POSEIDON study: a pilot, safety and feasibility trial of high-dose omega3 fatty acids and high-dose cholecalciferol supplementation in type 1 diabetes.

The anti-inflammatory and immunomodulatory properties of high-dose omega-3 fatty acids and Vitamin D, and the initial encouraging results from case reports on the use of this supplementation in new-onset Type 1 Diabetes (T1D), support further testing of this combination strategy. This intervention appears to be well tolerated, affordable, and sufficiently safe to be further tested in randomized prospective trials to determine whether this combination therapy may be of assistance to halt progression of autoimmunity and/or preserve residual beta-cell function in subjects with new onset and established T1D of up to 10 years duration. In addition, the 1st PreDiRe T1D conference (Preventing Disease and its Recurrence in Type 1 Diabetes - see Editorial in this issue) was organized to discuss initial results and possible alternative/complementary strategies, for collaborative international expansion of these trials, to include strategies for disease prevention. Our POSEIDON clinical trial will test the use of high dose vitamin D3 and highly purified Omega-3 fatty acids in new onset and established T1D. The draft of the study protocol, in addition to the informed consent and assent, is now shared open access to facilitate its international implementation by interested physicians and centers that would like to further test this approach through clinical trials.

Mining functional information associated with expression arrays.

Deciphering the networks of interactions between molecules in biological systems has gained momentum with the monitoring of gene expression patterns at the genomic scale. Expression array experiments provide vast amounts of experimental data about these networks, the analysis of which requires new computational methods. In particular, issues related to the extraction of biological information are key for the end users. We propose here a strategy, implemented in a system called GEISHA (gene expression information system for human analysis) and able to detect biological terms significantly associated to different gene expression clusters by mining collections of Medline abstracts. GEISHA is based on a comparison of the frequency of abstracts linked to different gene clusters and containing a given term. Interpretation by the end user of the biological meaning of the terms is facilitated by embedding them in the corresponding significant sentences and abstracts and by establishing relations with other, equally significant terms. The information provided by GEISHA for the available yeast expression data compares favorably with the functional annotations provided by human experts, demonstrating the potential value of GEISHA as an assistant for the analysis of expression array experiments.

The potential use of SUISEKI as a protein interaction discovery tool.

Relevant information about protein interactions is stored in textual sources. This sources are commonly used not only as archives of what is already known but also as information for generating new knowledge, particularly to pose hypothesis about new possible interactions that can be inferred from the existing ones. This task is the more creative part of scientific work in experimental systems. We present a large-scale analysis for the prediction of new interactions based on the interaction network for the ones already known and detected automatically in the literature. During the last few years it has became clear that part of the information about protein interactions could be extracted with automatic tools, even if these tools are still far from perfect and key problems such as detection of protein names are not completely solved. We have developed a integrated automatic approach, called SUISEKI (System for Information Extraction on Interactions), able to extract protein interactions from collections of Medline abstracts. Previous experiments with the system have shown that it is able to extract almost 70% of the interactions present in relatively large text corpus, with an accuracy of approximately 80% (for the best defined interactions) that makes the system usable in real scenarios, both at the level of extraction of protein names and at the level of extracting interaction between them. With the analysis of the interaction map of Saccharomyces cerevisiae we show that interactions published in the years 2000/2001 frequently correspond to proteins or genes that were already very close in the interaction network deduced from the literature published before these years and that they are often connected to the same proteins. That is, discoveries are commonly done among highly connected entities. Some biologically relevant examples illustrate how interactions described in the year 2000 could have been proposed as reasonable working hypothesis with the information previously available in the automatically extracted network of interactions.

Can bibliographic pointers for known biological data be found automatically? Protein interactions as a case study.

The Dictionary of Interacting Proteins (DIP) (Xenarios et al., 2000) is a large repository of protein interactions: its March 2000 release included 2379 protein pairs whose interactions have been detected by experimental methods. Even if many of these correspond to poorly characterized proteins, the result of massive yeast two-hybrid screenings, as many as 851 correspond to interactions detected using direct biochemical methods.We used information retrieval technology to search automatically for sentences in Medline abstracts that support these 851 DIP interactions. Surprisingly, we found correspondence between DIP protein pairs and Medline sentences describing their interactions in only 30% of the cases. This low coverage has interesting consequences regarding the quality of annotations (references) introduced in the database and the limitations of the application of information extraction (IE) technology to Molecular Biology. It is clear that the limitation of analyzing abstracts rather than full papers and the lack of standard protein names are difficulties of considerably more importance than the limitations of the IE methodology employed. A positive finding is the capacity of the IE system to identify new relations between proteins, even in a set of proteins previously characterized by human experts. These identifications are made with a considerable degree of precision. This is, to our knowledge, the first large scale assessment of IE capacity to detect previously known interactions: we thus propose the use of the DIP data set as a biological reference to benchmark IE systems.

Expression profiles and biological function.

Expression arrays facilitate the monitoring of changes in expression patterns of large collections of genes. It is generally expected that genes with similar expression patterns would correspond to proteins of common biological function. We assess this common assumption by comparing levels of similarity of expression patterns and statistical significance of biological terms that describe the corresponding protein functions. Terms are automatically obtained by mining large collections of Medline abstracts. We propose that the combined use of the tools for expression profiles clustering and automatic function retrieval, can be useful tools for the detection of biologically relevant associations between genes in complex gene expression experiments. The results obtained using publicly available experimental data show how, in general, an increase in the similarity of the expression patterns is accompanied by an enhancement of the amount of specific functional information or, in other words, how the selected terms became more specific following an increase in the specificity of the expression patterns. Particularly interesting are the discrepancies from this general trend, i.e. groups of genes with similar expression patterns but very little in common at the functional level. In these cases the similarity of their expression profiles becomes the first link between previously unrelated genes.

Automatic extraction of biological information from scientific text: protein-protein interactions.

We describe the basic design of a system for automatic detection of protein-protein interactions extracted from scientific abstracts. By restricting the problem domain and imposing a number of strong assumptions which include pre-specified protein names and a limited set of verbs that represent actions, we show that it is possible to perform accurate information extraction. The performance of the system is evaluated with different cases of real-world interaction networks, including the Drosophila cell cycle control. The results obtained computationally are in good agreement with current biological knowledge and demonstrate the feasibility of developing a fully automated system able to describe networks of protein interactions with sufficient accuracy.