Application of Granger causality to gene regulatory network discovery

Article no. 6314142Granger causality (GC) has been applied to gene regulatory network discovery using DNA microarray time-series data. Since the number of genes is much larger than the data length, a full model cannot be applied in a straightforward manner, hence GC is often applied to genes pairwisely. In this paper, firstly we investigate with synthetic data and point out how spurious causalities (false discoveries) may emerge in pairwise GC detection. In addition, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. Therefore, besides using a suitable model order, we recommend a full model over pairwise GC. This is possible if pairwise GC is first used to identify a network of interactions among only a few genes, and then all these interactions are validated with a full model again. If a full model is not possible, we recommend using model validation techniques to remove spurious discoveries. Secondly, we apply pairwise GC with model validation to a real dataset (HeLa). To estimate the model order, the Akaike information criterion is found to be more suitable than the Bayesian information criterion. Degree distribution and network hubs are obtained and compared with previous publications. The hubs tend to act as sources of interactions rather than receivers of interactions. © 2012 IEEE.published_or_final_versio

Meta-analysis on gene regulatory networks discovered by pairwise Granger causality

Identifying regulatory genes partaking in disease development is important to medical advances. Since gene expression data of multiple experiments exist, combining results from multiple gene regulatory network discoveries offers higher sensitivity and specificity. However, data for multiple experiments on the same problem may not possess the same set of genes, and hence many existing combining methods are not applicable. In this paper, we approach this problem using a number of meta-analysis methods and compare their performances. Simulation results show that vote counting is outperformed by methods belonging to the Fisher's chi-square (FCS) family, of which FCS test is the best. Applying FCS test to the real human HeLa cell-cycle dataset, degree distributions of the combined network is obtained and compared with previous works. Consulting the BioGRID database reveals the biological relevance of gene regulatory networks discovered using the proposed method.published_or_final_versio

Nieuwe klassenstrijd

Evaluation of gene regulatory network (GRN) discovery methods relies heavily on synthetic time series. However, synthetic data generated by traditional method deviate a lot from real data, making such evaluation questionable. Guiding by decaying sinusoids, we propose a new method that generates synthetic data resembling human (HeLa) cell-cycle gene expression data. Using the new synthetic data, a simple comparison between four GRN discovery methods reveals that Granger causality (GC) methods substantially outperform Pearson correlation coefficient (PCC), while time-shifted PCC can give comparable performance as GC methods. The new synthetic data generation would also be useful for generating other kinds of cell-cycle time series. Using data generated by our proposed method, evaluation of GRN discovery methods should be more trustworthy for real-data applications.published_or_final_versio

Unfolded protein response leading to cataractogenesis in a microphthalmia cataract mouse mutant

Congenital cataract is a leading cause of visual disability among children worldwide, it has a heterogeneous genetic basis but the cellular and molecular bases for cataractogenesis remain elusive. In our laboratory we have a spontaneously occurring, autosomal dominant mouse mutant named Secc, which displays combined features of small eye, cataract and closed eyelid. By mapping and sequencing we identified a single point deletion at nucleotide 273 of the Cryga gene, leading to a frame-shift from the 3rd Greek Key motif of the gamma-A-crystallin. Cataract features were initiated in E14.5 Secc mutant, the nuclei of the primary lens fibres were scattered and failed to align in the equatorial region. By E16.5, the secondary lens fibre cells were abnormally arranged with poor lens suture formation. Apoptotic cells were found in the centre of the lens as shown by TUNEL assay, the cytoskeleton and cell adhesion in the lens centre were disturbed as shown in immunohistochemistry analysis. By western blotting we found that mutant gamma-crystallins were reduced in amount and enriched in the insoluble fraction, suggesting that mutant gamma-a-crystallins were misfolded and protein aggregates were formed. We found that the expression of genes involved in the unfolded protein response (UPR) pathways including BiP, CHOP and spliced variant of XBP-1 were all up-regulated significantly in E14.5 and 16.5 mutant lenses. Therefore, the mutant gamma-A-crystallin appeared to trigger UPR and cell death in the fibre cells. The mutant cells lost their normal cell adhesion, failed to maintain the proper lens architecture, leading to cataract formation.link_to_OA_fulltex

A mutant gA-crystallin leads to cataractogenesis through the unfolded protein response

Oral Presentation - Session 2: Reproduction & Development, Musculoskeletal System and Cell Biology: O.2.15The 15th Research Postgraduate Symposium (RPS 2010), the University of Hong Kong, Hong Kong, China, 1-2 December 2010