Dataset Information

A Comparison of the Costs and Benefits of Bacterial Gene Expression.

ABSTRACT: To study how a bacterium allocates its resources, we compared the costs and benefits of most (86%) of the proteins in Escherichia coli K-12 during growth in minimal glucose medium. The cost or investment in each protein was estimated from ribosomal profiling data, and the benefit of each protein was measured by assaying a library of transposon mutants. We found that proteins that are important for fitness are usually highly expressed, and 95% of these proteins are expressed at above 13 parts per million (ppm). Conversely, proteins that do not measurably benefit the host (with a benefit of less than 5% per generation) tend to be weakly expressed, with a median expression of 13 ppm. In aggregate, genes with no detectable benefit account for 31% of protein production, or about 22% if we correct for genetic redundancy. Although some of the apparently unnecessary expression could have subtle benefits in minimal glucose medium, the majority of the burden is due to genes that are important in other conditions. We propose that at least 13% of the cell's protein is "on standby" in case conditions change.

SUBMITTER: Price MN

PROVIDER: S-EPMC5053530 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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A Comparison of the Costs and Benefits of Bacterial Gene Expression.

Price Morgan N MN Wetmore Kelly M KM Deutschbauer Adam M AM Arkin Adam P AP

PloS one 20161006 10

To study how a bacterium allocates its resources, we compared the costs and benefits of most (86%) of the proteins in Escherichia coli K-12 during growth in minimal glucose medium. The cost or investment in each protein was estimated from ribosomal profiling data, and the benefit of each protein was measured by assaying a library of transposon mutants. We found that proteins that are important for fitness are usually highly expressed, and 95% of these proteins are expressed at above 13 parts per ...[more]

PMID: 27711251

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Project description:BackgroundMeasuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another.MethodsThis systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies.ResultsSignature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69-0.97 for viral classification. Signature size varied (1-398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months-1 year and 2-11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets.ConclusionsIn this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature's size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.

Project description:There is growing support for characterizing ecosystem services in order to link conservation and human well-being. However, few studies have explicitly included ecosystem services within systematic conservation planning, and those that have follow two fundamentally different approaches: ecosystem services as intrinsically-important targeted benefits vs. substitutable co-benefits. We present a first comparison of these two approaches in a case study in the Central Interior of British Columbia. We calculated and mapped economic values for carbon storage, timber production, and recreational angling using a geographical information system (GIS). These 'marginal' values represent the difference in service-provision between conservation and managed forestry as land uses. We compared two approaches to including ecosystem services in the site-selection software Marxan: as Targeted Benefits, and as Co-Benefits/Costs (in Marxan's cost function); we also compared these approaches with a Hybrid approach (carbon and angling as targeted benefits, timber as an opportunity cost). For this analysis, the Co-Benefit/Cost approach yielded a less costly reserve network than the Hybrid approach (1.6% cheaper). Including timber harvest as an opportunity cost in the cost function resulted in a reserve network that achieved targets equivalently, but at 15% lower total cost. We found counter-intuitive results for conservation: conservation-compatible services (carbon, angling) were positively correlated with each other and biodiversity, whereas the conservation-incompatible service (timber) was negatively correlated with all other networks. Our findings suggest that including ecosystem services within a conservation plan may be most cost-effective when they are represented as substitutable co-benefits/costs, rather than as targeted benefits. By explicitly valuing the costs and benefits associated with services, we may be able to achieve meaningful biodiversity conservation at lower cost and with greater co-benefits.

Dataset Information

A Comparison of the Costs and Benefits of Bacterial Gene Expression.

Publications

A Comparison of the Costs and Benefits of Bacterial Gene Expression.

Similar Datasets

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