Monthly Archives: June 2014

06.25.2014

This entry was posted in Abstracts discussion and tagged DNA methylation gene expression Giovanni Montana regression Systems Biology on by

Network-guided regression for detecting associations between DNA methylation and gene expression

 

Zi Wang1, Edward Curry2 and Giovanni Montana1,3,*

1Department of Mathematics, Imperial College London, London SW7 2AZ.

2 Division of Cancer, Imperial College London, Hammersmith Hospital, London, W12 0NN

3 Department of Biomedical Engineering, King’s College London, St Thomas’ Hospital, London SE1 7EH

*To whom correspondence should be addressed. Giovanni Montana, E-mail: giovanni.montana@kcl.ac.uk

 

Abstract 

Motivation: High-throughput profiling in biological research has resulted in the availability of a wealth of data cataloguing the genetic, epigenetic and transcriptional states of cells. This data could yield discoveries that lead to breakthroughs in the diagnosis and treatment of human disease, but requires statistical methods designed to find the most relevant patterns from millions of potential interactions. Aberrant DNA methylation is often a feature of cancer, and has been proposed as a therapeutic target. However, the relationship between DNA methylation and gene expression remains poorly understood.

Results: We propose Network-sparse Reduced-Rank Regression (NsRRR), a multivariate regression framework capable of using prior biological knowledge expressed as gene interaction networks to guide the search for associations between gene expression and DNA methylation signatures. We use simulations to show the advantage of our proposed model in terms of variable selection accuracy over alternative models that do not use prior network information. We discuss an application of NsRRR to TCGA datasets on primary ovarian tumours.

Availability: R code implementing the NsRRR model is available at http://www2.imperial.ac.uk/~gmontana/

06.11.2014

This entry was posted in Abstracts discussion and tagged BATF cellular plasticity Fosl2 IRF4 RORγt STAT3 TH17 transcription factor on by

A Validated Regulatory Network for Th17 Cell Specification

Maria Ciofani1, 10, Aviv Madar3, 4, 10, Carolina Galan1, MacLean Sellars1, Kieran Mace3, Florencia Pauli5, Ashish Agarwal3, Wendy Huang1, Christopher N. Parkurst1, Michael Muratet5, Kim M. Newberry5, Sarah Meadows5, Alex Greenfield2, Yi Yang1, Preti Jain5, Francis K. Kirigin2, Carmen Birchmeier6, Erwin F. Wagner7, Kenneth M. Murphy8, 9, Richard M. Myers5, Richard Bonneau3, 4, Corresponding author contact information, E-mail the corresponding author, Dan R. Littman1, 9, Corresponding author contact information, E-mail the corresponding author

1 Molecular Pathogenesis Program, The Kimmel Center for Biology and Medicine of the Skirball Institute, New York University School of Medicine, New York, NY 10016, USA

2 Computational Biology Program, The Sackler Institute, New York University School of Medicine, New York, NY 10016, USA

3 Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, 10003 USA

4 Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY, 10003 USA

5 HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA

6 Developmental Biology, Max Delbruck for Molecular Medicine, 13125 Berlin, Germany

7 Cancer Cell Biology Programme, Spanish National Cancer Research Centre (CNIO), E-28029 Madrid, Spain

8 Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63108, USA

9 The Howard Hughes Medical Institute

 

Summary

Th17 cells have critical roles in mucosal defense and are major contributors to inflammatory disease. Their differentiation requires the nuclear hormone receptor RORγt working with multiple other essential transcription factors (TFs). We have used an iterative systems approach, combining genome-wide TF occupancy, expression profiling of TF mutants, and expression time series to delineate the Th17 global transcriptional regulatory network. We find that cooperatively bound BATF and IRF4 contribute to initial chromatin accessibility and, with STAT3, initiate a transcriptional program that is then globally tuned by the lineage-specifying TF RORγt, which plays a focal deterministic role at key loci. Integration of multiple data sets allowed inference of an accurate predictive model that we computationally and experimentally validated, identifying multiple new Th17 regulators, including Fosl2, a key determinant of cellular plasticity. This interconnected network can be used to investigate new therapeutic approaches to manipulate Th17 functions in the setting of inflammatory disease.