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Predicting the dynamics of protein abundance.

ABSTRACT: Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates transcriptomic and proteomic data to predict protein abundance and to model the effects of its determinants. We aim to use this model to follow a molecular response over time, from condition-specific data, in order to understand adaptation during processes such as the cell cycle. With microarray data now available for many conditions, the general utility of a protein abundance predictor is broad. Whereas most quantitative proteomics studies have focused on higher organisms, we developed a predictive model of protein abundance for both Saccharomyces cerevisiae and Schizosaccharomyces pombe to explore the latitude at the protein level. Our predictor primarily relies on mRNA level, mRNA-protein interaction, mRNA folding energy and half-life, and tRNA adaptation. The combination of key features, allowing for the low certainty and uneven coverage of experimental observations, gives comparatively minor but robust prediction accuracy. The model substantially improved the analysis of protein regulation during the cell cycle: predicted protein abundance identified twice as many cell-cycle-associated proteins as experimental mRNA levels. Predicted protein abundance was more dynamic than observed mRNA expression, agreeing with experimental protein abundance from a human cell line. We illustrate how the same model can be used to predict the folding energy of mRNA when protein abundance is available, lending credence to the emerging view that mRNA folding affects translation efficiency. The software and data used in this research are available at http://bioinf.scmb.uq.edu.au/proteinabundance/.

SUBMITTER: Mehdi AM

PROVIDER: S-EPMC4014288 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Predicting the dynamics of protein abundance.

Mehdi Ahmed M AM Patrick Ralph R Bailey Timothy L TL Bodén Mikael M

Molecular & cellular proteomics : MCP 20140216 5

Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates ...[more]

PMID: 24532840

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Project description:BACKGROUND:Although the high-throughput sequencing technique is useful for evaluating gastric microbiome, it is difficult to use clinically. We aimed to develop a predictive model for gastric microbiome based on serologic testing. METHODS:This study was designed to analyze sequencing data obtained from the Hanyang University Gastric Microbiome Cohort, which was established initially to investigate gastric microbial composition according to the intragastric environment. We evaluated the relationship between the relative abundance of potential gastric cancer-associated bacteria (nitrosating/nitrate-reducing bacteria or type IV secretion system [T4SS] protein gene-contributing bacteria) and serologic markers (IgG anti-Helicobacter pylori [HP] antibody or pepsinogen [PG] levels). RESULTS:We included 57 and 26 participants without and with HP infection, respectively. The relative abundance of nitrosating/nitrate-reducing bacteria was 4.9% and 3.6% in the HP-negative and HP-positive groups, respectively, while that of T4SS protein gene-contributing bacteria was 20.5% and 6.5% in the HP-negative and HP-positive groups, respectively. The relative abundance of both nitrosating/nitrate-reducing bacteria and T4SS protein gene-contributing bacteria increased exponentially as PG levels decreased. Advanced age (only for nitrosating/nitrate-reducing bacteria), a negative result of IgG anti-HP antibody, low PG levels, and high Charlson comorbidity index were associated with a high relative abundance of nitrosating/nitrate-reducing bacteria and T4SS protein gene-contributing bacteria. The adjusted coefficient of determination (R2) was 53.7% and 70.0% in the model for nitrosating/nitrate-reducing bacteria and T4SS protein gene-contributing bacteria, respectively. CONCLUSION:Not only the negative results of IgG anti-HP antibody but also low PG levels were associated with a high abundance of nitrosating/nitrate-reducing bacteria and T4SS protein gene-contributing bacteria.

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Predicting the dynamics of protein abundance.

Publications

Predicting the dynamics of protein abundance.

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