Meghana M. Kulkarni

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. J. Cell. Biochem. 94: 890–898, 2005.

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.

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 ≥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.

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

ABSTRACT 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.

Protein complex-based analysis framework for high-throughput data sets.

An analysis tool maps network dynamics at the protein complex level in multiple species. Complexes Reveal Signaling Dynamics Analysis of high-throughput data sets can provide information about changes in gene expression, protein abundance, and signaling pathway activity. However, current data mining approaches do not identify changes to functional protein complexes within a pathway over time, a critical aspect for network analysis. Vinayagam et al. developed an interactive Web tool called COMPLEAT, which uses raw genome-wide, RNA interference data to map protein complex dynamics during the cellular response to stimuli in humans, flies, and yeast. Using phosphorylated extracellular signal–regulated kinase as a marker for pathway activation, COMPLEAT identified the Brahma complex in the cellular response to insulin, a prediction that was validated in a Drosophila cell line. Analysis of high-throughput data increasingly relies on pathway annotation and functional information derived from Gene Ontology. This approach has limitations, in particular for the analysis of network dynamics over time or under different experimental conditions, in which modules within a network rather than complete pathways might respond and change. We report an analysis framework based on protein complexes, which are at the core of network reorganization. We generated a protein complex resource for human, Drosophila, and yeast from the literature and databases of protein-protein interaction networks, with each species having thousands of complexes. We developed COMPLEAT (http://www.flyrnai.org/compleat), a tool for data mining and visualization for complex-based analysis of high-throughput data sets, as well as analysis and integration of heterogeneous proteomics and gene expression data sets. With COMPLEAT, we identified dynamically regulated protein complexes among genome-wide RNA interference data sets that used the abundance of phosphorylated extracellular signal–regulated kinase in cells stimulated with either insulin or epidermal growth factor as the output. The analysis predicted that the Brahma complex participated in the insulin response.

Cytotoxic T Cells in PD-L1–Positive Malignant Pleural Mesotheliomas Are Counterbalanced by Distinct Immunosuppressive Factors

In malignant pleural mesothelioma, immunohistochemical expression of PD-L1 does not accurately predict whether patients respond to treatment with PD-1 pathway inhibitors. Comprehensive immunoprofiling by flow cytometry uncovered immunophenotypes that improve our understanding of response and resistance to checkpoint blockade. PD-L1 immunohistochemical staining does not always predict whether a cancer will respond to treatment with PD-1 inhibitors. We sought to characterize immune cell infiltrates and the expression of T-cell inhibitor markers in PD-L1–positive and PD-L1–negative malignant pleural mesothelioma samples. We developed a method for immune cell phenotyping using flow cytometry on solid tumors that have been dissociated into single-cell suspensions and applied this technique to analyze 43 resected malignant pleural mesothelioma specimens. Compared with PD-L1–negative tumors, PD-L1–positive tumors had significantly more infiltrating CD45+ immune cells, a significantly higher proportion of infiltrating CD3+ T cells, and a significantly higher percentage of CD3+ cells displaying the activated HLA-DR+/CD38+ phenotype. PD-L1–positive tumors also had a significantly higher proportion of proliferating CD8+ T cells, a higher fraction of FOXP3+/CD4+ Tregs, and increased expression of PD-1 and TIM-3 on CD4+ and CD8+ T cells. Double-positive PD-1+/TIM-3+ CD8+ T cells were more commonly found on PD-L1–positive tumors. Compared with epithelioid tumors, sarcomatoid and biphasic mesothelioma samples were significantly more likely to be PD-L1 positive and showed more infiltration with CD3+ T cells and PD-1+/TIM-3+ CD8+ T cells. Immunologic phenotypes in mesothelioma differ based on PD-L1 status and histologic subtype. Successful incorporation of comprehensive immune profiling by flow cytometry into prospective clinical trials could refine our ability to predict which patients will respond to specific immune checkpoint blockade strategies. Cancer Immunol Res; 4(12); 1038–48. ©2016 AACR.

Interleukin-17A Promotes Lung Tumor Progression through Neutrophil Attraction to Tumor Sites and Mediating Resistance to PD-1 Blockade

Introduction Proinflammatory cytokine interleukin‐17A (IL‐17A) is overexpressed in a subset of patients with lung cancer. We hypothesized that IL‐17A promotes a protumorigenic inflammatory phenotype and inhibits antitumor immune responses. Methods We generated bitransgenic mice expressing a conditional IL‐17A allele along with conditional KrasG12D and performed immune phenotyping of mouse lungs, a survival analysis, and treatment studies with antibodies either blocking programmed cell death 1 (PD‐1) or IL‐6 or depleting neutrophils. To support the preclinical findings, we analyzed human gene expression data sets and immune profiled patient lung tumors. Results Tumors in IL‐17:KrasG12D mice grew more rapidly, resulting in a significantly shorter survival as compared with that of KrasG12D mice. IL‐6, granulocyte colony‐stimulating factor (G‐CSF), milk fat globule‐EGF factor 8 protein, and C‐X‐C motif chemokine ligand 1 were increased in the lungs of IL17:Kras mice. Time course analysis revealed that levels of tumor‐associated neutrophils were significantly increased, and lymphocyte recruitment was significantly reduced in IL17:KrasG12D mice as compared with in KrasG12D mice. In therapeutic studies PD‐1 blockade was not effective in treating IL‐17:KrasG12D tumors. In contrast, blocking IL‐6 or depleting neutrophils with an anti–Ly‐6G antibody in the IL17:KrasG12D tumors resulted in a clinical response associated with T‐cell activation. In tumors from patients with lung cancer with KRAS mutation we found a correlation between higher levels of IL‐17A and colony‐ stimulating factor 3 and a significant correlation among high neutrophil and lower T‐cell numbers. Conclusions Here we have shown that an increase in a single cytokine, IL‐17A, without additional mutations can promote lung cancer growth by promoting inflammation, which contributes to resistance to PD‐1 blockade and sensitizes tumors to cytokine and neutrophil depletion.

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.

Genetic Determinants of Phosphate Response in Drosophila

Phosphate is required for many important cellular processes and having too little phosphate or too much can cause disease and reduce life span in humans. However, the mechanisms underlying homeostatic control of extracellular phosphate levels and cellular effects of phosphate are poorly understood. Here, we establish Drosophila melanogaster as a model system for the study of phosphate effects. We found that Drosophila larval development depends on the availability of phosphate in the medium. Conversely, life span is reduced when adult flies are cultured on high phosphate medium or when hemolymph phosphate is increased in flies with impaired Malpighian tubules. In addition, RNAi-mediated inhibition of MAPK-signaling by knockdown of Ras85D, phl/D-Raf or Dsor1/MEK affects larval development, adult life span and hemolymph phosphate, suggesting that some in vivo effects involve activation of this signaling pathway by phosphate. To identify novel genetic determinants of phosphate responses, we used Drosophila hemocyte-like cultured cells (S2R+) to perform a genome-wide RNAi screen using MAPK activation as the readout. We identified a number of candidate genes potentially important for the cellular response to phosphate. Evaluation of 51 genes in live flies revealed some that affect larval development, adult life span and hemolymph phosphate levels.

PPIRank - an advanced method for ranking protein-protein interations in TAP/MS data

BackgroundTandem affinity purification coupled with mass-spectrometry (TAP/MS) analysis is a popular method for the identification of novel endogenous protein-protein interactions (PPIs) in large-scale. Computational analysis of TAP/MS data is a critical step, particularly for high-throughput datasets, yet it remains challenging due to the noisy nature of TAP/MS data.ResultsWe investigated several major TAP/MS data analysis methods for identifying PPIs, and developed an advanced method, which incorporates an improved statistical method to filter out false positives from the negative controls. Our method is named PPIRank that stands for PPI rank ing in TAP/MS data. We compared PPIRank with several other existing methods in analyzing two pathway-specific TAP/MS PPI datasets from Drosophila.ConclusionExperimental results show that PPIRank is more capable than other approaches in terms of identifying known interactions collected in the BioGRID PPI database. Specifically, PPIRank is able to capture more true interactions and simultaneously less false positives in both Insulin and Hippo pathways of Drosophila Melanogaster.