High-throughput phenomics: experimental methods for mapping fluxomes
Introduction
The genomics revolution has produced massive datasets, and large-scale experiments for generating gene disruptions and analyzing phenotypes are underway to ascertain gene function. Such functional analyses are also referred to as phenomics, meaning any form of phenotypic analysis of genomic information or entire mutant collections with the goal of understanding the relationship between genes and higher levels of organization in the cell. Beyond qualitative or semi-quantitative phenotype profiling, the development and implementation of genome-wide analytical techniques has spawned a generation of ‘omics’ enterprises with much of the present emphasis on the genome-wide mRNA, protein or organic metabolite complements of cells. The operational unit of function in complex biological systems, however, is more properly seen as fully assembled biochemical networks 1., 2.••, 3., as connectivity, interactions and dynamic properties such as kinetics and regulation are not defined by genome sequences or expression array information and cannot be inferred directly from measurement of the components (Figure 1).
The functional determinants of cellular physiology are in vivo molecular fluxes through metabolic pathways, because they reflect the integration of genetic and metabolic regulation (Figure 1). Measurement of metabolism-wide fluxes, the fluxome [4], thus allows us to observe the functional output of the compositional transcriptome, proteome and metabolome changes and addresses the missing link in contemporary functional analyses to the cellular phenotype. The broad connective nature has made it very difficult to study metabolism comprehensively, but isotopic tracer experiments in combination with material balances has enabled metabolism-wide quantification of in vivo molecular fluxes 3., 5., 6.. Until recently, fluxome analyses were labor- and instrumentation-intensive, and this review focuses on advances in tailoring -based flux analysis for high-throughput phenotypic characterization in microbes. In contrast to the analysis of dynamic flux responses in metabolic subsystems [2••], the emphasis is on metabolism-wide characterization of microbes in (quasi) steady state. This appears to be most pertinent for large-scale systems response analyses of the integrated metabolic regulation in functional genomics, metabolic engineering, and the nascent field of systems biology.
Section snippets
Established experimental approaches
From its early days when material fluxes were balanced within assumed reaction networks [7], metabolic network analysis 6., 8. has matured to actually identify the topology of active reactions and pathways and to quantify the molecular flux through them on a variety of substrates 9., 10., 11., 12., 13., 14.. The pivotal elements of this advance were elaborate labeling experiments, new measurement techniques, and mathematical data evaluation methods. Recent discoveries include the unexpected
High-throughput 13C pattern analysis
Although NMR and MS analyses were both used successfully, only the latter has the potential for high-throughput analysis at the microscale because of its extraordinary sensitivity, speed and comparatively low cost [33]. Thus, it has been shown that on the order of 0.5–1 mg of dry biomass — which can be generated easily in microcultivation systems — suffice to detect mass isotopomer distributions of proteinogenic amino acids by GC-MS 22.••, 34.. After sample pretreatment, GC-MS analysis of a
High-throughput fluxome mapping
Before fluxome mapping, the measured mass isotopomer data must be corrected for natural stable isotopes, and an elegant method and software for automated correction, statistical data treatment and error recognition of MS data was recently described [40•]. With the availability of MS data at an appropriate throughput, the next issue is the flux resolution to be achieved, unless simple mutant or condition discrimination by multivariate statistics is sufficient (Figure 2). Metabolic flux
High-throughput cultivation systems
Effectively, the above analytical and computational advances have shifted the limitation of high-throughput flux analysis to suitable microscale cultivation systems. For large-scale approaches, fluxes are best studied in physiological (quasi) steady state using cultivation systems that are amenable for high-throughput. Most accurate, but not applicable, are continuous bioreactor cultures that were used extensively for rigorous analysis of tracer experiments. Technically much simpler, a (quasi)
Conclusions and outlook
The methods and tools for high-throughput flux analysis of entire mutant collections or sets of environmental conditions (e.g. the presence of toxic chemicals) are seemingly all in place. For functional genomics, it may suffice to first discriminate mutants from massive mass isotopomer and physiological datasets by multivariate statistics. The available data for interesting outliers or discriminated groups are then biochemically interpreted by simple, rapid and robust methods such as metabolic
Update
Recent work demonstrated analytical robustness of -constrained flux balancing at different cultivation scales [53•]. In particular, it was shown that, upon proper batch culture handling, metabolic fluxes are directly comparable between aerobic bioreactors and 1 ml deep-well microtiter plates.
References and recommended reading
Papers of particular interest, published within the annual period of review, have been highlighted as:
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of special interest
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of outstanding interest
Acknowledgements
I am grateful to Lars Blank, Eliane Fischer, Martin Fussenegger and Nicola Zamboni for fruitful comments on the manuscript.
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