Artem Lysenko

Transcriptome and Metabolite Profiling of the Infection Cycle of Zymoseptoria tritici on Wheat Reveals a Biphasic Interaction with Plant Immunity Involving Differential Pathogen Chromosomal Contributions and a Variation on the Hemibiotrophic Lifestyle Definition

The temporal dynamics of Zymoseptoria tritici reproduction on Triticum aestivum involves a biphasic manipulation of plant defense responses. The hemibiotrophic fungus Zymoseptoria tritici causes Septoria tritici blotch disease of wheat (Triticum aestivum). Pathogen reproduction on wheat occurs without cell penetration, suggesting that dynamic and intimate intercellular communication occurs between fungus and plant throughout the disease cycle. We used deep RNA sequencing and metabolomics to investigate the physiology of plant and pathogen throughout an asexual reproductive cycle of Z. tritici on wheat leaves. Over 3,000 pathogen genes, more than 7,000 wheat genes, and more than 300 metabolites were differentially regulated. Intriguingly, individual fungal chromosomes contributed unequally to the overall gene expression changes. Early transcriptional down-regulation of putative host defense genes was detected in inoculated leaves. There was little evidence for fungal nutrient acquisition from the plant throughout symptomless colonization by Z. tritici, which may instead be utilizing lipid and fatty acid stores for growth. However, the fungus then subsequently manipulated specific plant carbohydrates, including fructan metabolites, during the switch to necrotrophic growth and reproduction. This switch coincided with increased expression of jasmonic acid biosynthesis genes and large-scale activation of other plant defense responses. Fungal genes encoding putative secondary metabolite clusters and secreted effector proteins were identified with distinct infection phase-specific expression patterns, although functional analysis suggested that many have overlapping/redundant functions in virulence. The pathogenic lifestyle of Z. tritici on wheat revealed through this study, involving initial defense suppression by a slow-growing extracellular and nutritionally limited pathogen followed by defense (hyper) activation during reproduction, reveals a subtle modification of the conceptual definition of hemibiotrophic plant infection.

Data integration for plant genomics—exemplars from the integration of Arabidopsis thaliana databases

The development of a systems based approach to problems in plant sciences requires integration of existing information resources. However, the available information is currently often incomplete and dispersed across many sources and the syntactic and semantic heterogeneity of the data is a challenge for integration. In this article, we discuss strategies for data integration and we use a graph based integration method (Ondex) to illustrate some of these challenges with reference to two example problems concerning integration of (i) metabolic pathway and (ii) protein interaction data for Arabidopsis thaliana. We quantify the degree of overlap for three commonly used pathway and protein interaction information sources. For pathways, we find that the AraCyc database contains the widest coverage of enzyme reactions and for protein interactions we find that the IntAct database provides the largest unique contribution to the integrated dataset. For both examples, however, we observe a relatively small amount of data common to all three sources. Analysis and visual exploration of the integrated networks was used to identify a number of practical issues relating to the interpretation of these datasets. We demonstrate the utility of these approaches to the analysis of groups of coexpressed genes from an individual microarray experiment, in the context of pathway information and for the combination of coexpression data with an integrated protein interaction network.

Representing and querying disease networks using graph databases

BackgroundSystems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data.ResultsWe show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes.ConclusionsOur study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.

Assessing the functional coherence of modules found in multiple-evidence networks from Arabidopsis

BackgroundCombining multiple evidence-types from different information sources has the potential to reveal new relationships in biological systems. The integrated information can be represented as a relationship network, and clustering the network can suggest possible functional modules. The value of such modules for gaining insight into the underlying biological processes depends on their functional coherence. The challenges that we wish to address are to define and quantify the functional coherence of modules in relationship networks, so that they can be used to infer function of as yet unannotated proteins, to discover previously unknown roles of proteins in diseases as well as for better understanding of the regulation and interrelationship between different elements of complex biological systems.ResultsWe have defined the functional coherence of modules with respect to the Gene Ontology (GO) by considering two complementary aspects: (i) the fragmentation of the GO functional categories into the different modules and (ii) the most representative functions of the modules. We have proposed a set of metrics to evaluate these two aspects and demonstrated their utility in Arabidopsis thaliana. We selected 2355 proteins for which experimentally established protein-protein interaction (PPI) data were available. From these we have constructed five relationship networks, four based on single types of data: PPI, co-expression, co-occurrence of protein names in scientific literature abstracts and sequence similarity and a fifth one combining these four evidence types. The ability of these networks to suggest biologically meaningful grouping of proteins was explored by applying Markov clustering and then by measuring the functional coherence of the clusters.ConclusionsRelationship networks integrating multiple evidence-types are biologically informative and allow more proteins to be assigned to a putative functional module. Using additional evidence types concentrates the functional annotations in a smaller number of modules without unduly compromising their consistency. These results indicate that integration of more data sources improves the ability to uncover functional association between proteins, both by allowing more proteins to be linked and producing a network where modular structure more closely reflects the hierarchy in the gene ontology.

Genetical and Comparative Genomics of Brassica under Altered Ca Supply Identifies Arabidopsis Ca-Transporter Orthologs

This work uses multiple-environment expression quantitative trait locus analysis of the Brassica rapa genome, combined with comparative genomics of Arabidopsis, to identify putative vacuolar calcium transporters with potential uses in biofortification to increase the accumulation of calcium in food crops. Although Ca transport in plants is highly complex, the overexpression of vacuolar Ca2+ transporters in crops is a promising new technology to improve dietary Ca supplies through biofortification. Here, we sought to identify novel targets for increasing plant Ca accumulation using genetical and comparative genomics. Expression quantitative trait locus (eQTL) mapping to 1895 cis- and 8015 trans-loci were identified in shoots of an inbred mapping population of Brassica rapa (IMB211 × R500); 23 cis- and 948 trans-eQTLs responded specifically to altered Ca supply. eQTLs were screened for functional significance using a large database of shoot Ca concentration phenotypes of Arabidopsis thaliana. From 31 Arabidopsis gene identifiers tagged to robust shoot Ca concentration phenotypes, 21 mapped to 27 B. rapa eQTLs, including orthologs of the Ca2+ transporters At-CAX1 and At-ACA8. Two of three independent missense mutants of BraA.cax1a, isolated previously by targeting induced local lesions in genomes, have allele-specific shoot Ca concentration phenotypes compared with their segregating wild types. BraA.CAX1a is a promising target for altering the Ca composition of Brassica, consistent with prior knowledge from Arabidopsis. We conclude that multiple-environment eQTL analysis of complex crop genomes combined with comparative genomics is a powerful technique for novel gene identification/prioritization.

Developing integrated crop knowledge networks to advance candidate gene discovery

The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

Network-Based Data Integration for Selecting Candidate Virulence Associated Proteins in the Cereal Infecting Fungus Fusarium graminearum

The identification of virulence genes in plant pathogenic fungi is important for understanding the infection process, host range and for developing control strategies. The analysis of already verified virulence genes in phytopathogenic fungi in the context of integrated functional networks can give clues about the underlying mechanisms and pathways directly or indirectly linked to fungal pathogenicity and can suggest new candidates for further experimental investigation, using a ‘guilt by association’ approach. Here we study 133 genes in the globally important Ascomycete fungus Fusarium graminearum that have been experimentally tested for their involvement in virulence. An integrated network that combines information from gene co-expression, predicted protein-protein interactions and sequence similarity was employed and, using 100 genes known to be required for virulence, we found a total of 215 new proteins potentially associated with virulence of which 29 are annotated as hypothetical proteins. The majority of these potential virulence genes are located in chromosomal regions known to have a low recombination frequency. We have also explored the taxonomic diversity of these candidates and found 25 sequences, which are likely to be fungal specific. We discuss the biological relevance of a few of the potentially novel virulence associated genes in detail. The analysis of already verified virulence genes in phytopathogenic fungi in the context of integrated functional networks can give clues about the underlying mechanisms and pathways directly or indirectly linked to fungal pathogenicity and can suggest new candidates for further experimental investigation, using a ‘guilt by association’ approach.

AIGO: Towards a unified framework for the Analysis and the Inter-comparison of GO functional annotations

BackgroundIn response to the rapid growth of available genome sequences, efforts have been made to develop automatic inference methods to functionally characterize them. Pipelines that infer functional annotation are now routinely used to produce new annotations at a genome scale and for a broad variety of species. These pipelines differ widely in their inference algorithms, confidence thresholds and data sources for reasoning. This heterogeneity makes a comparison of the relative merits of each approach extremely complex. The evaluation of the quality of the resultant annotations is also challenging given there is often no existing gold-standard against which to evaluate precision and recall.ResultsIn this paper, we present a pragmatic approach to the study of functional annotations. An ensemble of 12 metrics, describing various aspects of functional annotations, is defined and implemented in a unified framework, which facilitates their systematic analysis and inter-comparison. The use of this framework is demonstrated on three illustrative examples: analysing the outputs of state-of-the-art inference pipelines, comparing electronic versus manual annotation methods, and monitoring the evolution of publicly available functional annotations. The framework is part of the AIGO library (http://code.google.com/p/aigo) for the Analysis and the Inter-comparison of the products of Gene Ontology (GO) annotation pipelines. The AIGO library also provides functionalities to easily load, analyse, manipulate and compare functional annotations and also to plot and export the results of the analysis in various formats.ConclusionsThis work is a step toward developing a unified framework for the systematic study of GO functional annotations. This framework has been designed so that new metrics on GO functional annotations can be added in a very straightforward way.

Graph-based sequence annotation using a data integration approach

Summary The automated annotation of data from high throughput sequencing and genomics experiments is a significant challenge for bioinformatics. Most current approaches rely on sequential pipelines of gene finding and gene function prediction methods that annotate a gene with information from different reference data sources. Each function prediction method contributes evidence supporting a functional assignment. Such approaches generally ignore the links between the information in the reference datasets. These links, however, are valuable for assessing the plausibility of a function assignment and can be used to evaluate the confidence in a prediction. We are working towards a novel annotation system that uses the network of information supporting the function assignment to enrich the annotation process for use by expert curators and predicting the function of previously unannotated genes. In this paper we describe our success in the first stages of this development. We present the data integration steps that are needed to create the core database of integrated reference databases (UniProt, PFAM, PDB, GO and the pathway database Ara- Cyc) which has been established in the ONDEX data integration system. We also present a comparison between different methods for integration of GO terms as part of the function assignment pipeline and discuss the consequences of this analysis for improving the accuracy of gene function annotation. The methods and algorithms presented in this publication are an integral part of the ONDEX system which is freely available from http://ondex.sf.net/.

Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks

Summary: The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j‐based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java‐based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. Availability and Implementation: The Neo4j‐based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/. The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework. Contact: ibalaur@eisbm.org Supplementary information: Supplementary data are available at Bioinformatics online.