Epigenetic and Transcriptomic Regulation of Lung Repair during Recovery from Influenza Infection.

Seasonal and pandemic influenza is a cause of morbidity and mortality worldwide. Most people infected with influenza virus display mild-to-moderate disease phenotypes and recover within a few weeks. Influenza is known to cause persistent alveolitis in animal models; however, little is known about the molecular pathways involved in this phenotype. We challenged C57BL/6 mice with influenza A/PR/8/34 and examined lung pathologic processes and inflammation, as well as transcriptomic and epigenetic changes at 21 to 60 days after infection. Influenza induced persistent parenchymal lung inflammation, alveolar epithelial metaplasia, and epithelial endoplasmic reticulum stress that were evident after the clearance of virus and resolution of morbidity. Influenza infection induced robust changes in the lung transcriptome, including a significant impact on inflammatory and extracellular matrix protein expression. Despite the robust changes in lung gene expression, preceding influenza (21 days) did not exacerbate secondary Staphylococcus aureus infection. Finally, we examined the impact of influenza on miRNA expression in the lung and found an increase in miR-155. miR-155 knockout mice recovered from influenza infection faster than controls and had decreased lung inflammation and endoplasmic reticulum stress. These data illuminate the dynamic molecular changes in the lung in the weeks after influenza infection and characterize the repair process, identifying a novel role for miR-155.

T-ReCS: stable selection of dynamically formed groups of features with application to prediction of clinical outcomes.

Feature selection is used extensively in biomedical research for biomarker identification and patient classification, both of which are essential steps in developing personalized medicine strategies. However, the structured nature of the biological datasets and high correlation of variables frequently yield multiple equally optimal signatures, thus making traditional feature selection methods unstable. Features selected based on one cohort of patients, may not work as well in another cohort. In addition, biologically important features may be missed due to selection of other co-clustered features We propose a new method, Tree-guided Recursive Cluster Selection (T-ReCS), for efficient selection of grouped features. T-ReCS significantly improves predictive stability while maintains the same level of accuracy. T-ReCS does not require an a priori knowledge of the clusters like group-lasso and also can handle "orphan" features (not belonging to a cluster). T-ReCS can be used with categorical or survival target variables. Tested on simulated and real expression data from breast cancer and lung diseases and survival data, T-ReCS selected stable cluster features without significant loss in classification accuracy.

Spectral clustering strategies for heterogeneous disease expression data.

Clustering of gene expression data simplifies subsequent data analyses and forms the basis of numerous approaches for biomarker identification, prediction of clinical outcome, and personalized therapeutic strategies. The most popular clustering methods such as K-means and hierarchical clustering are intuitive and easy to use, but they require arbitrary choices on their various parameters (number of clusters for K-means, and a threshold to cut the tree for hierarchical clustering). Human disease gene expression data are in general more difficult to cluster efficiently due to background (genotype) heterogeneity, disease stage and progression differences and disease subtyping; all of which cause gene expression datasets to be more heterogeneous. Spectral clustering has been recently introduced in many fields as a promising alternative to standard clustering methods. The idea is that pairwise comparisons can help reveal global features through the eigen techniques. In this paper, we developed a new recursive K-means spectral clustering method (ReKS) for disease gene expression data. We benchmarked ReKS on three large-scale cancer datasets and we compared it to different clustering methods with respect to execution time, background models and external biological knowledge. We found ReKS to be superior to the hierarchical methods and equally good to K-means, but much faster than them and without the requirement for a priori knowledge of K. Overall, ReKS offers an attractive alternative for efficient clustering of human disease data.

mirConnX: condition-specific mRNA-microRNA network integrator.

mirConnX is a user-friendly web interface for inferring, displaying and parsing mRNA and microRNA (miRNA) gene regulatory networks. mirConnX combines sequence information with gene expression data analysis to create a disease-specific, genome-wide regulatory network. A prior, static network has been constructed for all human and mouse genes. It consists of computationally predicted transcription factor (TF)-gene associations and miRNA target predictions. The prior network is supplemented with known interactions from the literature. Dynamic TF- and miRNA-gene associations are inferred from user-provided expression data using an association measure of choice. The static and dynamic networks are then combined using an integration function with user-specified weights. Visualization of the network and subsequent analysis are provided via a very responsive graphic user interface. Two organisms are currently supported: Homo sapiens and Mus musculus. The intuitive user interface and large database make mirConnX a useful tool for clinical scientists for hypothesis generation and explorations. mirConnX is freely available for academic use at http://www.benoslab.pitt.edu/mirconnx.