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BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research

View Article: PubMed Central - PubMed

ABSTRACT

Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.

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The EGF and HRG induced GRN in BC cells and the clinical relevance of some of its transcriptional hubs.(A,B) EGF and HRG induced GRNs in MCF7 cells with node size proportional to outdegree (number of targets). (C) Kaplan Meier plot for BC patient survival probability for different levels of SIX5 expression. (D) Kaplan Meier plot for survival probabilities of BC patients who underwent endocrine therapy for different levels of CHD2 expression. (E) Kaplan Meier plot for HER2 positive BC patient survival for different levels of RFX5 expression. (F) Kaplan Meier plot for TNBC patients survival probabilities for different levels of RFX5 expression. In (C–F) the red and black curves show survival probabilities for higher and lower expression of the corresponding markers respectively. (G,H) EGF and HRG induced GRNs in MCF7 cells with node size proportional to their betweenness centralities. (I,J) EGF and HRG induced GRNs in MCF7 cells with node size proportional to their page-rank.
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f3: The EGF and HRG induced GRN in BC cells and the clinical relevance of some of its transcriptional hubs.(A,B) EGF and HRG induced GRNs in MCF7 cells with node size proportional to outdegree (number of targets). (C) Kaplan Meier plot for BC patient survival probability for different levels of SIX5 expression. (D) Kaplan Meier plot for survival probabilities of BC patients who underwent endocrine therapy for different levels of CHD2 expression. (E) Kaplan Meier plot for HER2 positive BC patient survival for different levels of RFX5 expression. (F) Kaplan Meier plot for TNBC patients survival probabilities for different levels of RFX5 expression. In (C–F) the red and black curves show survival probabilities for higher and lower expression of the corresponding markers respectively. (G,H) EGF and HRG induced GRNs in MCF7 cells with node size proportional to their betweenness centralities. (I,J) EGF and HRG induced GRNs in MCF7 cells with node size proportional to their page-rank.

Mentions: BGRMI found 22692 and 19016 regulatory interactions for the HRG and EGF stimulated cells (Fig. 3A,B). The complete list of interactions is available as Supplementary Data 1. 10804 and 8997 of all interactions in the HRG and EGF induced networks were inhibitory regulations and the remaining were activating regulations. Surprisingly, only 286 of all the inferred interactions were common in both networks. However, the number of common interactions depends on the cut-off probability and for lower cut-offs more interactions were found common between these networks (Supplementary Fig. S1). The large difference between EGF and HRG induced GRNs suggests that the same genes are regulated by different sets of TFs in these two networks.


BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research
The EGF and HRG induced GRN in BC cells and the clinical relevance of some of its transcriptional hubs.(A,B) EGF and HRG induced GRNs in MCF7 cells with node size proportional to outdegree (number of targets). (C) Kaplan Meier plot for BC patient survival probability for different levels of SIX5 expression. (D) Kaplan Meier plot for survival probabilities of BC patients who underwent endocrine therapy for different levels of CHD2 expression. (E) Kaplan Meier plot for HER2 positive BC patient survival for different levels of RFX5 expression. (F) Kaplan Meier plot for TNBC patients survival probabilities for different levels of RFX5 expression. In (C–F) the red and black curves show survival probabilities for higher and lower expression of the corresponding markers respectively. (G,H) EGF and HRG induced GRNs in MCF7 cells with node size proportional to their betweenness centralities. (I,J) EGF and HRG induced GRNs in MCF7 cells with node size proportional to their page-rank.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC5120305&req=5

f3: The EGF and HRG induced GRN in BC cells and the clinical relevance of some of its transcriptional hubs.(A,B) EGF and HRG induced GRNs in MCF7 cells with node size proportional to outdegree (number of targets). (C) Kaplan Meier plot for BC patient survival probability for different levels of SIX5 expression. (D) Kaplan Meier plot for survival probabilities of BC patients who underwent endocrine therapy for different levels of CHD2 expression. (E) Kaplan Meier plot for HER2 positive BC patient survival for different levels of RFX5 expression. (F) Kaplan Meier plot for TNBC patients survival probabilities for different levels of RFX5 expression. In (C–F) the red and black curves show survival probabilities for higher and lower expression of the corresponding markers respectively. (G,H) EGF and HRG induced GRNs in MCF7 cells with node size proportional to their betweenness centralities. (I,J) EGF and HRG induced GRNs in MCF7 cells with node size proportional to their page-rank.
Mentions: BGRMI found 22692 and 19016 regulatory interactions for the HRG and EGF stimulated cells (Fig. 3A,B). The complete list of interactions is available as Supplementary Data 1. 10804 and 8997 of all interactions in the HRG and EGF induced networks were inhibitory regulations and the remaining were activating regulations. Surprisingly, only 286 of all the inferred interactions were common in both networks. However, the number of common interactions depends on the cut-off probability and for lower cut-offs more interactions were found common between these networks (Supplementary Fig. S1). The large difference between EGF and HRG induced GRNs suggests that the same genes are regulated by different sets of TFs in these two networks.

View Article: PubMed Central - PubMed

ABSTRACT

Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.

No MeSH data available.