Compound signature detection on LINCS L1000 big data

Liu, Cheng‐Lin; Su, Jing; Yang, Fei; Wei, Kun; Ma, Jinwen; Zhou, Xiaobo

doi:10.1039/c4mb00677a

Cited by 80 publications

(75 citation statements)

References 29 publications

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“…Fig. 111 It would be therefore important to evaluate how much these different variations affect the results, and whether there would be an optimal combination of methods and options specically for natural product research. One possible limitation, highlighted already in the original CMap paper, 28 is the presence of potential batch effect, i.e.…”

Section: Discussionmentioning

confidence: 99%

Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products

et al. 2015

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It is widely accepted that drug discovery often requires a systems-level polypharmacology approach to tackle problems such as lack of efficacy and emerging resistance of single-targeted compounds. Network pharmacology approaches are increasingly being developed and applied to find new therapeutic opportunities and to re-purpose approved drugs. However, these recent advances have been relatively slow to be translated into the field of natural products. Here, we argue that a network pharmacology approach would enable an effective mapping of the yet unexplored target space of natural products, hence providing a systematic means to extend the druggable space of proteins implicated in various complex diseases. We give an overview of the key network pharmacology concepts and recent experimental-computational approaches that have been successfully applied to natural product research, including unbiased elucidation of mechanisms of action as well as systematic prediction of effective therapeutic combinations. We focus specifically on anticancer applications that use in vivo and in vitro functional phenotypic measurements, such as genome-wide transcriptomic response profiles, which enable a global modelling of the multi-target activity at the level of the biological pathways and interaction networks. We also provide representative examples of other disease applications, databases and tools as well as existing and emerging resources, which may prove useful for future natural product research. Finally, we offer our personal view of the current limitations, prospective developments and open questions in this exciting field.

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Section: Discussionmentioning

confidence: 99%

Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products

et al. 2015

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“…Since we found broad-scale changes in the transcriptome of CFTR −/− airway smooth muscle, we predicted that we could use these transcriptional alterations to identify novel therapeutics for airway smooth muscle dysfunction (25) (Figure 3A). To test this hypothesis, we looked With this approach, we generated a list of potential candidate small molecules that elicited a transcriptional signature negatively correlated to CFTR −/− airway smooth muscle ( Figure 3B).…”

Section: Loss Of Cftr Causes Differential Gene Expression In Porcinementioning

confidence: 99%

CF airway smooth muscle transcriptome reveals a role for PYK2

Cook¹,

Adam

Zarei

et al. 2017

JCI Insight

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“…LINCS contains gene expression profiles measured upon treatment with >11,000 compounds on 10 or more cell lines. The increasing gene expression profiles provided by LINCS are actively explored to understand drug action . A set of 1,000 landmark genes were selected for profiling in LINCS, and their expression measurements can be used to infer expression of the rest of the genome.…”

mentioning

confidence: 99%

Relating Chemical Structure to Cellular Response: An Integrative Analysis of Gene Expression, Bioactivity, and Structural Data Across 11,000 Compounds

Chen

Greenside

Paik

et al. 2015

CPT Pharmacom & Syst Pharma

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A central premise in systems pharmacology is that structurally similar compounds have similar cellular responses; however, this principle often does not hold. One of the most widely used measures of cellular response is gene expression. By integrating gene expression data from Library of Integrated Network-based Cellular Signatures (LINCS) with chemical structure and bioactivity data from PubChem, we performed a large-scale correlation analysis of chemical structures and gene expression profiles of over 11,000 compounds taking into account confounding factors such as biological conditions (e.g., cell line, dose) and bioactivities. We found that structurally similar compounds do indeed yield similar gene expression profiles. There is an ∼20% chance that two structurally similar compounds (Tanimoto Coefficient ≥ 0.85) share significantly similar gene expression profiles. Regardless of structural similarity, two compounds tend to share similar gene expression profiles in a cell line when they are administrated at a higher dose or when the cell line is sensitive to both compounds.

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Compound signature detection on LINCS L1000 big data

Cited by 80 publications

References 29 publications

Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products

Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products

CF airway smooth muscle transcriptome reveals a role for PYK2

Relating Chemical Structure to Cellular Response: An Integrative Analysis of Gene Expression, Bioactivity, and Structural Data Across 11,000 Compounds

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