Reactive Nitrogen Species-Dependent Effects on Soybean Chloroplasts

Nitric oxide (NO) generation by soybean (Glycine max, var ADM 4800) chloroplasts was studied by electron paramagnetic resonance (EPR) spin-trapping technique.¹ Both nitrite and L-arginine (arg) are the required substrates for enzymatic activities considered as possible sources of NO in plants. Soybean chloroplasts showed a NO production of 3.2 ± 0.2 nmol min⁻¹ mg⁻¹ protein in the presence of 1 mM NaNO₂. Chloroplasts incubated with 1 mM arg showed a NO production of 0.76 ± 0.04 nmol min⁻¹ mg⁻¹ protein. This production was inhibited when chloroplasts were incubated in presence of NOS-inhibitors L-NAME and L-NNA. In vitro exposure of chloroplasts to a NO-donor (GSNO) decreased both ascorbyl radical content and the activity of ascorbate peroxidase, without modification of the total ascorbate content. Exposure of the isolated chloroplasts to a NO-donor decreased lipid radical content in membranes, however, incubation in the presence of 25 µM peroxynitrite (ONOO⁻) led to an increase in lipid-derived radicals (34%). The effect of ONOO⁻ on protein oxidation was determined by western blotting, showing an increase in carbonyl content either in stroma or thylakoid proteins as compared to control. Taken as a whole, NO seems to be an endogenous metabolite in soybean chloroplasts and reactive nitrogen species could exert either antioxidant or prooxidant effects on chloroplasts, since both a decreased lipid radical content in membranes and a decrease in the activity of ascorbate peroxidase were observed after exposure to a NO donor.Key Words: ascorbate, ascorbate peroxidase, chloroplasts, nitric oxide, peroxynitriteThe origin of nitric oxide (NO) in plants under aerobic conditions is currently under study. Although plants with low level of arginine shows arg-stimulated NO accumulation,² the mechanism for arginine-dependent NO synthesis in plants is still unknown, because the detection of an animal-type NOS remains elusive to date.^3,4 Even though assimilatory nitrate reductase is an enzymatic source of NO, its role in vivo would be limited by both its cytosolic localization which difficult the availability for nitrite, and the relative high K_m for nitrite (100 µM).⁵Chloroplasts have been previously marked as NO sources based in nonquantitative studies employing fluorescence microscopy^6,7 and immunogold electron microscopy.⁸ In our work we employed an specific technique (EPR, electron paramagnetic resonance with spin trap⁹) to detect NO as an endogenous metabolite and to quantify its generation in the presence of different substrates. In order to gain insight on the mechanism leading to NO production both nitrite-dependent and arg-dependent pathways were evaluated. In the presence of 1 mM arg and 0.1 mM NADPH the rate of NO generation was 0.76 ± 0.04 nmol min⁻¹ mg⁻¹ prot (arg-dependent synthesis). The synthesis of NO resulted completely blocked in the presence of arg analogs (L-NAME and L-NNA). It is important to point out that the content of arg in the chloroplasts stroma is high as compared to the content of other amino acids (56.7 ± 0.8 nmol mg⁻¹ prot), suggesting that this pathway could be operative under physiological conditions.Soybean chloroplasts showed a NO production of 3.2 ± 0.2 nmol min⁻¹ mg⁻¹ prot in the presence of 1 mM NaNO₂. Furthermore, NO generation was detected in the presence of nitrite concentrations as low as 25 mM. Since nitrite-dependent NO generation resulted inhibited by 50% by the addition of DCMU, and no NO generation was measured in the stroma fraction, thylakoidal electron transport seems to be a key feature in NO synthesis.According to this scenario and assuming that the two independent pathways for NO generation in chloroplasts are operative, the total rate of production of NO could be understood as the generation by the activity of an arg-dependent enzyme and by a NO₂⁻ dependent pathway, as indicated by eq. 1.d[NO]dt=(d[NO]dt)NOS like+(d[NO]dt)NO2−(1)Regarding the NO disappearance, from a kinetic point of view, the rate of the reaction of NO with O₂⁻ to generate peroxynitrite seems to be the main pathway, since the reaction is diffusionally controlled. Thus, the rate of disappearance of NO could be estimated from the rate of generation of ONOO⁻ (eq. 2); however, other reactions should be considered under nonphysiological conditions.−d[NO]dt=d⌊ONOO−⌋dt=k[NO][O2−](2)NO generation rate should be equal to NO consumption rate in order to keep a physiological NO steady state concentration (eq. 3)d[NO]dt=−d[NO]dt(3)Thus, replacing NO generation and disappearance rates by those rates indicated in equations 1 and 2, (d[NO]dt)NOS like+(d[NO]dt)NO2−=k[NO][O2−](4) The data obtained under unrestricted availability of substrates, indicate a generation rate of NO by the activity of a NOS-like enzyme of 13 × 10⁻⁹ M s⁻¹. Chloroplastic NO generation rate in the presence of 100 µM NO₂⁻ was 14 × 10⁻⁹ M s⁻¹. Thus, according to equation 1, the rate of generation of NO is approximately 3 × 10⁻⁸ M s⁻¹. Assuming a steady state concentration for O₂⁻ of 1 nM in chloroplasts¹⁰ and a rate constant (k) of 6.9 × 10⁹ M⁻¹ s⁻¹ for the reaction between O₂⁻ and NO,¹¹ a steady state concentration of 4 nM for NO in the chloroplast could be estimated. Since under in vivo conditions chloroplasts may content the required substrates for the NO synthesis, the assays presented here strongly suggest that a feasible NO production could take place inside the chloroplasts. However, nonsupplemented chloroplasts did not show any NO-dependent EPR signal. This observation agrees with the fact that NO steady state concentration under physiological conditions as was calculated here (4 nM) is below the EPR detection limit (500 nM).¹²Further studies should be performed to characterize NO oxidative effects on chloroplasts. Scavenging of O₂⁻ and H₂O₂ is essential for chloroplasts to maintain their ability to fix CO₂ since several enzymes in the CO₂-reduction cycle are sensitive to active oxygen species.¹³ These organelles lacking catalase, contain a significant peroxidase activity.¹⁴ H₂O₂-reduction catalized by ascorbate peroxidase (AP) lead to ascorbate oxidation and produces ascorbyl radical (A^.).¹⁵ In isolated chloroplast the content of A^.. was evaluated in DMSO based extract by EPR¹⁶. Quantification of EPR signals indicated that A^. content in control chloroplasts (123 ± 5 pmol mg⁻¹ prot) decreased after exposure to NO (Fig. 1). The total content of ascorbate, assessed by an HPLC technique¹⁷ in chloroplasts isolated from soybean leaves exposed to NO was not significantly different from the measured content in chloroplasts not exposed to the NO donor (Fig. 1). The activity of AP was significantly decreased by 48, 53 and 54% after exposure of the chloroplasts to NO-donor. Previous data suggested that AP could be inactivated by NO via oxidation of functional thiols.¹⁸ Besides, the reversible inhibition of AP could be due to the formation of Fe-nitrosyl complexes between NO and the Fe atom of the heme group, as it was previously described for NO-mediated activation of guanylate cyclase and the inhibition of cytochrome P₄₅₀ and catalase in mammals.¹⁹ The data presented here showed that in isolated chloroplasts exposed to a NO donor, there could be either a limited damage associated to the decrease in the content of A^.. or an increased cellular deterioration by the decrease in the activity of the enzyme responsible for the scavenging of H₂O₂.Open in a separate windowFigure 1Ascorbate metabolism in soybean chloroplasts after NO exposure. A^.. content (▪), ascorbate content (▪), and AP activity (*) as a function of the exposure of isolated chloroplasts to GSNO in the presence of 50 µM DTT. * = significantly different at p ≥ 0.05 from the value obtained in the absence of GSNO + 50 µM DTT.Thus, in situ generation of NO could play a protective role in preventing the oxidation of chloroplastic lipids; however, the reaction of NO with O₂⁻ leading to ONOO⁻ production may result in a potential source of damage or as it is shown here by the significant decrease of the AP activity that consumes H₂O₂. NO is a suitable candidate to modulate cellular H₂O₂ level through the chloroplast function, as an initial step to regulate complex metabolic pathways directed to activate physiological responses, defense pathways or deleterious effects in the cytosol. Furthermore, an integrated study on the effect of nitrogen reactive species is required under stress conditions to characterize the metabolic pathways involved in the resulting cellular damage.     

Kinetics and Energetics of Biomolecular Folding and Binding

The ability of biomolecules to fold and to bind to other molecules is fundamental to virtually every living process. Advanced experimental techniques can now reveal how single biomolecules fold or bind against mechanical force, with the force serving as both the regulator and the probe of folding and binding transitions. Here, we present analytical expressions suitable for fitting the major experimental outputs from such experiments to enable their analysis and interpretation. The fit yields the key determinants of the folding and binding processes: the intrinsic on-rate and the location and height of the activation barrier.Dynamic processes in living cells are regulated through conformational changes in biomolecules—their folding into a particular shape or binding to selected partners. The ability of biomolecules to fold and to bind enables them to act as switches, assembly factors, pumps, or force- and displacement-generating motors (1). Folding and binding transitions are often hindered by a free energy barrier. Overcoming the barrier requires energy-demanding rearrangements such as displacing water from the sites of native contacts and breaking nonnative electrostatic contacts, as well as loss of configurational entropy. Once the barrier is crossed, the folded and bound states are stabilized by short-range interactions: hydrogen bonds, favorable hydrophobic effects, and electrostatic and van der Waals attractions (2).Mechanistic information about folding and binding processes is detailed in the folding and binding trajectories of individual molecules: observing an ensemble of molecules may obscure the inherent heterogeneity of these processes. Single-molecule trajectories can be induced, and monitored, by applying force to unfold/unbind a molecule and then relaxing the force until folding or binding is observed (3–5) (Fig. 1). Varying the force relaxation rate shifts the range of forces at which folding or binding occurs, thus broadening the explorable spectrum of molecular responses to force and revealing conformational changes that are otherwise too fast to detect. The measured force-dependent kinetics elucidates the role of force in physiological processes (6) and provides ways to control the timescales, and even the fate, of these processes. The force-dependent data also provides a route to understanding folding and binding in the absence of force—by extrapolating the data to zero force via a fit to a theory.Open in a separate windowFigure 1Schematic of the output from a force-relaxation experiment. The applied force is continuously relaxed from the initial value F₀ until the biomolecule folds or binds, as signified by a sharp increase in the measured force. From multiple repeats of this experiment, distributions of the folding or binding forces are collected (inset). Fitting the force distributions with the derived analytical expression yields the key parameters that determine the kinetics and energetics of folding or binding.In this letter, we derive an analytical expression for the distribution of transition forces, the major output of force-relaxation experiments that probe folding and binding processes. The expression extracts the key determinants of these processes: the on-rate and activation barrier in the absence of force. The theory is first developed in the context of biomolecular folding, and is then extended to cover the binding of a ligand tethered to a receptor. In contrast to unfolding and unbinding, the reverse processes of folding and binding require a theory that accounts for the compliance of the unfolded state, as well as the effect of the tether, to recover the true kinetic parameters of the biomolecule of interest.In a force-relaxation experiment, an unfolded biomolecule or unbound ligand-receptor complex is subject to a stretching force, which is decreased from the initial value F₀ as the pulling device approaches the sample at speed V until a folding or binding transition is observed (Fig. 1) (3–5). Define S(t) as the probability that the molecule has not yet escaped from the unfolded (implied: or unbound) state at time t. When escape is limited by one dominant barrier, S(t) follows the first-order rate equationS˙(t)≡dS(t)dt=−k←(F(t))S(t),where k_←(F(t)) is the on-rate at force F at time t. Because, prior to the transition, the applied force decreases monotonically with time, the distribution of transition forces, p(F), is related to S(t) through p(F)dF=S˙(t)dt, yieldingp(F)=−k←(F)F˙(F)e−∫F0Fk←(F′)F˙(F′)dF′.(1)Here F˙(F)≡dF(t)/dt<0 is the force relaxation rate. The proper normalization of p(F) is readily confirmed by integrating Eq. 1 from the initial force F₀ to negative infinity, the latter accounting for transitions that do not occur by the end of the experiment. Note that the expression for the distribution of folding/binding forces in Eq. 1 differs from its analog for the unfolding process (7) by the limits of integration and a negative sign, reflecting the property of a relaxation experiment to decrease the survival probability S(t) by decreasing the force. Converting the formal expression in Eq. 1 into a form suitable for fitting experimental data requires establishing functional forms for k_←(F) and F˙(F) and analytically solving the integral. These steps are accomplished below.The on-rate k_←(F) is computed by treating the conformational dynamics of the molecule as a random walk on the combined free energy profile G(x,t) = G₀(x) + G_pull(x,t) along the molecular extension x. Here G₀(x) is the intrinsic molecular potential and G_pull(x,t) is the potential of the pulling device. When G(x,t) features a high barrier on the scale of k_BT (k_B is the Boltzmann constant and T the temperature), the dynamics can be treated as diffusive. The unfolded region of the intrinsic potential for a folding process, unlike that for a barrierless process (8), can be captured by the functionG0(x)=ΔG‡ν1−ν(xx‡)11−ν−ΔG‡ν(xx‡),which has a sharp (if ν = 1/2, Fig. 2, inset) or smooth (if ν = 2/3) barrier of height ΔG^‡ and location x^‡. The potential of a pulling device of stiffness κ_S is G_pull(x,t) = κ_S/2(X₀ – Vt – x)² with an initial minimum at X₀ (corresponding to F₀). Applying Kramers formalism (9) to the combined potential G(x,t), we establish the analytical form of the on-rate at force F(t),k←(F)=k0(1+κSκU(F))1ν−12(1+νFx‡ΔG‡)1ν−1×eβΔG‡[1−(1+κSκU(F))2ν1−ν−1(1+νFx‡ΔG‡)1ν],where k₀ is the intrinsic on-rate, β ≡ (k_BT)⁻¹, andκU(F)=ν(1−ν)2ΔG‡x‡2(1+νFx‡ΔG‡)2−1νis the stiffness of the unfolded biomolecule under force F (see the Supporting Material for details on all derivations). The full nonlinear form of G_pull(x,t) was necessary in the derivation because, in contrast to the typically stiff folded state, the unfolded state may be soft (to be exact, 1/2κ_S x^‡2(F) << k_BT may not be satisfied) and thus easily deformed by the pulling device. Because of this deformation, the folding transition faces an extra contribution (regulated by the ratio κ_S/κ_U(F)) to the barrier height, typically negligible for unfolding, that decreases the on-rate in addition to the applied force F.Open in a separate windowFigure 2Contributions to the free energy profile for folding (inset) and binding (main figure). The derived expression (Eq. 2) extracts the on-rate and the location and height of the activation barrier to folding. When applied to binding data, the expression extracts the parameters of the ligand-tether-receptor (LTR) potential G˜0 (x); the proposed algorithm (Eqs. 3 and 4) removes the contribution of the tether potential G_teth(x) to recover the parameters of the intrinsic ligand-receptor (LR) potential G₀(x).The last piece required for Eq. 1, the loading rate F˙(F), is computed as the time derivative of the force F(t) on the unfolded molecule at its most probable extension at time t:F˙(F)=−κSV1+κS/κU(F).Finally, we realize that the integral in Eq. 1 can be solved analytically exactly, both for ν = 1/2 and ν = 2/3, resulting in the analytical expression for the distribution of folding forces:p(F)=k←(F)|F˙(F)|e−k←(F)β|F˙(F)|x‡(1+κSκU(F))νν−1(1+νFx‡ΔG‡)1−1ν.(2)Equation 2 can be readily applied to (normalized) histograms from force-relaxation experiments to extract the parameters of the intrinsic kinetics and energetics of folding. Being exact for ν = 1/2 and ν = 2/3, Eq. 2 is also an accurate approximation for any ν in the interval 1/2 < ν < 2/3 as long as κ_S ≲ κ_U (F) (see Fig. S1 in the Supporting Material). For simplicity, in Eq. 2 we have omitted the term containing F₀ as negligible if F₀ is large enough to prevent folding events.The solution in Eq. 2 reveals properties of the distribution of folding forces that distinguish it from its unfolding counterpart (7):

• 1.The distribution has a positive skew (Fig. 3), as intuitively expected: the rare folding events occur at high forces when the barrier is still high.Open in a separate windowFigure 3Force histograms from folding (left) and binding (right) simulations at several values of the force-relaxation speed (in nanometers per second, indicated at each histogram). Fitting the histograms with the analytical expression in Eq. 2 (lines) recovers the on-rate and activation barrier for folding or binding (2.Increasing the relaxation speed shifts the distribution to lower forces (Fig. 3): faster force relaxation leaves less time for thermal fluctuations to push the system over a high barrier, causing transitions to occur later (i.e., at lower forces), when the barrier is lower.
• 3.The stiffness κ_S and speed V enter Eq. 2 separately, providing independent routes to control the range of folding forces and thus enhance the robustness of a fit.

The application of the above framework to binding experiments on a ligand and receptor connected by a tether (3) involves an additional step—decoupling the effect of the tether—to reconstruct the parameters of ligand-receptor binding. Indeed, the parameters extracted from a fit of experimental histograms to Eq. 2 characterize the ligand-tether-receptor (LTR) potential (k˜0, x˜‡, ΔG˜‡, ν) (Fig. 2). The parameters of the natural ligand-receptor (LR) potential (k₀, x^‡, ΔG^‡) can be recovered using three characteristics of the tether: contour length L; persistence length p; and extension Δℓ of the tether along the direction of the force in the LTR transition state. The values of L and p can be determined from the force-extension curve of the tether (10); these define the tether potential G_teth(x) (Fig. 2). The value of Δℓ can be found from an unbinding experiment (7) on LTR and the geometry of the tether attachment points (see Fig. S3). Approximating the region of the LR potential between the transition and unbound states as harmonic, with no assumptions about the shape of the potential beyond x^‡, the ligand-receptor barrier parameters are thenx‡=α−1α−2x˜‡,ΔG‡=(α−1)22(α−2)x˜‡Fteth(Δℓ+x˜‡),(3)and the intrinsic unimolecular association rate isk0≈k˜0(βΔG‡)32(βΔG˜‡)1ν−12(x˜‡x‡)2eβ(ΔG˜‡−ΔG‡).(4)Here, the force value Fteth(Δℓ+x˜‡) is extracted from the force-extension curve of the tether at extension Δℓ+x˜‡ andα=2(ΔG˜‡−Gteth(Δℓ)+Gteth(Δℓ+x˜‡))x˜‡Fteth(Δℓ+x˜‡),where G_teth(x) is the wormlike-chain potential (see Eq. S13 in the Supporting Material). Equations 3–4 confirm that a tether decreases the height and width of the barrier (see Fig. 2), thus increasing the on-rate.In Fig. 3, the developed analytical framework is applied to folding and binding force histograms from Brownian dynamics simulations at parameters similar to those in the analogous experimental and computational studies (3,5,11) (for details on simulations and fitting procedure, see the Supporting Material). For the stringency of the test, the simulations account for the wormlike-chain nature of the molecular unfolded and LTR unbound states that is not explicitly accounted for in the theory. With optimized binning (12) of the histograms and a least-squares fit, Eqs. 2–4 recover the on-rate, the location and the height of the activation barrier, and the value of ν that best captures how the kinetics scale with force (

1.Multiple relaxation speeds,

2.Folding/binding events at low forces, and

3.A large number of events at each speed.

Table 1

On-rate and the location and height of the activation barrier from the fit of simulated data to the theory in Eq. 2

Introducing Titratable Water to All-Atom Molecular Dynamics at Constant?pH

Recent development of titratable coions has paved the way for realizing all-atom molecular dynamics at constant pH. To further improve physical realism, here we describe a technique in which proton titration of the solute is directly coupled to the interconversion between water and hydroxide or hydronium. We test the new method in replica-exchange continuous constant pH molecular dynamics simulations of three proteins, HP36, BBL, and HEWL. The calculated pK_a values based on 10-ns sampling per replica have the average absolute and root-mean-square errors of 0.7 and 0.9 pH units, respectively. Introducing titratable water in molecular dynamics offers a means to model proton exchange between solute and solvent, thus opening a door to gaining new insights into the intricate details of biological phenomena involving proton translocation.Solution pH is an important factor in biology. Although neutral pH in extracellular medium accounts for balanced electrostatics and proper folding of protein structures, pH gradients across cell membranes induce large conformational changes that are necessary for biological functions, such as ATP synthesis and efflux of small molecules out of the cell. To gain detailed insights into pH-dependent conformational phenomena, several constant pH molecular dynamics (pHMD) methods, based on either discrete or continuous titration coordinates, have been developed in the last decade (1–4). In the continuous pHMD (CpHMD) framework (2,4), a set of titration coordinates {λ_i} are simultaneously propagated along with the conformational degrees of freedom. Although the original CpHMD method based on the generalized Born (GB) implicit-solvent models (2,4) offers quantitative prediction of pK_a values and pH dependence of folding and conformational dynamics of proteins (5), its accuracy and applicability to highly charged systems and those with dominantly hydrophobic regions are limited due to the approximate nature of the underlying implicit-solvent models.Motivated by the above-mentioned need, three groups have made efforts to develop a CpHMD method using exclusively the explicit-solvent models (6–8). In our development, the titration of acidic and basic sites is coupled with that of coions to level the total charge of the system (8). To further improve physical realism, here we replace the coions by titratable water molecules, which not only absorb the excess charge but also enable direct modeling of solute-solvent proton exchange in classical molecular dynamics simulations.To illustrate the utility of the new methodology, we applied it to the titration simulations of three proteins that were previously used to benchmark the GB-based CpHMD. Although this work does not explore specific interactions between titratable waters and proteins, the methodology can be further tested or improved to provide a rigorous way for modeling proton transfer in molecular dynamics, which is a computationally efficient alternative to the empirical valence-bond theory-based methodologies (9,10).We define titration of water as:

• 1.Loss of a proton to give a negatively charged hydroxide,

H₂O ? OH^? + H⁺, (1)or

• 2.Gain of a proton to give a positively charged hydronium,

H₂O + H⁺ ? H₃O⁺.(2)We now couple the titration of hydroxide (Eq. 1) with that of an acidic site of the solute in the CpHMD simulation,HA+OH−⇌KaA−+H2O.(3)The use of hydronium is avoided here to prevent a potential artifact due to prolonged attraction with A⁻. Analogously, we couple the titration of hydronium (Eq. 2) with that of a basic site,BH++H2O⇌KbH3O++B.(4)Thus, effectively, a proton is transferred between the solute and solvent. However, we should note that in CpHMD simulations, titratable protons are represented by covalently attached dummies (2,4). Through varying the atomic charges and van der Waals interactions, they are seen by other atoms in the protonated state but not in the unprotonated state (see Table S1 in the Supporting Material). Furthermore, the solution proton concentration is implicitly modeled through a free energy term (2,4).In CpHMD, the reference potential of mean force (PMF) for titration is that of the model compound (blocked single amino acid in water) along λ (2,4). In the presence of cotitrating water molecules, it is necessary to add the PMF for the conversion of water to hydroxide or hydronium. One-nanosecond NPT simulations at ambient pressure and temperature were performed to calculate the average force, 〈dU/d,θ〉 at given θ-values, which are related to λ by λ = sin² θ (see Fig. S1 in the Supporting Material). Thermodynamic integration was then applied to calculate the PMF. We found that the average force can be accurately fit when assuming the PMF is quadratic in λ (Fig. 1). The same applies to the PMFs for titration of models Asp, Glu, and His. After testing on the titration of model compounds (see Table S2), we performed 10-ns all-atom CpHMD simulations with the pH replica-exchange protocol for three proteins: HP36, BBL and HEWL (see the Supporting Material for details). Most of the calculated pK_a values were converged in 10 ns per replica (see Fig. S3). Results are summarized in Fig. S4. Based on the 10-ns data, the root-mean-square (RMS) and average absolute errors are 0.9 and 0.7 pH units, respectively, while the largest absolute error is 2.5 (Glu³⁵ of HEWL). Linear regression of the calculation versus experiment gives R² of 0.8 and slope of 1.2.Open in a separate windowFigure 1Average force and potential of mean force for converting a water molecule to hydroxide (A) and hydronium. (B) (Data points) Average forces. (Dashed curves) Best fits using a linear function, 2A(λ − B). (Solid curves) Corresponding potential of mean force.

Table 1

Calculated and experimental pK_a values of three proteins

On the Adjacent Eccentric Distance Sum Index of Graphs

For a given graph G, ε(v) and deg(v) denote the eccentricity and the degree of the vertex v in G, respectively. The adjacent eccentric distance sum index of a graph G is defined as ξsv(G)=∑v∈V(G)ε(v)D(v)deg(v), where D(v)=∑u∈V(G)d(u,v) is the sum of all distances from the vertex v. In this paper we derive some bounds for the adjacent eccentric distance sum index in terms of some graph parameters, such as independence number, covering number, vertex connectivity, chromatic number, diameter and some other graph topological indices.     

Kinetics of H2O2-driven catalysis by a lytic polysaccharide monooxygenase from the fungus Trichoderma reesei

Owing to their ability to break glycosidic bonds in recalcitrant crystalline polysaccharides such as cellulose, the catalysis effected by lytic polysaccharide monooxygenases (LPMOs) is of major interest. Kinetics of these reductant-dependent, monocopper enzymes is complicated by the insoluble nature of the cellulose substrate and parallel, enzyme-dependent, and enzyme-independent side reactions between the reductant and oxygen-containing cosubstrates. Here, we provide kinetic characterization of cellulose peroxygenase (oxidative cleavage of glycosidic bonds in cellulose) and reductant peroxidase (oxidation of the reductant) activities of the LPMO TrAA9A of the cellulose-degrading model fungus Trichoderma reesei. The catalytic efficiency (kcat/Km(H2O2)) of the cellulose peroxygenase reaction (k_cat = 8.5 s⁻¹, and Km(H2O2)=30μM) was an order of magnitude higher than that of the reductant (ascorbic acid) peroxidase reaction. The turnover of H₂O₂ in the ascorbic acid peroxidase reaction followed the ping-pong mechanism and led to irreversible inactivation of the enzyme with a probability of 0.0072. Using theoretical analysis, we suggest a relationship between the half-life of LPMO, the values of kinetic parameters, and the concentrations of the reactants.     

Widespread Distribution of Cell Defense against d-Aminoacyl-tRNAs

Several l-aminoacyl-tRNA synthetases can transfer a d-amino acid onto their cognate tRNA(s). This harmful reaction is counteracted by the enzyme d-aminoacyl-tRNA deacylase. Two distinct deacylases were already identified in bacteria (DTD1) and in archaea (DTD2), respectively. Evidence was given that DTD1 homologs also exist in nearly all eukaryotes, whereas DTD2 homologs occur in plants. On the other hand, several bacteria, including most cyanobacteria, lack genes encoding a DTD1 homolog. Here we show that Synechocystis sp. PCC6803 produces a third type of deacylase (DTD3). Inactivation of the corresponding gene (dtd3) renders the growth of Synechocystis sp. hypersensitive to the presence of d-tyrosine. Based on the available genomes, DTD3-like proteins are predicted to occur in all cyanobacteria. Moreover, one or several dtd3-like genes can be recognized in all cellular types, arguing in favor of the nearubiquity of an enzymatic function involved in the defense of translational systems against invasion by d-amino acids.Although they are detected in various living organisms (reviewed in Ref. 1), d-amino acids are thought not to be incorporated into proteins, because of the stereospecificity of aminoacyl-tRNA synthetases and of the translational machinery, including EF-Tu and the ribosome (2). However, the discrimination between l- and d-amino acids by aminoacyl-tRNA synthetases is not equal to 100%. Significant d-aminoacylation of their cognate tRNAs by Escherichia coli tyrosyl-, tryptophanyl-, aspartyl-, lysyl-, and histidyl-tRNA synthetases has been characterized in vitro (3–9). Recently, using a bacterium, transfer of d-tyrosine onto tRNA^Tyr was shown to occur in vivo (10).With such misacylation reactions, the resulting d-aminoacyl-tRNAs form a pool of metabolically inactive molecules, at best. At worst, d-aminoacylated tRNAs infiltrate the protein synthesis machinery. Although the latter harmful possibility has not yet been firmly established, several cells were shown to possess a d-tyrosyl-tRNA deacylase, or DTD, that should help them counteract the accumulation of d-aminoacyl-tRNAs. This enzyme shows a broad specificity, being able to remove various d-aminoacyl moieties from the 3′-end of a tRNA (4–6, 11). Such a function makes the deacylase a member of the family of enzymes capable of editing in trans mis-aminoacylated tRNAs. This family includes several homologs of aminoacyl-tRNA synthetase editing domains (12), as well as peptidyl-tRNA hydrolase (13, 14).Two distinct deacylases have already been discovered. The first one, called DTD1, is predicted to occur in most bacteria and eukaryotes (see d-amino acids, including d-tyrosine (6). In fact, in an E. coli Δdtd strain grown in the presence of 2.4 mm d-tyrosine, as much as 40% of the cellular tRNA^Tyr pool becomes esterified with d-tyrosine (10).

TABLE 1

Distribution of DTD1 and DTD2 homologs in various phylogenetic groupsHomologs of DTD1 and DTD2 were searched for using a genomic Blast analysis against complete genomes in the NCBI Database (www.ncbi.nlm.nih.gov). Values in the table are number of species. For instance, E. coli is counted only once in γ-proteobacteria despite the fact that several E. coli strains have been sequenced.

Detection and Identification of tdh- and trh-Positive Vibrio parahaemolyticus Strains from Four Species of Cultured Bivalve Molluscs on the Spanish Mediterranean Coast

Presented here is the first report describing the detection of potentially diarrheal Vibrio parahaemolyticus strains isolated from cultured bivalves on the Mediterranean coast, providing data on the presence of both tdh- and trh-positive isolates. Potentially diarrheal V. parahaemolyticus strains were isolated from four species of bivalves collected from both bays of the Ebro delta, Spain.Gastroenteritis caused by Vibrio parahaemolyticus has been reported worldwide, though only sporadic cases have been reported in Europe (7, 14). The bacterium can be naturally present in seafood, but pathogenic isolates capable of inducing gastroenteritis in humans are rare in environmental samples (2 to 3%) (15) and are often not detected (10, 19, 20).The virulence of V. parahaemolyticus is based on the presence of a thermostable direct hemolysin (tdh) and/or the thermostable direct hemolysin-related gene (trh) (1, 5). Both are associated with gastrointestinal illnesses (2, 9).Spain is not only the second-largest producer in the world of live bivalve molluscs but also one of the largest consumers of bivalve molluscs, and Catalonia is the second-most important bivalve producer of the Spanish Autonomous Regions. Currently, the cultivation of bivalves in this area is concentrated in the delta region of the Ebro River. The risk of potentially pathogenic Vibrio spp. in products placed on the market is not assessed by existing legislative indices of food safety in the European Union, which emphasizes the need for a better knowledge of the prevalence of diarrheal vibrios in seafood products. The aim of this study was to investigate the distribution and pathogenic potential of V. parahaemolyticus in bivalve species exploited in the bays of the Ebro delta.Thirty animals of each species of Mytilus galloprovincialis, Crassostrea gigas, Ruditapes decussatus, and Ruditapes philippinarum were collected. They were sampled from six sites of the culture area, three in each bay of the Ebro River delta, at the beginning (40°37′112"N, 0°37′092"E [Alfacs]; 40°46′723"N, 0°43′943"E [Fangar]), middle (40°37′125"N, 0°38′570"E [Alfacs]; 40°46′666"N, 0°45′855"E [Fangar]), and end (40°37′309"N, 0°39′934"E [Alfacs]; 40°46′338"N, 0°44′941"E [Fangar]) of the culture polygon. Clams were sampled from only one site per bay as follows: in the Alfacs Bay from a natural bed of R. decussatus (40°37′44"N, 0°38′0"E) and in the Fangar Bay from an aquaculture bed of R. philippinarum (40°47′3"N, 0°43′8"E). In total, 367 samples were analyzed in 2006 (180 oysters, 127 mussels, 30 carpet shell clams, and 30 Manila clams) and 417 samples were analyzed in 2008 (178 oysters, 179 mussels, 30 carpet shell clams, and 30 Manila clams).All animals were individually processed and homogenized, and 1 ml of the homogenate was inoculated into 9 ml of alkaline peptone water (Scharlau, Spain). Following a 6-h incubation at 37°C, one loopful of the contents of each tube of alkaline peptone water was streaked onto CHROMagar vibrio plates (CHROMagar, France) and incubated for 18 h at 37°C. Mauve-purple colonies were purified, and each purified isolate was cryopreserved at −80°C (135 isolates in 2006 and 96 in 2008). From the initial homogenate portion, 100 μl was inoculated onto marine agar (Scharlau, Spain) and onto thiosulfate citrate-bile salts-sucrose agar (Scharlau, Spain) for total heterotrophic marine bacteria counts and total vibrio counts, respectively (Table ​(Table11).

TABLE 1.

Vibrio parahaemolyticus isolates, serotypes, and origins and total number of vibrios/heterotrophic bacteria contained in the bivalve^a

Analysis of Amylose-borate Interaction by Frontal Gel Filtration

Amylose-borate interaction has been analyzed by frontal gel chromatography, using the constituent velocity data alone. The constituent Velocity equation was reformulated in terms of elution volume for a type of interacting system described by$\text{A}+i\text{B}={\text{AB}}_i(i=1,2,3\dots n)$

Detailed examination of the binding data indicates that, in the complex formation between amylose and borate, this type of equilibria operates predominantly, if not solely. Use of the constituent elution volume equation enabled us, for the first time, to evaluate the association constant (K) and number of binding site pertaining to this system, i.e., K = 4.9 10² and n = 1. There was no evidence indicating the occurrence of the formation of inclusion complex.     

The effect of encapsulated fine grain sediment and test morphology on the resistance of planktonic foraminifera to dissolution

The tests of planktonic foraminifera recovered from deep-sea sediment are commonly observed to encapsulate fine grain carbonate sediment within their chambers. In sediment below the lysocline, the interstitial water within the chambers may not be as corrosive as the seawater in contact with the outer surface of the test due to slow continuous dissolution of the encapsulated carbonate. As a consequence, the pore walls of the foram dissolve more slowly than the outer surface of the test.Using published dissolution rate measurements for foraminifera and deep-sea sediment, the effect of diffusional reduction of pore wall dissolution was quantitatively estimated with a one-dimensional model for the steady state condition where diffusional flux out of the foraminifer's pores is balanced by the dissolution flux from the encapsulated fines and pore walls. The diffusional effect is found to principally depend on the structural parametric ratioT_wd_T/f, whereT_w is the wall thickness,d_T the test diameter andf is the test porosity. In the case of adult planktonic foraminifera, the ratio of the pore wall to outer surface dissolution flux is predicted to vary between 60% for the thin-walled porous species and 30% for thick-walled tests.Incorporation of the predicted pore to outer surface flux ratios into the morphologic index equation of Adelseck (1978) results in a very good prediction (73% of the variation) of the solution index of Berger (1975) obtained from ranking species counts from core tops. A simple empirical equation which may be useful for prediction of the resistance of extinct microfossils was found as follows:

$R=\frac{T_w}{\lambda \{1+[A_w/(A_s?A_p)][.74?.27log?(T_w{d}_T/f)]\}}$

is the measured ratio of pore wall area to outer surface of the test, andT_wd_T/f is in units of 10⁴ μm².     

Use of methotrexate therapy is not associated with decreased prevalence of metabolic syndrome

With great interest, we read the article by Toms and colleagues [1] in the previous issue of Arthritis Research & Therapy, in which they assessed prevalences of metabolic syndrome (MetS) in rheumatoid arthritis (RA) patients. Moreover, they identified demographic and clinical factors that may be associated with MetS. Toms and colleagues found prevalences of up to 45% of MetS and demonstrated older age and health status (health assessment questionnaire) to be associated with MetS irrespectively of the definition used. Of most interest, an association between methotrexate (MTX) use and decreased presence of MetS was observed in patients more than 60 years of age. The investigators hypothesized that this may be attributed to a drug-specific effect (and not to an anti-inflammatory effect) either by changing levels of adenosine, which is known to interact with glucose and lipid metabolism, or by an indirect effect mediated through concomitant folic acid administration, thereby decreasing homocysteine levels.Recently, we also examined the prevalence of MetS in (a subgroup of) RA patients in the CARRÉ investigation, a prospective cohort study on prevalent and incident cardiovascular disease and its underlying cardiovascular risk factors [2]. The findings of Toms and colleagues stimulated us to perform additional analyses in our total study population (n = 353).The prevalences of MetS were 35% and 25% (Table ​(Table1)1) according to criteria of National Cholesterol Education Program (NCEP) 2004 and NCEP 2001, respectively. In multivariate backward regression analyses, we found significant associations between body mass index, pulse rate, creatinine levels, hypothyroidism and diabetes mellitus and the presence of MetS independently of the criteria used (Table ​(Table2).2). However, an independent association between single use of MTX or use of MTX in combination with other disease-modifying antirheumatic drugs, on the one hand, and a decreased prevalence of MetS, on the other hand, could not be demonstrated (even in the subgroup of patients over the age of 60).

Table 1

Characteristics of the study population

Antimicrobial Activity of Simulated Solar Disinfection against Bacterial,Fungal, and Protozoan Pathogens and Its Enhancement by Riboflavin

Riboflavin significantly enhanced the efficacy of simulated solar disinfection (SODIS) at 150 watts per square meter (W m⁻²) against a variety of microorganisms, including Escherichia coli, Fusarium solani, Candida albicans, and Acanthamoeba polyphaga trophozoites (>3 to 4 log₁₀ after 2 to 6 h; P < 0.001). With A. polyphaga cysts, the kill (3.5 log₁₀ after 6 h) was obtained only in the presence of riboflavin and 250 W m⁻² irradiance.Solar disinfection (SODIS) is an established and proven technique for the generation of safer drinking water (11). Water is collected into transparent plastic polyethylene terephthalate (PET) bottles and placed in direct sunlight for 6 to 8 h prior to consumption (14). The application of SODIS has been shown to be a simple and cost-effective method for reducing the incidence of gastrointestinal infection in communities where potable water is not available (2-4). Under laboratory conditions using simulated sunlight, SODIS has been shown to inactivate pathogenic bacteria, fungi, viruses, and protozoa (6, 12, 15). Although SODIS is not fully understood, it is believed to achieve microbial killing through a combination of DNA-damaging effects of ultraviolet (UV) radiation and thermal inactivation from solar heating (21).The combination of UVA radiation and riboflavin (vitamin B₂) has recently been reported to have therapeutic application in the treatment of bacterial and fungal ocular pathogens (13, 17) and has also been proposed as a method for decontaminating donor blood products prior to transfusion (1). In the present study, we report that the addition of riboflavin significantly enhances the disinfectant efficacy of simulated SODIS against bacterial, fungal, and protozoan pathogens.Chemicals and media were obtained from Sigma (Dorset, United Kingdom), Oxoid (Basingstoke, United Kingdom), and BD (Oxford, United Kingdom). Pseudomonas aeruginosa (ATCC 9027), Staphylococcus aureus (ATCC 6538), Bacillus subtilis (ATCC 6633), Candida albicans (ATCC 10231), and Fusarium solani (ATCC 36031) were obtained from ATCC (through LGC Standards, United Kingdom). Escherichia coli (JM101) was obtained in house, and the Legionella pneumophila strain used was a recent environmental isolate.B. subtilis spores were produced from culture on a previously published defined sporulation medium (19). L. pneumophila was grown on buffered charcoal-yeast extract agar (5). All other bacteria were cultured on tryptone soy agar, and C. albicans was cultured on Sabouraud dextrose agar as described previously (9). Fusarium solani was cultured on potato dextrose agar, and conidia were prepared as reported previously (7). Acanthamoeba polyphaga (Ros) was isolated from an unpublished keratitis case at Moorfields Eye Hospital, London, United Kingdom, in 1991. Trophozoites were maintained and cysts prepared as described previously (8, 18).Assays were conducted in transparent 12-well tissue culture microtiter plates with UV-transparent lids (Helena Biosciences, United Kingdom). Test organisms (1 × 10⁶/ml) were suspended in 3 ml of one-quarter-strength Ringer''s solution or natural freshwater (as pretreated water from a reservoir in United Kingdom) with or without riboflavin (250 μM). The plates were exposed to simulated sunlight at an optical output irradiance of 150 watts per square meter (W m⁻²) delivered from an HPR125 W quartz mercury arc lamp (Philips, Guildford, United Kingdom). Optical irradiances were measured using a calibrated broadband optical power meter (Melles Griot, Netherlands). Test plates were maintained at 30°C by partial submersion in a water bath.At timed intervals for bacteria and fungi, the aliquots were plated out by using a WASP spiral plater and colonies subsequently counted by using a ProtoCOL automated colony counter (Don Whitley, West Yorkshire, United Kingdom). Acanthamoeba trophozoite and cyst viabilities were determined as described previously (6). Statistical analysis was performed using a one-way analysis of variance (ANOVA) of data from triplicate experiments via the InStat statistical software package (GraphPad, La Jolla, CA).The efficacies of simulated sunlight at an optical output irradiance of 150 W m⁻² alone (SODIS) and in the presence of 250 μM riboflavin (SODIS-R) against the test organisms are shown in Table ​Table1.1. With the exception of B. subtilis spores and A. polyphaga cysts, SODIS-R resulted in a significant increase in microbial killing compared to SODIS alone (P < 0.001). In most instances, SODIS-R achieved total inactivation by 2 h, compared to 6 h for SODIS alone (Table ​(Table1).1). For F. solani, C. albicans, ands A. polyphaga trophozoites, only SODIS-R achieved a complete organism kill after 4 to 6 h (P < 0.001). All control experiments in which the experiments were protected from the light source showed no reduction in organism viability over the time course (results not shown).

TABLE 1.

Efficacies of simulated SODIS for 6 h alone and with 250 μM riboflavin (SODIS-R)

Physicochemical Properties and Amino Acid Composition of Alkaline Proteinase from Aspergillus sojae

Some physicochemical properties and amino acid composition of alkaline proteinase from Aspergillus sojae were found to be as follows: The isoelectric point was at pH 5.1. The molecular weight was 25,500 using the Sheraga-Mandelkern’s formula, based upon the values of the sedimentation coefficient $(s_{20,w}^{°}=\text{?}2.82\text{?}\text{S})$, the intrinsic viscosity ([η] = 0.027 dl/g), and the partial specific volume $({\displaystyle \overline{V}}\text{?}=\text{?}0.726\text{?}\text{ml}/\text{g})$. The enzyme contains 16.8% of nitrogen and is composed of 250 residues of amino acid; Asp₃₁ Glu₁₉, Gly₂₇, Ala₃₂, Val₁₈, Leu₁₄, Ile₁₄, Ser₂₈, Thr₁₈, ${\text{(}{\displaystyle \stackrel{}{{\displaystyle \stackrel{?}{\text{Cys C}}}}}\text{ys})}_1$, Met₂, Pro₆, Phe₇, Tyr₈, Trp₂, His₅, Lys₁₄, Arg₃, (amide-NH₃)₂₀.     

Deactivation of a Negative Regulator: A Distinct Signal Transduction Mechanism,Pronounced in Akt Signaling

Kinase cascades, in which enzymes are sequentially activated by phosphorylation, are quintessential signaling pathways. Signal transduction is not always achieved by direct activation, however. Often, kinases activate pathways by deactivation of a negative regulator; this indirect mechanism, pervasive in Akt signaling, has yet to be systematically explored. Here, we show that the indirect mechanism has properties that are distinct from direct activation. With comparable parameters, the indirect mechanism yields a broader range of sensitivity to the input, beyond saturation of regulator phosphorylation, and kinetics that become progressively slower, not faster, with increasing input strength. These properties can be integrated in network motifs to produce desired responses, as in the case of feedforward loops.Phosphorylation of proteins and lipids, catalyzed by specific kinase enzymes, is ubiquitous in intracellular signal transduction. A classic example in eukaryotes is the canonical structure of the mitogen-activated protein kinase cascades, in which three kinases are sequentially activated by phosphorylation (1). Another example is the PI3K (phosphoinositide 3-kinase)/Akt pathway, which (like the mammalian mitogen-activated protein kinases) is prominently dysregulated in human cancers (2). Type-I PI3Ks phosphorylate a lipid substrate to produce the lipid second messenger, PIP₃, which recruits the protein kinase Akt and mediates its activation by phosphorylation (3,4). In no small part because of these important pathways, we typically think of phosphorylation as a direct means of activating molecular interactions and reactions in signal transduction. This is not the only way to increase the flux through a signaling pathway, however. Consider signaling downstream of Akt, which phosphorylates a host of protein substrates to affect diverse functions. A survey of the Akt signaling hub shows that many of these reactions result in a decrease, rather than an increase, in activity/function of the substrates (3). And, among those substrates, the four listed in Fig. S1 in the Supporting Material). Whereas negative regulators are appreciated for their roles in feedback adaptation of signaling, the implications of deactivating a negative regulator as an indirect mechanism of pathway activation has yet to be explored.

Table 1

Survey of Akt substrates and downstream signaling