Cytosolic phospholipase A2 and arachidonic acid metabolites modulate ventilator-induced permeability increases in isolated mouse lungs.

We previously reported that the cytosolic phospholipase A(2) (cPLA2) pathway is involved in ventilator-induced lung injury (VILI) produced by high peak inflation pressures (PIP) (J Appl Physiol 98: 1264-1271, 2005), but the relative contributions of the various downstream products of cPLA2 on the acute permeability response were not determined. Therefore, we investigated the role of cPLA2 and the downstream products of arachidonic acid metabolism in the high-PIP ventilation-induced increase in vascular permeability. We perfused isolated mouse lungs and measured the capillary filtration coefficient (K(fc)) after 30 min of ventilation with 9, 25, and 35 cmH2O PIP. In high-PIP-ventilated lungs, K(fc) increased significantly, 2.7-fold, after ventilation with 35 cmH2O PIP compared with paired baseline values and low-PIP-ventilated lungs. Also, increased phosphorylation of lung cPLA2 suggested enzyme activation after high-PIP ventilation. However, treatment with 40 mg/kg arachidonyl trifluoromethyl ketone (an inhibitor of cPLA2) or a combination of 30 microM ibuprofen [a cyclooxygenase (COX) inhibitor], 100 microM nordihydroguaiaretic acid [a lipoxygenase (LOX) inhibitor], and 10 microM 17-octadecynoic acid (a cytochrome P-450 epoxygenase inhibitor) prevented the high-PIP-induced increase in K(fc). Combinations of the inhibitors of COX, LOX, or cytochrome P-450 epoxygenase did not prevent significant increases in K(fc), even though bronchoalveolar lavage levels of the COX or LOX products were significantly reduced. These results suggest that multiple mediators from each pathway contribute to the acute ventilator-induced permeability increase in isolated mouse lungs by mutual potentiation.     

Benchmarking protein classification algorithms via supervised cross-validation

Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and random sampling was used to construct model datasets, suitable for algorithm comparison.

The datasets are available at http://hydra.icgeb.trieste.it/benchmark.     

Exploring Nitrilase Sequence Space for Enantioselective Catalysis

Nitrilases are important in the biosphere as participants in synthesis and degradation pathways for naturally occurring, as well as xenobiotically derived, nitriles. Because of their inherent enantioselectivity, nitrilases are also attractive as mild, selective catalysts for setting chiral centers in fine chemical synthesis. Unfortunately, <20 nitrilases have been reported in the scientific and patent literature, and because of stability or specificity shortcomings, their utility has been largely unrealized. In this study, 137 unique nitrilases, discovered from screening of >600 biotope-specific environmental DNA (eDNA) libraries, were characterized. Using culture-independent means, phylogenetically diverse genomes were captured from entire biotopes, and their genes were expressed heterologously in a common cloning host. Nitrilase genes were targeted in a selection-based expression assay of clonal populations numbering 10⁶ to 10¹⁰ members per eDNA library. A phylogenetic analysis of the novel sequences discovered revealed the presence of at least five major sequence clades within the nitrilase subfamily. Using three nitrile substrates targeted for their potential in chiral pharmaceutical synthesis, the enzymes were characterized for substrate specificity and stereospecificity. A number of important correlations were found between sequence clades and the selective properties of these nitrilases. These enzymes, discovered using a high-throughput, culture-independent method, provide a catalytic toolbox for enantiospecific synthesis of a variety of carboxylic acid derivatives, as well as an intriguing library for evolutionary and structural analyses.     

Safety Indices during Fetal Echocardiography at the Time of First-Trimester Scan Are Machine Dependent

The aim of our study was to evaluate the thermal index (TI) and mechanical index (MI), during the assessment of the fetal heart at the time of first-trimester scan, with different ultrasound machines. This was part of an observational study conducted in patients undergoing routine first-trimester screening. Cases were examined with Voluson E8 or 730Pro scanners using 2–8 MHz transabdominal probes. TI and MI were retrieved from the saved displays while in gray mode, color flow mapping and pulsed-wave (PW) Doppler examinations of the fetal heart and also from the ductus venosus (DV) assessment. We evaluated 552 fetal cardiac examinations, 303 (55%) performed with Voluson E8 and 249 (45%) with Voluson 730Pro ultrasound machines. The gray-scale exam of the heart and the PW Doppler DV assessment had TI values significantly lower for the Voluson E8 group (median, 0.04 vs. 0.2 and 0.1 vs. 0.2, respectively). The MI values from gray-scale and color flow mapping of the heart were significantly lower (median, 0.6 vs, 1.2 and 0.7 vs. 1) and for PW Doppler exam of the tricuspid flow were significantly higher (median 0.4 vs. 0.2) in the Voluson E8 group. The TI values from Doppler examinations of the heart, either color flow or PW imaging and MI values from DV assessment were not significantly different between the two groups. A different (newer) generation of ultrasound equipment provides lower or at least the same safety indices for most of the first-trimester heart examinations.     

Early Evaluation and Monitoring of Critical Patients with Acute Respiratory Distress Syndrome (ARDS) Using Specific Genetic Polymorphisms

A high percentage of critical patients are found to develop acute respiratory distress syndrome (ARDS). Several studies have reported high mortality rates in these cases which are most frequently associated with multiple organ dysfunctions syndrome. Lately, many efforts have been made to evaluate and monitor ARDS in critical patients. In this regard, the assessment of genetic polymorphisms responsible for developing ARDS present as a challenge and are considered future biomarkers. Early detection of the specific polymorphic gene responsible for ARDS in critically ill patients can prove to be a useful tool in the future, able to help decrease the mortality rates in these cases. Moreover, identifying the genetic polymorphism in these patients can help in the implementation of a personalized intensive therapy scheme for every type of patient, based on its genotype.     

Clara cell secretory protein and phospholipase A2 activity modulate acute ventilator-induced lung injury in mice.

Lung vascular permeability is acutely increased by high-pressure and high-volume ventilation. To determine the roles of mechanically activated cytosolic PLA2 (cPLA2)and Clara cell secretory protein (CCSP), a modulator of cPLA2 activity, we compared lung injury with and without a PLA2 inhibitor in wild-type mice and CCSP-null mice (CCSP-/-) ventilated with high and low peak inflation pressures (PIP) for 2- or 4-h periods. After ventilation with high PIP, we observed significant increases in the bronchoalveolar lavage albumin concentrations, lung wet-to-dry weight ratios, and lung myeloperoxidase in both genotypes compared with unventilated controls and low-PIP ventilated mice. All injury variables except myeloperoxidase were significantly greater in the CCSP-/- mice relative to wild-type mice. Inhibition of cPLA2 in wild-type and CCSP-/- mice ventilated at high PIP for 4 h significantly reduced bronchoalveolar lavage albumin and total protein and lung wet-to-dry weight ratios compared with vehicle-treated mice of the same genotype. Membrane phospho-cPLA2 and cPLA2 activities were significantly elevated in lung homogenates of high-PIP ventilated mice of both genotypes but were significantly higher in the CCSP-/- mice relative to the wild-type mice. Inhibition of cPLA2 significantly attenuated both the phospho-cPLA2 increase and increased cPLA2 activity due to high-PIP ventilation. We propose that mechanical activation of the cPLA2 pathway contributes to acute high PIP-induced lung injury and that CCSP may reduce this injury through inhibition of the cPLA2 pathway and reduction of proinflammatory products produced by this pathway.     

Caveolin-1 overexpression correlates with tumour progression markers in pancreatic ductal adenocarcinoma

The assessment of caveolin-1 (Cav-1) as a marker of tumor aggressiveness in pancreatic ductal adenocarcinoma (PDAC). In this study, we examined the expression of Cav-1 in 34 human PDAC tissue samples and the associated peritumoral tissues by immunohistochemistry and western blot. Additionally, we correlated Cav-1 expression with other tissue (Ki-67, p53) and serum (CA 19-9) tumor markers. In the tumor-derived tissue, both tumor cells and blood vessels expressed Cav-1. In contrast, in peritumoral tissue, Cav-1 expression was confined mainly to blood vessels and was only occasionally expressed in ductal or parenchymal cells. Western blot analysis confirmed the overexpression of Cav-1 in pancreatic tumors compared with peritumoral tissue. Cav-1 expression in tumor tissues was correlated with both the Ki-67 LI (r = 0.95, P < 0.0001) and p53 expression (χ2 = 9.91, P < 0.005). Overexpression of Cav-1 was associated with tumor size, grade and stage and Cav-1 expression in tumors was correlated with an increased serum level of CA 19-9 (r = 0.795, P < 0.001). Based on the results of this study, the inclusion of Cav-1 in a putative panel of biomarkers predicting pancreatic cancer aggressiveness is warranted.