Agilent Measurement Journal - Issue 3 - 2007 - (Page 59)

LC/MS or GC/MS analysis Find molecular features Compare sample sets Identification Validation Figure 3. This diagram describes the Agilent mass proﬁling analysis workﬂow for metabolomics. Features (compounds) that are differential between sample sets are identiﬁed. The validation step requires a large number of samples. atmospheric pressure chemical ionization (APCI) that have broad analyte applicability as well as the ability to produce both positive and negative ions to get full sample coverage. The separation power of HPLC is less than that of GC, however, resulting in longer analysis times. Also, comprehensive, publicly accessible spectral libraries are still in development and only currently becoming available. The Agilent metabolomics analytical workﬂow (Figure 3) usually begins with a proﬁling approach using GC/MS, LC/MS, or both, to ﬁnd statistically interesting “features” between different sample sets. A feature is an artiﬁcially constructed variable for each unidentiﬁed analyte and includes its retention time, mass and ion intensity. Tools such as Agilent MassHunter software are used to ﬁnd all the features in the raw total ion chromatograms (TICs) from LC/MS runs. A ﬁle is produced that contains a list of all the features found. Feature ﬁles from different samples are then compared using an application such as the GeneSpring-MS software. Features that are found to be statistically different are identiﬁed by searching the accurate masses against a metabolite database such as METLIN.2 If the metabolite is not present in the database, it is possible to perform MS/MS analysis on a Q-TOF instrument for spectral interpretation of the data to a structure by generating molecular formulae from the fragment ions. Finally, a relatively small number of identiﬁed metabolites can be puriﬁed, synthesized or purchased in a puriﬁed form for accurate quantitation on a QQQ mass spectrometer. Metabolomics analysis of rice leaf infection These methods were developed and tested during our collaboration with the team at UCD. The experiment design is outlined in Table 1 and includes appropriate controls. Two variants of PXO99 bacteria were used in this study: One was the wild-type, PXO99, and the other was a raxST gene knockout PXO99-raxST¯. The wild-type bacterium PX099 produces a peptide (AvrXa21+) that is recognized by a cell-surface receptor encoded by the Xa21 gene in TP309_Xa21 transgenic rice but not present in TP309 (Figure 4). The raxST gene is critical for the correct processing of AvrXa21 peptide. The ability of transgenic rice to defend against the PXO99 bacterial pathogen is due to binding of the bacterial peptide to the plant Xa21 receptor, triggering the mitogen-activated protein 3-kinase (MAP3K) pathway that mediates signal transduction from the cell surface to the nucleus. This activates an Table 1. Our experimental study design included two rice genotypes: wild-type TP309 (susceptible to infection) and transgenic TP309_Xa21 (resistant to infection). Rice were challenged with wild-type bacterium, PXO99, or PXO99 containing a raxST gene knock-out (raxST¯), media-only mock infection or no treatment (NT). Six rice leaf biological replicates for each condition were analyzed. The average rice leaf weights (mg) for each condition and their standard deviations (SD) are shown. Rice genotype TP309 Challenge PXO99 Mock NT PXO99 Mock NT PXO99-raxST¯ Average_leaf weight (mg) 17.0 16.6 19.0 17.3 18.3 19.3 18.4 SD_leaf weight (mg) 3.6 1.6 3.2 1.5 2.4 2.1 1.3 TP309_Xa21 Agilent Measurement Journal 59

Table of Contents Feed for the Digital Edition of Agilent Measurement Journal - Issue 3 - 2007

Applying Ingenuity to the Measurement of Emerging Technologies Contents Emerging Innovations Department Delivering Bigger Benefits by Optimizing Customer Workflows Measuring Material Properties at the Nanoscale Utilizing In Situ Atomic Force Microscopy in Life Science, Pharmaceutical and other Bio-Related Applications WiMAX™: Plotting a New Path to Global Mobility Addressing the Triple Complexity of Triple-Play Networks What Next for Mobile Telephony? Exploring the Inner Workings of Tire-Pressure Monitoring Systems Developing, Assessing and Applying a High-Resolution Thin-Film Magnetic Probe Making Accurate Settling-Time Measurements Using a Vector Network Analyzer Applying Metabolomics Methods to the Study of Bacterial Leaf Blight in Rice Plants Measuring Stream Dynamics with Fiber Optics survey

Agilent Measurement Journal - Issue 3 - 2007

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