Advancing serum peptidomic profiling by data-independent acquisition for clear-cell renal cell carcinoma detection and biomarker discovery

Journal of Proteomics 215 (2020) 103671

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Advancing serum peptidomic profiling by data-independent acquisition for clear-cell renal cell carcinoma detection and biomarker discovery ⁎

Lin Lina, , Jiaxin Zhengb, Fangjian Zhengb, Zonglong Caib, Quan Yuc,

T

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a

Sustech Core Research Facilities, Southern University of Science and Technology, Shenzhen 518055, China Department of Urology and Center of Urology, The First Affiliated Hospital of Xiamen University, Xiamen 361003, China c Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Serum Peptidome Data independent acquisition Cancer detection Biomarker discovery Clear-cell renal cell carcinoma

The analysis of serum endogenous peptides holds promise for disease study and drug discovery, whereas it is relatively unexplored given its challenges in analysis reproducibility and reliability. Here, we developed a streamlined detection platform for high-sensitive and reproducible serum peptidome profiling by data-independent acquisition (DIA) strategy. Compared to the classic data-dependent acquisition (DDA), our developed DIA approach can quantify almost twice the number of peptides with half the median coefficients of variation detected for DDA. The platform enables reproducible profiling of thousands of peptides and their post-translational modifications after simple sample preparation. The developed platform was subsequently utilized in the serum peptidome study of clear-cell renal cell carcinoma (ccRCC). A total of 31 ccRCC patients and 31 healthy volunteers were enrolled. Significant differences in serum peptidome patterns were observed between the two groups, allowing us to distinguish ccRCC patients from healthy volunteers clearly. A total of 833 modified peptides were found significantly changed in the ccRCC patients. The study demonstrated the high potential of serum peptidome in cancer detection and the feasibility and advantage of applying the DIA-based platform on large-scale serum peptidomic analysis for biomarker discovery. Significance: Serum peptidomic study proves to be challenging given its low abundance and instability of endogenous peptides. In this study, we developed a fast, reproducible and accurate detection platform by DIAbased MS method for streamlined serum peptidome profiling. The developed platform was then utilized in the serum peptidome study of ccRCC. To our knowledge, this is the first report to apply DIA strategy to disease related peptidomic studies. The large difference in serum peptidome profiles enabled us to distinguish ccRCC patients from healthy volunteers clearly, illuminating the great potential of serum peptidome in cancer diagnosis. The discovered significantly changed peptides provided a better understanding of the pathophysiological changes in ccRCC.

1. Introduction Human blood is the major resource for diagnostic analyses in clinical practice as it can be easily obtained and is rich in disease-related protein and peptide candidates. Blood plasma or serum proteomics has been intensively studied these years, whereas studies focusing on the low-molecular-weight (LMW) serum/plasma peptidome are relatively lagging. Peptidome refers to LMW endogenous peptides, proteolytic fragments, and small protein molecules weighing less than 10 kDa [1]. Bioactive peptides (such as cytokines, hormones, and growth factors) are known to play critical roles in the regulation of various physiological processes, and the proteolytic peptide fragments generated within the tissue microenvironment could reflect early-stage

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pathophysiological changes [2,3]. Different from large protein molecules, small-size peptidomes are easily secreted into the extracellular interstitium, readily pass through the vasculature barrier, and are finally released into circulation [4,5]. Thus, the study of blood peptidome features important clinical implications for providing specific and valuable information about dynamic physiological changes within the body. Blood serum or plasma is characterized by enormous complexity and extremely large abundance range. Blood peptidomic studies are challenging given the notably low abundance levels of endogenous peptides compared with high-abundance proteins. Considering cytokine interleukin-6 (IL-6) as example, the baseline levels of IL6 in plasma is 5 pg/mL, which is 109 times lower than the most abundant human

Corresponding authors. E-mail addresses: [email protected] (L. Lin), [email protected] (Q. Yu).

https://doi.org/10.1016/j.jprot.2020.103671 Received 7 October 2019; Received in revised form 28 December 2019; Accepted 26 January 2020 Available online 28 January 2020 1874-3919/ © 2020 Published by Elsevier B.V.

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2.2. Sample preparation

serum albumin protein (around 50 mg/mL) [6]. The coexistence of massive high-abundance proteins hinders the efficient detection of lowabundance peptides. Thus, efficient peptide extraction approaches and high-sensitive peptide detection techniques are needed to solve this problem. With the advantage of high-sensitive and wide dynamic range, mass spectrometry (MS) has become the mainstay for analysis of complex biological samples. Various MS-based analysis techniques, such as matrix-assisted laser desorption/ionization time-of-flight MS (MALDI-TOFMS) [7], capillary electrophoresis coupled to MS (CE-MS) [8] and liquid chromatography coupled to MS (LC-MS) [9], have been developed for endogenous peptide detection. In recent years, nanoliter liquid chromatography coupled to tandem MS (nanoLC- MS/MS) has been increasingly used in peptidomic study [3,10,11]. The highly efficient separation capacity of the nanoLC improves the detection sensitivity, and MS/MS fragmentation provides the possibility of accurate detection of peptide sequences. However, the unsatisfactory detection reproducibility and analysis throughput still limit its large-scale clinical application. Currently, data-dependent acquisition (DDA) is still the most widely used acquisition approach in LC-MS/MS analysis. In DDA, peptide precursors with Top N abundance detected in the MS1 scan are isolated and subjected successively for subsequent MS/MS analysis. The acquisition mode features the drawbacks of losing low-abundance peptide information, and the semi-randomness of peptide precursor selection leads to poor reproducibility and limited quantitative accuracy [12,13]. With recent advancement in MS technologies and bioinformatic approaches, data-independent acquisition (DIA) has arisen as an attractive strategy for proteomic study in recent years. The DIA performs MS/MS fragmentation in predefined consecutive isolation windows and data collection of all the resulting fragments. The resulting fragment ions in MS2 level was then used for quantification of respective peptide precursors, allowing for more sensitive and accurate protein quantification compared with DDA [14,15]. Furthermore, as all the precursors and fragments were acquired without omission, complete qualitative and quantitative information are preserved in a traceable mode, which is valuable for biomarker discovery studies [16]. To data, DIA has been successfully applied in proteomic- and metabonomic-based biomarker discovery studies [17–20], whereas peptidomic studies are rarely reported. Herein, we developed a DIA strategy-based detection platform for high-sensitive and reproducible serum peptidome profiling. The workflow allows thousands of peptides to be profiled with simple and fast sample preparation and DIA-based LC-MS/MS detection. The optimized detection platform was then used to a serum peptidomic analysis of clear-cell renal cell carcinoma (ccRCC).

For optimization of sample preparation method, three modified acetonitrile (ACN) precipitation methods and an ultrafiltration approach were compared. (1) “ACN + 0.1%TFA” method: 40 μL serum was precipitated with two volumes of acetonitrile containing 0.1% (v/ v) of trifluoroacetic acid (TFA). TFA was added as ion-pairing agents to dissociate protein–peptide interactions. (2) “ACN + 25Mm ammonium bicarbonate (ABC), 20%ACN” method: 40 μL serum was diluted to 1:5 with 200 μL 20% ACN (v/v) in 25 mM ABC buffer (pH 8.0) and vortexed for 10 s to disrupt the protein–peptide interactions. Then, 480 μL ice-cold ACN was added to precipitate large and abundant proteins. (3) “ACN + Urea buffer” method: 40 μL serum was diluted to 1:5 with 200 μL Urea Buffer (8 M urea, 20 mM dithiothreitol in 50 mM N-2hydroxyethylpiperazine-N-ethane-sulphonicacid (HEPES), pH 8.0) and vortexed for 10 s to disrupt the protein–peptide interactions. Then, 480 μL ice-cold ACN was added to precipitate abundant proteins. All of the above mixture were centrifuged at 3000 ×g for 10 min at 4 °C. The supernatant was collected and lyophilized to dryness. (4) “Ultrafiltration” method: 40 μL serum was diluted to 1:5 with 200 μL 20% ACN (v/v) in 25 mM ABC buffer (pH 8.0) and vortexed for 10 s to dissociate protein–peptide interactions. The mixture was spun through 10 kDa MWCO cutoff filters (Amicon® Ultra-0.5, Millipore) according to the instructions and then lyophilized to dryness. All lyophilized peptide samples were then re-dissolved in 0.1% (v/v) formic acid (FA) in water and desalted using in-house packed reverse-phase C18 stage tips (3 M Empore, USA). The eluted peptides were lyophilized and finally redissolved in 20 μL of 0.1% (v/v) FA for LC-MS/MS analysis. The iRT peptides (Biognosys) were added according to manufacturer's instructions. 2.3. LC-MS/MS analysis 2.3.1. DDA analysis DDA analysis was performed on an EASY-nLC 1000 coupled to a Q Exactive mass spectrometer (Thermo Scientific). Peptides were separated at a flow rate of 250 nL/min on a homemade 20 cm column (100 μm i.d.) packed with ReproSil-Pur 120 Å C18 resins 1.9 μm (Dr. Maisch GmbH). Mobile phase A was 0.1% FA in water, and mobile phase B was 0.1% FA in ACN. The separation gradient was 0 min, 3% B; 2 min, 7% B; 52 min, 22% B; 62 min, 35% B; 64 min, 90% B; 80 min, 90% B. The MS was operated in data-dependent TOP10 mode with the following settings: MS1 scan range m/z 350–1550; MS1 resolution, 70,000; MS/MS scan range m/z 200–2000; MS/MS resolution, 17,500; isolation window, 1.6 Da; fragmentation type, high-energy collisional dissociation (HCD); normalized collision energy (NCE), 27%; dynamic exclusion window, 30 s.

2. Materials and methods 2.3.2. DDA data analysis The DDA data was searched using the Mascot search engine integrated within the Proteome Discoverer (PD) software (V.1.4, Thermo Scientific). The Homo sapiens fasta database (70,947 entries, downloaded from Uniprot on Mar 10, 2017) appended with iRT peptides sequence was used. No enzyme specificity were set, and a maximum of two missed cleavages were permitted. The precursor and fragment mass tolerances were set as 10 ppm and 0.02 Da, respectively. Oxidation (M), deamidation (N/Q), acetylation (N-term), and Gln- > pyro-Glu (Nterm Q) were chosen as variable modifications. Peptide spectrum matches were validated using Percolator Node at a false discovery rate (FDR) of 1%. Label-free quantification analysis was performed using MaxQuant (v.1.5.2.8). No enzyme specificity was selected. The modification parameters were set as in the PD search. All spectrum were validated at the 1% FDR.

2.1. Serum sample collection Fasting blood samples were collected following the informed consent guidelines from the First Affiliated Hospital of Xiamen University. The clotted sample was centrifuged at 3000 ×g for 10 min at 4 °C. The sera (supernatant) were stored at −80 °C immediately until analysis. In the initial experiment, 11 ccRCC patients and 11 healthy controls were enrolled. For subsequent verification experiment, another 20 ccRCC patients and 20 healthy volunteers were recruited. All the ccRCC patients were histopathologically diagnosed, and the clinical information is provided in Table S1. Pooled serum samples were prepared by mixing equal volumes of all samples for spectral library building, and another serum sample was made with mixing equal amounts of sera from five RCC patients and five healthy volunteers for method development and evaluation.

2.3.3. Generation of spectral library The pooled serum samples were acquired with DDA in four 2

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organic solvent precipitation [10,11,22,23], ultrafiltration [2,24], solid-phase extraction (SPE) [25,26], chromatography [27,28], and affinity depletion [29–31], have been developed for serum peptidomic sample preparation. Among these methods, ACN precipitation is one of the most widely used methods due to its simplicity, low cost, and high efficiency to remove large-abundance proteins [32,33]. However, similar with other methods, ACN precipitation have the drawback of losing low-abundance peptides that non-specifically adsorbed to large proteins such as albumin [23]. As a result, various strategies have been raised to dissociate peptide-protein interactions, such as adding ionpairing agents (eg. 0.1%TFA) [22] or pre-dissociation with denatured buffer solutions (eg. urea buffer) [10]. In our study, three modified ACN precipitation approaches were compared using different auxiliary dissociation reagents. The methods were described in detail in the “Materials and methods” section and were termed as “ACN + 0.1%TFA”, “ACN + 25mM ABC, 20%ACN” and “ACN + Urea buffer” methods. The three precipitation methods were also compared to a widely used ultrafiltration approach using 10 kDa MWCO filters. A same serum sample were processed in three technical repeats by the four extraction methods and the resulting peptides were measured by DDA. The methods were compared based on the number of identified peptides and identification reproducibility. The results is shown in Fig. S1. Among the three modified ACN precipitation methods, the pre-dissociation with weak denatured buffer solution (20% ACN in 25 mM ABC) provides largest peptide identification number and identification reproducibility. The ultrafiltration approach can obtain comparable peptide identification number to the “ACN+ 25mM ABC, 20% ACN” method, while the identification reproducibility is inferior. As a result, the optimal “ACN+ 25 mM ABC, 20% ACN” method was used in our subsequent studies.

technical replicates to construct the serum peptidome library. The raw files were searched together using PD software, and the results were imported into Spectronaut 11.0 (Biognosys) for library generation. The unspecific digest type was selected, and the other parameters were set as default. A peptide was added into the library if at least three fragment ions can be detected, and the maximum of six best fragment ions can be kept for each peptide. The FDR was controlled as 1%. The generated library consisted of 2361 peptides, 2784 modified peptides, and 22,009 fragment ions. 2.3.4. DIA acquisition and data analysis The same instrument system and chromatograph conditions as DDA were used for DIA acquisition. A DIA method with variable isolation windows was developed specifically for serum peptidome samples. The window list was constructed according to the principle of averaging precursor ion distribution in each window (Table S2). The DIA method includes one MS1 scan and 30 DIA scans with the following settings: MS1 scan range m/z 350–1550; MS1 resolution, 70,000; DIA scan resolution, 17,500; fragmentation type, HCD; NCE, 27%. For verification analysis of additional sample set, the DIA acquisition was performed on an Orbitrap Fusion mass spectrometer with the same LC setup. A shorter 50 min gradient was applied as follows: 0 min, 3% B; 2 min, 7% B; 32 min, 22% B; 38 min, 35% B; 40 min, 90% B; 50 min, 90% B. For MS parameters, the same isolation window list was used as described above. The method involves a MS1 scan and 30 DIA scans with the following settings: MS1 scan range m/z 350–1550; MS1 resolution, 60,000; DIA scan resolution, 30,000; fragmentation type, HCD; NCE, 30%. All DIA data were analyzed using Spectronaut 11.0 (Biognosys). The following settings were applied: RT prediction type, dynamic iRT; interference correction on MS1 and MS2 level, enabled; correction factor, 1; cross run normalization, enabled; quantity MS level, MS2; quantity type, Area. The FDR threshold in peptide level was set as 1%. For peptide quantification, peptide intensity was calculated by summing the peak areas of respective fragments in MS2 level.

3.2. Development of the DIA method To improve the sensitivity and reproducibility of peptide measurement, a variable isolation window DIA (vDIA) method was developed in this study. The variable window list was built on the basis of the peptidome ion distribution of the pooled serum samples. The principle of equalizing the peptide ion number in each window was applied, which has been found to improve measurement sensitivity and reproducibility than fixed window DIA method in our previous proteomic study [34,35]. In order to examine the performance of the approach when compared to classic DDA, a serum sample pooled from five ccRCC patients and five healthy volunteers was measured by the two methods in three replicates. Performance comparison was carried out on the basis of peptide detection number, identification reproducibility, and quantitative accuracy (Table 1). Although the total number of identified peptides is comparable over three replicates, the DIA method shows an outstanding improvement in identification reproducibility and quantitative accuracy. The reproducibility for DDA was poor due to the semirandom scan nature and may also ascribe to the huge peptide diversity for both amount and composition of serum peptidomic sample. As shown in Fig. 1b, an excellent identification reproducibility of 95.4% was achieved by our DIA method over triplicate acquisitions. For

2.4. Statistical analysis Statistical analyses were performed using SPSS (version 18.0) and an online statistical tool powered by R language (http://www. omicsolution.org/wu-kong-beta-linux/main/). For statistical analysis of significantly changed peptides, a nonparametric Mann-Whitney U test was used with a p-value < 0.05 after Benjamini–Hochberg correction [21]. Gene Ontology (GO) analysis was carried out with an online tool DAVID (https://david.ncifcrf.gov/tools.jsp). 3. Results 3.1. Optimization of sample preparation methods The development of effective large abundance protein removal and LMW peptide extraction method is important for serum peptidome analysis. For decades, many sample preparation methods, including Table 1 Comparison of results of DDA and vDIA methods. Method

Identified stripped peptidesa

Overlapb

Identified modified peptidesc

Overlapd

Protein precursorse

Overlapf

Quantified stripped peptideg

Median CV(%)h

DDA vDIA

2270 2346

38.5% 95.4%

2473 2765

44.1% 95.4%

225 233

50.7% 97.0%

1236 2239

19.9% 10.2%

a,c,e

Total number of identified stripped peptides/modified peptides/annotated protein precursors over triplicate acquisition. Percentage of stripped peptide sequences commonly identified over three replicates. d Percentage of modified peptide sequences commonly identified over three replicates. f Percentage of protein precursors commonly identified over three replicates. g The peptides with valid quantitative values in all three replicates were considered quantifiable. h The mean value of CV of peptide intensity over triplicate acquisition. b

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Fig. 1. Performance comparison of developed DIA method to classic DDA. (a–b). Venn diagrams of the identified stripped peptides over three repeated acquisitions by DDA and vDIA methods, respectively. (c) The number of quantified stripped peptides over different CV levels.

intensity correlation of stripped peptides between replicates. An average correlation value of 0.995 was obtained, indicating the outstanding quantification accuracy of our serum peptidomic profiling platform. With the platform, 1997 (~2000) stripped peptides (covering 2331 modification-specific peptide variants) can be reproducibly quantified by single-run LC-MS/MS analysis of a pooled serum sample. The abundance range of the quantified peptides covered six orders of magnitude, with EGDFLAEGGGVR showing the most abundance and ESVGKGAVHDVK, which featured the lowest abundance (Table S3). The peptide length distribution was 8–51 amino acid residues, with 15 amino acid residues as the median value (Fig. 2c). This distribution was consistent with recent large-scale peptidomic study on rat hypothalamus [3]. Molecular weight statistics showed that most of the peptides detected by the workflow are distributed in the mass range of 1 K–3 K (Fig. 2d). The dataset also shows the characteristics of ladder series of peptides with one or several amino acids removed, which is typical for peptidome due to the nonspecific protease activity. Of the quantified peptides, 26.4% exhibited post translational modifications (PTMs) in

quantitative comparison, 2239 stripped peptides, which is almost twice the number of quantified peptides by DDA, were reproducibly quantified by DIA method. Furthermore, a substantially better quantitative accuracy was achieved by the DIA method, with 1101 and 1561 peptides showing coefficients of variation (CVs) of < 10% and < 20% (Fig. 1c), respectively. 3.3. Fast, reproducible and accurate serum peptidomic detection platform The purpose of this article is to develop a fast, reproducible and accurate serum peptidomic detection platform which is suitable for large-scale clinical application. Based on this, we optimized the sample preparation approach and developed high efficient single-shot DIA method. The whole workflow, including sample preparation and LCMS/MS analysis, was carried out in three repetitions on a same sample to evaluate the whole performance of the detection platform. As shown in Fig. 2a, a total of 2223 stripped peptide sequences (covering 2612 modification-specific peptide variants) were identified, with 1997 (90%) stripped peptides commonly identified. Fig. 2b shows the

Fig. 2. Performance of the whole detection platform. (a) Detection reproducibility of the platform by repeating the whole sample preparation and MS analysis in triplicate. Total numbers of identified stripped peptide sequences, modified peptide sequences, and annotated protein groups were plotted; each bar is divided into peptides/proteins that were commonly detected (dark green) and that only detected in 1 or 2 replicates (light green). (b) Intensity correlation of the stripped peptides between replicates (R) 1, 2, and 3. (c) Length distribution of the peptide sequences. (d) Molecular weight distribution of the quantified peptides. (e) PTMs distribution of the quantified peptides. (f) GO cellular component and biological process enrichment analysis of peptidome-derived proteins. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 4

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Fig. 3. Number of detected peptides in each sample from (a) the first and (c) second experiments. The inserted bar chart indicates the average number of detected modified peptides in the cancer (P) and healthy groups (N). Unsupervised PCA analysis based on the dataset of (b) the first and (d) second experiments (red, cancer patients; blue, healthy volunteers). Comparative analysis of (e) total peptides, (f) discovered significantly changed peptides, and (g) assigned differential proteins from the two experiments. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

groups was larger than expected. The number of peptides detected in RCC patients is significantly higher than that of healthy controls (see the inserted bar chart in Fig. 3a). As shown in Fig. 3b, the samples were clearly clustered into two categories with the unsupervised principal component analysis (PCA). Nonparametric Mann-Whitney U test was then performed to detect the significantly changed peptides. Among the total of 2224 quantified modified peptides, 1069 modified peptides were significantly changed between the two groups (p value with Benjamini–Hochberg correction < 0.05 and fold change > 2). Among the peptides, 487 modified peptides were only detected in ccRCC patients and 15 peptides only detected in controls; 314 peptides were found significantly up-regulated in ccRCC patients, while 253 peptides were significantly down-regulated in patients. This difference is considerably evident than what we expected. To rule out the contingency of the sample batch, a second sample set of 20 ccRCC patients and 20 controls were included in the study. The samples were handled with our workflow, and data acquisition was performed on an Orbitrap Fusion MS. Fig. 3c shows the peptide identification results. The number of endogenous peptides in the patients and controls from the additional sample set followed the same trend. Same as the first experiment, we can discriminate cancer patients from healthy volunteers with PCA (Fig. 3d). For peptide detection, 2406 modified peptides were detected in total, and 1437 of them were significantly changed. We compared the total peptides and significantly changed peptides from the two

one or two modification sites (Fig. 2e and Table S4). These modified specific peptide variants may play important roles in the regulation of many physiological processes. To study the functional profile of these peptides, the peptides were assigned to protein precursors, and GO enrichment analysis was carried out. The 1997 stripped peptides were mapped to 207 annotated protein precursors. As shown in Fig. 2f, GO terms related to extracellular exosome, blood microparticle, platelet alpha granule lumen, and extracellular space were most significantly enriched. For GO classifiers of biological process, platelet degranulation/aggregation, movement of cell or subcellular component, cell–cell adhesion, and platelet activation were most over-represented.

3.4. Serum peptidomic study of ccRCC The optimal detection platform was subsequently utilized in a proof of concept serum peptidome study of ccRCC. Eleven ccRCC patients and 11 healthy volunteers were enrolled in the initial experiment. The whole sample set was prepared and measured with the platform within 2 days. Fig. 3a shows the peptidome profiling result for each sample. On average, 1182 ± 192 stripped peptides (covering 1397 ± 221 modified peptides) per sample were detected from the cancer group, whereas 642 ± 85 stripped peptides (787 ± 98 modified peptides) were detected from the healthy group. The difference between the two 5

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bioactive peptide that reported to promote tumor cellular motility by sequestration of G-actin monomers and then affecting actin filament assembly and cytoskeletal organization [46–48]. The elevated levels of T β-4 proteolytic peptide fragments in our study corroborate the importance of this peptide in carcinoma development. In addition, interalpha-trypsin inhibitor heavy chain H4 (ITIH4)-derived peptides were notably down-regulated in the ccRCC patients in our study. ITIH4 is a plasma glycoprotein that is highly sensitive to kallikrein and has been proposed as a precursor for kallikrein-induced bioactive peptides [49]. The proteolytically derived peptides from ITIH4 have been associated with different types of cancer in various studies, and the proteolysis pattern has been observed to be cancer-type-specific due to tumorspecific exoproteases [49–51]. The differential peptidome was further mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to perform a pathway enrichment analysis. The result is shown in Fig. 4b. In addition to the aforementioned adhesion/junction associated pathways and actin-cytoskeleton regulation pathways, other cancer-related pathways, including platelet activation, viral carcinogenesis, proteoglycans in cancer and cholesterol metabolism, were also significantly enriched. Furthermore, the pathway of complement and coagulation cascades was notably enriched. The complement system is a proteolytic cascade in blood plasma and a mediator of innate immunity. Although several studies currently emphasize the importance of this pathway in cancer development, more components of complement system, such as C5a, C3a, C4a, and C3 [52–54], have been implicated in tumor growth. Recent findings suggest that the complement system may promote angiogenesis, cellular proliferation, and tumor growth [55]. A new concept of cancer treatment based on blocking the complement activity was also proposed [56].

experiments. As shown in Fig. 3e, the total peptides from the two experiments showed a high degree of overlap, illustrating the high stability and reproducibility of our detection platform in large-scale clinical application. As for significantly changed peptides, 833(77.9%) of 1069 modified peptides found in the initial experiment were also detected with significant changes in the second experiment (Fig. 3f). If assigned the significantly changed modified peptides into proteins, 133 of proteins were assigned in common (Fig. 3g). 3.5. Study of the significantly changed peptidome Surprisingly large differences in serum peptidome patterns were observed between ccRCC group and healthy controls in our study. The number of peptides detected in cancer patients is significantly higher than that of healthy controls. This phenomenon is not very common in conventional omics-studies, but can be explained by the results of recent proteomic studies on ccRCC. A surprisingly large number of exoproteases were found differential expression in the ccRCC tissue in von Hippel-Lindau patients [36]. The significant up-regulation of exoproteases, such as cytosolic non-specific dipeptidase (CNDP2) [37–39], tripeptidyl-peptidase (TPP-1) [37] and glutamate carboxypeptidase 2 (GCPII) [40], were also found in other proteomic studies on ccRCC. In addition to exopeptidases, extracellular matrix (ECM)-degrading enzymes, including metalloproteinases (MMPs) and heparanase, have been reported to be significantly overexpressed in RCC patients with activated enzyme activity [41–44]. MMPs were regarded as playing key roles in RCC angiogenesis, invasion and metastasis by degrading various cell adhesion molecules, basement membrane components and ECM proteins [41,45]. The serum peptidome is mainly composed of proteolytic peptide fragments from tissue microenvironment. Thus, the up-regulation of exoproteases or activated ECM endopeptidases in RCC patients will lead to the significant increase of peptide numbers in serum peptidome. Among the 833 co-detected significantly changed peptidome, 375 of them were only detected in ccRCC group in both experiments (Table S5). Further study were carried out on these peptides. The peptides were assigned to corresponding protein precursors and GO enrichment analysis was carried out to study the molecular function of these corresponding protein precursors. The result is show in Fig. S2, cadherin binding involved in cell-cell adhesion, actin filament binding, actin binding, protein binding and structural constituent of cytoskeleton were most over-represented. This result is consistent with the aforementioned proteolysis mechanism of MMPs on RCC. The MMPs decrease cell adhesion and promote migration by degrading the proteins involved in cell-cell adhesion and cytoskeletal physical barriers, resulting in an increase of corresponding proteolytic fragments. In addition, the enriched molecular functions of actin filament binding and actin binding were consistent with our previous serum proteome study on RCC [34]. The loss of expression of actin stress fiber-associated protein (Transgelin-2) and actin was found in RCC group in our previous study, indicating the disorganization of cytoskeletal actin filaments and cancer cell motility in RCC patients. Unsupervised hierarchical clustering analysis (HCA) was performed on the remaining significantly changed peptides that were detected in both groups. As shown in Fig. 4a, a significant peptidome difference was observed between the two groups. The remaining 458 modified peptides can be clustered into two major categories, with 287 peptides up-regulated in ccRCC patients and 171 down-regulated ones (Table S5). In additional to the proteolytic peptide fragments from tissue microenvironment, the serum peptidome also consists of endogenous bioactive peptides. The 833 modified peptides (corresponding to 739 stripped peptide sequences) were further mapped to a recently published plasma peptidome dataset of known bioactive peptides to investigate their potential biological function [10]. As a result, 135 stripped peptides can be mapped (Table S5). Among these peptides, 28 stripped sequences were assigned to thymosin beta-4 (T β-4), and all of them were up-regulated in ccRCC patients. T β-4 is a 43-amino-acid

4. Discussion Serum peptidomic study proves to be challenging given its low abundance and instability of endogenous peptides. Different from proteomics, as the quantitative comparisons need to be performed in the peptide level rather than the protein level, the research on peptidomics places higher demands on method sensitivity, reproducibility, and accuracy. At present, the poor analysis reproducibility and limited quantitative accuracy of DDA have restricted its clinical application in biomarker discovery. Based on this, we developed a high-sensitive and reproducible detection platform by DIA-based MS method for serum peptidome profiling. To our knowledge, this study is the first to apply a DIA strategy to peptidomic-based biomarker discovery study. Compared with classic DDA, our developed DIA method almost doubled the number of quantified peptides at half the median CV observed for DDA (DDA = 19.9%; DIA = 10.2%). The whole workflow, which includes sample preparation and MS acquisition, presented excellent identification reproducibility and quantification accuracy during the performance evaluation, suggesting the feasibility of applying the detection platform in clinical applications. The detection platform was then applied to a proof of concept peptidome study of ccRCC. As presented in the result, a large difference in serum peptidome patterns was observed between the two groups, which allowed us to discriminate the ccRCC patients from the healthy volunteers. Notably, the difference is larger than what we expected but reasonable. The higher number of peptides detected in the ccRCC patients could be the result of up-regulation of exoproteases or ECM degrading endoproteases in ccRCC tissue. Further function analysis on differential peptidome indicate the disorganization of cytoskeletal actin filaments and cancer cell motility in RCC patients, which was consistent with our previous serum proteome study on RCC. Different from proteome, the small size of the disease microenvironment associated LMW peptides provides them with almost unrestricted interstitial access and facile passage through the endothelial cell barrier of the vasculature [5]. Hence, the serum peptidome may 6

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Fig. 4. (a) HCA of the significantly changed peptides that were detected in both groups. Columns represent samples, and rows denote the peptides. “N” represents the healthy volunteers, and “P” denotes the ccRCC patients. (b) KEGG pathway enrichment analysis of the differential peptidome.

discovered biomarkers. Thus, in our opinion, larger sample cohorts and more comprehensive disease control group are necessary in peptidomicbased biomarker discovery researches to obtain reliable biomarkers. For our study, 833 modified peptides were differentially expressed in ccRCC patients. As mentioned above, these differentially expressed peptidome provided rich information about the pathophysiological changes of ccRCC. However, these peptides cannot be treated as biomarkers in the current study. Given the complexity of cancer pathophysiology, larger sample sets and non-malignant disease control

provide a specific signature that is closer to the phenotype than the proteome. This hypothesis has been confirmed to a certain extent in a recent study of exercise-regulated plasma peptidome [10]. Physiological perturbations such as exercise can induce dynamic and significant changes in the plasma peptidome. This high susceptibility of serum peptidome to pathophysiological perturbations is an advantage but also a challenge for its application in biomarker discovery. Although disease-related subtle changes can be desirably reflected in peptidome, the susceptibility to other factors may reduce the specificity of the

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samples must be included in future studies to further refine and validate these peptides.

[17]

5. Conclusions [18]

We developed a DIA-based detection platform for streamlined serum peptidome profiling and applied it to a proof-of-concept peptidomic study on ccRCC. To the best of our knowledge, this report is the first to apply a DIA strategy to disease related peptidomic studies. The large difference in serum peptidome profiles enabled us to distinguish ccRCC patients from healthy volunteers, illuminating the great potential of serum peptidome in cancer diagnosis. The discovered differential peptides provided a better understanding of the pathophysiological changes in ccRCC although further studies are still needed to refine and validate those peptides in larger cohorts. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jprot.2020.103671.

[19]

[20]

[21]

[22]

Declaration of Competing Interest [23]

There are no conflicts of interest to declare. Acknowledgments

[24]

This work was supported by the National Natural Science Foundation of China (21605076).

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