Report from the 2nd Summer School in Computational Biology organized by the Queen's University of Belfast

In this paper, we present a meeting report for the 2nd Summer School in Computational Biology organized by the Queen's University of Belfast. We describe the organization of the summer school, its underlying concept and student feedback we received after the completion of the summer school.

Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.

RMA with quantile normalization mixes biological signals between different sample groups in microarray data analysis

Quantile normalization (QN) is a technique for microarray data processing and is the default normalization method in the Robust Multi-array Average (RMA) procedure, which was primarily designed for analysing gene expression data from Affymetrix arrays. Given the abundance of Affymetrix microarrays and the popularity of the RMA method, it is crucially important that the normalization procedure is applied appropriately. In this study we carried out simulation experiments and also analysed real microarray data to investigate the suitability of RMA when it is applied to dataset with different groups of biological samples. From our experiments, we showed that RMA with QN does not preserve the biological signal included in each group, but rather it would mix the signals between the groups. We also showed that the Median Polish method in the summarization step of RMA has similar mixing effect. RMA is one of the most widely used methods in microarray data processing and has been applied to a vast volume of data in biomedical research. The problematic behaviour of this method suggests that previous studies employing RMA could have been misadvised or adversely affected. Therefore we think it is crucially important that the research community recognizes the issue and starts to address it. The two core elements of the RMA method, quantile normalization and Median Polish, both have the undesirable effects of mixing biological signals between different sample groups, which can be detrimental to drawing valid biological conclusions and to any subsequent analyses. Based on the evidence presented here and that in the literature, we recommend exercising caution when using RMA as a method of processing microarray gene expression data, particularly in situations where there are likely to be unknown subgroups of samples.

Prognostic significance of combining VEGFA, FLT1 and KDR mRNA expression in lung cancer

Angiogenesis is important in cancer progression. Promising results in clinical trials have indicated that targeting vascular epidermal growth factor (VEGF) signaling may prolong lung cancer patient survival. In particular, various studies have implicated VEGFA as a potential prognostic marker in lung cancer, although prognostication using the expression of VEGF receptors (VEGFRs), such as fms-related tyrosine kinase 1 (FLT1; also known as VEGFR1) and kinase insert domain receptor (KDR; also known as VEGFR2), has produced varied results in different lung cancer studies. The present study aimed to investigate the prognostic significance of these three factors, alone or in combination. mRNA expression data were extracted from four independent lung cancer cohorts totaling 583 patients, and the association between mRNA expression and survival was investigated by performing statistical analyses. When VEGFA, FLT1 and KDR expression were considered alone, only VEGFA demonstrated a significant association with patient survival consistently across all four datasets (P<0.05). Patients with a high expression of VEGFA and one of the two receptors were associated with significantly worse survival than patients expressing low levels of VEGFA and the particular receptor (P<0.05). Notably, patients with a high level expression of all three genes in their tumor specimens were associated with a significantly shorter survival time compared with patients exhibiting a low level expression of one, two or all three genes (P<0.05). The results indicate that a high level of VEGFA expression and its receptors may be required for cancer progression. Therefore, these three factors should be considered together as a prognostic indicator for lung cancer patients.

PRL-3 activates mTORC1 in Cancer Progression

PRL-3, a metastasis-associated phosphatase, is known to exert its oncogenic functions through activation of PI3K/Akt, which is a key regulator of the rapamycin-sensitive mTOR complex 1 (mTORC1), but a coherent link between PRL-3 and activation of mTOR has not yet been formally demonstrated. We report a positive correlation between PRL-3 expression and mTOR phospho-activation in clinical tumour samples and mouse models of cancer and demonstrate that PRL-3 increased downstream signalling to the mTOR substrates, p70S6K and 4E-BP1, by increasing PI3K/Akt-mediated activation of Rheb-GTP via TSC2 suppression. We also show that PRL-3 increases mTOR translocation to lysosomes via increased mTOR binding affinity to Rag GTPases in an Akt-independent manner, demonstrating a previously undescribed mechanism of action for PRL-3. PRL-3 also enhanced matrix metalloproteinase-2 secretion and cellular invasiveness via activation of mTOR, attributes which were sensitive to rapamycin treatment. The downstream effects of PRL-3 were maintained even under conditions of environmental stress, suggesting that PRL-3 provides a strategic survival advantage to tumour cells via its effects on mTOR.

Comparative Raman Study of Carbon Nanotubes Prepared by D.C. Arc Discharge and Catalytic Methods

The Raman spectra of carbon nanotubes prepared by catalytic (C-CNT) and d.c. arc discharge (D-CNT) methods are reported. A previously unnoticed third-order Raman peak at ca. 4248 cm-1 was observed in the Raman spectrum of D-CNT. The Raman features of D-CNT and C-CNT are similar to those of highly oriented pyrolytic graphite (HOPG) and active carbon, respectively. The data also suggest that the increase in disorder in D-CNT compared with HOPG is due to structural defects in D-CNT. © 1997 by John Wiley & Sons, Ltd.

SOPS: A System for Efficient Processing of Spatial-Keyword Publish/Subscribe

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. These geo-textual data cover a wide range of topics. Users are interested in receiving up-to-date geo-textual objects (e.g., geo-tagged Tweets) such that their locations meet users’ need and their texts are interesting to users. For example, a user may want to be updated with tweets near her home on the topic “dengue fever headache.” In this demonstration, we present SOPS, the Spatial-Keyword Publish/Subscribe System, that is capable of efficiently processing spatial keyword continuous queries. SOPS supports two types of queries: (1) Boolean Range Continuous (BRC) query that can be used to subscribe the geo-textual objects satisfying a boolean keyword expression and falling in a specified spatial region; (2) Temporal Spatial-Keyword Top-k Continuous (TaSK) query that continuously maintains up-to-date top-k most relevant results over a stream of geo-textual objects. SOPS enables users to formulate their queries and view the real-time results over a stream of geotextual objects by browser-based user interfaces. On the server side, we propose solutions to efficiently processing a large number of BRC queries (tens of millions) and TaSK queries over a stream of geo-textual objects.