This repository contains details about the bioinformatics project performed using StringDB, Cytoscape, and ImmuneCellAI as part of the BMI 5311: Foundations of Biomedical Information Sciences II course. The study, titled "Identification of immunomodulatory hub genes and cell signatures in mouse lungs exposed to cigarette smoke: An integrated bioinformatic analysis using StringDB, Cytoscape, and ImmuneCellAI", analyzed the temporal variations in gene expression in mouse lungs subjected to different durations of CS exposure, ranging from one day to nine months, focusing on differentially expressed genes (DEGs), pathway enrichment, and immune-related hub gene identification using a publicly available Bulk RNA sequencing dataset obtained from NCBI GEO.
Identification of immunomodulatory hub genes and cell signatures in mouse lungs exposed to cigarette smoke: An integrated bioinformatic analysis using StringDB, Cytoscape, and ImmuneCellAI
The mouse Bulk RNA sequencing dataset with the accession ID GSE76205 was accessed through the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO).
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Raw and normalized count data were retrieved using the NIH LINCS tool GREIN:GEO RNA-seq Experiments Interactive Navigator.
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Sample groups and sizes:
- At day one, there were five samples from the air-exposed control group and four from the Cigarette smoke (CS)-exposed group.
- At seven days, both air-exposed control and CS-exposed groups had five samples each.
- For the one-month and three-month intervals, the sample sizes remained consistent at five for each group.
- The air-exposed control group comprised four samples at six and nine months, while the CS-exposed group included five.
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Packages used: DESeq2 R package for differential expression analysis and EnhancedVolcano R package for data visualization.
- 1 day CS-exposed vs. 1 day Air-exposed control
- 7 day CS-exposed vs. 7 day Air-exposed control
- 1 month CS-exposed vs. 1 month Air-exposed control
- 3 month CS-exposed vs. 3 month Air-exposed control
- 6 month CS-exposed vs. 6 month Air-exposed control
- 9 month CS-exposed vs. 9 month Air-exposed control
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Criteria for Differentially Expressed Genes (DEGs):
- False Discovery Rate (FDR) ≤ 0.05
- |Fold Change| > 1.5
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R scripts can be found in this folder.
Figures:
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Analytical Tools used:
- Metascape was used for comparative analysis across experimental timepoints.
- GO Biological Process (BP), Cellular Compartment (CC), and Molecular Function (MF), and KEGG pathway enrichments.
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Key Parameters for Enrichment Significance:
- Minimum gene overlap = 3
- Enrichment p-value cutoff = 0.05
- Minimum enrichment factor = 1.5
Figures:
- Analytical Tools used:
- StringDB version 12 for protein-protein interaction (PPI) network visualization.
- Cytoscape for network visualization and hub gene analysis.
- Maximal clique centrality (MCC) algorithm from the cytoHubba plugin was utilized to determine the top ten hub genes in each PPI.
Figures:
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Analytical Tool used:
- ImmuneCellAI-mouse to determine immune cell types from gene expression data.
- Input data were derived from normalized gene expression counts obtained via GREIN.
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Statistical Evaluation
- Tool used: GraphPad Prism version 10.4
- Analysis conducted:
- Temporal dynamics of immune cell infiltration in CS-exposed mouse lungs.
- A two-way analysis of variance (ANOVA) test, followed by a post-hoc Tukey multiple comparison test.
Figures:
If you use the tools or dataset mentioned in this repository in your research, please cite the following references:
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Blighe K, Rana S, Lewis M (2024). EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. R package version 1.24.0, https://github.com/kevinblighe/EnhancedVolcano
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Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M., & Ishiguro-Watanabe, M. (2023). KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Research, 51(D1), D587–D592. https://doi.org/10.1093/NAR/GKAC963
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Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 28(1), 27–30. https://doi.org/10.1093/NAR/28.1.27
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Mahi, N. Al, Najafabadi, M. F., Pilarczyk, M., Kouril, M., & Medvedovic, M. (2019). GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data. Scientific Reports, 9(1). https://doi.org/10.1038/S41598-019-43935-8
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Miao, Y. R., Xia, M., Luo, M., Luo, T., Yang, M., & Guo, A. Y. (2022). ImmuCellAI-mouse: a tool for comprehensive prediction of mouse immune cell abundance and immune microenvironment depiction. Bioinformatics, 38(3), 785–791. https://doi.org/10.1093/BIOINFORMATICS/BTAB711
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Miller, M. A., Danhorn, T., Cruickshank-Quinn, C. I., Leach, S. M., Jacobson, S., Strand, M. J., Reisdorph, N. A., Bowler, R. P., Petrache, I., & Kechris, K. (2017). Gene and metabolite time-course response to cigarette smoking in mouse lung and plasma. PLoS ONE, 12(6), e0178281. https://doi.org/10.1371/JOURNAL.PONE.0178281
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Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504. https://doi.org/10.1101/GR.1239303
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Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Gable, A. L., Fang, T., Doncheva, N. T., Pyysalo, S., Bork, P., Jensen, L. J., & Von Mering, C. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research, 51(D1), D638–D646. https://doi.org/10.1093/NAR/GKAC1000
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For questions or issues, please contact the repository maintainer. Refer to the final course paper for detailed information and results.
This repository is solely for educational purposes and serves as a backup for my graduate school assignments related to the BMI 5311: Foundations of Biomedical Information Sciences II course at McWilliams School of Biomedical Informatics at UTHealth Houston.