A Simulation Framework to Investigate in vitro Viral Infection Dynamics

AbstractVirus infection is a complex biological phenomenon for which in vitro experiments provide a uniquely concise view where data is often obtained from a single population of cells, under controlled environmental conditions. Nonetheless, data interpretation and real understanding of viral dynamics is still hampered by the sheer complexity of the various intertwined spatio-temporal processes. In this paper we present a tool to address these issues: a cellular automata model describing critical aspects of in vitro viral infections taking into account spatial characteristics of virus spreading within a culture well. The aim of the model is to understand the key mechanisms of SARS-CoV infection dynamics during the first 24hours post infection. We interrogate the model using a Latin Hypercube sensitivity analysis to identify which mechanisms are critical to the observed infection of host cells and the release of measured virus particles

Profiling Heterogeneous Circulating Tumor Cells (CTC) Populations in Pancreatic Cancer Using a Serial Microfluidic CTC Carpet Chip

Although isolation of circulating tumor cells (CTCs) from pancreatic adenocarcinoma patients is feasible, investigating their clinical utility has proven less successful than other cancers due to the limitations of epithelial cellular adhesion molecule (EpCAM)‐only based CTC assays. An integrated technology‐ and biology‐based approach using a microfluidic “Carpet Chip” is presented to study the biological relevance of heterogeneous CTC populations. Both epithelial CTCs (EpCs) and epithelial‐to‐mesenchymal transition (EMT)‐like CTCs (EMTCs) are isolated simultaneously from the whole blood of pancreatic cancer (PaCa) patients (n = 35) by separately targeting two surface markers: EpCAM and CD133. Recovery of cancer cell lines spiked into whole blood is ≥97% with >76% purity. Thirty‐four patients had ≥5 EpCs mL−1 and 35 patients had ≥15 EMTCs mL−1. Overall, significantly higher numbers of EMTCs than EpCs are recovered, reflecting the aggressive nature of PaCa. Furthermore, higher numbers of EMTCs are observed in patients with lymph node involvement compared to patients without. Gene expression profiling of CTCs from 17 patients reveals that CXCR1 is significantly upregulated in EpCs, while known stem cell markers POU5F1/Oct‐4 and MYC are upregulated in EMTCs. In conclusion, successful isolation and genomic profiling of heterogeneous CTC populations are demonstrated, revealing genetic signatures relevant to patient outcomes.“Carpet Chip” uses sequential immunoaffinity‐based microfluidics to study the biological relevance of heterogeneous circulating tumor cell (CTCs). Both epithelial (EpCs) and epithelial‐to‐mesenchymal transition (EMT)‐like CTCs (EMTCs) are detectable from the blood of pancreatic cancer patients. Based on our observations of EMTCs and patient lymph node involvement, individualizing therapies targeting genes involved in EMT could reduce metastasis, thereby improving patient survival.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147173/1/adbi201800228-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147173/2/adbi201800228.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147173/3/adbi201800228_am.pd

Folyóirat vagy gyűjteményes kötet? (Csokonai Diétai Magyar Múzsája)

BACKGROUND: The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ anti-immune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation. RESULTS: We validated our predictions in 18 different knockout (KO) mouse strains, showing that network topology provides significant predictive power to identify genes that are important for viral infection. We identified a novel player in the immune response to virus infection, Kepi, an inhibitory subunit of the protein phosphatase 1 (PP1) complex, which protects against SARS-CoV pathogenesis. We also found that receptors for the proinflammatory cytokine tumor necrosis factor alpha (TNFα) promote pathogenesis, presumably through excessive inflammation. CONCLUSIONS: The current study provides validation of network modeling approaches for identifying important players in virus infection pathogenesis, and a step forward in understanding the host response to an important infectious disease. The results presented here suggest the role of Kepi in the host response to SARS-CoV, as well as inflammatory activity driving pathogenesis through TNFα signaling in SARS-CoV infections. Though we have reported the utility of this approach in bacterial and cell culture studies previously, this is the first comprehensive study to confirm that network topology can be used to predict phenotypes in mice with experimental validation

Mechanisms of Severe Acute Respiratory Syndrome Coronavirus-Induced Acute Lung Injury

ABSTRACT Systems biology offers considerable promise in uncovering novel pathways by which viruses and other microbial pathogens interact with host signaling and expression networks to mediate disease severity. In this study, we have developed an unbiased modeling approach to identify new pathways and network connections mediating acute lung injury, using severe acute respiratory syndrome coronavirus (SARS-CoV) as a model pathogen. We utilized a time course of matched virologic, pathological, and transcriptomic data within a novel methodological framework that can detect pathway enrichment among key highly connected network genes. This unbiased approach produced a high-priority list of 4 genes in one pathway out of over 3,500 genes that were differentially expressed following SARS-CoV infection. With these data, we predicted that the urokinase and other wound repair pathways would regulate lethal versus sublethal disease following SARS-CoV infection in mice. We validated the importance of the urokinase pathway for SARS-CoV disease severity using genetically defined knockout mice, proteomic correlates of pathway activation, and pathological disease severity. The results of these studies demonstrate that a fine balance exists between host coagulation and fibrinolysin pathways regulating pathological disease outcomes, including diffuse alveolar damage and acute lung injury, following infection with highly pathogenic respiratory viruses, such as SARS-CoV.IMPORTANCESevere acute respiratory syndrome coronavirus (SARS-CoV) emerged in 2002 and 2003, and infected patients developed an atypical pneumonia, acute lung injury (ALI), and acute respiratory distress syndrome (ARDS) leading to pulmonary fibrosis and death. We identified sets of differentially expressed genes that contribute to ALI and ARDS using lethal and sublethal SARS-CoV infection models. Mathematical prioritization of our gene sets identified the urokinase and extracellular matrix remodeling pathways as the most enriched pathways. By infecting Serpine1-knockout mice, we showed that the urokinase pathway had a significant effect on both lung pathology and overall SARS-CoV pathogenesis. These results demonstrate the effective use of unbiased modeling techniques for identification of high-priority host targets that regulate disease outcomes. Similar transcriptional signatures were noted in 1918 and 2009 H1N1 influenza virus-infected mice, suggesting a common, potentially treatable mechanism in development of virus-induced ALI

Release of Severe Acute Respiratory Syndrome Coronavirus Nuclear Import Block Enhances Host Transcription in Human Lung Cells

The severe acute respiratory syndrome coronavirus accessory protein ORF6 antagonizes interferon signaling by blocking karyopherin-mediated nuclear import processes. Viral nuclear import antagonists, expressed by several highly pathogenic RNA viruses, likely mediate pleiotropic effects on host gene expression, presumably interfering with transcription factors, cytokines, hormones, and/or signaling cascades that occur in response to infection. By bioinformatic and systems biology approaches, we evaluated the impact of nuclear import antagonism on host expression networks by using human lung epithelial cells infected with either wild-type virus or a mutant that does not express ORF6 protein. Microarray analysis revealed significant changes in differential gene expression, with approximately twice as many upregulated genes in the mutant virus samples by 48 h postinfection, despite identical viral titers. Our data demonstrated that ORF6 protein expression attenuates the activity of numerous karyopherin-dependent host transcription factors (VDR, CREB1, SMAD4, p53, EpasI, and Oct3/4) that are critical for establishing antiviral responses and regulating key host responses during virus infection. Results were confirmed by proteomic and chromatin immunoprecipitation assay analyses and in parallel microarray studies using infected primary human airway epithelial cell cultures. The data strongly support the hypothesis that viral antagonists of nuclear import actively manipulate host responses in specific hierarchical patterns, contributing to the viral pathogenic potential in vivo. Importantly, these studies and modeling approaches not only provide templates for evaluating virus antagonism of nuclear import processes but also can reveal candidate cellular genes and pathways that may significantly influence disease outcomes following severe acute respiratory syndrome coronavirus infection in vivo

Knowledge based identification of essential signaling from genome-scale siRNA experiments

<p>Abstract</p> <p>Background</p> <p>A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.</p> <p>Results</p> <p>We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK <it>cytoskeletal remodeling pathway</it>. Different branches of the <it>FAK </it>pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction.</p> <p>Conclusion</p> <p>PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets. </p

Dendrobium Swarz.(ラン科)の類縁に関する研究 : I. Eugenanthe Schlechter節内での交配親和性

1.ノビルタイプのデンドロビウム品種に.新しい遺伝子を導入する可能性を調べるため, Eugenanthe節内の22種と、D. moniliforme(セッコク), D. nobileとの交配を行なった.2.交配稔性からみて, 本節内にはD. moniliforme, D. nobileとは遠縁と思われる種が含まれていた.3.D. moniliformeは, D. nobileに比べ, 多くの種と交雑可能で, 今後の育種のために有用な種と考えられた.In order to check the possibility of introducing new genes into the modern nobile-type cultivars of Dendrobium, D. nobile Lindl. and D. moniliforme (L.) Swarz. were crossed with selected species of section Eugenanthe Schlechter. D. moniliforme showed a wider range of crossability with Eugenanthe species compared to D. nobile. Eugenanthe species were divided into two groups according to their crossability with D. moniliforme

A comprehensive collection of systems biology data characterizing the host response to viral infection

The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). By comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection

Computational modeling of cancer etiology and progression using neural networks and genetic cellular automata /by Armand Bankhead III.

Cancer is a pathological state characterized by abnormally increased cell reproduction and survival. Cancer is caused by mutations to tumor suppressor and apoptosis genes which inhibit cellular reproduction and proto-oncogenes that activate reproduction. The specific genes that are mutated vary between individuals and the originating tissue. It is difficult to diagnose when and where in the body these mutations will occur.;Computational models provide a framework for abstraction of biochemically complex, biological systems relevant to cancer etiology and progression. Computer simulations can be implemented to run much faster and in a more cost-effective manner than biological experiments while producing a greater amount of data. In silico models allow us to ask questions about features that would not be possible with classical in vitro experiments, such as lethal genotypes or unidentified genes. Four research papers are included in this dissertation that present novel and useful computational models of cancerous behavior.;Genes interact with other genes and gene products in the form of genetic networks. We simulate the well established G2/M genetic network by implementing it as a neural network. This neural network is trained to reproduce in vivo mouse knockout data by disabling nodes in the neural network. The trained neural network is analyzed to quantify the importance of knockout genes p53, BRCA1, and ATM, to mammary cancer susceptibility.;We use cellular automata (CA) to model a specific form of breast cancer called ductal carcinoma in situ (DCIS). We present a novel extension to CA design by implementing CA rules as heritable genes that are subject to mutation. We also implement a newly discovered progenitor hierarchy that allows only progenitor cell types to reproduce. To examine the effect of progenitor hierarchy structure on cancer incidence, genetic heterogeneity, and cancer growth, we use several hierarchical structures. We also examine the effects of hereditary genetic predisposition by running simulations with and without initial mutations.;The research presented uses established computational paradigms established for decades. These models have been extended in novel and biologically realistic ways to answer fundamental questions regarding cancer etiology and progression.Thesis (Ph. D., Bioinformatics and Computational Biology)--University of Idaho, December 2006