03-08-2013, 04:34 PM
Experimential ‘Genomics and Phenomics’ approaches
ABSTRACT
Cells are living systems full of various functional molecules, which eventually determine the phenotype of the cells. Such molecules include mRNA transcribed from DNA, proteins translated from mRNA, and various metabolites of small molecular mass generated by various enzymic activities. Obviously, only analyzing 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. 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.
Why Metatranscriptomics?
Like metagenomics, metatranscriptomics (or environmental transcriptomics) involves random sequencing of microbial community mRNA. Neither primers nor probes are needed, so there is no need to anticipate important genes before hand and transcripts from microbial assemblages are sequenced with little bias. Further, paralagous sequences which might cross hybridize on a microarray can be distinguished. The approach is particularly amenable to an experimental framework in which gene expression is monitored while a biotic or abiotic parameteris manipulated. Experimental metatranscriptomicsis one of the most powerful tools for understanding the timing and regulation of complex microbial processes within communities and consortia, as well as microbial dexterityin response to changing conditions. An important payoff of community expression profiling via direct sequencing is that the individual metatranscriptomic studies contribute to a growing community database that can be used to address intractable or unanticipated questions.
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. In addition, the interaction between proteins and DNA is crucial for regulating gene expression and thus key to cellular regulatory networks. The interactome of cells can generally be obtained by three approaches.
i) The first one is to apply computational methods for the systematic identification of protein interactions in bacteria. As one example of this approach, Rodriguez-Llorente et al. (2009) performed a large-scale reconstruction of interactomes involved in establishing symbiosis in Sinorhizobiummeliloti. 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.
Metabolomics
In cells, the rate of enzymatic reactions is also regulated by the concentrations of substrates and products. 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, whereas its genome contains ∼6200 protein-encoding genes. 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. 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. 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. 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. 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.
Concluding remarks
Due to financial constraints and availability issues, very few studies employed more than one ‘omics’ technology in any single investigation in the past. However, our survey of the literature suggests that more and more multi-‘omics’ studies have been performed in recent years. It shows that the use of these integrated approaches to analyse a complex process at different levels can provide new insights into microbial biology. Although more efforts are still needed to improve the ‘omics’ technologies in terms of their detection coverage, sensitivity and specificity, so far the results obtained from these microbe-based studies strongly suggest that integration of knowledge at different levels in the cascade from genes to proteins and further to metabolic fluxes at a genomic scale is a powerful tool, and will be pivotal for understanding how the individual components in the system interact and influence overall cell metabolism. It is also clear from these studies that the ability to conduct multi-‘omics’ analyses would represent an additional and novel means to generate discrete and testable biological hypotheses from large-scale high-throughput datasets. For example, a strong correlation between transcriptomic or proteomic data can serve as confirmation for the discovery of an induced response to a treatment, and the lack of a strong correlation can help detect experimental errors or suggest the possibility of a biological uncoupling between the corresponding levels of the respective mRNA and protein species.