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Mitogen-activated protein kinase-interacting kinases 1 and 2 (MNK1 and MNK2) phosphorylate the oncogene eIF4E on serine 209. This phosphorylation has been reported to be required for its oncogenic activity. To investigate if pharmacological inhibition of MNK1 could be useful for the treatment of cancers, we pursued a comprehensive virtual screening approach to rapidly identify pharmacological tools for target validation and to find optimal starting points for a plausible medicinal chemistry project. A collection of 1236 compounds, selected from a library of 42 168 compounds and a database of 18.8 million structures, were assayed. Of the identified hits, 26 were found to have IC(50) values less than 10 μM (2.10% hit rate). The most potent compound had an IC(50) value of 117 nM, and 73.1% of these hits were fragments. The hits were characterized by a high ligand efficiency (0.32-0.52 kcal/mol per heavy atom). Ten different chemical scaffolds were represented, giving a chemotype/hit ratio of 0.38.

Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction of toxicity based on nucleus pattern recognition. Deep learning algorithms obtain abstract representations of images through an automated process, allowing them to efficiently classify complex patterns, and have become the state-of-the art in machine learning for computer vision. Here, deep convolutional neural networks (CNN) were trained to predict toxicity from images of DAPI-stained cells pre-treated with a set of drugs with differing toxicity mechanisms. Different cropping strategies were used for training CNN models, the nuclei-cropping-based Tox_CNN model outperformed other models classifying cells according to health status. Tox_CNN allowed automated extraction of feature maps that clustered compounds according to mechanism of action. Moreover, fully automated region-based CNNs (RCNN) were implemented to detect and classify nuclei, providing per-cell toxicity prediction from raw screening images. We validated both Tox_(R)CNN models for detection of pre-lethal toxicity from nuclei images, which proved to be more sensitive and have broader specificity than established toxicity readouts. These models predicted toxicity of drugs with mechanisms of action other than those they had been trained for and were successfully transferred to other cell assays. The Tox_(R)CNN models thus provide robust, sensitive, and cost-effective tools for in vitro screening of drug-induced toxicity. These models can be adopted for compound prioritization in drug screening campaigns, and could thereby increase the efficiency of drug discovery.

Caveolin-1 (CAV1) is over-expressed in prostate cancer (PCa) and is associated with adverse prognosis, but the molecular mechanisms linking CAV1 expression to disease progression are poorly understood. Extensive gene expression correlation analysis, quantitative multiplex imaging of clinical samples, and analysis of the CAV1-dependent transcriptome, supported that CAV1 re-programmes TGFβ signalling from tumour suppressive to oncogenic (i.e. induction of SLUG, PAI-1 and suppression of CDH1, DSP, CDKN1A). Supporting such a role, CAV1 knockdown led to growth arrest and inhibition of cell invasion in prostate cancer cell lines. Rationalized RNAi screening and high-content microscopy in search for CAV1 upstream regulators revealed integrin beta1 (ITGB1) and integrin associated proteins as CAV1 regulators. Our work suggests TGFβ signalling and beta1 integrins as potential therapeutic targets in PCa over-expressing CAV1, and contributes to better understand the paradoxical dual role of TGFβ in tumour biology.

The influence of lyophilization on the aromatic profile of two different truffles from Spain (Tuber melanosporum and Tuber aestivum) and a cultivated truffle (Tuber indicum) was evaluated by means of the headspace analysis. The volatile compounds were separated by gas chromatography and identified by mass spectrometry. The truffle aroma contained the characteristic compounds, such as 2‐methyl‐1‐propanol, 2‐methyl‐1‐butanol and dimethyl sulfide. Lyophilization and the subsequent rehydration of the truffles did not affect significantly the aroma profile of T. melanosporum; however, the volatile contents of T. indicum were slightly modified and those of T. aestivum changed after the treatment, in terms of reducing the 2‐butanol and 2‐butanone percentages and increasing the 2‐methylpropanal, 2‐methylbutanal and 3‐methylbutanal concentrations. From this study, we can conclude that truffle aromatic profile of the species T. melanosporum and T. indicum is mainly maintained after lyophilization whereas T. aestivum profile is substantially modified. Practical Applications Truffles are products with limited shelf life and their sensory properties are rapidly lost so that these fungi become a less valuable product in a few days. Losses of volatile compounds, oxidation and enzymatic reactions are a considerable problem during their storage. Several techniques have been used to preserve their sensory properties; however, the aroma profile is commonly modified as a result of high temperature processes or enzymatic reactions. This article describes the use of freeze‐drying as a form of processing technique for truffles (Tuber melanosporum, Tuber aestivum and Tuber indicum) to be stored. The use of lyophilization avoids the loss and degradation of volatile compounds because the process is performed at low temperatures. The results showed that the truffle aromatic profile of the species T. melanosporum and T. indicum was mainly maintained after lyophilization, whereas T. aestivum profile was substantially modified.