Biology subjects

Chen, T.

Publications and source records attributed to Chen, T..

At least 289 records · Page 16Linked to original sources

bioRxiv2020

Likelihood Approximation Networks (LANs) for Fast Inference of Simulation Models in Cognitive Neuroscience

In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by the set of models with known likelihood functions. For many models, the lack of a closed-form likelihood typically impedes Bayesian inference methods. As a result, standard models are evaluated for convenience, even when other models might be superior. Likelihood-free methods exist but are limited by their computational cost or their restriction to particular inference scenarios. Here, we propose neural networks that learn approximate likelihoods for arbitrary generative models, allowing fast posterior sampling with only a one-off cost for model simulations that is amortized for future inference. We show that these methods can accurately recover posterior parameter distributions for a variety of neurocognitive process models. We provide code allowing users to deploy these methods for arbitrary hierarchical model instantiations without further training.

neuroscience↗

bioRxiv2020

Large-scale Gradient Dysfunction of the Functional Connectome in Major Depression

BackgroundPatients with major depressive disorder (MDD) exhibit concurrent deficits in sensory processing and high-order cognitive functions such as self-awareness and rumination. Connectome mapping studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this principal connectome gradient is disrupted in patients with MDD and how this disruption is associated with gene expression profiles remain unclear. MethodsUsing a large cohort of resting-state functional magnetic resonance imaging data from 2,234 participants (1,150 patients with MDD and 1,084 healthy controls) recruited at 10 sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth and postmortem gene expression data to assess the cognitive functions and transcriptional profiles related to the gradient alterations in MDD, respectively. ResultsRelative to controls, patients with MDD exhibited abnormal global topography of the principal primary-to-transmodal gradient, as indicated by reduced explanation ratio, gradient range, and gradient variation (Cohens d = -0.16[~]-0.21). Focal alterations of gradient scores were mostly in the primary systems involved in sensory processing and in the transmodal systems implicated in high-order cognition. The transcriptional profiles explained 53.9% of the spatial variance in the altered gradient patterns, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. ConclusionsThese results highlight the dysfunction of the core connectome hierarchy in MDD and its linkage with gene expression profiles, providing insights into the neurobiological and molecular genetic underpinnings of sensory-cognitive deficits in this disorder.

neuroscience↗

bioRxiv2020

Live poultry feeding and trading network and the transmission of avian influenza A(H5N6) virus in a large city in China, 2014-2015

To understand clearly the mechanism of H5N6 transmission, the role of a live poultry feeding and trading network should be explored deeply. However, there is little data to show the network in an area. In this study, we performed a field epidemiological investigation to collect the numbers of farms, wholesale markets, and live poultry retail markets, and the numbers of purchased (from where) and sold (to where) live birds in Changsha City, China in 2014. We also collected samples from the network in the city from January 2014 to March 2015, including the LPMs visited by the patient known to be infected with A(H5N6) virus, and sequenced the genomes of 10 A(H5N6) viral strains isolated from these environmental samples, to determine the source of virus that infected the patient reported in Changsha. Additionally, we collected and analyzed A(H5N6) virus genome sequence isolated from humans, poultry, and LPMs registered in National Center for Biotechnology Information (NCBI; NIH, Bethesda, MD, USA) 14, to determine the source of other human infections with A(H5N6) virus reported in China. Changsha City in China has a large live poultry feeding and trading network (LPFTN) which had 665 farms, 5 wholesale markets, and 223 live poultry retail markets in 2014. The network covered nine provinces and purchased and sold more than 150000 live birds every day. About 840 environmental samples collected from the LPFTN network from January 2014 to March 2015. About 8.45% (71/840) environmental samples were shown to be positive for N6 and 10 full genome sequences of H5N6 virus were analyzed. We performed phylogenetic analyses and virus characterization, which demonstrated that the H5N6 viral strains isolated from Chinese patients were closely related to those isolated from the poultry and environmental samples obtained from the LPFTN network. This indicates that the network with a large volume of live poultry provides a platform for the transmission of H5N6, and provides an infectious pool which makes the people in high risk. ImportanceAvian influenza A(H5N6) virus is an emerging threat to public health. With several human cases reported in recent years, it has become a dominant avian influenza virus (AIV) subtype in China. Live poultry feeding and trading network (LPFTN) contributes to the presence of novel AIV. By a field epidemiological investigation in the complex LPFTN in Changsha, we demonstrated that the H5N6 viral strains isolated from Chinese patients were closely related to those isolated from the poultry and environmental samples obtained from the network, suggesting the LPFTN with a large volume of live poultry provides a platform for the transmission of H5N6 and creates an infectious pool which makes people in high risk. Considering the wide circulation and dynamic reassortment of this highly pathogenic avian influenza H5N6 virus, it should be carefully monitored in poultry and humans due to the pandemic potential.

molecular biology↗

bioRxiv2020

Caloric Restriction Remodels Energy Metabolic Pathways of Gut Microbiota and Promotes Host Autophagy

Calorie restriction (CR) can improve the metabolic balance of adults and elevate the relative abundance of probiotic bacteria in the gut while promoting longevity. However, the interaction between remodeled intestinal flora and metabolic improvement, as well as the mechanism for probiotic bacterial increase, are still unclear. In this study, using a metabolomics platform, we demonstrate for the first time, that CR leads to increased levels of malate and its related metabolites in biological samples. Next, we investigated the effects of CR on the gut microbial genome and the expression of mRNA related to energy metabolism which revealed a partially elevated TCA cycle and a subsequently promoted glyoxylate cycle, from which large amounts of malate can be produced to further impact malate related pathways in the host liver. Through the identification of key "hungry" metabolites produced by the gut microbiota that function in the promotion of autophagy in the host, further insight has been gained about a functional metabolic network important for both host-microbial symbiosis and maintenance of host health.

systems biology↗

bioRxiv2020

ResistoXplorer: a web-based tool for visual, statistical and exploratory data analysis of resistome data

The study of resistomes using whole metagenomic sequencing enables high throughput identification of resistance genes in complex microbial communities, such as the human microbiome. Over recent years, sophisticated and diverse pipelines have been established to facilitate raw data processing and annotation. Despite the progress, there are no easy-to-use tools for comprehensive visual, statistical, and functional analysis of resistome data. Thus, exploration of the resulting large complex datasets remains a key bottleneck requiring robust computational resources and technical expertise, which creates a significant hurdle for advancements in the field. Here, we introduce ResistoXplorer, a user-friendly tool that integrates recent advancements in statistics and visualization, coupled with extensive functional annotations and phenotype collection, to enable high-throughput analysis of common outputs generated from metagenomic resistome studies. ResistoXplorer contains three modules- the Antimicrobial Resistance Gene Table module offers various options for composition profiling, functional profiling and comparative analysis of resistome data; the Integration module supports integrative exploratory analysis of resistome and microbiome abundance profiles derived from metagenomic samples; finally, the Antimicrobial Resistance Gene List module enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights. ResistoXplorer is publicly available at http://www.resistoxplorer.no.

microbiology↗

bioRxiv2020

Optogenetic activation of spinal microglia triggers chronic pain in mice

Spinal microglia are highly responsive to peripheral nerve injury and are known to be a key player in neuropathic pain. However, there has not been any direct evidence showing selective microglial activation in vivo is sufficient to induce chronic pain. Here we used optogenetic approaches in microglia to address this question employing CX3CR1creER/+: R26LSL-ReaChR/+ transgenic mice, in which red-activated channelrhodopsin (ReaChR) is inducibly and specifically expressed in microglia. We found that activation of ReaChR by red light in spinal microglia evoked reliable inward currents and membrane depolarization. In vivo optogenetic activation of microglial ReaChR in the spinal cord triggered chronic pain hypersensitivity lasting for 5-7 days. In addition, activation of microglial ReaChR upregulated neuronal c-fos expression and enhanced C-fiber responses. Mechanistically, ReaChR activation led to a reactive microglial phenotype with increased IL-1{beta} production. IL-1 receptor antagonist was able to reverse the pain hypersensitivity and neuronal hyperactivity induced by microglial ReaChR activation. Therefore, our work demonstrates that optogenetic activation of spinal microglia is sufficient to trigger chronic pain phenotypes by increasing neuronal activity via IL-1 signaling.

neuroscience↗

bioRxiv2020

T4SE-XGB: interpretable sequence-based prediction of type IV secreted effectors using eXtreme gradient boosting algorithm

Type IV secreted effectors (T4SEs) can be translocated into the cytosol of host cells via type IV secretion system (T4SS) and cause diseases. However, experimental approaches to identify T4SEs are time- and resource-consuming, and the existing computational tools based on machine learning techniques have some obvious limitations such as the lack of interpretability in the prediction models. In this study, we proposed a new model, T4SE-XGB, which uses the eXtreme gradient boosting (XGBoost) algorithm for accurate identification of type IV effectors based on optimal features based on protein sequences. After trying 20 different types of features, the best performance was achieved when all features were fed into XGBoost by the 5-fold cross validation in comparison with other machine learning methods. Then, the ReliefF algorithm was adopted to get the optimal feature set on our dataset, which further improved the model performance. T4SE-XGB exhibited highest predictive performance on the independent test set and outperformed other published prediction tools. Furthermore, the SHAP method was used to interpret the contribution of features to model predictions. The identification of key features can contribute to improved understanding of multifactorial contributors to host-pathogen interactions and bacterial pathogenesis. In addition to type IV effector prediction, we believe that the proposed framework can provide instructive guidance for similar studies to construct prediction methods on related biological problems. The data and source code of this study can be freely accessed at https://github.com/CT001002/T4SE-XGB.

bioinformatics↗

bioRxiv2020

SHP2 Inhibition Abrogates Adaptive Resistance to KRASG12C-Inhibition and Remodels the Tumor Microenvironment of KRAS-Mutant Tumors

KRAS is the most frequently mutated oncogene in human cancer, and KRAS inhibition has been a longtime therapeutic goal. Recently, inhibitors (G12C-Is) that bind KRASG12C-GDP and react with Cys-12 were developed. Using new affinity reagents to monitor KRASG12C activation and inhibitor engagement, we found that, reflecting its action upstream of SOS1/2, SHP2 inhibitors (SHP2-Is) increased KRAS-GDP occupancy, enhancing G12C-I efficacy. SHP2-Is abrogated feedback signaling by multiple RTKs and blocked adaptive resistance to G12C-Is in vitro, in xenografts, and in syngeneic KRASG12C-mutant pancreatic ductal adenocarcinoma (PDAC) and non-small cell lung cancer (NSCLC) models. Biochemical analysis revealed enhanced suppression of ERK-, MYC-, anti-apoptotic-, and cell-cycle genes, and increased pro-apoptotic gene expression in tumors from combination-treated mice. SHP2-I/G12C-I also evoked favorable changes in the immune microenvironment, decreasing myeloid suppressor cells, increasing CD8+ T cells, and sensitizing tumors to PD-1 blockade. Experiments using cells expressing inhibitor-resistant SHP2 showed that SHP2 inhibition in PDAC cells is required for tumor regression and remodeling of the immune microenvironment, but also revealed direct inhibitory effects on angiogenesis resulting in decreased tumor vascularity. Our results demonstrate that SHP2-I/G12C-I combinations confer a substantial survival benefit in PDAC and NSCLC and identify additional combination strategies for enhancing the efficacy of G12C-Is.

cancer biology↗

bioRxiv2020

Towards Mycobacterium tuberculosis detection at the point-of-care: a brighter solvatochromic probe permits the detection of mycobacteria within minutes

There is an urgent need for point-of-care tuberculosis (TB) diagnostic methods that are fast, inexpensive, and operationally simple. Here, we report on a bright solvatochromic dye trehalose conjugate that specifically detects Mycobacterium tuberculosis (Mtb) in minutes. 3-hydroxychromone (3HC) dyes, known to yield high fluorescence quantum yields, exhibit shifts in fluorescence intensity in response to changes in environmental polarity. We synthesized two analogs of 3HC-trehalose conjugates (3HC-2-Tre and 3HC-3-Tre) and determined that 3HC-3-Tre has exceptionally favorable properties for Mtb detection. 3HC-3-Tre-labeled mycobacterial cells displayed a 10-fold increase in fluorescence intensity compared to our previously reports on the dye 4,4-N,N-dimethylaminonapthalimide (DMN-Tre). Excitingly, we detected fluorescent Mtb cells within 10 minutes of probe treatment. Thus, 3HC-3-Tre permits rapid visualization of mycobacteria that ultimately could empower improved Mtb detection at the point-of-care in low-resource settings.

microbiology↗

bioRxiv2020

Chemogenetic manipulation of microglia inhibits neuroinflammation and neuropathic pain in mice

Microglia play an important role in the central sensitization and chronic pain. However, a direct connection between microglial function and the development of neuropathic pain in vivo remains incompletely understood. To address this issue, we applied chemogenetic approach by using CX3CR1creER/+:R26LSL-hM4Di/+ transgenic mice to enable expression of inhibitory Designer Receptors Exclusively Activated by Designer Drugs (Gi DREADD) exclusively in microglia. We found that microglial Gi DREADD activation inhibited spinal nerve transection (SNT)-induced microglial reactivity as well as chronic pain initiation and maintenance. Gi DREADD activation downregulated the transcription factor interferon regulatory factor 8 (IRF8) and its downstream target pro-inflammatory cytokine interleukin 1 beta (IL-1{beta}). Using in vivo spinal cord recording, we found that activation of microglial Gi DREADD attenuated synaptic transmission following SNT. Our results demonstrate that microglial Gi DREADD reduces neuroinflammation, synaptic function and neuropathic pain after peripheral nerve injury. Thus, chemogenetic approaches provide a potential opportunity for interrogating microglial function and neuropathic pain treatment.

neuroscience↗

bioRxiv2020

MEKK3-MEK5-ERK5 signaling promotes mitochondrial degradation

Mitochondria are vital organelles that coordinate cellular energy homeostasis and have important roles in cell death. Therefore, the removal of damaged or excessive mitochondria is critical for maintaining proper cellular function. The PINK1-Parkin pathway removes acutely damaged mitochondria through a well-characterized mitophagy pathway, but basal mitochondrial turnover occurs via distinct and less well-understood mechanisms. Here we report that the MEKK3-MEK5-ERK5 kinase cascade is required for mitochondrial degradation in the absence of exogenous damage. We demonstrate that genetic or pharmacological inhibition of the MEKK3-MEK5-ERK5 pathway increases mitochondrial content by reducing lysosome-mediated degradation of mitochondria under basal conditions. We show that the MEKK3-MEK5-ERK5 pathway plays a selective role in basal mitochondrial degradation but is not required for non-selective bulk autophagy, damage-induced mitophagy, or restraint of mitochondrial biogenesis. This illuminates the MEKK3-MEK5-ERK5 pathway as a positive regulator of mitochondrial degradation that acts independently of exogenous mitochondrial stressors.

cell biology↗

bioRxiv2020

Distributed implementation of Boolean functions by transcriptional synthetic circuits

Starting in the early 2000s, a sophisticated technology has been developed for the rational construction of synthetic genetic networks that implement specified logical functionalities. Despite impressive progress, however, the scaling necessary in order to achieve greater computational power has been hampered by many constraints, including repressor toxicity and the lack of large sets of mutually-orthogonal repressors. As a consequence, a typical circuit contains no more than roughly seven repressor-based gates per cell. A possible way around this scalability problem is to distribute the computation among multiple cell types, which communicate among themselves using diffusible small molecules (DSMs) and each of which implements a small sub-circuit. Examples of DSMs are those employed by quorum sensing systems in bacteria. This paper focuses on systematic ways to implement this distributed approach, in the context of the evaluation of arbitrary Boolean functions. The unique characteristics of genetic circuits and the properties of DSMs require the development of new Boolean synthesis methods, distinct from those classically used in electronic circuit design. In this work, we propose a fast algorithm to synthesize distributed realizations for any Boolean function, under constraints on the number of gates per cell and the number of orthogonal DSMs. The method is based on an exact synthesis algorithm to find the minimal circuit per cell, which in turn allows us to build an extensive database of Boolean functions up to a given number of inputs. For concreteness, we will specifically focus on circuits of up to 4 inputs, which might represent, for example, two chemical inducers and two light inputs at different frequencies. Our method shows that, with a constraint of no more than seven gates per cell, the use of a single DSM increases the total number of realizable circuits by at least 7.58-fold compared to centralized computation. Moreover, when allowing two DSMs, one can realize 99.995% of all possible 4-input Boolean functions, still with at most 7 gates per cell. The methodology introduced here can be readily adapted to complement recent genetic circuit design automation software.

synthetic biology↗

bioRxiv2020

Multiple approaches for massively parallel sequencing of HCoV-19 genomes directly from clinical samples

COVID-19 has caused a major epidemic worldwide, however, much is yet to be known about the epidemiology and evolution of the virus. One reason is that the challenges underneath sequencing HCoV-19 directly from clinical samples have not been completely tackled. Here we illustrate the application of amplicon and hybrid capture (capture)-based sequencing, as well as ultra-high-throughput metatranscriptomic (meta) sequencing in retrieving complete genomes, inter-individual and intra-individual variations of HCoV-19 from clinical samples covering a range of sample types and viral load. We also examine and compare the bias, sensitivity, accuracy, and other characteristics of these approaches in a comprehensive manner. This is, to date, the first work systematically implements amplicon and capture approaches in sequencing HCoV-19, as well as the first comparative study across methods. Our work offers practical solutions for genome sequencing and analyses of HCoV-19 and other emerging viruses.

genomics↗

bioRxiv2020

A simple, scalable approach to building a cross-platform transcriptome atlas

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows. Graphical abstractRecursive approach to generating a multi-scaled atlas. Top panel: The method integrates data from all cell types in the Stemformatics database, and shows clear division of samples into global categories of stromal, pluripotent or blood (inset) cell types. Bottom panel: Integration of only the blood cell subsets provides a blood atlas. Projection of external samples (green) onto the blood atlas. Samples are coloured by curated annotations derived from the original studies, and can be viewed at Stemformatics.org O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/984468v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@2de08dorg.highwire.dtl.DTLVardef@1f18284org.highwire.dtl.DTLVardef@afd32aorg.highwire.dtl.DTLVardef@1507502_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Single-chain lanthanide luminescence biosensors for cell-based imaging and screening of protein-protein interactions

Research tools that enable imaging or analysis of protein-protein interactions (PPIs) directly within living cells provide unique and valuable biological insights and can also aid drug discovery efforts. Here, we present lanthanide-based, Forster resonance energy transfer (lanthanide-based FRET, or LRET) biosensors for time-gated luminescence (TGL) imaging or multiwell plate analysis of PPIs. Polypeptide chains comprised of an alpha helical linker flanked by a Tb(III) complex, GFP and two binding domains exhibit large differences in long-lifetime, Tb(III)-to-GFP LRET-sensitized emission between open (unbound) and closed (bound) states. We used TGL microscopy to image ca. 500% increases in Tb(III)-to-GFP LRET following rapamycin addition to NIH 3T3 cells that expressed biosensors bearing FKBP12 and the rapamycin binding domain of m-Tor (FRB) at each terminus. Much larger signal changes, up to ca. 2500%, were observed when cells were grown in 96-well or 384-well plates and analyzed using a TGL plate reader. We also measured the interaction of p53 and HDM2 and its inhibition within intact HeLa cells grown in 96-well plates and estimated a z-factor of 0.5 for the assay. The modular design and high dynamic range of Tb(III)-based LRET biosensors will facilitate versatile imaging and cell-based screening of PPIs.

biochemistry↗

bioRxiv2020

redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-Cell RNA-Seq Data and Applications in Cancer

Recent advancement of single-cell RNA-seq technology facilitates the study of cell lineages in developmental processes as well as cancer. In this manuscript, we developed a computational method, called redPATH, to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm. Besides, we implemented a novel approach to visualize the trajectory development of cells and visualization methods to provide biological insights. We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancerous datasets, as well as other single-cell transcriptome data. In particular, we identified a subpopulation of malignant glioma cells, which are stem cell-like. These cells express known proliferative markers such as GFAP (also identified ATP1A2, IGFBPL1, ALDOC) and remain silenced in quiescent markers such as ID3. Furthermore, MCL1 is identified as a significant gene that regulates cell apoptosis, and CSF1R confirms previous studies for re-programming macrophages to control tumor growth. In conclusion, redPATH is a comprehensive tool for analyzing single-cell RNA-Seq datasets along a pseudo developmental time. The software is available via http://github.com/tinglab/redPATH.

bioinformatics↗

bioRxiv2020

Inferring Multiple Metagenomic Association Networks based on Variation of Environmental Factors

Identifying significant biological relationships or patterns is central to many metagenomic studies. Methods that estimate association networks have been proposed for this purpose, but they assume that associations are static, neglecting the fact that relationships in a microbial ecosystem may vary with changes in environmental factors, which can result in inaccurate estimations. We propose a computational model, k-Lognormal-Dirichlet-Multinomial model (kLDM), which estimates multiple association networks that correspond to specific environmental conditions according to values of environmental factors (EFs), and simultaneously infers microbe-microbe and environmental factor-microbe associations for each network. We showed the effectiveness of kLDM on synthetic data, a colorectal cancer dataset, as well as the TARA Oceans and American Gut project datasets. The results showed that the widely used Spearmans rank correlation coefficient (SCC) performed much worse than other methods, indicating the importance of separating samples by environmental conditions. We compared cancer fecal samples with cancer-free samples, and our estimation showed fewer associations among microbes but stronger associations between specific bacteria such as five colorectal cancer (CRC)-associated OTUs, indicating gut microbe translocation in cancer patients. Some environmental-factor-dependent associations were found within marine eukaryotic community, and gut microbial heterogeneity of irritable bowel disease (IBD) patients was detected. Results demonstrated that kLDM could successfully unravel the underlying biological associations. In summary, our study presents a computational framework that can elucidate the complex associations within microbial ecosystems. The kLDM program, R, and python scripts, together with all experimental datasets are all accessible at Github (https://github.com/tinglab/kLDM.git).

bioinformatics↗

bioRxiv2020

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity: Mount Sinai DToxS LINCS Center Dataset

The Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers of the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. A key aim of DToxS is to generate both proteomic and transcriptomic signatures that can predict adverse effects, especially cardiotoxicity, of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments (317 cell line/drug combinations + 64 control lysates) have been conducted at the Center for Advanced Proteomics Research at Rutgers University - New Jersey Medical School. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures generated in tandem to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and possible mitigation. Both raw and processed proteomics data have been carefully screened for quality and made publicly available via the PRIDE database. As such, this broad protein kinase inhibitor-stimulated cardiomyocyte proteomic data and signature set is valuable for the prediction of drug toxicities. Links to: Metadata Tables O_TBL View this table: org.highwire.dtl.DTLVardef@1cda9f8org.highwire.dtl.DTLVardef@1520e05org.highwire.dtl.DTLVardef@16a35borg.highwire.dtl.DTLVardef@3ee6e7org.highwire.dtl.DTLVardef@1a98664_HPS_FORMAT_FIGEXP M_TBL C_TBL

pharmacology and toxicology↗