A major objective of systems biology is to organize molecular interactions as networks and to characterize information flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the 'signs' of interactions (i.e., activation-inhibition relationships). We constructed a Drosophila melanogaster signed PPI network consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes enolase and aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation-inhibition relationships between physically interacting proteins within signaling pathways will affect our understanding of many biological functions, including signal transduction and mechanisms of disease.

Antigen stimulation of immune cells triggers Ca2+ entry through Ca2+ release-activated Ca2+ (CRAC) channels, promoting the immune response to pathogens by activating the transcription factor NFAT. We have previously shown that cells from patients with one form of hereditary severe combined immune deficiency (SCID) syndrome are defective in store-operated Ca2+ entry and CRAC channel function. Here we identify the genetic defect in these patients, using a combination of two unbiased genome-wide approaches: a modified linkage analysis with single-nucleotide polymorphism arrays, and a Drosophila RNA interference screen designed to identify regulators of store-operated Ca2+ entry and NFAT nuclear import. Both approaches converged on a novel protein that we call Orai1, which contains four putative transmembrane segments. The SCID patients are homozygous for a single missense mutation in ORAI1, and expression of wild-type Orai1 in SCID T cells restores store-operated Ca2+ influx and the CRAC current (I(CRAC)). We propose that Orai1 is an essential component or regulator of the CRAC channel complex.

Characterizing the extent and logic of signaling networks is essential to understanding specificity in such physiological and pathophysiological contexts as cell fate decisions and mechanisms of oncogenesis and resistance to chemotherapy. Cell-based RNA interference (RNAi) screens enable the inference of large numbers of genes that regulate signaling pathways, but these screens cannot provide network structure directly. We describe an integrated network around the canonical receptor tyrosine kinase (RTK)-Ras-extracellular signal-regulated kinase (ERK) signaling pathway, generated by combining parallel genome-wide RNAi screens with protein-protein interaction (PPI) mapping by tandem affinity purification-mass spectrometry. We found that only a small fraction of the total number of PPI or RNAi screen hits was isolated under all conditions tested and that most of these represented the known canonical pathway components, suggesting that much of the core canonical ERK pathway is known. Because most of the newly identified regulators are likely cell type- and RTK-specific, our analysis provides a resource for understanding how output through this clinically relevant pathway is regulated in different contexts. We report in vivo roles for several of the previously unknown regulators, including CG10289 and PpV, the Drosophila orthologs of two components of the serine/threonine-protein phosphatase 6 complex; the Drosophila ortholog of TepIV, a glycophosphatidylinositol-linked protein mutated in human cancers; CG6453, a noncatalytic subunit of glucosidase II; and Rtf1, a histone methyltransferase.

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutamine-mediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington's Disease (HD) model.

Given the diversity of autophagy targets and regulation, it is important to characterize autophagy in various cell types and conditions. We used a primary myocyte cell culture system to assay the role of putative autophagy regulators in the specific context of skeletal muscle. By treating the cultures with rapamycin (Rap) and chloroquine (CQ) we induced an autophagic response, fully suppressible by knockdown of core ATG genes. We screened D. melanogaster orthologs of a previously reported mammalian autophagy protein-protein interaction network, identifying several proteins required for autophagosome formation in muscle cells, including orthologs of the Rab regulators RabGap1 and Rab3Gap1. The screen also highlighted the critical roles of the proteasome and glycogen metabolism in regulating autophagy. Specifically, sustained proteasome inhibition inhibited autophagosome formation both in primary culture and larval skeletal muscle, even though autophagy normally acts to suppress ubiquitin aggregate formation in these tissues. In addition, analyses of glycogen metabolic genes in both primary cultured and larval muscles indicated that glycogen storage enhances the autophagic response to starvation, an important insight given the link between glycogen storage disorders, autophagy, and muscle function.

Chlamydia spp. are intracellular obligate bacterial pathogens that infect a wide range of host cells. Here, we show that C. caviae enters, replicates, and performs a complete developmental cycle in Drosophila SL2 cells. Using this model system, we have performed a genome-wide RNA interference screen and identified 54 factors that, when depleted, inhibit C. caviae infection. By testing the effect of each candidate's knock down on L. monocytogenes infection, we have identified 31 candidates presumably specific of C. caviae infection. We found factors expected to have an effect on Chlamydia infection, such as heparansulfate glycosaminoglycans and actin and microtubule remodeling factors. We also identified factors that were not previously described as involved in Chlamydia infection. For instance, we identified members of the Tim-Tom complex, a multiprotein complex involved in the recognition and import of nuclear-encoded proteins to the mitochondria, as required for C. caviae infection of Drosophila cells. Finally, we confirmed that depletion of either Tom40 or Tom22 also reduced C. caviae infection in mammalian cells. However, C. trachomatis infection was not affected, suggesting that the mechanism involved is C. caviae specific.

The androgen receptor (AR) is a mediator of both androgen-dependent and castration-resistant prostate cancers. Identification of cellular factors affecting AR transcriptional activity could in principle yield new targets that reduce AR activity and combat prostate cancer, yet a comprehensive analysis of the genes required for AR-dependent transcriptional activity has not been determined. Using an unbiased genetic approach that takes advantage of the evolutionary conservation of AR signaling, we have conducted a genome-wide RNAi screen in Drosophila cells for genes required for AR transcriptional activity and applied the results to human prostate cancer cells. We identified 45 AR-regulators, which include known pathway components and genes with functions not previously linked to AR regulation, such as HIPK2 (a protein kinase) and MED19 (a subunit of the Mediator complex). Depletion of HIPK2 and MED19 in human prostate cancer cells decreased AR target gene expression and, importantly, reduced the proliferation of androgen-dependent and castration-resistant prostate cancer cells. We also systematically analyzed additional Mediator subunits and uncovered a small subset of Mediator subunits that interpret AR signaling and affect AR-dependent transcription and prostate cancer cell proliferation. Importantly, targeting of HIPK2 by an FDA-approved kinase inhibitor phenocopied the effect of depletion by RNAi and reduced the growth of AR-positive, but not AR-negative, treatment-resistant prostate cancer cells. Thus, our screen has yielded new AR regulators including drugable targets that reduce the proliferation of castration-resistant prostate cancer cells.