Abstract
Ectopia lentis is a hallmark of Marfan syndrome (MFS), a genetic
connective tissue disorder affecting 1/5000 to 1/10 000 individuals
worldwide. Early detection in ophthalmology clinics and timely
intervention of cardiovascular complications can be lifesaving. In this
study, a modified proteomics workflow with liquid chromatography‐tandem
mass spectrometry (LC‐MS/MS)‐based data‐independent acquisition (DIA)
and field asymmetric ion mobility spectrometry (FAIMS) to profile the
proteomes of aqueous humor (AH) and lens tissue from MFS children with
ectopia lentis is utilized. Over 2300 and 2938 comparable proteins are
identified in AH and the lens capsule, respectively. Functional
enrichment analyses uncovered dysregulation of complement and
coagulation‐related pathways, collagen binding, and cell adhesion in
MFS. Through weighted correlation network analysis (WGCNA) and machine
learning, distinct modules associated with clinical traits are
constructed and a unique biomarker panel ([38]Q14376, [39]Q99972,
[40]P02760, [41]Q07507; gene names: GALE, MYOC, AMBP, DPT) is defined.
These biomarkers are further validated using advanced parallel reaction
monitoring (PRM) in an independent patient cohort. The results provide
novel insights into the proteome characterization of ectopia lentis and
offer a promising approach for developing a valuable biomarker panel to
aid in the early diagnosis of Marfan syndrome via AH proteome.
Keywords: aqueous humor proteomics, DIA, ectopia lentis, FAIMS, Marfan
syndrome
__________________________________________________________________
The rare disease Marfan syndrome (MFS) is explored using advanced mass
spectrometry. Over 2000 aqueous humor proteins in pediatric patients
are discovered, with an investigation into clinical correlations and a
potential biomarker panel for early MFS detection. This study reveals
valuable insights into MFS pathophysiology, enriching our understanding
of the intricate ocular and systemic interplay.
graphic file with name ADVS-11-2303161-g003.jpg
1. Introduction
Marfan syndrome (MFS) is an autosomal dominant connective tissue
disease with a prevalence of 1/5000 to 1/10 000 and is characterized by
extensive disorders of mesodermal tissues, including abnormalities of
the ocular, skeletal, and cardiovascular systems.^[ [42]^1 , [43]^2 ^]
Notably, it is also an age‐related disease, meaning that the
complications gradually progress with age.^[ [44]^3 ^] The
cardiovascular system, in particular, is responsible for the fact that
the average natural life expectancy of MFS patients was only 32 years
when there was no effective treatment.^[ [45]^4 , [46]^5 ^] The
thoracic aortic disorder generally starts as an asymptomatic
enlargement of the aortic root and progresses to an aneurysm over time.
As the aneurysm grows, it becomes unstable and may eventually result in
an acute ascending aortic dissection, a potentially fatal consequence
that can shorten lifespan.^[ [47]^3 ^] Thus, the gradual and
asymptomatic nature of such lethal complications renders diagnosis at
an early stage challenging. Therefore, there is a pressing need for
maximally multidimensional diagnostic strategies to screen patients in
at‐risk populations as early as possible. Importantly, ectopia lentis
(that is, luxation or subluxation of the lens) is a stable and
characteristic manifestation of MFS. Approximately 60% of patients
present with ectopia lentis at the age of 3 to 5 years.^[ [48]^6 ,
[49]^7 , [50]^8 , [51]^9 ^] Most children initially present to
ophthalmologists' clinics with poor or blurred vision. This finding has
prompted our group to focus on those pediatric cases, with the goal of
identifying potential patients, and cardiovascular follow‐up will then
be advised to parents so that timely therapeutic intervention may save
lives.
Aqueous humor (AH), a transparent fluid nourishing the avascular
tissues in the anterior segment of the eyes, is essential for many
physiological functions.^[ [52]^10 , [53]^11 ^] AH sampling and
analysis have been frequently used in clinical practice to aid the
diagnosis and treatment of ocular diseases.^[ [54]^12 ^] AH proteins
are derived from not only the lens and ciliary body but also filtered
plasma across the blood‐ocular barrier.^[ [55]^13 ^]
High‐accuracy mass spectrometry (MS)‐based quantitative proteomics is a
promising approach for discovering new biomarkers since it is
particularly effective at recognizing changes in protein abundance
levels in various specimens.^[ [56]^14 , [57]^15 ^] In the context of
complicated diseases, MS‐based proteomics could move the emphasis from
individual proteins to biomarker panels. However, proteomic analysis of
AH has proven difficult due to the extremely limited volume collected,
low protein concentrations, and wide dynamic range of protein levels,
resulting in less in‐depth coverage and lower throughput compared to
other body fluids, such as the cerebrospinal fluid, urine, or plasma.^[
[58]^16 , [59]^17 ^]
Recent technical advances in data‐independent acquisition (DIA) mode in
conjunction with field asymmetric ion mobility spectrometry (FAIMS)
interface have significantly improved the detectability of
low‐abundance proteins.^[ [60]^18 ^] Our team has recently integrated
these state‐of‐the‐art technologies into a modified workflow, extended
the pipeline of biomarker discovery, and employed high‐throughput
MS‐based proteomics to acquire proteome profiles of children's AH and
lens tissue.
Here, we improved the depth of protein coverage by a large amount and
first provided proteome profiling of the children with ectopia lentis.
More specifically, we detected over 2300 proteins from minimal sample
amounts both in AH and the anterior lens capsule. By employing weighted
correlation network analysis (WGCNA) and machine learning, we
characterized the proteome of the two biofluids, defined unique protein
panels, and constructed the module associated with MFS clinical traits.
Taken together, our findings provide evidence that modern quantitative
MS‐based proteomics can serve as a clinically useful approach for
identifying potential biomarkers of MFS as well as provide insights
into molecular processes related to MFS and/or FBN1 mutations.
2. Experimental Section
2.1. Clinical Cohorts and Ophthalmologic Examinations
The current study was a case‐control investigation that included 32
cases and 31 controls, encompassing both the discovery and validation
cohorts. The cases were MFS patients with confirmed FBN1 gene mutation
recruited from a long‐term case series study on ectopia lentis
conducted by Eye and Ear, Nose and Throat Hospital of Fudan University,
Shanghai, from 2017 to 2022. The controls were age‐matched cataract
patients without ectopia lentis or any other ocular comorbidities. All
participants’ guardians were given a verbal description of the study
before enrollment and completed an informed consent form. The study
procedures adhered to the tenets of the Helsinki Declaration.
After obtaining a detailed history, each participant underwent systemic
ophthalmological examinations including refraction, slit‐lamp, and
fundus examination. The anterior segmental parameters including axial
length (AL), flat keratometry value (K1), steep keratometry value (K2),
mean keratometry value (Km), cylinder diopter (Cyl), axis, and anterior
chamber depth (ACD) were assessed with a biometer (IOL Master 700, Carl
Zeiss Meditec, Jena, Germany) at a sitting position. All patients
underwent phacoemulsification and intraocular lens implantation. AH
samples (20–100 µL) and the anterior lens capsule were collected during
the surgery.
2.2. Sample Preparation
2.2.1. Protein Extraction
The lens capsules were sonicated on ice using a high‐intensity
ultrasonic processor (Scientz, Ningbo, China) in lysis buffer (8 M
urea, Sigma‐Aldrich; 1% protease inhibitor cocktail, Merck Millipore).
The cell debris was removed by centrifugation at 12 000 g at 4 °C for
10 min and the supernatant was collected. Protein concentration was
determined with a BCA kit according to the manufacturer's instructions
(Beyotime, Shanghai, China).
2.2.2. Trypsin Digestion
For digestion, the protein solution was reduced with 5 mM
dithiothreitol (Sigma‐Aldrich) for 30 min at 56 °C and alkylated with
11 mM iodoacetamide (Sigma‐Aldrich) for 15 min at room temperature in
darkness. The protein sample was then diluted by adding 100 mM
Tetraethylammonium bromide (TEAB, Sigma‐Aldrich) to urea concentration
less than 2 M. Finally, trypsin (Promega) was added at 1:50
trypsin‐to‐protein mass ratio for the first digestion overnight and
1:100 trypsin‐to‐protein mass ratio for a second 4 h digestion.
Finally, the peptides were desalted by the C18 SPE column.
2.2.3. High‐Performance Liquid Chromatography (HPLC) Fractionation (For AH
Samples Only)
The sample was fractionated by high pH reverse‐phase HPLC using Agilent
300 Extend C18 column (5 µm particles, 4.6 mm ID, 250 mm length). The
analysis was conducted at a wavelength of 214 nm with a column
temperature of 35 °C. Prior to sample injection, the chromatographic
column was equilibrated with 95% buffer A (a water solution containing
5% acetonitrile, ThermoFisher Scientific) for 30 minutes until the
baseline stabilized. Subsequently, a gradient elution method was
initiated, and peptide samples were introduced to the HPLC system.
Sample separation was carried out using a 1‐minute per tube collection
interval. Fractions 11 to 46, totaling 36 fractions, were collected and
subsequently combined into 12 fractions, followed by vacuum drying.
2.2.4. Liquid Chromatography‐Tandem Mass Spectrometry (LC‐MS/MS) Analysis
The tryptic peptides were dissolved in mobile phase A (containing 0.1%
formic acid, Fluka, and 2% acetonitrile, ThermoFisher Scientific) and
separated on Vanquish neo (for AH samples) or EASY‐nLC 1200 (for lens
capsule samples) ultra‐performance liquid chromatography (UPLC). Mobile
phase B consisted of a solution with 0.1% formic acid and 90%
acetonitrile. The liquid gradient was programmed as follows: 0–16 min,
7%−20% B; 16–24 min, 20%−32% B; 24–27 min, 32%−80% B; 27–30 min, 80% B,
with a constant flow rate of 500 nl/min (for AH samples). 0–68 min,
6%∼23% B; 68–82 min, 23%∼32% B; 82–86 min, 32%∼80% B; 86–90 min, 80% B,
with a constant flow rate of 500 nl/min (for lens capsule samples). The
peptides were subjected to a nano‐spray ionization source followed by
tandem mass spectrometry (MS/MS) in Orbitrap Exploris 480 Mass
Spectrometer equipped with a high FAIMS Pro interface (ThermoFisher
Scientific, Bremen, Germany).
MS spectra of lens capsule samples were acquired with a data‐dependent
acquisition (DDA) mode. The top 25 precursors were sequentially
isolated and fragmented in higher‐energy collisional dissociation (HCD)
with 27% collision energy. FAIMS compensation voltage (CV) was set to
−45 V, and −65 V. Automatic gain control (AGC) was set at 100%, with an
intensity threshold of 5E4 ions/s and a maximum injection time of
“Auto.” The raw MS data were processed using Thermo Proteome Discoverer
(v2.4.1.15). The database utilized was
“Homo_sapiens_9606_SP_20220107.fasta,” comprising 20376 protein
sequences. To assess and control for false positive identifications
resulting from random matches, a decoy database was included.
Additionally, a common contaminant database was incorporated into the
search to mitigate the impact of contaminant proteins on identification
results. The enzyme cleavage specificity was set to Trypsin (Full),
allowing for up to 2 missed cleavage sites. The minimum peptide length
was defined as 6 amino acid residues, with a maximum of 3 variable
modifications allowed per peptide. The tolerance for mass errors was
set at 10 ppm for the precursor ions and 0.02 Da for the fragment ions.
Fixed modification Carbamidomethyl (C) was applied, while variable
modifications included Oxidation (M), Acetyl (N‐terminus), Met‐loss
(M), and Met‐loss+acetyl (M). False Discovery Rates (FDR) for protein,
peptide, and Peptide‐Spectrum Match (PSM) identifications were all
established at 1%.
MS spectra of AH samples were acquired with DIA mode, where HCD
collision energy was set to 25,30,35. FAIMS CV was set to −45 V, −70 V.
AGC was set at 3E6 ions/s with a maximum injection time of “Auto.” The
resulting MS data were processed using Spectronaut (V16.3) with default
software parameters. The database utilized for this analysis was
“Homo_sapiens_9606_SP_20 230 103,” containing 20389 protein sequences.
Trypsin/P was selected as the enzyme cleavage specificity with up to 2
missed cleavage sites allowed. C modification was designated as a fixed
modification for cysteine residues, while variable modifications
included oxidation of methionine residues and acetylation at the
N‐terminus of proteins. A decoy database was introduced to calculate
the FDR resulting from random matches. FDR thresholds of 1% were
applied for protein, peptide, and PSM identifications.
2.2.5. AH Spectral Library Generation
To maximize the protein coverage identified in pooled samples and
enhance the depth of identification in formal samples, peptides were
uniformly extracted from 53 AH samples. An equal amount of peptides was
obtained from each sample, totaling 3.8 µg per sample. These peptides
were used to create a pooled sample with a total quantity of 200 µg for
library construction. The previously described HPLC gradient method was
employed for peptide separation. Peptides, dissolved in mobile phase A,
were separated using the EASY‐nLC 1200 UPLC. The liquid gradient was
programmed as follows: 0–16 min, 7%−20% B; 16–24 min, 20%−32% B;
24–27 min, 32%−80% B; 27–30 min, 80% B, with a constant flow rate of
500 nl/min. Following separation by the UPLC system, peptides were
ionized in the NSI source and then analyzed in Orbitrap Exploris 480
Mass Spectrometer equipped with a high FAIMS Pro interface
(ThermoFisher Scientific, Bremen, Germany). The ion source voltage was
set to 2300 V, and FAIMS CV were ‐45 V and ‐70 V. Both precursor ions
and their secondary fragments were detected and analyzed in the
high‐resolution Orbitrap. The first mass spectrometry scan ranged from
400–1200 m/z with a scan resolution of 60000. The secondary mass
spectrometry scan had a fixed starting point at 110 m/z with a
resolution of 30000, and TurboTMT was turned off. Data acquisition was
performed using the DDA approach. This involved selecting the top 15
precursor ions with the highest signal intensity following the first
scan, subjecting them sequentially to HCD with 27% collision energy,
and subsequently conducting secondary mass spectrometry analysis. To
optimize mass spectrometry efficiency, AGC was set at 75%, the signal
threshold was 10000 ions/s, and the maximum injection time was 100 ms.
Dynamic exclusion for tandem mass spectrometry scans was set at 30 s to
prevent repeated scanning of precursor ions and enhance spectral
utilization.
For DDA data analysis, the embedded Pulsar search engine within
Spectronaut (v 16.3) was utilized with default software parameters. The
database employed for this analysis consisted of 20389 protein
sequences from “Homo_sapiens_9606_SP_20 230 103.” A decoy database was
included to assess the FDR resulting from random matches. The enzyme
cleavage specificity was set to Trypsin/P, allowing for up to 2 missed
cleavage sites. The minimum peptide length was defined as 7 amino acid
residues and a maximum of 5 variable modifications were permitted per
peptide. Carbamidomethyl (C) was designated as a fixed modification for
cysteine residues, while variable modifications included the oxidation
of methionine residues and acetylation at the N‐terminus of proteins.
The FDR for protein, peptide, and PSM identifications was established
at 1%.
2.2.6. Quantitative Analysis and Differential Protein Selection
For DIA proteomic quantitative analysis (AH), search results yield the
Normalized Intensity for each protein across diverse samples (the
protein's original intensity values normalized across samples).
Relative Quantification (R) for proteins across various samples is
derived through a centering transformation on the Normalized Intensity
(I). The calculation formula is as follows, where i represents the
sample, and j represents the protein:
[MATH:
Rij=Iij/Mean(Ij) :MATH]
(1)
Similarly, in Label‐Free proteomic quantitative analysis (lens
capsule), search results provide the LFQ (Label‐Free Quantification)
Intensity for each protein across different samples (the protein's
original intensity values normalized across samples). Relative
Quantification (R) for proteins across diverse samples is acquired by
centering the LFQ Intensity (I). The calculation formula is as follows,
where i represents the sample, and j represents the protein:
[MATH:
Rij=Iij/Mean(Ij) :MATH]
(2)
To assess differential protein expression between sample groups, the
Fold Change (FC) is calculated as the ratio of the mean relative
quantitative values for each protein across multiple replicate samples.
For example, when comparing sample group A to sample group B, the
formula is as follows, where R represents the relative quantitative
values of proteins, i refers to samples, and k pertains to proteins:
[MATH:
FCA/B,k=Mean(Rik,i∈A)/Mean(Rik,i∈B) :MATH]
(3)
The statistical significance of these differences is determined by
conducting a t‐test on the relative quantitative values of each protein
within the compared sample groups. The resulting P‐value, with a
default threshold of P‐value < 0.05, serves as the measure of
significance. To meet the normal distribution assumptions required for
the t‐test, the relative quantitative values of proteins undergo a Log2
transformation prior to testing, following this formula:
[MATH: Pk=T·test(log2(Rik,i∈A),log2(Rik,i∈B)) :MATH]
(4)
In this differential analysis, protein expression changes are deemed
significant when the P‐value is less than 0.05. Changes exceeding a
1.5‐fold increase are considered significant upregulation, while
changes less than 1/1.5‐fold are considered significant downregulation.
2.2.7. Protein Annotation and Functional Enrichment
We annotated the subcellular structure of the protein using WoLF PSORT
software ([61]http://www.genscript.com/psort/wolf_psort.html) The Pfam
database ([62]http://pfam.xfam.org/) was used for protein domain
enrichment analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes
and Genomes (KEGG) databases were used for GO categories and KEGG
pathway enrichment analysis. Fisher's exact test was used to analyze
the significance of the above functional enrichment of differentially
expressed proteins (DEPs) (using the identified protein as the
background). A p‐Value < 0.05 was considered significant. For further
hierarchical clustering based on differentially expressed protein
functional classification, we first collated all the categories
obtained after enrichment along with their p‐Values and then filtered
for those categories that were at least enriched in one of the clusters
with a p‐Value < 0.05. This filtered p‐Value matrix was transformed by
the function x = −log10 (p‐Value). These p‐Values were then clustered
by one‐way hierarchical clustering (Euclidean distance, average linkage
clustering) in Genesis. Cluster membership was visualized by a heat map
using the “heatmap” function from the “ggplot2” R‐package.
2.2.8. Protein‐phenotype Correlation Analysis
We performed WGCNA to cluster proteins with similar expression
patterns, that is, modules, and investigate their association with
specific ocular traits. The co‐expression network construction process
involved the following steps: First, hierarchical clustering analysis
was conducted based on protein expression to detect outliers. Next, a
weighting coefficient, β, was selected to establish the adjacency
matrix and achieve a network with scale‐free topology characteristics.
Then, modules were defined as branches of a cluster tree using
hierarchical clustering and dynamic tree‐cutting methods. The R package
WGCNA was used to construct the network (version 1.69).
To identify key proteins, we employed a two‐step approach. Firstly, we
calculated the Pearson correlation coefficients between the module
eigengenes (MEs) and clinical traits to identify modules significantly
associated with the clinical trait (P < 0.05). Secondly, we calculated
the Pearson correlation coefficients between the protein expression
levels and clinical traits [Gene significance (GS)]. We defined the top
10 hub proteins with the highest absolute value of GS in each
clinically significant module. Hub proteins were then searched against
the STRING database version 11.5 for protein‐protein interactions
(PPIs). Functional enrichment analyses were conducted by the
corresponding database as described above.
2.2.9. Machine Learning Strategy
In the field of machine learning, feature selection plays a crucial
role in improving model performance and preventing overfitting. In this
study, we employed a feature screening process to eliminate irrelevant
and redundant features, thus reducing the risk of unstable model
outcomes and poor generalization ability. Firstly, we excluded features
with a missing rate greater than 50% and imputed the missing values of
the remaining data using the k‐nearest neighbor (KNN) algorithm.^[
[63]^19 , [64]^20 ^] Next, we removed features with zero variance, as
they provided no predictive value. To mitigate the effect of
collinearity between features, we computed the Pearson correlation
coefficient between features, and highly correlated features (r > 0.8)
were filtered such that only the feature with the highest correlation
with sample classification was retained. The resultant set of 770
features was used for subsequent machine‐learning analyses.
To construct a classifier, we utilized the support vector machine (SVM)
algorithm and divided the dataset into training and test sets using
hierarchical sampling.^[ [65]^20 ^] Model performance evaluation
metrics, including sensitivity, specificity, accuracy, and the area
under the receiver operating characteristic (ROC) curve, were
calculated to assess the model's predictive ability. Sensitivity,
specificity, and accuracy were computed using the standard formulas.
Additionally, to evaluate the discriminative ability of each feature
for sample classification, we applied the univariate feature analysis
method on the filtered data features, calculating the correlation
between each feature and the sample class using variance analysis.
Scores and corresponding p‐Values were calculated for each expression
feature, and expression features were sorted based on their calculated
p‐Values.
The aim was to identify the optimal subset of expression features to
achieve the best prediction accuracy. We used the incremental feature
selection (IFS) method to obtain the optimal subset of expression
features.^[ [66]^21 ^] The IFS method involved constructing a feature
subset in each iteration using the top i sorted expression features,
followed by calculating the prediction accuracy of the feature subset
for the sample using 10‐fold cross‐validation. The accuracy results
were plotted against the number of features in the expression feature
subset to obtain the IFS curve.
Finally, to visualize the prediction results on the optimal expression
feature subset and the locally optimal expression feature subset, we
used the confusion matrix of the model. The confusion matrix displayed
the predicted and actual sample classifications, providing an intuitive
understanding of the model's performance.
2.2.10. Parallel Reaction Monitoring (PRM) Validation
PRM analysis was developed and applied to validate the differentially
expressed peptides combined with clinically significant proteins as
determined by WGCNA and machine learning. Each protein was quantified
using two unique peptides. The synthesis of mixed samples of labeled
peptides (original samples and synthetic peptide segments) was carried
out using solid‐phase synthesis. Peptide synthesis occurs from the
C‐terminal to the N‐terminal (amino end), whereby the first amino acid
of the target peptide is linked to the solid support via a covalent
bond at the C‐terminal end. Subsequently, starting from the N‐terminal
of the first amino acid, the amino‐protecting group is removed, and
excess activated second amino acid is reacted to extend the peptide
chain. This process is repeated to achieve the desired length of the
synthesized peptide chain. Finally, the peptide chain is cleaved from
the resin, purified, and isolated to obtain the target peptide. PRM was
performed on Orbitrap Exploris 480 (Thermo Fisher Scientific) with
EASY‐nLC 1200. The resulting MS data were searched against
Homo_sapiens_9606_SP_20 230 103.fasta and processed using Skyline
(v.21.2). Peptide settings: enzyme was set as Trypsin [KR/P], and max
missed cleavage set as 0. The peptide length was set as 7–25, and the
cysteine alkylation was set as fixed modification. Transition settings:
precursor charges were set as 2, 3, ion charges were set as 1, and ion
types were set as b, y. The product ions were set from ion 3 to the
last ion, the ion match tolerance was set as 0.02 Da.
3. Results
3.1. Study Design and Clinical Synopsis
The overall workflow of this study is shown in Figure [67] 1 . During
the discovery phase, 53 AH samples (27 MFS and 26 cataract controls
(CC)) from 53 children and 10 lens capsule tissue samples were analyzed
by LC‐MS/MS‐based DIA and label‐free quantitation. The sex, age, AL,
K1, K2, Km, Cyl, axis, and ACD of cases and controls are summarized in
Table [68] 1 . First, we conducted differential abundance analysis and
compared DEPs between two paired samples in each group. Functional
enrichment analyses were used to reveal the biospecimen
characterization of ectopia lentis and to further explore the
underlying biological pathway of MF syndrome.
Figure 1.
Figure 1
[69]Open in a new tab
Overview of the Study Workflow.
Table 1.
Clinical characteristics of enrolled patients in the discovery phase.
Group/Characteristic/No.
MF[70] ^a)
N = 27
CC[71] ^b)
N = 26
Gender, Male (number) 13 14
Age (mean, SD) 6.93±2.97 5.15±3.17
AL[72] ^c) (mean, SD) 24.95±2.25 21.96±1.31
K1 (mean, SD) 39.28±1.41 42.99±2.40
K2 (mean, SD) 41.05±1.42 44.95±2.40
Km[73] ^d) (mean, SD) 40.16±1.36 43.97±2.36
Cyl[74] ^e) (mean, SD) −1.77±0.75 −2.03±0.79
Axis (mean, range) 94.41 (1180) 102.75 (1179)
ACD[75] ^f) (mean, SD) 3.38±0.46 3.34±0.43
[76]Open in a new tab
^^a)
Marfan syndrome;
^^b)
Cataract controls;
^^c)
Axial length;
^^d)
Mean value of K1 and K2;
^^e)
Cylinder diopter;
^^f)
Anterior chamber depth.
Next, we constructed network analyses to explore the association
between protein sets with highly synergistic changes and clinically
significant traits, and screened hub proteins in modulating ocular
abnormities.
Finally, we applied a machine learning strategy to further screen and
evaluate the features, and developed a valuable and robust biomarkers
panel to aid in the early diagnosis of MF syndrome. Proteins with
biological significance were externally validated using an independent
cohort of 10 patients (5 MFS and 5 CC) using the PRM method.
3.2. AH Spectral Library Generation and its Overall Characteristics
This study introduced an AH‐specific spectral library to support
protein identification and quantification. 12‐fractioned‐pooled samples
were collected by high pH reversed‐phase chromatography and acquired by
DDA mode using Orbitrap Exploris 480. Our AH‐specific spectral library
comprised 1,1041 precursors, 9655 peptides, 9044 proteotypic peptides,
2450 proteins, and 2306 protein groups. The investigators compared the
present results with those in previously published AH proteomics
studies (Table [77] 2 ). This new library was not only the first
children‐targeted but also led to a lot more protein identification
than previous ones (from 802 to 2306). The overall characteristics of
the spectral library were evaluated in Figure [78] 2 . The range of
precursor mass covered 400–1200 m/z, and ≈81.6% of the precursors were
between 450 and 800 m/z (Figure [79]2A). The precursors primarily
showed two (68.9%) or three (29.2%) charges (Figure [80]2B). 91.1% of
peptides were between 7 and 20 amino acids in length, consistent with
the general pattern based on enzymatic and mass spectrometric
fragmentation (Figure [81]2C). Carbamidomethyl was the most common
modification found in 2781 peptides. (Figure [82]2D). The majority of
proteins were identified with at least two proteotypic peptides, while
11464 proteins were found to have more than 20 proteotypic peptides
(Figure [83]2E). 94.1% of peptides possessed over six fragment ions
(Figure [84]2F). Moreover, fragments from y‐ions (84.5%) were more
frequently detected than those from b‐ions (15.5%) due to basic
residues at the C‐terminus digested by trypsin (Figure [85]2G). One
(74.0%) and two (24.9%) charges comprised the majority of fragment
charge distribution (Figure [86]2H).
Table 2.
Comparison of recent aqueous humor (AH) proteomic studies.
Year Reference Disease Methods Instrument FDR Identified proteins
2020 Xue, Min et al. Pathologic myopia Label‐free Triple TOF 6600 1%
583
2020 Yu, Mengxi et al. Cataracts Label‐free Orbitrap Fusion Lumos
Tribrid 1% 802
2021 Liu, Xiaoyan et al. Glaucoma DIA Orbitrap Fusion Lumos Tribrid 1%
636
2021 Xiao, Hu et al. Proliferative diabetic retinopathy Tandem Mass Tag
(TMT) Orbitrap Fusion 1% 591
2022 Chen, Huan et al. Diabetic Nephropathy DIA Orbitrap Fusion Tribrid
1% 692
2023 This study Ectopia Lentis DIA Orbitrap Exploris 480‐FAIMS Pro 1%
2306
[87]Open in a new tab
Figure 2.
Figure 2
[88]Open in a new tab
Aqueous humor (AH)‐spectral library generation and its overall
characteristics. A) Distribution of precursor m/z. B) Counts of
different precursor charge states. C) Distribution of peptide length.
D) The number of modified peptides and distribution of different
modifications. E) The number of proteotypic peptides for each protein.
F) Proportion of fragment ions per precursor ion. G) Percentage of b, y
ions. H) Proportion of different charges of fragment ions.
3.3. Integrative Proteomic Profiling and Functional Enrichment Analysis
A total of 2336 and 3853 proteins were identified in AH and lens
capsules, respectively, with 2300 and 2938 comparable proteins
(Appendix Files). Peptide length, numbers, and protein molecular weight
distribution are shown in Figures [89]S1A–C and [90]S2A–C (Supporting
Information). The intensity distribution along with its density
characteristics (Figures [91]S1D–F and [92]S2D–F, Supporting
Information) suggested that samples met quality control requirements.
Protein coverage distribution in capsules is additionally displayed in
Figure [93]S2G (Supporting Information). Orthogonal partial least
squares discriminant analysis (OPLS‐DA) effectively distinguished the
MFS group from the CC group (Figure [94]S1G, Supporting Information).
We performed a comprehensive functional annotation of these identified
proteins (Figures [95]S1H and [96]S2H, Supporting Information).
We then processed comparable analysis showing significantly and
differentially altered proteins between MFS and CC. As the volcano
plots in Figure [97] 3B show, by setting a cutoff value of a 1.5‐FC and
a threshold adjusted p‐Value of less than 0.05, we identified 449 DEPs
in AH, specifically, 155 upregulated and 294 downregulated proteins. Of
these, 38.84% were from the extracellular space, which is consistent
with the physiological properties of AH. In addition, the comparison
showed 326 DEPs (38.04% cytoplasmic subcellular localization) in lens
capsules, with 178 upregulated and 148 downregulated proteins
(Figure [98]3C). Interestingly, only 20 DEPs were commonly
differentially abundant between AH and capsule proteome (Figure [99]3A,
right), which is a much smaller number compared to the overall number
of commonly detected proteins (Figure [100]3A, left). Figure [101]3D
was used to compare the overlapping proteins in detail, by analyzing
their Log2 FC. Figure [102]3E revealed a linear correlation between
their relative abundance rank in MFS and CC. It is noteworthy that
these proteins were generally detected at much lower levels in AH than
in capsules, presumably reflecting tissue leakage.
Figure 3.
Figure 3
[103]Open in a new tab
Integrative Proteomic Profiling of Children AH and Lens Capsule. A)
Venn diagram showing overall proteins (Left) and DEPs (Right)
identification in AH and lens capsule. B) Volcano plots (Left) and
polar area diagram (Right) showing DEPs and their subcellular
localization in AH of MFS and CC patients. C) Volcano plots (Left) and
polar area diagram (Right) showing DEPs and their subcellular
localization in lens capsule of MFS and CC patients. D) Nine‐quadrant
chart showing the distribution of overlapping proteins in AH and
capsules with Log2 fold change. E) AH–capsule proteome abundance map
showing the median protein intensity (assessed by MS intensity) of
overlapping proteins. AH, aqueous humor; CC, cataract controls; DEPs,
differentially expressed proteins with a 1.5‐fold change and a
threshold‐adjusted p‐Value of less than 0.05; MFS, Marfan syndrome.
To further investigate the biological function of DEPs in MF syndrome,
we performed comprehensive functional enrichment analyses. Based on the
449 DEPs detected in AH (Figure [104] 4A), Beta/Gamma crystallins,
which constitute major components of lens proteins, were the most
significantly enriched protein domain, indicating that they may be
leaked or secreted into the AH. Those DEPs were also found to be
involved in several major molecular functions, namely, structural
constituent of the eye lens, lipid binding, calcium channel regulator
activity, ubiquitin‐specific protease binding, and C5a and C5L2
anaphylatoxin chemotactic receptor binding, as annotated in
Figure [105]4B. Additionally, pathway annotation and enrichment
analysis revealed that upregulated proteins were overrepresented in
complement and coagulation cascades and the PPAR signaling pathway,
while the downregulated proteins were enriched in
glycolysis/gluconeogenesis and pyruvate metabolism (Figure [106]4C).
Figure 4.
Figure 4
[107]Open in a new tab
Key Pathway, Function, and Proteins Characterized in MFS and CC
Patients. A) Enrichment chord graph of DEPs domain in AH (Left) and its
four sets of hierarchical clustering analysis (Right). B) Enrichment
chord graph of DEPs molecular function in AH (Left) and its four sets
of hierarchical clustering analysis (Right). C) Enrichment bubble plot
of DEPs KEGG pathway in AH (Left) and its four sets of hierarchical
clustering analysis (Right). D) Enrichment bubble plot of DEPs cellular
component in lens capsule (Left) and its four sets of hierarchical
clustering analysis (Right). AH, aqueous humor; CC, cataract controls;
DEPs, differentially expressed proteins with a 1.5‐fold change and a
threshold‐adjusted p‐value of < 0.05; KEGG, Kyoto Encyclopedia of Genes
and Genomes; MFS, Marfan syndrome.
DEPs in lens tissue were also analyzed to explore mechanisms underlying
ectopia lentis by mapping to the cellular component. The obtained
results showed dysregulation of proteins involved in focal adhesion,
RISC‐loading complex, and cell trailing edge (Figure [108]4D).
For further hierarchical clustering based on DEPs functional
classification, we first divided them into 4 categories according to
their differential expression folds, called Q1 to Q4 (< 0.5, 0.5‐0.667,
1.5‐2.0, > 2.0, respectively). Then, for each Q group, the molecular
function, cellular component, and the KEGG pathway were enriched
separately, and cluster analysis was performed to find the correlation
of protein functions with differential expression folds in the
comparison groups. The corresponding enrichment‐based clustering are
displayed in Figure [109]4A–D, right. Data suggested that DEPs in AH
linked to the complement and coagulation cascades were highlighted in
the Q3 cluster, while glycolysis/gluconeogenesis and pyruvate
metabolism‐related ones were in the Q1 cluster.
3.4. Identification of Clinically Significant Protein Modules
The hierarchical clustering analysis revealed close relationships among
samples, indicating that there was no need to exclude any samples and
that all samples could be used for WGCNA (Figure [110] 5A). A power
value (β) of 8 was selected as the soft threshold to construct the
adjacency matrix, and the resulting network based on β = 8 exhibited a
scale‐free topology (Figure [111]5B,C). Using hierarchical clustering
and dynamic tree‐cutting methods, a total of seven distinct
co‐expressed modules were obtained, each represented by a different
color, i.e., turquoise, brown, yellow, green, blue, red, and grey
modules, with grey indicating genes that could not be assigned to any
module (Figure [112]5D). Figure [113]5E displayed the modules’
topological overlap map (TOM). Distinct modules were differentiated
based on the clustering dendrogram of MEs and module‐module
associations are depicted in an eigengenes adjacency heat map in
Figure [114]2F.
Figure 5.
Figure 5
[115]Open in a new tab
Identification of clinically significant protein modules. A) Sample
clustering for outlier detection. To identify any sample outliers in
the AH dataset, average linkage hierarchical clustering was conducted.
Results indicate that there were no sample outliers. B) Power
transformation analysis and assessment of scale‐free topology criteria.
We tested powers ranging from 1 to 20 to determine the optimal
transformation. The red line (0.85) represents the scale‐free topology
criterion; values greater than this indicate satisfactory adherence to
the criterion. We observed that increasing values of β lead to
decreased mean connectivity, suggesting that the network comprises many
proteins with few connections. Moreover, the decay of mean connectivity
follows an inverse power law, further supporting the scale‐free
topology assumption. C) Scale‐free topology checking. Distribution of
nodes with the degree of connection, k. (Left) and correlation between
log (k) and log [P(k)] (Right). D) Seven distinct modules of highly
co‐expressed proteins were identified based on the hierarchical
clustering dendrogram. E) TOM for distinctive modules; red shades mark
higher topology overlap shared between the correlated proteins in the
network. F) Eigenprotein dendrogram and heat map where red and blue
represent high and low correlations of the eigenproteins, respectively.
G) The module‐trait correlation plot according to the clustering
dendrogram of module eigengenes. The heat map illustrated positive
correlations as red and negative correlations as blue. The values in
each grid represented the correlation coefficient and the corresponding
p‐value between the module and clinical traits, with the second row of
values indicating the significance level of the correlation. H) Chord
graph showing the correlation between ocular features and the top 10
proteins with the highest GS value in each feature. I)
Clinicopathological signatures of MFS focused in this study. J) PPI
network (Up) and molecular function enrichment analysis (Down) of
proteins in three core modules, MEbrown, MEyellow, and MEblue. AH,
aqueous humor; GS, gene significance; MFS, Marfan syndrome; PPI,
protein‐protein interaction; TOM, topological overlap map.
Furthermore, correlations between modules and phenotype data were
analyzed and displayed using a heat map in Figure [116]5G. MEbrown,
MEyellow, and MEblue exhibited significant correlations with Km
(correlation coefficient r = −0.48, −0.34, 0.28, respectively, p <
0.05). Strong associations with Cyl were observed for MEturquoise,
MEbrown, and MEred (r = −0.31, 0.3, 0.35, respectively, p < 0.05).
MEyellow and MEblue were highly correlated with ACD (r = 0.42, −0.36,
respectively, p < 0.01). Moreover, a significant positive correlation
with AL was observed for MEbrown and MEyellow (r = 0.56, 0.52,
respectively, p < 0.001), whereas a significant negative correlation
was found for MEblue and MEred (r = −0.54, −0.33, respectively, p <
0.001).
To narrow the scope of key proteins, we filtered for proteins with
absolute values of GS greater than 0.05. Higher GS values indicate a
more pronounced correlation between proteins and clinical traits.
Figure [117]5H displays the correlation between ocular features and the
top 10 proteins with the highest absolute value in each feature. The
three resulting crucial modules, MEbrown, MEyellow, and MEblue are
predominately linked with AL and corneal keratometry (K1, K2, Km), and
correspondingly, longer ALs and flatter corneas are two distinctive
features of patients with MFS (Figure [118]5I). Their biological
continuity is well exhibited among proteins in
AL‐or‐keratometry‐related modules by plotting the hub proteins in the
PPI network (Figure [119]5J, up). These modules are enriched in
collagen, complement, cytokine and opsonin binding, and ribosomal
structural molecular function (Figure [120]5J, down).
3.5. Machine‐learning–based Selection of Biomarker Panels
We applied a machine‐learning approach to detect protein subsets that
could potentially function as AH‐biopsy signatures and integrated them
into unified predictors for accurate discrimination of patients at
risk. Specifically, we assessed quantified proteins as input features
for machine learning and determined the most relevant ones using the
SVM algorithm.^[ [121]^20 ^] To this end, we performed a univariate
feature analysis to measure the importance of each protein in
discriminating between two classes of patients (MFS or CC) and
subsequently ranked them based on their scores and p‐Values, as shown
in Figure [122] 6A. The top 30 ranked proteins are displayed in
Figure [123]6B. To obtain the optimal subset of candidates, we employed
the IFS method (Figure [124]6C). We assessed the prediction performance
of selected proteins using AUC. A set of candidates that showed the
highest AUC was selected as being the most relevant features. Using
these proteins as features, the best diagnostic model was generated. We
found that the four top‐ranked features, mapping to the four respective
distinct gene products (CCN2, ARFIP1, GALE, and MYOC) showed the best
performance in discriminating patients. Heat maps depicting their
Pearson correlation coefficients and quantitative levels were created,
with the data suggesting that our model had no redundancy
(Figure [125]6D,E). In the test set, the diagnostic model showed a
combined AUC of 0.98 (Figure [126]6F). Box plots presented their
relative expression variations taken from all samples together
(Figure [127]6G).
Figure 6.
Figure 6
[128]Open in a new tab
Machine‐learning‐based Identification of Biomarker Panels Signatures.
A) Evaluation of feature discrimination ability and visualization of
sorted expression features: We used univariate feature analysis to sort
the expression features based on their score and p‐value. The x‐axis
represents the score of each expression feature, while the y‐axis
indicates the ‐log10‐transformed p‐value of the corresponding feature.
The expression features with p‐values <0.05 are highlighted in red,
while those with p‐Values > 0.05 are shown in gray. B) The bar plot
displays the top 30 features with the highest scores, and the colors of
the bars are filled based on the ‐log10 p‐value of the corresponding
feature. C) IFS curve was generated to identify the optimal subset of
expression features for predicting sample class with the highest
accuracy using the incremental feature selection method. The curve
shows the relationship between the number of features and the
corresponding accuracy achieved through 10‐fold cross‐validation. D)
Heat map showing the Pearson correlation coefficients of expression
data for the selected features across different sample classes,
indicating low correlation and no feature redundancy in the optimal
expression feature subset. E) Heat map showing the expression levels of
the proteins in the optimal feature subset across all samples. F) ROC
curves showing the ability of selected features to accurately classify
samples in the train and test dataset. G) Box plot illustrating the
distribution differences of protein expression among different sample
groups. IFS, Incremental Feature Selection; ROC, Receiver Operator
Characteristic.
3.6. Selected Biomarkers Validation and Clinical Correlation
PRM validation was performed on candidate proteins in an independent
cohort. Among the sixteen top‐ranked peptides selected as the most
relevant biomarker signatures for predicting MF syndrome in children,
twelve proteins were identified from WGCNA due to their significant
clinical values, while the other four proteins were derived from
machine learning. We have successfully quantified 15 out of the 16
target proteins. We were able to identify only one peptide for some of
the proteins due to sensitivity limitations. Notably, four proteins
([129]Q14376, [130]Q99972, [131]P02760, [132]Q07507; Gene name, GALE
(UDP‐galactose‐4‐epimerase), MYOC (Myocilin), AMBP
(Alpha‐1‐microglobulin/bikunin precursor), DPT (Dermatopontin),
respectively) were confirmed to be the most robust in this independent
sample set, as evidenced by the distribution of the ion peak area of
their unique peptides (TWNAVLLR, ELETAYSNLLR, TVAACNLPIVR, YFESVLDR,
respectively) in Figure [133] 7A–D, which showed significant
differences (p‐Value < 0.005 or less). Furthermore, the correlation
between the level of these potential biomarkers and clinical signatures
was analyzed, as shown in Figure [134]7E–G. The results indicated that
[135]Q14376, [136]Q99972, and [137]P02760 were negatively correlated
with keratometry (K1, K2, Km), while [138]P02760 and [139]Q07507
positively correlated with AL. These findings further corroborated our
proteomic discoveries regarding two distinctive features of patients
with MFS in the discovery cohort.
Figure 7.
Figure 7
[140]Open in a new tab
Biomarker Expression Plots via PRM Validation and Clinical Correlation.
A) Distribution of fragment ion peak areas of peptide TWNAVLLR
(corresponding to protein [141]Q14376) in the validation cohort. B)
Distribution of fragment ion peak areas of peptide ELETAYSNLLR
(corresponding to protein [142]Q99972) in the validation cohort. C)
Distribution of fragment ion peak areas of peptide TVAACNLPIVR
(corresponding to protein [143]P02760) in the validation cohort. D)
Distribution of fragment ion peak areas of peptide YFESVLDR
(corresponding to protein [144]Q07507) in the validation cohort. E‐G)
Pearson correlation analysis between clinical indicators and protein
biomarkers: E: [145]Q14376; F: [146]Q99972; G: [147]P02760 (Left,
Middle) and [148]Q07507 (Right). PRM, Parallel Reaction Monitoring.
4. Discussion
Here we set out to employ cutting‐edge MS technology to reveal the
proteomic profile of AH proteins in children, significantly improving
the identification depth and providing novel insights into the
complexity of the AH proteome. Integrated analysis of the crystalline
lens anterior capsule and AH deepened our understanding of the
molecular mechanisms underlying MFS. We identified protein modules
strongly correlated with MFS clinical features through WGCNA and
applied machine‐learning techniques to select highly robust biomarkers.
Validation of the biomarker panel using advanced PRM underscores its
potential utility in clinical settings.
Significant strides have been made in uncovering the heritable risk of
MFS through genetics, but the impact of these genetic findings on the
biological pathways that underpin MFS pathophysiology remains
unclear.^[ [149]^3 , [150]^22 ^] While our team has made considerable
contributions to investigating the genetic basis of ectopia lentis,^[
[151]^23 , [152]^24 , [153]^25 , [154]^26 ^] the ultimate effectors of
MFS genetic risk are often the proteins and the metabolic pathways that
they regulate. In recent years, proteomics has emerged as a powerful
tool for elucidating the molecular mechanisms that drive various
diseases and identifying key biomarkers for diagnosis and prognosis.^[
[155]^27 ^] Proteomics research is gaining momentum among scientists
due to the rapid advances in MS and sample preparation techniques.^[
[156]^28 , [157]^29 ^]
DIA is a MS‐based technique that has shown promise in enabling the
simultaneous and reproducible quantification of hundreds or thousands
of peptides across multiple samples. DIA offers several advantages over
other approaches, including unbiased and comprehensive proteomic
coverage, the detection and quantification of low‐abundance peptides,
and the ability to measure multiple samples in a single experiment.^[
[158]^30 , [159]^31 ^] Unlike conventional proteomics, DIA is less
susceptible to the influence of high‐abundance proteins, eliminating
the necessity for affinity‐based depletion methods that may introduce
bias or incur additional costs, particularly in samples such as plasma
that are rich in high‐abundance proteins.^[ [160]^32 ^]
AH is an important fluid that plays a vital role in maintaining the
ocular environment by providing nutrients and oxygen to the cornea and
lens, which lack their own blood supply.^[ [161]^33 ^] In addition, AH
assists in the removal of waste products from the eye, thereby
contributing to the overall health of the eye.^[ [162]^33 ^] This
clear, watery fluid located in the anterior and posterior chambers of
the eye is critical for the proper functioning of the visual system.
The unique properties of AH make it an attractive target for proteomics
studies aimed at identifying new biomarkers and therapeutic targets for
ocular diseases.^[ [163]^34 , [164]^35 , [165]^36 ^] The study of AH
proteomics is an emerging field that has yet to be fully explored, with
inconsistent depths of identification observed across different
studies.^[ [166]^17 , [167]^35 , [168]^37 ^] However, AH has been shown
to share similar protein compositions with plasma,^[ [169]^38 ^]
suggesting the potential to apply established plasma proteomic methods
to AH. Despite the low protein concentration in AH (0.02% in AH versus
10% in plasma),^[ [170]^34 , [171]^38 ^] the depth of identification in
previous studies might have been limited. In this study, we identified
over 2000 proteins in AH, which is significantly higher than previous
reports, due to the use of DIA and the emerging ion mobility MS (IM‐MS)
technology.^[ [172]^39 ^] The combination of differential mobility
spectrometry (DMS) with IM‐MS, specifically the FAIMS‐IM approach, was
employed to enhance the sensitivity and specificity of protein
identification.^[ [173]^18 , [174]^40 , [175]^41 ^] The FAIMS device
acts as a filter that selectively transmits ions based on their
mobility characteristics, allowing for the separation of ions with
similar mass‐to‐charge ratios but different mobilities.^[ [176]^18 ,
[177]^40 ^] We utilized the state‐of‐the‐art Orbitrap Exploris 480 mass
spectrometer equipped with a FAIMS Pro Interface to obtain DIA data
that leveraged DMS techniques and achieved higher detection
sensitivity. Our approach enabled the detection of low‐abundance
proteins in AH while reducing batch effects and ensuring experimental
parallelism.
The existing diagnosis criteria for MFS^[ [178]^42 ^] based on features
of the ocular, skeletal, and thoracic systems have limited accuracy in
the early disease stages, thus severely reducing opportunities for
timely disease detection and intervention. Asymptomatic aortic root
aneurysms, in cases where MFS is left undiagnosed due to the lack of
recognition of skeletal or ophthalmic manifestations, can eventually
progress to acute aortic dissections, which itself serve as another
crucial diagnostic feature.^[ [179]^3 , [180]^42 ^] In fact, MFS may go
undiagnosed until an individual suffers from an acute aortic
dissection, thus highlighting the importance of genetic testing for
FBN1 variants.^[ [181]^43 , [182]^44 ^] It is worth noting that the
criteria used to diagnose MFS according to Ghent II criteria,^[
[183]^42 ^] which take into account skeletal growth abnormalities such
as pectus deformities and scoliosis, may not become fully apparent
until an individual reaches skeletal maturity.^[ [184]^3 ^] It is also
worth noting that MFS patients of Hispanic and Asian origins have been
observed to present with fewer skeletal symptoms, yet still display
similar ophthalmic and thoracic features as Europeans who have MFS.^[
[185]^45 , [186]^46 ^] As a result, these patients may be less likely
to receive a referral for MFS evaluation based on skeletal symptoms.
Therefore, it is imperative to develop broader diagnostic approaches
that target at‐risk populations, including those with a history of
ectopia lentis, particularly in children.
In recent years, the heightened importance placed on early education by
parents, coupled with the popularity of vision screening, has resulted
in a noticeable upsurge in the number of ectopia lentis cases presented
to ophthalmology clinics. Consequently, ophthalmologists are often the
first to detect potential patients with MF syndrome by identifying
children with reduced visual acuity. Were a proximal body fluid (i.e.,
AH) protein biomarker to be identified, the diagnosis of this disease
could be ascertained in a timely manner. Such an advance would enable
ophthalmologists to inform parents of the child of the need to
carefully monitor the development of the cardiovascular and skeletal
systems over time, ultimately leading to early diagnosis, intervention
in life‐threatening comorbidities such as aortic dilatation, and,
eventually, preservation of the patient's life. However, no relevant
studies have reported the discovery of such biomarkers in samples
similar to those discussed in this paper.
Recent studies have highlighted the importance of considering AL and
corneal curvature in the ophthalmic evaluation of suspected and
diagnosed cases of MFS.^[ [187]^47 ^] While these parameters are not
currently included as diagnostic criteria in the revised Ghent‐2
nosology, the Marfan Eye Consortium of Chicago recommends that patients
with longer AL and flatter corneas should be considered as potential
MFS cases.^[ [188]^6 ^] In a study by Martin Heur et al., there was a
significant difference in corneal curvature values between MFS and
control patients, with values less than 42 D potentially serving as a
clinical diagnostic criterion for MFS.^[ [189]^48 ^] These findings
emphasize the importance of ophthalmologists being aware of the
potential diagnostic value of AL and corneal keratometry in the
diagnosis of MF syndrome.^[ [190]^6 , [191]^47 , [192]^48 ^] In this
context, we employed the WGCNA method to establish a gene co‐expression
network linked to the clinical characteristics of MFS. Surprisingly, we
discovered that the protein modules correlated with AL and corneal
curvature had significantly higher GS scores and were functionally
linked with each other.
Recent research on aortic aneurysms in MFS indicates that the
progression of aneurysms may involve extracellular matrix remodeling,
cell adhesion, and complement activation.^[ [193]^49 , [194]^50 ,
[195]^51 , [196]^52 ^] In 2022, Stijntje Hibender et al. detected a
complement gene C1R variant linked to aortic comorbidity through
whole‐genome sequencing (WGS) on a Marfan family.^[ [197]^51 ^] A
single‐cell RNA sequencing workflow applied to aortic aneurysm samples
collected in Fbn1C1041G/+ (MFS) mice and controls reported a cluster of
altered genes involved in extracellular matrix modulation, collagen
synthesis, and adhesion.^[ [198]^52 ^] In our study, through functional
enrichment analysis of DEPs in AH and lens tissue, we discovered that
MFS patients with lens dislocation exhibit significant dysregulation of
complement and coagulation‐related functions in AH. In contrast, the
DEPs identified in the lens tissue were primarily associated with cell
adhesion. Further analysis using WGCNA enabled us to examine the
clinical relevance of all quantified AH proteins rather than focusing
on DEPs. Our results demonstrated that the MEbrown and MEyellow
modules, which were highly correlated with key clinical indicators
including AL and corneal curvature as discussed above, were also
enriched for complement, cytokine, opsonin, and collagen binding.
Notably, highly interactive proteins in MEbrown and MEyellow, including
C1R, C1QB, C4A, C4B, and CD74, were associated with the complement
system, while PLG, SERPIND1, SERPINC1, PROZ, F2, F9 were associated
with coagulation, and VCAM1 with cell adhesion. Our findings suggest
that complement and coagulation‐related proteins may play critical
roles in the pathogenesis of MFS‐related ocular complications. To the
best of our knowledge, this is the first study to construct a
co‐expression network associated with the clinical features of MFS.
This innovative and comprehensive approach provides a new avenue for
discovering potential biomarkers, identifying critical genes, and
developing more effective diagnostic methods for MFS. It is noteworthy
that four proteins (GALE, MYOC, AMBP, and DPT) met our validation
criteria for PRM and are all associated with keratometry or AL. DPT,
which plays a role in collagen fibrillogenesis, may contribute to
connective tissue disorders, as previously reported.^[ [199]^53 ^]
Therefore, the potential involvement of DPT in MFS pathogenesis is a
plausible hypothesis. This indicates that targeted proteomics analysis
in clinically relevant tissue proximal fluids can identify peptide
signatures that predict the onset of MFS before the manifestation of
cardiovascular symptoms, thus facilitating early diagnosis.
In conclusion, our study has provided compelling evidence that the AH,
a crucial ocular biofluid, contains a significant amount of
unidentified proteins that can serve as biomarkers for ectopia lentis
in MF syndrome. Utilizing network analysis and machine learning
techniques can further assist in screening and assessing the biomarkers
with clinical relevance, which, when combined with PRM validation, can
lead to the development of a robust and valuable panel of biomarkers
for early detection of MF syndrome. Our AH proteomics workflow has been
streamlined by the introduction of an AH‐specific spectral library,
which can be easily scaled up for use in larger and more powerful
cohorts. Future studies should incorporate longitudinal data to
validate the increased levels of GALE, MYOC, AMBP, and DPT in MFS
patients as potential indicators of early‐stage disease and also to
explore new biomarkers for other ocular system syndromes.
Conflict of Interest
The authors declare no conflict of interest.
Supporting information
Supporting Information
[200]Click here for additional data file.^ (379KB, pdf)
Supporting Table 1
[201]Click here for additional data file.^ (3.5MB, xlsx)
Supporting Table 2
[202]Click here for additional data file.^ (26.8MB, xlsx)
Acknowledgements