Global Network Reorganization During Dynamic Adaptations of
            <i>Bacillus subtilis</i>
            Metabolism

Buescher, Joerg M.; Liebermeister, Wolfram; Jules, Matthieu; Uhr, M.; Muntel, Jan; Botella, Eric; Heßling, Bernd; Kleijn, Roelco J.; Chat, Ludovic Le; Lecointe, François; Mäder, Ulrike; Nicolas, Pierre; Piersma, Sjouke; Rügheimer, Frank; Becher, Dörte; Bessières, Philippe; Bidnenko, Elena; Denham, Emma L.; Dervyn, Etienne; Devine, Kevin M.; Doherty, Geoff; Drulhe, Samuel; Felicori, Liza; Fogg, Mark J.; Goelzer, Anne; Hansen, Annette G.; Harwood, Colin R.; Hecker, Michael; Hübner, Sebastian; Hultschig, Claus; Jarmer, Hanne Østergaard; Klipp, Edda; Leduc, Aurélie; Lewis, Peter J.; Molina, Frank; Naveilhan, Philippe; Pérès, Sabine; Pigeonneau, Nathalie; Pohl, Susanne; Rasmussen, Simon; Rinn, Bernd; Schaffer, Marc; Schnidder, Julian; Schwikowski, Benno; Dijl, Jan Maarten van; Veiga, Patrick; Walsh, Sean; Wilkinson, Anthony J.; Stelling, Jörg; Aymerich, Stéphane; Sauer, Uwe

doi:10.1126/science.1206871

Cited by 257 publications

(251 citation statements)

References 29 publications

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“…Second, the mechanistic basis allows model tests against data that highlight missing processes (as here) rather than considering data comparisons as a mechanism for parameter estimation, as may be the case in highly aggregated (e.g., nutrient-phytoplanktonzooplankton-detritus) models. Finally, the coarse-grained representation of cellular physiology provides a link to more detailed systems-biology approaches (Buescher et al 2012) and to omics data sets (Gilbert et al 2011). Ultimately, such mechanistic approaches may help us understand the likely response of the marine ecosystem to global change.…”

Section: Discussionmentioning

confidence: 99%

“…Biomass generation and growth rate then follow from biophysical constraints (e.g., diffusion limited nutrient uptake), the chain of component rate limitations, and environmental conditions (e.g., light or nutrient availability). To link to laboratory studies of phytoplankton physiology, we build on the work of Shuter (1979) and Geider et al (1996) while noting that the recent work (Buescher et al 2012) has also demonstrated the potential of coarse-grained models of subcellular physiology to capture key cost-benefit trade-offs, as determined by detailed omics-based investigations. Importantly, the approach, based on subcellular resource allocation, can represent individual acclimation (as dynamic reallocation to components (Geider et al 1997;Bonachela et al 2011), diversity (as constant allocation defined by traits), and adaptation (as changes in allocation through reproduction with variation).…”

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confidence: 99%

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Environmental selection and resource allocation determine spatial patterns in picophytoplankton cell size

Clark

Lenton

Williams

et al. 2013

Limnology & Oceanography

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Here we describe a new trait-based model for cellular resource allocation that we use to investigate the relative importance of different drivers for small cell size in phytoplankton. Using the model, we show that increased investment in nonscalable structural components with decreasing cell size leads to a trade-off between cell size, nutrient and light affinity, and growth rate. Within the most extreme nutrient-limited, stratified environments, resource competition theory then predicts a trend toward larger minimum cell size with increasing depth. We demonstrate that this explains observed trends using a marine ecosystem model that represents selection and adaptation of a diverse community defined by traits for cell size and subcellular resource allocation. This framework for linking cellular physiology to environmental selection can be used to investigate the adaptive response of the marine microbial community to environmental conditions and the adaptive value of variations in cellular physiology.A key challenge facing marine biogeochemical modelers is how best to represent the important role that diverse, rapidly evolving microbial populations play in marine biogeochemical cycles. Simple models, which may include just a single state variable for all phytoplankton (e.g., Fasham et al. 1990), often ignore the distinct functional role that different taxa perform (e.g., silicifying, or calcifying organisms), while more complicated models, which attempt to explicitly resolve multiple functional types (Le Quéré et al. 2005), can face severe practical problems in terms of the number of organism-level measurements and parameters required to describe the model (Anderson 2005;Flynn 2006). A still more fundamental problem lies in how best to represent adaptive or evolutionary processes, which are typically ignored in current state-of-the-art marine ecosystem models.Recent approaches have attempted to address some of these issues (Follows and Dutkiewicz 2011). For example, Follows et al. (2007) used a Monte-Carlo sampling method with a marine ecosystem model that included a diverse phytoplankton community, described by a set of empirically motivated traits with trade-offs. Emergent patterns in phytoplankton biogeography were in broad agreement with observations, illustrating the importance of environmental selection in determining spatial and temporal patterns in phytoplankton biogeography. The same model has also been used to examine drivers for latitudinal patterns in biodiversity (Barton et al. 2010) and the effect of chromatic adaptation on community structure in oligotrophic environments (Hickman et al. 2010). Meanwhile, Bruggeman and Kooijman (2007) described a biodiversity-based marine ecosystem model in which species were defined by a generic model with continuous trait values for investment in nutrient-and light-harvesting machinery. In their model, trade-offs between traits emerged naturally as a result of different allocation strategies. When configured for a representative oligotrophic open-ocean sit...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Environmental selection and resource allocation determine spatial patterns in picophytoplankton cell size

Clark

Lenton

Williams

et al. 2013

Limnology & Oceanography

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“…Metabolism is involved in many human diseases, including diabetes, cancer, and neurodegeneration (Hsu and Sabatini 2008;Grüning et al 2010;Hanahan and Weinberg 2011;Buescher et al 2012;Keller et al 2014). Yeast as a model organism continues to be of central importance to the study of metabolism.…”

Section: Resultsmentioning

confidence: 99%

Metabolomics in Yeast

Caudy

Mülleder

Ralser

2017

Cold Spring Harb Protoc

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Budding yeast has from the beginning been a major eukaryotic model for the study of metabolic network structure and function. This is attributable to both its genetic and biochemical capacities and its role as a workhorse in food production and biotechnology. New inventions in analytical technologies allow accurate, simultaneous detection and quantification of metabolites, and a series of recent findings have placed the metabolic network at center stage in the physiology of the cell. For example, metabolism might have facilitated the origin of life, and in modern organisms it not only provides nutrients to the cell but also serves as a buffer to changes in the cellular environment, a regulator of cellular processes, and a requirement for cell growth. These findings have triggered a rapid and massive renaissance in this important field. Here, we provide an introduction to analysis of metabolomics in yeast. METABOLOMICS IN YEASTResearch on metabolism and metabolic enzymes dominated the early days of molecular biology. Interest in the topic decreased with the appearance of polymerase chain reaction (PCR) and molecular genetics during the 1980s and 1990s, although the importance of budding yeast in biotechnology ensured continued progress in the understanding of its physiology. Yeast has been used in winemaking, brewing, and baking since ancient times. The economic importance of fermentation triggered the first attempts to breed and manipulate yeast species and led to the purification, crystallization, and characterization of the first enzymes in the early 20th century (with major contributions from Sumner and Northrop in the United States and Warburg in Germany). Yeast experiments were so important in the development of biochemistry that the word "enzyme" is derived from the Greek word for in leaven (yeast).The full set of metabolic reactions in the cell is referred to as the metabolic network. Although complex, the basic structure of the metabolic network is largely conserved among organisms, indicating that it is of a common evolutionary origin (Jeong et al. 2000;Ravasz et al. 2002). Compared with the large number of chemical reactions and possible mechanisms, metabolism operates with only a subset of the reactions and prefers short paths in its functionality (Noor et al. 2010). Overall, this conservation means that metabolomics is a particularly attractive technique, as in principle any analytical method can be applied to a large variety of different species. However, sample preparation methods are species specific, because composition of the cell membrane, presence of a cell wall, cellular resistance to physical parameters, and relative and absolute metabolite content differ from species to species.

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“…Furthermore, the comprehensive community-curated databases (SubtiWiki, EcoliWiki, BsubCyc and EcoCyc, with SubtiWiki and EcoCyc being the most frequently used) provide up-todate information about these two model organisms (Caspi et al, 2014;Keseler et al, 2013;McIntosh et al, 2012;Michna et al, 2014). B. subtilis and E. coli are both endowed with complex regulatory and metabolic networks allowing them to thrive in a broad spectrum of environments (Buescher et al, 2012;Kohlstedt et al, 2014;Nicolas et al, 2012;Wang et al, 2010). B. subtilis is a commensal bacterium able to form metabolically inactive dehydrated endospores allowing survival in nutrient-free environments (McKenney et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Bacillus subtilis and Escherichia coli essential genes and minimal cell factories after one decade of genome engineering

et al. 2014

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Investigation of essential genes, besides contributing to understanding the fundamental principles of life, has numerous practical applications. Essential genes can be exploited as building blocks of a tightly controlled cell ‘chassis’. Bacillus subtilis and Escherichia coli K-12 are both well-characterized model bacteria used as hosts for a plethora of biotechnological applications. Determination of the essential genes that constitute the B. subtilis and E. coli minimal genomes is therefore of the highest importance. Recent advances have led to the modification of the original B. subtilis and E. coli essential gene sets identified 10 years ago. Furthermore, significant progress has been made in the area of genome minimization of both model bacteria. This review provides an update, with particular emphasis on the current essential gene sets and their comparison with the original gene sets identified 10 years ago. Special attention is focused on the genome reduction analyses in B. subtilis and E. coli and the construction of minimal cell factories for industrial applications.

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Global Network Reorganization During Dynamic Adaptations of Bacillus subtilis Metabolism

Cited by 257 publications

References 29 publications

Environmental selection and resource allocation determine spatial patterns in picophytoplankton cell size

Environmental selection and resource allocation determine spatial patterns in picophytoplankton cell size

Metabolomics in Yeast

Bacillus subtilis and Escherichia coli essential genes and minimal cell factories after one decade of genome engineering

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