Heeju Noh1,2,3, Ziyi Hua1, Panagiotis Chrysinas4, Jason E Shoemaker5,6, Rudiyanto Gunawan7. 1. Institute for Chemical and Bioengineering, ETH Zurich, 8093, Zurich, Switzerland. 2. Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland. 3. Columbia University Medical Center, New York, NY, 10032, USA. 4. Department of Chemical and Biological Engineering, University at Buffalo - SUNY, Buffalo, NY, 14260, USA. 5. Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA. 6. Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA. 7. Department of Chemical and Biological Engineering, University at Buffalo - SUNY, Buffalo, NY, 14260, USA. rgunawan@buffalo.edu.
Abstract
BACKGROUND: Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes. RESULTS: In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains. CONCLUSION: DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus .
BACKGROUND: Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes. RESULTS: In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains. CONCLUSION: DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus .
Entities:
Keywords:
Drug discovery; Gene expression; Gene regulatory network; Gene targets; Mechanism of action
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