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Plant Physiology Preview. Published on December 2, 2013, as DOI:10.1104/pp.113.225524 Running Head: SRM to determine protein abundance in Arabidopsis CORRESPONDING AUTHOR: A. Harvey Millar ARC Centre of Excellence in Plant Energy Biology and Centre for Comparative Analysis of Biomolecular Networks, The University of Western Australia (M316), 35 Stirling Highway , Crawley, WA 6009, Australia Tel: +61 8 64887245 Fax: +61 8 64884401 E-mail: [email protected] Research Area : Breakthrough Technologies Downloaded from on June 17, 20171- Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. Copyright 2013 by the American Society of Plant Biologists Selected Reaction Monitoring (SRM) to determine protein abundance in Arabidopsis using the Arabidopsis Proteotypic Predictor (APP). Nicolas L. Taylor1,2, Ricarda Fenske1,2, Ian Castleden3, Tiago Tomaz1,2, Clark J. Nelson1,2, and A. Harvey Millar1,2 1 ARC Centre of Excellence in Plant Energy Biology and 2Centre for Comparative Analysis of Biomolecular Networks (CABiN), Centre of Excellence in Computational Systems Biology, Bayliss Building M316, The University of Western Australia, 35 Stirling Highway, Crawley WA 6009, Western Australia, Australia. One-Sentence Summary: The Arabidopsis Proteotypic Predictor (APP) and SRM mass spectrometry enable the quantitation of protein abundance in knockout and complemented lines of Arabidopsis when antibodies are unavailable. Downloaded from on June 17, 20172- Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. This work was supported by the Australian Research Council (ARC) ARC Centre of Excellence for Plant Energy Biology (CE0561495). AHM is supported by the Australian Research Council (ARC) as an ARC Future Fellow. Downloaded from on June 17, 20173- Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. ABSTRACT In reverse genetic knockout (KO) studies that aim to assign function to specific genes, confirming the reduction in abundance of the encoded protein will often aid the link between genotype and phenotype. However, measuring specific protein abundance is particularly difficult in plant research where only a limited number of antibodies are available. This problem is enhanced when studying gene families or different proteins derived from the same gene (isoforms), as many antibodies cross-react with more than one protein. We show that utilizing Selected Reaction Monitoring (SRM) mass spectrometry allows researchers to confirm protein abundance in mutant lines, even when discrimination between very similar proteins is needed. Selecting the best peptides for SRM analysis to ensure protein or gene-specific information can be obtained requires a series of steps, aids and interpretation. To enable this process in Arabidopsis we have built a web-based tool, the Arabidopsis Proteotypic Predictor to select candidate SRM transitions when no previous mass spectrometry evidence exists. We also provide an in depth analysis of the theoretical Arabidopsis proteome and its use in selecting candidate SRM peptides to establish assays for use in determining protein abundance. To test the effectiveness of SRM mass spectrometry in determining protein abundance in mutant lines we selected two enzymes with multiple isoforms, aconitase and malate dehydrogenase. Selected peptides were quantified to estimate the abundance of each of the two mitochondrial isoforms in wild type, KO, double KO and complemented plant lines. We show that SRM protein analysis is a sensitive and rapid approach to quantify protein abundance differences in Arabidopsis for specific and highly related enzyme isoforms. Downloaded from on June 17, 20174- Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. INTRODUCTION Upon the completion of the Arabidopsis thaliana genome in 2000 (AGI, 2000) only ~10% of the 25500 initially predicted genes had an experimentally assigned function (Alonso and Ecker, 2006). In the past decade, forward and reverse genetics have provided the basis to experimentally verify the functions of 9663 (35%) of the 27416 genes (The Multinational Coordinated Arabidopsis thaliana Functional Genomic Project Annual Report 2011). Key to this advance has been the large collections of TDNA insertional mutants (O'Malley and Ecker, 2010). Researchers generally aim to obtain a knockout of gene expression or a severe reduction in the gene’s function by T-DNA insertion (O'Malley and Ecker, 2010). However, an examination of published literature on over 1084 T-DNA insertion mutants, only 44% of insertions resulted in no transcript, while 42% resulted in reduced transcript abundance and 14% showed no change or an increase in transcript abundance (Wang, 2008). Of these studies, only 136 reported information on abundance of the protein encoded by the gene of interest and of these 80% showed no protein expression while one in five showed either no effect on or reduced protein abundance or the presence of a truncated protein product (Wang, 2008). As 20% of gene functions assigned to phenotypes are assumed to result from protein knockouts that do not occur but instead result from other changes in protein abundance, it has become an increasing requirement to determine the abundance of the gene’s product in these genetically altered lines. The classical means of assessing protein abundance has been quantitative western blotting using antibodies raised to the specific polypeptide of interest (Aebersold et al., 2013). Polyclonal antibodies typically recognize a series of primary or secondary structures of a polypeptide and while they can be highly sensitive in immunodetection assays, they can also cross-react with multiple proteins due to common epitopes. In most assays it is not possible to know which epitopes are responsible for the immuno-reactivity observed. Monoclonal antibodies recognize a single primary sequence or secondary structure of a polypeptide, providing greater clarity in the immuno-reaction, but at a significantly greater cost and require immune-reactivity of each peptide in an animal model. Selected reaction monitoring (SRM) mass spectrometry provides an alternative approach that allows researchers to target their protein of interest in a complex Downloaded from on June 17, 20175- Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. mixture and estimate its abundance by quantitation of peptides derived from its enzymatic digestion. In this approach a triple quadropole (Q or q) mass spectrometer (QqQ) transmits a peptide precursor ion in Q1 that is then fragmented in q2 and a single peptide fragment ion is selected in Q3 for quantitation. The precursor and fragment ion pair is referred to as a transition and its abundance is used to quantify the abundance of a specific peptide derived from a protein. During an SRM experiment, sequential gating of precursor and product in a triple quadrupole mass spectrometer (QqQ-MS) allows millions of precursor/fragment ion combinations (transitions) to be assessed in complex peptide mixtures generated by proteolysis of protein extracts from cells. To further confirm the origin of the quantifier product ion, two qualifier ions are also examined from the same precursor ion. This combination of filters gives SRM approaches their power in complex samples and allows quantification of many different proteins over 4 orders of magnitude in crude whole protein extracts from plant tissue samples (Picotti and Aebersold, 2012). SRM mass spectrometry, also referred to as Mass Westerns, has previously been used in plants to quantify a number of proteins including: sucrose-phosphate synthase isoforms in Arabidopsis (Lehmann et al., 2008), sucrose synthase isoforms and N-metabolism enzymes in Medicago (Wienkoop et al., 2008), a basic amino acid carrier involved in arginine metabolism in rice (Taylor et al., 2010), cytosolic and organelle markers in Arabidopsis (Ito et al., 2011) and the plasma membrane transportome in Arabidopsis (Monneuse et al., 2011). Label-free quantitation in this manner requires reproducibility in sample extraction, digestion, liquid chromatography and ionisation and has been widely reviewed (Lange et al., 2008, Picotti and Aebersold 2012). To assist Arabidopsis researchers to design SRM assays, Fan et al. (2012) calculated transitions for Arabidopsis experimental protein spectra submitted to EBI’s PRIDE database. These candidate transitions are made available through the web-based tool MRMaid (www.mrmaid.info, (Fan et al., 2012)) that can be searched by entering an accession number. Whilst MRMaid provides a valuable resource of transitions for a subset of Arabidopsis proteins with matching data in PRIDE, the next challenge is to establish SRM analysis for the other ~20000 Arabidopsis proteins. A key consideration in the selection of candidate SRM transitions for these ~20000 proteins is whether they are likely to be detectable by LC-MS, so-called ‘proteotypic’ peptides. MRMaid currently only allows unique peptides that have already been Downloaded from on June 17, 20176- Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. experimentally observed to be selected as candidate transitions, in some cases, such as limited numbers of available detectable peptides or when studying a group of closely related proteins, redundant peptides as candidate transitions may in fact be favorable, desirable or simply unavoidable. This likely to be the case, for example, when attempting to measure proteins translated from splice variants of a gene, highly related enzyme isoforms or multiple mature proteins derived by post-translational processing which may accumulate in different parts of a cell. In these cases, it is likely that only very small regions of the proteins differ from one another and thus robust quantitation is likely to require information from a set of unique and nonunique peptides analyses. In this study we have attempted to systematically address a series of the issues associated with designing a SRM experiment in Arabidopsis by first carrying out an in depth analysis of the theoretical in silico trypsin digested Arabidopsis proteome. We have examined; the number of candidate SRM peptides in each Arabidopsis protein, the number of times these candidate transitions occur in the Arabidopsis proteome as both tryptic and non-tryptic fragments, the presence of potential trypsin missed cleavage sites, the presence of potential oxidised Met and a prediction of whether the candidate transition is likely to be proteotypic. We have collated these data into a searchable web http://www.plantenergy.uwa.edu.au/APP/) based called database the (available Arabidopsis at Proteotypic Predictor (APP) to assist researchers in the design of SRM experiments in Arabidopsis. To show the utility of this approach we studied two abundant isoforms of the TCA cycle, mitochondrial aconitase (mACO1) and malate dehydrogenase (mMDH1), and two lower abundance isoforms for the same enzymatic steps, mACO2 and mMDH2 (Taylor et al., 2011). We quantified peptides of each of the isoforms, of each protein, in WT and KO plants, and in the case of mMDH, in dKO and complemented plants. We use the SRM analysis to demonstrate the relative sensitivity and reliability of this method to distinguish between similar isoforms of proteins. We also use SRM analysis to measure changes in protein abundance in genetic knockouts of each or all isoforms of a protein. Downloaded from on June 17, 20177- Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. RESULTS Theoretical trypsin digestion of all Arabidopsis proteins to define targets for SRM mass spectrometry. In choosing peptides for use in SRM mass spectrometry it is vital to determine whether they have been observed previously by LC-MS, are theoretically detectable by LC-MS and their uniqueness within the genome. To assess this the 1618830 redundant set of theoretical trypsin peptides representing 27383 proteins and having a range of peptides from 1 to 755 per protein (Figure 1A.) generated from an in silico trypsin digest of the Arabidopsis proteome (Tair10pep) were assessed for how many times they occurred at a single gene locus (Figure 1B.). 939417 (58%) peptides were unique as a translated product from a single genome locus, whereas 679413 (42%) peptides occurred more than once in Arabidopsis. Of the 939417 unique peptides, 560651 contained a potential trypsin missed cleavage site, that is they contain an arginine (R) or lysine (K) residue at a position other than the carboxy-terminus of the peptide. While such peptides are regularly observed in peptide mass spectrometry, they are not consistently obtained during trypsin digestion and are thought to be relatively poor candidates for selection as SRM transitions. This leaves 378766 or 40% of the total theoretical trypsin peptides that contain no missed cleavage site and are unique, and reduces the total number of unique proteins that could assessed by unique peptides SRM mass spectrometry approaches to 23777 (87%) (Figure 2A.). If a requirement is made for three peptides per protein to be amenable for analysis, this would further reduce the number of proteins that could be assessed by SRM approaches to 21115 (77%). To determine how these numbers of peptides/proteins may correspond to peptides that can be detected by LC-MS and thus are proteotypic, we analyzed their occurrence in two large Arabidopsis data sets, Baerenfaller et al (2008) (106155 peptides) and Castellana et al (2008) (72195 peptides), and our own data set enriched for peptides from organellar proteins (9205 peptides). When comparing these sets to the number of unique theoretical peptides without miss cleavages (378766) we found that 69128 peptides were present in the three datasets, 28776 peptides were only found in the Baerenfaller et al (2008) data set, 7869 were unique to the Castellana et al (2008) data set and only 512 were unique to our own data. Overall, 309638 unique peptides Downloaded from on June 17, 20178- Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. without miss cleavages (81%) have not been observed to date in Arabidopsis (Figure 3). The observed, gene locus specific peptides correspond to 12104 non-redundant proteins (Baerenfaller et al (2008) 8871 non-redundant proteins, Castellana et al (2008) 5080 non-redundant proteins, and from our own data 269 non-redundant proteins) with a range of peptides per protein of 1-132 (Figure 2B.). This represents less than 45% of the Arabidopsis proteome. If we further exclude peptides that have had only one or two peptides observed, this reduces the number of non-redundant proteins to 7067 (~26% of the At proteome). While this shows the potential limitations imposed by this approach, it also identifies a large set of proteins for which SRM analysis is theoretically and experimentally feasible today. The MRMaid database currently provides candidate transitions for 7165 Arabidopsis proteins, while candidate transitions would have to be manually collated for the remaining ~5000 observed peptides from the three dataset above. A key consideration in attempts to designing an SRM experiment beyond this observed peptide set and those supplied by MRMaid is to predict the likelihood that other peptides could be observed if they were analyzed in a targeted assay. To address this the proteotypic predictor software packages PeptideSieve (Mallick et al., 2007), CONSeQuence (Eyers et al., 2011) and STEPP (Webb-Robertson et al., 2010) were used to assess their ability to more broadly predict proteotypic peptides in Arabidopsis. Using 1000 randomly selected observed proteotypic peptides and 1000 peptides not observed to date but from the same proteins as the known proteotypic peptides, we found that STEPP (FDR = 36.7%) was the best predictor of proteotypic peptides of Arabidopsis. (Supplemental Material, Figure S1, Supplemental Material, Tables S1 and S2). Interestingly whilst the machine learning methods used in PeptideSieve (FDR = 37.5%) and CONSeQuence (FDR = 69.4%) were trained on yeast peptides (Mallick et al., 2007; Eyers et al., 2011) the STEPP predictor was trained using a combination of three bacterial proteomes (Salmonella typhimurium, Shewanella oneidensis and Yersinia pestis) (Webb-Robertson et al., 2010). Comparing the amino acids composition of the proteotypic and non-proteotypic data sets using the Pepstats package from EMBOSS showed that the presence and absence of a number of amino acids was influencing detection (Supplemental Material, Table S3.). The presence of the positively charged amino acids arginine, histidine and Downloaded from on June 17, 20179- Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. lysine, the non-polar methionine, tryptophan, leucine and phenylalanine and cysteine appear to have a negative influence on detection whereas the presence of the negatively charged amino acids aspartic and glutamic acids, the non-polar alanine, tyrosine and valine and glycine and proline appear to have a positive influence on detection (Supplemental Material, Table S3.). Using a Bayesian classifier approach that incorporates known proteotypic peptides and known non-proteotypic peptides and the three proteotypic predictions programs we were able to create an Arabidopsis proteotypic predictor (APP). Using this predictor against the same 1000 randomly selected known proteotypic and 1000 peptides not previously observed but from the same proteins, we found APP had a false discovery rate (FDR) of 30.8%. This represent a ~6% improvement over the STEPP predictor (Webb-Robertson et al., 2010) for the prediction of Arabidopsis proteotypic peptides. Using APP we can classify 168494 Arabidopsis peptides to be proteotypic. These predicted peptides correspond to 21830 proteins (with a range of peptides per protein = 1-148) (Figure 2C.) or ~80% of the Arabidopsis proteome. If we further include peptides that have been observed in previous studies this increases the number of non-redundant proteins to 22132 (~81%, with a range of peptides per protein = 1-177) (Figure 2D.)), and if we require 3 or more peptide per protein this reduces the number of non-redundant proteins to 17159 (~63% of the Arabidopsis proteome). The APP can be queried by entering an AGI identifier (or group of AGIs) or a protein sequence via a web browser interface, accessible at http://www.plantenergy.uwa.edu.au/APP/. A series of screen shots and basic instructions are available in Supplemental Material, Figure S2. In addition to providing a proteotypic prediction of the tryptic peptides from the AGI or sequence, the output from the APP also provides details of all resulting peptides, their mass, the number of potential missed cleavage sites, the presence and mass change of possible methionine oxidation and the number of times a resulting peptide is observed in the entire Arabidopsis proteome as either tryptic or non-tryptic fragments. Selection and optimization of candidate SRM peptides for aconitase and malate dehydrogenase. Downloaded from on June 17, 201710 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. To test the value of APP to SRM development, we selected unique peptides for mACO1, mACO2, mMDH1 and mMDH2 that we had previously shown to be detected by mass spectrometry. Their predicted collision energies (CE) were calculated using the Skyline software package (MacLean et al., 2010). Of the 12 peptides chosen, 9 were predicted by APP as being proteotypic while three were predicted to be non-proteotypic. QqQ mass spectrometry then assessed candidate peptides for detection and their collision energy was optimized using a sample of isolated Arabidopsis mitochondria. The validity of the transitions was also confirmed by examining the MSMS spectra of the precursors ions (Supplemental Material, Figure S3). From these results, a list of SRM transitions was finalized with three transitions per peptide, one quantifier and two qualifiers and a total of three peptides per protein (Table I.). An overview of the SRM workflow can be found in supplemental material, Figure S4. SRM analysis of protein abundance in mitochondria of aconitase and malate dehydrogenase knockout lines. A single maco1 knockout line of mACO1 (At2g05710.1) has previously been characterized by Moeder et al. (2007), where they showed it had no morphological phenotype but an absence of mACO1 transcript and a 70% loss in total ACO activity. No protein measurements for mACO in maco1 have been reported. The maco1 plants showed an enhance tolerance to oxidative stress induce by the superoxide generating agent paraquat (Moeder et al., 2007). A single maco2 knockout line (mACO2, At4g26970.1) has been characterized here (Supplemental Material, Figure S5.), briefly it showed no morphological phenotype, a ~30% loss in mACO activity and a significant decrease in mACO2 protein abundance based on 2D gel analysis. Single knockout lines (mmdh1, mmdh2), a double knockout (mmdh1mmdh2) and a complemented knockout line (mmdh1mmdh2-35s:MDH1) for mitochondrial malate dehydrogenase (mMDH) have been previously characterized by Tomaz et al. (2010). We isolated mitochondrial samples from each of these plant lines and protein samples were digested with trypsin for analysis. To examine the abundance of mACO1 we first examined the abundance of the peptide VVNFSFDGQPAELK in mitochondria isolated from WT, maco1 and maco2 plants (Figure 4.). The SRM 775.9 557.3 transition was used to quantify the Downloaded from on June 17, 201711 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. abundance and its elution occurred in WT mitochondria between 10.5 - 10.9 minutes (Figure 4Ai.), between 10.3 - 10.5 minutes from maco1 mitochondria (Figure 4Aii.) and between 10.0 - 10.3 minutes from maco2 mitochondria (Figure 4Aiii.). The area under the SRM 775.9 557.3 transitions was then used to calculate the abundance of VVNFSFDGQPAELK in each of the isolated mitochondrial samples. 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 4B to highlight the quantifier and qualifier ions. We then quantified the abundance of two other peptides of mACO1 SSGEDTIILAGAEYGSGSSR (979.4 970.4) LSVFDAAMR (505.3 710.3), Figure 5A., Supplemental Material, Table S4.) along with three peptides from mACO2 (GVISEDFNSYGSR (715.8 FSYNGQPAEIK (627.3 557.3) ILDWENTSTK (603.8 1161.5) 980.4), Figure 5B., Supplemental Material, Table S5.). These SRM transitions were designed to allow us to estimate the protein abundance of mACO1 in maco1 and confirm the protein knockdown of mACO2 in maco2 observed by 2D-PAGE analysis (Supplemental Material, Figure S1.). Examining mACO1 we saw 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 the maco1 when compared to WT (Figure 5A., Supplemental Material, Table S4.). We also saw a decrease in the abundance of mACO1 in maco2 mitochondria to ~75% (Average Err = 2.7%) of WT levels. A similar result was obtained for mACO2 where peptides from this protein 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 when compared to WT (Figure 5B., Supplemental Material, Table S4.). We also saw a slight decrease in the abundance of mACO2 in maco1 mitochondria to ~82% (Average Err = ~3.9%) of WT levels. To further verify these results heavy labeled peptides (heavy labeled K or R) corresponding to all mACO peptides were used to construct standard concentration curves in a WT background (Supplemental Material, Figure S6.). These standard curves were then used to calculate the absolute abundance of SRM transitions of mACO1 and mACO2 in mitochondria isolated from WT, maco1 and maco2 plants (Supplemental Material, Figure S7., Table S7). The absolute concentrations of the peptides confirmed previous observations that mACO1 was twice as abundant as mACO2 (Taylor et al., 2011) and corresponded well with the relative abundance results reported in Figure 5 confirming the knockout of ACO1 and ACO2 in their respective mutant lines. This also showed Downloaded from on June 17, 201712 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. that for analysis of specific protein abundance in mutants, both relative abundance and absolute abundance provided very similar insights. To examine the protein knockdown of mMDH1 in mmdh1 and mMDH2 in mmdh2 we carried out SRM analysis of SEVVGYMGDDNLAK (749.0 (415.3 peptide transitions 1083.5) EGLEALKPELK (409.6 VAILGAAGGIGQPLALLMK (897.0 (736.3 the (mMDH1; 486.3) 970.6), mMDH2 SQVSGYMGDDDLGK 1157.5) VVILGAAGGIGQPLSLLMK (613.0 801.5) NLSIAIAK 602.4)) to determine the protein abundance of each isoform in WT, mmdh1 and mmdh2 plants (Figure 6, Supplemental Material, Table S5). In each case, the similarity of the proteins was such that methionine containing peptides had to be chosen in order to ensure three specific peptides could be assessed. We also examined the abundance of mMDH1 and mMDH2 in mmdh1mmdh2 and in mMDH1 complemented mmdh1mmdh2 (mmdh1mmdh2-35s:MDH1) (Figure 6.). 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 loss of both the mMDH isoforms to a level similar to those observed in the single knockouts. In the complemented line we saw a dramatic increase in the abundance of mMDH1 to levels much greater to those observed in the WT (~400.0% (Average Err = ~19%)). SRM analysis of protein abundance of mMDH1 in leaf extracts from WT mmdh1 and mmdh2 plants. To assess the sensitivity of SRM mass spectrometry for the detection of this protein in leaf extracts from 5 week old WT, mmdh1 and mmdh2 plants. SRM analysis of the peptide transitions (mMDH1; EGLEALKPELK (409.6 SEVVGYMGDDNLAK 486.3) VAILGAAGGIGQPLALLMK (897.0 mMDH2; SQVSGYMGDDDLGK (736.3 (613.0 (749.0 801.5) NLSIAIAK (415.3 1083.5) 970.6), 1157.5) VVILGAAGGIGQPLSLLMK 602.4)) were used to determine the protein abundance in leaf extracts (Figure 7, Supplemental Material, Table S6). Similarly to the result we observed in isolated mitochondria we were able to confirm the knockout of mMDH1 in the leaf extracts from mmdh1 plants and show the detection of this Downloaded from on June 17, 201713 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. protein in the leaf extracts from WT and mmdh2 plants. Attempts to quantify the abundance of mMDH2, mACO1 and mACO2 in whole leaf extracts were unsuccessful due to insufficient signal-to-noise ratio (data not shown). Downloaded from on June 17, 201714 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. DISCUSSION The emergence of targeted proteomics now allows researchers to focus on using proteomics to address specific biological questions in a way that is fundamentally unlike traditional discovery proteomics pipelines (Marx, 2013). This led to the recognition of targeted proteomics as the 2012 Method of the Year in Nature Methods (Aebersold et al., 2013; Editorial, 2013). Here we show the utility of targeted proteomics approach in Arabidopsis to enable researchers to quantify the abundance of proteins of interest in T-DNA insertion lines, and to overcome the time and expense, and lack of specificity when relying on Western blotting to measure protein abundances in plant samples. In principle, SRM mass spectrometry also enables the development of specific assays for the quantitation of selected alternative forms of a protein such as, isoforms, splice variants, mutated versions or those containing PTMs (that cannot be distinguished by antibodies) as long as these different forms can be characterized as a mass difference (Picotti et al., 2013). At this time, where antibodies are available, they often retain the upper hand in terms of sensitivity for detection of low abundance proteins, but often lack the specificity required for some questions such as those outlined above. However, as equipment and approaches improve, targeted SRM mass spectrometry is likely to surpass Western blotting as the preferred method for protein quantitation in Arabidopsis. The term ‘Mass Western’ has previously been coined to describe the potential of SRM mass spectrometry to complement and perhaps replace the use of antibodies for protein quantitation (Lehmann et al., 2008). Further advancing the use of SRM mass spectrometry in Arabidopsis will require a series of underpinning datasets to help design assays and to interpret the results. The theoretical considerations, outlined in the development of APP, must be combined with practice and often compromise choices will need to be made between peptide quality, peak abundance, uniqueness and peptide/transition number in order to obtain datasets for analysis. Guidelines for analysis are still being developed, but the strong genetic resources of Arabidopsis will be very useful for testing and assessing SRM mass spectrometry as a tool for determining the abundance of specific proteins of interest. SRM mass spectrometry can be immediately helpful to explore the impact of genetic insertions and deletions on protein abundance. The data provided here allow Downloaded from on June 17, 201715 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. us to consider five key questions about the use of peptide SRM mass spectrometry in Arabidopsis and these are each discussed in turn below. Are there sufficient specific peptides to enable SRM mass spectrometry to quantify the entire Arabidopsis proteome? Gene duplication and large-scale expansion of specific protein families in Arabidopsis has resulted in a large number of similar proteins derived from distinct gene loci. This means that many tryptic peptides that can be observed in mass spectrometry can be mapped to multiple locations in the genome. To assess how significant the impact of this problem is on using SRM mass spectrometry as a measure of protein abundance, we considered the entire in silico trypic digest of Tair10pep and applied a series of filters to assess the impact of size, amino acid composition and uniqueness on the availability of peptides for SRM development. This showed ~90% of proteins contained at least one unique peptide sequence, and thus that ~24000 Arabidopsis proteins are theoretically quantifiable by SRM mass spectrometry. Typically, the longer the peptide the more likely it is to be unique. However, in some cases, certain biological questions can be addressed by, or even enhanced by, the use of redundant peptides that can combine the abundance of a group of defined or closely related proteins. Which peptides should be prioritized for assessing specific proteins of interest in Arabidopsis? The choice of peptides for assessment by SRM mass spectrometry of a given protein is often not intuitive and requires a series of considerations including uniqueness, size, detectability and the absence of commonly modified amino acids. To date only 20% (~70000 peptides) of the total number of theoretical trypsin peptides have been experimentally observed and reported, which limits the number of Arabidopsis proteins theoretically quantifiable by SRM mass spectrometry to only 12104 proteins (~44% of the proteome). To investigate whether the low number of peptides/proteins observed was due to a low number of collected spectra or a limitation in the detection of Arabidopsis peptides/proteins we developed the Arabidopsis proteotypic predictor (APP, http://www.plantenergy.uwa.edu.au/APP/) to predict which Arabidopsis proteins should be observable by mass spectrometry. This analysis highlighted over 168494 peptides that can be predicted to be proteotypic, which would increase the Downloaded from on June 17, 201716 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. number of proteins that could be quantified by SRM mass spectrometry by over 10,000 to nearly 80% of the Arabidopsis proteome. Together with the predictions of being proteotypic the APP provides researchers with key information for designing SRM mass spectrometry experiments including: theoretical trypsin fragments of a query protein, their mass, the number of potential missed cleavage sites, the presence and mass change of possible methionine oxidation and the number of times a resulting peptide is observed in the entire Arabidopsis proteome as either tryptic or non-tryptic fragments. The APP and MRMaid can serve as the first stops for Arabidopsis researchers embarking on designing an SRM mass spectrometry assay by providing them with complementary knowledge required to select candidate peptides. We also envisaged that the ability to interrogate the APP and MRMaid, select candidate peptides and collect the results as excel spreadsheets, will provide researchers with a rapid and data-rich starting point to approach mass spectrometry labs or services for custom SRM assay development. Interestingly, despite concerns and limitations we had when selecting candidate peptides for mMDH and mACO proteins, we were able to correlate the abundance of both peptides containing an oxidisable Met residue and a trypsin miss cleavage site to other peptides that did not contain them (Figures 5, 6 and 7; Supplemental Material, Tables S4, S5 and S6). We also used one peptide for mMDH1 which was miss-cleaved adjacent to an internal Pro residue (EGLEALKPELK). While there is evidence that trypsin can cut before Pro in some cases, cleavage at Pro in this case was rare (based on our MSMS spectral libraries) and the miss-cleaved peptide abundance correlated with changes in the abundance of other mMDH1 peptides (Figure 6). This suggests that despite the presence of possible PTM sites or trypsin miss cleavage sites on peptides, at least some such peptides may be suitable for the establishment of SRM assays when they can be confirmed by other peptides of the same proteins without these characteristics. How can SRM mass spectrometry assays be optimized and confirmed in Arabidopsis? After peptide selection, the optimization of transitions is essential to define quantifiers and qualifiers. We optimized assays for one quantifier and two qualifier ions per peptide and three peptides per protein. The use of qualifier ions validates that the Downloaded from on June 17, 201717 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. optimized assay is specific to the peptide for which is designed by confirming the generation of two further fragmentation ions from the peptide of interest with the elution time of the peptide during RP-HPLC and reduces the need to produce MSMS spectra of each precursor ion. The quantifier ions, as the name implies, are the only ions quantified and used to determine the abundance of the protein in the sample and typically are the optimized peptides with the highest signal to noise ratio. Undertaking this optimization work is greatly aided by using partially purified protein (e.g. bacterial overexpression), an enriched protein fraction (eg. we used a mitochondrial fraction for mitochondrial proteins) or a 35s-driven complemented plant line where high levels of the proteins are present. This increased peak signal intensity and helps ensure that the signal from the transitions dominate the spectra, allowing rapid definition of the optimal collision energy, retention time and optimization of the assay. Once assays are optimized they can be deployed to more complex mixtures and whole tissue assays carried out with greater confidence. The availability of confirmed knockout mutants in Arabidopsis is also a great asset to test if SRM mass spectrometry assay signals are removed when single genes are deleted to independently confirm SRM assay target specificity. What is the value of SRM mass spectrometry in assessing protein abundance in Arabidopsis mutant lines? T-DNA insertion transformation studies in Arabidopsis can provide rapid insight into gene function, but knowing if a mutant is a knockout or a knockdown or if the protein is made but truncated, can have a major bearing on the explanation of phenotype. We tested SRM mass spectrometry in two cases where pairs of highly related proteins were genetically manipulated leading to different impacts on their respective enzyme activities. Such differences are commonly attributed to knockdown rather than knockout, with compensatory responses or with evidence of the different abundance of isoforms fulfilling the same function. However, often such datasets are incompletely validated or largely conjecture due to the absence of specific and highly quantitative means of measuring the different protein products involved. We examined the relative abundance of two isoforms of mitochondrial aconitase and two isoforms of mitochondrial malate dehydrogenase in WT, KO, dKO and complemented plants that have been previously characterized (mACO1, mMDH1 and mMDH2) or characterized here (mACO2). The isoforms of mACO and mMDH have Downloaded from on June 17, 201718 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. a high sequence homology and mACO isoforms are indistinguishable using antibodies (Bernard et al., 2009), whilst the only commercially available mMDH antibody cross reacts with both isoforms (mMDH1/2, Agisera AS12 2371). We were able to confirm the protein knockout of all the proteins investigated in mitochondria isolated from their respective single knockout lines. In comparison, DIGE-based quantitative proteomics indicated a 3-20 fold difference in abundance but was not able to confirm the absence of proteins in the T-DNAs lines (Tomaz et al 2010, Supp Fig 1). Using the optimized SRM assays we were clearly able to distinguish between and to quantify both pairs of very similar proteins. Further we were able to confirm the protein knockout of both isoforms of mMDH in the double knockout to below 0.5% of WT levels, and to accurately measure the degree of overexpression of mMDH1 in the complemented line. Absolute quantitation of proteins is possible using heavy labelled synthetic peptides that can be spiked into biological samples. Undertaking this analysis for mACO1 and mACO2 provided similar data from the relative quantitation in terms of assessing knockout plants (Supplemental Figure 7). However, there will be circumstances where reproducibility of extraction, chromatography or ionization, or comparisons between proteins and stoichiometry within protein complexes would be aided by absolute abundance information. What are the limits of detection for SRM in assessing Arabidopsis proteins? The use of the APP for the selection of candidate SRM peptides and the implementation of this workflow is applicable for the assessment of a wide range of proteins in mutant lines of Arabidopsis, however, abundance will be a critical issue in its deployment for many proteins. Very low abundance proteins such as transcription factors would require very significant enrichment strategies until increases in MS sensitivity of ~2 to ~3 orders of magnitude are achieved. However, with increasingly more sensitive QqQ mass spectrometers being released each year this time is approaching. To determine the sensitivity of the detection of proteins by SRM mass spectrometry, we attempted to assess the abundance of all four proteins in Arabidopsis leaf total protein extracts. We were able to quantify the abundance of the most abundant of the 4 proteins, mMDH1, however we were not able to quantify the other three. We have previously shown that mACO1 and mACO2 together account for ~1.5% of mitochondrial protein with mACO1 twice as abundant as mACO2 and that mMDH1 and mMDH2 account for ~2% of mitochondrial protein with mMDH2 Downloaded from on June 17, 201719 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. representing only ~20% of the abundance of mMDH1 (Taylor et al., 2011). While mMDH1 is the most abundant protein measured in this study, it is likely it accounts for only <0.05% of total cellular protein (TCP), as mitochondrial protein represents only approximately 3% of TCP. Attempts to quantify the abundance of mMDH2 (~0.01% TCP), mACO1 (~0.03% TCP) and mACO2 (~0.015% TCP) in whole leaf extracts were unsuccessful, showing that this approach currently requires proteins of at least ~0.05% or 5 pg/g TCP, which represents approximately 0.5 ng in the 1 µg of protein on column in liquid chromatography. However, we have shown here that with some subcellular fractionation lower concentration proteins can be readily quantified. With the increasing adoption of instrumentation and software to enable SRM mass spectrometry approaches in a large number of labs around the world, scientists will increasingly be able to access different types of equipment for SRM assays. As a result quantitation of protein abundance in T-DNA insertion lines will soon become the norm rather than the exception. Downloaded from on June 17, 201720 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. MATERIALS AND METHODS Plant material. Seeds of Arabidopsis (Arabidopsis thaliana) ecotype Columbia wild type and homozygous T-DNA insertion lines, double knockout and complemented lines mmdh1 (mMDH1 KO, At1g53240.1, GABI_540F04), mmdh2 (mMDH2 KO, At3g15020.1, Salk_126994), mmdh1mmdh2 (mMDH double KO), mmdh1mmdh235s:mMDH1 (MDH double KO-1:35s MDH1) were obtained from ABRC or generated in-house and characterized as in Tomaz et al. (2010). maco1 (mACO1 KO, At2g05710.1, Salk_014661) was obtained from Moeder et al. (2007) and maco2 (mACO2 KO, At4g26970.1, Salk_090200) was obtained from ABRC and is characterised in Supplemental Material, Figure S1. Theoretical analysis of Arabidopsis thaliana proteome to identify candidate SRM peptides. The non-redundant Arabidopsis protein set was obtained from The Arabidopsis Information Resource (TAIR, release 10, TAIR10pep.fasta) (Lamesch et al., 2012) and a theoretical digestion was carried out on the whole genome using Expasy Peptide Mass (http://web.expasy.org/peptide_mass/) using trypsin as enzyme (C-terminal side of K or R except when P is C-terminal to K or R), maximum number of missed cleavages = 1, all cysteines in reduced form, variable methionine oxidation to form methionine sulfoxide. Monoisotopic masses of the occurring amino acid residues were used and peptide masses were recorded as [M+H]+. Resulting peptides with a Mr of 500-3000 Daltons were collected. All resulting peptide data (Arabidopsis Theoretical Trypsin Digest, ATTD) were then standard protein BLAST (blastp) searched against the TAIR10pep and the results were used to determine the number of times a peptide occurred in TAIR10pep. ATTD was then further interrogated as required for analysis. Dataset from Baerenfaller et al (2008) was obtained from http://fgcz-pep2pro.uzh.ch and dataset from Castellana et al (2008) was obtained from http://proteomics.ucsd.edu/Software/ArabidopsisProteogenomics.html. Downloaded from on June 17, 201721 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. Theoretical prediction of Arabidopsis thaliana proteotypic peptides. PeptideSieve software was obtained from http://tools.proteomecenter.org/wiki/index.php?title=Software:PeptideSieve (Mallick et al., 2007). PeptideSieve was run using a 2000 peptide training list or with ATTD and minMass=500, maxMass=3000, numAllowedMisCleavages=1 and pValue=0. CONSeQuence predictions were carried out using the website http://king.smith.man.ac.uk/CONSeQuence/ using the “Rank score” prediction type based on a linear SVM using a 2000 peptide training list or with ATTD (Eyers et al., 2011). STEPP software was downloaded from http://omics.pnl.gov/software/STEPP.php (Webb-Robertson et al., 2010). STEPP was run using a 2000 peptide training list in “Peptides in Excel” mode or with ATTD in “Proteins in FASTA” mode using “minimum sequence length” = 6, “max missed cleavages” = 1 and “max daltons” = 3000. The resulting peptides and proteotypic predictions were then further interrogated as required for analysis. The Arabidopsis Proteotypic Predictor (APP). The APP utilizes the database programming language SQL (Structured QueryLanguage) and is housed on a Linux server running Ubuntu 10.04 LTS. The APP web browser-based graphical user interface is written in Dynamic Hyper Text Markup Language that makes use of Asynchronous JavaScript and XML (AJAX) to interact with the APP server. The back-end of the APP utilizes a number of PHP scripts that interact with the MySQL tables housing the APP data. Making use of complex JavaScript, the interface works best via the Mozilla Firefox, Google Chrome or Safari web browsers but will work on Microsoft Internet Explorer (6 and above). The APP leverages open-source technologies in order to provide a freely available platform at http://www.plantenergy.uwa.edu.au/APP/. The APP was developed using an assessment of three previously published proteotypic predictors (PeptideSieve, (Mallick et al., 2007); CONSeQuence, (Eyers et al., 2011); STEPP, (Webb-Robertson et al., 2010)) on the ATTD and a 2000 peptide training list. The APP prediction is based on Bayesian-based classification of probabilities calculated from these predictions and the training list for each peptide. Downloaded from on June 17, 201722 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. Purification of mitochondria from hydroponic shoot tissue All relevant Arabidopsis genotypes were grown and maintained in exact accordance with a previously published protocol (Lee et al., 2008). Shoot mitochondria were isolated from 3-week-old hydroponically grown Arabidopsis using the method from (Tomaz et al., 2010). Isolation of protein extracts from leaf tissue Five week old leaf tissue was snap frozen and ground in liquid nitrogen. Proteins were extracted in 2 mL grinding buffer (300 mM sucrose, 50 mM Tris (pH 7.5), 1 mM DTT, 0.5 % (w/v) PVP and complete protease cocktail inhibitor (Roche, 1 tablet per 50 mL) per mg fresh tissue while rocking on ice for 20 min. Cell debris was removed by filtration through disposable, fritted syringe barrels containing Miracloth (Calbiochem). Soluble proteins were separated from the filtrate by a 20 minute centrifugation at 20000 x g and 4 °C. The protein concentration was then determined by Bradford assay and spectrophotometric measurement at a wavelength of 595 nm using BSA as a standard. Samples of 200 µg soluble protein were precipitated in 5 times volume of chilled (-20 °C) acetone overnight followed by 20 min centrifugation at 20000 x g and 4 °C. After two further acetone washes the samples were resuspended in 20 µL buffer (8 M urea, 50 mM NH4HCO3, 5 mM DTT) and incubated at room temperature for an hour. Iodoacetamide was added to a final concentration of 10 mM followed by 30 min incubation at room temperature in the dark. The sample solutions were then diluted to below 1 M urea with 50 mM NH4HCO3. For protein digestion 10 µg trypsin (dissolved in 0.01 % TFA to a concentration of 1 mg mL-1) were added to each sample and incubated at 37 °C overnight. The samples were acidified to 1 % (v/v) with formic acid and SPE cleaned using Silica C18 Macrospin columns (The Nest Group). After each of the following steps SPE columns were centrifuged for 3 min at 150 x g at room temperature. Before loading the samples columns were washed with 750 µL of 70 % (v/v) acetonitrile, 0.1 % (v/v) formic acid and charged with 750 µL of 5 % (v/v) acetonitrile, 0.1 % (v/v) formic acid. After loading the samples onto the columns two washes with 750 µL of 5 % (v/v) acetonitrile, 0.1 % (v/v) formic acid were carried out, followed by two elution steps with 750 µL 70 % (v/v) acetonitrile, 0.1 % (v/v) formic acid. The eluate was dried under vacuum and re-suspended in 5 % (v/v) acetonitrile, 0.01 % (v/v) formic acid to a final concentration of 1 µg µL-1 for mass spectrometry. Downloaded from on June 17, 201723 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. Optimization of SRM transitions SRM transitions were optimized using trypsin digested isolated mitochondrial extracts run on an Agilent 6430 QqQ mass spectrometer with an HPLC Chip Cube source (Agilent Technologies). The chip consisted 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 (B.03.01 (B2065), version 5.1.2600, SP3, build 2600; 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 5L/min, for each transition the fragmentor was set to 130 and dwell time was 5 ms. Based on a historical ‘in-house’ dataset and a theoretically digested background proteome of each of the proteins of interest (mACO1, mACO2, mMDH1 and mMDH2) possible peptide transitions were determined using Skyline (version 1.1.0.2905) (MacLean et al., 2010). These were then optimized for collision energy (CE) based on predicted values by Skyline following an algorithm specific for Agilent instruments. For each transition a total of five CEs were analyzed, including the predicted CE ± 4 V and 8 V. Having optimized all available SRM transitions optimized data was used to select candidates for quantitative data analysis (Table 1.). Selection of candidate SRM transitions Selection of candidate SRM transitions was carried out in two steps. First peptides were assessed by their uniqueness in the Arabidopsis proteome, number of miss cleavages and composition. Peptides were selected based on preference for uniqueness > number of miss cleavages > presence of M. Optimized SRM transitions of these peptides were then reviewed in Skyline for signal intensity, signal-to-noise ratio, dot product and y-ion ranking. For each peptide three transitions, one quantifier and two qualifiers were selected to validate it. A total of three peptides per protein were analyzed. Downloaded from on June 17, 201724 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. Protein analysis using SRM mass spectrometry Protein extracts from isolated mitochondrial and whole leaf digests were analyzed on an Agilent 6430 QqQ mass spectrometer as described above for method optimization. Heavy labeled standards for 3 mACO1 (VVNFSFDGQPAELK13C615N2, SSGEDTIILAGAEYGSGSSR13C615N4, LSVFDAAMR13C615N4) and 3 mACO2 (GVISEDFNSYGSR13C615N4, FSYNGQPAEIK13C615N2, ILDWENTSTK13C615N2) peptides were obtained from JPT Peptide Technologies and prepared following manufacturer’s instructions. Standard curves of heavy labeled peptides were constructed using various concentrations of peptides spiked into isolated WT mitochondria. Data Analysis Transitions that had an intensity greater than 1000 and s/n >50 were then further analyzed. Resulting total ion chromatograms were opened in MassHunter Workstation Qualitative Analysis (version B.01.04, build 1.4.126.0; 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. The area under the curve for each replicate was then averaged to obtain an abundance value for each peptide. The abundance of a peptide in each of the mutant lines was then compared to WT to calculate a relative abundance. Downloaded from on June 17, 201725 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. Supplemental Material Figure S1. Anaysis of prediction of peptide flying by PeptideSieve, CONSeQuence and STEPP of 1000 proteotypic & non-proteotypic peptides from Arabidopsis thaliana. Figure S2. Screenshots of the Arabidopsis Proteotypic Predictor (APP) web interface. Figure S3. MSMS Validation of mACO1 (standard and heavy labeled), mACO2 (standard and heavy labeled), mMDH1 and mMDH2 peptides. Figure S4. An overview of SRM workflow for relative and absolute quantitation Figure S5. mACO2 (At4g26970.1) T-DNA mutant characterization. Figure S6. Standard curves of mACO1 and mACO2 heavy labelled peptides. Figure S7. SRM analysis of absolute protein abundance of mACO1 and mACO2 in WT, maco1 and maco2 mitochondria A. Table S1. 1000 proteotypic peptides used to rate proteotypic predictors Table S2. 1000 non-proteotypic peptides used to rate proteotypic predictors Table S3. Amino acid composition of proteotypic peptides Table S4. Relative abundance of mACO1 and mACO2 peptides in mitochondria from genotypes Table S5. Relative abundance of mMDH1 and mMDH2 peptides in mitochondria from genotypes Table S6. Relative abundance of mMDH1 peptides in whole tissue extracts from genotypes Table S7. Absolute abundance of mACO1 and mACO2 peptides in mitochondria from genotypes. Downloaded from on June 17, 201726 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. LITERATURE CITED Aebersold R, Burlingame AL, Bradshaw RA (2013) Western blots versus selected reaction monitoring assays: time to turn the tables? Mol Cell Proteomics 12: 2381-2382 AGI (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408: 796-815 Alonso JM, Ecker JR (2006) Moving forward in reverse: genetic technologies to enable genome-wide phenomic screens in Arabidopsis. Nature Rev Genet 7: 524-536 Baerenfaller K, Grossmann J, Grobei MA, Hull R, Hirsch-Hoffmann M, Yalovsky S, Zimmermann P, Grossniklaus U, Gruissem W, Baginsky S (2008) Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science 320: 938-941 Bernard DG, Cheng Y, Zhao Y, Balk J (2009) An allelic mutant series of ATM3 reveals its key role in the biogenesis of cytosolic iron-sulfur proteins in Arabidopsis. Plant Physiol 151: 590-602 Castellana NE, Payne SH, Shen Z, Stanke M, Bafna V, Briggs SP (2008) Discovery and revision of Arabidopsis genes by proteogenomics. Proc Natl Acad Sci USA 105: 21034-21038 Eyers CE, Lawless C, Wedge DC, Lau KW, Gaskell SJ, Hubbard SJ (2011) CONSeQuence: prediction of reference peptides for absolute quantitative proteomics using consensus machine learning approaches. Mol Cell Proteomics 10: M110 003384 Fan J, Mohareb F, Jones AM, Bessant C (2012) MRMaid: The SRM assay design tool for Arabidopsis and other species. Front Plant Sci 3: 164 Ito J, Batth TS, Petzold CJ, Redding-Johanson AM, Mukhopadhyay A, Verboom R, Meyer EH, Millar AH, Heazlewood JL (2011) Analysis of the Arabidopsis cytosolic proteome highlights subcellular partitioning of central plant metabolism. J Prot Res 10: 1571-1582 Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R, Muller R, Dreher K, Alexander DL, Garcia-Hernandez M, Karthikeyan AS, Lee CH, Nelson WD, Ploetz L, Singh S, Wensel A, Huala E (2012) The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nuc Acids Res 40: D1202-1210 Lange V, Picotti P, Domon B, Aebersold R (2008) Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol. 2008; 4: 222 Lee CP, Eubel H, O'Toole N, Millar AH (2008) Heterogeneity of the mitochondrial proteome for photosynthetic and non-photosynthetic Arabidopsis metabolism. Mol Cell Proteomics 7: 1297-1316 Lehmann U, Wienkoop S, Tschoep H, Weckwerth W (2008) If the antibody fails a Mass western approach. Plant J 55: 1039-1046 MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, Kern R, Tabb DL, Liebler DC, MacCoss MJ (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26: 966-968 Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, Ranish J, Raught B, Schmitt R, Werner T, Kuster B, Aebersold R (2007) Computational prediction of proteotypic peptides for quantitative proteomics. Nature Biotech 25: 125-131 Marx V (2013) Targetted proteomics. Nature Meth 10: 19-22 Downloaded from on June 17, 201727 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. Moeder W, Del Pozo O, Navarre DA, Martin GB, Klessig DF (2007) Aconitase plays a role in regulating resistance to oxidative stress and cell death in Arabidopsis and Nicotiana benthamiana. Plant Mol Biol 63: 273-287 Monneuse JM, Sugano M, Becue T, Santoni V, Hem S, Rossignol M (2011) Towards the profiling of the Arabidopsis thaliana plasma membrane transportome by targeted proteomics. Proteomics 11: 1789-1797 O'Malley RC, Ecker JR (2010) Linking genotype to phenotype using the Arabidopsis unimutant collection. Plant J 61: 928-940 Picotti P, Aebersold R (2012) Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nature Meth 9: 555-566 Picotti P, Bodenmiller B, Aebersold R (2013) Proteomics meets the scientific method. Nature Meth 10: 24-27 Taylor NL, Heazlewood JL, Millar AH (2011) The Arabidopsis thaliana 2-D gel mitochondrial proteome: Refining the value of reference maps for assessing protein abundance, contaminants and post-translational modifications. Proteomics 11: 1720-1733 Taylor NL, Howell KA, Heazlewood JL, Tan TY, Narsai R, Huang S, Whelan J, Millar AH (2010) Analysis of the rice mitochondrial carrier family reveals anaerobic accumulation of a basic amino acid carrier involved in arginine metabolism during seed germination. Plant Physiol 154: 691-704 Tomaz T, Bagard M, Pracharoenwattana I, Linden P, Lee CP, Carroll AJ, Stroher E, Smith SM, Gardestrom P, Millar AH (2010) Mitochondrial malate dehydrogenase lowers leaf respiration and alters photorespiration and plant growth in Arabidopsis. Plant Physiol 154: 1143-1157 Wang YH (2008) How effective is T-DNA insertional mutagenesis in Arabidopsis ? J Biochem Tech 1: 11-20 Webb-Robertson BJ, Cannon WR, Oehmen CS, Shah AR, Gurumoorthi V, Lipton MS, Waters KM (2010) A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics. Bioinformatics 26: 1677-1683 Wienkoop S, Larrainzar E, Glinski M, Gonzalez EM, Arrese-Igor C, Weckwerth W (2008) Absolute quantification of Medicago truncatula sucrose synthase isoforms and N-metabolism enzymes in symbiotic root nodules and the detection of novel nodule phosphoproteins by mass spectrometry. J Exp Bot 59: 3307-3315 Downloaded from on June 17, 201728 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. FIGURE LEGENDS Figure 1. The number of peptides per protein and the number of times a peptide occurs in a theoretical trypsin digest of the Arabidopsis thaliana proteome. A. The number of peptides per protein of all theoretical peptides resulting from in silico trypsin digestion. B. The number of times a peptide occurs in the TAIR10pep following an in silico trypsin digestion. Figure 2. Analysis of theoretically observed unique peptides and predicted peptides of the Arabidopsis thaliana proteome. A. All theoretical peptides per protein resulting from in silico trypsin digestion that contain no missed cleavage and are unique. B. Number of peptides per protein in the 12104 proteins observed in Baerenfaller et al (2008), Castellana et al (2008) and our own data. C. Number of peptides per protein of the predicted proteotypic peptides calculated by APP. D. Number of peptides per protein of the predicted proteotypic peptides calculated by APP and proteins observed in Baerenfaller et al (2008), Castellana et al (2008) and our own data. Figure 3. Number of peptides from an in silico trypsin digest of the Arabidopsis proteome that have been observed experimentally by mass spectrometry. Peptides observed in any of the three data sets (black) as well as peptides unique to Baerenfaller et al (2008)(magenta), Castellana et al (2008)(yellow) and our own work (cyan). Figure 4. 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 and maco2 mitochondria. i. WT, ii. maco1, iii. maco2. 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). Figure 5. SRM analysis of protein abundance of mACO1 and mACO2 in WT, maco1 and maco2 mitochondria A. SRM analysis of unique peptides mACO1 Downloaded from on June 17, 201729 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. abundance using the quantifier ion transitions VVNKSFDGQPAELK (SRM 775.9 557.3) SSGEDTIILAGAEYGSGSSR (SRM 979.4 505.3 quantifier 970.4) LSVFDAAMR (SRM 710.3). B. SRM analysis of unique peptides mACO2 abundance using the ion transitions GVISEDFNSYGSR FSYNGQPAEIK (SRM 627.3 (SRM 715.8 1161.5) 557.3) ILDWENTSTK (SRM 603.8 980.4). Data presented is averages ± SE (n=3). Figure 6. SRM analysis of protein abundance of mMDH1 and mMDH2 in WT, mmdh1, mmdh2, mmdh1mmdh2 and mmdh1mmdh2 complemented with mMDH1 cDNA mitochondria. A. SRM analysis of unique peptides mMDH1 abundance using the quantifier ion transitions SEVVGYMGDDNLAK (SRM 749.0 EGLEALKPELK (SRM 409.6 1083.5) 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 VVILGAAGGIGQPLSLLMK (SRM 613.0 1157.5) 801.5) NLSIAIAK (SRM 415.3 602.4). Data presented is averages ± SE (n=3). Figure 7. SRM analysis of protein abundance of mMDH1 in WT, mmdh1 and mmdh2 leaf extracts. SRM analysis of mMDH1 abundance using the quantifier ion transitions SEVVGYMGDDNLAK (SRM 749.0 409.6 1083.5) EGLEALKPELK (SRM 486.3) VAILGAAGGIGQPLALLMK (SRM 897.0 presented is averages ± SE (n=3) Downloaded from on June 17, 201730 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. 970.6). Data Table I. Optimized SRM transitions for mACO1, mACO2, mMDH1, mMDH2. Downloaded from on June 17, 2017 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. All rights reserved. AGI Protein Sequence SRM Pre m/z At2g05710.1 mACO1 VVNFSFDGQPAELK 775.9 2 At2g05710.1 mACO1 SSGEDTIILAGAEYGSGSSR 979.0 2 At2g05710.1 mACO1 LSVFDAAMR 505.3 2 710.30 y6 809.40 y7 At4g26970.1 mACO2 GVISEDFNSYGSR 715.8 2 1161.50 y10 945.40 y8 At4g26970.1 mACO2 FSYNGQPAEIK 627.3 2 557.30 y5 724.40 y7 At4g26970.1 mACO2 ILDWENTSTK 603.8 2 980.40 y8 679.30 y6 At1g53240.1 mMDH1 SEVVGYMGDDNLAK 749.4 2 1083.50 y10 732.40 y7 963.40 At1g53240.1 mMDH1 EGLEALKPELK 409.6 3 486.30 y4 614.40 y5 727.50 SRM Pre z SRM Pro 1 m/z (Quantifier) SRM Pro 1 ion (Quantifier) SRM Pro 2 m/z (Qualifier) SRM Pro 2 ion (Qualifier) 557.33 y5 1094.54 y10 970.40 y10 1041.50 y11 SRM Pro 3 m/z (Qualifier) SRM Pro 3 ion (Qualifier) RT (minutes) Predicted CE (V) Optimized CE (V) Area Ratio P1/P2 Area Ratio P1/P3 857.44 y8 11.0 24.0 20.0 0.60 0.35 1154.50 y12 10.1 34.4 34.4 0.75 0.65 563.30 y5 10.0 10.2 14.2 0.60 0.60 830.40 y7 9.1 20.9 20.9 0.60 0.35 856.50 y8 8.2 16.4 16.4 0.45 0.40 865.40 y7 9.1 15.2 15.2 0.20 0.20 y8 8.9 22.7 18.7 0.60 0.60 y6 9.3 5.4 9.4 0.20 0.05 At1g53240.1 mMDH1 VAILGAAGGIGQPLALLMK 897.0 2 970.60 y9 1197.70 y12 785.50 y7 15.4 30.2 26.2 0.75 0.70 At3g15020.1 mMDH2 SQVSGYMGDDDLGK 736.3 2 1157.50 y11 1256.50 y12 850.40 y8 8.3 22.0 22.0 0.55 0.50 At3g15020.1 mMDH2 VVILGAAGGIGQPLSLLMK 613.0 3 801.50 y7 591.40 y5 986.60 y9 15.8 12.9 12.9 0.10 0.20 At3g15020.1 mMDH2 NLSIAIAK 415.3 2 602.40 y6 515.40 y5 402.30 y4 9.3 5.6 5.6 0.10 0.05 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; RT, retention time of peptide on column; Predicted CE, predicted collision energy from Skyline (MacLean et al., 2010); Optimized CE, optimized collision energy; Area Ratio P1/P2, ratio of the area of XIC of production ion 1/product ion 2; Area Ratio P1/P3, ratio of the area of XIC of production ion 1/product ion 3. 31 32 Downloaded from on June 17, 2017 - Published by www.plantphysiol.org Copyright © 2013 American Society of Plant Biologists. 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