Understanding the kinetic mechanism of RNA single base pair formation

RNA functions are intrinsically tied to folding kinetics. The most elementary step in RNA folding is the closing and opening of a base pair. Understanding this elementary rate process is the basis for RNA folding kinetics studies. Previous studies mostly focused on the unfolding of base pairs. Here, based on a hybrid approach, we investigate the folding process at level of single base pairing/stacking. The study, which integrates molecular dynamics simulation, kinetic Monte Carlo simulation, and master equation methods, uncovers two alternative dominant pathways: Starting from the unfolded state, the nucleotide backbone first folds to the native conformation, followed by subsequent adjustment of the base conformation. During the base conformational rearrangement, the backbone either retains the native conformation or switches to nonnative conformations in order to lower the kinetic barrier for base rearrangement. The method enables quantification of kinetic partitioning among the different pathways. Moreover, the simulation reveals several intriguing ion binding/dissociation signatures for the conformational changes. Our approach may be useful for developing a base pair opening/closing rate model.RNAs perform critical cellular functions at the level of gene expression and regulation (1–4). RNA functions are determined not only by RNA structure or structure motifs [e.g., tetraloop hairpins (5, 6)] but also by conformational distributions and dynamics and kinetics of conformational changes. For example, riboswitches can adopt different conformations in response to specific conditions of the cellular environment (7, 8). Understanding the kinetics, such as the rate and pathways for the conformational changes, is critical for deciphering the mechanism of RNA function (9–19). Extensive experimental and theoretical studies on RNA folding kinetics have provided significant insights into the kinetic mechanism of RNA functions (19–36). However, due to the complexity of the RNA folding energy landscape (37–46) and the limitations of experimental tools (47–55), many fundamental problems, including single base flipping and base pair formation and fraying, remain unresolved. These unsolved fundamental problems have hampered our ability to resolve other important issues, such as RNA hairpin and larger structure folding kinetics. Several key questions remain unanswered, such as whether the hairpin folding is rate-limited by the conformational search of the native base pairs, whose formation leads to fast downhill folding of the whole structure, or by the breaking of misfolded base pairs before refolding to the native structure (18, 19, 54–73).Motivated by the need to understand the basic steps of nucleic acids folding, Hagan et al. (74) performed forty-three 200-ps unfolding trajectories at 400 K and identified both on- and off-pathway intermediates and two dominant unfolding pathways for a terminal C-G base pair in a DNA duplex. In one of the pathways, base pairing and stacking interactions are broken concomitantly, whereas in the other pathway, base stacking is broken after base pairing is disrupted. Furthermore, the unfolding requires that the Cyt diffuse away from the pairing Gua to a distance such that the C-G hydrogen bond cannot reform easily. More recently, Colizzi and Bussi (75) performed molecular dynamics (MD) pulling simulations for an RNA duplex and construct free energy landscape from the pulling simulation. The simulation showed that the base pair opening reaction starts with the unbinding of the 5′-base, followed by the unbinding of the 3′-base (i.e., the 5′-base is less stable than the 3′-base). These previous unfolding simulations offered significant insights into the pathways and transition states. However, as shown below, several important issues remain.One intriguing problem is the rate model for base pairing. There are currently three main types of models. In the first type of model, the barrier ΔG+‡ for closing a base pair is dominated by the entropic cost ΔS for positioning the nucleotides to the base-paired configuration and the barrier ΔG−‡ for opening a base pair is the enthalpic cost ΔH for disrupting the hydrogen bonds and base stacking interactions (18, 59, 60). In the second type of model, ΔG+‡ is the net free energy change for base pairing ΔG = ΔH ? TΔS and ΔG−‡ is zero (76, 77). In the third type of model, ΔG±‡=±ΔG/2 is used (78). In addition to the above three main types, other models, such as more sophisticated hybrid rate models, have been proposed (29).In this paper, we report a hybrid method (see Fig. 1) to investigate the single base pairing process. In contrast to the previous simulations for temperature- or force-induced unfolding reactions, we directly model the folding process here (i.e., the base pair closing process). Specifically, we use MD simulations to identify the conformational clusters. Based on the network of the conformational clusters as a reduced conformational ensemble, we apply kinetic Monte Carlo (KMC) and master equation (ME) methods to elucidate the detailed roles of base pairing and stacking interactions, as well as the roles of water and ions (79–82). The study reveals previously unidentified kinetics pathways, misfolded states, and rate-limiting steps. A clear understanding of the microscopic details of the elementary kinetic move is a prerequisite for further rigorous study of large-scale RNA kinetic studies. The method described here may provide a feasible way to develop a rate model for the base pair/stack-based kinetic move set. Furthermore, the mechanism of RNA single base folding may provide useful insights into many biologically significant processes, such as nucleotide flipping (83) in helicases and base pair fraying (84) (as the possible first step for nucleic duplex melting in nucleic acid enzymatic processes).Open in a separate windowFig. 1.(A) Folding of a single nucleotide (G1, red) from the unfolded (Left) to the native folded (Right) state. (B) Exhaustive sampling for the (discrete) conformations of the G1 nucleotide (Right) through enumeration of the torsion angles (formed by the blue bonds). (C) Schematic plot shows the trajectories on the energy landscape (depicted with two reaction coordinates for clarity) explored by the MD simulations. The lines, open circles, and hexagons denote the trajectories; the initial states; and the (centroid structures of the) clusters, respectively. (D) Conformational network based on six clusters. (E) The rmsds to the different clusters provide information about the structural changes in a MD trajectory.     

Label-free probe of HIV-1 TAT peptide binding to mimetic membranes

The transacting activator of transduction (TAT) protein plays a key role in the progression of AIDS. Studies have shown that a +8 charged sequence of amino acids in the protein, called the TAT peptide, enables the TAT protein to penetrate cell membranes. To probe mechanisms of binding and translocation of the TAT peptide into the cell, investigators have used phospholipid liposomes as cell membrane mimics. We have used the method of surface potential sensitive second harmonic generation (SHG), which is a label-free and interface-selective method, to study the binding of TAT to anionic 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-1′-rac-glycerol (POPG) and neutral 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) liposomes. It is the SHG sensitivity to the electrostatic field generated by a charged interface that enabled us to obtain the interfacial electrostatic potential. SHG together with the Poisson–Boltzmann equation yielded the dependence of the surface potential on the density of adsorbed TAT. We obtained the dissociation constants K_d for TAT binding to POPC and POPG liposomes and the maximum number of TATs that can bind to a given liposome surface. For POPC K_d was found to be 7.5 ± 2 μM, and for POPG K_d was 29.0 ± 4.0 μM. As TAT was added to the liposome solution the POPC surface potential changed from 0 mV to +37 mV, and for POPG it changed from −57 mV to −37 mV. A numerical calculation of K_d, which included all terms obtained from application of the Poisson–Boltzmann equation to the TAT liposome SHG data, was shown to be in good agreement with an approximated solution.The HIV type 1 (HIV-1) transacting activator of transduction (TAT) is an important regulatory protein for viral gene expression (1–3). It has been established that the TAT protein has a key role in the progression of AIDS and is a potential target for anti-HIV vaccines (4). For the TAT protein to carry out its biological functions, it needs to be readily imported into the cell. Studies on the cellular internalization of TAT have led to the discovery of the TAT peptide, a highly cationic 11-aa region (protein transduction domain) of the 86-aa full-length protein that is responsible for the TAT protein translocating across phospholipid membranes (5–8). The TAT peptide is a member of a class of peptides called cell-penetrating peptides (CPPs) that have generated great interest for drug delivery applications (ref. 9 and references therein). The exact mechanism by which the TAT peptide enters cells is not fully understood, but it is likely to involve a combination of energy-independent penetration and endocytosis pathways (8, 10). The first step in the process is high-affinity binding of the peptide to phospholipids and other components on the cell surface such as proteins and glycosaminoglycans (1, 9).The binding of the TAT peptide to liposomes has been investigated using a variety of techniques, each of which has its own advantages and limitations. Among the techniques are isothermal titration calorimetry (9, 11), fluorescence spectroscopy (12, 13), FRET (12, 14), single-molecule fluorescence microscopy (15, 16), and solid-state NMR (17). Second harmonic generation (SHG), as an interface-selective technique (18–24), does not require a label, and because SHG is sensitive to the interface potential, it is an attractive method to selectively probe the binding of the highly charged (+8) TAT peptide to liposome surfaces. Although coherent SHG is forbidden in centrosymmetric and isotropic bulk media for reasons of symmetry, it can be generated by a centrosymmetric structure, e.g., a sphere, provided that the object is centrosymmetric over roughly the length scale of the optical coherence, which is a function of the particle size, the wavelength of the incident light, and the refractive indexes at ω and 2ω (25–30). As a second-order nonlinear optical technique SHG has symmetry restrictions such that coherent SHG is not generated by the randomly oriented molecules in the bulk liquid, but can be generated coherently by the much smaller population of oriented interfacial species bound to a particle or planar surfaces. As a consequence the SHG signal from the interface is not overwhelmed by SHG from the much larger populations in the bulk media (25–28).The total second harmonic electric field, E_2ω, originating from a charged interface in contact with water can be expressed as (31–33)E2ω∝∑iχc,i(2)EωEω+∑jχinc,j(2)EωEω+χH2O(3)EωEωΦ,[1]where χc,i(2) represents the second-order susceptibility of the species i present at the interface; χinc,j(2) represents the incoherent contribution of the second-order susceptibility, arising from density and orientational fluctuations of the species j present in solution, often referred to as hyper-Rayleigh scattering; χH2O(3) is the third-order susceptibility originating chiefly from the polarization of the bulk water molecules polarized by the charged interface; Φ is the potential at the interface that is created by the surface charge; and E_ω is the electric field of the incident light at the fundamental frequency ω. The second-order susceptibility, χc,i(2), can be written as the product of the number of molecules, N, at the surface and the orientational ensemble average of the hyperpolarizability α_i⁽²⁾ of surface species i, yielding χc,i(2)=N〈αi(2)〉 (18). The bracket ?? indicates an orientational average over the interfacial molecules. The third term in Eq. 1 depicts a third-order process by which a second harmonic field is generated by a charged interface. This term is the focus of our work. The SHG signal is dependent on the surface potential created by the electrostatic field of the surface charges, often called the χ⁽³⁾ contribution to the SHG signal. The χ⁽³⁾ method has been used to extract the surface charge density of charged planar surfaces and microparticle surfaces, e.g., liposomes, polymer beads, and oil droplets in water (21, 25, 34–39).In this work, the χ⁽³⁾ SHG method is used to explore a biomedically relevant process. The binding of the highly cationic HIV-1 TAT peptide to liposome membranes changes the surface potential, thereby enabling the use of the χ⁽³⁾ method to study the binding process in a label-free manner. Two kinds of liposomes, neutral 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and anionic 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-1′-rac-glycerol (POPG), were investigated. The chemical structures of TAT, POPC, and POPG lipids are shown in Scheme 1.Open in a separate windowScheme 1.Chemical structures of HIV-1 TAT (47–57) peptide and the POPC and POPG lipids.     

Transient conformational fluctuation of TePixD during a reaction

Knowledge of the dynamical behavior of proteins, and in particular their conformational fluctuations, is essential to understanding the mechanisms underlying their reactions. Here, transient enhancement of the isothermal partial molar compressibility, which is directly related to the conformational fluctuation, during a chemical reaction of a blue light sensor protein from the thermophilic cyanobacterium Thermosynechococcus elongatus BP-1 (TePixD, Tll0078) was investigated in a time-resolved manner. The UV-Vis absorption spectrum of TePixD did not change with the application of high pressure. Conversely, the transient grating signal intensities representing the volume change depended significantly on the pressure. This result implies that the compressibility changes during the reaction. From the pressure dependence of the amplitude, the compressibility change of two short-lived intermediate (I₁ and I₂) states were determined to be +(5.6 ± 0.6) × 10⁻² cm³⋅mol⁻¹⋅MPa⁻¹ for I₁ and +(6.6 ± 0.7)×10⁻² cm³⋅mol⁻¹⋅MPa⁻¹ for I₂. This result showed that the structural fluctuation of intermediates was enhanced during the reaction. To clarify the relationship between the fluctuation and the reaction, the compressibility of multiply excited TePixD was investigated. The isothermal compressibility of I₁ and I₂ intermediates of TePixD showed a monotonic decrease with increasing excitation laser power, and this tendency correlated with the reactivity of the protein. This result indicates that the TePixD decamer cannot react when its structural fluctuation is small. We concluded that the enhanced compressibility is an important factor for triggering the reaction of TePixD. To our knowledge, this is the first report showing enhanced fluctuations of intermediate species during a protein reaction, supporting the importance of fluctuations.Proteins often transfer information through changes in domain–domain (or intermolecular) interactions. Photosensor proteins are an important example. They have light-sensing domains and function by using the light-driven changes in domain–domain interactions (1). The sensor of blue light using FAD (BLUF) domain is a light-sensing module found widely among the bacterial kingdom (2). The BLUF domain initiates its photoreaction by the light excitation of the flavin moiety inside the protein, which changes the domain–domain interaction, causing a quaternary structural change and finally transmitting biological signals (3, 4). It has been an important research topic to elucidate how the initial photochemistry occurring in the vicinity of the chromophore leads to the subsequent large conformation change in other domains, which are generally apart from the chromophore.It may be reasonable to consider that the conformation change in the BLUF domain is the driving force in its subsequent reaction; that is, the change in domain–domain interaction. However, sometimes, clear conformational changes have not been observed for the BLUF domain; its conformation is very similar before and after photo-excitation (5–13). The circular dichroism (CD) spectra of BLUF proteins AppA and PixD from thermophilic cyanobacterium Thermosynechococcus elongatus BP-1 (TePixD) did not change on illumination (5, 13). Similarly, solution NMR studies of AppA and BlrB showed only small chemical shifts on excitation (9, 10). The solution NMR structure of BlrP1 showed a clear change, but this was limited in its C-terminal extension region and not core BLUF (11). Furthermore, the diffusion coefficient (D) of the BLUF domain of YcgF was not changed by photo-excitation (12), although D is sensitive to global conformational changes. These results imply that a minor structural change occurs in the BLUF domain. In such cases, how does the BLUF domain control its interdomain interaction? Recently, a molecular dynamics (MD) simulation on another light-sensing domain, the light-oxygen-voltage (LOV) sensing domain, suggested that fluctuation of the LOV core structure could be a key to understanding the mechanism of information transfer (14–16).Because proteins work at room temperature, they are exposed to thermal fluctuations. The importance of such structural fluctuations for biomolecular reactions has been also pointed out: for example, enzymatic activity (17–20). Experimental detections of such conformation fluctuations using single molecular detection (21) or NMR techniques such as the hydrogen-deuterium (H-D) exchange, relaxation dispersion method, and high-pressure NMR (22–24) have succeeded. However, these techniques could not detect the fluctuation of short-lived transient species. Indeed, single molecule spectroscopy can trace the fluctuation in real time, but it is still rather difficult to detect rapid fluctuations for a short-lived intermediate during a reaction. Therefore, information about the fluctuation of intermediates is thus far limited.A thermodynamic measurement is another way to characterize the fluctuation of proteins. In particular, the partial molar isothermal compressibility [K¯T=−(∂V¯/∂P)T] is essential, because this property is directly linked to the mean-square fluctuations of the protein partial molar volume by 〈(V¯−〈V¯〉)2〉≡〈δV¯2〉=kBTK¯T (25). (Here, <X> means the averaged value of a quantity of X.) Therefore, isothermal compressibility is thought to reflect the structural fluctuation of molecules (26). However, experimental measurement of this parameter of proteins in a dilute solution is quite difficult. Indeed, this quantity has been determined indirectly from the theoretical equation using the adiabatic compressibility of a protein solution, which was determined by the sound velocity in the solution (26–31). Although the relation between volume fluctuations and isothermal compressibility is rigorously correct only with respect to the intrinsic part of the volume compressibility, and not the partial molar volume compressibility (32), we considered that this partial molar volume compressibility is still useful for characterizing the fluctuation of the protein structure including its interacting water molecules. In fact, the relationship between β¯T and the volume fluctuation has been often used to discuss the fluctuation of proteins (17, 26–28), and the strong correlation of β¯T of reactants with the functioning for some enzymes (17, 33, 34) has been reported. These studies show the functional importance of the structural fluctuation represented by β¯T. However, thermodynamic techniques lack time resolution, and it has been impossible to measure the fluctuations of short-lived intermediate species.Recently, we developed a time-resolving method for assessing thermodynamic properties using the pulsed laser induced transient grating (TG) method. Using this method, we thus far succeeded in measuring the enthalpy change (ΔH) (35–38), partial molar volume change (ΔV¯) (12, 35, 37), thermal expansion change (Δα¯th) (12, 37), and heat capacity change (ΔC_p) (36–38) for short-lived species. Therefore, in principle, the partial molar isothermal compressibility change (ΔK¯T) of a short-lived intermediate become observable if we conduct the TG experiment under the high-pressure condition and detect ΔV¯ with varying external pressure.There are several difficulties in applying the traditional high-pressure cell to the TG method to measure thermodynamic parameters quantitatively. The most serious problem is ensuring the quantitative performance of the intensity of TG signals measured under the high-pressure condition. On this point, our group has developed a new high-pressure cell specially designed for TG spectroscopy (39) and overcome this problem. In this paper, by applying this high-pressure TG system to the BLUF protein TePixD, we report the first measurement, to our knowledge, of ΔK¯T of short-lived intermediates to investigate the mechanism underlying signal transmission by BLUF proteins, from the view point of the transient fluctuation.TePixD is a homolog of the BLUF protein PixD, which regulates the phototaxis of cyanobacterium (40) and exists in a thermophilic cyanobacterium Thermocynechococcus elongates BP-1 (Tll0078). TePixD is a relatively small (17 kDa) protein that consists only of the BLUF domain with two extended helices in the C-terminal region. In crystals and solutions, it forms a decamer that consists of two pentameric rings (41). The photochemistry of TePixD is typical among BLUF proteins (42–45); on blue light illumination, the absorption spectrum shifts toward red by about 10 nm within a nanosecond. The absorption spectrum does not change further, and the dark state is recovered with a time constant of ∼5 s at room temperature (40, 43). The spectral red shift was explained by the rearrangement of the hydrogen bond network around the chromophore (6, 46–48). The TG method has revealed the dynamic photoreaction mechanism, which cannot be detected by conventional spectroscopic methods. The TG signal of TePixD (Fig. S1) showed that there are two spectrally silent reaction phases: a partial molar volume expansion with the time constant of ∼40 μs and the diffusion coefficient (D) change with a time constant of ∼4 ms. Furthermore, it was reported that the pentamer and decamer states of TePixD are in equilibrium and that the final photoproduct of the decamer is pentamers generated by its dissociation (13, 49). On the basis of these studies, the reaction scheme has been identified as shown in Fig. 1. Here, I₁ is the intermediate of the spectrally red-shifted species (generated within a nanosecond) and I₂ is the one created on the subsequent volume expansion process of +4 cm³⋅mol⁻¹ (∼40 μs). Furthermore, an experiment of the excitation laser power dependence of its TG signal revealed that the TePixD decamer undergoes the original dissociation reaction when only one monomer in the decamer is excited (50). In this study, we investigated the transient compressibility of the intermediates I₁ and I₂ of the photoreaction of TePixD and found a direct link between their fluctuation and reactivity.Open in a separate windowFig. 1.Schematic illustration of the photoreaction of TePixD. Yellow circles represent the TePixD monomer in the ground state, which constructs the decamer and pentamer states. In the dark state, these two forms are in equilibrium. The excited, spectral red-shifted state of the TePixD monomer is indicated by a red circle. The square represents the I₂ state of the monomer, which is created by the volume expansion process.     

Residue level quantification of protein stability in living cells

The intracellular milieu differs from the dilute conditions in which most biophysical and biochemical studies are performed. This difference has led both experimentalists and theoreticians to tackle the challenging task of understanding how the intracellular environment affects the properties of biopolymers. Despite a growing number of in-cell studies, there is a lack of quantitative, residue-level information about equilibrium thermodynamic protein stability under nonperturbing conditions. We report the use of NMR-detected hydrogen–deuterium exchange of quenched cell lysates to measure individual opening free energies of the 56-aa B1 domain of protein G (GB1) in living Escherichia coli cells without adding destabilizing cosolutes or heat. Comparisons to dilute solution data (pH 7.6 and 37 °C) show that opening free energies increase by as much as 1.14 ± 0.05 kcal/mol in cells. Importantly, we also show that homogeneous protein crowders destabilize GB1, highlighting the challenge of recreating the cellular interior. We discuss our findings in terms of hard-core excluded volume effects, charge–charge GB1-crowder interactions, and other factors. The quenched lysate method identifies the residues most important for folding GB1 in cells, and should prove useful for quantifying the stability of other globular proteins in cells to gain a more complete understanding of the effects of the intracellular environment on protein chemistry.Proteins function in a heterogeneous and crowded intracellular environment. Macromolecules comprise 20–30% of the volume of an Escherichia coli cell and reach concentrations of 300–400 g/L (1, 2). Theory predicts that the properties of proteins and nucleic acids can be significantly altered in cells compared with buffer alone (3, 4). Nevertheless, most biochemical and biophysical studies are conducted under dilute (<10 g/L macromolecules) conditions. Here, we augment the small but growing list of reports probing the equilibrium thermodynamic stability of proteins in living cells (5–9), and provide, to our knowledge, the first measurement of residue-level stability under nonperturbing conditions.Until recently, the effects of macromolecular crowding on protein stability were thought to be caused solely by hard-core, steric repulsions arising from the impenetrability of matter (4, 10, 11). The expectation was that crowding enhances stability by favoring the compact native state over the ensemble of denatured states. Increased attention to transient, nonspecific protein-protein interactions (12–15) has led both experimentalists (16–19) and theoreticians (20–22) to recognize the effects of chemical interactions between crowder and test protein when assessing the net effect of macromolecular crowding. These weak, nonspecific interactions can reinforce or oppose the effect of hard-core repulsions, resulting in increased or decreased stability depending on the chemical nature of the test protein and crowder (23–26).We chose the B1 domain of streptococcal protein G (GB1) (27) as our test protein because its structure, stability and folding kinetics have been extensively studied in dilute solution (28–38). Its small size (56 aa; 6.2 kDa) and high thermal stability make GB1 well suited for studies by NMR spectroscopy.Quantifying the equilibrium thermodynamic stability of proteins relies on determining the relative populations of native and denatured states. Because the denatured state ensemble of a stable protein is sparsely populated under native conditions, stability is usually probed by adding heat or a cosolute to promote unfolding so that the concentration ratio of the two states can be determined (39). However, stability can be measured without these perturbations by exploiting the phenomenon of backbone amide H/D exchange (40) detected by NMR spectroscopy (41). The observed rate of amide proton (N–H) exchange, k_obs, is related to equilibrium stability by considering a protein in which each N–H exists in an open (exposed, exchange-competent) state, or a closed (protected, exchange-incompetent) state (40, 42):closed(N−H)⇌kclkopopen(N−H)→kintopen(N−D)⇌kopkclclosed(N−D).[1]Each position opens and closes with rate constants, k_op and k_cl (where K_op = k_op/k_cl), and exchange from the open state occurs with intrinsic rate constant, k_int. Values for k_int are based on exchange data from unstructured peptides (43, 44). If the test protein is stable (i.e., k_cl >> k_op), the observed rate becomes:kobs=kopkintkcl+kint.[2]Exchange occurs within two limits (42). At the EX1 limit, closing is rate determining, and k_obs = k_op. This limit is usually observed for less stable proteins and at basic pH (45). Most globular proteins undergo EX2 kinetics, where exchange from the open state is rate limiting (i.e., k_cl >> k_int), and k_obs values can be converted to equilibrium opening free energies, ΔGop°′ (46):kobs=kopkclkint=Kopkint[3]ΔGop°′=−RT⁡lnkobskint,[4]where RT is the molar gas constant multiplied by the absolute temperature.The backbone amides most strongly involved in H-bonded regions of secondary structure exchange only from the fully unfolded state, yielding a maximum value of ΔGop°′ (47–49). For these residues ΔGop°′ approximates the free energy of denaturation, ΔGden°′, providing information on global stability. Lower amplitude fluctuations of the native state can give rise to partially unfolded forms (50), resulting in residues with ΔGop°′ values less than those of the global unfolders.In summary, NMR-detected H/D exchange can measure equilibrium thermodynamic stability of a protein at the level of individual amino acid residues under nonperturbing conditions. Inomata et al. (51) used this technique to measure k_obs values in human cells for four residues in ubiquitin, but experiments confirming the exchange mechanism were not reported and opening free energies were not quantified. Our results fill this void and provide quantitative residue-level protein stability measurements in living cells under nonperturbing conditions.     

How amide hydrogens exchange in native proteins

Amide hydrogen exchange (HX) is widely used in protein biophysics even though our ignorance about the HX mechanism makes data interpretation imprecise. Notably, the open exchange-competent conformational state has not been identified. Based on analysis of an ultralong molecular dynamics trajectory of the protein BPTI, we propose that the open (O) states for amides that exchange by subglobal fluctuations are locally distorted conformations with two water molecules directly coordinated to the N–H group. The HX protection factors computed from the relative O-state populations agree well with experiment. The O states of different amides show little or no temporal correlation, even if adjacent residues unfold cooperatively. The mean residence time of the O state is ∼100 ps for all examined amides, so the large variation in measured HX rate must be attributed to the opening frequency. A few amides gain solvent access via tunnels or pores penetrated by water chains including native internal water molecules, but most amides access solvent by more local structural distortions. In either case, we argue that an overcoordinated N–H group is necessary for efficient proton transfer by Grotthuss-type structural diffusion.Before the tightly packed and densely H-bonded structure of globular proteins had been established, Hvidt and Linderstrøm-Lang (1) showed that all backbone amide hydrogens of insulin exchange with water hydrogens, implying that all parts of the polypeptide backbone are, at least transiently, exposed to solvent. In the following 60 y, hydrogen exchange (HX), usually monitored by NMR spectroscopy (2) or mass spectrometry (3), has been widely used to study protein folding and stability (4–10), structure (11, 12), flexibility and dynamics (13–15), and solvent accessibility and binding (16, 17), often with single-residue resolution. However, because the exchange mechanism is unclear, HX data from proteins can, at best, be interpreted qualitatively (18–25).Under most conditions, amide HX is catalyzed by hydroxide ions (26, 27) at a rate that is influenced by inductive and steric effects from adjacent side chains (28). For unstructured peptides, HX is a slow process simply because the hydroxide concentration is low. For example, at 25° C and pH 4, HX occurs on a time scale of minutes. Under similar conditions, amides buried in globular proteins exchange on a wide range of time scales, extending up to centuries. HX can only occur if the amide is exposed to solvent, so conformational fluctuations must be an integral part of the HX mechanism (18).Under sufficiently destabilizing conditions HX occurs from the denatured-state ensemble, but under native conditions few amides exchange by such global unfolding (9, 29–31). For example, in bovine pancreatic trypsin inhibitor (BPTI), 8 amides in the core β-sheet exchange by global unfolding under native conditions (7, 32), whereas the remaining 45 amides require less extensive conformational fluctuations. Much of the debate in the protein HX field over the past half-century has concerned the nature of these subglobal fluctuations and their frequency, duration, amplitude, and cooperativity (18–25).According to the standard HX model (18), each amide can exist in a closed (C) state, where exchange cannot occur, or in an open (O) state, where exchange proceeds at a rate k_int. The kinetic scheme for H exchange into D₂O then reads as(N−H)C⇌kclkop(N−H)O→kint(N−D)Oand the measured steady-state HX rate is k_HX = k_op?k_int/(k_op + k_cl + k_int). To make this phenomenological model practically useful, two auxiliary assumptions are needed to disentangle the conformational and intrinsic parts of the process: (i) The conformational fluctuations (k_op and k_cl) are independent of pH, and (ii) HX from the O state proceeds at the same rate as in model peptides with the same neighboring side chains, so that kint=kHX0.Two HX regimes are distinguished with reference to the pH dependence of k_HX (18). If k_HX is constant in some pH range, it follows that k_int ? k_op + k_cl so that k_HX = k_op. In this so-called EX1 limit, the HX experiment measures the opening rate, or the mean residence time (MRT), of the C state, τ_C = 1/k_op. For BPTI, such pH invariance has only been observed for the eight core amides, and then only in a narrow pH interval (32).More commonly, HX experiments are performed in the EX2 limit, where k_int ? k_op + k_cl. Then k_HX = k_int/(κ + 1), where κ ≡ k_cl/k_op = τ_C/τ_O is the protection factor (PF). At equilibrium, the fractional populations, f_C and f_O, and the rates are linked by detailed balance, k_op?f_C = k_cl?f_O, so the PF may also be expressed as κ = f_C/f_O. Clearly, 1/(κ + 1) is the probability of finding the amide in the O state, 1/κ is the C  ?  O equilibrium constant, and β?ΔG = ln?κ is the free energy difference between the O and C states in units of k_B?T ≡ 1/β. The PF can thus be deduced from the HX rates measured (under EX2 conditions) for the amide in the protein and in a model peptide as κ=kHX0/kHX−1.The vast majority of the available protein HX data pertains to the EX2 regime and thus provides no information about the time scales, τ_C and τ_O, of the conformational fluctuations, except for the EX2 bound: 1/τC+1/τO≫kint≈kHX0. In the typical case where kHX≪kHX0, so that τ_C ? τ_O, we therefore only know that τO≪1/kHX0, which is in the millisecond range at pH 9 (EX2 HX data are usually measured at lower pH, where 1/kHX0 is even longer). Our analysis indicates that τ_O is seven orders of magnitude shorter than this upper bound estimate.The HX experiment is unique in probing sparsely populated conformational states with single-residue resolution. However, the physical significance of the PF is obscured by our ignorance about the structure and dynamics of the O state. Several attempts have been made to correlate experimental PFs with physical attributes of the amides, such as solvent contact (33–37), burial depth (38), intramolecular H-bonds (35, 38–40), packing density (38, 41), or electric field (42). Where significant correlations have been found, they suggest that the chosen attribute can serve as a proxy for the propensity for C → O fluctuations. However, whether based on crystal structures or molecular dynamics (MD) trajectories, these studies examined the time-averaged protein structure, which is dominated by the C state and therefore provides little or no information about the nature of the C → O fluctuations.In principle, the O state can be identified from molecular simulations, but this requires extensive conformational sampling because most C → O transitions are exceedingly rare. To date, this approach has been tried only with coarse-grained and/or empirical protein models without explicit solvent (43–45), or for HX from the denatured-state ensemble (46). The recent availability of ultralong MD simulations with realistic force fields opens up new opportunities in the search for the elusive O state. We have thus analyzed the millisecond MD trajectory of fully solvated native BPTI performed by Shaw et al. (47). Fortunately, BPTI is also among the proteins that have been most thoroughly studied by HX experiments.     

SOD1 aggregation in ALS mice shows simplistic test tube behavior

A longstanding challenge in studies of neurodegenerative disease has been that the pathologic protein aggregates in live tissue are not amenable to structural and kinetic analysis by conventional methods. The situation is put in focus by the current progress in demarcating protein aggregation in vitro, exposing new mechanistic details that are now calling for quantitative in vivo comparison. In this study, we bridge this gap by presenting a direct comparison of the aggregation kinetics of the ALS-associated protein superoxide dismutase 1 (SOD1) in vitro and in transgenic mice. The results based on tissue sampling by quantitative antibody assays show that the SOD1 fibrillation kinetics in vitro mirror with remarkable accuracy the spinal cord aggregate buildup and disease progression in transgenic mice. This similarity between in vitro and in vivo data suggests that, despite the complexity of live tissue, SOD1 aggregation follows robust and simplistic rules, providing new mechanistic insights into the ALS pathology and organism-level manifestation of protein aggregation phenomena in general.So far, the difficulty to experimentally measure protein aggregation in live tissue has focused many researchers to infer mechanistic details of neurodegenerative disease from molecular studies in vitro. An important outcome of this in vitro development is the establishment of rational protocols for quantitative assessment of protein aggregation data (1–4), which now start to consolidate our view of what is happening (5). Protein aggregation follows general and simplistic rules dictated by the amino acid sequence. However, the sheer number of competing aggregation sites within a typical protein chain (6) makes the process intrinsically malleable and dependent on experimental conditions (7). The nagging concern is then to what extent these already complex in vitro data are transferable to the even more complex situation in vivo? Here, we shed light on this question by comparing directly in vitro aggregation kinetics with corresponding data from transgenic mice using a recently developed in vivo quantification strategy based on antibodies (8). Our model system is the aggregation of superoxide dismutase 1 (SOD1) associated with the motor neuron disease ALS (8) (Fig. 1). A key feature of this system is that the immature apoSOD1 monomer, which is also implicated as a precursor in human pathology (9–12), needs to be globally unfolded to fibrillate in vitro (7) (Fig. 1). This simplistic behavior presents the experimental advantage that the fibrillation kinetics of apoSOD1 show simple dependence on structural stability (13, 14):ΔGD−N=−RTlnKD−N=−RTln[N][D],[1]where N is the soluble native structure, and D is the aggregation-competent unfolded state. Accordingly, it has been shown that the in vitro fibrillation of apoSOD1 displays the characteristic fingerprint of fragmentation-assisted growth (15) with a square root dependence on [D] (7), consistent with the requirement of sample agitation to expedite the reaction (1–4, 10). Analogous fibrillation behavior is found for β2-microglobulin (2), yeast prions Sup35 (16) and Ure2p (17), insulin (18), WW domain (19), TI 127 (20), and α-synuclein (21). The main difference between these proteins seems to be that some are intrinsically disordered and constantly aggregation-competent by lacking the ability to hide sticky sequence material by folding. In this study, we see that this simplistic in vitro behavior also translates to the more complex conditions in live tissue: the survival times of ALS mice expressing SOD1 variants of different stabilities are directly correlated with the in vivo levels of globally unfolded protein. Also, spinal cords of mice expressing the human SOD1 mutation G93A show exponential buildup of SOD1 aggregates with a square root dependence on log[D] indistinguishable from the fibrillation kinetics observed in agitated test tubes. The data raise fundamental questions about not only the striking resemblance between mouse and test tube aggregation but also, the apparent differences with human ALS pathology, which seems to have less ordered progression. Clues to the latter, however, are hinted in data from homozygous D90A mice showing two strains of structurally distinct SOD1 aggregates.Open in a separate windowFig. 1.SOD1 aggregation in vitro and in ALS mice. (A) Aggregation of SOD in test tubes yields fibrillar structures similar to those of other proteins (7). (B) Immunohistochemistry of the ventral horn in the terminal hSOD1^G93A mouse showing characteristics of aggresomes (44). (C) Competition between SOD1 folding and fibrillation in vitro, where elongation occurs by unfolded monomers through an encounter complex (7). The question that we ask is how do the in vitro and in vivo aggregations compare mechanistically. (D) Agitation-induced fibrillation in vitro with representative data from an SOD1 mutant in 0 (blue) and 5 M (red) urea with the associated statistics of τ_1/2 for repeated measures. To account for this statistical variation, we use the distribution average (Table S1). (E) Log plot of ν_max vs. τ_1/2 for all individual measures in this study showing uniform behavior of the various SOD1 mutants and a slope of one characteristic for exponential growth (1–4). ALS-associated SOD1 mutations examined in ALS mice (red) (Table S1), other ALS-associated mutations (blue) (Table S1), and SOD1 control mutations (black) (Table S1).     

An alcohol-sensing site in the calcium- and voltage-gated,large conductance potassium (BK) channel

Ethanol alters BK (slo1) channel function leading to perturbation of physiology and behavior. Site(s) and mechanism(s) of ethanol–BK channel interaction are unknown. We demonstrate that ethanol docks onto a water-accessible site that is strategically positioned between the slo1 calcium-sensors and gate. Ethanol only accesses this site in presence of calcium, the BK channel’s physiological agonist. Within the site, ethanol hydrogen-bonds with K361. Moreover, substitutions that hamper hydrogen bond formation or prevent ethanol from accessing K361 abolish alcohol action without altering basal channel function. Alcohol interacting site dimensions are approximately 10.7 × 8.6 × 7.1 Å, accommodating effective (ethanol-heptanol) but not ineffective (octanol, nonanol) channel activators. This study presents: (i) to our knowledge, the first identification and characterization of an n-alkanol recognition site in a member of the voltage-gated TM6 channel superfamily; (ii) structural insights on ethanol allosteric interactions with ligand-gated ion channels; and (iii) a first step for designing agents that antagonize BK channel-mediated alcohol actions without perturbing basal channel function.Alcohol (ethyl alcohol, ethanol) is a psychoactive agent that has been overwhelmingly consumed by mankind across cultures and civilizations. Alcohol actions on central nervous system (CNS) physiology and behavior are largely independent of beverage type but due to ethanol itself (1). Ethanol alters cell excitability by modifying function of transmembrane (TM) ion channel proteins, including K⁺ channels. These channels constitute the most heterogeneous and extensive group of ion channels, its members belonging to TM2, TM4, and TM6 protein superfamilies. Within this myriad of proteins, several K⁺ channels have been shown to modify behavior in response to acute exposure to ethanol concentrations that reach the CNS and other excitable tissues during alcohol drinking (2–5). However, with the sole exception of the TM2, G protein-regulated inward rectifier K⁺ (GIRK) channel (6), there is no structural information on ethanol-K⁺ channel protein interacting sites currently available.Voltage/Ca²⁺-gated, large conductance K⁺ channels (BK), which are members of the TM6 voltage-gated ion channel superfamily, constitute major mediators of alcohol actions in excitable tissues. Acute exposure to ethanol levels reached in CNS during alcohol intoxication alters BK-mediated currents and thus, elicits widespread and profound modifications in physiology and behavior. In rodent models, acute ethanol exposure leads to reduced vasopressin, oxytocin and growth hormone release with consequent perturbation in physiology and behavior (7), altered firing rates in nucleus accumbens (8) and dorsal root ganglia neurons (9), and alcohol-induced cerebral artery constriction (10, 11). Moreover, studies in both mammals and invertebrate models demonstrate that ethanol targeting of neuronal BK is involved in development of alcohol tolerance and dependence (12–16). Although the physiological and behavioral consequences of ethanol disruption of BK function have been well documented, it remains unknown whether alcohol modification of BK function results from drug interaction with a defined recognition site(s) in a protein target vs. physical perturbation of the proteolipid environment where the BK protein resides. Thus, location and structural characteristics of the ethanol-recognition site(s), as well as nature of chemical bonds between ethanol and functional target that lead to modification of BK function, remain unknown.Ethanol-induced regulation of BK channels is fine-tuned by many factors, including the BK channel-forming slo1 protein (α subunit) isoform (17) and its modification by phosphorylation (18), BK channel accessory (β) subunits (11), the channel-activating ionic ligand (Cai2+) (19) and the lipid microenvironment around the BK protein complex (20). However, ethanol perturbation of BK function is sustained when the slo1 protein is probed with the alcohol in cell-free membrane patches (19–21) or after protein reconstitution into artificial lipid bilayers (22). We recently demonstrated that perturbation of slo1 function by ethanol concentrations reached in blood during alcohol intoxication does not extend to Na⁺-gated slo2 and pH-gated slo3 channels, which are phylogenetically and structurally related to slo1. However, ethanol sensitivity does extend to a prokaryotic K⁺ channel from Methanobacterium thermoautotrophicum (MthK) (23), a TM2 ion channel that shares basic Cai2+-driven gating mechanisms with slo1 (24). Collectively, these studies lead us to hypothesize that ethanol-recognition site(s) involved in alcohol modification of BK current exists in the slo1 cytosolic Cai2+-sensing tail domain (CTD).Based on crystallographic data of the slo1 CTD and primary alignment of slo1-related ion channels that share ethanol sensitivity, we first identified eight putative ethanol recognition regions in the slo1 CTD. Using computational modeling, point amino acid substitutions and electrophysiology, we identified a distinct pocket as the ethanol-recognition site that leads to alcohol modification of BK current. This site has a few common characteristics of alcohol-binding protein sequences (25), yet presents features that differ from those of the alcohol site described in GIRK (6). In opposition to GIRK currents, which can be potentiated by alcohol in absence of G proteins (6), ethanol modulation of BK currents is dependent on the presence of Cai2+ (19). Our data strongly suggest that ethanol access to the newly identified BK ethanol-recognition site depends on the Cai2+ levels associated with the slo1 CTD. Thus, current data not only provide a structural basis for understanding Cai2+–alcohol allosterism on BK channels but could render structural insights on other ligand-gated channels that are activated by ethanol in presence of their natural ligand (26–30). Finally, present data document that the newly identified site plays a critical role in BK channel sensitivity to long-chain alkanols and explain the reported chain length differential sensitivity (“cutoff”) of linear n-alkanols to modify BK current (31).Identification of a distinct alcohol-sensing site in BK channels opens the door for rational design of pharmaceuticals to counteract widespread effects of alcohol intoxication in the body without altering basal BK channel function. Because this site is present in human BK protein ({"type":"entrez-protein","attrs":{"text":"AAA92290.1","term_id":"758791","term_text":"AAA92290.1"}}AAA92290.1), it is possible that genetic, epigenetic or other modifications of the alcohol-sensing site in BK channels could contribute to differential sensitivity to alcohol intoxication in humans. In addition, considering that individuals with low alcohol sensitivity are prone to developing heavy drinking (32), an altered profile of alcohol-sensing site on BK channels might be included as a potential predictor, along with other targets, for developing alcohol preference.     

Kinetics of nucleotide-dependent structural transitions in the kinesin-1 hydrolysis cycle

To dissect the kinetics of structural transitions underlying the stepping cycle of kinesin-1 at physiological ATP, we used interferometric scattering microscopy to track the position of gold nanoparticles attached to individual motor domains in processively stepping dimers. Labeled heads resided stably at positions 16.4 nm apart, corresponding to a microtubule-bound state, and at a previously unseen intermediate position, corresponding to a tethered state. The chemical transitions underlying these structural transitions were identified by varying nucleotide conditions and carrying out parallel stopped-flow kinetics assays. At saturating ATP, kinesin-1 spends half of each stepping cycle with one head bound, specifying a structural state for each of two rate-limiting transitions. Analysis of stepping kinetics in varying nucleotides shows that ATP binding is required to properly enter the one-head–bound state, and hydrolysis is necessary to exit it at a physiological rate. These transitions differ from the standard model in which ATP binding drives full docking of the flexible neck linker domain of the motor. Thus, this work defines a consensus sequence of mechanochemical transitions that can be used to understand functional diversity across the kinesin superfamily.Kinesin-1 is a motor protein that steps processively toward microtubule plus-ends, tracking single protofilaments and hydrolyzing one ATP molecule per step (1–6). Step sizes corresponding to the tubulin dimer spacing of 8.2 nm are observed when the molecule is labeled by its C-terminal tail (7–10) and to a two-dimer spacing of 16.4 nm when a single motor domain is labeled (4, 11, 12), consistent with the motor walking in a hand-over-hand fashion. Kinesin has served as an important model system for advancing single-molecule techniques (7–10) and is clinically relevant for its role in neurodegenerative diseases (13), making dissection of its step a popular ongoing target of study.Despite decades of work, many essential components of the mechanochemical cycle remain disputed, including (i) how much time kinesin-1 spends in a one-head–bound (1HB) state when stepping at physiological ATP concentrations, (ii) whether the motor waits for ATP in a 1HB or two-heads–bound (2HB) state, and (iii) whether ATP hydrolysis occurs before or after tethered head attachment (4, 11, 14–20). These questions are important because they are fundamental to the mechanism by which kinesins harness nucleotide-dependent structural changes to generate mechanical force in a manner optimized for their specific cellular tasks. Addressing these questions requires characterizing a transient 1HB state in the stepping cycle in which the unattached head is located between successive binding sites on the microtubule. This 1HB intermediate is associated with the force-generating powerstroke of the motor and underlies the detachment pathway that limits motor processivity. Optical trapping (7, 19, 21, 22) and single-molecule tracking studies (4, 8–11) have failed to detect this 1HB state during stepping. Single-molecule fluorescence approaches have detected a 1HB intermediate at limiting ATP concentrations (11, 12, 14, 15), but apart from one study that used autocorrelation analysis to detect a 3-ms intermediate (17), the 1HB state has been undetectable at physiological ATP concentrations.Single-molecule microscopy is a powerful tool for studying the kinetics of structural changes in macromolecules (23). Tracking steps and potential substeps for kinesin-1 at saturating ATP has until now been hampered by the high stepping rates of the motor (up to 100 s⁻¹), which necessitates high frame rates, and the small step size (8.2 nm), which necessitates high spatial precision (7). Here, we apply interferometric scattering microscopy (iSCAT), a recently established single-molecule tool with high spatiotemporal resolution (24–27) to directly visualize the structural changes underlying kinesin stepping. By labeling one motor domain in a dimeric motor, we detect a 1HB intermediate state in which the tethered head resides over the bound head for half the duration of the stepping cycle at saturating ATP. We further show that at physiological stepping rates, ATP binding is required to enter this 1HB state and that ATP hydrolysis is required to exit it. This work leads to a significant revision of the sequence and kinetics of mechanochemical transitions that make up the kinesin-1 stepping cycle and provides a framework for understanding functional diversity across the kinesin superfamily.     

Structural interactions of a voltage sensor toxin with lipid membranes

Protein toxins from tarantula venom alter the activity of diverse ion channel proteins, including voltage, stretch, and ligand-activated cation channels. Although tarantula toxins have been shown to partition into membranes, and the membrane is thought to play an important role in their activity, the structural interactions between these toxins and lipid membranes are poorly understood. Here, we use solid-state NMR and neutron diffraction to investigate the interactions between a voltage sensor toxin (VSTx1) and lipid membranes, with the goal of localizing the toxin in the membrane and determining its influence on membrane structure. Our results demonstrate that VSTx1 localizes to the headgroup region of lipid membranes and produces a thinning of the bilayer. The toxin orients such that many basic residues are in the aqueous phase, all three Trp residues adopt interfacial positions, and several hydrophobic residues are within the membrane interior. One remarkable feature of this preferred orientation is that the surface of the toxin that mediates binding to voltage sensors is ideally positioned within the lipid bilayer to favor complex formation between the toxin and the voltage sensor.Protein toxins from venomous organisms have been invaluable tools for studying the ion channel proteins they target. For example, in the case of voltage-activated potassium (Kv) channels, pore-blocking scorpion toxins were used to identify the pore-forming region of the channel (1, 2), and gating modifier tarantula toxins that bind to S1–S4 voltage-sensing domains have helped to identify structural motifs that move at the protein–lipid interface (3–5). In many instances, these toxin–channel interactions are highly specific, allowing them to be used in target validation and drug development (6–8).Tarantula toxins are a particularly interesting class of protein toxins that have been found to target all three families of voltage-activated cation channels (3, 9–12), stretch-activated cation channels (13–15), as well as ligand-gated ion channels as diverse as acid-sensing ion channels (ASIC) (16–21) and transient receptor potential (TRP) channels (22, 23). The tarantula toxins targeting these ion channels belong to the inhibitor cystine knot (ICK) family of venom toxins that are stabilized by three disulfide bonds at the core of the molecule (16, 17, 24–31). Although conventional tarantula toxins vary in length from 30 to 40 aa and contain one ICK motif, the recently discovered double-knot toxin (DkTx) that specifically targets TRPV1 channels contains two separable lobes, each containing its own ICK motif (22, 23).One unifying feature of all tarantula toxins studied thus far is that they act on ion channels by modifying the gating properties of the channel. The best studied of these are the tarantula toxins targeting voltage-activated cation channels, where the toxins bind to the S3b–S4 voltage sensor paddle motif (5, 32–36), a helix-turn-helix motif within S1–S4 voltage-sensing domains that moves in response to changes in membrane voltage (37–41). Toxins binding to S3b–S4 motifs can influence voltage sensor activation, opening and closing of the pore, or the process of inactivation (4, 5, 36, 42–46). The tarantula toxin PcTx1 can promote opening of ASIC channels at neutral pH (16, 18), and DkTx opens TRPV1 in the absence of other stimuli (22, 23), suggesting that these toxin stabilize open states of their target channels.For many of these tarantula toxins, the lipid membrane plays a key role in the mechanism of inhibition. Strong membrane partitioning has been demonstrated for a range of toxins targeting S1–S4 domains in voltage-activated channels (27, 44, 47–50), and for GsMTx4 (14, 50), a tarantula toxin that inhibits opening of stretch-activated cation channels in astrocytes, as well as the cloned stretch-activated Piezo1 channel (13, 15). In experiments on stretch-activated channels, both the d- and l-enantiomers of GsMTx4 are active (14, 50), implying that the toxin may not bind directly to the channel. In addition, both forms of the toxin alter the conductance and lifetimes of gramicidin channels (14), suggesting that the toxin inhibits stretch-activated channels by perturbing the interface between the membrane and the channel. In the case of Kv channels, the S1–S4 domains are embedded in the lipid bilayer and interact intimately with lipids (48, 51, 52) and modification in the lipid composition can dramatically alter gating of the channel (48, 53–56). In one study on the gating of the Kv2.1/Kv1.2 paddle chimera (53), the tarantula toxin VSTx1 was proposed to inhibit Kv channels by modifying the forces acting between the channel and the membrane. Although these studies implicate a key role for the membrane in the activity of Kv and stretch-activated channels, and for the action of tarantula toxins, the influence of the toxin on membrane structure and dynamics have not been directly examined. The goal of the present study was to localize a tarantula toxin in membranes using structural approaches and to investigate the influence of the toxin on the structure of the lipid bilayer.     

From the Cover: The point of no return in vetoing self-initiated movements

In humans, spontaneous movements are often preceded by early brain signals. One such signal is the readiness potential (RP) that gradually arises within the last second preceding a movement. An important question is whether people are able to cancel movements after the elicitation of such RPs, and if so until which point in time. Here, subjects played a game where they tried to press a button to earn points in a challenge with a brain–computer interface (BCI) that had been trained to detect their RPs in real time and to emit stop signals. Our data suggest that subjects can still veto a movement even after the onset of the RP. Cancellation of movements was possible if stop signals occurred earlier than 200 ms before movement onset, thus constituting a point of no return.It has been repeatedly shown that spontaneous movements are preceded by early brain signals (1–8). As early as a second before a simple voluntary movement, a so-called readiness potential (RP) is observed over motor-related brain regions (1–3, 5). The RP was found to precede the self-reported time of the “‘decision’ to act” (ref. 3, p. 623). Similar preparatory signals have been observed using invasive electrophysiology (8, 9) and functional MRI (7, 10), and have been demonstrated also for choices between multiple-response options (6, 7, 10), for abstract decisions (10), for perceptual choices (11), and for value-based decisions (12). To date, the exact nature and causal role of such early signals in decision making is debated (12–20).One important question is whether a person can still exert a veto by inhibiting the movement after onset of the RP (13, 18, 21, 22). One possibility is that the onset of the RP triggers a causal chain of events that unfolds in time and cannot be cancelled. The onset of the RP in this case would be akin to tipping the first stone in a row of dominoes. If there is no chance of intervening, the dominoes will gradually fall one-by-one until the last one is reached. This has been coined a ballistic stage of processing (23, 24). A different possibility is that participants can still terminate the process, akin to taking out a domino at some later stage in the chain and thus preventing the process from completing. Here, we directly tested this in a real-time experiment that required subjects to terminate their decision to move once a RP had been detected by a brain–computer interface (BCI) (25–31).     

Fragile charge order in the nonsuperconducting ground state of the underdoped high-temperature superconductors

The normal state in the hole underdoped copper oxide superconductors has proven to be a source of mystery for decades. The measurement of a small Fermi surface by quantum oscillations on suppression of superconductivity by high applied magnetic fields, together with complementary spectroscopic measurements in the hole underdoped copper oxide superconductors, point to a nodal electron pocket from charge order in YBa₂Cu₃O6+δ. Here, we report quantum oscillation measurements in the closely related stoichiometric material YBa₂Cu₄O₈, which reveals similar Fermi surface properties to YBa₂Cu₃O6+δ, despite the nonobservation of charge order signatures in the same spectroscopic techniques, such as X-ray diffraction, that revealed signatures of charge order in YBa₂Cu₃O6+δ. Fermi surface reconstruction in YBa₂Cu₄O₈ is suggested to occur from magnetic field enhancement of charge order that is rendered fragile in zero magnetic fields because of its potential unconventional nature and/or its occurrence as a subsidiary to more robust underlying electronic correlations.The normal state of the underdoped copper oxide superconductors has proven to be even more perplexing than the d-wave superconducting state in these materials. At high temperatures in zero magnetic fields, the normal state of the underdoped cuprates comprises an unconventional Fermi surface of truncated “Fermi arcs” in momentum space, which is referred to as the pseudogap state (1). At low temperatures in high magnetic fields, quantum oscillations reveal the nonsuperconducting ground state in various families of underdoped hole-doped copper oxide superconductors to comprise small Fermi surface pockets (2–15). These small Fermi pockets in YBa₂Cu₃O6+δ have been identified as nodal electron pockets (2, 3, 11, 16, 17) originating from Fermi surface reconstruction associated with charge order measured by X-ray diffraction (18–20), ultrasound (21), nuclear magnetic resonance (22), and optical reflectometry (23). However, various aspects of the underlying charge order and the associated Fermi surface reconstruction remain obscure. A central question pertains to the origin of this charge order, curious features of which include a short correlation length in zero magnetic field that grows with increasing magnetic field and decreasing temperature (20). It is crucial to understand the nature of this ground-state order that is related to the high-temperature pseudogap state and delicately balanced with the superconducting ground state. Here, we shed light on the nature of this state by performing extended magnetic field, temperature, and tilt angle-resolved quantum oscillation experiments in the stoichiometric copper oxide superconductor YBa₂Cu₄O₈ (24). This material with double CuO chains has fixed oxygen stoichiometry, making it a model system to study. YBa₂Cu₄O₈ avoids disorder associated with the fractional oxygen stoichiometry in the YBa₂Cu₃O6+δ chains, which has been shown by microwave conductivity to be the dominant source of weak-limit (Born) scattering (25).Intriguingly, we find magnetic field- and angle-dependent signatures of quantum oscillations in YBa₂Cu₄O₈ (13, 14) that are very similar to those in YBa₂Cu₃O6+δ, indicating a similar nodal Fermi surface that arises from Fermi surface reconstruction by charge order with orthogonal wave vectors (16). However, the same X-ray diffraction measurements that show a Bragg peak characteristic of charge order in YBa₂Cu₃O6+δ for a range of hole dopings from 0.084≤p≤0.164 (19, 20, 26) have, thus far, not revealed a Bragg peak in the case of YBa₂Cu₄O₈ (19). We suggest that charge order enhanced by applied magnetic fields reconstructs the Fermi surface in YBa₂Cu₄O₈, whereas charge order is revealed even in zero magnetic fields in YBa₂Cu₃O6+δ because of pinning by increased disorder from oxygen vacancies.     

α-Synuclein assembles into higher-order multimers upon membrane binding to promote SNARE complex formation

Physiologically, α-synuclein chaperones soluble NSF attachment protein receptor (SNARE) complex assembly and may also perform other functions; pathologically, in contrast, α-synuclein misfolds into neurotoxic aggregates that mediate neurodegeneration and propagate between neurons. In neurons, α-synuclein exists in an equilibrium between cytosolic and membrane-bound states. Cytosolic α-synuclein appears to be natively unfolded, whereas membrane-bound α-synuclein adopts an α-helical conformation. Although the majority of studies showed that cytosolic α-synuclein is monomeric, it is unknown whether membrane-bound α-synuclein is also monomeric, and whether chaperoning of SNARE complex assembly by α-synuclein involves its cytosolic or membrane-bound state. Here, we show using chemical cross-linking and fluorescence resonance energy transfer (FRET) that α-synuclein multimerizes into large homomeric complexes upon membrane binding. The FRET experiments indicated that the multimers of membrane-bound α-synuclein exhibit defined intermolecular contacts, suggesting an ordered array. Moreover, we demonstrate that α-synuclein promotes SNARE complex assembly at the presynaptic plasma membrane in its multimeric membrane-bound state, but not in its monomeric cytosolic state. Our data delineate a folding pathway for α-synuclein that ranges from a monomeric, natively unfolded form in cytosol to a physiologically functional, multimeric form upon membrane binding, and show that only the latter but not the former acts as a SNARE complex chaperone at the presynaptic terminal, and may protect against neurodegeneration.α-Synuclein is an abundant presynaptic protein that physiologically acts to promote soluble NSF attachment protein receptor (SNARE) complex assembly in vitro and in vivo (1–3). Point mutations in α-synuclein (A30P, E46K, H50Q, G51D, and A53T) as well as α-synuclein gene duplications and triplications produce early-onset Parkinson''s disease (PD) (4–10). Moreover, α-synuclein is a major component of intracellular protein aggregates called Lewy bodies, which are pathological hallmarks of neurodegenerative disorders such as PD, Lewy body dementia, and multiple system atrophy (11–14). Strikingly, neurotoxic α-synuclein aggregates propagate between neurons during neurodegeneration, suggesting that such α-synuclein aggregates are not only intrinsically neurotoxic but also nucleate additional fibrillization (15–18).α-Synuclein is highly concentrated in presynaptic terminals where α-synuclein exists in an equilibrium between a soluble and a membrane-bound state, and is associated with synaptic vesicles (19–22). The labile association of α-synuclein with membranes (23, 24) suggests that binding of α-synuclein to synaptic vesicles, and its dissociation from these vesicles, may regulate its physiological function. Membrane-bound α-synuclein assumes an α-helical conformation (25–32), whereas cytosolic α-synuclein is natively unfolded and monomeric (refs. 25, 26, 31, and 32; however, see refs. 33 and 34 and Discussion for a divergent view). Membrane binding by α-synuclein is likely physiologically important because in in vitro experiments, α-synuclein remodels membranes (35, 36), influences lipid packing (37, 38), and induces vesicle clustering (39). Moreover, membranes were found to be important for the neuropathological effects of α-synuclein (40–44).However, the relation of membrane binding to the in vivo function of α-synuclein remains unexplored, and it is unknown whether α-synuclein binds to membranes as a monomer or oligomer. Thus, in the present study we have investigated the nature of the membrane-bound state of α-synuclein and its relation to its physiological function in SNARE complex assembly. We found that soluble monomeric α-synuclein assembles into higher-order multimers upon membrane binding and that membrane binding of α-synuclein is required for its physiological activity in promoting SNARE complex assembly at the synapse.     

Cell membrane deformation and bioeffects produced by tandem bubble-induced jetting flow

Cavitation with bubble–bubble interaction is a fundamental feature in therapeutic ultrasound. However, the causal relationships between bubble dynamics, associated flow motion, cell deformation, and resultant bioeffects are not well elucidated. Here, we report an experimental system for tandem bubble (TB; maximum diameter = 50 ± 2 μm) generation, jet formation, and subsequent interaction with single HeLa cells patterned on fibronectin-coated islands (32 × 32 μm) in a microfluidic chip. We have demonstrated that pinpoint membrane poration can be produced at the leading edge of the HeLa cell in standoff distance S_d ≤ 30 μm, driven by the transient shear stress associated with TB-induced jetting flow. The cell membrane deformation associated with a maximum strain rate on the order of 10⁴ s⁻¹ was heterogeneous. The maximum area strain (ε_A,M) decreased exponentially with S_d (also influenced by adhesion pattern), a feature that allows us to create distinctly different treatment outcome (i.e., necrosis, repairable poration, or nonporation) in individual cells. More importantly, our results suggest that membrane poration and cell survival are better correlated with area strain integral (∫​εA2dt) instead of ε_A,M, which is characteristic of the response of materials under high strain-rate loadings. For 50% cell survival the corresponding area strain integral was found to vary in the range of 56 ∼ 123 μs with ε_A,M in the range of 57 ∼ 87%. Finally, significant variations in individual cell’s response were observed at the same S_d, indicating the potential for using this method to probe mechanotransduction at the single cell level.Cavitation-induced bioeffects (1–3) have been well recognized to play a pivotal role in a broad range of biomedical applications, including blood–brain barrier opening by focused ultrasound (4), shock wave lithotripsy (5), histotripsy (6), sonoporation (7), laser surgery (8), characterization (9, 10), and manipulation of single cells (11). Despite this, the dynamic processes of cavitation bubble(s) interaction with biological tissue and cells are not well understood, primarily due to the lack of enabling techniques and experimental systems to resolve such inherently complex and fast responses, especially at the cellular level. Although progress has been made in better control of cavitation–cell interaction, and characterization of bubble dynamics, associated flow field, and bioeffects (7, 12–14), a quantitative assessment of the cell membrane deformation produced by cavitation bubbles is still lacking. Without this knowledge, a disconnect exists between bubble-generated mechanical stress and the resultant cell response that may initiate calcium transients (15, 16), membrane poration (17, 18) that may eventually lead to necrosis, survival, gene expression, or proliferation of the cells following cavitation exposure (2).The unique combination of high strain rate and large deformation of a cell produced by impulsive stretches from bubble oscillation (10, 19) presents a significant challenge to understanding the mechanism of action. Although cell mechanics have been extensively investigated under quasi-static and dynamic loading conditions with low strain rates (20, 21), recent evidence suggests that the classical area strain threshold under quasi-static loading conditions (about 3%; ref. 22) is not applicable to cavitation-produced membrane rupture (10, 23, 24). Considering the importance of mechanical stress in the growth and repair of cells and tissue to maintain their physiological functions (25–27), knowledge about cell mechanoresponse under high strain-rate loading is essential for understanding cavitation-produced bioeffects, and furthermore, for exploring viable biomedical applications by harnessing the beneficial potential of cavitation.In addition to high-strain rate and large membrane deformation, other technical challenges exist in dissecting the complex bubble(s)–cell interaction. Recent studies have used ultrasound-activated microbubbles (7, 15, 17, 18) and laser-generated bubbles in microfluidic systems (13, 28) to reduce the randomness in cavitation initiation and bubble dynamics. Even so, significant variations in cell shape, size, growth adhesion environment (29), and heterogeneity in cell population (30) may also alter the bioeffects produced, hindering efforts to identify the most critical factor responsible for the treatment outcome. All these challenges motivate us to develop new technologies and experimental systems to investigate and better understand cavitation-induced bioeffects, especially at the single cell level.In this study, we developed a versatile experimental system to investigate cell membrane deformation and bioeffects produced by laser-induced tandem bubbles (TBs) and resultant jetting flow at the single cell level. We characterized the heterogeneous deformation of the cell membrane produced by the jetting flow at different standoff distances and with different adhesion patterns. We further analyzed the correlations between the maximum area strain or area strain integral imposed on the cell membrane with cell viability following the TB treatment. The results provide insights into the mechanism responsible for the pinpoint membrane rupture produced by tandem bubbles and inertial cavitation-induced bioeffects under high strain-rate load conditions.     

From the Cover: Folding pathway of a multidomain protein depends on its topology of domain connectivity

How do the folding mechanisms of multidomain proteins depend on protein topology? We addressed this question by developing an Ising-like structure-based model and applying it for the analysis of free-energy landscapes and folding kinetics of an example protein, Escherichia coli dihydrofolate reductase (DHFR). DHFR has two domains, one comprising discontinuous N- and C-terminal parts and the other comprising a continuous middle part of the chain. The simulated folding pathway of DHFR is a sequential process during which the continuous domain folds first, followed by the discontinuous domain, thereby avoiding the rapid decrease in conformation entropy caused by the association of the N- and C-terminal parts during the early phase of folding. Our simulated results consistently explain the observed experimental data on folding kinetics and predict an off-pathway structural fluctuation at equilibrium. For a circular permutant for which the topological complexity of wild-type DHFR is resolved, the balance between energy and entropy is modulated, resulting in the coexistence of the two folding pathways. This coexistence of pathways should account for the experimentally observed complex folding behavior of the circular permutant.Topology of protein conformation, or the spatial arrangement of structural units and the chain connectivity among them, is a key determinant of the folding mechanisms of proteins (1–5). However, predicting a folding pathway is a subtle problem when a protein comprises multiple regions of cooperative structure formation (i.e., foldons or domains). Given that a protein has n such cooperative regions and each region tends to show a two-state–like structural transition between ordered and disordered states, the protein as a whole can have 2ⁿ conformation states and multiple folding routes passing through them are allowed. The statistical weights of these folding routes should be determined both by the interactions among structural regions and the strength of cooperativity within individual regions (6). When multiple competitive routes coexist, the observed folding pathway of an ensemble of molecules should be a superposition of these routes, and the dominant folding pathway should be flexibly changed by changing the solution conditions or by mutations. The multiplicity and flexibility of pathways are important, even for small single-domain proteins like ribosomal protein S6 (7, 8), and are evident for proteins that have repeating structures (9–13). For proteins comprising multiple domains (14), the multiplicity of possible folding pathways is significant. The relative importance among 2ⁿ conformation states in the folding process in proteins with n independently foldable domains should be determined by length, structure (13), the topological connectivity of linkers between domains (3), and the interactions at the interface between domains (3, 15, 16). Fig. 1A shows an example protein for the case n = 2.Open in a separate windowFig. 1.Examples of two-domain proteins with different topological complexities. (A) Human γD-crystallin (PDB ID: 1HK0), which has two independently foldable domains connected by a single linker. (B) DHFR (PDB ID: 1rx1), which is topologically more complex, comprising two domains, DLD (blue) and ABD (pink). DLD is a discontinuous domain comprising the N- and C-terminal parts of the chain, and ABD and DLD are connected by two linkers. The positions of linkers are designated by red arrows.The above mechanism for determining folding intermediates and pathways of multidomain proteins is not applicable when domains have mutually correlated folding tendencies. In particular, the correlation between domains may be significant in a topologically complex protein, which has a domain comprising multiple discontinuous parts of a chain. For example, consider one domain, a discontinuous domain, consisting of residues 1 ≤ i ≤ N₁ and N₂ ≤ i ≤ N, and another domain, a continuous domain, consisting of residues N₁ < i < N₂. Because there is a tendency that the continuous parts of the chain form “islands” of ordered structures (17, 18) and that these continuous parts of a sequence can be the nuclei for folding, the discontinuous domain may not be an independent folding unit, but may depend on the continuous domain. In this paper, we theoretically analyze the problem of how a folding pathway is selected in multidomain proteins that have a discontinuous domain by using Escherichia coli dihydrofolate reductase (DHFR) as an example and compare it with its circular permutant that consists only of continuous domains.As shown in Fig. 1B, DHFR is a 159-residue α/β protein consisting of two domains: a discontinuous loop domain (DLD) (residues 1–37 and 107–159) and an adenosine-binding domain (ABD) (residues 38–106). Because DLD does not include a single contiguous region of the chain, but rather includes separate N- and C-terminal parts, the structural ordering of DLD can be correlated with the structural ordering of ABD. As a model protein, DHFR has been intensively investigated (19–29), which has resulted in a picture that DHFR folds along the following pathway:U→τ7I6→τ6I5→τ5IHF→τ1,…,τ4{N}.[1]Here I₆ is an intermediate exhibiting heterogeneous compactness with DLD being only partially compacted but ABD attaining a native-like compactness (19). I₆ appeared in τ₇ < 35?μs after folding was initiated from the unfolded state (U). During τ₆ ～ 550?μs, further structural development was observed both in ABD and in DLD (19), which led to I₅ in which the secondary structures were reasonably formed (20–22) and two subsets of hydrogen-bonding networks were formed in ABD and DLD (23). During τ₅ ～ 200 ms, structures of ABD and DLD were further organized, which led to the hyperfluorescent intermediate state, I_HF, consisting of four substates, I₁, …, I₄, which matured through four parallel pathways on timescales of τ₁, …, τ₄ = 1 ? 100 s to reach the four native conformers, collectively denoted by {N} in Eq. 1 (24–27). It is plausible that the slow process (several hundred seconds) during the τ₁ ? τ₄ phases is due to intense “internal friction” (30–32) in the glassy dynamics of conformation (33), including formation/disruption of nonnative contacts, the effects of proline isomerization, and the cis–trans isomerization of Gly95 and Gly96. Apart from this complexity during the last phase, the folding scheme in Eq. 1 can be regarded as a hierarchical assembly of structures that begins from the ordering of each domain at the early phase of τ₇ and proceeds to the formation of the whole protein during the later phase of τ₅ (19). Therefore, the questions are the mechanisms for how such a sequential pathway is realized in DHFR and how the topological complexity of DHFR affects the pathway selection.     

Therapeutic mechanisms of high-frequency stimulation in Parkinson’s disease and neural restoration via loop-based reinforcement

High-frequency deep brain stimulation (HFS) is clinically recognized to treat parkinsonian movement disorders, but its mechanisms remain elusive. Current hypotheses suggest that the therapeutic merit of HFS stems from increasing the regularity of the firing patterns in the basal ganglia (BG). Although this is consistent with experiments in humans and animal models of Parkinsonism, it is unclear how the pattern regularization would originate from HFS. To address this question, we built a computational model of the cortico-BG-thalamo-cortical loop in normal and parkinsonian conditions. We simulated the effects of subthalamic deep brain stimulation both proximally to the stimulation site and distally through orthodromic and antidromic mechanisms for several stimulation frequencies (20–180 Hz) and, correspondingly, we studied the evolution of the firing patterns in the loop. The model closely reproduced experimental evidence for each structure in the loop and showed that neither the proximal effects nor the distal effects individually account for the observed pattern changes, whereas the combined impact of these effects increases with the stimulation frequency and becomes significant for HFS. Perturbations evoked proximally and distally propagate along the loop, rendezvous in the striatum, and, for HFS, positively overlap (reinforcement), thus causing larger poststimulus activation and more regular patterns in striatum. Reinforcement is maximal for the clinically relevant 130-Hz stimulation and restores a more normal activity in the nuclei downstream. These results suggest that reinforcement may be pivotal to achieve pattern regularization and restore the neural activity in the nuclei downstream and may stem from frequency-selective resonant properties of the loop.High-frequency (i.e., above 100 Hz) deep brain stimulation (HFS) of the basal ganglia (BG) and thalamus is clinically recognized to treat movement disorders in Parkinson’s disease (PD) (1–4), but its therapeutic mechanisms remain unclear (5, 6).Early hypotheses about HFS were derived from the rate-based model of the BG function (7, 8) and postulated the disruption of the output of the BG-thalamic system via either the inactivation of neurons in the stimulated site (target) (9–15), which would provide an effect similar to a surgical lesion, or the abnormal excitation of axons projecting out of the target (16–19), which would disrupt the neuronal activity in the structures downstream, including any pathophysiological activity (20).More recently, an ever-growing number of experiments in PD humans and animal models of Parkinsonism has indicated that HFS affects the firing patterns of the neurons rather than the mean firing rate both in the target and the structures downstream (18, 19, 21–31) and it replaces repetitive low-frequency (i.e., ≤50 Hz) bursting patterns with regularized (i.e., more tonic) patterns at higher frequencies (25, 26). It has been proposed that increased pattern regularity of neurons in the target may be therapeutic (5, 32–37), but it is still unknown how this regularity comes about with HFS.It has been suggested that an increased pattern regularity can deplete the information content of the target output and this lack of information would act as an “information lesion” (33) and prevent the pathological activity from being transmitted within the BG-thalamic system (22, 33, 36). As a result, an information lesion in the target [typically, one among the subthalamic nucleus (STN), internal globus pallidus (GPi), or thalamus] would have effects similar to those of a destructive lesion in the same site, which has been reported to alleviate the movement disorders (38).Instead, studies (32, 34, 35, 37) have suggested that an increased pattern regularity of the BG output partly compensates the PD-evoked impairment of the information-processing capabilities of the thalamo-cortical system, and this restores a more faithful thalamic relay of the sensorimotor information (35, 39).Although intriguing, these hypotheses remain elusive on (i) the neuronal mechanisms that would elicit pattern regularization (e.g., why regularization would be relevant only for HFS) and (ii) the effects that increased regularity would have on the cortico-BG-thalamo-cortical loop.It has been hypothesized that pattern regularization occurs because axons projecting out of the target follow the pattern of the stimulus pulses (40, 41) and, given the segregated organization of the BG-thalamic connections (42), it has been assumed that pattern regularization percolates straightforward from the target to the structures immediately downstream (34, 36). However, this representation of the pattern regularization as a “local” effect can hardly be reconciled with the fact that HFS of any structure of the cortico-BG-thalamo-cortical loop is therapeutic for at least some movement disorders (1–4, 43–47), nor does it explain why stimulation at frequencies above 160–180 Hz is not necessarily therapeutic despite the fact that the regularity of the axonal patterns may increase (48, 49). Moreover, coherence in the 8–30-Hz band among neurons across different structures may decrease under HFS but not for lower frequencies (26, 50–52), which suggests the emergence of diffused changes in neuronal activity that would be hardly accounted for with purely local effects.There is emerging evidence, instead, that HFS affects multiple structures simultaneously. First, it has been shown that deep brain stimulation (DBS) may antidromically activate afferent axons and fibers of passage (53–59), thus reaching structures not immediately downstream. Second, studies (57, 58) observed in 6-hydroxydopamine (6-OHDA)-intoxicated rats that the antidromic effects increase with the stimulation frequency and peak around 110–130 Hz. Third, it has been shown in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-intoxicated nonhuman primates (NHPs) that STN DBS may evoke similar poststimulus responses in different BG structures, both downstream from and upstream to the STN (5, 27, 28, 30, 60). Finally, it has been reported that the cortico-BG-thalamo-cortical system consists of multiple sets of reentrant, interconnected, and partially overlapping neuronal loops (5, 42, 61, 62), which means that the structures upstream to the target (e.g., the striatum) may play an important role in the therapeutic mechanisms of HFS.Altogether, these results suggest that (A) pattern regularization is a global effect that exploits the closed-loop nature of the cortico-BG-thalamo-cortical system and selectively emerges only for specific HFS values, and that (B) the therapeutic merit of pattern regularization has to deal with the restoration of a more normal functionality of the entire cortico-BG-thalamo-cortical loop rather than with variations in the information content of one specific structure.We explored hypotheses (A) and (B) and assessed the system-wide effects of DBS by constructing a computational model of the cortico-BG-thalamo-cortical loop in both normal and parkinsonian conditions and by simulating the effects of STN DBS both at low (20–80 Hz) and high (100–180 Hz) frequencies. The model includes populations of single-compartment neurons and interneurons from motor cortex, striatum, GPi, and thalamus according to a network topology derived from the NHP anatomy, and it simulates both the orthodromic and antidromic effects of DBS. As a result, this model reproduced both average activity and discharge patterns of single units in NHP and rats under normal and parkinsonian conditions, with and without DBS, for all modeled structures.We show through numerical simulation that hypothesis (A) is significantly contributed by reinforcement mechanisms in the striatum. These mechanisms are selectively elicited by HFS, facilitate the percolation of regularized discharge patterns from the striatum to the GPi, and have a primary role in (B), because the percolated striato-pallidal input combines with the local effects of STN DBS to restore the thalamic relay function (63).     

Scaling laws describe memories of host–pathogen riposte in the HIV population

The enormous genetic diversity and mutability of HIV has prevented effective control of this virus by natural immune responses or vaccination. Evolution of the circulating HIV population has thus occurred in response to diverse, ultimately ineffective, immune selection pressures that randomly change from host to host. We show that the interplay between the diversity of human immune responses and the ways that HIV mutates to evade them results in distinct sets of sequences defined by similar collectively coupled mutations. Scaling laws that relate these sets of sequences resemble those observed in linguistics and other branches of inquiry, and dynamics reminiscent of neural networks are observed. Like neural networks that store memories of past stimulation, the circulating HIV population stores memories of host–pathogen combat won by the virus. We describe an exactly solvable model that captures the main qualitative features of the sets of sequences and a simple mechanistic model for the origin of the observed scaling laws. Our results define collective mutational pathways used by HIV to evade human immune responses, which could guide vaccine design.Viruses can infect humans to cause infectious diseases, which, on occasion, lead to outbreaks that reach pandemic proportions resulting in millions of deaths. One prominent example of such a virus is HIV. Vaccination, a procedure that aims to protect humans from infectious pathogens, is one of the greatest triumphs of modern medicine. Vaccines induce human immune responses that are specific for a pathogen, which then lie ready and waiting to abort infection. However, no effective vaccine for HIV exists, and there is no known example of HIV being cleared by natural human immune responses. This is because of the extraordinarily high mutability of the virus and its ability to rapidly down-regulate the human immune system (1, 2). The high mutability enables HIV to evade natural or vaccine-induced immune responses (2, 3), while down-regulation of the host immune system hinders the development of potent responses (2). This is in contrast to many other viruses that can often be cleared by effective natural responses and vaccinated against successfully (4). These viruses accumulate mutations in a directed fashion, guided by selective pressure due to successful vaccine-induced or natural immune responses (1, 5). The lack of effective natural immune responses or successful vaccines, and the enormous diversity of human immune pressures [e.g., T-cell responses (6)], implies that HIV has evolved in the human population in response to myriad, usually ineffective, immune responses. We set out to study the properties of such a virus population.In past and current work (7–9), we have tried to define the functional constraints on HIV evolution with the practical goal of identifying its mutational vulnerabilities, and then harnessing this knowledge to inform vaccine design. Toward this end, we analyzed sequences of HIV proteins derived from virus samples extracted from diverse patients. Following a statistical approach pioneered in the study of neuronal networks (10, 11), we inferred a model for the probability of occurrence of mutant strains (a “prevalence landscape”) by maximizing the entropy of this inferred probability distribution subject to the constraints of reproducing the observed frequency of single and double mutations in the sequence data (8, 9). This model also accurately reproduces higher-order statistics characterizing the sequence data, such as the probability of observing sequences with a certain number of mutations, even though these quantities are not directly constrained in the inference procedure (Fig. S1 and SI Text). Theoretical studies suggest that, for HIV strains that are phylogenetically relatively close, the rank order of the prevalence of strains is the same as the rank order of their intrinsic replicative fitness (12). This may seem surprising because the viral sequences used to infer our model are samples obtained from patients during the course of nonequilibrium host–pathogen combat, and so the effective in-host fitness of a viral strain can be different from its intrinsic fitness. Although immune responses drive sequence evolution in each patient, they are a perturbative effect at the population level, making the rank order of prevalence and fitness statistically similar (12). This is because of the great diversity of human immune responses directed toward different regions of the viral proteome, and deleterious mutations made to evade the immune response in one host tend to revert upon transmission to another host (13). In vitro and in vivo studies testing our predictions for fitness support this conclusion (8, 9).The maximum entropy model for the prevalence/fitness is described by the following:P(z)=exp(−H(z))Q, H(z)=−∑i=1Nhizi−∑i=1N−1∑j=i+1NJijzizj,[1]where P(z) is the probability of observing a sequence of amino acids z = {z₁, z₂, …, z_N}, with N the total length of the protein sequence. Amino acids at each site i are identified as either consensus (z_i = 0) or mutant (z_i = 1). Here, the partition function Q ensures that the probabilities of all sequences sum to 1. The fields, h_i, and couplings, J_ij, in the Hamiltonian are obtained by fitting the observed probabilities of single and double mutations in sequences of HIV proteins (Methods). A positive coupling between a pair of sites implies that sequences with both sites mutated are observed more often than would be expected if mutations at these sites were independent. Thus, positive couplings indicate potentially synergistic or compensatory interactions between mutations. Mutations of both sites in a negatively coupled pair are observed less often than would be expected if the sites were independent, indicating a potential antagonistic or deleterious interaction between mutations at these sites. Similarly, point mutations are observed more often at sites with positive fields than at those with negative fields, when interactions with other sites in the sequence background are neglected. (Although related to epistasis, we emphasize that the overall effect of a particular mutation on fitness must be considered in the context of a particular sequence background: for example, mutation at a site i where the field h_i is positive may nonetheless lead to a decrease in viral fitness if there exist significant negative couplings between site i and other mutated sites in the sequence background.) For clarity and consistency, we use the language of fitness to describe the results presented below.     

Activation of cyclic electron flow by hydrogen peroxide in vivo

Cyclic electron flow (CEF) around photosystem I is thought to balance the ATP/NADPH energy budget of photosynthesis, requiring that its rate be finely regulated. The mechanisms of this regulation are not well understood. We observed that mutants that exhibited constitutively high rates of CEF also showed elevated production of H₂O₂. We thus tested the hypothesis that CEF can be activated by H₂O₂ in vivo. CEF was strongly increased by H₂O₂ both by infiltration or in situ production by chloroplast-localized glycolate oxidase, implying that H₂O₂ can activate CEF either directly by redox modulation of key enzymes, or indirectly by affecting other photosynthetic processes. CEF appeared with a half time of about 20 min after exposure to H₂O₂, suggesting activation of previously expressed CEF-related machinery. H₂O₂-dependent CEF was not sensitive to antimycin A or loss of PGR5, indicating that increased CEF probably does not involve the PGR5-PGRL1 associated pathway. In contrast, the rise in CEF was not observed in a mutant deficient in the chloroplast NADPH:PQ reductase (NDH), supporting the involvement of this complex in CEF activated by H₂O₂. We propose that H₂O₂ is a missing link between environmental stress, metabolism, and redox regulation of CEF in higher plants.In oxygenic photosynthesis, linear electron flow (LEF) is the process by which light energy is captured to drive the extraction of electrons and protons from water and transfer them through a system of electron carriers to reduce NADPH. LEF is coupled to proton translocation into the thylakoid lumen, generating an electrochemical gradient of protons (Δμ~H+) or proton motive force (pmf). The pmf drives the synthesis of ATP to power the reactions of the Calvin–Benson–Bassham (CBB) cycle and other essential metabolic processes in the chloroplast. The pmf is also a key regulator of photosynthesis in that it activates the photoprotective q_E response to dissipate excess light energy and down-regulates electron transfer by controlling the rate of oxidation of plastoquinol at the cytochrome b₆f complex (b₆f), thus preventing the buildup of reduced intermediates (1, 2).LEF results in the transfer or deposition into the lumen of three protons for each electron transferred through PSII, plastoquinone (PQ), b₆f, plastocyanin, and photosystem I (PSI) to ferredoxin (Fd). The synthesis of one ATP is thought to require the passage of 4.67 protons through the ATP synthase, so that LEF should produce a ratio of ATP/NADPH of about 1.33; this ratio is too low to sustain the CBB cycle or supply ATP required for translation, protein transport, or other ATP-dependent processes (3). In addition, the relative demands for ATP and NADPH can change dramatically depending on environmental, developmental, and other factors, leading to rapid energy imbalances that require dynamical regulation of ATP/NADPH balance.Several alternative electron flow pathways in the chloroplast have been proposed to augment ATP production, thus balancing the ATP/NADPH budget of the chloroplast (2, 3). Perhaps the most important and complicated of these pathways is cyclic electron flow around photosystem I (CEF), in which electron flow from the acceptor side of PSI is shunted back into the PQ pool, generating additional pmf that can power ATP production without net NADPH production. There are several proposed CEF pathways that may operate under different conditions or in different species (reviewed in refs. 2 and 3). In higher-plant chloroplasts, the most studied routes of CEF are the antimycin A-sensitive pathway, which involves a complex of two CEF-related proteins, PGR5 (Proton Gradient Regulation 5) and PGRL1 (PGR5-like 1), directly reducing the quinone pool (4–7), the respiratory complex I analog, the NADPH dehydrogenase (NDH) complex (8–10), which oxidizes Fd or NAD(P)H to reduce plastoquinone (8, 11), and through the Q_i site of b₆f (12, 13). Different CEF mechanisms seem to operate in other species. In Chlamydomonas, for example, CEF seems to be conducted by a supercomplex of PSI, b₆f, and the PGRL1 protein (14, 15), and the involvement of PGR5 has recently been described as important for CEF under hypoxia (16, 17).Regardless of the mechanism of CEF, the overall process must be well regulated to properly balance the production of ATP to match the demands of metabolism. The mechanism of this regulation is not known, but many general models have been proposed. Perhaps the most widely cited regulatory model is the antenna state transition, which was previously shown to be correlated with activation of CEF in Chlamydomonas reinhardtii (14, 18) and favor the formation of the PSI–b₆f supercomplex (14). However, it was recently shown that state transitions are not required for CEF activation, supporting models that include redox control (15–17, 19–22). Other possible regulatory mechanisms include sensing of ATP/ADP ratios (23, 24), the redox status of NAD(P)H or Fd (25), various CBB metabolic intermediates (reviewed in ref. 26), calcium signaling (15, 27), phosphorylation of CEF-related proteins (27), and the reactive oxygen species H₂O₂ (26–29).One possibility is that CEF may be at least partly regulated by H₂O₂ (26), which is produced by the light reactions of photosynthesis and already known to regulate other cellular processes such as plant growth, development, and defense (30–32). Based on in vitro studies, it was previously proposed that H₂O₂ could activate CEF or chlororespiration by modifying the NDH complex (27). It has also been shown that H₂O₂ can increase the expression of the NDH complex (29) and may further affect the accumulation of photosynthetic metabolites, indirectly activating CEF (26). Consistent with this possibility, H₂O₂ is a well-documented signaling molecule (33), possibly through its ability to oxidize thiols (34, 35). Furthermore, H₂O₂ is expected to be produced under many conditions that initiate CEF [e.g., under a deficit of ATP, when electrons should accumulate in the PSI acceptor pools, leading to superoxide production that can be converted to H₂O₂ by superoxide dismutase (36)].This study aims to test the hypothesis that CEF can be initiated in vivo by H₂O₂ using a combination of in vivo spectroscopy and genetic modifications to selectively and rapidly initiate H₂O₂ production in the chloroplast.     

Dopamine release from transplanted neural stem cells in Parkinsonian rat striatum in vivo

Embryonic stem cell-based therapies exhibit great potential for the treatment of Parkinson’s disease (PD) because they can significantly rescue PD-like behaviors. However, whether the transplanted cells themselves release dopamine in vivo remains elusive. We and others have recently induced human embryonic stem cells into primitive neural stem cells (pNSCs) that are self-renewable for massive/transplantable production and can efficiently differentiate into dopamine-like neurons (pNSC–DAn) in culture. Here, we showed that after the striatal transplantation of pNSC–DAn, (i) pNSC–DAn retained tyrosine hydroxylase expression and reduced PD-like asymmetric rotation; (ii) depolarization-evoked dopamine release and reuptake were significantly rescued in the striatum both in vitro (brain slices) and in vivo, as determined jointly by microdialysis-based HPLC and electrochemical carbon fiber electrodes; and (iii) the rescued dopamine was released directly from the grafted pNSC–DAn (and not from injured original cells). Thus, pNSC–DAn grafts release and reuptake dopamine in the striatum in vivo and alleviate PD symptoms in rats, providing proof-of-concept for human clinical translation.Parkinson’s disease (PD) is a chronic progressive neurodegenerative disorder characterized by the specific loss of dopaminergic neurons in the substantia nigra pars compacta and their projecting axons, resulting in loss of dopamine (DA) release in the striatum (1). During the last two decades, cell-replacement therapy has proven, at least experimentally, to be a potential treatment for PD patients (2–7) and in animal models (8–15). The basic principle of cell therapy is to restore the DA release by transplanting new DA-like cells. Until recently, obtaining enough transplantable cells was a major bottleneck in the practicability of cell therapy for PD. One possible source is embryonic stem cells (ESCs), which can develop infinitely into self-renewable pluripotent cells with the potential to generate any type of cell, including DA neurons (DAns) (16, 17).Recently, several groups including us have introduced rapid and efficient ways to generate primitive neural stem cells (pNSCs) from human ESCs using small-molecule inhibitors under chemically defined conditions (12, 18, 19). These cells are nonpolarized neuroepithelia and retain plasticity upon treatment with neuronal developmental morphogens. Importantly, pNSCs differentiate into DAns (pNSC–DAn) with high efficiency (∼65%) after patterning by sonic hedgehog (SHH) and fibroblast growth factor 8 (FGF8) in vitro, providing an immediate and renewable source of DAns for PD treatment. Importantly, the striatal transplantation of human ESC-derived DA-like neurons, including pNSC–DAn, are able to relieve the motor defects in a PD rat model (11–13, 15, 19–23). Before attempting clinical translation of pNSC–DAn, however, there are two fundamental open questions. (i) Can pNSC–DAn functionally restore the striatal DA levels in vivo? (ii) What cells release the restored DA, pNSC–DAn themselves or resident neurons/cells repaired by the transplants?Regarding question 1, a recent study using nafion-coated carbon fiber electrodes (CFEs) reported that the amperometric current is rescued in vivo by ESC (pNSC–DAn-like) therapy (19). Both norepinephrine (NE) and serotonin are present in the striatum (24, 25). However, CFE amperometry/chronoamperometry alone cannot distinguish DA from other monoamines in vivo, such as NE and serotonin (Fig. S1) (see also refs. 26–28). Considering that the compounds released from grafted ESC-derived cells are unknown, the work of Kirkeby et al. was unable to determine whether DA or other monoamines are responsible for the restored amperometric signal. Thus, the key question of whether pNSC–DAn can rescue DA release needs to be reexamined for the identity of the restored amperometric signal in vivo.Regarding question 2, many studies have proposed that DA is probably released from the grafted cells (8, 12, 13, 20), whereas others have proposed that the grafted stem cells might restore striatal DA levels by rescuing injured original cells (29, 30). Thus, whether the grafted cells are actually capable of synthesizing and releasing DA in vivo must be investigated to determine the future cellular targets (residual cells versus pNSC–DAn) of treatment.To address these two mechanistic questions, advanced in vivo methods of DA identification and DA recording at high spatiotemporal resolution are required. Currently, microdialysis-based HPLC (HPLC) (31–33) and CFE amperometric recordings (34, 35) have been used independently by different laboratories to assess evoked DA release from the striatum in vivo. The major advantage of microdialysis-based HPLC is to identify the substances secreted in the cell-grafted striatum (33), but its spatiotemporal resolution is too low to distinguish the DA release site (residual cells or pNSC–DAn). In contrast, the major advantage of CFE-based amperometry is its very high temporal (ms) and spatial (μm) resolution, making it possible to distinguish the DA release site (residual cells or pNSC–DAn) in cultured cells, brain slices, and in vivo (34–39), but it is unable to distinguish between low-level endogenous oxidizable substances (DA versus serotonin and NE) in vivo.In the present study, we developed a challenging experimental paradigm of combining the two in vivo methods, microdialysis-based HPLC and CFE amperometry, to identify the evoked substance as DA and its release site as pNSC–DAn in the striatum of PD rats.     

Distinct dopamine neurons mediate reward signals for short- and long-term memories

Drosophila melanogaster can acquire a stable appetitive olfactory memory when the presentation of a sugar reward and an odor are paired. However, the neuronal mechanisms by which a single training induces long-term memory are poorly understood. Here we show that two distinct subsets of dopamine neurons in the fly brain signal reward for short-term (STM) and long-term memories (LTM). One subset induces memory that decays within several hours, whereas the other induces memory that gradually develops after training. They convey reward signals to spatially segregated synaptic domains of the mushroom body (MB), a potential site for convergence. Furthermore, we identified a single type of dopamine neuron that conveys the reward signal to restricted subdomains of the mushroom body lobes and induces long-term memory. Constant appetitive memory retention after a single training session thus comprises two memory components triggered by distinct dopamine neurons.Memory of a momentous event persists for a long time. Whereas some forms of long-term memory (LTM) require repetitive training (1–3), a highly relevant stimulus such as food or poison is sufficient to induce LTM in a single training session (4–7). Recent studies have revealed aspects of the molecular and cellular mechanisms of LTM formation induced by repetitive training (8–11), but how a single training induces a stable LTM is poorly understood (12).Appetitive olfactory learning in fruit flies is suited to address the question, as a presentation of a sugar reward paired with odor induces robust short-term memory (STM) and LTM (6, 7). Odor is represented by a sparse ensemble of the 2,000 intrinsic neurons, the Kenyon cells (13). A current working model suggests that concomitant reward signals from sugar ingestion cause associative plasticity in Kenyon cells that might underlie memory formation (14–20). A single activation session of a specific cluster of dopamine neurons (PAM neurons) by sugar ingestion can induce appetitive memory that is stable over 24 h (19), underscoring the importance of sugar reward to the fly.The mushroom body (MB) is composed of the three different cell types, α/β, α′/β′, and γ, which have distinct roles in different phases of appetitive memories (11, 21–25). Similar to midbrain dopamine neurons in mammals (26, 27), the structure and function of PAM cluster neurons are heterogeneous, and distinct dopamine neurons intersect unique segments of the MB lobes (19, 28–34). Further circuit dissection is thus crucial to identify candidate synapses that undergo associative modulation.By activating distinct subsets of PAM neurons for reward signaling, we found that short- and long-term memories are independently formed by two complementary subsets of PAM cluster dopamine neurons. Conditioning flies with nutritious and nonnutritious sugars revealed that the two subsets could represent different reinforcing properties: sweet taste and nutritional value of sugar. Constant appetitive memory retention after a single training session thus comprises two memory components triggered by distinct reward signals.