The GCTx format and cmap{Py, R, M} packages: resources for the optimized storage and integrated traversal of dense matrices of data and annotations

Enache, Oana; Lahr, David L.; Natoli, Ted; Litichevskiy, Lev; Wadden, David; Flynn, Corey; Gould, Joshua; Asiedu, Jacob K.; Narayan, Rajiv; Subramanian, Aravind

doi:10.1101/227041

Cited by 13 publications

(8 citation statements)

References 21 publications

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“…Data L1000 expression data L1000 expression data (processed at "Level 5") for compounds, shRNA, and CRISPR were downloaded from clue.io in the form of GCTx files [7]. The L1000 assay directly measures the expression of 978 landmark genes, and allows for computational estimation of about 12,000 more genes [23].…”

Section: Methodsmentioning

confidence: 99%

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Jones

Tsherniak

2020

Preprint

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While chemical and genetic viability screens in cancer cell lines have identified many promising cancer vulnerabilities, simple univariate readouts of cell proliferation fail to capture the complex cellular responses to perturbations. Complementarily, gene expression profiling offers an informationrich measure of cell state that can provide a more detailed account of cellular responses to perturbations. Relatively little is known, however, about the relationship between transcriptional responses to perturbations and the long-term cell viability effects of those perturbations. To address this question, we integrated thousands of post-perturbational transcriptional profiles from the Connectivity Map with large-scale screens of cancer cell lines' viability response to genetic and chemical perturbations. This analysis revealed a generalized transcriptional signature associated with reduced viability across perturbations, which was consistent across post-perturbation time-points, perturbation types, and viability datasets. At a more granular level, we lay out the landscape of treatment-specific expression-viability relationships across a broad panel of drugs and genetic reagents, and we demonstrate that these postperturbational expression signatures can be used to infer long-term viability. Together, these results help unmask the transcriptional changes that are associated with perturbation-induced viability loss in cancer cell lines. Co-senior authors

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Section: Methodsmentioning

confidence: 99%

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Jones

Tsherniak

2020

Preprint

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“…Replicate-collapsed differential expression signatures (Level5 dataset) of the measured (landmark) genes were used in our analysis pipeline. For accessing L1000 signatures, we used cmapPy Python library (Enache et al, 2018) . Phase I and Phase II data were merged, and signatures corresponding to the same conditions (treatment, cell line, time and concentration in case of compounds, or treatment, cell line and time in case of shRNA) were averaged using the MODZ method.…”

Section: Databases and Data Preprocessingmentioning

confidence: 99%

Signatures of cell death and proliferation in perturbation transcriptomics data - from confounding factor to effective prediction

Szalai

Subramanian

Sáez-Rodríguez

2018

Preprint

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Transcriptomics perturbation signatures are valuable data sources for functional genomic studies. They can be effectively used to identify mechanism of action for new compounds and to infer functional activity of different cellular processes. Linking perturbation signatures to phenotypic studies opens up the possibility to model selected cellular phenotypes from gene expression data and to predict drugs interfering with the phenotype. At the same time, close association of transcriptomics changes with phenotypes can potentially mask the compound specific signatures. By linking perturbation transcriptomics data from the LINCS-L1000 project with cell viability phenotypic information upon genetic (from Achilles project) and chemical (from CTRP screen) perturbations for more than 90,000 signature -cell viability pairs, we show here that a cell death signature is a major factor behind perturbation signatures. We use this relationship to effectively predict cell viability from transcriptomics signatures, and identify and experimentally validate compounds that induce either cell death or proliferation. We also show that cellular toxicity can lead to an unexpected similarity of toxic compound signatures confounding the mechanism of action discovery. Consensus compound signatures predict cell-specific anti-cancer drug sensitivity, even if the drug signature is not measured in the same cell line. These signatures outperform conventional drug-specific features like nominal target and chemical fingerprints. Our results can help removing confounding factors of large scale transcriptomics perturbation screens and show that expression signatures boost prediction of drug sensitivity.

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“…Using the cmappy [63] and statsmodels [64] (version 0.9.0) Python packages, we computed the Pearson correlation (ρ) among the transcripts per million (TPM) values of RNA sequence expression of TLR4, MD2 and the HSP70 family genes occurring in the heart (left ventricle) and in the aorta of human donors. The correlation matrix visualization was generated using Python Matplotlib (version 3.0.0) [65].…”

Section: Materials and Methodsmentioning

confidence: 99%

Unveiling the Interplay between the TLR4/MD2 Complex and HSP70 in the Human Cardiovascular System: A Computational Approach

Oliveira

Faustino

Lima

et al. 2019

IJMS

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While precise mechanisms underlying cardiovascular diseases (CVDs) are still not fully understood, previous studies suggest that the innate immune system, through Toll-like receptor 4 (TLR4), plays a crucial part in the pathways leading to these diseases, mainly because of its interplay with endogenous molecules. The Heat-shock protein 70 family (HSP70-70kDa) is of particular interest in cardiovascular tissues as it may have dual effects when interacting with TLR4 pathways. Although the hypothesis of the HSP70 family members acting as TLR4 ligands is becoming widely accepted, to date no co-crystal structure of this complex is available and it is still unknown whether this process requires the co-adaptor MD2. In this study, we aimed at investigating the interplay between the TLR4/MD2 complex and HSP70 family members in the human cardiovascular system through transcriptomic data analysis and at proposing a putative interaction model between these proteins. We report compelling evidence of correlated expression levels between TLR4 and MD2 with HSP70 cognate family members, especially in heart tissue. In our molecular docking simulations, we found that HSP70 in the ATP-bound state presents a better docking score towards the TLR4/MD2 complex compared to the ADP-bound state (−22.60 vs. −10.29 kcal/mol, respectively). Additionally, we show via a proximity ligation assay for HSP70 and TLR4, that cells stimulated with ATP have higher formation of fluorescent spots and that MD2 might be required for the complexation of these proteins. The insights provided by our computational approach are potential scaffolds for future in vivo studies investigating the interplay between the TLR4/MD2 complex and HSP70 family members in the cardiovascular system.

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The GCTx format and cmap{Py, R, M} packages: resources for the optimized storage and integrated traversal of dense matrices of data and annotations

Cited by 13 publications

References 21 publications

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Signatures of cell death and proliferation in perturbation transcriptomics data - from confounding factor to effective prediction

Unveiling the Interplay between the TLR4/MD2 Complex and HSP70 in the Human Cardiovascular System: A Computational Approach

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