Biophysical journal最新文献

The random motion of molecules in living cells has consistently been reported to deviate from standard Brownian motion, a behavior coined as "anomalous diffusion." To study this phenomenon in living cells, fluorescence correlation spectroscopy (FCS) and single-particle tracking (SPT) are the two main methods of reference. In opposition to SPT, FCS, with its classical analysis methodology, cannot consider models of motion for which no analytical expression of the auto-correlation function is known. This excludes, for instance, anomalous continuous-time random walks and random walk on fractal. Moreover, the whole acquisition sequence of the classical FCS methodology takes several tens of minutes. Here, we propose a new analysis approach that frees FCS of these limitations. Our approach associates each individual FCS recording with a vector of features based on an estimator of the auto-correlation function and uses machine learning to infer the underlying model of motion and to estimate the values of the motion parameters. Using simulated recordings, we show that this approach endows FCS with the capacity to distinguish between a range of standard and anomalous random motions, including continuous-time random walk and random walk on fractal. Our approach exhibits performances comparable to the best-in-class state-of-the-art algorithms for SPT and can be used with a range of FCS setup parameters. Since it can be applied on individual recordings of short duration, we show that, with our method, FCS can be used to monitor rapid changes of the motion parameters. Finally, we apply our method on experimental FCS recordings of calibrated fluorescent beads in increasing concentrations of glycerol in water. Our results accurately predict that the beads follow Brownian motion with a diffusion coefficient and anomalous exponent, which agree with classical predictions from Stokes-Einstein law even at large glycerol concentrations. Taken together, our approach significantly augments the analysis power of FCS to capacities that are similar to state-of-the-art SPT approaches.

Photosynthetic organisms rely on a network of light-harvesting protein-pigment complexes to efficiently absorb sunlight and transfer excitation energy to reaction center proteins where charge separation occurs. In photosynthetic purple bacteria, these complexes are embedded within the cell membrane, with lipid composition affecting complex clustering, thereby impacting inter-complex energy transfer. However, the impact of the lipid bilayer on intra-complex excitation dynamics is less understood. Recent experiments have addressed this question by comparing photo-excitation dynamics in detergent-isolated light-harvesting complex 2 (LH2) to LH2 complexes embedded in membrane discs mimicking the biological environment, revealing differences in spectra and energy-transfer rates. In this paper, we use available quantum chemical and spectroscopy data to develop a complementary theoretical study on the excitonic structure and intra-complex energy-transfer kinetics of the LH2 of photosynthetic purple bacteria Rhodoblastus (Rbl.) acidophilus (formerly Rhodopseudomonas acidophila) in two different conditions: the LH2 in a membrane environment and detergent-isolated LH2. We find that dark excitonic states, crucial for B800-B850 energy transfer within LH2, are more delocalized in the membrane model. Using nonperturbative and generalized Förster calculations, we show that such increased quantum delocalization results in a 30% faster B800 to B850 transfer rate in the membrane model, in agreement with experimental results. We identify the dominant energy-transfer pathways in each environment and demonstrate how differences in the B800 to B850 transfer rate arise from changes in LH2's electronic properties when embedded in the membrane. Furthermore, by accounting for the quasi-static variations of electronic excitation energies in the LH2, we show that the broadening of the distribution of the B800-B850 transfer rates is affected by the lipid composition. We argue that such variation in broadening could be a signature of a speed-accuracy trade-off, commonly seen in biological process.

The polymerization of cytoskeletal filaments is regulated by both biochemical pathways, as well as physical factors such as crowding. The effect of crowding in vivo emerges from the density of intracellular components. Due to the complexity of the intracellular environment, most studies are based on either in vitro reconstitution or theory. Crowding agent (crowdants) size has been shown to influence polymerization of both actin and microtubules (MTs). Previously, the elongation rates of MT dynamics observed at single filament scale were reported to decrease with increasing concentrations of small but not large crowdants, and this correlated with in vivo viscosity increases. However, the exact nature of the connection between viscosity, crowdant size, nucleation, and MT elongation has remained unclear. Here, we use in vitro reconstitution of bulk MT polymerization kinetics and microscopy to examine the collective effect of crowdant molecular weight, volume occupancy, and viscosity on elongation and spontaneous polymerization. We find MT elongation rates obtained from bulk polymerization decrease in the presence of multiple low-molecular weight (LMW) crowdants, while increasing with high-molecular weight (HMW) crowdants. Lattice Monte Carlo simulations of an effective model of collective polymerization demonstrate reduced polymerization rates arise due to decrease in monomer diffusion due to small-sized crowdants. However, MT polymerization in the absence of nucleators, de novo, shows a crowdant size independence of polymerization rate and critical concentration, depending solely on concentration of the crowdant. In microscopy, we find LMW crowdants result in short but many filaments, while HMW crowdants increase filament density, but have little effect on lengths. The effect of crowdant volume fraction ϕ_C and size in de novo polymerization match simulations, demonstrating crowdants affect elongation independent of nucleation. Thus, the effect of viscosity on collective MT dynamics, i.e., filament numbers and lengths, shows crowdant size dependence for elongation, but independence for de novo polymerization.

Mechanical forces play key roles in biological processes such as cell migration and sensory perception. In recent years, molecular force sensors have been developed as tools for in situ force measurements. Here, we use all-atom steered molecular dynamics simulations to predict and study the relationship between design parameters and mechanical properties for three types of molecular force sensors commonly used in cellular biological research: two peptide and one DNA based. The peptide-based sensors consist of a pair of fluorescent proteins that can undergo Förster resonance energy transfer, linked by spider silk (GPGGA)_n or synthetic (GGSGGS)_n disordered regions. The DNA-based sensor consists of two fluorophore-labeled strands of DNA that can be unzipped or sheared upon force application with a Förster resonance energy transfer signal as readout of dissociation. We simulated nine sensors, three of each kind. After equilibration, flexible peptide linkers of three different lengths were stretched by applying forces to their N- and C-terminal Cα atoms in opposite directions. Similarly, we equilibrated a DNA-based sensor and pulled on the phosphate atom of the terminal guanine of one strand and a selected phosphate atom on the other strand for pulling in the opposite direction. These simulations were performed at constant velocity (0.01-10 nm/ns) and constant force (10-500 pN) for all versions of the sensors. Our results show how the force response of these sensors depends on their length, sequence, configuration, and loading rate. Mechanistic insights gained from simulations analyses indicate that interpretation of experimental results should consider the influence of transient formation of secondary structure in peptide-based sensors and of overstretching in DNA-based sensors. These predictions can guide optimal fluorophore choice and facilitate the rational design of new sensors for use in protein, DNA, hybrid systems, and molecular devices.

The Hsp100 family of protein disaggregases plays important roles in maintaining protein homeostasis in cells. E. coli ClpB is an Hsp100 protein that solubilizes protein aggregates. ClpB is proposed to couple the energy from ATP binding and hydrolysis to processively unfold and translocate protein substrates through its axial channel in the hexameric ring structure. However, many of the details of this reaction remain obscure. We have recently developed a transient state kinetics approach to study ClpB catalyzed protein unfolding and translocation. In the work reported here we have used the approach to examine how ATP is coupled to the protein unfolding reaction. Here we show that at saturating [ATP], ClpB induces the cooperative unfolding of a complete Titin I27 domain of 98 amino acids, which is represented by our measured kinetic step size m ∼ 100 amino acids. This unfolding event is followed by rapid and undetected translocation up to the next folded domain. At subsaturating [ATP], ClpB induces cooperative unfolding of a complete Titin I27 domain but translocation becomes partially rate limiting, which leads to an apparent reduced kinetic step size as small as ∼50 amino acids. Furthermore, we show that ClpB exhibits an unfolding processivity of P = 0.74 ± 0.06 independent of [ATP]. These findings advance our understanding of the ATP coupling to enzyme catalyzed protein unfolding by E. coli ClpB and present a strategy that is broadly applicable to a variety of Hsp100 family members and AAA+ superfamily members.

We numerically study three-dimensional colonies of nonmotile stress-responsive bacteria growing under confining isotropic pressure in a nutrient-rich environment. We develop a novel simulation method to demonstrate how imposing an external pressure leads to a denser aggregate and strengthens the mechanical interactions between bacteria. Unlike rigid confinements that prevent bacterial growth, confining pressure acts as a soft constraint and allows colony expansion with a nearly linear long-term population growth and colony size. Enhancing the mechanosensitivity reduces instantaneous bacterial growth rates and the overall colony size, though its impact is modest compared to pressure for our studied set of biologically relevant parameter values. The doubling time grows exponentially at low mechanosensitivity or pressure in our bacterial growth model. We provide an analytical estimate of the doubling time and develop a population dynamics model consistent with our simulations. Our findings align with previous experimental results for E. coli colonies under pressure. Understanding the growth dynamics of stress-responsive bacteria under mechanical stresses provides insight into their adaptive response to varying environmental conditions.

In the cell membrane, it is considered that saturated lipids and cholesterol organize liquid-ordered (L_o) domains in a sea of liquid-disordered (L_d) phases and proteins relevant to cellular functions are localized in the L_o domains. Since the diffusion of transmembrane proteins is regulated by the membrane viscosity, we investigate the temperature dependence of the membrane viscosity of the ternary giant unilamellar vesicles (GUVs) composed of the saturated lipid 1,2-dipalmitoyl-sn-glycero-3-phosphocholine, the unsaturated lipid 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), and cholesterol to understand the effect of the phase separation on the membrane viscosity using a microinjection technique. In the microinjection method, membrane viscosity is estimated by comparing the flow pattern induced on a spherical membrane with a hydrodynamic model. For phase-separated GUVs, the flow pattern is visualized by the motion of the domains. In this study, we developed a method to visualize the flow patterns of homogeneous GUVs above the phase separation temperature by using beads attached to the GUVs. We succeeded in measuring the membrane viscosity of ternary GUVs both above phase separation temperature and in the phase-separated region and found that the membrane viscosity decreases dramatically by phase separation. In the phase-separated region, i.e., GUVs with L_o domains, the membrane viscosity is determined by that of the L_d phase, η_Ld, and shows weak temperature dependence compared to that of the DOPC single-component GUV, which is a main component of the L_d phase. We revealed that the Moelwyn-Hughest model, which takes into account the effects of the membrane composition, viscosity of the pure component, and interaction between components, well describes the obtained membrane viscosity of the ternary GUV both above the phase separation temperature and in the phase-separated region. The drastic decrease of the membrane viscosity by the phase separation plays an important role in regulating the mobility of constituents in multi-component membranes.

Translocation across barriers and through constrictions is a mechanism that is often used in vivo for transporting material between compartments. A specific example is apicomplexan parasites invading host cells through the tight junction that acts as a pore, and a similar barrier crossing is involved in drug delivery using lipid vesicles penetrating intact skin. Here, we use triangulated membranes and energy minimization to study the translocation of vesicles through pores with fixed radii. The vesicles bind to a lipid bilayer spanning the pore, the adhesion-energy gain drives the translocation, and the vesicle deformation induces an energy barrier. In addition, the deformation-energy cost for deforming the pore-spanning membrane hinders the translocation. Increasing the bending rigidity of the pore-spanning membrane and decreasing the pore size both increase the barrier height and shift the maximum to smaller fractions of translocated vesicle membrane. We compare the translocation of initially spherical vesicles with fixed membrane area and freely adjustable volume to that of initially prolate vesicles with fixed membrane area and volume. In the latter case, translocation can be entirely suppressed. Our predictions may help rationalize the invasion of apicomplexan parasites into host cells and design measures to combat the diseases they transmit.

Structural heterogeneity in the distribution of ryanodine receptor (RyR) clusters in cardiac myocytes has been shown to have pro-arrhythmic effects. The presence of a mixture of large and small RyR clusters can potentiate arrhythmogenic calcium (Ca²⁺) waves. RyRs are subject to posttranslational modifications (PTMs), such as phosphorylation, that are linked to heart failure and other pathological conditions. This study aims to investigate how PTMs interact with the structural heterogeneity of RyR clusters and further increase heterogeneous Ca²⁺ release activities in cardiac myocytes. Using a physiologically detailed three-dimensional ventricular myocyte model containing approximately 2 million stochastic RyR channels, we simulated heterogeneous distributions of RyR clusters with and without PTMs. The results demonstrate that Ca²⁺ cycling and RyR phosphorylation by Ca²⁺/calmodulin-dependent protein kinase II (CaMKII) create a positive feedback loop, which increases functional heterogeneity in the Ca²⁺ spark size distribution. In large clusters, the Ca²⁺ leak is substantial due to the large flux (number of channels recruited), leading to increased local Ca²⁺ concentrations, CaMKII activation, and further RyR sensitization, amplifying the leak. Conversely, in small clusters, the leak is limited, and sensitization is restricted. Furthermore, CaMKII activation can enhance late sodium (Na⁺) currents, increasing Na⁺ influx and subsequently raising Ca²⁺ levels via the Na⁺-Ca²⁺ exchanger, further promoting the Ca²⁺ leak and functional heterogeneity. We conclude that such positive feedback processes play a crucial role in arrhythmogenic Ca²⁺ wave initiation and propagation, particularly in heart failure myocytes, where PTMs are often dysregulated.