Post-translational modifications within fibrinaloid microclot complexes distinguish Pre-COVID-19 Postural Orthostatic Tachycardia Syndrome (POTS), Long COVID, and Long COVID-POTS and reveal disease-specific molecular pathways
Bibliographic record
Abstract
SUMMARY Background Pre-COVID-19 Postural orthostatic tachycardia syndrome (PC-POTS), Long COVID, and their overlap (LC-POTS) are chronic post-viral conditions marked by debilitating symptoms despite frequently normal routine laboratory results. We previously identified insoluble fibrinaloid microclot complexes (FMCs) in Long COVID. It is not known whether FMCs are also present in PC-POTS, or whether post-translational modifications (PTMs) within FMC-entrapped proteins contribute to disease mechanisms. Methods Platelet-poor plasma from healthy controls, PC-POTS patients (collected prior to the COVID-19 pandemic), Long COVID (without POTS) and LC-POTS patients underwent fluorescence imaging flow cytometry to quantify FMCs. Proteomic analyses were performed on insoluble protein fractions using a double trypsin digestion strategy and data-independent liquid chromatography-tandem mass spectrometry (LC-MS/MS). Differential protein abundance, PTMs, and amyloidogenicity were compared across groups. Results Measured with imaging flowcytometry in objects/mL, higher levels of FMCs were present in disease groups compared to controls, although not statistically significant. Statistically significant differences potentially lay within FMC sizes and composition. Furthermore, despite only a few dysregulated proteins, FMC proteomics revealed extensive and disease-specific peptides with PTM dysregulation across coagulation, immune, and metabolic pathways. Long COVID displayed FMCs with PTMs of coagulation proteins including prominent advanced glycation end-products (AGE)- and oxidation-based modifications of fibrinogen subunits, particularly fibrinogen subunit A (FIBA), resembling diabetic glycation profiles. FMCs in PC-POTS showed fewer fibrinogen PTMs but markedly increased modifications in metabolic proteins, including oxidised apoA1 and apoB, and immune patterns with complement-related proteins (C3, C4A/B, IC1), immunoglobulin G1 (IGG1) and alpha 2 macroglobulin (A2MG). LC-POTS shared coagulation pathology with Long COVID and immune pathology with PC-POTS. Many dysregulated peptides were determined by in silco methods to be highly amyloidogenic, consistent with FMCs as β-sheet-rich aggregates. Protein-level differences were minimal compared with PTM changes. Conclusions This study provides the first evidence that post-translational modifications (PTMs) within fibrinaloid microclots complexes (FMCs) uniquely distinguish pre-COVID-19 POTS, Long COVID, and Long COVID-POTS. Because PC-POTS samples were collected before SARS-CoV-2, their PTM patterns reflect intrinsic disease biology, allowing a clear separation from Long COVID-related changes. PTM profiling revealed pro-coagulant fibrinogen modifications in Long COVID and LC-POTS, metabolic-oxidative disruptions in Long COVID and PC-POTS, and immune dysregulation in PC-POTS and LC-POTS. None of these is detectable with routine assays, and all are independent of protein abundance. The consistent presence of amyloidogenic peptides suggests a contribution to microvascular dysfunction. These findings define disease-specific PTM landscapes and support new diagnostic and therapeutic avenues across autonomic and post-viral disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".