Predicting individual-specific cardiotoxicity responses induced by tyrosine kinase inhibitors.

Introduction: Tyrosine kinase inhibitor drugs (TKIs) are highly effective cancer drugs, yet many TKIs are associated with various forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse events remain poorly understood. We studied mechanisms of TKI-induced cardiotoxicity by integrating several complementary approaches, including comprehensive transcriptomics, mechanistic mathematical modeling, and physiological assays in cultured human cardiac myocytes. Methods: Induced pluripotent stem cells (iPSCs) from two healthy donors were differentiated into cardiac myocytes (iPSC-CMs), and cells were treated with a panel of 26 FDA-approved TKIs. Drug-induced changes in gene expression were quantified using mRNA-seq, changes in gene expression were integrated into a mechanistic mathematical model of electrophysiology and contraction, and simulation results were used to predict physiological outcomes. Results: Experimental recordings of action potentials, intracellular calcium, and contraction in iPSC-CMs demonstrated that modeling predictions were accurate, with 81% of modeling predictions across the two cell lines confirmed experimentally. Surprisingly, simulations of how TKI-treated iPSC-CMs would respond to an additional arrhythmogenic insult, namely, hypokalemia, predicted dramatic differences between cell lines in how drugs affected arrhythmia susceptibility, and these predictions were confirmed experimentally. Computational analysis revealed that differences between cell lines in the upregulation or downregulation of particular ion channels could explain how TKI-treated cells responded differently to hypokalemia. Discussion: Overall, the study identifies transcriptional mechanisms underlying cardiotoxicity caused by TKIs, and illustrates a novel approach for integrating transcriptomics with mechanistic mathematical models to generate experimentally testable, individual-specific predictions of adverse event risk.

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity.

Kinase inhibitors (KIs) represent an important class of anti-cancer drugs. Although cardiotoxicity is a serious adverse event associated with several KIs, the reasons remain poorly understood, and its prediction remains challenging. We obtain transcriptional profiles of human heart-derived primary cardiomyocyte like cell lines treated with a panel of 26 FDA-approved KIs and classify their effects on subcellular pathways and processes. Individual cardiotoxicity patient reports for these KIs, obtained from the FDA Adverse Event Reporting System, are used to compute relative risk scores. These are then combined with the cell line-derived transcriptomic datasets through elastic net regression analysis to identify a gene signature that can predict risk of cardiotoxicity. We also identify relationships between cardiotoxicity risk and structural/binding profiles of individual KIs. We conclude that acute transcriptomic changes in cell-based assays combined with drug substructures are predictive of KI-induced cardiotoxicity risk, and that they can be informative for future drug discovery.

A Comparison of mRNA Sequencing with Random Primed and 3'-Directed Libraries.

Creating a cDNA library for deep mRNA sequencing (mRNAseq) is generally done by random priming, creating multiple sequencing fragments along each transcript. A 3'-end-focused library approach cannot detect differential splicing, but has potentially higher throughput at a lower cost, along with the ability to improve quantification by using transcript molecule counting with unique molecular identifiers (UMI) that correct PCR bias. Here, we compare an implementation of such a 3'-digital gene expression (3'-DGE) approach with "conventional" random primed mRNAseq. Given our particular datasets on cultured human cardiomyocyte cell lines, we find that, while conventional mRNAseq detects ~15% more genes and needs ~500,000 fewer reads per sample for equivalent statistical power, the resulting differentially expressed genes, biological conclusions, and gene signatures are highly concordant between two techniques. We also find good quantitative agreement at the level of individual genes between two techniques for both read counts and fold changes between given conditions. We conclude that, for high-throughput applications, the potential cost savings associated with 3'-DGE approach are likely a reasonable tradeoff for modest reduction in sensitivity and inability to observe alternative splicing, and should enable many larger scale studies focusing on not only differential expression analysis, but also quantitative transcriptome profiling.

Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics.

Tyrosine kinase inhibitors (TKIs) are highly potent cancer therapeutics that have been linked with serious cardiotoxicity, including left ventricular dysfunction, heart failure, and QT prolongation. TKI-induced cardiotoxicity is thought to result from interference with tyrosine kinase activity in cardiomyocytes, where these signaling pathways help to control critical processes such as survival signaling, energy homeostasis, and excitation-contraction coupling. However, mechanistic understanding is limited at present due to the complexities of tyrosine kinase signaling, and the wide range of targets inhibited by TKIs. Here, we review the use of TKIs in cancer and the cardiotoxicities that have been reported, discuss potential mechanisms underlying cardiotoxicity, and describe recent progress in achieving a more systematic understanding of cardiotoxicity via the use of mechanistic models. In particular, we argue that future advances are likely to be enabled by studies that combine large-scale experimental measurements with Quantitative Systems Pharmacology (QSP) models describing biological mechanisms and dynamics. As such approaches have proven extremely valuable for understanding and predicting other drug toxicities, it is likely that QSP modeling can be successfully applied to cardiotoxicity induced by TKIs. We conclude by discussing a potential strategy for integrating genome-wide expression measurements with models, illustrate initial advances in applying this approach to cardiotoxicity, and describe challenges that must be overcome to truly develop a mechanistic and systematic understanding of cardiotoxicity caused by TKIs.

RNase III nucleases from diverse kingdoms serve as antiviral effectors.

In contrast to the DNA-based viruses in prokaryotes, the emergence of eukaryotes provided the necessary compartmentalization and membranous environment for RNA viruses to flourish, creating the need for an RNA-targeting antiviral system. Present day eukaryotes employ at least two main defence strategies that emerged as a result of this viral shift, namely antiviral RNA interference and the interferon system. Here we demonstrate that Drosha and related RNase III ribonucleases from all three domains of life also elicit a unique RNA-targeting antiviral activity. Systemic evolution of ligands by exponential enrichment of this class of proteins illustrates the recognition of unbranched RNA stem loops. Biochemical analyses reveal that, in this context, Drosha functions as an antiviral clamp, conferring steric hindrance on the RNA-dependent RNA polymerases of diverse positive-stranded RNA viruses. We present evidence for cytoplasmic translocation of RNase III nucleases in response to virus in diverse eukaryotes including plants, arthropods, fish, and mammals. These data implicate RNase III recognition of viral RNA as an antiviral defence that is independent of, and possibly predates, other known eukaryotic antiviral systems.

microRNA Function Is Limited to Cytokine Control in the Acute Response to Virus Infection.

With the capacity to fine-tune protein expression via sequence-specific interactions, microRNAs (miRNAs) help regulate cell maintenance and differentiation. While some studies have also implicated miRNAs as regulators of the antiviral response, others have found that the RISC complex that facilitates miRNA-mediated silencing is rendered nonfunctional during cellular stress, including virus infection. To determine the global role of miRNAs in the cellular response to virus infection, we generated a vector that rapidly eliminates total cellular miRNA populations in terminally differentiated primary cultures. Loss of miRNAs has a negligible impact on both innate sensing of and immediate response to acute viral infection. In contrast, miRNA depletion specifically enhances cytokine expression, providing a posttranslational mechanism for immune cell activation during cellular stress. This work highlights the physiological role of miRNAs during the antiviral response and suggests their contribution is limited to chronic infections and the acute activation of the adaptive immune response.

In Vivo RNAi Screening Identifies MDA5 as a Significant Contributor to the Cellular Defense against Influenza A Virus.

Responding to an influenza A virus (IAV) infection demands an effective intrinsic cellular defense strategy to slow replication. To identify contributing host factors to this defense, we exploited the host microRNA pathway to perform an in vivo RNAi screen. To this end, IAV, lacking a functional NS1 antagonist, was engineered to encode individual siRNAs against antiviral host genes in an effort to rescue attenuation. This screening platform resulted in the enrichment of strains targeting virus-activated transcription factors, specific antiviral effectors, and intracellular pattern recognition receptors (PRRs). Interestingly, in addition to RIG-I, the PRR for IAV, a virus with the capacity to silence MDA5 also emerged as a dominant strain in wild-type, but not in MDA5-deficient mice. Transcriptional profiling of infected knockout cells confirmed RIG-I to be the primary PRR for IAV but implicated MDA5 as a significant contributor to the cellular defense against influenza A virus.

Stable heteroplasmy at the single-cell level is facilitated by intercellular exchange of mtDNA.

Eukaryotic cells carry two genomes, nuclear (nDNA) and mitochondrial (mtDNA), which are ostensibly decoupled in their replication, segregation and inheritance. It is increasingly appreciated that heteroplasmy, the occurrence of multiple mtDNA haplotypes in a cell, plays an important biological role, but its features are not well understood. Accurately determining the diversity of mtDNA has been difficult, due to the relatively small amount of mtDNA in each cell (<1% of the total DNA), the intercellular variability of mtDNA content and mtDNA pseudogenes (Numts) in nDNA. To understand the nature of heteroplasmy, we developed Mseek, a novel technique to purify and sequence mtDNA. Mseek yields high purity (>90%) mtDNA and its ability to detect rare variants is limited only by sequencing depth, providing unprecedented sensitivity and specificity. Using Mseek, we confirmed the ubiquity of heteroplasmy by analyzing mtDNA from a diverse set of cell lines and human samples. Applying Mseek to colonies derived from single cells, we find heteroplasmy is stably maintained in individual daughter cells over multiple cell divisions. We hypothesized that the stability of heteroplasmy could be facilitated by intercellular exchange of mtDNA. We explicitly demonstrate this exchange by co-culturing cell lines with distinct mtDNA haplotypes. Our results shed new light on the maintenance of heteroplasmy and provide a novel platform to investigate features of heteroplasmy in normal and diseased states.

The Mammalian response to virus infection is independent of small RNA silencing.

A successful cellular response to virus infection is essential for evolutionary survival. In plants, arthropods, and nematodes, cellular antiviral defenses rely on RNAi. Interestingly, the mammalian response to virus is predominantly orchestrated through interferon (IFN)-mediated induction of antiviral proteins. Despite the potency of the IFN system, it remains unclear whether mammals also have the capacity to employ antiviral RNAi. Here, we investigated this by disabling IFN function, small RNA function, or both activities in the context of virus infection. We find that loss of small RNAs in the context of an in vivo RNA virus infection lowers titers due to reduced transcriptional repression of the host antiviral response. In contrast, enabling a virus with the capacity to inhibit the IFN system results in increased titers. Taken together, these results indicate that small RNA silencing is not a physiological contributor to the IFN-mediated cellular response to virus infection.

Drosha as an interferon-independent antiviral factor.

Utilization of antiviral small interfering RNAs is thought to be largely restricted to plants, nematodes, and arthropods. In an effort to determine whether a physiological interplay exists between the host small RNA machinery and the cellular response to virus infection in mammals, we evaluated antiviral activity in the presence and absence of Dicer or Drosha, the RNase III nucleases responsible for generating small RNAs. Although loss of Dicer did not compromise the cellular response to virus infection, Drosha deletion resulted in a significant increase in virus levels. Here, we demonstrate that diverse RNA viruses trigger exportin 1 (XPO1/CRM1)-dependent Drosha translocation into the cytoplasm in a manner independent of de novo protein synthesis or the canonical type I IFN system. Additionally, increased virus infection in the absence of Drosha was not due to a loss of viral small RNAs but, instead, correlated with cleavage of viral genomic RNA and modulation of the host transcriptome. Taken together, we propose that Drosha represents a unique and conserved arm of the cellular defenses used to combat virus infection.

An in vivo RNAi screening approach to identify host determinants of virus replication.

RNA interference (RNAi) has been extensively used to identify host factors affecting virus infection but requires exogenous delivery of short interfering RNAs (siRNAs), thus limiting the technique to nonphysiological infection models and a single defined cell type. We report an alternative screening approach using siRNA delivery via infection with a replication-competent RNA virus. In this system, natural selection, defined by siRNA production, permits the identification of host restriction factors through virus enrichment during a physiological infection. We validate this approach with a large-scale siRNA screen in the context of an in vivo alphavirus infection. Monitoring virus evolution across four independent screens identified two categories of enriched siRNAs: specific effectors of the direct antiviral arsenal and host factors that indirectly dampened the overall antiviral response. These results suggest that pathogenicity may be defined by the ability of the virus to antagonize broad cellular responses and specific antiviral factors.

MiST: a new approach to variant detection in deep sequencing datasets.

MiST is a novel approach to variant calling from deep sequencing data, using the inverted mapping approach developed for Geoseq. Reads that can map to a targeted exonic region are identified using exact matches to tiles from the region. The reads are then aligned to the targets to discover variants. MiST carefully handles paralogous reads that map ambiguously to the genome and clonal reads arising from PCR bias, which are the two major sources of errors in variant calling. The reduced computational complexity of mapping selected reads to targeted regions of the genome improves speed, specificity and sensitivity of variant detection. Compared with variant calls from the GATK platform, MiST showed better concordance with SNPs from dbSNP and genotypes determined by an exonic-SNP array. Variant calls made only by MiST confirm at a high rate (>90%) by Sanger sequencing. Thus, MiST is a valuable alternative tool to analyse variants in deep sequencing data.