food allergies (James et al., 1997), and WDEIA (Hofmann et al., 2012). Additionally, one 2-DE spot was detected as PER1 which is a confirmed wheat allergen likely associated with Baker's asthma (Pahr et al., 2012). However, this study did not detect the 27 kDa albumin, which was shown to be associated with Baker's asthma (Weiss et al., 1993) or the alcohol-soluble gliadin proteins involved in celiac disease (Wieser, 1996; Allred and Ritter, 2010).

The majority of wheat grain allergenic proteins detected in the present study were not quantified in our recent MS-based study (Uvackova et al., 2013b). This finding is in agreement with a recent investigation of soybean under flooding stress, where only 9 out of 115 proteins were detected by both gel-based and gel-free proteomics approaches in root tips (Yin et al., 2014). Similar results have been shown in the analysis of the honey bees hemolymph proteome, where only 27% of proteins were detected with both approaches (Bogaerts et al., 2009).

Based on this, it is tempting to speculate that gel-based and gel-free approaches are complementary for the detection and quantification of wheat grain allergenic proteins. However, the complementarity of gel-based and gel-free proteomics approaches was suggested previously (Luque-Garcia et al., 2011; Abdallah et al., 2012). The combination of gel-based and gel-free proteomics was shown to be effective for the analyses of soybean under flooding (Yin et al., 2014), phytopathogenic fungus *Botrytis cinerea* (Gonzalez-Fernandez et al., 2013), *Nicotiana tabacum* trichomes (Van Cutsem et al., 2011), the honeybee hemolymph proteome (Bogaerts et al., 2009), or during soybean seed filling (Agrawal et al., 2008).

# References

# Conclusion

This study has demonstrated that phenol-based protein extraction in combination with 2-DE and 7 cm IPG strips is capable of determining clinically relevant proteins in wheat grain extracts. However, important clinically relevant proteins, such as alcohol-soluble gliadins were not detected with this approach. The comparison of these data with previous work suggests that gel-based and gelfree proteomics are complementary approaches for the determination of clinically relevant proteins in wheat grain extracts.

# Acknowledgments

This research was supported by the Scientific Grant Agency of the Ministry of Education of the Slovak Republic and Slovak Academy of Sciences (VEGA-2/0016/14) and European Community under project no<PHONE_NUMBER>0: Building Research Centre "AgroBioTech."

# Supplementary Material

The Supplementary Material for this article can be found online at: http://journal*.*frontiersin*.*org/article/10*.*3389/fpls*.* 2015*.*00433/abstract

patients suffering from wheat-induced respiratory allergy. *Clin. Exp. Allergy* 42, 597–609. doi: 10.1111/j.1365-2222.2012.03961.x

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Fekecsová, Danchenko, Uvackova, Skultety and Hajduch. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Time to dig deep into the plant proteome: a hunt for low-abundance proteins

### *Ravi Gupta1, Yiming Wang2, Ganesh K. Agrawal 3,4, Randeep Rakwal 3,4,5,6, Ick H. Jo7, Kyong H. Bang7 and Sun T. Kim1 \**

*1 Plant Functional Genomics Laboratory, Department of Plant Bioscience, Life and Industry Convergence Research Institute, Pusan National University, Miryang, South Korea*

*2 Plant Proteomics Group, Max Planck Institute for Plant Breeding Research, Cologne, Germany*

*3 Research Laboratory for Biotechnology and Biochemistry, Kathmandu, Nepal*

*4 Global Research Arch for Developing Education (GRADE) Academy Pvt. Ltd, Birgunj, Nepal*

*5 Organization for Educational Initiatives, University of Tsukuba, Tsukuba, Japan*

*6 Department of Anatomy I, Showa University School of Medicine, Tokyo, Japan*

*7 Department of Herbal Crop Research, Rural Development Administration, Eumseong, South Korea*

*\*Correspondence<EMAIL_ADDRESS>
### *Edited by:*

*Joshua L. Heazlewood, The University of Melbourne, Australia*

### *Reviewed by:*

*Holger Eubel, Leibniz Universität Hannover, Germany Christian Lindermayr, Helmholtz Zentrum München - German Research Center for Environmental Health, Germany*

**Keywords: low-abundance proteins, high-abundance proteins, two-dimensional gel electrophoresis, RuBisCO, post-translational modifications**

### **INTRODUCTION**

Two-dimensional gel electrophoresis (2- DGE) has come a long way since its introduction around 40 years by the pioneering work of these three researchers (Klose, 1975; O'Farrell, 1975; Scheele, 1975). 2-DGE was one of the major breakthroughs in proteomics, enabling researchers to detect, analyze and identify the whole set of proteins of a cell or tissue, simultaneously. With the advancement in technology, some modifications to this technique like development of immobilized pH gradient (IPG) strips were introduced, which undoubtedly, made this technique more simple, rapid and autonomous (Bjellqvist et al., 1982). After its introduction to the present, 2-DGE has been the method of choice for analyzing the complex proteomes of plants. 2-DGE has been used extensively to investigate the effects of biotic and abiotic stress, role of hormones, and developmental changes of plants, among others (Agrawal and Rakwal, 2008). However, it was slowly realized that identification of the plant proteins led to the repeated detection of high-abundance proteins (HAPs) including ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) and other housekeeping proteins, which are present at 106-105 order of magnitude (Gygi et al., 2000; Patterson and Aebersold, 2003; Görg et al., 2004). Signaling and other regulatory proteins are generally present 100 molecules per cells. Subsequently, these proteins are difficult to identify by either gel-based or gel-free proteomic approaches, even with access to the latest mass spectrometers. RuBisCO comprises of a large percentage in the total proteins and thus hinders the absorption of low-abundance proteins (LAPs) on the IPG strips, which subsequently results in the poor detection and identification of LAPs on 2D gels and by mass spectrometry (MS), respectively. Therefore, the time has come for all plant proteomers to realize the need to hunt for the LAPs, moving one step ahead from the present. As RuBisCO is the major HAP in plant leaves, here we recommend the incorporation of a RuBisCO depletion/removal method in every plant protein extraction step to look deeper into the plant proteome. RuBisCO depletion will definitely improve the proteome coverage and will lead to the detection of novel unidentified LAPs.

### **OVERVIEW OF THE METHODS DEVELOPED FOR ENRICHMENT OF LAPs: MERITS AND DE-MERITS RuBisCO DEPLETION METHODS**

A number of protein extraction methods with numerous depletion steps were developed in the last decade to remove RuBisCO, which accounts for nearly half of the total leaf protein content (summarized in **Figure 1**). Kim and co-workers initially developed a poly-ethylene glycol (PEG)-based method for the depletion of RuBisCO from rice leaves (Kim et al., 2001). Application of 20% PEG significantly precipitated the RuBisCO protein (large and small subunits) in the pellet fraction, resulting in the enrichment of LAPs in the supernatant fraction. Later, it was shown that 16% PEG was sufficient to deplete the RuBisCO from Arabidopsis leaves (Xi et al., 2006) indicating this method can be applied to all plants for the removal of RuBisCO. However, this PEG method is laborious and time consuming. Following the PEG fractionation approach, a new method using calcium and phytate was introduced for the removal of RuBisCO from leaves of soybean. Results revealed that a 10 min incubation of the leaf extract with 10 mM calcium and 10 mM phytate at 42◦C, depleted 86% of the RuBisCO protein in the pellet fraction (Krishnan and Natarajan, 2009). As incubation of the protein extract at 42◦C is absolutely essential for significant depletion of RuBisCO, this temperature condition can lead to the denaturation of some heat labile proteins. Incubation at lower temperatures significantly reduces

the RuBisCO precipitation ability of this method. For example, only 44% RuBisCO depletion was achieved at 4◦C (Krishnan and Natarajan, 2009). More recently, a protamine sulfate-based specific RuBisCO depletion method was introduced (Kim et al., 2013). It was shown that addition of 0.1% protamine sulfate differentially precipitates the RuBisCO in the pellet fraction and enriches the LAPs in the supernatant fraction. Using Western blotting, no RuBisCO was detected in the supernatant fraction, suggesting this method is able to deplete RuBisCO below the detection limit. 2-DGE analysis showed that application of this method in soybean resulted in visualization of 423 new spots in the supernatant fraction which were not discernible in the total fraction. Furthermore, in addition to soybean, this method was also applicable to another dicot Arabidopsis, and monocots rice and maize, suggesting that it can be universally applied in plants for the removal of RuBisCO. This protamine sulfate-based method is rapid, reliable, cost effective, and highly efficient and is more specific than the previously published PEG and calcium-phytate based methods (Kim et al., 2013).

### **IMMUNO-AFFINITY BASED METHODS**

Other than the above mentioned precipitation methods, an affinity-based method has been developed for RuBisCO depletion. This method utilizes the anti-RuBisCO antibodies, which are commercially available and supplied as columns (IgY RuBisCO column, Sigma Aldrich; Cellar et al., 2008). The beauty of this method is that it is highly specific to RuBisCO. However, this method is very expensive limiting its wide acceptance among the scientific community, especially laboratories in the developing countries.

In addition to the RuBisCO-depletion methods, other techniques have also been introduced for the enrichment of LAPs. "Combinatorial peptide ligand library" (CPLL) technology, developed over the years by the group of P.G. Righetti, is commercially available under the trade name of Proteominer (BioRad) (Boschetti and Righetti, 2013). Briefly, CPLLs consists of several million hexapeptides (prepared using 16 different amino acids) that are able to recognize complementary amino acid sequence in a bait protein harvesting it from the sample matrix. To put is simply, when the protein extract is loaded onto a CPLL column under large overloading conditions, beads having affinity with the abundant proteins saturate first and therefore, the major fractions of these proteins are washed out due to limited binding capacity of the beads. However, due to low concentrations of LAPs, these proteins keep on binding with their partner beads when additional protein extract is loaded to it and thus get enriched (Boschetti and Righetti, 2013). This method is not based on the specific removal of RuBisCO and removes all the general HAPs from the plant extract.

### **RuBisCO DEPLETION AND POST-TRANSLATIONAL MODIFICATION ANALYSIS**

In addition to the detection of LAPs under normal conditions, depletion of RuBisCO can also be fruitful for post-translational modification (PTM) analysis. RuBisCO being phosphorylated and nitrosylated, hinders the detection of PTMs of LAPs. Recently, it was shown that removal of RuBisCO from *Brassica juncea* leaves significantly improves the detection of novel nitrosylated LAPs (Sehrawat et al., 2013). Similarly, the plant phosphoproteome coverage can also be increased by incorporating the RuBisCO depletion step during the protein extraction step, as indicated in soybean. Application of calcium phytate during protein extraction in soybean led to the identification of 28 new phosphorylated proteins which were previously undetectable, suggesting the application of RuBisCO depletion methods in PTMs discovery as well (Krishnan and Natarajan, 2009).

### **CONCLUSIONS**

The methods (precipitation- and affinitybased) discussed here can deplete the RuBisCO protein, a major HAP in plants. These methods are schematically presented in **Figure 1**. We recommend the incorporation of RuBisCO depletion step during the sample preparation for proteomics analyses given the fact that RuBisCO depletion enriches the LAPs/rare proteins. Identification of these proteins will enrich our knowledge on plant biology.

### **ACKNOWLEDGMENTS**

This work was supported by grants from National Agenda Programs for Agricultural R&D (FTA, grant#: PJ01010401), Rural Development Administration (RDA), Republic of Korea.

### **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 05 December 2014; accepted: 12 January 2015; published online: 30 January 2015.*

*Citation: Gupta R, Wang Y, Agrawal GK, Rakwal R, Jo IH, Bang KH and Kim ST (2015) Time to dig deep into the plant proteome: a hunt for low-abundance proteins. Front. Plant Sci. 6:22. doi: 10.3389/fpls.2015.00022*

*This article was submitted to Plant Proteomics, a section of the journal Frontiers in Plant Science.*

*Copyright © 2015 Gupta, Wang, Agrawal, Rakwal, Jo, Bang and Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# **Multiplex staining of 2-DE gels for an initial phosphoproteome analysis of germinating seeds and early grown seedlings from a non-orthodox specie:** *Quercus ilex* **L. subsp.** *ballota* **[Desf.] Samp.**

*M. Cristina Romero-Rodríguez 1, 2, 3, Nieves Abril 1, Rosa Sánchez-Lucas 1, 2 and Jesús V. Jorrín-Novo1, 2\**

### *Edited by:*

Nicolas L. Taylor, The University of Western Australia, Australia

### *Reviewed by:*

Martin Hajduch, Slovak Academy of Sciences, Slovakia Deyou Qiu, Chinese Academy of Forestry, China

### *\*Correspondence:*

Jesús V. Jorrín-Novo, Department of Biochemistry and Molecular Biology, University of Cordoba, Campus de Rabanales, Ed. Severo Ochoa, Planta Baja, 14071 Cordoba, Spain<EMAIL_ADDRESS>
### *Specialty section:*

This article was submitted to Plant Proteomics, a section of the journal Frontiers in Plant Science

*Received:* 07 April 2015 *Accepted:* 27 July 2015 *Published:* 11 August 2015

### *Citation:*

Romero-Rodríguez MC, Abril N, Sánchez-Lucas R and Jorrín-Novo JV (2015) Multiplex staining of 2-DE gels for an initial phosphoproteome analysis of germinating seeds and early grown seedlings from a non-orthodox specie: Quercus ilex L. subsp. ballota [Desf.] Samp. Front. Plant Sci. 6:620. doi: 10.3389/fpls.2015.00620 1 Department of Biochemistry and Molecular Biology, University of Cordoba, Cordoba, Spain, 2 Agricultural and Plant Proteomics Research Group, Department of Biochemistry and Molecular Biology, Escuela Técnica Superior de Ingenieros Agrónomos y de Montes, University of Cordoba, Cordoba, Spain, 3 Centro Multidisciplinario de Investigaciones Tecnológicas, Universidad Nacional de Asunción, San Lorenzo, Paraguay

As a preliminary step in the phosphoproteome analysis of germinating seeds (0 and 24 h after seed imbibition) and early grown seedlings (216 h after seed imbibition) from a non-orthodox sp. Quercus ilex, a multiplex (SYPRO-Ruby and Pro-Q DPS) staining of high-resolution 2-DE gels was used. By using this protocol it was possible to detect changes in protein-abundance and/or phosphorylation status. This simple approach could be a good complementary alternative to the enrichment protocols used in the search for phosphoprotein candidates. While 482 spots were visualized with SYPRO-Ruby, 222 were with Pro-Q DPS. Statistically significant differences in spot intensity were observed among samples, these corresponding to 85 SYPRO-Ruby-, 20 Pro-Q-DPS-, and 35 SYPRO-Ruby and Pro-Q-DPS-stained spots. Fifty-five phosphoprotein candidates showing qualitative or quantitative differences between samples were subjected to MALDI-TOF-TOF MS analysis, with 20 of them being identified. Identified proteins belonged to five different functional categories, namely: carbohydrate and amino acid metabolism, defense, protein folding, and oxidation-reduction processes. With the exception of a putative cyclase, the other 19 proteins had at least one orthologous phosphoprotein in Arabidopsis thaliana, Medicago truncatula, N. tabacum, and Glycine max. Out of the 20 identified, seven showed differences in intensity in Pro-Q-DPS but not in SYPRO-Ruby-stained gels, including enzymes of the glycolysis and amino acid metabolism. This bears out that theory the regulation of these enzymes occurs at the post-translational level by phosphorylation with no changes at the transcriptional or translational level. This is different from the mechanism reported in orthodox seeds, in which concomitant changes in abundance and phosphorylation status have been observed for these enzymes.

**Keywords: holm oak, recalcitrant seeds, germination, phosphoproteomics, post translational modification**

# **Introduction**

Holm oak (*Quercus ilex* L. subsp. *ballota* [Desf.] Samp.) is the dominant tree species in natural forest ecosystems over large areas of the Western Mediterranean Basin (Pulido et al., 2001). Nowadays, forest restoration and reforestation are high priority objectives, with *Q. ilex* being one of the major tree species for such a purpose (MAPA, 2006), this requiring its nursery production at a high scale. *Q. ilex* forest maintenance and sustainability are facing important problems and challenges related to seed viability/conservation and mortality of adult trees and plantlets after field transplantation resulting from adverse environmental conditions like drought and the so-called decline syndrome (Gallego et al., 1999). As natural, non-domesticated, plant species with a great plasticity and phenotypic variability, a key challenge prior to massive clonal propagation is the establishment of techniques for the cataloging and selection of genotypes among provenances with a high survival percentage and productivity under adverse environmental conditions. In our group, a Proteomics Research Program with *Q. ilex* has been carried out in order to study variability of holm oak populations and response to stresses, and to select elite individuals to be used in reforestation programmes (Jorge et al., 2006; Valero Galván et al., 2011, 2012a,b).

Holm oak is a recalcitrant plant species whose germination and viability loss during storage, has been poorly studied at the molecular level if compared with to orthodox ones. This knowledge will help to understand biochemistry and metabolic status before and after the germination process, which could be important for the development and optimization of strategies for large scale propagation, germplasm conservation and seed conservation practices (Balbuena et al., 2011; Walters et al., 2013).

The germination process of plant seeds has been analyzed by using a proteomics approach both for comparative purposes and for characterisation of posttranslational modifications (PTMs), mainly phosphorylation. Phosphorylation is a ubiquitous and reversible PTM, which determines protein conformation, stability and activity (Kersten et al., 2006; Hunt et al., 2007; Bond et al., 2011). Phosphorylation events modulate a wide range of biological processes in plants and other organisms (Nakagami et al., 2010). Thus, in seed germination, phosphorylation has proven to be one of the mechanisms underlying the signaling cascade pathway mediated by ABA (Fujii et al., 2009; Cutler et al., 2010; Umezawa et al., 2013). Quantitative and qualitative profiling of phosphoproteins during seed germination and seedling development has been performed using different proteomic approaches (gel based and gel-free) in different plant species such as *Arabidopsis thaliana* (Sugiyama et al., 2008; Kersten et al., 2009; Reiland et al., 2009), *Medicago truncatula* (Kersten et al., 2009; Rose et al., 2012a), *Phaseolus vulgaris* (Alonso and Zapata, 2014), *Zea mays* (Lu et al., 2008), and *Oryza sativa* (Chen et al., 2014; Han et al., 2014). It is important to highlight that to the best of our knowledge, all previously investigated species produced orthodox seeds and no data on phosphoproteomic analysis of non-orthodox or recalcitrant seeds have been published.

The characterisation of the phosphoproteome includes the detection and identification of phosphoproteins and phosphopeptides, localisation of the exact phosphorylation sites and the quantitation of phosphorylation status, which can be performed by gel-based and gel-free approaches. Although several MS-based approaches for studying phosphoproteins, including down and bottom-up ones (Kaufmann et al., 2001; Woods Ignatoski, 2001; Agrawal and Thelen, 2005) have been used, phosphopeptides are notoriously difficult to analyse, especially in the presence of their non-phosphorylated counterparts. This is due, among other factors, to the low stoichiometry of phosphorylated proteins arising from the fact that only a small fraction of the protein will exist in a particular phosphorylated form (Wu et al., 2011; Rigbolt and Blagoev, 2012).

Phosphoproteomic experiments are being perfomed by using a phospho-protein/peptide enrichment preliminary step (Thingholm et al., 2009). These protocols require an excessive manipulation of the sample, thus reducing the confidence of the comparative results. It is for that reason, and as a complementary protocol, we propose the use of multiplexing (SYPRO-Ruby and Pro-Q DPS) staining of high-resolution 2-DE gels for a simultaneous analysis of protein changes in abundance and/or phosphorylation status.

In the present work we describe the use of that a technique to detect changes in the phosphoprotein profile throughout the *Q. ilex* seed germination and early seedling growth stages. After MALDI-TOF-TOF MS analysis, we have identified 20 proteins whose phosphorylation status varies during the seed developmental process, with seven of them showing no differences in abundance. This last group included enzymes of the glycolytic and amino acid pathways that were, respectively, more and less phosphorylated in seedlings than in seeds. This pattern was different from the one reported for orthodox seed species, in which concomitant changes in abundance and phosphorylation have been observed for enzymes of these two pathways.

# **Materials and Methods**

### **Plant Material**

Mature acorns were harvested during October–November from healthy holm oaks from Cerro Muriano-Córdoba (Córdoba, Spain 37◦59 57.74N, 4◦46 57.93W). Germination and seedling growth were performed at 22 ± 1◦C for up to 10 days in darkness as described in Liu et al. (2012). Undamaged, mature acorns were sterilized by immersion in 2.5% sodium hypochlorite, washed abundantly with water and finally dried with filter paper. In order to achive a homogeneous and synchronized germination (Liu et al., 2012), acorns were peeled, removing the pericarp and cutting off parts of the distal ends of the acorns, and then placed in plastic boxes containing one sheet of whatman No3 filter paper over wet perlite. The system was covered with filter paper to avoid water loss (Figure S1). Analyzed time course/periods, corresponding to different seed developmental stages (Figure S2) was selected based on morphology as assessed by microscopic observations (Romero-Rodríguez, 2015; Ph. D Thesis); these stages were selected because they were representative of the morphological changes that occur during germination and seedling growth (24 h after imbibition the emergence of radicle was visible and 216 h shoot seedling started to grow). The embryonic axis was removed from seeds at 0 and 24 h after imbibition and the whole seedling at 216 h after imbibition. Samples from each time were abundantly washed with water, blot dried and frozen in liquid nitrogen and stored at −80◦C until protein extraction. Three pooled samples per stage, each one corresponding to a biological replicate (1–2 g fresh weight per pool coming from 20 to 100 individuals), were performed.

## **Protein Extraction, 2-DE Electrophoresis and Multiplex Staining of the Gels**

Tissue samples were ground to a fine powder in liquid nitrogen using a mortar and pestle (three biological replicates per stage). Protein extracts were obtained from embryo axes of mature (0 h, un-imbibed seeds) and germinated seeds (24 h after imbibition when the radicle just emerged) and from seedling radicles (4.5–5 cm length, 216 h after imbibition), (Figure S2). Proteins were extracted using TCA/acetone-phenol according to the protocol of Wang et al. (2006). Protein content in samples was estimated by the method of Bradford (Bradford, 1976) with bovine serum albumin as a standard. Samples (400μg of protein) of each biological replicate per gel, were focused on 17 cm, 5–8 pH IPG strips using a Bio–Rad Protean IEF Cell system (Görg et al., 2004; Maldonado et al., 2008; Valero Galván et al., 2011). The second dimension, SDS-PAGE (Laemmli, 1970) was performed on 12% polyacrylamide gels (PROTEANR Plus Dodeca Cell). Gels were double stained, first with Pro-Q DPS and then with SYPRO-Ruby (Figure S3) following the procedure described in Agrawal and Thelen (2006) and Berggren et al. (2000). Images were captured with Molecular Imager FX (Bio-Rad Laboratories, Inc.). Experimental *Mr*-values were calculated by mobility comparisons with protein standard markers (SDS-PAGE Standards, 161-0304, Bio-Rad) run in a separate marker lane on the SDS-gel, while pI was determined by using a 5–8 linear scale over the total length of the IPG strips.

### **Gel Image Analysis and Statistical Tests**

Gel image (Pro-Q DPS and SYPRO-Ruby) analysis was performed with PDQuest 8.0.1 software (Bio-Rad) (Valledor and Jorrín, 2011). As reported by Agrawal and Thelen (2006) and in order to eliminate false positives, phosphoproteins spots (revealed with Pro-Q DPS) were only considered if the Pro-Q DPS/SYPRO-Ruby volume ratios were higher than those obtained for negative control, non-phosphorylated markers (βgalactosidase and serum albumin) and with ratios equal to or higher than those obtained for phosphorylated ovoalbumin used as positive control. Consistent spot volumes (those present in all biological replicates) were normalized based on total quantity in valid spots, calculated for each 2-DE gel and used for statistical assessments of differential phosphoprotein and total protein abundance. For statistical analysis (ANOVA, PCA), the webbased software NIA array analysis tool (http://lgsun.grc.nia.nih. gov/anova/index.html) (Sharov et al., 2005; Sghaier-Hammami et al., 2013) was employed.

### **MALDI-TOF/TOF Analysis**

Spots with differential abundance were automatically excised (Investigator ProPic, Genomic Solutions), transferred to multiwell 96 plates, and digested with modified porcine trypsin (sequencing grade; Promega) by using a ProGest (Genomics Solution) digestion station. In-gel digestion was performed as decribed by Shevchenko et al. (1996). Peptides were extracted from gel plugs by adding 10μL of 10% (v/v) trifluoracetic acid (15 min at room temperature). Solubilized peptides were desalted and concentrated by using μC-18 ZipTip columns (Millipore). Eluate was directly loaded onto the MALDI plate using α-cyano hydroxycinnamic acid as a matrix. Peptide mass analysis was performed with a MALDI-TOF/TOF (4800 Proteomics Analyzer, Applied Biosystems). The most abundant peptide ions were then subjected to fragmentation analysis (MS/MS), providing information that can be used to determine the peptide sequence. Proteins were assigned identification by peptide mass fingerprinting and confirmed by MS/MS analysis. Mascot 2.0 search engine (Matrix Science Ltd., London; http://www.matrixscience.com) was used for protein identification running over non-redundant NCBI protein, UniprotKB, and *Quercus* (Romero-Rodriguez et al., 2014) databases. The following parameters were allowed: taxonomy restrictions to *Viridiplantae* in public databases, one missed cleavage, 100 ppm mass tolerance in MS and 0.5 Da for MS/MS data, cysteine carbamidomethylation as a fixed modification, methionine oxidation, and the phosphorylation of Ser, Thr, and Tyr residues as a variable modification. The confidence in the peptide mass fingerprinting matches (*p* < 0.05) was based on the MOWSE score, and confirmed by the accurate overlapping of the matched peptides with the major peaks of the mass spectrum. Proteins with statistically significant (*p* < 0.05) hits were positively assigned identification. Identified phosphoprotein sequences downloaded from UniprotKB, NCBI nr or available in Quercus\_DB (Romero-Rodriguez et al., 2014) were subjected to BLAST analysis by using the phosphoprotein BLAST tool in the Plant Protein Phosphorylation DataBase (P3DB) (Gao et al., 2009) available at http://www.p3db.org/, to find orthologous proteins whose phosphorylation sites were described previously in other species. Proteins identified by MALDI TOF/TOF analysis were extracted and classified based on their putative function according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, using Blast2GO (Conesa et al., 2005) based on BLASTp results against NCBI nr protein database (*e* < 10−3), or according to annotations in UniProtKB protein database.

# **Results**

By using a multiplex double staining of the gels it was possible to detect changes in the protein abundance (SYPRO-Ruby-stained spots) and phosphorylation status (Pro-Q DPS-stained spots) throughout the seed germination and early seedling growth of *Q. ilex* in three different stages, mature seeds (just before imbibition), germinated seeds (24 h after imbibition), and early grown seedlings (216 h after imbibition) (Figure S2). In the analyzed stages the protein yield per fresh weight was around 2– 15 mg of protein per g, diminishing during the seedling growth (**Table 1**).

Proteins in the extract were separated by 2-DE, and were evenly distributed throughout along the whole pH (5–8) and *Mr* (6–116 kDa) ranges (**Figure 1**). A total of 482 spots were resolved after SYPRO-Ruby staining, with 222 of them also being stained with Pro-Q DPS, these corresponding to putative phosphoproteins (Table S1).

Consistent Pro-Q DPS stained spots, present in all the three biological replicates, were subjected to statistical, ANOVA and PCA, analysis, with 55, out of the 222, showing significant variations (spot volume) between samples (Table S1). Both qualitative and quantitative changes were observed (**Table 1**). Taking as a reference the mature seed phosphoprotein-profile, big changes occur after radicle emergence, with small differences in germinated seeds. At the seedling stage (216 h post imbibition), 33 qualitative (7 newly appeared and 26 disappeared), and 20 quantitative (12 up and 8 down) changes were observed (**Table 1**).

Two-dimensional biplots indicating associations between experimental samples and protein spots were generated by principal component analysis (PCA) in NIA array analysis tools (**Figure 2**). The consistent Pro-Q-DPS stained spots were different enough to establish groups of the samples analyzed. The three analyzed stages were separated from each other; the first component separated the mature (0 h, un-imbibed) and germinated (24 h after imbibition) stage from seedling stages (216 h post imbibition), and the second component separated all the three stages. PCA results showed that PC1 and PC2 explained 88.57 and 11.42% of total variance, respectively. The 55 putative phosphoprotein spots were selected for MALDI-TOF/TOF MS analysis. ANOVA tests of SYPRO-Ruby stained spots (Table S1 and Figure S4) revealed that 20 out of the 55 did not show any differences in abundance while 35 did.

### **Protein Identification**

After MALDI-TOF/TOF analysis, 20 putative phosphoproteins were identified (**Table 2**). Out of the 20, 13 changed in abundance, while seven did not. For the former the variation in phospho-signal could be simply due to a change in abundance while for the other seven results can only be explained by a modification in their phosphorylation status. To validate the phosphoprotein character/nature of the identified proteins, a BLAST against entries at Plant Protein Phosphorylation DataBase (P3DB; http://www.p3db.org/) (Kersten et al., 2009) was performed. With the only exception of spot 3103, a putative cyclase family protein, the other *Q. ilex* proteins had at least one orthologous phosphoprotein in *A. thaliana*, *M. truncatula*, *Nicotiana tabacum,* and *Glycine max*.

**FIGURE 1 | A virtual 2-DE gel showing the protein profile of** *Q. ilex* **mature seed embryo axis (0 h, un-imbibed) obtained by successive Pro-Q DPS and SYPRO-Ruby staining.** Proteins stained with SYPRO-Ruby appear in green, while Pro-Q DPS stained proteins appear in red. The statistically significant differential phosphoprotein spots are indicated with circles for quantitative differences and with triangles for qualitative (absence/presence) differences. Numbers in red indicate the protein spots that were identified by MALDI TOF/TOF.

**TABLE 1 | Electrophoretic analysis of changes in the protein and phosphoprotein profile during germination and seedling growth.**

Up and down accumulated proteins were calculated respect to the mature (0h) stage.

\* Phosphorylation profile was considered changed when no difference was observed in SYPRO-Ruby staining but was statistically different in Pro-Q DPS. In contrast, it was considered unchanged when a difference was observed in both staining methods.

Number of spots detected by SYPRO-Ruby and Pro-Q DPS in different analyzed stages and number of differential spots in total protein and phosphoprotein are shown.

The identified proteins were grouped into functional categories based on the KEGG pathways database (**Table 2**): carbohydrate and amino acid metabolism, defense, protein folding and oxidation-reduction processes.

# **Discussion**

As a preliminary step in the phosphoproteome analysis during the seed germination and early seedling growth processes of a non-orthodox sp. *Q. ilex*, a multiplex (SYPRO-Ruby and Pro-Q DPS) staining of high-resolution 2-DE gels was used. With this protocol it was possible to detect changes in protein-abundance and/or phosphorylation status, identifying, at the same time, candidate phosphoproteins. This simple technique could be a good complementary alternative to the enrichment protocols used in the search for phosphoprotein (Subba et al., 2013; Han et al., 2014; Li et al., 2015). Phosphoprotein enrichment apart from providing, as Pro-Q staining does, false positives, involves excessive manipulation of the sample that results in protein and PTM losses and possible biases. It is true that phosphoprotein validation requires the identification of the phosphorylated peptide, this not being possible or being moret difficult through the MALDI-TOF-TOF MS strategy employed in this work (Thingholm et al., 2009). The protocol presented suffers from the inherent limitations of the 2-DE coupled to the MALDI-TOF-TOF strategy, such as the possible existence and identification of commigrating spots. In at least one case, that of the cyclase, a phosphopeptide was identified, thus confirming its phosphoprotein nature. In any case, rather than identifying the site of phosphorylation, our objective was to search for putative phosphoroteins that showed changes in the phosphorylation status and interpreted those changes from a biological point of view. Different evidence confirmed that most of the Pro-Q–DPS stained spots identified corresponded to real phosphoproteins. Thus: (i) they are Pro-Q stained; (ii) they were only considered if the Pro-Q DPS/SYPRO-Ruby volume ratios were higher than those obtained for a negative control, non-phosphorylated markers (β-galactosidase and serum albumin in this work) and with a ratio equal to or higher than to those obtained for phosphorylated ovoalbumin used as a positive control (Agrawal and Thelen, 2006); (iii) phosphoprotein orthologs have been reported for four different plant species, including *A. thaliana, M. truncatula, N. tabacum,* and *G. max;* (iv) biological interpretation of the data, as discussed below, fits in very well with what is known about the regulation of the identified proteins by phosphorylation. The percentage of phosphorylated proteins detected in our experimental system was of 46%, with similar figures reported for chickpea seedlings (300, Subba et al., 2013) but lower than those reported for germinating rice seeds (500, Han et al., 2014). This could be due to the different methodological approaches and the experimental system used, rather than the system itself or the biological process used or the experimental conditions rather than differences in the number of detectable phosphorylated proteins.

The percentage of protein identification was lower than that obtained in Coomassie stained spots (Valero Galván et al., 2011, 2012b), this being related to the amount of protein present beyond the absence of sequences for *Quercus* in databases. The most important functional categories are discussed. In this work, we paid special attention to those proteins that showed variations in the phosphorylation pattern with no changes in protein abundance, so that they were supposed to be regulated at the post-translational levels. Independent (protein abundance or phosphorylation pattern) or simultaneous, multiplex (protein abundance and phosphorylation pattern) proteomics analysis by using a similar (bottom-up, 2-DE based) strategy has been used in the analysis of mature orthodox seed and seed germination process in model and crop plant species, including *Arabidopsis*, rice, soybean, rapeseed and maize (Lu et al., 2008; Meyer et al., 2012; Han et al., 2014).

Three proteins belonged to the *carbohydrate metabolism* category: pyrophosphate-dependent phosphofructokinase (PPi-PFK, spot 7502), phosphoglycerate kinase (PGK, spot 7318) and glucose-1-phosphate adenylyltransferase (AGP, spot 4304) (**Table 2** and **Figure 3**). PPi-PFK is a cytosolic enzyme that catalyzes the phosphorylation of fructose-6-phosphate to fructose-1,6-bisphosphate in the glycolytic direction, using inorganic pyrophosphate as the phosphoryl donor. This process makes fructose flow into glycolysis to provide energy. PGK catalyzes the conversion of 1,3-diphosphoglycerate to 3-phosphoglycerate, the first substrate-level phosphorylation reaction in the glycolytic pathway for production of ATP. AGP catalyzes the synthesis of ADP-glucose, which is the active glucoside for starch synthesis (**Figure 3**). Overall, the phosphorylation states of these three enzymes increased throughout the germination process. Phosphorylation modification of many glycolytic enzymes has been reported to cause a significant increase in enzyme activity (Li et al., 2011), incrementing the glycolysis rate and the generation of energy to supply the needs of the developing seedling. These results are in agreement with previous studies on rice germination and seedling (Nakagami et al., 2010; Chen et al., 2014; Han et al., 2014). An increased glycolytic activity in germinating *Q. ilex* seeds is supported by a decrease in sucrose content (Romero-Rodríguez, 2015).

**48**

(Continued)

**TABLE 3 | Continued**

On the contrary, for enzymes of the amino acid metabolism (Glutamate decarboxylase, spot 3612) and chaperones (Heat shock protein 60, spot 2606) a decrease in their phosphorylation signal was observed (**Table 2**). Glutamate decarboxylase (GDC) catalyzes the decarboxylation of glutamate to GABA, a nonprotein amino acid involved in stress tolerances that accumulates in germinating seeds of rice and tomato (Taji et al., 2002; Leitner et al., 2011). Some isoforms of this enzyme are inhibited by phosphorylation (Bao et al., 1995). If applicable to GDC, the reduction in its phosphorylation status observed here might imply an increase in the activity of this enzyme, to eliminate the

# **References**

excess of glutamate and glutamine originated by the high rates of stored proteins degradation occurring during germination.

The phosphorylation status of heat shock proteins (HSPs), involved in *protein folding*, has been described decreasing during rice germination (Han et al., 2014). In agreement with that, HSP60 showed high levels of phosphorylation in nonimbibed (0 h after imbibition) and germinated seeds (24 h after imbibition).

In conclusion, over 200 putative phosphoproteins spots were detected in our analysis. Among them, 20 proteins exhibited significant changes in their phosphorylation status, seven of which were identified. Identified enzymes of the glycolytic (pyrophosphate-dependent phosphofructokinase and phosphoglycerate kinase) and amino acid metabolic pathways (glutamate decarboxylase) and protein folding (heat shock protein 60) did not change in abundance during germination and growth but their phosphorylation status increased suggesting regulation at the post-translational level. Alterations in the phosphorylation status of proteins related to glycolysis and amino acid metabolism are in agreement considering that these pathways must increase from mature seeds to germinated seeds and seedling. To test these hypotheses it is necessary identify the phosphorylation sites, but this work constitutes an initiation in the study of the molecular mechanism involved in *Q. ilex* seed germination. The phosphoproteome analysis suggested that the metabolic machinery present in the recalcitrant seeds receives a signal to activate and resume/summarize the most important metabolic pathways in *Q. ilex* to start the germination and the establishment of the seedlings. In orthodox seeds, changes in abundance, together with differences in their phosphorylation status, were observed for these enzymes. Thus, this is one of the differences between orthodox and non-orthodox seeds that may explain their different behavior (Han et al., 2014).

# **Supplementary Material**

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2015. 00620

synthase in stress tolerance in *Arabidopsis thaliana*. *Plant J.* 29, 417–426. doi: 10.1046/j.0960-7412.2001.01227.x

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Romero-Rodríguez, Abril, Sánchez-Lucas and Jorrín-Novo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# MALDI-TOF MS profiling approach: how much can we get from it?

Angela Mehta\* and Luciano P. Silva\*

Embrapa Recursos Genéticos e Biotecnologia, Parque Estação Biológica, Brasília, Brazil

Keywords: MALDI-TOF MS profiling, plants, microorganisms

Mass spectrometry has brought unprecedented possibilities in the field of proteomics. The advances obtained in the last 10 years have been outstanding and have enabled faster and more reliable data acquisition and comparison. One powerful method developed was the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling (MALDI-TOF MS profiling) approach, primarily used for the rapid and accurate identification of microorganism species. At least three commercial manufacturers developed systems (instrument, processing method, and databases) for this purpose with relatively similar results although not completely comparable or exchangeable. These methods were conceived for species level identification; however we explored these approaches with some few modifications for expression profiling purposes. We tested different samples, from diverse organisms (not only microorganisms) and found that MALDI-TOF MS profiling methods also have the ability to differentiate samples submitted to different biological conditions (e.g., biotic or abiotic stresses). In the case of MALDI-TOF MS profiling traditional approach, the extraction procedure is based on the enrichment of ribosomal proteins. By using different extraction protocols, samples can also be enriched with different types of proteins. Indeed, when proteins considered for profiling were further analyzed by MALDI TOF/TOF methods, other proteins could also be detected. Although MALDI-TOF MS profiling methods have been used in several tissues and samples, this approach has been rarely employed in plants. One of the applications in plant research is the identification of biomarkers associated to disease, which could, for example, help on quarantine procedures. Overall, MALDI-TOF MS profiling has a high potential to contribute for sample differentiation and biomarker identification and should be better explored in plant proteomics.

# Advances and Applications of MALDI-TOF MS Profiling

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling (MALDI-TOF MS profiling) is an emerging approach based on rapid and high-throughput screening of ions from molecules directly detected in biological samples, including intact cells and crude extracts (Albrethsen, 2011). Although MALDI-TOF MS profiling is one of the most promising mass spectrometry techniques, it detects only the most intense molecular ions of low molecular mass (typically < m/z 20,000) and does not enable direct protein identification. MALDI-TOF MS profiling commonly leads to the detection of a large number of biomarkers and captures the fingerprints of cells, tissues, and biological fluids under normal or altered conditions. MALDI-TOF MS profiling has been initially targeted for the classification of clinically relevant medical applications, including cancer (Sidransky et al., 2003) and pathogenic microorganism (Ilina et al., 2009) diagnostics. Recently, it has proved to be a versatile tool toward an unprecedented number of applications. Clinical bacterial (Eigner et al., 2009) and mycobacterial (Oswald-Richter et al., 2012) isolates, entomopathogenic soil fungi (Lopes et al., 2014), environmental yeasts (Agustini et al., 2014), and viruses (Calderaro et al., 2014) are some of the biological systems which were identified

### Edited by:

Jesus V. Jorrin Novo, University of Cordoba, Spain

### Reviewed by:

Tiago Santana Balbuena, State University of São Paulo, Brazil Jesus V. Jorrin Novo, University of Cordoba, Spain Manuela Peukert, Institut für Pflanzengenetik und Kulturpflanzenforschung Gatersleben, Germany

### \*Correspondence:

Angela Mehta and Luciano P. Silva<EMAIL_ADDRESS><EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Plant Proteomics, a section of the journal Frontiers in Plant Science

> Received: 21 December 2014 Accepted: 06 March 2015 Published: 23 March 2015

### Citation:

Mehta A and Silva LP (2015) MALDI-TOF MS profiling approach: how much can we get from it? Front. Plant Sci. 6:184. doi: 10.3389/fpls.2015.00184 and clustered by MALDI-TOF MS profiling. In addition, it has been applied for quality control of foods including honey (Wang et al., 2009) and chocolate (Bonatto and Silva, 2014a).

Interestingly, it has also been demonstrated by several authors that MALDI-TOF MS profiling can exhibit resolution even toward bacterial strain-level identification (reviewed in Sandrin and Goldstein, 2013). MALDI-TOF MS profiling has also been applied for the detection of methicillinresistant strains of Staphylococcus aureus (Sparbier et al., 2013). Recently, Bonatto and Silva (2014b) reported the use of this technique for the differentiation of yeast cultures submitted to metal nanoparticle stress, showing that this method can have additional applications beyond microorganism species identification. We have further tested MALDI-TOF MS profiling for bacterial and plant samples and also successfully differentiated specific biological conditions (unpublished data).

The main prerequisite for improved identification of biological samples by MALDI-TOF MS profiling is a curated database of mass spectra. Commercial and public mass spectral libraries typically contain hundreds to thousands of entries which are clustered according to a subject category or biological taxa. Although there are several companies manufacturing MALDI instruments, there are only three major commercial systems available in the market (BioTyper— Bruker Daltonics, SARAMIS—Shimadzu and Anagnostec, and MicrobeLynx™—Waters Corporation) for which, equipment, software, and database are integrated aiming at the identification and classification of organisms. These systems typically apply robust algorithms that are associated with multivariate statistic approaches and show distinct quantitative levels of reliability. Indeed, some authors have compared the results obtained using two of the above mentioned approaches and their performances were overall similar (Lohmann et al., 2013).

# MALDI-TOF MS Profiling in Plant Proteomics

The use of MALDI-TOF MS profiling in plants has been restricted to metabolite profiles and has been rarely reported (Fraser et al., 2007). Therefore, to date there is no database sharing spectra from protein profiles of plant tissues or organs under physiological or altered states. However, the application of this technique can have high impacts in plant proteomic studies, contributing for sample differentiation and/or protein marker discovery in agriculture and industry. The identification of biological markers is an important field of study and can significantly help breeders select cultivars better adapted to diverse biotic and abiotic stresses or in different developmental stages.

One of the main challenges in applying MALDI-TOF MS profiling to plant studies is the high complexity observed in plant tissues. For example, leaves have a high ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) content, which seems to interfere in the reproducibility of protein profiles. We have analyzed Brassica oleracea leaves infected with the bacterium Xanthomonas campestris pv. campestris in comparison to non-infected leaves and found that the results did not always show a clear distinction and sometimes clustered different samples together (unpublished data). Reproducibility limitations, specially related to peak intensities, have also been reported in human disease diagnosis when using MALDI TOF MS (Albrethsen, 2007). However, we are confident that a fine tuning of the methodology may be able to solve this problem. Sample preparation and tissue origin, for example, may be crucial for the high reproducibility required during MALDI-TOF MS profiling experiments. The removal of highly abundant proteins such as RuBisCO may result in more reliable and consistent results. In addition, plant organs and tissues showing more constant proteomes (e.g., pollen and seeds) could be preferable when the molecular signature of a given species is the most important challenge. On the other hand, leaves and other more variable plant organs must be the choice if the main interest is to evaluate a specific biological state.

Another challenge is the identification of the proteins detected in the MALDI-TOF MS profiles since this approach is merely comparative. One possibility is the use of enzymatic hydrolysis to generate peptide fingerprints representing a given physiological stage. We recently used this approach in order to generate a representative pool of the peptides hydrolysable by trypsin from the B. oleracea proteins and further detected as a shotgun by the MALDI-TOF MS profiling approach. However, the proteins containing the peptides detected in the MALDI-TOF MS profiling could not be inferred based only on predicted peptide masses obtained by the hydrolysis due to the low mass accuracy and mainly the signal suppression associated with the high abundance of some molecular components.

The species identification based on MALDI-TOF MS profiling approaches is based on ribosomal proteins, which are enriched by the extraction method with formic acid. Indeed, for accurate identification purposes, this enrichment is crucial. However, when other extraction methods are used, it is possible to get a broader range of protein types, allowing a more global view of the proteins being expressed. We have already tested phenol extraction method followed by precipitation in ammonium acetate and methanol for bacterial and plant samples and have found that a higher diversity of proteins was obtained. Therefore, when using this approach for protein expression profiling, the use of such extraction methods seems preferable. It is noteworthy that ions from other biological molecules (e.g., lipids and secondary metabolites) and mass ranges (e.g., lower than m/z 1000) can be used for identification of biological conditions. Ion sources that do not require conventional MALDI matrices or approaches that are based on surfaceenhanced laser desorption/ionization (SELDI) or matrix-free methods are being currently developed by researchers and companies worldwide and forthcoming years will show significant evolution.

Overall, MALDI-TOF MS profiling is a powerful technique that can have various potential applications in plant proteomics, such as protein marker discovery, and provide substantial contributions for genetic breeding programs and biotechnology. Efforts need to be made in order to adapt the methodology for different plant tissues, however, our studies have shown that the use of this technology is feasible, fast and reliable and can be successfully applied in plant proteomic studies.

# References

# Acknowledgments

We thank Embrapa, CNPq and CAPES for the financial support. We acknowledge the Laboratory of Mass Spectrometry of Embrapa Recursos Genéticos e Biotecnologia for the support in the mass spectrometry analysis.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Mehta and Silva. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# **Beyond the Western front: targeted proteomics and organelle abundance profiling**

### *Harriet T. Parsons 1 and Joshua L. Heazlewood 2,3 \**

*1 Section for Plant Glycobiology, Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg, Denmark, 2 Joint BioEnergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA, 3 The Australian Research Council Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of Melbourne, Melbourne, VIC, Australia*

The application of westerns or immunoblotting techniques for assessing the composition, dynamics, and purity of protein extracts from plant material has become common practice. While the approach is reproducible, can be readily applied and is generally considered robust, the field of plant science suffers from a lack of antibody variety against plant proteins. The development of approaches that employ mass spectrometry to enable both relative and absolute quantification of many hundreds of proteins in a single sample from a single analysis provides a mechanism to overcome the expensive impediment in having to develop antibodies in plant science. We consider it an opportune moment to consider and better develop the adoption of multiple reaction monitoring (MRM)-based analyses in plant biochemistry.

### *Edited by:*

*Sabine Lüthje, University of Hamburg, Germany*

### *Reviewed by:*

*Martin Hajduch, Slovak Academy of Sciences, Slovakia Stefanie Wienkoop, University of Vienna, Austria*

### *\*Correspondence:*

*Joshua L. Heazlewood, The Australian Research Council Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of Melbourne, Swanston Street, Melbourne, VIC 3010, Australia<EMAIL_ADDRESS>
### *Specialty section:*

*This article was submitted to Plant Proteomics, a section of the journal Frontiers in Plant Science*

*Received: 17 March 2015 Accepted: 15 April 2015 Published: 05 May 2015*

### *Citation:*

*Parsons HT and Heazlewood JL (2015) Beyond the Western front: targeted proteomics and organelle abundance profiling. Front. Plant Sci. 6:301. doi: 10.3389/fpls.2015.00301* **Keywords: multiple reaction monitoring (MRM), organelle abundance, immunoblotting,** *Arabidopsis***, quantitative proteomics, proteomics**

Higher eukaryotic genomes encode tens of thousands of genes and after considering splice variants and post-translational modifications, likely produce hundreds of thousands of distinct protein products. In eukaryotic cells, proteins are found distributed amongst membrane bound organelles that undertake a multitude of specialized functions and often partition metabolic pathways. Understanding the functional roles of these organelles has given us a comprehensive overview of plant physiology, on to which the complex details of the dynamic regulation of plant-environment interactions can be mapped.

Subcellular fractionation and enrichment by density centrifugation has played a central role in elucidating the functional roles of subcellular compartments. The main biochemical processes were described years before the advent of electrophoretic transfer of proteins on to membranes or the use of antibodies to probe homogenates (Packer et al., 1970; Towbin et al., 1979; Burnette, 1981). Purity was typically assessed by a combination of electron microscopy and enzyme assays (Stocking, 1959; Douce et al., 1977; Mettler and Leonard, 1979) or, in some cases, radiolabeling (Galbraith and Northcote, 1977). Above a certain threshold of purity, maintenance of structural integrity and enzyme activity was the most important prerequisite during the fractionation process. However, for compartments that were less easily enriched than discrete organelles like the plastid or mitochondrion, assessment of contamination levels became more pressing and researchers turned toward immunoblotting, as well as enzyme assays and microscopy (Norman et al., 1986; Hahn et al., 1987; Meyer et al., 1988).

The advent of modern mass spectrometry and proteomics meant that not only could the main biochemical reactions or constituents of a compartment be investigated but many potentially functionally associated proteins could be identified, making organelle proteomics a valuable tool for reducing the complexity of the eukaryotic cell. The likelihood that a protein is correctly assigned to a location is either a function of the purity of the subcellular isolate (for review, see Millar and Taylor, 2014) or of the migration profile of an organelle on a continuous gradient, relative to other subcellular compartments (Dunkley et al., 2006; Nikolovski et al., 2012; Groen et al., 2014).This meant that accurate estimation of the organelle composition of samples became a critical question in this field as well as for biochemical analyses (Aronsson and Jarvis, 2002; Eubel et al., 2007; Parsons et al., 2012; Millar and Taylor, 2014). Although enzyme assays have proven useful in some contexts, as a general method for assessing organelle purity they are not suitable; maintenance of enzyme activity cannot always be assumed and, as reliable assays do not exists for all compartments, not all contaminants can be excluded. Purity is assayed most directly by electron microscopy, although as membranous vesicles can be difficult to distinguish, it cannot always provide a reliable answer. Furthermore, it is dependent on a considerable technical investment and knowledge that is not always possible for many research groups.

Immunoblotting provides a better means by which to address this question, but the qualitative nature of the signal detection makes it a poor choice for accurately assessing the proportional enrichment of a compartment. The availability of antibodies is not evenly distributed across the subcellular compartments in plants with some only being represented by one or two antibodies. Using multiple antibodies as representative markers for an organelle is an important control in situations where purity is paramount to the confidence placed in newly assigned proteins. Using publically available *Arabidopsis* proteomics data and spectral counting, it has been possible to estimate these confidence levels (Reumann et al., 2009; Parsons et al., 2012). However, quantification during fractionation nevertheless remains a limiting factor in this process.

The recent development of protein quantification methods by targeted mass spectrometry has revived discussions regarding the most efficient methods for the quantification of a protein in a sample (Lehmann et al., 2008; Aebersold et al., 2013). Targeted proteomics techniques aim to detect and determine the quantity of a limited set of predefined peptides in a complex mixture of peptides following enzymatic digestion of a protein samples by, e.g., trypsin. This is in contrast to data-dependent acquisition (commonly referred to as "shotgun proteomics") where the aim is to identify as many peptides, and therefore proteins, in a sample as possible. This, however, introduces a certain element of randomness into peptide detection, particularly for lower-abundance peptides and so makes for poor protein quantitation. In multiple reaction monitoring (MRM), or selected reaction monitoring (SRM), a triple quadrupole mass spectrometer is used to select a precursor ion and its resultant product ion(s) after fragmentation (Kondrat et al., 1978). Selection of the parent ion occurs in the first mass analyzing quadrupole (Q1), which is set to a narrow mass window according to the masses of the ion(s) of interest. Collision induced disassociation in the second quadrupole (q2) results in fragmentation of the parent ion in to product ions which are detected in the third quadrupole (Q3) which, again, is set to an appropriately narrow mass window. By focussing machine time on a defined number of peptides, and by requiring both the parent and product ion to be detected, this technique is sufficiently

sensitive and the background signal sufficiently low, that quantitation is possible for both high and moderately low-abundance peptides within the same complex starting mixture in a way that cannot be achieved using shotgun proteomics. The approach has been developed for proteomic studies, as demand for quantitative workflows has increased (Barnidge et al., 2003; Picotti et al., 2010; Maiolica et al., 2012). In recent years advocates have posited the technique as a superior alternative to immunoblotting (Maiolica et al., 2012; Aebersold et al., 2013; Picotti et al., 2013). Indeed, the application of MRM at the individual protein and protein isoform level has proved its ability to detect and quantify proteins against which raising antibodies would have been difficult (Zulak et al., 2009; Taylor et al., 2014). In *Arabidopsis* (Lehmann et al., 2008) and *Chlamydomonas* (Recuenco-Munoz et al., 2015), spiking samples with stable isotope-labeled versions of peptide targets has allowed absolute quantitation of proteins, referred to as a mass western as the results resemble the theoretical output of quantitative immunoblot but done using mass spectrometry.

Given the history of using approaches like westerns and enzyme assays to assess organelle contributions in a sample, the MRM technique could be extended from the individual protein to the compartment level by designing suites of peptide transitions covering marker proteins for multiple subcellular compartments. This would be akin to undertaking multiple immunoblots with suites of antibodies against major plant cellular compartments, like those currently available commercially (e.g., Agrisera AB) and would quickly and easily enable the estimation of the subcellular composition of a given sample. This perspective seeks to explore MRM as an alternative to immunoblotting for assessing the relative abundance of organelles in plant homogenates.

Unlike many targeted approaches using mass spectrometry where protein abundance is assayed in the context of a response, this survey describes the relative abundance of marker proteins between compartments in the same sample, without reference to their function. Several marker proteins and representative peptides per compartment were selected to ensure the overall signal would be representative of the compartment as a whole. Once adequately developed with a collection of reliable transitions that had been assessed for parameters such as limits of detection, limits of quantitation, matrix effects, ion suppression and linearity, the adoption of this technique could greatly benefit the plant community. The ability to assess both the contamination levels of an organelle preparation and track organelle migration during centrifugation would be incredibly useful, but it is imagined that it could also provide means for the rapid monitoring of changes in organelle populations (Yan et al., 2005; Castillo et al., 2008). Consequently, we sought to highlight the potential of the approach by developing an initial set of transitions for specific organelle marker proteins to assess the potential of this approach.

An organelle abundance profile was generated for the reference plant *Arabidopsis* by selecting and analyzing candidate MRM peptide transitions for a number of organelle marker proteins (**Figure 1**). Only proteins repeatedly localizing to a subcellular compartment (Tanz et al., 2013) and generating nonredundant peptides were selected as markers. As far as was possible, selected proteins were functionally unrelated, not coexpressed and within the top 40 most expressed transcripts for

rosettes **(B,D)**. MRM assays **(A,B)** were performed using two to five marker proteins per compartment, except the plastid where six marker proteins were used, including both the light-harvesting complex candidates (three proteins) and non-light harvesting complex candidates (three proteins). Error bars show standard error for *n* = 3 biological replicates. Spectral counts **(C,D)** were

**TABLE 1 | Summary of representative marker proteins and peptides used for detection of subcellular compartments by MRM.**

*1LHC, light harvesting complex.*

an organelle or compartment. This last point was important for comparisons between compartments. For this proof-of-concept study a minimum of three marker proteins per compartment was applied (with the exception of the vacuole); in some instances up to five were employed (e.g., for the PM) when available MRM transitions were readily identified (**Table 1**). A ribosomal category was included with the 10 major subcellular categories (**Table 1**) as these can be an appreciable source of sample contamination in subcellular proteomics. Using *in vitro* synthesis techniques (Brownridge et al., 2011), we have thus far validated the identity (retention time and fragment ions) of at least one peptide per subcellular compartment, i.e., 25 of the 72 peptides.

organelle/subcompartment were summed and expressed as a percentage of the total number of identified proteins. Cyt, cytosol; ER, endoplasmic reticulum; ExC, extracellular; Mt, mitochondria; Ncl, nucleus; Prx, peroxisome; Pld, plastid;

PM, plasma membrane; RP, ribosomal proteins; Vac, vacuole.

Typical differences in organelle abundance detected using this MRM method are demonstrated in two very different but popular experimental systems; heterotrophic *Arabidopsis* cellsuspension culture (**Figure 1A**) and 4-week old *Arabidopsis* rosettes (**Figure 1B**). As growth conditions varied dramatically between systems, particularly with respect to light and carbon source, both light-harvesting complex and non-light harvesting complex plastid markers were included. These MRM profiles of subcellular compartments were then compared to profiles generated by spectral counts of several 100 compartment marker proteins from data-dependent analyses of total protein extracts (**Figure 1**; **Table 1**). Although data-dependent acquisition approaches are known to favor medium/high abundance proteins (Wienkoop and Weckwerth, 2006; Ahn et al., 2007), since relatively abundant proteins had been selected as organelle markers for MRM such a comparison was considered meaningful. Both MRM and data-dependent analyses produced similar organelle profiles for each system (**Figure 1**), showing that using MRM to estimate the relative abundance of subcellular compartments is conceptually valid. Changes in relative abundance were detected in all subcellular categories, demonstrating the quantitative capacity and sensitivity of MRM. As expected given the physiological differences between the two systems, plastids were much less abundant, and mitochondria more so, in cell cultures compared to rosettes (**Figures 1A,B**). Ribosomal proteins and lower-abundance organelles such as the Golgi and peroxisome appeared more abundant in cell cultures (**Figure 1A**), as expected for cytoplasmic-dense, rapidly-dividing, undifferentiated cells grown in a relatively high-oxygen environment.

Some differences between spectral counting and MRM were observed. Lower-abundance organelles such as the Golgi, peroxisome and ER appeared lower when estimated by spectral counting (compare **Figures 1A,C**). The ratio of plastidic proteins to proteins from other compartments also appears lower in the MRM results compared to standard spectral counting approaches (**Figures 1B,D**). Detection is biased against very small, low abundance, or hydrophobic proteins using data-dependent acquisitions, particularly in complex samples, whilst heavily post-translationally modified proteins may never be detected. Undoubtedly, this will affect compartments disproportionately, potentially leading to misrepresentation using techniques such as spectral counting, which could explain these discrepancies between results such as over-representation of the plastid in photosynthetic tissues. This analysis demonstrates a proof of concept for this application of MRM in determining relative organelle abundance, and shows how it could potentially lead to a more accurate estimation of organelle abundance when compared to immunoblotting, enzyme assays, and other mass spectrometry techniques such as spectral counting. However, these results do also point to some potential drawbacks of this technique in its current format.

This technique relies on the assumption that changes in abundance of an entire subcellular compartment can be represented by a small number of proteins. Therefore, this makes the appropriate selection of proteins a critical consideration when designing suites of transitions for detecting subcellular compartments. Tissue- or environment specific changes in gene expression can be largely avoided by consulting publically available microarray data; however how this compares to proteins levels is harder to predict. The disproportionate decrease in light-harvesting complex proteins (**Figures 1A,B**) may reflect environmental influence on protein expression. However, the similarities between **Figures 1A,C** and **Figures 1B,D** suggest that by applying stringent criteria during MRM marker selection, such effects may be minimized. For

# **References**

example, had markers been selected entirely from non-light harvesting complex proteins, our results would have looked very different.

Through this proof-of-concept study, which paves the way for an in-depth analysis of the applicability of MRM to estimating the subcellular composition of samples, we demonstrate that relative quantitation can provide a sufficient overview. One established such approaches could enable profiling of organelles abundance between treatments or as a means to assess the purity of a subcellular fraction. Although this technique does not offer absolute peptide quantitation, as can be achieved by using labeled peptides (Lehmann et al., 2008), this technique likely represents an appreciable increase in accuracy compared to immunoblotting and would already be applicable to a well-annotated species such as *Arabidopsis*. By avoiding isotope-labeled peptides used for absolute quantification, e.g., AQUA (Gerber et al., 2003), which requires additional levels of cost, we aim to increase accessibility of this technique. The triple quadrupole mass spectrometers required are relatively affordable, further increasing the potential popularity of MRM for such approaches. Future design of peptide transitions will be facilitated by the MRMaid and *Arabidopsis* Proteotypic Predictor resources (Fan et al., 2012; Taylor et al., 2014) and validation of transitions facilitated by the development of synthetic peptides libraries (Picotti et al., 2010) and the emergence of techniques such as QconCAT (Beynon et al., 2005; Brownridge et al., 2011). The investment required for quality antibody development will likely prevent production keeping pace with the number of emerging organisms of research interest. Genomic annotation of several more plant species is approaching the level of completion required for non-redundant peptide selection (Goodstein et al., 2012), whilst ever accumulating data in PRIDE (Vizcaino et al., 2013) will facilitate the design of marker peptides for other species, meaning that MRM-based techniques will have a sizable impact on plant cellular biology in the coming years.

# **Acknowledgments**

This work was supported by the U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the U. S. Department of Energy. JH is supported by an Australian Research Council Future Fellowship [FT130101165]. HP is supported by a Marie Curie Intra European Fellowship 2012 [FP7-PEOPLE-2011-IEF 301401].

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Parsons and Heazlewood. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Characterization of protein N-glycosylation by tandem mass spectrometry using complementary fragmentation techniques

### Kristina L. Ford1 , Wei Zeng1 , Joshua L. Heazlewood1, 2 and Antony Bacic1 \*

1 ARC Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of Melbourne, Melbourne, VIC, Australia, 2 Physical Biosciences Division, Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

### Edited by:

Katja Baerenfaller, Swiss Federal Institute of Technology Zurich, Switzerland

### Reviewed by:

Cécile Albenne, Université Toulouse III - Paul Sabatier, France Friedrich Altmann, University of Natural Resources and Life Sciences, Austria

### \*Correspondence:

Antony Bacic, ARC Centre of Excellence in Plant Cell Walls, School of BioSciences, The University of Melbourne, Building 122, Melbourne, Victoria 3010, Australia<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Plant Proteomics, a section of the journal Frontiers in Plant Science

Received: 08 April 2015 Accepted: 15 August 2015 Published: 28 August 2015

### Citation:

Ford KL, Zeng W, Heazlewood JL and Bacic A (2015) Characterization of protein N-glycosylation by tandem mass spectrometry using complementary fragmentation techniques. Front. Plant Sci. 6:674. doi: 10.3389/fpls.2015.00674 The analysis of post-translational modifications (PTMs) by proteomics is regarded as a technically challenging undertaking. While in recent years approaches to examine and quantify protein phosphorylation have greatly improved, the analysis of many protein modifications, such as glycosylation, are still regarded as problematic. Limitations in the standard proteomics workflow, such as use of suboptimal peptide fragmentation methods, can significantly prevent the identification of glycopeptides. The current generation of tandem mass spectrometers has made available a variety of fragmentation options, many of which are becoming standard features on these instruments. We have used three common fragmentation techniques, namely CID, HCD, and ETD, to analyze a glycopeptide and highlight how an integrated fragmentation approach can be used to identify the modified residue and characterize the N-glycan on a peptide.

Keywords: glycosylation, fragmentation, electron-transfer dissociation, post-translational modification, tandem mass spectrometry

The identification, characterization and quantification of post-translational modifications (PTMs) in proteins are a major challenge in the field of proteomics (Heazlewood, 2011). Protein glycosylation is one of the most commonly occurring PTMs with estimates of around 50% of the cellular proteome predicted to be glycosylated (Van den Steen et al., 1998). As well as being a commonly occurring PTM, protein glycosylation is one of the more difficult protein modifications to investigate due to the heterogeneity of the glycan structure (Song et al., 2011). Indeed, the examination of protein glycosylation is further complicated by the variations in the glycan structure (glycoforms) that can occur on a given polypeptide (Rudd and Dwek, 1997). Aberrations in protein glycosylation result in severe developmental abnormalities in diverse species including mammals (Furuichi et al., 2009) and plants (Lerouxel et al., 2005). Protein glycosylation primarily occurs in the secretory system (ER and Golgi) and can result in functional changes, influence subcellular localization, and protein stability (Oxley et al., 2004; Zhou et al., 2005). Identifying and profiling protein glycosylation is thus essential to define the underlying subtleties of a complex proteome.

The application of tandem mass spectrometry (MSn ) as a technique for the characterization of protein glycosylation has occurred for decades (Reddy et al., 1988). However, it has been the development of proteomics-based MS that has enabled the high-throughput analyses of protein glycosylation (glycoproteomics) to occur (Morelle and Michalski, 2007; Zhou et al., 2007; Zielinska et al., 2012). To secure peptide sequences, these high-throughput approaches usually require the enzymatic removal of the glycan prior to analysis by MS (Zhang and Aebersold, 2006), thus losing information on the glycan structure and the specific site of glycosylation. The removal of the glycan is necessary since commonly applied fragmentation techniques produce spectra yielding information from the carbohydrate structure rather than the peptide backbone. This is generally due to the labile nature of the glycan moiety (Ruiz-May et al., 2012b; Saba et al., 2012) and without removal of the glycan, it is difficult to match resultant fragmentation spectra using a standard data analysis workflow. As a result, these types of approaches have thus far dominated the area of glycoproteomics.

As with other eukaryotes, plants contain an array of glycoproteins that are mainly produced in the secretory pathway and contain both N- and/or O-linked glycan structures (Ruiz-May et al., 2012a; Strasser, 2014). A number of targeted plant glycosylation studies have revealed the importance of these motifs to protein function, including enzyme activity (Kimura et al., 1999), thermal stability and folding (Lige et al., 2001), and protein solubility (Welinder and Tams, 1995). However, the prevalence of O-linked glycosylation and the diversity of N-glycan structures found in plant glycoproteins poses distinct challenges for their global analysis in plants (Gomord et al., 2010). The range of N-glycans identified on plant proteins are separated into three broad types; each type represents a variety of structural permutations. The high mannose type comprises structures of GlcNAc2Man5–9 and they mainly occur in the ER and represent immature or precursors of the complex N-glycan structures. The complex type of plant N-glycan structures represents the mature form found in the late Golgi and on extracellular proteins and have been described as comprising GlcNAc4Xyl1Fuc1Man3 to GlcNAc4Xyl1Fuc3Man3Gal2. Lastly, the paucimannose type represents the processing of terminal residues (GlcNAc/Fuc) from complex N-glycan structures resulting in a processed N-glycan comprising GlcNAc2Xyl1Fuc1Man3; these structures are thought to be present in vacuolar localized proteins (Rayon et al., 1998). Detailed knowledge about O-linked glycan structures found on plant proteins is still limited. The most widely studied proteins with O-linked glycans structures in plants are the extensins and arabinogalactan proteins (AGPs), which belong to the hydroxyproline-rich glycoprotein (HRGP) superfamily, where the O-glycan is mainly attached to modified Pro residues (hydroxyproline). The extensins are glycosylated on a series of adjacent hydroxyprolines with Ara4−1 followed by an O-linked Gal on a Ser residue (Gomord et al., 2010). In contrast, the AGPs contain large branched Gal structures also O-linked through hydroxyprolines that contain terminal Ara but can also include Fuc, GlcA, Rha (Nguema-Ona et al., 2014). The complexity and diversity of O-glycan structures and resulting glycopeptides derived from these O-linked glycoproteins has resulted in few MS-based studies; indeed their characterization usually requires a multifaceted approach (Hijazi et al., 2012). Consequently, we intend to address the role of complementary fragmentation techniques by MS for the characterization of N-glycans.

The distinct structural feature found in complex plant N-glycans e.g., the presence of an α-1,3-linked fucose in N-glycans (Tretter et al., 1991), has resulted in few glycoproteomic studies as the established workflows require some adaptation (Song et al., 2013). Consequently, the first attempt at applying the emerging glycoproteomic technologies to plant material was only undertaken a few years ago (Zielinska et al., 2012). Prior to this, some plant glycoproteomics had been conducted including in Arabidopsis (Minic et al., 2007) and tomato (Catalá et al., 2011), however these studies only characterized the lectin enriched sub-proteomes from these species. An attempt to characterize the modified residue and the N-glycan structures by MS represented the first real advancement in the area (Zhang et al., 2011). Using a combination of various glycoprotein enrichment strategies and informatics, 127 putative glycoproteins were identified by MS, with N-glycan sites and structures determined by prediction and re-analysis of MS1 scans (Zhang et al., 2011). Only a year later, a significant advance in the identification of N-glycosylation sites in plant proteins occurred as part of a large-scale survey of model eukaryotic organisms applying the developed glycoproteomic strategies (Zielinska et al., 2012). While a standard workflow of lectin affinity to enrich glycopeptides followed by treatment with peptide-Nglycosidase (PNGase) F was used for non-plant species, enriched plant glycopeptides from Arabidopsis were treated with PNGase A to remove the more complex fucose containing N-glycans. Significantly, removal of N-glycan structures was undertaken in the presence of H18 2 O resulting in an isotopic signature on the modified residue. Thus, in combination with the deamidation of Asn to Asp by the PNGase reaction, both the site and presence of the N-glycan could be validated in the resultant peptides after analysis by MS. Over 2000 N-glycosylation sites were mapped to Arabidopsis proteins by this study, however, no structural information about the glycans was achieved (Zielinska et al., 2012). While lectin enrichment of glycans has been widely employed in glycoproteomic studies, complementary technologies have been developed including the enrichment of glycopeptides by crosslinking chemically activated carbohydrates to hydrazide beads (Zhang et al., 2003). The technique has been applied to plant samples (Arabidopsis) to both assess the specificity of PNGase (F and A) and profile N-glycans from wild-type and the cgl N-glycosylation mutant (Song et al., 2013). A total of 330 glycopeptides from 173 Arabidopsis proteins were identified using this approach. Surprisingly, it was found that the activities of commercially available PNGase A were ineffective with glycoproteomic workflows and that the identified glycopeptides were likely to have harbored immature or mannose-type glycans, like those found in the Arabidopsis cgl mutant (Song et al., 2013). It should be noted that this ineffective removal of complex N-glycans with PNGase A occurred with enriched glycopeptide fractions rather than with glycoproteins, which are known to be difficult to digest with PNGases. The focus in plant glycoproteomics has thus far mainly concentrated on the reference plant Arabidopsis with about 2500 N-glycan sites mapped (Mann et al., 2013). Nonetheless, the small number of glycoproteomic studies undertaken in plants and the limited species analyzed indicate that the current workflows are providing limited opportunities to study plant glycoproteomes and new or complementary approaches need to be explored.

The development of different fragmentation techniques available in the current generation of MS instruments may provide an alternative approach to the limitations of current glycoproteomic procedures (Scott et al., 2011). The most widely employed fragmentation technique in proteomic surveys to produce tandem mass spectra is collision-induced dissociation (CID). The process involves the acceleration of molecules (peptides) which are collided with a neutral gas (e.g., nitrogen) resulting in the breaking of molecular bonds and the generation of tandem mass spectra (Sleno and Volmer, 2004). The term CID generally encompasses trap-type (derived from an ion trap instrument) and beam-type [derived from a quadrupoletime-of-flight (Q-ToF) instruments]. With the development of the Orbitrap MS, a CID-based approach termed higherenergy collisional dissociation (HCD) was developed where fragmentation spectra are produced outside the iontrap, namely in the C-trap (Olsen et al., 2007). The resultant HCD fragmentation spectra are similar to those when producing CID in a Q-ToF mass spectrometer (Michalski et al., 2012). A further fragmentation technique used in proteomics is electron-transfer dissociation (ETD) or electron-capture dissociation (ECD) which causes fragmentation via the transfer of electrons to the gas phase ion (Zubarev et al., 1998; Syka et al., 2004). The application of ETD in proteomics is mainly associated with the analysis of PTMs since fragment ions tend to retain the modifications which are often lost in CID approaches (Wiesner et al., 2008). All these fragmentation techniques are now common options for most modern tandem MS.

Consequently, with the recent availability of differential MS-based fragmentation techniques, we were interested in exploring these approaches for the structural characterization of glycoproteins. The amount and purity of a specific glycoprotein is crucial for the adoption of the LC-MSn approach when applying complementary fragmentation techniques. As a result, we have been using in planta synthesis in Nicotiana benthamiana to transiently express proteins with suspected N-glycan modifications followed by immunoprecipitation as a means to adequately enrich proteins prior to analysis by MS. Recent glycoproteomic analyses in Arabidopsis (Zielinska et al., 2012) had suggested that many enzymes found in the plant Golgi apparatus and involved in cell wall biosynthesis may themselves be glycosylated. Consequently, we have been attempting to identify N-glycans from recently characterized plant glycosyltransferases (Song et al., 2015) after in planta synthesis and immunoprecipitation. Agrobacterium harboring a construct comprising IRREGULAR XYLEM 9 from Asparagus (AoIRX9, KJ556998) conjugated to the yellow fluorescent protein (Venus) was used to infiltrate N. benthamiana leaves and after 4 days, infected leaves were harvested and microsomal protein isolated. The AoIRX9 protein was enriched by immunoprecipitation with GFP-Trap R (ChromoTek) and digested with trypsin. The analysis of the digested protein lysate by standard LC-MSn (CID) resulted in the identification of AoIRX9 with high sequence coverage and indicated it was considerably enriched in the lysate. The utilization of different fragmentation techniques provides complementary information to sequence a glycopeptide. For example, the utilization of ETD in combination with CID (MS3 ) has been used to produce relevant structural and sequence information from an N-glycopeptide (Catalina et al., 2007). The AoIRX9 digested lysate was analyzed by HCD and spectra manually inspected for the presence of an oxonium ion (204.09 m/z) likely derived from a HexNAc. A putative glycopeptide was identified with multiple charge states (770.94 m/z [M + 5H]5+, 963.43 [M + 4H]4+ and 1284.23 [M + 3H]3+) with [M + 5H]5+ the predominant form. The 770.94 m/z [M + 5H]5+ ion was subsequently analyzed using ETD to determine the peptide sequence. The three fragmentation modes, (CID, HCD, and ETD) used to characterize this enriched plant glycopeptide from AoIRX9 are outlined in **Figure 1**.

The application of CID (trap-type) produced poor peptide and glycan fragmentation resulting in little peptide backbone information (b6 and y2) and glycopeptide fragments with high charge states, as previously reported (Catalina et al., 2007; Desaire, 2013). The glycan derived peaks could only be assigned after analysis of the complementary fragmentation spectra was undertaken (**Figure 1A**). The HCD-derived spectra (**Figure 1B**) revealed glycan signatures, such as the HexNAc (GlcNAc) oxonium ion (204.09 m/z) and importantly resulted in an ion representing the mass of the peptide without the glycan structure (1137.54 m/z [M + 2H]2+). This information is essential for the calculation of the mass of the glycan, 1576.6 Da, corresponding to a glycan structure of HexNAc4Pent1Deoxy1Hex3, which is most likely GlcNAc4Xyl1Fuc1Man3, a commonly found plant N-glycan (Oxley et al., 2004; Song et al., 2011). To accurately determine the structure of the glycan, a coupled approach employing detailed analysis of fragment ions and enzymatic sequencing (e.g., exoglycosidase) are necessary (Harvey, 2005). Similar to CID, the HCD spectrum provides minimal information about the peptide backbone, yielding masses for b2, y3, and y5. In contrast, the ETD spectrum (**Figure 1C**) was essential for fragmenting the peptide backbone revealing virtually every z- and a majority of c-ions, enabling confirmation of the peptide sequence as has been previously demonstrated for ETD (Catalina et al., 2007). Lastly, the ETD spectra enabled the determination of the residue (Asn, N) where the glycan structure was attached. In this instance, only a single likely site of N-glycan attachment is possible, however this situation is not always the case.

A considerable bottleneck when attempting to characterize protein glycosylation by MS is the limitations of commonly used software for high confidence matching of glycopeptide fragmentation spectra (Woodin et al., 2013). This issue becomes even more problematic when tandem spectra are derived from fragmentation techniques such as ETD. Although there is an assortment of software options ranging from commercial to open source, each has its limitations depending on the workflow (Dallas et al., 2013). Nonetheless, high throughput matching of glycopeptide spectra is still a considerable challenge which requires extensive manual curation. Generally, we have found that the presence of oxonium ions by manual inspection of HCD derived spectra is still the most reliable approach, however the identity of the glycopeptide is still required. We have attempted to analyze ETD spectra derived from glycopeptides with commonly used programs such as Mascot (Matrix Science, UK), such software is often limited in the number of modifications and the expanded search space results in significant increases in processing time. Consequently, we have found that "boutique

method incorporating a specific activation type (A) CID, (B) HCD, and (C) ETD. The resultant MSn data (CID) were used to confirm the presence of the purified protein in the sample. Unmatched spectra from

.

revealed the sequence of the peptide. This complementary approach identified the peptide HLTYKENFTDAKAEADHQR with a complex N-linked glycan comprising HexNAc4Pent1Deoxy1Hex3

software," such as Byonic (Protein Metrics, USA) outperforms many of the more widely utilized programs for the analysis of ETD spectra containing a highly variable PTM. Overall, the issue of separate analyses e.g., HCD and ETD, the requirement to cross-reference data and variable modifications creates a complicated workflow which may be overcome in the future through software enhancements.

The application of complementary fragmentation techniques to characterize glycopeptides provides an effective approach in glycoproteomics (Desaire, 2013). The utilization and success of complementary fragmentation techniques for the analysis of PTMs including glycopeptides has been widely discussed (Catalina et al., 2007; Chi et al., 2007; Alley et al., 2009; Sobott et al., 2009; Snovida et al., 2010; Scott et al., 2011; Ruiz-May et al., 2012b), however the approach is yet to be adopted by the plant proteomics community. The glycopeptide example outlined here further highlights and confirms the effectiveness of HCD and ETD in revealing crucial information necessary for the characterization of both the glycan and the peptide (Scott et al., 2011). The utilization of CID is generally not of great assistance, although could contribute important structural information about the glycan from resultant fragment ions. The presence of the oxonium ion derived from the glycan in the HCD spectra (e.g., HexNAc, 204.09 m/z) should enable more advanced workflows to be employed for the characterization of glycopeptides (Wu et al., 2007; Scott et al., 2011). For example, such an application could involve the presence of the oxonium ion in the HCD fragmentation spectra triggering the ETD fragmentation of the precursor (Saba et al., 2012). A further advancement could be gained by coupling hydrophilic interaction liquid chromatography (HILIC) as the

# References

pre-MS separation procedure (Scott et al., 2011). There is a clear advantage for glycopeptide separation as features like oligosaccharide branching provide the necessary requirements for hydrophilic interactions (Zauner et al., 2011).

Finally, with advancements in hardware, it is simple to envision future instruments that simultaneously undertake a variety of fragmentation processes during a standard proteomics workflow. This improvement would also likely result in the development of software that could better deal with this complicated workflow. Such a feature could enable a global analysis of PTMs in conjunction with protein identification and label free quantification. The example outlined above applying complementary analytical approaches will likely be an essential part of future proteomic workflows for the accurate characterization of protein structure and function.

# Acknowledgments

The interpretation of ETD spectra was conducted with the assistance of Ms. Yin Ying Ho (The University of Melbourne). This work was funded by grants from the Australian Research Council (ARC) to the ARC Centre of Excellence in Plant Cell Walls [CE110001007] and the U. S. Department of Energy, Office of Science, Office of Biological, and Environmental Research, through contract DE-AC02- 05CH11231 between Lawrence Berkeley National Laboratory and the U. S. Department of Energy. JH is supported by an ARC Future Fellowship [FT130101165]. The MS spectra were acquired at the Mass Spectrometry and Proteomics Facility (MSPF), Bio21 Institute, The University of Melbourne with the help of Dr. Ching-Seng Ang.

spondylodysplastic dysplasias group diseases. J. Med. Genet. 46, 562–568. doi: 10.1136/jmg.2008.065201

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Ford, Zeng, Heazlewood and Bacic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# An improved protocol to study the plant cell wall proteome

Bruno Printz 1, 2, Raphaël Dos Santos Morais 1 , Stefanie Wienkoop3 , Kjell Sergeant 1 , Stanley Lutts 2 , Jean-Francois Hausman1 \* and Jenny Renaut 1

1 Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, Belvaux, Luxembourg, 2 Groupe de Recherche en Physiologie Végétale, Earth and Life Institute Agronomy, Universiteì catholique de Louvain, Louvain-la-Neuve, Belgium, 3 Department for Molecular Systems Biology, University of Vienna, Vienna, Austria

Cell wall proteins were extracted from alfalfa stems according to a three-steps extraction procedure using sequentially CaCl2, EGTA, and LiCl-complemented buffers. The efficiency of this protocol for extracting cell wall proteins was compared with the two previously published methods optimized for alfalfa stem cell wall protein analysis. Following LC-MS/MS analysis the three-steps extraction procedure resulted in the identification of the highest number of cell wall proteins (242 NCBInr identifiers) and gave the lowest percentage of non-cell wall proteins (about 30%). However, the three protocols are rather complementary than substitutive since 43% of the identified proteins were specific to one protocol. This three-step protocol was therefore selected for a more detailed proteomic characterization using 2D-gel electrophoresis. With this technique, 75% of the identified proteins were shown to be fraction-specific and 72.7% were predicted as belonging to the cell wall compartment. Although, being less sensitive than LC-MS/MS approaches in detecting and identifying low-abundant proteins, gel-based approaches are valuable tools for the differentiation and relative quantification of protein isoforms and/or modified proteins. In particular isoforms, having variations in their amino-acid sequence and/or carrying different N-linked glycan chains were detected and characterized. This study highlights how the extracting protocols as well as the analytical techniques devoted to the study of the plant cell wall proteome are complementary and how they may be combined to elucidate the dynamism of the plant cell wall proteome in biological studies. Data are available via ProteomeXchange with identifier PXD001927.

### Keywords: proteomics, cell wall, plant, glycosylation, EGTA

# Introduction

Cell walls are biological composites developing outside the cells and forming a rigid frame protecting the cell. Plant cell walls fulfill a wide variety of roles which differ between cell types, plants, and species (Cosgrove, 2005; Guerriero et al., 2014a,b). Cell walls are generally composed of cellulose, lignin, and hemicellulose embedded in an aqueous glue of pectins. The growth of the cell wall is further determined by the presence of minerals (in particular calcium) and the activity of enzymes and structural proteins that account for up to 10% of the mass of the wall of growing cells (Wolf et al., 2012).

Since the first study directed at the proteome of the plant cell wall, more than 55 papers (http:// www.polebio.lrsv.ups-tlse.fr/WallProtDB/index.php/links) have been published and extensive

### Edited by:

Ganesh Kumar Agrawal, Research Laboratory for Biotechnology and Biochemistry, Nepal

### Reviewed by:

Laurence Veronique Bindschedler, Royal Holloway University of London, UK Sixue Chen, University of Florida, USA

### \*Correspondence:

Jean-Francois Hausman, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology, 5, Avenue des Hauts-Fourneaux, L-4362 Esch/Alzette, Belvaux, Luxembourg<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Plant Proteomics, a section of the journal Frontiers in Plant Science

> Received: 28 January 2015 Accepted: 25 March 2015 Published: 10 April 2015

### Citation:

Printz B, Dos Santos Morais R, Wienkoop S, Sergeant K, Lutts S, Hausman JF and Renaut J (2015) An improved protocol to study the plant cell wall proteome. Front. Plant Sci. 6:237. doi: 10.3389/fpls.2015.00237 research has been carried out on the model species Arabidopsis thaliana (Albenne et al., 2013). Nonetheless, the remarkable diversity in composition and function of the wall across cells, organs and species and the regain of interest in plant by-products in the industrial field [as source of bioethanol (Sreenath et al., 2001), building components (Nozahic et al., 2012), and biopolymers (Hühns and Broer, 2010)] foster the analysis of the cell wall proteome.

The extracellular nature of the wall and the range of bindingaffinities that proteins have for the extracellular matrix make the purification of cell wall proteins in a one-step procedure difficult. The major steps made to improve the enrichment of cell wall proteins (CWPs) have previously been reviewed (Feiz et al., 2006; Jamet et al., 2008; Albenne et al., 2013). Although, CWPs enrichment can be done using non-destructive techniques which preserve membrane integrity, the use of destructive methods that require the grinding of the plant material and consequently the disruption of the plasma membranes is commonly preferred. In these protocols, CWPs are extracted from the ground plant material by washes in buffers of various ionic strengths. In 2004, Watson et al. used a washing procedure using sodium acetate, sodium chloride, and ascorbic acid followed by successive vacuum filtrations on nylon mesh membranes with sodium chloride, water, acetone, and sodium acetate (Watson et al., 2004). In 2006, Feiz et al. introduced a procedure which combines extractions with low ionic strength acidic buffers with different washes in increased sucrose concentration shown to considerably limit the contamination of the wall fraction with intracellular proteins, probably by helping the elimination of organelles and other vesicles less dense than cell wall polysaccharides (Feiz et al., 2006).

In the first studies on CWPs (Bozarth et al., 1987), proteins were extracted with a CaCl2 solution, later studies proposed the enrichment of CWPs and the reduction of the complexity of the extracts by using CaCl2, cyclohexylenedinitrilotetraacetate (CDTA), DTT, NaCl and borate buffers to sequentially extract proteins with various wall-binding affinities (Robertson et al., 1997). Quicker methods involving only a two-steps fractionation using sodium acetate buffers with CaCl2 or LiCl, known to be efficient extractants of CWPs, were then developed on crushed plant material (Watson et al., 2004; Feiz et al., 2006). Recently, CaCl2 was replaced by the chelating agent EGTA to remove the proteins associated with the pectin fraction (Verdonk et al., 2012). Regarding the high degree of variability of the cell wall across species, organs and growing conditions, a broad comparison of these protocols starting from the same initial material appears essential.

Independent of the extraction procedure, CWPs can be identified using different methods involving either 2D LC-MS/MS analysis of the total digested proteins or a separation on gels, followed by a digestion step and MS/MS analysis (Jamet et al., 2008). Although, the basic glycoproteins that are found in the cell wall may be poorly resolved on 2D-gels, this method allows the separation and the relative quantification of different isoforms of a protein and eases the identification of post-translational modifications that may have occurred during the maturation of these proteins.

In this study, the protocols used in the two major studies dealing with the alfalfa stem cell wall proteome (Watson et al., 2004; Verdonk et al., 2012) were tested and compared with a third, hybrid protocol. In this latter protocol, adapted from Verdonk et al. (2012) and Feiz et al. (2006), a sequential three-step extraction based on low ionic strength buffers with additional CaCl2, EGTA, and LiCl is performed. The three protocols are compared by a direct analysis of the digested proteins using LC-MS/MS. Two-Dimensional electrophoresis were further carried out on the extracts from the hybrid protocol to highlight how this technique can complement LC-MS/MS analysis of plant CWPs.

# Methods

### Plant Material

Alfalfa stems (Medicago sativa L.) were harvested from a local field (49◦ 33′ 39.1′′N, 5◦ 41′ 38.0′′E, Musson, Belgium) in early spring 2014. After removal of the leaves, stems were ground to a homogeneous powder in a mortar filled with liquid nitrogen. In this study, about 30 g of field-grown fresh alfalfa stems were frozen in liquid nitrogen, ground, and divided in 3 × 2 (protocols × replicates) samples of about 5.0 g and stored at −80◦C prior to analysis.

# Cell Wall Protein Extraction

CWPs were extracted as described in Watson et al. (2004) (protocol 1) and in (Verdonk et al., 2012) (protocol 2). A third hybrid protocol adapted from Feiz et al. (2006) and Verdonk et al. (2012) was established (protocol 3) to analyze the role of the chelating agent EGTA used by Verdonk et al. (2012). Each extraction procedure was carried out in 2 replicates and performed as summarized in **Figure 1**. Minor modifications were done to the previously described protocols, these were done to focus on the differences induced by the extraction and less on differences in the first steps of the sample preparation. Another adaptation done was the use of the same, 2D-DIGE compatible, buffer for the final resolubilization of the extracted proteins.

## Protocol 1 (Adapted from Watson et al., 2004)

The CWPs isolation and extraction were adapted from Watson et al. (2004), Modifications include the absence of PVPP in the grinding buffer (Watson and Sumner, 2007), the filtering of the plant material through 30µm2 pore size nylon mesh and the resuspension of the proteins in 7 M urea, 2 M thiourea, 2% w/v CHAPS, 30 mM Tris. The plant material was placed in a 50 ml Falcon R tube with 10 ml of buffer (50 mM Na acetate, 50 mM NaCl, and 30 mM ascorbic acid, pH 5.5, 4◦C). After vigorous shaking (24 Hz, 2 min), the slurry was filtered through a nylon mesh membrane (30µm2 pore size) under vacuum and washed sequentially with (a) 100 mL of 50 mM Na acetate, 50 mM NaCl, and 30 mM ascorbic acid, pH 5.5, 4◦C, (b) 50 mL of NaCl (0.1 M, 4◦C), (c) 100 mL of cold water (4◦C), (d) 250 mL of cold acetone (4◦C), and (e) 100 mL of cold water. The retentates were transferred to 30 ml tubes prior to protein extraction.

Cell wall protein extraction was carried out by resuspending the retentates in 7.5 mL of extraction buffer 1 (50 mM Na acetate, 200 mM CaCl2, pH 5.5, 4◦C). Samples were placed on a rocking platform (30 min, 4◦C), centrifuged (10,000 g, 15 min, 4◦C) and the supernatants were saved. The pellets were re-extracted once with extraction buffer 1 and both supernatants of the same sample were pooled to form the CaCl2 fraction.

The pellets were resuspended in 15 mL of extraction buffer 2 (50 mM Na acetate, 3 M LiCl, pH 5.5, 4◦C), placed on a rocking platform (overnight, 4◦C) and centrifuged (10,000 g, 15 min, 4 ◦C). The supernatants were saved, forming the LiCl fraction of the samples.

### Protocol 2 (Adapted from Verdonk et al., 2012)

Cell wall proteins were extracted as presented in Verdonk et al. (2012) with the following adaptations. Only 5 g of fresh matter were used for extraction, proteins were precipitated and washed using ReadyPrep™ 2-D Cleanup Kit (Bio-Rad) and proteins were resuspended in 7 M urea, 2 M thiourea, 2% w/v CHAPS, 30 mM Tris.

Briefly, the plant material was placed in a 50 ml Falcon R tube with 20 mL of buffer A (5 mM Na acetate, 0.4 M sucrose, pH 4.6, 4◦C), shaken vigorously (24 Hz, 2 min) and placed on a rocking platform (overnight, 4◦C). Samples were then centrifuged (1000 g, 15 min, 4◦C) and supernatants were discarded. Both pellets were resuspended in 10 mL of buffer B (5 mM Na acetate, 0.6 M sucrose, pH 4.6, 4◦C) and placed on a rocking platform (30 min, 4◦C) and centrifuged again (1000 g, 15 min, 4◦C). Supernatants were discarded. This washing step was repeated respectively with buffer C (5 mM Na acetate, 1 M sucrose, pH 4.6, 4 ◦C) and twice with buffer D (5 mM Na acetate, pH 4.6, 4◦C). The isolated cell wall fractions (pellet) were then transferred to 30 mL tubes.

Proteins were extracted with 10 mL of extraction buffer 3 (5 mM Na acetate, 50 mM EGTA, pH 4.6) and samples were shaken vigorously at 37◦C for 1 h. After centrifugation (10,000 g, 15 min, 4◦C), supernatants were saved. This extraction step was repeated twice and all supernatants were pooled, leading to the EGTA fraction.

The remaining pellet was resuspended in 10 mL of extraction buffer 4 (5 mM Na acetate, 3 M LiCl, pH 4.6, 4◦C), placed on a rocking platform (overnight, 4◦C) and centrifuged (10,000 g, 15 min, 4◦C). Supernatants were saved and pellets were reextracted twice using the same procedure with a shaking step lasting at least 8 h. All supernatants were pooled, leading to the LiCl fraction.

## Protocol 3 (Adapted from Feiz et al., 2006 and Verdonk et al., 2012)

The isolation of the cell wall fraction was carried out using sequential washes in increased sucrose concentration as described in protocol 2.

The proteins from the isolated cell wall fraction (pellet) were extracted with 7.5 mL of extraction buffer 5 (5 mM Na acetate, 200 mM CaCl2, pH 4.6, 4◦C) and placed on a rocking platform (30 min, 4◦C). Samples were then centrifuged (10,000 g, 15 min, 4 ◦C) and supernatants saved. This step was repeated once and supernatants were pooled, leading to the CaCl2 fraction.

Proteins were further extracted with 10 mL of extraction buffer 3 (5 mM Na acetate, 50 mM EGTA, pH 4.6) and shaken vigorously at 37◦C for 1 h. After centrifugation (10,000 g, 15 min, 4 ◦C), supernatants were saved. This extraction step was repeated twice and supernatants were pooled leading to the EGTA fraction.

The remaining pellet was finally resuspended in 15 mL of extraction buffer 4 (5 mM Na acetate, 3 M LiCl, pH 4.6, 4◦C), placed on a rocking platform (overnight, 4◦C) and centrifuged (10,000 g, 15 min, 4◦C). Supernatants were saved, forming the LiCl fraction.

### Concentration and Desalting of the Extracts

Each cell wall enriched protein fraction was concentrated by using an Amicon Ultra-15 10K Centrifugal Filter Device (Millipore) and centrifuged (4700 g, 4◦C) until reaching a final volume of approximately 200µL. Proteins were further washed and desalted with a ReadyPrep 2-D Cleanup Kit (Bio-Rad) according to manufacturer instructions. After drying, proteins were solubilized in 100µL labeling buffer (7 M urea, 2 M thiourea, 2% w/v CHAPS, 30 mM Tris) and protein concentrations were assessed by using the Bradford protein assay with BSA as standard (Bradford, 1976).

### Protein Analysis SDS-Page

The reproducibility of the extractions was assessed by SDS-PAGE. Proteins, 20µg of each sample (2 replicates by fraction) were loaded on Criterion™ XT precast 1D gel 12% Bis-Tris, 12 + 2 wells, 45µL, 1.0 mm (Bio-Rad) according to manufacturer's instructions. Proteins were allowed to migrate for 1 h at a constant voltage of 200 V. After migration, gels were stained for 45 min with 100 ml of InstantBlue solution (Expedeon). Gels were rinsed twice with deionized water and scanned using a Typhoon FLA 9500 scanner (GE Healthcare).

## Focus on the Hybrid Three-Steps Protocol Using 2-Dimensional Gel Electrophoresis Protein Separation

CWPs, 50µg per fraction (1 replicate), were mixed with 9µL Servalyte, pH 3-10 (Serva Electrophoresis GmbH) and 2.7µL of Destreak Reagent (GE Healthcare) and volumes were completed to 450µL with lysis buffer (7 M urea, 2 M thiourea, 0.5% (w/v) CHAPS). Samples were loaded onto Immobiline™ DryStrip 3-10 NL, 24 cm (GE Healthcare) during overnight rehydration.

Isoelectric focusing was carried out in a five step-program: (1) constant 100 V for 3 h, (2) linear gradient from 100 to 1000 V for 4 h, (3) constant 1000 V for 6 h, (4) linear gradient from 1000 to 10,000 V for 6 h, and (5) constant 10,000 V until reaching a total of 95,000 Vh. During IEF, the current was limited to 75µA per strip.

Strips were then equilibrated 15 min in equilibration buffer (Serva Electrophoresis GmbH) complemented with 6 M Urea and 1% w/v DTT and further 15 min in equilibration buffer complemented with 6 M Urea and 2.5% w/v IAA. Strips were loaded on 2D-HPE™ Large-Gels NF 12.5% (Serva Electrophoresis GmbH) and electrophoresis was carried out using an HPE™ Tower System according to manufacturer's instructions. After the front reached the bottom of the gel, the gels were placed in fixation solution containing 15% v/v ethanol complemented with 1% (m/v) of citric acid. Gels were subsequently placed for 90 min in a LavaPurple (Serva Electrophoresis GmbH) staining solution (0.005% v/v) containing 100 mM NaOH, 100 mM boric acid. After staining, gels were washed twice with 15% EtOH for 15 min, acidified again for 15 min in fixation solution and rehydrated in deionized water. Gels were subsequently scanned at 473 nm using a Typhoon FLA 9500 scanner (GE-Healthcare), and quantitative analysis was carried out using the DeCyder software (v7.0, GE-Healthcare).

### Protein Digestion and Analysis

Following spot detection, all visible spots of each of the 3 gels were stored in a picklist and picked with an Ettan Spotted Picker (GE Healthcare). Digestion and MALDI spotting were carried out using a Freedom EVO II workstation (Tecan). Briefly, gels plugs were washed for 20 min in a 50 mM ammonium bicarbonate solution in 50% v/v MeOH/MQ water and dehydrated for 20 min with 75% ACN. After dehydration, proteins were digested with trypsin Gold (Promega), 8µl of a solution containing 5 ng/µL trypsin in 20 mM ammonium bicarbonate (overnight, 37◦C). After digestion, peptides were extracted from the gel plugs with 50% v/v ACN containing 0.1% v/v TFA and dried. Peptides were then solubilized in 2µL of 50% v/v ACN containing 0.1% v/v TFA and 0.7µL was spotted on MALDI-TOF targets. A volume of 0.7µL of 7 mg/mL αcyano-4-hydroxycinnamic acid in 50% v/v ACN containing 0.1% v/v TFA was added. A MALDI peptide mass spectrum was acquired using the AB Sciex 5800 TOF/TOF (AB Sciex), and the 10 most abundant peaks, excluding known contaminants, were automatically selected and fragmented. MS analyses were carried out as described by Printz et al. (2013). MS and MS/MS spectra were submitted for NCBInr database-dependent identification using the taxonomy viridiplantae (http://www.ncbi. nlm.nih.gov) downloaded on September 23, 2013 and containing 32,770,904 sequences on an in-house MASCOT server (Matrix Science, www.matrixscience.com). A second search was carried out against an EST fabacea database downloaded on December 17, 2013 and containing 19,932,450 sequences. The parameters used for these searches were mass tolerance MS 100 ppm, mass tolerance MS/MS 0.75 Da, fixed modifications cysteine carbamidomethylation, and variable modifications methionine oxidation, double oxidation of tryptophan, and tryptophan to kynurenine. Proteins were considered as identified when at least two peptides passed the MASCOTcalculated 0.05 threshold scores (respectively a score of 50 for all NCBI viridiplantae queries and 57 for the EST fabacea queries).

# Liquid Chromatography Protein Digestion

The digestion of proteins was performed using Amicon Ultra-4 10K Centrifugal Filter Devices (Millipore) (Abdallah et al., 2012). CWPs, 25µg per fraction (1 replicate), were reduced for 20 min in 200µL 10 mM DTT dissolved in 100 mM ammonium bicarbonate. After centrifugation, (30 min, 4700 g, 4◦C) the sample was washed with 200µL of 100 mM ammonium bicarbonate and again centrifuged. The reduced proteins (at the top of the filter) were alkylated with 50 mM iodoacetamide in 100 µL of 100 mM ammonium bicarbonate for 30 min in the dark and after centrifugation washed twice with 100µL 100 mM ammonium bicarbonate. After the last centrifugation, 50µL of trypsin Gold (Promega), 5 ng/mL trypsin in 50 mM ammonium bicarbonate, was added and the filter device incubated overnight at 37◦C. Following digestion, 100µL of deionized H2O were added, filter devices were centrifuged (40 min, 4700 g, 4◦C), and the peptides collected at the bottom of the tube. The mixture of peptides was dried under vacuum and solubilized in 45µL of a solution containing 2% v/v ACN and 0.1% v/v formic acid.

### Peptide Separation and Analysis

A volume of 5µL of the extracted peptides were desalted and separated by reverse phase separation using an Eksigent nano 1DLC (AB Sciex) coupled with a LTQ-OrbiTrap XL mass spectrometer (Thermo scientific) operated with Xcalibur (2.0.7 SP1). Peptide desalting was carried out on C18 OMIX tips (100µl, Agilent Technologies) and separation was carried out at a flow rate of 400 nl.min−1 on a Peptide ES-C18 column (15×0.1 mm, 2.7µm; Sigma-Aldrich) using a linear binary gradient (solvent A: 0.1% formic acid (FA); solvent B: 80% ACN 0.1% FA). MS and MS/MS analyses were performed online, in data-dependent mode with automatic switching between MS and MS/MS. Full scan MS spectra (300–1500 m/z) were acquired at 30,000 (m/z 400) resolution. Internal mass calibration was performed using Cyclomethicone (m/z 371.101230) as lock mass. Dynamic exclusion was enabled with exclusion size list of 500 and exclusion duration of 90 s. The eight most intense precursors were selected for subsequent fragmentation with normalized collision energy of 35%. Fragmentation spectra were acquired in the ion trap with an isolation window of 2.0 m/z, a target value of 1000, an activation Q of 0.25 and an activation time of 30 ms.

CID spectra were processed in an in-house Mascot server (Version 2.1, Matrix Science, www.matrixscience.com, London, UK) using Proteome Discoverer (version <IP_ADDRESS>8, Thermo scientific) by searching against the NCBInr database using the taxonomy viridiplantae (http://www.ncbi.nlm.nih.gov) downloaded on June 06, 2014 and containing 40,910,947 sequences. The searches were performed with the following parameters: used enzyme: trypsin, 2 missed cleavages, mass accuracy precursor: 10 ppm, mass accuracy fragments: 0.8 Da, fixed modifications: Carbamidomethyl (C), dynamic modifications: Dioxidation (W), Gln->pyro-Glu (N-term Q), Glu->pyro-Glu (N-term E), Oxidation (HW), Trp-> Kynurenin (W). Identifications were filtered using the following settings; high peptide confidence (minimum confidence: 95%, peptide decoy database search: Target FDR (Strict): 0.01; Target FDR (Relaxed): 0.05, Validation based on: q-Value), with minimum two peptides per protein. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (Vizcaino et al., 2014) via the PRIDE partner repository with the dataset identifier PXD001927 and 10.6019/PXD001927. Finally, all identifications obtained based on the NCBI database were matched on the Medicago truncatula reference genome using an online platform available at http://plantgrn.noble.org/LegumeIP/v2/blasttranscript. jsp. Sequences were imported as FASTA sequences and blasted to the Mt4.0v1\_GenesCDSSeq\_20130731\_1800 database. Searches were performed with an E-value cut-off of 1e-04 and blast results were accepted in case more than 50% identity was reported.

## Protein Localization

Proteins were considered to be secreted in case the 2 servers SignalP 4.1 (http://www.cbs.dtu.dk/services/SignalP) and TargetP 1.1 (http://www.cbs.dtu.dk/services/TargetP) predicted the presence of a signal peptide cleavage site and an extracellular location with the standard search parameters.

# Results and Discussion

In 2006, Feiz et al. performed a comparative analysis of CWPs isolation protocols (Feiz et al., 2006). This comparison, based on published data, remains theoretical since all protocols were not tested in the same conditions, with a same analytical method and on a same initial plant material. So far two different protocols have been applied to study the cell wall proteome of alfalfa stems, both start with a purification of the cell wall followed by two steps of cell wall protein extraction (Watson et al., 2004; Verdonk et al., 2012). Watson et al. (2004) proposed the sequential use of 200 mM CaCl2 and 3 M LiCl in 50 mM sodium acetate buffers and succeeded to limit the contamination with intracellular proteins to less than 50%. In the second strategy, described by Verdonk et al. (2012), CWPs are sequentially extracted with 50 mM EGTA and 3 M LiCl in 5 mM sodium acetate buffers and the percentage of proteins predicted to be targeted to the cell wall increased significantly to reach about 70%. Although, the sequential use of CaCl2 and LiCl buffers to extract CWPs and glycosylated proteins has been frequently reported (Irshad et al., 2008; Day et al., 2013; Calderan-Rodrigues et al., 2014), the use of a chelating agent as first extractor as proposed by Verdonk et al. is rarely depicted.

In our study, we compare these two previously published methods with a third "hybrid" protocol based on the sequential use of low ionic strength buffers (5 mM sodium acetate) complemented with 200 mM CaCl2, 50 mM EGTA, and 3 M LiCl respectively. This three-steps fractionation should first allow the release of the most loosely attached proteins by saturating the pectin-fraction with Ca2+ ions. The subsequent use of the chelating agent EGTA, that exhibits a high affinity for Ca2+ ions, loosens the pectin network and frees up proteins associated with it. Finally, the last extraction with a high concentration of LiCl should release proteins that are more tightly bound to the wall matrix (Verdonk et al., 2012). The isolation of the cell wall and washing were performed according to Watson et al. (2004) for protocol 1—or using the washes in different sucrose

### TABLE 1 | Amount of proteins extracted by fraction and by protocol.

Proteins were extracted from approximately 5 g of fresh alfalfa stem crushed into powder in liquid nitrogen. \*The mass is expressed in µg per g of fresh material.

concentrations proposed by Feiz et al. (2006) for the protocols 2 and 3. For each protocol and fraction, the total mass of proteins extracted per g of fresh weight is presented in **Table 1**.

As already described by Feiz et al. (2006), the use of NaCl salt in an early step of the protocol, as proposed by Watson et al. (2004), decreased the amount of proteins extracted and thus potentially the number of CWPs present in the extract. The difference in the amount of protein extracted in the two replicates of protocol 1 is remarkable, and in our opinion stems from the fact that the isolation of the cell wall fraction using a Büchner filter is more difficult to control compared to the use of sucrose washes. In comparison the washes with sucrose led to extract the highest amount of proteins. The mass of extracted proteins was however significantly higher for the three-step hybrid protocol. For each protocol, the SDS-PAGE profiles of the two replicates were compared (**Figure 2**). Both fractions (CaCl2 and LiCl) of the protocol adapted from Watson et al. (2004) have a similar protein profile although the intensity of some gel bands varies between fractions (**Figure 2A**). In contrast, the use of EGTA followed by LiCl in protocol 2 (adapted from Verdonk et al., 2012) and 3 (adapted from Feiz et al., 2006 and Verdonk et al., 2012) results in distinct protein patterns between the fractions (**Figure 2B**). This indicates that the use of the chelating agent allows the extraction of set of proteins different from those extracted by CaCl2. The similarity

et al. (2004) (B) protocol 2, adapted from Verdonk et al. (2012) (C) protocol 3 (hybrid), adapted from Verdonk et al. (2012) and Feiz et al. (2006): each lane was loaded with 20µg of proteins on Criterion™ XT precast 1D gel 12% Bis-Tris (Bio-Rad). The gels were stained with Coomassie blue (InstantBlue, Expedeon).

between the replicate profiles indicates the reproducibility of the different extraction steps.

# LC-MS/MS Shotgun Analysis