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PLoS Computational Biology (1)
The Journal of Cell Biology (1)
Fink, Marc Y. (2)
Bradley, Ann E. (1)
Cassella, Melanie R. (1)
Choi, Soon-gang (1)
Felsenfeld, Dan P. (1)
Gil, Orlando D. (1)
Kuo, James A. (1)
Sakurai, Takeshi (1)
Sealfon, Stuart C. (1)
Shimoni, Yishai (1)
von Mering, Christian (1)
Year of Publication
Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows
Sealfon, Stuart C.
von Mering, Christian
PLoS Computational Biology
Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.
Elucidating the biochemical interactions in living cells is essential to understanding their behavior under various external conditions. Some of these interactions occur between signaling components with many active states, and their activity levels may be difficult to measure directly. However, most methods to reverse engineer interaction networks rely on measuring gene activity at steady state under various cellular stimuli. Such gene measurements therefore ignore the intermediate effects of signaling components, and cannot reliably convey the interactions between the signaling components themselves. We propose using the changes in activity of early genes shortly after the stimulus to infer the functional interactions between the unmeasured signaling components. The change in expression in such genes at these times is directly and linearly affected by the signaling components, since there is insufficient time for other genes to be transcribed and interfere with the early genes' expression. We present an algorithm that uses such measurements to reverse engineer the functional interaction network between signaling components, and also provides a means for testing these predictions. The algorithm therefore uses feasible experiments to reconstruct functional networks. We applied the algorithm to experimental measurements and uncovered known interactions, as well as novel interactions that were then confirmed experimentally.
Ankyrin binding mediates L1CAM interactions with static components of the cytoskeleton and inhibits retrograde movement of L1CAM on the cell surface
Gil, Orlando D.
Bradley, Ann E.
Cassella, Melanie R.
Kuo, James A.
Felsenfeld, Dan P.
The Journal of Cell Biology
The function of adhesion receptors in both cell adhesion and migration depends critically on interactions with the cytoskeleton. During cell adhesion, cytoskeletal interactions stabilize receptors to strengthen adhesive contacts. In contrast, during cell migration, adhesion proteins are believed to interact with dynamic components of the cytoskeleton, permitting the transmission of traction forces through the receptor to the extracellular environment. The L1 cell adhesion molecule (L1CAM), a member of the Ig superfamily, plays a crucial role in both the migration of neuronal growth cones and the static adhesion between neighboring axons. To understand the basis of L1CAM function in adhesion and migration, we quantified directly the diffusion characteristics of L1CAM on the upper surface of ND-7 neuroblastoma hybrid cells as an indication of receptor–cytoskeleton interactions. We find that cell surface L1CAM engages in diffusion, retrograde movement, and stationary behavior, consistent with interactions between L1CAM and two populations of cytoskeleton proteins. We provide evidence that the cytoskeletal adaptor protein ankyrin mediates stationary behavior while inhibiting the actin-dependent retrograde movement of L1CAM. Moreover, inhibitors of L1CAM–ankyrin interactions promote L1CAM-mediated axon growth. Together, these results suggest that ankyrin binding plays a crucial role in the anti-coordinate regulation of L1CAM-mediated adhesion and migration.
cell migration; single particle tracking; traction force; cell adhesion; axon growth
Results 1-2 (2)
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