Jeremy E. Purvis

p53 dynamics control cell fate

Dynamic Responses Expression of the tumor suppressor p53 is activated in response to cell stress. The dynamics of p53 activation can vary, depending on the stressor, resulting in either pulsatile or constant p53 levels; however, the functional consequence of these different dynamics is unclear. Purvis et al. (p. 1440) developed a method to control p53 dynamics in human cells. Pulsing p53 selectively activated genes involved in cell cycle arrest and DNA repair, allowing recovery from DNA damage. In contrast, sustained p53 promoted induction of terminal genes leading to cellular senescence. Thus, protein dynamics can affect cell fate decisions. After DNA damage, pulses of p53 allow recovery, whereas sustained levels lead to senescence. Cells transmit information through molecular signals that often show complex dynamical patterns. The dynamic behavior of the tumor suppressor p53 varies depending on the stimulus; in response to double-strand DNA breaks, it shows a series of repeated pulses. Using a computational model, we identified a sequence of precisely timed drug additions that alter p53 pulses to instead produce a sustained p53 response. This leads to the expression of a different set of downstream genes and also alters cell fate: Cells that experience p53 pulses recover from DNA damage, whereas cells exposed to sustained p53 signaling frequently undergo senescence. Our results show that protein dynamics can be an important part of a signal, directly influencing cellular fate decisions.

Encoding and decoding cellular information through signaling dynamics.

A growing number of studies are revealing that cells can send and receive information by controlling the temporal behavior (dynamics) of their signaling molecules. In this Review, we discuss what is known about the dynamics of various signaling networks and their role in controlling cellular responses. We identify general principles that are emerging in the field, focusing specifically on how the identity and quantity of a stimulus is encoded in temporal patterns, how signaling dynamics influence cellular outcomes, and how specific dynamical patterns are both shaped and interpreted by the structure of molecular networks. We conclude by discussing potential functional roles for transmitting cellular information through the dynamics of signaling molecules and possible applications for the treatment of disease.

Enhanced Trehalose Production Improves Growth of Escherichia coli under Osmotic Stress

ABSTRACT The biosynthesis of trehalose has been previously shown to serve as an important osmoprotectant and stress protectant in Escherichia coli. Our results indicate that overproduction of trehalose (integrated lacI-Ptac-otsBA) above the level produced by the native regulatory system can be used to increase the growth of E. coli in M9-2% glucose medium at 37°C to 41°C and to increase growth at 37°C in the presence of a variety of osmotic-stress agents (hexose sugars, inorganic salts, and pyruvate). Smaller improvements were noted with xylose and some fermentation products (ethanol and pyruvate). Based on these results, overproduction of trehalose may be a useful trait to include in biocatalysts engineered for commodity chemicals.

Gene array-based identification of changes that contribute to ethanol tolerance in ethanologenic Escherichia coli: Comparison of KO11 (parent) to LY01 (resistant mutant)

Escherichia coli KO11 (parent) and LY01 (mutant) have been engineered for the production of ethanol. Gene arrays were used to identify expression changes that occurred in the mutant, LY01, during directed evolution to improve ethanol tolerance (defined as extent of growth in the presence of added ethanol). Expression levels for 205 (5%) of the ORFs were found to differ significantly ( p < 0.10) between KO11 and LY01 under each of six different growth conditions ( p < 0.000001). Statistical evaluation of differentially expressed genes according to various classification schemes identified physiological areas of importance. A large fraction of differentially expressed ORFs were globally regulated, leading to the discovery of a nonfunctional fnrgene in strain LY01. In agreement with a putative role for FNR in alcohol tolerance, increasing the copy number of fnr+ in KO11(pGS196) decreased ethanol tolerance but had no effect on growth in the absence of ethanol. Other differences in gene expression provided additional clues that permitted experimentation. Tolerance appears to involve increased metabolism of glycine (higher expression of gcv genes) and increased production of betaine (higher expression of betIBAand betT encoding betaine synthesis from choline and choline uptake, respectively). Addition of glycine (10 mM) increased ethanol tolerance in KO11 but had no effect in the absence of ethanol. Addition of betaine (10 mM) increased ethanol tolerance by over 2‐fold in both LY01 and KO11 but had no effect on growth in the absence of ethanol. Both glycine and betaine can serve as protective osmolytes, and this may be the basis of their beneficial action. In addition, the marAB genes encoding multiple antibiotic resistance proteins were expressed at higher levels in LY01 as compared to KO11. Interestingly, overexpression of marAB in KO11 made this strain more ethanol‐sensitive. Overexpression of marAB in LY01 had no effect on ethanol tolerance. Increased expression of genes encoding serine uptake ( sdaC) and serine deamination ( sdaB) also appear beneficial for LY01. Addition of serine increased the growth of LY01 in the presence and absence of ethanol but had no effect on KO11. Changes in the expression of several genes concerned with the synthesis of the cell envelope components were also noted, which may contribute to increased ethanol tolerance.

Pairwise agonist scanning predicts cellular signaling responses to combinatorial stimuli.

Prediction of cellular response to multiple stimuli is central to evaluating patient-specific clinical status and to basic understanding of cell biology. Cross-talk between signaling pathways cannot be predicted by studying them in isolation and the combinatorial complexity of multiple agonists acting together prohibits an exhaustive exploration of the complete experimental space. Here we describe pairwise agonist scanning (PAS), a strategy that trains a neural network model based on measurements of cellular responses to individual and all pairwise combinations of input signals. We apply PAS to predict calcium signaling responses of human platelets in EDTA-treated plasma to six different agonists (ADP, convulxin, U46619, SFLLRN, AYPGKF and PGE2) at three concentrations (0.1, 1 and 10 × EC50). The model predicted responses to sequentially added agonists, to ternary combinations of agonists and to 45 different combinations of four to six agonists (R = 0.88). Furthermore, we use PAS to distinguish between the phenotypic responses of platelets from ten donors. Training neural networks with pairs of stimuli across the dose-response regime represents an efficient approach for predicting complex signal integration in a patient-specific disease milieu.Patient-specific prediction of cellular response to multiple stimuli is central to evaluating clinical risk, disease progression, and response to therapy. We deployed Pairwise Agonist Scanning (PAS) to measure calcium signaling of human platelets in EDTA-treated plasma exposed to 6 different agonists (at 0.1, 1, and 10×EC50) used individually or in 135 pairwise combinations. With 154 traces, we trained a neural network (NN) model to accurately predict the entire 6-dimensional response to ADP, convulxin, U46619, SFLLRN, AYPGKF, and PGE2. The NN successfully predicted calcium responses to sequential agonist additions, all ternary combinations of [ADP]+[convulxin]+[SFLLRN] (R=0.88), and 45 different combinations of 4 to 6 agonists (R=0.88). Furthermore, PAS provided 135 pairwise synergy values that allowed a unique phenotypic scoring and differentiation of 10 donors. Training of NNs with pairs of stimuli across the dose-response regime represents a highly efficient approach to predict integration of multiple, complex signals in a patient-specific disease milieu.

A molecular signaling model of platelet phosphoinositide and calcium regulation during homeostasis and P2Y1 activation

To quantify how various molecular mechanisms are integrated to maintain platelet homeostasis and allow responsiveness to adenosine diphosphate (ADP), we developed a computational model of the human platelet. Existing kinetic information for 77 reactions, 132 fixed kinetic rate constants, and 70 species was combined with electrochemical calculations, measurements of platelet ultrastructure, novel experimental results, and published single-cell data. The model accurately predicted: (1) steady-state resting concentrations for intracellular calcium, inositol 1,4,5-trisphosphate, diacylglycerol, phosphatidic acid, phosphatidylinositol, phosphatidylinositol phosphate, and phosphatidylinositol 4,5-bisphosphate; (2) transient increases in intracellular calcium, inositol 1,4,5-trisphosphate, and G(q)-GTP in response to ADP; and (3) the volume of the platelet dense tubular system. A more stringent test of the model involved stochastic simulation of individual platelets, which display an asynchronous calcium spiking behavior in response to ADP. Simulations accurately reproduced the broad frequency distribution of measured spiking events and demonstrated that asynchronous spiking was a consequence of stochastic fluctuations resulting from the small volume of the platelet. The model also provided insights into possible mechanisms of negative-feedback signaling, the relative potency of platelet agonists, and cell-to-cell variation across platelet populations. This integrative approach to platelet biology offers a novel and complementary strategy to traditional reductionist methods.

A Small-Molecule Oxocarbazate Inhibitor of Human Cathepsin L Blocks Severe Acute Respiratory Syndrome and Ebola Pseudotype Virus Infection into Human Embryonic Kidney 293T cells

A tetrahydroquinoline oxocarbazate (PubChem CID 23631927) was tested as an inhibitor of human cathepsin L (EC 3.4.22.15) and as an entry blocker of severe acute respiratory syndrome (SARS) coronavirus and Ebola pseudotype virus. In the cathepsin L inhibition assay, the oxocarbazate caused a time-dependent 17-fold drop in IC50 from 6.9 nM (no preincubation) to 0.4 nM (4-h preincubation). Slowly reversible inhibition was demonstrated in a dilution assay. A transient kinetic analysis using a single-step competitive inhibition model provided rate constants of kon = 153,000 M−1s−1 and koff = 4.40 × 10−5 s−1 (Ki = 0.29 nM). The compound also displayed cathepsin L/B selectivity of >700-fold and was nontoxic to human aortic endothelial cells at 100 μM. The oxocarbazate and a related thiocarbazate (PubChem CID 16725315) were tested in a SARS coronavirus (CoV) and Ebola virus-pseudotype infection assay with the oxocarbazate but not the thiocarbazate, demonstrating activity in blocking both SARS-CoV (IC50 = 273 ± 49 nM) and Ebola virus (IC50 = 193 ± 39 nM) entry into human embryonic kidney 293T cells. To trace the intracellular action of the inhibitors with intracellular cathepsin L, the activity-based probe biotin-Lys-C5 alkyl linker-Tyr-Leu-epoxide (DCG-04) was used to label the active site of cysteine proteases in 293T lysates. The reduction in active cathepsin L in inhibitor-treated cells correlated well with the observed potency of inhibitors observed in the virus pseudotype infection assay. Overall, the oxocarbazate CID 23631927 was a subnanomolar, slow-binding, reversible inhibitor of human cathepsin L that blocked SARS-CoV and Ebola pseudotype virus entry in human cells.

A Multiscale Computational Approach to Dissect Early Events in the Erb Family Receptor Mediated Activation, Differential Signaling, and Relevance to Oncogenic Transformations

We describe a hierarchical multiscale computational approach based on molecular dynamics simulations, free energy-based molecular docking simulations, deterministic network-based kinetic modeling, and hybrid discrete/continuum stochastic dynamics protocols to study the dimer-mediated receptor activation characteristics of the Erb family receptors, specifically the epidermal growth factor receptor (EGFR). Through these modeling approaches, we are able to extend the prior modeling of EGF-mediated signal transduction by considering specific EGFR tyrosine kinase (EGFRTK) docking interactions mediated by differential binding and phosphorylation of different C-terminal peptide tyrosines on the RTK tail. By modeling signal flows through branching pathways of the EGFRTK resolved on a molecular basis, we are able to transcribe the effects of molecular alterations in the receptor (e.g., mutant forms of the receptor) to differing kinetic behavior and downstream signaling response. Our molecular dynamics simulations show that the drug sensitizing mutation (L834R) of EGFR stabilizes the active conformation to make the system constitutively active. Docking simulations show preferential characteristics (for wildtype vs. mutant receptors) in inhibitor binding as well as preferential enhancement of phosphorylation of particular substrate tyrosines over others. We find that in comparison to the wildtype system, the L834R mutant RTK preferentially binds the inhibitor erlotinib, as well as preferentially phosphorylates the substrate tyrosine Y1068 but not Y1173. We predict that these molecular level changes result in preferential activation of the Akt signaling pathway in comparison to the Erk signaling pathway for cells with normal EGFR expression. For cells with EGFR over expression, the mutant over activates both Erk and Akt pathways, in comparison to wildtype. These results are consistent with qualitative experimental measurements reported in the literature. We discuss these consequences in light of how the network topology and signaling characteristics of altered (mutant) cell lines are shaped differently in relationship to native cell lines.

KINETIC CHARACTERIZATION AND MOLECULAR DOCKING OF A NOVEL, POTENT, AND SELECTIVE SLOW-BINDING INHIBITOR OF HUMAN CATHEPSIN L

A novel small molecule thiocarbazate (PubChem SID 26681509), a potent inhibitor of human cathepsin L (EC 3.4.22.15) with an IC50 of 56 nM, was developed after a 57,821-compound screen of the National Institutes of Health Molecular Libraries Small Molecule Repository. After a 4-h preincubation with cathepsin L, this compound became even more potent, demonstrating an IC50 of 1.0 nM. The thiocarbazate was determined to be a slow-binding and slowly reversible competitive inhibitor. Through a transient kinetic analysis for single-step reversibility, inhibition rate constants were kon = 24,000 M-1s-1 and koff = 2.2 × 10-5 s-1 (Ki = 0.89 nM). Molecular docking studies were undertaken using the experimentally derived X-ray crystal structure of papain/CLIK-148 (1cvz. pdb). These studies revealed critical hydrogen bonding patterns of the thiocarbazate with key active site residues in papain. The thiocarbazate displayed 7- to 151-fold greater selectivity toward cathepsin L than papain and cathepsins B, K, V, and S with no activity against cathepsin G. The inhibitor demonstrated a lack of toxicity in human aortic endothelial cells and zebrafish. In addition, the thiocarbazate inhibited in vitro propagation of malaria parasite Plasmodium falciparum with an IC50 of 15.4 μM and inhibited Leishmania major with an IC50 of 12.5 μM.

Steady-state kinetic modeling constrains cellular resting states and dynamic behavior.

A defining characteristic of living cells is the ability to respond dynamically to external stimuli while maintaining homeostasis under resting conditions. Capturing both of these features in a single kinetic model is difficult because the model must be able to reproduce both behaviors using the same set of molecular components. Here, we show how combining small, well-defined steady-state networks provides an efficient means of constructing large-scale kinetic models that exhibit realistic resting and dynamic behaviors. By requiring each kinetic module to be homeostatic (at steady state under resting conditions), the method proceeds by (i) computing steady-state solutions to a system of ordinary differential equations for each module, (ii) applying principal component analysis to each set of solutions to capture the steady-state solution space of each module network, and (iii) combining optimal search directions from all modules to form a global steady-state space that is searched for accurate simulation of the time-dependent behavior of the whole system upon perturbation. Importantly, this stepwise approach retains the nonlinear rate expressions that govern each reaction in the system and enforces constraints on the range of allowable concentration states for the full-scale model. These constraints not only reduce the computational cost of fitting experimental time-series data but can also provide insight into limitations on system concentrations and architecture. To demonstrate application of the method, we show how small kinetic perturbations in a modular model of platelet P2Y1 signaling can cause widespread compensatory effects on cellular resting states.