Tag: long covid vs POTS

Post-translational modifications within fibrinaloid microclot complexes distinguish Pre-COVID-19 Postural Orthostatic Tachycardia Syndrome, Long COVID, and Long COVID-POTS and reveal disease-specific molecular pathways

Abstract:

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 have 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 flow cytometry 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 beta-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.

Source: Renata Madre Booyens, Mare Vlok, Cecile Bester, Rashmin Hira, M Asad Khan, Douglas B Kell, Satish R Raj, Etheresia Pretorius. Post-translational modifications within fibrinaloid microclot complexes distinguish Pre-COVID-19 Postural Orthostatic Tachycardia Syndrome, Long COVID, and Long COVID-POTS and reveal disease-specific molecular pathways.
bioRxiv 2025.12.29.696828; doi: https://doi.org/10.64898/2025.12.29.696828 https://www.biorxiv.org/content/10.64898/2025.12.29.696828v1 (Full text available as PDF file)

Metabolomics-Based Machine Learning Diagnostics of Post-Acute Sequelae of SARS-CoV-2 Infection

Abstract:

Background: COVID-19 has taken millions of lives and continues to affect people worldwide. Post-Acute Sequelae of SARS-CoV-2 Infection (also known as Post-Acute Sequelae of COVID-19 (PASC) or more commonly, Long COVID) occurs in the aftermath of COVID-19 and is poorly understood despite its widespread effects.

Methods: We created a machine-learning model that distinguishes PASC from PASC-similar diseases. The model was trained to recognize PASC-dysregulated metabolites (p ≤ 0.05) using molecular descriptors.

Results: Our multi-layer perceptron model accurately recognizes PASC-dysregulated metabolites in the independent testing set, with an AUC-ROC of 0.8991, and differentiates PASC from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Lyme disease, postural orthostatic tachycardia syndrome (POTS), and irritable bowel syndrome (IBS). However, it was unable to differentiate fibromyalgia (FM) from PASC.

Conclusions: By creating and testing models pairwise on each of these diseases, we elucidated the unique strength of the similarity between FM and PASC relative to other PASC-similar diseases. Our approach is unique to PASC diagnosis, and our use of molecular descriptors enables our model to work with any metabolite where molecular descriptors can be identified, as these descriptors can be generated and compared for any metabolite. Our study presents a novel approach to PASC diagnosis that partially circumvents the lengthy process of exclusion, potentially facilitating faster interventions and improved patient outcomes.

Source: Cai E, Kouznetsova VL, Tsigelny IF. Metabolomics-Based Machine Learning Diagnostics of Post-Acute Sequelae of SARS-CoV-2 Infection. Metabolites. 2025 Dec 17;15(12):801. doi: 10.3390/metabo15120801. PMID: 41441042; PMCID: PMC12734907. https://pmc.ncbi.nlm.nih.gov/articles/PMC12734907/ (Full text)