Spinning Gland Transcriptomics from Two Main Clades of Spiders (Order: Araneae) - Insights on Their Molecular, Anatomical and Behavioral Evolution

Characterized by distinctive evolutionary adaptations, spiders provide a comprehensive system for evolutionary and developmental studies of anatomical organs, including silk and venom production. Here we performed cDNA sequencing using massively parallel sequencers (454 GS-FLX Titanium) to generate ∼80,000 reads from the spinning gland of Actinopus spp. (infraorder: Mygalomorphae) and Gasteracantha cancriformis (infraorder: Araneomorphae, Orbiculariae clade). Actinopus spp. retains primitive characteristics on web usage and presents a single undifferentiated spinning gland while the orbiculariae spiders have seven differentiated spinning glands and complex patterns of web usage. MIRA, Celera Assembler and CAP3 software were used to cluster NGS reads for each spider. CAP3 unigenes passed through a pipeline for automatic annotation, classification by biological function, and comparative transcriptomics. Genes related to spider silks were manually curated and analyzed. Although a single spidroin gene family was found in Actinopus spp., a vast repertoire of specialized spider silk proteins was encountered in orbiculariae. Astacin-like metalloproteases (meprin subfamily) were shown to be some of the most sampled unigenes and duplicated gene families in G. cancriformis since its evolutionary split from mygalomorphs. Our results confirm that the evolution of the molecular repertoire of silk proteins was accompanied by the (i) anatomical differentiation of spinning glands and (ii) behavioral complexification in the web usage. Finally, a phylogenetic tree was constructed to cluster most of the known spidroins in gene clades. This is the first large-scale, multi-organism transcriptome for spider spinning glands and a first step into a broad understanding of spider web systems biology and evolution

Hagit Shatkay

Current genomic research is characterized by immense volume of data, accompanied by a tremendous increase in the number of gene-related publications. This wealth of information presents a major data-analysis challenge. An ultimate goal is to understand the complex biological interrelationships among all discovered genes and proteins. Scanning the abundant literature available about each gene, and plenty of human expertise are currently required as a step towards meeting this goal. As has been recently noted by several research groups, automated systems for extracting relevant information from the literature can complement existing techniques, speed-up the analysis process, and greatly enhance our understanding of genetic processes. We present a new approach, based on probabilistic information retrieval, which uses the literature to establish functional relationships among genes on a genome-wide scale. Experiments applied to documents discussing yeast genes, and a comparison of the results to well-established gene functions, demonstrate the effectiveness of our approach.

Concentrated Announcements on Clustered Data: An Event Study on Biotechnology Stocks

In spring 2000, three events-two political statements by Bill Clinton and Tony Blair and a breakthrough announcement by Celera Genomics-had a major impact on biotechnology stocks. We analyze their effects over a comprehensive set of biopharmaceutical companies, using a composite return-generating model with an industry-specific patent-based factor. Our results show that stocks can be clustered according to their responsiveness to political and scientific events. Furthermore, we emphasize different impacts on the market value of intangible assets for each cluster, suggesting that growth options are valued with different criteria for therapeutics, and technology-based subsectors. Copyright (c) 2006 Financial Management Association International.

The sequence of the Human Genome

International audienc