Human Basal Tear Peptidome Characterization by CID, HCD, and

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Human basal tear peptidome characterization by CID, HCD and ETD followed by in silico and in vitro analyses for antimicrobial peptide identification Mikel Azkargorta, Javier Soria, Claudia Ojeda, Fanny Guzman, Arantxa Acera, Ibon Iloro, Tatiana Suárez, and Felix Elortza J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00179 • Publication Date (Web): 06 May 2015 Downloaded from http://pubs.acs.org on May 7, 2015

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Journal of Proteome Research

Human basal tear peptidome characterization by CID, HCD and ETD followed by in silico and in vitro analyses for antimicrobial peptide identification

Mikel Azkargorta1, Javier Soria2, Claudia Ojeda4, Fanny Guzmán3, Arantxa Acera2, Ibon Iloro1, Tatiana Suárez2, Felix Elortza1*

1

Proteomics Platform, CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Bizkaia Science and

Technology Park, Derio, Spain 2

Bioftalmik Applied Research, Bizkaia Science and Technology Park, 48160, Derio, Spain

3

Núcleo Biotecnológico de Curauma (NBC), Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

4

Instituto de Biología, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

Running title: Human basal tear peptidome analysis by mass spectrometry

Keywords: Tear, CID, HCD, ETD, Antimicrobial Peptides (AMP), Peptidome, Natural Peptides, Degradome, Top-down proteomics

*Corresponding Author: Felix Elortza CIC bioGUNE Bizkaia Science and Technology Park, Building 800, 48160, Derio Spain Email: [email protected] Tel: +34 944061315

1 ACS Paragon Plus Environment


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ABSTRACT Endogenous peptides are valuable targets in the analysis of biological processes. The tear film contains proteins and peptides released by the tear duct mucosal cells, including antimicrobial peptides involved in the protection against exogenous pathogens; however, the peptide content of the tear liquid remains poorly characterized. We analyzed naturally occurring peptides isolated from human basal tears. Mass spectrometry analysis of endogenous peptides present a number of drawbacks, including size heterogeneity, and non-predictable fragmentation patterns, among others. Therefore, CID, ETD and HCD methods were used for the characterization of the tear peptide content. The contribution of DMSO as an additive of the chromatographic solvents was also evaluated. We identified 157, 131, and 122 peptides using CID-, ETD-, and HCDbased methods, respectively. Altogether, 234 different peptides were identified, leading to the generation of the biggest dataset of endogenous tear peptides to date. The antimicrobial activity prediction analysis performed in silico revealed different putative antimicrobial peptides. Two of the extracellular glycoprotein lacritin peptides were de novo synthesized, and their antimicrobial activity was confirmed in vitro. Our findings demonstrate the benefits of using different fragmentation methods for the analysis of endogenous peptides, and provide a useful approach for the discovery of peptides with antimicrobial activity.

INTRODUCTION Proteolytic processing is an important post-translational mechanism increasing the functional diversity of proteins.1 The cleavage of precursor proteins by proteases generates peptides that often gain specialized functions not ascribed to their precursors. These peptides are considered as part of the degradome and have important roles in many, if not all, biological processes, such as antimicrobial activity or intercellular signaling.2, 7 Endogenously generated secreted peptides may reflect the state of a cell under certain conditions, and, therefore, may be potential biomarkers of specific physiological and/or pathological processes. 8, 9


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The tear film is a complex lacrimal gland fluid that contains lipids, proteins, peptides, electrolytes, and other small organic molecules. It coats the cornea and conjunctiva, and serves a wide range of important functions including lubrication, nourishment of the ocular surface epithelia, and protection from external injury and infection by pathogens. Thus, the tear film acts as a protective barrier between the epithelia and the outside environment. 10 This protective function is partly fulfilled by antimicrobial peptides (AMPs), which have the capacity to kill bacteria. 11, 12 AMPs are produced constitutively or induced when a tissue is injured or exposed to microbes. 13 As the bacteria have not developed effective resistance mechanisms against these peptides, they are promising alternatives to classical antibiotics. 14 Therefore, the identification and characterization of these peptides might be of great value in the development of new antibiotics. 15 Some high-throughput proteomic analyses have been carried out to characterize the protein composition of tears, but most of these studies have relied on bottom-up proteomic strategies, utilizing enzymatic digestion-based approaches. 16, 17 Hayakawa et al. have characterized the naturally occurring peptides obtained from reflex tears using LC-MALDI-TOF-TOF methods

18

but the peptidome of basal tears remains poorly characterized. The analysis of endogenous peptides by MS is still challenging due to their size heterogeneity. The sizes range approximately from 3 to 100 residues. Moreover, the proteolytically processed peptides often contain multiple internal basic residues that limit their effective fragmentation by CID 19. In contrast to the enzymatically digested protein fragments obtained for bottom-up analysis, Ctermini of the endogenous peptides are not restricted to specific residues, increasing the complexity of the analysis. 6 CID, ETD, and HCD have been reported as complementary peptide fragmentation methods, each of them having their own advantages and limitations. 20-24 However, the reported analyses have mostly used the peptides obtained by tryptic digestion, and it is unclear whether these dissociation methods are suitable for the analysis of endogenous peptides. Sasaki and coworkers have reported successful use of CID and ETD in the analysis of endogenous peptides derived from human secretory granules, identifying 795 and 569 unique peptides, respectively. 6 3 ACS Paragon Plus Environment


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Shen et al. have also shown that CID, HCD, and ETD can be used for the analysis of such peptides, achieving a substantial number of complementary identifications. 25 Here, we present the results of the analysis of naturally occurring peptides isolated from basal human tears. Samples were analyzed using CID, HCD, and ETD fragmentation methods in high-high mode (high-resolution mass measurement for both MS and MS/MS), leading to the identification of 234 different peptide species originating from 25 proteins. DMSO, a supercharging reagent which increases the intensity and sensitivity in LC-MS experiments, 26, 27 was used in some of the analyses. We looked for potential AMPs among the identified peptides and confirmed in vitro antimicrobial activity of two peptides derived from the C-terminus of the extracellular glycoprotein lacritin.

MATERIALS AND METHODS Tear collection and sample preparation Sample collection was conducted by medically qualified personnel after approval by the Institutional Review Board (IRB)/Ethics Committee. Approval was obtained in strict accordance with the tenets of the Declaration of Helsinki on Biomedical Research Involving Human Subjects. Tear samples were obtained from the inferior temporal tear meniscus, minimizing irritation of the ocular surface or lid margin, without anesthesia, using 10-µl calibrated glass microcapillary tubes (Blaubrand intraMARK, Wertheim, Germany). Two different sample preparations were made. Sample 1 was made with aliquots of tear liquid from 4 age-matched males (age range 35–45). Sample 2 was obtained from 5 age-matched individuals (3 male and 2 female, age range 35–45). The tear samples were placed in Eppendorf tubes, pooled as Sample1 and Sample2, and stored at -80 °C until analysis. The samples (approx. 90 µl) were acidified by adding 9 µl of 1% trifluoroacetic acid (TFA), reduced with 50 mM dithiothreitol (30-min incubation at 56 ºC with agitation), and alkylated with 50 mM iodoacetamide (30 min at room temperature, in the dark). Sample 1 was processed using custom-made microcolumns filled with a mixture of Poros R2 and R3 (Applied Biosystems, now Thermo Fisher Scientific, MA, USA) as described previously. 28 Sample 2 was 4 ACS Paragon Plus Environment

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divided into 10 aliquots, each of which was independently processed using Reversed-Phase C18 ZipTips (Millipore), as described by the manufacturer. All eluates were pooled, dried in an RVC2 25 SpeedVac concentrator (Christ), and resuspended in 0.1% FA. LC-MS analysis Peptides were separated using on-line NanoLC (nLC), and analyzed using electrospray tandem mass spectrometry. Peptide separation was performed on a nanoACQUITY UPLC system (Waters) connected to an LTQ Orbitrap XL ETD mass spectrometer (Thermo Electron, Bremen, Germany). Samples were loaded onto a Symmetry 300 C18 UPLC Trap column, 180 µm × 20 mm, 5 µm (Waters). The precolumn was connected to a BEH130 C18 column, 75 µm × 200 mm, 1.7 µm (Waters), equilibrated in 3% acetonitrile and 0.1% FA. The peptides were eluted at 300 nl/min, using either a 60-min or a 120-min linear gradient of 3–50% ACN. Chromatographic solvents containing 5% DMSO were used for the analysis of sample 2, when stated. Samples were loaded directly onto the nanoelectrospray ion source (Proxeon Biosystems, Odense, Denmark). The mass spectrometer automatically switched between MS and MS/MS acquisition in DDA mode. Methods were derived from those described by Sasaki et al. 6 with minor modifications. Full MS survey spectra (m/z 400–2000) were acquired in the orbitrap with 100,000 resolution at m/z 400. Automatic Gain Control (AGC) target was set up at 7 × 10e5 with a maximum ion injection time (IT) of 150 ms for all methods. Data for each fragmentation method were acquired in independent runs, and all MS/MS events were acquired in the orbitrap with 100,000 resolution at m/z 400. In CID- and HCD-based procedures, the three most intense ions from each survey scan were subjected to fragmentation with normalized collision energy of 35.0 %, activation Q of 0.250, and activation time of 30.000 ms. MS/MS AGC target was 2 × 10e5 with IT of 1000 ms for both fragmentation methods. ETD was performed over the 2 most intense ions, with supplemental activation and charge-state dependent ETD time allowed. MS/MS AGC was set up at 5 × 10e5 with IT of 1500 ms, and fluoranthene AGC was 1 × 10e6 with IT of 100 ms. A minimal signal of 1000 counts was required for triggering an MS/MS event for all methods. Precursors with charge states equal to or greater than 2 were specifically selected for fragmentation. The ions were obtained with an isolation window of 5 m/z units and 5 ACS Paragon Plus Environment


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provided in a dynamic exclusion list for 2400 s after being selected for two MS/MS scans. Exclusion lists were used for Sample 1 analysis. For this purpose, a exclusion list with the m/z of the peptides identified with ion scores greater than 100 was generated, and these were excluded from selection in new runs under a mass tolerance of 10 ppm. Data analysis Searches were performed using Mascot search engine (www.matrixscience.com, Matrix Science, London, UK) with Proteome Discoverer v.1.2. software (Thermo Electron, Bremen, Germany). Spectra were deconvoluted prior to search using Xtract with default parameters. Carbamidomethyl (C) was selected as fixed modification, and Oxidation (M), Acetyl (N-term), Amidated (C-term), Gln->pyro-Glu (N-term Q), and Glu->pyro-Glu (N-term E) as variable modifications. Peptide mass tolerance of 5 ppm and 50-mmu fragment mass tolerance were adopted as search parameters. Spectra were searched against human UniProt/Swiss-Prot version 2012_09 (538010 entries). Only rank-1 unambiguously assigned unique peptides passing the identity threshold (p < 0.01) in Mascot searches were further considered. A non-deconvoluted search was performed in order to determine the charge state of the input and the successfully identified MS/MS events obtained in each run. Chromatogram intensity and charge state distribution of the LC-MS/MS runs were analyzed using RawMeat software (VAST SCIENTIFIC). Functional analysis was performed using DAVID (http://david.abcc.ncifcrf.gov/). Cellular Component (CC), Biological Process (BP), and Molecular Function (MF) categories were used in Gene Ontology (GO) analysis. In addition, protein domains in the identified proteins were analyzed using Interpro and Prosite databases, with the same software tool. Only terms enriched with an FDR 3 (6). Shen and co-workers have also shown that CID and HCD surpassed ETD performance for z = 2 peptides from blood plasma and that all three methods performed similarly with z = 3 peptides. 25

In accord with the published reports, 22, 41 our analysis of the m/z distribution of the identified

peptides using the 3 fragmentation methods shows that ETD performs better than other methods with m/z values below 800. Contribution of DMSO We examined the benefits of DMSO as chromatographic additive in the analysis of tear peptides. As it has been previously reported, the use of DMSO increases the signal intensity and the number of MS/MS events per run. 26 Using DMSO, we identified 44 additional peptides in Sample 2. We observed an enhancement in the overall charge state of the detected ions, shifting the sampling of ions toward lower m/z and higher MH+ values. DMSO induces coalescence of peptide charge state to the number of charge-accepting sites in its sequence. 26, 27, 42 The maximum number of charges a peptide can accept depends on the number of basic residues in its sequence plus the N-terminal amine. 43 However, the peptides are often seen with lower charge states since the number of charges in the final stages of desolvation might be reduced. 44 Thus, one possible explanation for the observed benefits conferred by DMSO could be the presence of large and very basic peptides. These peptides would be in low charge states in the absence of DMSO (in relation to the number of basic residues). After addition of DMSO, the number of occupied charge-accepting sites would increase, approaching completeness. We examined Sample 2 peptides that were identified both with and without DMSO. The charge states of these peptides remained the same or increased when DMSO was added to the chromatographic solvents (Suppl. Table 6). This effect was prominent among large peptides capable of acquiring a large number of charges, such as those with z = 6 or higher in the absence of DMSO. No significant effect was observed for the peptides with low charges. Despite the increase in the z and m/z of the peptides under analysis in the presence of DMSO, only CID seems to get a clear benefit from DMSO use. More data on the analysis of this sample 13 ACS Paragon Plus Environment


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with ETD in the presence of DMSO should be compiled in order to provide a clear conclusion, but the increase in the size of the peptides under analysis in the presence of DMSO (suppl. Fig. 8) could partially explain the phenomenon observed, since ETD has been shown to be less effective than CID for the analysis of large natural occurring peptides. 6 Biological perspective Consistently with the nature of the sample, the functional analysis demonstrated that most of the identified proteins belonged to the extracellular environment. Protective functions were clearly prevalent among these proteins. There were some well-known mediators of defensive responses, such as dermcidin, 45 lysozymes, 46 lactoferrin, 47and cystatins. 48, 49 Regarding protein cleavage specificity, most of the cleavage sites were not assignable to specific proteases. The identification of AMPs among the peptides characterized in our study might be of particular interest 15. Interestingly, none of the peptides with putative antimicrobial activity was found in the bioactive peptides databases. AMPs might supply a powerful template for the development of new antibiotics. These peptides cause cell death mainly by disrupting the microbial cell membrane; this unique mechanism of action makes the development of bacterial resistance very difficult. 12, 29 No information on secretoglobin family 1D member, proline-rich protein 4, extracellular glycoprotein lacritin and antileukoproteinase is available in the bioactive peptides databases searched, suggesting that their putative antimicrobial activity remains poorly characterized. Direct evidences on the antimicrobial activity of antileukoproteinase are not available in the literature. However, a closely related skin-derived antileukoproteinase (SKALP, elafin), also involved in elastase inhibition, is known to display antimicrobial activity, 50, 51 linking this protein to bactericidal activity. In addition, even if absent from the databases searched, lacritin is a well-known antimicrobial protein 52. We identified two antimicrobial peptides derived from extracellular glycoprotein, lacritin. Our results confirm once more the benefits conferred by the use of alternative fragmentation methods. Our antimicrobial activity analysis reveals that these two peptides can compromise the growth of both Gram-positive and Gram-negative bacteria. Interestingly, Gram-negative bacteria are the main cause of contact lens-related microbial keratitis (MK), where P. 14 ACS Paragon Plus Environment

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aeruginosa is the most common pathogen. S. aureus is the Gram-positive bacterium most frequently isolated from MK. 53 Lacritin promotes the survival of corneal epithelial cells, basal tearing, and epithelial cell proliferation. 54-56 Recently, bactericidal peptides released from lacritin C-terminal side have been described in basal tears. 52 This study has reported that the antimicrobial activity of N-65, a form of lacritin lacking 65 amino acids from its N-terminus, has a potent antimicrobial activity. Moreover, the antimicrobial core activity has been mapped to the last 15 amino acids of lacritin (N-15), comprising the sequence AQKLLKKFSLLKPWA. This peptide, which was also identified in our study (Suppl. Table 1, Suppl. Fig 1 and 2), consists of an N-terminal amphipathic α-helix and a hydrophobic C-terminal coiled coil tail, appropriate for bacterial membrane contact and insertion. 52 The predicted structures of the two peptides tested in this study, which lack the first two and three amino acids from N-15, also form α -helices followed by a turn (Suppl. Fig. 12). Thus, they retain the same structural properties. Interestingly, our antimicrobial activity prediction analysis revealed that the theoretical weighted scores for the two peptides characterized here were higher than for N-15 (0.928 for N-15, Table 2, Suppl. Table 4). This result suggests that these peptides should be more active than the N-15. A direct comparison of their activities should be performed to examine this possibility. As the peptide AQKLLKKFSLLKPWA might be the result of serine protease-dependent processing 52, further processing may be needed to obtain the forms we detect.

CONCLUSION We present the largest dataset of human basal tear naturally occurring peptides known to date. The use of alternative fragmentation methods is shown to be beneficial for this purpose. We characterize the antimicrobial activity of two peptides derived from the extracellular glycoprotein lacritin, confirming that the strategy presented in this work constitutes a useful workflow for the identification of AMPs. The workflow presented here could be applied to different biofluids to discover novel antibacterial peptides, and even for the identification and



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discovery of peptides with other interesting biological functions, such as cytoprotection, antiinflammation, or wound healing, widening the available array of applications.

SUPPLEMENTARY MATERIAL Supplementary Figure 1: Spectra corresponding to the peptides identified in Sample 1 Supplementary Figure 2: Spectra corresponding to the peptides identified in Sample 2 Supplementary Figure 3: Comparison of the proteins and peptides identified in Sample 1 and 2 Supplementary Figure 4: Sequence coverage obtained for the proteins identified in this work Supplementary Figure 5: Distribution of z, m/z and MH+ of the ionization input in the absence of DMSO Supplementary Figure 6: Distribution of z, m/z and MH+ of the peptides identified by CID, HCD and ETD Supplementary Figure7: Analysis of the chromatogram intensity and the number of MSMS events upon DMSO treatment in Sample 2 Supplementary Figure 8: Distribution of z, m/z and MH+ of the ionization input in the presence and absence of DMSO in Sample 2 Supplementary Figure 9: Comparison of the results and the distribution of z, m/z and MH+ of the peptides identified by CID in Sample 2 in the presence and absence of DMSO Supplementary Figure 10: Comparison of the results and the distribution of z, m/z and MH+ of the peptides identified by ETD in Sample 2 in the presence and absence of DMSO Supplementary Figure 11: Comparison of the results and the distribution of z, m/z and MH+ of the peptides identified by HCD in Sample 2 in the presence and absence of DMSO Supplementary Figure 12: Pepfold structure prediction of KLLKKFSLLKPWA and LLKKFSLLKPWA. Red: Alpha-helix , Green: Beta-sheet and analogues, Blue: Turns and analogues. Supplementary Table 1: Data regarding the identification of proteins and peptides in Sample 1 and Sample 2. Supplementary Table 2: Data regarding m/z and charge state of the input and the identified peptides by CID, ETD and HCD in the absence of DMSO. Supplementary Table 3: Data regarding m/z and charge state of the input and the identified peptides by CID, ETD and HCD in the presence of DMSO. Supplementary Table 4: Results for the Gene Ontology analysis of the identified proteins.


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Supplementary Table 5: Antimicrobial activity and peptidase cleavage prediction analysis results. Supplementary Table 6: Analysis of the effect of DMSO over charge-binding site occupancy in the peptides identified by CID, ETD and HCD. Supporting Information , this material is available free of charge via the internet http://pubs.acs.org.

Acknowledgements We thank Iraide Escobes from Proteomics Platform at CIC bioGUNE for technical assistance in sample preparation. We also thank ProteoRed-ISCIII, Basque Government, and Bizkaia County for financial support.

FIGURE LEGENDS: Figure 1: Summary of the analysis of the basal human tear peptidome. A) Overview of the followed strategy. B) Details of sample processing and MS analysis performed for Sample 1 and Sample 2. Figure 2: Contribution of CID, HCD, and ETD to peptide identification. Data from all the runs were used, including Sample 2 data obtained with 5% DMSO in the chromatographic solvents. Figure 3: Annotated spectra for the peptides derived from lacritin assessed for antimicrobial activity. A) CID fragmentation for KLLKKFSLLKPWA. B) ETD fragmentation for KLLKKFSLLKPWA. C) HCD fragmentation for LLKKFSLLKPWA. Figure 4: Antimicrobial activity of peptides KLLKKFSLLKPWA (▲) and LLKKFSLLKPWA (■) derived from extracellular glycoprotein lacritin. Activity was tested against E. coli (A), P. aeruginosa (B), L. monocytogenes (C), and S. aureus (D). The inactive peptide WLQEGGQECECKDWFLRAPR (●) from VEGF coregulated chemokine was used as



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a negative control in all cases. Surviving bacteria are shown as colony-forming units/ml (CFU/ml) for each peptide concentration. Measurements were performed in duplicate.

TABLES Table 1: Protein identification summary. The searches were performed using Mascot search engine. Accession: protein accession number in the UniProt database. Name: protein name. Entry: entry symbol in the UniProt database. Seq. Cov: sequence coverage obtained for that protein (%). # Peptides: number of peptides identified for that protein. # PSMs: number of spectral counts obtained for that protein. Accession

Name

Entry

O75556

Mammaglobin-B

SG2A1_HUMAN

Seq. Cov. # Peptides # PSMs 42.11

3

48

O95968

Secretoglobin family 1D member 1

SG1D1_HUMAN

76.67

13

104

P01036

Cystatin-S

CYTS_HUMAN

40.43

5

86

P01037

Cystatin-SN

CYTN_HUMAN

40.43

5

71

P01591

Immunoglobulin J chain

IGJ_HUMAN

17.61

2

53

P01833

Polymeric immunoglobulin receptor

PIGR_HUMAN

9.95

26

380

P01834

Ig kappa chain C region

IGKC_HUMAN

38.68

1

53

P02788

Lactotransferrin

TRFL_HUMAN

17.46

9

119

P03973

Antileukoproteinase

SLPI_HUMAN

29.5

1

1

P09228

Cystatin-SA

CYTT_HUMAN

16.31

1

1

P0CG04

Ig lambda-1 chain C regions

LAC1_HUMAN

19.81

1

1

P12273

Prolactin-inducible protein

PIP_HUMAN

32.19

7

60

P20930

Filaggrin

FILA_HUMAN

2.36

4

10

P31025

Lipocalin-1

LCN1_HUMAN

33.52

15

213

P61626

Lysozyme C

LYSC_HUMAN

51.35

16

140

P61769

Beta-2-microglobulin

B2MG_HUMAN

8.4

1

1

P62328

Thymosin beta-4

TYB4_HUMAN

97.73

1

13

P68871

Hemoglobin subunit beta

HBB_HUMAN

8.84

1

1

P81605

Dermcidin

DCD_HUMAN

78.18

3

26

Q13421

Mesothelin

MSLN_HUMAN

2.38

1

1

Q16378

Proline-rich protein 4

PROL4_HUMAN

59.7

53

667

Q6UXB2

VEGF coregulated chemokine 1

VCC1_HUMAN

46.22

3

44

Q99935

Proline-rich protein 1

PROL1_HUMAN

8.87

1

5

Q9GZZ8

Extracellular glycoprotein lacritin

LACRT_HUMAN

57.97

51

470

1.2

1

112

Q9UGM3 Deleted in malignant brain tumors 1 protein DMBT1_HUMAN

Table 2: Data for the peptides with predicted antimicrobial activity. Entry: UniProt entry for the protein from which the peptide originates. Sequence: peptide sequence. Mascot Score: Score 18 ACS Paragon Plus Environment

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obtained for each of the fragmentation methods used, if the peptide was identified. Prob: weighted probability in the AMP analysis. Mascot Score Entry

Sequence

CID

ETD

HCD

Prob

LACRT_HUMAN

LLKKFSLLKPWA

_

_

63.3

0.992

LACRT_HUMAN

KLLKKFSLLKPWA

55.87

75.45

_

0.977

LYSC_HUMAN

AVACAKRVVRDPQGIRAWVAWRNRCQNRDVRQYVQGCGV

_

83.25

_

0.965

LYSC_HUMAN

ACAKRVVRDPQGIRAWVAWRNRCQNRDVRQYVQGCGV

_

69.49

_

0.96

LACRT_HUMAN

GGKQFIENGSEFAQKLLKKFSLLKPWA

_

_

52.27

0.944

LYSC_HUMAN

AKRVVRDPQGIRAWVAWRNRCQNRDVRQYVQGCGV

_

116.23

_

0.939

LACRT_HUMAN

GSEFAQKLLKKFSLLKPWA

91.21

_

73.06

0.938

LACRT_HUMAN

LKKFSLLKPWA

57.79

54.85

_

0.936

LYSC_HUMAN

CAKRVVRDPQGIRAWVAWRNRCQNRDVRQYVQGCGV

_

62.35

_

0.933

LACRT_HUMAN

AQKLLKKFSLLKPWA

44.3

93.05

_

0.928

LACRT_HUMAN

GVPGGKQFIENGSEFAQKLLKKFSLLKPWA

51.97

_

76.31

0.901

LACRT_HUMAN

QALAKAGKGMHGGVPGGKQFIENGSEFAQKLLKKFSLLKPWA

106.24

_

_

0.897

LYSC_HUMAN

QDNIADAVACAKRVVRDPQGIRAWVAWRNRCQNRDVRQYVQGCGV

_

64.41

_

0.897

LACRT_HUMAN

QKLLKKFSLLKPWA

_

43.23

_

0.885

LACRT_HUMAN

AGKGMHGGVPGGKQFIENGSEFAQKLLKKFSLLKPWA

131.08

86.67

78.56

0.877

PROL4_HUMAN

GPQQRPPKPGGHHRHPPPPPFQNQQRPPRRGHR

87.16

_

_

0.839

PROL4_HUMAN

GPQQRPPKPGGHHRHPPPPPFQNQQRPPRRGH

67.75

81.69

0.837

PROL4_HUMAN

_

58.16

LYSC_HUMAN

DGPQQRPPKPGGHHRHPPPPPFQNQQRPPRRGH HLSCSALLQDNIADAVACAKRVVRDPQGIRAWVAWRNRCQNRDVRQYVQG CGV

_ 119.9 3

62.38

_

73.09

0.826

DCD_HUMAN

LEKGLDGAKKAVGGLGKLGKDA

74.88

142.77

95.21

0.823

PROL4_HUMAN

112.18

_

_

_

_ 199.9 5

0.817

SG1D1_HUMAN

GPQQRPPKPGGHHRHPPPPPFQNQQRPPR VVCQALGSEITGFLLAGKPVFKFQLAKFKAPLEAVAAKMEVKKCVDTMAYE KRVLITKTLGKIAEKCDR

LACRT_HUMAN

AGKGMHGGVPGGKQFIENGSEFAQKLLKK

53.01

105.16

60.33

0.796

PROL4_HUMAN

DGPQQRPPKPGGHHRHPPPPPFQNQQRPPR

78.16

_

_

0.793

LACRT_HUMAN

SEFAQKLLKKFSLLKPWA

85.07

79.59

_

0.786

LACRT_HUMAN

PGGKQFIENGSEFAQKLLKKFSLLKPWA

_

_

56.82

0.782

TRFL_HUMAN

CFQWQRNMRKVRGPPVSCIK

_

70.46

_

0.778

LACRT_HUMAN

GVPGGKQFIENGSEFAQKLLKK

_

52.38

0.77

LACRT_HUMAN

SILLTEQALAKAGKGMHGGVPGGK

65.36

123.1

LACRT_HUMAN

AGKGMHGGVPGGKQFIENGSEFAQKLLK

157.78

150.63

_ 114.3 3 100.0 6

0.764

SG1D1_HUMAN

VVCQALGSEITGFLLAGKPVFKFQLAKFKAPLEAV

117.39

_

_

0.76

LACRT_HUMAN

SILLTEQALAKAGKGMHGGVPGGKQFIENGSEFAQKLLKKFSLLKPWA

86.54

_

53.92

0.758


0.837

0.8

0.767


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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22. Swaney, D. L.; McAlister, G. C.; Coon, J. J., Decision tree-driven tandem mass spectrometry for shotgun proteomics. Nat Methods 2008, 5, (11), 959-64. 23. Syka, J. E.; Coon, J. J.; Schroeder, M. J.; Shabanowitz, J.; Hunt, D. F., Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry. Proc Natl Acad Sci U S A 2004, 101, (26), 9528-33. 24. Michalski, A.; Neuhauser, N.; Cox, J.; Mann, M., A systematic investigation into the nature of tryptic HCD spectra. J Proteome Res 2012, 11, (11), 5479-91. 25. Shen, Y.; Tolic, N.; Xie, F.; Zhao, R.; Purvine, S. O.; Schepmoes, A. A.; Moore, R. J.; Anderson, G. A.; Smith, R. D., Effectiveness of CID, HCD, and ETD with FT MS/MS for degradomic-peptidomic analysis: comparison of peptide identification methods. J Proteome Res 2011, 10, (9), 3929-43. 26. Hahne, H.; Pachl, F.; Ruprecht, B.; Maier, S. K.; Klaeger, S.; Helm, D.; Medard, G.; Wilm, M.; Lemeer, S.; Kuster, B., DMSO enhances electrospray response, boosting sensitivity of proteomic experiments. Nat Methods 2013, 10, (10), 989-91. 27. Meyer, J. G.; E, A. K., Charge state coalescence during electrospray ionization improves peptide identification by tandem mass spectrometry. J Am Soc Mass Spectrom 2012, 23, (8), 1390-9. 28. Gobom, J.; Nordhoff, E.; Mirgorodskaya, E.; Ekman, R.; Roepstorff, P., Sample purification and preparation technique based on nano-scale reversed-phase columns for the sensitive analysis of complex peptide mixtures by matrix-assisted laser desorption/ionization mass spectrometry. J Mass Spectrom 1999, 34, (2), 105-16. 29. Thomas, S.; Karnik, S.; Barai, R. S.; Jayaraman, V. K.; Idicula-Thomas, S., CAMP: a useful resource for research on antimicrobial peptides. Nucleic Acids Res 2010, 38, (Database issue), D774-80. 30. Waghu, F. H.; Gopi, L.; Barai, R. S.; Ramteke, P.; Nizami, B.; Idicula-Thomas, S., CAMP: Collection of sequences and structures of antimicrobial peptides. Nucleic Acids Res 2014, 42, (Database issue), D1154-8. 31. Thevenet, P.; Shen, Y.; Maupetit, J.; Guyon, F.; Derreumaux, P.; Tuffery, P., PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Nucleic Acids Res 2012, 40, (Web Server issue), W288-93. 32. Rawlings, N. D.; Waller, M.; Barrett, A. J.; Bateman, A., MEROPS: the database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res 2014, 42, (Database issue), D503-9. 33. Houghten, R. A., General method for the rapid solid-phase synthesis of large numbers of peptides: specificity of antigen-antibody interaction at the level of individual amino acids. Proc Natl Acad Sci U S A 1985, 82, (15), 5131-5. 34. Mastroianni, J. R.; Ouellette, A. J., Alpha-defensins in enteric innate immunity: functional Paneth cell alpha-defensins in mouse colonic lumen. J Biol Chem 2009, 284, (41), 27848-56. 35. Malhotra, S.; Mehta, D. K.; Kumar, P., Spectrum and antibiotic susceptibility pattern of bacterial isolates from conjunctival swabs. Indian J Pathol Microbiol 2005, 48, (4), 538-41. 36. Zaidi, T. S.; Priebe, G. P.; Pier, G. B., A live-attenuated Pseudomonas aeruginosa vaccine elicits outer membrane protein-specific active and passive protection against corneal infection. Infect Immun 2006, 74, (2), 975-83. 37. Soria, J.; Duran, J. A.; Etxebarria, J.; Merayo, J.; Gonzalez, N.; Reigada, R.; Garcia, I.; Acera, A.; Suarez, T., Tear proteome and protein network analyses reveal a novel pentamarker panel for tear film characterization in dry eye and meibomian gland dysfunction. J Proteomics 2013, 78, 94-112. 38. von Thun Und Hohenstein-Blaul, N.; Funke, S.; Grus, F. H., Tears as a source of biomarkers for ocular and systemic diseases. Exp Eye Res 2013, 117, 126-37. 39. Zhou, L.; Beuerman, R. W., Tear analysis in ocular surface diseases. Prog Retin Eye Res 2012, 31, (6), 527-50. 21 ACS Paragon Plus Environment


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40. Badamchian, M.; Damavandy, A. A.; Damavandy, H.; Wadhwa, S. D.; Katz, B.; Goldstein, A. L., Identification and quantification of thymosin beta4 in human saliva and tears. Ann N Y Acad Sci 2007, 1112, 458-65. 41. Good, D. M.; Wirtala, M.; McAlister, G. C.; Coon, J. J., Performance characteristics of electron transfer dissociation mass spectrometry. Mol Cell Proteomics 2007, 6, (11), 1942-51. 42. Sterling, H. J.; Prell, J. S.; Cassou, C. A.; Williams, E. R., Protein conformation and supercharging with DMSO from aqueous solution. J Am Soc Mass Spectrom 2011, 22, (7), 117886. 43. N.E., K. M. a. S., Protein Sequencing and Identification Using Tandem Mass Spectrometry. John Wiley & Sons, Inc.: Canada, 2000. 44. Wilm, M., Principles of electrospray ionization. Mol Cell Proteomics 2011. 45. Kim, K. A.; Ka, S. O.; Moon, W. S.; Yi, H. K.; Lee, Y. H.; Kwon, K. B.; Park, J. W.; Park, B. H., Effect of dermcidin, an antimicrobial peptide, on body fat mobilization in normal mice. J Endocrinol 2008, 198, (1), 111-8. 46. Nakatsuji, T.; Gallo, R. L., Antimicrobial peptides: old molecules with new ideas. J Invest Dermatol 2012, 132, (3 Pt 2), 887-95. 47. Sinha, M.; Kaushik, S.; Kaur, P.; Sharma, S.; Singh, T. P., Antimicrobial lactoferrin peptides: the hidden players in the protective function of a multifunctional protein. Int J Pept 2013, 2013, 390230. 48. Fabian, T. K.; Hermann, P.; Beck, A.; Fejerdy, P.; Fabian, G., Salivary defense proteins: their network and role in innate and acquired oral immunity. Int J Mol Sci 2012, 13, (4), 4295320. 49. Ochieng, J.; Chaudhuri, G., Cystatin superfamily. J Health Care Poor Underserved 2010, 21, (1 Suppl), 51-70. 50. Baranger, K.; Zani, M. L.; Chandenier, J.; Dallet-Choisy, S.; Moreau, T., The antibacterial and antifungal properties of trappin-2 (pre-elafin) do not depend on its protease inhibitory function. FEBS J 2008, 275, (9), 2008-20. 51. Simpson, A. J.; Maxwell, A. I.; Govan, J. R.; Haslett, C.; Sallenave, J. M., Elafin (elastasespecific inhibitor) has anti-microbial activity against gram-positive and gram-negative respiratory pathogens. FEBS Lett 1999, 452, (3), 309-13. 52. McKown, R. L.; Coleman Frazier, E. V.; Zadrozny, K. K.; Deleault, A. M.; Raab, R. W.; Ryan, D. S.; Sia, R. K.; Lee, J. K.; Laurie, G. W., A cleavage-potentiated fragment of tear lacritin is bactericidal. J Biol Chem 2014, 289, (32), 22172-82. 53. Dutta, D.; Willcox, M. D., Antimicrobial contact lenses and lens cases: a review. Eye Contact Lens 2014, 40, (5), 312-24. 54. Samudre, S.; Lattanzio, F. A., Jr.; Lossen, V.; Hosseini, A.; Sheppard, J. D., Jr.; McKown, R. L.; Laurie, G. W.; Williams, P. B., Lacritin, a novel human tear glycoprotein, promotes sustained basal tearing and is well tolerated. Invest Ophthalmol Vis Sci 2011, 52, (9), 6265-70. 55. Wang, J.; Wang, N.; Xie, J.; Walton, S. C.; McKown, R. L.; Raab, R. W.; Ma, P.; Beck, S. L.; Coffman, G. L.; Hussaini, I. M.; Laurie, G. W., Restricted epithelial proliferation by lacritin via PKCalpha-dependent NFAT and mTOR pathways. J Cell Biol 2006, 174, (5), 689-700. 56. Wang, N.; Zimmerman, K.; Raab, R. W.; McKown, R. L.; Hutnik, C. M.; Talla, V.; Tyler, M. F. t.; Lee, J. K.; Laurie, G. W., Lacritin rescues stressed epithelia via rapid forkhead box O3 (FOXO3)-associated autophagy that restores metabolism. J Biol Chem 2013, 288, (25), 1814661.


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Abstract Graphic



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 1: Summary of the analysis of the basal human tear peptidome. A) Overview of the followed strategy. B) Details of sample processing and MS analysis performed for Sample 1 and Sample 2. 96x59mm (300 x 300 DPI)

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Page 25 of 27

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Figure 2: Contribution of CID, HCD, and ETD to peptide identification. Data from all the runs were used, including Sample 2 data obtained with 5% DMSO in the chromatographic solvents. 52x61mm (300 x 300 DPI)



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Figure 3: Annotated spectra for the peptides derived from lacritin assessed for antimicrobial activity. A) CID fragmentation for KLLKKFSLLKPWA. B) ETD fragmentation for KLLKKFSLLKPWA. C) HCD fragmentation for LLKKFSLLKPWA. 100x53mm (300 x 300 DPI)


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Figure 4: Antimicrobial activity of peptides KLLKKFSLLKPWA (▲) and LLKKFSLLKPWA (■) derived from extracellular glycoprotein lacritin. Activity was tested against E. coli (A), P. aeruginosa (B), L. monocytogenes (C), and S. aureus (D). The inactive peptide WLQEGGQECECKDWFLRAPR (●) from VEGF coregulated chemokine was used as a negative control in all cases. Surviving bacteria are shown as colonyforming units/ml (CFU/ml) for each peptide concentration. Measurements were performed in duplicate. 72x64mm (300 x 300 DPI)


Human Basal Tear Peptidome Characterization by CID, HCD, and

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