Computation-Driven Analysis of Model Polyextremo-tolerant Fungus Exophiala dermatitidis: Defensive Pigment Metabolic Costs and Human Applications

Schroeder, Wheaton L.; Harris, Steven D.; Saha, Rajib

doi:10.1016/j.isci.2020.100980

Cited by 10 publications

(60 citation statements)

References 58 publications

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“…For FTP needs, Mac OS users in the authors’ laboratory use Cyberduck, available at https://cyberduck.io/ . Codes related to this protocol as well as to Schroeder and Saha (2020) and Schroeder et al. (2020) can be found in two GitHub repositories associated with these works: GitHub repositories for both the i Ede2091 model (doi: 10.5281/zenodo.3608172 , also available and periodically updated on GitHub at https://github.com/ssbio/E_dermatitidis_model ) and OptFill (doi: 10.5281/zenodo.3518501 , also available and periodically updated on GitHub at https://github.com/ssbio/OptFill ).…”

Section: Before You Beginmentioning

confidence: 99%

“…Important to this reconstruction is OptFill, a recently developed alternative gap-filling method useful in the holistic and conservative reconstruction of genome-scale models of metabolism, particularly for understudied organisms like E. dermatitidis where gaps in metabolic knowledge are abundant. For complete details on the use and execution of this protocol, please refer to Schroeder and Saha (2020) and Schroeder et al. (2020) .…”

mentioning

confidence: 99%

“…For complete details on the use and execution of this protocol, please refer to Schroeder and Saha (2020) and Schroeder et al. (2020) .…”

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confidence: 99%

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Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis

Schroeder¹,

Saha²

2020

STAR Protocols

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Exophiala dermatitidis is a polyextremotolerant fungus with a small genome, thus suitable as a model system for melanogenesis and carotenogensis. A genome-scale model, i Ede2091, is reconstructed to increase metabolic understanding and used in a shadow price analysis of pigments, as detailed here. Important to this reconstruction is OptFill, a recently developed alternative gap-filling method useful in the holistic and conservative reconstruction of genome-scale models of metabolism, particularly for understudied organisms like E. dermatitidis where gaps in metabolic knowledge are abundant. For complete details on the use and execution of this protocol, please refer to Schroeder and Saha (2020) and Schroeder et al. (2020) .

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Section: Before You Beginmentioning

confidence: 99%

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confidence: 99%

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Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis

Schroeder¹,

Saha²

2020

STAR Protocols

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“…In silico evaluation and analysis utilizing mathematical relation-based modeling allow for a highresolution understanding of the biological processes in a microbial community. The availability of genome-scale metabolic network models combined with biological constraints provide multiple methods to analyze, perform in silico experiment, develop hypotheses, and redesign biological systems at a genome-level (Zomorrodi & Maranas, 2012;Zomorrodi, Islam & Maranas, 2014;Maranas & Zomorrodi, 2016;Islam & Saha, 2018;(Alsiyabi et al 2019;Islam et al 2020;Schroeder et al 2020;Schroeder & Saha 2019). To develop effective multi-species community models, significant and comprehensive knowledge of inter-species interactions and experimental data must be utilized.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Investigation of microbial community interactions between Lake Washington methanotrophs using genome-scale metabolic modeling (v0.1)"

2020

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Background. The role of methane in global warming has become paramount to the environment and the human society, especially in the past few decades. Methane cycling microbial communities play an important role in the global methane cycle, which is why the characterization of these communities is critical to understand and manipulate their behavior. Methanotrophs are a major player in these communities and are able to oxidize methane as their primary carbon source. Results. Lake Washington is a freshwater lake characterized by a methane-oxygen countergradient that contains a methane cycling microbial community. Methanotrophs are a major part of this community involved in assimilating methane from lake water. Two significant methanotrophic species in this community are Methylobacter and Methylomonas. In this work, these methanotrophs are computationally studied via developing highly curated genome-scale metabolic models. Each model was then integrated to form a community model with a multi-level optimization framework. The competitive and mutualistic metabolic interactions among Methylobacter and Methylomonas were also characterized. The community model was next tested under carbon, oxygen, and nitrogen limited conditions in addition to a nutrient-rich condition to observe the systematic shifts in the internal metabolic pathways and extracellular metabolite exchanges. Each condition showed variations in the methane oxidation pathway, pyruvate metabolism, and the TCA cycle as well as the excretion of formaldehyde and carbon dioxide in the community. Finally, the community model was simulated under fixed ratios of these two members to reflect the opposing behavior in the two-member synthetic community and in sediment-incubated communities. The community simulations predicted a noticeable switch in intracellular carbon metabolism and formaldehyde transfer between community members in sediment-incubated vs. synthetic condition. Conclusion. In this work, we attempted to predict the response of a simplified methane cycling microbial community from Lake Washington to varying

show abstract

“…In silico evaluation and analysis utilizing mathematical relation-based modeling allow for a high-resolution understanding of the biological processes in a microbial community. The availability of genome-scale metabolic network models combined with biological constraints provide multiple methods to analyze, perform in silico experiment, develop hypotheses, and redesign biological systems at a genome-level (Zomorrodi & Maranas, 2012;Zomorrodi, Islam & Maranas, 2014;Maranas & Zomorrodi, 2016;Islam & Saha, 2018;Alsiyabi, Immethun & Saha, 2019;Schroeder, Harris & Saha, 2020;Schroeder & Saha, 2019). To develop effective multi-species community models, significant and comprehensive knowledge of inter-species interactions and experimental data must be utilized.…”

Section: Introductionmentioning

confidence: 99%

Investigation of microbial community interactions between Lake Washington methanotrophs using genome-scale metabolic modeling

Islam

Daggumati

et al. 2020

PeerJ

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Background The role of methane in global warming has become paramount to the environment and the human society, especially in the past few decades. Methane cycling microbial communities play an important role in the global methane cycle, which is why the characterization of these communities is critical to understand and manipulate their behavior. Methanotrophs are a major player in these communities and are able to oxidize methane as their primary carbon source. Results Lake Washington is a freshwater lake characterized by a methane-oxygen countergradient that contains a methane cycling microbial community. Methanotrophs are a major part of this community involved in assimilating methane from lake water. Two significant methanotrophic species in this community are Methylobacter and Methylomonas. In this work, these methanotrophs are computationally studied via developing highly curated genome-scale metabolic models. Each model was then integrated to form a community model with a multi-level optimization framework. The competitive and mutualistic metabolic interactions among Methylobacter and Methylomonas were also characterized. The community model was next tested under carbon, oxygen, and nitrogen limited conditions in addition to a nutrient-rich condition to observe the systematic shifts in the internal metabolic pathways and extracellular metabolite exchanges. Each condition showed variations in the methane oxidation pathway, pyruvate metabolism, and the TCA cycle as well as the excretion of formaldehyde and carbon di-oxide in the community. Finally, the community model was simulated under fixed ratios of these two members to reflect the opposing behavior in the two-member synthetic community and in sediment-incubated communities. The community simulations predicted a noticeable switch in intracellular carbon metabolism and formaldehyde transfer between community members in sediment-incubated vs. synthetic condition. Conclusion In this work, we attempted to predict the response of a simplified methane cycling microbial community from Lake Washington to varying environments and also provide an insight into the difference of dynamics in sediment-incubated microcosm community and synthetic co-cultures. Overall, this study lays the ground for in silico systems-level studies of freshwater lake ecosystems, which can drive future efforts of understanding, engineering, and modifying these communities for dealing with global warming issues.

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Computation-Driven Analysis of Model Polyextremo-tolerant Fungus Exophiala dermatitidis: Defensive Pigment Metabolic Costs and Human Applications

Cited by 10 publications

References 58 publications

Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis

Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis

Peer Review #1 of "Investigation of microbial community interactions between Lake Washington methanotrophs using genome-scale metabolic modeling (v0.1)"

Investigation of microbial community interactions between Lake Washington methanotrophs using genome-scale metabolic modeling

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Computation-Driven Analysis of Model Polyextremo-tolerant Fungus Exophiala dermatitidis: Defensive Pigment Metabolic Costs and Human Applications

Cited by 10 publications

References 58 publications

Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis

Protocol for Genome-Scale Reconstruction and Melanogenesis Analysis of Exophiala dermatitidis

Peer Review #1 of "Investigation of microbial community interactions between Lake Washington methanotrophs using ­­­­­­­genome-scale metabolic modeling (v0.1)"

Investigation of microbial community interactions between Lake Washington methanotrophs using ­­­­­­­genome-scale metabolic modeling

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Peer Review #1 of "Investigation of microbial community interactions between Lake Washington methanotrophs using genome-scale metabolic modeling (v0.1)"

Investigation of microbial community interactions between Lake Washington methanotrophs using genome-scale metabolic modeling