U2019.3_ISSF
Ontology highlight
ABSTRACT:
SUBMITTER: Uriel Urquiza Garcia
PROVIDER: 773 | FAIRDOMHub | 2021-03-01
REPOSITORIES: FAIRDOMHub
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Project description:The TOL system encoded by plasmid pWW0 of Pseudomonas putida mt-2 is able to sense a large number of both exogenous and endogenous signals as inputs for the genetic and metabolic circuit that determines the biodegradation of m-xylene. However, whether the enormous combinatorial space of inputs is translated into an equally variable response landscape or is processed into very few outcomes remains unclear. To address this question, we set out to define the number of states that can be obtained by a network of a given set of genes under the control of a specified regulatory circuit that is exposed to all possible combinations of inputs. To this end, the TOL network and its regulatory wiring were formalized as a synchronous logic Boolean circuit that had 10 signals (i.e. pathway substrates, temperature, sugars, amino acids, metabolic regimes and global regulators) as possible inputs. The analysis of the attractors of the circuit using a satisfiability (SAT) algorithm revealed that only eight transcriptional states are reached in response to the 1024 possible combinations of stimuli. The experimental validation resulted in a refinement of the model through the addition of a previously unknown interaction that controls the meta catabolic pathway. The full induction of the two xyl operons occurred with only 1.6% of the input combinations, which suggests that the architecture of the network allows the expression of the xyl genes only under a very narrow range of conditions. These data not only explain much of the unusual layout of the TOL circuit but also strengthen the view of the regulatory circuits of environmental bacteria as digital decision-making devices.
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Project description:Background In the yeast Saccharomyces cerevisiae, the presence of high levels of glucose leads to an array of down-regulatory effects known as glucose repression. This process is complex due to the presence of feedback loops and crosstalk between different pathways, complicating the use of intuitive approaches to analyze the system. Results We established a logical model of yeast glucose repression, formalized as a hypergraph. The model was constructed based on verified regulatory interactions and it includes 50 gene transcripts, 22 proteins, 5 metabolites and 118 hyperedges. We computed the logical steady states of all nodes in the network in order to simulate wildtype and deletion mutant responses to different sugar availabilities. Evaluation of the model predictive power was achieved by comparing changes in the logical state of gene nodes with transcriptome data. Overall, we observed 71% true predictions, and analyzed sources of errors and discrepancies for the remaining. Conclusion Though the binary nature of logical (Boolean) models entails inherent limitations, our model constitutes a primary tool for storing regulatory knowledge, searching for incoherencies in hypotheses and evaluating the effect of deleting regulatory elements involved in glucose repression.
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Project description:The blood cancer T cell large granular lymphocyte (T-LGL) leukemia is a chronic disease characterized by a clonal proliferation of cytotoxic T cells. As no curative therapy is yet known for this disease, identification of potential therapeutic targets is of immense importance. In this paper, we perform a comprehensive dynamical and structural analysis of a network model of this disease. By employing a network reduction technique, we identify the stationary states (fixed points) of the system, representing normal and diseased (T-LGL) behavior, and analyze their precursor states (basins of attraction) using an asynchronous Boolean dynamic framework. This analysis identifies the T-LGL states of 54 components of the network, out of which 36 (67%) are corroborated by previous experimental evidence and the rest are novel predictions. We further test and validate one of these newly identified states experimentally. Specifically, we verify the prediction that the node SMAD is over-active in leukemic T-LGL by demonstrating the predominant phosphorylation of the SMAD family members Smad2 and Smad3. Our systematic perturbation analysis using dynamical and structural methods leads to the identification of 19 potential therapeutic targets, 68% of which are corroborated by experimental evidence. The novel therapeutic targets provide valuable guidance for wet-bench experiments. In addition, we successfully identify two new candidates for engineering long-lived T cells necessary for the delivery of virus and cancer vaccines. Overall, this study provides a bird's-eye-view of the avenues available for identification of therapeutic targets for similar diseases through perturbation of the underlying signal transduction network.
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Project description:Caveolin-1 (CAV1) is a vital scaffold protein heterogeneously expressed in both healthy and malignant tissue. We focus on the role of CAV1 when overexpressed in T-cell leukemia. Previously, we have shown that CAV1 is involved in cell-to-cell communication, cellular proliferation, and immune synapse formation; however, the molecular mechanisms have not been elucidated. We hypothesize that the role of CAV1 in immune synapse formation contributes to immune regulation during leukemic progression, thereby warranting studies of the role of CAV1 in CD4(+) T-cells in relation to antigen-presenting cells. To address this need, we developed a computational model of a CD4(+) immune effector T-cell to mimic cellular dynamics and molecular signaling under healthy and immunocompromised conditions (i.e., leukemic conditions). Using the Cell Collective computational modeling software, the CD4(+) T-cell model was constructed and simulated under CAV1 (+/+), CAV1 (+/-), and CAV1 (-/-) conditions to produce a hypothetical immune response. This model allowed us to predict and examine the heterogeneous effects and mechanisms of CAV1 in silico. Experimental results indicate a signature of molecules involved in cellular proliferation, cell survival, and cytoskeletal rearrangement that were highly affected by CAV1 knock out. With this comprehensive model of a CD4(+) T-cell, we then validated in vivo protein expression levels. Based on this study, we modeled a CD4(+) T-cell, manipulated gene expression in immunocompromised versus competent settings, validated these manipulations in an in vivo murine model, and corroborated acute T-cell leukemia gene expression profiles in human beings. Moreover, we can model an immunocompetent versus an immunocompromised microenvironment to better understand how signaling is regulated in patients with leukemia.
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