Digging More Missing Proteins Using an Enrichment Approach with

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Digging More Missing Proteins Using an Enrichment Approach with ProteoMiner Siqi Li,†,§ Yanbin He,†,§ Zhilong Lin,† Shaohang Xu,† Ruo Zhou,† Feng Liang,† Jian Wang,† Huanming Yang,†,‡ Siqi Liu,*,† and Yan Ren*,† †

BGI-Shenzhen, Beishan Industrial Zone 11th building, Yantian District, Shenzhen, Guangdong 518083, China James D. Watson Institute of Genome Sciences, Hangzhou 310008, China

‡

S Supporting Information *

ABSTRACT: Human Proteome Project (HPP) aims at mapping entire human proteins with a systematic effort upon all the emerging techniques, which would enhance understanding of human biology and lay a foundation for development of medical applications. Until now, 2563 missing proteins (MPs, PE2−4) are still undetected even using the most sensitive approach of protein detection. Herein, we propose that enrichment of low-abundance proteins benefits MPs finding. ProteoMiner is an equalizing technique by reducing high-abundance proteins and enriching lowabundance proteins in biological liquids. With triton X-100/TBS buffer extraction, ProteoMiner enrichment, and peptide fractionation, 20 MPs (at least two non-nested unique peptides with more than eight a.a. length) with 60 unique peptides were identified from four human tissues including eight membrane/secreted proteins and five nucleus proteins. Then 15 of them were confirmed with two non-nested unique peptides (≥9 a.a.) identified by matching well with their chemically synthetic peptides in PRM assay. Hence, these results demonstrated ProteoMiner as a powerful means in discovery of MPs. KEYWORDS: missing proteins, ProteoMiner protein enrichment kit, human tissue, LC−MS/MS, Triton X-100

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INTRODUCTION Launched by the Human Proteome Organization (HUPO), the Human Proteome Project (HPP, http://www.thehpp.org) aims to stepwisely complete a thorough human proteome “map” by identifying and characterizing all the proteins of the 20 179 encoding genes at its initial phase.1,2 HPP considered that the protein identified through MS signals required high-stringency evidence and proposed its own interpretation guidelines of MS data for protein identification.2,3 Although in 2014 Kim et al. claimed the protein products encoded by 17 294 genes from 30 histologically normal human tissue samples were identified by MS-based approaches,4 some proteins in the reservoir were not recognized as identified ones under the strict criteria. The proteins in UniProtKB/Swiss-Prot are broadly classified into five categories (PE1−5) according to the protein existence (PE) evidence. The proteins in PE 2 to 4 are defined as missing proteins (MPs) because of lack of the protein existence evidence.3,5,6 In the newest release (2017 April) of neXtProt (https://www. nextprot.org/about/protein-existence), total of 17 045 proteins are defined as the identified proteins confirmed with at least two non-nested unique peptides of over eight residue length (PE1). There are still 2563 missing proteins to be detected and confirmed yet. It is generally accepted that at current MS with high resolution is a good solution to dig MPs. This technique, however, has © 2017 American Chemical Society

met some obstacles in the digging process, (1) the proteins at low-abundance whose MS signals are suppressed by other proteins with higher abundance or are undetected due to MS detection restriction, (2) the proteins with higher hydrophobicity that are difficultly extracted and poorly digested, (3) the proteins having higher sequence homology so that they have few unique peptides to highlight the corresponding identity, (4) the proteins with short amino acid sequences that contain a few of or no tryptic peptides easily detected by MS, and (5) the protein existence being time- and space-dependent.7−12 Since HPP was launched in 2010, the strategies and approaches to discover MPs have been made a great progress, such as enrichment of lowabundance proteins in subcellular level, deep fractionation of peptides to reduce complexity, extension of LC gradient, and tuning resolution, sensitivity and accuracy in MS.7 Corresponding to such efforts from the HPP teams, the number of MPs has been shrunk from 3868 (2013) to 2563 (April 2017).5,6 On the other hand, we must realize that exploring MPs becomes more and more difficult and badly needs new strategies or techniques.13 As peptide ionization and fragmentation are a random process, peptide abundance is a key factor that impacts the Special Issue: Chromosome-Centric Human Proteome Project 2017 Received: May 31, 2017 Published: September 29, 2017 4330

DOI: 10.1021/acs.jproteome.7b00353 J. Proteome Res. 2017, 16, 4330−4339

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Journal of Proteome Research

submitted to and approved by The Ethics Committees in the three hospitals. Permission of BGI Animal Ethics Committee has been obtained for the use of animal tissues for this study. The scheme which stated the tissue sample pathological information and experimental flowchart was provided in Supplementary Figure 1. All of the reagents were purchased from Sigma-Aldrich (St. Louis, MO) or Thermo Fisher Scientific (Waltham, MA) unless specified otherwise. The ProteoMiner Protein Enrichment Kits were purchased from Bio-Rad (Hercules, CA). Protease inhibitor cocktails were ordered from Roche (Basel, Switzerland). Trypsin for protein digestion was ordered from Progema (Madison, WI). Bovine serum albumin was obtained from Sangon Biotech (Shanghai, China). The peptides used for PRM assay were chemically synthesized by GL Biochem (Shanghai, China). Solutions for proteome extraction, purification, proteolysis, and fractionation were prepared with Milli-Q water (Merk Millipore, Darmstadt, Germany), and solutions for subsequent steps were prepared with HPLC grade water (Fisher Scientific, Waltham, MA).

feasibility of peptide detection by MS. If the peptide abundance diversity over three orders of magnitude, the peptides at lower abundance were difficult to be identified by MS.14 Doubtlessly reducing dynamic range of protein abundance in a protein complex is an efficient way for detection of low-abundance proteins. On the basis of the interaction of highly diverse hexapeptides coated on beads with the sequences of complex proteins, the ProteoMiner kit was designed for protein equalization by reducing high-abundance proteins and enriching low-abundance proteins in body liquids,14−16 although Keidel proved that the equalization was performed by a hydrophobic interaction mechanism, not by the diverse interaction of surface ligands of hexapeptides for reduction of complexity, dynamic range, and high abundance proteins.17 ProteoMiner has been successfully applied to high-abundance protein depletion of human serum,18,19 urine,20 synovial fluid,21 bile fluid,22 bovine colostrums,23 bacteria outer membrane proteins,24 and cell culture supernatants.25 The treatment with ProteoMiner did offer a good opportunity to identify the low-abundance proteins. In fact, the ProteoMiner method has been seldom adopted for profiling proteins in tissues or cells due to free of a large diverse abundance of proteins interfering. Thus, a question is naturally raised whether the abundance normalization for proteins could facilitate the MPs discovery in tissues or cells. As well-known, membrane proteins with low copy and high hydrophobicity are not easily identified by MS due to poor performance in protein extraction and tryptic digestion.26−28 TX-100, a nonionic detergent, is widely used for solubilizing membrane proteins under a nondenature condition.29−31 It was reported that the membrane proteins in the TX-100 extracts occupied higher percentage than that extracted by urea or SDS.32 Notably, TX-100 well matches with the experimental conditions required for ProteoMiner treatment to proteins at nondenature status. Hence, we postulate that the ProteoMiner treatment in a TX-100 solution followed by elution with urea solution under denatured condition could benefit not only for enrichment of lowabundance proteins but also for digestion and detection of membrane proteins. In the communication, 20 MPs were identified with at least two non-nested unique peptides (≥9 a.a.) from the four human tissues with TX-100 extraction and ProteoMiner enrichment. These MPs mapping to 15 chromatins included eight membrane/ secreted proteins, five nucleus proteins, and four MPs without cellular location definition. Then 15 of these MPs were further verified by matching with their chemically synthesized peptide identification (two non-nested unique peptides/protein, ≥ 9 a.a.) in PRM assay. The results indicated that TX-100 extraction and ProteoMiner capture benefited the identification of MPs, especially those with higher hydrophobicity or low-abundance in nucleus.

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Tissue Protein Extraction and Lower Abundance Protein Enrichment by ProteoMiner kit

Tissues were ground in a tissue lyser and further lysed by probe sonication in the extraction buffer of PBS (pH 7.5), HEPES (20 mM, pH 8.0), NH4HCO3 (50 mM, pH 8.0), or triton buffer (1% Triton X-100, 20 mM Tris, 150 mM NaCl, pH 7.4) with protease inhibitor cocktail at 0−4 °C. Bradford assay was adopted to measure the concentrations of extracted proteins with bovine serum albumin as a standard. Lower abundance proteins were enriched with ProteoMiner Protein Enrichment Kits according to manufacturer’s protocol. In brief, the columns were conditioned by the extraction buffer and then the extracted tissue proteins (5 mg) were loaded onto them (50 μL) under gravity speed. The nonspecific binding proteins were washed out by the same extraction buffer, and then the specific binding proteins were eluted by the elution buffer provided in the kits. The effects of depletion on higher abundance proteins were evaluated by protein recovery ratio and the images of SDS-PAGE gels to show the protein pattern changes with ProteoMiner treatment. Protein Digestion and Peptide Desalting

After the enriched lower abundance proteins were eluted from the column, they were reduced in 10 mM dithiothreitol (30 °C, 2 h) prior to alkylation in 55 mM iodoacetamide (room temperature in the dark, 45 min). The mixture of protein solutions was diluted with 50 mM NH4HCO3 buffer to reduce the concentration of urea in the elution buffer to improve the efficiency of trypsin. The proteins were digested by trypsin (1:50) at 37 °C with overnight incubation. The resultant solution containing tryptic peptides was adjusted to pH 2−3 with TFA and desalted using Waters Sep-Pak C18 cartridges (Milford, MA) following the procedure of conditioning (0.1% TFA), loading sample, washing (0.1% TFA), and elution (75% ACN with 0.1% TFA). The peptides were then dried using a speed-vac (LaboGene, Lynge, Denmark), dissolved by 5% ACN in ammonia (pH 9.8) for first dimensional separation.

MATERIALS AND METHODS

Materials, Chemicals, and Reagents

Human hepatoma and their adjacent tissues, colorectal cancer and their adjacent tissues were provided by China Xijing Hospital; human bladder cancer and adjacent tissues were obtained from Shenzhen second People’s Hospital (China); and human kidney tissues were supported by Nanjing General Hospital (China). Mice were cultured in the animal house of BGI, Shenzhen. All the clinical samples in this study were taken under surgery and all the patients signed informed consent forms. The protocol of sample collection and the data usage were

First Dimensional Fractionation of Peptides

Desalted peptides were separated by a Gemini high-pH RP column (5 μm, 110 Å, 250 × 4.6 mm2, Phenomenex, Torrance, CA) equipped on a Shimadzu UFLC system (Kyoto, Japan). Fractionation was performed over a 60 min period at a constant 4331

DOI: 10.1021/acs.jproteome.7b00353 J. Proteome Res. 2017, 16, 4330−4339

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Journal of Proteome Research

Figure 1. Images of SDS-PAGE gels to display the effects of ProteoMiner enrichmemnt on mouse liver proteins under four extraction conditions.

flow rate of 1 mL/min using a gradient of 5% B for 10 min, 5%−35% B for 40 min, 35%−95% B in 1 min, 95% B for 3 min, and dropped to 5% within 1 min before equilibrating with 5% B for 10 min (buffer A, 5% ACN in ammonia, pH 9.8; buffer B, 95% ACN in ammonia, pH 9.8). All 60 fractions were collected and pooled into 20 fractions by a concatenation mode.33 Fractions were dried in a speed-vac (LaboGene, Lynge, Denmark).

(PSMs) and the Occam’s razor approach to assemble the identified peptide sequences into a set of confident proteins.34,35 The protein level FDR was calculated by using the picked protein FDR strategy with setting of less than 1%.36 Confirmation of MP Identification by PRM

The .dat files of DDA data search results were put into Skyline and used to direct the extraction of peptides for PRM assay.37 In this situation, only MP unique peptide sequences were input as baits to get their MS identification information used for direct PRM confirmation. The list of target peptides with m/z and sequence obtained from Skyline analysis was imported for targeted identification (Supplementary Table 1). PRM assay was also performed on a Q Exactive HF hybrid quadrupoleOrbitrap mass spectrometer coupled with the same column used for DDA identification. The fractions with MP unique peptide identification were selected for PRM confirmation and separated with a 60 min gradient: 5 min with buffer A (2% ACN and 0.1% formic acid), from 5% B (98% ACN and 0.1% formic acid) to 30% B in 44 min, to 80% B in 3 min, at 80% B for 2 min, dropped to 5% within 1 min, and then kept at 5% B for 5 min. During PRM scan, the PRM mode was set, while most of MS parameters were kept as same as DDA identification, such as spray voltage, capillary temperature, resolution, polarity, AGC target, and scan range, except the maximum IT was set up at 250 ms for improvement of the detection sensitivity. The PRM data were processed by Skyline software with the following steps, selecting the settings for peptides and fragments based upon the guidelines of the software, inputting the information on target peptide list, importing PRM raw data files, locking the native peptides identification based on the database search, and the chromatographic and MS behaviors of synthetic peptides and finally exporting the results of comparison on RT and product ions peak area percentage for the native and synthetic peptides.

Identification of Missing Proteins on Mass Spectrometry

The peptides (2 μg) were passed onto a Q Exactive HF hybrid quadrupole-Orbitrap mass spectrometer coupled to an UltiMate 3000 UHPLC (Thermo Scientific, Waltham, MA) for identification, in which a 300 μm × 5 mm C18 TRAP column (μ-Precolumn, Thermo Scientific, Waltham, MA) and a 75 μm × 25 cm in-house packed analytical column containing 3 μm Ultimate LP-C18 particles (120 Å) from Welch (Shanghai, China) were used. Each fraction was loaded on enrichment column at flow rate 5 μL/min for 5 min with buffer A (2% ACN and 0.1% formic acid), followed by a 115 min gradient at 300 nL/min: from 5% B (98% ACN and 0.1% formic acid) to 26% B in 85 min, to 35% B in 10 min, to 80% B in 10 min, at 80% B for 5 min, dropped to 5% within 0.5 min, and then kept at 5% B for 4.5 min. MS parameters were listed as following: spray voltage 2 kV, capillary temperature 320 °C, positive mode, scan range 350−1500 m/z, loop count 30, NCE 26, MS resolution 120 000, MS/MS HCD scans with resolution 30 000, dynamic exclusion duration 30 s; isolation window 2.0 m/z; intensity threshold 1.0 e4; charge exclusion, exclude 1,7,8,>8. Each fraction was injected twice for more confident identification. Database Searching

Acquired MS data were converted to MGF files by Proteome Discoverer 1.4 (Thermo Scientific, Waltham, MA) and the exported MGF files were searched using Mascot version 2.3.02 against the Swiss-prot human database (released on 2017_04 with 20 183 protein sequences) with a decoy database involved. The false discovery rate (FDR) was set to less than 1% at both PSM and protein level during searching and automatically calculated by the software. Trypsin was selected as the specific enzyme with a maximum of two missed cleavages permitted per peptide. Parameters included fixed modification: Carbamidomethylation (C); variable modification: Oxidation (M), Deamidatioin (N, Q). Data were searched with a peptide mass tolerance of 20 ppm and a fragment mass tolerance of 0.05 Da. The Mascot results were further processed by IQuant software by the Mascot Percolator to rescore the peptide spectrum matches

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RESULTS AND DISCUSSION

Optimization of Proteominer Protocol for Equalizing the Abundance of Tissue Proteins

ProteoMiner technology was adopted to improve the detection rate toward the low-abundance proteins in samples through expelling the high-abundance proteins.15,17,38 It is often implemented in a body fluid, whereas is not extensively used for abundance equalization of the tissue proteins.20,38,39 Rivers et. al adopted the equalization approach to eliminate the bias in 4332

DOI: 10.1021/acs.jproteome.7b00353 J. Proteome Res. 2017, 16, 4330−4339

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Journal of Proteome Research

Figure 2. Images of SDS-PAGE gels to display the effects of ProteoMiner enrichmemnt on the four human tissue proteins with TX-100 or NH4HCO3 extraction. (A) Human liver proteins; (B) human colon proteins; (C) human kidney proteins; (D) human bladder proteins.

alternative buffer choice to have more hydrophobic proteins in ProteoMiner. Furthermore, we applied ProteoMiner coupled with the two buffers, TX-100/TBS and NH4HCO3, to extract and equalize the proteins in human liver. As presented in Figure 2A, similar to the extraction result for mouse liver proteins by TX-100/TBS, the proteins extracted by this buffer displayed the SDS-PAGE images with obvious enriched proteins with low molecular masses. The protein recoveries for human liver by TX-100/TBS and NH4HCO3 were 4.6% and 2.6%, respectively. The two sets of the protein extracted were tryptic digested and the digested peptides were fractioned into 20 parts on a high pH reverse-phase chromatograph followed by peptide identification by Q Exactive HF hybrid quadrupole-Orbitrap mass spectrometer. Totals of 6200 and 6778 proteins with 67 091 and 76 251 peptides were identified in NH4HCO3 and TX-100/TBS, respectively, in which 5576 and 6007 proteins identified with at least two unique peptides (≥9 a.a., Supplementary Table 1). Notably, two and five MPs with at least two non-nested unique peptides (≥9 a.a.) were found in the NH4HCO3 and TX-100 extraction, respectively, in which one protein (Ras-related protein Rap-1b-like protein) was coidentified in the two extractions, and five proteins were assigned as membrane or secretary ones. These results demonstrated that the ProteoMiner treatment offered an assistant for discovery of MPs. In addition, as ProteoMiner in TX-100/TBS was able to find more MPs, we chose the buffer system in later experiments for protein extraction from other human tissues.

protein abundance in the soluble fraction of skeletal muscle with a dramatic asymmetry in the range of abundances of proteins and found that loading at different buffer conditions on the beads led to capture of different subpopulations of proteins.40 For the sake of optimization condition that allows an efficient protein equalization, we first evaluated several extraction buffers for the nondenatured proteins in mouse liver, which would be also used for binding of extracted proteins and hexapeptides. HEPES and phosphorylated buffer saline (PBS) or Tris-HCl buffer saline (TBS) are generally taken to extract proteins under nondenatured condition, while NH4HCO3 works for effectively extracting those proteins with higher solubility. Three buffers, 50 mM NH4HCO3 (pH 8.0), 20 mM HEPES (pH 8.0), and PBS pH 7.5, were employed in protein extraction and ProteoMiner binding. SDS-PAGE gels were used as a primary tool to overview the equalization effects of ProteoMiner and check whether a new asymmetry of protein expression was reached as reported in a soluble fraction of skeletal muscle.40 The SDS-PAGE images with Coomassie blue staining for the equalized proteins and the protein recovery ratio (the eluted binding-proteins/total of in-put proteins) were regarded as the evaluation parameters for depletion efficiency. As depicted in Figure 1, the electrophoretic images of the staining bands corresponding to three lanes (lysate, flowthrough, and binding fractions) exhibited similar patterns in the all three buffers (HEPES, PBS, and NH4HCO3) coupled with ProteoMiner column. The protein recoveries at 4.3% in HEPES and 4.1% in PBS out of 5 mg original proteins were comparable, however, NH4HCO3 was found in the lowest protein recovery at 2.9% in the three extraction buffers. NH4HCO3 therefore appears a better nondenature buffer to equalize the tissue protein abundance in the ProteoMiner treatment. Triton X-100 (TX-100) is widely accepted as a mild detergent to extract the proteins with higher hydrophobicity.29,41 We further inquired to whether TX-100 could get an improvement of enrichment of low-abundance and hydrophobic proteins in ProteoMiner.14,42 The electrophoretic bands derived from the mouse liver extraction by TX-100/TBS showed an obviously different pattern with more proteins at lower molecular masses, as compared with the SDS-PAGE images from the other three extraction buffers (Figure 1), while the treatment of TX-100/TBS resulted in 3.84% protein recovery. TX-100/TBS seems another

MPs Identification from Treated Proteins by ProteoMiner

In approximately 20 000 human genes, about 50% of the genes are detectable for their transcripts in all analyzed tissues and approximately 40% show tissue priority with an elevated expression in one of the analyzed tissues.43,44 The genes with a significant elevated level of expression in a particular tissue or a group of related tissues consist of three major subtypes: the tissue enriched genes, the group enriched genes and the tissue enhanced genes. The amount of tissue enriched genes (at least 5-fold higher mRNA levels in a particular tissue as compared to all other tissues) is highly variable between the analyzed tissue types.45,46 The testis, brain and liver tissues are ranked at the top tissues with the enriched genes in the list of all 34 human tissues.44 C-HPP teams have found a lot of MPs in testis since 2012.47−50 On the basis of the human tissues collected in our 4333

DOI: 10.1021/acs.jproteome.7b00353 J. Proteome Res. 2017, 16, 4330−4339

cytoplasm nucleus membrane

PE2

sp|P57058|HUNK_HUMAN

4334

PE4

PE2

PE2

sp|A6NES4|MRO2A_HUMAN

sp|Q9NUC0|SRTD4_HUMAN

sp|A6NK89|RASFA_HUMAN

R2

R2

R2

R1,2

R2

R1,2

R1,2

R1,2

R1,2

R1,2

R1,2

R1,2

R2

R1

R1,2

R1,2

R1

R1,2

AHTALSEYRPILSQEHR; APAPSKPGESQESQGTPGELPST

ATVSEQLSQDLLR;NIKNYEEEILR

AALSQGHDGAPLALQQK; DVEPAVVGQLVDFVYTGR

EEPLEPDGGPDGELLLEQER;GAPARPSLAMTQEK; LNTDLEAVKSDLDYSQQQWDSK; LWAAWGEEQENVR;METLVHLVLSQDHTIR

AHILYMSLEK;FIDDPEVYLR

FLLETMAYVK;MTVFQTTMCSILTR; NSLQELQLDPDPGVRR

FTEEVIEYFQK;ILLTSDEAWKR;LTATSTDQLEALR; NLTPYVAIEDKDMQQK; QWAQELEENLNELTHIHQSLK

IQQLAFVYPELLAGEFTR;LLGTLAVSR; LYMANAGDSR;RDEIRPLSFEFTPETER

CLTETPIEYLR;ESSQHVTQLVLSNK

ACQMMLDIR;APETIESVAQGIR; SYSEVANHILDTAAISNWAFIPNK

EEGARPGTLLGTFNAMDPDSQIR;GNYLVPLFIGDK; IVDTSLIFNIR;LLVQDRDSPFTSAWR; NWGQSVELLTLR;RWVITTLELEEEDPGPFPK; YELVHDPANWVSVDK

AFHLFGGFR;LTLLPTLYEIHSK

DSNQSSNLIIHQR;TFNQSSDLLR

ALEGQLPPLQENWYGR;DMYFDIPLEHR; METWLHEQEAQGQLLWDSSSSDSDEQGK

LNLSQQLEAWQDDMHR; LSATLEENDLLQGTVEELQDR; MDMMSLNSQLLDAIQQK; SLQSSAATSTSLLSEIEQSMEAEELEQEREQLR

EYITSSLVQQVSSSR;NILSVIAVR

FPMMGIGQMLRK;ISLEDLSPSVVLHMTEK; MVDKEMNPLPTQLSTGAISFLR

EELVTILEEEEESSKEEEEDQEPQR;EVSPVEIPGQTLR; QLNQAGLVPPGLGPPPQALR

EIFTKPLNFQETETDASKSDYELQALR; YIFQLNEIEQEQNLR

ALGTSDSPVLFIHCPGAAGTAQGLEYR; APPALVVTANIGQAGGSSSR

non-nested uni_pep_ sequecne

Q14916−1

Y

Y

Y

Y

N

Y

Y

Y

Y

Y

bladder 1p36.22

bladder 1p22.1

bladder 2q37.3

bladder 11p15.3

bladder 1q32.2

bladder 2q37.1

bladder 22q12.3

bladder 3p21.2

bladder 8q24.13

bladder 6p12.3

NA

Q5XKL5−1/2

NA

NA

NA

NA

Q9BPW4−1/2/3

Q96MI6−1/4

NA

NA

bladder 20q13.33 Q8IXH8−1/4

Q86T96−1

NA

NA

Y

5q12.3

17p13.2

14q22.3

12q24.23 Q6ZP65−1

6p22.2

isoform

kidney

kidney

kidney

NA

NA

NA

21q22.11 NA

19q13.2

Xq26.2

11p15.5

chro_ location

N

Y

Y

kidney kidney

Y

kidney

kidney

kidney

liver

tissue source

Y

N

N

Y

N

confirmed by PRMb

714

130

325

200

335

378/305

578

507

356

1674

351/347/348

270/123/169/247

552

695

852/165/124/832

592/416/96

807

296

573/270

467/413

length

11.94

6.35

7.09

16.77

7.87

2.75

22.22

20

7.79

6.62

12.56

3.72

2.85

21.28

15.18

6.64

8.26

44.62

12.92

23.5

coverage (%)

R1 or R2, MPs were identified with two non-nested unique peptides (≥9 a.a.) in only one of the two LC−MS injections; R1,2, MPs were identified with two non-nested unique peptides (≥9 a.a.) in both two LC−MS injections. bWhether the MPs were confirmed by synthetic peptides in PRM assay. cNA, not available.

a

nucleus

PE2

PE2

sp|Q5XKL5|BTBD8_HUMAN

sp|Q5UAW9|GP157_HUMAN

membrane

cytosol

cytoplasm

nucleus

NA

secreted

PE2

sp|Q0D2K2|KLH30_HUMAN

PE2

sp|Q9BPW4|APOL4_HUMAN

nucleus

membrane

PE2

PE2

sp|Q92819|HYAS2_HUMAN

membrane

membrane

membrane

PE2

sp|Q96MI6|PPM1M_HUMAN

sp|Q8IZF3|AGRF4_HUMAN

PE2

sp|Q8IXH8|CAD26_HUMAN

nucleus

PE2

PE2

sp|Q96JF6|ZN594_HUMAN

sp|Q86T96|RN180_HUMAN

NA

PE2

sp|Q9NVL8|CN105_HUMAN

centrosome cytoplasm

PE2

PE2

sp|Q14916|NPT1_HUMAN

sp|Q6ZP65|BICL1_HUMAN

NA

PE4

sp|A6NGS2|ERIC4_HUMAN

R1

NAc

PE3

sp|A6NGH7|CC160_HUMAN

R1

iden_duplicatesa

secreted

cellular localization

PE2

MP level

sp|F8WCM5|INSR2_HUMAN

protein ID

Table 1. List of Missing Proteins Identified from Four Human Tissues

Journal of Proteome Research Article

DOI: 10.1021/acs.jproteome.7b00353 J. Proteome Res. 2017, 16, 4330−4339

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Journal of Proteome Research

Figure 3. Comparison of hydrophobicity and amino acid length for different protein groups. (A) Analysis of protein size; (B) analysis of protein hydrophobicity.

Figure 4. Illustration for the confirmation of MP identification by their synthetic peptides in PRM assay.

were enriched (Figure 2B−D). In total, 8432, 7985, and 7598 proteins with 102 964, 104 390, 62 454 peptides were identified from the tissue of kidney, bladder, and colon, respectively (Supplementary Tables 2 and 3), in which 8302, 7845, and 6740 proteins were identified with at least two unique peptides (≥9 a.a.). In these results, the unique peptides assigned to MPs might be shared by MPs and PE1 proteins, which should be deleted from the unique peptide list. Even for the identified unique peptides only assigned to MPs, those shared by the different MP groups should be also discarded from the final results. The identification of MPs in the final list should be only contributed by the unique peptides belonging to a single MP

laboratory, four tissues were chosen for MP searching in this study, in which liver and kidney have more tissue-enriched genes, while bladder and colon have less ones.44−46 The tumor and its adjacent tissue from human kidney, bladder, or colon were first mixed and the corresponding proteins were extracted by TX-100/TBS and then treated by ProteoMiner. The protein quantification showed that the protein recoveries for kidney, bladder, and colon proteins through ProteoMiner columns were 3.08%, 3.12%, and 5.02%, respectively. The SDS-PAGE images clearly exhibited after the ProteoMiner treatment that the band intensities for the high-abundance proteins were obviously attenuated, while that with low-abundance proteins 4335

DOI: 10.1021/acs.jproteome.7b00353 J. Proteome Res. 2017, 16, 4330−4339

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hydrophobic, the corresponding peptide detection would be difficultly conducted.5 In our primary identification list for 35 MPs without any filtering, Ras-related protein Rap-1b-like protein was defined as a potential MP with four unique peptides (non-nested, ≥ 9 a.a.) detected in liver and colon tissue. After examining by checker, three of them were filtrated out due to the shared peptides that have high homology sequences with a PE1 protein, Ras-related protein Rap-1b. As a matter of fact, the two proteins have only 2 a.a. difference in 184 a.a. length. The peptide with one a.a. difference in Ras-related protein Rap-1blike protein was identified in our experiment, however, the other one a.a. difference was difficult to identify because of its tryptic peptide too short to be detected. As shown in Figure 3A, the 2563 MPs have the amino acid sequence lengths obviously shorter than that in the MPs and PE1 proteins identified in this study. In this study, the distribution of MP a.a. sequence lengths is similar to that of PE1 proteins, suggesting that ProteoMiner enrichment did not show a bias on protein lengths and the protein binding to the resin was mainly based on the interactions of the hexapeptide structures and the protein complexes. The hydrophobicity analysis by ProPAS (http://bioinfo. hupo.org.cn/tools/ProPAS/propas.htm) clearly revealed that the MPs found in this study had a higher hydrophobicity distribution due to 40% membrane protein identified in these MPs, while the 2563 MPs appeared two peaks in hydrophobicity (Figure 3B). As some of the MPs yet to be identified have higher hydrophobicity than all the proteins already identified, there is still a long way to get all MP identified if they are not well extracted or enriched.

group. Recently, Schaefferin et al. developed a peptide uniqueness checker to help scientists define which peptides can be used as a unique peptide to validate the existence of human MPs51 (https://www.nextprot.org/tools/peptide-uniquenesschecker). Finally, the quality of all the unique peptide spectra were manually checked to exclude those with low intensity or less fragment information for peptide full sequence coverage. After filtering with these checking, 20 MPs with 60 peptides were finally confirmed in identification with high-stringency evidence of at least two non-nested unique peptides (≥9 a.a.) including 17 PE2, one PE3, and two PE4 proteins, in which 11 MPs were both identified from the two injections of LC− MS/MS with at least two non-nested unique peptides (≥9 a.a.). The detailed information for these MPs was listed in Table 1, including identified peptide sequence, chromatin location, cellular location, and unique isoform identification. The spectra of all the unique peptides labeled with pLable software were summarized in Supplementary Figure 2,52 and the precursor m/z, mass error, and expect value for each spectrum were provided in Supplementary Table 4. The 20 MPs (1 from liver; 0 from colon; 8 from kidney; 11 from bladder) were mapped to 15 chromatins, three of which were mapped to the chromatin 1 (3PE2), and two of which were mapped to each of the chromatin 2 (PE4/PE2), 6 (2PE2), 11 (2PE2). The MPs were composed of two major subclasses in cellular localization, membrane/secreted and nucleus. In total, eight MPs were identified as membrane or secreted proteins, five identified as nucleus proteins, in which one, two, and five membrane/ secreted MPs from liver, kidney, and bladder, and two and three nucleus MPs from kidney and bladder, respectively. It is reasonable to find that 40% of the MPs are membrane proteins due to the efficient extraction of TX-100 to membrane proteins. Why is the ProteoMiner enrichment also efficient to nucleus proteins? We speculate that the structure or hydrophobicity of these nucleus proteins is the favorite of hexapeptides. The kidney, which has more tissue enriched genes and expresses a large number of membrane bound transport proteins, is supposed to give more membrane MP identification.53,54 It is surprising that 11 MPs are identified from bladder with five membrane/secreted proteins. It might be due to the efficient enrichment of ProteoMiner beads to these low-abundance membrane proteins which had relatively higher expression in bladder. One MP-coding gene may have more than one protein product expressed and these protein products share with the same accession number and higher homology. In the results of MP identification shown in Table 1, the sequences of the identified peptides for APOL4 and BTBD8 did not show any isoform characteristics, whereas for NPT1, BICL1, RNF180, CAD26, and PPMIM displayed some specific features for isoforms, which led to the identification of partial members in the MP groups. These results reached the HPP goal for identifying and charactering at least one protein product from each protein-coding gene.

Confirmation of MP Identification by Parallel Reaction Monitoring (PRM)

PRM assay, which provides high selectivity, high sensitivity with confident targeted peptide confirmation, was adopted as an orthogonal method to validate the MPs identified by DDA.55−57 The identified unique peptides for MPs in Table 1 were used as the targets for PRM verification. The two typical PRM traces derived from native tissue and synthetic peptides were shown in Figure 4, in which the two unique peptides of sodium-dependent phosphate transport protein 1 (Q14916/ NPT1, PE2, kidney membrane protein) were obtained confirmation with more than eight peptide fragments, and the two unique peptides of a PE2 bladder secreted protein, apolipoprotein L4 (Q9BPW4/APOL4) were also successfully detected in PRM assay. In total, 16 MPs with at least two non-nested unique peptides and four MPs with only one unique peptide were perceived in this assay. Then 32 peptides from the 16 MPs were synthesized (2 peptides/protein) and put into the matrix of the equalized peptides for PRM testing. The chromatographic and MS behaviors of these synthetic peptides were compared with that of native peptides from human tissues. Figure 4 revealed the good match of the synthetic peptides and tissue native peptides from NPT1 and APOL4 in RT and fragment abundance patterns. In total, 15 MPs were verified with two non-nested unique peptides whose chromatographic and MS behaviors in RT and fragment abundance patterns were matched well with that of their corresponding synthetic peptides (Table 1, Figure 4, and Supplementary Figure 3). To evaluate the reproducibility of ProteoMiner depletion performance, the PRM assays were performed to evaluate the equalized peptides that were generated from human bladder and kidney proteins, which were twice treated with ProteoMiner. As shown in Supplementary Figure 4, 10 peptides were identified with a

Characterization of New MPs Found in This Study

Baker MS et al. summarized the top five MP families as uncharacterized proteins, olfactory receptors, zinc finger proteins, nonGPCR transmembrane proteins, and coil−coil domain proteins.7 Out of 20 identified MPs, one uncharacterized protein, one zinc finger protein, two GPCR membrane proteins, and one coil−coil domain protein were identified. If the proteins have smaller molecular weight with few or no unique tryptic peptides of 9−30 amino acids or if the proteins are highly 4336

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good reproducibility in the biological duplicates and with a good consistence at the chromatographic and MS behaviors compared with their corresponding synthetic peptides.

CONCLUSIONS For the first time, we proposed that ProteoMiner is capable of equalizing protein abundance in human tissues for MP discovery. Combined with TX-100/TBS and ProteoMiner for protein extraction, we achieved 20 MPs with higher scores of confidential identification in four human tissues, including eight membrane/secreted proteins and five nucleus proteins, 15 of which were confirmed to be identified by matching with their chemically synthetic peptides in PRM assay. The results support our hypothesis that equalization of protein abundance with ProteoMiner prefers in finding of some membrane or nucleus MPs with lower abundance. ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.7b00353. Scheme of tissue sample pathological information and experimental flowchart; labeled spectra with MS identification information on all identified unique peptides from 20 MPs; comparison of RT and fragment abundance pattern for native peptides in human tissues and synthetic peptides from 16 MPs identified in PRM assay; evaluation for reproducibility of ProteoMiner depletion treatment by PRM assay; list of target peptides with m/z and sequence obtained from Skyline analysis and used for the PRM assay; identification summary of equalized proteins by ProteoMiner from human four tissues; list of proteins identified from four human tissues in this study; precursor m/z, mass error and E-value of all identified unique peptides from 20 MPs (PDF) Proteins identified from Triton X-100 extraction of human bladder (XLSX)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. Phone: 86-755-36307403. *E-mail: [email protected]. Phone: 86-755-36307403. ORCID

Yan Ren: 0000-0002-4007-8625 Author Contributions §

These authors contributed equally to this work.

Notes

The authors declare no competing financial interest. All the shotgun proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD006833, and the PRM data have been uploaded to PeptideAtlas (http://www.peptideatlas.org) with the dataset identifier PASS01068 and PASS01095.

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ACKNOWLEDGMENTS This work was supported by the National Basic Research Program of China (2014CBA02002, 2014CBA02005) and the National Natural Science Foundation of China (31500670). 4337

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