Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris.
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SUBMITTER: Li F
PROVIDER: S-EPMC3114432 | biostudies-literature | 2011
REPOSITORIES: biostudies-literature
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Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris.
Li Feng F Nie Lei L Wu Gang G Qiao Jianjun J Zhang Weiwen W
Comparative and functional genomics 20110504
Proteomic datasets are often incomplete due to identification range and sensitivity issues. It becomes important to develop methodologies to estimate missing proteomic data, allowing better interpretation of proteomic datasets and metabolic mechanisms underlying complex biological systems. In this study, we applied an artificial neural network to approximate the relationships between cognate transcriptomic and proteomic datasets of Desulfovibrio vulgaris, and to predict protein abundance for the ...[more]
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Project description:Missing data are a common problem for both the construction and implementation of a prediction algorithm. Pattern submodels (PS)-a set of submodels for every missing data pattern that are fit using only data from that pattern-are a computationally efficient remedy for handling missing data at both stages. Here, we show that PS (i) retain their predictive accuracy even when the missing data mechanism is not missing at random (MAR) and (ii) yield an algorithm that is the most predictive among all standard missing data strategies. Specifically, we show that the expected loss of a forecasting algorithm is minimized when each pattern-specific loss is minimized. Simulations and a re-analysis of the SUPPORT study confirms that PS generally outperforms zero-imputation, mean-imputation, complete-case analysis, complete-case submodels, and even multiple imputation (MI). The degree of improvement is highly dependent on the missingness mechanism and the effect size of missing predictors. When the data are MAR, MI can yield comparable forecasting performance but generally requires a larger computational cost. We also show that predictions from the PS approach are equivalent to the limiting predictions for a MI procedure that is dependent on missingness indicators (the MIMI model). The focus of this article is on out-of-sample prediction; implications for model inference are only briefly explored.
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Project description:BackgroundDesulfovibrio vulgaris Hildenborough is a sulfate-reducing bacterium (SRB) that is intensively studied in the context of metal corrosion and heavy-metal bioremediation, and SRB populations are commonly observed in pipe and subsurface environments as surface-associated populations. In order to elucidate physiological changes associated with biofilm growth at both the transcript and protein level, transcriptomic and proteomic analyses were done on mature biofilm cells and compared to both batch and reactor planktonic populations. The biofilms were cultivated with lactate and sulfate in a continuously fed biofilm reactor, and compared to both batch and reactor planktonic populations.ResultsThe functional genomic analysis demonstrated that biofilm cells were different compared to planktonic cells, and the majority of altered abundances for genes and proteins were annotated as hypothetical (unknown function), energy conservation, amino acid metabolism, and signal transduction. Genes and proteins that showed similar trends in detected levels were particularly involved in energy conservation such as increases in an annotated ech hydrogenase, formate dehydrogenase, pyruvate:ferredoxin oxidoreductase, and rnf oxidoreductase, and the biofilm cells had elevated formate dehydrogenase activity. Several other hydrogenases and formate dehydrogenases also showed an increased protein level, while decreased transcript and protein levels were observed for putative coo hydrogenase as well as a lactate permease and hyp hydrogenases for biofilm cells. Genes annotated for amino acid synthesis and nitrogen utilization were also predominant changers within the biofilm state. Ribosomal transcripts and proteins were notably decreased within the biofilm cells compared to exponential-phase cells but were not as low as levels observed in planktonic, stationary-phase cells. Several putative, extracellular proteins (DVU1012, 1545) were also detected in the extracellular fraction from biofilm cells.ConclusionsEven though both the planktonic and biofilm cells were oxidizing lactate and reducing sulfate, the biofilm cells were physiologically distinct compared to planktonic growth states due to altered abundances of genes/proteins involved in carbon/energy flow and extracellular structures. In addition, average expression values for multiple rRNA transcripts and respiratory activity measurements indicated that biofilm cells were metabolically more similar to exponential-phase cells although biofilm cells are structured differently. The characterization of physiological advantages and constraints of the biofilm growth state for sulfate-reducing bacteria will provide insight into bioremediation applications as well as microbially-induced metal corrosion.
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Project description:Desulfovibrio vulgaris has been studied extensively for its potential in the bioremediation of heavy metals and radionuclides. Hydrocarbons and solvents, as frequent environmental co-contaminants, have been reported to inhibit microbial activities and thereby pose a limitation on bioremediation efficiency. As a part of the Genomes: GTL project to deduce the stress response pathways in metal/radionuclide reducing bacteria, we studied the responses of D. vulgaris to ethanol, which is a solvent and co-contaminant frequently encountered at contaminated DOE sites. Growth experiments in closed vessels at 37 °C indicated that D. vulgaris could maintain normal growth with 1%(v/v) ethanol. The growth rates were reduced with increasing ethanol concentrations at 2% and 5%. Growth ceased when ethanol concentration was raised to 5%(v/v). Cell lysis was apparent with decreasing optical density following 10% ethanol addition. To assess the mechanism of ethanol inhibition, genome-wide transcriptional profiles were analyzed from D. vulgaris cultures following ethanol (5% v/v) treatment using whole-genome microarrays. The ethanol treatment altered the expression of a large number of genes in the D. vulgaris genome, of which 354 were up-regulated greater than 2 fold and 217 were down-regulated by over 2 fold. As expected, changes in the transcriptional profile were similar to those of the stress response to acetone, which is also a solvent. Transcripts highly up-regulated included genes encoding the flagella structural subunits, suggesting motility as a mechanism of solvent stress response. Another group of genes highly induced were chaperones, such as dnaJ, groES, and hsp20, indicating the importance of maintaining proper protein folding under ethanol stress. Down-regulated genes included two groups of genes, ribosomal proteins and amino acid transporters, consistent with the growth inhibition by ethanol observed in growth studies. These results were interpreted that D. vulgaris responds to elevated solvent levels by increased motility and maintenance of proper protein functions. Current work is focused on the analysis of regulatory pathways based on temporal transcriptional dynamics. Keywords: Stress response
2009-08-08 | GSE7489 | GEO