Cyclooxygenase-2 (COX-2) oxygenates arachidonic acid (AA) and its ester analog, 2-arachidonoylglycerol (2-AG), to prostaglandins (PGs) and prostaglandin glyceryl esters (PG-Gs), respectively. Although the efficiency of oxygenation of these substrates by COX-2 in vitro is similar, cellular biosynthesis of PGs far exceeds that of PG-Gs. Evidence that the COX enzymes are functional heterodimers suggests that competitive interaction of AA and 2-AG at the allosteric site of COX-2 might result in differential regulation of the oxygenation of the two substrates when both are present. Modulation of AA levels in RAW264.7 macrophages uncovered an inverse correlation between cellular AA levels and PG-G biosynthesis. In vitro kinetic analysis using purified protein demonstrated that the inhibition of 2-AG oxygenation by high concentrations of AA far exceeded the inhibition of AA oxygenation by high concentrations of 2-AG. An unbiased systems-based mechanistic model of the kinetic data revealed that binding of AA or 2-AG at the allosteric site of COX-2 results in a decreased catalytic efficiency of the enzyme toward 2-AG, whereas 2-AG binding at the allosteric site increases COX-2’s efficiency toward AA. The results suggest that substrates interact with COX-2 via multiple potential complexes involving binding to both the catalytic and allosteric sites. Competition between AA and 2-AG for these sites, combined with differential allosteric modulation, gives rise to a complex interplay between the substrates, leading to preferential oxygenation of AA.

This paper reports on a significant improvement of a new structural biology approach designed to probe the secondary structure of membrane proteins using the pulsed EPR technique of Electron Spin Echo Envelope Modulation (ESEEM) spectroscopy. Previously, we showed that we could characterize an α-helical secondary structure with ESEEM spectroscopy using a 2H-labeled Val side chain coupled with site-directed spin-labeling (SDSL). In order to further develop this new approach, molecular dynamic (MD) simulations were conducted on several different hydrophobic residues that are commonly found in membrane proteins. 2H-SL distance distributions from the MD results indicated that 2H-labeled Leu was a very strong candidate to significantly improve this ESEEM approach. In order to test this hypothesis, the secondary structure of the α-helical M2δ peptide of the acetylcholine receptor (AChR) incorporated into a bicelle was investigated with 2H-labeled Leu d10 at position 10 (i) and nitroxide spin labels positioned 1, 2, 3 and 4 residues away (denoted i+1 to i+4) with ESEEM spectroscopy. The ESEEM data reveal a unique pattern that is characteristic of an α-helix (3.6 residues per turn). Strong 2H modulation was detected for the i+3 and i+4 samples, but not for the i+2 sample. The 2H modulation depth observed for 2H-labeled d10 Leu was significantly enhanced (x4) when compared to previous ESEEM measurements that used 2H-labeled d8 Val. Computational studies indicate that deuterium nuclei on the Leu sidechain are closer to the spin label when compared to Val. The enhancement of 2H modulation and the corresponding Fourier Transform (FT) peak intensity for 2H-labeled Leu significantly reduces the ESEEM data acquisition time for Leu when compared to Val. This research demonstrates that a different 2H-labeled amino acid residue can be used as an efficient ESEEM probe further substantiating this important biophysical technique. Finally, this new method can provide pertinent qualitative structural information on membrane proteins in a short time (few minutes) at low sample concentrations (~50 μM).

SummaryBiological models contain many parameters whose values are difficult to measure directly via experimentation and therefore require calibration against experimental data. Markov chain Monte Carlo (MCMC) methods are suitable to estimate multivariate posterior model parameter distributions, but these methods may exhibit slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM, a Python implementation of the (Multiple-Try) Differential Evolution Adaptive Metropolis [DREAM(ZS)] algorithm developed by Vrugt and ter Braak (2008) and Laloy and Vrugt (2012). PyDREAM achieves excellent performance for complex, parameter-rich models and takes full advantage of distributed computing resources, facilitating parameter inference and uncertainty estimation of CPU-intensive biological models.Availability and implementationPyDREAM is freely available under the GNU GPLv3 license from the Lopez lab GitHub repository at http://github.com/LoLab-VU/PyDREAM.Supplementary information Supplementary data are available at Bioinformatics online.

A biological reaction network may serve multiple purposes, processing more than one input and impacting downstream processes via more than one output. These networks operate in a dynamic cellular environment in which the levels of network components may change within cells and across cells. Recent evidence suggests that protein concentration variability could explain cell fate decisions. However, systems with multiple inputs, multiple outputs, and changing input concentrations have not been studied in detail due to their complexity. Here, we take a systems biochemistry approach, combining physiochemical modeling and information theory, to investigate how cyclooxygenase-2 (COX-2) processes simultaneous input signals within a complex interaction network. We find that changes in input levels affect the amount of information transmitted by the network, as does the correlation between those inputs. This, and the allosteric regulation of COX-2 by its substrates, allows it to act as a signal integrator that is most sensitive to changes in relative input levels.

10A biological reaction network may serve multiple purposes, processing more than one input and impacting downstream processes via more than one output. These networks operate in a dynamic cellular environment in which the levels of network components may change within cells and across cells. Recent evidence suggests that protein concentration variability could explain cell fate decisions. However, systems with multiple inputs, multiple outputs, and changing input concentrations have not been studied in detail due to their complexity. Here, we take a systems biochemistry approach, combining physiochemical modeling and information theory, to investigate how cyclooxygenase-2 (COX-2) processes simultaneous input signals within a complex interaction network. We find that changes in input levels affect the amount of information transmitted by the network, as does the correlation between those inputs. This, and the allosteric regulation of COX-2 by its substrates, allows it to act as a signal integrator that is most sensitive to changes in relative input levels.

As of May 2021, over 286 million coronavirus 2019 (COVID-19) vaccine doses have been administered across the country. This data is promising, however there are still populations that, despite availability, are declining vaccination. We reviewed vaccine likelihood and receptiveness to recommendation from a doctor or nurse survey responses from 101,048 adults (≥18 years old) presenting to 442 primary care clinics in 8 states and the District of Columbia. Occupation was self-reported and demographic information extracted from the medical record, with 58.3% (n = 58,873) responding they were likely to receive the vaccine, 23.6% (n = 23,845) unlikely, and 18.1% (n = 18,330) uncertain. We found that essential workers were 18% less likely to receive the COVID-19 vaccination. Of those who indicated they were not already “very likely” to receive the vaccine, a recommendation from a nurse or doctor resulted in 16% of respondents becoming more likely to receive the vaccine, although certain occupations were less likely than others to be receptive to recommendations. To our knowledge, this is the first study to look at vaccine intent and receptiveness to recommendations from a doctor or nurse across specific essential worker occupations, and may help inform future early phase, vaccine rollouts and public health measure implementations.

Cancer cells within a tumor environment exhibit a complex and adaptive nature whereby genetically and epigenetically distinct subpopulations compete for resources. The probabilistic nature of gene expression and intracellular molecular interactions confer a significant amount of stochasticity in cell fate decisions. This cellular heterogeneity is believed to underlie cases of cancer recurrence, acquired drug resistance, and so-called exceptional responders. From a population dynamics perspective, clonal heterogeneity and cell-fate stochasticity are distinct sources of noise, the former arising from genetic mutations and/or epigenetic transitions, extrinsic to the fate decision signaling pathways and the latter being intrinsic to biochemical reaction networks. Here, we present our results and ongoing work of a kinetic modeling study based on experimental time course data for EGFR-addicted non-small cell lung cancer (PC9) cells in both parental and isolated sublines. When PC9 cells are treated with erlotinib, an EGFR inhibitor, a complex array of division and death cell decisions arise within a given population in response to treatment. Although deterministic (ODE) simulations capture the ef-fects of clonal heterogeneity and describe the overall trends of experimentally treated tumor cell popu-lations, these are not capable of explaining the observed variability of drug response trajectories, in-cluding response magnitude and time to rebound. Our stochastic simulations, instead, capture the ef-fects of intrinsically noisy cell fate decisions that cause significant variability in cell population trajecto-ries. These findings indicate that stochastic simulations are necessary to distinguish the contribution of extrinsic (clonal heterogeneity) and intrinsic (cell fate decisions) noise to understand the variability of cancer-cell response treatment. Furthermore, they suggest that, whereas tumors with distinct clon-al structures are expected to behave differently in response to drug, two clonally identical tumors may also experience vastly different outcomes, such as exceptional response vs. rapid relapse, due to the intrinsic noise of cell fate decisions. The results of this study underscore the need to improve our un-derstanding of intracellular signaling networks that govern division vs. death decisions in cancer cells at the single-cell level. We will present our ongoing work to develop mechanistic models that link erlo-tinib-driven EGFR inhibition and intrinsic apoptotic execution. Our preliminary results give insight into what signaling interactions are required to generate the intrinsic apoptotic response to EGFR inhibi-tion with erlotinib. Citation Format: Erin M. Shockley, Leonard A. Harris, Carlos F. Lopez. How stochastic single-cell fate decisions drive population dynamics in oncogene-addicted cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3763. doi:10.1158/1538-7445.AM2015-3763