A systems approach to model natural variation in reactive properties of bacterial ribosomes

Jackson, Julius H.; Schmidt, Thomas M.; Herring, Patricia A.

doi:10.1186/1752-0509-2-62

Cited by 5 publications

(7 citation statements)

References 20 publications

(19 reference statements)

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“…cgi) and other sources (Klappenbach et al 2001), and these reports demonstrate that genetic variation among different target species and strains could contribute to uncertainty in such recovery estimates. However, the results from this study, as well as others (Jackson et al 2008;Kurmayer and Kutzenberger 2003;Ludwig and Schleifer 2000;Mazel et al 1990), indicate that the actual copy numbers per cell may vary even more substantially in response to the physiological status of the organisms. For example, in this study, the mean estimate of the rRNA gene copy numbers recovered per cultured E. faecalis cell in the calibrator samples was ∼3.5 times greater than the reported copy number per genome based on sequencing studies.…”

Section: Discussioncontrasting

confidence: 44%

Comparison of Fecal Indicator Bacteria Densities in Marine Recreational Waters by QPCR

et al. 2009

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The US EPA is currently investigating the use of quantitative PCR (qPCR) analysis techniques to estimate densities of fecal indicator bacteria (FIB) in recreational waters. Present water quality guidelines, based on culturable FIB, prevent same day water quality determination, whereas results from qPCR-based approaches are available within several hours. Epidemiological studies at Publicly-Owned Treatment Works (POTW)-impacted freshwater beaches have also indicated correlations between qPCR determined Enterococcus densities and swimming-related illness rates. Similar qPCR assays are now available for several other accepted or emerging FIB groups. This study provides an initial assessment of qPCR estimated Enterococcus, Bacteroidales, E. coli and Clostridium spp. densities in marine water and sand samples collected over one summer from two POTW-impacted recreational beaches. Relative target sequence densities of these organisms in the samples did not correspond with their relative estimated cell densities. These observations were attributable to differences in target sequences recovered from the calibrator cells of the different types of organisms. Comparative cycle threshold (CT) qPCR analyses of whole cell calibrator samples provide a simple and standardizable approach for estimating both total cell and target sequence densities of different types of FIB in water. Cell density estimates obtained by this approach are subject to uncertainty due to potential variability in absolute numbers of target sequences in the target organisms under different physiological or environmental conditions, but still

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Section: Discussioncontrasting

confidence: 44%

Comparison of Fecal Indicator Bacteria Densities in Marine Recreational Waters by QPCR

et al. 2009

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“…In equation 3, e g is the initial efficiency of the SU, that is, the growth per metabolite availability at low metabolite concentration (p X ), and G max (maximum G) is proportional to the amount of growth machinery g ( ) and its max-1 Ϫ z imum capacity of biomass synthesis (f G ), which is a function of the maximal translational activity of the ribosomes (Jackson et al 2008):…”

Section: Cell Processesmentioning

confidence: 99%

Optimization of Biomass Composition Explains Microbial Growth-Stoichiometry Relationships

Franklin

Hall

Kaiser

et al. 2011

The American Naturalist

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Integrating microbial physiology and biomass stoichiometry opens far-reaching possibilities for linking microbial dynamics to ecosystem processes. For example, the growth-rate hypothesis (GRH) predicts positive correlations among growth rate, RNA content, and biomass phosphorus (P) content. Such relationships have been used to infer patterns of microbial activity, resource availability, and nutrient recycling in ecosystems. However, for microorganisms it is unclear under which resource conditions the GRH applies. We developed a model to test whether the response of microbial biomass stoichiometry to variable resource stoichiometry can be explained by a trade-off among cellular components that maximizes growth. The results show mechanistically why the GRH is valid under P limitation but not under N limitation. We also show why variability of growth rate-biomass stoichiometry relationships is lower under P limitation than under N or C limitation. These theoretical results are supported by experimental data on macromolecular composition (RNA, DNA, and protein) and biomass stoichiometry from two different bacteria. In addition, compared to a model with strictly homeostatic biomass, the optimization mechanism we suggest results in increased microbial N and P mineralization during organic-matter decomposition. Therefore, this mechanism may also have important implications for our understanding of nutrient cycling in ecosystems.

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“…In a recent work, Jackson et al [47] modelled the process of translation as an enzymatic reaction. However, there are crucial differences between their formulation of translation and our interpretation of the mechanochemical cycle in our model.…”

Section: A Comparison With Some Earlier Workmentioning

confidence: 99%

“…However, there are crucial differences between their formulation of translation and our interpretation of the mechanochemical cycle in our model. In their formulation, Jackson et al [47] treated the completely synthesized protein as the product of the enzymatic reaction, i.e., the run of a single ribosome from the initiation site to the termination site was treated as a single enzymatic reaction. In contrast, translocation of a ribosome from one codon to the next, and the associated elongation of the growing polypeptide by one amino acid has been treated in our calculation here as a single enzymatic reaction.…”

Section: A Comparison With Some Earlier Workmentioning

confidence: 99%

Stochastic kinetics of ribosomes: Single motor properties and collective behavior

Garai

Chowdhury

et al. 2009

Phys. Rev. E

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Synthesis of protein molecules in a cell are carried out by ribosomes. A ribosome can be regarded as a molecular motor which utilizes the input chemical energy to move on a messenger RNA (mRNA) track that also serves as a template for the polymerization of the corresponding protein. The forward movement, however, is characterized by an alternating sequence of translocation and pause. Using a quantitative model, which captures the mechanochemical cycle of an individual ribosome, we derive an exact analytical expression for the distribution of its dwell times at the successive positions on the mRNA track. Inverse of the average dwell time satisfies a "Michaelis-Menten-like" equation and is consistent with the general formula for the average velocity of a molecular motor with an unbranched mechano-chemical cycle. Extending this formula appropriately, we also derive the exact force-velocity relation for a ribosome. Often many ribosomes simultaneously move on the same mRNA track, while each synthesizes a copy of the same protein. We extend the model of a single ribosome by incorporating steric exclusion of different individuals on the same track. We draw the phase diagram of this model of ribosome traffic in 3-dimensional spaces spanned by experimentally controllable parameters. We suggest new experimental tests of our theoretical predictions.

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A systems approach to model natural variation in reactive properties of bacterial ribosomes

Cited by 5 publications

References 20 publications

Comparison of Fecal Indicator Bacteria Densities in Marine Recreational Waters by QPCR

Comparison of Fecal Indicator Bacteria Densities in Marine Recreational Waters by QPCR

Optimization of Biomass Composition Explains Microbial Growth-Stoichiometry Relationships

Stochastic kinetics of ribosomes: Single motor properties and collective behavior

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