Genetic dissection of the rf2a-mediated fertility restoration pathway in Maize

As the first practical cytoplasmic male sterility (cms) system used in hybrid seed production, the first molecularly characterized cms gene and the first cms system for which a restorer was cloned, cms-T and its restorer system have been intensively studied since their identification. The accumulated knowledge on the cms-T system mainly includes: (i) The sterility-causing TURF13 protein is associated with mitochondrial inner membrane. (ii) Both functional alleles of the rf1 and rf2a restorer genes are required to counteract the TURF13\u27s effect and to restore fertility. (iii) rf2a encodes a mitochondrial aldehyde dehydrogenase (ALDH) that is required for fertility restoration. (iv) rf1 interferes with the expression of the T-urf13 gene, and the profile leads to a reduction in the accumulation of TURF13 protein. However, the mechanism that underlines how the mitochondrial TURF13 protein disrupts microspore development and how nuclear restorer genes restore the fertility is not fully understood.;This dissertation is aimed at a better understanding of underlying pathways for fertility restoration and anther development: (i) We used global transcriptome profiling to analyze gene expression in the tapetum, where the action of cms-T and the restoration take place. Prior studies have not been able to focus on this critical single cell layer and all the related data have been based on transcript analysis in either vegetative tissues or mixed reproductive tissues. Our study was able to reveal differentially expressed genes in the tapetal cells of cms plants versus restored plants. (ii) We detected mitochondrial transcripts in our expression profiling analysis and our results suggest that the rf2a nuclear gene affects the stability of T-urf13 transcripts. (iii) We used both forward and reverse genetic approaches to analyze the rso gene, a enhancer of rf2a. Both functional alleles of rf2a and rso are required for normal anther development. Our results establish that rso encodes a maize homologue of mammalian bax inhibitor I that is involved in mitochondrial-mediated programmed cell death (PCD).;In the end, we propose an integrated working hypothesis on the pathways involved in rf2a-mediated fertility restoration in cms-T and in normal anther development

Gene loss rate: a probabilistic measure for the conservation of eukaryotic genes

The rate of conservation of a gene in evolution is believed to be correlated with its biological importance. Recent studies have devised various conservation measures for genes and have shown that they are correlated with several biological characteristics of functional importance. Specifically, the state-of-the-art propensity for gene loss (PGL) measure was shown to be strongly correlated with gene essentiality and its number of protein–protein interactions (PPIs). The observed correlation between conservation and functional importance varies however between conservation measures, underscoring the need for accurate and general measures for the rate of gene conservation. Here we develop a novel maximum-likelihood approach to computing the rate in which a gene is lost in evolution, motivated by the same principles as those underlying PGL. However, in difference to PGL which considers only the most parsimonious ancestral states of the internal nodes of the phylogenetic tree relating the species, our approach weighs in a probabilistic manner all possible ancestral states, and includes the branch length information as part of the probabilistic model. In application to data of 16 eukaryotic genomes, our approach shows higher correlations with experimental data than PGL, including data on gene lethality, level of connectivity in a PPI network and coherence within functionally related genes

ModHMM: A Modular Supra-Bayesian Genome Segmentation Method

Genome segmentation methods are powerful tools to obtain cell type or tissue-specific genome-wide annotations and are frequently used to discover regulatory elements. However, traditional segmentation methods show low predictive accuracy and their data-driven annotations have some undesirable properties. As an alternative, we developed ModHMM, a highly modular genome segmentation method. Inspired by the supra-Bayesian approach, it incorporates predictions from a set of classifiers. This allows to compute genome segmentations by utilizing state-of-the-art methodology. We demonstrate the method on ENCODE data and show that it outperforms traditional segmentation methods not only in terms of predictive performance, but also in qualitative aspects. Therefore, ModHMM is a valuable alternative to study the epigenetic and regulatory landscape across and within cell types or tissues

Repeaterator: A tool for visualizing DNA repeat motifs in actinobacteriophage genomes

Horizontal gene transfer plays a large role in microbial genetic diversity. Bacteriophages can mediate diversity within their hosts through transduction, the uptake and dispersal of microbial host DNA between bacterial hosts. However, bacteriophages themselves experience horizontal gene transfer through mobile genetic elements and recombination. Unlike their hosts, bacteriophages cannot easily be mapped onto a phylogenic tree as they do not all possess a common trait like the 16s RNA gene. However, their genomes are typically small enough to be analyzed usingThere are tools to compare bacteriophages such as Gepard and Phamerator that compare nucleotide identity across bacteriophage entire genomes. However, Gepard lacks the ability to contextualize the analysis with respect to annotated genes, , but none that fully capture evidence of horizontal gene transfer and display it in an intuitive manner. and Phamerator, by its very nature as a comparison tool, cannot highlight repeats within a single genome. Programs Many programs to identify repeat motifs also exist, but many lack the ability to display genomic information and regard repeats in an isolated manner. To address this problem, I have developed Repeaterator, a tool to visualize DNA repeat motifs within Actinobacteriophage genomes. Much like Phamerator, Repeaterator displays bacteriophage genomes and their annotations visually using the D3.js library from a Mongo database. Instead of comparing multiple genomes, Repeaterator compares a genome to itself to map the occurrence of DNA repeat motifs in the context of gene annotations. Additionally, other genomic information can easily be overlaid on the visualization, including GC content or the strand the gene is coded on. Repeaterator provides powerful new insight into the evolutionary origins of Actinobacteriophage genomes and can easily be easily adapted to analyze other genomes. Much like Phamerator, Repeaterator displays bacteriophage genomes and their annotations visually using the D3.js library from a Mongo database.as an interactive data visualization. It uses the same underlying technologies as Phamerator itself and will be available along with our other tools on http://phamerator.org for general use

A \u3cem\u3eColletotrichum graminicola\u3c/em\u3e Mutant Deficient in the Establishment of Biotrophy Reveals Early Transcriptional Events in the Maize Anthracnose Disease Interaction

Background: Colletotrichum graminicola is a hemibiotrophic fungal pathogen that causes maize anthracnose disease. It progresses through three recognizable phases of pathogenic development in planta: melanized appressoria on the host surface prior to penetration; biotrophy, characterized by intracellular colonization of living host cells; and necrotrophy, characterized by host cell death and symptom development. A “Mixed Effects” Generalized Linear Model (GLM) was developed and applied to an existing Illumina transcriptome dataset, substantially increasing the statistical power of the analysis of C. graminicola gene expression during infection and colonization. Additionally, the in planta transcriptome of the wild-type was compared with that of a mutant strain impaired in the establishment of biotrophy, allowing detailed dissection of events occurring specifically during penetration, and during early versus late biotrophy. Results: More than 2000 fungal genes were differentially transcribed during appressorial maturation, penetration, and colonization. Secreted proteins, secondary metabolism genes, and membrane receptors were over-represented among the differentially expressed genes, suggesting that the fungus engages in an intimate and dynamic conversation with the host, beginning prior to penetration. This communication process probably involves reception of plant signals triggering subsequent developmental progress in the fungus, as well as production of signals that induce responses in the host. Later phases of biotrophy were more similar to necrotrophy, with increased production of secreted proteases, inducers of plant cell death, hydrolases, and membrane bound transporters for the uptake and egress of potential toxins, signals, and nutrients. Conclusions: This approach revealed, in unprecedented detail, fungal genes specifically expressed during critical phases of host penetration and biotrophic establishment. Many encoded secreted proteins, secondary metabolism enzymes, and receptors that may play roles in host-pathogen communication necessary to promote susceptibility, and thus may provide targets for chemical or biological controls to manage this important disease. The differentially expressed genes could be used as ‘landmarks’ to more accurately identify developmental progress in compatible versus incompatible interactions involving genetic variants of both host and pathogen

Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery

Biological organisms are complex systems that are composed of functional networks of interacting molecules and macro-molecules. Complex phenotypes are the result of orchestrated, hierarchical, heterogeneous collections of expressed genomic variants. However, the effects of these variants are the result of historic selective pressure and current environmental and epigenetic signals, and, as such, their co-occurrence can be seen as genome-wide correlations in a number of different manners. Biomass recalcitrance (i.e., the resistance of plants to degradation or deconstruction, which ultimately enables access to a plant’s sugars) is a complex polygenic phenotype of high importance to biofuels initiatives. This study makes use of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomic and pyMBMS data across this population, as well as co-expression and co-methylation networks in order to better understand the molecular interactions involved in recalcitrance, and identify target genes involved in lignin biosynthesis/degradation. A Lines Of Evidence (LOE) scoring system is developed to integrate the information in the different layers and quantify the number of lines of evidence linking genes to target functions. This new scoring system was applied to quantify the lines of evidence linking genes to lignin-related genes and phenotypes across the network layers, and allowed for the generation of new hypotheses surrounding potential new candidate genes involved in lignin biosynthesis in P. trichocarpa, including various AGAMOUS-LIKE genes. The resulting Genome Wide Association Study networks, integrated with Single Nucleotide Polymorphism (SNP) correlation, co-methylation, and co-expression networks through the LOE scores are proving to be a powerful approach to determine the pleiotropic and epistatic relationships underlying cellular functions and, as such, the molecular basis for complex phenotypes, such as recalcitrance

Comparative genomic analysis of toxin-negative strains of Clostridium difficile from humans and animals with symptoms of gastrointestinal disease

Background: Clostridium difficile infections (CDI) are a significant health problem to humans and food animals. Clostridial toxins ToxA and ToxB encoded by genes tcdA and tcdB are located on a pathogenicity locus known as the PaLoc and are the major virulence factors of C. difficile. While toxin-negative strains of C. difficile are often isolated from faeces of animals and patients suffering from CDI, they are not considered to play a role in disease. Toxin-negative strains of C. difficile have been used successfully to treat recurring CDI but their propensity to acquire the PaLoc via lateral gene transfer and express clinically relevant levels of toxins has reinforced the need to characterise them genetically. In addition, further studies that examine the pathogenic potential of toxin-negative strains of C. difficile and the frequency by which toxin-negative strains may acquire the PaLoc are needed. Results: We undertook a comparative genomic analysis of five Australian toxin-negative isolates of C. difficile that lack tcdA, tcdB and both binary toxin genes cdtA and cdtB that were recovered from humans and farm animals with symptoms of gastrointestinal disease. Our analyses show that the five C. difficile isolates cluster closely with virulent toxigenic strains of C. difficile belonging to the same sequence type (ST) and have virulence gene profiles akin to those in toxigenic strains. Furthermore, phage acquisition appears to have played a key role in the evolution of C. difficile. Conclusions: Our results are consistent with the C. difficile global population structure comprising six clades each containing both toxin-positive and toxin-negative strains. Our data also suggests that toxin-negative strains of C. difficile encode a repertoire of putative virulence factors that are similar to those found in toxigenic strains of C. difficile, raising the possibility that acquisition of PaLoc by toxin-negative strains poses a threat to human health. Studies in appropriate animal models are needed to examine the pathogenic potential of toxin-negative strains of C. difficile and to determine the frequency by which toxin-negative strains may acquire the PaLoc

Phylogenomics of plant genomes: a methodology for genome-wide searches for orthologs in plants

<p>Abstract</p> <p>Background</p> <p>Gene ortholog identification is now a major objective for mining the increasing amount of sequence data generated by complete or partial genome sequencing projects. Comparative and functional genomics urgently need a method for ortholog detection to reduce gene function inference and to aid in the identification of conserved or divergent genetic pathways between several species. As gene functions change during evolution, reconstructing the evolutionary history of genes should be a more accurate way to differentiate orthologs from paralogs. Phylogenomics takes into account phylogenetic information from high-throughput genome annotation and is the most straightforward way to infer orthologs. However, procedures for automatic detection of orthologs are still scarce and suffer from several limitations.</p> <p>Results</p> <p>We developed a procedure for ortholog prediction between <it>Oryza sativa </it>and <it>Arabidopsis thaliana</it>. Firstly, we established an efficient method to cluster <it>A. thaliana </it>and <it>O. sativa </it>full proteomes into gene families. Then, we developed an optimized phylogenomics pipeline for ortholog inference. We validated the full procedure using test sets of orthologs and paralogs to demonstrate that our method outperforms pairwise methods for ortholog predictions.</p> <p>Conclusion</p> <p>Our procedure achieved a high level of accuracy in predicting ortholog and paralog relationships. Phylogenomic predictions for all validated gene families in both species were easily achieved and we can conclude that our methodology outperforms similarly based methods.</p

Inference of the genetic network regulating lateral root initiation in Arabidopsis thaliana

Regulation of gene expression is crucial for organism growth, and it is one of the challenges in Systems Biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyse two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants to infer their regulatory network. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale-free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation