Selective Chemical Intervention in the Proteome of Caenorhabditis

Selective Chemical Intervention in the Proteome of Caenorhabditis elegans Holger Husi,† Fiona McAllister,‡ Nicos Angelopoulos,† Victoria J. Butler,§ Kevin R. Bailey,| Kirk Malone,| Logan MacKay,‡ Paul Taylor,† Antony P. Page,§ Nicholas J. Turner,| Perdita E. Barran,*,†,‡ and Malcolm Walkinshaw*,† Centre for Translational and Chemical Biology, University of Edinburgh, Edinburgh EH9 3JJ, United Kingdom, School of Chemistry, University of Edinburgh, Edinburgh EH9 3JJ, United Kingdom, Faculty of Veterinary Medicine, University of Glasgow, 464 Bearsden Road, Glasgow G61 1QH, United Kingdom, and School of Chemistry, University of Manchester, 131 Princess Street, Manchester, M1 7DN, United Kingdom Received May 10, 2010

Abstract: We present the first study of protein regulation by ligands in Caenorhabditis elegans. The ligands were peptidyl-prolyl isomerase inhibitors of cyclophilins. Upregulation is observed for several heat shock proteins and one ligand in particular caused a greater than 2-fold enhancement of cyclophilin CYN-5. Additionally, several metabolic enzymes display elevated levels. This approach, using label-free relative quantification, provides an extremely attractive way of measuring the effect of ligands on an entire proteome, with minimal sample pretreatment, which could be applicable to large-scale studies. In this initial study, which compares the effect of three ligands, 54 unique proteins have been identified that are up- (51) or down- (3) regulated in the presence of a given ligand. A total of 431 C. elegans proteins were identified. Our methodology provides an intriguing new direction for in vivo screening of the effects of novel and untested ligands at the whole organism level. Keywords: C. elegans • cyclophilin • ligand intervention on intact proteome • label-free profiling

Introduction Proteomics research has as a primary objectivesthe systematic identification of proteins in a given proteomesand increasingly such investigations are concerned with quantifying the regulation of proteins within the chosen system. One of the particular benefits of global mass spectrometry-based quantitative proteomics analysis is the potential to identify the interactions that occur between proteins in a given organism and hence define biological pathways.1 Alternatives, such as gene arrays, large-scale comparisons of mRNA and protein expression in yeast2 and mammalian cells3 have demonstrated that the differential expression of mRNA (up- and downregulation) can capture at most 40% of the variation of protein * To whom correspondence should be addressed. E-mail: perdita.barran@ ed.ac.uk; [email protected]. † Centre for Translational and Chemical Biology, University of Edinburgh. ‡ School of Chemistry, University of Edinburgh. § University of Glasgow. | University of Manchester.

6060 Journal of Proteome Research 2010, 9, 6060–6070 Published on Web 08/30/2010

expression. Proteomic analysis can therefore provide tangible advantages in quantifying changes in protein expression, though this is still not a straightforward process. A myriad of different proteomic methodologies have been developed over the past few years based on mass spectrometry (MS). Stepwise improvements have been made to the core technique of peptide-mass fingerprinting along with highthroughput strategies for comparative analysis of multiple protein samples with high complexity. Early experimental approaches employed 2D-gels to observe changing patterns due to shifts in protein expression followed by MS-based identification.4,5 Alternative approaches have been developed which rely on mass spectrometry for relative quantitation as well as identification and have in the main involved a labeling step. These include stable isotope labeling with amino acids in cell cultures (SILAC)5-7 and protein labeling with isotopecoded affinity tags (ICAT, iTRAQ, or CILAT),8-11 chemical labeling via modifying sulfhydryl-groups of peptide chains,12 or even a combination13,14 for quantitation of proteins/peptides of interest. Alongside these labeling approaches, there has been considerable efforts made to quantify proteomic change based on label-free measurements of protein/peptide quantitative changes, relying instead on exact matching of elution times of peptides and comparing relative intensities of peptide peaks in a mass spectrum.15-18 Such approaches are practical, with minimal sample preparation, for relative quantitation, or simply to recognize pattern changes between samples.19,20 Such labelfree approaches are particularly useful when quantifying protein abundance changes in global proteomic analyses,15,21 and this is the route we have taken here. The nematode Caenorhabditis elegans is a well-characterized model system for the study of phenotypic and gene expression changes after molecular intervention. Proteome-wide analysis of this animal by LC-MS/MS identified several hundred proteins22 and a similar strategy provided valuable insights to identify changes of protein expression patterns after environmental changes23 as well as quantification of protein levels using 2D-gel approaches,24 iTRAQ,25 and SILAC metabolic labeling.26 Although the genome has been completely sequenced, little is known about mechanisms of action of drugs or toxins on C. elegans. Previous studies with Cyclosporin A (CsA) have been 10.1021/pr100427c

 2010 American Chemical Society

Selective Chemical Intervention in C. elegans shown to cause moulting, cuticle and gut structural defects in C. elegans which is possibly the effect of the inhibition of a cyclophilin (CYP), CsA’s main protein target. CYPs (also abbreviated as CYN in C. elegans) are peptidyl-prolyl isomerases (PPIases), which are crucial in the 3D folding of polypeptide chains and/or intracellular protein transport. In total, 18 CYNs (or molecules containing cyclophilin-like domains) can be identified in the C. elegans genome, compared to around 20 in humans.27 Inhibition of this class of molecules results in an accumulation of misfolded proteins, including structurally significant collagens.28 The phenotypic effects after CsA treatment were also observed by us with a new family of synthetic dimedone-derived cyclophilin inhibitors.29 Here we investigate the biological role of cyclophilins in C. elegans by identifying and quantifying proteins that show a change in expression after treatment by cyclophilin-specific inhibitors. We also demonstrate that mass spectrometry is a suitable approach to observe such chemical-induced quantitative changes in the overall proteome of C. elegans. The labelfree methodology described below provides an alternative to more costly labeling by conventional techniques.

Experimental Procedures Ligands. The synthetic inhibitors of cyclophilin as shown in Figure 2 were synthesized in house as previously described.29 Cyclosporin (Figure 2) was a kind donation from Novartis Basel, Switzerland. Material for N2 Culture. The solutions (S media, superbroth, S basal and rich nematode growth agarose (RNGM))30 and methods followed for liquid culture are described in detail by Lewis and Fleming.31 Liquid Culture Compound Treatment of C. elegans Wild Type Strain (N2) Worms. Mixed stage N2 worms were washed off five, almost starved, large RNGM agarose plates with S basal and added to 1 L of S basal with 12.5 mL of bacteria and incubated in a shaking incubator at 20 °C for four days until a large population of mixed staged worms were obtained that were predominantly gravid adults. The worms were pelleted and bleach treated to kill all the adults and larvae leaving only the eggs and then washed three times with 50 mL of 0.1 M NaCl to remove any traces of bleach. The eggs were resuspended in 20 mL of S basal and 3 mL of egg suspension was added to 100 mL of liquid culture containing S basal supplemented with penicillin/streptomycin and nystatin and bacteria. Each ligand (as shown in Figure 2) was dissolved in ethanol to 100 µM resulting in 3% ethanol in the final culture. Controls were carried out with a final ethanol concentration of 3%. The staged worms were then incubated with the ligands in a shaking incubator at 20 °C for 72 h. After this time the worm populations were a mixture of packed adults, young gravid adults and pregravid young adults, as reported previously.32 Liquid cultures were spun down at 3000 rpm for three minutes and the supernatant removed. The pellet was resuspended in 25 mL of 0.1 M NaCl and left on ice to chill, 25 mL of ice cold 60% sucrose was added mixed then spun at 3500 rpm for five minutes leaving a layer of worms at the top which were then removed and washed in 50 mL 0.1 M NaCl four times with spinning at 3100 rpm for 3 min between washes then finally spun down to give a packed worm pellet (containing a mixture of packed adults, young gravid adults and pregravid young adults) of 1-2 mL in 0.1 M NaCl. This was stored at -80 °C until processed for proteomic analysis.

technical notes This represents a homogeneous, supply of material from a single genotype, thus maximizing biological reproducibility for subsequent relative quantitation experiments. We can estimate the number of worms in each pellet. If we consider that the volume of an adult N2 worm is approximately 2 × 10-15 m3,33 then if the pellet contained entirely adult worms we would have on the order of 500 000 worms (half a million biological replicates) in a 1 mL tightly packed pellet. Cytosolic Protein Extraction, Digestion and Mass Spectrometry. Proteins were extracted following a protocol described in detail elsewhere.34 Briefly, a worm pellet was resuspended in cold 10 mM Tris buffer pH 7.8. Proteins were extracted by sonication, and the cytosolic fraction obtained by centrifugation. All chemicals were obtained from Sigma Aldrich U.K. unless stated. Samples were clarified by 0.2 µm filtration, reduced with DTT and iodoacetamide alkylated as described by Pandey et al.,35 prior to digestion with trypsin (TrypsinGold sequencing grade, Promega, U.K.). The digested material was conditioned for mass spectrometric analysis, loaded onto a C18 PepMap reverse phase trap column (Dionex, U.K.) and chromatographically separated at 350nL/min on a PepMap capillary column (15 cm × 75 µm) (Dionex, UK) using an Ultimate 3000 capillary LC system (Dionex, UK) over a 2 h gradient (2-80% acetonitrile, 0.1% formic acid in water) prior to injection into an ion trap mass analyzer fitted with a picospray source (Esquire HCT Bruker Daltonics, Coventry, U.K.) and metalcoated source needle (New Objective, USA) in either MS or MS/ MS mode. For each sample (3 ligands and an untreated sample) we performed 4 analyses, 1 for MS2 data and 3 MS runs for quantitation. The instrument was externally calibrated using the Agilent Tune Mix. MS2 data was acquired using data dependent acquisition software (HCT Plus, Esquire Control, Bruker Daltonics U.K.). The acquisition parameters for the instrument were as follows, capillary voltage 1300 V; plate offset -500 V; the drying gas was nitrogen at a flow of 10 L/min; the source temperature was 150; the aimed ion charge control was 150 000; and the maximum fill time was set to 200 ms. To generate fragment ions, low energy CID was performed on isolated multiply charged precursor ions with a peak width greater than 0.1 and of a minimum intensity of 100, the fragmentation was set to amplitude to 1 V. In MS mode the instrument was scanned from 300-1500 m/z. at a rate of 8100 m/z /s and the maximum charge state for isotope deconvolution set to 4 with a minimum of 3 peaks needed to identify this. For MS2, the instrument was scanned from 100 to 1500 m/z at a rate of 26 000 m/z /s. The MS and MS2 spectra were the sum of 3 scans. Data Convergence and Analysis. Prior to database searching, the raw data was processed using peaklist-generating software Bruker DataAnalysis Version 3.4 (Bruker Daltonics U.K.) and Biotools Version 3.0. (Bruker Daltonics U.K.). For both of these their default parameters were used for generating peak lists. Database searches (using NCBI-nr version NCBInr_20071130) were performed using the Mascot software 2.2.1 (Matrix Science, Oxford, U.K.)36 with the following parameters: charge states [M + H]+, [M + 2H]2+ and [M + 3H]3+, mass tolerance in MS mode (1.2 Da, and for MS/MS data a tolerance 0.6 Da. A signal-to-noise threshold of 5, an area threshold of 10, and an intensity threshold of 10 were used. In all searches the enzyme was specified as trypsin and up to two missed cleavages were permitted in each peptide. No fixed modifications were considered but carbamidomethylation of cysteines and oxidation of methionine were included as optional modifications (for Journal of Proteome Research • Vol. 9, No. 11, 2010 6061

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Husi et al.

Figure 1. Workflow of the present study using a label-free approach to quantify protein expression changes.

the former no difference to the score was found if it was included as a fixed modification). The search of the NCBInr database was restricted to C. elegans. The database had at the time of search 27 919 protein sequences listed. All other parameters were kept as default. A Mascot cutoff score of 37 for proteins was used. Using these settings, we achieved low false positive rates thus justifying our choice. In all but one of our data sets, the false discovery rate for the number of peptide matches above identity threshold was less than 0.6% (in the outlier it was 1.06%). We obtained these values following the method described by Elias et al.37 For quantitative analysis we used the XCMS software package version 1.9.2 developed by Smith and co-workers.38,39 XCMS was used to perform retention time reconciliation between equivalent samples, and between non equivalent (i.e., treated and untreated) sample sets. It was also used to provide statistical measures of precision in fold differences of treated and untreated samples sets. XCMS was chosen for this as it is open source software written using the R statistical computing system. This allowed us the opportunity to extend the system if required and embed the system within the larger web based data handling system we are developing in-house. XCMS performs four major steps: peak identification; data grouping; retention time correction; and quantification of relative expression levels. In all these stages we used default XCMS values as reported previously.39 Notably: for peak identification a “matchedFilter” function and “bin” profile method are used. The minimum criteria for data to be used for quantitation are found within the matched filter function, the parameter “fwhm” (full width at half-maximum of matched filtration Gaussian model peak) is set to 30 and the signal-tonoise threshold parameter “snthresh” to 10. Subsequent quantitation is based on integrated peak areas. For data grouping XCMS does not take any values, it collates all (for example) untreated samples to one group. Each LC-MS analysis was performed in triplicate and quantification was performed on grouped data sets from each of the three runs. For retention time correction the default “missing value” of 1 was used. To select significant differences the XCMS p-value cutoff was set to 0.1 for data analysis and reporting. Data searching and merging from XCMS output with Mascot files was done with in-house AWASH software, whereby m/z values and retention time pairs from the Mascot output were 6062

Journal of Proteome Research • Vol. 9, No. 11, 2010

matched with corresponding peaks from the XCMS output. AWASH software (binary obtainable from the Proteomic Analysis DataBase (PADB40) is a data handling package used to match the peaks in the LC-MS data with the LC-MS/MS peaks; combining the quantitative information (XCMS output) with the identification information (Mascot output). For the work presented here, we searched the absolute m/z and retention time pairs, as reported by Mascot analysis, of the data sets against the ranges of m/z and retention time pairs of statistically significant peak hits as reported by the XCMS output file of each individual compound treated data set. The algorithm in AWASH uses iterative searches of the m/z and retention time data pairs and fits the highest scoring hits to the corresponding XCMS data points. AWASH allows an extension of both the m/z and retention time data ranges of each individual data point as well as the order of how the extension is applied. A maximum of 1 Da for the m/z and 180s for the retention time range was used, and the iterative searching used 0.1 Da and 30s respectively. The order was set to extend first the retention time prior to the m/z for unmatched data points. If more than one Mascot peptide hit matched the LC-MS data range then the best match was selected. Error margins were set to (1 Da for m/z and (120s for the retention time. Subsequent data convergence and nonredundancy was done using the BLAST search program of identified sequences against the nonredundant RefSeq database and subsequent mapping to WormBase version WS186.

Results Many independent studies have utilized a label-free mass spectrometry approach to quantify and profile protein levels (see for example work by Silva,41 Cutillas and Vanhaesebroeck42,43 and Old15). The general workflow used in this study is shown in Figure 1, where extracted proteins were digested with trypsin and subjected to LC-MS analysis. Three different cyclophilin inhibitors (Figure 2) were tested: the immunosuppressant drug CsA has a Kd of around 12nM while EM234 and KM184, are dimedone derivative cyclophilin A (CypA) inhibitors29 with dissociation constants of 27 µM and 22 µM respectively. At least three runs were performed for each compound treated set, and also for the corresponding untreated samples. LC-MS/MS analysis was used for protein identification. LC-MS runs were combined and analyzed using XCMS and cross-compared to

Selective Chemical Intervention in C. elegans

Figure 2. Ligands used in this study. Cyclosporin (CsA) is shown at the top, and below are the two dimedone based ligands EM234 and KM184 synthesized in house following a computational search and score strategy and extensive in vitro screening.29 Kd values for binding to human cyclophilin A and in vivo IC50 values against C. elegans are given for CsA and KM184. This data and how it was obtained has been reported previously.29

Figure 3. Total ion count chromatogram traces of CsA treated (black) and untreated (gray) samples accumulated over the entire run time. The reverse phase elution gradient (in % B) is denoted as a dashed line. The x-axis denotes the run time in minutes, y-axis on the left is the total ion intensity and the y-axis on the right is the percentage of the reverse-phase elution buffer.

identify peptide masses and their corresponding proteins, which were obtained from Mascot analysis of LC-MS/MS runs. This latter step was performed by hand. Tryptic protein fragments were introduced into an LC-MS system, separated on a reverse phase capillary column over a two hour organic solvent gradient and in-line subjected to MS analysis. Spectra were recorded from a total of 12 individual runs. A representative normalized total ion count chromatogram, for both CsA treated and untreated (control) samples, is shown in Figure 3. It can be seen that the total ion traces are similar. The collected data from three individual runs per sample was then subjected to XCMS analysis (the results from each analysis are available as supplementary data sets). From ∼12 500 peaks present in each sample set, XCMS identified

technical notes ∼9500 peaks. A representative scatter plot of identified peaks versus the retention time for CsA treated worms is shown in Figure 4A. All of these plots indicate an average m/z of approximately 800, eluting at about 75 min (4500 s) which corresponds to approximately 30% organic solvent in the elution buffer. We observe a correlation between higher m/z and longer elution times which is probably explained by an increase of hydrophobicity with increasing peptide length of the fragmented proteins. Figure 4B shows the linear distribution of cumulative identified peaks over a m/z range of 400 to 900, for each of the four samples. The distribution trails off at higher m/z, indicating a good correlation to the median of the statistical test. A number of XCMS identified hits were selected for manual analysis and verified by extracting and overlaying MS traces (see Figure 4C). For example, the peptide with m/z of 732, which was identified by Mascot as derived from F09E5.2, a potential glycosyltransferase involved in embryonic development and growth showed a 2-fold change in the CsA treated sample. The same peptide displayed a 4-fold increase in KM184 treated samples. This enzyme is a paralogue of BUS-8, an essential glycosyltransferase that is required for cuticle integrity in C. elegans.44 The same samples were also analyzed by LC-MS/MS. We identified a total of 14 090 peptides in four individual runs (one for each ligand), and Mascot analysis, with a peptide match of at least two peptides to positively match a protein, identified 413 nonredundant proteins. The output Mascot files, for each of the 3 treated sample sets and the untreated sample are available as supplementary data. Mascot scores ranged from 49 to 1964. Lower score Mascot identified peptide sequences were also searched manually using the BLAST search algorithm against C. elegans sequences to extend potential hits found by XCMS (see below). To assign protein tags to the identified peptide peaks (see Supplemental Table 1) we searched the XCMS data for matching m/z and retention time pairs against the Mascot results We set the statistically relevant XCMS p-value cutoff to 0.1 (approximately 90% confidence) for analysis in all data sets and discarded any potential hits with higher values. These results were then merged to give a nonredundant list of identified proteins. Next we identified which of these proteins had been up or down regulated after treatment with cyclophilin inhibitor. Table 1 lists 54 unambiguously identified proteins that have been up or down regulated. If a peptide is identified as being regulated by more that one of the ligands the Mascot score given is the highest that was found. We considered 1.4 as the minimum fold change that was statistically significant. A further 176 proteins that show up or down regulation but were only identified by a single peptide match are not included in this table. It is apparent from inspection of Table 1 that using XCMS on our LC-MS data sets, we do not identify as many peptides that are regulated after ligand treatment as we do to identify a given protein. The discrepancy can be attributed to the difference between the peak intensity threshold criteria for selection by the HCT Plus, Esquire Control software for MS2 and the fairly stringent “snthresh” value of 10 (see above) we have applied for significance in XCMS data processing. We have grouped the 54 proteins which do have high MASCOT scores, into functional classifications based on gene ontology and KOG, including the up- or down-regulation of proteins, (Figure 5A). The percentage distribution of the Journal of Proteome Research • Vol. 9, No. 11, 2010 6063

technical notes

Husi et al. proteins in each data set indicates that the ligands induce similar protein profile changes with some small differences. For example, EM234 appears to favor an altered expression level for transcriptional molecules (approximately 25% of all proteins observed for this ligand, compared to 17% for CsA and KM184), and a much reduced amount of chaperones (20% for CsA compared with 8% for EM234).

Discussion

Figure 4. Scatter plot of XCMS analyzed data, peak distribution and manual verification of one identified peak. XCMS identified peptides are plotted as their m/z (x-axis) versus the retention time in seconds (y-axis) for untreated samples versus treated samples, in (A) we show the scatter plot for the sample subjected to CsA treatment. The most intense peaks are shown in blue, then green, followed by red with white for the lowest intensity peaks. (B) Distribution graph of m/z (xaxis) versus accumulated number of XCMS detected peaks (y-axis), as for Figure 5 below, data from CsA is shown in blue, KM184 in yellow and EM234 in red and for the untreated set in green. The error bars show the difference between each of the three data sets acquired for each sample. (C) One peak identified by XCMS was selected for manual analysis at m/z of 732 and the combined spectra for CsA and untreated samples are overlaid in the appropriate region. Green/red are untreated traces, purple/brown CsA treated. The x axis in this Figure is m/z and the y axis is intensity (in arbitrary units). 6064


The dimedone family of cyclophilin inhibitors (including KM184 and EM234) are the first chemically synthesized series of molecules to show cyclophilin binding and biological activity.29,45 Measured Kd values for binding of the ligands to human cyclophilin A, are 12nM (CsA), 27 µM (EM234) and 22 µM (KM184) respectively.29 It is well established that C. elegans is relatively impermeable to bioactive compounds, being up to 1000-fold less sensitive than mammalian cells a feature that relates to the presence of the protective cuticle.46 In line with this, the in vivo IC50 for CsA when tested against C. elegans is 28 µM and is significantly greater than the in vitro cyclophilin binding constant. KM184 has an in vivo IC50 of 190 µM which shows an unexpectedly high biological activity compared with its relatively weak binding to cyclophilin A in vitro. Despite the large differences in chemical structure and in cyclophilin binding affinities, both CsA and KM184 cause similar phenotypes in C. elegans including reduced fecundity, reduced growth and cuticle structural defects. The compound EM234 is chemically related to KM184 and was included as a third ligand in the screen with the expectation that there would be a overlap in the biological effects with KM184. The Venn-diagram of the three ligand-treated data sets (Figure 5B) indicates, however, the rather unexpected result that each ligand appears to induce rather different changes in protein expression profiles. There are only three proteins significantly up regulated by all three cyclophilin ligands: vit-6 (an egg yolk protein which is one of over 80 genes upregulated by the dbl-1 pathway that controls fat metabolism:47 EFT-3 (an elongation factor involved in translation) and RPS-17 (a ribosomal protein). The ribosomal protein family are the largest group to be upregulated by all three ligands (Figure 5, Table 1) probably reflecting the effect of augmented translational activity due to cellular stress. Interestingly a number of vitellogenin family members are also significantly upregulated by the three ligands: vit-1 and -2 by CsA and KM184 and vit-4 and -5 by KM184 and EM234. CsA treated worms show a 6-fold increase in most of the VIT proteins which is the most pronounced change for all of the protein families (Table 1). We have previously reported that several morphological changes are seen in the worms following ligand treatment.29 The increase in the VIT proteins fits with the appearance of vacuole formation in the worms after treating with both KM184 and CsA as presumably lipid transport vitellogenins would be required in the construction of the lipid vacuole surfaces. Treatment by CsA and KM184 also changes the structure of the worm gut which swells and shows significant defects, including the inability to process bacteria. Such changes to organ shape will also cause a change in lipid regulation and may also help explain the large changes in vitellogenin production. It is also significant to note that the adult gut is the sole site of vitellogenin synthesis in C. elegans.48 Interestingly a previously published proteomics study on the effect of azacoprostane treatment in C. elegans showed 5 to 7


technical notes

Figure 5. Classification of identified proteins in this study. The individual proteins identified are listed in detail in Table 1 along with their Wormbase accepted gene names and associated accession numbers. (A) Total number of proteins were sorted according to functional classifications and plotted based on up- or down-regulation. Abbreviated classifications are: RIB, ribosomal protein; TF, transcriptional/translational/DNA binding function; TP, transport; CHA, chaperones/chaperonins/protein folding; CS, cytoskeletal component; SCA, scaffolder; UK, unknown function and/or involvement. In this figure, the table underneath lists the number of molecules classified. (B) Summary of the total numbers of identified regulated proteins after treatment by each of the three ligands given in Figure 2. In brackets is the additional number of proteins identified by single peptide hits. For the EM234 ligand 4 (43) unique proteins are identified, and of an additional 14 (22), 8 (16) of these are also regulated by just KM184, 3 (3) by just CsA and 3 (3) by both of the additional ligands.

fold reduction in the expression of vit-2 and vit-6 despite a 3to 5-fold increase in their transcription levels as measured by quantitative PCR.49 Thus it seems that up-regulation of vit-2 and vit-6 may be a standard stress response by C. elegans, but that azacoprostane treatment disturbs sterol metabolism by inhibiting the synthesis of selected proteins including the vitellogenins. Another family of proteins affected by all three ligands (though each showing a different profile) are the chaperones and heat shock proteins (Figure 5 and Table 1). For both CsA and KM184-treated worms there was also notable up-regulation of HSP-1, HSP-3, HSP-4, HSP-70 and F44E5.4 which are ER localized chaperones that play an important role in folding of peptide chains in the secretory pathway. Potential substrates for these chaperones could be secreted structural proteins, including the collagens which make up the nematode exoskeleton, a structure that is severely disrupted in the presence of all three compounds.29 It may be significant that the essential cuticle collagen DPY-14 is downregulated in the presence of KM184. In one of the few published drug-related proteomics studies in C. elegans, treatment with the antibiotic tunicamycin has been found to induced an unfolded protein response in the endoplasmic reticulum resulting in up regulation of the chaperone calreticulin (CRT-1).50 In our study, KM184 also shows a 2.5-fold increase in CRT-1 expression. KM184 also upregulates the chaperone DAF-21 (the homologue of human hsp-90), the protein disulfide isomerase PDI-2 and the cyclophilin CYN-5. Both PDI-2 and CYN-5 are ER-resident enzymes/ chaperones that play a role in protein folding in the nematode cuticle;51 and gut52 respectively. Up-regulation of HSPs has also been induced in RNAi knockdown experiments causing a mitochondrial stress response in C. elegans. The suggestion has been made from experiments with maize that the primary signaling for this stress response may originate from a reduction in mitochondrial transmembrane potential53 which is regulated in part by an isoform of cyclophilin.54 As an indication of the power of this approach, we also detect a 2-fold increase in RACK-1 after both EM234 and KM184 treatment. RACK-1 is a well characterized scaffolder for the PKC signal transduction pathway and plays an active role in tethering and also in the signal cascade. It is not clear how this response could be related directly to cyclophilin inhibition, especially as it seems to be restricted to the two chemically

similar small molecule dimedone ligands which may also be acting on another pathway. In addition since the culture of C. elegans requires the inclusion of bacteria as food source, we cannot rule out the possibility that the bacteria may modify the cyclophillin inhibitors in some way prior to their effect on the worms, however the fact that these ligands have been designed to interact with cyclophilin supports a more direct inhibition of its action. These findings certainly merit future investigation. A future logical next step would be the generation of C. elegans mutants in the main targets identified in this study in order to further characterize the phenotypes induced by these compounds. In addition, we will now be able to generate reporter lines in the key targets and again assess the effect of the compounds on their expression level. In summary, this work shows that label-free quantitative proteomics in conjunction with successive automated data analysis is a viable alternative to conventional methods such as iTRAQ and ICAT. Of course the reproducibility of this method for relative quantitation is governed by the reliability of liquid chromatography-mass spectrometry (LC-MS) based differential analysis, but this is also a major issue with labeling strategies. Comparing two or more complex protein mixtures using LC-MS requires multiple analysis steps to locate and quantitate natural peptides within a single experiment.16,55 Benefits are brought here not only in reduced sample handling and chemical modification, but also in avoiding protein profile responses due to alterations in the growth environment of the organism under investigation. However, it is crucial that multiple LC-MS runs are as identical as possible in terms of column and machine performance, and the software used for data analysis is flexible enough to allow slight changes in the acquired data both for retention times as well as m/z ranges. We also demonstrate here for the first time to our knowledge, the effects of profiling the changes on an entire organism as a result of ligand inhibition. This global approach will lend itself well to the mapping of interaction networks revealed by selective chemical intervention.

Acknowledgment. This work was supported by the BBSRC and the EPSRC. P.E.B. thanks the EPSRC for the award of an Advanced Research Fellowship, and FEM thanks the Caledonian Research Foundation/Carnegie Trust fund for the award of a PhD studentship. We are grateful for the Journal of Proteome Research • Vol. 9, No. 11, 2010 6065

6066

WormBase


Tubulin, Beta family member TuBulin, Alpha family member

Heat Shock Protein family member Heat Shock Protein family member Heat Shock Protein family member Heat Shock Protein family member F44E5.4 Protein Disulfide Isomerase family member CalReTiculin family member F31C3.1 abnormal DAuer Formation family member Heat Shock Protein family member

name

Enzymatic function/metabolic processes WBGene00020166 T02G5.7 WBGene00000114 ALdehyde deHydrogenase family member WBGene00018519 F46H5.3b WBGene00019322 K02F2.2 WBGene00002183 3-Ketoacyl-coA Thiolase family member WBGene00018491 F46E10.10a WBGene00003162 Malate DeHydrogenase family member WBGene00008506 F01G10.1 WBGene00000833 CiTrate Synthase family member WBGene00014095 ZK829.4 WBGene00010317 F59B8.2

WBGene00006528

Cytoskeletal WBGene00006536

WBGene00002025

WBGene00000881 WBGene00000915

WBGene00000802


WBGene00002026

WBGene00002008

WBGene00002007

Chaperones/chaperonins WBGene00002005

function

49 92 325 376 52 138 134 42 93 182 88

F46H5.3 K02F2.2 kat-1 F46E10.10 mdh-1 F01G10.1 cts-1 ZK829.4 F59B8.2

149

227

210

41 183

89

100 98

110

85

93

378

T02G5.7 alh-8

tba-1

tbb-1

hsp-60

cyn-5 daf-21

crt-1

F44E5.4 pdi-2

hsp-70

hsp-4

hsp-3

hsp-1

gene name

Mascot score

12 5

5 10

10 5

15 18 3

3 7

5

13

9

3 11

5

5 7

5

4

6

23

Mascot peptide count (MS/MS)

20.9 14.8

8.3 30.1

25.9 23.8

53.2 42.6 10.3

7.2 14.3

14.9

35

18.8

17 18.7

21.5

10.9 15.6

7.5

8.5

10.7

42.8

sequence coverage (%)

Table 1. Summary of Up- and Down-Regulated Proteins after Ligand Treatment of C. elegansa

2.4 2.4 1.4

2.4 3.9

2.0

1.9

1.5

1.4

1.9

fold change

n/a n/a n/a

n/a n/a

0.3

0.4

0.2

n/a

0.5

standard error

CsA

1 1 1

1 1

4

3

2

1

2

XCMS peptide hits

2.0

2.8 2.3

3.5

2.3

fold change

n/a

n/a n/a

n/a

n/a

standard error

EM234

1

1 1

1

1

XCMS peptide hits

4.7 4.6

3.3 5.2

5.6 2.5

3.5 1.8 3.2

0.5

10.0

0.6

2.5 2.6

2.5

2.7 2.4

2.9

3.1

3.2

3.3

fold change

n/a n/a

n/a n/a

n/a 0.2

0.5 1.2 n/a

n/a

n/a

n/a

n/a 2.5

n/a

0.5 n/a

0.3

0.1

n/a

0.5

standard error

KM184

1 1

1 1

1 2

2 2 1

1

1

1

1 2

1

3 1

3

2

1

5

XCMS peptide hits

technical notes Husi et al.

WBGene00001564

RACK1 (Receptor of Activated C Kinase) homologue family member

Transcriptional/translational involvement WBGene00001167 Elongation FacTor family member (eft-2)

Scaffolder WBGene00010556

WBGene00004409

WBGene00004434

WBGene00004478

WBGene00004473

WBGene00004487

WBGene00004415

WBGene00004474

WBGene00004416

WBGene00004418

WBGene00004421

WBGene00004417

WBGene00004486

WBGene00004472

Ribosomal Protein, Small subunit family member Ribosomal Protein, Small subunit family member Ribosomal Protein, Small subunit family member Ribosomal Protein, Large subunit family member Ribosomal Protein, Large subunit family member Ribosomal Protein, Large subunit family member Ribosomal Protein, Large subunit family member Ribosomal Protein, Small subunit family member Ribosomal Protein, Large subunit family member Ribosomal Protein, Small subunit family member Ribosomal Protein, Small subunit family member Ribosomal Protein, Small subunit family member Ribosomal Protein, Large subunit family member Ribosomal Protein, Acidic family member

name

ASpartyl Protease family member GEX Interacting protein family member

WormBase

WBGene00000214

Ribosomal components WBGene00004481

function

Table 1. Continued

eft-2

rack-1

rla-1

rpl-22

rps-9

rps-4

rps-18

rpl-4

rps-5

rpl-5

rpl-7

rpl-10

rpl-6

rps-17

rps-3

rps-12

gei-7

asp-1

gene name

262

68

44

70

58

94

88

134

69

144

53

99

145

60

98

45

170

94

Mascot score

17

7

3

5

5

8

7

9

5

6

5

6

6

2

7

3

9

4


19.2

25.5

21.6

37.7

18.5

32

29.9

22.9

21

25.9

22.5

28.5

41

20

27.1

49.3

8.9

18.4


2.4

2.5

1.7

2.4

3.6

fold change

n/a

n/a

n/a

n/a

n/a

standard error

CsA

1

1

1

1

1

XCMS peptide hits

2.3

2.1

2.3

2.5

3.8

2.5

3.7

fold change

n/a

n/a

n/a

n/a

n/a

n/a

n/a

standard error

EM234

1

1

1

1

1

1

1

XCMS peptide hits

2.4

0.3

2.9

3.1

3.1

4.6

6.4

14.1

1.8

3.0

2.5

4.0

fold change

n/a

n/a

n/a

n/a

n/a

n/a

6.2

10.7

n/a

n/a

n/a

n/a

standard error

KM184

1

1

1

1

1

1

2

2

1

1

1

1

XCMS peptide hits


technical notes

Journal of Proteome Research • Vol. 9, No. 11, 2010 6067

6068

WBGene00008920


Y38H6C.1

dct-16

vha-13

tct-1

vit-5

vit-4

vit-3

vit-2 vit-6

vit-1

vha-12

F17C11.9

eft-3

eft-4

gene name

143

138

126

312

347

245

575 761

330

94

49

263

131

Mascot score

11

7

7

45

44

43

50 67

35

6

2

19

7


61.1

19.3

45.3

25.5

16

24.3

31.8 42.3

18.5

12.6

13.6

32.4

21.3


3.2

6.7 3.2

6.7

5.9

2.4

2.4

fold change

n/a

4.2 n/a

4.2

n/a

n/a

n/a

standard error

CsA

1

3 1

3

1

1

1

XCMS peptide hits

2.8

2.8

2.8

3.9

3.5

3.7

3.7

fold change

0.0

0.0

n/a

1.6

n/a

n/a

n/a

standard error

EM234

2

2

1

6

1

1

1

XCMS peptide hits

1.4

3.1

4.4

4.4

3.7 14.5

5.6

3.4

fold change

1.1

n/a

n/a

n/a

1.7 6.3

n/a

n/a

standard error

KM184

2

1

1

1

3 5

1

1

XCMS peptide hits

a Entries are sorted according to molecular functions. Gene name, Wormbase accepted gene name; WormBase, associated accession number; name, abbreviated RefSeq protein group name (with gene names removed where appropriate); Mascot score obtained from the untreated data; number of peptides from MS2 data identified the peptide in Mascot; the sequence coverage obtained; for each of the three ligands, CsA, EM234, KM184; fold change, averaged fold differences compared to untreated samples; CsA, EM234, KM184 standard error, statistical standard error; CsA, EM234, KM184 the number of peptides identified in XCMS above threshold in relation to protein identified by Mascot search.

Unknown/other function WBGene00012615

WBGene00013025

WBGene00009122

WBGene00006929

WBGene00006928

WBGene00006927


WBGene00006925

Vacuolar H ATPase family member VITellogenin structural genes (yolk protein genes) family member C42D8.2 VITellogenin structural genes (yolk protein genes) family member VITellogenin structural genes (yolk protein genes) family member VITellogenin structural genes (yolk protein genes) family member VITellogenin structural genes (yolk protein genes) family member TCTP (translationally controlled tumor protein) homologue family member Vacuolar H ATPase family member

Elongation FacTor family member (eft-4) Elongation FacTor family member (eft-3) F17C11.9b

WBGene00001169

WBGene00001168

name

WormBase

Transport WBGene00006921

function

Table 1. Continued

technical notes Husi et al.

technical notes

Selective Chemical Intervention in C. elegans help of Yana Berezovskaya in setting up the LC-MS instrumentation. Bruker Daltonics are also thanked for their ongoing support of mass spectrometry at Edinburgh.

Supporting Information Available: XCMS output files for each of the LC-MS data sets run on each of the four samples (three ligand treated and the untreated data) and Mascot output files from LC-MS/MS analysis of each of the sample sets are available on request. This material is available free of charge via the Internet at http://pubs.acs.org.

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PR100427C

Selective Chemical Intervention in the Proteome of Caenorhabditis

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