Clustering gene expression regulators: new approach to disease subtyping.

One of the main challenges in modern medicine is to stratify different patient groups in terms of underlying disease molecular mechanisms as to develop more personalized approach to therapy. Here we propose novel method for disease subtyping based on analysis of activated expression regulators on a sample-by-sample basis. Our approach relies on Sub-Network Enrichment Analysis algorithm (SNEA) which identifies gene subnetworks with significant concordant changes in expression between two conditions. Subnetwork consists of central regulator and downstream genes connected by relations extracted from global literature-extracted regulation database. Regulators found in each patient separately are clustered together and assigned activity scores which are used for final patients grouping. We show that our approach performs well compared to other related methods and at the same time provides researchers with complementary level of understanding of pathway-level biology behind a disease by identification of significant expression regulators. We have observed the reasonable grouping of neuromuscular disorders (triggered by structural damage vs triggered by unknown mechanisms), that was not revealed using standard expression profile clustering. For another experiment we were able to suggest the clusters of regulators, responsible for colorectal carcinoma vs adenoma discrimination and identify frequently genetically changed regulators that could be of specific importance for the individual characteristics of cancer development. Proposed approach can be regarded as biologically meaningful feature selection, reducing tens of thousands of genes down to dozens of clusters of regulators. Obtained clusters of regulators make possible to generate valuable biological hypotheses about molecular mechanisms related to a clinical outcome for individual patient.

Novel approach to meta-analysis of microarray datasets reveals muscle remodeling-related drug targets and biomarkers in Duchenne muscular dystrophy.

Elucidation of new biomarkers and potential drug targets from high-throughput profiling data is a challenging task due to a limited number of available biological samples and questionable reproducibility of differential changes in cross-dataset comparisons. In this paper we propose a novel computational approach for drug and biomarkers discovery using comprehensive analysis of multiple expression profiling datasets.The new method relies on aggregation of individual profiling experiments combined with leave-one-dataset-out validation approach. Aggregated datasets were studied using Sub-Network Enrichment Analysis algorithm (SNEA) to find consistent statistically significant key regulators within the global literature-extracted expression regulation network. These regulators were linked to the consistent differentially expressed genes.We have applied our approach to several publicly available human muscle gene expression profiling datasets related to Duchenne muscular dystrophy (DMD). In order to detect both enhanced and repressed processes we considered up- and down-regulated genes separately. Applying the proposed approach to the regulators search we discovered the disturbance in the activity of several muscle-related transcription factors (e.g. MYOG and MYOD1), regulators of inflammation, regeneration, and fibrosis. Almost all SNEA-derived regulators of down-regulated genes (e.g. AMPK, TORC2, PPARGC1A) correspond to a single common pathway important for fast-to-slow twitch fiber type transition. We hypothesize that this process can affect the severity of DMD symptoms, making corresponding regulators and downstream genes valuable candidates for being potential drug targets and exploratory biomarkers.

A genetically encoded photosensitizer.

Photosensitizers are chromophores that generate reactive oxygen species (ROS) upon light irradiation. They are used for inactivation of specific proteins by chromophore-assisted light inactivation (CALI) and for light-induced cell killing in photodynamic therapy. Here we report a genetically encoded photosensitizer, which we call KillerRed, developed from the hydrozoan chromoprotein anm2CP, a homolog of green fluorescent protein (GFP). KillerRed generates ROS upon irradiation with green light. Whereas known photosensitizers must be added to living systems exogenously, KillerRed is fully genetically encoded. We demonstrate the utility of KillerRed for light-induced killing of Escherichia coli and eukaryotic cells and for inactivating fusions to beta-galactosidase and phospholipase Cdelta1 pleckstrin homology domain.

Far-red fluorescent proteins evolved from a blue chromoprotein from Actinia equina.

Proteins of the GFP (green fluorescent protein) family demonstrate a great spectral and phylogenetic diversity. However, there is still an intense demand for red-shifted GFP-like proteins in both basic and applied science. To obtain GFP-like chromoproteins with red-shifted absorption, we performed a broad search in blue-coloured Anthozoa species. We revealed specimens of Actinia equina (beadlet anemone) exhibiting a bright blue circle band at the edge of the basal disc. A novel blue chromoprotein, aeCP597, with an absorption maximum at 597 nm determining the coloration of the anemone basal disk was cloned. AeCP597 carries a chromophore chemically identical with that of the well-studied DsRed (red fluorescent protein from Discosoma sp.). Thus a strong 42-nm bathochromic shift of aeCP597 absorption compared with DsRed is determined by peculiarities of chromophore environment. Site-directed and random mutagenesis of aeCP597 resulted in far-red fluorescent mutants with emission maxima at up to 663 nm. The most bright and stable mutant AQ143 possessed excitation and emission maxima at 595 and 655 nm respectively. Thus aeCP597 and its fluorescent mutants set a new record of red-shifted absorption and emission maxima among GFP-like proteins.