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1 Center for Ecogenomics, Biodesign Institute, Arizona State University, Tempe, AZ 85287-6501, USA
2 Division of Biometrics II, Office of Biometrics/OTS/CDER/FDA, Silver Spring, MD 20993-0002, USA
3 Division of Biometrics IV, Office of Biometrics/OTS/CDER/FDA, Silver Spring, MD 20993-0002, USA
Correspondence
Weiwen Zhang
Weiwen.Zhang{at}asu.edu
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ABSTRACT |
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 TOP ABSTRACT IntroductionExperimential 'omics' approachesRecent application of integrated...Computational methodologies...Databases and bioinformatic...Concluding remarksDisclaimerREFERENCES |
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Introduction |
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TOPABSTRACTIntroductionExperimential 'omics' approachesRecent application of integrated...Computational methodologies...Databases and bioinformatic...Concluding remarksDisclaimerREFERENCES |
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; Kyrpides, 2009). The computational-based annotation and comparative genomic analyses of DNA sequences have provided biologists with information regarding gene function, genome structures, biological pathways, metabolic and regulatory networks, and evolution of microbial genomes, which has greatly enhanced our understanding of microbial metabolism (Schoolnik, 2001; Ward & Fraser, 2005; Sharan & Ideker, 2006; Cardenas & Tiedje, 2008; Rocha, 2008). However, to fully elucidate microbial metabolism and its responses to environmental factors, it is necessary to include functional characterization and accurate quantification of all levels of gene products, mRNA, proteins and metabolites, as well as their interaction. In the past decade, significant efforts in improving analytical technologies pertaining to measuring mRNA, proteins and metabolites have been made. These efforts have led to the generation of several new ‘omics’ research fields: transcriptomics, proteomics, metabolomics, interactomics and so on (Fiehn, 2001; Singh & Nagaraj, 2006; Lin & Qian, 2007; Kandpal et al., 2009; Ishii & Tomita, 2009; Tang et al., 2009a) (Fig. 1). In general, all experimental ‘omics’ approaches can be considered to share three major features in contrast to traditional procedures. The first feature is that, unlike traditional methods, ‘omics’ approaches are high-throughput, data-driven, holistic and top-down methodologies; the second feature is the attempt to understand the cell metabolism as one ‘integrated system’ rather than as mere collections of different parts by using information of the relationships between many measured molecular species (Ishii & Tomita, 2009); and the last feature is that these high-throughput ‘omics’ approaches generate large amounts of data and the analysis of these data often requires significant statistical and computational efforts. To date, a great deal of information regarding cellular metabolism has been acquired through application of individual ‘omics’ approaches (Park et al., 2005).
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). For example, the expression level of a given gene does not indicate the amount of protein produced, nor its location, biological activity or functional relationship with metabolomes. Moreover, in cells many levels of regulation occur after genes have been transcribed, such as post-transcriptional, translational and post-translational regulation, and all forms of biochemical control such as allosteric or feedback regulation. For example, in a study by Ter Kuile & Westerhoff (2001), control of glycolysis was shown to be shared between metabolic, proteomic and genomic levels. Taking this view into account, it is hard to believe that functional genomics can stop at the mRNA level or any single level of information. Integrated multi-‘omics’ approaches have been applied recently and the studies have enabled researchers to unravel global regulatory mechanisms and complex metabolic networks in various eukaryotic organisms (Hegde et al., 2003; Mootha et al., 2003a, b; Alter & Golub, 2004). While more work still needs to be done in order to improve experimental protocols and computational methodologies so that integrative analysis of multiple large-scale ‘omics’ datasets can be used to generate new knowledge not accessible by analysis of a single data type alone, these early studies have clearly demonstrated that integrated ‘omics’ analysis may be a key to decipher complex biological systems. Some of the progress and challenges associated with integrated ‘omics’ studies have been reviewed previously in several excellent articles (Joyce & Palsson, 2006; De Keersmaecker et al., 2006; Steinfath et al., 2007). In this article we review recent progress, with a focus on the application of integrated ‘omics’ approaches in various prokaryotic microbial systems and on the advance in various computational methods in dealing with integrated ‘omics’ data. A brief introduction to various experimental ‘omics’ platforms is also presented. |
Experimential ‘omics’ approaches |
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TOPABSTRACTIntroductionExperimential 'omics' approachesRecent application of integrated...Computational methodologies...Databases and bioinformatic...Concluding remarksDisclaimerREFERENCES |
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). Obviously, only analysing the DNA sequences of microbial genomes is not enough to obtain crucial information regarding the functionality of these molecules and the regulatory mechanisms involved in generating these molecules (Nierman et al., 2000). To address the limitation in genome analysis, the last decade has witnessed significant growth in technologies pertaining to molecular biological assays to measure various cellular molecules, and these efforts have led to the establishment of various experimental ‘omics’ strategies. Several well-established ‘omics’ platforms that have been used in various integrated ‘omics’ studies are briefly described below.
Transcriptomics
Transcriptomics, also called global analysis of gene expression or genome-wide expression profiling, is one of the tools to measure the whole set of all mRNA molecules, or ‘transcripts’, produced in one cell or a population of cells. Unlike genome sequencing and comparative genomics technologies that focus on DNA, which is static information for any given microbial species and normally does not change significantly in response to short-term external environmental changes, transcriptomics has enabled quantitative measurements of the dynamic expression of mRNA molecules and their variation between different states at the genome scale, thus reflecting the genes that are being actively expressed at any given time, with the exception of mRNA degradation phenomena (Ye et al., 2001; Horak & Snyder, 2002). Several popular high-throughput transcriptomics strategies involve: (i) first identifying mRNAs that differ in their expression status under different experimental conditions and later defining the identity of the respective genes, for example by differential display or serial analysis of gene expression (SAGE); (ii) alternatively assessing changes in the expression of previously defined genes, for example by cDNA or oligonucleotide microarrays (Kagnoff & Eckmann, 2001), or chip-based nanolitre-volume reverse-transcript (RT)-PCR, which measures gene expression for several thousands of genes simultaneously at higher sensitivity and accuracy than microarrays (Stedtfeld et al., 2008); (iii) a recent approach employing next-generation sequencers (i.e. Roche 454 Sequencer, Illumina Genome Analyser and Applied Biosystems SOLiD System) for direct sequencing of the cDNA converted from whole transcriptomes (Frias-Lopez et al., 2008; Gilbert et al., 2008); and (iv) a more recent direct RNA sequencing (DRS) technology, which allows massively parallel sequencing of RNA molecules directly without prior synthesis of cDNA or the need for ligation/amplification steps (i.e. Helicos Genetic Analysis System) (Ozsolak et al., 2009). In DRS both the abundance and the identity of mRNA molecules can be determined in one analytical process. In addition to a several thousand times higher sequencing throughput, a major advantage of next-generation sequencing over the traditional sequencing method is the dramatically increased degree of parallelism, which can be represented by the number of reads (i.e. the number of DNA templates that can be sequenced simultaneously) in a single sequencing run and the number of sequenced bases per day. Transcriptomics technologies have been used in various microbial systems to explore genome-wide transcriptional activity, and to define regulons, delineate operon structure and perform comparative genotyping (Ye et al., 2001).
Combined with the chromatin immune-precipitation (ChIP) procedure, transcriptomics technology can also be used to investigate the genome-wide location and function of DNA-binding proteins. This so-called ChIP-Chip technology was initially established in yeast (Ren et al., 2000), but has recently been applied to various bacterial systems (Grainger et al., 2004; Herring et al., 2005; Bruscella et al., 2008; Engels et al., 2008; Uyar et al., 2009). Considering the smaller size of bacterial genomes, the technology could be even more powerful in studying genome-wide binding and gene expression regulatory systems in bacteria.
Proteomics
Proteins are vital parts of living organisms, as they are the major components for building the cellular structure and they also serve as catalytic enzymes in metabolic pathways, and as signal transduction proteins in regulatory pathways of cells (Graham et al., 2007). The ability to identify and measure protein molecules on a large scale depends on recent advances in high-throughput proteomics methodologies, which aim at the simultaneous analysis of all proteins expressed by a cell, tissue or organism in a specific physiological condition. The term proteomics was coined to make an analogy with genomics and transcriptomics as a tool for the large-scale study of proteins, particularly their functions and structures (Wilkins et al., 1996; James, 1997). Proteomics has heavily relied on two major strategies for the separation and visualization of proteins: (i) two-dimensional PAGE (2D-PAGE), in which proteins are separated according to their isoelectric point and mass, followed by mass spectrometric identification, and (ii) gel-free profiling procedures such as multidimensional separations coupling micro-scale separations [most commonly micro-capillary liquid chromatographic (mLC) fractionation of protein tryptic digests] with automated tandem mass spectrometry (LC-MS/MS) (Baggerman et al., 2005; Nie et al., 2008). 2D-PAGE was first introduced in 1975 (O'Farrell, 1975), and typically allows the detection of only a few hundred proteins after their separation, thereby covering only ‘low-complexity’ proteomes. With recent application of various fluorescent dyes in this approach, such as 2D fluorescence difference gel electrophoresis (2D DIGE), the coverage and sensitivity of gel-based protein analysis have been significantly improved (Sonck et al., 2009); however, it is still not compatible with gel-free proteomics techniques that can detect several thousand proteins of large dynamic ranges in a single run and that can also provide better detection for low-abundance proteins and insoluble membrane-spanning proteins (Wolff et al., 2006). Even with some improvement, currently neither the 2D-PAGE nor the LC-MS/MS approach is close to saturating identifications in even small bacterial proteomes: typically only 20–40 % of the proteome can be detected, and these are identification approaches and do not provide quantitative information. In order to obtain more accurate proteome measurements, quantitative proteomic methods, such as stable isotope labelling-based isotope-coded affinity tags (ICAT), isobaric tag for relative and absolute quantification (iTRAQ) (Yan et al., 2008) or label-free comparative quantitative proteomics, need to be employed (Haqqani et al., 2008). Proteomics has been extensively used to explore microbial metabolism, differentiation and relationship with environments (for recent reviews see Cash, 2000; Fraser & Rappuoli, 2005; Norbeck et al., 2006; Graham et al., 2007; Zhao & Poh, 2008; Lacerda & Reardon, 2009).
Interactomics
It has been suggested that cellular life is organized through a complex protein interaction network, in which many proteins participating in the same functional pathways tend to aggregate together in multi-component protein complexes. The large-scale detection of these aggregated proteins, i.e. ‘interactomics’, thus represents one of the important directions of functional proteomics and it could provide novel insight into microbial cellular metabolism (Singh & Nagaraj, 2006). In addition, the interaction between proteins and DNA is crucial for regulating gene expression and thus key to cellular regulatory networks (Missiuro et al., 2009). The interactome of cells can generally be obtained by three approaches. The first one is to apply computational methods for the systematic identification of protein interactions in bacteria (Parrish et al., 2007; Kim et al., 2008; Rodriguez-Llorente et al., 2009). As one example of this approach, Rodriguez-Llorente et al. (2009) recently performed a large-scale reconstruction of interactomes involved in establishing symbiosis in Sinorhizobium meliloti. The study identified 263 novel proteins potentially associated with the Symbiosis Interactome, and the topology of the Symbiosis Interactome was used to guide experimental techniques attempting to validate novel proteins involved in different stages of symbiosis. The second approach is proteomic analysis of the protein complexes, typically performed using affinity tag/pull-down of large protein complexes and then MS/MS characterization at a proteome level (Butland et al., 2005; Gingras et al., 2005; Krogan et al., 2006; Díaz-Mejía et al., 2009). Using this approach, some subsets of the interactomes, such as cell-envelope-associated proteome and interactome in Escherichia coli, have been studied (Díaz-Mejía et al., 2009). However, only recently the first bacterial genome-scale interactome has been characterized in E. coli K-12. In this study, Hu et al. (2009) performed an extensive proteomic survey using affinity-tagged E. coli K-12 strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome, consisting of 5993 putative physical interactions and 74 776 putative functional associations. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multi-protein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. The third approach is large-scale genetic interaction screening by phenotype analysis of gene deletion mutants (Tong et al., 2004; Boone et al., 2007; Butland et al., 2008; Typas et al., 2008). Using the genetic interaction screening approach, two research groups independently performed large-scale quantitative analyses of genetic interactions using double mutant strains in E. coli (Butland et al., 2008; Typas et al., 2008). These studies have revealed the presence of new reactions and novel pathways that involve not only functionally uncharacterized genes, but also well-characterized genes. Together, they have improved our understanding of gene function and network connections in bacteria.
Metabolomics and fluxomics
In cells, the rate of enzymic reactions is also regulated by the concentrations of substrates and products (Ter Kuile & Westerhoff, 2001). Moreover, for most organisms, there is no direct relationship between cellular metabolites and genes in the same way as for mRNA and proteins. For example, Saccharomyces cerevisiae has fewer than 600 low-molecular-mass cellular intermediates (Oliver et al., 1998), whereas its genome contains 
6200 protein-encoding genes (Raamsdonk et al., 2001). Metabolomics, as a method to define the small-molecule diversity in the cell and to display differences in small molecule abundance, shows many advantages in terms of metabolic analyses because metabolites are the functional entities within the cells and their concentration levels vary as a consequence of genetic or physiological changes (Raamsdonk et al., 2001). Metabolomics analysis is typically performed by employing gas chromatography time-of-flight mass spectrometry (GC-TOF), high-performance liquid chromatography-mass spectrometry (LC-MS) or capillary electrophoresis-mass spectrometry (CE-MS) instruments, nuclear magnetic resonance (NMR) spectroscopy, and more recently vibrational spectroscopy (of which the resolution and sensitivity are considered as being lower than mass spectrometry). Metabolomics analysis can also be performed through a combined application of several technologies together in order to achieve high coverage and better identification (Kell, 2004; Dunn et al., 2005). Several metabolomic studies aimed at the non-biased comprehensive study of metabolites have been reported in recent years for various microbes including E. coli and S. cerevisiae (Tweeddale et al., 1998, 1999; Raamsdonk et al., 2001; Soga et al., 2003; Castrillo et al., 2003; van der Werf et al., 2005; Garcia et al., 2008). Compared with transcriptomics and proteomics, technologies used to profile end products of gene expression (e.g. metabolites) are less mature, and most of the metabolomics studies done so far are not yet sufficiently comprehensive (on average only a few dozen metabolites identified in most of these studies) and their measurement accuracy also needs further improvement (Fiehn, 2001; Kell, 2004; Cascante & Marin, 2008). However, these studies have demonstrated that microbial metabolomics could be a powerful tool in deciphering microbial metabolism and bridging the phenotype–genotype gap since it amplifies changes in the proteome and provides a better representation of the phenotype of an organism than any other methods (Cascante & Marin, 2008).
Moreover, in some recent applications, microbial metabolomics studies using isotope-labelled intermediate metabolites combined with various types of dynamic metabolic flux modelling have been suggested as a useful tool in investigations of complex and large-scale metabolic systems, and the term ‘fluxomics’ has been coined to describe this study area (Forster et al., 2002; Toya et al., 2007; Ishii & Tomita, 2009; Tang et al., 2009a, b). Improvements are still needed in both instruments and data-analysing software to improve identification coverage and accuracy in order to make metabolomics compatible with other ‘omics’ technologies (Dunn, 2008).
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Recent application of integrated ‘omics’ in microbial systems |
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TOPABSTRACTIntroductionExperimential 'omics' approachesRecent application of integrated...Computational methodologies...Databases and bioinformatic...Concluding remarksDisclaimerREFERENCES |
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) or have observed only a weak correlation (Ideker et al., 2001; Greenbaum et al., 2002; Washburn et al., 2003; Zhang et al., 2006b; Nie et al., 2007). It has been suggested that the discrepancy arises from several factors, including protein regulation by post-translational modification, post-transcriptional regulation of protein synthesis, differences in the half-lives of mRNA and proteins, possible functional requirement for protein binding, and significant levels of experimental error (Greenbaum et al., 2002; Beyer et al., 2004; Park et al., 2005). However, this discrepancy also highlights the importance of applying transcriptomic analysis in combination with proteomic analysis, since the biological systems may not be faithfully represented by either of the single individual approaches (Park et al., 2005). Integrated transcriptomics and proteomics is the most commonly used method among various multi-‘omics’ approaches. Several recent combined transcriptomic and proteomic analyses in microbial systems are summarized in Table 1. The studies can be classified into three major categories based on how the data were analysed.
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). By using the integrated ‘omics’ approach, the expression of c-type cytochromes involved in electron-transport processes was demonstrated in Desulfovibrio vulgaris (Nie et al., 2006c; Zhang et al., 2006a, b). This is especially important since most of these studies are aimed at obtaining global metabolic responses after certain treatments (e.g. stress or drug) (Mader et al., 2002a; Budde et al., 2006; Scherl et al., 2006; Brown et al., 2006) (Table 1).
The second type of study is to use transcriptomic and proteomic data for cross-validation purposes. When a good correlation or consensus at both RNA and protein level is found, conclusions can then be made with high confidence (Corbin et al., 2003; Lee et al., 2003; Yoon et al., 2003; Nunez et al., 2006).
The third type of study is to utilize transcriptomic and proteomic data to reveal novel biological insights that are not accessible through any single type of ‘omics’ dataset. For example, the discrepancy between transcriptomics and proteomics data has been used to suggest possible post-transcription regulation involved in chill-adaptation in Bacillus subtilis (Budde et al., 2006) and physiological adjustment of Halobacterium salinarum in response to changes in oxygen availability (Schmid et al., 2007). In one recent study using proteomic and transcriptomic data, Nie et al. (2006a) used multiple regression analysis of whole-genome mRNA expression and LC-MS/MS proteome abundance data collected from D. vulgaris grown in three conditions, to gain insights into how the mRNA–protein correlation may be affected by various sequence features related to translation efficiency. The analysis suggested that the mRNA–protein correlation is affected primarily by the factors important during the elongation stage, i.e. codon usage and amino acid composition. In contrast, factors related to translation initiation and termination, such as stop codon context and the Shine–Dalgarno sequence, appear to be less important in D. vulgaris (Nie et al., 2006a). In another recent study of growth and stationary phase adaptation in Streptomyces coelicolor, independent principal components analyses of isobaric stable isotope labelled peptides (iTRAQ)-derived shotgun proteomic data and DNA microarray-derived transcriptome data revealed that the prominent patterns in both protein and mRNA domains are surprisingly well correlated (Jayapal et al., 2008). By employing a systematic concordance analysis, the researchers estimated that over 30 % of the analysed genes likely exhibited significantly divergent patterns, of which nearly one-third even displayed opposing trends. Integrating the data with biological information, they discovered that certain groups of functionally related genes exhibit mRNA–protein discordance in a similar fashion. These observations suggest that differences between mRNA and protein synthesis/degradation mechanisms are prominent in microbes while reaffirming the plausibility of such mechanisms acting in a concerted fashion at a protein complex or subpathway level (Jayapal et al., 2008). Overall, these studies constitute excellent examples of how large-scale transcriptomic and proteomic data can be integrated to gain novel insights into metabolic and regulatory mechanisms of microbial cellular processes.
Integrated transcriptomics and metabolomics
Integrated transcriptomics and metabolomics analysis is a powerful tool to build the relationship between information elements – genes/transcripts – and functional elements – metabolites – in cells. It was first shown by fungal and plant biologists that transcript and metabolic profiles can be analysed in parallel, and from pairwise transcript–metabolite correlation analysis functionally important metabolites (Askenazi et al., 2003) and genes (Urbanczyk-Wochniak et al., 2003) could be identified (Hoefgen & Nikiforova, 2008). In recent years, the integrated transcriptomics and metabolomics approach has also been applied in various microbial systems. In one study, an integrated transcriptomics, metabolomics and fluxomics analysis of lysine-producing Corynebacterium glutamicum ATCC 13287 was performed at different stages of batch culture (Kromer et al., 2004). Good correlation was found between fluxomics and transcriptomics data for certain pathways. For example, the phase shift from growth to lysine production was accompanied by a decrease in glucose uptake flux and the redirection of flux from the tricarboxylic acid (TCA) cycle towards anaplerotic carboxylation and lysine biosynthesis; a significant correlation between flux and expression was also observed for glucose-6-phosphate dehydrogenase, transaldolase and transketolase and for most TCA cycle genes. However, cytoplasmic malate dehydrogenase expression increased despite a reduction of the TCA cycle flux, probably related to its contribution to NADH regeneration under conditions of reduced growth. In addition, most genes for lysine biosynthesis showed a constant expression level, despite a marked change of the metabolic flux, indicating that they are strongly regulated at the metabolic level. Glyoxylate cycle genes were continuously expressed, but the pathway exhibited in vivo activity only in the later stage. The most pronounced changes in gene expression during cultivation were found for enzymes at entry points into glycolysis, the pentose phosphate pathway, the TCA cycle and lysine biosynthesis, indicating that these might be of special importance for transcriptional control in C. glutamicum. The results demonstrated that the metabolic changes at different layers are not always the same, and that an integrated approach is valuable for fully understanding the microbial systems (Kromer et al., 2004).
In another recent integrated transcriptomics and metabolomics study targeting the infection mechanism of Rhodococcus fascians into Arabidopsis thaliana, the transcript results showed that R. fascians caused a pronounced cytokinin response, illustrated by the activation of cytokinin perception, signal transduction and homeostasis. Gene Ontology categorization of the differentially expressed genes hinted at a significant impact of infection on the primary metabolism of the host. This was confirmed by subsequent metabolite profiling; for example, invertase transcripts and activities were strongly enhanced upon infection, which probably accounted for the observed increase in the hexose : sucrose ratio and illustrating the establishment of a sink (Depuydt et al., 2009). In a study to compare the aerobic and anaerobic fermentations of Zymomonas mobilis, Yang et al. (2009) found that greater amounts of end products such as acetate, lactate and acetoin were detected under aerobic conditions and there was only 1.7 % of the amount of ethanol present aerobically as there was anaerobically. However, they did not observe any significant differences in gene expression between aerobic and anaerobic conditions in the early exponential growth phase. They also found that differences in energy-spilling pathways due to oxygen, and uncoupled growth, later led to large differences in ethanol yield and transcriptomic profiles between anaerobic and aerobic stationary-phase cultures (Yang et al., 2009).
Other integrated ‘omics’ studies
To obtain a complete view of microbial metabolism, researchers have recently made efforts to utilize more than two layers of information to seek insights into biological systems. Ishii et al. (2007) studied the response of E. coli cells to genetic and environmental perturbations. In this study, multiple ‘omics’ measurements were performed, including DNA microarray for 4213 E. coli genes and qRT-PCR for 85 genes involved in central carbon metabolism, 2D DIGE for approximately 2000 proteins and quantitative methods using liquid chromatography-mass spectrometry/mass spectrometry (LCMS/MS) for 57 proteins involved in central carbon metabolism, CE-TOFMS for 579 metabolites and 13C-labelled glucose for fluxome analysis. Analysis of metabolic enzyme gene disruptants revealed unexpectedly small changes in mRNA and proteins for most disruptants. Overall, metabolite levels were also stable, reflecting the rerouting of fluxes in the metabolic network. In contrast, the cells actively regulated enzyme levels to maintain a stable metabolic state in response to changes in growth rate. E. coli thus seems to use complementary strategies that result in a metabolic network robust against perturbations (Ishii et al., 2007). In another study, Trauger et al. (2008) studied the adaptation response of Pyrococcus furiosus when shifting from its optimal 95 °C to 72 °C using integrated transcriptomic, proteomic and metabolomic analysis. The complementary information obtained by the various ‘omics’ techniques was used to catalogue and correlate the overall molecular changes.
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Computational methodologies related to integrated ‘omics’ analyses |
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TOPABSTRACTIntroductionExperimential 'omics' approachesRecent application of integrated...Computational methodologies...Databases and bioinformatic...Concluding remarksDisclaimerREFERENCES |
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; Brockmann et al., 2007). In addition to various biological factors and technical limitations, it has been suggested that the poor correlation between transcriptomic and proteomic data may stem from the inadequacy of available statistical tools to compensate for biases in the data collection methodologies. As summarized in one review (Nie et al., 2007), chances of capturing correlation patterns between transcriptomic and proteomic data could be improved if some of the statistical challenges can be properly addressed, such as proper data transformation and normalization, better statistical tools to deal with experimental measurement errors and missing proteomic data, and tools for handling the nonlinearity property of correlation between transcriptomic and proteomic data. Some progress has been made in recent years; for example, Nie et al. (2006a) proposed a zero-inflated Poisson regression model to address issues with missing proteomics data. The key improvement of this model is that the undetected proteins are also taken into consideration, thus allowing an estimation of protein expression even when the proteins are experimentally undetectable due to technical limitations. In addition, the predicted values can be used to correct the measured protein abundance level for experimentally detected proteins by considering their mRNA levels. Fagan et al. (2007) used multivariate statistical methods to study gene expression and protein abundance data from studies of the human malarial parasite life cycle and the NCI-60 cancer cell line. They used the co-inertia analysis (CIA) method to visualize gene and proteomic expression data stemming from the same biological samples and to explore the relationships between two or more datasets, and used principal components analysis or correspondence analysis for data exploration on single datasets. After projecting gene ontology (GO) information onto these plots, the approach can visualize gene expression, protein abundance and GO classes in the same low-dimensional projections and identify GO classes that are likely to be of biological importance (Fagan et al., 2007). Recently, Torres-García et al. (2009) used a stochastic Gradient Boosted Trees approach to uncover possible nonlinear relationships between transcriptomic and proteomic data, and to predict protein abundance for the proteins not experimentally detected based on relevant predictors such as mRNA abundance, cellular role, molecular mass, sequence length, protein length, G+C content and triple codon counts.
In addition to the data-driven exploratory approaches mentioned above, some studies have taken biologically based approaches which aim to describe the relationship between proteomic data and mRNA through a mechanistic and dynamic model based on biological relationship. For example, Mehra et al. (2003) used a mechanistic genome-wide model for translation that provides mapping between changes in mRNA levels and changes in protein levels. In the model, the results present the correlation between mRNA and protein levels as a function of both the kinetic parameters and concentration of ribosomes at the reference state. In another study, Mogilevskaya et al. (2009) presented an approach to developing mathematical description of metabolic pathways, and then to integrate reaction kinetics and different types of experimental data including proteomic, mRNA and metabolite data.
Methodologies for integrated transcriptomics and metabolomics
The multivariate analysis methods applied to integrating transcriptomics and proteomics (introduced in the previous section) can be well adapted here. In attempts to encompass the profiling of RNA and metabolites of potato, Urbanczyk-Wochniak et al. (2003, 2007) used the principal components analysis method to compare the discriminatory power of metabolic and RNA profiling to distinguish different potato tuber systems, and used Spearman's rank-based correlation to determine the relationship between genes and their paired metabolite. In another study, a batch-learning self-organizing network was used to classify the metabolites and the transcripts of Arabidopsis according to their time-dependent pattern of changes; the results showed that the metabolites and genes regulated by the same mechanism tended to be clustered together (Hirai et al., 2005). Pir et al. (2006) used partial least squares (PLS) to model the metabolic variables as a function of the transcriptome profile. The experimental design allowed the analyses to discriminate between the effects that the growth medium, dilution rate and deletion of specific genes had on the transcriptome and metabolite profiles (Pir et al., 2006).
Before integrating multi-‘omics’ data, each ‘omics’ dataset must be preprocessed, involving procedures such as normalization or scaling, missing value imputation, and dimension reduction or feature selection. Lê Cao et al. (2008, 2009) proposed a sparse PLS approach to combine integration and simultaneous variable (e.g. gene) selection in one step. In this approach, the PLS was penalized by the sum of the absolute values of the coefficients through least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996), therefore automatically eliminating variables (e.g. genes) with negligible effects. The model selection approach, together with the smoothly clipped absolute deviation approach (Fan & Li, 2001) is effective in analysing data with sparsity (e.g. only a few genes have significant effects).
Methodologies for integrated proteomics and metabolomics
Some general statistical tools, such as unsupervised multivariate exploratory data mining, correlation network topology analysis, pattern recognition, principal components analysis and sparse partial least squares regression have been used for integrative proteomic and metabolomic analysis (Weckwerth et al., 2004; Morgenthal et al., 2006; Weckwerth & Morgenthal, 2005; Morgenthal et al., 2007; Wienkoop et al., 2008; Lê Cao et al., 2008, 2009). The results demonstrated that good pattern recognition and association identification through proper statistical tools are important for the biological interpretation. In a recent effort to define complex signatures of disease that involve signals from multiple types of biomolecules, Webb-Robertson et al. (2009) presented a Bayesian approach to integration that uses posterior probabilities to assign class memberships to samples using individual and multiple data sources; these probabilities are based on lower-level likelihood functions derived from standard statistical learning algorithms. The approach was demonstrated by integrating two proteomic datasets and one metabolic dataset from microbial infections of mice; the results showed that integration of the different datasets improved classification accuracy to 89 % from the best individual dataset at 83 %.
Methodologies for integrating interactomics with other ‘omics’
The interactomics data reveal the physical or functional interaction between components of different layers in a cell. Integrating interactomics with other ‘omics’ data and combining biological mechanistic models leads to more complete insight into the reconstruction of the biological regulation network and thus helps more accurate prediction of the cellular dynamics. While so far no study integrating interactomics with other ‘omics’ data has been published in microbial systems, it is expected that such integrative studies will appear in the near future. To address the computational capacity needed for this type of integration, some established methodologies, such as dynamic Bayesian networks, artificial neural networks and dynamic control system theory, can play a role, although further exploration is still needed.
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Databases and bioinformatic infrastructures for integrated ‘omics’ analysis |
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TOPABSTRACTIntroductionExperimential 'omics' approachesRecent application of integrated...Computational methodologies...Databases and bioinformatic...Concluding remarksDisclaimerREFERENCES |
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). In the past few years, several such online databases have been established and made available for microbiology studies (Martínez-Cruz et al., 2003; Jones et al., 2004; Faria-Campos et al., 2007; Adler et al., 2008). Among them, several are dedicated to specific microbial species, such as ChlamyCys for Chlamydomonas reinhardtii (May et al., 2009) and EchoBASE for E. coli (Misra et al., 2005), while others are more general and can be used for cognate ‘omics’ data from any species. One good example of these databases is PARE (Protein Abundance and mRNA Expression), which can be used to determine the global correlation between transcriptomic and proteomic data and identify correlation outliers, and also allows users to select subsets of proteins to carry out the analysis (Yu et al., 2007). Currently, almost all of these databases are for integrated transcriptomics and proteomics studies, although some allow incorporation of genomic data (Watson, 2005) or protein–protein interactome data (Maraziotis et al., 2007). Most of the current databases are not equipped with enough statistical tools for integrative analysis. Immediate effort is needed to develop databases which can integrate metabolomics or other ‘omics’ data, and to incorporate various novel computational methods or software specifically designed for integrated ‘omics’ study. |
Concluding remarks |
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TOPABSTRACTIntroductionExperimential 'omics' approachesRecent application of integrated...Computational methodologies...Databases and bioinformatic...Concluding remarksDisclaimerREFERENCES |
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). In the future, such integrated ‘omics’ investigation of various cellular molecules (i.e. transcriptome, proteome and metabolome) and their interaction in cells (i.e. interactomes) could lead to a quantified description/model of cellular metabolism at a genome scale that can serve as a foundation for further hypothesis-driven investigation (Schilling et al., 1999; Ishii & Tomita, 2009). Eventually those efforts will lead to fundamental new insights into microbial cellular metabolism. |
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TOPABSTRACTIntroductionExperimential 'omics' approachesRecent application of integrated...Computational methodologies...Databases and bioinformatic...Concluding remarksDisclaimerREFERENCES |
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TOPABSTRACTIntroductionExperimential 'omics' approachesRecent application of integrated...Computational methodologies...Databases and bioinformatic...Concluding remarksDisclaimerREFERENCES |
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