization. Here we show how WikiPathways can serve as a supportive tool to make omics data interoperable with the AOP-Wiki, part of the AOP-KB. Manual matching of key events (KEs) indicated that 67% could be linked with molecular pathways. Automatic connection through linkage of identifiers between the databases showed that only 30% of AOP-Wiki chemicals were found on WikiPathways. More loose linkage through gene names in KE and Key Event Relationships descriptions gave an overlap of 70 and 71%, respectively. This shows many opportunities to create more direct connections, for example with extended ontology annotations, improving its interoperability. This interoperability allows the needed integration of omics data linked to the molecular pathways with AOPs. A new AOP Portal on WikiPathways is presented to allow the community of AOP developers to collaborate and populate the molecular pathways that underlie the KEs of AOP-Wiki. We conclude that the integration of WikiPathways and AOP-Wiki will improve risk assessment because omics data will be linked directly to KEs and therefore allow the comprehensive understanding and description of AOPs. To make this assessment reproducible and valid, major changes are needed in both WikiPathways and AOP-Wiki.

Transcriptome Analyses of Two Citrus Cultivars (Shiranuhi and Huangguogan) in Seedling Etiolation

Citrus species are among the most important fruit crops. However, gene regulation and signaling pathways related to etiolation in this crop remain unknown. Using Illumina sequencing technology, modification of global gene expression in two hybrid citrus cultivars—Huangguogan and Shiranuhi, respectively—were investigated. More than 834.16 million clean reads and 125.12 Gb of RNA-seq data were obtained, more than 91.37% reads had a quality score of Q30. 124,952 unigenes were finally generated with a mean length of 1,189 bp. 79.15%, 84.35%, 33.62%, 63.12%, 57.67%, 57.99% and 37.06% of these unigenes had been annotated in NR, NT, KO, SwissProt, PFAM, GO and KOG databases, respectively. Further, we identified 604 differentially expressed genes in multicoloured and etiolated seedlings of Shiranuhi, including 180 up-regulated genes and 424 down-regulated genes. While in Huangguogan, we found 1,035 DEGs, 271 of which were increasing and the others were decreasing. 7 DEGs were commonly up-regulated, and 59 DEGs down-regulated in multicoloured and etiolated seedlings of these two cultivars, suggesting that some genes play fundamental roles in two hybrid citrus seedlings during etiolation. Our study is the first to provide the transcriptome sequence resource for seedlings etiolation of Shiranuhi and Huangguogan.

Pathway Analysis and Omics Data Visualization Using Pathway Genome Databases: FragariaCyc, a Case Study

The species-specific plant Pathway Genome Databases (PGDBs) based on the BioCyc platform provide a conceptual model of the cellular metabolic network of an organism. Such frameworks allow analysis of the genome-scale expression data to understand changes in the overall metabolisms of an organism (or organs, tissues, and cells) in response to various extrinsic (e.g. developmental and differentiation) and/or extrinsic signals (e.g. pathogens and abiotic stresses) from the surrounding environment. Using FragariaCyc, a pathway database for the diploid strawberry Fragaria vesca, we show (1) the basic navigation across a PGDB; (2) a case study of pathway comparison across plant species; and (3) an example of RNA-Seq data analysis using Omics Viewer tool. The protocols described here generally apply to other Pathway Tools-based PGDBs.

Reactome from a WikiPathways Perspective

Reactome and WikiPathways are two of the most popular freely available databases for biological pathways. Reactome pathways are centrally curated with periodic input from selected domain experts. WikiPathways is a community-based platform where pathways are created and continually curated by any interested party. The nascent collaboration between WikiPathways and Reactome illustrates the mutual benefits of combining these two approaches. We created a format converter that converts Reactome pathways to the GPML format used in WikiPathways. In addition, we developed the ComplexViz plugin for PathVisio which simplifies looking up complex components. The plugin can also score the complexes on a pathway based on a user defined criterion. This score can then be visualized on the complex nodes using the visualization options provided by the plugin. Using the merged collection of curated and converted Reactome pathways, we demonstrate improved pathway coverage of relevant biological processes for the analysis of a previously described polycystic ovary syndrome gene expression dataset. Additionally, this conversion allows researchers to visualize their data on Reactome pathways using PathVisio's advanced data visualization functionalities. WikiPathways benefits from the dedicated focus and attention provided to the content converted from Reactome and the wealth of semantic information about interactions. Reactome in turn benefits from the continuous community curation available on WikiPathways. The research community at large benefits from the availability of a larger set of pathways for analysis in PathVisio and Cytoscape. The pathway statistics results obtained from PathVisio are significantly better when using a larger set of candidate pathways for analysis. The conversion serves as a general model for integration of multiple pathway resources developed using different approaches.

Plant Pathway Databases

Pathway databases provide information about the role of chemicals, genes, and gene products in the form of protein or RNA, their interactions leading to the formulation of metabolic, transport, regulatory, and signaling reactions. The reactions can then be tethered by the principle of inputs and outputs of one or more reaction to create pathways. This chapter provides a list of various online databases that carry information about plant pathways and provides a brief overview of how to use the pathway databases such as WikiPathways Plants Portal, MapMan and the cereal crop pathway databases like RiceCyc and MaizeCyc, that were developed using the Pathway Tools software.

Aging Chart: a community resource for rapid exploratory pathway analysis of age-related processes

Aging research is a multi-disciplinary field encompassing knowledge from many areas of basic, applied and clinical research. Age-related processes occur on molecular, cellular, tissue, organ, system, organismal and even psychological levels, trigger the onset of multiple debilitating diseases and lead to a loss of function, and there is a need for a unified knowledge repository designed to track, analyze and visualize the cause and effect relationships and interactions between the many elements and processes on all levels. Aging Chart (http://agingchart.org/) is a new, community-curated collection of aging pathways and knowledge that provides a platform for rapid exploratory analysis. Building on an initial content base constructed by a team of experts from peer-reviewed literature, users can integrate new data into biological pathway diagrams for a visible, intuitive, top-down framework of aging processes that fosters knowledge-building and collaboration. As the body of knowledge in aging research is rapidly increasing, an open visual encyclopedia of aging processes will be useful to both the new entrants and experts in the field.

WikiPathways: capturing the full diversity of pathway knowledge

WikiPathways (http://www.wikipathways.org) is an open, collaborative platform for capturing and disseminating models of biological pathways for data visualization and analysis. Since our last NAR update, 4 years ago, WikiPathways has experienced massive growth in content, which continues to be contributed by hundreds of individuals each year. New aspects of the diversity and depth of the collected pathways are described from the perspective of researchers interested in using pathway information in their studies. We provide updates on extensions and services to support pathway analysis and visualization via popular standalone tools, i.e. PathVisio and Cytoscape, web applications and common programming environments. We introduce the Quick Edit feature for pathway authors and curators, in addition to new means of publishing pathways and maintaining custom pathway collections to serve specific research topics and communities. In addition to the latest milestones in our pathway collection and curation effort, we also highlight the latest means to access the content as publishable figures, as standard data files, and as linked data, including bulk and programmatic access.

De novo transcriptome assembly and analyses of gene expression during photomorphogenesis in diploid wheat Triticum monococcum

BACKGROUND

Triticum monococcum (2n) is a close ancestor of T. urartu, the A-genome progenitor of cultivated hexaploid wheat, and is therefore a useful model for the study of components regulating photomorphogenesis in diploid wheat. In order to develop genetic and genomic resources for such a study, we constructed genome-wide transcriptomes of two Triticum monococcum subspecies, the wild winter wheat T. monococcum ssp. aegilopoides (accession G3116) and the domesticated spring wheat T. monococcum ssp. monococcum (accession DV92) by generating de novo assemblies of RNA-Seq data derived from both etiolated and green seedlings.

PRINCIPAL FINDINGS

The de novo transcriptome assemblies of DV92 and G3116 represent 120,911 and 117,969 transcripts, respectively. We successfully mapped ∼90% of these transcripts from each accession to barley and ∼95% of the transcripts to T. urartu genomes. However, only ∼77% transcripts mapped to the annotated barley genes and ∼85% transcripts mapped to the annotated T. urartu genes. Differential gene expression analyses revealed 22% more light up-regulated and 35% more light down-regulated transcripts in the G3116 transcriptome compared to DV92. The DV92 and G3116 mRNA sequence reads aligned against the reference barley genome led to the identification of ∼500,000 single nucleotide polymorphism (SNP) and ∼22,000 simple sequence repeat (SSR) sites.

CONCLUSIONS

De novo transcriptome assemblies of two accessions of the diploid wheat T. monococcum provide new empirical transcriptome references for improving Triticeae genome annotations, and insights into transcriptional programming during photomorphogenesis. The SNP and SSR sites identified in our analysis provide additional resources for the development of molecular markers.

Integrated network analysis and effective tools in plant systems biology

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.