Altered Serum Proteins Suggest Inflammation, Fibrogenesis and Angiogenesis in Adult Patients with a Fontan Circulation
<p>Distinct abundance variations of proteins in patient and control serum samples (volcano plot). The plot visualizes the adjusted <span class="html-italic">p</span>-values and corresponding log-fold changes (|logFC|). <span class="html-italic">p</span> < 0.05 was considered statistically significant (horizontal red line). The |logFC| cutoffs are indicated as vertical lines. Proteins with a positive |logFC| had a higher abundance in patient samples; proteins with a negative value were in control samples. Proteins with |logFC| < 0.5 and a significant adjusted <span class="html-italic">p</span>-value are defined as differential and are displayed in blue. Proteins indicated in green either feature a |logFC| > 0.5, while not reaching the significance threshold, or feature a significant difference, while not reaching the |logFC| threshold.</p> "> Figure 2
<p>Individual array values for the four proteins with differential abundance in patient and control samples (see <a href="#ijms-25-05416-t002" class="html-table">Table 2</a>). Each sample is measured by four replicate spots per array. Rhombs indicate sample group means. Whiskers indicate one standard deviation.</p> "> Figure 3
<p>Heatmap displaying the relative expression of proteins identified as differential. Values were centered and scaled by proteins.</p> "> Figure 4
<p>Principal component analysis for differential proteins. The scatter plot displays the first two principal components of the samples’ protein signal data based exclusively on the four differentially abundant proteins GLPA, LIF, SDC1, and NGF-β. In the plot, the location of the samples is defined by their first two principal components, i.e., linear combinations of protein features with the largest variance across the samples. Samples with a similar profile are located in close proximity. The percentages given in the axis labels describe the ratio of total variance explained by the respective principal component. Note that in the principal component analysis of the four differential proteins, the distribution of the probands suggests a clustering of patients or controls. Green, controls; red, patients.</p> "> Figure 5
<p>ROC curves for selected parameters. Receiver operating characteristic curves and area under curves for selected traditional and proteomics serum parameters and for combinations thereof with regard to group assignment (patient vs. control). Note the exceeding performance of combinations including γGT, uric acid, or triglyceride serum concentration in combination with one or two of the proteomics variables. The dashed line represents an area under the curve of 0.5.</p> "> Figure 6
<p>Normalized cumulated MDM (proteomics). Cumulative impact of the 526 proteomics-derived analytes examined on classification competence into diseased vs. non-diseased proband serum (dotted curve, red). The linear line (blue) represents the analytes’ rank standardized to 100%. Open circle: the 27/526 topmost ranked proteins (5% of analytes) account for 80% of classification optimum. MDM, mean decrease in the margin (classification impact score).</p> ">
Abstract
:1. Introduction
2. Results
2.1. Clinical Examination, Routine Laboratory Analytes, Imaging, Exercise Capacity Testing
2.2. Proteomics Analysis
2.2.1. Differentially Abundant Proteins between Patients and Controls
2.2.2. Principal Component Analysis
2.2.3. Receiver Operating Characteristic (ROC) Analysis
2.2.4. Random Forest Analysis of Proteomics Parameters with Respect to Group Assignment
3. Discussion
3.1. Fontan Is Never Normal—‘Good’ Fontan Patients Exhibit a Specific Proteomics Phenotype
3.2. Four Proteins as Inflammatory Biomarkers at Different Levels in Fontan Patients
3.2.1. Elevated SDC1—Inflammation, Cardiac and Hepatic Fibrosis, Angiogenesis
3.2.2. Elevated GLPA—Inflammation, Mutagenicity, Red Blood Cell Structure
3.2.3. Decreased LIF—While Pleiotropic, Expression of Cardioprotection
3.2.4. Decreased NGF-β—Alteration of the Autonomic Nervous System, from Cognitive Development to Cardiac Rhythm Abnormalities, and Metalloproteinase Inhibitor
3.3. Combining Four Proteomics-Based Proteins with Traditional Biomarkers for a Global Picture
3.3.1. Uric Acid—Antioxidative Scavenger
3.3.2. γ-Glutamyl-Transferase (γGT)—Liver Disease, and Antioxidative Glutathione Status
3.3.3. Cholesterol—Fontan Dyslipidemia Requiring Further Lipidology Research
3.4. Limitations, Confounders
4. Materials and Methods
4.1. Study Design and Inclusion and Exclusion Criteria
4.2. Routine Examination
4.3. Proteomics Analysis
4.3.1. Samples and Protein Extraction and Labelling
4.3.2. Sample Incubation
4.4. Data Acquisition and Analysis
4.5. Pathway Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Unit | Patient | Control | p-Value |
---|---|---|---|---|
minimum SpO2 | % | 90 ± 3 | 98 ± 1 | <0.00001 |
maximum SpO2 | % | 93 ± 3 | 99 ± 1 | <0.00001 |
VO2AT | ml/kg/min | 24.5 ± 4.9 | 30.1 ± 3.6 | <0.00001 |
VO2max | ml/kg/min | 28.8 ± 10.1 | 45.7 ± 6.4 | <0.00001 |
heart rate at rest | 1/min | 83 ± 17 | 86 ± 19 | 0.08 |
BP systolic at rest | mmHg | 123 ± 10 | 119 ± 12 | 0.04 |
BP diastolic at rest | mmHg | 68 ± 8 | 71 ± 8 | 0.04 |
hematocrit | % | 47 ± 5 | 39 ± 4 | <0.00001 |
hemoglobin | g/dL | 16.4 ± 2.1 | 12.7 ± 1.4 | <0.00001 |
platelet count | 1000/nL | 171 ± 73 | 279 ± 88 | 0.0002 |
leucocytes | 1/nL | 6.7 ± 3.2 | 7.2 ± 2.6 | 0.23 |
γGT | U/L | 86 ± 43 | 35 ± 19 | 0.00002 |
alkaline phosphatase | U/l | 103 ± 53 | 99 ± 73 | 0.2 |
ALT | U/L | 39 ± 11 | 31 ± 10 | 0.04 |
AST | U/L | 35 ± 8 | 32 ± 8 | 0.12 |
GLDH | U/L | 3.7 ± 1.9 | 3.5 ± 1.5 | 0.26 |
total bilirubin | mg/dL | 1.22 ± 0.67 | 0.3 ± 0.29 | <0.00001 |
CRP | mg/dL | 0.18 ± 0.2 | 0.16 ± 0.14 | 0.47 |
fibrinogen | mg/dL | 239 ± 78 | 259 ± 57 | 0.17 |
antithrombin III | % | 101 ± 11 | 105 ± 9 | 0.38 |
INR | 2.1 ± 0.7 | 1 ± 0.04 | <0.00001 | |
PTT | sec | 35 ± 6 | 27 ± 4 | 0.00004 |
creatine kinase | U/L | 129 ± 66 | 95 ± 38 | 0.09 |
uric acid | mg/dL | 5.9 ± 1.4 | 3.9 ± 1.3 | 0.0003 |
creatinine | mg/dL | 0.8 ± 0.12 | 0.53 ± 0.18 | <0.00001 |
urea | mg/dL | 32 ± 7 | 23 ± 9 | 0.0008 |
triglycerides | mg/dL | 128 ± 86 | 47 ± 22 | 0.0003 |
total cholesterol | mg/dL | 145 ± 27 | 149 ± 34 | 0.77 |
HDL-cholesterol | mg/dL | 42 ± 15 | 51 ± 22 | 0.03 |
non-HDL-cholesterol | mg/dL | 85 ± 25 | 73 ± 21 | 0.2 |
total protein | g/dL | 7.2 ± 0.5 | 7.0 ± 0.7 | 0.31 |
albumin | mg/dL | 4145 ± 492 | 4215 ± 218 | 0.64 |
NT-proBNP * | pg/mL | 53 ± 69 | 41 ± 32 | 0.41 |
Protein | Antibody ID | Uniprot-Entry-Name | Uniprot ID | logFC | AveExp | adj. p-Value | HGNC |
---|---|---|---|---|---|---|---|
SDC1 | ab1576 | SDC1_HUMAN | P18827 | 0.96 | 11.14 | 2.2 × 10−2 | SDC1 |
GLPA | ab1491 | GLPA_HUMAN | P02724 | 0.85 | 11.09 | 2.18 × 10−2 | GYPA |
LIF | ab1725 | LIF_HUMAN | P15018 | −0.6 | 15.2 | 5.61 × 10−8 | LIF |
NGF-beta | ab1602 | NGF_HUMAN | P01138 | −0.75 | 12.44 | 6.69 × 10−3 | NGF |
Protein | Antibody ID | Uniprot-Entry-Name | Uniprot ID | logFC | AveExp | adj. p-Value | HGNC |
---|---|---|---|---|---|---|---|
LYAM3 | ab1570 | LYAM3_HUMAN | P16109 | 1.08 | 11.45 | 2.17 × 10−1 | SELP |
CD28 | ab1420 | CD28_HUMAN | P10747 | 0.82 | 12.14 | 2.14 × 10−1 | CD28 |
B3GA1 | ab1568 | B3GA1_HUMAN | Q9P2W7 | 0.75 | 11.25 | 6.28 × 10−2 | B3GAT1 |
TNR8 | ab1423 | TNR8_HUMAN | P28908 | 0.73 | 10.92 | 7.14 × 10−2 | TNFRSF8 |
IL18 | ab1511 | IL18_HUMAN | Q14116 | 0.69 | 11.19 | 2.17 × 10−1 | IL18 |
CD33 | ab1562 | CD33_HUMAN | P20138 | 0.66 | 11.31 | 2.24 × 10−1 | CD33 |
ITAE | ab1573 | ITAE_HUMAN | P38570 | 0.66 | 10.88 | 1.08 × 10−1 | ITGAE |
IL2RB | ab1575 | IL2RB_HUMAN | P14784 | 0.65 | 11.18 | 1.13 × 10−1 | IL2RB |
SLAF1 | ab2132 | SLAF1_HUMAN | Q13291 | 0.63 | 10.35 | 4.10 × 10−1 | SLAMF1 |
FCG2A | ab1561 | FCG2A_HUMAN | P12318 | 0.61 | 10.85 | 2.46 × 10−1 | FCGR2A |
PRTN3 | ab1501 | PRTN3_HUMAN | P24158 | 0.60 | 11.04 | 2.10 × 10−1 | PRTN3 |
BDNF | ab1502 | BDNF_HUMAN | P23560 | 0.55 | 10.96 | 2.21 × 10−1 | BDNF |
CRLF2 | ab1498 | CRLF2_HUMAN | Q9HC73 | 0.53 | 11.73 | 2.47 × 10−1 | CRLF2 |
ENTP1 | ab1111 | ENTP1_HUMAN | P49961 | 0.53 | 11.36 | 5.89 × 10−1 | ENTPD1 |
VGFR1 | ab1618 | VGFR1_HUMAN | P17948 | 0.50 | 11.63 | 2.46 × 10−1 | FLT1 |
S10A8/9 | ab1624 | −0.42 | 14.37 | 3.94 × 10−2 | |||
pan HLA-class II | ab1496 | −0.46 | 9.86 | 4.53 × 10−2 | |||
SDF1 | ab2491 | SDF1_HUMAN | P48061 | −0.46 | 11.20 | 5.39 × 10−3 | CXCL12 |
TNFB | ab1921 | TNFB_HUMAN | P01374 | −0.50 | 12.00 | 8.04 × 10−2 | LTA |
Lactoferin | ab1611 | TRFL_HUMAN | P02788 | −0.52 | 13.03 | 8.10 × 10−2 | LTF |
PLF4 | ab1841 | PLF4_HUMAN | P02776 | −0.56 | 12.51 | 8.61 × 10−1 | PF4 |
CD53 | ab1453 | CD53_HUMAN | P19397 | −0.56 | 13.55 | 3.50 × 10−1 | CD53 |
I17RA | ab2407 | I17RA_HUMAN | Q96F46 | −0.61 | 12.62 | 2.47 × 10−1 | IL17RA |
TNR6 | ab1478 | TNR6_HUMAN | P25445 | −0.66 | 14.34 | 1.55 × 10−1 | FAS |
Random Forest | Classical Statistics | Protein | ||||
---|---|---|---|---|---|---|
Rank | Score | Rank (n = 64) | Fold Change | Adj. p | Rank (Only Adj. p < 0.05, n = 7) | |
1 | 0.0085 | 5 | −0.75 | 0.0067 | 3 | NGF-β |
2 | 0.0068 | 11 | −0.6 | 0.000000056 | 4 | LIF |
3 | 0.0052 | 28 | 0.36 | 0.25 | CCL14 | |
4 | 0.0048 | 21 | 0.44 | 0.22 | PD1L1 | |
5 | 0.0044 | 25 | −0.39 | 0.22 | CCL5 | |
6 | 0.0041 | 43 | −0.21 | 0.61 | CD38 | |
7 | 0.0028 | 19 | −0.46 | 0.0054 | 6 | SDF1 |
8 | 0.0024 | 25 | −0.2 | 0.76 | AMPN | |
9 | 0.0023 | 38 | −0.26 | 0.98 | LYAM1 | |
10 | 0.0018 | 23 | 0.42 | 0.25 | CADH2 | |
11 | 0.0017 | 28 | 0.36 | 0.51 | VCAM1 | |
12 | 0.0016 | 35 | 0.09 | 0.74 | CD27 | |
13 | 0.0015 | 1 | 1.08 | 0.98 | LYAM3 | |
14 | 0.0013 | 41 | −0.23 | 0.59 | DPP4 | |
15 | 0.0011 | 21 | 0.1 | 0.98 | CEAM1,3,5,6,8 | |
16 | 0.001 | 20 | −0.45 | 0.25 | BMP4 | |
17 | 0.001 | 30 | −0.34 | 0.55 | VGFR2 | |
18 | 0.001 | 20 | −0.45 | 0.62 | ITA2B (CD41a) | |
19 | 0.0009 | 28 | −0.36 | 0.22 | CD9 | |
20 | 0.0008 | 35 | −0.29 | 0.54 | IL34 | |
21 | 0.0008 | 9 | 0.63 | 0.41 | SLAF1 | |
22 | 0.0007 | 2 | 0.96 | 0.02 | 1 | SDC1 |
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Michel, M.; Renaud, D.; Schmidt, R.; Einkemmer, M.; Laser, L.V.; Michel, E.; Dubowy, K.O.; Karall, D.; Laser, K.T.; Scholl-Bürgi, S. Altered Serum Proteins Suggest Inflammation, Fibrogenesis and Angiogenesis in Adult Patients with a Fontan Circulation. Int. J. Mol. Sci. 2024, 25, 5416. https://doi.org/10.3390/ijms25105416
Michel M, Renaud D, Schmidt R, Einkemmer M, Laser LV, Michel E, Dubowy KO, Karall D, Laser KT, Scholl-Bürgi S. Altered Serum Proteins Suggest Inflammation, Fibrogenesis and Angiogenesis in Adult Patients with a Fontan Circulation. International Journal of Molecular Sciences. 2024; 25(10):5416. https://doi.org/10.3390/ijms25105416
Chicago/Turabian StyleMichel, Miriam, David Renaud, Ronny Schmidt, Matthias Einkemmer, Lea Valesca Laser, Erik Michel, Karl Otto Dubowy, Daniela Karall, Kai Thorsten Laser, and Sabine Scholl-Bürgi. 2024. "Altered Serum Proteins Suggest Inflammation, Fibrogenesis and Angiogenesis in Adult Patients with a Fontan Circulation" International Journal of Molecular Sciences 25, no. 10: 5416. https://doi.org/10.3390/ijms25105416