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Article

The Constitutive Proteome of Human Aqueous Humor and Race Specific Alterations

1
Center for Biotechnology and Genomic Medicine, Augusta University, Augusta, GA 30912, USA
2
Department of Ophthalmology, Augusta University, Augusta, GA 30912, USA
3
Department of Population Health Sciences, Augusta University, Augusta, GA 30912, USA
*
Author to whom correspondence should be addressed.
Submission received: 15 October 2020 / Revised: 11 November 2020 / Accepted: 13 November 2020 / Published: 18 November 2020
(This article belongs to the Special Issue Human Proteomics)

Abstract

:
Aqueous humor (AH) is the fluid in the anterior and posterior chambers of the eye that contains proteins regulating ocular homeostasis. Analysis of aqueous humor proteome is challenging, mainly due to low sample volume and protein concentration. In this study, by utilizing state of the art technology, we performed Liquid-Chromatography Mass spectrometry (LC-MS/MS) analysis of 88 aqueous humor samples from subjects undergoing cataract surgery. A total of 2263 unique proteins were identified, which were sub-divided into four categories that were based on their detection in the number of samples: High (n = 152), Medium (n = 91), Low (n = 128), and Rare (n = 1892). A total of 243 proteins detected in at least 50% of the samples were considered as the constitutive proteome of human aqueous humor. The biological processes and pathways enriched in the AH proteins mainly include vesicle mediated transport, acute phase response signaling, LXR/RXR activation, complement system, and secretion. The enriched molecular functions are endopeptidase activity, and various binding functions, such as protein binding, lipid binding, and ion binding. Additionally, this study provides a novel insight into race specific differences in the AH proteome. A total of six proteins were upregulated, and five proteins were downregulated in African American subjects as compared to Caucasians.

1. Introduction

Aqueous humor (AH) is the fluid in the anterior and posterior chambers of the eye. It is produced by the non-pigmented ciliary body epithelium primarily through active transport of ions and solutes into the posterior chamber [1,2,3,4]. From the posterior chamber, the AH enters the anterior chamber via the lens and iris. After supporting the metabolic requirements of the avascular tissues of the anterior segment, the AH mainly exits the eye via the trabecular meshwork/Schlemm’s canal into the episcleral veins, known as conventional outflow. AH outflow also occurs via an alternative route through the ciliary muscle bundles into the supraciliary and suprachoroidal spaces, which is known as uveoscleral outflow [5].
AH is an integral component in many ocular health functions, including nutrient and oxygen supply, the removal of metabolic waste, ocular immunity, and ocular shape and refraction [6,7,8]. The dynamics of AH and the fine balance between production and drainage is essential in maintaining the physiological intraocular pressure (IOP) [2].
The major constituents of AH are water, electrolytes, organic solutes, cytokines, growth factors, and proteins [3,9,10,11]. The protein concentration in AH is in the range of 150 to 500 μg/mL [2]. Although proteins in AH are present in relatively low concentrations when compared to blood plasma, they are vital in the maintenance of anterior segment homeostasis [2,8,12,13,14,15,16,17,18,19]. Previous studies have shown significant alterations in several proteins in the AH obtained from eyes with glaucoma [12,13,14,15,20,21] and other eye disorders, including age-related macular degeneration [14,16,22,23,24,25].
Therefore, identifying the protein contents of AH is vital in understanding their physiological and pathological roles in the eye. However, given the low protein concentration and small volume, traditional low throughput approaches are not suitable for the proteomic analysis of AH. Liquid- chromatography Mass spectrometry (LC-MS/MS) has emerged as the analytical method of choice because of its high throughput nature, sensitivity, high dynamic range, and ability to identify complex mixtures even from small sample volumes [26,27]. However, many experimental and data-analytical hurdles exist, hampering the reliability and reproducibility of the data. In LC-MS/MS profiles, the proteins with high concentrations have a higher chance of detection, whereas the proteins with lower concentrations are detected only in a smaller percentage of samples due to random chance. It is exceedingly difficult to draw statistical conclusions for the proteins with a very low detection rate and should be excluded at the data analysis step. However, making such decisions that are based on the smaller sample set can lead to poor reproducibility. Therefore, a reference list of AH proteins detected reliably while using a larger sample set may be helpful in alleviating these concerns.
In this study, we identified the constitutive proteome of human aqueous humor, which may be useful as a reference for future studies, by utilizing a large sample set, state of the art technology, and revolutionary data analysis methods. Based on their abundance, the proteins were sub-divided into four (high, medium, low, and rare) categories. Interestingly, a comparison of African American and Caucasian subjects led us to the discovery of race-specific differences in the AH proteome, which are also presented in this study.

2. Materials and Methods

2.1. Human Subjects and Sample Collection

Aqueous humor samples were collected from 88 subjects undergoing cataract surgery at the Department of Ophthalmology, Medical College of Georgia at Augusta University. During these surgical procedures, a corneal incision is made, through which the aqueous humor fluid is evacuated from the anterior chamber and discarded. Instead of discarding, the AH samples were aspirated from the anterior chamber and collected in Eppendorf tubes. This method of sample collection is safe and efficient and it does not pose any risk to the subjects. The study was approved by the Institutional Review Board (IRB# 611480-13) at Augusta University, and written informed consent was obtained from all of the study participants. A chart review was conducted for all subjects to record their age, race, gender, smoking history, presence of systemic and ocular diseases, and IOP levels. Table 1 shows the characteristics of the study participants.

2.2. Aqueous Humor Sample Preparation

The aim of this study was to characterize all of the proteins present in the human aqueous humor and we did not utilize immunodepletion to remove abundant proteins. Aqueous humor samples (60 µL) were lyophilized and subsequently reconstituted in 30 µL of 8 M urea in 50 mM Tris-HCl (pH 8). 20 mM Dithiothreitol (DTT) was then added to the mixture in order to reduce cysteine residues, followed by alkylation with 55 mM iodoacetamide. 240 µL of 50 mM ammonium bicarbonate buffer was added in order to reduce urea concentration to below 1 M. Total protein concentration was measured while using a Bradford assay kit (Pierce, Rockford, IL, USA), according to the manufacturer’s instructions. The digestion of proteins was performed using a 1:20 ratio (w/w) of Trypsin (Pierce, Rockford, IL, USA) at 37 °C overnight. Figure 1 shows a schematic of the workflow involved in the AH sample preparation and proteomic quantification.

2.3. LC-MS/MS Analysis

The trypsin-digested samples were analyzed using an Orbitrap Fusion Tribrid mass spectrometer coupled with an Ultimate 3000 nano-UPLC system in order to perform in-depth proteomic characterization. Reconstituted peptides (6 μL) were trapped and washed on a Pepmap100 C18 trap at the rate of 20 μL/min using a gradient of 2% acetonitrile in water with 0.1% formic acid for 10 min. Subsequently, the peptide mixture was separated on a Pepmap100 RSCLC C18 column using a gradient of 2% to 40% acetonitrile with 0.1% formic acid for 120 min at a flow rate of 300 nL/min. Eluted peptides from the column were introduced into the Mass Spectrometer via nano-electrospray ionization source (temperature: 275 °C; spray voltage: 2000 V) and analyzed via data-dependent acquisition in positive mode. Orbitrap MS analyzer was used for precursor scan at 120,000 FWHM from 300 to 1500 m/z. MS/MS scans were taken while using an ion-trap MS analyzer in top speed mode (2-s cycle time) with dynamic exclusion settings (repeat count 1, repeat duration 15 s, and exclusion duration 30 s). Collision-induced dissociation (CID) was used as a fragmentation method with 30% normalized collision energy. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [28] partner repository with the dataset identifier PXD022463.

2.4. Protein Identification and Quantification

For the protein identification and quantification, raw MS data were processed using Proteome Discoverer software (ver 1.4; Thermo Scientific, Waltham, MA, USA) and then submitted for SequestHT search against the reviewed-manually annotated Uniprot- SwissProt human database with 20,385 entries. The following search parameters were used: 10 ppm precursor mass tolerance and 0.6 Da product ion tolerance; static carbidomethylation (+57.021 Da) for cysteine, dynamic oxidation (+15.995 Da) for methionine, and dynamic phosphorylation (+79.966 Da) for serine, threonine, and tyrosine. Proteins that contain similar peptides, which cannot be differentiated based on MS/MS analysis alone, were grouped in order to satisfy the principles of parsimony. A report comprising the identities and spectrum counts (number of peptide-spectrum match) for each protein was then exported as a semi-quantitative measure for relative protein levels that were detected in the AH sample. Figure 2 shows an example of LC-MS/MS analysis of one AH sample.

2.5. Statistical Analysis

The peptide-spectrum match (PSM) values from LC-MS/MS analysis were quantile normalized, and then log2 transformed to achieve normal distribution. For each protein, the detection rate (proportion of samples in which the protein was detected) was quantified. The proteins that were detected in a majority of samples (>50%) were examined in detail to see whether certain protein families were enriched in human AH. These commonly expressed proteins were also associated with gene ontology terms, including biological processes, cellular components, and molecular functions, using the “goana” function from “limma” (ver.3.40.6) R package. Adjusting for confounding variables, including age, sex and hypertension, differential expression analyses were performed using negative binomial regression, in order to discover differences in protein levels between African American and Caucasian subjects. The p-values were adjusted for multiple testing using the FDR method. All of the statistical analyses were performed using the R Project for Statistical Computing (version 3.5.1).

3. Results

3.1. Protein Content of the Human Aqueous Humor

A total of 2263 unique proteins were identified in 88 aqueous humor samples (Table S1). These proteins were divided into four categories that were based on their detection in the number of samples: High (n = 152; detected in >75% of samples), Medium (n = 91; detected in 50–75% of samples), Low (n = 128; detected in 25–50% of samples), and Rare (n = 1892, detected in <25% of samples) (Figure 3A). Figure 3B shows the sample-to-sample variation in the levels of these proteins (the coefficient of variation). The majority of proteins in the “High” group show low sample-to-sample variation, indicating the uniformity of expression across samples. As the mean expression decreases, the coefficient of variation increases from high to rare proteins. Table 2 shows a complete list of 152 proteins found in at least 75% of AH samples.

3.2. Major Protein Families Detected in the Human Aqueous Humor

Five major protein families were found to be enriched in human aqueous humor, including Immunoglobulins (61 proteins), Complement proteins (25 proteins), Apolipoproteins (12 proteins), Serine Protease Inhibitors (16 proteins), and Insulin Growth Factor family (10 proteins). Table 3 lists all of the members of these five protein families detected in AH.

3.3. Gene Ontology Enrichment Analysis

A total of 243 proteins that were detected in at least 50% of the samples were considered as the constitutive proteome of human aqueous humor. Gene ontology enrichment analysis was performed in order to discover the biological processes, cellular components, and molecular functions associated with the constitutive proteome (Figure 4). The top enriched categories among the biological processes include organonitrogen metabolic process (136 proteins), protein metabolic process (127 proteins), transport (112 proteins), and establishment of localization (112 proteins). The most enriched cellular components are extracellular region (190 proteins), organelle (182 proteins), vesicle (161 proteins), extracellular vesicle (148), and extracellular exosome (146 proteins). Furthermore, protein binding (166 proteins), ion binding (90 proteins), molecular function regulator (56 proteins), signaling receptor binding (48 proteins), and enzyme regulator activity (47 proteins) were the top enriched molecular functions.

3.4. Network and Pathway Analysis

Ingenuity Pathway Analysis (IPA) was used to discover the protein-protein interaction networks in the constitutive proteome (243 proteins) of human aqueous humor. Figure 5 presents the three top-scoring networks. Several members of the Apolipoprotein, Complement, and SERPIN families were part of the top-scoring network (Figure 5A). The second-highest scoring network consisted of 56 proteins, which are involved in tissue development, protein synthesis, and cellular compromise (Figure 5B). The third network includes several members of the Immunoglobulin and IGF families and other proteins that are involved in protein synthesis, humoral immune, and inflammatory responses (Figure 5C). IPA analysis also revealed that 21 canonical pathways were significantly enriched among the constitutive proteins observed in the AH (Table 4). The highly enriched canonical pathways include acute phase response signaling (40 proteins), LXR/RXR activation (33 proteins), FXR/RXR activation (32 proteins), clathrin-mediated endocytosis signaling (18 proteins), complement system (17 proteins), and coagulation system (14 proteins).

3.5. Aqueous Humor Proteins Associated with Race

Analyses were performed in order to discover race-specific differences in the AH proteome (differentially expressed in African Americans as compared to Caucasian subjects). A total of six proteins were upregulated and 5 proteins were downregulated in African American subjects (Table 5). Proteins significantly upregulated in African Americans subjects include Immunoglobulin kappa variable 1D-33 (IGKV1-33; FC = 2.191), Extracellular superoxide dismutase (SOD3; FC = 2.190), Complement C1r subcomponent (C1R; FC = 2.182), Complement Factor H (CFH; FC = 1.865), Alpha-2-macroglobulin (A2M; FC = 1.489), and Complement C3 (C3; FC = 1.289). The proteins significantly downregulated in African American subjects include Tetraspanin-14 (TSPAN14; FC = −2.089), Retinol-binding protein 4 (RBP4; FC = −1.753), Transthyretin (TTR; FC = −1.751), Ribonuclease pancreatic (RNASE1; FC = −1.636), and Prostaglandin D2 synthase (PTGDS; FC = −1.435). Figure 6 shows the boxplots depicting the distribution of these proteins in the African American and Caucasian subjects.

4. Discussion

This study provides the proteomic repertoire of human AH while using a larger sample set and highly sensitive mass spectrometry technology. The low abundant proteins have higher variation and poor reproducibility due to random nature of detection of proteins in mass spectrometry analysis. This study provides a reference AH proteome, which can be used in order to enhance the interpretation of results in future studies. We identified 243 proteins in at least 50% of samples, which we refer to as the constitutive proteome of human aqueous humor.
A comparison of our study with a previously published study by Chowdhury et. al. [2] revealed significant overlap in the proteins identified in human AH. We detected more than 79% of the 355 AH proteins that were identified in the previous study using nano-LC-ESI-MS/MS. Also, in the previous study, the samples were divided into three matched groups and 206 proteins were found in all three groups. A comparison of these 206 proteins with the constitutive AH proteome of our study (243 proteins) revealed >70% overlap [2].
Our comprehensive proteomic analysis revealed that five protein families are highly enriched in human aqueous humor, including apolipoproteins, complement proteins, immunoglobulins, IGF family proteins, and serine protease inhibitors (SERPINs). Apolipoproteins are proteins that bind and transport lipids in biological fluids. Seven apolipoproteins, including APOA1, APOA2, APOA4, APOC3, APOD, APOE, and APOH, were highly abundant, whereas five apolipoproteins APOB, APOC1, APOLD1, APOF, and APOL1 were detected in less than 25% of samples. Consistent to our findings, APOA1, APOA2, APOA4, APOD, APOE, and APOH were also identified in previous studies [2,29,30]. Several members of this family were part of the top scoring protein interaction network identified while using IPA analysis.
The anterior chamber is immune privileged and relies on AH to maintain a pathogen-free environment. Our analysis identified 25 complement proteins from both the classical and alternative pathways. Eleven complement proteins, including CFI, C4B, C6, C8A, and C9, were detected in more than 75% of the samples. Similar to the blood plasma, several members of the Immunoglobulin family of proteins were also identified in the AH. Immunoglobulins are involved in cell communication, defense response, and the regulation of metabolic processes. The presence of a wide array of immunoglobulins has been reported in previous studies indicating their existence in the AH of cataract and glaucoma patients [12,31,32].
Insulin-like growth factors and their binding proteins have been shown to play an important role in ocular functions. Ten IGF family proteins were identified in our analyses. Several IGFBPs in vitreous and aqueous humor have been previously reported [33]. However, the predominant serum carrier protein, IGFBP3, was present in less than 50% of AH samples, whereas IGFBP7 and IGFBP6 were highly abundant, indicating quantitative differences between the two fluids. IGFBP7 has been linked to hypertensive retinopathy and familial retinal macroaneurysms, indicating its role in retinal vascular pathology [34,35]. Furthermore, IGFBP7 was elevated in the AH of exudative age-related macular degenerative patients and is considered to be an anti-angiogenic agent in these patients [36].
The SERPIN family of proteins are ubiquitous in the body and their abnormalities are associated with ‘serpinopathies’. Ten proteins of this family, including SERPINC1, SERPINF1, SERPINA3, SERPINA1, SERPING1, SERPINF2, SERPIND1, SERPINA6, SERPINA4, and SERPINI1 were detected in at least 75% of samples. SERPINA3 is an acute phase response protein, which is involved in retinal angiogenesis and inflammation [37,38]. SERPINC1 or Antithrombin III deficiency has been associated with retinal vein occlusion, consistent with its role as an anti-clotting agent [39]. A decrease in SERPINF1 was associated with neovascularization and high myopia [40,41]. Overall, this glycoprotein is known to possess beneficial effects, such as potent anti-angiogenic, anti-thrombotic, anti-tumorigenic, anti-inflammatory, and neuroprotective properties [40,41,42,43,44,45,46,47]. SERPING1, which is the largest member of the superfamily, has been associated with suppressing inflammatory conditions, fibrinolysis and blood coagulation [48,49].
Interestingly, after adjusting for confounding variables, including age, sex, and hypertension, we found 11 proteins that were differentially expressed between African American and Caucasian subjects, indicating race-specific differences in the AH proteome. Overall, six proteins were significantly upregulated, while five proteins were downregulated in African American subjects. Proteins related to immune responses such as IGKV1D-33, C6, C8A were present at elevated levels in African American subjects. Among the downregulated proteins, TSPAN-14 was at least two-fold lower in African Americans. A member of this family, TSPAN-12, was discovered as a therapeutic target for retinal vascular diseases, such as age-related macular degeneration and diabetic retinopathy [50]. Three other vision-related proteins, including RBP4, TTR, and PTGDS, were present in lower levels in the African American population. RBP4, a retinol transporter protein, is known to be involved in congenital eye disease [51]. TTR is a transport protein, which carries retinol-binding protein and is essential for the maintenance of photoreceptors, visual cycle, and perception [52]. PTGDS is a secretory retinoid transporter that is involved in the maintenance of the blood-retinal barrier. The difference in the vision-related proteins might be one of the contributing factors for increased risk of eye-related ailments in the African American population.

5. Conclusions

In conclusion, this study characterized the human aqueous humor proteome using the latest technology and a larger sample set. A total of 243 proteins, which were detected in at least half of the samples, were considered to be the constitutive proteome of human aqueous humor. Five protein families were highly enriched in the human aqueous humor proteome. Eleven proteins were significantly altered between African American and Caucasian subjects, indicating race-specific differences. The highly abundant aqueous humor proteins are involved in immune-mediated responses, transport, metabolism, and binding. The reliable characterization of the aqueous humor proteome will provide new insights into the factors that govern anterior segment homeostasis and aid in biomarker discovery in various eye disorders.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/2227-7382/8/4/34/s1, Table S1: Complete list of proteins detected in 88 aqueous humor samples.

Author Contributions

Conceptualization, A.S., S.K.K., S.S.; methodology, S.K.K., W.Z., L.C.; software, A.S., T.J.L.; formal analysis, T.J.L., S.K.K.; resources, L.U., A.E., K.B., D.B., A.S., S.S.; data curation, L.C., S.K.K.; writing—original draft preparation, S.K.K.; writing—review and editing, A.S., S.S., L.U.; visualization, T.J.L., L.C.; supervision, A.S.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported by the National Institutes of Health, National Eye Institute (Bethesda, MD, USA) grant# R01 EY029728 awarded to Ashok Sharma and grant# P30 EY031631 Center Core Grant for Vision Research.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Liquid chromatography/Mass Spectrometry (LC-MS/MS) workflow for proteomic analysis of human aqueous humor. Samples were digested using trypsin and were analyzed using an Orbitrap Fusion Tribrid mass spectrometer coupled with an Ultimate 3000 nano-UPLC system. Proteins were identified and quantified using Proteome Discoverer (ver 1.4; Thermo Scientific, Waltham, MA, USA) followed by statistical analysis using the R Project for statistical computing (https://www.r-project.org/).
Figure 1. Liquid chromatography/Mass Spectrometry (LC-MS/MS) workflow for proteomic analysis of human aqueous humor. Samples were digested using trypsin and were analyzed using an Orbitrap Fusion Tribrid mass spectrometer coupled with an Ultimate 3000 nano-UPLC system. Proteins were identified and quantified using Proteome Discoverer (ver 1.4; Thermo Scientific, Waltham, MA, USA) followed by statistical analysis using the R Project for statistical computing (https://www.r-project.org/).
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Figure 2. Example of LC-MS/MS analysis of human aqueous humor sample. (A) LC-MS/MS total ion current chromatogram. The retention time (RT) elution of one reporter peptide indicative of A2M protein is marked for illustration purposes. (B) MS spectra of selected precursor peptide 444.78 m/z with a distinct isotopic pattern benefitted from the high resolution of the Orbitrap MS analyzer. (C) MS/MS spectra using collision-induced dissociation (CID) fragmentation of A2M (444.78 m/z) precursor peptide, colored peaks (red for b ions and blue for y ions) indicate matches between experimental and theoretical/calculated values.
Figure 2. Example of LC-MS/MS analysis of human aqueous humor sample. (A) LC-MS/MS total ion current chromatogram. The retention time (RT) elution of one reporter peptide indicative of A2M protein is marked for illustration purposes. (B) MS spectra of selected precursor peptide 444.78 m/z with a distinct isotopic pattern benefitted from the high resolution of the Orbitrap MS analyzer. (C) MS/MS spectra using collision-induced dissociation (CID) fragmentation of A2M (444.78 m/z) precursor peptide, colored peaks (red for b ions and blue for y ions) indicate matches between experimental and theoretical/calculated values.
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Figure 3. Distribution of the mean values (A) and coefficient of variation (B) of proteins detected in the human aqueous humor samples. The proteins were subdivided into four categories, based on their detection rate. High: detected in >75%; Medium: detected in 50–75%; Low: detected in 25–50%; Rare: detected in <25% of the samples. Coefficient of variation decreases as mean protein expression increases.
Figure 3. Distribution of the mean values (A) and coefficient of variation (B) of proteins detected in the human aqueous humor samples. The proteins were subdivided into four categories, based on their detection rate. High: detected in >75%; Medium: detected in 50–75%; Low: detected in 25–50%; Rare: detected in <25% of the samples. Coefficient of variation decreases as mean protein expression increases.
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Figure 4. Biological processes (A), cellular components (B), and molecular functions (C) associated with the highly abundant aqueous humor proteins (detected in >50% samples). Bioinformatics analysis was performed in order to associate significantly enriched Gene Ontology (GO) terms to the constitutive aqueous humor proteome. The horizontal bars represent the number of proteins annotated to each GO term, and the black lines represent the p-value of enrichment.
Figure 4. Biological processes (A), cellular components (B), and molecular functions (C) associated with the highly abundant aqueous humor proteins (detected in >50% samples). Bioinformatics analysis was performed in order to associate significantly enriched Gene Ontology (GO) terms to the constitutive aqueous humor proteome. The horizontal bars represent the number of proteins annotated to each GO term, and the black lines represent the p-value of enrichment.
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Figure 5. Three top-scoring interaction networks of highly abundant aqueous humor proteins. Ingenuity Pathway Analysis (IPA) was performed on the 243 proteins detected in at least half of the aqueous humor samples. (A) Network 1: includes several members of the Apolipoprotein, Complement, and SERPIN families. (B) Network 2: Network of proteins involved in tissue development, protein synthesis, and cellular compromise. (C) Network 3: Protein cluster associated with humoral immune response, inflammatory response, and protein synthesis. Each protein is represented as a node, and edges represent interactions between proteins. The intensity of color represents the relative levels of proteins (brighter red nodes indicate higher levels). Proteins are separated based on the cellular compartments.
Figure 5. Three top-scoring interaction networks of highly abundant aqueous humor proteins. Ingenuity Pathway Analysis (IPA) was performed on the 243 proteins detected in at least half of the aqueous humor samples. (A) Network 1: includes several members of the Apolipoprotein, Complement, and SERPIN families. (B) Network 2: Network of proteins involved in tissue development, protein synthesis, and cellular compromise. (C) Network 3: Protein cluster associated with humoral immune response, inflammatory response, and protein synthesis. Each protein is represented as a node, and edges represent interactions between proteins. The intensity of color represents the relative levels of proteins (brighter red nodes indicate higher levels). Proteins are separated based on the cellular compartments.
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Figure 6. Race-specific differences in human aqueous humor proteins. A total of six proteins were upregulated (A) and five proteins were downregulated (B) in African American subjects as compared to Caucasian subjects.
Figure 6. Race-specific differences in human aqueous humor proteins. A total of six proteins were upregulated (A) and five proteins were downregulated (B) in African American subjects as compared to Caucasian subjects.
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Table 1. Characteristics of study participants.
Table 1. Characteristics of study participants.
CharacteristicsCount
Subjects, (n)88
Sex: F/M55/33
Age (years)67.0 ± 9.56
Race: AA/Caucasian66/22
Hypertension, N/Y41/47
Smoking, N/Y56/32
Cardiovascular disease, N/Y81/7
Cerebrovascular disease, N/Y87/1
Collagen vascular disease, N/Y75/13
Intraocular Pressure (IOP)19.5 ± 7.01
Table 2. Most abundant proteins present in the human aqueous humor.
Table 2. Most abundant proteins present in the human aqueous humor.
Uniprot IDGene SymbolDescriptionDetected in Proportion of Samples (%)Mean PSM Value
P02768ALBAlbumin100.004202.61
P02787TFSerotransferrin100.00765.31
P01024C3Complement C3100.00255.00
P01009SERPINA1Alpha-1-antitrypsin100.00233.24
P02790HPXHemopexin100.00176.64
P01859IGHG2Immunoglobulin heavy constant gamma 2100.00168.92
P10745RBP3Retinol-binding protein 3100.00166.25
P00450CPCeruloplasmin100.00165.20
P01834IGKCImmunoglobulin kappa constant100.00161.42
P02766TTRTransthyretin100.00156.30
P41222PTGDSProstaglandin-H2 D-isomerase100.00142.25
P36955SERPINF1Pigment epithelium-derived factor100.00141.33
P01860IGHG3Immunoglobulin heavy constant gamma 3100.00127.65
P02763ORM1Alpha-1-acid glycoprotein 1100.00119.16
P02774GCVitamin D-binding protein100.00115.89
P0DOX7N/AImmunoglobulin kappa light chain100.00108.22
P10909CLUClusterin100.00102.33
P02647APOA1Apolipoprotein A-I100.0096.78
P01034CST3Cystatin-C100.0082.73
P02765AHSGAlpha-2-HS-glycoprotein100.0073.27
P01023A2MAlpha-2-macroglobulin100.0072.77
P19652ORM2Alpha-1-acid glycoprotein 2100.0068.70
P06396GSNGelsolin100.0062.66
P01008SERPINC1Antithrombin-III100.0062.65
P02749APOHBeta-2-glycoprotein 1100.0059.09
P04217A1BGAlpha-1B-glycoprotein100.0059.04
P22352GPX3Glutathione peroxidase 3100.0058.23
Q9UBP4DKK3Dickkopf-related protein 3100.0056.91
P01011SERPINA3Alpha-1-antichymotrypsin100.0053.15
P01876IGHA1Immunoglobulin heavy constant alpha 1100.0051.83
Q12805EFEMP1EGF-containing fibulin-like extracellular matrix protein 1100.0050.79
P00751CFBComplement factor B100.0050.35
Q13822ENPP2Ectonucleotide pyrophosphatase/phosphodiesterase family member 2100.0045.77
P00747PLGPlasminogen100.0045.45
Q9UBM4OPTCOpticin100.0043.07
P07339CTSDCathepsin D100.0039.10
P00734F2Prothrombin100.0038.21
P05155SERPING1Plasma protease C1 inhibitor100.0035.94
P10451SPP1Osteopontin100.0035.94
P06727APOA4Apolipoprotein A-IV100.0033.93
P02649APOEApolipoprotein E100.0032.43
P01042KNG1Kininogen-1100.0030.10
P04196HRGHistidine-rich glycoprotein100.0029.90
P25311AZGP1Zinc-alpha-2-glycoprotein100.0029.67
P07998RNASE1Ribonuclease pancreatic100.0029.30
O94985CLSTN1Calsyntenin-1100.0027.54
P01019AGTAngiotensinogen100.0026.00
P02760AMBPProtein AMBP100.0024.23
P02652APOA2Apolipoprotein A-II100.0020.83
P36222CHI3L1Chitinase-3-like protein 1100.0020.03
P23142FBLN1Fibulin-1100.0019.91
P02750LRG1Leucine-rich alpha-2-glycoprotein100.0018.87
P05156CFIComplement factor I100.0018.82
P02753RBP4Retinol-binding protein 4100.0017.09
P04004VTNVitronectin100.0016.58
Q16270IGFBP7Insulin-like growth factor-binding protein 7100.0015.10
P51884LUMLumican100.0013.89
P00746CFDComplement factor D100.0013.10
P05090APODApolipoprotein D100.0012.66
O43505B4GAT1Beta-1,4-glucuronyltransferase 1100.0012.09
P24592IGFBP6Insulin-like growth factor-binding protein 6100.0011.28
Q96PD5PGLYRP2N-acetylmuramoyl-L-alanine amidase100.0011.19
Q14515SPARCL1SPARC-like protein 1100.0011.01
P61916NPC2NPC intracellular cholesterol transporter 2100.009.57
Q99969RARRES2Retinoic acid receptor responder protein 2100.008.03
P61769B2MBeta-2-microglobulin100.007.77
Q99714HSD17B103-hydroxyacyl-CoA dehydrogenase type-2100.007.61
Q06481APLP2Amyloid-like protein 298.8630.95
P43652AFMAfamin98.8617.18
Q8IZJ3CPAMD8C3 and PZP-like alpha-2-macroglobulin domain-containing protein 898.8617.14
Q14767LTBP2Latent-transforming growth factor beta-binding protein 298.8615.82
Q15582TGFBITransforming growth factor-beta-induced protein ig-h398.8614.68
Q9Y5W5WIF1Wnt inhibitory factor 198.8614.39
Q08629SPOCK1Testican-198.868.93
P07602PSAPProsaposin98.867.48
Q08380LGALS3BPGalectin-3-binding protein98.866.67
P00748F12Coagulation factor XII98.866.06
P0DOY2IGLC2Immunoglobulin lambda constant 297.7375.13
P16870CPECarboxypeptidase E97.7318.17
Q9HCB6SPON1Spondin-197.7310.63
P06312IGKV4-1Immunoglobulin kappa variable 4-197.736.06
P51888PRELPProlargin97.735.19
P0DOX8N/AImmunoglobulin lambda-1 light chain96.5964.10
P00738HPHaptoglobin96.5943.35
P08603CFHComplement factor H96.5920.12
Q8NG11TSPAN14Tetraspanin-1496.5917.09
Q14624ITIH4Inter-alpha-trypsin inhibitor heavy chain H496.5914.89
Q99972MYOCMyocilin96.598.76
Q9NQ79CRTAC1Cartilage acidic protein 196.598.24
P01861IGHG4Immunoglobulin heavy constant gamma 495.4599.95
P35555FBN1Fibrillin-195.4512.27
Q9BSG5RTBDNRetbindin95.456.64
P05452CLEC3BTetranectin95.456.41
Q92520FAM3CProtein FAM3C95.455.67
Q86UP8GTF2IRD2General transcription factor II-I repeat domain-containing protein 2A95.455.51
P02748C9Complement component C994.3213.93
O14773TPP1Tripeptidyl-peptidase 194.325.24
P01033TIMP1Metalloproteinase inhibitor 194.323.64
Q15113PCOLCEProcollagen C-endopeptidase enhancer 194.323.45
P01700IGLV1-47Immunoglobulin lambda variable 1-4793.184.61
P0C0L5C4BComplement C4-B92.05124.58
P08697SERPINF2Alpha-2-antiplasmin92.059.18
A0A0C4DH25IGKV3D-20Immunoglobulin kappa variable 3D-2092.056.28
P10643C7Complement component C792.054.08
P19022CDH2Cadherin-292.053.83
P19823ITIH2Inter-alpha-trypsin inhibitor heavy chain H290.9110.06
Q5T3U5ABCC10Multidrug resistance-associated protein 790.915.73
P51693APLP1Amyloid-like protein 189.774.08
P07357C8AComplement component C8 alpha chain89.773.88
P20273CD22B-cell receptor CD2288.6415.02
P98160HSPG2Basement membrane-specific heparan sulfate proteoglycan core protein88.645.82
Q5T8P6RBM26RNA-binding protein 2688.644.68
Q9Y287ITM2BIntegral membrane protein 2B88.643.43
P39060COL18A1Collagen alpha-1(XVIII) chain88.642.36
P18065IGFBP2Insulin-like growth factor-binding protein 287.503.78
P10645CHGAChromogranin-A87.503.71
P05067APPAmyloid-beta precursor protein86.3610.08
P08294SOD3Extracellular superoxide dismutase [Cu-Zn]86.365.86
Q92765FRZBSecreted frizzled-related protein 386.364.79
Q96S96PEBP4Phosphatidylethanolamine-binding protein 486.363.76
P01780IGHV3-7Immunoglobulin heavy variable 3-785.234.97
Q9HCJ0TNRC6CTrinucleotide repeat-containing gene 6C protein85.233.70
O15240VGFNeurosecretory protein VGF85.232.74
P02675FGBFibrinogen beta chain84.096.75
P55083MFAP4Microfibril-associated glycoprotein 484.092.81
P22914CRYGSGamma-crystallin S82.956.40
P00441SOD1Superoxide dismutase [Cu-Zn]82.952.84
P34096RNASE4Ribonuclease 482.951.81
Q9Y6R7FCGBPIgGFc-binding protein81.826.89
O75326SEMA7ASemaphorin-7A81.825.49
P00736C1RComplement C1r subcomponent81.824.49
Q9BZV3IMPG2Interphotoreceptor matrix proteoglycan 280.683.07
A0A087WSY6IGKV3D-15Immunoglobulin kappa variable 3D-1580.682.17
P61626LYZLysozyme C79.554.83
P43251BTDBiotinidase79.552.71
P06733ENO1Alpha-enolase78.4112.05
P29622SERPINA4Kallistatin78.415.03
P05546SERPIND1Heparin cofactor 278.415.03
P08185SERPINA6Corticosteroid-binding globulin78.413.86
Q7Z7G0ABI3BPTarget of Nesh-SH378.412.72
P04406GAPDHGlyceraldehyde-3-phosphate dehydrogenase77.275.63
P01593IGKV1D-33Immunoglobulin kappa variable 1D-3377.272.93
P02679FGGFibrinogen gamma chain76.144.92
Q66K66TMEM198Transmembrane protein 19876.144.40
P02656APOC3Apolipoprotein C-III76.143.19
P13671C6Complement component C676.142.96
Q99574SERPINI1Neuroserpin76.142.38
P04264KRT1Keratin, type II cytoskeletal 175.0036.86
Q9UHG2PCSK1NProSAAS75.003.28
P08571CD14Monocyte differentiation antigen CD1475.002.48
Q86UD1OAFOut at first protein homolog75.002.12
P98164LRP2Low-density lipoprotein receptor-related protein 275.001.84
Table 3. Five most abundant protein families in the human aqueous humor.
Table 3. Five most abundant protein families in the human aqueous humor.
FamilyLevelProteins
ApolipoproteinsHighAPOA1APOA2APOA4APOC3APOD
APOEAPOH
Medium
Low
RareAPOBAPOC1APOFAPOL1APOLD1
Complement ProteinsHighC1RC3C4BC6C7
C8AC9CFBCFDCFH
CFI
MediumC1SC2C4AC5C8B
C8G
LowC1QCCFHR1CFHR2
RareC1QBC1QTNF6C1QTNF7C1RLCD55
ImmunoglobulinsHighIGHA1IGHG2IGHG3IGHG4IGHV3-7
IGKCIGKV1D-33IGKV3D-15IGKV3D-20IGKV4-1
IGLC2IGLV1-47
MediumIGHV3-49IGHV5-51IGHV6-1IGKV1-8IGKV2-28
IGLV1-40IGLV3-9
LowIGHG1IGHMIGHV3-66IGHV4-59IGKV1-6
IGKV1D-13IGKV3D-11IGLV6-57
RareIGDCC4IGHA2IGHV1-2IGHV1-3IGHV1-18
IGHV1-46IGHV1-69IGHV2-26IGHV2-70DIGHV3-9
IGHV3-15IGHV3-64DIGHV3-72IGHV3-74IGKV1-5
IGKV1-16IGKV1-17IGKV1-27IGKV3-11IGKV3-20
IGLV1-44IGLV1-51IGLV2-11IGLV2-14IGLV2-18
IGLV3-10IGLV3-19IGLV3-21IGSF10IGSF21
IGSF22ILDR1ISLRJCHAIN
Insulin Growth Factor (IGF) FamilyHighIGFBP2IGFBP6IGFBP7
MediumIGF2IGFBP5
LowIGFALSIGFBP3
RareIGF1RIGF2BP1IGFBP4
Serine Protease Inhibitors (SERPINs)HighSERPINA1SERPINA3SERPINA4SERPINA6SERPINC1
SERPIND1SERPINF1SERPINF2SERPING1SERPINI1
MediumSERPINA5SERPINA7
Low
RareSERPINB3SERPINB13SERPINE3SERPINH1
Table 4. Canonical pathways enriched in the constitutive aqueous humor proteome.
Table 4. Canonical pathways enriched in the constitutive aqueous humor proteome.
Canonical Pathwayp-Value# of Proteins
Acute Phase Response Signaling5.01 × 10−4240
LXR/RXR Activation5.01 × 10−3833
FXR/RXR Activation1.00 × 10−3532
Complement System1.00 × 10−2417
Coagulation System2.00 × 10−1914
Clathrin-mediated Endocytosis Signaling1.58 × 10−1218
Atherosclerosis Signaling3.98 × 10−1215
IL-12 Signaling and Production in Macrophages1.00 × 10−1014
Extrinsic Prothrombin Activation Pathway1.15 × 10−107
Intrinsic Prothrombin Activation Pathway3.55 × 10−109
Production of Nitric Oxide and Reactive Oxygen Species in Macrophages1.02 × 10−814
Hepatic Fibrosis/Hepatic Stellate Cell Activation6.92 × 10−813
Maturity Onset Diabetes of Young (MODY) Signaling1.74 × 10−78
Airway Pathology in Chronic Obstructive Pulmonary Disease3.63 × 10−69
Systemic Lupus Erythematosus Signaling4.68 × 10−612
Neuroprotective Role of THOP1 in Alzheimer’s Disease2.63 × 10−58
GP6 Signaling Pathway2.29 × 10−47
Iron homeostasis signaling pathway5.37 × 10−47
Actin Cytoskeleton Signaling0.0028
Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis0.0168
Glucocorticoid Receptor Signaling0.02110
Table 5. Proteins with significant differences between African American and Caucasian subjects.
Table 5. Proteins with significant differences between African American and Caucasian subjects.
UniProt IDGene SymbolDescriptionFold ChangeAdj. p-ValuePathway
Upregulated in African American subjects
P01593IGKV1D-33Immunoglobulin kappa variable 1D-332.1910.024Complement activation
P08294SOD3Extracellular superoxide dismutase [Cu-Zn]2.1900.017Antioxidant, Heparin binding
P00736C1RComplement C1r subcomponent2.1820.014Complement activation, classical pathway
P08603CFHComplement factor H1.8650.021Complement activation, alternative pathway
P01023A2MAlpha-2-macroglobulin1.4890.047Blood coagulation
P01024C3Complement C31.2890.047Endopeptidase inhibitor activity
Downregulated in African American subjects
Q8NG11TSPAN14Tetraspanin-14−2.0890.005Cellular protein metabolic process
P02753RBP4Retinol-binding protein 4−1.7530.002Retinal and retinol binding
P02766TTRTransthyretin−1.751<0.001Hormone activity
P07998RNASE1Ribonuclease pancreatic−1.6360.002Nucleic acid binding
P41222PTGDSProstaglandin-H2 D-isomerase−1.4350.002Fatty acid binding
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Kodeboyina, S.K.; Lee, T.J.; Churchwell, L.; Ulrich, L.; Bollinger, K.; Bogorad, D.; Estes, A.; Zhi, W.; Sharma, S.; Sharma, A. The Constitutive Proteome of Human Aqueous Humor and Race Specific Alterations. Proteomes 2020, 8, 34. https://0-doi-org.brum.beds.ac.uk/10.3390/proteomes8040034

AMA Style

Kodeboyina SK, Lee TJ, Churchwell L, Ulrich L, Bollinger K, Bogorad D, Estes A, Zhi W, Sharma S, Sharma A. The Constitutive Proteome of Human Aqueous Humor and Race Specific Alterations. Proteomes. 2020; 8(4):34. https://0-doi-org.brum.beds.ac.uk/10.3390/proteomes8040034

Chicago/Turabian Style

Kodeboyina, Sai Karthik, Tae Jin Lee, Lara Churchwell, Lane Ulrich, Kathryn Bollinger, David Bogorad, Amy Estes, Wenbo Zhi, Shruti Sharma, and Ashok Sharma. 2020. "The Constitutive Proteome of Human Aqueous Humor and Race Specific Alterations" Proteomes 8, no. 4: 34. https://0-doi-org.brum.beds.ac.uk/10.3390/proteomes8040034

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