Revealing molecular mechanisms by integrating high-dimensional functional screens with protein interaction data

A New Pathway Promotes Adaptation of Human Glioblastoma Cells to Glucose Starvation

Adaptation of glioblastoma to caloric restriction induces compensatory changes in tumor metabolism that are incompletely known. Here we show that in human glioblastoma cells maintained in exhausted medium, SHC adaptor protein 3 (SHC3) increases due to down-regulation of SHC3 protein degradation. This effect is reversed by glucose addition and is not present in normal astrocytes. Increased SHC3 levels are associated to increased glucose uptake mediated by changes in membrane trafficking of glucose transporters of the solute carrier 2A superfamily (GLUT/SLC2A). We found that the effects on vesicle trafficking are mediated by SHC3 interactions with adaptor protein complex 1 and 2 (AP), BMP-2-inducible protein kinase and a fraction of poly ADP-ribose polymerase 1 (PARP1) associated to vesicles containing GLUT/SLC2As. In glioblastoma cells, PARP1 inhibitor veliparib mimics glucose starvation in enhancing glucose uptake. Furthermore, cytosol extracted from glioblastoma cells inhibits PARP1 enzymatic activity in vitro while immunodepletion of SHC3 from the cytosol significantly relieves this inhibition. The identification of a new pathway controlling glucose uptake in high grade gliomas represents an opportunity for repositioning existing drugs and designing new ones.

Omics Data Complementarity Underlines Functional Cross-Communication in Yeast

Mapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker’s yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein–protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO). We utilize a simple framework that identifies functional cross-communication lines in each of the three data types, in GO, and collectively in the integrated model of the three omics data types; we present each of them in our new Functional Organization Map (FOM) model. We compare the FOMs of the three omics datasets with the FOM of GO and find that GI is in best agreement with GO, followed COEX and PPI. We integrate the three FOMs into a unified FOM and find that it is in better agreement with the FOM of GO than those of any omics dataset alone, demonstrating functional complementarity of different omics data.

Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects

RNA interference and morphological profiling-the measurement of thousands of phenotypes from individual cells by microscopy and image analysis-are a potentially powerful combination. We show that morphological profiles of RNAi-induced knockdown using the Cell Painting assay are in fact highly sensitive and reproducible. However, we find that the magnitude and prevalence of off-target effects via the RNAi seed-based mechanism make morphological profiles of RNAi reagents targeting the same gene look no more similar than reagents targeting different genes. Pairs of RNAi reagents that share the same seed sequence produce image-based profiles that are much more similar to each other than profiles from pairs designed to target the same gene, a phenomenon previously observed in small-scale gene-expression profiling experiments. Various strategies have been used to enrich on-target versus off-target effects in the context of RNAi screening where a narrow set of phenotypes are measured, mostly based on comparing multiple sequences targeting the same gene; however, new approaches will be needed to make RNAi morphological profiling (that is, comparing multi-dimensional phenotypes) viable. We have shared our raw data and computational pipelines to facilitate research.