Inhibition of calcium ATPase by phencyclidine in rat brain

Phencyclidine (PCP) is a potent psychotomimetic drug of abuse and has profound effect on the functioning of the central nervous system (CNS). Many of the CNS functions are known to be mediated by calcium (Ca2+). In the present study we have investigated the effects of PCP on Ca2+ ATPase activity in rat brain both in vitro and in vivo. For in vitro studies, synaptic membrane fractions prepared from normal rat brain were incubated with PCP at different concentrations (25-100 M) before the addition of substrate. For n vivo studies, rats were treated with a single moderate dose of PCP (10 mg/kg, IP) and animals were sacrificed at 1,2, 6 and 12 h after treatment. Ca2+ ATPase activity in synaptic membrane fractions was assayed by estimation of inorganic phosphate. PCP inhibited the Ca2+ ATPase in vitro in a concentration dependent manner with significant effect at 50 and 100 M. A significant time-dependent reduction of the Ca2+ ATPase activity was evident in vivo. As early as 2 h after the treatment of rats with PCP the ATPase activity was significantly reduced. The reduction of Ca2+ ATPase observed even at 12 h after treatment suggesting a prolonged presence of the drug in the brain tissue. Further, kinetic studies in vitro indicated PCP to be a competitive inhibitor of Ca2+ ATPase with respect to the substrate, ATP. The present findings indicate that PCP inhibits synaptic membrane Ca2+ ATPase thus altering cellular Ca2+ homeostasis in CNS which may partially explain the pharmacological effects of the drug and/or its neurotoxicity.     

QTL mapping of yield, agronomic and quality traits in tetraploid potato (Solanum tuberosum subsp. tuberosum)

Interval mapping of quantitative trait loci (QTL) for 16 yield, agronomic and quality traits in potato was performed on a tetraploid full-sib family comprising 227 clones from a cross between processing clone 12601ab1 and table cultivar Stirling. Thirty-eight AFLP primer combinations and six SSRs provided 514 informative markers which formed a molecular marker map comprising 12 linkage groups (LGs) in 12601ab1 (nine with four homologous chromosomes) which were aligned with 12 in Stirling (11 with four homologous chromosomes), with four partial groups remaining in 12601ab1. Two LGs were identified unequivocally as chromosomes IV and V and eight others were tentatively assigned with chromosomes VII and X unidentified. All of the traits scored had moderately high heritabilities with 54–92% of the variation in clone means over 3 years and two replicates being due to genetic differences. A total of 39 QTLs were identified. A QTL for maturity was identified on chromosome V which explained 56% of the phenotypic variance, whereas the other QTLs individually explained between 5.4 and 16.5%. However, six QTLs were detected for after-cooking blackening and four for each of regularity of tuber shape, fry colour both after storage at 4 and 10°C and sprouting. Just two QTLs were found for each of yield, the two ‘overall’ scores, crop emergence, tuber size and common scab and just one QTL was detected for each of dry matter content, keeping quality, growth cracks and internal condition. The implications for practical potato breeding and for practical linkage and QTL analysis in autotetraploids are discussed.     

Ectomychorrizal DB: a symbiotic association database

Ectomycorrhizal (ECM) fungal species, a "Symbiotic" relationship between tress and fungi in forest has a great ecological and economic importance. Here is an attempt to describe database named "EctomycorrhizalDB", addressing ECM diversity of Central Himalaya (Kumaun region), with special emphasis on their characterization, physical properties and morphological features along with specifications. This database would help the scientific community to draw a better understanding of the environmental factors that affects species diversity. AVAILABILITY: The database is available for free at http://www.kubic.nic.in/ectomychorhiza.     

Mechanical unfolding of a beta-hairpin using molecular dynamics

Single-molecule mechanical unfolding experiments have the potential to provide insights into the details of protein folding pathways. To investigate the relationship between force-extension unfolding curves and microscopic events, we performed molecular dynamics simulations of the mechanical unfolding of the C-terminal hairpin of protein G. We have studied the dependence of the unfolding pathway on pulling speed, cantilever stiffness, and attachment points. Under conditions that generate low forces, the unfolding trajectory mimics the untethered, thermally accessible pathway previously proposed based on high-temperature studies. In this stepwise pathway, complete breakdown of backbone hydrogen bonds precedes dissociation of the hydrophobic cluster. Under more extreme conditions, the cluster and hydrogen bonds break simultaneously. Transitions between folding intermediates can be identified in our simulations as features of the calculated force-extension curves.     

The Roles of Entropy and Kinetics in Structure Prediction

Background

Here we continue our efforts to use methods developed in the folding mechanism community to both better understand and improve structure prediction. Our previous work demonstrated that Rosetta''s coarse-grained potentials may actually impede accurate structure prediction at full-atom resolution. Based on this work we postulated that it may be time to work completely at full-atom resolution but that doing so may require more careful attention to the kinetics of convergence.

Methodology/Principal Findings

To explore the possibility of working entirely at full-atom resolution, we apply enhanced sampling algorithms and the free energy theory developed in the folding mechanism community to full-atom protein structure prediction with the prominent Rosetta package. We find that Rosetta''s full-atom scoring function is indeed able to recognize diverse protein native states and that there is a strong correlation between score and Cα RMSD to the native state. However, we also show that there is a huge entropic barrier to folding under this potential and the kinetics of folding are extremely slow. We then exploit this new understanding to suggest ways to improve structure prediction.

Conclusions/Significance

Based on this work we hypothesize that structure prediction may be improved by taking a more physical approach, i.e. considering the nature of the model thermodynamics and kinetics which result from structure prediction simulations.     

Cleistanthin B causes G1 arrest and induces apoptosis in mammalian cells

Cleistanthin B is a potential anticancer agent isolated from the tropical plant Cleistanthus collinus. We have previously shown that cleistanthin B is clastogenic and induces micronuclei formation and chromosomal aberrations. We now show that this compound inhibits DNA synthesis in Chinese hamster ovary (CHO) cells and induces apoptosis in cervical carcinoma (SiHa) cells. Flow cytometric analysis of cleistanthin treated CHO cells revealed that they were blocked in G1. Cervical carcinoma (SiHa) cells exposed to cleistanthin B shrank, rounded up and had condensed chromatin and fragmented nuclei. DNA isolated from cleistanthin treated cells exhibited the characteristic apoptotic ladder when electrophoresed in agarose gels. These results were confirmed by flow cytometry. Etoposide, a structurally similar compound also induced apoptosis in these cells although with a difference. Etoposide induced apoptosis after permitting cells to enter into S phase, while cleistanthin B stopped entry of cells into S phase and subsequently drove them to apoptosis.     

MSMBuilder: Statistical Models for Biomolecular Dynamics

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.     

Effects of Familial Mutations on the Monomer Structure of Aβ42

Amyloid beta (Aβ) peptide plays an important role in Alzheimer’s disease. A number of mutations in the Aβ sequence lead to familial Alzheimer’s disease, congophilic amyloid angiopathy, or hereditary cerebral hemorrhage with amyloid. Using molecular dynamics simulations of ∼200 μs for each system, we characterize and contrast the consequences of four pathogenic mutations (Italian, Dutch, Arctic, and Iowa) for the structural ensemble of the Aβ monomer. The four familial mutations are found to have distinct consequences for the monomer structure.Amyloid beta (Aβ) peptides have long been thought to play a central role in Alzheimer’s disease (AD). Usually 40 or 42 residues in length, Aβ peptides are proteolytic products of the Aβ precursor protein and they aggregate to form the fibrillar plaques in AD patients’ brains. Besides fibrillar plaques, Aβ oligomers are also neurotoxic. The significance and nature of Aβ oligomerization has recently become a focus of intensive research studies and debates (1,2). Notably, numerous pathogenic mutations have been identified in the Aβ precursor protein sequence and in the enzymes involved in Aβ processing (3). These mutations generally lead to early onset of AD or cerebral amyloid angiopathy. Understanding how the pathogenic mutations alter Aβ oligomerization/aggregation is essential to our understanding of the disease mechanism.Four of these pathogenic mutations (Italian E22K, Dutch E22Q, Arctic E22G, and Iowa D23N) cluster in the region of E22 and D23 in the Aβ sequence (distal from proteolytic cleavage sites) and they have higher neurotoxicity compared to wild-type (WT) Aβ (4). These mutations are thought to modify the physicochemistry of the peptide. For example, kinetic studies (4) show that the E22K and E22Q mutations lead to faster peptide aggregation, whereas the E22G and D23N mutations result in slightly slower aggregation than WT Aβ₄₂ (although the E22G mutation shows increased protofibril formation (5)). Recent solid-state NMR studies also suggest that rather than the in-register β-sheet conformation adopted by WT Aβ, the Iowa D23N mutant forms amyloid fibrils with antiparallel β-sheet structure (6).To understand how the mutations modify the peptide oligomerization/aggregation it is critical to characterize the starting point of the process, the monomers. Unfortunately, investigating the early phase of the oligomerization process experimentally is a challenging task due to the high aggregation propensity of Aβ and its intrinsic disorder. Therefore, a number of computational approaches have been adopted to investigate the consequences of mutations for the monomer structure (7–16). However, due to the high computational demands of explicit-solvent molecular dynamics (MD) simulations to simulate full-length Aβ peptides, most of these computational studies are either on Aβ fragments (to decrease the system size) using explicit-solvent simulations (8–12) or on full-length Aβ using implicit-solvent simulations (which are less computationally demanding and enable longer simulation times, but lack explicit water molecules in the simulations to fully describe water-peptide interactions) (13–15). In a very recent report, explicit-solvent simulations were used to study the effects of the E22Q mutation on full-length Aβ; however, rather limited data (<10 μs) were collected (16). Thus, characterizing full-length Aβ monomers remains quite a daunting task even with simulations.To characterize the effects of mutations on full-length Aβ monomer using explicit-solvent MD simulations, we employed distributed computing (17) to simulate the WT Aβ₄₂, Aβ₄₂-E22K, Aβ₄₂-E22Q, Aβ₄₂-E22G, and Aβ₄₂-D23N monomers. MD simulations of >200 μs were performed for each system and AMBER ff99sb (18) and the tip3p water model (19) were used for force field parameters. Peptide configurations in the MD trajectories were clustered with the root mean-square deviation metric to identify representative conformations (i.e., states) and transitions between these states were counted. Markov state model analysis was then performed where the master equations were solved and the equilibrium population of each state deduced (20). Details of the MD simulation procedures and Markov state model analysis can be found in the Supporting Material.Each of the five Aβ monomer systems exhibits great structural diversity and can only be characterized in an ensemble fashion (rather than described by a handful of representative configurations). This is in accord with the notion that full-length Aβ peptides are intrinsically disordered (21,22). Using the Dictionary of Secondary Structure of Proteins program (23) to assign secondary structure, it is clear that the five Aβ monomer systems are found overall not well structured, although small β-hairpins and α-helices are observed. In Fig. 1 we plot the residue-dependent extended β propensity and α-helix propensity, in the top and bottom panels, respectively, for each Aβ monomer system. Although we are reasonably confident of the convergence behavior of the α-helix propensity, we note that the convergence of the extended β-propensity might be more challenging and demand a much longer sampling time than the current aggregate simulation time of ∼200 μs (24).Open in a separate windowFigure 1Ensemble-averaged %population of β-strand (top) and α-helix (bottom) propensity for all five monomer systems. The sequence of the WT Aβ₄₂ is given on the x axis.We observe in Fig. 1 that all five Aβ monomer systems share a rather similar residue-dependent tendency to form an extended β-structure, although minor differences are present. On the other hand, these pathogenic mutations alter the α-helix propensity quite significantly. The E22K and E22Q mutations increase the α-helix propensity in the region of residues 20–23. All four mutations (E22K, E22Q, E22G, and D23N) decrease the α-helix propensity in the region of residues 33–36.Notably, we find that in all five systems only short stretches of α-helices are formed. That is, when a residue is involved in α-helix formation, it participates in forming mostly short helical segments (consisting of only four helical residues). To provide more insight into the changes of α-helix propensity due to the mutations, in Fig. S1 we plot the tendency of forming short α-helices along the sequence for all five systems. Each data point in Fig. S1 represents the propensity to form an α-helix of four residues in length, ending at the specific residue. For example, in the structural ensemble adopted by the WT peptide, ∼5.5% of the conformations have a short α-helix of size four, involving residues 15–18. We see from Fig. S1 that the E22K and E22Q mutations induce the formation of two short helices in residues 19–22 and 20–23. The higher α-helix propensity in this region for the E22K mutant compared to the WT was previously attributed to the elimination of the electrostatic repulsion between E22 and D23 in the WT by the mutation and the longer aliphatic chain of K22 in the mutant compared to E22 in the WT (9,22). This is consistent with the observation that the E22Q mutation also induces helix formation in this region (by eliminating the electrostatic repulsion between E22 and D23 in the WT) but to a lesser extent, possibly due to the shorter aliphatic chain of Q22 compared to K22.In the E22G mutant, although the mutation eliminates the electrostatic repulsion between E22 and D23 in the WT peptide, glycine is known to be a helix breaker (25), leading to diminished α-helix propensity in the region around residue G22 seen in Fig. S1.In the D23N mutant, although the mutation eliminates the electrostatic repulsion between E22 and D23 in the WT peptide, it does not induce (or rather even slightly decreases) helix formation around residue 23. This may be due to the short aliphatic chain of N23 but it is possible that the mutation induces some nonlocal effects on the peptide structure, disfavoring helix formation in this region.It is worth noting that all four mutations (E22K, E22Q, E22G, and D23N) virtually eliminate the α-helix propensity in the region of residues 33–36. This region is rather far away from the mutation sites in sequence but its α-helix propensity is nonetheless affected. The origin of such a nonlocal effect is less straightforward to explain and further analysis will aid untangling this behavior. Nonetheless, the diminished α-helix propensity in the region of residues 33–36 appears to be a consistent feature across all four mutants.The four mutations studied here (E22K, E22Q, E22G, and D23N) have been thought to modify the physicochemistry of the peptide and alter the oligomerization/aggregation process, leading to higher neurotoxicity. In predicting intrinsic aggregation propensities using peptide sequences, all four mutants are suggested to be more aggregation prone (26). On the other hand, kinetic studies show that only the E22K and E22Q mutants aggregate more quickly, whereas the E22G and D23N mutations result in slightly slower aggregation than WT Aβ₄₂ (4). Our simulation results suggest these pathogenic mutations have complicated effects on the monomer structure—all four mutations decrease helix propensity in residues 33–36, whereas only the E22K and E22Q mutations increase helix propensity in residues 20–23. It is interesting to note that α-helix propensity is generally thought to anticorrelate with aggregation propensity; however, recent studies have suggested an important role of α-helical intermediates in amyloid oligomerization (27–29). Our studies suggest that it would be of great value to investigate how the distinct patterns of α-helix propensity in these five systems may propagate to give rise to different oligomerization kinetics or even mechanisms. The pathogenic mutations studied here have complex effects on the oligomerization of the peptide. The characterization of the monomer structural ensembles reported here should aid understanding of such an important and complicated process.     

Mean growth rate when rare is not a reliable metric for persistence of species

The coexistence of many species within ecological communities poses a long‐standing theoretical puzzle. Modern coexistence theory (MCT) and related techniques explore this phenomenon by examining the chance of a species population growing from rarity in the presence of all other species. The mean growth rate when rare, , is used in MCT as a metric that measures persistence properties (like invasibility or time to extinction) of a population. Here we critique this reliance on and show that it fails to capture the effect of temporal random abundance variations on persistence properties. The problem becomes particularly severe when an increase in the amplitude of stochastic temporal environmental variations leads to an increase in , since at the same time it enhances random abundance fluctuations and the two effects are inherently intertwined. In this case, the chance of invasion and the mean extinction time of a population may even go down as increases.