1
|
Richelle A, Joshi C, Lewis NE. Assessing key decisions for transcriptomic data integration in biochemical networks. PLoS Comput Biol 2019; 15:e1007185. [PMID: 31323017 PMCID: PMC6668847 DOI: 10.1371/journal.pcbi.1007185] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 07/31/2019] [Accepted: 06/14/2019] [Indexed: 12/21/2022] Open
Abstract
To gain insights into complex biological processes, genome-scale data (e.g., RNA-Seq) are often overlaid on biochemical networks. However, many networks do not have a one-to-one relationship between genes and network edges, due to the existence of isozymes and protein complexes. Therefore, decisions must be made on how to overlay data onto networks. For example, for metabolic networks, these decisions include (1) how to integrate gene expression levels using gene-protein-reaction rules, (2) the approach used for selection of thresholds on expression data to consider the associated gene as "active", and (3) the order in which these steps are imposed. However, the influence of these decisions has not been systematically tested. We compared 20 decision combinations using a transcriptomic dataset across 32 tissues and showed that definition of which reaction may be considered as active (i.e., reactions of the genome-scale metabolic network with a non-zero expression level after overlaying the data) is mainly influenced by thresholding approach used. To determine the most appropriate decisions, we evaluated how these decisions impact the acquisition of tissue-specific active reaction lists that recapitulate organ-system tissue groups. These results will provide guidelines to improve data analyses with biochemical networks and facilitate the construction of context-specific metabolic models.
Collapse
Affiliation(s)
- Anne Richelle
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, California, United States of America
| | - Chintan Joshi
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, California, United States of America
| | - Nathan E. Lewis
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, California, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| |
Collapse
|
2
|
Jamialahmadi O, Hashemi-Najafabadi S, Motamedian E, Romeo S, Bagheri F. A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism. PLoS Comput Biol 2019; 15:e1006936. [PMID: 31009458 PMCID: PMC6497301 DOI: 10.1371/journal.pcbi.1006936] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 05/02/2019] [Accepted: 03/11/2019] [Indexed: 02/07/2023] Open
Abstract
Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.
Collapse
Affiliation(s)
- Oveis Jamialahmadi
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Sameereh Hashemi-Najafabadi
- Department of Biomedical Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
- * E-mail: (SHN); (EM)
| | - Ehsan Motamedian
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
- * E-mail: (SHN); (EM)
| | - Stefano Romeo
- Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
- Clinical Nutrition Unit, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
- Cardiology Department, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Fatemeh Bagheri
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| |
Collapse
|
3
|
Richelle A, Chiang AWT, Kuo CC, Lewis NE. Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions. PLoS Comput Biol 2019; 15:e1006867. [PMID: 30986217 PMCID: PMC6483243 DOI: 10.1371/journal.pcbi.1006867] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/25/2019] [Accepted: 02/13/2019] [Indexed: 12/26/2022] Open
Abstract
Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.
Collapse
Affiliation(s)
- Anne Richelle
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
| | - Austin W. T. Chiang
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
| | - Chih-Chung Kuo
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Nathan E. Lewis
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| |
Collapse
|
4
|
Kaliamurthi S, Demir-Korkmaz A, Selvaraj G, Gokce-Polat E, Wei YK, Almessiere MA, Baykal A, Gu K, Wei DQ. Viewing the Emphasis on State-of-the-Art Magnetic Nanoparticles: Synthesis, Physical Properties, and Applications in Cancer Theranostics. Curr Pharm Des 2019; 25:1505-1523. [PMID: 31119998 DOI: 10.2174/1381612825666190523105004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 05/16/2019] [Indexed: 02/07/2023]
Abstract
Cancer-related mortality is a leading cause of death among both men and women around the world. Target-specific therapeutic drugs, early diagnosis, and treatment are crucial to reducing the mortality rate. One of the recent trends in modern medicine is "Theranostics," a combination of therapeutics and diagnosis. Extensive interest in magnetic nanoparticles (MNPs) and ultrasmall superparamagnetic iron oxide nanoparticles (NPs) has been increasing due to their biocompatibility, superparamagnetism, less-toxicity, enhanced programmed cell death, and auto-phagocytosis on cancer cells. MNPs act as a multifunctional, noninvasive, ligand conjugated nano-imaging vehicle in targeted drug delivery and diagnosis. In this review, we primarily discuss the significance of the crystal structure, magnetic properties, and the most common method for synthesis of the smaller sized MNPs and their limitations. Next, the recent applications of MNPs in cancer therapy and theranostics are discussed, with certain preclinical and clinical experiments. The focus is on implementation and understanding of the mechanism of action of MNPs in cancer therapy through passive and active targeting drug delivery (magnetic drug targeting and targeting ligand conjugated MNPs). In addition, the theranostic application of MNPs with a dual and multimodal imaging system for early diagnosis and treatment of various cancer types including breast, cervical, glioblastoma, and lung cancer is reviewed. In the near future, the theranostic potential of MNPs with multimodality imaging techniques may enhance the acuity of personalized medicine in the diagnosis and treatment of individual patients.
Collapse
Affiliation(s)
- Satyavani Kaliamurthi
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou High-tech Industrial Development Zone, 100 Lianhua Street, Zhengzhou, Henan 450001, China
- College of Chemistry, Chemical and Environmental Engineering, Henan University of Technology, Zhengzhou Hightech Industrial Development Zone, 100 Lianhua Street, Zhengzhou, Henan 450001, China
| | - Ayse Demir-Korkmaz
- Department of Chemistry, Istanbul Medeniyet University, 34700 Uskudar, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou High-tech Industrial Development Zone, 100 Lianhua Street, Zhengzhou, Henan 450001, China
- College of Chemistry, Chemical and Environmental Engineering, Henan University of Technology, Zhengzhou Hightech Industrial Development Zone, 100 Lianhua Street, Zhengzhou, Henan 450001, China
| | - Emine Gokce-Polat
- Department of Engineering Physics, Istanbul Medeniyet University, 34700 Uskudar, Istanbul, Turkey
| | - Yong-Kai Wei
- College of Science, Henan University of Technology, Zhengzhou High-tech Industrial Development Zone, 100 Lianhua Street, Zhengzhou, Henan 450001, China
| | - Munirah A Almessiere
- Department of Physics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441 Dammam, Saudi Arabia
| | - Abdulhadi Baykal
- Department of Nano-Medicine Research, Institute for Research & Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441 Dammam, Saudi Arabia
| | - Keren Gu
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou High-tech Industrial Development Zone, 100 Lianhua Street, Zhengzhou, Henan 450001, China
- College of Chemistry, Chemical and Environmental Engineering, Henan University of Technology, Zhengzhou Hightech Industrial Development Zone, 100 Lianhua Street, Zhengzhou, Henan 450001, China
| | - Dong-Qing Wei
- Center of Interdisciplinary Sciences-Computational Life Sciences, College of Food Science and Engineering, Henan University of Technology, Zhengzhou High-tech Industrial Development Zone, 100 Lianhua Street, Zhengzhou, Henan 450001, China
- The State Key Laboratory of Microbial Metabolism, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, No: 800 Dongchuan Road, Minhang, Shanghai, 200240, China
| |
Collapse
|