Super-size flies.

The increasing prevalence of obesity and other nutrition-related chronic diseases has prompted considerable efforts to understand their pathogenesis and treatment. One experimental approach is to overexpress, inactivate, or manipulate specific genes that regulate energy metabolism and fat storage. Many such techniques are fully established, routine tools in Drosophila and C. elegans, which provide elegant models for dissecting endocrine problems and metabolic pathways.

cis-regulatory logic of short-range transcriptional repression in Drosophila melanogaster.

Bioinformatics analysis of transcriptional control is guided by knowledge of the characteristics of cis-regulatory regions or enhancers. Features such as clustering of binding sites and co-occurrence of binding sites have aided enhancer identification, but quantitative predictions of enhancer function are not yet generally feasible. To facilitate the analysis of regulatory sequences in Drosophila melanogaster, we identified quantitative parameters that affect the activity of short-range transcriptional repressors, proteins that play key roles in development. In addition to the previously noted distance dependence, repression is strongly influenced by the stoichiometry, affinity, spacing, and arrangement of activator binding sites. Repression is insensitive to the type of activation domain, suggesting that short-range repression may primarily affect activators at the level of DNA binding. The activity of several short-range, but not long-range, repressors is circumscribed by the same quantitative parameters. This cis-regulatory "grammar" may aid the identification of enhancers regulated by short-range repressors and facilitate bioinformatic prediction of the functional output of transcriptional regulatory sequences.

An Integrative Analysis of the InR/PI3K/Akt Network Identifies the Dynamic Response to Insulin Signaling.

Insulin regulates an essential conserved signaling pathway affecting growth, proliferation, and metabolism. To expand our understanding of the insulin pathway, we combine biochemical, genetic, and computational approaches to build a comprehensive Drosophila InR/PI3K/Akt network. First, we map the dynamic protein-protein interaction network surrounding the insulin core pathway using bait-prey interactions connecting 566 proteins. Combining RNAi screening and phospho-specific antibodies, we find that 47% of interacting proteins affect pathway activity, and, using quantitative phosphoproteomics, we demonstrate that ∼10% of interacting proteins are regulated by insulin stimulation at the level of phosphorylation. Next, we integrate these orthogonal datasets to characterize the structure and dynamics of the insulin network at the level of protein complexes and validate our method by identifying regulatory roles for the Protein Phosphatase 2A (PP2A) and Reptin-Pontin chromatin-remodeling complexes as negative and positive regulators of ribosome biogenesis, respectively. Altogether, our study represents a comprehensive resource for the study of the evolutionary conserved insulin network.

Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints.

Despite compelling antitumour activity of antibodies targeting the programmed death 1 (PD-1): programmed death ligand 1 (PD-L1) immune checkpoint in lung cancer, resistance to these therapies has increasingly been observed. In this study, to elucidate mechanisms of adaptive resistance, we analyse the tumour immune microenvironment in the context of anti-PD-1 therapy in two fully immunocompetent mouse models of lung adenocarcinoma. In tumours progressing following response to anti-PD-1 therapy, we observe upregulation of alternative immune checkpoints, notably T-cell immunoglobulin mucin-3 (TIM-3), in PD-1 antibody bound T cells and demonstrate a survival advantage with addition of a TIM-3 blocking antibody following failure of PD-1 blockade. Two patients who developed adaptive resistance to anti-PD-1 treatment also show a similar TIM-3 upregulation in blocking antibody-bound T cells at treatment failure. These data suggest that upregulation of TIM-3 and other immune checkpoints may be targetable biomarkers associated with adaptive resistance to PD-1 blockade.

Multiparametric profiling of non-small-cell lung cancers reveals distinct immunophenotypes.

BACKGROUND. Immune checkpoint blockade improves survival in a subset of patients with non-small-cell lung cancer (NSCLC), but robust biomarkers that predict response to PD-1 pathway inhibitors are lacking. Furthermore, our understanding of the diversity of the NSCLC tumor immune microenvironment remains limited. METHODS. We performed comprehensive flow cytometric immunoprofiling on both tumor and immune cells from 51 NSCLCs and integrated this analysis with clinical and histopathologic characteristics, next-generation sequencing, mRNA expression, and PD-L1 immunohistochemistry (IHC). RESULTS. Cytometric profiling identified an immunologically "hot" cluster with abundant CD8+ T cells expressing high levels of PD-1 and TIM-3 and an immunologically "cold" cluster with lower relative abundance of CD8+ T cells and expression of inhibitory markers. The "hot" cluster was highly enriched for expression of genes associated with T cell trafficking and cytotoxic function and high PD-L1 expression by IHC. There was no correlation between immunophenotype and KRAS or EGFR mutation, or patient smoking history, but we did observe an enrichment of squamous subtype and tumors with higher mutation burden in the "hot" cluster. Additionally, approximately 20% of cases had high B cell infiltrates with a subset producing IL-10. CONCLUSIONS. Our results support the use of immune-based metrics to study response and resistance to immunotherapy in lung cancer. FUNDING. The Robert A. and Renée E. Belfer Family Foundation, Expect Miracles Foundation, Starr Cancer Consortium, Stand Up to Cancer Foundation, Conquer Cancer Foundation, International Association for the Study of Lung Cancer, National Cancer Institute (R01 CA205150), and the Damon Runyon Cancer Research Foundation.

Digital multiplexed gene expression analysis using the NanoString nCounter system.

This unit presents the protocol for the NanoString nCounter Gene Expression Assay, a robust and highly reproducible method for detecting the expression of up to 800 genes in a single reaction with high sensitivity and linearity across a broad range of expression levels. The methodology serves to bridge the gap between genome-wide (microarrays) and targeted (real-time quantitative PCR) expression profiling. The nCounter assay is based on direct digital detection of mRNA molecules of interest using target-specific, color-coded probe pairs. It does not require the conversion of mRNA to cDNA by reverse transcription or the amplification of the resulting cDNA by PCR. Each target gene of interest is detected using a pair of reporter and capture probes carrying 35- to 50-base target-specific sequences. In addition, each reporter probe carries a unique color code at the 5' end that enables the molecular barcoding of the genes of interest, while the capture probes all carry a biotin label at the 3' end that provides a molecular handle for attachment of target genes to facilitate downstream digital detection. After solution-phase hybridization between target mRNA and reporter-capture probe pairs, excess probes are removed and the probe/target complexes are aligned and immobilized in the nCounter cartridge, which is then placed in a digital analyzer for image acquisition and data processing. Hundreds of thousands of color codes designating mRNA targets of interest are directly imaged on the surface of the cartridge. The expression level of a gene is measured by counting the number of times the color-coded barcode for that gene is detected, and the barcode counts are then tabulated.

Evidence of off-target effects associated with long dsRNAs in Drosophila melanogaster cell-based assays.

To evaluate the specificity of long dsRNAs used in high-throughput RNA interference (RNAi) screens performed at the Drosophila RNAi Screening Center (DRSC), we performed a global analysis of their activity in 30 genome-wide screens completed at our facility. Notably, our analysis predicts that dsRNAs containing > or = 19-nucleotide perfect matches identified in silico to unintended targets may contribute to a significant false positive error rate arising from off-target effects. We confirmed experimentally that such sequences in dsRNAs lead to false positives and to efficient knockdown of a cross-hybridizing transcript, raising a cautionary note about interpreting results based on the use of a single dsRNA per gene. Although a full appreciation of all causes of false positive errors remains to be determined, we suggest simple guidelines to help ensure high-quality information from RNAi high-throughput screens.

Transcriptional enhancers: Intelligent enhanceosomes or flexible billboards?

In higher eukaryotes, transcriptional enhancers play critical roles in the integration of cellular signaling information, but apart from a few well-studied model enhancers, we lack a general picture of transcriptional information processing by most enhancers. Here we discuss recent studies that have provided fresh insights on information processing that occurs on enhancers, and propose that in addition to the highly cooperative and coordinate action of "enhanceosomes", a less integrative, but more flexible form of information processing is mediated by information display or "billboard" enhancers. Application of these models has important ramifications not only for the biochemical analysis of transcription, but also for the wider fields of bioinformatics and evolutionary biology.

Information display by transcriptional enhancers.

Transcriptional enhancers integrate positional and temporal information to regulate the complex expression of developmentally controlled genes. Current models suggest that enhancers act as computational devices, receiving multiple inputs from activators and repressors and resolving them into a single positive or a negative signal that is transmitted to the basal transcriptional machinery. We show that a simple, compact enhancer is capable of representing both repressed and activated states at the same time and in the same nucleus. This finding suggests that closely apposed factor binding sites, situated within compact cis-elements, can be independently interpreted by the transcriptional machinery, possibly through successive enhancer-promoter interactions. These results provide clear evidence that the computational functions usually ascribed to the enhancer itself are actually shared with the basal machinery. In contrast to the autonomous computer model of enhancer function, an information-display or 'billboard' model of enhancer activity may better describe many developmentally regulated transcriptional enhancers.