Library Of Integrated Network-based Cellular Signatures Research Articles

LINCS dataset-based repositioning of rosiglitazone as a potential anti-human adenovirus drug

Human adenoviruses (HAdVs) often cause mild respiratory infections. These infections, however, can potentially become fatal in immunosuppressive patients. Unfortunately, there has been no specific anti-HAdV drug approved for treatment of HAdV infections. In this study, a time-course transcriptome of HAdV-infected human lung epithelial cells (A549 cells) was performed and compared with perturbation datasets of 890 drug-treated A549 cells from the library of integrated network-based cellular signatures (LINCS) database to predict previously unknown therapeutic drug-HAdV relationships using a characteristic direction (CD) algorithm. We performed experiments to validate a prediction for the anti-diabetic drug rosiglitazone as a candidate drug for treatment of anti-HAdV both in vivo and in vitro. The Type I interferon (IFNs) signaling pathway was negatively regulated during the course of HAdV infection and rosiglitazone increased STAT1 phosphorylation for antiviral IFN response induction. Taken together, this study confirmed the prospect for re-exploitation of this FDA-approved drug as a potential therapeutic for HAdV infections.

Connectivity Enhanced Structure Activity Relationship (ceSAR): A Novel Approach to Increase Accuracy and Speed of Virtual Drug Discovery

Experimental drug discovery and repurposing is a costly and time‐consuming process. Here, a novel approach, dubbed connectivity enhanced structure activity relationship (ceSAR) is introduced. ceSAR combines traditional virtual screening methodologies with cheminformatics and Library of Integrated Network‐Based Cellular Signatures (LINCS) gene expression signatures of genetic and chemical perturbations. This methodology combines structural similarity of small molecules with transcriptional connectivity and virtual screening‐predicted specific interacting molecules for the enhancement of structure activity relationship (SAR) and lead compound discovery. The transcriptional connectivity aspect of ceSAR is implemented as an R Shiny package, titled Sig2Lead, that employs connectivity between chemical and genetic signatures included in the LINCS library to identify small molecule inhibitors of user‐targeted pathways. Chemical libraries are reduced with ceSAR by first measuring concordance of transcriptomic signatures between genetic and chemical perturbation, identifying putative pathway inhibitors, followed by traditional docking approaches to re‐score the remaining library to identify compounds likely to interact with the target of interest. This combination identifies compounds that are both pathway inhibitors and have shape complementarity to the target of interest, making the most likely scenario that the molecules are directly interacting with the target protein of interest to modulate the pathway. Through integration of these orthogonal approaches, both an increased accuracy and speed can be observed, with the speed increase being several orders of magnitude.Support or Funding InformationThis work was supported in part by the National Institutes of Health grants U54 HL127624ceSAR combines transcriptional connectivity and virtual screening to identify novel inhibitors from a screened chemical library. The library is first reduced using transcriptional connectivity to a knockdown of the target gene to restrict the search to putative pathway inhibitors. These pathway inhibitors are then reduced further using traditional virtual screening approaches to find compounds with shape complementarity to the protein of interest.Figure 1Comparison of results of an early drug screen using a combination of docking with AutoDock4.2.6 and Sig2Lead shows a dramatic reduction in compounds without losing the best‐performing compounds. If used in this case, the ceSAR method would have excluded 51 of the 147 tested compounds, of which 45 had no observable IC50 via a Fluorescence Polarization assay, thus driving library enrichment. This reduction would have saved time and reagents through preventing testing of as many spurious compounds.Figure 2

Bioactivity Evaluation of a Novel Formulated Curcumin.

Curcumin has been used as a traditional medicine and/or functional food in several cultures because of its health benefits including anticancer properties. However, poor oral bioavailability of curcumin has limited its oral usage as a food supplement and medical food. Here we formulated curcumin pellets using a solid dispersion technique. The pellets had the advantages of reduced particle size, improved water solubility, and particle porosity. This pellet form led to an improvement in curcumin’s oral bioavailability. Additionally, we used the C-Map and Library of Integrated Network-Based Cellular Signatures (LINCS) Unified Environment (CLUE) gene expression database to determine the potential biological functions of formulated curcumin. The results indicated that, similar to conventional curcumin, the formulated curcumin acted as an NF-κB pathway inhibitor. Moreover, ConsensusPathDB database analysis was used to predict possible targets and it revealed that both forms of curcumin exhibit similar biological functions, including apoptosis. Biochemical characterization revealed that both the forms indeed induced apoptosis of hepatocellular carcinoma (HCC) cell lines. We concluded that the formulated curcumin increases the oral bioavailability in animals, and, as expected, retains characteristics similar to conventional curcumin at the cellular level. Our screening platform using big data not only confirms that both the forms of curcumin have similar mechanisms but also predicts the novel mechanism of the formulated curcumin.

LINCS Data Portal 2.0: next generation access point for perturbation-response signatures.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program with the goal of generating a large-scale and comprehensive catalogue of perturbation-response signatures by utilizing a diverse collection of perturbations across many model systems and assay types. The LINCS Data Portal (LDP) has been the primary access point for the compendium of LINCS data and has been widely utilized. Here, we report the first major update of LDP (http://lincsportal.ccs.miami.edu/signatures) with substantial changes in the data architecture and APIs, a completely redesigned user interface, and enhanced curated metadata annotations to support more advanced, intuitive and deeper querying, exploration and analysis capabilities. The cornerstone of this update has been the decision to reprocess all high-level LINCS datasets and make them accessible at the data point level enabling users to directly access and download any subset of signatures across the entire library independent from the originating source, project or assay. Access to the individual signatures also enables the newly implemented signature search functionality, which utilizes the iLINCS platform to identify conditions that mimic or reverse gene set queries. A newly designed query interface enables global metadata search with autosuggest across all annotations associated with perturbations, model systems, and signatures.

Gene Expression Signature-Based Approach Identifies Antifungal Drug Ciclopirox As a Novel Inhibitor of HMGA2 in Colorectal Cancer.

Human high-mobility group A2 (HMGA2) encodes for a non-histone chromatin protein which influences a variety of biological processes, including the cell cycle process, apoptosis, the DNA damage repair process, and epithelial–mesenchymal transition. The accumulated evidence suggests that high expression of HMGA2 is related to tumor progression, poor prognosis, and a poor response to therapy. Thus, HMGA2 is an important molecular target for many types of malignancies. Our recent studies revealed the positive connections between heat shock protein 90 (Hsp90) and HMGA2 and that the Hsp90 inhibitor has therapeutic potential to inhibit HMGA2-triggered tumorigenesis. However, 43% of patients suffered visual disturbances in a phase I trial of the second-generation Hsp90 inhibitor, NVP-AUY922. To identify a specific inhibitor to target HMGA2, the Gene Expression Omnibus (GEO) database and the Library of Integrated Network-based Cellular Signatures (LINCS) L1000platform were both analyzed. We identified the approved small-molecule antifungal agent ciclopirox (CPX) as a novel potential inhibitor of HMGA2. In addition, CPX induces cytotoxicity of colorectal cancer (CRC) cells by induction of cell cycle arrest and apoptosis in vitro and in vivo through direct interaction with the AT-hook motif (a small DNA-binding protein motif) of HMGA2. In conclusion, this study is the first to report that CPX is a novel potential inhibitor of HMGA2 using a drug-repurposing approach, which can provide a potential therapeutic intervention in CRC patients.

Influence of batch effect correction methods on drug induced differential gene expression profiles

BackgroundBatch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods.ResultsDifferences in average signature size were observed with different methods applied. The gene signatures identified by the Latent Effect Adjustment after Primary Projection (LEAPP) method and the methods fitted with Linear Models for Microarray Data (limma) software demonstrated little agreement. The external validity of the gene signatures was evaluated by connectivity mapping between the CMAP database and the Library of Integrated Network-based Cellular Signatures (LINCS) database. The results of connectivity mapping indicate that the genes identified were not reliable for drugs with total sample size (drug + control samples) smaller than 40, irrespective of the batch effect correction method applied. With total sample size larger than 40, the methods correcting for batch effects produced significantly better results than the method with no batch effect correction. In a simulation study, the power was generally low for simulated data with sample size smaller than 40. We observed best performance when using the limma method correcting for two principal components.ConclusionBatch effect correction methods strongly impact differential gene expression analysis when the sample size is large enough to contain sufficient information and thus the downstream drug repositioning. We recommend including two or three principal components as covariates in fitting models with limma when sample size is sufficient (larger than 40 drug and controls combined).

Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data.

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression.

A new LSTM-based gene expression prediction model: L-GEPM.

Molecular biology combined with in silico machine learning and deep learning has facilitated the broad application of gene expression profiles for gene function prediction, optimal crop breeding, disease-related gene discovery, and drug screening. Although the acquisition cost of genome-wide expression profiles has been steadily declining, the requirement generates a compendium of expression profiles using thousands of samples remains high. The Library of Integrated Network-Based Cellular Signatures (LINCS) program used approximately 1000 landmark genes to predict the expression of the remaining target genes by linear regression; however, this approach ignored the nonlinear features influencing gene expression relationships, limiting the accuracy of the experimental results. We herein propose a gene expression prediction model, L-GEPM, based on long short-term memory (LSTM) neural networks, which captures the nonlinear features affecting gene expression and uses learned features to predict the target genes. By comparing and analyzing experimental errors and fitting the effects of different prediction models, the LSTM neural network-based model, L-GEPM, can achieve low error and a superior fitting effect.

Abstract 5103: The dark cancer kinome - untapped opportunities for the development of novel drugs

Abstract Kinases are firmly established drug targets in cancer. There are currently 44 FDA approved kinase drug and hundreds of compounds are in clinical development. However, less than 10% of the Kinome is currently targeted and a large proportion is considered understudied by the NIH Illuminating the Druggable Genome Program (https://druggablegenome.net/). No small molecule inhibitors are known for these “dark” proteins, yet many may be opportune novel cancer targets.We developed a computational pipeline to identify and prioritize understudied kinases as cancer drug targets. We analyzed the complete set of tumors in The Cancer Genome Atlas (TCGA). For 33 different cancers we performed differential expression analysis and identified 39 dark kinases that exhibit significant upregulation in at least four types. Using co-expression analysis we built functional networks prioritizing drug targets. To identify small molecules that reverse their expression levels, we leveraged transcriptional response signatures obtained from dozens of human cancer cell lines exposed to tens of thousands of small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). To identify small molecules that directly bind to and inhibit dark kinases, we have have combined an advanced AI (artificial intelligence) model trained on activity data from across the Kinome with structure-based simulations.Using the computational pipeline, we identified the dark Ca2+/Calmodulin dependent kinase PNCK as the most differentially overexpressed kinase in kidney cancer patients. Our analyses have demonstrated statistically significant correlation between PNCK mRNA levels and various clinical and pathological outcomes, including histologic grade, clinical staging and overall survival. We have confirmed high levels of PNCK expression in 5 renal cell carcinoma cell lines (Caki-1, ACHN, 786-O, A704 and A498). Knockdown and overexpression studies have suggested PNCK and the CaMK pathway may contribute to cellular proliferation and cell cycle progression. We have applied our AI-based screening pipeline to a library of >20 million commercially available compounds and confirmed three PNCK inhibiting chemotypes. In summary, using a novel computational pipeline, we have identified and experimentally validated PNCK as a prospective novel drug target in an understudied pathway that is highly upregulated in kidney cancer. We identified first in class small molecules that target this previously dark kinase as prospective starting points for optimization into a clinical candidate. Citation Format: Derek J. Essegian, Rimpi Khurana, Vasileios Stathias, Valery Chavez, Jaime R. Merchan, Stephan Schürer. The dark cancer kinome - untapped opportunities for the development of novel drugs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5103.

31-LB: Identification of Repurposable Drug Candidate for Diabetic Peripheral Neuropathy Using High-Throughput Drug-Perturbation Data

Diabetic peripheral neuropathy (DPN) is a common complication of prediabetes and type 2 diabetes (T2D) for which there are limited treatment options. We recently demonstrated that dietary reversal (DR) corrects the DPN phenotype using mouse models of prediabetes and T2D. However, it is still important to develop therapeutic agents for treating DPN in addition to life style changes such as DR. In this study, we employed an in silico omics-based systems approach to identify small-molecule drugs with a high potential of reversing or halting DPN. The NIH Library of Integrated Network-based Cellular Signatures (LINCS) drug-perturbation transcriptomic data were obtained, which measured gene expression changes after treatment of 83 human cells with over 20,000 small-molecule compounds using the L1000 assay platform. Gene expression signature of DPN, consisting of highly up- or down-regulated genes, was defined based on the dysregulated genes, identified from sciatic nerves RNA-Seq data of C57BL/6J mice fed with high-fat diet (60% kcal fat for 19 weeks). This DPN signature was compared against the LINCS perturbation data using the CLUE Query app from the Broad Institute and our in-house analysis pipeline. The top small-molecule drugs, demonstrating opposite expression profiles of the DPN signature, included various protein synthesis inhibitors, calcium channel blockers, and adrenergic receptor antagonists such as cephaeline, levetiracetam, and esmolol, respectively. Although the anti-DPN effects of these drugs need to be systematically examined, our systems approach provides a list of drugs with a potential for being used to treat DPN. Disclosure K. Guo: None. E.L. Feldman: None. J. Hur: None. Funding National Institutes of Health (R24DK082841); University of Michigan

Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data: Drug Association Networks

Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.

Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data.

The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be predictive proxy for adverse drug reactions. Depending on the way that different drugs may contribute to adverse drug reactions, different options may exist in the clinical setting. Here, we formulate a network framework to integrate the relationships between drugs, biological functions, and adverse drug reactions based on the high-throughput drug perturbation data from the Library of Integrated Network-Based Cellular Signatures (LINCS) project. We present network-based parameters that indicate whether a given reaction may be related to the effect of a single drug or to the combination of several drugs, as well as the relative risk of adverse drug reaction manifestation given a certain drug combination.

Drug Effect Prediction by Integrating L1000 Genomic and Proteomic Big Data.

The library of integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction. Gene expression and proteomic data in LINCS L1000 are cataloged for human cancer cells treated with compounds and genetic reagents. For understanding the related cell pathways and facilitating drug discovery, we developed binary linear programming (BLP) to infer cell-specific pathways and identify compounds' effects using L1000 gene expression and phosphoproteomics data. A generic pathway map for the MCF7 breast cancer cell line was built. Within them, BLP extracted the cell-specific pathways, which reliably predicted the compounds' effects. In this way, the potential drug effects are revealed by our models.

Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data.

The library of integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction. L1000 big datasets provide gene expression profiles induced by over 10,000 compounds, shRNAs, and kinase inhibitors using L1000 platform. We developed a systematic compound signature discovery pipeline named csNMF, which covers from raw L1000 data processing to drug screening and mechanism generation. The discovered compound signatures of breast cancer were consistent with the LINCS KINOMEscan data and were clinically relevant. In this way, the potential mechanisms of compounds' efficacy are elucidated by our computational model.

Identification of alterations in macrophage activation associated with disease activity in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is characterized by abnormalities in B cell and T cell function, but the role of disturbances in the activation status of macrophages (Mϕ) has not been well described in human patients. To address this, gene expression profiles from isolated lymphoid and myeloid populations were analyzed to identify differentially expressed (DE) genes between healthy controls and patients with either inactive or active SLE. While hundreds of DE genes were identified in B and T cells of active SLE patients, there were no DE genes found in B or T cells from patients with inactive SLE compared to healthy controls. In contrast, large numbers of DE genes were found in myeloid cells (MC) from both active and inactive SLE patients. Among the DE genes were several known to play roles in Mϕ activation and polarization, including the M1 genes STAT1 and SOCS3 and the M2 genes STAT3, STAT6, and CD163. M1-associated genes were far more frequent in data sets from active versus inactive SLE patients. To characterize the relationship between Mϕ activation and disease activity in greater detail, weighted gene co-expression network analysis (WGCNA) was used to identify modules of genes associated with clinical activity in SLE patients. Among these were disease activity-correlated modules containing activation signatures of predominantly M1-associated genes. No disease activity-correlated modules were enriched in M2-associated genes. Pathway and upstream regulator analysis of DE genes from both active and inactive SLE MC were cross-referenced with high-scoring hits from the drug discovery Library of Integrated Network-based Cellular Signatures (LINCS) to identify new strategies to treat both stages of SLE. A machine learning approach employing MC gene modules and a generalized linear model was able to predict the disease activity status in unrelated gene expression data sets. In summary, altered MC gene expression is characteristic of both active and inactive SLE. However, disease activity is associated with an alteration in the activation of MC, with a bias toward the M1 proinflammatory phenotype. These data suggest that while hyperactivity of B cells and T cells is associated with active SLE, MC potentially direct flare-ups and remission by altering their activation status toward the M1 state.

Drug and disease signature integration identifies synergistic combinations in glioblastoma

Glioblastoma (GBM) is the most common primary adult brain tumor. Despite extensive efforts, the median survival for GBM patients is approximately 14 months. GBM therapy could benefit greatly from patient-specific targeted therapies that maximize treatment efficacy. Here we report a platform termed SynergySeq to identify drug combinations for the treatment of GBM by integrating information from The Cancer Genome Atlas (TCGA) and the Library of Integrated Network-Based Cellular Signatures (LINCS). We identify differentially expressed genes in GBM samples and devise a consensus gene expression signature for each compound using LINCS L1000 transcriptional profiling data. The SynergySeq platform computes disease discordance and drug concordance to identify combinations of FDA-approved drugs that induce a synergistic response in GBM. Collectively, our studies demonstrate that combining disease-specific gene expression signatures with LINCS small molecule perturbagen-response signatures can identify preclinical combinations for GBM, which can potentially be tested in humans.

The Natural Compound Neobractatin Induces Cell Cycle Arrest Through Multiple Signaling Pathways Including RNA Binding Protein CELF6

Background: We previously identified neobractatin (NBT), a caged prenylxanthone isolated from Garcinia bracteata, is a novel cancer cell proliferation inhibitor. However, the detailed biological effect of NBT is largely unknown. Methods: RNA-Seq was performed by comparing the mRNA expression and the RNA splicing variants changes before and after NBT treatment in cancer cells. Bioinformatics analysis was performed by GO, KEGG analysis and rMATS software. The preclinical evaluation of NBT was analyzed using the Library of Integrated Network-based Cellular Signatures (LINCS) program. Findings: We found that NBT could cause both G1/S and G2/M arrest through several mechanisms including E2F1, Gadd45ɑ. LINCS analysis indicated that the transcriptome signature of NBT is approximately 70% similar to that of the antileukemic drug homoharringtonine (HHT). NBT exhibited similar effects with HHT on leukemia in vitro and in vivo, suggesting that NBT might be useful for treating leukemia. Interestingly, we demonstrated that the upregulation of the RNA-binding protein CELF6 following NBT treatment led to p21 WAF1/CIP1 increase and subsequent cell cycle arrest in vitro, indicating the novel function of CELF6 in cancer. Interpretation: By demonstrating the biological effect of NBT, we provided evidences for further investigations of this novel natural compound with anticancer potential and for the novel definition of CELF6 in p21WAF1/CIP1 upregulation. Funding Statement: This work was supported by National Natural Science Foundation of China (NSFC81773951, NSFC81602990) and Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning for financial support. Declaration of Interests: The authors declare no potential conflicts of interest Ethics Approval Statement: The experimental procedures were approved by the Shanghai University of Traditional Chinese Medicine Committee on the Use of Live Animals for Teaching and Research (SZY201704006).

Exploiting Genomic Relations in Big Data Repositories by Graph-Based Search Methods

We are living at a time that allows the generation of mass data in almost any field of science. For instance, in pharmacogenomics, there exist a number of big data repositories, e.g., the Library of Integrated Network-based Cellular Signatures (LINCS) that provide millions of measurements on the genomics level. However, to translate these data into meaningful information, the data need to be analyzable. The first step for such an analysis is the deliberate selection of subsets of raw data for studying dedicated research questions. Unfortunately, this is a non-trivial problem when millions of individual data files are available with an intricate connection structure induced by experimental dependencies. In this paper, we argue for the need to introduce such search capabilities for big genomics data repositories with a specific discussion about LINCS. Specifically, we suggest the introduction of smart interfaces allowing the exploitation of the connections among individual raw data files, giving raise to a network structure, by graph-based searches.

LINCS L1000 dataset-based repositioning of CGP-60474 as a highly potent anti-endotoxemic agent

Sepsis is one of the most common clinical syndromes that causes death and disability. Although many studies have developed drugs for sepsis treatment, none have decreased the mortality rate. The aim of this study was to identify a novel treatment option for sepsis using the library of integrated network-based cellular signatures (LINCS) L1000 perturbation dataset based on an in vitro and in vivo sepsis model. Sepsis-related microarray studies of early-stage inflammatory processes in patients and innate immune cells were collected from the Gene Expression Omnibus (GEO) data repository and used for candidate drug selection based on the LINCS L1000 perturbation dataset. The anti-inflammatory effects of the selected candidate drugs were analyzed using activated macrophage cell lines. CGP-60474, an inhibitor of cyclin-dependent kinase, was the most potent drug. It alleviated tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in activated macrophages by downregulating the NF-κB activity, and it reduced the mortality rate in LPS induced endotoxemia mice. This study shows that CGP-60474 could be a potential therapeutic candidate to attenuate the endotoxemic process. Additionally, the virtual screening strategy using the LINCS L1000 perturbation dataset could be a cost and time effective tool in the early stages of drug development.

Systematic exploration of cell morphological phenotypes associated with a transcriptomic query.

Cell morphological phenotypes, including shape, size, intensity, and texture of cellular compartments have been shown to change in response to perturbation with small molecule compounds. Image-based cell profiling or cell morphological profiling has been used to associate changes of cell morphological features with alterations in cellular function and to infer molecular mechanisms of action. Recently, the Library of Integrated Network-based Cellular Signatures (LINCS) Project has measured gene expression and performed image-based cell profiling on cell lines treated with 9515 unique compounds. These data provide an opportunity to study the interdependence between transcription and cell morphology. Previous methods to investigate cell phenotypes have focused on targeting candidate genes as components of known pathways, RNAi morphological profiling, and cataloging morphological defects; however, these methods do not provide an explicit model to link transcriptomic changes with corresponding alterations in morphology. To address this, we propose a cell morphology enrichment analysis to assess the association between transcriptomic alterations and changes in cell morphology. Additionally, for a new transcriptomic query, our approach can be used to predict associated changes in cellular morphology. We demonstrate the utility of our method by applying it to cell morphological changes in a human bone osteosarcoma cell line.