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Wounding of the oral mucosa occurs frequently in a highly septic environment. Remarkably, these wounds heal quickly and the oral cavity, for the most part, remains healthy. Deciphering the normal human oral epithelial cell (NHOEC) proteome is critical for understanding the mechanism(s) of protection elicited when the mucosal barrier is intact, as well as when it is breached. Combining 2D gel electrophoresis with shotgun proteomics resulted in identification of 1662 NHOEC proteins. Proteome annotations were performed based on protein classes, molecular functions, disease association and membership in canonical and metabolic signaling pathways. Comparing the NHOEC proteome with a database of innate immunity-relevant interactions (InnateDB) identified 64 common proteins associated with innate immunity. Comparison with published salivary proteomes revealed that 738/1662 NHOEC proteins were common, suggesting that significant numbers of salivary proteins are of epithelial origin. Gene ontology analysis showed similarities in the distributions of NHOEC and saliva proteomes with regard to biological processes, and molecular functions. We also assessed the interindividual variability of the NHOEC proteome and observed it to be comparable with other primary cells. The baseline proteome described in this study should serve as a resource for proteome studies of the oral mucosa, especially in relation to disease processes.

Unlabelled: Bimodal patterns of expression have recently been shown to be useful not only in prioritizing genes that distinguish phenotypes, but also in prioritizing network models that correlate with proteomic evidence. In particular, subgroups of strongly coexpressed gene pairs result in an increased variance of the correlation distribution. This variance, a measure of association between sets of genes (or proteins), can be summarized as the bimodality of coexpression (BiC). We developed an online tool to calculate the BiC for user-defined gene lists and associated mRNA expression data. BiC is a comprehensive application that provides researchers with the ability to analyze both publicly available and user-collected array data., Availability: The freely available web service and the documentation can be accessed at http://gurkan.case.edu/software., Contact: gurkan@case.edu.

Background: In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity., Results: In this paper, we propose a phylogenetic framework for analyzing network modules, with applications that extend well beyond network-based phylogeny reconstruction. Our approach is based on identification of modular network components from each network separately, followed by projection of these modules onto the networks of other species to compare different networks. Subsequently, we use the conservation of various modules in each network to assess the similarity between different networks. Compared to traditional methods that rely on topological comparisons, our approach has key advantages in (i) avoiding intractable graph comparison problems in comparative network analysis, (ii) accounting for noise and missing data through flexible treatment of network conservation, and (iii) providing insights on the evolution of biological systems through investigation of the evolutionary trajectories of network modules. We test our method, MOPHY, on synthetic data generated by simulation of network evolution, as well as existing protein-protein interaction data for seven diverse species. Comprehensive experimental results show that MOPHY is promising in reconstructing evolutionary histories of extant networks based on conservation of modularity, it is highly robust to noise, and outperforms existing methods that quantify network similarity in terms of conservation of network topology., Conclusion: These results establish modularity and network proximity as useful features in comparative network analysis and motivate detailed studies of the evolutionary histories of network modules.

Osteoarthritis, characterized by the breakdown of articular cartilage in synovial joints, has long been viewed as the result of 'wear and tear'. Although low-grade inflammation is detected in osteoarthritis, its role is unclear. Here we identify a central role for the inflammatory complement system in the pathogenesis of osteoarthritis. Through proteomic and transcriptomic analyses of synovial fluids and membranes from individuals with osteoarthritis, we find that expression and activation of complement is abnormally high in human osteoarthritic joints. Using mice genetically deficient in complement component 5 (C5), C6 or the complement regulatory protein CD59a, we show that complement, specifically, the membrane attack complex (MAC)-mediated arm of complement, is crucial to the development of arthritis in three different mouse models of osteoarthritis. Pharmacological modulation of complement in wild-type mice confirmed the results obtained with genetically deficient mice. Expression of inflammatory and degradative molecules was lower in chondrocytes from destabilized joints from C5-deficient mice than C5-sufficient mice, and MAC induced production of these molecules in cultured chondrocytes. Further, MAC colocalized with matrix metalloprotease 13 (MMP13) and with activated extracellular signal-regulated kinase (ERK) around chondrocytes in human osteoarthritic cartilage. Our findings indicate that dysregulation of complement in synovial joints has a key role in the pathogenesis of osteoarthritis. [ABSTRACT FROM AUTHOR]

The article discusses a research study which identified the germline mutations in MSR1, ASCC1 and CTHRC1 genes in patients with Barrett esophagus (BE) and esophageal adenocarcinoma (EAC). Data analysis identified MSR1 as the most frequently mutated gene, with eight of 116 cases, while germline MSR1 mutations in two of 58 cases was confirmed by an independent validation series. Findings also showed the upregulation of CCND1 in peripheral-protein lysate resulting from MSR1 mutation. The researchers observed an increased in the nuclear expression of CCND1 as shown by the immunohistochemistry of BE tissues in MSR1-mutation carriers.

Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called ''driver'' genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections - both precedented and novel - between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/-) or Cdkn1a (Cdkn1a-/-), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data. [ABSTRACT FROM AUTHOR]