Plasma Proteome of Long-covid Patients Indicates Hypoxia-mediated Vasculo-proliferative Disease With Impact on Brain and Heart Function

doi:10.21203/rs.3.rs-2448315/v1

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Research Article

Plasma Proteome of Long-covid Patients Indicates Hypoxia-mediated Vasculo-proliferative Disease With Impact on Brain and Heart Function

https://doi.org/10.21203/rs.3.rs-2448315/v1

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Journal Publication

published 10 Jun, 2023

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Aims

Long-COVID occurs after SARS-CoV-2 infection and results in diverse, prolonged symptoms. The present study aims to determine the underlying mechanisms, and to inform prognosis and treatment.

Methods

Plasma proteome from Long-COVID outpatients was analyzed in comparison to acutely ill COVID-19 (mild and severe) inpatients and healthy control subjects. The expression of approximately 3000 protein biomarkers was determined with proximity extension assays and then deconvoluted with multiple bioinformatics tools into both cell types and signaling mechanisms, as well as organ specificity.

Results

Compared to age- and sex-matched acutely ill COVID-19 inpatients and healthy control subjects, Long-COVID outpatients showed natural killer cells with a resting phenotype, as opposed to active, and neutrophils that formed extracellular traps. This resetting of cell phenotypes was reflected in vascular events mediated by both angiopoietin-1 (ANGPT1) and vascular-endothelial growth factor-A (VEGFA). Levels of ANGPT1 and VEGFA were validated by serological methods in different patient cohorts. Silent signaling of transforming growth factor-β1 with elevated EP300 favored not only vascular inflammation, but also tumor necrosis factor-α driven pathways. In addition, a vascular proliferative state associated with hypoxia inducible factor 1 pathway was predicted that progressed from COVID-19 to Long-COVID. The vasculo-proliferative process identified in Long-COVID was associated with significant changes in the organ-specific proteome reflective of neurological and cardiometabolic dysfunction.

Conclusions

Taken together, our study uncovered a vasculo-proliferative process in Long-COVID initiated by prior hypoxia, and identified potential organ-specific prognostic biomarkers and therapeutic targets.

The "Long-COVID” syndrome is also referred to as long-haul COVID, post-COVID-19 condition, chronic COVID and post-acute sequelae of SARS-CoV-2 (PASC) (1). Symptoms of Long-COVID can be either similar or dissimilar from those of acute COVID-19. While some patients have symptoms that last for weeks, months or years after the initial diagnosis [2, 3] some recovered from COVID-19 and then endure a return of the symptoms, or they acquire new symptoms [4–7]. Additionally, individuals who had no symptoms when they were infected can still develop symptoms at a later date [1, 2]. Mostly, a spectrum of COVID-19 severities drive Long-COVID symptoms [1–3].

Long-COVID investigations are hampered by a variety of symptoms reported, as well as other contributing factors such as i) biological sex, ii) older age, iii) severity of initial COVID-19 illness, iv) amplitude of the immune response to initial infection, v) vaccination status, vi) the SARS-CoV-2 variant that caused the initial infection, vii) the severity of any preexisting health conditions (diabetes, lung problems, autoimmune diseases, or obesity), and/or viii) health care inequities [1, 2, 4–7]. A number of mechanisms have been proposed for Long-COVID, including i) reactivated SARS-CoV-2 particles [8, 9], ii) epigenetically programmed, overactive immune cells that release inflammatory substances [1], iii) autoimmune disease triggered by SARS-CoV-2 infection (10–13), or iv) a combination of the above factors originating from the initial COVID-19 disease [5, 14–19]. We recently reported that a panel of vascular transformation biomarkers was significantly elevated in plasma from Long-COVID outpatients (e.g., ANGPT1, MMP1, VEGF-A, etc), suggesting that altered angiogenesis may be a common mechanism in these patients with prolonged, diffuse, diverse symptoms [4].

In this study, we analyzed the plasma protein profiles of Long-COVID outpatients, and compared with age- and sex-matched acutely ill COVID-19 inpatients and healthy control subjects. The plasma proteome was deconvoluted into cell-type profiles and signaling pathways, as well as specific organ systems based on previously curated biomarkers [20–29]. Several biomarkers were dramatically elevated in Long-COVID patients and taken together these markers pinpointed immune triggered vascular proliferation which is mediated by hypoxia with a strong effect on brain and heart function [30–33].

Clinical assessment strategies. This study was approved by Western University, Human Research Ethics Board (Long-COVID REB ID# 120084; COVID-19 REB ID# 1670; Healthy Control Subjects REB ID# 6963). All patients were screened and enrolled from our tertiary care hospitals (London Health Sciences Centre, London, Ontario, Canada). Both Long-COVID outpatients and acutely ill COVID-19 inpatients had their COVID-19 status confirmed by standard hospital testing for SARS-CoV-2 viral genes using polymerase chain reaction (PCR). Long-COVID outpatients were referred to a specialty clinic based on prolonged, diffuse symptoms. Venous blood work was drawn once as part of a larger clinical screen, and subsequent analysis was performed on excess plasma collected for routine blood work by Pathology and Laboratory Medicine (PaLM). COVID-19 inpatients were enrolled on hospital admission on day-1n, either to the medical ward or to the intensive care unit (ICU). Blood from COVID-19 inpatients on day-1 was obtained from indwelling catheters or a venipuncture as required. The healthy control subjects were individuals without disease, acute illness or prescription medications and were previously banked in the Translational Research Centre, London, ON (Directed by Dr. D.D. Fraser; https://translationalresearchcentre.com/). These latter samples were obtained prior to the emergence of SARS-CoV-2 in our region and therefore, were considered not to have been exposed to the virus. Final participant groups were matched by age and gender (Long-COVID outpatients with acutely ill COVID-19 inpatients and healthy control subjects) [4]. Blood was centrifuged and plasma was isolated and aliquoted in 250 µL, and frozen at − 80°C until analysis.

Targeted Proteomics

Proximity Extension Assay (PEA) was used to measure plasma protein expression and included immune-recognition with dNTP-labeled antibodies, extension mediated by polymerases, amplification and detection as described by Lundberg et al [34–38]. All clinical samples were analyzed on the same 88 well plate. A control was used to estimate precision, a negative control was used to set background levels and to calculate limit of detection, a plate control to correct levels between plates, and a reference plasma control was used to estimate CV between runs. The relative protein quantification is presented as a Normalized Protein Expression (NPX) on a log2 scale. Data generation of NPX consists of normalization to the extension control, log2-transformation, and level adjustment using the plate control. PEA was outsourced to OLINK laboratories (Boston, MA) [38–40].

Bioinformatic Analyses. Normalized Protein eXpression (NPX) data was processed to determine the following: i) differentially expressed biomarkers, ii) Gene Ontology (GO) and pathways enrichment, and ii) affiliation with different cell types. Selected biomarkers were investigated for candidate drugs. The NPX of each peptide was normalized within the specified protein. All normalization computations used the medians to multiply and/or normalize the data. Multiple Mann Whitney Test analysis was performed to determine the differentially expressed proteins. For the functional annotation analysis data was trained in three steps to be certain that multiple platforms identify similar patterns: 1) analysis DAVID Bioinformatics Resources (version 6.8; https://david.ncifcrf.gov/) and PANTHER Classification System (version 14.0; http://www.pantherdb.org/), respectively; while the protein-protein interaction network analysis was carried out with STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) (version 11.0; https://string-db.org/) [41]; next 2) analysis with Metascape software, a gene annotation and analysis resource (https://metascape.org/gp/index.html#/main/step1) and GSEA, Gene Set Enrichment Analysis platform (https://www.gsea-msigdb.org/gsea/index.jsp); 3) selected biomarkers were analyzed with Kyoto Encyclopedia of Genes and Genomes (KGG) Mapper software (https://www.genome.jp/kegg/mapper/). Although the results were converging to the same findings we also validated our findings using i) iPathwayGuide (iPG) (https://www.advaitabio.com) a software based on KEGG charts, basically an improced version of KEGG Mapper [42–46] and ii) Qlucore (https://qlucore.com/geneexpressions). Using iPG, the analysis included the selection of differentially expressed proteins (DEPs) based on a fold change greater than 0.6 (in log2 scale) as well as a p-value lower than 0.05 after the correction for multiple experiments. The pathways were analyzed with the impact analysis approach published by Draghici et al [42]. This approach is able to take into consideration and position of each gene on each pathway, as well as the type and direction of all signals throughout the pathway [42]. Adjusted P values less than 0.05 were considered significant for both GO and pathway analysis.

Immune-cell deconvolution - was completed by CIBERSORT analysis mapping proteins onto genes (https://cibersort.stanford.edu) an analytical tool provided by Stanford University (“Stanford”) [47].

Matrix visualization and analysis was performed by Morpheus software and iteracting tools, from Broad Institute (https://software.broadinstitute.org/morpheus). Hierarchical clustering was performed by row, using Pierson one correlation algorithms both for the regular maps and similarity matrixes. K-means analysis was applied for columns to analyze the delineation of the study groups.

Principal componenet analysis was performed with ClustVis software: https://biit.cs.ut.ee/clustvis/.

Other statistical analyses. Bar graphs summarize data analyzed using by Mann-Whitney U test for unpaired data (two-sided). *P < 0.05; ***P < 0.0001 (GraphPad Prism, version 9). In the analysis process, several public R-studio packages from Bioconductor, were used for the initial global data analysis, as follows: data load (DBI, odbc); data manipulation (tidyr); data visualization (ggplot2, rgl, leaflet); data modeling (tidymodels); data report (shiny); spatial data (maps); web work (xml, github); R writing (devtools, curl).

Marker validation. ANGPT1 and VEGFA were measured in human plasma using a custom multiplexed immunoassay kit according to manufacturer’s instructions (Endothelial Injury Marker 12-Plex Human ProcartaPlex™ Panel, EPX120-15849-901).

Study model and dimensionality reduction of the data sets. The patient demographic and clinical data are shown in Supplementary Tables 1 (Long-COVID outpatients) and 2 (COVID-19 inpatients). Figure 1A (left panel) illustrates the experimental model consisting of four cohorts of patients, Long-COVID outpatients, acutely ill COVID-19 inpatients (evere), mild COVID-19 and healthy control subjects. Figure 1A (right panel) shows the data processing strategy. Dimensionality reduction analysis including all data sets, with complet hierarchical clustering, is shown in Supplementary Figure S1, where all data points that contributed to the PCA are shown grouped by disease severity and unit variance scaling was applied to rows and SVD with imputation is used to calculate principal components. In Figure S1, X and Y axis show principal component 1 and principal component 2 that explain 27.5% and 15.1% of the total variance, respectively (N = 88 data points). To heighten the differences between groups, in Fig. 1B we present the principal components analysis (PCA) and hierarchical clustering of averaged data points. The X and Y axes show principal component 1 and principal component 2 which explain 67.6% and 21.6% of the total variance, respectively. Data was processed by ClustVis software for both Figire S1 and Fig. 1B.

Overall, the data indicated that Long-COVID is a distinct disease compared to COVID-19 ICU patients (severe disease), although the cohorts did share some mechanisms. In contrast, the COVID-19 medical ward inpatients (mild disease) shared mechanisms closer to the healthy control subjects.

Natural killer (NK) cells from Long-COVID diseasechanged phenotye from activated to resting. The plasma proteome was deconvoluted to various immune cell-types from tissues based on their complex OMICS profile using the CIBERSORT analysis tool. The proportion of each cell-type in Long-COVID was compared to healthy control subjects (Fig. 2A). One major finding in Long-COVID outpatients is the shift in NK cells from activated to resting, indicating a shift in phenotype. In addition, memory B cells, memory CD4 activated cells and neutrophils were significantly elevated in Long-COVID, whereas resting dendritic cells, CD4 memory resting cells and M1 macrophages were down-regulated. The NK cell cytotoxic pathways, output of the iPathwaysGuide software, is shown in Fig. 2B. Overexpressed biomarkers are illustrated in red and the down-regulated ones in blue. The original NK-cell signaling KEGG map is presented in Supplementary Fig. 2. Investigations on the expression of individual markers showed down-regulations of SLP76, an immune response adaptor, while individual markers such as PAK1, MEK1/2 and ERK1/2 were upregulated and interconnected in signaling cascades that target the secretion of TNFα, IFNγ and GM-CSF. Furthermore, the VAV1 exchange factor for Rho-GTP-ases that connects directly to targets like ITGB2 and ITGAM was upregulated, thus predicting a subsequent increase in cellular adhesion and movement. Figure 2C (left panel) shows hierarchical clustering heatmaps, including NK cell signature markers (NCAM/CD56, killer receptors KLRK1 and KLRD1, IFNγ, IL-22, natural cytotoxicity receptor NCR1 and KIT stem cell factor). These above markers shared similarity in their behavior based on Pearson correlation algorithms (Fig. 2C, right panel), and all were upregulated in Long COVID (Fig. 2C, lower panel). Figure 2D (top left panel) illustrate the pathways analysis scoring with TNFα as the top hit; this graph shows the enrichment p-value on the horizontal axis (in a negative log scale) and the perturbation p-value on the vertical axis [48].

Prediction from the enrichment graph proved to be true, as the TNFα plasma expression was elevated in Long-COVID, as opposed to heathy control subjects (next plot, top row, Fig. 2D). The remaining plots in Fig. 2D portrayed elevated ANGPT1, whereas ANGPT2 did not change suggesting an ANGPT1/ANGPT2 conversion phenomen, as illustrated by the cartoon (Fig. 2D, bottom, right corner). The ANGPT1/2-Tie axis is critical regulator of endothelial inflammation and vascular leakage [49–51], and excess of ANGPT1 may reduce this process. Potential ‘vascular perturbation’ in Long-COVID pivoting around ANGPT/VEGF axis was validated by ANGPT1 and VEGFA measurement in plasma provided by a different group of patients than those approached by targeted proteomics (Supplementary Fig. 3, Heatmaps).

Resetting of immune-cell type proportions is reflected in vascular events mediated by VEGFA. Along with ANGPT1, VEGF signaling is also related to TNFα-linked pathways. Supplementary Fig. 4A shows that VEGFA pathway could regulate endothelial cell migration (signaling map is a crop-out of the KEGG chart presented in Supplemetary Fig. 5, as an output of iPathwaysGuide software). Supplementary Fig. 4C illustrates the upregulated markers that are members of the VEGFA pathway in Long-COVID, and included PXN, SRC, NOS3, and HSPB1. Other key markers such as Flt1 (VEGF receptor 1), AKT and MAPK, indicate endothelial cell survival, migration and proliferation. Key markers capable of protein-protein interactions are highlighted in the bottom left diagram and predicts VEGFA induced cell proliferation mediated by SRC and MAP-Kinases, and subsequent AKT signaling to influence cell survival and/or migration.

Neutrophil extracellular trap formation in Long-COVID. The features of the neutrophil extracellular traps (NETs) include: i) a defense mechanism against both micro-organisms and sterile triggers, ii) a DNA scaffold with granule-derived proteins, such as enzymatically active proteases and anti-microbial peptides, iii) an important role in inflammation, autoimmunity and other pathophysiological conditions (either detrimental or beneficial), and/or iv) it can be prompted by many triggers and via multiple distinct pathways with often unknown interrelationship [52–55]. The NET pathways shown in Fig. 3A represent a summary of the KEGG NET map output of iPathwayGuide, presented in Supplimentary Fig. 6. In red are the overexpressed biomarkers. Individual NET-specific/related biomarkers were featured in single expression graphs, where phospholipase (PLC1 and PLCγ), CAS1, and PDA4 were significantly upregulated in Long COVID, while NFkB, CR1, C3 and SLPG were down-regulated in this cohort when compared to severe COVID-19. While these results appear conflictual, our group and others previously reported a repurposed neutrophil phenotype in severe COVID-19 which may further differentiate in Long-COVID [52, 53].

Heatmaps representing the profiling of the neutrophil phenotype (based on a manually curated set of markers taken from specific literature reports) are presented in Fig. 3B [52–55]. The heatmaps show the hierarchical clustering of these markers in healthy control subjects and in COVID-19 patients along with their predicted interdependence (Pearson correlation algorhithm; Fig. 3B, right panel). Figure 3C highlights increased expression of neutrophil CD177, HLA-DR, ITGAM, ITGB2, and TLR2 manually curated markers in Long-COVID, as compared to the other patient cohorts. Interestingly, the expression of CD14, which is an innate immunity marker produced by macrophages and neutrophils was similar in both healthy control subjects and Long-COVID patients, but significantly down-regulated in mild and severe COVID-19 patients.

Silent TGFβ signaling and reduced EP300 favors vascular inflammation via TNF signaling and subsequent glucocorticoid resistance (GCR). Figure 4A illustrates TGFβ1 signaling pathway with both up-regulated (in red) and down-regulated (in blue) markers, per KEGG iPathwaysGuide output (Supplementary Fig. 7). Figure 4B graphs represent the expression of the individual markers associated with the TGFβ1 pathways. Key marker interactions are presented in the diagram (upper right). TGFβ1 and TGFBR2 were unchanged from healthy control subjects, thereby favoring TNFα pro-inflammatory signaling to induce an acute form of GCR [54, 56, 57]. TNFα has a significant and broad impact on the transcriptional performance of GR, but no impact on nuclear translocation, dimerization, or DNA binding capacity of GR [57]. A GR cofactor that interacts significantly less with the receptor under GCR conditions is EP/p300, a down-regulated biomarker in Long COVID that regulates the vascular bed via HIF under hypoxic conditions. EP/p300 strongly influences NFκB activation and thus, mediates inflamation [54]. EP/p300 knockdown reduces the transcriptional output of GCR, whereas its overexpression followed by NFκB inhibition reverts TNFα-induced GCR, trailing an authentic rearrangement model [57]. EP/p300 actions are quite vast, for instance it interacts with Smad1 to bridge coactivators such as NFκB transcribing IL-6, or it may interact with HIF1α/VEGFA axis [59, 60].

Vascular proliferation via HIF signaling pathway. The Long-COVID-19 proteome has been intersected with the mild and severe COVID-19 data sets, as well as the healthy control group, in a meta-analysis performed by iPathwaysGuide software. The resulted Venn diagram demonstrated that the Long-COVID group shared 80 signaling pathways among groups, 60 with the Severe COVID-19, but still retains 32 unique or independent pathways (Fig. 5A left diagram). Among all the pathways, HIF signaling was a major mechanism that progressed proportionally with disease severity, to a maximum in Long-COVID, as seen Fig. 5A and Supplementary Fig. 8 (where red arrows represent coherent cascades). HIF signaling in mild COVID-19 was initiated by IL-6 and STAT3, and targeted ANGPT and Flt1, followed by possible modifications in angiogenesis, iron metabolism (TFRC effector), vascular tone (HMOX1 effector), and cell survival (through BCL2 survival factor). In severe COVID-19, HIF signaling was triggered by IL-6, IFNγ and growth factors (VEGF and/or EGF) as ligands of receptor tyrosine kinases. Possible down stream modifications include erythropoietin (EPO), angiogenesis (Flt1, EGF, ANGPT1), vascular tone (eNOS, HMOX1), aerobic metabolism (GAPDH, ENO1) and survival (BCL2). HIF activation, and its consequences, in Long-COVID were significantly affected by the simultaneous decrease in (EP)p300, predicting extracellular matrix consequences via TIMP1, CD18-dependent inflammation (Integrin β2, ITGB2), and Tie2 regulated angiogenesis. HIF is a leading regulator of hypoxic/ischemic vascular responses, driving transcriptional activation of genes involved in vascular reactivity, angiogenesis, and the deployment of bone marrow-derived angiogenic cells. In parallel, (EP)p300 may function as a histone acetyltransferase with epigenetic functions reflected in endothelial cell proliferation and differentiation [54, 56–60], perhaps contributing to epigenetic modifications induced by SARS-CoV-2 infection. (EP)p300 may also affect the stimulation of hypoxia-inducible genes such as VEGF. While there are not therapeutic agents that target (EP)p300, it was predicted by the iPathwayGuide software that the HIF pathway can be however regulated by multiple re-purposed drugs presented in Fig. 5B.

Proliferative pathways in Long-COVID pathology. To establish the nature of the vascular disease and its proliferative aspects, Long-COVID data was investigated with the KEGG cancer pathways (Supplementary Fig. 9), with the most representative signaling cascade led by HIF as highlighted in Fig. 6A (left panel). In this network, the up-regulated biomarkers are shown in red and down-regulated in blue. Plots in Fig. 6A (right panel) illustrate markers elevated in Long-COVID responsible for the sustainability of the vascular bed. Next, Fig. 6B heatmaps reflect the levels of growth-factors that support cell proliferation through RTK membrane receptors located within the vascular bed. Markers have been curated by OLINK. The diagram in the right panel illustrates correlations between these factors, in a similarity matrix based on Pearson correlation principles. Figure 6C graphs show the levels of the IGFBPs that regulate the bioavailability of IGF, a factor known to regulate both angiogenesis and sustainability of the vascular bed. Except IGFBP4, IGFBP6 and IGFBPL1, which seem Long-COVID specific, the other IGFBPs are common to severe COVID-19. The status of the IGFs and their binding proteins (IGFBPs) could reflect the tissue repair capacity.

Long-COVID pathology implies brain dysfunction. The neurologic manifestations of the COVID-19 are well characterized and a comprehensive evaluation of the post-acute neurologic sequelae at 1 year was recently undertaken [61–64]. COVID-19 increased the risk of numerous neurologic sequelae such as ischemic and hemorrhagic stroke, cognition and memory disorders, peripheral nervous system disorders, episodic disorders (migraine and seizures), extrapyramidal and movement disorders, mental health disorders, musculoskeletal disorders, sensory disorders, Guillain–Barré syndrome, encephalitis, and/or encephalopathy. In Fig. 7A, hierarchical clustering heatmaps reflect the levels of neurological markers across the patient groups (markers have been curated by OLINK). The values of the PEA expression levels were hierarchically clustered based on Pearson correlation algorithms. Markers selected through the above methodology were investigated for functional annotation using tools from the GSEA platform and MSigDB data positories (Fig. 7B). This latest analysis demonstrated that functional clusters were formed around leukocyte migration, positive immune signals, glial cell differentiation, neurogenesis and MAPK regulatory modules. Taken together, these pathways predict a possible brain-blood barrier dysfunctionality grounded on cell proliferation.Graphs in Fig. 7C illustrate the expression levels of individual markers from the functional groups presented in Fig. 7B. One of the highly expressed markers, was the amyloid precursor protein (APP; Supplementary Fig. 10) which is known to be a pathognomonic marker for both Alzheimer disease and brain inflammation [61–65]. Additional markers for brain dysfunction include JAM2 (endothelial tight junctions protein), SNAPIN (a mediator of neuronal autophagy-lysosomal function in developing neurons), KCNH2 (potassium channel), S100A14 (involved in cell motility adhesion and growth), KIAA0319 (language impairment biomarker), and IROR1 (a receptor tyrosine kinase like orphan receptor 1, which regulates neurites growth in the central nervous system having also WNT-signaling pathway functions, and being crucial for the auditive apparatus maintenance).

Long-COVID disease implies cardiometabolic injury based on vasculo-proliferative events. We confirm and further clarify previousy discovered COVID-19 mechanisms excerted in the heart [66]. In Fig. 8A, hierarchical clustering heatmaps reflect the levels and the dynamics of cardio-metabolic markers across all patient cohorts. Markers were curated by OLINK and their expression levels were hierarchically clustered using Morpheus software tools. Topographically, on this map three critical clusters can be observed, and functionally annotated (Fig. 8B) by a standard screening using GSEA/MSigDB tools. This analysis also yielded three functional clusters. Taken in order from 1 to 3, clusters refer to i) extracellular matrix remodeling, ii) cell adhesion and motility and iii) angiogenesis (tube formation). These functional clusters were then investigated for protein-protein interaction (Fig. 8C), where it can be seen the that the extracellular matrix remodeling and cell-cell adhesion functions were dominated by integrins (ITGB1, ITGA5, ITGA1, ITGB6, ITGB1B1) which can potentially interact with fibronectin (FN1), filamin (FLNA) and calcium binding markers, essentially mediating Ca²⁺-independent - cell matrix interactions. Graphs in Fig. 8D represent individual marker expression levels in plasma. The integrin changes depicted in these graphs may result into disbalanced extracellular-matrix, leading to apoptosis of the endothelial cells.

SARS-CoV2 infection has proclivity for long term disease, with profound impact on brain and heart (Supplementary Figure 11). To elucidate potential mechanisms, the plasma proteome of Long-COVID patients was deconvoluted into different cell types and signaling mechanisms, as compared to age- and sex-matched acutely ill COVID-19 inpatients and healthy control subjects. Individual biomarker expression was also analyzed among the patient cohorts to identify potential value in diagnostic and prognostic targets. We found that in Long-COVID patients, natural killer cells switched phenotype from activated to a resting status, and the neutrophils were predisposed to extracellular trap formation. The cell-type proportions and their resetting were directly reflected in vascular events mediated by TNFa, ANGPT1, VEGFA, TGFb1, and EP300 biomarkers; and the vasculo-proliferative state was anchored by HIF1-pathways. Concurrrently, the down-regulation of EP300 may have dramatially influenced HIF1 functions in Long-COVID patients. The “E1A Binding Protein P300 (EP/p300)” is a large protein with multiple cellular functions, including stem cell effector efficacy. Thus, EP/p300 plays a major role in the reprogramming events, leading to a proliferative phenotype (for instance in cancer) with the acquisition of drug resistance and cell plasticity[67]. Down-regulated EP/p300, as a part of various transcriptional complexes, can alter critical biological functions such as cellular proliferation, cell cycle regulation, apoptosis, DNA damage repair, cell fate determination and stem cell pluripotency [67,68].

Our study may provides insight with regards to Long-COVID pathophysiology and potential preventative strategies. Our latest studies suggests ANGPT1 is up-regulated in Long-COVID [4], and this may play an important role in vascular development and angiogenesis, by activating the endothelial cell-specific tyrosine-protein kinase receptor (TEK/Tie receptor) [51]. ANGPT1 may be a critical compensatory protein in Long-COVID, mediating reciprocal interactions between endothelium and the surrounding extracellular matrix (ECM), interacting with the mesenchyme to inhibit endothelial permeability, while sustaining angiogenesis, endothelial cell survival, proliferation, motility and vascular quiescence [51]. In quiescent vessels, ANGPT1 usually forms complexes with TEK kinases from contiguous cells, leading to preferential activation of phosphatidylinositol 3-kinase and the AKT1 signaling cascades. Migrating endothelial cells that lack cell-cell adhesion capacity, rely on ANGT1 to recruit TEK and influence the ECM, resulting in the formation of focal adhesion complexes. However, their interactions with ECM would be impaired in Long-COVID due to down-regulated MMP7, thereby reducing migration and tissue repair. Moreover, depressed MMP7 would impede the proteolytic release of TNF from macrophages, which is typical for hypoxia, and the release of VEGF from its receptor (VEGFR1/Flt1) [69-71] Elevated ANGPT1 in Long-COVID might be compensatory to the above alterations, thereby contributing to the activation of PTK2/FAK and downstream kinases MAPK1/ERK2 and MAPK3/ERK1; events that ultimately stimulate angiogenesis and blood vessel maturation. Hypoxic conditions occurring in acute COVID-19 would induce growth factors such as VEGF, PDGF, EGF and IGF, ultimately potentiating cell proliferation in the vascular bed, and may hold true for thos patients with “happy hypoxia” [72,73]. Alternatively, ANGPT1 may be a critical link between the VEGFA, HIF and TNFa signaling pathways that were impacted in Long-COVID. VEGFA for instance could play a crucial role in mediating reciprocal interactions between the endothelium and surrounding matrix and mesenchyme, due to the ECM remodeling detected in Long-COVID [4,51,54,56-6].

Our data also indicate that Long-COVID has the potential to severely impact the functionality of multiple organs via these aforementioned vasculo-proliferative actions. Differential protein expression and network analysis showed disruption of the tissue recovery mechanisms that could be directly related to concerted HIF, TNFa and VEGFA signaling actions linked by ANGPT1. Besides providing system-level insights into the mechanism of Long-COVID pathology, our study identified potential biomarkers and therapeutic strategies that might be tailored to specific immune and organ mechanisms to combat tissue hypoxia.

The APP gene encodes a cell surface receptor and transmembrane precursor protein that is cleaved by secretases to form a number of peptides, some of which are secreted and can bind to the acetyltransferase complex to promote transcriptional activation, while others form the protein basis of the amyloid plaques found in the brains of patients with Alzheimer disease [61-64]. In addition, two of the peptides are antimicrobial peptides [61]. Mutations in this gene have been implicated in autosomal dominant Alzheimer disease and cerebro-arterial amyloidosis (cerebral amyloid angiopathy). Amyloids were shown to be highly toxic to neuronal cells [61-64]. In this context, we suggest that cytotoxic aggregates may trigger the neurological symptoms in COVID-19 which are then exacerbated in Long-COVID.

Our study also indicates cardiometabolic changes that were based on integrin signaling, where integrins ITGBP1 and INGA5 engage FN1, possibly triggering ITGB1/Focal adhesion (FAK)-related apoptosis pathways.

Elevated CCL5 may maintain an inflammatory state during Long-COVID. Resting NK cells, which we have shown to predominate in Long-COVID, express CCR5 [73] that interacts with CCL5 to promote angiogenesis via PI3K/AKT, NF-κB, HIF, RAS-ERK-MEK, JAK-STAT and TGFβ-smad pathways [74]. However, these above mechanisms were most likely independent of the myeloid lines and their inflammatory activity since the myeloid marker MNDA was down-regulated in Long-COVID. Furthermore, the increased levels of HEBP1 suggested a vascular proliferative disease, as this protein includes a natural chemoattractant peptide acting as a natural ligand for formyl peptide receptor-like receptor 2 (FPRL2). The FPRL2 receptor promotes calcium mobilization and chemotaxis in monocytes and dendritic cells, potentially instigating myocarditis[74]. FPRL2 peptides are powerful neutrophil chemotactic factors and activators [74], and possibly neutrophil trap formation. Dysregulated inflammation can potentiate maladaptive healing and pathological remodeling of cardiac tissue, leading to long term cardiac dysfunction and failure, features sometimes present in Long-COVID patients. Since there are key preclinical studies that support the use of FPR2 agonists in heart disease, these therapeutic agents may be applicable for the prevention and treatment of cardio-metabolic pathology in Long-COVID [74].

In conclusion, our report provides a pathophysiological framework to better understand the functional heterogenicity of the Long-COVID and provides clues to the neurological and cardio-metabolic basis of this disease (Figure 9). This study also provides a valuable resource for the exploration of biomarkers in Long-COVID and the development of potential therapeutic targets for the prevention and/or treatment of the disease based on multiple patholophysiological mechanisms.

AKNOWLEDGEMENTS: The authors are grateful to the frontline health professionals at London Health Science Center (London, ON, Canada) whom aided the collection of biological samples.

FUNDING: This study was supported by the London Health Sciences Foundation and Academic Medical Organization of Southwestern Ontario (AMOSO) Innovation Fund to DDF, Principal Investigator.

ETHICS: This study was approved by Western University, Human Research Ethics Board (Long-COVID REB ID# 120084; COVID-19 REB ID# 1670; Healthy Control Subjects REB ID# 6963)

CONSENT FOR PUBLICATION: All authors consented for publication.

DATA AND MATERIALS AVAILABILITY: By request.

COMPETING INTERESTS: There are no competing interests.

AUTHORS CONTRIBUTION: CI and DDF conceived, designed and performed the analysis, and wrote the manuscript; DDF supervised data collection and analysis; SD contributed with analysis tools and reviewed the analysis principles; MK, MN, LRvN collected data and critically reviewed data and the manuscript; GC and VKM critically reviewed the data and the manuscript.

Castanares-Zapatero D, Chalon P, Kohn L, Dauvrin M, Detollenaere J, et al. Pathophysiology and mechanism of Long-COVID: a comprehensive review. Ann Med. 2022; 54 (1):1473-1487.
Brown K, Yahyouche A, Haroon S, Camaradou J, Turner J. Long-COVID and self-management. Lancet. 2022; 22 (399):10322-355.
World Health Organization WHO R&D Blueprint. Novel Coronavirus. COVID-19 Therapeutic Trial Synopsis.https://cdn.who.int/media/docs/default-source/blue-print/covid-19-therapeutic-trial-synopsis.pdf?sfvrsn=44b83344_1&download=true.
Patel MA, Knauer MJ, Nicholson M, Daley M, Van Nynatten LR, Martin C, et al. Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism. Molec Med. 2022; 28 (1):122 doi: 10.1186/s10020-022-00548-8.
Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of post-acute sequelae of COVID-19. Nature. 2021; 594 (7862):259-264.
Xie Y, Bowe B, Al-Aly Z. Burdens of post-acute sequelae of COVID-19 by severity of acute infection, demographics and health status. Nature Comm. 2021; 12, 12(1):6571.
Yong SJ and Shiliang L. Proposed subtypes of post-COVID-19 syndrome (or long-COVID) and their respective potential therapies. Rev Med Virol. 2022; 32(4):e2315.
M. Antonelli, J.C. Pujol, T.D. Spector, S. Ourselin, C.J. Steves, Risk of Long-COVID associated with delta versus omicron variants of SARS-CoV-2. Lancet. 18;399(10343):2263-2264 (2022)
Couzin-Frankel J, Vogel G. Vaccines may cause rare, Long-COVID-like symptoms. Science. 2022; 28;375(6579):364-366.
Dotan A, David P, Arnheim D, Shoenfeld Y. The autonomic aspects of the post-COVID19 syndrome. Autoimmun Rev. 2022; 21(5):103071.
Ledford H. Long-COVID treatments: why the world is still waiting. Nature. 2022; 608(7922):258-260.
Y. Su Y, D. Yuan, D.G. Chen, R.H. Ng, K. Wang, J. Choi, S. Li, S. Hong, R. Zhang, J. Xie, S.A. Kornilov, K. Scherler, et al. Multiple early factors anticipate post-acute COVID-19 sequelae. Cell. 2022; 3;185(5):881-895.
B. Vijayakumar, K. Boustani, P.P. Ogger, A. Papadaki, J. Tonkin, C.M. Orton, P. Ghai, K. Suveizdyte, et al. Immuno-proteomic profiling reveals aberrant immune cell regulation in the airways of individuals with ongoing post-COVID-19 respiratory disease. Immunity. 2022; 8;55(3):542-556.e5.
Fraser DD, M.R. Miller, C.M. Martin, M. Slessarev, P. Hahn, I. Higgins, et al. Cohort-Specific Serological Recognition of SARS-CoV-2 Variant RBD Antigens, Ann Clin Lab Sci. 2022; 52(4):651-662.
Dong E, Du H, Gardner H. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect 2020; Dis. 20:533–534.
A. Nalbandian, K. Sehgal, A. Gupta, M.V. Madhavan, C. McGroder, J.S. Stevens , J.R. Cook, A.S. Nordvig AS, Shalev D, et al. Post-acute COVID-19 syndrome. Nat Med. Nat Med. 2021; 27(4):601-615.
Taribagil P, Creer D, Tahir D. 'Long-COVID' syndrome. BMJ Case Rep. 2021; 14:e241485. doi: 10.1136/bcr-2020-241485.
Al-Aly Z, Bowe B, Xie Y. Long-COVID after breakthrough SARS-CoV-2 infection. Nat Med. 2022; 28(7):1461-1467.
Wilk AJ, Rustagi A, Zhao NQ, Roque J, Martínez-Colón GJ, McKechnie JL, et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat Med. 2020; 26:1070-1076.
Kruger A, Vlok M, Turner S, Venter C, Laubscher GJ, Kell DB, Pretorius E. Proteomics of fibrin amyloid microclots in Long-COVID/post-acute sequelae of COVID-19 (PASC) shows many entrapped pro-inflammatory molecules that may also contribute to a failed fibrinolytic system. Cardiovasc Diabeto. 2022; 21(1):190. doi: 10.1186/s12933-022-01623-4.
Demichev V, Tober-Lau P, Lemke O, Nazarenko T, Thibeault C, Whitwell H, Röhl A, Freiwald A, Szyrwiel L, Ludwig D, Correia-Melo C, et al. PA-COVID-19 Study group, Ralser M, Kurth F.. A time-resolved proteomic and prognostic map of COVID-19. Cell Syst. 2021; 12(8):780-794.
Filbin MR, et al. Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions. Cell Rep Med. 2021;2(5):100287.
Filbin MR, Mehta A, Schneider AM, Kays KR, Guess JR, Gentili M, Fenyves BG, Charland NC, Gonye ALK, et al. Novel outcome biomarkers identified with targeted proteomic analyses of plasma from critically Ill coronavirus disease 2019 patients. Cell Rep Med. 2021; 18;2(5):100287.
Memon D, Barrio-Hernandez I, Beltrao P. Individual COVID-19 disease trajectories revealed by plasma proteomics. EMBO Mol Med. 2021; 13(8):e14532.
Vedula P, Tang HY, Speicher DW, Kashina A. UPenn COVID Processing Unit. Protein Posttranslational Signatures Identified in COVID-19 Patient Plasma. Front Cell Dev Biol. 2022; 10:807149.
Al-Nesf MAY, Abdesselem HB, Bensmail I, Ibrahim S, Saeed WAH, Mohammed SSI, Razok A, Alhussain H, et al. Prognostic tools and candidate drugs based on plasma proteomics of patients with severe COVID-19 complications. Nat Commun. 2022; 13(1):946 3(1):946. doi: 10.1038/s41467-022-28639-4.
Feyaerts D, Hédou J, Gillard J, Chen H, Tsai ES, Peterson LS, et al. Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19. bioRxiv. 2021.02.09.430269 .
Lam SM, Zhang C, Wang Z, Ni Z, Zhang S, Yang S, Huang X, Mo L, et al. A multi-omics investigation of the composition and function of extracellular vesicles along the temporal trajectory of COVID-19. Nat Metab. 2021; 3(7):909-922.
Rostron AJ, Simpson AJ, Hambleton S, Laurenti E, Lyons PA, Meyer KB, Nikolić MZ, Duncan CJA, Smith KGC, et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat Med. 2021; 27(5):904-916.
Mathew D, Giles JR, Baxter AE, Oldridge DA, Greenplate AR, Wu JE, Alanio C, Kuri-Cervantes L, Pampena MB, D'Andrea K, Manne S, et al. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science. 2020; 369: 369(6508):eabc8511.
Laing AG, Lorenc A, Del Molino Del Barrio I, Das A, Fish M, Monin L, Muñoz-Ruiz M, McKenzie DR, Hayday TS, Francos-Quijorna I, Kamdar S, et al. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat Med. 2020; 26:1623-1635.
Fraser DD, Cepinskas G, Slessarev M, Martin CM, Daley M, Patel MA, et al. Detection and Profiling of Human Coronavirus Immunoglobulins in Critically Ill Coronavirus Disease 2019 Patients. Crit Care Explor. 2021; 3(3):e0369. doi: 10.1097/CCE.0000000000000369.
Lupisella LA, Parvin SS, Wurtz TR, Garcia RA. Formyl peptide receptor 2 and heart disease. Seminars in Immunol. 2022; 101602; https://doi.org/10.1016/j.smim.2022.101602.
Lundberg M, Eriksson A, Tran B, Assarsson E, Fredrickson S. Homogenous antibody-based proximity ligation assay provides sensitive and specific detection or low abundant proteins in human blood. Nucl Acid Research. 2011; 39(15):e102. doi: 10.1093/nar/gkr424.
Arunachalam PS, Wimmers F, Mok CKP, Perera RAPM, Scott M, Hagan T, Sigal N, Feng Y, Bristow L, et al. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science. 2020. 369(6508):1210-1220. doi: 10.1126/science.abc6261.
Møller PL, Rohde PD, Winther S, Breining P, Nissen L, Nykjaer A, Bøttcher M, Nyegaard M, Kjolby M. Sortilin as a Biomarker for Cardiovascular Disease Revisited. Front Cardiovasc Med. 2021; 16;8:652584. doi: 10.3389/fcvm.2021.652584.
Zhao J, Schank M, Wang L, Dang X, Cao D, Khanal S, Nguyen LNT, Zhang Y, Wu XY, Adkins JL, Pelton BJ, et al. Plasma biomarkers for systemic inflammation in COVID-19 survivors. Proteomics Clin Appl.2022; 16(5):e2200031. doi: 10.1002/prca.202200031.
Shu T, Ning W, Wu D, Xu J, Han Q, Huang M, Zou X, Yang Q, Yuan Y, Bie Y, Pan S, Mu J, Han Y, et al. Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19. Immunity. 2020; 17;53(5):1108-1122.e5. doi: 10.1016/j.immuni.2020.10.008.
Williams SA, Kivimaki M, Langenberg C, Hingorani AD, Casas JP, Bouchard C, Jonasson C, Sarzynski MA, et al. Plasma protein patterns as comprehensive indicators of health. Nat Med. 2019; 25 (12):1851-1857.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering CV. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019. 47(D1):D607-D613. doi: 10.1093/nar/gky1131.
Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, Georgescu C, Romero R. A systems biology approach for pathway level analysis. Genome Res. 2007; 17(10): 1537–1545.
Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, Burgess S, Jiang T, Paige E, et al. Genomic atlas of the human plasma proteome. Nature. 2018; 558(7708):73-79. doi: 10.1038/s41586-018-0175-2.
Shafi A, Nguyen T, Peyvandipour A, Nguyen H, Draghici S. A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures. Front Genet.2019; 19;10:159. doi: 10.3389/fgene.2019.00159.
Nguyen T, Shafi A, Nguyen TM, Schissler AG, Draghici S. NBIA: a network-based integrative analysis framework - applied to pathway analysis. Sci Rep. 2020; 10(1):4188. doi: 10.1038/s41598-020-60981-9.
Pfaff ER, Girvin AT, Bennett TD, Bhatia A, Brooks IM, Deer RR, Dekermanjian JP, Jolley SE, Kahn MG, Kostka K, et al. N3C Consortium. Identifying who has long COVID in the USA: a machine learning approach using N3C data. Lancet Digit Health. 2022; 4(7):e532-e541. doi: 10.1016/S2589-7500(22)00048-6.
Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015; 12(5):453-7. doi: 10.1038/nmeth.3337.
Mathew D, Giles JR, Baxter AE, Oldridge DA, Greenplate AR, Wu JE, Alanio C, Kuri-Cervantes L, Pampena MB, D'Andrea K, Manne S, et al. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science. 2020; 369 (6508):eabc8511. doi: 10.1126/science.abc8511.
Laing AG, Lorenc A, Del Molino Del Barrio I, Das A, Fish M, Monin L, Muñoz-Ruiz M, McKenzie DR, Hayday TS, et al. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat Med. 2020; 26:1623-1635.
Parikh SM. The angiopoietins and Tie in vascular inflammation. Curr Oppinion Hematol. 24 (5): 432–438 (2017).
Silvin A, Chapuis N, Dunsmore G, Goubet AG, Dubuisson A, Derosa L, Almire C, Hénon C, Kosmider O, Droin N, Rameau P, Catelain C, Alfaro A, et al. Elevated calprotection and abnormal myeloid cell subsets discriminate severe from mild COVID19. Cell. 2020; 182:1401-1418.
Rice CM, Lewis P, Ponce-Garcia FM, Willem Gibbs D, Cela F, et al. Neutrophils in COVID19 are characterized by hyperactive immature state and maintained CXR2 expression. medRix: doi.org/10.1101/2022.03.23.22272828.
Sinha S, Rosin NL, Arora R, Labit E, Jaffer A, Cao L, Farias R, Nguyen AP, de Almeida LGN, et al. Dexamethasone modulates immature neutrophils and interferon programming in severe COVID-19. Nat Med. 2022; 28(1):201-211.
Meizlish ML, Pine AB, Bishai JD, Goshua G, Nadelmann ER, Simonov M, Chang CH, Zhang H, Shallow M, Bahel P, Owusu K, et al. A neutrophil activation signature predicts critical illness and mortality in COVID-19. Blood Adv. 2021; 5(5):1164-1177.
Liu ZW, Zhang YM, Zhang LY, Zhou T, Li YY, Zhou GC, Miao ZM, et al. Duality of Interactions Between TGF-β and TNF-α During Tumor Formation. Frontientrs Immunol. 2021; doi.org/10.3389/fimmu. 12:810286. doi: 10.3389/fimmu.2021.810286.
Portuguez Aj, Grbesa I, Tal M, Deitch R, Raz D, Kliker L, Weismann R, Schwartz M, Loza O, Cohen L, Marchenkov-Flam L,et al. Ep300 sequestration to functionally distinct glucocorticoid receptor binding loci underlie rapid gene activation and repression. Nucl Ac Res. 2022; 50(12), 6702–6714.
Dendoncker K, Timmermans S, Vandewalle J, Eggermont M, Lempiäinen J, Paakinaho V, Van Hamme E, Dewaele S, Vandevyver S,et al. TNF-α inhibits glucocorticoid receptor-induced gene expression by reshaping the GR nuclear cofactor profile. Proc Natl Acad Sci USA. 2019; 116(26): 12942–12951.
Morikawa et al. Genome-wide mechanisms of Smad binding. Oncogene. 2013; 32:1609–1615.
ENTREZ: https://www.ncbi.nlm.nih.gov/gene/4088; https://www.ncbi.nlm.nih.gov/gene?Db=gene&Cmd=ShowDetailView&TermToSearch=2033
Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of post-acute sequelae of COVID-19. Nature. 2021; 594(7862):259-264.
Hugon J, Msika EF, M. Queneau, K. Farid K, C. Paquet. Long-COVID: cognitive complaints (brain fog) and dysfunction of the cingulate cortex. J Neurol. 2022; 269(1):44-46. doi: 10.1007/s00415-021-10655-x.
Boldrini M, Canoll PD, Klein RS. How COVID-19 affects the brain. JAMA Psychiat. 2021; doi: 10.1001/jamapsychiatry.2021.0500.
Ellul MA, Benjamin L, Singh B, Lant S, Michael BD, Easton A, Kneen R, Defres S, Sejvar J, Solomon T.. Neurological associations of COVID-19. Lancet Neurol. 2020; 19:767–783.doi: 10.1016/S1474-4422(20)30221-0.
Charnley M, Islam S, Bindra GK, Engwirda J, Ratcliffe J, Zhou J, Mezzenga R, Hulett MD, Han K, Berryman JT, Reynolds NP.. Neurotoxic amyloidogenic peptides in the proteome of SARS-COV2: potential implications for neurological symptoms in COVID-19. Nat Commun. 2022; 13 (1):3387. doi: 10.1038/s41467-022-30932-1.
Asaduzzaman M, Constantinou S, Min H, Gallon J, Lin ML, Singh P, Raguz S, Ali S, Shousha S, Coombes RC, Lam EW, Hu Y, Yagüe E. Tumour suppressor EP300, a modulator of paclitaxel resistance and stemness, is downregulated in metaplastic breast cancer. Breast Cancer Res Treat. 2018; 167(2):605-606.
Xie Y, Xu E, Bowe B, Al-Aly Z. Long-term cardiovascular outcomes of COVID-19. Nat. Med. 2022; 28:583–590. doi: 10.1038/s41591-022-01689-3.
Ring A, P. Kaur, J.E. Lang JE. EP300 knockdown reduces cancer stem cell phenotype, tumor growth and metastasis in triple negative breast cancer. BMC Cancer.2020; 10;20(1):1076.
Ito TK, Ishi G, Saito S, Iano K, Suziki T, Ochiai A. Ito TK, Ishi G, Saito S, Iano K, Suziki T, Ochiai A. Degradation of soluble VEGF receptor-1 by MMP-7 allows VEGF access to endothelial cells. Blood. 2009; 5;113(10):2363-9.
Patterson BK, Guevara-Coto J, Yogendra R, Francisco EB, Long E, Pise A, Rodrigues H, Parikh P, Mora J, Mora-Rodríguez RA. Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning. Front. Immunol. 2021; 28;12:700782. doi: 10.3389/fimmu.2021.700782.
Patterson BK, Francisco EB, Yogendra R, Long E, Pise A, Rodrigues H, Hall E, et al. Persistence of SARS CoV-2 S1 Protein in CD16+ Monocytes in Post-Acute Sequelae of COVID-19 (PASC) up to 15 Months Post-Infection. Front. Immunol. 2022; 10;12:746021. doi: 10.3389/fimmu.2021.746021.
Dhont S, Derom E, Van Braeckel E, Depuydt P, Lambrecht BN. The pathophysiology of ‘happy’ hypoxemia in COVID-19. Respir Res. 2020; 21:198. doi.org/10.1186/s12931-020-01462-5.
Akoumianaki E, Vaporidi K, Bolaki M, Georgopoulos D. Happy or Silent Hypoxia in COVID-19–A Misnomer Born in the Pandemic Era. Front. Physiol. Sec. Respiratory Physiology and Pathophysiology. 2021; 18;12:745634. doi: 10.3389/fphys.2021.745634.
Robertson M. Role of chemokines in the biology of natural killer cells. J Leukoc Biol. 71(2):173-83 (2002).
Zeng Z, Lan T, Wei Y, Wei X. CCL5/CCR5 axis in human diseases and related treatments. Genes and Diseases. 2022; 9(1): 12-27.

IosefetalSupplementaryFigure1.jpg
S1. (Supplememnbtary Figure 1)- Study model and dimensionality reduction of data sets including all patients (22 per group). (A) Model in the left panel suggests three types of Covid-19 disease data sets (mild, severe and long-Covid-19) run against a healthy control group. Data represents plasma proteome obtained by Proximity Extension Assay (PEA), the average counts for all patients of each group. Right panel informs on type of data processing. (B) Principal components analysis and hierarchical clustering. Unit variance scaling was applied to rows; SVD with imputation is used to calculate principal components. X and Y axis show principal component 1 and principal component 2 that explain 27.5% and 15.1% of the total variance, respectively. N = 88 data points. Data was processed by Clustvis software (https://github.com/taunometsalu/ClustVis).
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S2. (Supplementary Figure 2) Natural killer cell mediated cytotoxicity (KEGG Mapper output, confirm by iPG map). S3. (Supplemantary Figure 3). Serological validation of key markers (Angiopoetin 1 and Vasculo-Endothelial Growth Factorb A). Each block in the heatmaps represent the mean of three technical replicates. Markers were measured in human plasma using a custom multiplexed immunoassay kit according to manufacturer’s instructions (Endothelial Injury Marker 12-Plex Human ProcartaPlex™ Panel, EPX120-15849-901)
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S4. (Supplementary Figure 4). Resetting of cell types proportions is reflected in vascular events mediated by VEGFA. (A) VEGF signaling pathways as described by the KEGG are presented. Up-regulated biomarkers are in red and down-regulated biomarkers in blue. Analysis was done with KEGG Mapper and confirmed with iPathwayGuide software. (B) Graphs show individual marker expression in Long-COVID plasma, as compared to healthy control subjects. Statistical significance was established using GraphPad-9, and P-value was considered significant if <0.05 in Mann-Whitney U test. Key markers were VEGFA, VEGFR2, AKT and MAPK, indicating endothelial cell activity, survival and migration. Key markers protein-protein interactions are presented in the diagram from the bottom panel, left (done with STRING and confirmed with iPathwayGuide).
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S5. (Supplementary Figure 5) VEGF Signaling pathway (KEGG Mapper, iPG confirmed map). S6. (Supplementary Figure 6) Neutrophil Extracellular Trap Formation (KEGG Mapper, iPG confirmed map).
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S7. (Supplementary Figure 7) TGFB1 Signaling pathway (KEGG Mapper, iPG confirmed map).
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S8. (Supplementary Figure 8) HIF1 Signaling pathway (KEGG Mapper, iPG confirmed map) as resulted from the pathways meta-analysis between HCTR,Mild-, Severe- and Long-COVID-19 groups.
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S9. (Supplementary Figure 9) Cancer pathway and biomarkers that appear in Long-COVID19 patient plasma (KEGG Mapper, iPG confirmed map)
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S10. (Supplementary Figure 10) Neurological dysfunction pathway and biomarkers that appear in Long-COVID19 patient plasma (KEGG Mapper, iPG confirmed map)
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Plasma Proteome of Long-covid Patients Indicates Hypoxia-mediated Vasculo-proliferative Disease With Impact on Brain and Heart Function

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