Characterization of the cytochromes C from Desulfovibrio desulfuricans G201

Hemoproteins in dissimilatory sulfate- and sulfur-reducing prokaryotes

Dissimilatory sulfate and sulfur reduction evolved billions of years ago and while the bacteria and archaea that use this unique metabolism employ a variety of electron donors, H(2) is most commonly used as the energy source. These prokaryotes use multiheme c-type proteins to shuttle electrons from electron donors, and electron transport complexes presumed to contain b-type hemoproteins contribute to proton charging of the membrane. Numerous sulfate and sulfur reducers use an alternate pathway for heme synthesis and, frequently, uniquely specific axial ligands are used to secure c-type heme to the protein. This review presents some of the types and functional activities of hemoproteins involved in these two dissimilatory reduction pathways.

Genetics and molecular biology of the electron flow for sulfate respiration in desulfovibrio

Progress in the genetic manipulation of the Desulfovibrio strains has provided an opportunity to explore electron flow pathways during sulfate respiration. Most bacteria in this genus couple the oxidation of organic acids or ethanol with the reduction of sulfate, sulfite, or thiosulfate. Both fermentation of pyruvate in the absence of an alternative terminal electron acceptor, disproportionation of fumarate and growth on H₂ with CO₂ during sulfate reduction are exhibited by some strains. The ability to produce or consume H₂ provides Desulfovibrio strains the capacity to participate as either partner in interspecies H₂ transfer. Interestingly the mechanisms of energy conversion, pathways of electron flow and the parameters determining the pathways used remain to be elucidated. Recent application of molecular genetic tools for the exploration of the metabolism of Desulfovibrio vulgaris Hildenborough has provided several new datasets that might provide insights and constraints to the electron flow pathways. These datasets include (1) gene expression changes measured in microarrays for cells cultured with different electron donors and acceptors, (2) relative mRNA abundances for cells growing exponentially in defined medium with lactate as carbon source and electron donor plus sulfate as terminal electron acceptor, and (3) a random transposon mutant library selected on medium containing lactate plus sulfate supplemented with yeast extract. Studies of directed mutations eliminating apparent key components, the quinone-interacting membrane-bound oxidoreductase (Qmo) complex, the Type 1 tetraheme cytochrome c₃ (Tp1-c₃), or the Type 1 cytochrome c₃:menaquinone oxidoreductase (Qrc) complex, suggest a greater flexibility in electron flow than previously considered. The new datasets revealed the absence of random transposons in the genes encoding an enzyme with homology to Coo membrane-bound hydrogenase. From this result, we infer that Coo hydrogenase plays an important role in D. vulgaris growth on lactate plus sulfate. These observations along with those reported previously have been combined in a model showing dual pathways of electrons from the oxidation of both lactate and pyruvate during sulfate respiration. Continuing genetic and biochemical analyses of key genes in Desulfovibrio strains will allow further clarification of a general model for sulfate respiration.

Prediction and Characterization of Missing Proteomic Data in Desulfovibrio vulgaris

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 proteins not experimentally detected, based on several relevant predictors, such as mRNA abundance, cellular role and triple codon counts. The results showed that the coefficients of determination for the trained neural network models ranged from 0.47 to 0.68, providing better modeling than several previous regression models. The validity of the trained neural network model was evaluated using biological information (i.e. operons). To seek understanding of mechanisms causing missing proteomic data, we used a multivariate logistic regression analysis and the result suggested that some key factors, such as protein instability index, aliphatic index, mRNA abundance, effective number of codons (N(c)) and codon adaptation index (CAI) values may be ascribed to whether a given expressed protein can be detected. In addition, we demonstrated that biological interpretation can be improved by use of imputed proteomic datasets.

Integrating multiple 'omics' analysis for microbial biology: application and methodologies

Recent advances in various 'omics' technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. Several popular 'omics' platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single 'omics' analysis can fully unravel the complexities of fundamental microbial biology. Therefore, integration of multiple layers of information, the multi-'omics' approach, is required to acquire a precise picture of living micro-organisms. In spite of this being a challenging task, some attempts have been made recently to integrate heterogeneous 'omics' datasets in various microbial systems and the results have demonstrated that the multi-'omics' approach is a powerful tool for understanding the functional principles and dynamics of total cellular systems. This article reviews some basic concepts of various experimental 'omics' approaches, recent application of the integrated 'omics' for exploring metabolic and regulatory mechanisms in microbes, and advances in computational and statistical methodologies associated with integrated 'omics' analyses. Online databases and bioinformatic infrastructure available for integrated 'omics' analyses are also briefly discussed.

Integrative analysis of transcriptomic and proteomic data: challenges, solutions and applications

Recent advances in high-throughput technologies enable quantitative monitoring of the abundance of various biological molecules and allow determination of their variation between biological states on a genomic scale. Two popular platforms are DNA microarrays that measure messenger RNA transcript levels, and gel-free proteomic analyses that quantify protein abundance. Obviously, no single approach can fully unravel the complexities of fundamental biology and it is equally clear that integrative analysis of multiple levels of gene expression would be valuable in this endeavor. However, most integrative transcriptomic and proteomic studies have thus far either failed to find a correlation or only observed a weak correlation. In addition to various biological factors, it is suggested that the poor correlation could be quite possibly due to the inadequacy of available statistical tools to compensate for biases in the data collection methodologies. To address this issue, attempts have recently been made to systematically investigate the correlation patterns between transcriptomic and proteomic datasets, and to develop sophisticated statistical tools to improve the chances of capturing a relationship. The goal of these efforts is to enhance understanding of the relationship between transcriptomes and proteomes so that integrative analyses may be utilized to reveal new biological insights that are not accessible through one-dimensional datasets. In this review, we outline some of the challenges associated with integrative analyses and present some preliminary statistical solutions. In addition, some new applications of integrated transcriptomic and proteomic analysis to the investigation of post-transcriptional regulation are also discussed.

A proteomic view ofDesulfovibrio vulgaris metabolism as determined by liquid chromatography coupled with tandem mass spectrometry

Direct LC-MS/MS was used to examine the proteins extracted from exponential or stationary phase Desulfovibrio vulgaris cells that had been grown on a minimal medium containing either lactate or formate as the primary carbon source. Across all four growth conditions, 976 gene products were identified with high confidence, which is equal to approximately 28% of all predicted proteins in the D. vulgaris genome. Bioinformatic analysis showed that the proteins identified were distributed among almost all functional classes, with the energy metabolism category containing the greatest number of identified proteins. At least 154 ORFs originally annotated as hypothetical proteins were found to encode the expressed proteins, which provided verification for the authenticity of these hypothetical proteins. Proteomic analysis showed that proteins potentially involved in ATP biosynthesis using the proton gradient across membrane, such as ATPase, alcohol dehydrogenases, heterodisulfide reductases, and [NiFe] hydrogenase (HynAB-1) of the hydrogen cycling were highly expressed in all four growth conditions, suggesting they may be the primary pathways for ATP synthesis in D. vulgaris. Most of the enzymes involved in substrate-level phosphorylation were also detected in all tested conditions. However, no enzyme involved in CO cycling or formate cycling was detected, suggesting that they are not the primary ATP-biosynthesis pathways under the tested conditions. This study provides the first proteomic overview of the cellular metabolism of D. vulgaris. The complete list of proteins identified in this study and their abundances (peptide hits) is provided in Supplementary Table 1.

Integrated analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: zero-inflated Poisson regression models to predict abundance of undetected proteins

MOTIVATION

Integrated analysis of global scale transcriptomic and proteomic data can provide important insights into the metabolic mechanisms underlying complex biological systems. However, because the relationship between protein abundance and mRNA expression level is complicated by many cellular and physical processes, sophisticated statistical models need to be developed to capture their relationship.

RESULTS

In this study, we describe a novel data-driven statistical model to integrate whole-genome microarray and proteomic data collected from Desulfovibrio vulgaris grown under three different conditions. Based on the Poisson distribution pattern of proteomic data and the fact that a large number of proteins were undetected (excess zeros), zero-inflated Poisson (ZIP)-based models were proposed to define the correlation pattern between mRNA and protein abundance. In addition, by assuming that there is a probability mass at zero representing unexpressed genes and expressed proteins that were undetected owing to technical limitations, a Potential ZIP model was established. Two significant improvements introduced by this approach are (1) the predicted protein abundance level values for experimentally detected proteins are corrected by considering their mRNA levels and (2) protein abundance values can be predicted for undetected proteins (in the case of this study, approximately 83% of the proteins in the D.vulgaris genome) for better biological interpretation. We demonstrated the use of these statistical models by comparatively analyzing proteomic and microarray results from D.vulgaris grown on lactate-based versus formate-based media. These models correctly predicted increased expression of Ech hydrogenase and decreased expression of Coo hydrogenase for D.vulgaris grown on formate.

Multi-heme cytochromes--new structures, new chemistry

Heme is one of the most pervasive cofactors in nature and the c-type cytochromes represent one of the largest families of heme-containing proteins. Recent progress in bacterial genomic analysis has revealed a vast range of genes encoding novel c-type cytochromes that contain multiple numbers of heme cofactors. The genome sequence of Geobacter sulfurreducens, for example, includes some one hundred genes encoding c-type cytochromes, with around seventy of these containing two, or more, heme groups and with one protein containing an astonishing twenty seven heme groups. This wealth of cytochromes is of great significance in the respiratory flexibility shown by bacteria such as Geobacter. In addition, we are now discovering that many of these multi-heme cytochromes have associated enzymatic activities and in some cases this is revealing new chemistries. The purpose of this perspective is to describe recent progress in the structural and functional analyses of these new multi-heme cytochromes. To illustrate this we have chosen to focus on three of these cytochromes which exhibit catalytic activities; nitrite reductase, hydroxylamine oxidoreductase and tetrathionate reductase. In addition we consider the multi-heme cytochromes from Geobacter and Desulfovibrio species. Finally, we consider and contrast the repeating structural modules found in these multi-heme cytochromes.

Interaction and electron transfer between the high molecular weight cytochrome and cytochrome c3 from Desulfovibrio vulgaris Hildenborough: Kinetic, microcalorimetric, EPR and electrochemical studies

The complex formation between the tetraheme cytochrome c3 and hexadecaheme high molecular weight cytochrome c (Hmc), the structure of which has recently been resolved, has been characterized by cross-linking experiments, EPR, electrochemistry and kinetic analysis, and some key parameters of the interaction were determined. The analysis of electron transfer between [Fe] hydrogenase, cytochrome c3 and Hmc demonstrates a redox-shuttling role of cytochrome c3 in the pathway from hydrogenase to Hmc, and shows an effect of redox state on the interaction between the two cytochromes. The role of polyheme cytochromes in electron transfer from periplasmic hydrogenase to membrane redox proteins is assessed. A model with cytochrome c3 as an intermediate between hydrogenase and various polyheme cytochromes is proposed and its physiological consequences are discussed.

The genome sequence of the anaerobic, sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough

Desulfovibrio vulgaris Hildenborough is a model organism for studying the energy metabolism of sulfate-reducing bacteria (SRB) and for understanding the economic impacts of SRB, including biocorrosion of metal infrastructure and bioremediation of toxic metal ions. The 3,570,858 base pair (bp) genome sequence reveals a network of novel c-type cytochromes, connecting multiple periplasmic hydrogenases and formate dehydrogenases, as a key feature of its energy metabolism. The relative arrangement of genes encoding enzymes for energy transduction, together with inferred cellular location of the enzymes, provides a basis for proposing an expansion to the 'hydrogen-cycling' model for increasing energy efficiency in this bacterium. Plasmid-encoded functions include modification of cell surface components, nitrogen fixation and a type-III protein secretion system. This genome sequence represents a substantial step toward the elucidation of pathways for reduction (and bioremediation) of pollutants such as uranium and chromium and offers a new starting point for defining this organism's complex anaerobic respiration.

Uranium reduction by Desulfovibrio desulfuricans strain G20 and a cytochrome c3 mutant

Previous in vitro experiments with Desulfovibrio vulgaris strain Hildenborough demonstrated that extracts containing hydrogenase and cytochrome c3 could reduce uranium(VI) to uranium(IV) with hydrogen as the electron donor. To test the involvement of these proteins in vivo, a cytochrome c3 mutant of D. desulfuricans strain G20 was assayed and found to be able to reduce U(VI) with lactate or pyruvate as the electron donor at rates about one-half of those of the wild type. With electrons from hydrogen, the rate was more severely impaired. Cytochrome c3 appears to be a part of the in vivo electron pathway to U(VI), but additional pathways from organic donors can apparently bypass this protein.

Cytochrome c(3) mutants of Desulfovibrio desulfuricans

To explore the physiological role of tetraheme cytochrome c(3) in the sulfate-reducing bacterium Desulfovibrio desulfuricans G20, the gene encoding the preapoprotein was cloned, sequenced, and mutated by plasmid insertion. The physical analysis of the DNA from the strain carrying the integrated plasmid showed that the insertion was successful. The growth rate of the mutant on lactate with sulfate was comparable to that of the wild type; however, mutant cultures did not achieve the same cell densities. Pyruvate, the oxidation product of lactate, served as a poor electron source for the mutant. Unexpectedly, the mutant was able to grow on hydrogen-sulfate medium. These data support a role for tetraheme cytochrome c(3) in the electron transport pathway from pyruvate to sulfate or sulfite in D. desulfuricans G20.