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Rondel FM, Farooq H, Hosseini R, Juyal A, Knyazev S, Mangul S, Rogovskyy AS, Zelikovsky A. Estimating Enzyme Expression and Metabolic Pathway Activity in Borreliella-Infected and Uninfected Mice. J Comput Biol 2024. [PMID: 38934087 DOI: 10.1089/cmb.2024.0564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024] Open
Abstract
Evaluating changes in metabolic pathway activity is essential for studying disease mechanisms and developing new treatments, with significant benefits extending to human health. Here, we propose EMPathways2, a maximum likelihood pipeline that is based on the expectation-maximization algorithm, which is capable of evaluating enzyme expression and metabolic pathway activity level. We first estimate enzyme expression from RNA-seq data that is used for simultaneous estimation of pathway activity levels using enzyme participation levels in each pathway. We implement the novel pipeline to RNA-seq data from several groups of mice, which provides a deeper look at the biochemical changes occurring as a result of bacterial infection, disease, and immune response. Our results show that estimated enzyme expression, pathway activity levels, and enzyme participation levels in each pathway are robust and stable across all samples. Estimated activity levels of a significant number of metabolic pathways strongly correlate with the infected and uninfected status of the respective rodent types.
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Affiliation(s)
| | - Hafsa Farooq
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Roya Hosseini
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Akshay Juyal
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Sergey Knyazev
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, USA
| | - Serghei Mangul
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, California, USA
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, California, USA
- Department of Quantitative and Computational Biology, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, California, USA
| | - Artem S Rogovskyy
- Department of Veterinary Pathobiology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
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Sahoo B, Pinnix Z, Sims S, Zelikovsky A. Identifying Biomarkers Using Support Vector Machine to Understand the Racial Disparity in Triple-Negative Breast Cancer. J Comput Biol 2023; 30:502-517. [PMID: 36716280 PMCID: PMC10325814 DOI: 10.1089/cmb.2022.0422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
With the properties of aggressive cancer and heterogeneous tumor biology, triple-negative breast cancer (TNBC) is a type of breast cancer known for its poor clinical outcome. The lack of estrogen, progesterone, and human epidermal growth factor receptor in the tumors of TNBC leads to fewer treatment options in clinics. The incidence of TNBC is higher in African American (AA) women compared with European American (EA) women with worse clinical outcomes. The significant factors responsible for the racial disparity in TNBC are socioeconomic lifestyle and tumor biology. The current study considered the open-source gene expression data of triple-negative breast cancer samples' racial information. We implemented a state-of-the-art classification Support Vector Machine (SVM) method with a recurrent feature elimination approach to the gene expression data to identify significant biomarkers deregulated in AA women and EA women. We also included Spearman's rho and Ward's linkage method in our feature selection workflow. Our proposed method generates 24 features/genes that can classify the AA and EA samples 98% accurately. We also performed the Kaplan-Meier analysis and log-rank test on the 24 features/genes. We only discussed the correlation between deregulated expression and cancer progression with a poor survival rate of 2 genes, KLK10 and LRRC37A2, out of 24 genes. We believe that further improvement of our method with a higher number of RNA-seq gene expression data will more accurately provide insight into racial disparity in TNBC.
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Affiliation(s)
- Bikram Sahoo
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Zandra Pinnix
- Department of Biology and Marine Biology, University of North Carolina at Wilmington, Wilmington, North Carolina, USA
| | - Seth Sims
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
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