Combination of a Proteomics Approach and Reengineering of Meso Scale Network Models for Prediction of Mode-of-Action for Tyrosine Kinase Inhibitors

In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs.     

Transcriptional Cofactor TBLR1 Controls Lipid Mobilization in White Adipose Tissue

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Highlights? TBLR1 controls cAMP-dependent lipolysis in adipocytes ? Adipocyte-specific deletion of TBLR1 in mice impairs fasting-induced lipolysis ? Lack of TBLR1 in adipocytes aggravates diet-induced obesity and metabolic dysfunction ? TBLR1 mRNA levels in WAT are elevated under lipolytic conditions in mice and humans     

Effect of Isovolemic,Isothermic Hemodialysis on Cerebral Perfusion and Vascular Stiffness Using Contrast Computed Tomography and Pulse Wave Velocity

Background

Patients undergoing hemodialysis treatment have a six-fold increased risk for stroke relative to the general population. However, the effect of hemodialysis on cerebral blood flow is poorly studied and confounding factors like blood pressure and ultrafiltration as well as temperature changes have rarely been accounted for. The aim of our study was to use state-of-the-art technology to evaluate the effect of a single dialysis session on cerebral perfusion as well as on vascular stiffness.

Methods

Chronic hemodialysis patients (7 male/3 female, mean age 58 years) were recruited. Cerebral blood flow and arterial pulse wave velocity were measured before and immediately after a hemodialysis session. To exclude effects of volume changes we kept ultrafiltration to a minimum, allowing no change in body weight. Isothermic conditions were maintained by using the GENIUS single-pass batch-dialysis system with a high-flux polysulfone dialyser. Cerebral blood flow was measured by contrast-enhanced computed tomography. Pulse wave velocity was measured using the SphygmoCor (AtCor Medical, USA) device by a single operator.

Results

This study shows for the first time that isovolemic, isothermic hemodialysis neither affected blood pressure or heart rate, nor total or regional cerebral perfusion. There was also no change in pulse wave velocity.

Conclusions

Mechanisms other than the dialysis procedure itself might be causative for the high incidence of ischemic strokes in this patient population. Moreover, the sole removal of uremic toxins does not lead to short-term effects on vascular stiffness, underlying the importance of volume control in this patient population.     

Process,correlation and parameter fitting in species distribution models: a response to Kriticos et al

In a recent article (Dormann et al., 2012, Journal of Biogeography, 39, 2119–2131), we compared different approaches to species distribution modelling and depicted modelling approaches along an axis from purely ‘correlative’ to ‘forward process‐based’ models. In their correspondence, Kriticos et al. (2013, Journal of Biogeography, doi: 10.1111/j.1365‐2699.2012.02791.x ) challenge this view, claiming that our continuum representation neglects differences among models and does not consider the ability of fitted process‐based models to combine the advantages of both process‐based and correlative modelling approaches. Here we clarify that the continuum view resulted from recognition of the manifold differences between models. We also reinforce the point that the current trend towards combining different modelling approaches may lead not only to the desired combination of the advantages but also to the accumulation of the disadvantages of those approaches. This point has not been made sufficiently clear previously.     

Temporal variability of ecological niches: a study on intertidal macrobenthic fauna

The determination of temporal niche dynamics under field conditions is an important component of a species’ ecology. Recent developments in niche mapping, and the possibility to account for spatial autocorrelation in species distributions, hold promise for the statistical approach explored here. Using species counts from a landscape‐scale benthic monitoring programme in the western Dutch Wadden Sea during 1997–2005 in combination with sediment characteristics and tidal height as explanatory variables, we statistically derive realised niches for two bivalves, two crustaceans and three polychaetes, encompassing predators, suspension and bottom feeding functional groups. Unsurprisingly, realized niches varied considerably between species. Intraspecific temporal variation was assessed as overlap between the year‐specific niche and the overall mean niche, and this analysis revealed considerable variation between years. The main functional groups represented by these species showed idiosyncratic and wide variability through the study period. There were no strong associations between niche characteristics and mean abundance or body size. Our assessment of intraspecific niche variability has ramifications for species distribution models in general and offers advances from previous methods. 1) By assessing species’ realized niches in the multivariate environmental space, analyses are independent from the relative availability of particular environments. Predicted realized niches present differences between years, rather than annual differences in environmental conditions. 2) Using spatially explicit models to predict species habitat preferences provide more precise and unbiased estimates of species–environment relationships. 3) Current niche models assume constant niches, whereas we illustrate how much these can vary over only a few generations. This emphasizes the potentially limited scope of global change studies with forecasts based on single‐time species distribution snapshots.