Comprehensive Quantification of Monolignol-Pathway Enzymes in

Article pubs.acs.org/jpr

Comprehensive Quantification of Monolignol-Pathway Enzymes in Populus trichocarpa by Protein Cleavage Isotope Dilution Mass Spectrometry Christopher M. Shuford,† Quanzi Li,‡ Ying-Hsuan Sun,‡ Hsi-Chuan Chen,‡ Jack Wang,‡ Rui Shi,‡ Ronald. R. Sederoff,‡ Vincent L. Chiang,‡ and David C. Muddiman*,† †

W.M. Keck FT-ICR Mass Spectrometry Laboratory, Department of Chemistry, and ‡Forest Biotechnology Group, Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina 27695, United States S Supporting Information *

ABSTRACT: The economic value of wood/pulp from many tree species is largely dictated by the quantity and chemical properties of lignin, which is directly related to the composition and linkages of monolignols comprising the polymer. Although much is known regarding the monolignol biosynthetic pathway, our understanding is still deficient due to the lack of quantitative information at the proteomic level. We developed an assay based on protein cleavage isotope dilution mass spectrometry (PC-IDMS) for the determination of all potential, primary enzymes involved in the biosynthesis of monolignols and the peroxidases responsible for their polymerization to form lignin in the model tree species, Populus trichocarpa. Described is the identification of quantitative surrogate peptides through shotgun analysis of native and recombinant proteins, optimization of trypsin proteolysis using fractional factorial design of experiments, and development of a liquid chromatography-selected reaction monitoring method for specific detection of all targeted peptides. Of the 25 targeted enzymes, three were undetected in the normal xylem tissues, and all but two of the detectable species showed good day-to-day precision (CV < 10%). This represents the most comprehensive assay for quantification of proteins regulating monolignol biosynthesis and will lead to a better understanding of lignin formation at a systems level. KEYWORDS: quantification, isotope dilution mass spectrometry, selected reaction monitoring, filter-aided sample preparation, monolignol biosynthesis

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cellulosic polysaccharides in forage by ruminants12 and must be reduced or processed to make cellulosics accessible for the production of ethanol as biofuel.13 As a direct fuel source, lignin is about 28 kJ/kg, which is 1.5 times more “energy rich” than the cellulosic components in wood14 and can be equivalent to the energy content of some kinds of coal.15,16 Lignin is produced by oxidative polymerization of three precursor monolignols to produce a high molecular mass crosslinked polymer.17 These monolignols are synthesized by a series of up to 10 enzymatic reactions2,18,19 that successively deaminate phenylalanine, hydroxylate the phenyl ring at the 3, 4, and 5 positions, and reduce the acid end group of the propane side chain to an alcohol.20 The result of this pathway (Figure 1), or metabolic grid,21,22 is three predominant monolignols, p-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol.23,24 The enzymes of the monolignol biosynthetic pathway have been intensively studied for half a century.1,23 In

INTRODUCTION Lignin is a plant polymer composed of phenylpropane subunits (C6C3)1 and is one of the most abundant biopolymers on Earth2 due to its presence in the supporting tissues of plant cell walls. Second only to cellulose, lignin represents about onefourth to one-third of the dry weight of wood3 and is also found in herbaceous plants, though in lesser amounts.1 Due to the fact that it is a highly heterogeneous cross-linked polymer, lignin is resistant to microbial degradation4 and accumulates in soils as humic biomass.5 In plants, this same property allows lignin to form a physical barrier to microbial infection.6 In fact, only a few specialized fungi and bacteria are able to degrade lignin.7 Lignin also serves to form a hydrophobic surface inside vascular tissues of plants to facilitate water transport8 and acts as an embedding matrix around cellulose and hemicellulose to produce a strong layered secondary cell wall,9 thereby providing support to plants including very large trees.10 From a practical point of view, the major concern regarding lignin is in its removal from wood for the production of paper and industrial pulp.11 Additionally, it serves as a barrier for digestion of © 2012 American Chemical Society

Received: March 2, 2012 Published: April 23, 2012 3390

dx.doi.org/10.1021/pr300205a | J. Proteome Res. 2012, 11, 3390−3404

Journal of Proteome Research

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Figure 1. Shown is the biosynthetic pathway leading to the synthesis of p-hydroxyphenyl (H), guaiacyl (G), and syringyl (S) monolignols, which are subsequently polymerized to form lignin. The highlighted pathway is understood to be the predominant pathway in woody plants, such as P. trichocharpa.

the pathway.20,29 To accomplish this, quantitative estimates of the pathway components are needed for the genes, transcripts, proteins, and metabolites. We have chosen Populus trichocarpa for such a comprehensive analysis of monolignol biosynthesis. P. trichocarpa was the first woody plant to have a full, wellannotated genome sequence.30 On the basis of this sequence and tissue-specific transcriptome analysis, we have identified the major genes and proteins that direct the biosynthesis of monolignols in the formation of wood in this species.20 Here, we present the first comprehensive quantitative determination of the proteins of P. trichocarpa in monolignol biosynthesis during wood formation. Utilizing protein cleavage isotope dilution mass spectrometry (PC-IDMS),31−33 we

the past few decades, many enzymes have been purified and characterized, often as targets for the reduction or modification of lignin by genetic engineering.25 Several of the monolignol biosynthetic enzymes have been identified by two-dimensional gel electrophoresis and mass spectrometry (MS).26 However, MS analysis of proteins from secondary cell walls of tobacco cells did not identify any of the monolignol biosynthetic enzymes.27 In contrast, the relative abundance of transcripts for the monolignol pathway genes has been estimated in many species.20,28 Few attempts have been made to describe monolignol biosynthesis in an integrated model, to predict flux through the pathway, as a step toward a comprehensive understanding of 3391



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mM IPTG at 28 °C for 8 h. These proteins possessed an Nterminal GST-fusion tag and were purified utilizing the incorporated thrombin cleavage site (see Supporting Information). All recombinant proteins purified via a fusion tag were subsequently precipitated using ethanol and then reconstituted in 6 M urea before measuring the protein concentration with a Coomassie Plus Bradford Assay (Thermo Scientific, Rockford, IL). Plasmids for the expression of the membrane-bound hydroxylases were transformed into S. cerevisiae WAT1146 using a LiAc method.47 Yeast culture, protein expression induction, and microsome isolation were conducted as described previously.45 The amino acid sequences for each recombinant protein are provided in the Supporting Information.

developed a multiplexed assay for quantification of 25 proteins across the 11 enzyme families directly involved in the biosynthesis of monolignols and their subsequent polymerization to form lignin. PC-IDMS was first demonstrated by Barr and co-workers31 and is based on the premise that a protein quantity is equivalent to the concentration of its unique peptide(s) produced via enzymatic digestion. The peptide concentration is determined by spiking in a known amount of its stable isotope-labeled (SIL) analogue as an internal standard. This method has been used successfully for the absolute quantitation of proteins,34 protein biomarkers,33,35−39 and posttranslational modified states of proteins32,40 and for determination of stoichiometric ratios of protein complexes.41 Moreover, PC-IDMS has been used in a variety of biological systems including plant tissues.42,43 Development of the present assay was carried out by first selecting tryptic peptides to target as quantitative surrogates (i.e., surrogate peptides) utilizing experimental data obtained from LC−MS/MS-based shotgun proteomics of recombinant and native xylem proteins. The selected reaction monitoring (SRM) detection scheme44 for each surrogate peptide was then optimized to ensure both sensitive and specific detection of each peptide. Complete proteolysis of each target enzyme was ensured by optimization of the trypsin digestion conditions using a fractional factorial design (FracFD) of experiments. Finally, the robustness of the quantitative assay was validated during analysis of xylem tissue from wild-type trees.

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Stable Isotope-labeled Peptide Standards

Natural (NAT) and stable isotope-labeled (SIL) peptides were synthesized by the Mayo Clinic Proteomics Research Center (Rochester, MN) (see Table 2). With the exception of C3H3.125−134 and CCoAOMT3.217−232, all peptides were utilized as received. For these two cysteinyl peptides, 50 mM solutions of each peptide were carbamidomethylated in the presence of 200 mM iodoacetamide and 100 mM ammonium bicarbonate (pH 8.0) for 1 h at 37 °C (in the dark) and then purified using an offline Macro reversed-phase desalting column (Michrom Bioresources, Auburn, CA) according to the manufacturer’s protocol. The purity of each modified peptide was confirmed by direct infusion MS and MS/MS (data not shown). For all peptide standards, stock solutions were produced by dissolving ∼2 mg in water, and the absolute concentrations were confirmed by spectrophotometry using the Scopes method.48 Two mixtures of SIL peptides were utilized here for quantitative analysis and are referred to as SIL cocktail A and SIL cocktail B (see Table 2). SIL cocktail A was an equimolar mixture of all preliminary SIL peptides and was employed only for digest optimization. SIL cocktail B consisted of all final SIL peptides and was utilized in the final quantitative assay. Each peptide standard in SIL cocktail B was present at a concentration such that when added to the control sample each SIL peptide would provide a comparable signal (i.e., within an order of magnitude) to the corresponding native, surrogate peptide following proteolysis. Following production of the bulk SIL cocktails, both were fractionated into several aliquots (>100) that were concentrated to dryness under vacuum and stored at −80 °C for long-term stability. Each aliquot had enough material to accommodate ∼18 samples of 200 μg.

EXPERIMENTAL SECTION

Materials

Unless otherwise stated, all reagents were purchased from Sigma-Aldrich (St. Louis, MO). All solvents used were HPLCgrade from Honeywell Burdick & Jackson (Muskegon, MI). Production and Purification of Recombinant Proteins

The full length coding sequence of each target gene was amplified from cDNA of a P. trichocarpa stem differentiating xylem (SDX) using PfuUltra high-fidelity DNA polymerase (Stratagene, La Jolla, CA).20 In some instances, the constructs were amplified directly from P. trichocarpa cDNA clones. Coding regions of each amplified sequence were either cloned into pGEM-T easy cloning vectors (Promega, Madison, WI) or TOPO cloned directly into pET101/D-TOPO expression vectors (Invitrogen, Carlsbad, CA). After sequence confirmation, the coding regions from the pGEM-T vectors were excised and cloned into pGEXKG-1 (GE Life Sciences, Piscataway, NJ) or pYeD60 M vectors45 for expression. For CAld5H1, CAld5H2, CAD1, CAD2, HCT1, HCT6, and CCR2, the coding regions were excised from their intermediate vectors using NotI/SalI, NotI/SalI, SalI/SacI, NcoI/SalI, SalI/HindIII, SalI/HindIII, and NcoI/SalI, respectively. Details are provided in the Supporting Information (Table S1) regarding the specific primer pairs utilized for cDNA amplification as well as the specific vectors used for sequencing and expression of each protein. Amplification, sequencing, and expression of C3H3, C4H1, and C4H2 are described elsewhere.45 Production of recombinant proteins utilizing pET101 vectors was performed in E. coli BL21 (DE3) using the Champion pET1010 TOPO Expression Kit (Invitrogen). These proteins were expressed with C-terminal His-tags and were purified using a Probond Purification Kit (Invitrogen). Constructs utilizing pGEXKG-1 expression vectors were also introduced into BL21 (DE3) cells, and expression was induced using 0.5

Xylem Protein Extraction

All tissues were harvested from 6-month-old trees of P. trichocarpa (genotype Nisqually-1) maintained in a greenhouse.20 After removing the bark, normal vertically developing xylem tissue was collected by scraping a 100 cm long segment of the stem that was cut from 30 cm above the soil. Protein extracts were produced by grinding 3 g of xylem tissue in liquid nitrogen followed by 2 min of homogenization on ice in 15 mL of extraction buffer. The extraction buffer was composed of 50 mM Bis-Tris (pH 8.0), 20 mM sodium ascorbate, 0.4 M sucrose, 100 mM NaCl, 5 mM DTT, and 10% (w/w) polyvinylpolypyrrolidone. Cellular debris was pelleted and removed twice by centrifugation for 15 min at 3000g and 4 °C. The final protein concentration was measured using a Coomassie Plus Bradford assay (Thermo Scientific, Rockford, 3392



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IL), and the samples were stored at −80 °C until further analysis.

(FDR) calculations. Data from SDX protein extracts were searched against the JGI P. trichocarpa v2.2 database, which was modified to contain the sequences recently cloned from cDNA of P. trichocarpa.20 Data from the recombinant proteins were searched against NCBI E. coli or S. cerevisiae databases containing the respective recombinant protein sequences. Database search parameters included: trypsin as the enzyme, fixed carbamidomethyl modifications of cysteine, variable oxidation of methionine, variable deamidation of asparagine and glutamine, maximum of two missed cleavages, 5 ppm peptide tolerance, and 0.6 Da MS/MS tolerance. Finally, the search results (.dat files) were imported into ProteoIQ v2.3.01 (BioInquire, Athens, GA) for final protein identifications at a protein FDR of 1%.

Shotgun Proteomic Analysis of Native and Recombinant Proteins

SDX protein extracts from normal trees and select, recombinant proteins (HCT1, HCT6, PO8, CCoAOMT2, C3H3, C4H1, C4H2, CAld5H1, and CAld5H2) were subjected to in-gel digestion by trypsin.49 For global analysis of native SDX proteins, the entire gel lane was excised and divided into 12 fractions, while only the target molecular weight band was excised for each recombinant protein (Figure S1, Supporting Information). All protein-containing bands were excised, cut into ∼1 mm cubes, and destained with 100 mM ammonium bicarbonate (pH 8.0):acetonitrile (50:50, v/v). Subsequently, the bands were reduced in 10 mM dithiothreitol (30 min, 56 °C), alkylated in 55 mM iodoacetamide (20 min, room temperature, in the dark), and digested with 875 ng of bovine trypsin (16 h, 37 °C). All reagents were prepared fresh in 100 mM ammonium bicarbonate buffer (pH 8.0), and prior to each step, gel pieces were dehydrated using acetonitrile. Digestion was quenched by adding acetonitrile and formic acid to a final concentration of 50 and 1% (v/v), respectively, and incubating for 30 min at 37 °C. After aspirating the peptide-containing supernatant, the gel fragments were dehydrated in acetonitrile and then rehydrated in 100 mM ammonium bicarbonate (pH 8.0):acetonitrile (2:1, v/v). These three resulting liquid fractions were then combined, dried under vacuum, and stored at −20 °C until required for LC−MS/MS analysis. Additionally, all purified recombinant proteins were prepared using in-solution trypsin digestion. Approximately 60 μg of purified recombinant protein was denatured in 8 M urea and 10 mM dithiothreitol (1 h, 37 °C). Alkylation (1 h, 37 °C) was carried out by iodoacetamide at a concentration of 100 mM, prior to quenching with excess cysteine and diluting to 2 M urea. Overnight digestion (37 °C) was performed by adding 3 μg of trypsin (1:20 enzyme/substrate, w/w). Finally, samples were quenched by addition of formic acid (1%) and stored at −20 °C until LC−MS/MS analysis. For LC−MS/MS analysis, 10 μL of each tryptic digest was subjected to online desalting and reverse-phase nanoLC separation using a vented column configuration50 on a nanoLC-2D system with an AS1 autosampler (Eksigent, Dublin, CA). Trap and analytical columns were created using 100 μm × 3 cm IntegraFrit and 75 μm × 15 cm PicoFrit columns (New Objective, Woburn, MA), respectively, and were self-packed with Magic C18AQ particles (5 μm, 200 Ǻ , Michrom Bioresources). Mobile phase A (98:2:0.2) and mobile phase B (2:98:0.2) were comprised of water/acetonitrile/ formic acid (v/v/v). Samples were loaded/desalted via a 12 μL metered injection at 4 μL/min in 2% mobile phase B, and subsequently, peptides were eluted over the course of a 55 min ramp from 2 to 45% mobile phase B at 350 nL/min. Datadependent acquisition was performed on an LTQ-FT Ultra (Thermo Scientific, San Jose, CA) essentially as previously described51 with the exception that a FT resolving power of 100 000fwhm was used. All LC−MS/MS data files (.RAW files) were processed and searched using Mascot Daemon (Matrix Science, Boston, MA). Peak-list files (.mgf files) were created in Mascot Daemon using the default “Orbitrap − MS2 Low Res 2” settings and were searched against the corresponding database, modified to contain a concatenated reverse database for false discovery rate

Filter-Aided Sample Preparation

All samples for quantification were processed using a modified filter-aided sample preparation (FASP) scheme.52 Protein extracts were diluted 2-fold with a denaturing solution (100 mM DTT, 8 M urea, and 50 mM Tris-HCl, pH 8.0) and incubated for 30 min at 56 °C. After cooling, alkylation solution (1 M iodoacetamide, 8 M urea, 50 mM Tris-HCl, pH 8.0) was added to give a final iodoacetamide concentration of 200 mM, and alkylation proceeded for 60 min at 37 °C. Next, an aliquot containing 200 μg of total protein was added to an Amicon 10 kDa MWCO-filter unit (Millipore, Billerica, MA) and concentrated via centrifugation for 15 min at 14 000g. The sample was then buffer exchanged, three times, by addition 400 μL of digestion buffer and centrifugation. After addition of trypsin, digestion proceeded at 37 °C, after which 100 μL of 1% formic acid containing 0.001% zwittergent 3-16 (Calbiochem, La Jolla, CA) was added to the filter unit to quench the digestion. All peptides were then eluted from the filter unit by centrifugation as above. To ensure maximum recovery, the volume retained in the filter unit (∼30 μL) was diluted with 400 μL of the quenching solution and then eluted and combined with the original eluent. Lastly, the samples were diluted to a final volume of 1 mL to give an approximate peptide concentration of 0.2 mg/mL and analyzed by LC− SRM without freezing. During the FracFD experiments, 16 different digest conditions were tested according to this FASP protocol. The specific conditions for each are in the Supporting Information (Table S2). During this preliminary optimization, 10 μL of the SIL cocktail A was added to each sample immediately after quenching the digest to calculate the resulting peptide recovery. Following the FracFD study, the concentration of trypsin and digestion time were more thoroughly examined. In these latter experiments, the optimized digestion buffer (10 mM CaCl2, 2 M urea, 50 mM Tris-HCl, pH 8.0) was used for all digestions, and 10 μL of SIL cocktail A was added concurrently with 90 μL of trypsin solution to provide a total digest volume of ∼100 μL. This is in contrast to the FracFD experiments where SIL cocktail A was added after quenching the digest. When testing different trypsin concentrations, solutions of 2222, 444, 222, 111, or 44.4 μg/mL were added to give final trypsin concentrations of 2000, 400, 200, 100, or 40 μg/mL, respectively, and the digests were quenched after 16 h. When testing different digest times, all digests contained 40 μg of trypsin (400 μg/mL final concentration) and were quenched at either 1, 2, 3, 4, 5, 6, 7, 8, or 9 h. The final, optimized digestion protocol for protein quantification consisted of consecutive reduction and alkylation 3393



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standards were added following the digestions to estimate the peptide recovery, which is related to the digestion efficiency as well as the peptides’ stability under the various digestion conditions (note, the SIL peptides were added concurrently with the trypsin in all other experiments to account for any peptide decay during the digestion process and, thus, provide a more accurate estimate of the protein quantity). Each individual protein’s measured concentration was utilized as the response factor to assess each individual peptide’s recovery, while the median concentration measured for all proteins was utilized to assess the overall peptide recovery. In the latter case, the protein concentrations were normalized to the respective digest that yielded the highest protein concentration, so that each was expressed as a percentage. This was done so proteins with high or low absolute protein concentrations could not bias the results. These data were imported into JMP to calculate the pvalues and contrast values associated with each main effect and second-order interaction.

steps as described above, followed by buffer exchange with the optimized digestion buffer (10 mM CaCl2, 2 M urea, 50 mM Tris-HCl, pH 8.0). Subsequently, 10 μL of SIL cocktail B and 90 μL of 444 μg/mL trypsin (40 μg) were added to the filter units, and digestion proceeded for 8 h prior to quenching. In all cases, samples were stored at 4 °C and were analyzed by LC− SRM within 48 h. LC−SRM Quantitative Analysis

Analysis by LC−SRM was performed using a nanoLC-2D system equipped with an AS1 autosampler and cHiPLCnanoflex system (Eksigent, Dublin, CA), which was coupled to a TSQ Vantage triple stage quadrupole mass spectrometer (Thermo Scientific, San Jose, CA) using a 20 μm i.d. SilicaTip ESI emitter (New Objective, Woburn, MA). For each sample, loading and desalting were carried out on 5 μL of sample using a 9 μL metered injection with 100% mobile phase A at 1.5 μL/ min. Peptides were subsequently eluted at 400 nL/min with a 22 min ramp from 5 to 38.5% mobile phase B, after which the column was rinsed with 95% mobile phase B for 5 min prior to re-equilibrating both columns to initial conditions. Mobile phase A (98:2:0.2) and mobile phase B (2:98:0.2) were comprised of water/acetonitrile/formic acid (v/v/v). The column eluent was ionized using a 1400 V ESI potential and capillary temperature of 200 °C. Scheduled SRM was performed using the EZ Method with a defined cycle time of 1.5 s, chrom filter of 30 s, Q1 peak width (fwhm) of 0.7, collision pressure of 1.5 mTorr, and a 2.5 min retention time window for each peptide. The entire list of peptide transitions is provided (Table S3, Supporting Information) along with their optimized collision energies. LC−SRM method development and data analysis were performed using Skyline v.1.1.0.2095.53 Spectral libraries were created in Skyline by importing the .dat files obtained from the shotgun proteomic analysis. LC−SRM data files (.RAW files) were imported into Skyline for automatic peak detection and integration; however, all chromatograms were manually inspected within Skyline to ensure no obvious coeluting contaminants were selected by the software. Peak areas for each transition were exported to Excel 2010 (Microsoft, Redmond, WA) for assessment of peak purity and quantitative determinations. Quantification was performed using the peak area sum for all specific (i.e., noncontaminated) transitions according to the following equation [ProteinNAT] ≈ [Peptide NAT] =

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RESULTS A unique nomenclature is used here to efficiently identify peptides and their corresponding protein. For example, ENZYME2.xx-yy corresponds to the peptide found in the generic protein “ENZYME2” beginning at residue “xx” and ending at residue “yy” of the cloned protein sequence (Supporting Information). Alternatively, ENZYME1|2.xx-yy refers to a shared peptide sequence that is found in both proteins, “ENZYME1” and “ENZYME2”. In cases where the shared peptide is found at different locations in the two proteins, “xx-yy” refers to the location in the first protein’s sequence (“ENZYME1” in this example). Selection of Surrogate Peptides

Surrogate peptide selection was performed using a systematic workflow (Figure 2) in conjunction with Skyline, a freely available SRM-assay development software.53 Each target enzyme sequence20 was subjected to in silico digestion by trypsin, allowing for no missed cleavages and excluding peptides containing residues within the first 25 amino acids of the protein’s N-terminus. The resulting tryptic peptides having between 7 and 25 residues were compared to the annotated proteome for P. trichocarpa (JGI v2.2) to compile a list of unique, tryptic peptides that could potentially serve as surrogates (Table S4, Supporting Information). In many cases, proteins having high sequence similarity with other members of the same enzyme family were inherently limited in their number of potential surrogate peptides. For instance, PAL4 and PAL5 are differentiated by only two amino acid residues (99.86% shared sequence identity) and, consequently, only have two potential surrogate peptides that could uniquely quantify each protein. Similarly, CCoAOMT1 and CCoAOMT2 are distinguished by 13 amino acids (94.74% sequence identity) and have only three potential surrogate peptides. To determine which potential surrogate peptides could be effectively detected by LC−MS/MS, we sought to identify them through a shotgun analysis of stem-differentiating xylem (SDX). A pooled sample of three xylem protein extracts (from genetically identical trees grown under the same conditions) was subjected to in-gel tryptic digestion followed by LC−MS/ MS analysis.49 Nine hundred and seven unique proteins (i.e., protein groups) were identified during this experiment, including 20 of 25 target enzymes where PAL4 and PAL5

∑ ANAT [PeptideSIL] ∑ ASIL (1)

This equation shows that the calculated concentration for the native/natural (NAT) proteolytic peptide is assumed to approximate the native protein concentration. All calculated protein concentrations were normalized to the total protein content of the SDX protein extract, which was determined using a Bradford assay (vide supra). Fractional Factorial Design of Experiments

The FracFD employed for digest optimization was created using a DOE custom design within JMP v9.0.0 (SAS Institute Inc., Cary, NC). Six factors were each assessed at two levels, and the software was manually set to model all main effects as well as 9 of 15 second-order interactions. This resulted in 16 different digest conditions being evaluated, which were performed in duplicate for a total of 32 experiments (Table S2, Supporting Information). In this study, the SIL peptide 3394



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constituted one identified protein group (Table 1). The database search results (.dat files) from these experiments were uploaded into Skyline to create a spectral library of all identified peptides (peptide score >0.85), which could be crossreferenced with all potential surrogates. In most cases, the number of potential surrogates identified was relatively small (less than 5) due to poor sequence coverage of the native protein. To provide a larger pool of identified candidate peptides, we also produced each target enzyme recombinantly and subjected the purified form to shotgun analysis. Overall, much better sequence coverage was obtained during these analyses resulting in more potential surrogate peptides being identified (Table 1). The main exceptions to this were the hydroxylases (C3H, C4H, and CAld5H), which yielded no identifications from the recombinant proteins. This was presumably due to their low relative abundance in the yeast microsomal fractions that were interrogated for protein/peptide identification. Utilizing the combined spectral libraries of peptides identified in vivo (SDX extracts) and in vitro (purified recombinant proteins), a final list of candidate peptides was created (Table S4, Supporting Information). Various additional criteria were considered to select the final surrogate peptide from among this list of identified candidates (Figure 2). This included avoiding sites of known chemical modifications (e.g., Met, Asn, Gln, Cys) or highly probably post-translational modifications54,55 and favoring peptides with higher detected ion abundances. The candidate peptide best matching all criteria was selected as the surrogate peptide and then screened to determine the feasibility of synthesis.

Figure 2. Surrogate peptide selection workflow. The cloned protein sequences20 were utilized as the starting protein sequences in this workflow. In a few cases, there were discrepancies between this sequence and the sequence annotated in the JGI database. Given the higher probability of error during genome sequencing, we always deferred to the cloned sequence when selecting surrogate peptides. The cloned protein sequences are provided in the Supporting Information. The Basic Local Alignment Search Tool (BLAST) was performed in Skyline using the “Unique Peptides” tool.

Table 1. Results for Shotgun Analysis of Native and Recombinant Target Pathway Enzymes # candidates identifiedc

% sequence coverage enzyme family

#

gene model

4-coumarate-CoA ligase (4CL)

3 5 3 1 2 1 2 1 2 1 2 3 2 2 1 6 1 2 3 4 5 1 2 3 8

POPTR_0001s07400.1 POPTR_0003s18710.1d POPTR_0006s03180.1 POPTR_0013s15380.2 POPTR_0019s15110.1 POPTR_0009s09870.1 POPTR_0016s07910.1 POPTR_0005s11950.1 POPTR_0007s13720.1 POPTR_0009s10270.1 POPTR_0001s31220.1 POPTR_0008s13600.1 POPTR_0003s17980.1 POPTR_0012s00670.1 POPTR_0003s18210.1 POPTR_0001s03440.1 POPTR_0006s12870.1 POPTR_0008s03810.1 POPTR_0016s09230.1 POPTR_0010s23100.1 POPTR_0010s23110.1 POPTR_0004s01510.1 POPTR_0006s13190.1 POPTR_0005s11070.1 POPTR_0005s21740.1

p-coumarate 3-hyroxylase (C3H) cinnamate 4-hydroxylase (C4H) cinnamyl-alcohol dehydrogenase (CAD) coniferaldehyde 5-hydroxelase (CAld5H)a caffeoyl-CoA O-methyltransferase (CCoAOMT)

cinnamoyl CoA reductase (CCR) caffeic acid 3-O-methyltransferase (COMT) hydroxycinnamoyl transferase (HCT) phenylalanine ammonium-lyase (PAL)

peroxidase (PO)

a

native

recomb

23.0 7.0 13.0 26.5 18.6 37.3 7.6 11.7 24.7 24.7 22.6 28.1 57.1 15.6 22.9 26.5 41.2 23.1 45.0e 45.0e 22.0

57.2 62.4 84.4 84.5 78.5 19.0 83.4 81.4 82.7 44.4 69.5 82.8 94.9 65.7 38.3 46.3 39.3 65.9 33.1 13.3

b

native

recombb

total

5 1 3 1 1 9 1 2 1 3 7 2 3 4 3 4 5

8 8 13 9 1 7 7 9 5 5 12 8 8 1 7 8 5 2

8 of 10 8 of 13 3 of 17 1 of 11 1 of 11 13 of 13 9 of 10 1 of 12 2 of 12 1 of 3 0 of 3 7 of 10 8 of 8 9 of 11 5 of 9 5 of 8 12 of 15 8 of 15 8 of 17 0 of 2 1 of 2 7 of 14 8 of 14 5 of 11 5 of 9

a

JGI v2.2. bRecombinant; calculation does not include detection of a fusion tag. cPeptide Probability > 0.85. dC-terminal portion of this protein is also annotated in POPTR_0003s18720.1. eThese proteins were identified as a single protein group. 3395



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Table 2. Preliminary and Final Surrogate Peptides Selected for Each Target Enzyme final surrogate peptides (SIL cocktail B)

preliminary surrogate peptides (SIL cocktail A) sequence

[SIL]a

peptide

sequence

[SIL]a

4CL3.262−273 4CL5.262−273 C3H3.125−134 C4H1.255−261 C4H2.255−261 CAD1.184−198 CAD2.186−201b CAld5H1.426−435 CAld5H2.L.427−436 CAld5H2.M.427−436 CCoAOMT1.182−206 CCoAOMT2.182−206

FDIGTLLGLIEK FEIGSLLGLIEK VCTLELFSPK DYFVDER DYFVEER GGILGLGGVGHMGVK HIGIVGLGGLGHVAVK FLEPGVPDFK FLKPGVPDFK FMKPGVPDFK VGGLIGYDNTLWNGSVVAPPDAPMR VGGLIGYDNTLWNGSVVAPADAPMR

10 10 10 10 10 10 10 10 10 10 10 10

CCoAOMT3.217−232b CCR2.299−308 COMT2.51−69 HCT1.338−354 HCT6.338−354 PAL1.238−251b PAL2.661−672 PAL3.239−252b PAL4|5.614−622 PO1.136−149 PO2.213−230 PO3.300−310 PO8.113−121

VEISQISIGDGVTLCR DLGFEFTPVK AGPGAFLSTSEIASHLPTK SALDFLELQPDLSALVR SALDYLELQPDLSALVR AAGIDSGFFELQPK EELGTILLTGEK AAGIESGFFELQPK IGSFEEELK DGIVSLGGPHIPLK IYPTVDPTMDPDYAEYLK MSSITGGQEVR AFEIIEDLR

10 10 10 10 10 10 10 10 10 10 10 10 10

4CL3.262−273 4CL5.262−273 C3H3.125−134 C4H1.255−261 C4H2.255−261 CAD1.184−198 CAD2.177−185 CAld5H1.426−435 CAld5H2.L.427−436 CAld5H2.M.427−436 CCoAOMT1.182−206 CCoAOMT2.182−206 CCoAOMT1|2.115−126 CCoAOMT3.59−65 CCR2.299−308 COMT2.51−69 HCT1.338−354 HCT6.338−354 PAL1.664−675 PAL2.661−672 PAL3.665−676 PAL4|5.614−622 PO1.136−149 PO2.213−230 PO3.300−310 PO8.113−121

FDIGTLLGLIEK FEIGSLLGLIEK VCTLELFSPK DYFVDER DYFVEER GGILGLGGVGHMGVK YFGLDEPGK FLEPGVPDFK FLKPGVPDFK FMKPGVPDFK VGGLIGYDNTLWNGSVVAPPDAPMR VGGLIGYDNTLWNGSVVAPADAPMR ENYELGLPVIQK FLSMLLK DLGFEFTPVK AGPGAFLSTSEIASHLPTK SALDFLELQPDLSALVR SALDYLELQPDLSALVR EELGTGLLTGEK EELGTILLTGEK EELGTVLLTGEK IGSFEEELK DGIVSLGGPHIPLK IYPTVDPTMDPDYAEYLK MSSITGGQEVR AFEIIEDLR

50 10 10 10 10 20 10 10 10 10 200 200 100 10 10 200 10 10 10 10 20 10 2 10 10 10

peptide

a

Concentration of SIL peptide added to each sample with units of femtomoles per microgram of total SDX protein. bFinal surrogate peptide is different due to rapid degradation or interference observed with the preliminary surrogate peptide.

In many cases, the peptide initially selected to serve as the surrogate could not be synthesized due to potential insolubility or aggregation, in which case an alternative peptide was selected from the pool of identified candidates. For C3H3, C4H1, and C4H2, no candidate peptide identified during these studies was amenable to synthesis. Fortunately, we were able to identify surrogate peptides (C3H3.125−134 and C4H1.255−261) during the course of a separate study.45 Although we did not detect any surrogate peptide for C4H2 during either work, we elected to use the analogous surrogate peptide (C4H2.255− 261) to that selected for C4H1. For CAld5H2, the only viable surrogate peptide meeting all essential criteria was CAld5H2.427−436. After selecting and synthesizing this peptide, we learned it contained an amino acid substitution (L428M) resulting from a single-nucleotide polymorphism (data unpublished), indicating that Nisqually1 is a heterozygote. In the absence of any other viable surrogate, both forms of CAld5H2.427−436 were synthesized, and the total CAld5H2 concentration was calculated as the sum from both peptides (referred to as CAld5H2.L.427−436 and CAld5H2.M.427−436). In the case of PAL4 and PAL5, neither of the two potential surrogate peptides from each protein could be made synthetically. Consequently, we decided to quantitate the sum of both proteins using a shared peptide, PAL4|5.614− 622 (Figure S2, Supporting Information). A list of all preliminary surrogate peptides selected and synthesized is shown (Table 2). After optimization of their SRM transitions, this preliminary set of peptides was utilized for optimizing the digestion protocol (vide infra). In the course of this work, five of the preliminary peptides selected were observed to decay rapidly (Figure S3, Supporting Information)

and prompted the selection of new surrogate peptides to provide more reliable quantification. For PAL1 and PAL3, the final surrogate peptides we elected to use for quantification (PAL1.664−675 and PAL3.665−676) were analogues of the preliminary surrogate chosen for PAL2 (PAL2.661−672). In the case of CCoAOMT1 and CCoAOMT2, the only alternative peptide capable of being synthesized was CCoAOMT2.153− 166. Unfortunately, the SIL form of this peptide had an isobaric contaminant coelute with it, which prevented its use for quantification. Consequently, we elected to use the preliminary peptides selected for CCoAOMT1 and 2 despite their rapid decay but added a shared surrogate peptide (CCoAOMT1| 2.115−126) to provide a composite measurement of both proteins (Figure S2, Supporting Information). Three alternative surrogate peptides were also tested for CCoAOMT3, of which the first two had poor stability (CCoAOMT3.104−115) or failed synthesis (CCoAOMT3.44−58); however, the final surrogate selected (CCoAOMT3.59−65) yielded good results. An alternative surrogate peptide was selected for CAD2 because the preliminary surrogate peptide (CAD2.186−201) was masked by an abundant, coeluting isobaric contaminant (Figure S4, Supporting Information). SRM Transition Development

Although Skyline affords the ability to automatically select SRM transitions based on MS/MS data obtained via shotgun analyses, low-energy dissociation can differ between platforms utilizing resonance excitation to induce dissociation (i.e., ion traps) and those using beam-type dissociation (i.e., nontrapping linear quadrupoles).56 Given this, we elected to systematically develop and optimize each surrogate peptide’s SRM transitions 3396



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Figure 3. SRM transition development workflow. SRM transitions were determined empirically using synthetic peptides to identify (A) the most abundant precursor and (B) product ions. (C) Collision energy breakdown curves were produced in Skyline (ref 57), and the optimal collision energy was defined where the summed intensities of all transitions (gray bars) was greatest. (D) The relative abundance (RA, eq 2) of each transition at the optimal collision energy was determined using the synthetic peptides. The RA of each transition was subsequently monitored in every replicated injection (Rep 1−3) of every sample. Transitions were deemed nonspecific if they were not within a certain tolerance (indicated with error bars) of the RA value determined using the standard peptide (Std). Such transitions are shown here and are indicated by an asterisk.

SRM Transition Validation

(Figure 3). Mixtures containing 4 to 6 synthetic peptides were prepared at final concentrations of 2 μM (each) and analyzed by direct-infusion ESI on our triple quadrupole instrument. After identifying the most abundant precursor ion charge state for each peptide (Figure 3A), product ion spectra were acquired to identify the six most abundant product ions to monitor for SRM analysis (Figure 3B). When the doubly charged and triply charged precursor ions were observed to have comparable abundances, SRM transitions were developed for both species, and the precursor ion resulting in the most intense SRM transitions was selected. Following manual selection of the six SRM transitions for each surrogate peptide within Skyline, collision energy (CE) optimization was performed.57 The software was utilized to generate an unscheduled SRM transition list where the CE value for each peptide was predicted using the default “TSQ Vantage” regression equation. Preliminary LC−SRM runs (n = 3) were carried out with this transition list on a mixture of all synthetic peptides (40 nM each) to determine their retention times. After importing the resulting data files into Skyline, several scheduled SRM transition lists were generated in which every transition was monitored over a range of CE values (range = 22 eV; ΔCE = 2 eV). The range for each peptide was centered at the optimum predicted by the default equation. Each transition list was run in duplicate, and the resulting data were imported into Skyline to generate breakdown curves and empirically determine the optimal collision energy for each precursor ion (Figure 3C). The final, optimized collision energies are provided along with the SRM transitions list (Table S3, Supporting Information).

Although SRM transitions are intended to be specific to the molecular species they target, contamination of SRM transition can often occur from coeluting, isobaric species (Figure S4, Supporting Information).58 This is of particular concern when short gradients are employed, as they were here to maximize sample throughput. To determine if quantification is specific, the relative abundances of each peptide’s SRM transitions must be validated in every sample to ensure no contamination is present.59−61 Here, all SRM transitions were first tested on a tryptic digest of SDX protein extract, and any transitions having poor signal-to-noise (NAT or SIL) were excluded. Next, we determined the relative abundance for each peptide’s remaining transitions through replicate injections (n = 5) of a neat mixture of all synthetic peptides (Figure 3D and Table S3, Supporting Information). The relative abundance (RA) of each transition was defined by the following quotient n

RA = (∑ A i − A t )/A t i=1

(2)

In this equation, At represents the peak area measured for the transition being investigated, and the numerator is the summed peak areas for all (n) transitions minus At. This quotient results in abundant transitions having small RA values, while less abundant transitions have large RA values. During quantitative analysis of SDX proteins, the RA value of all SRM transitions was monitored in each run and compared to the expected value obtained with the synthetic peptides (Figure 3D). Any transition deviating significantly from the 3397



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Figure 4. (A) Statistical results obtained when using the median protein concentration as the metric (i.e., response factor) for digestion efficiency during FracFD experiments. The terms are the 6 main factors and 9 second-order interactions assessed using this experimental design (Table S2, Supporting Information) listed in order of their contrast value. The contrast represents the magnitude of the influence each term has on the experimental outcome, and the sign designates which level (+ or −) was favored. The individual p-values indicate the significance of each term. (B) FracFD results are shown for each main factor when the individual protein concentrations were used as the metric for digestion efficiency. The bar graph shows the number of proteins determined to be significantly impacted by each main factor (p-value < 0.05). More specifically, the number of proteins favoring a specific level of each main factor is indicated. (C) The minimum concentration of trypsin required to achieve complete digestion in 16 h was determined experimentally (Table S5, Supporting Information). Shown is the number of proteins achieving complete digestion using each concentration of trypsin. (D) The minimum time required to obtain complete digestion when using 400 μg/mL of trypsin was determined (Table S5, Supporting Information). The chart shows the number of proteins reaching complete digestion at each time point. (C and D) The red graphs show the cumulative number of proteins having achieved complete digestion by the specified point, and the blue graphs show the specific number of proteins achieving complete digestion at the specified point.

expected RA value was deemed to be nonspecific and was excluded from use in calculating the quantitative NAT:SIL ratio, as was its corresponding transition in the opposite peptide species (NAT or SIL). As such, the NAT:SIL ratio utilized for quantification (eq 1) was calculated with transition pairs where both the NAT and SIL species were confirmed free of contamination. The significance threshold used to determine whether a transition was contaminated was determined by the expected RA value for that transition. For highly abundant transitions (RA ≤ 1), the tolerance was set at ±15%. For moderate abundance (9 < RA < 1) and low abundance transitions (RA ≥ 9), the tolerances were set to ±25% and ±50%, respectively. More stringent tolerances were utilized for higher intensity transitions due to their larger contribution to the summed intensities for all transitions (Figure S6, Supporting Information). The necessity of this SRM validation procedure was exemplified during the digest optimization experiments (vide infra) in which the background peptide matrix was constantly changing due to differences in the digestion efficiencies. For

instance, when comparing different concentrations of trypsin, a prominent contaminant was observable in the a2+ transition for CCR2.299−308 when using 40 μg/mL of trypsin but disappeared when using higher concentrations of trypsin (Figure S5, Supporting Information). In monitoring the relative abundance for this transition, the RA values fell outside the tolerance range and confirmed the presence of contamination when using lower concentrations of trypsin. Rather than excluding this transition altogether, we only excluded it in the samples where it was determined to be nonspecific. In general, either exclusion method gave nearly identical quantitation (Table S5, Supporting Information); however, we reasoned it would be best to utilize the a2+ transition where possible due to it being the most abundant transition. Moreover, it would not be appropriate to assume this transition would always be contaminated given that the background peptide matrix will not necessarily be constant across all samples. 3398



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FASP-Digest Optimization

ratios were achieved here by using smaller volumes of the same concentration of trypsin while using the same total amount of substrate protein (Table S2, Supporting Information). Consequently, this result is more likely a reflection of higher substrate concentrations rather than higher substrate/enzyme ratios. We concluded that the digestion buffer should contain 10 mM Ca2+ and 2 M urea but not methanol. We fixed the digestion volume to 100 μL (for practical purposes) and proceeded to further evaluate the concentration of trypsin and time required to achieve complete digestion using the optimized conditions (10 mM CaCl2, 2 M Urea, 50 mM Tris-HCl, pH 8). Other concentrations of Ca2+ or urea were not tested because the concentrations we employed have already been deemed optimal for trypsin by previous studies.62,63,66,67 The concentrations of trypsin tested were 2000, 400, 200, 100, and 40 μg/mL, which corresponded with enzyme/substrate ratios of 1:1, 1:5, 1:10, 1:20, and 1:50. Complete digestion was achieved in the presence of 2000 and 400 μg/mL of trypsin for all 17 proteins whose surrogate peptide was detectable after the 16 h digestion period (Figure 4C, Supporting Information Figure S7 and Table S6). Next, we determined the minimum time for complete proteolysis using 400 μg/mL of trypsin (1:5 enzyme/substrate, w/w). Nine parallel digests were performed, and one was stopped consecutively after every hour for 9 h. For 16 of 18 detectable proteins, the digestions were complete after 7 h, while 2 proteins (4CL3 and 4CL5) required 11−13 h for completion (Figure 4D, Supporting Information, Figure S8 and Table S6). We subsequently compared an 8 and 12 h digestion and observed no difference (p-value > 0.05) in the measured protein concentrations for either 4CL3 or 4CL5. We concluded that an 8 h digestion would provide complete proteolysis and, thus, accurate quantification of all target proteins.

When employing SIL peptide standards in a PC-IDMS assay, accurate quantification can only be achieved when target proteins are rapidly and completely digested to form their proteolytic peptides. Utilizing a FASP-digestion scheme and a fractional factorial design (FracFD) of experiments, we tested various parameters known to impact trypsin digestions to identify the best conditions for our quantitative assay. We tested six digestion parameters to determine which were most important. These parameters (i.e., factors) were: digestion time, concentration of trypsin, substrate/enzyme ratio, and the presence/absence of urea,62,63 methanol,63−65 and calcium66,67 in the digestion buffer. These parameters were selected because they have been shown to improve the digestion efficiency of trypsin, did not require any additional sample preparation steps, and are compatible with the filter units used for FASP digestion. Utilizing a statistical software package (JMP), an experimental design was created in which each factor was tested at two levels across 16 different digests (Table S2, Supporting Information). Each digest was performed in duplicate, and the measured protein concentrations were utilized as the metric for digestion efficiency. This experimental design allowed the 6 main factors and 9 of 15 possible second-order interactions to be evaluated (Figure 4A). When considering the median concentration measured for all proteins, the impact of each factor on the overall digestion efficiency was quickly identified according to their contrast value and associate p-value (Figure 4A). The contrast value for each factor describes its influence on the observed variation between the different samples and, thus, the factor’s influence on the overall peptide recovery. The sign indicates which level favors higher peptide recoveries, and the p-value indicates the significance of the observed contrast value. For example, the presence of 10 mM Ca2+ in the digestion buffer drastically and significantly improved the overall digestion efficiency as indicated by its larger, positive contrast value and low p-value, respectively. In comparison, use of 50% methanol drastically reduced the digestion efficiency. To a lesser extent, 2 M urea in the digestion buffer and longer digestion times improved the digestion efficiency, while the concentration of trypsin and enzyme/substrate ratio did not significantly impact the median protein concentration measured in these experiments. Several second-order interactions were also observed to significantly improve the overall digestion efficiency. The digestion time played a significant role in combination with each of the variable buffer constituents. To determine if any factors were protein-specific, each protein’s concentration was independently used as the metric for digestion efficiency (Figure 4B). In nearly all cases, proteins were either affected similarly by any given parameter or were not affected at all. For instance, 18 of 21 proteins analyzed during this study showed 50% MeOH decreased their production/recovery, while 3 proteins were determined to be unaffected. The one exception to this was CCoAOMT1, which favored lower concentrations of trypsin, while all other proteins either favored higher concentrations or were unaffected. This is in contrast to the result observed when using the median protein concentration as the metric, which showed that the concentration of trypsin did not impact the overall digestion efficiency. Similarly, higher substrate/enzyme ratios were observed to result in improved digestion efficiency for four proteins even though they did not impact the median measured protein concentration. This latter result is counterintuitive and was presumably due to the fact that higher substrate/enzyme

Assay Validation in Wild-Type SDX

Given this assay is intended for large numbers of samples that are likely to span several days or weeks for collection and processing, we tested the analytical robustness of our assay with replication of an identical sample over three days. Using our optimized FASP-LC−SRM protocol, a pooled sample of three SDX protein extracts was processed in triplicate on three separate days (3 digests × 3 days = 9 analyses) to estimate the analytical variation. All reagents were prepared fresh, and a new vial of SIL Cocktail B (Table 2) was used each day. For the majority of proteins/peptides, very good analytical variation (CV < 10%) was observed both within and between days (Table S7, Supporting Information). The only exceptions were CCoAOMT1 and 2, whose measurements showed much larger variation (CV > 10%). This result was somewhat expected given these peptides had been observed to decay rapidly over the course of the digestion period (Figure S3, Supporting Information). Nonetheless, the shared surrogate peptide for these two proteins showed considerably better precision (CV < 3%). Interestingly, the individual protein quantities measured for CCoAOMT1 and 2 did not add up to provide the same protein quantity measured by their shared surrogate peptide. When summing the individual measurements, a total protein quantity of 303 fmol/μg was obtained, compared to 228 fmol/μg for the shared peptide. This discrepancy could be explained by incomplete production of the shared peptide during the digestion. However, the rapid decay observed for the individual 3399



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Figure 5. Displayed are the protein concentrations measured in normal stem differentiating xylem of P. trichocarpa. (A) The protein concentration measured by each final surrogate peptide (Table 2) is plotted on a log-10 scale and shows the large dynamic range of monolignol enzyme concentrations being evaluated. (B) Shown are the protein concentrations measured for each enzyme family. Note that the linear scale for each enzyme family plot is different. (A and B) The quantities are the mean values obtained from 9 replicate digestions performed across 3 days, and the error bars represent the 95% confidence interval of the mean. The protein concentration for CAld5H2.427−436 was calculated by summing the average for CAld5H2.M.427−436 and CAld5H2.L.427.436. Likewise, the standard deviation for this measurement was calculated by summing the standard deviations for the two means. The exact numerical values are provided in the Supporting Information (Table S7).

combined) and 4CL3 (112 fmol/μg). Of particular interest are several enzymes that are phylogenetic pairs (Figure 5B), with greater than 88% sequence similarity.20 Equivalent amounts of protein for the related pairs of enzymes in the same tissue extract suggest functional redundancy for these enzymes in this tissue. Of six phylogenetically close pairs, five had close protein quantities: respectively, PAL 1 and 3 had levels of 5.93 and 6.27 fmol/μg; C4H1 and 2 had quantities of 10.6 and 4.04 fmol/μg; HCT 1 and 6 had quantities of 23.6 and 13.0 fmol/μg; CCoAOMT 1 and 2 have corrected quantities of 107 and 120 fmol/μg; and CAld5H1 and 2 have quantities of 16.4 and 16.4 fmol/μg. The exception is 4CL3 and 5, which had quantities of 112−5.27 fmol/μg, respectively. Moreover, the concentrations for two allele forms of CAld5H2 were estimated by using both forms of the surrogate peptide sequence containing the polymorphism (L428M). It was determined that the leucine-containing form was approximately 3-fold more abundant than the methionine-containing form, which suggests differential expression of the two alleles.

surrogate peptides (Figure S3, Supporting Information) suggests the SIL peptide decayed (i.e., degraded or precipitated) to a greater extent over the course of the digestion than the native peptide, resulting in an overestimation of the individual protein quantities. Similar biases have been reported previously for PC-IDMS assays.68 For this reason, we concluded the estimate provided by the shared peptide was most accurate. Using the relative abundance of CCoAOMT1 and 2 as measured by their individual peptides (1:1.12, respectively), the shared peptide’s concentration was apportioned to provide a corrected estimate for the respective proteins. These were 107 and 120 fmol/mg for CCoAOMT1 and 2, respectively. From this initial quantitation, it was determined that the protein levels for different pathway enzymes spanned a large dynamic range of concentrations (Figure 5A), from 0.558 fmol/ μg of SDX protein for PO1 to 1740 fmol/μg for COMT2 (Table S7, Supporting Information). The abundance of COMT2 was roughly 10-fold greater than the next most abundant proteins, CCoAOMT1 and 2 (228 fmol/μg 3400



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proteins are known to have low transcript abundances20 and were not identified during shotgun proteomic analysis of SDX. As such, no additional surrogate peptides were tested for these three proteins. To ensure specific quantification of each protein, specific detection of each surrogate peptide and its SIL standard must be validated. This means that each SRM transition used for quantification must be specific only to the targeted species. Often it is assumed that detection of multiple SRM transitions confirms the specificity of detection, yet in reality this only implies the targeted peptide is present. Given that coeluting isobaric peptides can contribute a signal to one or more of the detected transitions, each transition must be validated individually for the presence of contamination that could otherwise bias quantification. This was accomplished here by assessing the relative ion abundances for all SRM transitions within each peptide species. The values measured in each run were compared to the anticipated values determined using synthetic peptide standards, and any transition deemed to be nonspecific (i.e., contaminated) was not utilized for quantification. Ion ratio assessments have been utilized previously during SRM-based quantification of proteins.37 Rather than excluding nonspecific transitions completely from the assay, we elected to only exclude these transitions where they were identified as contaminated. Because future applications of this assay are expected to involve the assessment of various tissue types or transgenic constructs, it was reasoned that contamination may fluctuate as the background peptide matrix changes. In other words, it is not possible to unequivocally say any transition will be specific in every sample and every sample type simply because it was deemed pure in the few sample types analyzed here. However, this raises the question of whether or not quantification will be biased by using different SRM transitions across different runs and samples. Consequently, we compared the quantitative results when a contaminated transition was systematically (all runs) or uniquely (one or more run) excluded from quantification and did not observe any statistically significant difference in the measured protein concentrations with either method. Thus, it seemed prudent and practical to monitor multiple SRM transitions for each peptide and continuously assess each for the presence of contamination (i.e., nonspecificity). It is well-known that the digestion efficiency can impact the measured protein concentrations, and it is now common for the digestion process to be optimized to ensure quantitative production of surrogate peptides. Generally, this is accomplished by simply increasing the amount of trypsin until the protein estimates plateau; however, the required amount of trypsin can often be exceedingly high, and in our experience, the high concentration of autolytic peptides can lead to ion suppression and poor chromatographic performance. To circumvent the use of high trypsin concentrations, we elected to test six digestion parameters to increase the digestion efficiency. We employed a FracFD to efficiently assess which parameters had the greatest positive influence on the digestion efficiency. FracFD is a powerful statistical tool, allowing a large experimental space to be evaluated in a relatively small number of experiments,71−73 and has been successfully employed for optimizing a variety of processes in MS-based workflows.51,74−76 To evaluate all possible combinations of these 6 factors would require 26 or 64 experiments based on a full factorial design; however, we were able to perform only 16

DISCUSSION The main hindrance to the development of a PC-IDMS assay is the selection of the proper surrogate peptides for quantification. To employ a proteolytic peptide as a quantitative surrogate, the peptide must meet certain necessary criteria. First, the peptide should not contain any “missed cleavage” sites and must be unique to the target protein to ensure quantification is both accurate and specific. Potential surrogate peptides must also perform well by LC−MS/MS to allow for sensitive detection. Although these properties can be predicted with some certainty using sequence-specific criteria,69 successful prediction is not guaranteed. We confirmed these criteria by identifying the peptide(s) through preliminary shotgun analyses. During our analysis of SDX, we were able to confidently identify at least one protein from each of the 11 enzyme families in monolignol biosynthesis and polymerization. Although this comprises the most comprehensive in vivo proteomic analysis of monolignol pathway enzymes, relatively few candidate surrogate peptides were identified. To improve this process, MacCoss and coworkers recently demonstrated in vitro identification and selection of surrogate peptides utilizing recombinant proteins.70 To this same end, we expressed each protein recombinantly to interrogate for potential surrogate peptides and identified a much larger pool of candidates. Despite best efforts to select the “ideal” surrogate peptide for each protein, options were often limited by high degrees of shared sequence identity among enzyme families. Moreover, many peptides initially selected were not amenable to synthesis or performed poorly under the FASP−LC−SRM conditions employed. In two cases, the combination of these obstacles actually precluded selection of sequence-specific surrogate peptides. For CCoAOMT1 and CCoAOMT2, the initial surrogate peptides selected were unstable and decayed rapidly over the course of the digestion period (Figure S4, Supporting Information), while the other possible surrogates could not be produced synthetically or had nonspecific SRM transitions (Figure S5, Supporting Information). As a result, we were forced to utilize the former surrogate peptides despite their poor stability and subpar precision. To augment these measurements, however, we also selected a shared surrogate peptide to provide a more accurate and precise measurement of both proteins simultaneously. In a similar situation, no sequence-specific surrogate peptides could be synthesized for either PAL4 or PAL5. Given the lack of a sequence-specific surrogate peptide for either protein, we were again limited to a composite measurement via a shared surrogate peptide. It should be noted that we have observed recombinant forms of these protein pairs (PAL4-PAL5 and CCoAOMT1CCoAOMT2) to have nearly identical activities (data unpublished). Consequently, it is not yet clear if it will be necessary to determine these individual protein quantities to accurately predict the metabolic flux through their respective steps of the pathway. Even with careful selection, the final surrogate peptides for three proteins (CAD2, PO2, and PO3) were not detectable in any tryptic digests of SDX protein extracts despite detection of their SIL standards. This result is potentially due to poor detection sensitivity of the LC−SRM assay and/or modification of the native surrogate peptide during the sample preparation; however, the lack of detection for these native enzymes could also be due to their low absolute concentrations in SDX. Indeed, this latter explanation seems most probable given these 3401



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sequences. This material is available free of charge via the Internet at http://pubs.acs.org.

different experiments to accomplish the same goal. Through this study, we determined that both Ca2+ and urea improved the production of the surrogate peptides, while methanol was detrimental. The poor performance in the presence of methanol is in contrast to previous reports63−65 and may have resulted from our use of a FASP-digestion scheme and poor stability of the membrane filters in the presence of the organic solvent. Given this assay is intended for large-scale studies, the use of expensive reagents such as acid-labile surfactants or modified trypsin (i.e., acetylated or permethylated trypsin) was avoided to reduce cost and to increase sample throughput.

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*Phone: 919-513-0084. Fax: 919-513-7993. E-mail: david_ [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank North Carolina State University, the W.M. Keck Foundation, and the National Science Foundation Plant Genome Research Program (Grant DBI-0922391) for supporting this work.

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CONCLUSIONS A collection of systematic techniques were applied to develop a thorough quantitative assay for all potential, primary enzymes involved in monolignol biosynthesis and polymerization. Many of the techniques have not been applied in the context of PCIDMS assay development (e.g., FracFD) or have received little attention (e.g., SRM validation). As such, the process utilized here may serve as a model in development of similar assays. Following development of the optimized protocol, the analytical variation of this assay was assessed for each protein both within and across multiple days (Table S7, Supporting Information). Coefficients of variation (CVs) less than 10% were obtained for all detectable enzymes, except for CCoAOMT1 (CVinterday = 10.9%) and CCoAOMT2 (CVinterday = 17.2%), demonstrating this assay is highly robust. To the best of our knowledge, this work constitutes the first protein-level evidence and quantities for many of the targeted enzymes and is the first large-scale quantitative protein assay in trees. Accurate and precise estimation of absolute quantities of protein for monolignol enzymes will be an important component to systems biology studies of lignin biosynthesis. With reliable quantitation of each functional protein in the pathway, it will soon be possible to compare the abundance of expressed protein with the relative abundance of transcripts, levels of enzyme activity, and metabolites and, thereby, to provide insight into regulation and mechanisms of function and evolution. This assay will prove beneficial in more detailed studies, which are needed to investigate variation in protein abundance during tree development and differentiation, different times of the day, and seasonal development, and in response to environmental stress. These efforts are intended to be the subject of future publications.

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AUTHOR INFORMATION

Corresponding Author

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REFERENCES

(1) Sarkanen, K. V.; Ludwig, C. H. Lignins: Occurrence, Formation, Structure and Reactions; John Wiley and Sons: New York, 1971. (2) Boerjan, W.; Ralph, J.; Baucher, M. Lignin Biosynthesis. Annu. Rev. Plant Biol. 2003, 54, 519−546. (3) Sjostrom, E. Wood Chemistry: Fundamentals and Applications; Ann Arbor Science Publishers: Ann Arbor, MI, 1993. (4) Talbot, J. M.; Yelle, D. J.; Nowick, J.; Treseder, K. K. Litter Decay Rates are Determined by Lignin Chemistry. Biogeochemsitry 2012, 108 (1−3), 279−295. (5) Hurst, H. M.; Burges, N. A. Lignin and Humic Acids. In Soil Biochemistry; McLaren, A. D., Peterson, G. H., Eds.; Marcel Dekker Inc.: New York, 1967; Vol. 11, pp 260−286. (6) Vance, C. P.; Kirk, T. K.; Sherwood, R. T. Lignification as a Mechanism of Disease Resistance. Annu. Rev. Phytopathol. 1980, 18, 259−288. (7) Erikson, K., -E., L.; Blanchette, R. A.; Ander, P. Microbial and Enzymatic Degradation of Wood and Wood Components; SpringerVerlag: Berlin, Germany, 1990. (8) Koch, G. W.; Sillett, S. C.; Jennings, G. M.; Davis, S. D. The Limits to Tree Height. Nature 2004, 428 (6985), 851−854. (9) Schniewind, A. P.; Berndt, H. The Composite Nature of Wood; Marcel Dekker Inc.: New York, 1991. (10) Fry, W.; White, J. B. Big Trees; Stanford University Press: Standford, CA, 1938. (11) Biermann, C. J. Essentials of Pulping and Papermaking; Academic Press: San Diego, CA, 1993. (12) Jung, H. G.; Allen, M. S. Characteristics of Plant Cell Walls Affecting Intake and Digestibility of Forages by Ruminants. J. Anim. Sci. 1995, 73 (9), 2774−2790. (13) Chen, F.; Dixon, R. A. Lignin Modification Improves Fermentable Sugar Yields for Biofuel. Nat. Biotechnol. 2007, 25 (7), 759−761. (14) Tillman, D. A. Wood as an Energy Resource; Academic Press: New York, 1978. (15) B., M. S.; Samson, R.; Bransby, D.; Wiselogel, A. In Evaluating Physical, Chemical, and Energetic Properties of Perennial Grasses as Biofuels, Bioenergy ’96 - The Seventh National Bioenergy Conference: Partnerships to Develop and Apply Biomass Technologies, Nashville, TN, 1996; Oak Ridge National Laboratory: Nashville, TN, 1996; pp 1−8. (16) Novaes, E.; Kirst, M.; Chiang, V.; Winter-Sederoff, H.; Sederoff, R. Lignin and Biomass: A Negative Correlation for Wood Formation and Lignin Content in Trees. Plant Physiol. 2010, 154 (2), 555−561. (17) Ralph, J.; Ludnquist, K.; Brunow, G.; Lu, F.; Kim, H.; Schatz, P. F.; Marita, J. M.; Hatfield, R. D.; Ralph, S. A.; Christensen, J. H.; Boerjan, W. Lignins: Natural Polymers from Oxidative Coupling of 4hyroxyphenylpropanoids. Phytochem. Rev. 2004, 3 (1−2), 29−60. (18) Higuchi, T. Lignin Biochemistry: Biosynthesis and Biodegradation. Wood Sci. Technol. 1990, 24 (1), 23−63.

ASSOCIATED CONTENT

S Supporting Information *

Additional experimental details are provided that pertain to: (1) production of the recombinant proteins, (2) production of tension and opposite wood, (3) 1D-SDS-PAGE of native SDX and recombinant proteins, (4) fractional factorial design (FracFD) of experiment for digest optimization, and (5) selected reaction monitoring (SRM) transitions. Furthermore, supplemental results are shown for the: (1) selection of surrogate peptides, (2) validation of SRM transition specificity, (3) optimization of the digestion procedure, and 4) quantitative assessment of intra- and inter-day variations of the final FASPLC-SRM method. Figures and tables are provided at the end of the text; however, Tables S3 and S4 are provided as a separate file, which also contains all the cloned/recombinant protein 3402



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