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Using ToxCast data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure
Citation:
Shah, I., Woodrow Setzer, J. Jack, K. Houck, R. Judson, T. Knudsen, J. Liu, M. Martin, D. Reif, Ann M. Richard, Russell S. Thomas, K. Crofton, David J. Dix, AND Robert J. Kavlock. Using ToxCast data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, 124(7):910-919, (2016).
Impact/Purpose:
High-throughput in vitro screening is an important tool for evaluating the potential biological activity of the thousands of existing chemicals in commerce and the hundreds more introduced each year. Among the assay technologies available, high-content imaging (HCI) allows multiplexed measurements of cellular phenotypic changes induced by chemical exposures. For a large chemical inventory having limited concentration-time series data, the deconvolution of cellular response profiles into transitive or irrevocable state trajectories is an important consideration for estimating points of departure (POD).
Description:
AbstractBackground. High-throughput in vitro screening is an important tool for evaluating the potential biological activity of the thousands of existing chemicals in commerce and the hundreds more introduced each year. Among the assay technologies available, high-content imaging (HCI) allows multiplexed measurements of cellular phenotypic changes induced by chemical exposures. For a large chemical inventory having limited concentration-time series data, the deconvolution of cellular response profiles into transitive or irrevocable state trajectories is an important consideration. Objectives. Our goal was to analyze temporal and concentration-related cellular changes measured using HCI to identify the “tipping point” at which the cells did not show recovery towards a normal phenotypic state. Methods. The effects of 976 chemicals (ToxCast Phase I and II) were evaluated using HCI as a function of concentration and time in HepG2 cells over a 72-hr exposure period to concentrations ranging from 0.4- to 200 µM. The cellular endpoints included nuclear p53 accumulation, JNK, markers of oxidative stress, cytoskeletal changes, mitochondrial energization and density, cell viability and cell cycle progression. A novel computational model was developed to interpret dynamic multidimensional system responses as cell-state trajectories. Results. Analysis of cell-state trajectories showed that HepG2 cells were resilient to the effects of 178 chemicals up to the highest concentration tested (200 µM). A total of 340 chemicals showed altered cellular trajectories that did not recover to normal over the test period, and concentration-dependent transitions in system recovery. The critical concentration was generally between 3.5 and 18 times (25th and 75th percentiles) lower than the concentration that produced 50% cell loss. Results were inconclusive for the remaining 458 chemicals and may require more extensive data collection before they can be placed in either of the former categories. Conclusions. These findings reveal that HCI data can be used to reconstruct cell state trajectories, and provide insight into adaptation and resilience of in vitro cellular systems based on tipping points in a cellular systems biology context. With additional research, cellular tipping points could be used to define a point of departure for risk-based prioritization of environmental chemicals.
