Three-dimensional stochastic model of actin–myosin binding in the sarcomere lattice

The effect of molecule tethering in three-dimensional (3-D) space on bimolecular binding kinetics is rarely addressed and only occasionally incorporated into models of cell motility. The simplest system that can quantitatively determine this effect is the 3-D sarcomere lattice of the striated muscle, where tethered myosin in thick filaments can only bind to a relatively small number of available sites on the actin filament, positioned within a limited range of thermal movement of the myosin head. Here we implement spatially explicit actomyosin interactions into the multiscale Monte Carlo platform MUSICO, specifically defining how geometrical constraints on tethered myosins can modulate state transition rates in the actomyosin cycle. The simulations provide the distribution of myosin bound to sites on actin, ensure conservation of the number of interacting myosins and actin monomers, and most importantly, the departure in behavior of tethered myosin molecules from unconstrained myosin interactions with actin. In addition, MUSICO determines the number of cross-bridges in each actomyosin cycle state, the force and number of attached cross-bridges per myosin filament, the range of cross-bridge forces and accounts for energy consumption. At the macroscopic scale, MUSICO simulations show large differences in predicted force-velocity curves and in the response during early force recovery phase after a step change in length comparing to the two simplest mass action kinetic models. The origin of these differences is rooted in the different fluxes of myosin binding and corresponding instantaneous cross-bridge distributions and quantitatively reflects a major flaw of the mathematical description in all mass action kinetic models. Consequently, this new approach shows that accurate recapitulation of experimental data requires significantly different binding rates, number of actomyosin states, and cross-bridge elasticity than typically used in mass action kinetic models to correctly describe the biochemical reactions of tethered molecules and their interaction energetics

Exposure to a Virtual Environment Induces Biological and Microbiota Changes in Onset-of-Lay Hens

Increasing demand for cage-free eggs arises from goals to provide hens with better welfare, particularly in terms of natural behavior. However, most laying hens are kept in conventional cages, and cage-free systems can present challenges, such as injuries, floor eggs, and bacterial infections. We proposed using virtual reality (VR) as a feasible means for combining the positive attributes of natural environments while mitigating health risks. To our knowledge, no animal study has provided evidence that VR can trigger biological changes to improve animal health and well-being nor whether VR can affect the gut microbiota. In this study, we used VR technology to simulate a natural environment in laying hen housing. Early-lay White Leghorn hens were placed in pens with (VR) or without (CON) video projections displaying free-range chickens interacting with indoor and outdoor environmental features over 5 days. Using in vitro blood bactericidal assays, VR hens exhibited higher resistance against avian pathogenic Escherichia coli versus CON (p < 0.05), which was positively associated with corticosterone levels (p < 0.01). Analyzing intestinal neurochemicals via ultra-high pressure liquid chromatography, salsolinol was the only neurochemical metabolite affected by VR, being greater in CON ileal content (p < 0.0001), in VR ileal mucus (p < 0.01), and in VR ceca tissue (p < 0.05). Using 16S rRNA sequencing and QIIME2 analyses, no differences in alpha nor beta diversity were determined between groups. Although several genera (Megamonas, Ruminococcus, Slackia) were reduced in VR hens versus CON, Mucispirillum schaedleri (member of Deferribacteres Phylum) was the only taxon increased in VR hens, being elevated in ileal mucus (p < 0.05). Lastly, using the QIIME2 plugin mmvec to map microbe-metabolite co-occurrences, we identified several positive relationships between bacterial phyla and neurochemical metabolites, notably finding dopamine and salsolinol levels were related to Deferribacteres and Tenericutes levels. In conclusion, we found that several biological parameters were influenced by VR treatment in hens, suggesting that VR can be used to improve host resistance to pathogens and gut health in poultry.This article is published as Redweik, Graham AJ, Suzanne T. Millman, Rebecca L. Parsons, Alejandro N. Hurtado Terminel, Rafael Radkowski, Karrie Daniels, Mark Lyte, James Oliver, and Melha Mellata. "Exposure to a Virtual Environment Induces Biological and Microbiota Changes in Onset-of-Lay Hens." Frontiers in Virtual Reality (2022): 69. DOI: 10.3389/frvir.2022.891584. Copyright 2022 Redweik, Millman, Parsons, Hurtado Terminel, Radkowski, Daniels, Lyte, Oliver and Mellata. Attribution 4.0 International (CC BY 4.0). Posted with permission

The atypical β‐blocker S‐oxprenolol reduces cachexia and improves survival in a rat cancer cachexia model

Background: Beta‐blockers and selected stereoisomers of beta‐blockers, like bisoprolol and S‐pindolol (ACM‐001), have been shown to be effective in preclinical cancer cachexia models. Here, we tested the efficacy of stereoisomers of oxprenolol in two preclinical models of cancer cachexia—the Yoshida AH‐130 rat model and the Lewis lung carcinoma (LLC) mouse model. Methods and Results: In the Yoshida AH130 hepatoma rat cancer cachexia model and compared with placebo, 50 mg/kg/d S‐oxprenolol (HR: 0.49, 95% CI: 0.28–0.85, P = 0.012) was superior to 50 mg/kg/d R‐oxprenolol (HR: 0.83, 95% CI 0.38–1.45, P = 0.51) in reducing mortality (= reaching ethical endpoints). Combination of the three doses (12.5, 25 and 50 mg/kg/d) that had a significant effect on body weight loss in the S‐oxprenolol groups vs the same combination of the R‐oxprenolol groups lead to a significantly improved survival of S‐oxprenolol vs R‐oxprenolol (HR: 1.61, 95% CI: 1.08–2.39, P = 0.0185). Interestingly, there is a clear dose dependency in S‐oxprenolol‐treated (5, 12.5, 25 and 50 mg/kg/d) groups, which was not observed in groups treated with R‐oxprenolol. A dose‐dependent attenuation of weight and lean mass loss by S‐oxprenolol was seen in the Yoshida rat model, whereas R‐oxprenolol had only had a significant effect on fat mass. S‐oxprenolol also non‐significantly reduced weight loss in the LLC model and also improved muscle function (grip strength 428 ± 25 and 539 ± 37 g/100 g body weight for placebo and S‐oxprenolol, respectively). However, there was only a minor effect on quality of life indicators food intake and spontaneous activity in the Yoshida model (25 mg/kg/S‐oxprenolol: 11.9 ± 2.5 g vs placebo: 4.9 ± 0.8 g, P = 0.013 and also vs 25 mg/kg/d R‐oxprenolol: 7.5 ± 2.6 g, P = 0.025). Both enantiomers had no effects on cardiac dimensions and function at the doses used in this study. Western blotting of proteins involved in the anabolic/catabolic homoeostasis suggest that anabolic signalling is persevered (IGF‐1 receptor, Akt) and catabolic signalling is inhibited (FXBO‐10, TRAF‐6) by S‐pindolol, but not he R‐enantiomer. Expression of glucose transporters Glut1 and Glut 4 was similar in all groups, as was AMPK. Conclusions: S‐oxprenolol is superior to R‐oxprenolol in cancer cachexia animal models and shows promise for a human application in cancer cachexia

Second Harmonic Generation Microscopy Probes Different States of Motor Protein Interaction in Myofibrils

The second harmonic generation (SHG) signal intensity sourced from skeletal muscle myosin II strongly depends on the polarization of the incident laser beam relative to the muscle fiber axis. This dependence is related to the second-order susceptibility χ(2), which can be described by a single component ratio γ under generally assumed symmetries. We precisely extracted γ from SHG polarization dependence curves with an extended focal field model. In murine myofibrillar preparations, we have found two distinct polarization dependencies: With the actomyosin system in the rigor state, γrig has a mean value of γrig = 0.52 (SD = 0.04, n = 55); in a relaxed state where myosin is not bound to actin, γrel has a mean value of γrel = 0.24 (SD = 0.07, n = 70). We observed a similar value in an activated state where the myosin power stroke was pharmacologically inhibited using N-benzyl-p-toluene sulfonamide. In summary, different actomyosin states can be visualized noninvasively with SHG microscopy. Specifically, SHG even allows us to distinguish different actin-bound states of myosin II using γ as a parameter