Investigating the role of interleukin-1 beta and glutamate in inflammatory bowel disease and epilepsy using discovery browsing

Abstract Background Structured electronic health records are a rich resource for identifying novel correlations, such as co-morbidities and adverse drug reactions. For drug development and better understanding of biomedical phenomena, such correlations need to be supported by viable hypotheses about the mechanisms involved, which can then form the basis of experimental investigations. Methods In this study, we demonstrate the use of discovery browsing, a literature-based discovery method, to generate plausible hypotheses elucidating correlations identified from structured clinical data. The method is supported by Semantic MEDLINE web application, which pinpoints interesting concepts and relevant MEDLINE citations, which are used to build a coherent hypothesis. Results Discovery browsing revealed a plausible explanation for the correlation between epilepsy and inflammatory bowel disease that was found in an earlier population study. The generated hypothesis involves interleukin-1 beta (IL-1 beta) and glutamate, and suggests that IL-1 beta influence on glutamate levels is involved in the etiology of both epilepsy and inflammatory bowel disease. Conclusions The approach presented in this paper can supplement population-based correlation studies by enabling the scientist to identify literature that may justify the novel patterns identified in such studies and can underpin basic biomedical research that can lead to improved treatments and better healthcare outcomes

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

<div><p>Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The <i>p53</i> pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.</p></div

The KEGG <i>p53</i> signaling pathway entry showing the pathway map and some reference citations.

<p>The PMIDs for each reference citation were used to identify MeSH terms with PubMed (see section <i>Predication network generation</i>). <a href="http://www.genome.jp/kegg-bin/show_pathway?hsa04115" target="_blank">http://www.genome.jp/kegg-bin/show_pathway?hsa04115</a>.</p

Literature validation of newly included interactions.

<p>Each interaction was evaluated against source literature to evaluate its accuracy. Evaluation included whether an interaction between the indicated genes was actually contained in the sentence, and whether the resulting sign (−/inhibits or +/stimulates) corresponded to the assertion in the sentence. The upper half includes every interaction regardless of weight, whereas the lower half refers to only interactions with weight >0.1.</p

Artificial neural network fitness function.

<p>Each contributory gene's expression in the current time point is magnified by its weight of interaction with the target gene. The sum of these contributions determines the expression level of the target gene at the subsequent time point. <i>t</i> indicates the time point in the microarray dataset, <i>n</i> is the number of genes in the pathway, is the weight of the interaction between gene <i>i</i> and gene <i>j</i>, is the gene expression value in the microarray set for gene <i>i</i> at time point <i>t</i>.</p

Comparison of fitness to microarray data.

<p>Fitness of our model is compared to that of the Keedwell et al. model and the TDSRC model on our microarray dataset. Fitness is significantly better (p<0.05) in 4 out of 6 time points and for all time points combined (overall).</p

Comparison of fitness to yeast cell cycle microarray data.

<p>Fitness of our model is compared to that of the Keedwell et al. model on a yeast cell cycle microarray dataset. Fitness is significantly better (p<0.05) over all points.</p

Precision of yeast interactions.

<p>Each gene and interaction predicted by our model was compared against interactions contained in the Biogrid dataset. Predicted: number of genes and interactions predicted by our model. In Biogrid: number of genes and interactions predicted by our model and found in the Biogrid dataset. Precision: number found/number predicted.</p

Literature-based gene regulatory network discovery schema.

<p>Step 1) a network is formed from semantic predications extracted from MEDLINE by SemRep for each set of citations related to a given pathway; Step 2) each network is filtered by predication argument distance and frequency; Step 3) a genetic algorithm uses gene expression data to quantify the weight of the interactions of the gene regulatory network.</p