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\begin{document}

\title{Comparative proteome analysis of retinal hypoxia--ischemia in acute
ocular hypertension model with TMT-based quantitative proteomics}



\author[1]{Guangyi Huang}%
\author[1]{Yunru Lin}%
\author[1]{Fen Tang}%
\author[1]{Hui Huang}%
\author[1]{Qi Chen}%
\author[1]{Ning Su}%
\author[1]{Ling Cui}%
\author[1]{Fan Xu}%
\author[1]{Chaolan Shen}%
\affil[1]{People's Hospital of Guangxi Zhuang Autonomous Region}%


\vspace{-1em}



  
  \date{June 16, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Acute glaucoma's main sign is acute ocular hypertension (AOH), leading
to retinal ganglion cell (RGC) death and irreversible visual loss.
However, there is currently no approved effective therapy for this
condition. This research aimed to identify the major regulators and the
overall protein changes involved in AOH-induced RGC death.
Mass-spectrometry was used to analyze proteomic patterns in the retinal
protein extracts from the AOH and sham-group, and then Gene Ontology
(GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway studies
were performed. In proteomics analysis, we identified 92 proteins in the
AOH group compared to the control group, with 58 proteins being
up-regulated and 34 proteins being down-regulated. Western blot and
biochemical assay analyses identified changes in Fatty acid-binding
protein 7 (FABP7), and caveolin-1(Cav-1) that were related to fatty acid
metabolism and ocular inflammatory signaling. Moreover, variations in
the expression of the proteins Galectin-1 (Gal-1), S100 calcium-binding
protein A6 (S100a6), and Visinin-like protein-1 (VILIP) was shown, all
of which were associated to the process of neuronal ischemia. Our
investigation demonstrated that neuroinflammation and fatty acid
metabolism were involved in retinal impairment following AOH, offering a
potential therapeutic strategy for acute glaucoma.%
\end{abstract}%



\sloppy


\textbf{Comparative proteome~analysis~of~retinal hypoxia--ischemia in
acute ocular hypertension model with~TMT-based~quantitative}
~\textbf{proteomics}

\textbf{Guangyi Huang\textsuperscript{1,2}\#;Yunru
Lin\textsuperscript{1,3}\#;Fen Tang\textsuperscript{1};Hui
Huang\textsuperscript{1};Qi Chen\textsuperscript{1};Ning
Su\textsuperscript{1}; Ling Cui\textsuperscript{1};Fan
Xu\textsuperscript{1};Chaolan Shen\textsuperscript{1}*}

\textsuperscript{1}Department of Ophthalmology, the People's Hospital of
Guangxi Zhuang Autonomous Region \& Guangxi Key Laboratory of Eye Health
\& Guangxi Health Commission Key Laboratory of Ophthalmology and Related
Systemic Diseases Artificial Intelligence Screening Technology
\&Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences,
Nanning 530021, China

2 Guilin Medical University, Guilin 541000, China

3 Youjiang Medical University for Nationalities, Baise 533000, China

\textbf{*} Corresponding author: Chaolan Shen, Tel.:0086-0771-2186320,
E-mail:\emph{shenchaolaneye@163.com} , Department of Ophthalmology, the
People's Hospital of Guangxi Zhuang Autonomous Region, No. 6 Taoyuan
Road, Qingxiu District, Nanning 530021, PR China

\# These authors contributed equally to this work.

\textbf{Abstract}

Acute glaucoma's main sign is acute ocular hypertension (AOH), leading
to retinal ganglion cell (RGC) death and irreversible visual loss.
However, there is currently no approved effective therapy for this
condition. This research aimed to identify the major regulators and~the
overall protein changes involved in AOH-induced RGC death.
Mass-spectrometry was used to analyze proteomic patterns in the retinal
protein extracts from the AOH and sham-group, and then Gene Ontology
(GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway studies
were performed.

In proteomics analysis, we identified 92 proteins in the AOH group
compared to the control group, with 58 proteins being up-regulated and
34 proteins being down-regulated. Western blot and biochemical assay
analyses identified changes in Fatty acid-binding protein 7 (FABP7), and
caveolin-1(Cav-1) that were related to fatty acid metabolism and ocular
inflammatory signaling. Moreover, variations in the expression of the
proteins Galectin-1 (Gal-1), S100 calcium-binding protein A6 (S100a6),
and Visinin-like protein-1 (VILIP) was shown, all of which were
associated to the process of neuronal ischemia. Our investigation
demonstrated that neuroinflammation and fatty acid metabolism were
involved in retinal impairment following AOH, offering a potential
therapeutic strategy for acute glaucoma.

Significance: Ischemia-reperfusion (I/R) retinal disorders play a
significant role in acute glaucoma. Pathological cell death in the
retina due to I/R injury may involve fatty acid metabolism and ocular
inflammation. This study suggests that AOH-induced retinal neuronal cell
death might be influenced by the interaction between FABP7 and
Caveolin-1 in the PPAR/JAK-STAT signaling pathway. Neuroinflammation and
fatty acid metabolism appear to be critical factors in retinal
impairment following AOH, offering promising therapeutic targets for
acute glaucoma.

\textbf{Keywords:} Acute glaucoma, Proteomics, Retina,
Neurodegeneration, FABP7, Caveolin-1

\textbf{Abbreviations:}

AOH: acute ocular hypertension

Cav-1: caveolin-1

DHA: docosahexaenoic acid

FABP7: Fatty acid-binding protein 7

GO: Gene Ontology

Gal-1: Galectin-1

IOP: intraocular pressure

KEGG: Kyoto Encyclopedia of Genes and Genomes

OGDR: glucose deprivation/reoxygenation

PUFAs: polyunsaturated fatty acids

RGC: retinal ganglion cell

RIR: retinal ischemia/ reperfusion

S100a6: S100 calcium-binding protein A6

TMT: Tandem mass tag

VILIP: Visinin-like protein-1

\textbf{Introduction}

Glaucoma, a major cause of vision impairment, is expected to cause
blindness in around 111.8 million individuals worldwide by 2040{[}1{]}.
Acute glaucoma, which is more prevalent among Asians and East Indians,
is a subtype of primary angle-closure glaucoma{[}2{]}. In acute
glaucoma, the intraocular pressure (IOP) increases at a faster pace than
the retinal perfusion pressure, which could lead to retinal ischemia/
reperfusion (RIR) injury, optic nerve damage, and death of RGCs.
However, the intricate pathophysiology of IOP-induced optic nerve injury
is still not fully elucidated. The current recommended treatment for
glaucoma is persistent IOP reduction, which can slow but not halt the
disease's progression{[}3,4{]}.

The analysis of protein changes plays a critical role in the analytical
tools used to comprehend the mechanisms of pathogenesis underlying
glaucomatous neurodegeneration. Multiplexing high-throughput protein
profiling is an invaluable aid in this area, facilitating a better
understanding of this intricate neurodegenerative disease and the
development of treatments for it. Therefore, glaucoma researchers
frequently investigate protein expression, modifications, and
protein-protein interactions in experimental models and postmortem human
tissues from individuals with glaucoma, alongside the expanding use of
genetic and transcriptome analysis{[}5-9{]}.

Tandem mass tag (TMT) labeling coupled with liquid chromatography-tandem
mass spectrometry (LC-MS/MS) has emerged as the preferred method for
protein quantitation. The utilization of TMT labeling in conjunction
with LC-MS/MS offers several distinct advantages over the outdated
2D-DIGE method. This innovative technique provides enhanced accuracy and
reliability in quantitative data analysis, enabling the identification
of proteins with a broader range of molecular weights and abundance
levels. Moreover, this advanced method is applicable to a wide array of
samples, including cellular extracts and complex mixtures, and
demonstrates superior sensitivity, specificity, accuracy, and
reproducibility in its analyses.

Considering the complexity of glaucoma and the limitations associated
with human samples, it is essential to establish an animal model that
directly replicates the pathogenesis of human acute glaucoma. The mouse
acute ocular hypertension model is a commonly used animal model that can
mimic the typical alterations in retinal hypoxia-ischemia phenotypes
seen in acute glaucoma \textsuperscript{{[}10,11{]}}. In this study, the
proteome of retinal tissue from an AOH model was examined using a
TMT-based quantitative proteomic approach and compared to a control
group to understand the biochemical changes that occur during the
evolution of acute glaucoma and to identify potential biomarkers. Our
results identified DEPs in the retina of AOH mice, which were involved
in the dysregulation of specific metabolic pathways significantly. In
particular, FABP7 and Cav-1 were significantly up-regulated in injured
retinas. Therefore, we propose that FABP7 and Cav-1 may serve as
potential targets for treating acute glaucoma.

\textbf{2 Material and methods}

\textbf{2.1 Animal model of retinal ischemic injure and grouping}

For this study, we selected male C57BL/6J mice aged between 6 to 8 weeks
(20--25 g), and purchased from the Guangxi Medical University. The mice
were maintained in a 12-h light/dark cycle (lights on from 8:00 a.m. to
8:00 p.m.) with food and water ad libitum. The study protocols had the
approval by ethical committee of The People's Hospital of Guangxi Zhuang
Autonomous Region (No. KY-GZR-2019-053). All animal cares and
experimentations were carried out based on the Association for Research
in Vision and Ophthalmology Statement for the Use of Animals in
Ophthalmic and Vision Research.

To induce AOH injury in the mice, a precise and careful protocol was
followed.{[}12{]} The mice were first anesthetized with a 1\%
pentobarbital solution. To further minimize any discomfort, a topical
anesthetic of 0.5\% tetracaine hydrochloride was applied to the cornea,
and 0.5\% tropicamide phenylephrine eye drops were used to dilate the
pupil. Using a 32-gauge sterile needle, the anterior chamber of the
right eye was perfused with sterilized normal saline, which was
connected to a reservoir in the nasal direction. The intraocular
pressure was then raised and maintained at 110 mmHg by elevating the
reservoir to a height of 150 cm. This ensured that the AOH injury was
induced and sustained over a period of one hour. Intraocular pressure
was raised to 110 mmHg for 60 minutes; the needle was then removed to
allow natural retinal reperfusion{[}12,13{]}.After this time, the needle
was gently withdrawn. The left eye, which was cannulated without
elevating the reservoir, served as the control eye or the sham group.

\textbf{2.2 Protein Extraction and Trypsin Digestion and LC-MS/MS}

In this study, retinal tissues from five AOH and Sham mice were combined
into a single tube to create pooled samples for proteomics analyses.
Three replicates of pooled samples were used for each group, resulting
in a total of 15 retinas per group. The samples were flash-frozen in
liquid nitrogen and stored at -80\selectlanguage{ngerman}°C until use. To extract the proteins,
the retinal sample was added to Sodium Dodecyl sulfate and Tris buffer
\selectlanguage{english}(SDT\selectlanguage{english})buffer and then sonicated and boiled for 10 minutes. After
centrifugation at 14,000 xg for 15 minutes at 4degC, the supernatant was
collected and the protein concentration was determined using the BCA
Protein Assay Kit (Beyotime). Any remaining debris was removed during
the centrifugation step.

The digestion process for the samples involved reducing 150-200 \selectlanguage{greek}μ\selectlanguage{english}g of
proteins with DL-dithiothreitol (Sigma, USA) at 100 \selectlanguage{ngerman}°C for 5 minutes.
The resulting mixture was then purified using UA buffer (8 M Urea, 150
mM Tris-HCl pH 8.5) through multiple rounds of ultrafiltration
(Sartorius, 30 kD) to remove unwanted low-molecular-weight components.
To block reduced cysteine residues, 100 \selectlanguage{greek}μ\selectlanguage{english}l of iodoacetamide (100 mM IAA
in UA buffer) was added to the samples, which were then incubated in
darkness for 30 minutes. After washing the filters three times with 100
\selectlanguage{greek}μ\selectlanguage{english}l of UA buffer, followed by two washes with 100 \selectlanguage{greek}μ\selectlanguage{english}l of 0.1M TEAB buffer,
the protein suspensions were digested overnight at 37 \selectlanguage{ngerman}°C with 4 \selectlanguage{greek}μ\selectlanguage{english}g
trypsin (Promega) in 40 \selectlanguage{greek}μ\selectlanguage{english}l 0.1M TEAB buffer. The peptide content was
estimated by measuring the UV light spectral density at 280 nm, using an
extinction coefficient of 1.1 of 0.1\% (g/l) solution, which was
calculated based on the frequency of tryptophan and tyrosine in
vertebrate proteins. We used the TMT 10-plex system (Thermo Fisher
Scientific) for TMT-labeling, 100 \selectlanguage{greek}μ\selectlanguage{english}g of each sample was used with TMT
reagent.

LC-MS/MS analysis was conducted using a Q Exactive PLUS mass
spectrometer (Thermo Fisher Scientific) that was coupled to an Easy nLC
system (Thermo Fisher Scientific) for 90 minute. The mass spectrometer
was operated in positive ion mode, and survey scans were acquired at a
resolution of 70000 at m/z 200 with an AGC target of 3e6 and a maxIT of
50 ms. MS2 scans were acquired at a resolution of either 17500 or 35000
for HCD spectra at m/z 200 with an AGC target of 2e5 and a maxIT of 50
ms, and the isolation width was set to 2.0 m/z. Only ions with a charge
state between 2-6 and a minimum intensity of 2e3 were selected for
fragmentation, and dynamic exclusion for selected ions was set to 30
seconds. The normalized collision energy was set to 30 eV.

\textbf{2.3 Data Analysis}

The MS/MS raw files were processed using the MASCOT engine (Matrix
Science, London, UK; version 2.6) integrated into Proteome Discoverer
2.2 and searched against the database
(Uniprot\_MusMusculus\_17082\_20210928\_swissprot). The search
parameters included trypsin as the enzyme used to generate peptides,
with a maximum of 2 missed cleavages permitted. A precursor mass
tolerance of 10 ppm was specified, along with a 0.05 Da tolerance for
MS2 fragments. Carbamidomethyl (C) was set as a fixed modification,
while variable modifications included Oxidation (M) and Acetyl (Protein
N-term), except for TMT labels. A peptide and protein false discovery
rate of 1\% was enforced using a reverse database search strategy.

Data Availability

The MS proteomics data have been deposited to the PRIDE Archive
(http://www.ebi.ac.uk/pride/archive/) via the PRIDE partner repository
with the data set identifier PXD042988.

\textbf{2.4 Bioinformatics Analysis}

The identified differentially abundance proteins were analyzed using GO
terms (database version: go\_201504.obo) of the sequence with the top
Bit-Score by Blast2GO. The annotation from GO terms to proteins was
completed by Blast2GO Command Line. After the elementary annotation,
InterProScan was used to search the EBI database by motif, and the
functional information of the motif was added to proteins to improve
annotation. ANNEX was used to further improve the annotation and
connection between GO terms. Pathway analysis was performed using the
KEGG database, and protein clusters with a False discovery rate
\textless{} 0.05, and correlation value[?]0.7 were considered
statistically significant compared to the original ertapenem untreated
time point.

\textbf{2.5 Verification of Candidate Proteins by qRT-PCR}

The decidual immune cells were subjected to RNA extraction using TRIzol
reagent (Takara Biotechnology, Inc., Japan) following the manufacturer's
protocol. Primers were designed by Takara Biotechnology and the
sequences were blasted (Table 1). The resulting cDNA was synthesized
using the PrimeScript RT--PCR Kit (Takara) and amplified with duplicate
SYBR Green real-time PCR reagent (Roche). The \selectlanguage{greek}β-\selectlanguage{english}actin gene was used as
an endogenous control to normalize the data.

Table 1 List primers of identified proteins by real-time PCR\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
FABP7 & forward: TGAAACCACTGCAGATGATAGAA\tabularnewline
\midrule
\endhead
& reverse: TTTCTTTGCCATCCCATTTC\tabularnewline
Caveolin1 & forward: CACACCAAGGAGATTGACCTGG\tabularnewline
& reverse: CCTTCCAGATGCCGTCGAAACT\tabularnewline
S100a6 & forward: TACTCTGGCAAGGAAGGT\tabularnewline
& reverse: CAGGAAGGCGACATACTC\tabularnewline
Galectin & forward: GTAACACCAAGGAAGATGGGACC\tabularnewline
& reverse: TCATGTCCGTCTGGCAGCTTGA\tabularnewline
VILIP-1 & forward: CCTGCTCAACTACTCATTAAG\tabularnewline
& reverse: GGACATCTATTGCCACCTT\tabularnewline
XBP-1 & forward: CGCAGCACTCAGACTATG\tabularnewline
& reverse: GGTCCAACTTGTCCAGAAT\tabularnewline
\bottomrule
\end{longtable}

\textbf{2.6 Validation of Candidate Proteins by Western Blot Analysis}

Retinas were dissected and centrifuged at 4\selectlanguage{ngerman}°C for 20 min. Protein
samples were separated by sodium dodecyl\selectlanguage{english}-sulfate polyacrylamide gel
electrophoresis and transferred to polyvinylidene difluoride filter
membranes (Millipore). The membranes were then blocked with 5\% nonfat
dry milk in TBS-T at room temperature for 2.5 h. Primary antibodies for
FABP7 (1:500, CST,13347, United States), Caveolin1(1:500, Abcam,
ab32577, United States), S100A6 (1:500, CST,13162, United States),
Galectin-1 (1:500, CST,12936, United States), Visinin-like protein-1
(1:500, CST,49468, United States), XBP-1 (1:500, CST, 40435, United
States), and GAPDH (1:2,000, CST,5174, United States) were added to the
membranes and incubated with gentle rocking at room temperature for 1.5
h. The corresponding secondary antibody was then added and incubated at
room temperature for 1 h. Finally, protein visualization was performed
using an enhanced chemiluminescence system (Millipore) and
quantification was carried out with Image Lab 6.0 software (Bio\selectlanguage{english}-Rad).

\textbf{2.7 Immunofluorescence of Retinal Cryosections}

Eyes were prepared for cryosectioning by enucleation and embedding in
optimal cutting temperature compound (Sakura Finetek, Torrance,CA) at
-80degC. Sections of 5\selectlanguage{greek}μ\selectlanguage{english}m thickness were cut on a cryostat and placed on
poly-L-lysine coated slides, followed by fixation in cold acetone on ice
for 8 minutes. The sections were permeabilized using PBS with 0.05\%
Triton-X for 5 minutes at room temperature (RT) and then blocked with
5\% BSA in PBS at RT for 1 hour. Primary antibodies were incubated
overnight at 4\selectlanguage{ngerman}°C in a humidity chamber, followed by incubation with
secondary antibodies conjugated with a fluorescent dye (Alexa Fluor 488;
Molecular Probes/Invitrogen) for 1 hour at RT. Nuclei were stained with
4\selectlanguage{english}',6\selectlanguage{english}'-diamidino-2-phenindole\selectlanguage{english}(DAPI\selectlanguage{english}) for 6 min at room temperature.
Images were captured using a confocal microscope (LSM 980; Carl Zeiss
Meditec, Oberkochen, Germany) with excitation filters for DAPI and green
set at 450 nm and 549 nm respectively. The levels of staining were
determined by calculating the percentage of area with positive
fluorescence in the samples.

\textbf{2.8 Cell culture and Oxygen and glucose
deprivation/reoxygenation (OGDR) treatment}

The human trabecular meshwork (TM) cells and human retinal endothelial
cells (HRECs) were purchased from Procell Life Science \& Technology
Co., Ltd. (Wuhan, China), while the 661W photoreceptor cell line was
obtained from the American Type Culture Collection (Manassas, VA, USA).

In brief, Cell cultures were maintained in complete TM cell medium
(Procell, Wuhan, China), which contained 5\% (v/v) heat-inactivated
fetal bovine serum, 1\% TM cell growth supplement. Human retinal
endothelial cells (HRECs) were grown in endothelial cell medium (ECM,
ScienCell, USA) especially containing 1\% endothelial cell growth
supplement. Mouse retinal 661W cells were cultured in Dulbecco's
modified Eagle's medium (DMEM, Gibco, CA, USA) supplemented with 10\%
fetal bovine serum (FBS; Gibco, Carlsbad, CA, USA). The cells were
maintained at 37 degC in a humidified 5\% CO2 incubator. When cells grew
to 80\% \textasciitilde{} 90\% fusion, the cells at logarithmic growth
stage were used for the experiment.

To mimic the murine model of RIR~in vitro, cells were subjected to
oxygen and glucose deprivation for 3 h and subsequently returned to
their normal environment (37 degC, normoxic) and normal medium (DMEM
supplemented with 10\% FBS and glucose {[}4.5 g/L{]}) for 24 h prior to
harvest. The cells were transferred into serum and glucose-free medium
and placed under hypoxic conditions (5\% CO2 and 95\% N2) in a 37 degC
incubator for 3 h. Reperfusion was performed by exposing the cells to
normoxic culture conditions and culturing them in DMEM supplemented with
10\% FBS and glucose (4.5 g/L) in a 37 degC incubator for 24 h.

\textbf{2.9 Cell immunofluorescence assay}

The mouse TM, HRECs and 661W cells were subjected to oxygen and glucose
deprivation for 3 h followed by 24 h of OGDR. After OGDR, the cells were
fixed with 4\% PFA for 30 min and washed several times with PBS. The
primary antibodies targeted FABP7 (1: 200) and Caveolin1 (1:200). Cell
nuclei were stained with DAPI. Fluorescence images were acquired by two
independent observers with a Zeiss fluorescence microscope.

\textbf{2.10 Statistical Analysis}

The study data were presented as mean +- standard deviation (SD).
Statistical analysis was conducted using SPSS 23.0 software (SPSS Inc.,
USA). Student's t-test was performed to compare the normally distributed
variables between the two groups. Group differences were considered
significant if \emph{p} \textless{} 0.05. For functional enrichment
analysis of GO and pathway analysis, Fisher's exact test (two-tailed)
was used to examine the enrichment of differentially expressed proteins
compared to all identified proteins, and a corrected \emph{p} -value of
\textless{}0.05 was considered statistically significant. Proteins with
a fold change greater than 1.5 and a \emph{p} -value \textless{} 0.05
were considered differentially abundance proteins.

\textbf{3. Results}

\textbf{3.1 Protein Abundance on Retinas in AOH Mice}

The experimental workflow is depicted in Fig. 1, where MS-based
proteomics was utilized to investigate the AOH retina proteome to
identify molecules potentially involved in the pathogenic changes in
retinal hypoxia-ischemia caused by acute glaucoma. Using a cutoff of
1.5-fold changes and a p value \textless{}0.05, 92 Differentially
abundance proteins were identified in the AOH group compared to the
control group. To visualize the distribution of proteins with
differential expression, a volcano plot was generated (Fig. 2A). Out of
the 92 differentially abundance proteins, 58 were significantly
up-regulated while 34 were down-regulated in the AOH retina (Table 2).
Notably, the expression levels of Cav-1, S100a6, FABP7, and Galectin-1
were up-regulated, while VILIP-1 and XBP-1 were among the downregulated
proteins with a fold change greater than 1.5.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1} . Proteomics workflow schematic. Mass
spectrometry-based proteomics of retinal samples collected from C57BL/6
adult mice (control: n = 3; AOH: n = 3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} functional classification of the differentially
expressed retinal proteins. (\textbf{A)} Volcano plot showing variations
in protein abundances. x-axis, log2; y-axis, -log of P-value. Green,
downregulated proteins; red, upregulated proteins; grey, unchanged
proteins. (\textbf{B)} Histogram representing the distribution of
differentially expressed proteins. (\textbf{C)} The subcellular
localization of altered proteins. (\textbf{D)} Categories of
differentially expressed proteins according to Gene ontology (GO),
including biological process, molecular function, and cellular
component.

\textbf{Table 2.} List of differentially abundance proteins in AOH group
compared with control group.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
UniProt accession & Gene name & Protein description & AOH vs.control &
AOH vs.control\tabularnewline
\midrule
\endhead
& & & Fold Change & P-value\tabularnewline
P98086 & C1qa & Complement C1q subcomponent subunit A & 2.7657 &
0.0068\tabularnewline
Q3ULW8 & Parp3 & Protein mono-ADP-ribosyltransferase PARP3 & 2.5903 &
0.0001\tabularnewline
Q9CYL5 & Glipr2 & Golgi-associated plant pathogenesis-related protein 1
& 2.4901 & 0.0007\tabularnewline
Q91WP6 & Serpina3n & Serine protease inhibitor A3N & 2.2593 &
0.02737\tabularnewline
P14106 & C1qb & Complement C1q subcomponent subunit B & 2.1725 &
0.0080\tabularnewline
P49935 & Ctsh & Pro-cathepsin H & 2.1260 & 0.0027\tabularnewline
P50543 & S100a10 & Protein S100-A10 & 2.0532 & 0.0018\tabularnewline
Q9Z0E6 & Gbp2 & Guanylate-binding protein 2 & 2.0358 &
0.0000\tabularnewline
Q64282 & Ifit1 & Interferon-induced protein with tetratricopeptide
repeats 1 & 1.9625 & 0.0004\tabularnewline
P20065 & Tmsb4x & Thymosin beta-4 & 1.9551 & 0.0204\tabularnewline
Q06890 & Clu & Clusterin & 1.9322 & 0.0041\tabularnewline
Q9R118 & Htra1 & Serine protease HTRA1 & 1.8763 & 0.0045\tabularnewline
P03995 & Gfap & Glial fibrillary acidic protein & 1.8761 &
0.0003\tabularnewline
P13597 & Icam1 & Intercellular adhesion molecule 1 & 1.8351 &
0.0039\tabularnewline
O89017 & Lgmn & Legumain & 1.7948 & 0.0031\tabularnewline
Q64345 & Ifit3 & Interferon-induced protein with tetratricopeptide
repeats 3 & 1.7940 & 0.0003\tabularnewline
P24452 & Capg & Macrophage-capping protein & 1.7847 &
0.0004\tabularnewline
Q61233 & Lcp1 & Plastin-2 & 1.7613 & 0.0466\tabularnewline
P14901 & Hmox1 & Heme oxygenase 1 & 1.7487 & 0.00092\tabularnewline
P15655 & Fgf2 & Fibroblast growth factor 2 & 1.7442 &
0.0002\tabularnewline
O70370 & Ctss & Cathepsin S & 1.7409 & 0.0042\tabularnewline
P28076 & Psmb9 & Proteasome subunit beta type-9 & 1.7077 &
0.0032\tabularnewline
P29533 & Vcam1 & Vascular cell adhesion protein 1 & 1.7005 &
0.0002\tabularnewline
Q9QZ03 & Slc39a1 & Zinc transporter ZIP1 & 1.6969 &
0.0116\tabularnewline
Q641K1 & Agtpbp1 & Cytosolic carboxypeptidase 1 & 1.6939 &
0.0003\tabularnewline
O35309 & Nmi & N-myc-interactor & 1.6711 & 0.0005\tabularnewline
O35955 & Psmb10 & Proteasome subunit beta type-10 & 1.6686 &
0.0006\tabularnewline
P15379 & Cd44 & CD44 antigen & 1.6664 & 0.0000\tabularnewline
P14069 & S100a6 & Protein S100-A6 & 1.6624 & 0.0004\tabularnewline
Q64339 & Isg15 & Ubiquitin-like protein ISG15 & 1.6408 &
0.0002\tabularnewline
P51642 & Cntf & Ciliary neurotrophic factor & 1.6376 &
0.0021\tabularnewline
Q61263 & Soat1 & Sterol O-acyltransferase 1 & 1.6310 &
0.0004\tabularnewline
Q9WVA4 & Tagln2 & Transgelin-2 & 1.6300 & 0.0009\tabularnewline
P42227 & Stat3 & Signal transducer and activator of transcription 3 &
1.6238 & 0.0000\tabularnewline
O54983 & Crym & Ketimine reductase mu-crystallin & 1.6208 &
0.0006\tabularnewline
P10833 & Rras & Ras-related protein R-Ras & 1.6205 &
0.0035\tabularnewline
O70458 & Osmr & Oncostatin-M-specific receptor subunit beta & 1.6204 &
0.0005\tabularnewline
P26041 & Msn & Moesin & 1.6162 & 0.0003\tabularnewline
P21981 & Tgm2 & Protein-glutamine gamma-glutamyltransferase 2 & 1.6036 &
0.0105\tabularnewline
Q8R2Q8 & Bst2 & Bone marrow stromal antigen 2 & 1.5996 &
0.0001\tabularnewline
P08226 & Apoe & Apolipoprotein E & 1.5945 & 0.0009\tabularnewline
Q9QZU9 & Ube2l6 & Ubiquitin/ISG15-conjugating enzyme E2 L6 & 1.5680 &
0.0003\tabularnewline
Q9WUU7 & Ctsz & Cathepsin Z & 1.5625 & 0.0114\tabularnewline
Q6Q899 & Ddx58 & Antiviral innate immune response receptor RIG-I &
1.5620 & 0.0004\tabularnewline
Q9WV32 & Arpc1b & Actin-related protein 2/3 complex subunit 1B & 1.5537
& 0.0104\tabularnewline
Q9EQK5 & Mvp & Major vault protein & 1.5457 & 0.0003\tabularnewline
P21958 & Tap1 & Antigen peptide transporter 1 & 1.5429 &
0.0010\tabularnewline
Q5U4C3 & Scaf1 & Splicing factor, arginine/serine-rich 19 & 1.5420 &
0.0017\tabularnewline
P29391 & Ftl1 & Ferritin light chain 1 & 1.5394 & 0.0001\tabularnewline
P08905 & Lyz2 & Lysozyme C-2 & 1.5333 & 0.0361\tabularnewline
P42225 & Stat1 & Signal transducer and activator of transcription 1 &
1.5315 & 0.0007\tabularnewline
Q00493 & Cpe & Carboxypeptidase E & 1.5253 & 0.0335\tabularnewline
P50427 & Sts & Steryl-sulfatase & 1.5240 & 0.0001\tabularnewline
Q91VJ2 & Cavin3 & Caveolae-associated protein 3 & 1.5205 &
0.0032\tabularnewline
P35918 & Kdr & Vascular endothelial growth factor receptor 2 & 1.5076 &
0.0008\tabularnewline
P49817 & Cav1 & Caveolin-1 & 1.5060 & 0.0001\tabularnewline
Q61107 & Gbp4 & Guanylate-binding protein 4 & 1.5015 &
0.0016\tabularnewline
P16045 & Lgals1 & Galectin-1 & 1.5014 & 0.0011\tabularnewline
O35426 & Xbp1 & X-box-binding protein 1 & 0.7938 & 0.0455\tabularnewline
P62761 & Vsnl1 & Visinin-like protein 1 & 0.7011 & 0.0007\tabularnewline
Q61554 & Fbn1 & Fibrillin-1 & 0.6651 & 0.0076\tabularnewline
Q8BTI9 & Pik3cb & Phosphatidylinositol 4,5-bisphosphate 3-kinase
catalytic subunit beta isoform & 0.6647 & 0.0100\tabularnewline
P14094 & Atp1b1 & Sodium/potassium-transporting ATPase subunit beta-1 &
0.6624 & 0.0013\tabularnewline
Q56A07 & Scn2b & Sodium channel subunit beta-2 & 0.6553 &
0.0004\tabularnewline
P17879 & Hspa1b & Heat shock 70 kDa protein 1B & 0.6519 &
0.0019\tabularnewline
B2RSH2 & Gnai1 & Guanine nucleotide-binding protein G(i) subunit alpha-1
& 0.6503 & 0.0002\tabularnewline
Q9WVB0 & Rbpms & RNA-binding protein with multiple splicing & 0.6494 &
0.0004\tabularnewline
Q9JJZ8 & Cnga3 & Cyclic nucleotide-gated cation channel alpha-3 & 0.6484
& 0.0016\tabularnewline
Q80TL4 & Phf24 & PHD finger protein 24 & 0.6473 & 0.00023\tabularnewline
O55042 & Snca & Alpha-synuclein & 0.6459 & 0.0003\tabularnewline
Q6PHS9 & Cacna2d2 & Voltage-dependent calcium channel subunit
alpha-2/delta-2 & 0.6441 & 0.0003\tabularnewline
Q7TQD2 & Tppp & Tubulin polymerization-promoting protein & 0.6416 &
0.0006\tabularnewline
P0C192 & Lrrc4b & Leucine-rich repeat-containing protein 4B & 0.6401 &
0.0001\tabularnewline
Q3V384 & Afg1l & AFG1-like ATPase & 0.6391 & 0.0016\tabularnewline
Q9WUC3 & Ly6h & Lymphocyte antigen 6H & 0.6386 & 0.0008\tabularnewline
P11798 & Camk2a & Calcium/calmodulin-dependent protein kinase type II
subunit alpha & 0.6352 & 0.0004\tabularnewline
O35526 & Stx1a & Syntaxin-1A & 0.6332 & 0.0011\tabularnewline
Q3USH1 & Insyn2a & Inhibitory synaptic factor 2A & 0.6316 &
0.0012\tabularnewline
Q91VR8 & Brk1 & Protein BRICK1 & 0.6305 & 0.0055\tabularnewline
O55240 & Rdh5 & Retinol dehydrogenase 5 & 0.6113 & 0.0011\tabularnewline
Q9EP96 & Slco1a4 & Solute carrier organic anion transporter family
member 1A4 & 0.6038 & 0.0052\tabularnewline
Q08331 & Calb2 & Calretinin & 0.5821 & 0.0008\tabularnewline
P15409 & Rho & Rhodopsin & 0.5745 & 0.0004\tabularnewline
P63040 & Cplx1 & Complexin-1 & 0.5654 & 0.0023\tabularnewline
P15499 & Prph2 & Peripherin-2 & 0.5577 & 0.0008\tabularnewline
Q9ESG4 & Cltrn & Collectrin & 0.5573 & 0.0010\tabularnewline
Q9CX11 & Utp23 & rRNA-processing protein UTP23 homolog & 0.5561 &
0.0177\tabularnewline
Q5EBJ4 & Ermn & Ermin & 0.5536 & 0.0019\tabularnewline
Q8C838 & Trarg1 & Trafficking regulator of GLUT4 1 & 0.5380 &
0.0018\tabularnewline
Q9D387 & Lamp5 & Lysosome-associated membrane glycoprotein 5 & 0.5375 &
0.0006\tabularnewline
O35599 & Opn1mw & Medium-wave-sensitive opsin 1 & 0.5180 &
0.0013\tabularnewline
P86046 & Kcnj13 & Inward rectifier potassium channel 13 & 0.4900 &
0.0004\tabularnewline
Q8BHB9 & Clic6 & Chloride intracellular channel protein 6 & 0.4892 &
0.0007\tabularnewline
\bottomrule
\end{longtable}

\textbf{2.2 GO Enrichment Analysis of Differentially} Abundance

\textbf{Proteins in AOH Mice}

The differentially abundance proteins in this study were annotated using
GO categories, including biological process, cellular component, and
molecular function, as well as subcellular localization, to elucidate
their properties and functions. The top four GO keywords in biological
process were biological regulation, cellular process, response to
stimulus, and developmental process, with 87, 84, 63, and 63
differentially abundance proteins, respectively. The cellular component
analysis revealed that the DEPs were enriched in cell, organelle, and
organelle part, with 91, 70, and 56 differentially abundance proteins,
respectively. The most prevalent terms in molecular function were
binding (87 proteins), catalytic activity (36 proteins), and molecular
function regulator (18 proteins). Subcellular localization predictions
showed that the greater percentages of differentially abundance proteins
were enriched in the cytoplasm (37.0\%), plasma membrane (20.7\%), and
extracellular (16.3\%) (Fig. 2C). Based on GO functional classification,
the differentially abundance proteins were categorized into 55 GO terms,
including 25 biological process terms, 16 cellular component terms, and
14 molecular function terms. (Fig. 2D)

\textbf{3.3 Functional Enrichment Analysis of Differentially Expressed
Proteins in AOH Mice}

The study employed KEGG analysis to identify biological pathways for the
differentially abundance proteins between the two groups. The top 20
highly enriched pathways were displayed in Fig. 3A and 3B, including the
JAK-STAT signaling pathway, neuroactive ligand-receptor interaction,
GABAergic synapse, phototransduction, glutamatergic synapse, and cell
adhesion molecules. Domain enrichment analysis revealed that the
differentially abundance proteins were mainly linked to
Guanylate-binding protein, C-terminal domain superfamily, Visual
pigments (opsins) retinal binding site, Opsin, Guanylate-binding
protein/Atlastin, and C-terminal, Intercellular adhesion
molecule/vascular cell adhesion molecule, N-terminal (Fig. 3C).
Furthermore, the study used the STRING program to identify the PPI
networks for both the up- and down-regulated genes, and the results
suggested a close relationship among GFAP, FABP7, Calb2, Snca, Fgf2, and
Caveolin1 (Figure 3D).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. KEGG pathway~enrichment\emph{~} analysis\emph{~} of\emph{~}
differentially\emph{~} expressed\emph{~} proteins\emph{.}\textbf{A}
Bubble diagram representing the enrichment analysis of differentially
expressed proteins in KEGG pathways. \textbf{B} Pathways analysis of
significantly changed proteins. The top 20 signaling pathways were
determined by using the KOALA(KEGG Orthology And Links Annotation)soft
well. \textbf{C} Bubble diagram of differentially abundant proteins
enriched by Protein Domain. \textbf{D} In total 51 proteins formed a
protein-protein-interacting (PPI) network and PPI~network~between~6 key
targets.

\textbf{2.4 Validation of Candidate Proteins}

The study focused on investigating the JAK-STAT signaling pathway and
PPAR signaling pathway as both are involved in neuronal survival,
inflammation, proliferation, and vascular morphology and function.
Several proteins associated with these pathways, including Caveolin1,
FABP7, S100a6, Galectin-1, VILIP, and xbp-1, were selected for
validation using quantitative real-time PCR (qRT-PCR) and western
blotting. The results showed that Caveolin1, FABP7, S100a6, and
Galectin-1 were significantly up-regulated in AOH retinal tissue
compared to the sham group, while VILIP and xbp-1 were down-regulated
(Fig. 4A). The western blotting results were consistent with the qRT-PCR
results (Fig. 4B-G).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} Verification of differentially abundant
proteins.\textbf{(A)} Quantitative analysis of Caveolin1, FABP7, S100a6,
Galectin-1, VILIP and xbp-1 mRNA level. \textbf{(B-H)} Representative
western blot images and quantification of Caveolin1, FABP7, S100a6,
Galectin-1, VILIP and xbp-1. All tests were analyzed by a \emph{t} -test
with two-tailed and all data were expressed as the mean \selectlanguage{ngerman}± SEM (n = 3
each group). *\emph{p} \textless{} 0.05, **\emph{p} \textless{} 0.01.
***\emph{p} \selectlanguage{english}<0.001.

Immunofluorescence staining of retinal sections was used to show the
expression and location of FABP7 and Cav-1 after AOH. FABP7 was found to
be present in the inner retinal layer and showed increased intensity
after AOH. Anti-Cav-1 staining showed increased immunoreactivity of
Cav-1 located in the retinal endothelium (Fig. 5A-B). Our research has
demonstrated that the expression of Cav-1 progressively increases in
trabecular meshwork cells and human retinal endothelial cells during the
development of retinal hypoxia/ischemia injury. Furthermore, exposure of
photoreceptor cells to OGDR resulted in a significant upregulation of
FABP7(Fig. 5C-D).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Protein immunofluorescence analyses of retinal
sections. \textbf{(A)} Representative images of Caveolin-1and FABP7
staining in retinal sections of AOH and control group. Blue indicated
the DAPI staining whereas green indicated the Caveolin1 staining. Scale
bar: 20\selectlanguage{greek}μ\selectlanguage{english}m (n = 3). \textbf{(B)} The quantification of the percentage of
area with positive green fluorescence for samples shown in\textbf{.(C)}
Representative images of immunofluorescence staining targeting
Caveolin-1in TM, REC cells and FABP7 in 661W cell line exposed to OGDR.
Scale bar: 20 \selectlanguage{greek}μ\selectlanguage{english}m (n = 3). \textbf{(D)} Analysis of quantification of
percentage of staining positive cells with Cav-1(green) and FABP7(red).

\textbf{3. Discussion}

The present study utilized an unbiased proteomics approach to
investigate the molecular mechanisms underlying glaucomatous
neurodegeneration in an experimental glaucoma model induced by AOH.
Experimentally induced retinal I/R injury is an established model of
glaucoma injury that ultimately leads to RGC apoptosis{[}14{]}.
Proteomics, which is centered around the analysis of proteins, plays a
crucial role in enhancing our understanding of pathophysiological
pathways in glaucoma. By examining protein abundant, modifications, and
interactions, proteomics can provide a comprehensive link between
genotypic and phenotypic changes. Furthermore, since many therapies
target proteins, proteomics can contribute to drug development as well
as providing insights into cellular signaling and function. Glaucoma
researchers have long been studying protein expression, modifications,
and interactions in experimental models and postmortem human tissues
with glaucoma{[}5,6,9{]}. TMT proteomics, a novel method of protein mass
spectrometry, has emerged as a powerful tool for screening
differentially expressed proteins in various fields, including tumor
metastasis{[}15{]} , autoimmune disease{[}16{]} and HIV
infection{[}17{]}. This study is the first to use TMT quantitative
proteomics technology combined with 2D LC-MS/MS to screen for
differentially expressed proteins in the AOH-induced glaucoma model.

Table 2 presents a comprehensive list of significantly altered proteins,
including their fold changes and corresponding p-values. To validate the
results, Caveolin1, FABP7, S100a6, Galectin-1, VILIP, and xbp-1
proteins, known for their association with neurodegeneration,
neuroinflammation, and lipogenesis, were selected for further
investigation. The western blotting and real-time PCR results confirmed
the proteomics findings. The S100 protein family plays a significant
role in various diseases such as cancer, metabolic disease, neurological
disease, and vascular calcification. S100 proteins may facilitate cell
signal transduction in response to internal or external stimuli. S100a6,
a member of the S100 protein family, has been implicated in the
inflammatory response to ischemic conditions{[}18{]}. A study by
Stepanov et al. found that the pro-inflammatory S100a6 levels are
directly proportional to the severity of retinal ischemia{[}19{]}. The
findings from our study provide valuable insights into the mechanisms
underlying retinal ischemia and the related inflammatory response. Our
data suggest that the increase in S100a6 levels in the AOH retinal under
ischemic conditions could be indicative of inflammation being a major
factor in the development of this condition. The LGALS1 gene encodes
galectin-1, a member of the galactoside-binding lectin family that
is~crucial for angiogenesis. The statement of retinal ischemia causes
the release of a variety of inflammatory and angiogenic cytokines, which
in consequence encouraged galectin-1 up-expression{[}20{]}.
Interestingly, galectin-1 has been found to enhance cerebral ischemia
recovery by targeting vascular remodeling, which highlights its
potential as a therapeutic target for retinal ischemia{[}21{]}.
Galectin-1 has also been shown to aid cerebral ischemia recovery by
focusing on vascular remodeling.

Another protein that we investigated was VILIP-1, a member of the
neuronal calcium sensor family that regulates various neuronal
functions, including ion channel regulation, learning, and neuronal
growth. However, in the pathogenesis of stroke, VILIP-1 acts as a
neurotoxic factor for neurons when transient ischemia disturbs calcium
homeostasis{[}22{]}. These results have important implications for
developing strategies to prevent neuronal damage during ischemia.
Furthermore, our study also highlighted the critical role of X box
binding protein 1 (XBP 1), a key transcription regulator of the unfolded
protein response, in mediating adaptation to endoplasmic reticulum
stress {[}23{]}. Based on our findings, we hypothesize that a feedback
loop may exist between XBP 1, ERS, and lipogenesis, which could provide
novel insights into the mechanisms underlying the development of retinal
ischemia.

The STRING data analysis provided valuable insights into the potential
functions of FABP7 and Caveilin1 in the PPAR/JAK-STAT signaling pathway,
as they were found to be in the center of the proteomics function
network (fig.3D). An intriguing recent study suggests that FABP7 may
have an impact on Cav-1 expression, thereby regulating lipid raft
function in astrocytes, which could have important implications for the
pathogenesis of neurodegenerative disorders{[}24{]}. FABPs are small
cytosolic nonenzymatic proteins (14-15 kDa) that bind to long-chain
fatty acids and play critical roles in cellular uptake and transport of
fatty acids, intracellular signal transduction, and regulation of gene
expression{[}25{]}. After cerebral ischemia, FABP7 expression is
upregulated in neural stem/progenitor cells{[}26{]} , and our findings
indicate that FABP7 is also significantly upregulated in the retina
during the pathogenesis of retinal ischemia. Immunofluorescence data
revealed that FABP7 is primarily expressed in the photoreceptor layer of
the retina, where the most prevalent polyunsaturated fatty acids (PUFAs)
are found in photoreceptor membrane phospholipids. Interestingly, FABP7
has been demonstrated to bind to docosahexaenoic acid (DHA) in vitro,
which is one of the most abundant PUFAs in the retina and critical for
retinal function{[}27{]}. Furthermore, in FABP7-knockout mice, there
were notable changes in retinal morphology, including increased
acellular strands, less branching of the microvasculature, and
endothelial cell loss, which are characteristic of early diabetic
retinopathy{[}28{]}. Taken together, our findings suggest that FABP7 may
play a crucial role in regulating photoreceptor function under 'stress'
conditions, such as retinal ischemia, by modulating fatty acid uptake
and transport and influencing lipid raft function.

The scaffolding protein Caveolin-1, which has been discovered to be
increased in the AOH group, interacts with numerous signaling molecules.
The ability of Caveolin-1 to interact with numerous signaling molecules
and its presence in a variety of ocular cells, such as retinal vascular
cells, M\selectlanguage{ngerman}üller glia, retinal pigment epithelium, and typical aqueous
humor outflow cells, highlights its importance in transcellular
transport, endocytosis, cell proliferation, membrane lipid homeostasis,
and signal transduction. As a critical regulator of caveolae and their
molecular components, Caveolin-1 is essential for maintaining the
integrity of the blood-retinal barrier, regulating ocular inflammatory
signaling, preventing pathogen entry at the ocular surface, and
facilitating aqueous humor drainage. Moreover, the importance of Cav-1's
cell-specific inflammatory regulatory characteristics is exemplified in
numerous studies of acute lung injury, where its deficiency has been
shown to reduce local cytokine production {[}29{]}. In the retina,
retina-specific Cav-1 deficiency has been demonstrated to suppress STAT3
phosphorylation after retinal damage, highlighting the protein's role in
retinal neuroprotection{[}30{]} . The observed increase in Cav-1
expression in the AOH model retina suggests that it may act as a
protective factor for retinal neuroprotection, underscoring its
importance in maintaining the delicate balance of ocular homeostasis.

In response to acute IOP elevation, the retina undergoes significant
changes in several key processes, including cellular and developmental
processes, as well as its response to stimulus. Our KEGG pathway
enrichment analysis identified several pathways that were primarily
involved in the JAK-STAT signaling pathway, neuroactive ligand-receptor
interaction, GABAergic synapse, and phototransduction. By using a
combination of functional pathways and protein-protein interaction
analysis tools, we were able to identify specific pathways that are
impacted by high IOP. Interestingly, our analysis revealed a close
relationship among GFAP, FABP7, and Caveolin1, which are all associated
with the JAK-STAT and PPAR signaling pathways that were upregulated or
induced in the animal model of high IOP (fig.6). These findings suggest
that there may be complex interactions between these proteins that play
a crucial role in the retinal response to elevated IOP. Further studies
on these pathways and proteins may shed light on potential targets for
therapeutic interventions to prevent or treat ocular diseases associated
with high IOP.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 6. Diagram illustrating the pathway by which FABP7 and Caveolin1
are involved in the biological functions of neuroinflammation and
retinal lipid homeostasis, contributing to the pathogenesis of acute
glaucoma. Fabp-7, Fatty acid-binding protein 7; MMPs, matrix
metalloproteinases; Cav-1, caveolin-1; LD, lipid droplet; FAs, fatty
acids; ROS, Reactive Oxygen Species,; eNOS, Nitric oxide synthase;
STAT3, signal transducer and activator of transcription 3; JAK, janus
kinase; IL-6, Interleukin-6; IL-10, Interleukin-10; TNF, tumour necrosis
factor.

The JAK/STAT signaling pathway is a fascinating and multifaceted
intracellular signal transduction pathway that plays a crucial role in
many biological processes, including hematopoiesis, immune fitness,
tissue repair, inflammation, apoptosis, and adipogenesis{[}31{]}.
Besides, PPAR family of nuclear receptors plays a major regulatory role
in energy homeostasis and metabolic function. Research conducted by Sun
X et al. has shown that the activation of PPAR signal transduction in
macrophages can lead to the downregulation of JAK-STAT signals and the
enhancement of fatty acid metabolism{[}32{]}. Cholesterol homeostasis is
crucial for neuronal survival{[}33{]}. Retinal lipid homeostasis is also
tightly regulated by several mechanisms implicated in retinal lipid
trafficking between neurons and glial cells and eliminating lipids
through blood circulation{[}34{]}. A drop in cholesterol level in the
cell's membrane causes dissociation and dysfunction of glutamate
transporter lipid rafts. The excess glutamate level at the synaptic
level leads to neurotoxicity. We can assume that modulations of cell
membrane proteins in lipid rafts are possible in glaucomatous
neuropathy. JAK-STAT signals and PPAR also exhibit marked
anti-inflammatory capacities{[}35{]}, which can play a role in
protecting the neurons from neuroinflammation and subsequent neuronal
loss, an essential pathophysiological process in glaucomatous
neurodegeneration. Moreover, PPRA-mediated STAT3 downregulation has been
shown to interfere with STAT-dependent transcriptional activity, which
may also contribute to the anti-inflammatory action of PPAR
activation{[}36{]}. Neuroinflammation leading to neuronal loss is also
an important pathophysiological process of glaucomatous
neurodegeneration.

Using a multi-step data analysis procedure, the researchers were able to
identify and quantify the relative abundances of proteins and compare
them between experimental groups. This approach allowed for a thorough
evaluation of potential differences in protein expression and provided a
statistically sound framework for interpretation of the data.
Furthermore, the reliance on current protein databases and previously
published data from a wide range of biological systems highlights the
interdisciplinary nature of modern scientific research. By pooling
information from various sources, researchers were able to identify
potential protein-protein interactions and pathways that may be relevant
to the observed findings. It is important to note that the results
presented in this study are based on interpretations of the data and
predicted pathways. However, these findings represent an important step
forward in understanding the mechanisms underlying conditions such as
acute glaucoma and elevations in intraocular pressure. As with any
scientific study, further investigation is necessary to confirm and
expand upon these findings, but the potential implications for future
research and treatment are promising.

\textbf{5. Conclusions}

In conclusion, we discovered many protein candidates that may be related
to retinal ischemic injury in acute glaucoma using quantitative
proteomics to analyze protein modifications in AOH retinas. For patients
with acute glaucoma, the neuroinflammation and retinal lipid homeostasis
may be involved and represent potential therapeutic approaches. The
upregulation of FABP7 and Cav-1 levels in ischemia retinas highlights
the intricate network of signaling pathways involved in retinal
dysfunction. While our study provides a foundation for understanding the
pathogenesis of retinal ischemia, further research is needed to fully
elucidate the underlying mechanisms and identify novel therapeutic
interventions to treat acute glaucoma.

\textbf{Author Contributions:} G.H.: Data curation; formal analysis;
investigation. Y.L.: Methodology; investigation; project administration.
F.T.: Investigation; methodology; data curation. H.H.: Investigation;
resources. Q.C.: Validation; data curation. N.S.: Investigation;
resources; project administration. L.C.: Supervision; project
administration. F.X.: Project administration; funding acquisition. C.S.:
Conceptualization; funding acquisition; writing---review and editing;
writing---original draft. All authors have read and agreed to the
published version of the manuscript.

\textbf{Funding:} This work was supported by Guangxi Natural Science
Foundation Program (Grant No. 2020GXNSFBA159057), Guangxi Clinical
Ophthalmic Research Center (No. Guike AD19245193), and the National
Natural Science Foundation (No. 82000925, 82070982,82060179).

\textbf{Declaration of Competing Interest:} The authors declare no
competing financial interest.

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