An R package for generic modular response analysis and its application to estrogen and retinoic acid receptor crosstalk

AbstractModular response analysis (MRA) is a widely used inference technique developed to uncover directions and strengths of connections in molecular networks under a steady-state condition by means of perturbation experiments. We devised several extensions of this methodology to search genomic data for new associations with a biological network inferred by MRA, to improve the predictive accuracy of MRA-inferred networks, and to estimate confidence intervals of MRA parameters from datasets with low numbers of replicates. The classical MRA computations and their extensions were implemented in a freely available R package called aiMeRA (https://github.com/bioinfo-ircm/aiMeRA/). We illustrated the application of our package by assessing the crosstalk between estrogen and retinoic acid receptors, two nuclear receptors implicated in several hormone-driven cancers, such as breast cancer. Based on new data generated for this study, our analysis revealed potential cross-inhibition mediated by the shared corepressors NRIP1 and LCoR. We designed aiMeRA for non-specialists and to allow biologists to perform their own analyses.

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aiMeRA: A generic modular response analysis R package and its application to estrogen and retinoic acid receptors crosstalk

10.1101/2020.01.30.925800 ◽

2020 ◽

Author(s):

Gabriel Jimenez-Dominguez ◽

Patrice Ravel ◽

Stéphan Jalaguier ◽

Vincent Cavaillès ◽

Jacques Colinge

Keyword(s):

Transcription Factor ◽

Retinoic Acid ◽

R Package ◽

Retinoic Acid Receptors ◽

Response Analysis ◽

Sequencing Data ◽

Genome Wide ◽

Genome Wide Data ◽

Modular Response Analysis ◽

Mathematical Derivation

AbstractModular response analysis (MRA) is a widely used modeling technique to uncover coupling strengths in molecular networks under a steady-state condition by means of perturbation experiments. We propose an extension of this methodology to search genomic data for new associations with a network modeled by MRA and to improve the predictive accuracy of MRA models. These extensions are illustrated by exploring the cross talk between estrogen and retinoic acid receptors, two nuclear receptors implicated in several hormone-driven cancers such as breast. We also present a novel, rigorous and elegant mathematical derivation of MRA equations, which is the foundation of this work and of an R package that is freely available at https://github.com/bioinfo-ircm/aiMeRA/. This mathematical analysis should facilitate MRA understanding by newcomers.Author summaryEstrogen and retinoic acid receptors play an important role in several hormone-driven cancers and share co-regulators and co-repressors that modulate their transcription factor activity. The literature shows evidence for crosstalk between these two receptors and suggests that spatial competition on the promoters could be a mechanism. We used MRA to explore the possibility that key co-repressors, i.e., NRIP1 (RIP140) and LCoR could also mediate crosstalk by exploiting new quantitative (qPCR) and RNA sequencing data. The transcription factor role of the receptors and the availability of genome-wide data enabled us to explore extensions of the MRA methodology to explore genome-wide data sets a posteriori, searching for genes associated with a molecular network that was sampled by perturbation experiments. Despite nearly two decades of use, we felt that MRA lacked a systematic mathematical derivation. We present here an elegant and rather simple analysis that should greatly facilitate newcomers’ understanding of MRA details. Moreover, an easy-to-use R package is released that should make MRA accessible to biology labs without mathematical expertise. Quantitative data are embedded in the R package and RNA sequencing data are available from GEO.

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Modelling Signalling Networks from Perturbation Data

10.1101/243600 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mathurin Dorel ◽

Bertram Klinger ◽

Anja Sieber ◽

Anirudh Prahallad ◽

Torsten Gross ◽

...

Keyword(s):

Colon Cancer ◽

Cancer Cell Line ◽

Network Models ◽

Colon Cancer Cell Line ◽

R Package ◽

Response Analysis ◽

Knock Out ◽

Perturbation Data ◽

Using Data ◽

Modular Response Analysis

AbstractMotivationIntracellular signalling is realized by complex signalling networks which are almost impossible to understand without network models, especially if feedbacks are involved. Modular Response Analysis (MRA) is a convenient modelling method to study signalling networks in various contexts.ResultsWe developed a derivative of MRA that is suited to model signalling networks from incomplete perturbation schemes and multi-perturbation data. We applied the method to study the effect of SHP2, a protein that has been implicated in resistance to targeted therapy in colon cancer, using data from a knock out and parental colon cancer cell line. We find that SHP2 is required for MAPK signalling, whereas AKT signalling only partially depends on SHP2.AvailabilityAn R-package is available at https://github.com/MathurinD/[email protected]

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