Influence of substrate supply on cardiac efficiency, as measured by pressure-volume analysis in ex vivo mouse hearts

In the present study, we tested the reliability of measurements of pressure-volume area (PVA) and oxygen consumption (MVo(2)) in ex vivo mouse hearts, combining the use of a miniaturized conductance catheter and a fiber-optic oxygen sensor. Second, we tested whether we could reproduce the influence of increased myocardial fatty acid (FA) metabolism on cardiac efficiency in the isolated working mouse heart model, which has already been documented in large animal models. The hearts were perfused with crystalloid buffer containing 11 mM glucose and two different concentrations of FA bound to 3% BSA. The initial concentration was 0.3 +/- 0.1 mM, which was subsequently raised to 0.9 +/- 0.1 mM. End-systolic and end-diastolic pressure-volume relationships were assessed by temporarily occluding the preload line. Different steady-state PVA-MVo(2) relationships were obtained by changing the loading conditions (pre- and afterload) of the heart. There were no apparent changes in baseline cardiac performance or contractile efficiency (slope of the PVA-MVo(2) regression line) in response to the elevation of the perfusate FA concentration. However, all hearts (n = 8) showed an increase in the y-intercept of the PVA-MVo(2) regression line after elevation of the palmitate concentration, indicating an FA-induced increase in the unloaded MVo(2). Therefore, in the present model, unloaded MVo(2) is not independent of metabolic substrate. This is, to our knowledge, the first report of a PVA-MVo(2) relationship in ex vivo perfused murine hearts, using a pressure-volume catheter. The methodology can be an important tool for phenotypic assessment of the relationship among metabolism, contractile performance, and cardiac efficiency in various mouse models.     

Is oxygen supply a limiting factor for survival during rewarming from profound hypothermia?

It has been postulated that unsuccessful resuscitation of victims of accidental hypothermia is caused by insufficient tissue oxygenation. The aim of this study was to test whether inadequate O2 supply and/or malfunctioning O2 extraction occur during rewarming from deep/profound hypothermia of different duration. Three groups of rats (n = 7 each) were used: group 1 served as normothermic control for 5 h; groups 2 and 3 were core cooled to 15 degrees C, kept at 15 degrees C for 1 and 5 h, respectively, and then rewarmed. In both hypothermic groups, cardiac output (CO) decreased spontaneously by > 50% in response to cooling. O2 consumption fell to less than one-third during cooling but recovered completely in both groups during rewarming. During hypothermia, circulating blood volume in both groups was reduced to approximately one-third of baseline, indicating that some vascular beds were critically perfused during hypothermia. CO recovered completely in animals rewarmed after 1 h (group 2) but recovered to only 60% in those rewarmed after 5 h (group 3), whereas blood volume increased to approximately three-fourths of baseline in both groups. Metabolic acidosis was observed only after 5 h of hypothermia (15 degrees C). A significant increase in myocardial tissue heat shock protein 70 after rewarming in group 3, but not in group 2, indicates an association with the duration of hypothermia. Thus mechanisms facilitating O2 extraction function well during deep/profound hypothermia, and, despite low CO, O2 supply was not a limiting factor for survival in the present experiments.     

BOX elements modulate gene expression in Streptococcus pneumoniae: impact on the fine-tuning of competence development

More than 100 BOX elements are randomly distributed in intergenic regions of the pneumococcal genome. Here we demonstrate that these elements can affect expression of neighboring genes and present evidence that they are mobile. Together, our findings show that BOX elements enhance genetic diversity and genomic plasticity in Streptococcus pneumoniae.     

Goodness-of-fit diagnostics for Bayesian hierarchical models

This article proposes methodology for assessing goodness of fit in Bayesian hierarchical models. The methodology is based on comparing values of pivotal discrepancy measures (PDMs), computed using parameter values drawn from the posterior distribution, to known reference distributions. Because the resulting diagnostics can be calculated from standard output of Markov chain Monte Carlo algorithms, their computational costs are minimal. Several simulation studies are provided, each of which suggests that diagnostics based on PDMs have higher statistical power than comparable posterior-predictive diagnostic checks in detecting model departures. The proposed methodology is illustrated in a clinical application; an application to discrete data is described in supplementary material.     

A single-molecule Hershey-Chase experiment

Ever since Hershey and Chase used phages to establish DNA as the carrier of genetic information in 1952, the precise mechanisms of phage DNA translocation have been a mystery. Although bulk measurements have set a timescale for in vivo DNA translocation during bacteriophage infection, measurements of DNA ejection by single bacteriophages have only been made in vitro. Here, we present direct visualization of single bacteriophages infecting individual Escherichia coli cells. For bacteriophage λ, we establish a mean ejection time of roughly 5 min with significant cell-to-cell variability, including pausing events. In contrast, corresponding in vitro single-molecule ejections are more uniform and finish within 10 s. Our data reveal that when plotted against the amount of DNA ejected, the velocity of ejection for two different genome lengths collapses onto a single curve. This suggests that in vivo ejections are controlled by the amount of DNA ejected. In contrast, in vitro DNA ejections are governed by the amount of DNA left inside the capsid. This analysis provides evidence against a purely intrastrand repulsion-based mechanism and suggests that cell-internal processes dominate. This provides a picture of the early stages of phage infection and sheds light on the problem of polymer translocation.     

Genetic noise control via protein oligomerization

Background

Biochemical equilibria are usually modeled iteratively: given one or a few fitted models, if there is a lack of fit or over fitting, a new model with additional or fewer parameters is then fitted, and the process is repeated. The problem with this approach is that different analysts can propose and select different models and thus extract different binding parameter estimates from the same data. An alternative is to first generate a comprehensive standardized list of plausible models, and to then fit them exhaustively, or semi-exhaustively.

Results

A framework is presented in which equilibriums are modeled as pairs (g, h) where g = 0 maps total reactant concentrations (system inputs) into free reactant concentrations (system states) which h then maps into expected values of measurements (system outputs). By letting dissociation constants K _d be either freely estimated, infinity, zero, or equal to other K _d , and by letting undamaged protein fractions be either freely estimated or 1, many g models are formed. A standard space of g models for ligand-induced protein dimerization equilibria is given. Coupled to an h model, the resulting (g, h) were fitted to dTTP induced R1 dimerization data (R1 is the large subunit of ribonucleotide reductase). Models with the fewest parameters were fitted first. Thereafter, upon fitting a batch, the next batch of models (with one more parameter) was fitted only if the current batch yielded a model that was better (based on the Akaike Information Criterion) than the best model in the previous batch (with one less parameter). Within batches models were fitted in parallel. This semi-exhaustive approach yielded the same best models as an exhaustive model space fit, but in approximately one-fifth the time.

Conclusion

Comprehensive model space based biochemical equilibrium model selection methods are realizable. Their significance to systems biology as mappings of data into mathematical models warrants their development.