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Integrated tear proteome and metabolome reveal panels of inflammatoryrelated molecules via key regulatory pathways in dry eye syndrome Xueli Chen, Jun Rao, Zhi Zheng, Yan Yu, Shang Lou, Liping Liu, Qinsi He, Luhua Wu, and Xinghuai Sun J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.9b00149 • Publication Date (Web): 09 Apr 2019 Downloaded from http://pubs.acs.org on April 9, 2019

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Journal of Proteome Research

Integrated tear proteome and metabolome reveal panels of inflammatory-related molecules via key regulatory pathways in dry eye syndrome Xueli Chen1,†, Jun Rao2,†, Zhi Zheng2,†, Yan Yu2, Shang Lou2, Liping Liu2, Qinsi He2, Luhua Wu3,*, Xinghuai Sun1,* 1Department

of Ophthalmology & Visual Science, Eye & ENT Hospital, Shanghai

Medical College, Fudan University, Shanghai, China 2Jiangxi

Provincial Key Laboratory of Translational Medicine and Oncology, Jiangxi

Cancer Hospital, Jiangxi Cancer Center, Nanchang, 330029, People’s Republic of China 3Department

of Ophthalmology, The Third Affiliated Hospital of Beijing University

of Chinese Medicine, Beijing 100029, China †These authors contributed equally to this work. *Corresponding Authors: Xinghuai Sun: [email protected] Luhua Wu: [email protected]

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Abstract Dry eye syndrome (DES) is a growing public health concern with a high global prevalence; however, the fundamental processes involved in its pathogenic mechanisms remain poorly understood. In the present study, we applied nanoscale liquid chromatography and quadrupole time-of-flight tandem mass spectrometry (nanoLC/Q-TOF-MS/MS) and ultra-performance LC/Q-TOF-MS/MS technologies on tear samples obtained from 18 dry eye patients and 19 healthy controls for integrated proteomic and metabolomic analyses. Overall, 1,031 tear proteins were detected, while 190 proteins were determined to be significantly expressed in dry eye patients. Further functional analysis suggested various biological processes were highly expressed and involved in the pathogenesis of DES, especially immune and inflammatory processes. Totally, 156 named metabolites were identified, among which 34 were found to be significantly changed in dry eye patients. The results highlighted the key elements, especially inflammatory-related proteins and metabolites that played important roles in the development of DES. Further, the regulatory roles of primary pathways, including complement and coagulation cascades, glycolysis/gluconeogenesis, and amino acid metabolism, were also identified as processes involved in DES. Collectively, our work not only provided insight into the potential biomarkers of DES for diagnostic and prognostic purposes, but extended our knowledge of the physiopathology of this syndrome. Keywords: dry eye; metabolomics; proteomics; SWATH-MS; inflammatory

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Introduction Dry eye syndrome (DES), also referred to as keratoconjunctivitis sicca, is a multifactorial disorder of the tears and ocular surface, resulting in ocular discomfort and visual disturbance.1 Other common symptoms of DES include conjunctival redness, burning/stinging, foreign-body sensation, ocular fatigue, and blurred vision, which can significantly impair patients’ quality of life and increase the economic burden for both patients and society. Generally, the diagnosis of DES is based on questionnaire, ocular surface staining, non-invasive tear film breakup time (TFBUT) assessments, and osmolality measurements.1 Supplementary diagnostic procedures are employed for subtype classification by means of the Schirmer test and meibomian gland examination. However, the diagnosis and treatment of DES are difficult due to the poor correlation between reported symptoms and clinical signs.2 To date, significant work has been done to understand the molecular changes underlying the pathogenesis of DES and to identify the potential biomarkers that can facilitate earlier diagnosis and the initiation of novel therapies. For example, both tear osmolarity and matrix metalloproteinase (MMP)-9 were reported to be biomarkers for DES; these play important roles in inflammatory processes.3 Recently, advanced high-throughput omics approaches (genomics, transcriptomics, proteomics, and metabolomics) have dramatically expanded our understanding of complex diseases. For instance, proteomics, which enables the simultaneous study of thousands of proteins and/or peptides in biological samples, shows great potential for its application in large-scale clinical ophthalmology studies. Kliuchnikova et al. (2016) 3



employed LC-high resolution MS analysis to perform proteomic analysis of 29 human aqueous humor (AH) samples from patients with cataracts and glaucoma, with and without pseudoexfoliation syndrome.4 The results revealed no significant difference between glaucoma and cataract AH proteomes, and apolipoprotein D was determined to be a putative biomarker of pseudoexfoliation syndrome. Another high-throughput and powerful analysis method is metabolomics, which aims to simultaneously analyze all metabolites in a biological sample; it holds great potential for determining the biomarkers and functional pathways of various diseases, thus leading to novel therapy development.5 Our previous study used GC-MS for metabolomic analysis to identify the potential biomarkers associated with high myopia. A total of 242 metabolites were identified in 40 AH samples from patients with high myopia and controls. Of the total humor samples analyzed, 29 demonstrated significantly changed metabolites, and the regulatory aspects of their metabolic pathways were further determined to be key regulatory elements or pathways involved in the development of high myopia. Moreover, Vehof et al. (2017) used gas chromatography and LC coupled with MS (Metabolon Inc., Durham, NC, USA) to profile serum samples from 2,819 individuals.6 The results indicated strong associations between all 5 androgens and DES, which were found in all 222 detected metabolites; the authors thus proposed 1-palmitoylglycerophosphocholine as a potential biomarker. Previous proteomics and metabolomics studies have typically been performed on ocular fluids, including the vitreous humor, AH, and tears.7 Vitreous humor is the fluid found closest to sites of damage, accounting for over 80% of the eyeball volume. 4


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The profiling of vitreous humor has been intensively analyzed in glaucoma, age-related macular degeneration (AMD), diabetic retinopathy, and following cataract surgery; this method aims to identify disease-associated biomarkers.8 Another important intraocular fluid, AH, is generated by the ciliary epithelium, supplying nutrients and removing metabolic wastes from avascular tissues in the eye.5 Significant correlations have been demonstrated between alterations within AH and the prognosis of certain eye diseases, such as wet AMD, glaucoma, and myopia. A series of proteins and metabolites were identified to play crucial roles in regulating homeostasis in the eye and may act as potential biomarkers for early diagnosis of wet AMD.9 Additionally, tear fluid is composed of proteins, carbohydrates, lipids, electrolytes, and some small organic molecules, which play numerous roles, such as protecting and maintaining the ocular surface.10 More importantly, defective tear quality and quantity an lead to dry eye; in this way, tear fluids serve as a valuable source for establishing disease biomarkers, which is of increased interest for many researchers, as they aim to characterize the proteomic and metabolomic changes in tears, especially in patients with dry eye.10-14 In the present study, we took advantage of non-targeted technology to perform integrated

proteomic

(nanoLC/Q-TOF-MS/MS)

and

metabolomic

(UPLC/Q-TOF-MS/MS) analyses on tear samples obtained from 18 patients with dry eye and from 19 normal individuals, who served as controls. Our objectives were to determine the key regulatory elements (proteins or metabolites) and functional pathways involved in the pathogenesis of DES, which may provide additional 5



potential biomarkers and new therapeutic strategies for this syndrome.

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Materials and Methods Sample collection Thirty-seven subjects were recruited in this study, including 18 patients with DES and 19 normal individuals, who served as controls (Table 1). The ophthalmic examination included patients’ subjective symptoms, visual acuity, a Schirmer I test (SIT) with anesthesia (basis secretory test [BST]), and tear breakup time (BUT). Each patient was asked to report their subjective symptoms, such as burning, itching, foreign-body sensation, dryness, and photophobia. Patients were classified as having dry eye if they had the aforementioned symptoms, in addition to abnormalities of test dynamics, as determined by SIT (90%, FDR < 1%, excluded shared peptides, and Ion Library Mass Tolerance 100 ppm. The quantification data was normalized by the median.

Data analysis All the raw mass spectrometry data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD012917. Metabolomics data were normalized and analyzed, as in our previous reports.5 The normalization step was performed by registering the median level of each compound equal to one (1.00), and missing values (if any) were imputed with the observed minimum. Moreover, Mev 11



(MultiExperiment Viewer) 4.8 software was used for hierarchical cluster analysis, while SIMCA-P software (v13.0; Umetrics, Malmö, Sweden) was employed for both principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). An independent t-test (SPSS 17.0 software; IBM Corporation, Armonk, NY, USA) was also used to identify significantly different metabolites. For proteomic data analysis, a significantly changed protein was defined as a fold-change ≥1.5 or ≤0.67 with a q-value ≤0.05. The categories of identified proteins were determined according to the online PANTHER (protein annotation through evolutionary relationship) classification system (www.pantherdb.org). Additionally, a multi-omics data analysis tool, OmicsBean (http://www.omicsbean.com), was employed here for bioinformatics analysis, which included Gene Ontology (GO) enrichment analysis, KEGG pathway enrichment analysis, and protein-protein interaction network analysis. P-values

Integrated Tear Proteome and Metabolome Reveal Panels of

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