1
|
Rondel FM, Hosseini R, Sahoo B, Knyazev S, Mandric I, Stewart F, Măndoiu II, Pasaniuc B, Porozov Y, Zelikovsky A. Pipeline for Analyzing Activity of Metabolic Pathways in Planktonic Communities Using Metatranscriptomic Data. J Comput Biol 2021; 28:842-855. [PMID: 34264744 PMCID: PMC8575064 DOI: 10.1089/cmb.2021.0053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
In this article, we present our novel pipeline for analysis of metabolic activity using a microbial community's metatranscriptome sequence data set for validation. Our method is based on expectation-maximization (EM) algorithm and provides enzyme expression and pathway activity levels. Further expanding our analysis, we consider individual enzymatic activity and compute enzyme participation coefficients to approximate the metabolic pathway activity more accurately. We apply our EM pathways pipeline to a metatranscriptomic data set of a plankton community from surface waters of the Northern Gulf of Mexico. The data set consists of RNA-seq data and respective environmental parameters, which were sampled at two depths, six times a day over multiple 24-hour cycles. Furthermore, we discuss microbial dependence on day-night cycle within our findings based on a three-way correlation of the enzyme expression during antipodal times-midnight and noon. We show that the enzyme participation levels strongly affect the metabolic activity estimates: that is, marginal and multiple linear regression of enzymatic and metabolic pathway activity correlated significantly with the recorded environmental parameters. Our analysis statistically validates that EM-based methods produce meaningful results, as our method confirms statistically significant dependence of metabolic pathway activity on the environmental parameters, such as salinity, temperature, brightness, and a few others.
Collapse
Affiliation(s)
| | - Roya Hosseini
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Bikram Sahoo
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Sergey Knyazev
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Igor Mandric
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Frank Stewart
- Department of Microbiology and Immunology, Montana State University, Bozeman, Montana, USA
| | - Ion I. Măndoiu
- Computer Science & Engineering Department, University of Connecticut, Storrs, Connecticut, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Yuri Porozov
- World-Class Research Center “Digital biodesign and personalized healthcare,” I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia
| | - Alexander Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
- World-Class Research Center “Digital biodesign and personalized healthcare,” I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| |
Collapse
|
2
|
Ovacik MA, Sen B, Euling SY, Gaido KW, Ierapetritou MG, Androulakis IP. Pathway modeling of microarray data: a case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP). Toxicol Appl Pharmacol 2010; 271:386-94. [PMID: 20850466 DOI: 10.1016/j.taap.2010.09.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2010] [Revised: 09/03/2010] [Accepted: 09/08/2010] [Indexed: 10/19/2022]
Abstract
Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.
Collapse
Affiliation(s)
- Meric A Ovacik
- Chemical and Biochemical Engineering Department, Rutgers University, Piscataway, NJ 08854, USA
| | | | | | | | | | | |
Collapse
|