APPLICATION SPOTLIGHT INTRODUCTION NICOLAS L. TAYLOR

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1 APPLICATION SPOTLIGHT Measuring protein abundance by peptide selected reaction monitoring () mass spectrometry in knock-out organisms when antibodies are unavailable/ ineffective - An example from plant science. 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 INTRODUCTION The 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 () 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 mass spectrometry in determining protein abundance in mutant lines we selected two enzymes with multiple isoforms that cannot be distinguished by 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 mass spectrometry is a sensitive and rapid approach to quantify the protein abundance for highly related enzyme isoforms. Selection and optimization of candidate peptides for aconitase (maco) and malate dehydrogenase (mmdh). Unique peptides for, maco, and mmdh 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 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 100 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 transitions (Table 1.) were used for quantitative data analysis.

2 analysis of protein abundance of aconitase (maco) and malate dehydrogenase (mmdh) in mitochondria from knockout and complemented lines. To determine the abundance of we first examined the abundance of the peptide VVNFSFDGQPAELK in mitochondria isolated from WT (wild-type), maco1 ( knockout) and maco (maco 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 transition ( ) was used to quantify its abundance and the resulting data files were opened in MassHunter Workstation Qualitative Analysis (Agilent Technologies), and chromatograms were obtained using the Extract Chromatogram feature using default settings. Each 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 ( ) eluted in WT mitochondria between minutes (Figure 1, Ai.), between minutes from maco1 mitochondria (Figure 1, Aii.) and between minutes from maco 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 SSGEDTIILAGAEYGSGSSR ( ) LSVFDAAMR ( ) along with three peptides from maco (GVISEDFNSYGSR ( ) FSYNGQPAEIK ( ) ILDWENTSTK ( ) was then quantified (Figure.). Examining 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 ~0.1% in maco1 mitochondria when compared to WT. A similar result was obtained for maco where peptides from this protein were seen to decrease in abundance to between 0.0% to 0.1% with an average of ~0.05% and an average error of ~0.0% in maco mitochondria when compared to WT. To determine the abundance of and mmdh, the abundance of the peptides from ; SEVVGYMGDDNLAK ( ) EGLEALKPELK ( ) VAILGAAGGIGQPLALLMK ( ) and mmdh SQVSGYMGDDDLGK ( ) VVILGAAGGIGQPLSLLMK ( ) NLSIAIAK ( ) were quantified in WT, mmdh1 ( knockout), mmdh (mmdh knockout), mmdh1mmdh ( and mmdh knockout, mmdh double knockout) and mmdh1mmdh-35s: (mmdh double knockout complemented with ) plants (Figure 3.). We saw a significant reduction in protein abundance in each of the knockout lines for their respective proteins with reduced to ~0.1% (Average Err = ~0.09%) of WT and mmdh reduced to ~0.5% (Average Err = ~0.03%) of WT. In mmdh1mmdh 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 to levels much greater to those observed in the WT (~400.0% (Average Err = ~19%)).

3 CONCLUSION In this study peptide 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 antibodies, whilst the only commercially available mmdh antibody cross reacts with both isoforms (/, Agisera AS1 371). Using peptide 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 in the complemented line. Overall this study demonstrates the utility of an 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 transitions for, maco,, mmdh. AGI Protein Sequence Pre m/z Pre z Pro 1 m/z Pro 1 ion Pro m/z Pro ion Pro 3 m/z Pro 3 ion Predicted CE (V) Optimized CE (V) Atg Atg Atg At4g At4g At4g At1g At1g At1g At3g At3g At3g maco maco maco mmdh mmdh mmdh VVNFSFDGQPAELK SSGEDTIILAGAEYGSGSSR LSVFDAAMR GVISEDFNSYGSR FSYNGQPAEIK ILDWENTSTK SEVVGYMGDDNLAK EGLEALKPELK VAILGAAGGIGQPLALLMK SQVSGYMGDDDLGK VVILGAAGGIGQPLSLLMK NLSIAIAK y4 y9 y y11 y1 y y1 y9 y AGI, Arabidopsis Genome Initiative identifier; Protein, protein name; Sequence, peptide sequence; Pre m/z, peptide precursor ion mass/charge ratio; Pre z, peptide precursor ion mass; Pro 1 m/z, peptide product ion 1 mass/charge ratio; Pro 1 ion, peptide product ion 1 fragmentation series location; Pro m/z (Qualifier), peptide product ion (Qualifier) mass/charge ratio; Pro ion (Qualifier), peptide product ion (Qualifier) fragmentation series location; Pro 3 m/z (Qualifier), peptide product ion 3 (Qualifier) mass/charge ratio; Pro 3 ion (Qualifier), peptide product ion 3 (Qualifier) fragmentation series location; Predicted CE, predicted collision energy from Skyline 1 ; Optimized CE, optimized collision energy. Adapted from Taylor et al. 3 Copyright American Society of Plant Biologists.

4 Figure 1. The elution of VVNFSFDGQPAELK of in WT and knockout lines and the MSMS spectra showing quantifier and qualifier ions. A transition of VVNFSFDGQPAELK peptide of in WT, KO and maco KO mitochondria. i. WT, ii. KO, iii. maco KO. B. The MS/MS spectrum of VVNFSFDGQPAELK showing the y-series ions and the selected quantifier ion () and the two qualifier ions ( and ). Reproduced from Taylor et al. 3 www. plantphysiol.org, Copyright American Figure 1 Figure. analysis of protein abundance of and maco in WT, KO and maco KO mitochondria A. analysis of unique peptides abundance using the quantifier ion transitions VVNKSFDGQPAELK ( ) SSGEDTIILAGAEYGSGSSR ( ) LSVFDAAMR ( ). B. analysis of unique peptides maco abundance using the quantifier ion transitions GVISEDFNSYGSR ( ) FSYNGQPAEIK ( ) ILDWENTSTK ( ). Data presented is averages ± SE (n=3). Reproduced from Taylor et al. 3 Copyright American

5 Figure 3. analysis of protein abundance of and mmdh in WT, KO, mmdh KO, mmdh dko and mmdh dko line complemented with cdna mitochondria. A. analysis of unique peptides abundance using the quantifier ion transitions SEVVGYMGDDNLAK ( ) EGLEALKPELK ( ) VAILGAAGGIGQPLALLMK ( ). B. analysis of unique peptides mmdh abundance using the quantifier ion transitions SQVSGYMGDDDLGK ( ) VVILGAAGGIGQPLSLLMK ( ) NLSIAIAK ( ). Data presented is averages ± SE (n=3). Reproduced from Taylor et al. 3 Copyright American Figure 3 ACKNOWLEDGEMENTS This 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 010, 6, (7), () 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 009, 151, (), (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 014, 164, (),