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Assessing developmental and transcriptional effects of PM2.5 on zebrafish embryos
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Metadata
Document Title
Assessing developmental and transcriptional effects of PM2.5 on zebrafish embryos
Author
Techapichetvanich P., Sillapaprayoon S., Vivithanaporn P., Pimtong W., Khemawoot P.
Affiliations
Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand; School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand; Biotechnology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand; National Nanotechnology Center, National Science and Technology Development Agency (NSTDA), Pathum Thani, Thailand; Theoretical and Computational Physics Group, Center of Excellence in Theoretical and Computational Science, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
Type
Article
Source Title
PLoS ONE
ISSN
19326203
Year
2024
Volume
19
Issue
2-¡.¾.
Open Access
All Open Access, Gold
Publisher
Public Library of Science
DOI
10.1371/journal.pone.0298788
Abstract
Breast cancer is one of the most common types of cancer in females. While drug combinations have shown potential in breast cancer treatments, identifying new effective drug pairs is challenging due to the vast number of possible combinations among available compounds. Efforts have been made to accelerate the process with in silico predictions. Here, we developed a Boolean model of signaling pathways in breast cancer. The model was tailored to represent five breast cancer cell lines by integrating information about cell-line specific mutations, gene expression, and drug treatments. The models reproduced cell-line specific protein activities and drug-response behaviors in agreement with experimental data. Next, we proposed a calculation of protein synergy scores (PSSs), determining the effect of drug combinations on individual proteins’ activities. The PSSs of selected proteins were used to investigate the synergistic effects of 150 drug combinations across five cancer cell lines. The comparison of the highest single agent (HSA) synergy scores between experiments and model predictions from the MDA-MB-231 cell line achieved the highest Pearson’s correlation coefficient of 0.58 with a great balance among the classification metrics (AUC = 0.74, sensitivity = 0.63, and specificity = 0.64). Finally, we clustered drug pairs into groups based on the selected PSSs to gain further insights into the mechanisms underlying the observed synergistic effects of drug pairs. Clustering analysis allowed us to identify distinct patterns in the protein activities that correspond to five different modes of synergy: 1) synergistic activation of FADD and BID (extrinsic apoptosis pathway), 2) synergistic inhibition of BCL2 (intrinsic apoptosis pathway), 3) synergistic inhibition of MTORC1, 4) synergistic inhibition of ESR1, and 5) synergistic inhibition of CYCLIN D. Our approach offers a mechanistic understanding of the efficacy of drug combinations and provides direction for selecting potential drug pairs worthy of further laboratory investigation. ? 2024 Public Library of Science. All rights reserved.
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License
CC BY
Rights
Authors
Publication Source
WoS