Large-scale analysis of Arabidopsis transcription reveals a basal co-regulation network

<p>Abstract</p> <p>Background</p> <p>Analyses of gene expression data from microarray experiments has become a central tool for identifying co-regulated, functional gene modules. A crucial aspect of such analysis is the integration of data from different experiments and different laboratories. How to weigh the contribution of different experiments is an important point influencing the final outcomes. We have developed a novel method for this integration, and applied it to genome-wide data from multiple Arabidopsis microarray experiments performed under a variety of experimental conditions. The goal of this study is to identify functional globally co-regulated gene modules in the Arabidopsis genome.</p> <p>Results</p> <p>Following the analysis of 21,000 Arabidopsis genes in 43 datasets and about 2 × 10⁸gene pairs, we identified a globally co-expressed gene network. We found clusters of globally co-expressed Arabidopsis genes that are enriched for known Gene Ontology annotations. Two types of modules were identified in the regulatory network that differed in their sensitivity to the node-scoring parameter; we further showed these two pertain to general and specialized modules. Some of these modules were further investigated using the <it>Genevestigator </it>compendium of microarray experiments. Analyses of smaller subsets of data lead to the identification of condition-specific modules.</p> <p>Conclusion</p> <p>Our method for identification of gene clusters allows the integration of diverse microarray experiments from many sources. The analysis reveals that part of the <it>Arabidopsis </it>transcriptome is globally co-expressed, and can be further divided into known as well as novel functional gene modules. Our methodology is general enough to apply to any set of microarray experiments, using any scoring function.</p

Adaptive Evolution of the Venom-Targeted vWF Protein in Opossums that Eat Pitvipers

The rapid evolution of venom toxin genes is often explained as the result of a biochemical arms race between venomous animals and their prey. However, it is not clear that an arms race analogy is appropriate in this context because there is no published evidence for rapid evolution in genes that might confer toxin resistance among routinely envenomed species. Here we report such evidence from an unusual predator-prey relationship between opossums (Marsupialia: Didelphidae) and pitvipers (Serpentes: Crotalinae). In particular, we found high ratios of replacement to silent substitutions in the gene encoding von Willebrand Factor (vWF), a venom-targeted hemostatic blood protein, in a clade of opossums known to eat pitvipers and to be resistant to their hemorrhagic venom. Observed amino-acid substitutions in venom-resistant opossums include changes in net charge and hydrophobicity that are hypothesized to weaken the bond between vWF and one of its toxic snake-venom ligands, the C-type lectin-like protein botrocetin. Our results provide the first example of rapid adaptive evolution in any venom-targeted molecule, and they support the notion that an evolutionary arms race might be driving the rapid evolution of snake venoms. However, in the arms race implied by our results, venomous snakes are prey, and their venom has a correspondingly defensive function in addition to its usual trophic role

Interactome mapping provides a network of neurodegenerative disease proteins and uncovers widespread protein aggregation in affected brains

Interactome maps are valuable resources to elucidate protein function and disease mechanisms. Here, we report on an interactome map that focuses on neurodegenerative disease (ND), connects ∼5,000 human proteins via ∼30,000 candidate interactions and is generated by systematic yeast two-hybrid interaction screening of ∼500 ND-related proteins and integration of literature interactions. This network reveals interconnectivity across diseases and links many known ND-causing proteins, such as α-synuclein, TDP-43, and ATXN1, to a host of proteins previously unrelated to NDs. It facilitates the identification of interacting proteins that significantly influence mutant TDP-43 and HTT toxicity in transgenic flies, as well as of ARF-GEP(100) that controls misfolding and aggregation of multiple ND-causing proteins in experimental model systems. Furthermore, it enables the prediction of ND-specific subnetworks and the identification of proteins, such as ATXN1 and MKL1, that are abnormally aggregated in postmortem brains of Alzheimer's disease patients, suggesting widespread protein aggregation in NDs

Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.National Institutes of Health (U.S.) (Grant GM081871