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

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

Effect of Quantum Barrier Thickness in the Multiple-Quantum-Well Active Region of GaInN/GaN Light-Emitting Diodes

The dependence of the polarization-induced electric field in GaInN/GaN multiple-quantum-well light-emitting diodes (LEDs) on the GaN quantum barrier (QB) thickness is investigated. Electrostatic arguments and simulations predict that a thin QB thickness reduces the electric field in the quantum wells (QWs) and also improves the LED efficiency. We experimentally demonstrate that the QW electric field decreases with decreasing QB thickness. The lower electric field results in a better overlap of electron and hole wave functions and better carrier confinement in the QWs. A reduced efficiency droop and enhanced internal quantum efficiency is demonstrated for GaInN/GaN LEDs when the QB thickness is reduced from 24.5 to 9.1 nm.X111210sciescopu

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

SummaryResponding 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

An In Vivo RNAi Screening Approach to Identify Host Determinants of Virus Replication

SummaryRNA 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

Regional Features of Long-Term Exposure to PM2.5 Air Quality over Asia under SSP Scenarios Based on CMIP6 Models

This study investigates changes in fine particulate matter (PM2.5) concentration and air-quality index (AQI) in Asia using nine different Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from historical and future scenarios under shared socioeconomic pathways (SSPs). The results indicated that the estimated present-day PM2.5 concentrations were comparable to satellite-derived data. Overall, the PM2.5 concentrations of the analyzed regions exceeded the WHO air-quality guidelines, particularly in East Asia and South Asia. In future SSP scenarios that consider the implementation of significant air-quality controls (SSP1-2.6, SSP5-8.5) and medium air-quality controls (SSP2-4.5), the annual PM2.5 levels were predicted to substantially reduce (by 46% to around 66% of the present-day levels) in East Asia, resulting in a significant improvement in the AQI values in the mid-future. Conversely, weak air pollution controls considered in the SSP3-7.0 scenario resulted in poor AQI values in China and India. Moreover, a predicted increase in the percentage of aged populations (>65 years) in these regions, coupled with high AQI values, may increase the risk of premature deaths in the future. This study also examined the regional impact of PM2.5 mitigations on downward shortwave energy and surface air temperature. Our results revealed that, although significant air pollution controls can reduce long-term exposure to PM2.5, it may also contribute to the warming of near- and mid-future climates

Regional Features of Long-Term Exposure to PM2.5 Air Quality over Asia under SSP Scenarios Based on CMIP6 Models.

This study investigates changes in fine particulate matter (PM2.5) concentration and air-quality index (AQI) in Asia using nine different Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from historical and future scenarios under shared socioeconomic pathways (SSPs). The results indicated that the estimated present-day PM2.5 concentrations were comparable to satellite-derived data. Overall, the PM2.5 concentrations of the analyzed regions exceeded the WHO air-quality guidelines, particularly in East Asia and South Asia. In future SSP scenarios that consider the implementation of significant air-quality controls (SSP1-2.6, SSP5-8.5) and medium air-quality controls (SSP2-4.5), the annual PM2.5 levels were predicted to substantially reduce (by 46% to around 66% of the present-day levels) in East Asia, resulting in a significant improvement in the AQI values in the mid-future. Conversely, weak air pollution controls considered in the SSP3-7.0 scenario resulted in poor AQI values in China and India. Moreover, a predicted increase in the percentage of aged populations (>65 years) in these regions, coupled with high AQI values, may increase the risk of premature deaths in the future. This study also examined the regional impact of PM2.5 mitigations on downward shortwave energy and surface air temperature. Our results revealed that, although significant air pollution controls can reduce long-term exposure to PM2.5, it may also contribute to the warming of near- and mid-future climates

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