Death Induced by CD95 or CD95 Ligand Elimination

SummaryCD95 (Fas/APO-1), when bound by its cognate ligand CD95L, induces cells to die by apoptosis. We now show that elimination of CD95 or CD95L results in a form of cell death that is independent of caspase-8, RIPK1/MLKL, and p53, is not inhibited by Bcl-xL expression, and preferentially affects cancer cells. All tumors that formed in mouse models of low-grade serous ovarian cancer or chemically induced liver cancer with tissue-specific deletion of CD95 still expressed CD95, suggesting that cancer cannot form in the absence of CD95. Death induced by CD95R/L elimination (DICE) is characterized by an increase in cell size, production of mitochondrial ROS, and DNA damage. It resembles a necrotic form of mitotic catastrophe. No single drug was found to completely block this form of cell death, and it could also not be blocked by the knockdown of a single gene, making it a promising way to kill cancer cells

Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome

Abstract Background To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data, which is less biologically relevant than sampling by mass spectrometry, and other tools are limited by incomplete exploration of machine learning approaches. Herein, we assemble publicly available data describing human peptides discovered by sampling the MHC-I immunopeptidome with mass spectrometry and use this database to train random forest classifiers (ForestMHC) to predict presentation by MHC-I. Results As measured by precision in the top 1% of predictions, our method outperforms NetMHC and NetMHCpan on test sets, and it outperforms both these methods and MixMHCpred on new data from an ovarian carcinoma cell line. We also find that random forest scores correlate monotonically, but not linearly, with known chemical binding affinities, and an information-based analysis of classifier features shows the importance of anchor positions for our classification. The random-forest approach also outperforms a deep neural network and a convolutional neural network trained on identical data. Finally, we use our large database to confirm that gene expression partially determines peptide presentation. Conclusions ForestMHC is a promising method to identify peptides bound by MHC-I. We have demonstrated the utility of random forest-based approaches in predicting peptide presentation by MHC-I, assembled the largest known database of MS binding data, and mined this database to show the effect of gene expression on peptide presentation. ForestMHC has potential applicability to basic immunology, rational vaccine design, and neoantigen binding prediction for cancer immunotherapy. This method is publicly available for applications and further validation

Walk-through experiment results for 78 ATS siRNA duplexes.

<p>Results of walk-through experiments measured at day 6 post transfection with synthetic siRNA duplexes using EGFPB reporter cell line. (<b>A</b>) Clustered heatmap to show % gain in EGFP signal conferred by 78 ATS siRNA duplexes, tested as singles. (<b>B</b>) Clustered heatmap to show % gain in EGFP signal conferred by siRNA duplexes segregated into three pools that of duplexes active as singles, inactive as singles or with all inclusive. Rep stands for replicate, AVG stands for average of the four replicates.</p

Schematics for Alternate Targeting Sequence Generator (ATSG).

<p>Hairpin inside the cell gets cleaved at its theoretical site and silences its target specifically. Inefficiencies in cleavage would lead to ATSG, generating random targeting sequencing which silence alternate targets, making it extremely difficult to comprehend the eventual phenotypic outcomes.</p

Chemical & RNAi Screening at MSKCC: A Collaborative Platform to Discover & Repurpose Drugs to Fight Disease

Memorial Sloan-Kettering Cancer Center (MSKCC) has implemented the creation of a full service state-of-the-art High-throughput Screening Core Facility (HTSCF) equipped with odern robotics and custom-built screening data management resources to rapidly store and uery chemical and RNAi screening data outputs. The mission of the facility is to provide ncology clinicians and researchers alike with access to cost-effective HTS solutions for both hemical and RNAi screening, with an ultimate goal of novel target identification and drug iscovery. HTSCF was established in 2003 to support the institution’s commitment to growth in olecular pharmacology and in the realm of therapeutic agents to fight chronic diseases such as ancer. This endeavor required broad range of expertise in technology development to establish obust and innovative assays, large collections of diverse chemical and RNAi duplexes to probe pecific cellular events, sophisticated compound and data handling capabilities, and a profound nowledge in assay development, hit validation, and characterization. Our goal has been to strive or constant innovation, and we strongly believe in shifting the paradigm from traditional drug iscovery towards translational research now, making allowance for unmet clinical needs in patients. Our efforts towards epurposing FDA-approved drugs fructified when digoxin, identified through primary HTS, was administered in the clinic for treatment of stage Vb retinoblastoma. In summary, the overall aim of our facility is to identify novel chemical probes, to study cellular processes relevant to investigator’s research interest in chemical biology and functional genomics, and to be instrumental in accelerating the process of drug discovery in academia