LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org

LegumeGRN: a gene regulatory network prediction server for functional and comparative studies

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org.Oklahoma Center for The Advancement of Science and Technology: (OCAST Grant No. PSB11-031)

Quantitative Resistance to Verticillium Wilt in Medicago truncatula Involves Eradication of the Fungus from Roots and Is Associated with Transcriptional Responses Related to Innate Immunity

Resistance mechanisms to Verticillium wilt are well studied in tomato, cotton and Arabidopsis, but much less in legume plants. Because legume plants establish nitrogen-fixing symbioses in their roots, resistance to root-attacking pathogens merits particular attention. The interaction between the soil-borne pathogen Verticillium alfalfae and the model legume Medicago truncatula was investigated using a resistant (A17) and a susceptible (F83005.5) line. As shown by histological analyses, colonization by the pathogen was initiated similarly in both lines. Later on, the resistant line A17 eliminated the fungus, whereas the susceptible F83005.5 became heavily colonized. Resistance in line A17 does not involve homologs of the well-characterized tomato Ve1 and V. dahliae Ave1 genes. A transcriptomic study of early root responses during initial colonization (i.e. until 24 h post-inoculation) similarly was performed. Compared to the susceptible line, line A17 displayed already a significantly higher basal expression of defense-related genes prior to inoculation, and responded to infection with up-regulation of only a small number of genes. Although fungal colonization was still low at this stage, the susceptible line F83005.5 exhibited a disorganized response involving a large number of genes from different functional classes. The involvement of distinct phytohormone signaling pathways in resistance as suggested by gene expression patterns was supported by experiments with plant hormone pretreatment before fungal inoculation.Gene co-expression network analysis highlighted five main modules in the resistant line, whereas no structured gene expression was found in the susceptible line. One module was particularly associated to the inoculation response in A17. It contains the majority of differentially expressed genes, genes associated with PAMP perception and hormone signaling, and transcription factors. An in silico analysis showed that a high number of these genes also respond to other soil-borne pathogens in M. truncatula, suggesting a core of transcriptional response to root pathogens. Taken together, the results suggest that resistance in M. truncatula line A17 might be due to innate immunity combining preformed defense and PAMP-triggered defense mechanisms, and putative involvement of abscisic acid

MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

Discovery AP2/ERF family genes in silico in Medicago truncatula

Medicago truncatula is a legume model plant due to its small genome and it has been used to study the molecular events of legume biology. As a crucial plant-specific gene family, AP2/EREBP transcription factors (TFs) are important for plant development and biotic and abiotic stress responses. The purpose of the work was to determine AP2/ERF family genes in silico of M. truncatula, and also sheds light on molecular mechanism of stress responses of AP2/EREBPs. We investigated AP2/ERF family genes of M. truncatula using BLAST search. Thirty-seven (37) AP2/ERF family genes were identified and sorted into the corresponding subfamily or subgroup, with sequences alignment and phylogenetic analysis of the AP2-like TFs proteins between in Arabidopsis and in M. truncatula, and expression patterns of putative 35 AP2/ERF family genes in M. truncatula were revealed. Identification of AP2/ERF family genes would make them easier to clone and position those functional genes, and which also would open new opportunities for the study of molecular regulatory network of stress resistance in M. truncatula.Keywords: Medicago truncatula, transcription factor, AP2/ERFAfrican Journal of Biotechnology Vol. 12(23), pp. 3636-364

Computational analysis of small RNAs and the RNA degradome with application to plant water stress

Water shortage is one of the most important environmental stress factors that affects plants, limiting crop yield in large areas worldwide. Plants can survive water stress by regulating gene expression at several levels. One of the recently discovered regulatory mechanisms involves small RNAs (sRNAs), which can regulate gene expression by targeting messenger RNAs (mRNAs) and directing endonucleolytic cleavage resulting in mRNA degradation. A snapshot of an mRNA degradation profile (degradome) can be captured through a new high-throughput technique called Parallel Analysis of RNA Ends (PARE) by using next generation sequencing technologies. In this thesis we describe a new user friendly degradome analysis software tool called PAREsnip that we have used for the rapid genome-wide discovery of sRNA/target interactions evidenced through the degradome. In addition to PAREsnip and based upon PAREsnip’s rapid capability, we also present a new software tool for the construction, analysis and visualisation of sRNA regulatory interaction networks. The two new tools were used to analyse PARE datasets obtained fromMedicago truncatula and Arabidopsis thaliana. In particular, we have used PAREsnip for the high-throughput analysis of PARE data obtained from Medicago when subjected to dehydration and found several sRNA/mRNA interactions that are potentially responsive to water stress. We also present how we used our new network visualisation and analysis tool with PARE datasets obtained from Arabidopsis and discovered several novel sRNA regulatory interaction networks. In building tools and using them for this kind of analysis, we gain a better understanding of the processes and mechanisms involved in sRNA mediated gene regulation and how plants respond to water stress which could lead to new strategies in improving stress tolerance

The metabolism of nitrogen assimilation in Medicago truncatula : a quest for sensors and regulators

Trabalho de investigação desenvolvido no Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto, na Faculdade de Engenharia da Universidade do Porto e no Instituto de Biologia Molecular e Celular da Universidade do PortoTese de mestrado integrado. Bioengenharia - Ramo de Biotecnologia Molecular. Faculdade de Engenharia. Universidade do Porto. 201

Effects of microRNA156 on Flowering Time and Plant Architecture in Medicago sativa

MiR156 regulates plant biomass production through regulation of members of Squamosa-Promoter Binding Protein-Like (SPL) genes. In this study, I investigated function of miR156 in Medicago sativa (alfalfa). Alfalfa plants overexpressing alfalfa miR156 and Lotus japonicus miR156 were generated, and the miR156 cleavage targets were validated. In silico analysis showed that some alfalfa sequence reads (~ 60 bp) are similar to miR156 precursors but the hairpin secondary structure could not be produced from these sequences. Of the five predicted target SPLs genes, three (SPL6, SPL12 and SPL13) contain miR156 cleavage sites and their expression was downregulated in transgenic alfalfa overexpressing miR156. These transgenic alfalfa genotypes had reduced internode length, enhanced shoot branching, and elevated biomass. Although alfalfa miR156 had little effect on nodulation and flowering time, L. japonicus miR156 reduced nodulation and delayed flowering time (up to 50 days). Our observations imply that miR156 could be employed in improving alfalfa biomass

Automatic and manual functional annotation in a distributed web service environment

While the number of genomic sequences becoming available is increasing exponentially, most genes are not functionally well characterized. Finding out more about the function of a gene and about functional relationships between genes will be the next big bottleneck in the post-genomic era. On the one hand improved pipelines and tools are needed in this context, because running experiments for all predicted genes is not feasible. On the other hand manual curation of the automatic predictions is necessary to judge the reliability of the automatic annotation and to get a more comprehensive view on the function of each individual gene. For the automatic functional annotation often a homology based function transfer from functionally characterized genes is applied using methods like Blast. However, this approach has many drawbacks and makes systematic errors by not taking care of speciation and duplication events. Phylogenomics has shown to improve the functional prediction accuracy by taking the evolutionary history of genes in a phylogenetic tree context into account. In this thesis the manual process from the assembly of the DNA sequence to the functional characterization of genes and the identification and comparison of shared syntenic regions, including the identification of candidate genes for pathogen resistance in potato chromosome V, is explained and problems discussed. To improve the automatic functional annotation in genome projects, a phylogenomic pipeline, which includes SIFTER one of the best phylogenomic tools in this area, is introduced, improved and tested in the Medicago truncatula, Sorghum bicolor and Solanum lycopersicum genome projects. To obtain new candidate genes for the development of new drugs and crop protection products, non-plant specific genes, like the transferrin family which is not known in plants yet, are extracted from the M. truncatula and S. bicolor genomes and further investigated. For further improvement of the annotation, a new phylogenomic approach is developed. This approach makes use of annotated functional attributes to calculate the functional mutation rate between genes and groups of genes in a phylogenetic tree and to find out if the function of a gene can be transferred or not. The new approach is integrated into the SIFTER tool and tested on the blue-light photoreceptor/photolyase family and on a test set of manually curated Arabidopsis thaliana genes. Using both test sets the prediction accuracy could be significantly improved and a more comprehensive view on the gene function could be obtained. But because still no tool is able to annotate all functions of a gene with 100% accuracy, I introduce a system for manual functional annotation, called AFAWE. AFAWE runs different web services for the functional annotation and displays the results and intermediate results in a comprehensive web interface that facilitates comparison. It can be used for any organism and any kind of gene. The inputs are the amino acid sequence and the corresponding organism. Because of its flexible structure, new web services and workflows can be easily integrated. Besides Blast searches against different databases and protein domain prediction tools, AFAWE also includes the phylogenomic pipeline. Different filters help to identify trustworthy results from each analysis. Furthermore a detailed manual annotation can be assigned to each protein, which will be used to update the functional annotation in public databases like MIPSPlantsDB

Identification And Functional Characterization Of Plant Small Secreted Proteins During Arbuscular Mycorrhizal Symbiosis

Plant small secreted proteins (SSPs) are sequences of 50 – 250 amino acids in size which are transported out of cells to fulfill multiple functions related to plant growth and development and response to various stresses. With the development of more accurate and affordable genome sequencing technology, an increasing number of SSPs have been predicted using diverse computational tools based on machine learning. Although experimentally validated plant SSPs are still limited, some studies have reported that plant SSPs can be induced and involved in mutualistic relationships between plants and microbes. In Chapter I, known SSPs and their functions in various plant species are reviewed. Additionally, current computational tools and experimental methods that have been widely applied to identify plant SSPs are summarized. A new, robust, and integrated pipeline to discover plant SSPs is proposed. Furthermore, strategies for elucidating the biological functions of SSPs in plants are discussed in Chapter I. Chapter II presents predicted SSPs from 60 plant species and elucidates the evolutionary convergence of changes in SSP sequences. Furthermore, the expression of SSPs induced by arbuscular mycorrhizal fungi (AMF) which correspond to the convergent abilityfor different plants to form mutualistic association with AMF are explored. Overall, this study provides insightful ideas to understand functions of plant SSPs that occur during symbiosis between plants and fungi