Seeded Bayesian Networks: Constructing genetic networks from microarray data

<p>Abstract</p> <p>Background</p> <p>DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results.</p> <p>Results</p> <p>Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data.</p> <p>Conclusion</p> <p>The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.</p

The 5′ Leader of the mRNA Encoding the Mouse Neurotrophin Receptor TrkB Contains Two Internal Ribosomal Entry Sites that Are Differentially Regulated

A single internal ribosomal entry site (IRES) in conjunction with IRES transactivating factors (ITAFs) is sufficient to recruit the translational machinery to a eukaryotic mRNA independent of the cap structure. However, we demonstrate that the mouse TrkB mRNA contains two independent IRESes. The mouse TrkB mRNA consists of one of two 5′ leaders (1428 nt and 448 nt), both of which include the common 3′ exon (Ex2, 344 nt). Dicistronic RNA transfections and in vitro translation of monocistronic RNA demonstrated that both full-length 5′ leaders, as well as Ex2, exhibit IRES activity indicating the IRES is located within Ex2. Additional analysis of the upstream sequences demonstrated that the first 260 nt of exon 1 (Ex1a) also contains an IRES. Dicistronic RNA transfections into SH-SY5Y cells showed the Ex1a IRES is constitutively active. However, the Ex2 IRES is only active in response to retinoic acid induced neural differentiation, a state which correlates with the synthesis of the ITAF polypyrimidine tract binding protein (PTB1). Correspondingly, addition or knock-down of PTB1 altered Ex2, but not Ex1a IRES activity in vitro and ex vivo, respectively. These results demonstrate that the two functionally independent IRESes within the mouse TrkB 5′ leader are differentially regulated, in part by PTB1

Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers

BACKGROUND: Although prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis algorithms, helps to identify common factors in molecular oncology. Similarities of Ordered Gene Lists (SOGL) is a recently proposed approach to meta-analysis suitable for identifying features shared by two data sets. Here we extend the idea of SOGL to the detection of significant prognostic marker genes from microarrays of multiple data sets. Three data sets for leukemia and the other six for different solid tumors are used to demonstrate our method, using established statistical techniques. RESULTS: We describe a set of significantly similar ordered gene lists, representing outcome comparisons for distinct types of cancer. This kind of similarity could improve the diagnostic accuracies of individual studies when SOGL is incorporated into the support vector machine algorithm. In particular, we investigate the similarities among three ordered gene lists pertaining to mesothelioma survival, prostate recurrence and glioma survival. The similarity-driving genes are related to the outcomes of patients with lung cancer with a hazard ratio of 4.47 (p = 0.035). Many of these genes are involved in breakdown of EMC proteins regulating angiogenesis, and may be used for further research on prognostic markers and molecular targets of gene therapy for cancers. CONCLUSION: The proposed method and its application show the potential of such meta-analyses in clinical studies of gene expression profiles

p16INK4A Positively Regulates Cyclin D1 and E2F1 through Negative Control of AUF1

/pRB/E2F pathway, a key regulator of the critical G1 to S phase transition of the cell cycle, is universally disrupted in human cancer. However, the precise function of the different members of this pathway and their functional interplay are still not well defined. -dependent manner, and several of these genes are also members of the AUF1 and E2F1 regulons. We also present evidence that E2F1 mediates p16-dependent regulation of several pro- and anti-apoptotic proteins, and the consequent induction of spontaneous as well as doxorubicin-induced apoptosis. is also a modulator of transcription and apoptosis through controlling the expression of two major transcription regulators, AUF1 and E2F1

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE)

PURPOSE: Gene-expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by non-standardized methods which are not applicable in a clinical setting. We sought to generate a clinical-grade minimal gene-set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. EXPERIMENTAL DESIGN: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene-expression data from 1650 tumors. We applied resulting models to NanoString data on 3829 HGSOCs from the Ovarian Tumor Tissue Analysis Consortium. We further developed, confirmed, and validated a reduced, minimal gene-set predictor, with methods suitable for a single patient setting. RESULTS: Gene-expression data was used to derive the Predictor of high-grade-serous Ovarian carcinoma molecular subTYPE (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor infiltrating lymphocytes, and outcome. The locked-down clinical-grade PrOTYPE test includes a model with 55 genes that predicted gene-expression subtype with >95% accuracy that was maintained in all analytical and biological validations. CONCLUSIONS: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical-grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications

Calcified Algae for Tissue Engineering.

This book presents the latest advances in marine structures and related biomaterials for applications in both soft- and hard-tissue engineering, as well as controlled drug delivery