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The existence of liquid carbon as an intermediate phase preceding the formation of novel carbon materials has been a point of contention for several decades. Experimental observation of such a liquid state requires nonthermal melting of solid carbon materials at various laser fluences and pulse properties. Reflectivity experiments performed in the mid-1980s reached opposing conclusions regarding the metallic or insulating properties of the purported liquid state. Time-resolved X-ray absorption studies showed shortening of C-C bonds and increasing diffraction densities, thought to evidence a liquid or glassy carbon state, respectively. Nevertheless, none of these experiments provided information on the electronic structure of the proposed liquid state. Herein, we report the results of time-resolved resonant inelastic X-ray scattering (RIXS) and time-resolved X-ray emission spectroscopy (XES) studies on amorphous carbon (a-C) and ultrananocrystalline diamond (UNCD) as a function of delay time between the irradiating pulse and X-ray probe. For both a-C and UNCD, we attribute decreases in RIXS or XES signals to transition blocking, relaxation, and finally, ablation. Increased signal at 20 ps following the irradiation of the UNCD is attributed to the probable formation of nanoscale structures in the ablation plume. Differences in the amount of signal observed between a-C and UNCD are explained by the difference in sample thickness and, specifically, incomplete melting of the UNCD film. Comparisons to spectral simulations based on MD trajectories at extreme conditions indicate that the carbon state in our experiments is crystalline. Normal mode analysis confirmed that symmetrical bending or stretching of the C-C bonds in the diamond lattice results in XES spectra with small intensity differences. Overall, we observed no evidence of melting to a liquid state, as determined by the lack of changes in the spectral properties for up to 100 ps delays following the melting pulses.

Evolution of nitrogen under shock compression up to 100 GPa is revisited via molecular dynamics simulations using a machine-learned interatomic potential. The model is shown to be capable of recovering the structure, dynamics, speciation, and kinetics in hot compressed liquid nitrogen predicted by first-principles molecular dynamics, as well as the measured principal shock Hugoniot and double shock experimental data, albeit without shock cooling. Our results indicate that a purely molecular dissociation description of nitrogen chemistry under shock compression provides an incomplete picture and that short oligomers form in non-negligible quantities. This suggests that classical models representing the shock dissociation of nitrogen as a transition to an atomic fluid need to be revised to include reversible polymerization effects., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly, and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

Siloxane systems consisting primarily of polydimethylsiloxane (PDMS) are versatile, multifaceted materials that play a key role in diverse applications. However, open questions exist regarding the correlation between their varied atomic-level properties and observed macroscale features. To this effect, we have created a systematic workflow to determine coarse-grained simulation models for crosslinked PDMS in order to further elucidate the effects of network changes on the system's rheological properties below the gel point. Our approach leverages a fine-grained united atom model for linear PDMS, which we extend to include crosslinking terms, and applies iterative Boltzmann inversion to obtain a coarse-grain "bead-spring-type" model. We then perform extensive molecular dynamics simulations to explore the effect of crosslinking on the rheology of silicone fluids, where we compute systematic increases in both density and shear viscosity that compare favorably to experiments that we conduct here. The kinematic viscosity of partially crosslinked fluids follows an empirical linear relationship that is surprisingly consistent with Rouse theory, which was originally derived for systems comprised of a uniform distribution of linear chains. The models developed here serve to enable quantitative bottom-up predictions for curing- and age-induced effects on macroscale rheological properties, allowing for accurate prediction of material properties based on fundamental chemical data.

Adsorption of n -nonane/1-hexanol (C9/C6OH) mixtures into the lamellar phase formed by a 50/50 w/w triethylene glycol mono- n -decyl ether (C10E3)/water system was studied using configurational-bias Monte Carlo simulations in the osmotic Gibbs ensemble. The interactions were described by the Shinoda-Devane-Klein coarse-grained force field. Prior simulations probing single-component adsorption indicated that C9 molecules preferentially load near the center of the bilayer, increasing the bilayer thickness, whereas C6OH molecules are more likely to be found near the interface of the polar and nonpolar moieties, swelling the bilayer in the lateral dimension. Here, we extend this work to binary C9/C6OH adsorption to probe whether the difference in the spatial preferences may lead to a synergistic effect and enhanced loadings for the mixture. Comparing loading trends and the thermodynamics of binary adsorption to unary adsorption reveals that C9-C9 interactions lead to the largest enhancement, whereas C9-C6OH and C6OH-C6OH interactions are less favorable for this bilayer system. Ideal adsorbed solution theory yields satisfactory predictions of the binary loading.

A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ∼0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

There is significant interest in establishing a capability for tailored synthesis of next-generation carbon-based nanomaterials due to their broad range of applications and high degree of tunability. High pressure (e.g., shockwave-driven) synthesis holds promise as an effective discovery method, but experimental challenges preclude elucidating the processes governing nanocarbon production from carbon-rich precursors that could otherwise guide efforts through the prohibitively expansive design space. Here we report findings from large scale atomistically-resolved simulations of carbon condensation from C/O mixtures subjected to extreme pressures and temperatures, made possible by machine-learned reactive interatomic potentials. We find that liquid nanocarbon formation follows classical growth kinetics driven by Ostwald ripening (i.e., growth of large clusters at the expense of shrinking small ones) and obeys dynamical scaling in a process mediated by carbon chemistry in the surrounding reactive fluid. The results provide direct insight into carbon condensation in a representative system and pave the way for its exploration in higher complexity organic materials. They also suggest that simulations using machine-learned interatomic potentials could eventually be employed as in-silico design tools for new nanomaterials., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH 2 as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

We describe a machine learning approach to rapidly tune density functional tight binding models for the description of detonation chemistry in organic molecular materials. Resulting models enable simulations on the several 10s of ps scales characteristic to these processes, with "quantum-accuracy." We use this approach to investigate early shock chemistry in 3,4-bis(3-nitrofurazan-4-yl)furoxan, a hydrogen-free energetic material known to form onion-like nanocarbon particulates following detonation. We find that the ensuing chemistry is significantly characterized by the formation of large C x N y O z species, which are likely precursors to the experimentally observed carbon condensates. Beyond utility as a means of investigating detonation chemistry, the present approach can be used to generate quantum-based reference data for the development of full machine-learned interatomic potentials capable of simulation on even greater time and length scales, i.e., for applications where characteristic time scales exceed the reach of methods including Kohn-Sham density functional theory, which are commonly used for reference data generation.

HN 3 is a unique liquid energetic material that exhibits ultrafast detonation chemistry and a transition to metallic states during detonation. We combine the Chebyshev interaction model for efficient simulation (ChIMES) many-body reactive force field and the extended-Lagrangian multiscale shock technique molecular dynamics method to calculate the detonation properties of HN 3 with the accuracy of Kohn-Sham density-functional theory. ChIMES is based on a Chebyshev polynomial expansion and can accurately reproduce density-functional theory molecular dynamics (DFT-MD) simulations for a wide range of unreactive and decomposition conditions of liquid HN 3 . We show that addition of random displacement configurations and the energies of gas-phase equilibrium products in the training set allows ChIMES to efficiently explore the complex potential energy surface. Schemes for selecting force field parameters and the inclusion of stress tensor and energy data in the training set are examined. Structural and dynamical properties and chemistry predictions for the resulting models are benchmarked against DFT-MD. We demonstrate that the inclusion of explicit four-body energy terms is necessary to capture the potential energy surface across a wide range of conditions. Our results generally retain the accuracy of DFT-MD while yielding a high degree of computational efficiency, allowing simulations to approach orders of magnitude larger time and spatial scales. The techniques and recipes for MD model creation we present allow for direct simulation of nanosecond shock compression experiments and calculation of the detonation properties of materials with the accuracy of Kohn-Sham density-functional theory.

Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. Here, we present an active learning approach based on cluster analysis and inspired by Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. The use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training database management approach enables development of models exhibiting excellent agreement with Kohn-Sham density functional theory in terms of structure, dynamics, and speciation.

We describe the development of a reactive force field for C/O systems under extreme temperatures and pressures, based on the many-body Chebyshev Interaction Model for Efficient Simulation (ChIMES). The resulting model, which targets carbon condensation under thermodynamic conditions of 6500 K and 2.5 g cm -3 , affords a balance between model accuracy, complexity, and training set generation expense. We show that the model recovers much of the accuracy of density functional theory for the prediction of structure, dynamics, and chemistry when applied to dissociative condensed phase systems at 1:1 and 1:2 C:O ratios, as well as molten carbon. Our C/O modeling approach exhibits a 10 4 increase in efficiency for the same system size (i.e., 128 atoms) and a linear system size scalability over standard quantum molecular dynamics methods, allowing the simulation of significantly larger systems than previously possible. We find that the model captures the condensed-phase reaction-coupled formation of carbon clusters implied by recent experiments, and that this process is susceptible to strong finite size effects. Overall, we find the present ChIMES model to be well suited for studying chemical processes and cluster formation at pressures and temperatures typical of shock waves. We expect that the present C/O modeling paradigm can serve as a template for the development of a broader high pressure-high temperature force-field for condensed phase chemistry in organic materials.

Carbon nanoallotropes are important nanomaterials with unusual properties and promising applications. High pressure synthesis has the potential to open new avenues for controlling and designing their physical and chemical characteristics for a broad range of uses but it remains little understood due to persistent conceptual and experimental challenges, in addition to fundamental physics and chemistry questions that are still unresolved after many decades. Here we demonstrate sub-nanosecond nanocarbon synthesis through the application of laser-induced shock-waves to a prototypical organic carbon-rich liquid precursor-liquid carbon monoxide. Overlapping large-scale molecular dynamics simulations capture the atomistic details of the nanoparticles' formation and evolution in a reactive environment and identify classical evaporation-condensation as the mechanism governing their growth on these time scales.

Proteinogenic amino acids can be produced on or delivered to a planet via impacting abiotic sources and consequently were likely present before the emergence of life on Earth. However, the role that these materials played in prebiotic scenarios remains an open question, in part because little is known about the survivability and reactivity of astrophysical organic compounds upon impact with a planetary surface. To this end, we use a force-matched semi-empirical quantum simulation method to study impacts of aqueous proteinogenic amino acids at conditions reaching 48 GPa and 3000 K. Here, we probe a relatively unstudied mechanism for prebiotic synthesis where sudden heating and pressurization causes condensation of complex carbon-rich structures from mixtures of glycine, the simplest protein-forming amino acid. These carbon-containing clusters are stable on short timescales and undergo a fundamental structural transition upon expansion and cooling from predominantly sp 3 -bonded tetrahedral-like moieties to those that are more sp 2 -bonded and planar. The recovered sp 2 -bonded structures include large nitrogen containing polycyclic aromatic hydrocarbons (NPAHs) with a number of different functional groups and embedded bonded regions akin to oligo-peptides. A number of small organic molecules with prebiotic relevance are also predicted to form. This work presents an alternate route to gas-phase synthesis for the formation of NPAHs of high complexity and highlights the significance of both the thermodynamic path and local chemical self-assembly in forming prebiotic species during shock synthesis. Our results help determine the role of comets and other celestial bodies in both the delivery and synthesis of potentially significant life building compounds on early Earth.

Two-dimensional (2D) liquid chromatography (2DLC) methods have grown in popularity due to their enhanced peak capacity that allows for resolving complex samples. Given the large number of commercially available column types, one of the major challenges in implementing 2DLC methods is the selection of suitable column pairs. Column selection is typically informed by chemical intuition with subsequent experimental optimization. In this work a computational screening method for 2DLC is proposed whereby virtual 2D chromatograms are calculated utilizing the Snyder-Dolan hydrophobic subtraction model (HSM) for reversed-phase column selectivity. Towards this end, 319 225 column pairs resulting from the combination of 565 columns and 100 sets of 1000 diverse analytes are examined. Compared to other screening approaches, the present method is highly predictive for column pairs that are able to resolve the largest number of analytes. This approach shows a strong sensitivity to the choice of the second dimension column (having a shorter operating time) and a preference for those with embedded polar moieties, whereas a relatively weak preference for C 18 and phenyl columns is found for the first dimension., (Copyright © 2018 Elsevier B.V. All rights reserved.)

We demonstrate development of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) for molecular systems through application to water under ambient conditions (298 K, 1 g/cm 3 ). These models, which are comprised of linear combinations of Chebyshev polynomials explicitly describing two- and three-body interactions, are largely fit by force matching to Kohn-Sham Density Functional Theory (DFT). Protocols for selecting user-specified parameters and inclusion of stress tensor data are investigated, and structural and dynamical property prediction for resulting models is benchmarked against DFT. We show that the present ChIMES force fields yield excellent agreement with DFT without the need for additional terms such as those for Coulomb interactions. Overall, we show that tractable parametrization and subsequent accuracy of the present models make ChIMES an ideal candidate for extension of DFT dynamics to larger system sizes and longer time scales.

Understanding solute uptake into soft microstructured materials, such as bilayers and worm-like and spherical micelles, is of interest in the pharmaceutical, agricultural, and personal care industries. To obtain molecular-level insight on the effects of solutes loading into a lamellar phase, we utilize the Shinoda-Devane-Klein (SDK) coarse-grained force field in conjunction with configurational-bias Monte Carlo simulations in the osmotic Gibbs ensemble. The lamellar phase is comprised of a bilayer formed by triethylene glycol mono- n-decyl ether (C10E3) surfactants surrounded by water with a 50:50 surfactant/water weight ratio. We study both the unary adsorption isotherm and the effects on bilayer structure and stability caused by n-nonane, 1-hexanol, and ethyl butyrate at several different reduced reservoir pressures. The nonpolar n-nonane molecules load near the center of the bilayer. In contrast, the polar 1-hexanol and ethyl butyrate molecules both load with their polar bead close to the surfactant head groups. Near the center of the bilayer, none of the solute molecules exhibits a significant orientational preference. Solute molecules adsorbed near the polar groups of the surfactant chains show a preference for orientations perpendicular to the interface, and this alignment with the long axis of the surfactant molecules is most pronounced for 1-hexanol. Loading of n-nonane leads to an increase of the bilayer thickness, but does not affect the surface area per surfactant. Loading of polar additives leads to both lateral and transverse swelling. The reduced Henry's law constants of adsorption (expressed as a molar ratio of additive to surfactant per reduced pressure) are 0.23, 1.4, and 14 for n-nonane, 1-hexanol, and ethyl butyrate, respectively, and it appears that the SDK force field significantly overestimates the ethyl butyrate-surfactant interactions.

We detail the creation of a multicenter density functional tight binding (DFTB) model for hydrogen on δ-plutonium, using a framework of new Slater-Koster interaction parameters and a repulsive energy based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES), where two- and three-center atomic interactions are represented by linear combinations of Chebyshev polynomials. We find that our DFTB/ChIMES model yields a total electron density of states for bulk δ-Pu that compares well to that from Density Functional Theory, as well as to a grid of energy calculations representing approximate H 2 dissociation paths on the δ-Pu (100) surface. We then perform molecular dynamics simulations and minimum energy pathway calculations to determine the energetics of surface dissociation and subsurface diffusion on the (100) and (111) surfaces. Our approach allows for the efficient creation of multicenter repulsive energies with a relatively small investment in initial DFT calculations. Our efforts are particularly pertinent to studies that rely on quantum calculations for interpretation and validation, such as experimental determination of chemical reactivity both on surfaces and in condensed phases.

We present a new force field and development scheme for atomistic simulations of materials under extreme conditions. These models, which explicitly include two- and three-body interactions, are generated by fitting linear combinations of Chebyshev polynomials through force matching to trajectories from Kohn-Sham density functional theory (DFT). We apply our method to liquid carbon near the diamond/graphite/liquid triple point and at higher densities and temperatures, where metallization and many-body effects may be substantial. We show that explicit inclusion of three-body interaction terms allows our model to yield improved descriptions of both dynamic and structural properties over previous empirical potential efforts, while exhibiting transferability to nearby state points. The simplicity of our functional form and subsequent efficiency of parameter determination allow for extension of DFT to experimental time and length scales while retaining most of its accuracy.

Over the past 20 years, molecular simulation methods have been applied to the modeling of reversed-phase liquid chromatography (RPLC). The purpose of these simulations was to provide a molecular-level understanding of: (i) the structure and dynamics of the bonded phase and its interface with the mobile phase, (ii) the interactions of analytes with the bonded phase, and (iii) the retention mechanism for different analytes. However, the investigation of chromatographic systems poses significant challenges for simulations with respect to the accuracy of the molecular mechanics force fields and the efficiency of the sampling algorithms. This review discusses a number of aspects concerning molecular simulation studies of RPLC systems including the historical development of the subject, the background needed to understand the two prevalent techniques, molecular dynamics (MD) and Monte Carlo (MC) methods, and the wealth of insight provided by these simulations. Examples from the literature employing MD approaches and from the authors' laboratory using MC methods are discussed. The former can provide information on chain dynamics and transport properties, whereas the latter techniques are uniquely suited for the investigation of phase and sorption equilibria that underly RPLC retention, and both can be used to elucidate the bonded-chain conformations and solvent distributions., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Previous studies suggest that post-transcriptional events play an important role in estrogen-induced loss of estrogen receptor expression. The present study shows that treatment of MCF-7 cells with estradiol resulted in a six-fold decrease in estrogen receptor mRNA half-life from 4 h in control cells to 40 min in estradiol treated cells. To determine the role of protein synthesis in the regulation of estrogen receptor mRNA stability, several translational inhibitors were utilized. Pactamycin and puromycin, which prevent ribosome association with mRNA, inhibited the effect of estradiol on receptor mRNA stability, whereas cycloheximide, which has no effect on ribosome association with mRNA, had no effect on estradiol regulation of estrogen receptor mRNA stability. In control cells, the total cellular content of estrogen receptor mRNA was associated with high molecular weight polyribosomes. Treatment with estradiol resulted in a 70% decrease in estrogen receptor mRNA associated with polyribosomes but had no effect on the polyribosome distribution of estrogen receptor mRNA. In an in vitro degradation assay, polyribosomes isolated from estradiol-treated cells degraded ER mRNA faster than polyribosomes isolated from control cells. The nuclease activity associated with the polysome fraction appeared to be Mg2+ independent and inhibited by RNasin. Freeze-thawing and heating at 90 degrees C for 10 min resulted in the loss of nuclease activity. These studies suggest that an estrogen-regulated nuclease activity associated with ribosomes alters the stability of estrogen receptor mRNA.

One of the most prevalent of cancers, breast cancer, is characterized by hormonal control of its growth. Expression of the estrogen receptor (ER) in MCF-7 breast cancer cells appears to be a complex process involving multiple steps subject to hormonal regulation by estrogen. Treatment of MCF-7 cells with estradiol results in the suppression of estrogen receptor protein. By 6 hours, the receptor protein declined by about 60% from a level of approximately 3.6 to 1.2 fmol/micrograms DNA and remained suppressed for 24-48 hours. Similar results were obtained with an estrogen receptor binding assay. Estrogen treatment also resulted in a decrease of receptor mRNA to approximately 10% of control values by 6 hours. Estrogen receptor remained at the suppressed level for up to 48 hours. Transcription run-on experiments demonstrated a transient decrease of about 90% in receptor gene transcription after 1 hour. By 3-6 hours transcription increased approximately 2-fold and remained elevated for at least 48 hours. These data suggest that estrogen suppresses ER mRNA by inhibition of ER gene transcription at early times and by a post-transcriptional effect on receptor mRNA at later times. To determine whether post-transcriptional regulation of ER gene expression is mediated by an ER-dependent mechanism independent of protein synthesis, we used the competitive estrogen antagonist, 4-hydroxytamoxifen, and the inhibitor of protein synthesis, cycloheximide, to study the regulation of ER mRNA by estradiol. 4-Hydroxytamoxifen had no effect on the steady-state level of receptor mRNA and effectively blocked the suppression of ER mRNA by estradiol. The metabolic inhibitor, cycloheximide, was unable to prevent the estrogen induced decrease in ER mRNA. These data provide evidence that the post-transcriptional suppression of ER expression through estradiol is mediated through the ER independent of protein synthesis.

The effect of 12-O-tetradecanoylphorbol-13-acetate (TPA) on the regulation of the estrogen receptor (ER) was investigated in this study. After treatment with 100 nM TPA the concentration of receptor protein was measured using an enzyme immunoassay. By 24 h the receptor protein declined by about 80% from a level of approximately 236 fmol of ER/mg of protein in control cells to 50 fmol of ER/mg of protein in cells treated with TPA. Similar results were obtained with an estrogen receptor ligand binding assay. After removal of TPA, the level of ER returned to control values. 4-alpha-Phorbol, a compound related to TPA, had no effect on ER. The effects of TPA on ER expression appear to be mediated by activation of protein kinase C as H-7, an inhibitor of protein kinase C, blocks these effects. In addition to the effect on ER protein, TPA treatment also resulted in a decrease in the steady-state level of ER mRNA as determined by a RNase protection assay. The metabolic inhibitor cycloheximide was unable to prevent the TPA-induced decrease in ER mRNA. Transcription run-off experiments demonstrated that TPA had no effect on ER gene transcription. A half-life study demonstrated that TPA decreased ER mRNA half-life by a factor of 6 from approximately 4 h in control cells to 40 min in TPA-treated cells. These data suggest that the decline in ER expression is mediated by post-transcriptional destabilization of ER mRNA.

We have previously demonstrated that regulation of estrogen receptor (ER) expression in MCF-7 breast cancer cells is a complex process involving transcriptional and posttranscriptional regulation by estradiol. Treatment of MCF-7 cells with estradiol results in the down-regulation of receptor expression; posttranscriptional suppression of receptor mRNA appears to be the predominant mechanism. To determine whether posttranscriptional regulation of ER gene expression is mediated by an ER-dependent mechanism independent of protein synthesis, we have used the competitive estrogen antagonist, 4-hydroxytamoxifen, and the inhibitor of protein synthesis, cycloheximide, to study regulation of ER mRNA by estradiol. 4-Hydroxytamoxifen had no effect on the steady-state level of receptor mRNA and effectively blocked the suppression of ER mRNA by estradiol. The metabolic inhibitor, cycloheximide, was unable to prevent the estrogen induced decrease in ER mRNA. These data provide evidence that the posttranscriptional suppression of ER expression through estradiol is mediated through the ER independent of protein synthesis. A study of the effects of estradiol on the steady-state levels of nuclear and cytoplasmic receptor mRNA suggest that posttranscriptional suppression is a nuclear event.