Simulation methods with extended stability for stiff biochemical Kinetics

Background  

With increasing computer power, simulating the dynamics of complex systems in chemistry and biology is becoming increasingly routine. The modelling of individual reactions in (bio)chemical systems involves a large number of random events that can be simulated by the stochastic simulation algorithm (SSA). The key quantity is the step size, or waiting time, τ, whose value inversely depends on the size of the propensities of the different channel reactions and which needs to be re-evaluated after every firing event. Such a discrete event simulation may be extremely expensive, in particular for stiff systems where τ can be very short due to the fast kinetics of some of the channel reactions. Several alternative methods have been put forward to increase the integration step size. The so-called τ-leap approach takes a larger step size by allowing all the reactions to fire, from a Poisson or Binomial distribution, within that step. Although the expected value for the different species in the reactive system is maintained with respect to more precise methods, the variance at steady state can suffer from large errors as τ grows.     

Sensitive fluorescence in situ hybridization signal detection in maize using directly labeled probes produced by high concentration DNA polymerase nick translation

The signal produced by fluorescence in situ hybridization (FISH) often is inconsistent among cells and sensitivity is low. Small DNA targets on the chromatin are difficult to detect. We report here an improved nick translation procedure for Texas red and Alexa Fluor 488 direct labeling of FISH probes. Brighter probes can be obtained by adding excess DNA polymerase I. Using such probes, a 30?kb yeast transgene, and the rp1, rp3 and zein multigene clusters were clearly detected.     

Biodeterioration of Mayan buildings at uxmal and tulum,Mexico

Uxmal and Tulum are two important Mayan sites in the Yucatan peninsula. The buildings are mainly composed of limestone and grey/black discoloration is seen on exposed walls and copious greenish biofilms on inner walls. The principal microorganisms detected on interior walls at both Uxmal and Tulum were cyanobacteria; heterotrophic bacteria and filamentous fungi were also present. A dark‐pigmented mitosporic fungus and Bacillus cereus, both isolated from Uxmal, were shown to be acidogenic in laboratory cultures. Cyanobacteria belonging to rock‐degrading genera Synechocystis and Gloeocapsa were identified at both sites. Surface analysis previously showed that calcium ions were present in the biofilms on buildings at Uxmal and Tulum, suggesting the deposition of biosolubilized stone. Apart from their potential to degrade the substrate, the coccoid cyanobacteria supply organic nutrients for bacteria and fungi, which can produce organic acids, further increasing stone degradation.     

Alpha-Synuclein Induces Lysosomal Rupture and Cathepsin Dependent Reactive Oxygen Species Following Endocytosis

α-synuclein dysregulation is a critical aspect of Parkinson''s disease pathology. Recent studies have observed that α-synuclein aggregates are cytotoxic to cells in culture and that this toxicity can be spread between cells. However, the molecular mechanisms governing this cytotoxicity and spread are poorly characterized. Recent studies of viruses and bacteria, which achieve their cytoplasmic entry by rupturing intracellular vesicles, have utilized the redistribution of galectin proteins as a tool to measure vesicle rupture by these organisms. Using this approach, we demonstrate that α-synuclein aggregates can induce the rupture of lysosomes following their endocytosis in neuronal cell lines. This rupture can be induced by the addition of α-synuclein aggregates directly into cells as well as by cell-to-cell transfer of α-synuclein. We also observe that lysosomal rupture by α-synuclein induces a cathepsin B dependent increase in reactive oxygen species (ROS) in target cells. Finally, we observe that α-synuclein aggregates can induce inflammasome activation in THP-1 cells. Lysosomal rupture is known to induce mitochondrial dysfunction and inflammation, both of which are well established aspects of Parkinson''s disease, thus connecting these aspects of Parkinson''s disease to the propagation of α-synuclein pathology in cells.     

Protein contact prediction using patterns of correlation

We describe a new method for using neural networks to predict residue contact pairs in a protein. The main inputs to the neural network are a set of 25 measures of correlated mutation between all pairs of residues in two "windows" of size 5 centered on the residues of interest. While the individual pair-wise correlations are a relatively weak predictor of contact, by training the network on windows of correlation the accuracy of prediction is significantly improved. The neural network is trained on a set of 100 proteins and then tested on a disjoint set of 1033 proteins of known structure. An average predictive accuracy of 21.7% is obtained taking the best L/2 predictions for each protein, where L is the sequence length. Taking the best L/10 predictions gives an average accuracy of 30.7%. The predictor is also tested on a set of 59 proteins from the CASP5 experiment. The accuracy is found to be relatively consistent across different sequence lengths, but to vary widely according to the secondary structure. Predictive accuracy is also found to improve by using multiple sequence alignments containing many sequences to calculate the correlations.     

A multi-scaled approach for simulating chemical reaction systems

In this paper we give an overview of some very recent work, as well as presenting a new approach, on the stochastic simulation of multi-scaled systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge–Kutta methods and the balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and present a new approach which couples the three regimes mentioned above. We then apply this approach to a biologically inspired problem involving the expression and activity of LacZ and LacY proteins in E. coli, and conclude with a discussion on the significance of this work.     

A model system for increased meiotic nondisjunction in older oocytes

For at least 5% of all clinically recognized human pregnancies, meiotic segregation errors give rise to zygotes with the wrong number of chromosomes. Although most aneuploid fetuses perish in utero, trisomy in liveborns is the leading cause of mental retardation. A large percentage of human trisomies originate from segregation errors during female meiosis I; such errors increase in frequency with maternal age. Despite the clinical importance of age-dependent nondisjunction in humans, the underlying mechanisms remain largely unexplained. Efforts to recapitulate age-dependent nondisjunction in a mammalian experimental system have so far been unsuccessful. Here we provide evidence that Drosophila is an excellent model organism for investigating how oocyte aging contributes to meiotic nondisjunction. As in human oocytes, nonexchange homologs and bivalents with a single distal crossover in Drosophila oocytes are most susceptible to spontaneous nondisjunction during meiosis I. We show that in a sensitized genetic background in which sister chromatid cohesion is compromised, nonrecombinant X chromosomes become vulnerable to meiotic nondisjunction as Drosophila oocytes age. Our data indicate that the backup pathway that normally ensures proper segregation of achiasmate chromosomes deteriorates as Drosophila oocytes age and provide an intriguing paradigm for certain classes of age-dependent meiotic nondisjunction in humans.     

Left Ventricular Global Longitudinal Strain (GLS) Is a Superior Predictor of All-Cause and Cardiovascular Mortality When Compared to Ejection Fraction in Advanced Chronic Kidney Disease

BackgroundEchocardiographic global longitudinal strain (GLS) is increasingly recognised as a more effective technique than conventional ejection fraction (EF) in detecting subtle changes in left ventricular (LV) function. This study investigated the prognostic value of GLS over EF in patients with advanced Chronic Kidney Disease (CKD).MethodsThe study included 183 patients (57% male, 63% on dialysis) with CKD stage 4, 5 and 5Dialysis (D). 112 (61%) of patients died in a follow up of 7.8 ± 4.4 years and 41% of deaths were due to cardiovascular (CV) disease. GLS was calculated using 2-dimensional speckle tracking and EF was measured using Simpson’s biplane method. Cox proportional hazard models were used to assess the association of measures of LV function and all- cause and CV mortality.ResultsThe mean GLS at baseline was -13.6 ± 4.3% and EF was 45 ± 11%. GLS was a significant predictor of all-cause [Hazard Ratio (HR) 1.09 95%; Confidence Interval (CI) 1.02–1.16; p = 0.01] and CV mortality (HR 1.16 95%; CI 1.04–1.30; p = 0.008) following adjustment for relevant clinical variables including LV mass index (LVMI) and EF. GLS also had greater predictive power for both all- cause and CV mortality compared to EF. Impaired GLS (>-16%) was associated with a 5.6-fold increased unadjusted risk of CV mortality in patients with preserved EF.ConclusionsIn this cohort of patients with advanced CKD, GLS is a more sensitive predictor of overall and CV mortality compared to EF. Studies of larger populations in CKD are required to confirm that GLS provides additive prognostic value in patients with preserved EF.     

Identification of conformational B-cell Epitopes in an antigen from its primary sequence

Background

One of the major challenges in the field of vaccine design is to predict conformational B-cell epitopes in an antigen. In the past, several methods have been developed for predicting conformational B-cell epitopes in an antigen from its tertiary structure. This is the first attempt in this area to predict conformational B-cell epitope in an antigen from its amino acid sequence.

Results

All Support vector machine (SVM) models were trained and tested on 187 non-redundant protein chains consisting of 2261 antibody interacting residues of B-cell epitopes. Models have been developed using binary profile of pattern (BPP) and physiochemical profile of patterns (PPP) and achieved a maximum MCC of 0.22 and 0.17 respectively. In this study, for the first time SVM model has been developed using composition profile of patterns (CPP) and achieved a maximum MCC of 0.73 with accuracy 86.59%. We compare our CPP based model with existing structure based methods and observed that our sequence based model is as good as structure based methods.

Conclusion

This study demonstrates that prediction of conformational B-cell epitope in an antigen is possible from is primary sequence. This study will be very useful in predicting conformational B-cell epitopes in antigens whose tertiary structures are not available. A web server CBTOPE has been developed for predicting B-cell epitope http://www.imtech.res.in/raghava/cbtope/.