Mechanistic insights into the inhibition of prostate specific antigen by beta-lactam class compounds

Prostate Specific Antigen (PSA) is a biomarker used in the diagnosis of prostate cancer and to monitor therapeutic response. However, its precise role in prostate carcinogenesis and metastasis remains largely unknown. A number of studies arguing in the favor of an active role of PSA in prostate cancer development and progression have implicated this serine protease in the release and activation of growth factors such as insulin-like growth factor 1 (IGF1) through cleavage of insulin like growth factor binding protein 3 and Transforming Growth Factor beta (TGF-beta) through cleavage of Latent TGF-beta. In contrast, other studies suggest that PSA activity might hinder tumor development and progression. In light of these contradictory findings, efficient inhibitors of PSA are needed for exploring its biological role in tumor development and metastasis. Towards the goal of developing potent inhibitors of PSA, we have explored the molecular mechanism of a series of beta-lactam based compounds on binding to PSA using activity assays, matrix assisted laser desorption ionization with a time-of-flight mass spectrometry, and GOLD docking methodology. The mass spectrometry experiments and the activity assays confirmed the time-dependent and covalent nature of beta-lactam binding. To gain insights on the reaction intermediates at the molecular level, we docked beta-lactam inhibitors to a homology modeled PSA using the GOLD docking program in noncovalent and covalent binding modes. The docking studies elucidated the molecular details of the early noncovalent Michaelis complex, the acyl-enzyme covalent complex, and the nature of conformational reorganization required for the long term stability of the covalent complex. Additionally, the molecular basis for the effect of stereochemistry of the lactam ring on the inhibitory potency was elucidated through docking of beta-lactam enantiomers. As a validation of our docking methodology, two novel enantiomers were synthesized and evaluated for their inhibitory potency using fluorogenic substrate based activity assays. Additionally, cis enantiomers of eight beta-lactam compounds reported in a previous study were docked and their GOLD scores and binding modes were analyzed in order to assess the general applicability of our docking results. The close agreement of our docking results with the experimental data validates the mechanistic insights revealed through the docking studies and paves the way for the design and development of potent and specific inhibitors of PSA.     

A dynamic model of thoracic differentiation for the control of turning in the stick insect

Leg movements of stick insects (Carausius morosus) making turns towards visual targets are examined in detail, and a dynamic model of this behaviour is proposed. Initial results suggest that front legs shape most of the body trajectory, while the middle and hind legs just follow external forces (Rosano H, Webb B, in The control of turning in real and simulated stick insects, vol. 4095, pp 145–156, 2006). However, some limitations of this explanation and dissimilarities in the turning behaviour of the insect and the model were found. A second set of behavioural experiments was made by blocking front tarsi to further investigate the active role of the other legs for the control of turning. The results indicate that it is necessary to have different roles for each pair of legs to replicate insect behaviour. We demonstrate that the rear legs actively rotate the body while the middle legs move sideways tangentially to the hind inner leg. Furthermore, we show that on average the middle inner and hind outer leg contribute to turning while the middle outer leg and hind inner leg oppose body rotation. These behavioural results are incorporated into a 3D dynamic robot simulation. We show that the simulation can now replicate more precisely the turns made by the stick insect. This work was supported by CONACYT México and the European Commission under project FP6-2003-IST2-004690 SPARK.     

Multiscale landscape genomic models to detect signatures of selection in the alpine plant Biscutella laevigata

Plant species are known to adapt locally to their environment, particularly in mountainous areas where conditions can vary drastically over short distances. The climate of such landscapes being largely influenced by topography, using fine‐scale models to evaluate environmental heterogeneity may help detecting adaptation to micro‐habitats. Here, we applied a multiscale landscape genomic approach to detect evidence of local adaptation in the alpine plant Biscutella laevigata. The two gene pools identified, experiencing limited gene flow along a 1‐km ridge, were different in regard to several habitat features derived from a very high resolution (VHR) digital elevation model (DEM). A correlative approach detected signatures of selection along environmental gradients such as altitude, wind exposure, and solar radiation, indicating adaptive pressures likely driven by fine‐scale topography. Using a large panel of DEM‐derived variables as ecologically relevant proxies, our results highlighted the critical role of spatial resolution. These high‐resolution multiscale variables indeed indicate that the robustness of associations between genetic loci and environmental features depends on spatial parameters that are poorly documented. We argue that the scale issue is critical in landscape genomics and that multiscale ecological variables are key to improve our understanding of local adaptation in highly heterogeneous landscapes.     

Host plant quality,selection history and trade-offs shape the immune responses of Manduca sexta

Immune defences are an important component of fitness. Yet susceptibility to pathogens is common, suggesting the presence of ecological and evolutionary limitations on immune defences. Here, we use structural equation modelling to quantify the direct effects of resource quality and selection history, and their indirect effects mediated via body condition prior to an immune challenge on encapsulation and melanization immune defences in the tobacco hornworm, Manduca sexta. We also investigate allocation trade-offs among immune defences and growth rate following an immune challenge. We found considerable variation in the magnitude and direction of the direct effects of resource quality and selection history on immune defences and their indirect effects mediated via body condition and allocation trade-offs. Greater resource quality and evolutionary exposure to pathogens had positive direct effects on encapsulation and melanization. The indirect effect of resource quality on encapsulation mediated via body condition was substantial, whereas indirect effects on melanization were negligible. Individuals in better condition prior to the immune challenge had greater encapsulation; however, following the immune challenge, greater encapsulation traded off with slower growth rate. Our study demonstrates the importance of experimentally and analytically disentangling the relative contributions of direct and indirect effects to understand variation in immune defences.     

Impacts of imperfect reference data on the apparent accuracy of species presence–absence models and their predictions

Aim To explore the impacts of imperfect reference data on the accuracy of species distribution model predictions. The main focus is on impacts of the quality of reference data (labelling accuracy) and, to a lesser degree, data quantity (sample size) on species presence–absence modelling. Innovation The paper challenges the common assumption that some popular measures of model accuracy and model predictions are prevalence independent. It highlights how imperfect reference data may impact on a study and the actions that may be taken to address problems. Main conclusions The theoretical independence of prevalence of popular accuracy measures, such as sensitivity, specificity, true skills statistics (TSS) and area under the receiver operating characteristic curve (AUC), is unlikely to occur in practice due to reference data error; all of these measures of accuracy, together with estimates of species occurrence, showed prevalence dependency arising through the use of a non‐gold‐standard reference. The number of cases used also had implications for the ability of a study to meet its objectives. Means to reduce the negative effects of imperfect reference data in study design and interpretation are suggested.     

Respiratory system dynamical mechanical properties: modeling in time and frequency domain

The mechanical properties of the respiratory system are important determinants of its function and can be severely compromised in disease. The assessment of respiratory system mechanical properties is thus essential in the management of some disorders as well as in the evaluation of respiratory system adaptations in response to an acute or chronic process. Most often, lungs and chest wall are treated as a linear dynamic system that can be expressed with differential equations, allowing determination of the system’s parameters, which will reflect the mechanical properties. However, different models that encompass nonlinear characteristics and also multicompartments have been used in several approaches and most specifically in mechanically ventilated patients with acute lung injury. Additionally, the input impedance over a range of frequencies can be assessed with a convenient excitation method allowing the identification of the mechanical characteristics of the central and peripheral airways as well as lung periphery impedance. With the evolution of computational power, the airway pressure and flow can be recorded and stored for hours, and hence continuous monitoring of the respiratory system mechanical properties is already available in some mechanical ventilators. This review aims to describe some of the most frequently used models for the assessment of the respiratory system mechanical properties in both time and frequency domain.     

Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.     

Protein Sequence Modules

Abstract

Conserved protein sequence segments are commonly believed to correspond to functional sites in the protein sequence. A novel approach is proposed to profile the changing degree of conservation along the protein sequence, by evaluating the occurrence frequencies of all short oligopeptides of the given sequence in a large proteome database. Thus, a protein sequence conservation profile can be plotted for every protein. The profile indicates where along the sequences the potential functional (conserved) sites are located. The corresponding oligopeptides belonging to the sites are very frequent across many prokaryotic species. Analysis of a representative set of such profiles reveals a common feature of all examined proteins: they consist of sequence modules represented by the peaks of conservation. Typical size of the modules (peak-to-peak distance) is 25–30 amino acid residues.