Insights into differential modulation of receptor function by hinge region using novel agonistic lutropin receptor and inverse agonistic thyrotropin receptor antibodies

We report two antibodies, scFv 13B1 and MAb PD1.37, against the hinge regions of LHR and TSHR, respectively, which have similar epitopes but different effects on receptor function. While neither of them affected hormone binding, with marginal effects on hormone response, scFv 13B1 stimulated LHR in a dose-dependent manner, whereas MAb PD1.37 acted as an inverse agonist of TSHR. Moreover, PD1.37 could decrease the basal activity of hinge region CAMs, but had varied effects on those present in ECLs, whereas 13B1 was refractory to any CAMs in LHR. Using truncation mutants and peptide phage display, we compared the differential roles of the hinge region cysteine box-2/3 as well as the exoloops in the activation of these two homologus receptors. (C) 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Statistical properties of entropy-consuming fluctuations in jammed states of laponite suspensions: Fluctuation relations and generalized Gumbel distribution

We report a universal large deviation behavior of spatially averaged global injected power just before the rejuvenation of the jammed state formed by an aging suspension of laponite clay under an applied stress. The probability distribution function (PDF) of these entropy consuming strongly non-Gaussian fluctuations follow an universal large deviation functional form described by the generalized Gumbel (GG) distribution like many other equilibrium and nonequilibrium systems with high degree of correlations but do not obey the Gallavotti-Cohen steady-state fluctuation relation (SSFR). However, far from the unjamming transition (for smaller applied stresses) SSFR is satisfied for both Gaussian as well as non-Gaussian PDF. The observed slow variation of the mean shear rate with system size supports a recent theoretical prediction for observing GG distribution.

Protein-Protein Interactions in Clathrin Vesicular Assembly: Radial Distribution of Evolutionary Constraints in Interfaces

In eukaryotic organisms clathrin-coated vesicles are instrumental in the processes of endocytosis as well as intracellular protein trafficking. Hence, it is important to understand how these vesicles have evolved across eukaryotes, to carry cargo molecules of varied shapes and sizes. The intricate nature and functional diversity of the vesicles are maintained by numerous interacting protein partners of the vesicle system. However, to delineate functionally important residues participating in protein-protein interactions of the assembly is a daunting task as there are no high-resolution structures of the intact assembly available. The two cryoEM structures closely representing intact assembly were determined at very low resolution and provide positions of C alpha atoms alone. In the present study, using the method developed by us earlier, we predict the protein-protein interface residues in clathrin assembly, taking guidance from the available low-resolution structures. The conservation status of these interfaces when investigated across eukaryotes, revealed a radial distribution of evolutionary constraints, i.e., if the members of the clathrin vesicular assembly can be imagined to be arranged in spherical manner, the cargo being at the center and clathrins being at the periphery, the detailed phylogenetic analysis of these members of the assembly indicated high-residue variation in the members of the assembly closer to the cargo while high conservation was noted in clathrins and in other proteins at the periphery of the vesicle. This points to the strategy adopted by the nature to package diverse proteins but transport them through a highly conserved mechanism.

Hydration dynamics of protein molecules in aqueous solution: Unity among diversity

Dielectric dispersion and NMRD experiments have revealed that a significant fraction of water molecules in the hydration shell of various proteins do not exhibit any slowing down of dynamics. This is usually attributed to the presence of the hydrophobic residues (HBR) on the surface, although HBRs alone cannot account for the large amplitude of the fast component. Solvation dynamics experiments and also computer simulation studies, on the other hand, repeatedly observed the presence of a non-negligible slow component. Here we show, by considering three well-known proteins (lysozyme, myoglobin and adelynate kinase), that the fast component arises partly from the response of those water molecules that are hydrogen bonded with the backbone oxygen (BBO) atoms. These are structurally and energetically less stable than those with the side chain oxygen (SCO) atoms. In addition, the electrostatic interaction energy distribution (EIED) of individual water molecules (hydrogen bonded to SCO) with side chain oxygen atoms shows a surprising two peak character with the lower energy peak almost coincident with the energy distribution of water hydrogen bonded to backbone oxygen atoms (BBO). This two peak contribution appears to be quite general as we find it for lysozyme, myoglobin and adenylate kinase (ADK). The sharp peak of EIED at small energy (at less than 2 k(B)T) for the BBO atoms, together with the first peak of EIED of SCO and the HBRs on the protein surface, explain why a large fraction (similar to 80%) of water in the protein hydration layer remains almost as mobile as bulk water Significant slowness arises only from the hydrogen bonds that populate the second peak of EIED at larger energy (at about 4 k(B)T). Thus, if we consider hydrogen bond interaction alone, only 15-20% of water molecules in the protein hydration layer can exhibit slow dynamics, resulting in an average relaxation time of about 5-10 ps. The latter estimate assumes a time constant of 20-100 ps for the slow component. Interestingly, relaxation of water molecules hydrogen bonded to back bone oxygen exhibit an initial component faster than the bulk, suggesting that hydrogen bonding of these water molecules remains frustrated. This explanation of the heterogeneous and non-exponential dynamics of water in the hydration layer is quantitatively consistent with all the available experimental results, and provides unification among diverse features.

Distribution of transverse chain fluctuations in harmonically confined semiflexible polymers

Two different experimental studies of polymer dynamics based on single-molecule fluorescence imaging have recently found evidence of heterogeneities in the widths of the putative tubes that surround filaments of F-actin during their motion in concentrated solution. In one J. Glaser, D. Chakraborty, K. Kroy, I. Lauter, M. Degawa, N. Kirchesner, B. Hoffmann, R. Merkel, and M. Giesen, Phys. Rev. Lett. 105, 037801 (2010)], the observations were explained in terms of the statistics of a worm-like chain confined to a potential determined self-consistently by a binary collision approximation, and in the other B. Wang, J. Guan, S. M. Anthony, S. C. Bae, K. S. Schweizer, and S. Granick, Phys. Rev. Lett. 104, 118301 (2010)], they were explained in terms of the scaling properties of a random fluid of thin rods. In this paper, we show, using an exact path integral calculation, that the distribution of the length-averaged transverse fluctuations of a harmonically confined weakly bendable rod (one possible realization of a semiflexible chain in a tube), is in good qualitative agreement with the experimental data, although it is qualitatively different in analytic structure from the earlier theoretical predictions. We also show that similar path integral techniques can be used to obtain an exact expression for the time correlation function of fluctuations in the tube cross section. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4712306]

The force distribution function of an oscillator model of polymer stretching at constant velocity

Recent simulations of the stretching of tethered biopolymers at a constant speed v (Ponmurugan and Vemparala, 2011 Phys. Rev. E 84 060101(R)) have suggested that for any time t, the distribution of the fluctuating forces f responsible for chain deformation is governed by a relation of the form P(+ f)/ P(- f) = expgamma f], gamma being a coefficient that is solely a function of v and the temperature T. This result, which is reminiscent of the fluctuation theorems applicable to stochastic trajectories involving thermodynamic variables, is derived in this paper from an analytical calculation based on a generalization of Mazonka and Jarzynski's classic model of dragged particle dynamics Mazonka and Jarzynski, 1999 arXiv:cond-\textbackslashmat/9912121v1]. However, the analytical calculations suggest that the result holds only if t >> 1 and the force fluctuations are driven by white rather than colored noise; they further suggest that the coefficient gamma in the purported theorem varies not as v(0.15)T-(0.7), as indicated by the simulations, but as vT(-1).

Linearization and Reconstruction of Non-linear Diffuse Optical Tomographic Image

Diffuse optical tomography (DOT) is one of the ways to probe highly scattering media such as tissue using low-energy near infra-red light (NIR) to reconstruct a map of the optical property distribution. The interaction of the photons in biological tissue is a non-linear process and the phton transport through the tissue is modelled using diffusion theory. The inversion problem is often solved through iterative methods based on nonlinear optimization for the minimization of a data-model misfit function. The solution of the non-linear problem can be improved by modeling and optimizing the cost functional. The cost functional is f(x) = x(T)Ax - b(T)x + c and after minimization, the cost functional reduces to Ax = b. The spatial distribution of optical parameter can be obtained by solving the above equation iteratively for x. As the problem is non-linear, ill-posed and ill-conditioned, there will be an error or correction term for x at each iteration. A linearization strategy is proposed for the solution of the nonlinear ill-posed inverse problem by linear combination of system matrix and error in solution. By propagating the error (e) information (obtained from previous iteration) to the minimization function f(x), we can rewrite the minimization function as f(x; e) = (x + e)(T) A(x + e) - b(T)(x + e) + c. The revised cost functional is f(x; e) = f(x) + e(T)Ae. The self guided spatial weighted prior (e(T)Ae) error (e, error in estimating x) information along the principal nodes facilitates a well resolved dominant solution over the region of interest. The local minimization reduces the spreading of inclusion and removes the side lobes, thereby improving the contrast, localization and resolution of reconstructed image which has not been possible with conventional linear and regularization algorithm.

Molecular Epidemiology of HIV-1 Subtypes in India: Origin and Evolutionary History of the Predominant Subtype C

Background: India has the third largest HIV-1 epidemic with 2.4 million infected individuals. Molecular epidemiological analysis has identified the predominance of HIV-1 subtype C (HIV-1C). However, the previous reports have been limited by sample size, and uneven geographical distribution. The introduction of HIV-1C in India remains uncertain due to this lack of structured studies. To fill the gap, we characterised the distribution pattern of HIV-1 subtypes in India based on data collection from nationwide clinical cohorts between 2007 and 2011. We also reconstructed the time to the most recent common ancestor (tMRCA) of the predominant HIV-1C strains. Methodology/Principal Findings: Blood samples were collected from 168 HIV-1 seropositive subjects from 7 different states. HIV-1 subtypes were determined using two or three genes, gag, pol, and env using several methods. Bayesian coalescent-based approach was used to reconstruct the time of introduction and population growth patterns of the Indian HIV-1C. For the first time, a high prevalence (10%) of unique recombinant forms (BC and A1C) was observed when two or three genes were used instead of one gene (p<0.01; p = 0.02, respectively). The tMRCA of Indian HIV-1C was estimated using the three viral genes, ranged from 1967 (gag) to 1974 (env). Pol-gene analysis was considered to provide the most reliable estimate 1971, (95% CI: 1965-1976)]. The population growth pattern revealed an initial slow growth phase in the mid-1970s, an exponential phase through the 1980s, and a stationary phase since the early 1990s. Conclusions/Significance: The Indian HIV-1C epidemic originated around 40 years ago from a single or few genetically related African lineages, and since then largely evolved independently. The effective population size in the country has been broadly stable since the 1990s. The evolving viral epidemic, as indicated by the increase of recombinant strains, warrants a need for continued molecular surveillance to guide efficient disease intervention strategies.

Bimodal distribution of motility and cell fate in Dictyostelium discoideum

Pre-starvation amoebae of Dictyostelium discoideum exhibit random movements. Starved cells aggregate by directed movements (chemotaxis) towards cyclic AMP and differentiate into live spores or dead stalk cells. Many differences between presumptive spore and stalk cells precede differentiation. We have examined whether cell motility-related factors are also among them. Cell speeds and localisation of motility-related signalling molecules were monitored by live cell imaging and immunostaining (a) in nutrient medium during growth, (b) immediately following transfer to starvation medium and (c) in nutrient medium that was re-introduced after a brief period of starvation. Cells moved randomly under all three conditions but mean speeds increased following transfer from nutrient medium to starvation medium; the transition occurred within 15 min. The distribution of speeds in starvation medium was bimodal: about 20% of the cells moved significantly faster than the remaining 80%. The motility-related molecules F-actin, PTEN and PI3 kinase were distributed differently in slow and fast cells. Among starved cells, the calcium content of slower cells was lower than that of the faster cells. All differences reverted within 15 min after restoration of the nutrient medium. The slow/fast distinction was missing in Polysphondylium pallidum, a cellular slime mould that lacks the presumptive stalk and spore cell classes, and in the trishanku (triA(center dot)) mutant of D. discoideum, in which the classes exist but are unstable. The transition from growth to starvation triggers a spontaneous and reversible switch in the distribution of D. discoideum cell speeds. Cells whose calcium content is relatively low (known to be presumptive spore cells) move slower than those whose calcium levels are higher (known to be presumptive stalk cells). Slow and fast cells show different distributions of motility-related proteins. The switch is indicative of a bistable mechanism underlying cell motility.

A mixture model approach for formant tracking and the robustness of student's-t distribution

We address the problem of robust formant tracking in continuous speech in the presence of additive noise. We propose a new approach based on mixture modeling of the formant contours. Our approach consists of two main steps: (i) Computation of a pyknogram based on multiband amplitude-modulation/frequency-modulation (AM/FM) decomposition of the input speech; and (ii) Statistical modeling of the pyknogram using mixture models. We experiment with both Gaussian mixture model (GMM) and Student's-t mixture model (tMM) and show that the latter is robust with respect to handling outliers in the pyknogram data, parameter selection, accuracy, and smoothness of the estimated formant contours. Experimental results on simulated data as well as noisy speech data show that the proposed tMM-based approach is also robust to additive noise. We present performance comparisons with a recently developed adaptive filterbank technique proposed in the literature and the classical Burg's spectral estimator technique, which show that the proposed technique is more robust to noise.

A numerical approach to determine the sufficiency of given boundary data sets for uniquely estimating interior elastic properties

We consider an inverse elasticity problem in which forces and displacements are known on the boundary and the material property distribution inside the body is to be found. In other words, we need to estimate the distribution of constitutive properties using the finite boundary data sets. Uniqueness of the solution to this problem is proved in the literature only under certain assumptions for a given complete Dirichlet-to-Neumann map. Another complication in the numerical solution of this problem is that the number of boundary data sets needed to establish uniqueness is not known even under the restricted cases where uniqueness is proved theoretically. In this paper, we present a numerical technique that can assess the sufficiency of given boundary data sets by computing the rank of a sensitivity matrix that arises in the Gauss-Newton method used to solve the problem. Numerical experiments are presented to illustrate the method.

A broad pore size distribution mesoporous SnO2 as anode for lithium-ion batteries

We demonstrate here that mesoporous tin dioxide (abbreviated M-SnO2) with a broad pore size distribution can be a prospective anode in lithium-ion batteries. M-SnO2 with pore size ranging between 2 and 7.5 nm was synthesized using a hydrothermal procedure involving two different surfactants of slightly different sizes, and characterized. The irreversible capacity loss that occurs during the first discharge and charge cycle is 890 mAh g(-1), which is smaller than the 1,010-mAh g(-1) loss recorded for mesoporous SnO2 (abbreviated S-SnO2) synthesized using a single surfactant. After 50 cycles, the discharge capacity of M-SnO2 (504 mAh g(-1)) is higher than that of S-SnO2 (401 mAh g(-1)) and solid nanoparticles of SnO2 (abbreviated nano-SnO2 < 4 mAh g(-1)) and nano-SnO2. Transmission electron microscopy revealed higher disorder in the pore arrangement in M-SnO2. This, in turn imparts lower stiffness to M-SnO2 (elastic modulus, E (R) a parts per thousand aEuro parts per thousand 14.5 GPa) vis-a-vis S-SnO2 (E (R) a parts per thousand aEuro parts per thousand 20.5 GPa), as obtained using the nanoindentation technique. Thus, the superior battery performance of M-SnO2 is attributed to its intrinsic material mechanical property. The fluidity of the internal microstructure of M-SnO2 resulted in a lower degree of aggregation of Sn particles compared to S-SnO2 and nano-SnO2 structural stabilization and long-term cyclability.

Effect of pressure on the ionic conductivity of Li+ and Cl- ions in water

A molecular dynamics simulation study of aqueous solution of LiCl is reported as a function of pressure. Experimental measurements of conductivity of Li+ ion as a function of pressure shows an increase in conductivity with pressure. Our simulations are able to reproduce the observed trend in conductivity. A number of relevant properties have been computed in order to understand the reasons for the increase in conductivity with pressure. These include radial distribution function, void and neck distributions, hydration or coordination numbers, diffusivity, velocity autocorrelation functions, angles between ion-oxygen and dipole of water as well as OH vector, mean residence time for water in the hydration shell, etc. These show that the increase in pressure acts as a structure breaker. The decay of the self part of the intermediate scattering function at small wave number k shows a bi-exponential decay at 1 bar which changes to single exponential decay at higher pressures. The k dependence of the ratio of the self part of the full width at half maximum of the dynamic structure factor to 2Dk(2) exhibits trends which suggest that the void structure of water is playing a role. These support the view that the changes in void and neck distributions in water can account for changes in conductivity or diffusivity of Li+ with pressure. These results can be understood in terms of the levitation effect. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4756909]