Reverse-engineering transcriptional modules from gene expression data

"Module networks" are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions. We present a method to infer ensembles of such networks and an averaging procedure to extract the statistically most significant modules and their regulators. We show that the inferred probabilistic models extend beyond the data set used to learn the models.Comment: 5 pages REVTeX, 4 figure

Coordinated functional divergence of genes after genome duplication in Arabidopsis thaliana

Gene and genome duplications have been rampant during the evolution of flowering plants. Unlike small-scale gene duplications, whole-genome duplications (WGDs) copy entire pathways or networks, and as such create the unique situation in which such duplicated pathways or networks could evolve novel functionality through the coordinated sub-or neofunctionalization of its constituent genes. Here, we describe a remarkable case of coordinated gene expression divergence following WGDs in Arabidopsis thaliana. We identified a set of 92 homoeologous gene pairs that all show a similar pattern of tissue-specific gene expression divergence following WGD, with one homoeolog showing predominant expression in aerial tissues and the other homoeolog showing biased expression in tip-growth tissues. We provide evidence that this pattern of gene expression divergence seems to involve genes with a role in cell polarity and that likely function in the maintenance of cell wall integrity. Following WGD, many of these duplicated genes evolved separate functions through subfunctionalization in growth/development and stress response. Uncoupling these processes through genome duplications likely provided important adaptations with respect to growth and morphogenesis and defense against biotic and abiotic stress

Gene duplicability of core genes is highly consistent across all angiosperms

Gene duplication is an important mechanism for adding to genomic novelty. Hence, which genes undergo duplication and are preserved following duplication is an important question. It has been observed that gene duplicability, or the ability of genes to be retained following duplication, is a nonrandom process, with certain genes being more amenable to survive duplication events than others. Primarily, gene essentiality and the type of duplication (small-scale versus large-scale) have been shown in different species to influence the (long-term) survival of novel genes. However, an overarching view of "gene duplicability" is lacking, mainly due to the fact that previous studies usually focused on individual species and did not account for the influence of genomic context and the time of duplication. Here, we present a large-scale study in which we investigated duplicate retention for 9178 gene families shared between 37 flowering plant species, referred to as angiosperm core gene families. For most gene families, we observe a strikingly consistent pattern of gene duplicability across species, with gene families being either primarily single-copy or multicopy in all species. An intermediate class contains gene families that are often retained in duplicate for periods extending to tens of millions of years after whole-genome duplication, but ultimately appear to be largely restored to singleton status, suggesting that these genes may be dosage balance sensitive. The distinction between single-copy and multicopy gene families is reflected in their functional annotation, with single-copy genes being mainly involved in the maintenance of genome stability and organelle function and multicopy genes in signaling, transport, and metabolism. The intermediate class was overrepresented in regulatory genes, further suggesting that these represent putative dosage-balance-sensitive genes

Nosema neumanni n. sp. (Microsporidia, Nosematidae), a new microsporidian parasite of honeybees, Apis mellifera in Uganda

The microsporidium Nosema neumanni n. sp., a new parasite of the honeybee Apis mellifera is described based on its ultra structural and molecular characteristics. Structures resembling microsporidian spores were found by microscopic examination of honeybees from Uganda. Molecular confirmation failed when PCR primers specific for Nosema apis and Nosema ceranae were used, but was successful with primers covering the whole family of Nosematidae. We performed transmission electron microscopy and found typical microsporidian spores which were smaller (length: 2.36 +/- 0.14 m and width: 1.78 +/- 0.06 p.m; n = 6) and had fewer polar filament coils (10-12) when compared to those of known species infecting honeybees. The entire 16S SS(J rRNA region was amplified, cloned and sequenced and was found to be unique with the highest resemblance (97% identity) to N. apis, The incidence of N. neumanni n. sp. in Ugandan honeybees was found to be much higher than of the two other Nosema species

MarkerMiner 1.0: a new application for phylogenetic marker development using angiosperm transcriptomes

Premise of the study: Targeted sequencing using next-generation sequencing (NGS) platforms offers enormous potential for plant systematics by enabling economical acquisition of multilocus data sets that can resolve difficult phylogenetic problems. However, because discovery of single-copy nuclear (SCN) loci from NGS data requires both bioinformatics skills and access to high-performance computing resources, the application of NGS data has been limited. Methods and Results: We developed MarkerMiner 1.0, a fully automated, open-access bioinformatic workflow and application for discovery of SCN loci in angiosperms. Our new tool identified as many as 1993 SCN loci from transcriptomic data sampled as part of four independent test cases representing marker development projects at different phylogenetic scales. Conclusions: MarkerMiner is an easy-to-use and effective tool for discovery of putative SCN loci. It can be run locally or via the Web, and its tabular and alignment outputs facilitate efficient downstream assessments of phylogenetic utility, locus selection, intron-exon boundary prediction, and primer or probe development

Sequence-specific protein aggregation generates defined protein knockdowns in plants

Protein aggregation is determined by short (5-15 amino acids) aggregation-prone regions (APRs) of the polypeptide sequence that self-associate in a specific manner to form beta-structured inclusions. Here, we demonstrate that the sequence specificity of APRs can be exploited to selectively knock down proteins with different localization and function in plants. Synthetic aggregation-prone peptides derived from the APRs of either the negative regulators of the brassinosteroid (BR) signaling, the glycogen synthase kinase 3/Arabidopsis SHAGGY-like kinases (GSK3/ASKs), or the starch-degrading enzyme alpha-glucan water dikinase were designed. Stable expression of the APRs in Arabidopsis (Arabidopsis thaliana) and maize (Zea mays) induced aggregation of the target proteins, giving rise to plants displaying constitutive BR responses and increased starch content, respectively. Overall, we show that the sequence specificity of APRs can be harnessed to generate aggregation-associated phenotypes in a targeted manner in different subcellular compartments. This study points toward the potential application of induced targeted aggregation as a useful tool to knock down protein functions in plants and, especially, to generate beneficial traits in crops

Query-based biclustering of gene expression data using Probabilistic Relational Models

Background: With the availability of large scale expression compendia it is now possible to view own findings in the light of what is already available and retrieve genes with an expression profile similar to a set of genes of interest (i.e., a query or seed set) for a subset of conditions. To that end, a query-based strategy is needed that maximally exploits the coexpression behaviour of the seed genes to guide the biclustering, but that at the same time is robust against the presence of noisy genes in the seed set as seed genes are often assumed, but not guaranteed to be coexpressed in the queried compendium. Therefore, we developed ProBic, a query-based biclustering strategy based on Probabilistic Relational Models (PRMs) that exploits the use of prior distributions to extract the information contained within the seed set.status: publishe

Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks

We have compared a recently developed module-based algorithm LeMoNe for reverse-engineering transcriptional regulatory networks to a mutual information based direct algorithm CLR, using benchmark expression data and databases of known transcriptional regulatory interactions for Escherichia coli and Saccharomyces cerevisiae. A global comparison using recall versus precision curves hides the topologically distinct nature of the inferred networks and is not informative about the specific subtasks for which each method is most suited. Analysis of the degree distributions and a regulator specific comparison show that CLR is 'regulator-centric', making true predictions for a higher number of regulators, while LeMoNe is 'target-centric', recovering a higher number of known targets for fewer regulators, with limited overlap in the predicted interactions between both methods. Detailed biological examples in E. coli and S. cerevisiae are used to illustrate these differences and to prove that each method is able to infer parts of the network where the other fails. Biological validation of the inferred networks cautions against over-interpreting recall and precision values computed using incomplete reference networks.Comment: 13 pages, 1 table, 6 figures + 6 pages supplementary information (1 table, 5 figures

Lack of GLYCOLATE OXIDASE1, but Not GLYCOLATE OXIDASE2, Attenuates the Photorespiratory Phenotype of CATALASE2-Deficient Arabidopsis

The genes coding for the core metabolic enzymes of the photorespiratory pathway that allows plants with C3-type photosynthesis to survive in an oxygen-rich atmosphere, have been largely discovered in genetic screens aimed to isolate mutants that are unviable under ambient air. As an exception, glycolate oxidase (GOX) mutants with a photorespiratory phenotype have not been described yet in C3 species. Using Arabidopsis (Arabidopsis thaliana) mutants lacking the peroxisomal CATALASE2 (cat2-2) that display stunted growth and cell death lesions under ambient air, we isolated a second-site loss-of-function mutation in GLYCOLATE OXIDASE1 (GOX1) that attenuated the photorespiratory phenotype of cat2-2. Interestingly, knocking out the nearly identical GOX2 in the cat2-2 background did not affect the photorespiratory phenotype, indicating that GOX1 and GOX2 play distinct metabolic roles. We further investigated their individual functions in single gox1-1 and gox2-1 mutants and revealed that their phenotypes can be modulated by environmental conditions that increase the metabolic flux through the photorespiratory pathway. High light negatively affected the photosynthetic performance and growth of both gox1-1 and gox2-1 mutants, but the negative consequences of severe photorespiration were more pronounced in the absence of GOX1, which was accompanied with lesser ability to process glycolate. Taken together, our results point toward divergent functions of the two photorespiratory GOX isoforms in Arabidopsis and contribute to a better understanding of the photorespiratory pathway.Peer reviewe