Incorporating topological information for predicting robust cancer subnetwork markers in human protein-protein interaction network

BACKGROUND: Discovering robust markers for cancer prognosis based on gene expression data is an important yet challenging problem in translational bioinformatics. By integrating additional information in biological pathways or a protein-protein interaction (PPI) network, we can find better biomarkers that lead to more accurate and reproducible prognostic predictions. In fact, recent studies have shown that, “modular markers,” that integrate multiple genes with potential interactions can improve disease classification and also provide better understanding of the disease mechanisms. RESULTS: In this work, we propose a novel algorithm for finding robust and effective subnetwork markers that can accurately predict cancer prognosis. To simultaneously discover multiple synergistic subnetwork markers in a human PPI network, we build on our previous work that uses affinity propagation, an efficient clustering algorithm based on a message-passing scheme. Using affinity propagation, we identify potential subnetwork markers that consist of discriminative genes that display coherent expression patterns and whose protein products are closely located on the PPI network. Furthermore, we incorporate the topological information from the PPI network to evaluate the potential of a given set of proteins to be involved in a functional module. Primarily, we adopt widely made assumptions that densely connected subnetworks may likely be potential functional modules and that proteins that are not directly connected but interact with similar sets of other proteins may share similar functionalities. CONCLUSIONS: Incorporating topological attributes based on these assumptions can enhance the prediction of potential subnetwork markers. We evaluate the performance of the proposed subnetwork marker identification method by performing classification experiments using multiple independent breast cancer gene expression datasets and PPI networks. We show that our method leads to the discovery of robust subnetwork markers that can improve cancer classification. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1224-1) contains supplementary material, which is available to authorized users

Key mechanisms governing resolution of lung inflammation

Innate immunity normally provides excellent defence against invading microorganisms. Acute inflammation is a form of innate immune defence and represents one of the primary responses to injury, infection and irritation, largely mediated by granulocyte effector cells such as neutrophils and eosinophils. Failure to remove an inflammatory stimulus (often resulting in failed resolution of inflammation) can lead to chronic inflammation resulting in tissue injury caused by high numbers of infiltrating activated granulocytes. Successful resolution of inflammation is dependent upon the removal of these cells. Under normal physiological conditions, apoptosis (programmed cell death) precedes phagocytic recognition and clearance of these cells by, for example, macrophages, dendritic and epithelial cells (a process known as efferocytosis). Inflammation contributes to immune defence within the respiratory mucosa (responsible for gas exchange) because lung epithelia are continuously exposed to a multiplicity of airborne pathogens, allergens and foreign particles. Failure to resolve inflammation within the respiratory mucosa is a major contributor of numerous lung diseases. This review will summarise the major mechanisms regulating lung inflammation, including key cellular interplays such as apoptotic cell clearance by alveolar macrophages and macrophage/neutrophil/epithelial cell interactions. The different acute and chronic inflammatory disease states caused by dysregulated/impaired resolution of lung inflammation will be discussed. Furthermore, the resolution of lung inflammation during neutrophil/eosinophil-dominant lung injury or enhanced resolution driven via pharmacological manipulation will also be considered

A point mutation in the pre-ZRS disrupts sonic hedgehog expression in the limb bud and results in triphalangeal thumb-polysyndactyly syndrome

Item does not contain fulltextPURPOSE: The zone of polarizing activity regulatory sequence (ZRS) is an enhancer that regulates sonic hedgehog during embryonic limb development. Recently, mutations in a noncoding evolutionary conserved sequence 500 bp upstream of the ZRS, termed the pre-ZRS (pZRS), have been associated with polydactyly in dogs and humans. Here, we report the first case of triphalangeal thumb-polysyndactyly syndrome (TPT-PS) to be associated with mutations in this region and show via mouse enhancer assays how this mutation leads to ectopic expression throughout the developing limb bud. METHODS: We used linkage analysis, whole-exome sequencing, Sanger sequencing, fluorescence in situ hybridization, multiplex ligation-dependent probe amplification, single-nucleotide polymorphism array, and a mouse transgenic enhancer assay. RESULTS: Ten members of a TPT-PS family were included in this study. The mutation was linked to chromosome 7q36 (LOD score 3.0). No aberrations in the ZRS could be identified. A point mutation in the pZRS (chr7:156585476G>C; GRCh37/hg19) was detected in all affected family members. Functional characterization using a mouse transgenic enhancer essay showed extended ectopic expression dispersed throughout the entire limb bud (E11.5). CONCLUSION: Our work describes the first mutation in the pZRS to be associated with TPT-PS and provides functional evidence that this mutation leads to ectopic expression of this enhancer within the developing limb