Presenilin-Based Genetic Screens in Drosophila melanogaster Identify Novel Notch Pathway Modifiers

Presenilin is the enzymatic component of γ-secretase, a multisubunit intramembrane protease that processes several transmembrane receptors, such as the amyloid precursor protein (APP). Mutations in human Presenilins lead to altered APP cleavage and early-onset Alzheimer's disease. Presenilins also play an essential role in Notch receptor cleavage and signaling. The Notch pathway is a highly conserved signaling pathway that functions during the development of multicellular organisms, including vertebrates, Drosophila, and C. elegans. Recent studies have shown that Notch signaling is sensitive to perturbations in subcellular trafficking, although the specific mechanisms are largely unknown. To identify genes that regulate Notch pathway function, we have performed two genetic screens in Drosophila for modifiers of Presenilin-dependent Notch phenotypes. We describe here the cloning and identification of 19 modifiers, including nicastrin and several genes with previously undescribed involvement in Notch biology. The predicted functions of these newly identified genes are consistent with extracellular matrix and vesicular trafficking mechanisms in Presenilin and Notch pathway regulation and suggest a novel role for γ-tubulin in the pathway

Correction: Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages

<p>Correction: Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages</p

CYP27A1 dependent anti-melanoma activity of limonoid natural products targets mitochondrial metabolism

Three limonoid natural products with selective anti-proliferative activity against BRAF(V600E) and NRAS(Q61K)-mutation dependent melanoma cell lines were identified. Differential transcriptome analysis revealed dependency of compound activity on expression of the mitochondrial cytochrome P450 oxidase CYP27A1, a transcriptional target of MITF. We determined that CYP27A1 activity is necessary for the generation of a reactive metabolite that proceeds to inhibit cellular proliferation. A genome-wide siRNA screen in combination with chemical proteomics experiments revealed genedrug functional epistasis, suggesting that these compounds target mitochondrial biogenesis and inhibit tumor bioenergetics. Our work suggests a strategy for melanoma specific targeting by exploiting the expression of MITF target gene CYP27A1 and inhibiting mitochondrial oxidative phosphorylation in BRAF mutant melanomas

GREP reveals informative relationships between genes.

<p>(<i>A</i>) Network representation of ratios that significantly differentiate response identified by GREP^DR5. Genes are connected if they are involved in a ratio, sized based on the number of ratios in which they appear, and colored based on their positivity (%times they appear in the numerator of ratios; ratios were ordered so that they are positively correlated with sensitivity). Red indicates positive, while blue indicates negative. Ratios used in the classifier are shown as bold connections. (<i>B</i>) Importance of individual genes in GREP^DR5. Importance of individual features, each assessed using the receiver operator characteristics area under curve (AUC_ROC) accuracy measure between the full GREP^DR5 and one built with that feature excluded.</p

GREP improves predictions and translatability over standard approaches for feature selection and classification.

<p>(<i>A</i>) GREP^DR5 compared to ratio classifiers with random gene sets (N = 100) of the same size as the hypothesis gene set. 90% of the models were not significantly better than random (Fisher’s test p-value>0.05). Examination of some random models that performed significantly showed that they included DR5-related genes. (<i>B</i>) Challenging the GREP modeling assumptions in predicting <i>in vitro</i> response. GREP^DR5 was compared to four models, each built without one or more of its key assumptions (error bars show 95% confidence from cross-validation). GREP^DR5 outperforms random and 2-gene classifiers, but a standard gene expression predictor that used ratios for feature selection performed just as well in cell lines. (<i>C</i>) Challenging the GREP modeling assumptions in predicting <i>in vivo</i> response. Validation of the classifier predictions in pancreatic patient-derived tumor xenograft models (PTX) compared to classifiers built with single genes. Assuming a 30% margin of error on the PPV calculation for 11 samples (95% confidence), GREP outperforms both the 2-gene classifier and a standard gene expression predictor that used ratios for feature selection. Vertical dotted line denotes AUC of random classifier (0.5).</p

GREP^DR5 accurately translates across model systems (<i>in vitro</i> to <i>in vivo</i>) and platforms (microarray to RNA-seq).

<p>GREP^DR5 predictions trained on cell lines, has been applied to pancreatic primary tumor xenograft (PTX) samples using microarray and RNA-seq data. <i>(A)</i> Performance of DR5+Casp8 predictor on 11 PTX models results in a PPV = 50%. <i>(B)</i> GREP^DR5 prediction probability accurately predicts %T/C in 11 pancreatic PTX models (PPV = 100%). Additionally, GREP^DR5 predictions correlates linearly with the anti-tumor activity (%T/C or %Regression) (Pearson’s R = -0.91, p = 10⁻⁵). <i>(C)</i> GREP^DR5 predictions between the microarray and RNA-seq platforms are highly correlated (R = 0.87), showing that GREP can be readily used for translation to another platform. It should be noted that the data was not transformed (e.g. scaled or batch corrected) before applying the predictions.</p

New insights from old data - Hunting for compounds with novel mechanisms using cellular high-throughput screening profiles with Grey Chemical Matter

Identifying high quality chemical starting points is a critical and challenging step in drug discovery, which typically involves screening large compound libraries or repurposing of compounds with known mechanisms of actions (MoAs). Here we introduce a novel cheminformatics approach that mines existing large-scale, phenotypic high throughput screening (HTS) data. Our method aims to identify bioactive compounds with distinct and specific MoAs, serving as a valuable complement to existing focused library collections. This approach identifies chemotypes with selectivity across multiple cell-based assays and characterized by persistent and broad structure activity relationships (SAR). We prospectively demonstrate the validity of the approach in broad cellular profiling assays (cell painting, DRUG-seq, Promotor Signature Profiling) and chemical proteomics experiments where the compounds behave similarly to known chemogenetic libraries, but with a bias towards novel protein targets and required no synthetic effort to improve compound properties. A public set of such compounds is provided based on the PubChem BioAssay dataset for use by the scientific community

DR5 and CASP8 expression are top ranking features correlated with sensitivity and together improve predictions.

<p>(<i>A</i>) Differential association of gene expression using p-values to assess the significance of gene expression features correlated with DR5Nb1-tetra sensitivity. Inset shows the top 20 genes (dotted line = FDR < 0.05). Colors are based on association with sensitives (red) and insensitives (blue). (<i>B</i>) Comparison of relative surface protein levels of DR5 to mRNA expression in 25 pancreatic cancer cell lines. Points are colored based on sensitivity to DR5Nb1-tetra: sensitives (red), intermediates (orange) and insensitives (blue). <i>(C)</i> Induction of Casp8 activity compared to DR5 gene expression in 27 pancreatic cell lines. (<i>D</i>) DR5+Casp8 expression compared to sensitivity. DR5 and Casp8 are individually significantly associated with response, as shown in the marginal histograms for sensitives (red), insensitives (blue), with overlap shown in purple (DR5: p = 10⁻¹², Casp8: p = 10⁻⁹). Scatterplot of DR5 and Casp8 shows that cell lines with high expression of both genes (marked by red box) are enriched in sensitives (PPV = 44%) compared to high DR5 (PPV = 34%) and unselected (PPV = 34%).</p

DR5Nb1-tetra is selective with responses in multiple tumor lineages.

<p>(<i>A</i>) Schematic diagram of DR5Nb1-tetra Nanobody. <i>(B)</i> Composition of <i>in vitro</i> pan-cancer screen tested for response to DR5Nb1-tetra. <i>(C)</i> DR5Nb1-tetra response in the CLiP, CRXX and Lab screens. Response is shown as A_max relative to IC₅₀. A_max cut-offs for sensitive, intermediate, and insensitive classes are drawn. (<i>D</i>) Consistency of A_max values across the three screens. A_max values for each screen (CLIP, CRXX and Lab) are shown as a heatmap colored to represent sensitive (red), intermediate (yellow) and insensitive (blue) categories defined using the same thresholds for A_max across the three screens. Missing values are shown in gray. (<i>E</i>) Response rates (% sensitives) are plotted for each of the lineages (#cell lines ≥10). Lineages with significant (p<0.05 using Fisher’s exact test) enrichment are denoted by *.</p