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Bhandari G, Sharma M, Negi S, Gangola S, Bhatt P, Chen S. System biology analysis of endosulfan biodegradation in bacteria and its effect in other living systems: modeling and simulation studies. J Biomol Struct Dyn 2022; 40:13171-13183. [PMID: 34622744 DOI: 10.1080/07391102.2021.1982773] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Endosulfan is a broadly applied cyclodiene insecticide which has been in use across 80 countries since last 5 decades. Owing to its recalcitrant nature, endosulfan residues have been reported from air, water and soil causing toxicity to various non-target organisms. Microbial decontamination of endosulfan has been reported previously by several authors. In the current study, we have evaluated the pathways of endosulfan degradation and its hazardous impact on other living beings including insects, humans, plants, aquatic life and environment by in-silico methods. For establishment of the endosulfan metabolism in different ecosystems, cell designer was employed. The established model was thereafter assessed and simulated to understand the biochemical and physiological metabolism of the endosulfan in various systems of the network. Topological investigation analysis of the endosulfan metabolism validated the presence of 207 nodes and 274 edges in the network. We have concluded that biomagnification of the endosulfan generally occurs in the various elements of the ecosystem. Dynamics study of endosulfan degrading enzymes suggested the important role of monooxygenase I, II and hydrolase in endosulfan bioremediation. Endosulfan shows toxicity in human beings, fishes and plants, however it is biodegraded by the microbes. To date, there are no reports of in- silico analysis of bioremediation of endosulfan and its hazardous effects on the environment. Thus, this report can be important in terms of modelling and simulation of biodegradation network of endosulfan and similar compounds and their impact on several other systems.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Geeta Bhandari
- Department of Biochemistry and Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Mukund Sharma
- Department of Biochemistry and Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Shalini Negi
- Department of Biochemistry and Biotechnology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India
| | - Saurabh Gangola
- School of Agriculture, Graphic Era Hill University, Bhimtal Campus, Uttarakhand, India
| | - Pankaj Bhatt
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China
| | - Shaohua Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China
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Hanspers K, Kutmon M, Coort SL, Digles D, Dupuis LJ, Ehrhart F, Hu F, Lopes EN, Martens M, Pham N, Shin W, Slenter DN, Waagmeester A, Willighagen EL, Winckers LA, Evelo CT, Pico AR. Ten simple rules for creating reusable pathway models for computational analysis and visualization. PLoS Comput Biol 2021; 17:e1009226. [PMID: 34411100 PMCID: PMC8375987 DOI: 10.1371/journal.pcbi.1009226] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Kristina Hanspers
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, California, United States of America
| | - Martina Kutmon
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Susan L. Coort
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Daniela Digles
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Lauren J. Dupuis
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Friederike Ehrhart
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Finterly Hu
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Elisson N. Lopes
- Instituto de Ciencias Biologicas, Departamento de Bioquimica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Marvin Martens
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Nhung Pham
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Woosub Shin
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Denise N. Slenter
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | | | - Egon L. Willighagen
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Laurent A. Winckers
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Chris T. Evelo
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Alexander R. Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, California, United States of America
- * E-mail:
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Correia Y, Scheel J, Gupta S, Wang K. Placental mitochondrial function as a driver of angiogenesis and placental dysfunction. Biol Chem 2021; 402:887-909. [PMID: 34218539 DOI: 10.1515/hsz-2021-0121] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022]
Abstract
The placenta is a highly vascularized and complex foetal organ that performs various tasks, crucial to a healthy pregnancy. Its dysfunction leads to complications such as stillbirth, preeclampsia, and intrauterine growth restriction. The specific cause of placental dysfunction remains unknown. Recently, the role of mitochondrial function and mitochondrial adaptations in the context of angiogenesis and placental dysfunction is getting more attention. The required energy for placental remodelling, nutrient transport, hormone synthesis, and the reactive oxygen species leads to oxidative stress, stemming from mitochondria. Mitochondria adapt to environmental changes and have been shown to adjust their oxygen and nutrient use to best support placental angiogenesis and foetal development. Angiogenesis is the process by which blood vessels form and is essential for the delivery of nutrients to the body. This process is regulated by different factors, pro-angiogenic factors and anti-angiogenic factors, such as sFlt-1. Increased circulating sFlt-1 levels have been linked to different preeclamptic phenotypes. One of many effects of increased sFlt-1 levels, is the dysregulation of mitochondrial function. This review covers mitochondrial adaptations during placentation, the importance of the anti-angiogenic factor sFlt-1in placental dysfunction and its role in the dysregulation of mitochondrial function.
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Affiliation(s)
- Yolanda Correia
- Aston Medical School, College of Health & Life Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK
| | - Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, D-18051 Rostock, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, D-18051 Rostock, Germany
| | - Keqing Wang
- Aston Medical School, College of Health & Life Sciences, Aston University, Aston Triangle, Birmingham B4 7ET, UK
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Touré V, Dräger A, Luna A, Dogrusoz U, Rougny A. The Systems Biology Graphical Notation: Current Status and Applications in Systems Medicine. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11515-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Stanford NJ, Scharm M, Dobson PD, Golebiewski M, Hucka M, Kothamachu VB, Nickerson D, Owen S, Pahle J, Wittig U, Waltemath D, Goble C, Mendes P, Snoep J. Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices. Methods Mol Biol 2019; 2049:285-314. [PMID: 31602618 DOI: 10.1007/978-1-4939-9736-7_17] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR-findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.
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Affiliation(s)
| | - Martin Scharm
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Paul D Dobson
- School of Computer Science, University of Manchester, Manchester, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Michael Hucka
- Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | | | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Stuart Owen
- School of Computer Science, University of Manchester, Manchester, UK
| | - Jürgen Pahle
- BIOMS/BioQuant, Heidelberg University, Heidelberg, Germany.
| | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Dagmar Waltemath
- Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Carole Goble
- School of Computer Science, University of Manchester, Manchester, UK
| | - Pedro Mendes
- Centre for Quantitative Medicine, University of Connecticut, Farmington, CT, USA
| | - Jacky Snoep
- School of Computer Science, University of Manchester, Manchester, UK.,Biochemistry, Stellenbosch University, Stellenbosch, South Africa
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