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Title: Flux balance modeling to predict bacterial survival during pulsed-activity events

Desert biological soil crusts (BSCs) are cyanobacteria-dominated surface soil microbial communities common to plant interspaces in arid environments. The capability to significantly dampen their metabolism allows them to exist for extended periods in a desiccated dormant state that is highly robust to environmental stresses. However, within minutes of wetting, metabolic functions reboot, maximizing activity during infrequent permissive periods. Microcoleus vaginatus, a primary producer within the crust ecosystem and an early colonizer, initiates crust formation by binding particles in the upper layer of soil via exopolysaccharides, making microbial dominated biological soil crusts highly dependent on the viability of this organism. Previous studies have suggested that biopolymers play a central role in the survival of this organism by powering resuscitation, rapidly forming compatible solutes, and fueling metabolic activity in dark, hydrated conditions. To elucidate the mechanism of this phenomenon and provide a basis for future modeling of BSCs, we developed a manually curated, genome-scale metabolic model of Microcoleus vaginatus (iNJ1153). To validate this model, gas chromatography–mass spectroscopy (GC–MS) and liquid chromatography–mass spectroscopy (LC–MS) were used to characterize the rate of biopolymer accumulation and depletion in in hydrated Microcoleus vaginatus under light and dark conditions. Constraint-based flux balance analysis showed agreement between modelmore » predictions and experimental reaction fluxes. A significant amount of consumed carbon and light energy is invested into storage molecules glycogen and polyphosphate, while β-polyhydroxybutyrate may function as a secondary resource. Pseudo-steady-state modeling suggests that glycogen, the primary carbon source with the fastest depletion rate, will be exhausted if M. vaginatus experiences dark wetting events 4 times longer than light wetting events.« less
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [2] ;  [1] ;  [1] ;  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Arizona State Univ., Tempe, AZ (United States)
Publication Date:
Grant/Contract Number:
AC02-05CH11231
Type:
Published Article
Journal Name:
Biogeosciences (Online)
Additional Journal Information:
Journal Name: Biogeosciences (Online); Journal Volume: 15; Journal Issue: 7; Related Information: © Author(s) 2018.; Journal ID: ISSN 1726-4189
Publisher:
European Geosciences Union
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES
OSTI Identifier:
1433274
Alternate Identifier(s):
OSTI ID: 1462973

Jose, Nicholas A., Lau, Rebecca, Swenson, Tami L., Klitgord, Niels, Garcia-Pichel, Ferran, Bowen, Benjamin P., Baran, Richard, and Northen, Trent R.. Flux balance modeling to predict bacterial survival during pulsed-activity events. United States: N. p., Web. doi:10.5194/bg-15-2219-2018.
Jose, Nicholas A., Lau, Rebecca, Swenson, Tami L., Klitgord, Niels, Garcia-Pichel, Ferran, Bowen, Benjamin P., Baran, Richard, & Northen, Trent R.. Flux balance modeling to predict bacterial survival during pulsed-activity events. United States. doi:10.5194/bg-15-2219-2018.
Jose, Nicholas A., Lau, Rebecca, Swenson, Tami L., Klitgord, Niels, Garcia-Pichel, Ferran, Bowen, Benjamin P., Baran, Richard, and Northen, Trent R.. 2018. "Flux balance modeling to predict bacterial survival during pulsed-activity events". United States. doi:10.5194/bg-15-2219-2018.
@article{osti_1433274,
title = {Flux balance modeling to predict bacterial survival during pulsed-activity events},
author = {Jose, Nicholas A. and Lau, Rebecca and Swenson, Tami L. and Klitgord, Niels and Garcia-Pichel, Ferran and Bowen, Benjamin P. and Baran, Richard and Northen, Trent R.},
abstractNote = {Desert biological soil crusts (BSCs) are cyanobacteria-dominated surface soil microbial communities common to plant interspaces in arid environments. The capability to significantly dampen their metabolism allows them to exist for extended periods in a desiccated dormant state that is highly robust to environmental stresses. However, within minutes of wetting, metabolic functions reboot, maximizing activity during infrequent permissive periods. Microcoleus vaginatus, a primary producer within the crust ecosystem and an early colonizer, initiates crust formation by binding particles in the upper layer of soil via exopolysaccharides, making microbial dominated biological soil crusts highly dependent on the viability of this organism. Previous studies have suggested that biopolymers play a central role in the survival of this organism by powering resuscitation, rapidly forming compatible solutes, and fueling metabolic activity in dark, hydrated conditions. To elucidate the mechanism of this phenomenon and provide a basis for future modeling of BSCs, we developed a manually curated, genome-scale metabolic model of Microcoleus vaginatus (iNJ1153). To validate this model, gas chromatography–mass spectroscopy (GC–MS) and liquid chromatography–mass spectroscopy (LC–MS) were used to characterize the rate of biopolymer accumulation and depletion in in hydrated Microcoleus vaginatus under light and dark conditions. Constraint-based flux balance analysis showed agreement between model predictions and experimental reaction fluxes. A significant amount of consumed carbon and light energy is invested into storage molecules glycogen and polyphosphate, while β-polyhydroxybutyrate may function as a secondary resource. Pseudo-steady-state modeling suggests that glycogen, the primary carbon source with the fastest depletion rate, will be exhausted if M. vaginatus experiences dark wetting events 4 times longer than light wetting events.},
doi = {10.5194/bg-15-2219-2018},
journal = {Biogeosciences (Online)},
number = 7,
volume = 15,
place = {United States},
year = {2018},
month = {4}
}