Abstract

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL: https://bmbl.bmi.osumc.edu/HBFP/

Background

Human body fluids are thought to be rich resources of disease-associated proteins that are secreted or leaked from pathological tissues across the body, many of which are commonly obtainable through non-invasive procedures (1, 2). Driven by these factors, research interests have soared a few decades ago toward biomarker discovery by examining body fluid proteomes. It is highly plausible that empowered by innovative high-throughput technologies, modern proteomic studies have successfully identified a large number of proteins in various body fluids such as plasma, serum, saliva and urine (3).

With great effort by a few large consortiums, several community-based proteomic databases have been developed in the past decades. For example, in 2002, the international Human Proteome Organization initiated the Human Plasma Proteome Project and reported human plasma and serum protein constituents in its online databases (4). Another similar database, named Plasma Proteome Database, archived more than 10 000 proteins detected in human blood (5). Additionally, the Proteomics Identifications database (6) and Human Plasma PeptideAtlas (7) report a total of 3509 high-confidence plasma proteins. More recently, the extracellular vesicles community also reports new proteins identified in exosomes in multiple different resources including blood and breast milk, e.g. in ExoCarta (8). Additionally, the global Human Proteome Project (HPP) announces a set of mass spectrometry (MS) data interpretation guidelines that are presented to the broader research community (9).

Our team has recently conducted a systematical assessment of human proteome identified using quantitative proteomics tools such as MS and computational predictive models, as documented in a recent review article (10). To expand this effort, we developed a new human body fluid proteome (HBFP) database to organize 11 827 unique proteins reported in 164 scientific articles since 2001, which has a maximal false discovery rate (FDR) of 0.01 on both the peptide and protein levels. Until today, this database stores information about proteins from 17 types of body fluids including plasma/serum, saliva, urine, cerebrospinal fluid (CSF), seminal fluid (SF), amniotic fluid, tear fluid, bronchoalveolar lavage fluid (BALF), milk, synovial fluid, nipple aspirate fluid, cervical-vaginal fluid, pleural effusion, sputum, exhaled breath condensate, pancreatic juice and sweat. For each protein entry, description about protein secretion information, literature source, abundances, confidence and functional annotation is provided. This database system also provides users easy access to data visualization and download and functional analysis based on Gene Ontology (GO) and pathways.

Database content and design

Protein entries

We have manually collected proteins reported in 17 types of body fluids by carefully reviewing 164 scientific references published since 2001 based on a PubMed search with FDR ≤1% on both the peptide and protein levels.

In the HBFP database, each protein is assigned with a unique identifier of UniProtKB/Swiss-Prot accession (UniProt release 2020_06) (11). Since different identifiers have been mixed used in the referenced studies, we first used conversion tools at BioDBnet (https://biodbnet-abcc.ncifcrf.gov/) (12) and UniProt (https://www.UniProt.org/) to confidently convert different identifiers to UniProt accession numbers. The common identifiers involved in this study include International Protein Index ID [hosted at European Bioinformatics Institute (EBI) (closed in 2011)], GI number (from Genbank database), RefSeq protein accession (from RefSeq database), Gene name/symbol (from NCBI Gene database) and UniProt protein/entry name (from UniProt database). The ID conversion process is shown in Figure 1. During the conversion, poorly curated proteins with ambiguous identifiers were eliminated. For examples, many International Protein Index ID links to unclearly described instances that cannot be mapped to a UniProt entry are excluded.

Workflow of protein identifier conversion.
Figure 1.

Workflow of protein identifier conversion.

Database utilities

The interface of the HBFP database is constructed by PHP, while the database system is based on MySQL. The main contents of the current database include query and browse pages described as follows.

Querying page

As one of the most important functions, the querying page allows users to search for body fluid proteins based on different types of input including protein ID, gene name, and protein or gene sequence. When given a FASTA input, BLASTp or BLASTn is used to translate sequence input to the best-match protein entry. The top hit (the highest bit score) from the BLAST search is considered the best match of the query. Figure 2 illustrates the workflow and content of querying page.

Construction workflow and utilities of querying page.
Figure 2.

Construction workflow and utilities of querying page.

The annotation of each protein contains the following information:

  • Protein ID/name/entry name

  • Gene name

  • Associated body fluid type along with indicated discovery confidence (explained in the next section)

  • References and protein abundance information where the protein is reported

  • External links to public databases including UniProt, PeptideAtlas and NeXtProt (13), MassIVE (14)

  • Functional annotation based on the KEGG pathway (15) and GO (16)

Browsing page

This page provides an overview list of proteins associated with 17 types of body fluids and links to view and download selected proteins.

Database highlights

Data statistics

When determining the inclusion of reported proteins, we applied the following criteria for credibility of the MS evidence. First, for papers that issued peptide sequence details, we remapped all those peptide sequences to neXtProt (release 2021-02-15) using the neXtProt peptide uniqueness checker to remove unreliable matches (17). Specifically, we applied guideline #15 of HPP Guidelines 2.1 (9) to include proteins that contain at least two non-nested uniquely mapping peptides of nine amino acids into the HBFP database. According to this criterion, 7354 proteins were confirmed confidently. Another 4473 proteins were also included as they were not explicitly provided with peptide sequence information but have more than two unique peptides.

The overall statistics about the protein entries and references in terms of each body fluid are summarized in Table 1. The current HBFP database contains 11 827 distinct proteins from 17 types of body fluids. Note that urine exceeds all other body fluids in terms of protein counts while blood is at the second rank. All data are made publicly available in the HBFP and via links at https://bmbl.bmi.osumc.edu/HBFP/.

Table 1.

Overall statistics

Statistics
Body fluid typesNumber of protein entriesNumber of referencesReferences
1Plasma/serum579038(18–55)
2Saliva275821(19, 56–75)
3Urine733023(19, 76–97)
4CSF436412(19, 90, 98–107)
5SF40845(108–112)
6Amniotic fluid30256(19, 113–117)
7Tear fluid (TF)188211(118–128)
8BALF34346(41, 129–133)
9Milk245714(134–147)
10Synovial fluid16377(148–154)
11Nipple aspirate fluid17345(155–159)
12Cervical–vaginal fluid9494(160–163)
13Pleural effusion15193(164–166)
14Sputum18093(167–169)
15Exhaled breath condensate3515(170–174)
16Pancreatic juice7024(175–178)
17Sweat12443(179–181)
Total (non-redundant)11 827164
Statistics
Body fluid typesNumber of protein entriesNumber of referencesReferences
1Plasma/serum579038(18–55)
2Saliva275821(19, 56–75)
3Urine733023(19, 76–97)
4CSF436412(19, 90, 98–107)
5SF40845(108–112)
6Amniotic fluid30256(19, 113–117)
7Tear fluid (TF)188211(118–128)
8BALF34346(41, 129–133)
9Milk245714(134–147)
10Synovial fluid16377(148–154)
11Nipple aspirate fluid17345(155–159)
12Cervical–vaginal fluid9494(160–163)
13Pleural effusion15193(164–166)
14Sputum18093(167–169)
15Exhaled breath condensate3515(170–174)
16Pancreatic juice7024(175–178)
17Sweat12443(179–181)
Total (non-redundant)11 827164
Table 1.

Overall statistics

Statistics
Body fluid typesNumber of protein entriesNumber of referencesReferences
1Plasma/serum579038(18–55)
2Saliva275821(19, 56–75)
3Urine733023(19, 76–97)
4CSF436412(19, 90, 98–107)
5SF40845(108–112)
6Amniotic fluid30256(19, 113–117)
7Tear fluid (TF)188211(118–128)
8BALF34346(41, 129–133)
9Milk245714(134–147)
10Synovial fluid16377(148–154)
11Nipple aspirate fluid17345(155–159)
12Cervical–vaginal fluid9494(160–163)
13Pleural effusion15193(164–166)
14Sputum18093(167–169)
15Exhaled breath condensate3515(170–174)
16Pancreatic juice7024(175–178)
17Sweat12443(179–181)
Total (non-redundant)11 827164
Statistics
Body fluid typesNumber of protein entriesNumber of referencesReferences
1Plasma/serum579038(18–55)
2Saliva275821(19, 56–75)
3Urine733023(19, 76–97)
4CSF436412(19, 90, 98–107)
5SF40845(108–112)
6Amniotic fluid30256(19, 113–117)
7Tear fluid (TF)188211(118–128)
8BALF34346(41, 129–133)
9Milk245714(134–147)
10Synovial fluid16377(148–154)
11Nipple aspirate fluid17345(155–159)
12Cervical–vaginal fluid9494(160–163)
13Pleural effusion15193(164–166)
14Sputum18093(167–169)
15Exhaled breath condensate3515(170–174)
16Pancreatic juice7024(175–178)
17Sweat12443(179–181)
Total (non-redundant)11 827164

Protein abundance

In order to provide users experimental evidence from the original study, this database also displays relatively abundant information from the corresponding literature studies. General proteomics approaches using MS identify proteins by matching identified peptides against predefined protein sequence databases. The qualitative measures of protein reported in the original reference include the following: (i) peptide information: most of cited studies provide explicit information about peptide sequence, the total number of peptides, MS counts or the percent sequence coverage; (ii) differential expression information including fold change (positive value demonstrates up-regulated expression and negative value indicates down-regulated expression), up- or down-regulated expression in case vs. control or (normalized) spectral counts and (iii) other statistical information including FDR, relative standard deviation and the number of times across different samples or experiments, as shown in Figure 3.

Distribution of protein abundance methods in HBFP database based on a number of original quantitative analysis methods from the original literature studies. Note that the sum of protein abundance is not 100% since not all of the literature studies provide quantitative analysis information.
Figure 3.

Distribution of protein abundance methods in HBFP database based on a number of original quantitative analysis methods from the original literature studies. Note that the sum of protein abundance is not 100% since not all of the literature studies provide quantitative analysis information.

Confidence score

In the HBFP database, to evaluate the confidence level of each discovered protein in each body fluid, a new statistical measure is calculated based on Guideline # 9 of HPP guidelines 2.1 for the combined datasets. It is a well-known phenomenon that when taking N datasets with a substantial FDR and piling them all together, the overall FDR increases with the number of datasets. For example, for plasma, there are 38 papers with plasma protein lists, each with a substantial FDR (⁠|$ \le $|1%). It is probably a conservative estimate to suppose that the FDR of such a combined result is 1% + 0.5%|$ \times $|(N datasets|$ - $|1) (9). It means that 50% of the correct identifications overlap and none of the incorrect ones does, so the resulting FDR is added in a 0.5% increment. Meanwhile, the confidence level of protein in the combined datasets is also reduced. Otherwise, considering the overlap of the true positives, the larger the number of datasets in which a protein is associated with a specific fluid, the more reliable this protein is. In the end, a confidence score |$C$| is calculated as follows:
(1)
(2)
(3)
where |${N_i}$| is the number of relevant literature studies (datasets) of a specific fluid |$i$|⁠; |$FD{R_i}$| represents the overall FDR of multiple datasets in body fluid |$i$|⁠; |${A_i}$| means the confidence level of proteins in the combined datasets of body fluid |$i$| and |${M_j}$| refers to the number of literature studies in which a protein |$j$| is identified in body fluid |$i$|⁠.

For example, there are 38 literature studies related to blood in the HBFP, so |${N_i} = 38$|⁠, |$FD{R_i} = 0.195$| and |${A_i} = 0.805$|⁠. The protein O14791 is identified in blood by 19 independent studies, i.e. |${M_j} = 19$|⁠. As a result, the calculated |${C_{i,j}}$| score for O14791 in blood is |$0.895$|⁠. Meanwhile, protein Q9UJV9 only is identified in one paper for blood, so |${M_j} = 1$| and |${C_{i,j}} = {A_i} = 0.805$|⁠. It means that protein Q9UJV9 maintains only the confidence level in the combined datasets of blood. Specifically, protein P01833 is identified in milk by 14 studies, and a total of 14 literature studies on milk are included in the HBFP, so protein P01833 maintains the original confidence level, i.e. 0.99. The larger the |$C$| score, the higher the confidence that a protein reported in that fluid will be. Note that this score can only be compared within the same type of body fluid.

Database applications

Data access

The website can be accessed through https://bmbl.bmi.osumc.edu/HBFP/.

Query

All proteins can be easily accessed by searching protein ID, gene name, protein sequence (FASTA) or gene sequence (FASTA) (<50 items per query) (Figure 4A and B as an example). A BLAST (182) is performed locally to find the best match when the sequence FASTA format is given. For each protein, detailed information is displayed (Figure 4C). Users can connect directly to the PubMed or Google Scholar to view the original study through the provided links. Four databases (UniProt, PeptideAtlas, NeXtProt and MassIVE) are cross-linked for additional protein annotation, while the KEGG pathway and GO are focused on the functional aspects (Figure 4D).

Example of query response with input as ‘P58340’ in the protein ID and protein sequence box.
Figure 4.

Example of query response with input as ‘P58340’ in the protein ID and protein sequence box.

Download

HBFP allows users to browse the entire protein list in each body fluid, where the proteins are ordered based on descending confidence scores. Users can check and download all entries of the selected body fluid type in one go, as shown in Figure 5.

Download illustration where user can choose the body fluid name and download the proteins of interest or all proteins.
Figure 5.

Download illustration where user can choose the body fluid name and download the proteins of interest or all proteins.

Demo of comparative analysis using the HBFP database

Body fluid analysis

Many proteins in the HBFP database have a broad distribution in terms of body fluid types. An internal comparative analysis across different fluids can provide further information regarding the specificity of a proposed marker protein. Of 11 827 identified proteins, 66.8% are identified in at least two body fluids (Figure 6). A total of 93 proteins (0.79%) are shared among all analyzed body fluids, which may indicate that these proteins are essential for various life activities (Table 2).

Comparative analysis across different body fluids. Seven thousand eight hundred and ninety-nine (7899) proteins are presented in at least two body fluids and 5733 proteins existed in at least three body fluids. Only 93 proteins exist in all 17 body fluids.
Figure 6.

Comparative analysis across different body fluids. Seven thousand eight hundred and ninety-nine (7899) proteins are presented in at least two body fluids and 5733 proteins existed in at least three body fluids. Only 93 proteins exist in all 17 body fluids.

Table 2.

List of 93 proteins shared among all 17 body fluids

UniProt accession numberProtein nameGene name
1P11021Endoplasmic reticulum chaperone BiPHSPA5
2P55072Transitional endoplasmic reticulum ATPaseVCP
3P13647Keratin, type II cytoskeletal 5KRT5
4O00299Chloride intracellular channel protein 1CLIC1
5P02787SerotransferrinTF
6P22314Ubiquitin-like modifier-activating enzyme 1UBA1
7P13645Keratin, type I cytoskeletal 10KRT10
8P02533Keratin, type I cytoskeletal 14KRT14
9P07237Protein disulfide-isomeraseP4HB
10P06576ATP synthase subunit beta, mitochondrialATP5F1B
11P30041Peroxiredoxin-6PRDX6
12P6310414-3-3 protein zeta/deltaYWHAZ
13P6225814-3-3 protein epsilonYWHAE
14P14923Junction plakoglobinJUP
15P04040CatalaseCAT
16P01834Immunoglobulin kappa constantIGKC
17P06702Protein S100-A9S100A9
18P522096-Phosphogluconate dehydrogenase, decarboxylatingPGD
19P18669Phosphoglycerate mutase 1PGAM1
20P14618Pyruvate kinase PKMPKM
21P6198114-3-3 protein gammaYWHAG
22P07384Calpain-1 catalytic subunitCAPN1
23P50395Rab GDP dissociation inhibitor betaGDI2
24Q00610Clathrin heavy chain 1CLTC
25P26641Elongation factor 1-gammaEEF1G
26P32119Peroxiredoxin-2PRDX2
27P19971Thymidine phosphorylaseTYMP
28P26038MoesinMSN
29P40121Macrophage-capping proteinCAPG
30P35754Glutaredoxin-1GLRX
31P01009Alpha-1-antitrypsinSERPINA1
32P01860Immunoglobulin heavy constant gamma 3IGHG3
33P06753Tropomyosin alpha-3 chainTPM3
34P68871Hemoglobin subunit betaHBB
35P62805Histone H4H4C1
36P30086Phosphatidylethanolamine-binding protein 1PEBP1
37P35579Myosin-9MYH9
38P01023Alpha-2-macroglobulinA2M
39Q06830Peroxiredoxin-1PRDX1
40P02042Hemoglobin subunit deltaHBD
41P07737Profilin-1PFN1
42P80188Neutrophil gelatinase-associated lipocalinLCN2
43P02679Fibrinogen gamma chainFGG
44P40925Malate dehydrogenase, cytoplasmicMDH1
45P08758Annexin A5ANXA5
46P46940Ras GTPase-activating-like protein IQGAP1IQGAP1
47P01833Polymeric immunoglobulin receptorPIGR
48P31949Protein S100-A11S100A11
49P04792Heat shock protein beta-1HSPB1
50P07339Cathepsin DCTSD
51P01857Immunoglobulin heavy constant gamma 1IGHG1
52P06733Alpha-enolaseENO1
53P23284Peptidyl-prolyl cis-trans isomerase BPPIB
54P02647Apolipoprotein A-IAPOA1
55O43707Alpha-actinin-4ACTN4
56P30740Leukocyte elastase inhibitorSERPINB1
57Q16610Extracellular matrix protein 1ECM1
58P60709Actin, cytoplasmic 1ACTB
59P15924DesmoplakinDSP
60P62937Peptidyl-prolyl cis-trans isomerase APPIA
61P17931Galectin-3LGALS3
62P00491Purine nucleoside phosphorylasePNP
63P04080Cystatin-BCSTB
64P02788LactotransferrinLTF
65P13639Elongation factor 2EEF2
66P35527Keratin, type I cytoskeletal 9KRT9
67P06396GelsolinGSN
68P59998Actin-related protein 2/3 complex subunit 4ARPC4
69P25311Zinc-alpha-2-glycoproteinAZGP1
70P02768AlbuminALB
71P61160Actin-related protein 2ACTR2
72P04406Glyceraldehyde-3-phosphate dehydrogenaseGAPDH
73P60174Triosephosphate isomeraseTPI1
74P18206VinculinVCL
75P08670VimentinVIM
76P10599ThioredoxinTXN
77P11142Heat shock cognate 71 kDa proteinHSPA8
78P01011Alpha-1-antichymotrypsinSERPINA3
79P04075Fructose-bisphosphate aldolase AALDOA
80P04264Keratin, type II cytoskeletal 1KRT1
81P37837TransaldolaseTALDO1
82P35908Keratin, type II cytoskeletal 2 epidermalKRT2
83P02545Prelamin-A/CLMNA
84P69905Hemoglobin subunit alphaHBA1
85P07900Heat shock protein HSP 90-alphaHSP90AA1
86P29401TransketolaseTKT
87P00558Phosphoglycerate kinase 1PGK1
88P00338L-lactate dehydrogenase A chainLDHA
89P01861Immunoglobulin heavy constant gamma 4IGHG4
90P05109Protein S100-A8S100A8
91P04083Annexin A1ANXA1
92P01024Complement C3C3
93P09211Glutathione S-transferase PGSTP1
UniProt accession numberProtein nameGene name
1P11021Endoplasmic reticulum chaperone BiPHSPA5
2P55072Transitional endoplasmic reticulum ATPaseVCP
3P13647Keratin, type II cytoskeletal 5KRT5
4O00299Chloride intracellular channel protein 1CLIC1
5P02787SerotransferrinTF
6P22314Ubiquitin-like modifier-activating enzyme 1UBA1
7P13645Keratin, type I cytoskeletal 10KRT10
8P02533Keratin, type I cytoskeletal 14KRT14
9P07237Protein disulfide-isomeraseP4HB
10P06576ATP synthase subunit beta, mitochondrialATP5F1B
11P30041Peroxiredoxin-6PRDX6
12P6310414-3-3 protein zeta/deltaYWHAZ
13P6225814-3-3 protein epsilonYWHAE
14P14923Junction plakoglobinJUP
15P04040CatalaseCAT
16P01834Immunoglobulin kappa constantIGKC
17P06702Protein S100-A9S100A9
18P522096-Phosphogluconate dehydrogenase, decarboxylatingPGD
19P18669Phosphoglycerate mutase 1PGAM1
20P14618Pyruvate kinase PKMPKM
21P6198114-3-3 protein gammaYWHAG
22P07384Calpain-1 catalytic subunitCAPN1
23P50395Rab GDP dissociation inhibitor betaGDI2
24Q00610Clathrin heavy chain 1CLTC
25P26641Elongation factor 1-gammaEEF1G
26P32119Peroxiredoxin-2PRDX2
27P19971Thymidine phosphorylaseTYMP
28P26038MoesinMSN
29P40121Macrophage-capping proteinCAPG
30P35754Glutaredoxin-1GLRX
31P01009Alpha-1-antitrypsinSERPINA1
32P01860Immunoglobulin heavy constant gamma 3IGHG3
33P06753Tropomyosin alpha-3 chainTPM3
34P68871Hemoglobin subunit betaHBB
35P62805Histone H4H4C1
36P30086Phosphatidylethanolamine-binding protein 1PEBP1
37P35579Myosin-9MYH9
38P01023Alpha-2-macroglobulinA2M
39Q06830Peroxiredoxin-1PRDX1
40P02042Hemoglobin subunit deltaHBD
41P07737Profilin-1PFN1
42P80188Neutrophil gelatinase-associated lipocalinLCN2
43P02679Fibrinogen gamma chainFGG
44P40925Malate dehydrogenase, cytoplasmicMDH1
45P08758Annexin A5ANXA5
46P46940Ras GTPase-activating-like protein IQGAP1IQGAP1
47P01833Polymeric immunoglobulin receptorPIGR
48P31949Protein S100-A11S100A11
49P04792Heat shock protein beta-1HSPB1
50P07339Cathepsin DCTSD
51P01857Immunoglobulin heavy constant gamma 1IGHG1
52P06733Alpha-enolaseENO1
53P23284Peptidyl-prolyl cis-trans isomerase BPPIB
54P02647Apolipoprotein A-IAPOA1
55O43707Alpha-actinin-4ACTN4
56P30740Leukocyte elastase inhibitorSERPINB1
57Q16610Extracellular matrix protein 1ECM1
58P60709Actin, cytoplasmic 1ACTB
59P15924DesmoplakinDSP
60P62937Peptidyl-prolyl cis-trans isomerase APPIA
61P17931Galectin-3LGALS3
62P00491Purine nucleoside phosphorylasePNP
63P04080Cystatin-BCSTB
64P02788LactotransferrinLTF
65P13639Elongation factor 2EEF2
66P35527Keratin, type I cytoskeletal 9KRT9
67P06396GelsolinGSN
68P59998Actin-related protein 2/3 complex subunit 4ARPC4
69P25311Zinc-alpha-2-glycoproteinAZGP1
70P02768AlbuminALB
71P61160Actin-related protein 2ACTR2
72P04406Glyceraldehyde-3-phosphate dehydrogenaseGAPDH
73P60174Triosephosphate isomeraseTPI1
74P18206VinculinVCL
75P08670VimentinVIM
76P10599ThioredoxinTXN
77P11142Heat shock cognate 71 kDa proteinHSPA8
78P01011Alpha-1-antichymotrypsinSERPINA3
79P04075Fructose-bisphosphate aldolase AALDOA
80P04264Keratin, type II cytoskeletal 1KRT1
81P37837TransaldolaseTALDO1
82P35908Keratin, type II cytoskeletal 2 epidermalKRT2
83P02545Prelamin-A/CLMNA
84P69905Hemoglobin subunit alphaHBA1
85P07900Heat shock protein HSP 90-alphaHSP90AA1
86P29401TransketolaseTKT
87P00558Phosphoglycerate kinase 1PGK1
88P00338L-lactate dehydrogenase A chainLDHA
89P01861Immunoglobulin heavy constant gamma 4IGHG4
90P05109Protein S100-A8S100A8
91P04083Annexin A1ANXA1
92P01024Complement C3C3
93P09211Glutathione S-transferase PGSTP1
Table 2.

List of 93 proteins shared among all 17 body fluids

UniProt accession numberProtein nameGene name
1P11021Endoplasmic reticulum chaperone BiPHSPA5
2P55072Transitional endoplasmic reticulum ATPaseVCP
3P13647Keratin, type II cytoskeletal 5KRT5
4O00299Chloride intracellular channel protein 1CLIC1
5P02787SerotransferrinTF
6P22314Ubiquitin-like modifier-activating enzyme 1UBA1
7P13645Keratin, type I cytoskeletal 10KRT10
8P02533Keratin, type I cytoskeletal 14KRT14
9P07237Protein disulfide-isomeraseP4HB
10P06576ATP synthase subunit beta, mitochondrialATP5F1B
11P30041Peroxiredoxin-6PRDX6
12P6310414-3-3 protein zeta/deltaYWHAZ
13P6225814-3-3 protein epsilonYWHAE
14P14923Junction plakoglobinJUP
15P04040CatalaseCAT
16P01834Immunoglobulin kappa constantIGKC
17P06702Protein S100-A9S100A9
18P522096-Phosphogluconate dehydrogenase, decarboxylatingPGD
19P18669Phosphoglycerate mutase 1PGAM1
20P14618Pyruvate kinase PKMPKM
21P6198114-3-3 protein gammaYWHAG
22P07384Calpain-1 catalytic subunitCAPN1
23P50395Rab GDP dissociation inhibitor betaGDI2
24Q00610Clathrin heavy chain 1CLTC
25P26641Elongation factor 1-gammaEEF1G
26P32119Peroxiredoxin-2PRDX2
27P19971Thymidine phosphorylaseTYMP
28P26038MoesinMSN
29P40121Macrophage-capping proteinCAPG
30P35754Glutaredoxin-1GLRX
31P01009Alpha-1-antitrypsinSERPINA1
32P01860Immunoglobulin heavy constant gamma 3IGHG3
33P06753Tropomyosin alpha-3 chainTPM3
34P68871Hemoglobin subunit betaHBB
35P62805Histone H4H4C1
36P30086Phosphatidylethanolamine-binding protein 1PEBP1
37P35579Myosin-9MYH9
38P01023Alpha-2-macroglobulinA2M
39Q06830Peroxiredoxin-1PRDX1
40P02042Hemoglobin subunit deltaHBD
41P07737Profilin-1PFN1
42P80188Neutrophil gelatinase-associated lipocalinLCN2
43P02679Fibrinogen gamma chainFGG
44P40925Malate dehydrogenase, cytoplasmicMDH1
45P08758Annexin A5ANXA5
46P46940Ras GTPase-activating-like protein IQGAP1IQGAP1
47P01833Polymeric immunoglobulin receptorPIGR
48P31949Protein S100-A11S100A11
49P04792Heat shock protein beta-1HSPB1
50P07339Cathepsin DCTSD
51P01857Immunoglobulin heavy constant gamma 1IGHG1
52P06733Alpha-enolaseENO1
53P23284Peptidyl-prolyl cis-trans isomerase BPPIB
54P02647Apolipoprotein A-IAPOA1
55O43707Alpha-actinin-4ACTN4
56P30740Leukocyte elastase inhibitorSERPINB1
57Q16610Extracellular matrix protein 1ECM1
58P60709Actin, cytoplasmic 1ACTB
59P15924DesmoplakinDSP
60P62937Peptidyl-prolyl cis-trans isomerase APPIA
61P17931Galectin-3LGALS3
62P00491Purine nucleoside phosphorylasePNP
63P04080Cystatin-BCSTB
64P02788LactotransferrinLTF
65P13639Elongation factor 2EEF2
66P35527Keratin, type I cytoskeletal 9KRT9
67P06396GelsolinGSN
68P59998Actin-related protein 2/3 complex subunit 4ARPC4
69P25311Zinc-alpha-2-glycoproteinAZGP1
70P02768AlbuminALB
71P61160Actin-related protein 2ACTR2
72P04406Glyceraldehyde-3-phosphate dehydrogenaseGAPDH
73P60174Triosephosphate isomeraseTPI1
74P18206VinculinVCL
75P08670VimentinVIM
76P10599ThioredoxinTXN
77P11142Heat shock cognate 71 kDa proteinHSPA8
78P01011Alpha-1-antichymotrypsinSERPINA3
79P04075Fructose-bisphosphate aldolase AALDOA
80P04264Keratin, type II cytoskeletal 1KRT1
81P37837TransaldolaseTALDO1
82P35908Keratin, type II cytoskeletal 2 epidermalKRT2
83P02545Prelamin-A/CLMNA
84P69905Hemoglobin subunit alphaHBA1
85P07900Heat shock protein HSP 90-alphaHSP90AA1
86P29401TransketolaseTKT
87P00558Phosphoglycerate kinase 1PGK1
88P00338L-lactate dehydrogenase A chainLDHA
89P01861Immunoglobulin heavy constant gamma 4IGHG4
90P05109Protein S100-A8S100A8
91P04083Annexin A1ANXA1
92P01024Complement C3C3
93P09211Glutathione S-transferase PGSTP1
UniProt accession numberProtein nameGene name
1P11021Endoplasmic reticulum chaperone BiPHSPA5
2P55072Transitional endoplasmic reticulum ATPaseVCP
3P13647Keratin, type II cytoskeletal 5KRT5
4O00299Chloride intracellular channel protein 1CLIC1
5P02787SerotransferrinTF
6P22314Ubiquitin-like modifier-activating enzyme 1UBA1
7P13645Keratin, type I cytoskeletal 10KRT10
8P02533Keratin, type I cytoskeletal 14KRT14
9P07237Protein disulfide-isomeraseP4HB
10P06576ATP synthase subunit beta, mitochondrialATP5F1B
11P30041Peroxiredoxin-6PRDX6
12P6310414-3-3 protein zeta/deltaYWHAZ
13P6225814-3-3 protein epsilonYWHAE
14P14923Junction plakoglobinJUP
15P04040CatalaseCAT
16P01834Immunoglobulin kappa constantIGKC
17P06702Protein S100-A9S100A9
18P522096-Phosphogluconate dehydrogenase, decarboxylatingPGD
19P18669Phosphoglycerate mutase 1PGAM1
20P14618Pyruvate kinase PKMPKM
21P6198114-3-3 protein gammaYWHAG
22P07384Calpain-1 catalytic subunitCAPN1
23P50395Rab GDP dissociation inhibitor betaGDI2
24Q00610Clathrin heavy chain 1CLTC
25P26641Elongation factor 1-gammaEEF1G
26P32119Peroxiredoxin-2PRDX2
27P19971Thymidine phosphorylaseTYMP
28P26038MoesinMSN
29P40121Macrophage-capping proteinCAPG
30P35754Glutaredoxin-1GLRX
31P01009Alpha-1-antitrypsinSERPINA1
32P01860Immunoglobulin heavy constant gamma 3IGHG3
33P06753Tropomyosin alpha-3 chainTPM3
34P68871Hemoglobin subunit betaHBB
35P62805Histone H4H4C1
36P30086Phosphatidylethanolamine-binding protein 1PEBP1
37P35579Myosin-9MYH9
38P01023Alpha-2-macroglobulinA2M
39Q06830Peroxiredoxin-1PRDX1
40P02042Hemoglobin subunit deltaHBD
41P07737Profilin-1PFN1
42P80188Neutrophil gelatinase-associated lipocalinLCN2
43P02679Fibrinogen gamma chainFGG
44P40925Malate dehydrogenase, cytoplasmicMDH1
45P08758Annexin A5ANXA5
46P46940Ras GTPase-activating-like protein IQGAP1IQGAP1
47P01833Polymeric immunoglobulin receptorPIGR
48P31949Protein S100-A11S100A11
49P04792Heat shock protein beta-1HSPB1
50P07339Cathepsin DCTSD
51P01857Immunoglobulin heavy constant gamma 1IGHG1
52P06733Alpha-enolaseENO1
53P23284Peptidyl-prolyl cis-trans isomerase BPPIB
54P02647Apolipoprotein A-IAPOA1
55O43707Alpha-actinin-4ACTN4
56P30740Leukocyte elastase inhibitorSERPINB1
57Q16610Extracellular matrix protein 1ECM1
58P60709Actin, cytoplasmic 1ACTB
59P15924DesmoplakinDSP
60P62937Peptidyl-prolyl cis-trans isomerase APPIA
61P17931Galectin-3LGALS3
62P00491Purine nucleoside phosphorylasePNP
63P04080Cystatin-BCSTB
64P02788LactotransferrinLTF
65P13639Elongation factor 2EEF2
66P35527Keratin, type I cytoskeletal 9KRT9
67P06396GelsolinGSN
68P59998Actin-related protein 2/3 complex subunit 4ARPC4
69P25311Zinc-alpha-2-glycoproteinAZGP1
70P02768AlbuminALB
71P61160Actin-related protein 2ACTR2
72P04406Glyceraldehyde-3-phosphate dehydrogenaseGAPDH
73P60174Triosephosphate isomeraseTPI1
74P18206VinculinVCL
75P08670VimentinVIM
76P10599ThioredoxinTXN
77P11142Heat shock cognate 71 kDa proteinHSPA8
78P01011Alpha-1-antichymotrypsinSERPINA3
79P04075Fructose-bisphosphate aldolase AALDOA
80P04264Keratin, type II cytoskeletal 1KRT1
81P37837TransaldolaseTALDO1
82P35908Keratin, type II cytoskeletal 2 epidermalKRT2
83P02545Prelamin-A/CLMNA
84P69905Hemoglobin subunit alphaHBA1
85P07900Heat shock protein HSP 90-alphaHSP90AA1
86P29401TransketolaseTKT
87P00558Phosphoglycerate kinase 1PGK1
88P00338L-lactate dehydrogenase A chainLDHA
89P01861Immunoglobulin heavy constant gamma 4IGHG4
90P05109Protein S100-A8S100A8
91P04083Annexin A1ANXA1
92P01024Complement C3C3
93P09211Glutathione S-transferase PGSTP1

Venn diagram and GO annotation

To take a closer look at this comparison, we focused on five body fluids that have the most protein counts, including blood, urine, CSF, SF) and BALF. An interesting discovery is that urine shares large numbers of common proteins with other fluids (Figure 7). A total of 4109, 3212, 2990 and 2950 proteins overlapped between the plasma and the other four body fluids (blood, CSF, SF and BALF, respectively). There are 965 proteins commonly detected in all five body fluids. The functional analysis using the BiNGO tool (183) in Cytoscape (184), reflecting information about cellular localization, molecular function and biological process of these proteins (Figure 8).

Venn diagram showing the common proteins among five body fluids (blood, urine, CSF, SF and BALF) that have the most number of proteins in the HBFP.
Figure 7.

Venn diagram showing the common proteins among five body fluids (blood, urine, CSF, SF and BALF) that have the most number of proteins in the HBFP.

Example of GO annotation based on the 965 proteins common in five body fluids.
Figure 8.

Example of GO annotation based on the 965 proteins common in five body fluids.

Conclusions

The new HBFP database developed in this study represents the first of its kind as a comprehensive reference resource of HBFP. All data are available through an open-access user-friendly Web platform. All protein entries were manually curated, which can be easily traced back to the original literature. Users can query and download proteins of interest to verify discovery in their own study or conduct an in silico analysis on human secretomes. We currently schedule a regular update every 6 months. The future plan is to include computationally identified proteins using statistical and machine learning approaches (185–191). In the past decade, many computational studies have revealed unique strengths in overcoming challenges in profiling-based proteomics research in terms of discovering new protein bioavailability and functions. Those computationally predicted proteins can serve as a secondary resource for biomarker discovery. In summary, by providing a wealth of information and functional analysis, we believe the HBFP database can be an excellent tool for the research community to explore human proteome in various body fluids.

Funding

National Natural Science Foundation of China (no. 62072212); Development Project of Jilin Province of China (nos 20200401083GX, 2020LY500L06 and 2020C003); Guangdong Key Project for Applied Fundamental Research (grant 2018KZDXM076); Jilin Province Key Laboratory of Big Data Intelligent Computing (no. 20180622002JC).

Conflict of interest

The authors declare that they have no competing interests.

References

1.

Anderson
N.L.
(
2010
)
The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum
.
Clin. Chem.
,
56
,
177
185
.

2.

Lathrop
J.
,
Anderson
N.
,
Anderson
N.
et al.  (
2003
)
Therapeutic potential of the plasma proteome
.
Curr. Opin. Mol. Ther.
,
5
,
250
257
.

3.

Hu
S.
,
Loo
J.
and
Wong
D.
(
2006
)
Human body fluid proteome analysis
.
Proteomics
,
6
,
6326
6353
.

4.

Omenn
G.S.
,
States
D.J.
,
Adamski
M.
et al.  (
2005
)
Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database
.
Proteomics
,
5
,
3226
3245
.

5.

Nanjappa
V.
,
Thomas
J.K.
,
Marimuthu
A.
et al.  (
2014
)
Plasma Proteome Database as a resource for proteomics research: 2014 update
.
Nucleic Acids Res.
,
42
,
D959
D965
.

6.

Vizcaíno
J.
,
Côté
R.
,
Reisinger
F.
et al.  (
2010
)
The Proteomics Identifications database: 2010 update
.
Nucleic Acids Res.
,
38
,
D736
D742
.

7.

Schwenk
J.M.
,
Omenn
G.S.
,
Sun
Z.
et al.  (
2017
)
The human plasma proteome draft of 2017: building on the Human Plasma PeptideAtlas from mass spectrometry and complementary assays
.
J. Proteome Res.
,
16
,
4299
4310
.

8.

Keerthikumar
S.
,
Chisanga
D.
,
Ariyaratne
D.
et al.  (
2016
)
ExoCarta: a web-based compendium of exosomal cargo
.
J. Mol. Biol.
,
428
,
688
692
.

9.

Deutsch
E.W.
,
Overall
C.M.
,
Eyk
J.V.
et al.  (
2016
)
Human Proteome Project mass spectrometry data interpretation guidelines 2.1
.
J. Proteome Res.
,
15
,
3961
3970
.

10.

Huang
L.
,
Shao
D.
,
Wang
Y.
et al.  (
2021
)
Human body-fluid proteome: quantitative profiling and computational prediction
.
Brief. Bioinformatics
,
22
,
315
333
.

11.

UniProt Consortium
(
2015
)
UniProt: a hub for protein information
.
Nucleic Acids Res.
,
43
,
D204
D212
.

12.

Mudunuri
U.
,
Che
A.
,
Yi
M.
et al.  (
2009
)
bioDBnet: the biological database network
.
Bioinformatics
,
25
,
555
556
.

13.

Lydie
L.
,
Ghislaine
A.P.
,
Aurore
B.
et al.  (
2012
)
neXtProt: a knowledge platform for human proteins
.
Nucleic Acids Res.
,
40
,
D76
D83
.

14.

Wang
M.
,
Wang
J.
,
Carver
J.
et al.  (
2018
)
Assembling the community-scale discoverable human proteome
.
Cell Syst.
,
7
,
412
421
.

15.

Minoru
K.
,
Michihiro
A.
,
Susumu
G.
et al.  (
2008
)
KEGG for linking genomes to life and the environment
.
Nucleic Acids Res.
,
36
,
D480
D484
.

16.

Carbon
S.
,
Ireland
A.
,
Mungall
C.J.
et al.  (
2009
)
AmiGO: online access to ontology and annotation data
.
Bioinformatics
,
25
,
288
289
.

17.

Mathieu
S.
,
Alain
G.
,
Daniel
T.
et al.  (
2017
)
The neXtProt peptide uniqueness checker: a tool for the proteomics community
.
Bioinformatics
,
33
,
3471
3472
.

18.

Zhao
Y.
,
Chang
C.
,
Qin
P.
et al.  (
2016
)
Mining the human plasma proteome with three-dimensional strategies by high-resolution Quadrupole Orbitrap Mass Spectrometry
.
Anal. Chim. Acta
,
904
,
65
75
.

19.

Zhao
M.
,
Yang
Y.
,
Guo
Z.
et al.  (
2018
)
A comparative proteomics analysis of five body fluids: plasma, urine, cerebrospinal fluid, amniotic fluid and saliva
.
Proteomics Clin. Appl.
,
12
, e1800008.

20.

Yan
W.
,
Apweiler
R.
,
Balgley
B.M.
et al.  (
2010
)
Systematic comparison of the human saliva and plasma proteomes
.
Proteomics Clin. Appl.
,
3
,
116
134
.

21.

Moreno
S.O.
,
Cominetti
O.
,
Galindo
A.N.
et al.  (
2017
)
The differential plasma proteome of obese and overweight individuals undergoing a nutritional weight loss and maintenance intervention
.
Proteomics Clin. Appl.
,
12
, 1600150.

22.

Li
L.
,
Xu
Y.
and
Yu
C.X.
(
2012
)
Proteomic analysis of serum of women with elevated Ca-125 to differentiate malignant from benign ovarian tumors
.
Asian Pac. J. Cancer Prev.
,
13
,
3265
3270
.

23.

Chen
L.Z.
,
Gu
H.
,
Li
J.
et al.  (
2016
)
Comprehensive maternal serum proteomics identifies the cytoskeletal proteins as non-invasive biomarkers in prenatal diagnosis of congenital heart defects
.
Sci. Rep.
,
6
, 19248.

24.

Acosta-Martin
A.E.
,
Panchaud
A.
,
Chwastyniak
M.
et al.  (
2011
)
Quantitative mass spectrometry analysis using PAcIFIC for the identification of plasma diagnostic biomarkers for abdominal aortic aneurysm
.
PLoS One
,
6
, e28698.

25.

Pietzner
M.
,
Engelmann
B.
,
Kacprowski
T.
et al.  (
2017
)
Plasma proteome and metabolome characterization of an experimental human thyrotoxicosis model
.
BMC Med.
,
15
, 6.

26.

Boichenko
A.P.
,
Govorukhina
N.
,
Klip
H.G.
et al.  (
2014
)
A panel of regulated proteins in serum from patients with cervical intraepithelial neoplasia and cervical cancer
.
J. Proteome Res.
,
13
,
4995
5007
.

27.

Geyer
P.E.
,
Kulak
N.A.
,
Pichler
G.
et al.  (
2016
)
Plasma proteome profiling to assess human health and disease
.
Cell Syst.
,
2
,
185
195
.

28.

Geyer
P.E.
,
Wewer Albrechtsen
N.J.
,
Tyanova
S.
et al.  (
2016
)
Proteomics reveals the effects of sustained weight loss on the human plasma proteome
.
Mol. Syst. Biol.
,
12
, 901.

29.

Yadav
A.K.
,
Bhardwaj
G.
,
Basak
T.
et al.  (
2011
)
A systematic analysis of eluted fraction of plasma post immunoaffinity depletion: implications in biomarker discovery
.
PLoS One
,
6
, e24442.

30.

Liu
Z.
,
Fan
S.
,
Liu
H.
et al.  (
2016
)
Enhanced detection of low-abundance human plasma proteins by integrating polyethylene glycol fractionation and immunoaffinity depletion
.
PLoS One
,
11
, e0166306.

31.

Limonier
F.
,
Steendam
K.V.
,
Waeterloos
G.
et al.  (
2016
)
An application of mass spectrometry for quality control of biologicals: highly sensitive profiling of plasma residuals in human plasma-derived immunoglobulin
.
J. Proteomics
,
152
,
312
320
.

32.

Bjelosevic
S.
,
Pascovici
D.
,
Ping
H.
et al.  (
2017
)
Quantitative age-specific variability of plasma proteins in healthy neonates, children and adults
.
Mol. Cell Proteomics
,
16
,
924
935
.

33.

Farrah
T.
,
Deutsch
E.W.
,
Omenn
G.S.
et al.  (
2011
)
A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas
.
Mol. Cell. Proteomics
,
152
,
312
320
.

34.

Gautam
P.
,
Nair
S.C.
,
Ramamoorthy
K.
et al.  (
2013
)
Analysis of human blood plasma proteome from ten healthy volunteers from Indian population
.
PLoS One
,
8
, e72584.

35.

Cheon
D.H.
,
Nam
E.J.
,
Park
K.H.
et al.  (
2016
)
Comprehensive analysis of low-molecular-weight human plasma proteome using top-down mass spectrometry
.
J. Proteome Res.
,
15
,
229
244
.

36.

Zhou
M.
,
Prieto
D.A.
,
Lucas
D.A.
et al.  (
2006
)
Identification of the SELDI ProteinChip human serum retentate by microcapillary liquid chromatography-tandem mass spectrometry
.
J. Proteome Res.
,
5
,
2207
2216
.

37.

Zeng
Z.
,
Hincapie
M.
,
Pitteri
S.J.
et al.  (
2011
)
A proteomics platform combining depletion, multi-lectin affinity chromatography(M-LAC), and isoelectric focusing to study the breast cancer proteome
.
Anal. Chem.
,
83
,
4845
4854
.

38.

Pan
S.
,
Chen
R.
,
Crispin
D.A.
et al.  (
2011
)
Protein alterations associated with pancreatic cancer and chronic pancreatitis found in human plasma using global quantitative proteomics profiling
.
J. Proteome Res.
,
10
,
2359
2376
.

39.

Surinova
S.
,
Choi
M.
,
Tao
S.
et al.  (
2015
)
Prediction of colorectal cancer diagnosis based on circulating plasma proteins
.
EMBO Mol. Med.
,
7
,
1166
1178
.

40.

Harel
M.
,
Oren-Giladi
P.
,
Kaidar-Person
O.
et al.  (
2015
)
Proteomics of microparticles with SILAC Quantification (PROMIS-Quan): a novel proteomic method for plasma biomarker quantification
.
Mol. Cell. Proteomics
,
14
,
1127
1136
.

41.

Carvalho
A.S.
,
Cuco
C.M.
,
Lavareda
C.
et al.  (
2017
)
Bronchoalveolar lavage proteomics in patients with suspected lung cancer
.
Sci. Rep.
,
7
, 42190.

42.

Zhou
B.
,
Zhou
Z.
,
Chen
Y.
et al.  (
2019
)
Plasma proteomics-based identification of novel biomarkers in early gastric cancer
.
Clin. Biochem.
,
76
,
5
10
.

43.

Du
Z.
,
Liu
X.
,
Wei
X.
et al.  (
2020
)
Quantitative proteomics identifies a plasma multi-protein model for detection of hepatocellular carcinoma
.
Sci. Rep.
,
10
, 15552.

44.

Park
J.
,
Kim
H.
,
Kim
S.Y.
et al.  (
2020
)
In-depth blood proteome profiling analysis revealed distinct functional characteristics of plasma proteins between severe and non-severe COVID-19 patients
.
Sci. Rep.
,
10
, 22418.

45.

Garay-Baquero
D.J.
,
White
C.H.
,
Walker
N.F.
et al.  (
2020
)
Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis
.
JCI Insight
,
5
, e137427.

46.

Kumar
V.
,
Ray
S.
,
Ghantasala
S.
et al.  (
2020
)
An integrated quantitative proteomics workflow for cancer biomarker discovery and validation in plasma
.
Front. Oncol.
,
10
, 543997.

47.

Geyer
P.E.
,
Arend
F.M.
,
Doll
S.
et al.  (
2021
)
High-resolution serum proteome trajectories in COVID-19 reveal patient-specific seroconversion
.
EMBO Mol. Med.
, 13, e14167.

48.

Messner
C.B.
,
Demichev
V.
,
Wendisch
D.
et al.  (
2020
)
Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection
.
Cell Syst.
,
11
, 11–24.e4.

49.

Ming
C.
,
Yi
L.
,
Hla
B.
et al.  (
2020
)
Quantitative proteomics and reverse engineer analysis identified plasma exosome derived protein markers related to osteoporosis
.
J. Proteomics
,
228
, 103940.

50.

Yang
T.
,
Fu
Z.
,
Zhang
Y.
et al.  (
2020
)
Serum proteomics analysis of candidate predictive biomarker panel for the diagnosis of trastuzumab-based therapy resistant breast cancer
.
Biomed. Pharmacother.
,
129
, 110465.

51.

Dey
K.K.
,
Wang
H.
,
Niu
M.
et al.  (
2019
)
Deep undepleted human serum proteome profiling toward biomarker discovery for Alzheimer’s disease
.
Clin. Proteomics
,
16
, 16.

52.

Smolarz
M.
,
Pietrowska
M.
,
Matysiak
N.
et al.  (
2019
)
Proteome profiling of exosomes purified from a small amount of human serum: the problem of co-purified serum components
.
Proteomes
,
7
, 18.

53.

Lin
L.
,
Zheng
J.
,
Yu
Q.
et al.  (
2017
)
High throughput and accurate serum proteome profiling by integrated sample preparation technology and single-run data independent mass spectrometry analysis
.
J. Proteomics
,
174
,
9
16
.

54.

Ren
J.
,
Zhao
G.
,
Sun
X.
et al.  (
2017
)
Identification of plasma biomarkers for distinguishing bipolar depression from major depressive disorder by iTRAQ-coupled LC-MS/MS and bioinformatics analysis
.
Psychoneuroendocrinology
,
86
,
17
24
.

55.

Liu
C.W.
,
Bramer
L.
,
Webb-Robertson
B.J.
et al.  (
2017
)
Temporal expression profiling of plasma proteins reveals oxidative stress in early stages of Type 1 diabetes progression
.
J. Proteomics
,
172
,
100
110
.

56.

Rao
P.V.
,
Reddy
A.P.
,
Lu
X.
et al.  (
2009
)
Proteomic identification of salivary biomarkers of type-2 diabetes
.
J. Proteome Res.
,
8
,
239
245
.

57.

Guo
T.
,
Rudnick
P.A.
,
Wang
W.J.
et al.  (
2006
)
Characterization of the human salivary proteome by capillary isoelectric focusing/nanoreversed-phase liquid chromatography coupled with ESI-tandem MS
.
J. Proteome Res.
,
5
,
1469
1478
.

58.

Wilmarth
P.A.
,
Riviere
M.A.
,
Rustvold
D.L.
et al.  (
2004
)
Two-dimensional liquid chromatography study of the human whole saliva proteome
.
J. Proteome Res.
,
3
,
1017
1023
.

59.

Gonzalezbegne
M.
,
Lu
B.W.
,
Liao
L.J.
et al.  (
2011
)
Characterization of the human submandibular/sublingual saliva glycoproteome using lectin affinity chromatography coupled to Multidimensional Protein Identification Technology
.
J. Proteome Res.
,
10
,
5031
5046
.

60.

Sivadasan
P.
,
Gupta
M.K.
,
Sathe
G.J.
et al.  (
2015
)
Data from human salivary proteome – a resource of potential biomarkers for oral cancer
.
J. Proteomics
,
4
,
374
378
.

61.

Cho
H.R.
,
Kim
H.S.
,
Park
J.S.
et al.  (
2017
)
Construction and characterization of the Korean whole saliva proteome to determine ethnic differences in human saliva proteome
.
PLoS One
,
12
, e0181765.

62.

Winck
F.V.
,
Ribeiro
A.C.P.
,
Domingues
R.R.
et al.  (
2015
)
Insights into immune responses in oral cancer through proteomic analysis of saliva and salivary extracellular vesicles
.
Sci. Rep.
,
5
, 16305.

63.

Aboodi
G.M.
,
Sima
C.
,
Moffa
E.B.
et al.  (
2016
)
Salivary cytoprotective proteins in inflammation and resolution during experimental gingivitis—a pilot study
.
Front. Cell. Infect. Microbiol.
,
5
, 92.

64.

Xie
H.
,
Rhodus
N.L.
,
Griffin
R.J.
et al.  (
2005
)
A catalogue of human saliva proteins identified by free flow electrophoresis-based peptide separation and tandem mass spectrometry
.
Mol. Cell. Proteomics
,
4
,
1826
1830
.

65.

Bandhakavi
S.
,
Stone
M.D.
,
Onsongo
G.
et al.  (
2009
)
A dynamic range compression and three-dimensional peptide fractionation analysis platform expands proteome coverage and the diagnostic potential of whole saliva
.
J. Proteome Res.
,
8
,
5590
5600
.

66.

De Jong
E.P.
,
Xie
H.W.
,
Onsongo
G.
et al.  (
2010
)
Quantitative proteomics reveals myosin and actin as promising saliva biomarkers for distinguishing pre-malignant and malignant oral lesions
.
PLoS One
,
5
, e11148.

67.

Franco-Martínez
L.
,
Hernández
J.M.G.
,
Horvati
A.
et al.  (
2019
)
Differences on salivary proteome at rest and in response to an acute exercise in men and women: a pilot study
.
J. Proteomics
,
214
, 103629.

68.

Contini
C.
,
Olianas
A.
,
Serrao
S.
et al.  (
2021
)
Top-down proteomics of human saliva highlights anti-inflammatory, antioxidant, and antimicrobial defense responses in alzheimer disease
.
Front. Neurosci.
,
15
, 668852.

69.

Sembler-Mller
M.L.
,
Belstrm
D.
,
Locht
H.
et al.  (
2020
)
Proteomics of saliva, plasma, and salivary gland tissue in Sjögren’s syndrome and non-Sjögren patients identify novel biomarker candidates
.
J. Proteomics
,
225
, 103877.

70.

Xiao
X.
,
Liu
Y.
,
Guo
Z.
et al.  (
2017
)
Comparative proteomic analysis of the influence of gender and acid stimulation on normal human saliva using LC/MS/MS
.
Proteomics Clin. Appl.
,
11
, 1600142.

71.

Sun
Y.
,
Huo
C.
,
Qiao
Z.
et al.  (
2018
)
Comparative proteomic analysis of exosomes and microvesicles in human saliva for lung cancer
.
J. Proteome Res.
,
17
,
1101
1107
.

72.

Wu
C.C.
,
Chu
H.W.
,
Hsu
C.W.
et al.  (
2015
)
Saliva proteome profiling reveals potential salivary biomarkers for detection of oral cavity squamous cell carcinoma
.
Proteomics
,
15
,
3394
3404
.

73.

Suresh
A.
(
2015
)
Human salivary proteome — a resource of potential biomarkers for oral cancer
.
J. Proteomics
,
127
,
89
95
.

74.

Cecchettini
A.
,
Finamore
F.
,
Ucciferri
N.
et al.  (
2019
)
Phenotyping multiple subsets in Sjögren’s syndrome: a salivary proteomic SWATH-MS approach towards precision medicine
.
Clin. Proteomics
,
16
, 26.

75.

Jehmlich
N.
,
Dinh
K.
,
Gesell-Salazar
M.
et al.  (
2013
)
Quantitative analysis of the intra- and inter-subject variability of the whole salivary proteome
.
J. Periodont. Res.
,
48
,
392
403
.

76.

Castagna
A.
,
Cecconi
D.
,
Sennels
L.
et al.  (
2005
)
Exploring the hidden human urinary proteome via ligand library beads
.
J. Proteome Res.
,
4
,
1917
1930
.

77.

Alamgir
K.
and
Packer
N.H.
(
2006
)
Simple urinary sample preparation for proteomic analysis
.
J. Proteome Res.
,
5
,
2824
2838
.

78.

Li
Q.R.
,
Fan
K.X.
,
Li
R.X.
et al.  (
2010
)
A comprehensive and non-prefractionation on the protein level approach for the human urinary proteome: touching phosphorylation in urine
.
Rapid Commun. Mass Spectrom. RCM
,
24
,
823
832
.

79.

Guo
Z.
,
Wang
Z.
,
Lu
C.
et al.  (
2018
)
Analysis of the differential urinary protein profile in IgA nephropathy patients of Uygur ethnicity
.
BMC Nephrol.
,
19
, 358.

80.

Hogan
M.C.
,
Johnson
K.L.
,
Zenka
R.M.
et al.  (
2014
)
Subfractionation, characterization, and in-depth proteomic analysis of glomerular membrane vesicles in human urine
.
Kidney Int.
,
85
,
1225
1237
.

81.

Nielsen
H.H.
,
Beck
H.C.
,
Kristensen
L.P.
et al.  (
2015
)
The urine proteome profile is different in neuromyelitis optica compared to multiple sclerosis: a clinical proteome study
.
PLoS One
,
10
, e0139659.

82.

Lin
L.
,
Yu
Q.
,
Zheng
J.X.
et al.  (
2018
)
Fast quantitative urinary proteomic profiling workflow for biomarker discovery in kidney cancer
.
Clin. Proteomics
,
15
, 42.

83.

Zhao
M.D.
,
Li
M.L.
,
Yang
Y.H.
et al.  (
2017
)
A comprehensive analysis and annotation of human normal urinary proteome
.
Sci. Rep.
,
7
, 3024.

84.

Onile
O.S.
,
Calder
B.
,
Soares
N.C.
et al.  (
2017
)
Quantitative label-free proteomic analysis of human urine to identify novel candidate protein biomarkers for schistosomiasis
.
PLoS Negl. Trop. Dis.
,
11
, e0006045.

85.

Simona
P.
,
Yunee
K.
,
Simona
F.
et al.  (
2012
)
Identification of prostate-enriched proteins by in-depth proteomic analyses of expressed prostatic secretions in urine
.
J. Proteome Res.
,
11
,
2386
2396
.

86.

Adachi
J.
,
Kumar
C.
,
Zhang
Y.L.
et al.  (
2006
)
The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins
.
Genome Biol.
,
7
, R80.

87.

Liu
X.J.
,
Shao
C.
,
Wei
L.L.
et al.  (
2012
)
An individual urinary proteome analysis in normal human beings to define the minimal sample number to represent the normal urinary proteome
.
Proteome Sci.
,
10
, 70.

88.

Zheng
J.H.
,
Liu
L.G.
,
Wang
J.
et al.  (
2013
)
Urinary proteomic and non-prefractionation quantitative phosphoproteomic analysis during pregnancy and non-pregnancy
.
BMC Genomics
,
14
, 777.

89.

Marimuthu
A.
,
O’Meally
R.N.
,
Chaerkady
R.
et al.  (
2011
)
A comprehensive map of the human urinary proteome
.
J. Proteome Res.
,
10
,
2734
2743
.

90.

Guo
Z.G.
,
Zhang
Y.
,
Zou
L.L.
et al.  (
2015
)
A proteomic analysis of individual and gender variations in normal human urine and cerebrospinal fluid using iTRAQ quantification
.
PLoS One
,
10
, e0133270.

91.

Prikryl
P.
,
Satrapova
V.
,
Frydlova
J.
et al.  (
2020
)
Mass spectrometry-based proteomic exploration of the small urinary extracellular vesicles in ANCA-associated vasculitis in comparison with total urine
.
J. Proteomics
,
233
, 104067.

92.

Swensen
A.C.
,
He
J.
,
Fang
A.C.
et al.  (
2021
)
A comprehensive urine proteome database generated from patients with various renal conditions and prostate cancer
.
Front. Med.
,
8
, 548212.

93.

Li
Y.
,
Wang
Y.
,
Liu
H.
et al.  (
2021
)
Urine proteome of COVID-19 patients
.
URINE
,
2
,
1
8
.

94.

Chen
R.
,
Yi
Y.
,
Xiao
W.
et al.  (
2021
)
Label-free liquid chromatography–mass spectrometry proteomic analysis of urinary identification in diabetic vascular dementia in a han chinese population
.
Front. Aging Neurosci.
,
13
, 619945.

95.

Huo
S.
,
Wang
H.X.
,
Yan
M.X.
et al.  (
2021
)
Urinary proteomic characteristics of hyperuricemia and their possible links with the occurrence of its concomitant diseases
.
ACS Omega
,
6
,
9500
9508
.

96.

Ahn
H.S.
,
Kim
J.H.
,
Jeong
H.
et al.  (
2020
)
Differential urinary proteome analysis for predicting prognosis in type 2 diabetes patients with and without renal dysfunction
.
Int. J. Mol. Sci.
,
21
, 4236.

97.

Chen
C.J.
,
Chou
C.Y.
,
Shu
K.H.
et al.  (
2021
)
Discovery of novel protein biomarkers in urine for diagnosis of urothelial cancer using iTRAQ proteomics
.
J. Proteome Res.
,
20
,
2953
2963
.

98.

Pan
S.
,
Wang
Y.
,
Quinn
J.F.
et al.  (
2006
)
Identification of glycoproteins in human cerebrospinal fluid with a complementary proteomic approach
.
J. Proteome Res.
,
5
,
2769
2779
.

99.

Guldbrandsen
A.
,
Vethe
H.
,
Farag
Y.
et al.  (
2014
)
In-depth characterization of the cerebrospinal fluid (CSF) proteome displayed through the CSF proteome resource (CSF-PR)
.
Mol. Cell. Proteomics Mcp
,
13
,
3152
3163
.

100.

Mouton-Barbosa
E.
,
Roux-Dalvai
F.
,
Bouyssié
D.
et al.  (
2010
)
In-depth exploration of cerebrospinal fluid by combining peptide ligand library treatment and label-free protein quantification
.
Mol. Cell. Proteomics
,
9
,
1006
1021
.

101.

Schutzer
S.E.
,
Liu
T.
,
Natelson
B.H.
et al.  (
2010
)
Establishing the proteome of normal human cerebrospinal fluid
.
PLoS One
,
5
, e10980.

102.

Borg
J.
,
Campos
A.
,
Diema
C.
et al.  (
2011
)
Spectral counting assessment of protein dynamic range in cerebrospinal fluid following depletion with plasma-designed immunoaffinity columns
.
Clin. Proteomics
,
8
, 6.

103.

Hu
Z.Y.
,
Zhang
H.Y.
,
Zhang
Y.
et al.  (
2014
)
Nanoparticle size matters in the formation of plasma protein coronas on Fe3O4 nanoparticles
.
Colloids Surf B Biointerfaces
,
121
,
354
361
.

104.

Schutzer
S.E.
,
Angel
T.E.
,
Liu
T.
et al.  (
2011
)
Distinct cerebrospinal fluid proteomes differentiate post-treatment lyme disease from chronic fatigue syndrome
.
PLoS One
,
6
, e17287.

105.

Pan
S.
,
Zhu
D.
,
Quinn
J.F.
et al.  (
2007
)
A combined dataset of human cerebrospinal fluid proteins identified by multi-dimensional chromatography and tandem mass spectrometry
.
Proteomics
,
7
,
469
473
.

106.

Begcevic
I.
,
Brinc
D.
,
Drabovich
A.P.
et al.  (
2016
)
Identification of brain-enriched proteins in the cerebrospinal fluid proteome by LC-MS/MS profiling and mining of the Human Protein Atlas
.
Clin. Proteomics
,
13
, 11.

107.

Charlotte
M.
,
Lydie
L.
,
Antonio
N.G.
et al.  (
2018
)
Deep dive in the proteome of human cerebrospinal fluid: a valuable data resource for biomarker discovery and missing protein identification
.
J. Proteome Res.
,
17
,
4113
4126
.

108.

Yang
C.
,
Guo
W.B.
,
Zhang
W.S.
et al.  (
2017
)
Comprehensive proteomics analysis of exosomes derived from human seminal plasma
.
Andrology
,
5
,
1007
1015
.

109.

Ashok
A.
,
Ahmet
A.
,
Luna
S.
et al.  (
2015
)
Comparative proteomic network signatures in seminal plasma of infertile men as a function of reactive oxygen species
.
Clin. Proteomics
,
12
, 23.

110.

Pilch
B.
and
Mann
M.
(
2006
)
Large-scale and high-confidence proteomic analysis of human seminal plasma
.
Genome Biol.
,
7
, R40.

111.

Wang
G.
,
Guo
Y.
,
Tao
Z.
et al.  (
2013
)
In-depth proteomic analysis of the human sperm reveals complex protein compositions
.
J. Proteomics
,
79
,
114
122
.

112.

Zhang
X.G.
,
Vos
H.R.
,
Tao
W.
et al.  (
2020
)
Proteomic profiling of two distinct populations of extracellular vesicles isolated from human seminal plasma
.
Int. J. Mol. Sci.
,
21
, 7957.

113.

Lee
J.
,
Lee
J.E.
,
Choi
J.W.
et al.  (
2020
)
Proteomic analysis of amniotic fluid proteins for predicting the outcome of emergency cerclage in women with cervical insufficiency
.
Reprod. Sci.
,
27
,
1318
1329
.

114.

Cho
C.K.
,
Smith
C.R.
and
Diamandis
E.P.
(
2010
)
Amniotic fluid proteome analysis from Down syndrome pregnancies for biomarker discovery
.
J. Proteome Res.
,
9
,
3574
3582
.

115.

Liu
X.
,
Song
Y.J.
,
Guo
Z.G.
et al.  (
2019
)
A comprehensive profile and inter-individual variations analysis of the human normal amniotic fluid proteome
.
J. Proteomics
,
192
,
1
9
.

116.

Jeon
H.S.
,
Lee
S.M.
,
Jung
Y.M.
et al.  (
2020
)
Proteomic biomarkers in mid-trimester amniotic fluid associated with adverse pregnancy outcomes in patients with systemic lupus erythematosus
.
PLoS One
,
15
, e0235838.

117.

Hong
S.
,
Ji
E.L.
,
Yu
M.K.
et al.  (
2020
)
Identifying potential biomarkers related to pre-term delivery by proteomic analysis of amniotic fluid
.
Sci. Rep.
,
10
, 19648.

118.

Zhou
L.
,
Zhao
S.Z.
,
Koh
S.K.
et al.  (
2012
)
In-depth analysis of the human tear proteome
.
J. Proteomics
,
75
,
3877
3885
.

119.

Liu
Q.
,
Liu
J.
,
Ren
C.
et al.  (
2017
)
Proteomic analysis of tears following acupuncture treatment for menopausal dry eye disease by two-dimensional nano-liquid chromatography coupled with tandem mass spectrometry
.
Int. J. Nanomed.
,
12
,
1663
1671
.

120.

Huang
Z.
,
Du
C.X.
and
Pan
X.D.
(
2018
)
The use of in-strip digestion for fast proteomic analysis on tear fluid from dry eye patients
.
PLoS One
,
13
, e0200702.

121.

Soria
J.
,
Acera
A.
,
Merayo-Lloves
J.
et al.  (
2017
)
Tear proteome analysis in ocular surface diseases using label-free LC-MS/MS and multiplexed-microarray biomarker validation
.
Rep
,
7
, 17478.

122.

Nttinen
J.
,
Mkinen
P.
,
Aapola
U.
et al.  (
2020
)
Early changes in tear film protein profiles after femtosecond LASIK surgery
.
Clin. Proteomics
,
17
, 36.

123.

Csősz
É.
,
Boross
P.
,
Csutak
A.
et al.  (
2012
)
Quantitative analysis of proteins in the tear fluid of patients with diabetic retinopathy
.
J. Proteomics
,
75
,
2196
2204
.

124.

Chen
X.L.
,
Rao
J.
,
Zheng
Z.
et al.  (
2019
)
Integrated tear proteome and metabolome reveal panels of inflammatory-related molecules via key regulatory pathways in dry eye syndrome
.
J. Proteome Res.
,
18
,
2321
2330
.

125.

Tong
L.
,
Zhou
X.Y.
,
Jylha
A.
et al.  (
2015
)
Quantitation of 47 human tear proteins using high resolution multiple reaction monitoring (HR-MRM) based-mass spectrometry
.
J. Proteomics
,
115
,
36
48
.

126.

Boerger
M.
,
Funke
S.
,
Leha
A.
et al.  (
2019
)
Proteomic analysis of tear fluid reveals disease-specific patterns in patients with Parkinson’s disease – a pilot study
.
Parkinsonism Relat. Disord.
,
63
,
3
9
.

127.

Cheung
J.K.W.
,
Bian
J.F.
,
Sze
Y.H.
et al.  (
2021
)
Human tear proteome dataset in response to daily wear of water gradient contact lens using SWATH-MS approach
.
Data Brief
,
36
, 107120.

128.

Dor
M.
,
Eperon
S.
,
Lalive
P.H.
et al.  (
2018
)
Investigation of the global protein content from healthy human tears
.
Exp. Eye Res.
,
179
,
64
74
.

129.

Almatroodi
S.A.
,
Mcdonald
C.F.
,
Collins
A.L.
et al.  (
2015
)
Quantitative proteomics of bronchoalveolar lavage fluid in lung adenocarcinoma
.
Cancer Genomics Proteomics
,
12
,
39
48
.

130.

Sim
S.Y.
,
Choi
Y.R.
,
Lee
J.H.
et al.  (
2019
)
In-depth proteomic analysis of human bronchoalveolar lavage fluid toward the biomarker discovery for lung cancers. Proteomics
.
Clin. Appl.
,
13
, e1900028.

131.

Foster
M.W.
,
Thompson
J.W.
,
Que
L.G.
et al.  (
2013
)
Proteomic analysis of human bronchoalveolar lavage fluid after subsgemental exposure
.
J. Proteome Res.
,
12
,
2194
2205
.

132.

Ortea
I.
,
Rodríguez-Ariza
A.
,
Chicano-Gálvez
E.
et al.  (
2016
)
Discovery of potential protein biomarkers of lung adenocarcinoma in bronchoalveolar lavage fluid by SWATH MS data-independent acquisition and targeted data extraction
.
J. Proteomics
,
138
,
106
114
.

133.

Foster
M.W.
,
Morrison
L.D.
,
Todd
J.L.
et al.  (
2015
)
Quantitative proteomics of bronchoalveolar lavage fluid in idiopathic pulmonary fibrosis
.
J. Proteome Res.
,
14
,
1238
1249
.

134.

Yang
M.
,
Cong
M.
,
Peng
X.
et al.  (
2016
)
Quantitative proteomic analysis of milk fat globule membrane (MFGM) proteins in human and bovine colostrum and mature milk samples through iTRAQ labeling
.
Food Funct.
,
7
,
2438
2450
.

135.

Liao
Y.
,
Alvarado
R.
,
Phinney
B.
et al.  (
2011
)
Proteomic characterization of human milk whey proteins during a twelve-month lactation period
.
J. Proteome Res.
,
10
,
1746
1754
.

136.

Beck
K.L.
,
Weber
D.
,
Phinney
B.S.
et al.  (
2015
)
Comparative proteomics of human and macaque milk reveals species-specific nutrition during postnatal development
.
J. Proteome Res.
,
14
,
2143
2157
.

137.

Liao
Y.L.
,
Alvarado
R.
,
Phinney
B.
et al.  (
2011
)
Proteomic characterization of specific minor proteins in the human milk casein fraction
.
J. Proteome Res.
,
10
,
5409
5415
.

138.

Zhang
Q.
,
Cundiff
J.K.
,
Maria
S.D.
et al.  (
2013
)
Quantitative analysis of the human milk whey proteome reveals developing milk and mammary-gland functions across the first year of lactation
.
Proteomes
,
1
,
128
158
.

139.

Molinari
C.E.
,
Casadio
Y.S.
,
Hartmann
B.T.
et al.  (
2012
)
Proteome mapping of human skim milk proteins in term and preterm milk
.
J. Proteome Res.
,
11
,
1696
1714
.

140.

Kim
B.J.
and
Dallas
D.C.
(
2021
)
Systematic examination of protein extraction, proteolytic glycopeptide enrichment and MS/MS fragmentation techniques for site-specific profiling of human milk N-glycoproteins
.
Talanta
,
224
, 121811.

141.

Dallas
D.C.
,
Guerrero
A.
,
Khaldi
N.
et al.  (
2013
)
Extensive in vivo human milk peptidomics reveals specific proteolysis yielding protective antimicrobial peptides
.
J. Proteome Res.
,
12
,
2295
2304
.

142.

Picariello
G.
,
Ferranti
P.
,
Mamone
G.
et al.  (
2012
)
Gel-free shotgun proteomic analysis of human milk
.
J. Chromatogr. A
,
1227
,
219
233
.

143.

Liao
Y.
,
Alvarado
R.
,
Phinney
B.
et al.  (
2011
)
Proteomic characterization of human milk fat globule membrane proteins during a 12 month lactation period
.
J. Proteome Res.
,
10
,
3530
3541
.

144.

Dayon
L.
,
Macron
C.
,
Lahrichi
S.
et al.  (
2021
)
Proteomics of human milk: definition of a discovery workflow for clinical research studies
.
J. Proteome Res.
,
20
,
2283
2290
.

145.

Goonatilleke
E.
,
Huang
J.
,
Xu
G.
et al.  (
2019
)
Human milk proteins and their glycosylation exhibit quantitative dynamic variations during lactation
.
J. Nutr.
,
149
,
1317
1325
.

146.

Zhou
Y.H.
,
Le
Z.
,
Yu
Z.B.
et al.  (
2019
)
Peptidomic analysis reveals multiple protection of human breast milk on infants during different stages
.
J. Cell. Physiol.
,
234
,
15510
15526
.

147.

Gan
J.
,
Robinson
R.C.
,
Wang
J.
et al.  (
2018
)
Peptidomic profiling of human milk with LC-MS/MS reveals pH-specific proteolysis of milk proteins
.
Food Chem.
,
274
,
766
774
.

148.

Balakrishnan
L.
,
Nirujogi
R.S.
,
Ahmad
S.
et al.  (
2014
)
Proteomic analysis of human osteoarthritis synovial fluid
.
Clin. Proteomics
,
11
, 6.

149.

Balakrishnan
L.
,
Bhattacharjee
M.
,
Ahmad
S.
et al.  (
2014
)
Differential proteomic analysis of synovial fluid from rheumatoid arthritis and osteoarthritis patients
.
Clin. Proteomics
,
11
, 1.

150.

Rydholm
U.
(
2016
)
Synovial fluid proteome in rheumatoid arthritis
.
Acta Orthop.
,
77
,
1
11
.

151.

Mahendran
S.M.
,
Keystone
E.C.
,
Krawetz
R.J.
et al.  (
2019
)
Elucidating the endogenous synovial fluid proteome and peptidome of inflammatory arthritis using label-free mass spectrometry
.
Clin. Proteomics
,
16
, 23.

152.

Birkelund
S.
,
Bennike
T.B.
,
Kastaniegaard
K.
et al.  (
2020
)
Proteomic analysis of synovial fluid from rheumatic arthritis and spondyloarthritis patients
.
Clin. Proteomics
,
17
, 29.

153.

Foers
A.D.
,
Dagley
L.F.
,
Chatfield
S.
et al.  (
2020
)
Proteomic analysis of extracellular vesicles reveals an immunogenic cargo in rheumatoid arthritis synovial fluid
.
Clin. Transl. Immunol.
,
9
, e1185.

154.

Lee
J.H.
,
Jung
J.H.
,
Kim
J.
et al.  (
2020
)
Proteomic analysis of human synovial fluid reveals potential diagnostic biomarkers for ankylosing spondylitis
.
Clin. Proteomics
,
17
, 20.

155.

Brunoro
G.V.
,
Carvalho
P.C.
,
Ferreira
A.T.
et al.  (
2015
)
Proteomic profiling of nipple aspirate fluid (NAF): exploring the complementarity of different peptide fractionation strategies
.
J. Proteomics
,
117
,
86
94
.

156.

Alexander
H.
,
Stegner
A.L.
,
Wagner-Mann
C.
et al.  (
2004
)
Proteomic analysis to identify breast cancer biomarkers in nipple aspirate fluid
.
Clin. Cancer Res.
,
10
,
7500
7510
.

157.

Kurono
S.
,
Kaneko
Y.
,
Matsuura
N.
et al.  (
2016
)
Identification of potential breast cancer markers in nipple discharge by protein profile analysis using two-dimensional nano-liquid chromatography/nanoelectrospray ionization-mass spectrometry
.
Proteomics Clin. Appl.
,
10
,
605
613
.

158.

Shaheed
S.U.
,
Tait
C.
,
Kyriacou
K.
et al.  (
2017
)
Nipple aspirate fluid - a liquid biopsy for diagnosing breast health
.
Proteomics Clin. Appl.
,
11
, 1700015.

159.

Pavlou
M.P.
,
Kulasingam
V.
,
Sauter
E.R.
et al.  (
2010
)
Nipple aspirate fluid proteome of healthy females and patients with breast cancer
.
Clin. Chem.
,
56
,
848
855
.

160.

Kim
Y.E.
,
Kim
K.
,
Han
B.O.
et al.  (
2021
)
Quantitative proteomic profiling of Cervicovaginal fluid from pregnant women with term and preterm birth
.
Proteome Sci.
,
19
, 3.

161.

Muytjens
C.
,
Yu
Y.
and
Diamandis
E.P.
(
2017
)
Discovery of antimicrobial peptides in cervical-vaginal fluid from healthy nonpregnant women via an integrated proteome and peptidome analysis
.
Proteomics
,
17
, 1600461.

162.

Federi
C.C.
,
Valerie
W.
,
Silvia
T.
et al.  (
2013
)
Proteome profiles of vaginal fluids from women affected by bacterial vaginosis and healthy controls: outcomes of rifaximin treatment
.
J. Antimicrob. Chemother.
,
68
,
2648
2659
.

163.

Starodubtseva
N.L.
,
Brzhozovzkiy
A.G.
,
Bugrova
A.E.
et al.  (
2019
)
Label-free cervicovaginal fluid proteome profiling reflects the cervix neoplastic transformation
.
J. Mass Spectrom.
,
54
,
693
703
.

164.

Hosako
M.
,
Muto
T.
,
Nakamura
Y.
et al.  (
2012
)
Proteomic study of malignant pleural mesothelioma by laser microdissection and two-dimensional difference gel electrophoresis identified cathepsin D as a novel candidate for a differential diagnosis biomarker
.
J. Proteomics
,
75
,
833
844
.

165.

Mundt
F.
,
Johansson
H.J.
,
Forshed
J.
et al.  (
2013
)
Proteome screening of pleural effusions identifies galectin 1 as a diagnostic biomarker and highlights several prognostic biomarkers for malignant mesothelioma
.
Mol. Cell. Proteomics
,
13
,
701
715
.

166.

Park
J.O.
,
Choi
D.Y.
,
Choi
D.S.
et al.  (
2013
)
Identification and characterization of proteins isolated from microvesicles derived from human lung cancer pleural effusions
.
Proteomics
,
13
,
2125
2134
.

167.

Burg
D.
,
Schofield
J.P.R.
,
Brandsma
J.
et al.  (
2018
)
Large-scale label-free quantitative mapping of the sputum proteome
.
J. Proteome Res.
,
17
,
2072
2091
.

168.

Hailemariam
M.
,
Yu
Y.B.
,
Singh
H.
et al.  (
2020
)
Protein and microbial biomarkers in sputum discern acute and latent tuberculosis in investigation of pastoral Ethiopian cohort
.
Front. Cell. Infect. Microbiol.
,
11
, 595554.

169.

Gray
R.D.
,
Macgregor
G.
,
Noble
D.
et al.  (
2008
)
Sputum proteomics in inflammatory and suppurative respiratory diseases
.
Am. J. Respir. Crit. Care Med.
,
178
,
444
452
.

170.

Muccilli
V.
,
Saletti
R.
,
Cunsolo
V.
et al.  (
2015
)
Protein profile of exhaled breath condensate determined by high resolution mass spectrometry
.
J. Pharm. Biomed. Anal.
,
105
,
134
149
.

171.

Hayes
S.A.
,
Haefliger
S.
,
Harris
B.
et al.  (
2016
)
Exhaled breath condensate for lung cancer protein analysis: a review of methods and biomarkers
.
J. Breath Res.
,
10
, 034001.

172.

Lacombe
M.
,
Marie-Desvergne
C.
,
Combes
F.
et al.  (
2018
)
Proteomic characterization of human exhaled breath condensate
.
J. Breath Res.
,
12
, 021001.

173.

Chen
D.P.
,
Bryden
W.A.
and
Mcloughlin
M.
(
2020
)
A novel system for the comprehensive collection of nonvolatile molecules from human exhaled breath
.
J. Breath Res.
,
15
, 016001.

174.

Gade
I.L.
,
Schultz
J.G.
,
Cehofski
L.J.
et al.  (
2020
)
Exhaled breath condensate in acute pulmonary embolism; a porcine study of effect of condensing temperature and feasibility of protein analysis by mass spectrometry
.
J. Breath Res.
,
15
, 026005.

175.

Paulo
J.A.
,
Lee
L.S.
,
Banks
P.A.
et al.  (
2011
)
Difference gel electrophoresis identifies differentially expressed proteins in endoscopically collected pancreatic fluid
.
Electrophoresis
,
32
,
1939
1951
.

176.

Paulo
J.A.
,
Lee
L.S.
,
Wu
B.
et al.  (
2010
)
Identification of pancreas-specific proteins in endoscopic (ePFT) collected pancreatic fluid with mass spectrometry (GeLC-MS/MS)
.
Pancreas
,
39
, 889.

177.

Marchegiani
G.
,
Paulo
J.A.
,
Sahora
K.
et al.  (
2015
)
The proteome of postsurgical pancreatic juice
.
Pancreas
,
44
,
574
582
.

178.

Paulo
J.A.
,
Kadiyala
V.
,
Gaun
A.
et al.  (
2013
)
Analysis of endoscopic pancreatic function test (ePFT)-collected pancreatic fluid proteins precipitated via ultracentrifugation
.
J. Pancreas
,
14
,
176
186
.

179.

Csősz
É.
,
Emri
G.
,
Kalló
G.
et al.  (
2015
)
Highly abundant defense proteins in human sweat as revealed by targeted proteomics and label-free quantification mass spectrometry
.
J. Eur. Acad. Dermatol. Venereol.
,
29
,
2024
2031
.

180.

Raiszadeh
M.M.
,
Ross
M.M.
,
Russo
P.S.
et al.  (
2012
)
Proteomic analysis of eccrine sweat: implications for the discovery of schizophrenia biomarker proteins
.
J. Proteome Res.
,
11
,
2127
2139
.

181.

Yu
Y.J.
,
Prassas
I.
,
Muytjens
C.
et al.  (
2017
)
Proteomic and peptidomic analysis of human sweat with emphasis on proteolysis
.
J. Proteomics
,
155
,
40
48
.

182.

Johnson
M.
,
Zaretskaya
I.
,
Raytselis
Y.
et al.  (
2008
)
NCBI BLAST: a better web interface
.
Nucleic Acids Res.
,
36
,
W5
W9
.

183.

Maere
S.
,
Heymans
K.
and
Kuiper
M.
(
2005
)
BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks
.
Bioinformatics
,
2
,
3448
3449
.

184.

Shannon
P.
,
Markiel
A.
,
Ozier
O.
et al.  (
2003
)
Cytoscape: a software environment for integrated models of biomolecular interaction networks
.
Genome Res.
,
13
,
2498
2504
.

185.

Cui
J.
,
Liu
Q.
,
Puett
D.
et al.  (
2008
)
Computational prediction of human proteins that can be secreted into the bloodstream
.
Bioinformatics
,
24
,
2370
2375
.

186.

Hong
C.S.
,
Cui
J.
,
Ni
Z.H.
et al.  (
2011
)
A computational method for prediction of excretory proteins and application to identification of gastric cancer markers in urine
.
PLoS One
,
6
, e16875.

187.

Hu
L.L.
,
Huang
T.
,
Cai
Y.D.
et al.  (
2011
)
Prediction of body fluids where proteins are secreted into based on protein interaction network
.
PLoS One
,
6
, e22989.

188.

Liu
Q.
,
Cui
J.
,
Yang
Q.
et al.  (
2010
)
In-silico prediction of blood-secretory human proteins using a ranking algorithm
.
BMC Bioinform.
,
11
, 250.

189.

Sun
Y.
,
Du
W.
,
Zhou
C.
et al.  (
2015
)
A computational method for prediction of saliva-secretory proteins and its application to identification of head and neck cancer biomarkers for salivary diagnosis
.
IEEE Trans. Nanobiosci.
,
14
,
167
174
.

190.

Wang
J.X.
,
Liang
Y.C.
,
Wang
Y.
et al.  (
2013
)
Computational prediction of human salivary proteins from blood circulation and application to diagnostic biomarker identification
.
PLoS One
,
8
, e80211.

191.

Wang
Y.
,
Du
W.
,
Liang
Y.C.
et al.  (
2016
)
PUEPro: a computational pipeline for prediction of urine excretory proteins
. In:
2016 Advanced Data Mining and Applications (ADMA)
.
Gold Coast, QLD, Australia
.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.