Analysis of COVID-19 vaccine non-intent by essential vs non-essential worker, demographic, and socioeconomic status among 101,048 US adults.

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.

Signal integration and information transfer in an allosterically regulated network.

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.

PyDREAM: high-dimensional parameter inference for biological models in python.

Summary: Biological 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 implementation: PyDREAM is freely available under the GNU GPLv3 license from the Lopez lab GitHub repository at http://github.com/LoLab-VU/PyDREAM. Contact: c.lopez@vanderbilt.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Competition and allostery govern substrate selectivity of cyclooxygenase-2.

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.

Enhancement of electron spin echo envelope modulation spectroscopic methods to investigate the secondary structure of membrane proteins.

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 (2)H-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. (2)H-SL distance distributions from the MD results indicated that (2)H-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 (2)H-labeled Leu d(10) 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 (2)H modulation was detected for the i+3 and i+4 samples, but not for the i+2 sample. The (2)H modulation depth observed for (2)H-labeled d(10) Leu was significantly enhanced (×4) when compared to previous ESEEM measurements that used (2)H-labeled d(8) Val. Computational studies indicate that deuterium nuclei on the Leu side chain are closer to the spin label when compared to Val. The enhancement of (2)H modulation and the corresponding Fourier Transform (FT) peak intensity for (2)H-labeled Leu significantly reduces the ESEEM data acquisition time for Leu when compared to Val. This research demonstrates that a different (2)H-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).