Biology subjects

Sorger, P. K.

Publications and source records attributed to Sorger, P. K..

5 recordsLinked to original sources

bioRxiv2018

Drug adaptation influences cardiotoxicity caused by tyrosine kinase inhibitors in iPSC-derived human cardiomyocytes

Cardiotoxicity induced by anti-cancer drugs is of increasing concern as the durability of therapeutic responses increases. The molecular basis of cardiotoxicity remains poorly understood, particularly when due to drug classes that do not inhibit the hERG potassium channel or cause the arrhythmias associated with long QT syndrome. This paper describes systematic molecular profiling of one such class of drugs, tyrosine kinase inhibitors (TKIs), which are widely used to treat solid tumors. Human cardiomyocytes differentiated from induced pluripotent stem cells (hiPSC-CMs) were exposed to one of four TKIs (Sunitinib, Sorafenib, Lapatinib and Erlotinib) observed to cause different levels of human cardiotoxicity and profiled by RNA sequencing (RNA-Seq) and mass spectroscopy-based proteomic analysis. We find that TKIs have diverse effects on hiPSC-CMs but genes involved in cardiac metabolism are particularly sensitive. In the case of Sorafenib, many genes involved in oxidative phosphorylation are down regulated resulting in a profound defect in mitochondrial metabolism. Cells adapt to this by upregulating aerobic glycolysis. Metabolic remodeling makes cells less acutely sensitive to Sorafenib and the effect is reversible upon drug withdrawal. Thus, the response of cardiomyocytes to Sorafenib is characterized by adaptive drug resistance previously described in tumor cells.

pharmacology and toxicology↗

bioRxiv2018

Genome-encoded Cytoplasmic Double-Stranded RNAs, Found in C9ORF72 ALS-FTD Brain, Provoke Propagated Neuronal Death

Innate immune signaling activation and DNA damage are pathological hallmarks of aging that may herald multiple adult-onset neurodegenerative diseases. Here, we report that both cell autonomous and non-autonomous neuronal death are triggered by the production of cytoplasmic double-stranded RNA (cdsRNA) from a regulated, disarticulated transgene in the setting of type I interferon (IFN-I) signaling. CdsRNA is a pathogen associated molecular pattern that induces IFN-I in many cell types. Transfection of a dsRNA mimetic into cultured human neurons also induces IFN-I signaling and cell death in a dose-dependent manner. Direct relevance to human disease is found in neurons of ALS-FTD patients carrying C9ORF72 intronic hexanucleotide expansions; cdsRNA isolated from these tissues is comprised of repeat sequences. Together, these findings implicate cdsRNA generated from genomic sequences in neurons as a trigger for sterile, viral-mimetic IFN-I induction and propagated neuronal death within in a neural circuit in the aging nervous system.

neuroscience↗

bioRxiv2017

A multi-center study on factors influencing the reproducibility of in vitro drug-response studies

Evidence that some influential biomedical results cannot be repeated has increased interest in practices that generate data meeting findable, accessible, interoperable and reproducible (FAIR) standards. Multiple papers have identified examples of irreproducibility, but practical steps for increasing reproducibility have not been widely studied. Here, seven research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cell biology, and patient stratification. While many experimental and computational factors have an impact on intra- and inter-center reproducibility, the factors most difficult to identify and correct are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways of identifying such context-sensitive factors, thereby advancing the conceptual and practical basis for greater experimental reproducibility.

cancer biology↗

bioRxiv2017

From word models to executable models of signaling networks using automated assembly

Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage; (ii) adaptive drug resistance in BRAF-V600E mutant melanomas; and (iii) the RAS signaling pathway. The use of natural language for modeling makes routine tasks more efficient for modeling practitioners and increases the accessibility and transparency of models for the broader biology community.\n\nStandfirst textINDRA uses natural language processing systems to read descriptions of molecular mechanisms and assembles them into executable models.\n\nHighlightsO_LIINDRA decouples the curation of knowledge as word models from model implementation\nC_LIO_LIINDRA is connected to multiple natural language processing systems and can draw on information from curated databases\nC_LIO_LIINDRA can assemble dynamical models in rule-based and reaction network formalisms, as well as Boolean networks and visualization formats\nC_LIO_LIWe used INDRA to build models of p53 dynamics, resistance to targeted inhibitors of BRAF in melanoma, and the Ras signaling pathway from natural language\nC_LI

systems biology↗

bioRxiv2017

Maintaining the provenance of microscopy metadata using OMERO.forms software

The creation of datasets that are findable, accessible, interoperable and reproducible (the FAIR standard) requires that data provenance be maintained1. Provenance is particularly important for microscopy data, whose interpretation is dependent on the biological context (e.g. cell state) and detection reagent (e.g. antibody.) This paper describes a new software tool, OMERO.forms, that extends the OMERO microscopy data management system2 to simplify and enhance metadata entry and provenance tracking.

scientific communication and education↗