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Johns Hopkins Bloomberg School of Public HealthCAAT

CAAT Grants Program

Research Grants 2017-2018: Summaries

  • Rebecca Clewell, ScitoVation (new)
    A System Biology Approach to Evaluating the DNA Damage Response Pathway
     
  • Christopher Vulpe, University of Florida, Gainesville (new)
    Improving Metabolic Accuracy of In Vitro Assays Using CRISPER/Cas-9
     
  • Hao Zhu, Rutgers University Camden (renewal)
    Advance Predictive Modeling of Acute Toxicity by Big Data

Abstracts

Rebecca Clewell, ScitoVation (new)

A System Biology Approach to Evaluating the DNA Damage Response Pathway

We are working to develop a computational systems biology model for DNA damage, repair, and mutation. Development of this model is an iterative process: data are collected and used to test model assumptions, and the model is then used to prescribe collection of new data to improve confidence in the model. Through this process, we developed numerous fit-for-purpose in vitro assays that provide dose-response information on DNA damage, kinase activation, and transcriptional, post-translational, and phenotypic responses. However, the model is limited by an inability to measure DNA damage with sufficient resolution. Current methods either rely on indirect measurements (repair foci, micronucleus) or are not sufficiently quantitative (Comet Assay) to provide required resolution to support a model of response at low, human relevant doses. Our laboratory is developing a human cell-based assay that directly labels DNA damage using a patented methodology. This assay labels DNA double strand breaks that are then measured using flow cytometry. Preliminary data indicates that our assay (direct DNA labeling assay, DDL) can detect DNA damage with greater resolution and less false positives than traditional assays. With the prototype chemical mitomycin C, the DDL assay detects damage at doses 10-fold lower than the micronucleus assay (EC50; 0.022 μM, 0.29 μM, respectively). This proposal focuses on developing dose-response data from the assay for use in the computational model, through Two Specific Aims. First, in depth dose-response data will be collected for radioactive iodine exposure in HT-1080 human fibrosarcoma cells, to determine the relationship between fluorescent signal in the DDL assay and number of radioactive disintegrations to help build the current model. HT-1080 cells were selected because they are human derived, express wild-type p53 and MDM2, and have been shown to recapitulate all of the major cellular defense mechanisms to DNA damage (p53, cell cycle arrest, apoptosis, DNA repair) Second, this data will be used together with data previously collected for the DNA damage response pathway to validate the current computational systems biology model. The goal of this systems approach is to provide a quantitative description of the processes that protect cells at low levels of damage and predict doses that will lead to increased risk of adversity (mutation). Use of the DDL assay will provide measures of DNA damage data at doses not detectable with current assays and provide valuable information for model development to ensure reduced need for animal studies.

Christopher Vulpe, University of Florida, Gainesville (new)

Improving Metabolic Accuracy of In Vitro Assays Using CRISPER/Cas-9

In vitro testing provides a powerful means of conducting rapid, targeted toxicological evaluations for pharmaceuticals and industrial chemicals, without the fiscal and moral costs of whole-animal testing. Unfortunately, immortalized cell lines have very different metabolic profiles from primary cells in the normal body environment, particularly in the case of cytochrome P450 enzymes (CYP450s) and other enzymes involved in detoxication pathways. These metabolic discrepancies limit the accuracy of in vitro toxicity tests and misrepresent the likely fate of a compound in a human body. The long-term objective of this project is to improve the predictive accuracy of existing HepG2-based assays by using a synergistic activation mediator (SAM) CRISPR-Cas9 system to restore metabolic competence and physiological levels of CYP450 expression. We will use a catalytically-inactive Cas9 fused to a transcriptional activation domain to enhance CYP450 gene expression when the fused Cas9 is co-expressed with an sgRNA targeted to the CYP450 gene promoter. A lentiviral system will be used to introduce the SAM components and the targeting sgRNAs to the HepG2 cells. This system will allow us to enhance the expression of multiple genes simultaneously by providing the sgRNAs for desired genes. As a proof of principle, we will use multiple sgRNAs to express one or more of the following CYP450s known for their prominent role in xenobiotic metabolism: CYP1A1, CYP1A2, and CYP3A4.

We envision two possible approaches for transfecting HepG2 cells with SAM: In the first, transient transfections will be used to provide the required components for CYP450 expression immediately prior to each screen. We anticipate that this approach would provide stable CYP expression for 48-72 hours – sufficient time to complete a screen. In the second approach, a HepG2 cell line will be stably transfected to produce SAM components allowing it to be used to activate CYP450 genes when the corresponding sgRNAs are introduced. sgRNA will be introduced via direct transfection or lentiviral transduction using a vector containing the corresponding sgRNA sequence. The transcriptional induction of single or multiple genes will be validated by qPCR, western blots, and enzyme activity assays using metabolically competent reporter cells and non-targeting sgRNA modified cells used as controls.

Hao Zhu

Advance Predictive Modeling of Acute Toxicity by Big Data (renewal)

As one of the major potential alternatives to animal models, the read-across of toxicity data within groups of similar compounds represents a promising direction to fill the data gap in chemical safety assessment. While read-across can play a key role in complying with legislation (e.g. the European REACH regulation), most of the current read-across tools only rely on chemical structure information. With more and more available biological data, biosimilarity can add extra strength to this process. In this project, we will develop an automated computational approach 1) to explore the public big data sources; 2) to generate the bioprofiles; 3) and to perform a read-cross study, using both chemical structure information and bioprofiles, for the target 7,300 compounds with animal acute toxicity data. Then the external acute toxicity database, which contains around 2,000 new compounds, will be used to validate the resulting read-across approach. Moreover, we will share the toxicology community the developed read-across tool via Chemical In vitro-In vivo Profiling (CIIPro) portal (ciipro.rutgers.edu). The toxicologists can use the CIIPro portal to directly evaluate acute toxicity of new compounds before animal testing and to prioritize toxic compounds of environmental interest for future animal studies.