The diastatic pressure-volume relationship is not the same as the end-diastolic pressure-volume relationship

The end-diastolic pressure-volume (P-V) relationship (EDPVR) is routinely used to determine the passive left ventricular (LV) stiffness, although the diastatic P-V relationship (D-PVR) has also been measured. Based on the physiological difference between diastasis (the LV and atrium are relaxed and static) and end diastole (LV volume increased by atrial systole and the atrium is contracted), we hypothesized that, although both D-PVR and EDPVR include LV chamber stiffness information, they are two different, distinguishable P-V relations. Cardiac catheterization determined LV pressures, and conductance volumes in 31 subjects were analyzed. Physiological, beat-to-beat variation of the diastatic and end-diastolic P-V points were fit by linear and exponential functions to generate the D-PVR and EDPVR. The extrapolated exponential D-PVR underestimated LVEDP in 82% of the heart beats (P < 0.001). The extrapolated EDPVR overestimated pressure at diastasis in 84% of the heart beats (P < 0.001). If each subject's diastatic and end-diastolic P-V data were combined to form a continuous data set to be fit by one exponential relation, the goodness of fit was always worse than if the diastatic and end-diastolic data were grouped separately and fit by two distinct exponential relations. Diastatic chamber stiffness was less than EDPVR stiffness (defined by the slope of P-V relation) for all 31 subjects (0.16 +/- 0.11 vs. 0.24 +/- 0.15 mmHg/ml, P < 0.001). We conclude that the D-PVR and EDPVR are distinguishable. Because it is not coupled to a contracted atrium, the D-PVR conveys passive LV stiffness better than the EDPVR. Additional studies that fully elucidate the physiology and biology of diastasis in health and disease are in progress.     

Single-beat estimation of end-diastolic pressure-volume relationship: a novel method with potential for noninvasive application

Whereas end-systolic and end-diastolic pressure-volume relations (ESPVR, EDPVR) characterize left ventricular (LV) pump properties, clinical utility of these relations has been hampered by the need for invasive measurements over a range of pressure and volumes. We propose a single-beat approach to estimate the whole EDPVR from one measured volume-pressure (Vm and Pm) point. Ex vivo EDPVRs were measured from 80 human hearts of different etiologies (normal, congestive heart failure, left ventricular assist device support). Independent of etiology, when EDPVRs were normalized (EDPVRn) by appropriate scaling of LV volumes, EDPVRns were nearly identical and were optimally described by the relation EDP = An.EDV (Bn), with An = 28.2 mmHg and Bn = 2.79. V0 (the volume at the pressure of approximately 0 mmHg) was predicted by using the relation V0 = Vm.(0.6 - 0.006.Pm) and V30 by V30 = V0 + (Vm,n - V0)/(Pm/An) (1/Bn). The entire EDPVR of an individual heart was then predicted by forcing the curve through Vm, Pm, and the predicted V0 and V30. This technique was applied prospectively to the ex vivo human EDPVRs not used in determining optimal An and Bn values and to 36 in vivo human, 12 acute and 14 chronic canine, and 80 in vivo and ex vivo rat studies. The root-mean-square error (RMSE) in pressure between measured and predicted EDPVRs over the range of 0-40 mmHg was < 3 mmHg of measured EDPVR in all settings, indicating a good predictive value of this approach. Volume-normalized EDPVRs have a common shape, despite different etiology and species. This allows the entire curve to be predicted by a new method with a potential for noninvasive application. The results are most accurate when applied to groups of hearts rather than to individual hearts.     

Left ventricular end-diastolic pressure-volume relationship in septic rats with open thorax

To estimate changes in compliance, we evaluated the effects of sepsis on the end-diastolic pressure-volume relationship (EDPVR) in the left ventricle of rats that had undergone an open thorax procedure. Sepsis was induced in male Wistar Hannover rats (n = 7; 240 to 270 g) by intraperitoneal administration of a slurry of cecal contents; control rats (n = 7) were given 5% dextrose only. On the third day after induction of sepsis, left ventricular (LV) pressure and LV dimensions were recorded simultaneously in animals of both groups. Using a micromanometer and ultrasonic crystals, measurements were obtained at baseline and during the increase of afterload. Blood samples were taken for determination of complete blood count, white blood cell differential count, and lactate concentration, and for bacteriologic examination. Septic rats lost weight, and developed changes in body temperature, ascites, and abscesses in the abdominal and thoracic cavities, gram-negative bacteremia, and increase in heart rate. On the third day after induction of sepsis, LV EDPVR decreased, compared with that in the control rats (regression coefficients: control group, 8.41 to 23.95; sepsis group, 3.94 to 7.92). Myocardial compliance in the left ventricle increased on the third day of sepsis in the open-thorax rat model, as evidenced by the downward shift of LV EDPVR in rats with sepsis, compared with controls.     

Load as an acute determinant of end-diastolic pressure-volume relation

Afterload-induced changes in myocardial relaxation are a mechanism for diastolic dysfunction when afterload is elevated beyond certain limits. The present study investigated the effects of acute afterload and preload changes on the position of the end-diastolic (ED) pressure-volume (P-V) relation. Beat-to-beat afterload elevations were induced in seven open-chest rabbits by gradually occluding the ascending aorta to increase peak left ventricular pressure (LVP) from baseline to isovolumetric level. Afterload elevations were performed at three ED LVP: 2.0 +/- 0.2 (low), 5.7 +/- 0.2 (mid), and 9.6 +/- 0.6 (high) mmHg. Preload was altered with caval occlusions and/or intravenous dextran. Afterload elevations induced an upward shift of the diastolic P-V relation, which became more important as afterload and/or preload increased. For instance, maximal afterload elevations shifted this relation upward 2.2 +/- 0. 5, 5.1 +/- 0.8, and 12.1 +/- 1.7 mmHg at low, mid, and high preload, respectively. These effects were partially due to changes in relaxation rate and time available to relax. In conclusion, load is an acute determinant of the ED P-V relation, which, therefore, does not provide a load-independent assessment of diastolic function.     

A computational method for the analysis and prediction of protein:phosphopeptide-binding sites

Phosphopeptide-binding domains, including the FHA, SH2, WW, WD40, MH2, and Polo-box domains, as well as the 14-3-3 proteins, exert control functions in important processes such as cell growth, division, differentiation, and apoptosis. Structures and mechanisms of phosphopeptide binding are generally diverse, revealing few general principles. A computational method for analysis of phosphopeptide-binding domains was therefore developed to elucidate the physical and chemical nature of phosphopeptide binding, given this lack of structural similarity. The surfaces of nine phosphopeptide-binding proteins, representing seven distinct classes of phosphopeptide-binding modules, were discretized, and encoded with information about amino acid identity, surface curvature, and electrostatic potential at every point on the surface in order to identify local surface properties enriched in phosphoresidue contact sites. Cross-validation indicated that propensities corresponding to this enrichment calculated from a subset of the training data could be used to predict the phosphoresidue contact site on proteins not used in training with no false negative results, and with few unconfirmed positive predictions. The locations of phosphoresidue contact sites were then predicted on the surfaces of the checkpoint kinase Chk1 and the BRCA1 BRCT repeat domain, and these predictions are consistent with recent experimental evidence.     

Factors affecting the end-systolic pressure-volume relationship

The end-systolic pressure-volume relationship (ESPVR) defines the systolic limits of cardiac performance. Using an isolated cross-circulated canine heart preparation, we examined the influence of afterload impedance changes, changes in coronary artery pressure (CAP), and regional ischemia on the ESPVR. We found that afterload impedance did not change the slope of the ESPVR but that increases in resistance and characteristic impedance did shift the relation slightly to the left. There was no change in the ESPVR with changes in CAP above a certain critical value. A decrease in CAP below this value caused a progressive decline in the slope of the ESPVR. With regional ischemia the ESPVR became nonlinear, and there was a near parallel downward shift of the ESPVR in the high-volume range. This shift was directly proportional to the extent of ischemic area. We conclude that an adequate measurement of the ESPVR demands at least three pressure-volume points to check for linearity and characterization of both the slope and volume intercept.     

New computational method for prediction of interacting protein loop regions

Flexible loop regions of proteins play a crucial role in many biological functions such as protein–ligand recognition, enzymatic catalysis, and protein–protein association. To date, most computational methods that predict the conformational states of loops only focus on individual loop regions. However, loop regions are often spatially in close proximity to one another and their mutual interactions stabilize their conformations. We have developed a new method, titled CorLps, capable of simultaneously predicting such interacting loop regions. First, an ensemble of individual loop conformations is generated for each loop region. The members of the individual ensembles are combined and are accepted or rejected based on a steric clash filter. After a subsequent side‐chain optimization step, the resulting conformations of the interacting loops are ranked by the statistical scoring function DFIRE that originated from protein structure prediction. Our results show that predicting interacting loops with CorLps is superior to sequential prediction of the two interacting loop regions, and our method is comparable in accuracy to single loop predictions. Furthermore, improved predictive accuracy of the top‐ranked solution is achieved for 12‐residue length loop regions by diversifying the initial pool of individual loop conformations using a quality threshold clustering algorithm. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

GeneSplicer: a new computational method for splice site prediction

GeneSplicer is a new, flexible system for detecting splice sites in the genomic DNA of various eukaryotes. The system has been tested successfully using DNA from two reference organisms: the model plant Arabidopsis thaliana and human. It was compared to six programs representing the leading splice site detectors for each of these species: NetPlantGene, NetGene2, HSPL, NNSplice, GENIO and SpliceView. In each case GeneSplicer performed comparably to the best alternative, in terms of both accuracy and computational efficiency.     

A brief review of computational gene prediction methods

With the development of genome sequencing for many organisms, more and more raw sequences need to be annotated. Gene prediction by computational methods for finding the location of protein coding regions is one of the essential issues in bioinformatics. Two classes of methods are generally adopted: similarity based searches and ab initio prediction. Here, we review the development of gene prediction methods, summarize the measures for evaluating predictor quality, highlight open problems in this area, and discuss future research directions.     

Single-beat estimation of right ventricular end-systolic pressure-volume relationship

Assessment of right ventricular (RV) contractility from end-systolic pressure-volume relationships (ESPVR) is difficult due to problems in measuring RV instantaneous volume and to effects of changes in RV preload or afterload. We therefore investigated in anesthetized dogs whether RV ESPVR and contractility can be determined without measuring RV volume and without changing RV preload or afterload. The maximal RV pressure of isovolumic beats (P(max)) was predicted from isovolumic portions of RV pressure during ejecting beats and compared with P(max) measured during the first beat after pulmonary artery clamping. In RV pressure-volume loops obtained from RV pressure and integrated pulmonary arterial flow, end-systolic elastance (E(es)) was assessed as the slope of P(max)-derived ESPVR, pulmonary artery effective elastance (E(a)) as the slope of end-diastolic to end-systolic relation, and coupling efficiency as the E(es)-to-E(a) ratio (E(es)/E(a)). Predicted P(max) correlated with observed P(max) (r = 0.98 +/- 0.02). Dobutamine increased E(es) from 1.07 to 2.00 mmHg/ml and E(es)/E(a) from 1.64 to 2.49, and propranolol decreased E(es)/E(a) from 1.64 to 0.91 (all P < 0.05). After adrenergic blockade, preload reduction did not affect E(es), whereas hypoxia and arterial constriction markedly increased E(a) and somewhat increased E(es) due to the Anrep effect. Low preload did not affect E(es)/E(a) and high afterload decreased E(es)/E(a). In conclusion, in the right ventricle 1) P(max) can be calculated from normal beats, 2) P(max) can be used to determine ESPVR without change in load, and 3) P(max)-derived ESPVR can be used to assess ventricular contractility and ventricular-arterial coupling efficiency.     

A comprehensive overview of computational protein disorder prediction methods

Over the past decade there has been a growing acknowledgement that a large proportion of proteins within most proteomes contain disordered regions. Disordered regions are segments of the protein chain which do not adopt a stable structure. Recognition of disordered regions in a protein is of great importance for protein structure prediction, protein structure determination and function annotation as these regions have a close relationship with protein expression and functionality. As a result, a great many protein disorder prediction methods have been developed so far. Here, we present an overview of current protein disorder prediction methods including an analysis of their advantages and shortcomings. In order to help users to select alternative tools under different circumstances, we also evaluate 23 disorder predictors on the benchmark data of the most recent round of the Critical Assessment of protein Structure Prediction (CASP) and assess their accuracy using several complementary measures.     

The computational prediction of toxicity

Recent developments in the prediction of toxicity from chemical structure have been reviewed. Attention has been drawn to some of the problems that can be encountered in the area of predictive toxicology, including the need for a multi-disciplinary approach and the need to address mechanisms of action. Progress has been hampered by the sparseness of good quality toxicological data. Perhaps too much effort has been devoted to exploring new statistical methods rather than to the creation of data sets for hitherto uninvestigated toxicological endpoints and/or classes of chemicals.     

A new computational approach for real protein folding prediction

An effective and fast minimization approach is proposed for the prediction of protein folding, in which the 'relative entropy' is used as a minimization function and the off-lattice model is used. In this approach, we only use the information of distances between the consecutive Calpha atoms along the peptide chain and a generalized form of the contact potential for 20 types of amino acids. Tests of the algorithm are performed on the real proteins. The root mean square deviations of the structures of eight folded target proteins versus the native structures are in a reasonable range. In principle, this method is an improvement on the energy minimization approach.     

Exploring the antiviral activity of juglone by computational method

Abstract

Nature has been the best source of medicines for a long time. Many plant extracts have been used as drugs. Juglone occurs in all parts of the Juglandaceae family and is found extensively in the black walnut plants. It possesses antifungal, antimalarial, antibacterial and antiviral properties besides exhibiting cytotoxic effects. Juglone has gained interest by the researchers for its anticancer properties. This article elucidates the antiviral activity of the Juglone by the computational method.     

Validation of the reliability of computational O-GlcNAc prediction

O-GlcNAcylation is an inducible, highly dynamic and reversible posttranslational modification, which regulates numerous cellular processes such as gene expression, translation, immune reactions, protein degradation, protein–protein interaction, apoptosis, and signal transduction. In contrast to N-linked glycosylation, O-GlcNAcylation does not display a strict amino acid consensus sequence, although serine or threonine residues flanked by proline and valine are preferred sites of O-GlcNAcylation. Based on this information, computational prediction tools of O-GlcNAc sites have been developed. Here, we retrospectively assessed the performance of two available O-GlcNAc prediction programs YinOYang 1.2 server and OGlcNAcScan by comparing their predictions for recently discovered experimentally validated O-GlcNAc sites. Both prediction programs efficiently identified O-GlcNAc sites situated in an environment resembling the consensus sequence P-P-V-[ST]-T-A. However, both prediction programs revealed numerous false negative O-GlcNAc predictions when the site of modification was located in an amino acid sequence differing from the known consensus sequence. By searching for a common sequence motif, we found that O-GlcNAcylation of nucleocytoplasmic proteins preferably occurs at serine and threonine residues flanked downstream by proline and valine and upstream by one to two alanines followed by a stretch of serine and threonine residues. However, O-GlcNAcylation of proteins located in the mitochondria or in the secretory lumen occurs at different sites and does not follow a distinct consensus sequence. Thus, our study indicates the limitations of the presently available computational prediction methods for O-GlcNAc sites and suggests that experimental validation is mandatory. Continuously update and further development of available databases will be the key to improve the performance of O-GlcNAc site prediction.