A Meta-Analysis of Human Transcriptomics Data in the Context of Peritoneal Dialysis Identifies Novel Receptor-Ligand Interactions as Potential Therapeutic Targets
Abstract
:1. Introduction
2. Results and Discussion
2.1. Transcriptomics Studies Included in Our Meta-Analysis
2.2. Differentially Expressed Genes
2.3. Functional Analysis—Gene Set Enrichment Analysis
2.4. Receptor-Ligand Interaction Analysis
2.4.1. Receptor-Receptor Complex between Leukemia Inhibitory Factor Receptor (LIFR) and IL6ST
2.4.2. Receptor-Ligand Interaction between the Atypical Chemokine Receptor 2 (ACKR2) and C-C motif Chemokine Ligand 2 (CCL2)
2.4.3. Receptor-Ligand Interactions between Bone Morphogenic Protein Receptor Type 2 (BMPR2) and Growth Differentiation Factor 6 (GDF6) as Well as Bone Morphogenic Protein 6 (BMP6)
2.4.4. Receptor-Ligand Interactions between the Epidermal Growth Factor Receptor (EGFR) and Epiregulin (EREG) as Well as the Epithelial Mitogen (EPGN)
2.4.5. Receptor-Ligand Interaction between Frizzled Class Receptor 4 (FZD4) and Wnt Family Member 7B (WNT7B)
2.4.6. Receptor-Ligand Interactions between KDR and Semaphorin 6D (SEMAD6) as Well as TYROBP and SEMAD6
2.4.7. Receptor-Receptor Complex between F11R and ITGB2
2.4.8. Receptor-Ligand Interaction between EPH Receptor A4 (EPHA4) and Ephrin A1 (EFNA1)
2.4.9. Receptor-Ligand Interactions between NTRK1 and NTF3 as Well as NTRK2 and NTF3
2.5. Study Limitations and Planned Next Steps
3. Materials and Methods
3.1. Defining the Set of Eligible Transcriptomics Studies in the Context of Peritoneal Dialysis for the Current Meta-Analysis
3.2. Consolidating the Sets of Differentially Expressed Genes (DEGs)
3.3. Functional Gene Set Enrichment Analysis
3.4. Receptor-Ligand Interactome Analysis
3.5. Statistical Analysis and Data Manipulation
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Study Acronym | Platform | Sample Type | Group 1 | Group 2 | References |
---|---|---|---|---|---|
Zaza2008 | Affymetrix U133A | patient PBMCs | PD | CKD5 (predialysis) | [6] |
Reimold2013 | Affymetrix U113 Plus PM | patient mesothelial cells | PD | Uremic | [7] |
Scherer2013 | Affymetrix U133 Plus 2.0 | patient PBMCs | PD | CKD5 (predialysis) | [8] |
Büchel2015 | Affymetrix U133 Plus 2.0 | primary mesothelial cells | PD fluids | control medium | [4] |
Kokoroishi2016 | Affymetrix U133 Plus 2.0 | primary mesothelial cells | high glucose | normal glucose | [9] |
Herzog2017 | Illumina HiSeq 2000 | patient peritoneal cells | PD without AlaGln | PD with AlaGln | [5] |
Ruiz-Carpio2017 | Agilent Whole Human Genome Microarrays Kit 4 × 44 K | patient mesothelial cells | epithelioid | non-epithelioid | [10] |
Bartosova2018 | Illumina Human Sentrix Beads | patient omental arterioles | PD | CKD5 (predialysis) | [11] |
Han2019 | Illumina HumanRef-8 v2.0 | primary peritoneal cells | TGFB1 stimulated | wt | [12] |
Liu2019 | Agilent SurePrint G3 Human GE 8 × 60 K Microarray kit | mesothelial cell culture | without HPG | with HPG | [13] |
Parikova2020 | Illumina Human HT-12 v4 Expression BeadChips | patient peritoneal cells | long-term PD | short-term PD | [14] |
Strippoli2020 | GRCm38.76 | primary mesothelial cells | stretched | non-stretched | [15] |
GO Biological Process | All | M | OA | P | PBMC |
---|---|---|---|---|---|
angiogenesis | 64 (4.59) | 56 (3.53) | 2 | 8 | - |
cell adhesion | 110 (4.59) | 88 (1.84) | - | 23 (2.97) | 3 |
positive regulation of cell migration | 56 (4.59) | 49 (3.53) | - | 9 | - |
positive regulation of smooth muscle cell proliferation | 28 (4.59) | 23 (3.32) | 3 | 4 | - |
inflammatory response | 92 (3.99) | 71 (1.33) | - | 14 | 7 (5.66) |
positive regulation of cell proliferation | 107 (3.84) | 90 (2.2) | 9 | 12 | 1 |
extracellular matrix organization | 55 (3.46) | 47 (2.39) | - | 12 (1.4) | - |
positive regulation of angiogenesis | 38 (3.46) | 33 (2.66) | 2 | 3 | 1 |
cell migration | 49 (3.1) | 39 (1.47) | - | 10 | - |
leukocyte migration | 38 (2.85) | 31 (1.62) | 4 | 4 | 1 |
response to estradiol | 30 (2.28) | 25 (1.54) | 1 | 3 | 1 |
positive regulation of ERK1 and ERK2 cascade | 47 (2.25) | 37 | 2 | 9 | - |
cell division | 79 (2.2) | 76 (2.9) | 1 | 2 | - |
cell-cell signaling | 61 (2.04) | 52 | 2 | 8 | 1 |
response to drug | 70 (2.04) | 60 (1.41) | 4 | 8 | 1 |
positive regulation of MAP kinase activity | 22 (2.03) | 17 | 1 | 4 | - |
receptor internalization | 18 (1.96) | 17 (1.83) | - | - | 1 |
regulation of phosphatidylinositol 3-kinase signaling | 26 (1.93) | 20 | - | 7 | - |
phosphatidylinositol phosphorylation | 29 (1.76) | 25 | - | 4 | - |
osteoblast differentiation | 31 (1.76) | 27 (1.46) | 1 | 4 | - |
regulation of apoptotic process | 52 (1.76) | 46 (1.5) | 1 | 4 | 1 |
protein autophosphorylation | 44 (1.67) | 39 (1.31) | - | 5 | - |
cell-cell adhesion | 62 (1.65) | 54 | 6 | 2 | - |
positive regulation of tyrosine phosphorylation of Stat3 protein | 16 (1.64) | 15 (1.84) | 1 | - | - |
response to hydrogen peroxide | 19 (1.59) | 16 | - | 3 | - |
positive regulation of vascular endothelial growth factor production | 13 (1.57) | 11 (1.33) | 2 | - | - |
response to nutrient | 24 (1.57) | 21 | - | 3 | - |
negative regulation of apoptotic process | 93 (1.56) | 77 | 9 | 10 | 1 |
cellular response to retinoic acid | 23 (1.55) | 20 (1.31) | 2 | 1 | - |
positive regulation of protein kinase B signaling | 26 (1.55) | 23 (1.38) | - | 3 | - |
wound healing | 25 (1.51) | 19 | 2 | 5 | 1 |
response to ethanol | 30 (1.47) | 27 | 2 | 2 | 1 |
extracellular matrix disassembly | 24 (1.46) | 22 | 3 | - | - |
positive regulation of MAPK cascade | 25 (1.46) | 21 | 1 | 3 | - |
positive regulation of fibroblast proliferation | 19 (1.38) | 13 | 3 | 4 | - |
sprouting angiogenesis | 12 (1.36) | 12 (1.51) | - | 2 | - |
mitotic nuclear division | 56 (1.35) | 54 (1.76) | 1 | 1 | - |
collagen catabolic process | 21 (1.33) | 19 (1.34) | 1 | 2 | - |
platelet degranulation | 29 (1.33) | 28 (1.76) | 4 | - | - |
complement activation, alternative pathway | 7 | 5 | 3 (1.85) | - | - |
Rho protein signal transduction | 15 | 11 | 4 (1.67) | - | - |
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Evgeniou, M.; Sacnun, J.M.; Kratochwill, K.; Perco, P. A Meta-Analysis of Human Transcriptomics Data in the Context of Peritoneal Dialysis Identifies Novel Receptor-Ligand Interactions as Potential Therapeutic Targets. Int. J. Mol. Sci. 2021, 22, 13277. https://doi.org/10.3390/ijms222413277
Evgeniou M, Sacnun JM, Kratochwill K, Perco P. A Meta-Analysis of Human Transcriptomics Data in the Context of Peritoneal Dialysis Identifies Novel Receptor-Ligand Interactions as Potential Therapeutic Targets. International Journal of Molecular Sciences. 2021; 22(24):13277. https://doi.org/10.3390/ijms222413277
Chicago/Turabian StyleEvgeniou, Michail, Juan Manuel Sacnun, Klaus Kratochwill, and Paul Perco. 2021. "A Meta-Analysis of Human Transcriptomics Data in the Context of Peritoneal Dialysis Identifies Novel Receptor-Ligand Interactions as Potential Therapeutic Targets" International Journal of Molecular Sciences 22, no. 24: 13277. https://doi.org/10.3390/ijms222413277