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In This Issue: Featured Research 2-4 Application Spotlight 5-16 In the News 17-19 Events 20-23

AGILENT AND ACADEMIA DISCOVER THE WORLD TOGETHERIssue 1

Professor Hookeun Lee at Lee Gil Ya Cancer and Diabetes Institute (LCDI), College of pharmacy, Gachon University utilizes proteomics and metabolomics platforms to explore novel biomarkers from human diseases.

The proteomics laboratory at LCDI serves as the Agilent’s reference laboratory for complete glycol-proteomics and biomarker discovery workflows.

Professor Lee is focusing on delivering solutions for personalized drugs for individual and development of automated sample preparation workflow’s to handle large number of clinical samples. This can only be possible with the collaboration of Agilent to provide access to the latest technologies to Gachon University like automated sample preparation platform Assay-Map Bravo, and LC-MS/MS coupled Q-TOF and MRM machines from Agilent.

Automation of the entire proteomic pipeline after sample preparation to process for LC-MS/MS can result in minimal loss of sample information. The assay bravo can handle 384 samples at a time for trypsinization which can drastically reduce the time required for analysis.

With the platforms, his recent researches focus on establishment of high-throughput dual omics analysis of proteomics and metabolomics. This has been enabled by recently released Mass Profiler Professional software, in which quantitative proteomic and metabolomics data were efficiently correlated and analyzed.

As a one of study, we used this automated trypsinization protocol to apply combined phospho- and glycoproteome analysis in necprocalcinosis tissue of phytate-fed rate.

By using 1 mg of protein from kidney tissue lysates from normal and diseased rats, we concurrently identified 437 glycosites/358 phosphosites and 468 glycosites/369

PROF. HOOKEUN LEE PH.D Lee Gil Ya Cancer and Diabetes Institute College of Pharmacy Gachon University

FEATURE RESEARCH

Developing a novel sample preparation method for LC-MS/MS based biomarker discovery approaches.

phosphosites in normal and disease kidneys, respectively, by liquid chromatography/tandem mass spectrometric analysis.

Compared with individual PTM analyses, the combined PTM analysis clearly provides more broad implications for PTMs related to the pathological status and discovery of biomarker candidates. Furthermore, the combined protocol thoroughly showed its advantages in enrichment efficiency and biological interpretation compared with current methods.

Using Agilent chip-cube nano LC-QTOF MS system, intensity based label-free quantification of peptides were carried out for quantitative proteome analysis. High reproducibility and sensitivity of the LC-MS system induced to analyze specific signal pathways enrichments from the proteome quantifications.

RELATED INFORMATION: Tran T.; Kim O.-K.; Park J.-M.; Kim B.; Choi D.-Y.; Lee J.; Kim K.; Oh B.-C.; Lee H. “Combined phospho- and glycoproteome enrichment in nephrocalcinosis tissues of phytate-fed rats” Rapid Commun. Mass Spectrom., 2013, 27, 2767-2776.

Arul A.-B.; Han N. Y.; Lee H. “An Automated High Throughput Proteolysis and Desalting Platform for Quantitative Proteomic Analysis” Mass Spectrom. Lett., 2013, 4(2), 25-29.

Development of MS-based analytical methods for glycomics & glycoproteomics

BACKGROUND OF RESEARCHDr Hyun Joo An is the director of the Asia Glycomics Reference Site (AGRS), which develops and validates new analytical platforms for glycomic and glycoproteomic analysis in collaboration with government agencies and regional industry. Since 2011, Dr An has been an associate professor at Chungnam National University. She received her BS and MS degrees from Inha University (Incheon, Korea) and her PhD from the University of California at Davis (United States).

Dr. An’s research focuses on bioanalytical mass spectrometry, with applications to glycomics, proteomics, and glycoproteomics. Her specific research interests include bioanalytical method development, biopharmaceutical characterization, disease biomarker discovery, clinical glycomics, and glycobiology. She has authored and co-authored over a 60 peer-reviewed publications on these subjects, and holds multiple related patents.

The Asia Glycomics Reference Site (AGRS), under the directorship of Prof. Hyun Joo An, specializes in mass spectrometric characterization of glycans, glycoproteins, and proteins. The AGRS develops and validates new analytical platforms for glycomic and glycoproteomic analysis, using cutting-edge facilities and equipment. In addition to providing core glyco-analytical support to biopharmaceutical companies as well as governmental regulatory agencies, the AGRS conducts basic glycobiological research in collaboration with academic labs around the world.

OBJECTIVES OF RESEARCH- Efficient and selective method to capture O-glycans from complex mixture

- Isomer-specific glycan separation and quantitation

- Selective enrichment of phosphoglycans using newly designed LC/MS chip

PROF. HYUN JOO AN Department: AGRS, GRAST, Chungnam National University Phone Number: 82-42-821-8547 Email: hjan@cnu.ac.kr

FEATURE RESEARCH

RESULT OF RESEARCHDespite recent advances, site-specific profiling of protein glycosylation remains a significant analytical challenge for conventional proteomic methodology. To alleviate the issue, we propose glyco-analytical multispecific proteolysis (Glyco-AMP) as a strategy for glycoproteomic characterization. Glyco-AMP consists of rapid, in-solution digestion of an analyte glycoprotein (or glycoprotein mixture) by a multispecific protease (or protease cocktail). Resulting glycopeptides are chromatographically separated by isomer-specific porous graphitized carbon nano-LC, quantified by high-resolution MS, and structurally elucidated by MS/MS. To demonstrate the consistency and customizability of Glyco-AMP methodology, the glyco-analytical performances of multispecific proteases subtilisin, pronase, and proteinase K were characterized in terms of quantitative accuracy, sensitivity, and digestion kinetics. Glyco-AMP was shown be effective on glycoprotein mixtures as well as glycoproteins with multiple glycosylation sites, providing detailed, quantitative, site- and structure-specific information about protein glycosylation.

The AGRS has developed new and improved analytical techniques for characterization of the glycoproteome, including (in order of increasing complexity and information content) preliminary site mapping, compositional glycan profiling, isomer-specific glycan profiling, glycosite-specific glycopeptide profiling, and glycoproteomic profiling using Agilent 6500 Series Accurate-Mass Quadrupole Time-of-Flight (Q-TOF) LC/MS interfaced with 1260 HPLC Chip cube system

that can be applied in glycoproteomics and glycomic research. These new strategies will help us gain a greater understanding of an important yet largely unexplored portion of our biology: the glycome.

REFERENCE OF PUBLICATION1. Hua, S., Hu, C.Y., Kim, B.J., Totten, S.M., Oh, M.J., Yun, N.Y., Nwosu, C.C., Yoo, J.S., Lebrilla, C.B., An, H.J., “Glyco-Analytical Multispecific Proteolysis (Glyco-AMP): A Simple Method for Detailed and Quantitative Glycoproteomic Characterization”, J. Proteome Res, 2013.

2. Strum, J.S., Nwosu, C.C., Hua, S., Kronewitter, S.R., Seipert, R.R., Bachelor, R.J., An, H.J., Lebrilla, C.B., “Automated Assignments of N- and O-Site Specific Glycosylation with Extensive Glycan Heterogeneity of Glycoprotein Mixtures”, Anal. Chem, 2013, 85, 5666−5675.

3. Hua, S., Oh, M.J., An, H.J., “Multi-Level Characterization of Protein Glycosylation”, Mass Spectrom. Lett, 2013, 4(1), 10–17.

4. Nwosu, C.C., Seipert, R.R., Strum, J.S., Hua, S., An, H.J., Zivkovic, A.M., German, B.J., Lebrilla, C.B., “Simultaneous and Extensive Site-specific N- and O-Glycosylation Analysis in Protein Mixtures”, J. Proteome Res, 2011, 10, 2612–2624.

5. Hua, S., Lebrilla, C.B., An, H.J., “Application of nano-LC-based glycomics towards biomarker discovery”, Bioanalysis, 2011, 3(22), 2573–2585.

APPLICATION SPOTLIGHT

ABSTRACTThis application note presents a simple, user-friendly technique for a comprehensive two-dimensional analysis of complex matrices when the operator only has access to a one-dimensional liquid chromatography system. In this example an 1260 Agilent Infinity chromatograph coupled to a fraction collector was used for an offline study of biochemical components of mushrooms; however, the approach is easily transferable to other sample types since all that is required is a standard Agilent one-dimensional HPLC system (consisting of an autosampler, degasser, a binary or quaternary pump, thermostatted column compartment, a diode array detector, a fraction collector) and appropriate data processing software.

INTRODUCTIONThe analysis of complex matrices such as natural/food products, petrochemicals and many environmental and biological samples is crucial in many areas of both industrial and academic research. The main aim of such investigations is usually the identification of particular compounds of interest, such a specific contaminant (or group of contaminants) that may be present in conjunction with a multitude of other substances, or simply the separation and quantitation of the components of a particular sample. These types of analyses require chromatographic techniques with high peak capacity in order to achieve the separation of as many compounds

Jessica Pandohee,1 Paul G. Stevenson,2 Xavier A. Conlan2 and Oliver A.H Jones1* 1School of Applied Sciences, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia 2School of Life and Environmental Sciences, Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia* Author to whom correspondence may be addressed

E-mail: Oliver.Jones@rmit.edu.au Phone: +61 3 9925 2632 Fax: +61 3 9925 3747

Two-dimensional HPLC analysis of complex matrices using a standard 1260Agilent Infinity LC system.

as possible. This is a challenging task since the samples often consist of thousands of compounds from many differing classes, which may vary greatly in factors such as concentration, mass and polarity. The analysis of many compound classes is further complicated by the fact that many of their constituent members are isomeric. A pertinent example are the sugars, for instance glucose, galactose and mannose are isomeric aldohexoses while arabinose and xylose are aldopentoses and sorbitol is the aldehyde-reduced derivative of glucose [1]. With the exception of sorbitol, these compounds exist in solution mainly in ring-closed form with various ratios of pyranose (6-membered) to furanose (5-membered) rings. The difficulties described above often result in co-elution of closely related compounds and/or insufficient separation during chromatographic analysis (peak overlapping), resulting in poor resolution of compounds of interest and in some cases masking of compounds of low concentration if/when they co-elute.

A solution to the analysis of complex samples is the use of multidimensional liquid chromatography; this technique uses two columns with differing phases. A sequential collection of aliquots is made from the first column (the first dimension) and reinjection onto a second column (the second dimension), the resulting data are then plotted in either 2D or 3D space. The total peak capacity of such a system is the product of the combined peak capacities of each dimension. However, two-dimensional LC-LC systems are new technology and can therefore be complex and expensive to set up. The price for such equipment may be up to US$130,000 and users may need to undergo extra training and become acquainted with new software and a complicated and unfamiliar set up.

It is however possible, to perform a two dimensional analysis using only a standard one-dimensional HPLC system. This format also has some advantages in that the second dimension is independent of the first, which simplifies the optimisation of the

Sample Preparation

First Dimension

Fraction Collection

Second Dimension

Data Analysis

Crush sample (1 g) under liquid nitrogen

Boil for 1.5 h with 300 mL ethyl acetate

Remove (and keep) solvent, repeat extraction with a further 300 mLof ethyl acetate for 1 h

Combine extracts and evaporate to dryness under vacuumat room temperature

Reconstitute in 1 mL of methanol and filter through 0.45 µmsyringe filter before analysis

Run sample on CN column twice, each time injecting 100 µL andcollecting in 250 µL clear plastic vials

Set fraction collector conditions to 0.1 min/tube with start timebefore the first peak is eluted and stop time just after the last peak is

eluted (in this case start time is 0.6 min and stop time is 5.6 min)

Ensure that there are no air bubbles in vials, run all collectedfractions as a sequance on C18 column

Export data as CSV, plot using Mathematica

EXPERIMENTALSample preparationThe sample preparation procedure used in this study is outlined in figure 1. A methanol/chloroform/water extract [2] may also work for less robust sample types e.g. blood.

Figure 1. Flow chart for non-automated two dimensional liquid chromatography analysis

ChemicalsAll mobile phases were HPLC grade solvents. Milli-Q water (18.2 MΩ) was obtained in-house. Methanol (Isocratic, HPLC-grade 254 nm) was purchased from Scharlau Chemie (South Australia, Australia) and acetonitrile from Ajax Fine Chem (New South Wales, Australia). All mushrooms were purchased from a local supermarket in the Melbourne CBD.

first dimension parameters); additionally, the second separation is not time limited meaning longer columns with greater peak capacity can be utilised, resulting in a larger total peak capacity than is possible using a short columns in the second dimension; N.B. Short, second dimension columns are required in an automated 2D LC-LC system since such instruments require the separation of a sub-sample in the second dimension to be completed before the subsequent sub-sample from the first dimension is injected.

There are therefore clear advantages in both cost and resolution to using a standard HPLC to perform two-dimensional separation. This application note outlines the steps for the method development of a reversed phase, non-automated, comprehensive two-dimensional liquid chromatography analysis. The button mushroom (Agaricus bisporus) is used as a model sample in this case but the technique is easily transferred to any other sample matrix.

ColumnsFirst dimension Phenomenex Luna CN, 150 mm x 4.6 mm x 5 µm (run at room temparature)Second dimension Phenomenex Kinetex C18, 150 mm x 4.6 mm x 5 µm (run at room temparature)

First dimension pumpSolvent A WaterSolvent B MethanolFlow rate 1 mL/minGradient 5% B at 0 mins; 100% B at 3 mins; 100% B at 6 minsStop time 6 mins

Second dimension pumpSolvent A WaterSolvent B AcetonitrileFlow rate 3 mL/minGradient 5% B at 0 mins; 100% B at 1 mins; 100% B at 4 minsStop time 4 mins

AutosamplerInjection volumn for first dimension 100 µLInjection volumn for second dimension 100 µL (combined extracts)

Fraction collectorRate of collection 0.1 min/tubeNumber of fractions 50Start time 0.6 minEnd time 5.6 min

Diode array detectorWavelength 280nmSlit 4 nmData rate 40 HzFlow cell 10 mm

SoftwareOpenLAB CDS, ChemStation Edition (Agilent Technologies, Mulgrave, Australia)Wolfram Mathematica (version 9) - (Wolfram Research, Champaign, IL, USA)

ChromatograhyA 1260 Agilent Infinity HPLC with 1260 Infinity autosampler, thermostatted column compartment, diode array detector, a 1290 binary pump and a Gilson analytical-scale fraction collector was used for this study. Table 1 lists the chromatographic conditions used.

Table 1. HPLC set up conditions

Result and DiscussionFigure 2 shows an initial one-dimensional separation of the mushroom extract using a C18 Luna column with a gradient protocol starting from 5% methanol at 0 mins, to 100% methanol at 30 mins. Five classes of interest are marked as follows: A-sugars, B-organic acids, C-amino acids, D-phenolics and E-fatty acids. The significant overlapping of the sugars, organic acids and amino acids in the first 10 mins of the run makes it difficult for identification and quantification of metabolites, even when an LC-MS was used. A closer look at the separation from 21 to 27 mins (inset panel) also shows that the peaks are not baseline resolved.

Figure 2. One-dimensional separation of mushroom extract on a C18 column (150 mm x 4.6 μm x 5 μm)

One of the most important factors to consider for 2DLC method development is the phases of the two columns (dimensions). To ensure high-resolution separation the columns should consist of different (and unrelated) phases so that the peaks are spread across the maximum separation area. A selectivity study of four stationary phases (CN, NH2, PFP and C18) using the mushroom extract was therefore undertaken and the results are shown in Figure 3.

The combination of CN and the C18 were found to demonstrate the greatest the separation of peaks in this case (although alternative column combinations may be necessary for different sample types). Moreover the chromatogram profiles are different, even though the CN was run under reverse phase conditions the CN groups offer sufficient polar characteristics to alter the interactions between the stationary phase and the mobile phase compared to a C18 based column.

Two common challenges faced by 2DLC users are that the total analysis run times are long; and the fact that samples often become highly diluted in the second column. To counteract this, both dimension run times were adjusted so that the run time was as short as possible and the columns were purposely overloaded with injection volume of 100 μL. This results in the unsymmetrical peak broadening seen in figure 4 (below).

Figure 3. Selectivity study of 4 Luna columns with stationary phases of; A) CN, B) NH2, C) PFP and D) C18. All columns had dimensions of 150 mm x 4.6 mm x 5 µm. The mobile phase in this part of the study started at 95% H2O: 5% methanol through to 100% methanol by 20 mins, which was then held for 10 mins

Figure 4. One-dimensional chromatogram of the mushroom extract using a CN column. This data is the first dimension (x-axis) in figures 6 and 7

Although the separation in figure 4 at first appears poor, the, peak separation and total capacity is later enhanced due to use of the second (C18) column which provided a second dimension of separation.

We chose to use a Kinetex C18 with core-shell particles instead of the Luna C18 phase (which has fully porous particles) for the second dimension separation since this resulted in less band broadening and provided better resolution, sensitivity and peak capacity.

The next step is to take the data from each fraction and combine to a data matrix with time in column 1 as the x dimension and time in column 2 in the y dimension and the intensity or response at each time point as the z value (either colour or height) [3]. The resulting 2DLC offline separations are shown in Figures 6 and 7 as a 2D contour plot and a 3D intensity plot, each created using Mathmatica. The separation of a total of 221 components was achieved.

Figure 5. One-dimensional chromatogram of the mushroom extract using a Kinetex C18 column. This data is the second dimension (y-axis) in figures 6 and 7

Figure 6. Two-dimensional contour plot using data from separation run using CN and Kinetex C18 as first and second dimensions respectively.

CONCLUSIONThis method detailed here demonstrates that a one-dimensional Agilent system coupled with a fraction collector can be used for the non-automated, two-dimensional comprehensive analysis of complex matrices. Although the total analysis time of the non-automated approach is longer than a fully automated separation, a greater peak capacity is obtained since the length of second dimension is not restricted, meaning that longer columns can be used in both dimensions. The greatest separation of peaks was obtained using reversed phase HPLC with CN and C18 based columns for the first and second dimensions respectively. The method was used to profile metabolites in mushrooms could easily be adapted and applied to other complex sample matrices.

REFERENCES1. Jones, O.A.H. and H.M. Hügel, Bridging the Gap: Basic Metabolomics Methods for Natural Product Chemistry, in Metabolomics Tools for Natural Product Discovery, U. Roessner, Dias, D.A., Editor 2013, Humana Press: London, UK. p. 245-266.

2. Le Belle, J., N. Harris, S. Williams, and K. Bhakoo, A Comparison Of Cell And Tissue Extraction Techniques Using High- Resolution 1H-NMR Spectroscopy. NMR in Biomedicine, 2002. 15: p. 37-44.

3. Stevenson, P.G., M. Mnatsakanyan, G. Guiochon, and R.A. Shalliker, Peak Picking And The Assessment Of Separation Performance In Two-Dimensional High Performance Liquid Chromatography. Analyst, 2010. 135: p. 1541-50.

Figure 7. Data from figure 6 plotted as a three dimensional chromatogram

APPLICATION SPOTLIGHT

INTRODUCTIONThe aim of reverse genetic knockout (KO) studies is to assign function(s) to specific gene(s) and confirmation of the reduction in abundance of the encoded protein aids the link between genotype and phenotype. However, measuring specific protein abundance is particularly difficult in research where limited numbers of antibodies are available. This problem is further exacerbated when studying gene families (that have very similar sequences) or different proteins derived from the same gene (isoforms), as many antibodies typically cross-react with more than one protein. Selected Reaction Monitoring (SRM) mass spectrometry allows researchers to confirm the abundance of their protein of interest in mutant lines, even when discrimination between very similar proteins is needed. To test the performance of SRM mass spectrometry in determining protein abundance in mutant lines we selected two enzymes with multiple isoforms that cannot be distinguished by

Measuring protein abundance by peptide selected reaction monitoring (SRM) mass spectrometry in knock-out organisms when antibodies are unavailable/ineffective - An example from plant science.

commercial antibodies, mitochondrial aconitase (mACO) and mitochondrial malate dehydrogenase (mMDH). Three peptides for each enzyme were quantified to estimate the abundance of each protein in wild type (WT), KO, double KO and complemented lines. We show that SRM mass spectrometry is a sensitive and rapid approach to quantify the protein abundance for highly related enzyme isoforms.

Selection and optimization of candidate SRM peptides for aconitase (mACO) and malate dehydrogenase (mMDH). Unique peptides for mACO1, mACO2, mMDH1 and mMDH2 that had previously been detected by mass spectrometry were selected and their predicted collision energies (CE) were calculated using the Skyline software package1 to yield putative transitions. The putative SRM transitions were then optimized using trypsin digested isolated mitochondrial extracts run on an Agilent 6430 QqQ mass spectrometer with an HPLC Chip Cube source. The chip is composed of a 160 nL enrichment column (Zorbax 300SB-C18, 5-mm pore size) and a 150 mm separation column (Zorbax 300SB-C18, 5 mm pore size) driven by Agilent Technologies 1200 series nano/capillary LC system. Both systems were controlled by MassHunter Workstation Data Acquisition for QqQ (Agilent Technologies). Peptides were loaded onto the trapping column at 3 mL/min in 5% (v/v) acetonitrile and 0.1% (v/v) formic acid with the chip switched to enrichment and using the capillary pump. The chip was then switched to separation, and peptides were eluted during a 15.5-min gradient (5% [v/v] acetonitrile to 100% [v/v] acetonitrile) directly into the mass spectrometer. The mass spectrometer was run in positive ion mode, with a drying gas temperature of 365 °C and flow rate of 5 L/min, for each transition the fragmentor was set to 130 and dwell time was 5 ms. Each transition was then optimized for collision energy (CE) based on predicted values by Skyline following an algorithm specific for Agilent Technologies instruments. For each transition a total of five CEs were analyzed, including the predicted CE ± 4 V and ± 8 V. The optimized SRM transitions (Table 1.) were used for quantitative data analysis.

NICOLAS L. TAYLOR ARC Centre of Excellence in Plant Energy Biology and Centre for Comparative Analysis of Biomolecular Networks (CABiN), Bayliss Building M316, The University of Western Australia, 35 Stirling Highway, Crawley WA 6009, Western Australia, Australia

SRM analysis of protein abundance of aconitase (mACO) and malate dehydrogenase (mMDH) in mito-chondria from knockout and complemented lines.To determine the abundance of mACO1 we first examined the abundance of the peptide VVNFSFDGQPAELK in mitochondria isolated from WT (wild-type), maco1 (mACO1 knockout) and maco2 (mACO2 knockout) plants (Figure 1.). Protein extracts from isolated mitochondrial were analyzed on an Agilent 6430 QqQ mass spectrometer as described above for method optimization. The SRM transition (775.9 557.3) was used to quantify its abundance and the resulting data files were opened in MassHunter Workstation Qualitative Analysis (Agilent Technologies), and SRM chromatograms were obtained using the Extract Chromatogram feature using default settings. Each SRM chromatogram was then integrated, and the area under the peak within 30 s of the expected retention time was calculated.

Transitions that had an intensity greater than 1000 and s/n >50 were then averaged to obtain an abundance value for each peptide. VVNFSFDGQPAELK (775.9 557.3) eluted in WT mitochondria between 10.5 - 10.9 minutes (Figure 1, Ai.), between 10.3 - 10.5 minutes from maco1 mitochondria (Figure 1, Aii.) and between 10.0 - 10.3 minutes from maco2 mitochondria (Figure 1, Aiii.). In addition to this quantifier transition two qualifier ions were used to confirm the peptide identity, with an example of the MS/MS spectrum of VVNFSFDGQPAELK provided in Figure 1B to highlight the quantifier and qualifier ions. The abundance of two other peptides of mACO1 SSGEDTIILAGAEYGSGSSR (979.4 970.4) LSVFDAAMR (505.3 710.3) along with three peptides from mACO2 (GVISEDFNSYGSR (715.8 1161.5) FSYNGQPAEIK (627.3 557.3) ILDWENTSTK (603.8 980.4) was then quantified (Figure 2.). Examining mACO1 we observed that the peptides from this protein decreased in abundance to between 0.5% to 6.7% with an average of ~4.63% and an average error of ~0.12% in maco1 mitochondria when compared to WT. A similar result was obtained for mACO2 where peptides from this protein were seen to decrease in abundance to between 0.02% to 0.1% with an average of ~0.05% and an average error of ~0.02% in maco2 mitochondria when compared to WT.

To determine the abundance of mMDH1 and mMDH2, the abundance of the peptides from mMDH1; SEVVGYMGDDNLAK (749.0 1083.5) EGLEALKPELK (409.6 486.3) VAILGAAGGIGQPLALLMK (897.0 970.6) and mMDH2 SQVSGYMGDDDLGK (736.3 1157.5) VVILGAAGGIGQPLSLLMK (613.0 801.5) NLSIAIAK (415.3 602.4) were quantified in WT, mmdh1 (mMDH1 knockout), mmdh2 (mMDH2 knockout), mmdh1mmdh2 (mMDH1 and mMDH2 knockout, mMDH double knockout) and mmdh1mmdh2-35s:mMDH1 (mMDH double knockout complemented with mMDH1) plants (Figure 3.). We saw a significant reduction in protein abundance in each of the knockout lines for their respective proteins with mMDH1 reduced to ~0.1% (Average Err = ~0.09%) of WT and mMDH2 reduced to ~0.5% (Average Err = ~0.03%) of WT. In mmdh1mmdh2 we saw the disappearance of both of the mMDH isoforms to a level similar to those observed in the single knockouts. In the complemented line we saw the expected dramatic increase in the abundance of mMDH1 to levels much greater to those observed in the WT (~400.0% (Average Err = ~19%)).

AGI

At2g05710.1At2g05710.1At2g05710.1At4g26970.1At4g26970.1At4g26970.1At1g53240.1At1g53240.1At1g53240.1At3g15020.1At3g15020.1At3g15020.1

Protein

mACO1mACO1mACO1mACO2mACO2mACO2mMDH1mMDH1mMDH1mMDH2mMDH2mMDH2

SRM Pre m/z

775.9979.0505.3715.8627.3603.8749.4409.6897.0736.3613.0415.3

SRM Pre z

222222232232

SRM Pro 1 m/z(Quantifier)

557.33970.40710.301161.50557.30980.401083.50486.30970.601157.50801.50602.40

SRM Pro 1 ion(Quantifier)

y5y10y6y10y5y8y10y4y9y11y7y6

SRM Pro 2 m/z(Quantifier)

1094.541041.50809.40945.40724.40679.30732.40614.401197.701256.50591.40515.40

SRM Pro 2 ion(Quantifier)

y10y11y7y8y7y6y7y5y12y12y5y5

SRM Pro 3 m/z(Quantifier)

857.441154.50563.30830.40856.50865.40963.40727.50785.50850.40986.60402.30

SRM Pro 3 ion(Quantifier)

y8y12y5y7y8y7y8y6y7y8y9y4

PredictedCE (V)

24.034.410.220.916.415.222.75.430.222.012.95.6

OptimizedCE (V)

20.034.414.220.916.415.218.79.426.222.012.95.6

Sequence

VVNFSFDGQPAELKSSGEDTIILAGAEYGSGSSRLSVFDAAMRGVISEDFNSYGSRFSYNGQPAEIKILDWENTSTKSEVVGYMGDDNLAKEGLEALKPELKVAILGAAGGIGQPLALLMKSQVSGYMGDDDLGKVVILGAAGGIGQPLSLLMKNLSIAIAK

CONCLUSIONIn this study peptide SRM mass spectrometry was used to attempt to quantify the abundance of two pairs of highly related proteins. The isoforms of mACO and mMDH have a high sequence homology and mACO isoforms are indistinguishable using antibodies2, whilst the only commercially available mMDH antibody cross reacts with both isoforms (mMDH1/2, Agisera AS12 2371). Using peptide SRM mass spectrometry the protein knockout of all the proteins investigated in mitochondria isolated from their respective single knockout lines were confirmed. Further it was shown that the protein knockout of both isoforms of mMDH in the double knockout resulted in a protein abundance below 0.5% of WT levels, and the approach could accurately measure the degree of overexpression of mMDH1 in the complemented line. Overall this study demonstrates the utility of an SRM mass spectrometry approach to enable researchers to quantify the abundance of proteins of interest in knock-out organisms and to overcome the time, expense and lack of specificity when relying on Western blotting to measure protein abundances.

Table 1. Optimized SRM transitions for mACO1, mACO2, mMDH1, mMDH2.

AGI, Arabidopsis Genome Initiative identifier; Protein, protein name; Sequence, peptide sequence; SRM Pre m/z, peptide precursor ion mass/charge ratio; SRM Pre z, peptide precursor ion mass; SRM Pro 1 m/z (Quantifier), peptide product ion 1 (Quantifier) mass/charge ratio; SRM Pro 1 ion (Quantifier), peptide product ion 1 (Quantifier) fragmentation series location; SRM Pro 2 m/z (Qualifier), peptide product ion 2 (Qualifier) mass/charge ratio; SRM Pro 2 ion (Qualifier), peptide product ion 2 (Qualifier) fragmentation series location; SRM Pro 3 m/z (Qualifier), peptide product ion 3 (Qualifier) mass/charge ratio; SRM Pro 3 ion (Qualifier), peptide product ion 3 (Qualifier) fragmentation series location; Predicted CE, predicted collision energy from Skyline1; Optimized CE, optimized collision energy. Adapted from Taylor et al.3 www.plantphysiol.org, Copyright American Society of Plant Biologists.

Figure 1

Figure 1. The elution of VVNFSFDGQPAELK of mACO1 in WT and knockout lines and the MSMS spectra showing quantifier and qualifier ions.

A. SRM 775.9 557.3 transition of VVNFSFDGQPAELK peptide of mACO1 in WT, mACO1 KO and mACO2 KO mitochondria. i. WT, ii. mACO1 KO, iii. mACO2 KO.

B. The MS/MS spectrum of VVNFSFDGQPAELK showing the y-series ions and the selected quantifier ion (y5) and the two qualifier ions (y8 and y10). Reproduced from Taylor et al.3 www.plantphysiol.org, Copyright American Society of Plant Biologists.

Figure 2. SRM analysis of protein abundance of mACO1 and mACO2 in WT, mACO1 KO and mACO2 KO mitochondria

A. SRM analysis of unique peptides mACO1 abundance using the quantifier ion transitions VVNKSFDGQPAELK (SRM 775.9

557.3) SSGEDTIILAGAEYGSGSSR (SRM 979.4 970.4) LSVFDAAMR (SRM 505.3

710.3).

B. SRM analysis of unique peptides mACO2 abundance using the quantifier ion transitions GVISEDFNSYGSR (SRM 715.8 1161.5) FSYNGQPAEIK (SRM 627.3 557.3) ILDWENTSTK (SRM 603.8

980.4). Data presented is averages ± SE (n=3). Reproduced from Taylor et al.3 www.plantphysiol.org, Copyright American Society of Plant Biologists.

Figure 3. SRM analysis of protein abundance of mMDH1 and mMDH2 in WT, mMDH1 KO, mMDH2 KO, mMDH dKO and mMDH dKO line complemented with mMDH1 cDNA mitochondria.

A. SRM analysis of unique peptides mMDH1 abundance using the quantifier ion transitions SEVVGYMGDDNLAK (SRM 749.0 1083.5) EGLEALKPELK (SRM 409.6

486.3) VAILGAAGGIGQPLALLMK (SRM 897.0 970.6).

B. SRM analysis of unique peptides mMDH2 abundance using the quantifier ion transitions SQVSGYMGDDDLGK (SRM 736.3 1157.5) VVILGAAGGIGQPLSLLMK (SRM 613.0 801.5) NLSIAIAK (SRM 415.3 602.4). Data presented is averages ± SE (n=3). Reproduced from Taylor et al.3 www.plantphysiol.org, Copyright American Society of Plant Biologists.

ACKNOWLEDGEMENTSThis work was supported by the Australian Research Council (ARC) ARC Centre of Excellence for Plant Energy Biology. NLT is supported by the ARC as an ARC Future Fellow.

REFERENCES(1) MacLean, B.; Tomazela, D. M.; Shulman, N.; Chambers, M.; Finney, G. L.; Frewen, B.; Kern, R.; Tabb, D. L.; Liebler, D. C.; MacCoss, M. J. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010, 26, (7), 966-8.

(2) Bernard, D. G.; Cheng, Y.; Zhao, Y.; Balk, J. An allelic mutant series of ATM3 reveals its key role in the biogenesis of cytosolic iron-sulfur proteins in Arabidopsis. Plant physiology 2009, 151, (2), 590-602.

(3) Taylor, N. L.; Fenske, R.; Castleden, I.; Tomaz, T.; Nelson, C. J.; Millar, A. H. Selected reaction monitoring to determine protein abundance in Arabidopsis using the Arabidopsis proteotypic predictor. Plant Physiol 2014, 164, (2), 525-36.

Figure 3

Agilent Technologies Collaborates with Seoul National University on New Research on New Research Center - Facility to Support New Drug Development Center in College of Pharmacy

IN THE NEWS

Agilent Technologies Inc. (NYSE: A) announced a collaboration with Seoul National University, one of the world’s leading universities and Korea’s top research university, on a new research center that will support the College of Pharmacy’s New Drug Development Center.

As stated in a memorandum of understanding signed by Seoul National University’s College of Pharmacy and Agilent, the collaboration aims to conduct drug metabolism studies; research, evaluate and develop new compounds; understand the remedial effects and toxicity; assess pharmacokinetics; and conduct clinical tests for drugs.

“The College of Pharmacy at Seoul National University has a long tradition of driving innovation,” said professor Bong-Jin Lee, dean of the college. “We have been leading Korea’s pharmaceutical industry through the strategic use of technology and driving the development of new drugs. As we prepare students for the future, we will push the frontiers of drug development by expanding our research facilities, producing outstanding research, and leading the development of technology. We are pleased to collaborate with Agilent, as our scientists and researchers require the latest, most sophisticated instruments for their work.”

FACILITY TO SUPPORT NEW DRUG DEVELOPMENT CENTER IN COLLEGE OF PHARMACY

Agilent will provide bio-analytical instruments and expertise in pharmaceutical drug testing and multi-omic biological systems to the College of Pharmacy. The jointly established research center will also serve as Agilent’s reference site for drug development.

“One of the megatrends fueling life science research is the aging population,” said Agilent’s Rod Minett, general manager, Life Sciences, South Korea and South Asia-Pacific region. “Through continual research, trials and developments, we believe that newer, more effective drugs can be developed. As such, Agilent is pleased to support SNU College of Pharmacy’s drive to create better drugs for a safer and healthier population.”

This new research facility will be located at the New Drug Development Center at SNU’s College of Pharmacy. The New Drug Development Center is an advanced multi-purpose research center for drug development, from identification of new drugs through to clinical trial testing. The six-story center houses facilities for analysis, efficacy testing, and medicinal ingredient research; it also contains a medicinal chemistry laboratory.

Agilent Technologies to Support National University of Singapore- Led Research Consortium to Identify, Map Lipid Levels in Healthy People - New International Effort to Develope World’s First Integrated Systems Biology Database for Different Races and Ethnicities

IN THE NEWS

Agilent Technologies Inc. (NYSE: A) announced its support for a new international research consortium, led by the National University of Singapore (NUS), that aims to develop the world’s first lipid1 database for healthy persons of different racial and ethnic groups.

Using the lipid database, scientists and researchers hope to better understand the healthy and unhealthy “fat” levels in people of different racial and ethnic backgrounds. This knowledge will pave the way for medical professionals to leverage such key information as diagnostic markers for their patients in future.

The Lipidomic Natural Variation (L-NAVA) consortium is founded by the Singapore Lipidomics Incubator (SLING) at NUS. Other founding members include South Korea’s Graduate School of Analytical Science and Technology (GRAST) at Chungnam National University; Baker IDI Heart and Diabetes Institute, a medical research institute located in Melbourne, Australia; and Agilent, which is L-NAVA’s preferred technology partner.

Using methodology created by SLING, the teams at GRAST and Baker IDI will undertake similar studies in their domestic markets; the results will be compiled

NEW INTERNATIONAL EFFORT TO DEVELOP WORLD’S FIRST INTEGRATED SYSTEMSBIOLOGY DATABASE FOR DIFFERENT RACES AND ETHNICITIES

From left to right: Dr Rudolf Grimm of Agilent Technologies, Professor Hyun Joo An of GRAST at Chungnam National University, Associate Professor Markus Wenk of SLING at National University of Singapore and Associate Professor Peter Meikle of Baker IDI.

into a database. (SLING recently concluded a study of 360 healthy subjects from three major ethnic groups-Chinese, Indian and Malay-in Singapore. Through that study, SLING was able to identify the upper and lower limits of the normal fat levels for healthy people in the three groups.)

The lipid information (L-NAVA) will be integrated with glycomic (G-NAVA) and proteomic (P-NAVA) studies, to provide insight into natural variation within glycans and proteins as well.

“Understanding natural variations is a major aim of SLING,” said associate professor Markus Wenk, director of SLING. “This network allows us to extend, and hopefully connect, our studies on lipids with others that address variability at the level of genes, proteins and sugars. Doing this in healthy individuals will provide a broad, foundational basis relevant for a better understanding of onset of diseases.” Dr. Wenk is also a faculty member at the Department of Biochemistry at the Yong Loo Lin School of Medicine at NUS and the Department of Biological Sciences at the NUS Faculty of Science.

“We are honored to be part of this new consortium, supporting its goal to systematically determine lipid profiles across different groups of humans,” said Agilent’s Rod Minett, general manager, Life Sciences, South Korea and the South Asia-Pacific region. “Agilent’s innovations in bio-analytical instruments will help consortium members in their research on the natural variations using different methods. We hope this resource will help medical professionals provide better-quality care to their patients.”

Principal investigators in the L-NAVA consortium are professor Wenk; professor Hyun Joo An, head of department at GRAST; associate professor Peter Meikle, head of Metabolomics at Baker IDI; and Dr. Rudolf Grimm, Agilent’s director of Science and Technology and manager of collaborations in the Asia-Pacific region.

“Lipidomics has the potential to deliver significant new advances in medicine,” said Professor Meikle. “These include adjust treatments more effectively. However, to achieve these being able to predict a person’s risk of disease, understanding what causes that disease and being able to monitor and advances we must first understand the natural variation within different ethnic groups. We can then identify more accurately where abnormal lipid metabolism may be contributing to diseases including heart disease and diabetes.”

“We are happy to be a founding member of this international consortium for lipidomics research,” said professor An.

“Although we have been focusing on the natural variation on serum glycoproteins to predict disease, we believe that the variation of glycans on lipid molecules can play a critical role in helping the scientific community gain deeper insights into the biological aspects of life. Our team at Asia Glycomics Reference Site is keen and ready to expand our research to lipidoglycomics, as glycolipids are closely involved in the development of neurons and their aging. This is a diversified yet interesting field that will benefit the global scientific and even medical communities.”

All members of the consortium use Agilent’s industry-leading triple quadrupole LC/MS system with ion funnel (iFunnel) technology in their lipidomics research. The Agilent iFunnel technology offers scientists and researchers the highest sensitivity and detection levels in their quest for scientific breakthroughs.

About National University of Singapore (NUS)A leading global university centered in Asia, the National University of Singapore (NUS) is Singapore’s flagship university, which offers a global approach to education and research, with a focus on Asian perspectives and expertise.

NUS has 16 faculties and schools across three campuses. Its transformative education includes a broad-based curriculum underscored by multi-disciplinary courses and cross-faculty enrichment. Over 37,000 students from 100 countries enrich the community with their diverse social and cultural perspectives.

NUS has three Research Centres of Excellence (RCE) and 23 university-level research institutes and centres. It is also a partner in Singapore’s fifth RCE. NUS shares a close affiliation with 16 national-level research institutes and centres. Research activities are strategic and robust, and NUS is well-known for its research strengths in engineering, life sciences and biomedicine, social sciences and natural sciences. It also strives to create a supportive and innovative environment to promote creative enterprise within its community.

EVENTS

Young Scientist Forum 2014

The inaugural Agilent Young Scientists Forum for Asia Pacific kicked off in Melbourne, Australia on 28th March. A month later on 29th April, it was held in Singapore and on 24th May, the forum was held in Seoul, Korea.

This event is unique for Agilent in Asia, and it is a new extension of the company’s commitment as a strategic partner to the research community, to nurture talent, and connect students to opportunities. During the event and even post-event, both the young and established scientists interact, and can continue collaborate and expand horizons across distinct areas within Life Sciences, such as genomics, proteomics and metabolomics. The forum is open only to young scientists and researchers in honors, doctorate and post-doctorate programs in life science and integrated biology industries.

The program was put together with the intent to provide insights into how to shape a career in science. The participating students put forth their research findings via oral presentations, while the invited mentors (who are established professors/scientists in their respective academic institutions) will provide their feedback and guidance to these students. In fact, we witnessed much lively discussions and inputs in all three countries in which we ran the event. Overall, we had received very positive feedback from the participants and mentors, as certainly an event worth participating in because no instrument companies have organized a similar event that is done with the intent to nurture and develop the science and research community.

The life science researchers who joined us as mentors included Prof. Tony Bacic of the University of Melbourne, Prof. Graham Mitchell of Foursight Associates for Australia, Professor Peter Preiser of Nanyang Technological University and Assoc. Prof. Markus Wenk of National University of Singapore. In Korea, we had Prof. Eugene Lee of Seoul National University and Prof. Hyun-Joo An from the Graduate School of Analytical Science and Technology (GRAST) at Chungnam National University.

In a press release issued to announce this event, Rod Minett, General Manager for Agilent Life Sciences Group, South Asia Pacific and Korea region was quoted, “As a technology partner to the research community, it is important that Agilent nurtures tomorrow’s upcoming scientists and researchers. The Young Scientist Forum will provide the opportunity for young and senior scientists to interact, collaborate, share analytical issues and cover more areas of biological and clinical research. These interactions are the steps toward future developments for a healthier population.”

1st Winner on the left: Eugenia Ong, PhD student, Duke-NUS Graduate Medical School, Program in Emerging Infectious Disease

EVENTS

Mass Spectrometry Education Webinar Series

Mass spectrometry is a powerful and sensitive analytical technology that has been successfully adopted into many clinical research and forensic toxicology laboratories around the world. They provide specific and accurate quantitation of many molecules of interest in complex biomatrices.

View our free education webinar sessions which contains three 20-minute presentations to get you well-versed with the latest products and techniques in using this emerging analytical technology to apply them in your daily analyses.

Who should attend:• Lab administrators, directors, managers, technologists, and clinical researchers, who are interested in understanding mass spectrometry and its many uses in the lab

After attending this program, you will be able to:• Have a basic understanding of how mass spectrometry works and how it is used to solve a variety of analytical challenges

• Choose which technique (ICPMS, GC/MS, LC/MS) is most suitable for your analytical needs

• Select appropriate columns and sample preparation options to get the best results from challenging biomatrices

PART 1: View now at www.agilent.com/chem/clinical_webinarAnalytical Basics, Optimize your clinical research experiment• How to Choose Columns/Sample Prep Options for Clinical Research Analyses by Jason Link, LC Columns Product Manager

This presentation will be an introduction on how to choose columns and sample prep options for LC/MS analyses of challenging biomatrices such as plasma, urine, and oral fluids.

• Introduction to GC/MS Technology by Terry Sheehan, Director of Marketing, GC/MS

This presentation will be a brief introduction to the basic fundamentals and applications for gas chromatography-mass spectrometry technology.

• Introduction to ICPMS Technology by Amir Liba, Life Science Business Development Manager, ICP-MS and ICP-QQQ

This presentation will be an introduction to the basic fundamentals and applications for inductively coupled plasma mass spectrometry technology

PART 2: View now at www.agilent.com/chem/clinical_webinar

New Technology to accelerate your clinical research• Introduction to the RapidFireMS system for Ultrafast MS Analysis by Vaughn Miller, RapidFire Application Development Manager

This presentation will be an overview of the ultrafast SPE-MS technology in the RapidFireMS system

• Introduction to Examiner Software for Fast Data Processing and Review of Flow Injection Analyses by Steve Madden, Examiner Software Product Manager

This presentation will be an introductory overview of the new Examiner software application that can be used for quantitative data processing using ion intensity ratios typical in flow injection experiments

• Introduction to the New StreamSelect LC/MS System for Increasing LC/MS Throughput by Maria Vandamme, StreamSelect Product Manager

This presentation is an introduction to the new dual-channel LC system for increasing LC/MS analysis throughput

PART 3: View now at www.agilent.com/chem/clinical_webinar

Analytical by Mass Spectrometry• Cyclosporin A, everolimus, sirolimus, tacrolimus, mycophenolic acid analysis in Whole Blood by LC/MS by Linda Cote, Applications Scientist LC/MS

This presentation will be an introduction to the analysis of a variety of drugs in whole blood by LC/MS

• Heavy Metals analysis in Blood by ICPMS by Amir Liba, Life Science Business Development Manager, ICP-MS and ICP-QQQ

This presentation will be an introduction to the analysis of heavy metals in blood by ICPMS

• Organic Acids Analysis in Urine by GC/MS by Terry Sheehan, Director of Marketing, GC/M

This presentation will be a brief introduction to the basic fundamentals and applications for gas chromatography-mass spectrometry technology

UPCOMING EVENTSDate: 16 July - 19 July 2014 Title: 5th AOMSC & 33rd CMSS Annual Conference Peking University, Beijing http://aomsc.cmss.org.cn

Date: 6 August - 8 August 2014 Title: AOHUPO 2014 Bangkok, Thailand www.aohupo2014.com/#!about/c1enr

Date: 20 August - 22 August 2014 Title: Aug KSMS (Korea Mass Spectrometry Conference) Jeju, Korea website: www.ksms.org/

Date: 12 August - 28 August 2014 Title: Mass Spectrometry Technology Seminar Register at www.agilent.com/chem/MSTech2014

Date: 24 August - 29 August 2014 Title: IMSC 2014 (20th IMSC) (International Mass Spectrometry Conference) International Conference Centre of Geneva, Geneva, Switzerland www.imsc2014.ch

Free Subscription:To receive regular updates on the latest news in academia research, please visit www.agilent.com/chem/sapkacademia to subscribe.