Investigation of the global protein content from healthy human tears

Accepted Manuscript Investigation of the global protein content from healthy human tears Marianne Dor, Simone Eperon, Patrice H. Lalive, Yan Guex-Crosier, Mehrad Hamedani, Cindy Salvisberg, Natacha Turck PII:

S0014-4835(18)30356-7

DOI:

10.1016/j.exer.2018.10.006

Reference:

YEXER 7504

To appear in:

Experimental Eye Research

Received Date: 11 May 2018 Revised Date:

1 September 2018

Accepted Date: 11 October 2018

Please cite this article as: Dor, M., Eperon, S., Lalive, P.H., Guex-Crosier, Y., Hamedani, M., Salvisberg, C., Turck, N., Investigation of the global protein content from healthy human tears, Experimental Eye Research (2018), doi: https://doi.org/10.1016/j.exer.2018.10.006. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Investigation of the global protein content from healthy human

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tears

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Authors: Marianne Dor1,2, Simone Eperon3, Patrice H. Lalive4, Yan Guex-Crosier3,

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Mehrad Hamedani3, Cindy Salvisberg2, Natacha Turck1,2*.

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Affiliations:

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University of Geneva, Geneva, Switzerland

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OPTICS Group, Department of Internal Medicine Specialties, Faculty of Medicine,

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OPTICS Group, Department of Human Protein Science, Faculty of Medicine,

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University of Geneva, Geneva, Switzerland

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Fondation Asile des Aveugles, Lausanne, Switzerland

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Switzerland

Department of Ophthalmology, University of Lausanne; Jules-Gonin Eye Hospital,

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Division of Neurology, Geneva University Hospitals, Faculty of Medicine, Geneva,

*Corresponding author:

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Email: [email protected].

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Abstract

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Considering absence of invasiveness and side effects, tears emerge as a particularly

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attractive fluid for biomarker discovery and therefore for daily clinical use. However,

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to date this fluid remains poorly studied in healthy condition. Here, we present an

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updated in-depth characterisation of the human healthy tear protein composition

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using proteomics approach. Both eyes of 8 healthy controls (4 men and 4 women,

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average age: 38 ± 18) were collected using the Schirmer’s strip method. After liquid

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digestion and off-gel electrophoresis fractionation, three independent proteomics

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analyses were performed on a LTQ-Orbitrap Velos Pro. Globally, 1351 proteins were

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identified with 2 unique peptides and 1% FDR. Gene ontology analyses showed up

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that 39% of the tear proteins were enzymes, with high numbers of dehydrogenases,

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phosphatases, kinases and ligases. Immunoglobulins, serpins and 14-3-3 domains

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proteins also emerged as the main tear protein families. The glycolysis and the

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coagulation and complement cascades, which were already shown in tears as

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involved in ocular and systemic diseases, were highlighted performing pathway

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analyses. Our study therefore complements the existing data on healthy tears

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proteome. Nevertheless, extensive studies for deeply and definitively characterise

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this promising fluid are required in the near future in order to be able to routinely use

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this fluid in clinics. A better understanding of its protein content will probably open

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new avenues in the biomarker discovery and clinical practice in the near future.

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Keywords: tears; proteome; Off-Gel Electrophoresis; Gas-Phase Fractionation;

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integrative pathways. 2

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1. Introduction

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It is now well known that tears play a key role in the correct function and health status

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of the eye by providing oxygen and nutriments to the ocular surface cells and

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improving the optical properties of the eye (lubricating eye surface). Furthermore,

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tear fluid protects the eye’s epithelium thanks to antibacterial properties and by

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flushing contaminants from the ocular surface. Surprisingly, until now tears and their

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clinical relevance have been relatively poorly studied. This is particularly illustrated by

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searching the keywords “Eye tears and biomarkers” in Pubmed. Only 268 items were

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found during the two last decades (between 1998/01 and 2018/01). In the same

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period, 264’224 publications containing the words “blood” and “biomarkers” were

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published. However, with non-invasive, easy and rapidly collected samples, tear-

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based approaches open up new routes for diagnostic methods and for deeper

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understanding both ocular and systemic diseases. Differences in the tear protein

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pattern of patients suffering from diabetic retinopathy (1-3), Graves’ Orbitopathy (4,

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5), dry eye disease (6-11), multiple sclerosis (12, 13) and even breast cancer (14) or

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renal failure (15) have already been highlighted. Consequently, the ability to measure

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modifications in human tear content offers promising opportunities for screening not

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only ocular but also systemic diseases and for discovering potentially new

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biomarkers for these clinical situations.

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At this stage, in order to be as performing as possible, the use of tears for biomarker

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discovery and clinics requires well knowing and understanding their global protein

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content, mainly in healthy subjects. To the best of our knowledge, to date, only three

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major studies (16-18) deeply investigated the proteome of human healthy tears. Even

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ACCEPTED MANUSCRIPT if these studies are technically and biologically relevant, this is probably not enough

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to definitively close the question of the complex composition of this fluid in healthy

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condition. However, in the past three years, there was an increasing number of

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published papers about tear proteomics, in both ocular and systemic diseases (10,

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11, 19-22). It is not easy to understand or explain why tears, unlike other biological

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fluids, just start to arouse real clinical interest. It is likely that the relatively low

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volumes available for collection contributed significantly to the low numbers of ‘omics’

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studies reported so far. Indeed, an average between 5 and 15 µl of tears can be

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collected per eye, which is not sufficient for some ‘omics’ technics (for example,

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Nuclear Magnetic Resonance usually required around 100µl). Due to their

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localisation close to the eye, for a long time tears were probably not considered as a

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relevant biofluid to study systemic diseases. Moreover, such as albumin within blood,

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tears contain some abundant proteins that makes more difficult the detection of other

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proteins. In previous studies investigating tear proteome, either the pool of samples

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were rather small (one sample for De Souza et al.(17) and four samples for Zhou et

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al.(18)) or the protein list was obtained with several extraction methods from the

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Schirmer strip (Aass et al.(16)). In this context, we designed our study with eight

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samples divided in three pools for our experiments and we used only one extraction

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method from the Schirmer. Moreover, we fractionated our peptides thanks to the off-

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gel electrophoresis method, which was not well described for tear investigation yet.

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To conclude, compared to the previous articles, we made improvements that allowed

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to enhance tear understanding. In this context, using mass spectrometry-based

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approaches, our study proposes an updated and extended list of tear proteins which

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could be used as additional reference list for clinical applications in the field of the

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biomarker research.

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2. Material and methods

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2.1.Subjects.

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Healthy subjects (N=8; experiment 1: 2 women aged 59 and 61 years; experiment 2:

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1 woman, 2 men, aged 21, 24 and 60 years respectively; experiment 3: 1 woman, 2

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men aged 26, 24 and 26 years respectively) were included in this study. Inclusion

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criteria were: subjects aged ≥ 18 and ≤ 70 years, any gender and ethnicity, mm of

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Schirmer >10mm in 5 minutes. Exclusion criteria were: any eye allergy, conjunctivitis,

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any eye pathology, acute infectious disease, make-up or contact lenses, cancer

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and/or chronic autoimmune disease. In accordance with the Declaration of Helsinki,

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written informed consents were obtained from these subjects. The cantonal ethics

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committee for research on human beings has approved the patient’s informed

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consent form and the use of biological material (approbation N°516/12, Dec 2012).

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2.2. Sample collection.

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Tears were collected using Schirmer-Plus® paper strip (Biotech Vision Care PVT

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LTD, Gujarat, India). To avoid any discomfort to subjects, collection was restricted to

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a maximum of 5 minutes. No external stimulation was done to collect the basal fluid.

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Topical anaesthesia should be avoided because it reduces tear production. For each

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patient, tear samples were taken from both eyes. Persons collecting tears wore

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gloves in order to avoid any contamination. Care was taken to avoid damage to the

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conjunctive surface and local eye irritation. External factors such as harsh lighting,

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ACCEPTED MANUSCRIPT background noise and extreme room temperature, all known to affect the content of

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samples, were strictly supervised in order to ensure satisfactory reproducibility. The

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strip was then inserted in a tube on ice and centrifuged at 7840 g for 7 min at 4°C

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without any additional buffer, as described elsewhere (21, 23, 24). After

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centrifugation, tear samples were immediately stored at -80°C until analysis. In order

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to verify the absence of cellular contamination in our samples, we performed a

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haematoxylin and eosin (H&E) stain in order to color the potential cells. 5µl of tears

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were deposited on a slide. The slide was heated 5 minutes at 60°C then fixed with 50

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µl of 10% formalin (10 minutes). 3 washes were done with H2O and another stain

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was done with 20µl of Hemalun (5 min). The slide was washed 3 times with H2O,

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stained with 20µl of Eosin (5 min), washed 3 times with H2O then 3 times with 10%

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alcohol. The slide was finally washed 3 times with the Neo-Clear solution (Merck,

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Darmstadt, Deutschland). The same protocol was applied to the positive control, i.e.

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human astrocytes (around 5’000 cells, suspended in PBS 1X, added on a Schirmer

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strip and centrifuged under the same conditions as for control tear strip) and negative

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control, i.e. PBS. The conclusion was that the Schirmer’s method did not induce cell

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contamination in our samples.

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Table 1: Moisture length (mm) of Schirmer strip for the cohort.

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Subject

Age

Sex*

mm Schirmer (right eye)

mm Schirmer (left eye)

C1

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W

12

13

C2

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W

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19

C3

21

W

15

18

C4

24

M

25

25

C5

60

M

25

15

C6

26

W

16

14

C7

24

M

32

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*: M: man; W: woman

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2.3. Total protein assay.

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The protein concentration of the pooled tear sample (from both eyes of each subject)

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was determined by performing a Bradford Assay according to manufacturer’s

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recommendations (Protein assay Dye reagent concentrate, Bio-Rad, Hercules, US-

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CA). The absorbance was measured at 595 nm with a spectrophotometer (Ultrospec

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2100 Pro, Amersham Biosciences) and the protein concentrations of the three pools

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were determined using a bovine serum albumin (BSA) calibration curve. They were

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estimated at 12.9 µg/µl, 11.06 µg/µl and 9.4 µg/µl for the experiments 1, 2 and 3

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respectively.

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2.4. Reduction, alkylation and digestion.

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Three independent proteomics experiments were done. Experiment 1 was performed

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with a pool of two healthy subjects, experiments 2 and 3 were done using two

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different pools of three healthy subjects (both eyes of the subjects in all three

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experiments). 60 µg of proteins were used for all experiments. They were dried under

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speed-vacuum; then urea (33 µl of 6M; Merck, Darmstadt, Deutschland) diluted in

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Triethylammonium bicarbonate buffer (0.1M; TEAB, Sigma-Aldrich, Saint Louis, US-

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MO) and tris-(2-carboxyethyl)phosphine (2 µl of 50 mM; TCEP, Sigma-Aldrich, Saint

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Louis, US-MO) were added in each tube. After incubation at 37°C during 1 hour,

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iodoacetamid (1 µl of 400 mM; Sigma-Aldrich, Saint Louis, US-MO) was added and

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tubes were incubated 30 minutes of in the dark. TEAB (67 µl of 0.1M) was added,

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then a liquid trypsin digestion (1:20 ratio, 1 microgram of enzyme to 20 micrograms of

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protein, porcine origin, Promega Corporation, Madison, US-WI) was done overnight

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at 37°C.

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2.5. Off-gel electrophoresis (OGE).

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Before OGE, samples were dried under speed-vacuum then purified by using

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Macrospin columns (Harvard Apparatus, Holliston, US-MA) according to

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manufacturer’s recommendations. Tubes were dried under speed-vacuum and A

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3100 OFFGEL Fractionator (Agilent technologies, Santa Clara, US-CA) was

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performed over night to separate the sample. Guidelines available in Agilent

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datasheet were followed, using a 13cm IPG strip (Immobiline DryStrip pH 3-10, 13cm

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GE Healthcare, Little Chalfont, UK) and 12 OGE wells (25, 26). After fractionation,

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microspin columns (Harvard Apparatus, Holliston, US-MA) were used according to

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the manufacturer’s recommendations and the 12 fractions of each experiment were

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dried under speed-vacuum. Peptide concentration of the fractions was theoretically

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approximated, considering that 1/12 of the pooled sample was found in each fraction

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after OGE.

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2.6. Mass spectrometry (MS) analyses.

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The fractions resulting from OGE were dissolved in 94.9% H2O / 5% Acetonitrile

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(ACN) / 0.1% Formic acid (FA). For the three experiments, 1µg of fraction was

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injected in GPF4 mode (27), meaning that each fraction of each experiment was

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injected four times (GPF1, GPF2, GPF3 and GPF4). They were analysed by tandem

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MS (Liquid Chromatography-MS/MS) using a Linear Trap Quadrupole (LTQ) Orbitrap

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Velos Pro (ThermoFisher instruments, San Jose, US-CA) coupled to a nanoflow high

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pressure liquid chromatography (HPLC, NanoAcquity system from Waters, Milford,

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US-MA). Peptides were trapped on a home-made 5 µm 200 Å Magic C18 AQ

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(Michrom) 0.1 × 20 mm pre-column and separated on a commercial 0.075 x 150 mm

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ACCEPTED MANUSCRIPT Nikkyo (Nikkyo Technology, Tokyo, JPN) analytical nanocolumn (C18, 5 µm, 100 Å).

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More precisely, trapping was done during 15 minutes with a flow rate of 3 µl/min

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using a gradient of H2O/FA 99.9%/0.1% (solvent A) and CH3CN/FA 99.9%/0.1%

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(solvent B), where 95% of solvent A were mixed with 5% of solvent B. Then the

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analytical separation was run for 85 min with a flow rate of 220 nl/min as follows: 0–1

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min 95% A and 5% B, 1-55 min 65% A and 35% B, 55-65 min 20% A and 80% B, 65-

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67 min 20% A and 80% B, 67-69 min 95% A and 5% B and 69-85 min 95% A and

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5% B. min. For MS survey scans, the OT resolution was set to 60000 and the ion

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population was set to 5 × 105 with an m/z window from 400 to 2000. Five precursor

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ions were selected for collision-induced dissociation (CID) in the LTQ. The

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normalised collision energies were set to 35% for CID. The different m/z windows for

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the gas-phase fractions were set as following: 400-520 for GPF1, 515-690 for GPF2,

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685-979 for GPF3 and 974-2000 for GPF4. Exclusion duration was set at 45

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seconds, with a repeat count of 1. Exclusion was done by mass, with a low m/z = 0.1

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and a high m/z = 1.1. Peak lists and resulting files, combined from the different

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experiments, were searched against the uniprot_sprot (2017_05_10) database using

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Thermo Proteome Discoverer (version 2.2.0388; Thermo Fisher Scientific, San Jose,

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US-CA). Oxidised methionine was set as variable amino acid modifications and

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carbamidomethylation of cysteines were set as fixed modification. Trypsin was

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selected as the enzyme, with one potential missed cleavage. The precursor mass

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tolerance and the fragment mass tolerance were 10 ppm and 0.6 Da respectively. A

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false discovery rate (FDR) of 1% was selected at peptide and protein levels and only

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the Master proteins were kept. The list of identified protein was generated containing

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proteins matching with two different and unique peptide sequences. The datasets

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supporting the conclusions of this article are available in the ProteomeXchange

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Consortium repository via the PRIDE database (submission identifier: PXD008702).

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The reviewer account details are the following: username: [email protected];

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password: ycE67P81.

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2.7. Process and pathway analyses.

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Database for Annotation, Visualization and Integrated Discovery (DAVID), a web-

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accessible program, was used to perform the Gene Ontology (GO) Analysis

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classification of the identified proteins (28). Options were the following: high

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classification stringency and p-Value for the GO term > 0.05. Some subcategories

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were grouped in order to reduce the numbers of GO categories and simplify the pie

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charts.

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Search Tool for the Retrieval of Interacting Genes/Proteins /STRING) database was

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used to classify the proteins into protein families (29). We considered a count in gene

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set > 5 and a p-Value > 0.05 for relevant protein families.

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Kyoto Encyclopedia of Genes and Genomes (KEGG) was also used to study and

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visualise the pathways in which the proteins were involved (30). We set a threshold

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count of 10, with a p-Value and adjusted p-Value (Benjamini-Hochberg correction)

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>0.05.

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A manual merge of the protein lists obtained with the three experiments was done

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using Venny (http://bioinfogp.cnb.csic.es/tools/venny/index.html).

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Top 10, 20, 50, 100 and 200 of the most abundant proteins for the three experiments

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were obtained by classifying the proteins according to their Peptide Spectrum

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Matches (PSM). PSM of a protein corresponds to the number of identified peptide

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spectra matched for this protein. The same classification was done to obtain the top-

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100 and top-200 of the less abundant proteins of the different experiments. 10

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3. Results

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In order to obtain a robust in-depth characterisation, three independent proteomics

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experiments were performed on three different pools of healthy tears. Individual and

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general lists of proteins were then generated, analysed and compared to the

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literature. The study design is summarised in the graphical abstract. In addition,

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comparisons between the tear protein content with other ocular fluids will be

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proposed.

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3.1. Characterisation of the human tear proteome

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By combining all the experiments, 1351 proteins with 2 unique peptides were

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identified. By excluding the keratins, which are suspected to come from

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contamination during the tear collect, the original list was finally reduced to 1337

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proteins with 2 unique peptides (874, 837 and 1143 proteins for experiment 1, 2, and

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3 respectively; global and detailed lists in S1 dataset).

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Interestingly, 45.5% (608 proteins) were in common between the three different

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experiments, which is a good overlapping considering the variabilities induced by

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intervariability. Other variabilities could also have been induced during trypsin

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digestion (31), off-gel fractionation and mass spectrometry injections (32). For further

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analyses, only list of proteins without keratins was considered.

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Top-10 of the most abundant proteins for each experiment was highly stable with 7

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proteins always present whatever the experiment observed (Table 2). Interestingly, 11

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3) and 36.7% (experiment 1) of the total PSM detected. As expected, the rank one

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protein was the lactotransferrin, one of the major proteins of the tears, involved in the

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anti-inflammatory and antimicrobial processes (33). This point highlighted the

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importance of tears to maintain a very clean environment for the eyes. The other six

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common proteins between our three experiments were lipocalin-1, also known for its

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antimicrobial activity (34), serum albumin, lysozyme C, immunoglobulin heavy

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constant alpha 1, immunoglobulin kappa constant and polymeric immunoglobulin

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receptor. Moreover, even if some differences in term of abundance (PSM number)

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have been observed, the presence of the 5 first proteins (lactotransferrin, lipocalin-1,

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lysozyme C, serum albumin, immunoglobulin heavy constant alpha 1) were largely

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conserved in all experiments. The principal tear proteins are directly secreted by

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lacrimal glands (lactotransferrin, epidermal growth factor (EGF), tear lipocalin,

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secretory immunoglobulin A (sIgA)) or by lysosomes (lysozyme, which was found in

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the three experiments in the top 10 more abundant proteins). However, some serum

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proteins such as albumin, transferrin, IgG and IgM were also found in the tear fluid

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probably as a result of passive transport from the blood and are suggestive of serum

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leakage. It is also known that cell infiltrating conjunctiva (T cells, B cells among

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others) secrete Igs and cytokines in various conditions (35). Finally, local contribution

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of the neighbouring cells releasing proteins in tears cannot be excluded.

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By expanding the comparison to the top-20, top-50 or top-100, the percentages of

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proteins in common were still relatively high (64%, 45.1% and 52.42%, respectively).

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At the opposite, top-100 and top-200 of the less abundant proteins presented only

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0.1% and 1.6% of proteins in common between the three experiments. The relatively

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low numbers of unique peptides of these less abundant proteins may explain why

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they were found in some experiments but not in all three.

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Table 2: Top 10 proteins of the three experiments. Protein names Lactotransferrin Lipocalin-1 Lysozyme C Serum albumin Immunoglobulin heavy constant alpha 1 Immunoglobulin alpha-2 heavy chain Polymeric immunoglobulin receptor Immunoglobulin heavy constant alpha 2 Prolactin-inducible protein Immunoglobulin kappa constant

PSMs

AC P02788 P02768 P61626 P01876 P31025 P01834 P01833 P0DOX2 P25311 P01024

Protein names Lactotransferrin Serum albumin Lysozyme C Immunoglobulin heavy constant alpha 1 Lipocalin-1 Immunoglobulin kappa constant Polymeric immunoglobulin receptor Immunoglobulin alpha-2 heavy chain Zinc-alpha-2-glycoprotein Complement C3

PSMs

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Protein names Lactotransferrin Lysozyme C Serum albumin Lipocalin-1 Immunoglobulin heavy constant alpha 1 Prolactin-inducible protein Polymeric immunoglobulin receptor Immunoglobulin kappa constant Zinc-alpha-2-glycoprotein Immunoglobulin kappa light chain

AC C

Pep

3074 1252 1109 1021 857 546 541 521 505 389

AC P02788 P61626 P02768 P31025 P01876 P12273 P01833 P01834 P25311 P0DOX7

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PSMs

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AC P02788 P31025 P61626 P02768 P01876 P0DOX2 P01833 P01877 P12273 P01834

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60 14 12 54 13 8 28 2 9 2

Pep 1570 906 751 649 558 487 385 344 268 244

60 13 57 12 14 12 33 2 21 3 Pep

1913 1358 1179 677 586 432 353 336 305 288

54 52 10 11 10 2 27 8 20 75

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From top to bottom: experiment 1,2 and 3. Bold: same position in the three lists.

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Italic: present in the three lists. AC: accession number on UniProt; PSMs: Peptide

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Spectrum Matches for each protein; Pep: number of unique peptides for each protein.

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Using the functional annotation-clustering tool from DAVID, the 1337 proteins were

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classified according to their molecular functions, cellular components and biological

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processes (Figs 1a, b and c respectively). The cadherin-binding involved in cell-cell

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adhesion (62%, with for instance some eukaryotic translation elongation factors and

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several capping actin proteins) and the enzyme activity (16%, containing a lot of

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alcohol dehydrogenases and aldo-keto reductase) were the two main subcategories

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emerging from GO molecular functions. For cellular components, half of the proteins

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were linked to the cell-cell adherens junction (56%), an observation that was

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confirmed by the biological process classification (cell-cell adhesion 46%).

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Around 39% (518 proteins) of the 1337 proteins appeared to be part of the enzyme

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protein classes (detailed lists are available as S2 dataset). By classifying these

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enzymes using UniProt, the three major subclasses were hydrolases (41%, 212

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proteins), transferases (27%, 138 proteins) and oxidoreductases (15%, 80 proteins)

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(Fig 2). In the hydrolases category, we highlighted for example the phospholipase A2

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(26, 15 and 15 PSMs in the three experiments) but also the lactotransferrin and the

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lysozyme C, which were present in the three top 10 proteins. Several mitogen-

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activated protein kinases (mitogen-activated protein kinase 1, 3, 13, 14) were found

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in the transferases category and the oxidoreductases contained a lot of

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dehydrogenases such as 4 alcohol dehydrogenases (alcohol dehydrogenase, alcohol

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dehydrogenase 1B, alcohol dehydrogenase 1C, alcohol dehydrogenase class 4,

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alcohol dehydrogenase class 3) and retinal dehydrogenase 1.

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ACCEPTED MANUSCRIPT After noticing the large amount of enzymes in our proteins, we were interested by

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studying the other major protein families. Using STRING, which is a biological

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database for protein-protein interactions, it appeared that serpins (10 proteins) and

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14-3-3 domain (7 proteins) were among the top five protein families. Both families

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have been extensively studied for various cellular functions. 14-3-3 family has been

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shown as a potential marker for ocular hypertension induced by glaucoma (36)

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andseveral serpins such as serpinA1 (serpin peptidase inhibitor clade A member 1)

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or serpinB1 (plasminogen activator inhibitor-1) were proposed as targets for

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diagnosis but also therapy in cancer (37-39). Tracing these proteins in tears, in

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response to a treatment or to predict patient outcome for examples, could be

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clinically relevant and open new way to manage cancer patients. Until now, no

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information related to the eye can be found concerning these protein families, but we

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may suggest that it is related to the weak overall investigation of tears.

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Furthermore, KEGG analyses highlighted two notable biological pathways, the

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glycolysis/gluconeogenesis (31 proteins, p-Value = 4.6E-14, with phosphoglycerate

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kinase (EC 2.7.2.3) and the ATP-dependent 6-phosphofructokinase (EC 2.7.1.11))

340

(Fig 3) and the coagulation and complement cascades (38 proteins, p-Value = 1.7E-

341

20

342

glycoprotein) (Fig 4). Alterations of these pathways have been already shown to be

343

involved in some eye diseases such as Age-Related Macular Degeneration (AMD) or

344

diabetic retinopathy (40, 41). Yokosako et al.(40) described the urinary levels of

345

lactate and pyruvate as deregulated in AMD patients. In the present study, we were

346

able to identify the lactate dehydrogenase and the pyruvate kinase, which may play a

347

role in the deregulation observed by Yokosako et al.(40). As the AMD is an ocular

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, among other the complement factor H, the complement factor 3 and the CD59

15

ACCEPTED MANUSCRIPT disease, it could be relevant to investigate if the levels of these two proteins are also

349

deregulated in tears. Karamichos et al.(42) highlighted the

350

glycolysis/gluconeogenesis as part of the most significantly affected pathways in

351

case of keratoconus (KC), which is a non-inflammatory corneal disease. They noticed

352

the 1,3 diphosphoglycerate and the 3-phosphoglycerate as significantly up-regulated

353

in patients suffering from KC compared to healthy controls. Interestingly, we

354

identified in tears the glyceraldehyde-3- phosphate dehydrogenase and the

355

phosphoglycerate kinase, which may have an influence on the metabolites studied by

356

Karamichos et al.(42). De Souza et al.(17) also reported in their tear study a high

357

number of hydrolases involved in the glycolysis. Since the glycolysis takes place

358

within the cells, the source of these proteins might be damaged cells. These cells

359

could be epithelial cells covering the eyes but we cannot exclude that lacrimal cells

360

released them. The fact that these proteins were deregulated in a disease context

361

emphasises their role or importance. Regarding the complement and coagulation

362

pathways, proteins that are involved in play a major role in the protection of the

363

ocular surface (43, 44) but also in several diseases (41). As one of the tear main

364

functions is protection and lubrication of the eye, it is completely relevant to find some

365

proteins related to these processes. Detecting in patient tears involved at different

366

levels in these pathways is clinically relevant and offer new therapeutic strategies.

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348

368

3.3. Consistency with the existing knowledge

369

To date, only three major publications focused on healthy tear proteome and

370

reported 491, 1543 and 1526 identified proteins respectively (16-18). Summary of

371

these studies compared to our data were described in Table 3. In order to compare

372

them with our study, the same stringent identification criteria that we used were 16

ACCEPTED MANUSCRIPT applied: identification of proteins with at least 2 unique peptides, only reviewed

374

proteins and exclusion of keratins. Subsequent new lists of identified proteins were

375

generated for each study, greatly reducing their lists at 478, 1026 and 662 proteins.

376

By merging all the information (the 3 published studies and our study), 1620 proteins

377

were identified in tears (Fig 5). Only 197 proteins (11.2%; S3 dataset) were in

378

common across all 4 studies. Among them, a mean of 55% were found in the top-200

379

of the most abundant proteins of our experiments (53.6%, 53.3% and 55.3% in the

380

experiment 1, 2 and 3 respectively). At the opposite, between 1% and 7.1% of these

381

197 proteins were found in the 200 less abundant proteins of the experiments (4.6%,

382

7.1% and 1% in the experiment 1, 2 and 3 respectively). Therefore, the less

383

abundant proteins are probably the main cause of variability between these studies.

384

This result is in correlation with the low number of shared proteins between the three

385

top-100 and top-200 less abundant proteins of our own experiments. It is then

386

important to enhance the identification of these low abundant proteins, as they are

387

more likely to contain potential biomarkers for diseases than abundant proteins.

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Furthermore, 425 proteins (24.1%) were described for the first time in our study (S4

390

dataset), including some translation initiation factors (eukaryotic translation initiation

391

factors 2A and subunit 2, factor 3 subunits A, B, C, D, E, F, M, factors 4 gamma 1

392

and 2, factor 5), ribosomal proteins (60S ribosomal protein L14, L17, L18a, L22, L23,

393

L24, L3, L30, L32, L9, 40S ribosomal protein S2, S21, S4, S5, S7 and ribosomal

394

protein S6 kinase alpha-1, alpha-3) and mitogen-activated protein kinases 1, 13 and

395

14. An important point is that around 40% of these 425 proteins were identified at

396

least in two out of our three experiments (S4 dataset), reinforcing our results. If we

397

exclude these 425 specific proteins from our three lists of top-100 and three lists of

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ACCEPTED MANUSCRIPT top-200 less abundant proteins, it does not greatly change the low percentages of

399

shared proteins which were previously found (2% instead of 0.1% between the three

400

top-100 and 5.7% instead of 1.6% between the three top-200). We specifically

401

identified some ribosomal proteins, which are part of small guanosine

402

trisphosphatases (GTPases) (45). Depending on the binding to guanosine

403

trisphosphate (GTP) or guanosine diphosphate (GDP), Rab proteins (Ras-related in

404

brain) are active or inactive, respectively (46). They are involved in the membrane

405

traffic and vesicles formation and transport. Several mitogen-activated protein

406

kinases which are in charge of the signal transduction by the phosphorylation of

407

serine and threonine protein residues were also found (47). They could be released

408

after apoptosis of the cells in contact with tears. Why did we find these proteins

409

specifically in our study and how could we explain the low number of proteins in

410

common (192) between the studies? The use of different technical workflows (Table

411

3) may bring some answers to these questions.

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Firstly, the method of sample collection has an impact on tear composition (23). De

414

Souza et al.(17) used microcapillary glass tubes whereas the other studies sampled

415

tears with Schirmer’s strip. Schirmer method is reported to be more comfortable and

416

pleasant for the collected persons, easier to handle and can not wound neither

417

cornea, nor conjunctiva. On the other side, capillary tube was described as requiring

418

more technical expertise from the person who collected tears. The good angle and

419

position of the capillary on the ocular surface are not trivial, which frequently need

420

capillary tube repositioning. Thereby, more risks to seriously damage the conjunctiva

421

and/or the cornea are associated to this technique. Finally, collection with a capillary

422

tube takes more time since it must be stopped when the patient moves or blinks.

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ACCEPTED MANUSCRIPT Even if this technique is suspected to limit cell contamination from conjunctival or

424

epithelial cells compared to Schirmer collection, this cannot be completely excluded.

425

Posa et al. (23) concluded that both methods were suitable for protein analyses. Yet,

426

they reported that all subjects experienced the Schirmer strip much more pleasant

427

compared to the capillary tube. Our personal data also support the conclusion of

428

Posa et al. (23), so regarding the balances between various advantages and few

429

limitations, we chose the Schirmer test. However, the use of capillary tubes in the

430

study of De Souza et al.(17) could be part of the reasons why only 197 proteins were

431

commonly found in the 4 studies. Indeed, in the literature higher protein contents and

432

concentrations were obtained using Schirmer strip method (48-50). Another

433

parameter that was different between the studies is the Schirmer extraction method.

434

Whereas we only centrifuged the Schirmer’s strip to collect tears, Zhou et al.(18)

435

used extraction buffer comprising 100 mM of ammonium bicarbonate and protease

436

inhibitor. Aass et al. (16) worked with different buffers containing NH4HCO3, NaCl, a

437

surfactant, or a combination of the three, the consequences on recovered proteins

438

following these extraction methods were not investigated yet.

439

Another different technical point could be the off-gel electrophoresis fractionation

440

(based on the isoelectric point of the peptides) while Aass et al.(16) and Zhou et

441

al.(18) used strong cation exchange (SCX) chromatography. It has been proven that

442

both methods were suitable to separate a complex sample and enhance the number

443

of identified proteins (51, 52). A few studies compared these two methods (53, 54).

444

The off-gel electrophoresis provides the isoelectric point of the peptides, a useful

445

information, and shows a high resolution, a high sample loading and a flexibility

446

concerning the choice of the pH gradient (25, 51, 55). This allows to focus on a

447

specific pH range, to better separate the samples in this pH gradient. The SCX

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19

ACCEPTED MANUSCRIPT method is described to be a faster method (25), but evidences about enhancing the

449

number of identified proteins are contradictory (53, 55, 56). As expected, the

450

methods lead to different protein profiles (55, 57). We also would like to mention that

451

we did not measure peptide concentration of the fractions after OG. Regarding the

452

difficulty in quantifying peptides (58, 59) and the low quantity of material per fraction,

453

we estimated the concentration by considering a homogeneous quantity of peptides

454

in each fraction.

455

Peptide length and tryptic missed cleavage sites could also explain the differences

456

between the 4 studies that we observed. While we used LTQ-Orbitrap like Aass et

457

al.(16), Zhou et al.(18) performed their analyses with a TripleTOF 5600. On another

458

hand, Ass et al.(16) supplemented the trypsin with Lys-C enzyme. This combination

459

of two proteases was described to enhance the efficiency of the digestion, and

460

influences the identified proteins (60).

461

Like Zhou et al.(18) and Aass et al.(16), we pooled different collected samples, while

462

De Souza et al.(17) analysed one single subject. Pooling different samples permitted

463

to reach a sufficient quantity and work with an average sample in term of peptide

464

content (61) in the analysed sample. However, pooling loses the sample identity and

465

the inter-variability information. In our case, we were interested in the global tear

466

proteome so we chose to pool samples.

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467

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448

468

In our pie charts, we did not obtain the same category names as Zhou and al. (18),

469

probably because of a different software version. Nevertheless, the main categories

470

seemed similar for each GO classification, which was completely relevant with the

471

fact that almost 50% (776) of proteins were found in common in their study and ours. 20

ACCEPTED MANUSCRIPT This observation strengthened our results but also emphasised the need to analyse

473

more samples, to consider the potential effects of physiological parameters (age,

474

gender, circadian rhythm and potential drugs) and to standardise the proteomics

475

workflow.

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476

Our method presents the advantage not to use extraction buffer, compared to Zhou

478

et al.(18) and Aass et al.(16). Therefore, the samples will be available for any kind of

479

experiments and analysis methods, without any interferences. Moreover, no study

480

checked whether the extraction buffer well preserved the samples. We recommend

481

including an equal number of men and women of various ages, to properly cover tear

482

proteome. Indeed, some recent studies started to investigate how both age and sex

483

could influence both tear composition and characteristics (62-64). The use of OGE

484

was also an added value, as this method was only described for tears in another

485

article from our team in a disease context (13). This alternative fractionation method

486

probably contributed to the specifically identified proteins of our study. However, the

487

current methodology has limitations. First, Schirmer strip extraction without buffer will

488

not allow studying ocular diseases leading to dry eye. Indeed, Schirmer moisturing

489

could be too short, resulting in 1 or 2-µl sample, not suitable for further analyses. In

490

these cases, using extraction buffers or other collecting methods such as a polyester

491

fiber rod or microcapillary tube would be more efficient (10, 11). Moreover, due to its

492

multiple steps, this methodology is not easy to handle in a clinical study with many

493

samples. However, our goal was to describe a methodology which can be used in the

494

first step of research projects, usually done with limited number of samples.

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495 496

Table 3: Comparison of the four tears proteome studies. 21

ACCEPTED MANUSCRIPT De Souza et al.

Zhou et al.

Aass et al.

Dor et al.

1 healthy patient

4 healthy subjects

3 healthy subjects

8 healthy subjects

Microcapillary tube

Schirmer's strip

Schirmer's strip

Schirmer's strip

Protein quantity (µg)

NM (13 µl)

400

NM

180

Pool

/

Yes

Yes

Yes

In-gel digestion

In-solution

In-solution

In-solution

Trypsin

Trypsin

Lys-C+ trypsin

Trypsin

SDS-PAGE

Offline SCX

Offline SCX

Digestion

RI PT

Collection

Off-Gel

Fractionation

NM LTQ-Orbitrap +

Mass spectrometer

2% ACN

Triple TOF 5600

LTQ-FT FDR <0.1%, 2

NM

5% ACN

LTQ-Orbitrap

LTQ-Orbitrap

M AN U

Injection MS buffer

SC

Electrophoresis

FDR <1%,

Identification

unique peptides

conditions

(Orbitrap), 1 peptide

FDR <1%, 1 peptide unspecified number FDR <1%, 1 peptide of peptide

identification

†

Stringent

497

*

‡

1543

1526

1351

‡

478

1026

662

1337

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identification

491

EP

Maximum

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(FT)

498

NM: not mentioned; Lys-C: proteinase lys-C; FT: Fourier transform; †: with 1 peptide

499

and non-reviewed proteins; ‡: with 2 peptides; *: with 2 unique peptides, without

500

keratins and reviewed proteins.

501 502

3.4. The tear proteome and the other ocular fluids

22

ACCEPTED MANUSCRIPT The tear proteome generated in our study was then compared to the proteome of

504

vitreous and aqueous humors (VH and AH respectively), investigated among others

505

by Murphy et al. (65, 66). So far, VH and AH were not very much studied in term of

506

proteomics studies. VH is a transparent and gelatinous substance situated between

507

the lens and the retina (67). Its global proteome, meaning in healthy subjects, was

508

described in only two main studies (65, 68). This fluid should contain some

509

information about the physiological condition of the retina. AH is situated at the

510

anterior and posterior chambers of the eye and plays a crucial role in cleaning the

511

lens and the cornea, but also permitting the distribution of nutriments and drugs to

512

several ocular structures (69). Interestingly, 291 (21.8%) and 197 (14.7%) tear

513

proteins were also found in VH and AH respectively (% of proteins we found in tears;

514

with two peptides, only reviewed and without keratins; S5 and S6 datasets). Proteins

515

found in common were represented by clusterin, recently demonstrated in VH as a

516

potential biomarker of the AMD (70) and suspected to be a marker of Alzheimer

517

disease in plasma (71), but also proteins involved in the glycolysis (among others the

518

lactate dehydrogenases A and B chains and the enolase 1) and complement and

519

coagulation pathways (6 proteins of the serpin family and 16 complement factors).

520

Several cathepsins (B, D, L1 and Z) were also commonly found in both tears, VH and

521

AH. Cathepsin D, identified in the three different ocular fluids, was also recently

522

proposed as biomarker candidate in AH for neovascular AMD (72). Finding in tears

523

some proteins also present in VH and AH and related to certain diseases is very

524

promising. Indeed collecting VH and AH requires surgery, which is an invasive

525

process. Using tears to find the same information could be a real advantage for both

526

clinicians and patients. Moreover, it could allow easier and better identification of

527

proteins of interest in certain pathologies.

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23

ACCEPTED MANUSCRIPT The comparisons between tears, AH and VH have some limitations. Indeed, two

529

studies of Murphy (65, 66) were done with AH and VH of patients with cataract,

530

because the need to a surgery does not permit to collect complete healthy persons.

531

We do not know how the cataract status could affect the results obtained in these

532

studies, and so the comparisons that we made. However, we are aware that these

533

remarks are somewhat speculative and that more experiments will be needed to

534

support our hypothesis.

RI PT

528

SC

535

5. Conclusion

537

To conclude, the merge of all tear protein lists has enabled the identification of 1620

538

proteins (with stringent conditions), suggesting that there is probably room for

539

improvement in tear protein identification. Indeed, the fact that the combination of

540

different studies greatly enhances our knowledge about this fluid clearly

541

demonstrates that additional experiments will be required for the establishment of a

542

reliable proteome for the tears. Through this study, we first complemented the

543

knowledge on tear proteome but we also proposed a global overview of their great

544

potential for clinical research and biomarker discovery.

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Acknowledgments

547

Authors thank Patrizia Arboit, Carla Pasquarello, Alexandre Hainard (Proteomics

548

Core Facility of the University of Geneva), Catherine Fouda, Christophe Tati and

549

Hortense Delacretaz for their technical assistance.

550

24

ACCEPTED MANUSCRIPT 551

Figure captions

552

Graphical Abstract. Summary of the experimental workflow followed in this

554

study. GPF: gas-phase fractionation mode; LTQ: linear trap quadrupole; IPG:

555

immobilized pH gradient. DAVID: Database for Annotation, Visualization and

556

Integrated Discovery; STRING: Search Tool for the Retrieval of Interacting

557

Genes/Proteins; KEGG: Kyoto Encyclopedia of Genes and Genomes.

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Fig 1. Pie charts of molecular function (a), cellular component (b) and

560

biological process (c) obtained with the 1337 proteins using DAVID software.

561

Some subcategories were grouped to reduce the number of GO categories

562

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Fig 2. Classification of the 518 enzymes identified in our study. Data were

564

obtained using the “enzymes classes” available on UniProt. Details of the content of

565

each category could be found in S2 dataset.

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567

Fig 3. Glycolysis/Gluconeogenesis pathway obtained using KEGG database

568

(30). Number of genes that are involved in is higher than the number of bold boxes

569

on the figures due to the isoforms of the proteins. It means that one bold box can

570

encompass several proteins. 5.4.2.2: phosphoglucomutase 1, 2; 5.3.1.9: glucose-6-

571

phosphate isomerase; 5.1.3.3: galactose mutarotase; 3.1.3.11: fructose-

25

ACCEPTED MANUSCRIPT bisphosphatase 1; 2.7.1.11: phosphofructokinase muscle, platelet, liver types;

573

4.1.2.13: aldolase fructose-bisphosphate C, A; 5.3.1.1: triosephosphate isomerase 1;

574

1.2.1.12: glyceraldehyde-3-phosphate dehydrogenase; 2.7.2.3: phosphoglycerate

575

kinase 1; 5.4.2.11: phosphoglycerate mutase 1; enolase 1; 2.7.1.40: pyruvate kinase

576

(muscle); 1.1.1.27: lactate dehydrogenase A, B; 6.2.1.1: acyl-CoA synthetase short-

577

chain family member 2; 1.2.1.3: aldehyde dehydrogenase 9 family member A1, 7

578

family member A1; 1.2.1.5: aldehyde dehydrogenase 1 family member A3, 3 family

579

member A1; 1.1.1.1: Alcohol dehydrogenase 1B, 1C, 5, 7; 1.1.1.2: aldo-keto

580

reductase family 1 member A1.

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581

Fig 4. Coagulation and complement cascades obtained using KEGG database

583

(30). Number of genes that are involved in is higher than the number of bold boxes

584

on the figures due to the isoforms of the proteins. It means that one bold box can

585

encompass several proteins. AT3: serpin family C member 1; A1AT: serpin family A

586

member 1; A2M: alpha-2-macroglobulin; CD59: CD59 molecule; CPB2:

587

carboxypeptidase B2; CR3: fibrinogen gamma chain; CR4: fibrinogen gamma chain;

588

C1INH: serpin family G member; C1qrs: complement C1q B chain, C1q C chain, C1r,

589

C1s; C2: complement factor 2; C3: complement C3; C4: complement C4A; C5:

590

complement C5; C6, 7, 8, 9: complement C6, C7, C8 alpha chain, C8 beta chain, C9;

591

DAF: CD55 molecule; FB: complement factor B; FD: complement factor D; FH:

592

complement factor H; FI: complement factor I; Fibrinogen: fibrinogen gamma chain,

593

beta chain, alpha chain; F2: coagulation factor 2 (thrombin); F5: coagulation factor 5;

594

F12: coagulation factor 12; F13: coagulation factor 13 B chain; HCII: serpin family D

595

member 1; kallikrein: kallikrein B1; kininogen: kininogen 1; PCI: serpin family A

596

member 5; PLG: plasminogen; α2AP: serpin family F member 2.

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ACCEPTED MANUSCRIPT 597

Fig 5. Comparison between the studies of De Souza et al.(17), Zhou et al.(18),

599

Aass et al.(16) and us. The following criteria were applied to the four protein lists:

600

only reviewed proteins, 2 unique peptides and exclusion of keratins. For each

601

category, percentage represents the ratio between the proteins contain in this

602

category and the total number of proteins. Major information is the proteins in

603

common to the four studies (197; 11.2%) and our specific proteins (425; 24.1%).

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References

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1. Csosz E, Boross P, Csutak A, Berta A, Toth F, Poliska S, et al. Quantitative analysis of proteins in the tear fluid of patients with diabetic retinopathy. J Proteomics. 2012;75(7):2196-204. 2. Kim HJ, Kim PK, Yoo HS, Kim CW. Comparison of tear proteins between healthy and early diabetic retinopathy patients. Clin Biochem. 2012;45(1-2):60-7. 3. Park KS, Kim SS, Kim JC, Kim HC, Im YS, Ahn CW, et al. Serum and tear levels of nerve growth factor in diabetic retinopathy patients. Am J Ophthalmol. 2008;145(3):432-7. 4. Pieragostino D, D'Alessandro M, di Ioia M, Di Ilio C, Sacchetta P, Del Boccio P. Unraveling the molecular repertoire of tears as a source of biomarkers: beyond ocular diseases. Proteomics Clin Appl. 2015;9(1-2):169-86. 5. Ujhelyi B, Gogolak P, Erdei A, Nagy V, Balazs E, Rajnavolgyi E, et al. Graves' orbitopathy results in profound changes in tear composition: a study of plasminogen activator inhibitor-1 and seven cytokines. Thyroid. 2012;22(4):407-14. 6. Perumal N, Funke S, Pfeiffer N, Grus FH. Proteomics analysis of human tears from aqueousdeficient and evaporative dry eye patients. Sci Rep. 2016;6:29629. 7. Zhou L, Beuerman RW, Chan CM, Zhao SZ, Li XR, Yang H, et al. Identification of tear fluid biomarkers in dry eye syndrome using iTRAQ quantitative proteomics. J Proteome Res. 2009;8(11):4889-905. 8. Aluru SV, Agarwal S, Srinivasan B, Iyer GK, Rajappa SM, Tatu U, et al. Lacrimal proline rich 4 (LPRR4) protein in the tear fluid is a potential biomarker of dry eye syndrome. PLoS One. 2012;7(12):e51979. 9. Postnikoff CK, Huisingh C, McGwin G, Nichols KK. Leukocyte Distribution in the Open Eye Tears of Normal and Dry Eye Subjects. Curr Eye Res. 2018. 10. Aluru SV, Shweta A, Bhaskar S, Geetha K, Sivakumar RM, Utpal T, et al. Tear Fluid Protein Changes in Dry Eye Syndrome Associated with Rheumatoid Arthritis: A Proteomic Approach. Ocul Surf. 2017;15(1):112-29. 11. Jung JH, Ji YW, Hwang HS, Oh JW, Kim HC, Lee HK, et al. Proteomic analysis of human lacrimal and tear fluid in dry eye disease. Sci Rep. 2017;7(1):13363.

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S1 dataset: Proteins identified in experiment 3 (2 peptides, no keratins)

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S2 dataset: details of the six enzyme categories

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S6 dataset: Proteins in common between tears and aqueous humor

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ACCEPTED MANUSCRIPT Highlights Tear fluid contains a high variety of proteins

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A lot of enzymes, more precisely hydrolases and transferases, were identified

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Pathways involved in different diseases can be greatly mapped in tears

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Combination of different studies is needed to establish a reliable tear

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proteome