SummaryThe zinc hyperaccumulator plant Arabidopsis halleri is able to naturally accumulate 100-fold higher leaf zinc concentrations when compared with non-accumulator species such as the closely related A. lyrata and A. thaliana, without showing toxicity symptoms. A novel member of the cation diffusion facilitator (CDF) protein family, an A. halleri metal tolerance protein 1 (MTP1), and the homologous A. thaliana Zn transporter (ZAT)/AtMTP1 metal-specifically complement the zinc hypersensitivity of a Saccharomyces cerevisiae zrc1 cot1 mutant strain. A fusion of the AhMTP1 protein to green fluorescent protein (GFP) localizes to the vacuolar membrane of A. thaliana protoplasts. When compared with A. lyrata and A. thaliana, the total MTP1 transcript levels are substantially higher in the leaves and upregulated upon exposure to high zinc concentrations in the roots of A. halleri. The high MTP1 transcript levels in A. halleri can be primarily attributed to two genetically unlinked genomic AhMTP1 gene copies. The two corresponding loci co-segregate with zinc tolerance in the back-cross 1 generation of a cross between the zinc-tolerant species A. halleri and the zinc-sensitive species A. lyrata. In contrast, a third MTP1 gene in the genome of A. halleri generates only minor amounts of MTP1 transcripts and does not co-segregate with zinc tolerance. Our data suggests that zinc tolerance in A. halleri involves an expanded copy number of an ancestral MTP1 gene, encoding functional proteins that mediate the detoxification of zinc in the cell vacuole. At the transcript level, MTP1 gene copies of A. halleri are regulated differentially and in response to changes in zinc supply.

BackgroundMetabolic flux analysis has become an established method in systems biology and functional genomics. The most common approach for determining intracellular metabolic fluxes is to utilize mass spectrometry in combination with stable isotope labeling experiments. However, before the mass spectrometric data can be used it has to be corrected for biases caused by naturally occurring stable isotopes, by the analytical technique(s) employed, or by the biological sample itself. Finally the MS data and the labeling information it contains have to be assembled into a data format usable by flux analysis software (of which several dedicated packages exist). Currently the processing of mass spectrometric data is time-consuming and error-prone requiring peak by peak cut-and-paste analysis and manual curation. In order to facilitate high-throughput metabolic flux analysis, the automation of multiple steps in the analytical workflow is necessary.ResultsHere we describe iMS2Flux, software developed to automate, standardize and connect the data flow between mass spectrometric measurements and flux analysis programs. This tool streamlines the transfer of data from extraction via correction tools to 13C-Flux software by processing MS data from stable isotope labeling experiments. It allows the correction of large and heterogeneous MS datasets for the presence of naturally occurring stable isotopes, initial biomass and several mass spectrometry effects. Before and after data correction, several checks can be performed to ensure accurate data. The corrected data may be returned in a variety of formats including those used by metabolic flux analysis software such as 13CFLUX, OpenFLUX and 13CFLUX2.ConclusioniMS2Flux is a versatile, easy to use tool for the automated processing of mass spectrometric data containing isotope labeling information. It represents the core framework for a standardized workflow and data processing. Due to its flexibility it facilitates the inclusion of different experimental datasets and thus can contribute to the expansion of flux analysis applications.

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In this chapter we illustrate the methodology for high-throughput metabolic flux analysis. Central to this is developing an end to end data pipeline, crucial for integrating the wet lab experiments and analytics, combining hardware and software automation, and standardizing data representation providing importers and exporters to support third party tools. The use of existing software at the start, data extraction from the chromatogram, and the end, MFA analysis, allows for the most flexibility in this workflow. Developing iMS2Flux provided a standard, extensible, platform independent tool to act as the "glue" between these end points. Most importantly this tool can be easily adapted to support different data formats, data verification and data correction steps allowing it to be central to managing the data necessary for high-throughput MFA. An additional tool was needed to automate the MFA software and in particular to take advantage of the course grained parallel nature of high-throughput analysis and available high performance computing facilities.In combination these methods show the development of high-throughput pipelines that allow metabolic flux analysis to join as a full member of the omics family.

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