Research data from the University of British Columbia Michael Smith Laboratories
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Protease-inhibitor interaction predictions: Lessons on the complexity of protein-protein interactionsby Fortelny, Nikolaus; Butler, Georgina; Overall, Christopher; Pavlidis, Paul

Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features. Thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of non-proteolytic and non-inhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task, and thereby highlight limitations of computational interaction prediction methods.

Production Date:2017
Last Released: Apr 3, 2017

Type 2 innate lymphoid cells (ILC2) potentiate adaptive immune responses however whether they have a role in mediating cancer immunity has not been assessed. Here, we report that mice genetically lacking ILC2s have significantly increased tumour growth rates and higher frequency of circulating tumour cells (CTCs) and metastasis to distal organs. Our data supports the conclusions that tumour-infiltrating ILC2s help mediate tumour immune-surveillance by promoting adaptive T cells responses and that ILC2s play a hitherto undescribed role in controlling metastasis. Furthermore, we demonstrate that adoptive transfer of ILC2s mediates a dramatic regression in cancer growth. Therefore, the adoptive transfer of ILC2s provides a new immunotherapeutic approach with the potential to aid in the eradication of cancers.

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Last Released: Mar 29, 2017
Transcriptomic correlates of neuron electrophysiological diversityby Tripathy, Shreejoy; Toker, Lilah; Li, Brenna; Crichlow, Cindy-Lee; Tebaykin, Dmitry; Mancarci, Ogan; Shreejoy Tripathy

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating transcriptomics with intracellular electrophysiology. Using a brain-wide dataset of 34 neuron types, we identified 653 genes whose expression levels significantly correlated with variability in one or more of 11 electrophysiological parameters. The majority of these correlations were further consistent in an independent sample of 12 visual cortex cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. These results suggest that despite the complexity linking gene expression to electrophysiology, there are likely some general principles that govern how individual genes establish phenotypic diversity across very different cell types.

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Last Released: May 4, 2017
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