Highlights d ERK signaling in the Drosophila embryo antagonizes repression in a two-step process d ERK activation leads to rapid loss of a transcriptional repressor from the DNA d Transcriptional repression is reestablished once signal is removed d Signal must persist for proper gene expression and pattern formation

The most frequent extracellular signal-regulated kinase 2 (ERK2) mutation occurring in cancers is E322K (E-K). ERK2 E-K reverses a buried charge in the ERK2 common docking (CD) site, a region that binds activators, inhibitors, and substrates. Little is known about the cellular consequences associated with this mutation, other than apparent increases in tumor resistance to pathway inhibitors. ERK2 E-K, like the mutation of the preceding aspartate (ERK2 D321N [D-N]) known as the sevenmaker mutation, causes increased activity in cells and evades inactivation by dual-specificity phosphatases. As opposed to findings in cancer cells, in developmental assays in Drosophila, only ERK2 D-N displays a significant gain of function, revealing mutation-specific phenotypes. The crystal structure of ERK2 D-N is indistinguishable from that of wild-type protein, yet this mutant displays increased thermal stability. In contrast, the crystal structure of ERK2 E-K reveals profound structural changes, including disorder in the CD site and exposure of the activation loop phosphorylation sites, which likely account for the decreased thermal stability of the protein. These contiguous mutations in the CD site of ERK2 are both required for docking interactions but lead to unpredictably different functional outcomes. Our results suggest that the CD site is in an energetically strained configuration, and this helps drive conformational changes at distal sites on ERK2 during docking interactions.

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Significance To understand developmental patterning of an organism, it is necessary to accurately measure how the state of a gene regulatory network is changing over time. One way of extracting dynamics of a network involves simultaneously imaging several reporters within fixed tissue. Reconstructing dynamics from such data requires staging many samples over time and often leads to low temporal resolution. Time-lapse microscopy of fluorescent transcriptional reporters has revolutionized studies of biological dynamics at the single-cell level. However, this method is limited by the number of reporters that can be imaged at one time. We present a computational method for addressing this problem and demonstrate its application by modeling the gene regulatory network underlying Drosophila posterior patterning and reconstructing its developmental dynamics.

Modern studies of embryogenesis are increasingly quantitative, powered by rapid advances in imaging, sequencing, and genome manipulation technologies. Deriving mechanistic insights from the complex datasets generated by these new tools requires systematic approaches for data-driven analysis of the underlying developmental processes. Here we use data from our work on signal-dependent gene repression in the Drosophila embryo to illustrate how computational models can compactly summarize quantitative results of live imaging, chromatin immunoprecipitation, and optogenetic perturbation experiments. The presented computational approach is ideally suited for integrating rapidly accumulating quantitative data and for guiding future studies of embryogenesis.

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Modern studies of embryogenesis are increasingly quantitative, powered by rapid advances in imaging, sequencing, and genome manipulation technologies. Deriving mechanistic insights from the complex datasets generated by these new tools requires systematic approaches for data-driven analysis of the underlying developmental processes. Here we use data from our work on signal-dependent gene repression in the fruit fly, Drosophila melanogaster, to illustrate how computational models can compactly summarize quantitative results of live imaging, chromatin immunoprecipitation, and optogenetic perturbation experiments. The presented computational approach is ideally suited for integrating rapidly accumulating quantitative data and for guiding future studies of embryogenesis.

How is a disease contracted, and how does it progress through the body? Answers to these questions are fundamental to understanding both basic biology and medicine. Advances in the biomedical sciences continue to provide more tools to address these fundamental questions and to uncover questions that have not been thought of before. Despite these major advances, we are still facing conceptual and technical challenges when learning about the etiology of disease, especially for genetic diseases. In this review, we illustrate this point by discussing the causal links between molecular mechanisms and systems-level phenotypes in molecular diseases. We begin with an examination of sickle cell anemia, and how mechanisms of the disease have been comprehended over the last century. While sickle cell anemia involves a mutation in a single protein in a single cell type, other diseases involve mutations in networks with many protein interactions and in diverse cell types. We introduce the challenges that result from these differences and illustrate the current obstacles by discussing the RASopathies, a recently discovered class of developmental syndromes that result from mutations in signaling networks. Methods to study mutant genotypes that lead to mutant phenotypes are discussed, particularly the use of model organisms and mutant proteins to study protein interactions that may be important for development of disease. These studies will point toward the future of diagnosing and treating genetic disease.