Tunneling Nanotubes Provide a Unique Conduit for Intercellular Transfer of Cellular Contents in Human Malignant Pleural Mesothelioma

Tunneling nanotubes are long, non-adherent F-actin-based cytoplasmic extensions which connect proximal or distant cells and facilitate intercellular transfer. The identification of nanotubes has been limited to cell lines, and their role in cancer remains unclear. We detected tunneling nanotubes in mesothelioma cell lines and primary human mesothelioma cells. Using a low serum, hyperglycemic, acidic growth medium, we stimulated nanotube formation and bidirectional transfer of vesicles, proteins, and mitochondria between cells. Notably, nanotubes developed between malignant cells or between normal mesothelial cells, but not between malignant and normal cells. Immunofluorescent staining revealed their actin-based assembly and structure. Metformin and an mTor inhibitor, Everolimus, effectively suppressed nanotube formation. Confocal microscopy with 3-dimensional reconstructions of sectioned surgical specimens demonstrated for the first time the presence of nanotubes in human mesothelioma and lung adenocarcinoma tumor specimens. We provide the first evidence of tunneling nanotubes in human primary tumors and cancer cells and propose that these structures play an important role in cancer cell pathogenesis and invasion

In silico characterization of human prion-like proteins: beyond neurological diseases

Prion-like behavior has been in the spotlight since it was first associated with the onset of mammalian neurodegenerative diseases. However, a growing body of evidence suggests that this mechanism could be behind the regulation of processes such as transcription and translation in multiple species. Here, we perform a stringent computational survey to identify prion-like proteins in the human proteome. We detected 242 candidate polypeptides and computationally assessed their function, protein-protein interaction networks, tissular expression, and their link to disease. Human prion-like proteins constitute a subset of modular polypeptides broadly expressed across different cell types and tissues, significantly associated with disease, embedded in highly connected interaction networks, and involved in the flow of genetic information in the cell. Our analysis suggests that these proteins might play a relevant role not only in neurological disorders, but also in different types of cancer and viral infections.SV was supported by Ministerio de Economía y Competitividad (MINECO) (BIO2016-78310-R) and by ICREA, ICREA ACADEMIA 2015 to SV. PA was supported by MINECO (BIO2016-77038-R) and the European Research Council Consolidator grant, SysPharmAD (614944). TJ-B is a recipient of an FPI-SO fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Learning protein binding affinity using privileged information

Abstract Background Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data. Results In this study, we propose a novel machine learning method for predicting binding affinity that uses protein 3D structure as privileged information at training time while expecting only protein sequence information during testing. Using the method, which is based on the framework of learning using privileged information (LUPI), we have achieved improved performance over corresponding sequence-based binding affinity prediction methods that do not have access to privileged information during training. Our experiments show that with the proposed framework which uses structure only during training, it is possible to achieve classification performance comparable to that which is obtained using structure-based features. Evaluation on an independent test set shows improved performance over the PPA-Pred2 method as well. Conclusions The proposed method outperforms several baseline learners and a state-of-the-art binding affinity predictor not only in cross-validation, but also on an additional validation dataset, demonstrating the utility of the LUPI framework for problems that would benefit from classification using structure-based features. The implementation of LUPI developed for this work is expected to be useful in other areas of bioinformatics as well