Reconstruction of Cell Type-Specific Genome-Scale Metabolic Models using Single-cell RNA-Seq Data to Investigate Tumor Metabolism

Download

HIBIT22_paper_25.pdf

Date

2022-10

Author

Abdik, Ecehan
Çakır, Tunahan
Pir, Pınar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

142
views

52
downloads

Cancer is a global health problem with high mortality and increasing prevalence. Mathematical modeling strategies have a great impact on the pharmaceutical industry through their cost and time-saving effects in drug discovery and development processes. Genome-scale metabolic models (GEMs) have been used to investigate complex disease mechanisms by integrating them with omics data [1]. Cancer has complex and dynamic biology including interactions between tumor cells and microenvironments. Each cell type has a different driver role in the clinical progression of cancer. Cell-specific computational strategies are crucial in exploring complex interactions in cancer mechanisms [2]. Through the single-cell RNA (scRNA) sequencing technologies, gene expression values of each cell that are taken from the same microenvironment can be measured. In this study, we used lung and liver cancer scRNA transcriptome datasets [3,4], which are publicly available in Gene Expression Omnibus (GEO) [5], to reconstruct cell type-specific genome-scale metabolic models for tumor and control tissues. Normalization and annotation of scRNA data were carried out in Seurat package in R [6]. iMAT (Integrative Metabolic Analysis Tool), an algorithm that can reconstruct context-specific GEMs based on the expression level of genes, was used to reconstruct cell type-specific GEMs [7]. Human-GEM version 1.11.0 model, consisting of 13,069 reactions, 3067 genes, and 8366 metabolites were used as a template model [8]. Basically, transcriptome data of each cell type was mapped to Human-GEM by iMAT to identify the reactions that are active in each cell type. After reconstruction of models, flux balance analysis (FBA) [9] was applied for each cell type specific model to predict their metabolic flux rates. Each cell type was found to have distinct metabolic rates in terms of growth rate and lactate production rate, and some cell types were observed to have differences in their metabolic pathway activities between control and tumor conditions.

Subject Keywords

Cancer, Tumor, Genome-scale Metabolic Model, Single-cell, Transcriptome

URI

https://hibit2022.ims.metu.edu.tr
https://hdl.handle.net/11511/101995

Conference Name

The International Symposium on Health Informatics and Bioinformatics

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Characterization of liposomal celecoxib formulation as a drug delivery system in colorectal cancer cell lines Erdoğ, Aslı; Banerjee, Sreeparna; Department of Biotechnology (2012) Colorectal carcinoma (CRC) is one of the most common cancers and is the leading cause of cancer deaths in much of the developed world. Owing to the high incidence of drug resistance and potential toxic effects of chemotherapy drugs, much research is currently underway to design better strategies for smart drug delivery systems. Cyclooxygenase-2 (COX-2) pathway is associated with poor prognosis in colon carcinomas. The selective COX-2 inhibitor drug Celecoxib (CLX) has been shown to posses COX-2 independent ...
Development and Characterization of PEG-B-PCL Micelles Carrying Anticancer Agents Işık, Gülhan; Tezcaner, Ayşen; Hasırcı, Nesrin; Department of Biotechnology (2022-2-9) Cancer is a disease that decreases the quality of life. Many cancer drugs are either toxic or not effective due to their fast removal by reticuloendothelial system. Therefore, nano-sized drug delivery systems, especially the ones carrying the drugs directly to tumor, gained attention in the last decades. The aim of the study was to prepare nano-sized drug carrying micelles (drug conjugated and drug loaded) from methoxy polyethylene glycol-block-polycaprolactone (mPEG-b-PCL). In order to conjugate drugs, mPE...
The Mechanism of anti tumorigenic effects of 15-lox-1 in colon cancer Çimen, İsmail; Banerjee, Sreeparna; Department of Biology (2012) Colorectal cancer is the 4th most widespread cause of cancer mortality. One of the pathways that are involved in the development of colorectal cancer is the arachidonic acid metabolizing lipoxygenase (LOX) pathway. Inflammatory molecules formed from this pathway exert profound effects that may exacerbate the development and progression of colon and other cancers. 15 lipoxygenase-1 (15-LOX-1) is a member of LOX protein family that metabolizes primarily linoleic acid to 13-(S)-HODE. Several lines of evidence ...
The effects of phenolic compound tannic acid on phase ii and cytochrome p450 dependent enzymes in rabbit liver and kidney Karakurt, Serdar; Adalı, Orhan; Department of Biochemistry (2008) Cancer is the second leading cause of death after cardiovascular diseases in the world. Many of the chemical carcinogens need metabolic activation that catalyzed by cytochrome P450 and Phase II enzymes in order to exert their genotoxic and carcinogenic effects. Hence one possible mechanism is that phenolic compounds may alter anticarcinogenic effects is through an interaction with these enzymes either by the inhibition or activation of certain forms, leading to a reduced production of the ultimate carcinoge...
Investigaton of chemopreventive properties of Urtica Dioica L., in MCF-7 and MDA231 breast cancer cell lines Güler, Elif; İşcan, Mesude; Department of Biology (2011) Cancer is a major health problem in developing world with mostly unsufficient treatment. Cancer prevention through dietary modification appears to be a practical and cost effective possibility. The aim of present study is to investigate the chemical components of “Urtica diocia,L (U. diocia) grown in Turkey” and the possible protective potential of its aqueous extract against breast cancer cell lines. U. diocia was extracted by maceration method which was performed for 6,12, 24, and 36 hours, at 50°C, 37°C,...

Citation Formats

E. Abdik, T. Çakır, and P. Pir, “Reconstruction of Cell Type-Specific Genome-Scale Metabolic Models using Single-cell RNA-Seq Data to Investigate Tumor Metabolism,” Erdemli, Mersin, TÜRKİYE, 2022, p. 3025, Accessed: 00, 2023. [Online]. Available: https://hibit2022.ims.metu.edu.tr.