MALDI–MS Profiling to Address Honey Bee Health Status under Bacterial Challenge through Computational Modeling

Honey bees play a critical role in the maintenance of plant biodiversity and sustainability of food webs. In the past few decades, bees have been subjected to biotic and abiotic threats causing various colony disorders. Therefore, monitoring solutions to help beekeepers to improve bee health are necessary. Matrix‐assisted laser desorption ionization–mass spectrometry (MALDI–MS) profiling has emerged within this decade as a powerful tool to identify in routine micro‐organisms and is currently used in real‐time clinical diagnosis. MALDI BeeTyping is developed to monitor significant hemolymph molecular changes in honey bees upon infection with a series of entomopathogenic Gram‐positive and ‐negative bacteria. A Serratia marcescens strain isolated from one naturally infected honey bee collected from the field is also considered. A series of hemolymph molecular mass fingerprints is individually recorded and to the authors' knowledge, the first computational model harboring a predictive score of 97.92% and made of nine molecular signatures that discriminate and classify the honey bees’ systemic response to the bacteria is built. Hence, the model is challenged by classifying a training set of hemolymphs and an overall recognition of 91.93% is obtained. Through this work, a novel, time and cost saving high‐throughput strategy that addresses honey bee health on an individual scale is introduced.     

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model‐data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter‐model variation is generally large and model agreement varies with timescales. In severely water‐limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter‐model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.     

Overcoming resistance to rapalogs in gliomas by combinatory therapies

Glioblastoma is the most common and aggressive brain tumor type, with a mean patient survival of approximately 1 year. Many previous analyses of the glioma kinome have identified key deregulated pathways that converge and activate mammalian target of rapamycin (mTOR). Following the identification and characterization of mTOR-promoting activity in gliomagenesis, data from preclinical studies suggested the targeting of mTOR by rapamycin or its analogs (rapalogs) as a promising therapeutic approach. However, clinical trials with rapalogs have shown very limited efficacy on glioma due to the development of resistance mechanisms. Analysis of rapalog-insensitive glioma cells has revealed increased activity of growth and survival pathways compensating for mTOR inhibition by rapalogs that are suitable for therapeutic intervention. In addition, recently developed mTOR inhibitors show high anti-glioma activity. In this review, we recapitulate the regulation of mTOR signaling and its involvement in gliomagenesis, discuss mechanisms resulting in resistance to rapalogs, and speculate on strategies to overcome resistance. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases (2012).     

The growth-hormone inducible transmembrane protein (Ghitm) belongs to the Bax inhibitory protein-like family

The conserved protein domain UPF0005 is a protein family signature distributed among many species including fungi and bacteria. Although of unknown functionality this motif has been found in newly identified antiapoptotic proteins comprising the BI-1 family, namely Bax-inhibitory Protein-1 (BI-1), Lifeguard (LFG), and h-GAAP. In a search for vertebrate proteins presumably belonging to the BI-1 family, we found that Growth-hormone inducible transmembrane protein (Ghitm) is another prospective member of the BI-1 family. Here we characterise Ghitm in a first analysis regarding its phylogeny, expression in cancer cell lines, and proteomical properties.