Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action

Dušica Vidović, Amar Koleti, Stephan C Schuerer

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

The Library of Integrated Network-based Cellular Signatures (LINCS) project is a large-scale coordinated effort to build a comprehensive systems biology reference resource. The goals of the program include the generation of a very large multidimensional data matrix and informatics and computational tools to integrate, analyze, and make the data readily accessible. LINCS data include genome-wide transcriptional signatures, biochemical protein binding profiles, cellular phenotypic response profiles and various other datasets for a wide range of cell model systems and molecular and genetic perturbations. Here we present a partial survey of this data facilitated by data standards and in particular a robust compound standardization workflow; we integrated several types of LINCS signatures and analyzed the results with a focus on mechanism of action (MoA) and chemical compounds. We illustrate how kinase targets can be related to disease models and relevant drugs. We identified some fundamental trends that appear to link Kinome binding profiles and transcriptional signatures to chemical information and biochemical binding profiles to transcriptional responses independent of chemical similarity. To fill gaps in the datasets we developed and applied predictive models. The results can be interpreted at the systems level as demonstrated based on a large number of signaling pathways. We can identify clear global relationships, suggesting robustness of cellular responses to chemical perturbation. Overall, the results suggest that chemical similarity is a useful measure at the systems level, which would support phenotypic drug optimization efforts. With this study we demonstrate the potential of such integrated analysis approaches and suggest prioritizing further experiments to fill the gaps in the current data.

Original languageEnglish
Article number342
JournalFrontiers in Genetics
Volume5
Issue numberSEP
DOIs
StatePublished - Jan 1 2014

Fingerprint

Libraries
Genome
Growth
Pharmaceutical Preparations
Pharmacologic Actions
Informatics
Systems Biology
Workflow
Protein Binding
Molecular Biology
Phosphotransferases
Datasets
Surveys and Questionnaires

Keywords

  • Chemical similarity
  • Data integration
  • Drug profiling
  • Kinome profiles
  • Systems-biology
  • Transcriptional signatures

ASJC Scopus subject areas

  • Genetics
  • Molecular Medicine
  • Genetics(clinical)

Cite this

@article{067aec57aeb4463eb21f1a34badbb6ef,
title = "Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action",
abstract = "The Library of Integrated Network-based Cellular Signatures (LINCS) project is a large-scale coordinated effort to build a comprehensive systems biology reference resource. The goals of the program include the generation of a very large multidimensional data matrix and informatics and computational tools to integrate, analyze, and make the data readily accessible. LINCS data include genome-wide transcriptional signatures, biochemical protein binding profiles, cellular phenotypic response profiles and various other datasets for a wide range of cell model systems and molecular and genetic perturbations. Here we present a partial survey of this data facilitated by data standards and in particular a robust compound standardization workflow; we integrated several types of LINCS signatures and analyzed the results with a focus on mechanism of action (MoA) and chemical compounds. We illustrate how kinase targets can be related to disease models and relevant drugs. We identified some fundamental trends that appear to link Kinome binding profiles and transcriptional signatures to chemical information and biochemical binding profiles to transcriptional responses independent of chemical similarity. To fill gaps in the datasets we developed and applied predictive models. The results can be interpreted at the systems level as demonstrated based on a large number of signaling pathways. We can identify clear global relationships, suggesting robustness of cellular responses to chemical perturbation. Overall, the results suggest that chemical similarity is a useful measure at the systems level, which would support phenotypic drug optimization efforts. With this study we demonstrate the potential of such integrated analysis approaches and suggest prioritizing further experiments to fill the gaps in the current data.",
keywords = "Chemical similarity, Data integration, Drug profiling, Kinome profiles, Systems-biology, Transcriptional signatures",
author = "Dušica Vidović and Amar Koleti and Schuerer, {Stephan C}",
year = "2014",
month = "1",
day = "1",
doi = "10.3389/fgene.2014.00342",
language = "English",
volume = "5",
journal = "Frontiers in Genetics",
issn = "1664-8021",
publisher = "Frontiers Media S. A.",
number = "SEP",

}

TY - JOUR

T1 - Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action

AU - Vidović, Dušica

AU - Koleti, Amar

AU - Schuerer, Stephan C

PY - 2014/1/1

Y1 - 2014/1/1

N2 - The Library of Integrated Network-based Cellular Signatures (LINCS) project is a large-scale coordinated effort to build a comprehensive systems biology reference resource. The goals of the program include the generation of a very large multidimensional data matrix and informatics and computational tools to integrate, analyze, and make the data readily accessible. LINCS data include genome-wide transcriptional signatures, biochemical protein binding profiles, cellular phenotypic response profiles and various other datasets for a wide range of cell model systems and molecular and genetic perturbations. Here we present a partial survey of this data facilitated by data standards and in particular a robust compound standardization workflow; we integrated several types of LINCS signatures and analyzed the results with a focus on mechanism of action (MoA) and chemical compounds. We illustrate how kinase targets can be related to disease models and relevant drugs. We identified some fundamental trends that appear to link Kinome binding profiles and transcriptional signatures to chemical information and biochemical binding profiles to transcriptional responses independent of chemical similarity. To fill gaps in the datasets we developed and applied predictive models. The results can be interpreted at the systems level as demonstrated based on a large number of signaling pathways. We can identify clear global relationships, suggesting robustness of cellular responses to chemical perturbation. Overall, the results suggest that chemical similarity is a useful measure at the systems level, which would support phenotypic drug optimization efforts. With this study we demonstrate the potential of such integrated analysis approaches and suggest prioritizing further experiments to fill the gaps in the current data.

AB - The Library of Integrated Network-based Cellular Signatures (LINCS) project is a large-scale coordinated effort to build a comprehensive systems biology reference resource. The goals of the program include the generation of a very large multidimensional data matrix and informatics and computational tools to integrate, analyze, and make the data readily accessible. LINCS data include genome-wide transcriptional signatures, biochemical protein binding profiles, cellular phenotypic response profiles and various other datasets for a wide range of cell model systems and molecular and genetic perturbations. Here we present a partial survey of this data facilitated by data standards and in particular a robust compound standardization workflow; we integrated several types of LINCS signatures and analyzed the results with a focus on mechanism of action (MoA) and chemical compounds. We illustrate how kinase targets can be related to disease models and relevant drugs. We identified some fundamental trends that appear to link Kinome binding profiles and transcriptional signatures to chemical information and biochemical binding profiles to transcriptional responses independent of chemical similarity. To fill gaps in the datasets we developed and applied predictive models. The results can be interpreted at the systems level as demonstrated based on a large number of signaling pathways. We can identify clear global relationships, suggesting robustness of cellular responses to chemical perturbation. Overall, the results suggest that chemical similarity is a useful measure at the systems level, which would support phenotypic drug optimization efforts. With this study we demonstrate the potential of such integrated analysis approaches and suggest prioritizing further experiments to fill the gaps in the current data.

KW - Chemical similarity

KW - Data integration

KW - Drug profiling

KW - Kinome profiles

KW - Systems-biology

KW - Transcriptional signatures

UR - http://www.scopus.com/inward/record.url?scp=84917736530&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84917736530&partnerID=8YFLogxK

U2 - 10.3389/fgene.2014.00342

DO - 10.3389/fgene.2014.00342

M3 - Article

AN - SCOPUS:84917736530

VL - 5

JO - Frontiers in Genetics

JF - Frontiers in Genetics

SN - 1664-8021

IS - SEP

M1 - 342

ER -