Modeling Genome-Wide Dynamic Regulatory Network in Mouse Lungs with Influenza Infection Using High-Dimensional Ordinary Differential Equations

The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.     

Modeling of influenza-specific CD8+ T cells during the primary response indicates that the spleen is a major source of effectors

The biological parameters that determine the distribution of virus-specific CD8(+) T cells during influenza infection are not all directly measurable by experimental techniques but can be inferred through mathematical modeling. Mechanistic and semimechanistic ordinary differential equations were developed to describe the expansion, trafficking, and disappearance of activated virus-specific CD8(+) T cells in lymph nodes, spleens, and lungs of mice during primary influenza A infection. An intensive sampling of virus-specific CD8(+) T cells from these three compartments was used to inform the models. Rigorous statistical fitting of the models to the experimental data allowed estimation of important biological parameters. Although the draining lymph node is the first tissue in which Ag-specific CD8(+) T cells are detected, it was found that the spleen contributes the greatest number of effector CD8(+) T cells to the lung, with rates of expansion and migration that exceeded those of the draining lymph node. In addition, models that were based on the number and kinetics of professional APCs fit the data better than those based on viral load, suggesting that the immune response is limited by Ag presentation rather than the amount of virus. Modeling also suggests that loss of effector T cells from the lung is significant and time dependent, increasing toward the end of the acute response. Together, these efforts provide a better understanding of the primary CD8(+) T cell response to influenza infection, changing the view that the spleen plays a minor role in the primary immune response.     

Modeling the Dynamics and Migratory Pathways of Virus-Specific Antibody-Secreting Cell Populations in Primary Influenza Infection

The B cell response to influenza infection of the respiratory tract contributes to viral clearance and establishes profound resistance to reinfection by related viruses. Numerous studies have measured virus-specific antibody-secreting cell (ASC) frequencies in different anatomical compartments after influenza infection and provided a general picture of the kinetics of ASC formation and dispersion. However, the dynamics of ASC populations are difficult to determine experimentally and have received little attention. Here, we applied mathematical modeling to investigate the dynamics of ASC growth, death, and migration over the 2-week period following primary influenza infection in mice. Experimental data for model fitting came from high frequency measurements of virus-specific IgM, IgG, and IgA ASCs in the mediastinal lymph node (MLN), spleen, and lung. Model construction was based on a set of assumptions about ASC gain and loss from the sampled sites, and also on the directionality of ASC trafficking pathways. Most notably, modeling results suggest that differences in ASC fate and trafficking patterns reflect the site of formation and the expressed antibody class. Essentially all early IgA ASCs in the MLN migrated to spleen or lung, whereas cell death was likely the major reason for IgM and IgG ASC loss from the MLN. In contrast, the spleen contributed most of the IgM and IgG ASCs that migrated to the lung, but essentially none of the IgA ASCs. This finding points to a critical role for regional lymph nodes such as the MLN in the rapid generation of IgA ASCs that seed the lung. Results for the MLN also suggest that ASC death is a significant early feature of the B cell response. Overall, our analysis is consistent with accepted concepts in many regards, but it also indicates novel features of the B cell response to influenza that warrant further investigation.     

Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis

Osteoarthritis (OA) significantly influences the quality life of people around the world. It is urgent to find an effective way to understand the genetic etiology of OA. We used weighted gene coexpression network analysis (WGCNA) to explore the key genes involved in the subchondral bone pathological process of OA. Fifty gene expression profiles of GSE51588 were downloaded from the Gene Expression Omnibus database. The OA‐associated genes and gene ontologies were acquired from JuniorDoc. Weighted gene coexpression network analysis was used to find disease‐related networks based on 21756 gene expression correlation coefficients, hub‐genes with the highest connectivity in each module were selected, and the correlation between module eigengene and clinical traits was calculated. The genes in the traits‐related gene coexpression modules were subject to functional annotation and pathway enrichment analysis using ClusterProfiler. A total of 73 gene modules were identified, of which, 12 modules were found with high connectivity with clinical traits. Five modules were found with enriched OA‐associated genes. Moreover, 310 OA‐associated genes were found, and 34 of them were among hub‐genes in each module. Consequently, enrichment results indicated some key metabolic pathways, such as extracellular matrix (ECM)‐receptor interaction (hsa04512), focal adhesion (hsa04510), the phosphatidylinositol 3'‐kinase (PI3K)‐Akt signaling pathway (PI3K‐AKT) (hsa04151), transforming growth factor beta pathway, and Wnt pathway. We intended to identify some core genes, collagen (COL)6A3, COL6A1, ITGA11, BAMBI, and HCK, which could influence downstream signaling pathways once they were activated. In this study, we identified important genes within key coexpression modules, which associate with a pathological process of subchondral bone in OA. Functional analysis results could provide important information to understand the mechanism of OA.     

Influenza virus-specific antibody-secreting cells in the murine lung during primary influenza virus infection.

An enzyme-linked immunosorbent plaque assay is described which can reliably enumerate influenza virus-specific antibody-secreting cells and exhibits specificity similar to that of the indirect enzyme-linked immunosorbent assay. The assay was used to characterize the development of specific antibody-secreting cells, principally within lung tissue, during primary murine influenza virus infection after intranasal inoculation. Cells secreting influenza virus-specific immunoglobulin M (IgM), IgG, and IgA were detected in greatest numbers in lung tissue, and the data presented indicated that the cells may have originated from specific B-cell precursors in lung tissue which are demonstratable in vitro. At 11 months after infection, cells secreting IgG and IgA were still present in lung tissue. Influenza virus-specific antibody-secreting cells were also detected in spleen tissue and blood. Antibody-secreting cells appeared earlier in spleen than in lung tissue and declined more rapidly in spleen tissue.     

Reverse engineering and analysis of genome-wide gene regulatory networks from gene expression profiles using high-performance computing

Regulation of gene expression is a carefully regulated phenomenon in the cell. “Reverse-engineering” algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles (microarrays). Mammalian cells express tens of thousands of genes; hence, hundreds of gene expression profiles are necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to discover “communities” within the gene network. Network communities are enriched for genes involved in the same biological functions. The inferred network was used to identify two mitochondrial proteins.     

Identification of dynamic signatures associated with smoking‐related squamous cell lung cancer and chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a risk factor for the development of lung cancer. The aim of this study was to identify early diagnosis biomarkers for lung squamous cell carcinoma (SQCC) in COPD patients and to determine the potential pathogenetic mechanisms. The GSE12472 data set was downloaded from the Gene Expression Omnibus database. Differentially co‐expressed links (DLs) and differentially expressed genes (DEGs) in both COPD and normal tissues, or in both SQCC + COPD and COPD samples were used to construct a dynamic network associated with high‐risk genes for the SQCC pathogenetic process. Enrichment analysis was performed based on Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used the gene expression data and the clinical information to identify the co‐expression modules based on weighted gene co‐expression network analysis (WGCNA). In total, 205 dynamic DEGs, 5034 DLs and one pathway including CDKN1A, TP53, RB1 and MYC were found to have correlations with the pathogenetic progress. The pathogenetic mechanisms shared by both SQCC and COPD are closely related to oxidative stress, the immune response and infection. WGCNA identified 11 co‐expression modules, where magenta and black were correlated with the “time to distant metastasis.” And the “surgery due to” was closely related to the brown and blue modules. In conclusion, a pathway that includes TP53, CDKN1A, RB1 and MYC may play a vital role in driving COPD towards SQCC. Inflammatory processes and the immune response participate in COPD‐related carcinogenesis.     

Global host immune response: pathogenesis and transcriptional profiling of type A influenza viruses expressing the hemagglutinin and neuraminidase genes from the 1918 pandemic virus

To understand more fully the molecular events associated with highly virulent or attenuated influenza virus infections, we have studied the effects of expression of the 1918 hemagglutinin (HA) and neuraminidase (NA) genes during viral infection in mice under biosafety level 3 (agricultural) conditions. Using histopathology and cDNA microarrays, we examined the consequences of expression of the HA and NA genes of the 1918 pandemic virus in a recombinant influenza A/WSN/33 virus compared to parental A/WSN/33 virus and to an attenuated virus expressing the HA and NA genes from A/New Caledonia/20/99. The 1918 HA/NA:WSN and WSN recombinant viruses were highly lethal for mice and displayed severe lung pathology in comparison to the nonlethal New Caledonia HA/NA:WSN recombinant virus. Expression microarray analysis performed on lung tissues isolated from the infected animals showed activation of many genes involved in the inflammatory response, including cytokine, apoptosis, and lymphocyte genes that were common to all three infection groups. However, consistent with the histopathology studies, the WSN and 1918 HA/NA:WSN recombinant viruses showed increased up-regulation of genes associated with activated T cells and macrophages, as well as genes involved in apoptosis, tissue injury, and oxidative damage that were not observed in the New Caledonia HA/NA:WSN recombinant virus-infected mice. These studies document clear differences in gene expression profiles that were correlated with pulmonary disease pathology induced by virulent and attenuated influenza virus infections.     

Activation of superoxide dismutase in selenium-deficient mice infected with influenza virus.

Selenium (Se) deficiency is associated with decreased activities of Se-dependent antioxidant enzymes, glutathione peroxidase (GPx) and thioredoxin reductase (TR), and with changes in the cellular redox status. We have previously shown that host Se deficiency is responsible for increased virulence of influenza virus in mice due to changes in the viral genome. The present study examines the antioxidant defense systems in the lung and liver of Se-deficient and Se-adequate mice infected with influenza A/Bangkok/1/79. Results show that neither Se status nor infection changed glutathione (GSH) concentration in the lung. Hepatic GSH concentration was lower in Se-deficient mice, but increased significantly day 5 post infection. No significant differences due to Se status or influenza infection were found in catalase activities. As expected, Se deficiency was associated with significant decreases in GPx and TR activities in both lung and liver. GPx activity increased in the lungs and decreased in the liver of Se-adequate mice in response to infection. Both Se deficiency and influenza infection had profound effects on the activity of superoxide dismutase (SOD). The hepatic SOD activity was higher in Se-deficient than Se-adequate mice before infection. However, following influenza infection, hepatic SOD activity in Se-adequate mice gradually increased. Influenza infection was associated with a significant increase of SOD activity in the lungs of Se-deficient, but not Se-adequate mice. The maximum of SOD activity coincided with the peak of pathogenesis in infected lungs. These data suggest that SOD activation in the lung and liver may be a part of a compensatory response to Se deficiency and/or influenza infection. However, SOD activation that leads to increased production of H(2)O(2) may also contribute to pathogenesis and to influenza virus mutation in lungs of Se-deficient mice.     

Comparative gene marker selection suite

MOTIVATION: An important step in analyzing expression profiles from microarray data is to identify genes that can discriminate between distinct classes of samples. Many statistical approaches for assigning significance values to genes have been developed. The Comparative Marker Selection suite consists of three modules that allow users to apply and compare different methods of computing significance for each marker gene, a viewer to assess the results, and a tool to create derivative datasets and marker lists based on user-defined significance criteria. AVAILABILITY: The Comparative Marker Selection application suite is freely available as a GenePattern module. The GenePattern analysis environment is freely available at http://www.broad.mit.edu/genepattern.