Search

Background: There is significant interest in understanding the nature of the inflammatory response and its role in Alzheimers disease (AD) pathophysiology. Immune cell phenotypes and their key pathway activation by AD stage is unclear. We therefore evaluated immune cell phenotypes in the cerebrospinal fluid (CSF) and their transcriptional profile comparing AD‐dementia, Mild Cognitive Impairment (MCI)‐AD and normal cognition controls using transcriptomics. Method: We characterized the gene expression profiles of immune cells in the CSF using both bulk RNA sequencing (RNAseq) and complementary single cell transcriptomics (scRNAseq). First, in a cross‐sectional cohort study, including 6 individuals with MCI‐AD meeting NIA/AA 2011 criteria and 9 amyloid negative controls with normal cognition, (mean age 68.75 years (SD 5.2), 58.8% Female), we identified significant pathways using gene set enrichment analysis (GSEA). Next, using scRNAseq analysis on2 AD‐dementia, 2 MCI‐AD and 2 normal controls we further elucidated the gene expression level changes in distinct immune cells. After clustering into immune cell types, we assessed their activation by GSEA and Gene set variation analysis (GSVA). Result: RNAseq analysis identified 47 genes that were differentially expressed between MCI‐AD and normal control samples (adj‐p‐val<0.05). GSEA identified downregulation of antigen processing, interferon signaling pathways, and neutrophil degranulation (adj‐p‐val<0.05). Next, scRNAseq, analysis defined immune cells within each sample and identified altered pathways that were in each cell type. We observed that CD4+CD8+ T cells were highly represented in AD‐dementia (17%) and MCI‐AD(14%) but not in normal controls(11%) (p<0.00001). The immune cell types that differed by disease stage between AD‐dementia and MCI‐AD were monocytes; increasing disease severity was associated with a transition from more classical monocytes to intermediate and nonclassical monocytes. The key pathways associated with monocytes were IL6 JAK STAT3 signaling, particularly STAT3, which is involved in monocyte‐to‐macrophage differentiation and the transition of classic monocytes to other subsets. Additionally, the pathways include interferon gamma (IFNg) response and TNF signaling via NF‐kB, which play important roles in activating macrophages and monocytes and promoting differentiation. Conclusion: Our data show enrichment in the proportion of CD4+CD8+ T cells and pathways related to monocyte differentiation that differ across AD stage. [ABSTRACT FROM AUTHOR]

The precise molecular events initiating human lung disease are often poorly characterized. Investigating prenatal events that may underlie lung disease in later life is challenging in man, but insights from the well-characterized sheep model of lung development are valuable. Here, we determine the transcriptomic signature of lung development in wild-type sheep (WT) and use a sheep model of cystic fibrosis (CF) to characterize disease associated changes in gene expression through the pseudoglandular, canalicular, saccular, and alveolar stages of lung growth and differentiation. Using gene ontology process enrichment analysis of differentially expressed genes at each developmental time point, we define changes in biological processes (BP) in proximal and distal lung from WT or CF animals. We also compare divergent BP in WT and CF animals at each time point. Next, we establish the developmental profile of key genes encoding components of ion transport and innate immunity that are pivotal in CF lung disease and validate transcriptomic data by RT-qPCR. Consistent with the known pro-inflammatory phenotype of the CF lung after birth, we observe upregulation of inflammatory response processes in the CF sheep distal lung during the saccular stage of prenatal development. These data suggest early commencement of therapeutic regimens may be beneficial. [ABSTRACT FROM AUTHOR]

The pathogenesis of osteoarthritis is unclear, and there are currently no treatments that prevent the development of osteoarthritis. Seeking to gain insight into osteoarthritis, we used mass spectrometry to identify [...], Osteoarthritis, characterized by the breakdown of articular cartilage in synovial joints, has long been viewed as the result of 'wear and tear' (1). Although low-grade inflammation is detected in osteoarthritis, its role is unclear (2-4). 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 Background High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths. Results We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DADA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DADA outperforms existing methods in prioritizing candidate disease genes. Conclusions These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DADA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/.

Abstract Background Colon cancer is driven by mutations in a number of genes, the most notorious of which is Apc. Though much of Apc's signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in Apc. Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in Apc is observed. Results We developed a pipeline, PETALS, to predict and test likely signaling pathways connecting Apc to other CAN-genes, where the interaction network originating at Apc is defined as a "blossom," with each Apc-CAN-gene subnetwork referred to as a "petal." Known and predicted protein interactions are used to identify an Apc blossom with 24 petals. Then, using a novel measure of bimodality, the coexpression of each petal is evaluated against proteomic (2 D differential In Gel Electrophoresis, 2D-DIGE) measurements from the Apc1638N+/-mouse to test the network-based hypotheses. Conclusions The predicted pathways linking Apc and Hapln1 exhibited the highest amount of bimodal coexpression with the proteomic targets, prioritizing the Apc-Hapln1 petal over other CAN-gene pairs and suggesting that this petal may be involved in regulating the observed proteome-level effects. These results not only demonstrate how functional 'omics data can be employed to test in silico predictions of CAN-gene pathways, but also reveal an approach to integrate models of upstream genetic interference with measured, downstream effects.

Abstract 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.

Abstract Background A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem. Results In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules. Conclusion Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.