Journal of
Clinical Medicine
Article
Serum Autoantibodies in Patients with Dry and Wet
Age-Related Macular Degeneration
Christina A. Korb * , Sabine Beck, Dominik Wolters, Katrin Lorenz , Norbert Pfeiffer and Franz H. Grus
Department of Ophthalmology, University Medical Center, Johannes Gutenberg-University, 55131 Mainz, Germany
* Correspondence: christina.korb@unimedizin-mainz.de; Tel.: +49-6131-175741
Abstract: Background: To assess the serum autoantibody profile in patients with dry and exudative
age-related macular degeneration compared with healthy volunteers to detect potential biomarkers,
e.g., markers for progression of the disease. Materials and Methods: IgG Immunoreactivities were
compared in patients suffering from dry age-related macular degeneration (AMD) (n = 20), patients
with treatment-naive exudative AMD (n = 29) and healthy volunteers (n = 21). Serum was analysed
by customized antigen microarrays containing 61 antigens. The statistical analysis was performed
by univariate and multivariate analysis of variance, predictive data-mining methods and artificial
neuronal networks were used to detect specific autoantibody patterns. Results: The immunoreactiv-
ities of dry and wet AMD patients were significantly different from each other and from controls.
One of the most prominently changed reactivity was against alpha-synuclein (p ≤ 0.0034), which is
known from other neurodegenerative diseases. Furthermore, reactivities against glyceraldehyde-
3-phosphat-dehydrogenase (p ≤ 0.031) and Annexin V (p ≤ 0.034), which performs a major role in
apoptotic processes, were significantly changed. Some immunoreacitvities were antithetic regulated
in wet and dry-AMD, such as Vesicle transport-related protein (VTI-B). Conclusions: Comparison of
autoantibody profiles in patients with dry and wet AMD revealed significantly altered immunoreac-
tivities against proteins particularly found in immunological diseases, further neurodegenerative,
apoptotic and autoimmune markers could be observed. A validation study has to explore if these
antibody pattern can help to understand the underlying differences in pathogenesis, evaluate their
prognostic value and if those could be possibly useful as additional therapeutic targets.
Citation: Korb, C.A.; Beck, S.;
Wolters, D.; Lorenz, K.; Pfeiffer, N.;
Keywords: age-related macular degeneration (AMD); immunoreactivities; antigen microarray; serum
Grus, F.H. Serum Autoantibodies in
Patients with Dry and Wet autoantibody profile
Age-Related Macular Degeneration. J.
Clin. Med. 2023, 12, 1590. https://
doi.org/10.3390/jcm12041590
1. Introduction
Academic Editors: Takao Hirano
and Atsushi Mizota Age-related macular degeneration (AMD) is a leading cause of irreversible visual
impairment and severe vision loss in developed countries, classified as early stage with
Received: 5 January 2023 medium-size drusen and retinal pigmentary changes to late neovascular (wet or exuda-
Revised: 2 February 2023 tive AMD) or atrophic stages [1,2]. With trends towards increased life expectancy in the
Accepted: 14 February 2023 industrialised world, the number of people suffering from AMD is projected to reach
Published: 17 February 2023 288 million in 2040, therefore AMD represents a substantial, global healthcare burden [3,4].
The etiology of AMD has multi-factorial, old age, cigarette smoking, cardiovascular risk
factors and environmental, nutritional and genetic risk factors which contribute to disease
pathogenesis [4,5]. Several studies reported the involvement of anti-retinal autoantibodies
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland. in ocular disorders, such as non-infectious uveitis [6], paraneoplastic and autoimmune
This article is an open access article retinopathy [7–9], retinitis pigmentosa [10,11], myopic macular degeneration [12] and
distributed under the terms and age-related macular degeneration (AMD) [13–19]. Serum autoantibodies that bind retinal
conditions of the Creative Commons proteins have been detected in AMD patients in much higher frequencies than age matched
Attribution (CC BY) license (https:// controls [19–22]. AMD is a complex disease and these studies support the growing evi-
creativecommons.org/licenses/by/ dence that an immunological impact and inflammatory factors perform important roles
4.0/). in the pathogenesis of AMD. Accordingly, in a previous study, we could demonstrate
J. Clin. Med. 2023, 12, 1590. https://doi.org/10.3390/jcm12041590 https://www.mdpi.com/journal/jcm
J. Clin. Med. 2023, 12, 1590 2 of 15
a significant difference in IgG antibody patterns against retinal antigens between patients
with “wet” AMD and healthy volunteers [23]. The differences were expressed by up- and
down-regulations of antigen–antibody reactivities in the serum of AMD patients pointing
to a shift in autoimmunity, including a possible loss of protective antibody functions. Until
now the relevance and the role of autoantibodies in the pathogenesis of AMD is speculative
and it is unclear if antibodies perform a causative role during the pathogenesis of AMD or
appear as secondary effects during the disease’s progression.
The present study will point out the immunoproteomic differences between wet and
dry AMD patients. The analyses will give evidence on up- and down-regulations of
autoantibodies against retinal antigens and can possibly give insights into the panel of
optimal serum markers of disease activity and future therapeutic mechanisms. After all,
it could give valuable hints for a possible way towards an early detection of the disease
or towards a possible immunoproteomic shift by prognostic biomarkers and towards
a personalized medicine targeting specific pathways in the early stage.
2. Materials and Methods
Seventy subjects were recruited for this study, which was carried out in the Department
of Ophthalmology, University Medical Center, Mainz, Germany. Written informed consent
was obtained from all patients and the control group before study recruitment. The study
had institutional review board/ethics committee approval, and the trial was undertaken in
accordance with the Declaration of Helsinki.
Inclusion criteria for participation in this study were the following: age ≥ 50 years,
dry AMD in at least one eye in the dry AMD group (n = 20), in the neovascular AMD
groups, participants with treatment-naive predominantly classic, minimally classic and
occult lesions were eligible (n = 29) and age-matched healthy controls (n = 21). Table 1
shows the descriptive statistics of the study groups. Exclusion criteria included ophthalmic
surgery or laser treatment within three months prior to inclusion in the study, diabetic
retinopathy, retinal branch or central vein occlusion, current steroid medication, glaucoma,
pathologic myopia, history of allergy to fluorescein, history of allergy to ranibizumab and
current ocular or periocular infections.
Table 1. Descriptive statistics of study groups.
Group N (Sample Size) Gender (m/f) Age—Means Age—Std.Dev. Age—Minimum Age—Maximum
CTRL 10 f 71.8 9.36453 54 82
CTRL 11 m 74.5 10.98511 55 90
17 14 f 80.9 8.54722 64 97
17 6 m 73.2 7.19491 63 82
18 20 f 81.0 7.93339 59 92
18 9 m 81.4 5.11503 72 88
CTRL = healthy volunteers, 17 = dry AMD, 18 = newly diagnosed wet AMD.
Best-corrected visual acuity using Snellen charts at baseline and at every follow-up
were recorded. Fluorescein angiography was used for diagnosis and spectral-domain OCT
imaging (Spectralis OCT, Heidelberg Engineering, Heidelberg, Germany) was used for
diagnosis and follow-up if needed. A 15 mL blood sample was drawn at baseline.
A correlation analysis was carried out in advance to exclude an influence of age on the
antibodies. A significant effect could only be determined for Glial fibrillary acidic protein
(GFAP), which is not one of the significant antibodies. For the remaining antibodies, age has
no influence on the result. Therefore, the larger range in the control patients is remarkable,
but not relevant for the further statistics. A possible reason for the slightly younger age of
the patients in the control group could be the fact that other eye diseases, which served as
exclusion criteria, such as diabetic retinopathy, retinal branch or central vein occlusion or
glaucoma, occur more frequently in older age.
J. Clin. Med. 2023, 12, 1590 3 of 15
2.1. Seldi-TOF Analysis
The analysis of antigen-antibody profiles can be done in a reliable and sensitive
manner using a developed proteomics technology: protein G Dynabeads combined with
a ProteinChip system based on SELDI-TOF (surface enhanced laser desorption/ionization-
time of flight) mass spectrometry (MS) [24]. The magnetic beads are designed to capture
immunoglobulins [25] via a cell wall component, binding a wide range of IgG antibodies
during incubation with various body fluids, such as sera. During a subsequent incuba-
tion with homogenized antigens, it is possible to capture relevant antigens by secondary
binding to the antibodies. After elution antigens can be analysed by SELDI-TOF MS using
ProteinChips with different, separating chip surfaces, e.g., cationic and anionic exchangers,
hydrophobic surfaces and metal-ion affinity-chromatographic surfaces. Resulting mass
spectra can be statistically analysed and compared to gain significantly higher or lower
antigen–antibody reactivity peaks according to the study groups. The identification of po-
tential biomarkers was performedusing highly sensitive MALDI-TOF/TOF (matrix assisted
laser desorption/ionization–time of flight) MS (for more details see below).
2.2. Protagen Arrays
For the analysis of antibody patterns and identification of potential autoantibody
biomarker candidates we chose a highly sensitive antigen microarray, which is a promising
approach in this field of interest. This method has already been successfully used for the
discovery of autoantibodies targeting prostate cancer specific biomarkers [8] and to screen
sera of patients with, e.g., different pathological subtypes of multiple sclerosis or autoim-
mune hepatitis for autoreactive antibodies [26,27]. To screen autoantibody reactivities
in study, sera was used as an advanced high density microarray approach. The sera of
patients before treatment with ranibizumab(n = 10) were compared with the sera of the
same patients after treatment with ranibizumab(n = 10). Two pools of ten sera were created
for each group, which were incubated on nitrocellulose-coated slides (Grace Bio-Labs, Bend,
OR, USA) with 3800 immobilized randomly selected human proteins from the UNIclone®
library (UNIchip®, Protagen, Dortmund, Germany) as described below. Incubation and
washing steps were performed at 4 ◦C on an orbital shaker (Micromix 5, DPC, Los An-
geles, CA, USA). Slides were covered with one-pad FAST-frame hybridization chambers
(Whatman, Maidstone, UK) and blocked with PBS (Phosphate Buffered Saline, Invitrogen®,
Carlsbad, USA) containing 0.5% BSA (Sigma-Aldrich, Steinheim, Deutschland) for one hour.
Afterwards slides were washed three times ten minutes each time with PBS containing
0.5% Tween 20 (PBS-T, ICN Biomedicals, Meckenheim, Deutschland). Patients sera were
diluted 1:375 in PBS and incubated on the Protagen-Slides overnight. After three washing
steps with PBS-T, each time for ten minutes, slides were treated with fluorescence labelled
secondary antibody (1:500 diluted in PBS, goat anti-human IgG, Jackson ImmunoResearch
Laboratories, West Grove, PA, USA) for one hour in the dark. After three final washing
steps, two with PBS-T and one with HPLC-grade ultra-pure Water (Mallinckrodt Baker BV,
Holland) (ten minutes each time) slides were dried under vacuum. By using a high sensitive
laser microarray scanner 16-bit TIFF (Tagged Information File Format) were generated.
Spot intensities were quantified with ImaGene Software (ImaGene 5.5, Biodiscovery, El
Segundo, CA, USA, 2012). After data normalization to internal standards with algorithm
provided by Protagen, group differences were calculated and compared.
For visualization of the resultant antigen-antibody complexes, slides were treated with
a secondary fluorescence labelled antibody (Dylight 650, Pierce, Rockford, IL, USA) fol-
lowed by confocal laser scanning. After data normalization spot intensities were compared
and group differences were analysed.
2.3. Analysis
Blood samples were centrifuged at 1000× g for ten minutes and the supernatant
was stored at −80 ◦C for subsequent analysis. Magnetic protein G beads (Dynal, Oslo,
Norway) were incubated with the patient’s sera. After several washings, the patient’s
J. Clin. Med. 2023, 12, 1590 4 of 15
antibodies were covalently bound to the beads using ethanolamine. The bead-antibody
complexes were then incubated with homogenized retinal antigens. The antigens bound to
the patient’s autoantibodies were be eluted, concentrated and analysed by SELDI time-of-
flight (TOF) MS ProteinChips with two different chromatographic surfaces (CM10 cation
exchange and H50 reversed phase). The samples were measured with a SELDI-TOF MS
ProteinChip system (Bio-Rad, Hercules, CA, USA) on a PBS-IIc ProteinChip Reader. Raw
data was transferred to CiphergenExpress 2.1 database software (Bio-Rad, Hercules, CA,
USA) for workup and analysis. An in-house developed Proteomics Software Project (PSP)
statistically evaluated the spectra using different statistical approaches to guarantee a high
specificity and sensitivity of antibody patterns for the observed study groups. The PSP
additionally searched for highly significant biomarkers directing a Statistical based analysis
using above mentioned algorithms. The identification of biomarkers was performed by
MALDI-TOF/TOF MS analysis (Bruker, Billerica, MA, USA). We aimed to generate at least
eight highly specific biomarkers (significance level α = 0.05 and power (1 − ß) = 90%) for
“wet” AMD.
Statistical calculations of sample sizes were based on experiences from previous studies
(e.g., [28]): the calculated number of cases is sufficient to detect an effect on the serum
antibody profiles, given a significance level α = 0.05 and power (1− ß) = 90%. The statistical
analysis demonstrated that the antibody composition against retinal antigens within sera
change over time. A comparison to the control group showed if the modifications are
beneficial, i.e., the serum compositions become more similar to the serum of healthy
subjects or not. A subsequent biomarker identification using MALDI-TOF/TOF MS (Bruker,
Billerica, MA, USA). revealed valuable hints on the systemic effects. After electrophoretic
separation, proteins were be typically digested, crystallized on matrix and analysed on
a MALDI target. The obtained peptide mass fingerprint data were exported into BioTools
(Version 3.1, Bruker, MA, USA) and used for an internal Mascot database search (Matrix
Science, London, England; Uniprot release 07, 2012), leading to protein identifications.
2.4. Antigen Microarrays
In this study, we used highly purified proteins, purchased at Sigma-Aldrich (Taufkirchen,
Germany) and BioMol (Hamburg, Germany), as antigens. The antigen selection is based
on previous autoantigen identifications in glaucoma patients by our group and survey
of the literature related to identifications of autoantigens in autoimmune diseases and
age-related macular degeneration. Antigens were diluted to 1 µg/µL with PBS buffer
containing 1.5% Trehalose (ICN Biomedicals, Meckenheim, Deutschland) for optimal
printing conditions. The spotting of antigens was performed with both a non-contact
printing technology (sciFLEXARRAYER S3, Scienion, Berlin, Germany), based on piezo
dispensing, and the commonly used pin based contact printing technique (OmniGrid100,
Digilab Genomic Solutions, Ann Arbor, MI, USA). Results were comparatively evaluated
for spot morphology and spot to spot variability. For printing of the whole set of study
microarrays the piezo based spotting technique was used. Each antigen was spotted
in triplicate onto nitrocellulose-slides (Oncyte, nitrocellulose 16 multi-pad slides, Grace
Bio-Labs, Bend, USA). As a positive and negative control, we used mouse anti-human
IgG/A/M (10 µg/µL) and spotting buffer. The spotting process was performed at RT and
a humidity of 30%. A total of 1 nL of each antigen-dilution was applied onto the nitrocellu-
lose surface by spotting four times 250 pl on exactly the same position. The accurateness
of the spotting volume and the correct positioning of the droplets were monitored before
and after the spotting process of each antigen using the sciDrop-VOLUME and auto drop
detection software (Scienion, Berlin, Germany).
Incubation and washing steps were performed at 4 ◦C on an orbital shaker (Titramax
100, Heidolph, Schwabach, Germany). Slides were covered with 16 pad FAST frame
hybridization chambers (Whatmann, Maidstone, UK) and blocked with PBS containing
4% BSA for one hour. Afterwards slides were washed three times with PBS containing
0.5% Tween (PBS-T). Patient sera were diluted 1:250 in PBS and aqueous humour in a ratio
J. Clin. Med. 2023, 12, 1590 5 of 15
of 1:10 in PBS. A total of 120 µL of these dilutions were randomly incubated on prepared
antigen-slides overnight. After several washing steps with PBS-T, slides were incubated
with a fluorescent Cy-5 labelled secondary antibody (1:500 diluted in PBS-T, goat anti-
human IgG, Jackson ImmunoResearch Laboratories, West Grove, PA, USA) for one hour in
the dark. Two washing steps with PBS-T were followed by two final washing steps with
HPLC-grade water. All microarrays were air dried before scanning, using a microarray
scanner (Affymetrix 428 TM Array Scanner, High Wycombe, UK). Generated 16-bit TIFF
images (Tagged Information File Format) of slides were analysed using the Spotfinder
3.1.1 software (TM4, Dana-Faber Cancer Institute, Boston, MA, USA) [29,30]. Background
subtraction was performed according to the formula: spot intensity = mean intensitySP
− ((sumbkg − sumtop5bkg)/(number of pixelbkg − number of pixelstop5bkg)), where
SP represents any spot, bkg the corresponding background and top5bkg the top five
percent of background pixel. The coefficient of variance (CV) was calculated as follows: CV
= SDSP3/meanSPX . . . SPn, where SDSP3 represents the standard deviation across three
replicate spots of one antigen of one sample and meanSPX . . . SPn the mean of all spot
intensities [31]
3. Results
IgG immunoreactivities were measured in three groups: controls, dry AMD and newly
diagnosed treatment naive neovascular AMD.
In the present study, we first analysed the autoantibody patterns against retinal
antigens in “wet” AMD sera samples and compared them to control sera samples and
“dry” AMD sera samples by mass spectrometry (MS) approach. After successful de novo
screening of immunoreactivities using MS-based approach and high density Protagen
antigen microarrays, a customized antigen microarrays containing 61 antigens was built
(Table 2). Each microarray contained each antigen as triplicate.
Analysis of immunoreactivities of IgG against these 61 antigens was performed in
70 samples.
Comparison of Immunoreactivities in Dry and Wet AMD
In a first step, the immunoreactivities were analysed in samples from dry AMD and
wet AMD and compared to healthy control samples (Figure 1). Table 3 shows the results of
ANOVA analysis and their corresponding p-values for the most-significant antigens.
J. Clin. Med. 2023, 12, 1590 6 of 15
Table 2. List of antigens on customized microarray.
ID MW [kDa] Protein Name UniProt Protein Name Abbreviation in Study
P62937 18.0 Peptidyl-prolyl cis-trans isomerase A (Cyclophilin A) Cyclophilin A human Cyclophilin B
P61604 10.9 10 kDa heat shock protein, mitochondrial (Hsp10) Chaperonin 10, Recombinant, Human HSP 10
P00441 15.9 Superoxide dismutase [Cu-Zn] Superoxide Dismutase from bovine erythrocytes SOD
P02686 33.1 Myelin basic protein (MBP); Isoform 1 Myelin Basic Protein from bovine brain MBP
P04792 22.8 Heat shock protein beta-1 (Heat shock 27 kDa protein; Hsp27) Hsp27 Protein—Low Endotoxin HSP 27
P08107 70.1 Heat shock 70 kDa protein 1A/1B (Hsp70.1/Hsp70.2) Heat Shock Protein 70 from bovine brain HSP 70
P02751 262.6 Fibronectin; Isoform 1 Fibronectin from human plasma Fibronektin
P01009 46.7 Alpha-1-antitrypsin α1-Antitrypsin from human plasma Alpha-1-Antitrypsin
P08758 35.9 Annexin A5 Annexin V from human placenta Annexin V
Q14694 87.1 Ubiquitin carboxyl-terminal hydrolase 10 (USP10) Ubiquitin human Ubiquitin
P49773 13.8 Histidine triad nucleotide-binding protein 1 (Protein kinase C inhibitor 1) Protein Kinase C Inhibitor, Myristoylated PKC Inhibitor
P02766 15.9 Transthyretin (Prealbumin) Prealbumin from human plasma PreAlbumin
O76070 13.3 Gamma-synuclein γ-Synuclein human Gamma-Synuklein
P14136 49.9 Glial fibrillary acidic protein (GFAP) Anti-Glial Fibrillary Acidic Protein GFAP
P27797 48.1 Calreticulin Calreticulin from bovine liver Calretikulin
P02549 280.0 Spectrin alpha chain, erythrocyte Spectrin from human erythrocytes Spektrin
P12081 57.4 Histidine-tRNA ligase, cytoplasmic (JO-1) JO-1 human Jo-1
P10809 61.1 60 kDa heat shock protein, mitochondrial (Hsp60) HSP60 (human), (recombinant) HSP 60
P53674 28.0 Beta-crystallin B1 βL-Crystallin from bovine eye lens Beta-L-Chrystalin
P09211 23.4 Glutathione S-transferase P Glutathione S-Transferase from bovine liver GST
P68133 42.1 Actin, alpha skeletal muscle Actin from bovine muscle Actin
P15104 42.1 Glutamine synthetase Glutamine synthetase GLUL
Q99798 83.4 Aconitase 2, mitochondrial aconitase 2, mitochondrial ACO2
E5RFU4 18.3 Dihydropyrimidinase-like 2 Dihydropyrimidinase-like 2 DBYSL2
P09936 24.8 Ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) Ubiquitin carboxyl-terminal hydrolase isozyme L1 VCHC1
P30086 21.1 Phosphatidylethanolamine-binding protein 1 Phosphatidylethanolamine-binding protein 1 PBP
P00918 29.2 Carbonic anhydrase 2 Carbonic Anhydrase II CAZ
P12277 42.6 Creatine kinase B-type Creatine kinase B CKB
P62873 37.4 Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1 Guanine nucleotide-binding protein G(1)/G(S)/G(T)(GNB1) subunit beta 1 GNB1
P06733 47.2 Alpha-enolase Alpha-Enolase ENO1
P04406 36.1 Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) Glyceraldeyde (3-)phosphate dehydrogenase GAPDH
P60842 46.2 Eukaryotic initiation factor 4A-I Homo sapiens eukaryotic translation initiation factor 4Aisoform 1 (EIF4A1) mRNA EIFA1
J. Clin. Med. 2023, 12, 1590 7 of 15
Table 2. Cont.
ID MW [kDa] Protein Name UniProt Protein Name Abbreviation in Study
A8K318 59.2 Protein kinase C substrate 80K-H protein kinase C substrate 80K-H isoform 2 [Homosapiens] PKC80
Q68Y55 34.9 Poly(RC) binding protein 2 poly(rC) binding protein 2 isoform g [Homo sapiens] PolyRp2
P49761 58.6 CDC-like kinase 3 (CLK3), transcript variant phclk3, mRNA Homo sapiens CDC-like kinase 3 (CLK3); transcriptvariant phclk3; mRNA CLK3
Q9P2Z0 28.4 THAP domain-containing protein 10 THAP domain containing 10 [Homo sapiens] THAP
Q9BXS5 48.6 AP-1 complex subunit mu-1 (AP1M1) Homo sapiens adaptor-related protein complex 1; mu 1subunit (AP1M1); mRNA AP1M1
P63330 35.6 Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform protein phosphatase type 2A catalytic subunit alphaisoform [Mus musculus] pp2A
Q9NZT2 73.3 Opioid growth factor receptor Homo sapiens opioid growth factor receptor(OGFR). mRNA OGFR
Homo sapiens plasticity-related gene 2 (PRG2) mRNA PRG2
P43235 37.0 Cathepsin K cathepsin K preproprotein [Homo sapiens] Catepsin
Q53G92 50.4 Tubulin beta-3 chain Homo sapiens tubulin beta 3 (TUBB3) mRNA TUBB3
P37108 14.6 Signal recognition particle 14 kDa protein Homo sapiens signal recognition particle 14 kDa(homologous Alu RNA binding protein) (SRP14) mRNA SRP14
Q7Z6Z7 481.9 E3 ubiquitin-protein ligase HUWE1; Isoform 1 HUWE1 protein [Homo sapiens] HUWE1
Homo sapiens chromosome X genomic contig.
reference assembly ChromosomX
Q96S16 36.9 JmjC domain-containing protein 8 (Jumonji domain-containing protein 8) jumonji domain containing 8 [Homo sapiens] jumonji
Q96N21 55.1 Uncharacterized protein C17orf56 Homo sapiens chromosome 17 open reading frame 56(C17orf56). mRNA Chromosome 17
Q96HG3 54.6 Islet cell autoantigen 1, 69 kDa Homo sapiens islet cell autoantigen 1. 69 kDa (ICA1).transcript variant 2. mRNA ICA1
P10768 31.5 S-formylglutathione hydrolase (Esterase D) Homo sapiens esterase D/formylglutathionehydrolase (ESD) ESD
P25325 33.2 3-mercaptopyruvate sulfurtransferase mercaptopyruvate sulfurtransferase isoform2 [Homo sapiens] MSI 2
P27361 43.1 Mitogen-activated protein kinase 3; Isoform 1 Homo sapiens mitogen-activated protein kinase 3(MAPK3); transcript variant 1; mRNA MAPK3
P43304 80.9 Glycerol-3-phosphate dehydrogenase, mitochondrial (GPD2); Isoform 1 Homo sapiens glycerol-3-phosphate dehydrogenase 2(mitochondrial) (GPD2); mRNA GPD2
P36969 22.2 Phospholipid hydroperoxide glutathione peroxidase, mitochondrial Homo sapiens glutathione peroxidase 4 (phospholipid(Glutathione peroxidase 4 hydroperoxidase) (GPX4). transcript variant 1. mRNA GPX4
B7Z4U7 65.1 Sec1 family domain containing 1, isoform CRA_b vesicle transport-related protein isoform b [Homo sapiens] VTI-B
J. Clin. Med. 2023, 12, 1590 8 of 15
Table 2. Cont.
ID MW [kDa] Protein Name UniProt Protein Name Abbreviation in Study
Q9BVL4 73.5 Selenoprotein O Homo sapiens selenoprotein O (SELO) mRNA SELO
Q6PJ21 39.4 SPRY domain-containing SOCS box protein 3 SPRY domain-containing SOCS box protein SSB-3 SPRX
P35611 81.0 Alpha-adducin adducin 1 (alpha) isoform c [Homo sapiens] Adduccin
Q99798 85.4 Aconitate hydratase, mitochondrial Aconitate Hydratase 2 (mitochondrial) Aconitate Hydratase
P06576 56.6 ATP synthase subunit beta, mitochondrial ATP synthase ATP Synthase
P40926 35.5 Malate dehydrogenase, mitochondrial Malat dehydrogenase Malat Dehydrogenase
P37840 14.5 Alpha-synuclein alpha-synuclein Alpha Synuclein
P10636 78.9 Microtubule-associated protein tau tau TAU
P05067 86.9 Amyloid beta A4 protein (Alzheimer disease amyloid protein) beta-amyloid Beta-Amyloid
Q05923 34.4 Dual specificity protein phosphatase 2 DUSP2 dual specificity phosphatase 2 [Homo sapiens] DUSP2
Q14166 74.4 Tubulin-tyrosine ligase-like protein 12 Homo sapiens tubulin tyrosine ligase-like family member12 (TTLL12) mRNA TTLL2
J. Clin. Med. 2023, 11, x FOR PEER REVIEW 9 of 15 
 
Comparison of Immunoreactivities in Dry and Wet AMD 
In a first step, the immunoreactivities were analysed in samples from dry AMD and 
J. Clin. Med. 2023, 12, 1590 wet AMD and compared to healthy control samples (Figure 1). Table 3 shows the re9soufl1ts5 
of ANOVA analysis and their corresponding p-values for the most-significant antigens.  
 
Figure 1. Comparison of immunoreactivities from dry AMD and wet AMD samples compared to
CTRL. Top: all immunoreactivities; bottom: 20 most significant reactivities.
 
J. Clin. Med. 2023, 12, 1590 10 of 15
Table 3. ANOVA (Analysis of Variance) of the IgG immunoreactivities in wet AMD and dry AMD
samples compared to controls.
ANOVA
CTRL-AV CTRL-SE AMDDRY-AV AMDDRY-SE AMDWET-AV AMDWET-SE ANOVA-p
Alpha Synuclein 6428 487 10.667 1974 7221 251 0.003
SELO 31.155 1755 27.412 1733 26.330 397 0.01
SPRY 24.905 1445 21.287 808 22.305 265 0.028
GAPDH—H2 11.571 821 13.277 1137 14.926 360 0.031
Annexin V 14.977 1821 18.675 1422 15.258 321 0.034
THAP 14.422 1575 10.767 715 11.889 284 0.044
VTI-B 22.121 1816 19.333 1493 23.773 512 0.065
HSP 10 16.099 1313 20.613 1716 19.235 385 0.071
ESD 29.416 1393 24.942 995 26.008 424 0.082
PKC80 23.592 1798 21.236 1396 20.575 335 0.082
ACO2—C2 19.238 1478 17.569 1462 16.185 376 0.089
OGFR 18.774 2383 21.865 3017 17.521 516 0.115
PBP—I2 21.243 1455 24.279 1422 24.944 467 0.119
CAZ—C3 5373 595 7534 1274 6402 196 0.148
EIFA1 26.612 2773 21.695 2045 21.915 612 0.15
MAPK3 28.505 1571 25.186 1110 27.123 315 0.15
ENO1—H7 19.297 1419 25.493 2798 22.685 594 0.15
Chromosome X
reading frame 56 20.439 1587 18.881 1513 21.993 455 0.16
Aconitate
Hydratase 20.584 1720 18.805 1622 17.738 391 0.163
GPX4 19.207 1159 17.929 1277 17.190 283 0.177
The table reveals the most significant antigens and according p-values.
The immunoreactivities of dry and wet AMD samples are highly significantly different
from each other and from controls. Although, as part of the natural autoimmunity also
found in healthy subjects, complex antibody patterns against the tested antigens could
show that the patterns in both AMD groups are changed. One of the most prominently
changed reactivity is against alpha-synuclein. Alpha-synuclein is known from other neu-
rodegenerative diseases. Others are heat shock proteins (e.g., HSP10) and Annexin V, which
performs a major role in apoptotic processes. Some others are antithetic regulated in wet
and dry AMD, such as VTI-B, PBP-12 and OGFR.
Pathway comparison analysis revealed protein functions particularly found in im-
munological diseases, especially aconitase 2, which performs a role in citric acid cy-
cle, furthermoreenolase 1, Annexin V, mitogen-activated protein kinase C (MAPK3) and
Alpha-Synuclein. Mutations in Alpha-synuclein are associated with Parkinson’s disease,
Alzheimer’s disease and several other neurodegenerative illnesses. Annexin 5 is a phos-
pholipase A2 and protein kinase C inhibitory protein with calcium channel activity and
a potential role in cellular signal transduction, inflammation, growth and differentiation.
Apart from the immunological markers, bio functions performing a major role in
inflammation could be observed, e.g., Alpha-synuclein, aconitase 2, enolase 1 and GADPH
(glyceraldehyde-phosphate dehydrogenase), which acts in apoptotic processes and is also
known to perform a role in Alzheimer’s disease. Annexin V is proposed to have anti-
apoptotic and anti-inflammatory functions, comparison of immunoreactivities in our study
revealed higher reactivity in the dry AMD samples.
4. Discussion
In the present study, we first investigated differences in the immunoreactivities in dry
and exudative AMD serum samples to gain insight into the pathogenesis and contribute to
J. Clin. Med. 2023, 12, 1590 11 of 15
the understanding of the underlying disease mechanisms. In recent years, biomarkers have
become major indicators of personalized medicine, in particular serological biomarkers
for diagnosis, prognosis and response to treatment [20,32]. It is known that two-third of
serum immunoglobulins in healthy individuals are natural occurring autoantibodies, so
that complex profiles exist even in healthy individuals [33–35] and disease specific changes
of circulating autoantibodies are known from several other diseases, e.g., glaucoma or
Sicca syndrome [36–40]. Serum anti-retinal autoantibodies were detected at a much higher
incidence in patients with early AMD than in individuals without AMD, suggesting their
diagnostic value [16,17,20]. Moreover, it has been suggested that distinct autoantibody sig-
natures may exist between early AMD, geographic atrophy and neovascular AMD [17,18].
However, the role of anti-retinal autoantibodies in the induction or progress of retinal de-
generation is not well defined, although it has been shown that anti-retinal autoantibodies
can develop on average 3–15 years prior to the first clinical signs [20,41].
Inflammation and oxidative stress have been proposed as central mechanisms in
the pathophysiology of AMD [42]. In this study, one of the most prominently changed
reactivity was against alpha-synuclein. Alpha-synuclein (α-syn) modulates retinal iron
homeostasis and has implications for visual manifestations of Parkinson’s disease [43].
Alpha-synuclein is also widely distributed in the retina and previous studies suggested
a role of the synuclein family, including α-syn, in retinal neurodegeneration and partic-
ularly during remodelling [44,45]. Ferroptosis is an iron-dependent regulated cell-death
pathway and has been implicated in AMD pathogenesis [46,47]. In the recent study, im-
munoreactivity against α-syn was significantly upregulated in patients with dry AMD.
The differences in immunoreactivities in the group of patients with dry and wet AMD
compared to control may suggest that autoantibodies against α-syn participate in pathogen-
ity of AMD. Furthermore, an analysis of antibody titre in patients with different stages
of dry AMD might be valuable, especially if potential inactivation or removal of specific
antibodies may have a monitoring or therapeutic effect in reducing the progression of dry
AMD in the future.
Annexin V is a phospholipid-binding protein with an important role in the regulation
of apoptosis, it is highly expressed by vascular endothelial cells where it is thought to
function antithrombolically [48]. Different expression levels have been reported for several
annexins and a wide range of diseases, suggesting a potential use to determine disease
progression and therapeutic monitoring [49]. A role for Annexin V was identified in the
recognition and binding step of clearance phagocytosis, which is essential to retinal physi-
ology [50]. Neovascular AMD was associated with altered gene expression in peripheral
white blood cells, among others increased levels of AnnexinA5 mRNA transcripts were
found and it was postulated that Annexin A5 levels may increase in AMD as part of a heal-
ing response [51]. Anti-Annexin A5 was upregulated in serum of patients with early to
advanced AMD compared to control serum before and could contribute to AMD pathogen-
esis via an autophagy-mediated mechanism [18]. Furthermore, Annexin A1 and Annexin
A4 were upregulated in the tears of patients with neovascular AMD, possibly indicating
a disturbed proteostasis in AMD [52]. In our study, comparison of immunoreactivities
revealed higher reactivity in the dry AMD samples, but immunoreactivity in neovascular
AMD samples was also higher than in the control group, suggesting that in both groups
anti Annexin A5 autoantibodies might contribute to the pathogenesis of AMD.
Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a glycolytic enzyme and
is also involved in regulating cell death, its role in the development of retinal diseases
has been explored in previous studies [53]. GAPDH performs a significant role in the
development of diabetic retinopathy and its progression [54]. Moreover, overexpression of
GAPDH inhibited neurovascular degeneration after retinal injury [53]. In retinal pigment
epithelial cells, GAPDH was differentially expressed after exposure to UVA radiation,
possibly contributing to photoreceptor dysfunction [55]. Interestingly, autoantibodies
against GAPDH in patients with autoimmune retinopathy were associated with disease
severity [56]. In the present study, we found immunoreactivities of anti-GAPDH elevated
J. Clin. Med. 2023, 12, 1590 12 of 15
in AMD patients compared to control with the most pronounced elevation in patients
with neovascular AMD. Thus, anti-GAPDH autoantibodies especially in the group of
patients with neovascular AMD could possibly contribute to the pathogenesis of induction
of apoptosis and proliferation.
Autoantibodies against Opioid Growth Factor Receptor (OGFr) were antithetically
regulated in patients with dry and wet AMD compared to controls, with an upregulation
in patients with dry AMD. The Opioid Growth Factor (OGF), chemically termed [Met5]-
enkephalin, is an endogenous pentapeptide that binds to OGFr, the pathway is responsible
for homeostasis of cell replication and renewal [57]. OGF-OGFr axis performs a key role in
the homeostasis of cornea and retina [58,59]. Depending on the duration of OGFr block-
ade, opioid antagonists, such as naloxone, are effective therapies for cancer, autoimmune
diseases and complications associated with diabetes [60]. In mice, naloxone significantly
reduced the progress of retinal AMD-like lesions, as naloxone modulates microglia accu-
mulation and activation at the site of retinal degeneration [61]. In the present study, we
found an antithetical regulation of anti-OGFr in patients with dry and neovascular AMD.
These observations suggest that the OGF-OGFr axis might perform an important role in
the pathogenesis of AMD and verification of the role of autoantibodies is desirable.
Strengths of the present study are the prospective nature and the analysis by cus-
tomized antigen-microarrays containing 61 antigens after successful de novo screening
of immunoreactivities using MS-based approach. Antigen-Microarrays allows for fast
evaluation with high sensitivity of potential autoantibody biomarkers using small micro-
liter sample volumes. Furthermore, an aged-matched control group with participants
without age-related macular degeneration was included. Fluorescein angiography and
spectral-domain OCT imaging were used for diagnosis in all patients.
Despite the strengths, our study has several limitations. First, the presence of im-
munoreactivities is neither sensitive nor specific for the diagnosis of age related macular
degeneration. In general, it has to be elucidated whether the described autoantibodies
perform a causative role in the pathogenesis of AMD or appear as secondary effects during
the disease’s progression. Second, a group of patients with dry AMD was included without
further evaluating different AMD stages. Subsequent studies are planned and required
to evaluate immunoproteomic differences in different stages of the disease in order to
possibly act as a diagnostic tool, to establish prognostic markers of disease activity and
progression and to obtain therapeutic treatments. Third, this study elucidated immunore-
activities against 61 antigens, which were preselected after successful de novo screening
of immunoreactivities using MS-based approach and high density Protagen antigen mi-
croarrays. Although we found statistically significant immunoproteomic differences in
the group of patients with dry and wet AMD, natural occurring autoantibodies also exist
in healthy individuals. Fourth, in this study, patients were not eligible if they fulfilled
the stated exclusion criteria. Other comorbidities or factors, such as body mass index,
cholesterol level, blood pressure and blood glucose level, were not evaluated in this study.
Subsequent validation studies, including possible correlating individual comorbidities,
could provide further valuable insights in the immunoproteomic profiles in patients with
dry and exudative AMD a possible impact on diagnosis, progression and therapy.
In sum, our study provides huge differences in the immunoreactivities in both wet and
dry AMD samples. The limitations described above highlight the need for standardised
and appropriately designed studies, which are needed to explore if antibody patterns can
help understand the underlying differences in pathogenesis of dry and exudative AMD
and evaluate their prognostic or possibly therapeutic value.
J. Clin. Med. 2023, 12, 1590 13 of 15
Author Contributions: Conceptualization, C.A.K. and F.H.G.; methodology, C.A.K., S.B., D.W.,
K.L., N.P. and F.H.G.; software: S.B., D.W. and F.H.G.; validation, S.B., D.W. and F.H.G.; formal
analysis, S.B., D.W. and F.H.G.; investigation, C.A.K. and K.L.; writing—original draft preparation,
C.A.K.; writing—review and editing, C.A.K., S.B., D.W., K.L., N.P. and F.H.G.; visualization, D.W.;
supervision, F.H.G.; project administration, N.P. and F.H.G.; funding acquisition, C.A.K. and F.H.G.
All authors have read and agreed to the published version of the manuscript.
Funding: This research received financial support from Novartis Pharma GmbH, Nürnberg, Germany.
The funders had no role in the design of the study; in the collection, analyses, or interpretation of
data; in the writing of the manuscript; or in the decision to publish the results.
Institutional Review Board Statement: The study protocol and study documents were approved
by the local ethics committee of the Medical Chamber of Rhineland-Palatinate, Germany (reference
no. 837.304.08).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Not applicable.
Acknowledgments: We thank all study participants for their willingness to participate and provide
data for this research project. This paper includes results of the doctoral thesis entitled “Auswirkung
der intravitrealen Ranibizumab-Therapie auf die Antikörperverteilung im Serum von Patienten mit
exsudativer altersbedingter Makuladegeneration (AMD)” submitted by Eva Rebecca Schmitt to the
Johannes Gutenberg-University Mainz in 2013.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Mitchell, P.; Liew, G.; Gopinath, B.; Wong, T.Y. Age-related macular degeneration. Lancet 2018, 392, 1147–1159. [CrossRef]
[PubMed]
2. Ferris, F.L., 3rd; Wilkinson, C.P.; Bird, A.; Chakravarthy, U.; Chew, E.; Csaky, K.; Sadda, S.R. Clinical classification of age-related
macular degeneration. Ophthalmology 2013, 120, 844–851. [CrossRef] [PubMed]
3. Wong, W.L.; Su, X.; Li, X.; Cheung, C.M.; Klein, R.; Cheng, C.Y.; Wong, T.Y. Global prevalence of age-related macular degeneration
and disease burden projection for 2020 and 2040: A systematic review and meta-analysis. Lancet Glob. Health 2014, 2, e106–e116.
[CrossRef] [PubMed]
4. Ambati, J.; Ambati, B.K.; Yoo, S.H.; Ianchulev, S.; Adamis, A.P. Age-Related Macular Degeneration: Etiology, Pathogenesis, and
Therapeutic Strategies. Surv. Ophthalmol. 2003, 48, 257–293. [CrossRef] [PubMed]
5. Lim, L.S.; Mitchell, P.; Seddon, J.M.; Holz, F.G.; Wong, T.Y. Age-related macular degeneration. Lancet 2012, 379, 1728–1738.
[CrossRef] [PubMed]
6. Gibbs, E.; Matsubara, J.; Cao, S.; Cui, J.; Forooghian, F. Antigen-specificity of antiretinal antibodies in patients with noninfectious
uveitis. Can. J. Ophthalmol. 2017, 52, 463–467. [CrossRef] [PubMed]
7. Heckenlively, J.R.; Fawzi, A.A.; Oversier, J.; Jordan, B.L.; Aptsiauri, N. Autoimmune retinopathy: Patients with antirecoverin
immunoreactivity and panretinal degeneration. Arch. Ophthalmol. 2000, 118, 1525–1533. [CrossRef]
8. Savchenko, M.S.; Bazhin, A.V.; Shifrina, O.N.; Demoura, S.A.; Kogan, E.A.; Chuchalin, A.G.; Philippov, P.P. Antirecoverin
autoantibodies in the patient with non-small cell lung cancer but without cancer-associated retinopathy. Lung Cancer 2003, 41,
363–367. [CrossRef]
9. Adamus, G.; Ren, G.; Weleber, R.G. Autoantibodies against retinal proteins in paraneoplastic and autoimmune retinopathy. BMC
Ophthalmol. 2004, 4, 5. [CrossRef]
10. Galbraith, G.M.; Emerson, D.; Fudenberg, H.H.; Gibbs, C.J.; Gajdusek, D.C. Antibodies to neurofilament protein in retinitis
pigmentosa. J. Clin. Investig. 1986, 78, 865–869. [CrossRef]
11. Galbraith, G.M.; Fudenberg, H.H. One subset of patients with retinitis pigmentosa has immunologic defects. Clin Immunol.
Immunopathol. 1984, 31, 254–260. [CrossRef] [PubMed]
12. Sim, S.S.; Wong, C.W.; Hoang, Q.V.; Lee, S.Y.; Wong, T.Y.; Cheung, C.M.G. Anti-retinal autoantibodies in myopic macular
degeneration: A pilot study. Eye 2020, 35, 2254–2259. [CrossRef] [PubMed]
13. Penfold, P.L.; Provis, J.M.; Furby, J.H.; Gatenby, P.A.; Billson, F.A. Autoantibodies to retinal astrocytes associated with age-related
macular degeneration. Graefes. Arch. Clin. Exp. Ophthalmol. 1990, 228, 270–274. [CrossRef] [PubMed]
14. Chen, H.; Wu, L.; Pan, S.; Wu, D.Z. An immunologic study on age-related macular degeneration. Yan Ke Xue Bao 1993, 9, 113–120.
[PubMed]
15. Gurne, D.H.; Tso, M.O.; Edward, D.P.; Ripps, H. Antiretinal antibodies in serum of patients with age-related macular degeneration.
Ophthalmology 1991, 98, 602–607. [CrossRef]
16. Cherepanoff, S.; Mitchell, P.; Wang, J.J.; Gillies, M.C. Retinal autoantibody profile in early age-related macular degeneration:
Preliminary findings from the Blue Mountains Eye Study. Clin. Exp. Ophthalmol. 2006, 34, 590–595. [CrossRef]
J. Clin. Med. 2023, 12, 1590 14 of 15
17. Adamus, G.; Chew, E.Y.; Ferris, F.L.; Klein, M.L. Prevalence of anti-retinal autoantibodies in different stages of Age-related
macular degeneration. BMC Ophthalmol. 2014, 14, 154. [CrossRef]
18. Iannaccone, A.; Giorgianni, F.; New, D.D.; Hollingsworth, T.J.; Umfress, A.; Alhatem, A.; Neeli, I.; Lenchik, N.I.; Jennings,
B.J.; Calzada, J.I.; et al. Circulating Autoantibodies in Age-Related Macular Degeneration Recognize Human Macular Tissue
Antigens Implicated in Autophagy, Immunomodulation, and Protection from Oxidative Stress and Apoptosis. PLoS ONE 2015,
10, e0145323. [CrossRef]
19. Patel, N.; Ohbayashi, M.; Nugent, A.K.; Ramchand, K.; Toda, M.; Chau, K.-Y.; Bunce, C.; Webster, A.; Bird, A.C.; Ono, S.J.; et al.
Circulating anti-retinal antibodies as immune markers in age-related macular degeneration. Immunology 2005, 115, 422–430.
[CrossRef]
20. Adamus, G. Can innate and autoimmune reactivity forecast early and advance stages of age-related macular degeneration?
Autoimmune Rev. 2017, 16, 231–236. [CrossRef]
21. Camelo, S. Potential Sources and Roles of Adaptive Immunity in Age-Related Macular Degeneration: Shall We Rename AMD
into Autoimmune Macular Disease? Autoimmune Dis. 2014, 2014, 1–11. [CrossRef] [PubMed]
22. Gu, X.; Meer, S.G.; Miyagi, M.; Rayborn, M.E.; Hollyfield, J.G.; Crabb, J.W.; Salomon, R.G. Carboxyethylpyrrole protein adducts
and autoantibodies, biomarkers for age-related macular degeneration. J. Biol. Chem. 2003, 278, 42027–42035. [CrossRef] [PubMed]
23. Joachim, S.C.; Bruns, K.; Lackner, K.J.; Pfeiffer, N.; Grus, F.H. Analysis of IgG antibody patterns against retinal antigens and
antibodies to α-crystallin, GFAP, and α-enolase in sera of patients with “wet” age-related macular degeneration. Graefe′s Arch.
Clin. Exp. Ophthalmol. 2006, 245, 619–626. [CrossRef] [PubMed]
24. Grus, F.H.; Joachim, S.C.; Pfeiffer, N. Analysis of complex autoantibody repertoires by surface-enhanced laser desorption/ionization-
time of flight mass spectrometry. Proteomics 2003, 3, 957–961. [CrossRef] [PubMed]
25. Salomon, O.; Moisseiev, J.; Rosenberg, N.; Vidne, O.; Yassur, I.; Zivelin, A.; Treister, G.; Steinberg, D.M.; Seligsohn, U. Analysis
of genetic polymorphisms related to thrombosis and other risk factors in patients with retinal vein occlusion. Blood Coagul.
Fibrinolysis 1998, 9, 617–622. [CrossRef]
26. Quintana, F.J.; Farez, M.F.; Viglietta, V.; Iglesias, A.H.; Merbl, Y.; Izquierdo, G.; Lucas, M.; Basso, A.S.; Khoury, S.J.; Lucchinetti,
C.F.; et al. Antigen microarrays identify unique serum autoantibody signatures in clinical and pathologic subtypes of multiple
sclerosis. Proc. Natl. Acad. Sci. USA 2008, 105, 18889–18894. [CrossRef]
27. Song, Q.; Liu, G.; Hu, S.; Zhang, Y.; Tao, Y.; Han, Y.; Zeng, H.; Huang, W.; Li, F.; Chen, P.; et al. Novel Autoimmune Hepatitis-
Specific Autoantigens Identified Using Protein Microarray Technology. J. Proteome Res. 2009, 9, 30–39. [CrossRef]
28. Grus, F.H.; Kramann, C.; Bozkurt, N.; Wiegel, N.; Bruns, K.; Lackner, N.; Pfeiffer, N. Effects of multipurpose contact lens solutions
on the protein composition of the tear film. Contact Lens Anterior Eye 2005, 28, 103–112. [CrossRef]
29. Saeed, A.I.; Sharov, V.; White, J.; Li, J.; Liang, W.; Bhagabati, N.; Braisted, J.; Klapa, M.; Currier, T.; Thiagarajan, M.; et al. TM4:
A Free, Open-Source System for Microarray Data Management and Analysis. Biotechniques 2003, 34, 374–378. [CrossRef]
30. Saeed, A.I.; Bhagabati, N.K.; Braisted, J.C.; Liang, W.; Sharov, V.; Howe, E.A.; Li, J.; Thiagarajan, M.; White, J.A.; Quackenbush, J.
TM4 Microarray Software Suite. Methods Enzymol. 2006, 411, 134–193. [CrossRef]
31. Hamelinck, D.; Zhou, H.; Li, L.; Verweij, C.; Dillon, D.; Feng, Z.; Costa, J.; Haab, B.B. Optimized Normalization for Antibody
Microarrays and Application to Serum-Protein Profiling. Mol. Cell Proteom. 2005, 4, 773–784. [CrossRef] [PubMed]
32. Lambert, N.G.; ElShelmani, H.; Singh, M.K.; Mansergh, F.C.; Wride, M.A.; Padilla, M.; Keegan, D.; Hogg, R.E.; Ambati, B.K. Risk
factors and biomarkers of age-related macular degeneration. Prog. Retin. Eye Res. 2016, 54, 64–102. [CrossRef] [PubMed]
33. Li, W.-H.; Zhao, J.; Li, H.-Y.; Liu, H.; Li, A.-L.; Wang, H.-X.; Wang, J.; He, K.; Liang, B.; Yu, M.; et al. Proteomics-based identification
of autoantibodies in the sera of healthy Chinese individuals from Beijing. Proteomics 2006, 6, 4781–4789. [CrossRef] [PubMed]
34. Avrameas, S. Natural autoantibodies: From ‘horror autotoxicus’ to ‘gnothi seauton’. Immunol. Today 1991, 12, 154–159.
35. Nóbrega, A.; Haury, M.; Grandien, A.; Malanchère, E.; Sundblad, A.; Coutinho, A. Global analysis of antibody repertoires. II.
Evidence for specificity, self-selection and the immunological “homunculus” of antibodies in normal serum. Eur. J. Immunol.
1993, 23, 2851–2859. [CrossRef]
36. Joachim, S.C.; Grus, F.H.; Pfeiffer, N. Analysis of autoantibody repertoires in sera of patients with glaucoma. Eur. J. Ophthalmol.
2003, 13, 752–758. [CrossRef]
37. Grus, F.H.; Joachim, S.C.; Bruns, K.; Lackner, K.J.; Pfeiffer, N.; Wax, M.B. Serum Autoantibodies to α-Fodrin Are Present in
Glaucoma Patients from Germany and the United States. Investig. Opthalmol. Vis. Sci. 2006, 47, 968–976. [CrossRef]
38. Grus, F.H.; Joachim, S.C.; Hoffmann, E.M.; Pfeiffer, N. Diagnosis of Glaucoma by Analysis of Autoantibody Repertoires. Investig.
Opthalmol. Vis. Sci. 2003, 44, 2100.
39. Grus, F.H.; Joachim, S.C.; Hoffmann, E.M.; Pfeiffer, N. Complex autoantibody repertoires in patients with glaucoma. Mol. Vis.
2004, 10, 132–137.
40. Grus, F.H.; Augustin, A.J.; Pfeiffer, N. Analysis of Autoantibodies in Tears of Dry-Eye Patients. Investig. Opthalmol. Vis. Sci. 2002,
43, 64.
41. Deane, K.D. Preclinical Rheumatoid Arthritis (Autoantibodies): An Updated Review. Curr. Rheumatol. Rep. 2014, 16, 419.
[CrossRef]
42. Romero-Vazquez, S.; Llorens, V.; Soler-Boronat, A.; Figueras-Roca, M.; Adan, A.; Molins, B. Interlink between Inflammation
and Oxidative Stress in Age-Related Macular Degeneration: Role of Complement Factor H. Biomedicines 2021, 9, 763. [CrossRef]
[PubMed]
J. Clin. Med. 2023, 12, 1590 15 of 15
43. Baksi, S.; Tripathi, A.K.; Singh, N. Alpha-synuclein modulates retinal iron homeostasis by facilitating the uptake of transferrin-
bound iron: Implications for visual manifestations of Parkinson′s disease. Free. Radic. Biol. Med. 2016, 97, 292–306. [CrossRef]
[PubMed]
44. Martinez-Navarrete, G.C.; Martin-Nieto, J.; Esteve-Rudd, J.; Angulo, A.; Cuenca, N. Alpha synuclein gene expression profile in
the retina of vertebrates. Mol. Vis. 2007, 13, 949–961. [PubMed]
45. Pfeiffer, R.L.; Marc, R.E.; Jones, B.W. Persistent remodeling and neurodegeneration in late-stage retinal degeneration. Prog. Retin.
Eye Res. 2020, 74, 100771. [CrossRef] [PubMed]
46. Zhao, T.; Guo, X.; Sun, Y. Iron Accumulation and Lipid Peroxidation in the Aging Retina: Implication of Ferroptosis in Age-Related
Macular Degeneration. Aging Dis. 2021, 12, 529–551. [CrossRef] [PubMed]
47. Sun, Y.; Zheng, Y.; Wang, C.; Liu, Y. Glutathione depletion induces ferroptosis, autophagy, and premature cell senescence in
retinal pigment epithelial cells. Cell Death Dis. 2018, 9, 1–15. [CrossRef]
48. Iaccarino, L.; Ghirardello, A.; Canova, M.; Zen, M.; Bettio, S.; Nalotto, L.; Punzi, L.; Doria, A. Anti-annexins autoantibodies: Their
role as biomarkers of autoimmune diseases. Autoimmune Rev. 2011, 10, 553–558. [CrossRef]
49. Schloer, S.; Pajonczyk, D.; Rescher, U. Annexins in Translational Research: Hidden Treasures to Be Found. Int. J. Mol. Sci. 2018, 19, 1781.
[CrossRef]
50. Yu, C.; Muñoz, L.E.; Mallavarapu, M.; Herrmann, M.; Finnemann, S.C. Annexin A5 regulates surface αvβ5 integrin for retinal
clearance phagocytosis. J. Cell Sci. 2019, 132, jcs232439. [CrossRef]
51. Lederman, M.; Weiss, A.; Chowers, I. Association of Neovascular Age-Related Macular Degeneration with Specific Gene
Expression Patterns in Peripheral White Blood Cells. Investig. Opthalmol. Vis. Sci. 2010, 51, 53–58. [CrossRef] [PubMed]
52. Winiarczyk, M.; Winiarczyk, D.; Michalak, K.; Kaarniranta, K.; Adaszek, Ł.; Winiarczyk, S.; Mackiewicz, J. Dysregulated Tear
Film Proteins in Macular Edema Due to the Neovascular Age-Related Macular Degeneration Are Involved in the Regulation of
Protein Clearance, Inflammation, and Neovascularization. J. Clin. Med. 2021, 10, 3060. [CrossRef] [PubMed]
53. Cai, R.; Xue, W.; Liu, S.; Petersen, R.B.; Huang, K.; Zheng, L. Overexpression of glyceraldehyde 3-phosphate dehydrogenase
prevents neurovascular degeneration after retinal injury. FASEB J. 2015, 29, 2749–2758. [CrossRef] [PubMed]
54. Kanwar, M.; Kowluru, R.A. Role of Glyceraldehyde 3-Phosphate Dehydrogenase in the Development and Progression of Diabetic
Retinopathy. Diabetes 2009, 58, 227–234. [CrossRef] [PubMed]
55. Chen, J.-L.; Hung, C.-T.; Keller, J.J.; Lin, H.-C.; Wu, Y.-J. Proteomic analysis of retinal pigment epithelium cells after exposure to
UVA radiation. BMC Ophthalmol. 2019, 19, 1–13. [CrossRef] [PubMed]
56. Pawestri, A.R.; Arjkongharn, N.; Suvannaboon, R.; Tuekprakhon, A.; Srimuninnimit, V.; Udompunthurak, S.; Atchaneeyasakul,
L.-O.; Koolvisoot, A.; Trinavarat, A. Autoantibody profiles and clinical association in Thai patients with autoimmune retinopathy.
Sci. Rep. 2021, 11, 15047. [CrossRef]
57. McLaughlin, P.J.; Sassani, J.W.; Zagon, I.S. Dysregulation of the OGF-OGFr Pathway and Associated Diabetic Complications.
J. Diabetes Clin. Res. 2021, 3, 64–67. [CrossRef]
58. Alvarez-Rivera, F.; Serro, A.P.; Silva, D.; Concheiro, A.; Alvarez-Lorenzo, C. Hydrogels for diabetic eyes: Naltrexone loading,
release profiles and cornea penetration. Mater. Sci. Eng. C 2019, 105, 110092. [CrossRef]
59. Zagon, I.S.; Sassani, J.W.; McLaughlin, P.J. Adaptation of Homeostatic Ocular Surface Epithelium to Chronic Treatment with the
Opioid Antagonist Naltrexone. Cornea 2006, 25, 821–829. [CrossRef]
60. McLaughlin, P.J.; Zagon, I.S. Duration of opioid receptor blockade determines biotherapeutic response. Biochem. Pharmacol. 2015,
97, 236–246. [CrossRef]
61. Shen, D.; Cao, X.; Zhao, L.; Tuo, J.; Wong, W.T.; Chan, C.-C. Naloxone Ameliorates Retinal Lesions inCcl2/Cx3cr1Double-Deficient
Mice via Modulation of Microglia. Investig. Opthalmol. Vis. Sci. 2011, 52, 2897–2904. [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.