Thrombosis-related circulating miR-16-5p is associated with disease severity in patients hospitalised for COVID-19

Abstract

SARS-CoV-2 tropism for the ACE2 receptor, along with the multifaceted inflammatory reaction, is likely to drive the generalized hypercoagulable and thrombotic state seen in patients with COVID-19. Using the original bioinformatic workflow and network medicine approaches we reanalysed four coronavirus-related expression datasets and performed co-expression analysis focused on thrombosis and ACE2 related genes. We identified microRNAs (miRNAs) which play role in ACE2-related thrombosis in coronavirus infection and further, we validated the expressions of precisely selected miRNAs-related to thrombosis (miR-16-5p, miR-27a-3p, let-7b-5p and miR-155-5p) in 79 hospitalized COVID-19 patients and 32 healthy volunteers by qRT-PCR. Consequently, we aimed to unravel whether bioinformatic prioritization could guide selection of miRNAs with a potential of diagnostic and prognostic biomarkers associated with disease severity in patients hospitalized for COVID-19. In bioinformatic analysis, we identified EGFR, HSP90AA1, APP, TP53, PTEN, UBC, FN1, ELAVL1 and CALM1 as regulatory genes which could play a pivotal role in COVID-19 related thrombosis. We also found miR-16-5p, miR-27a-3p, let-7b-5p and miR-155-5p as regulators in the coagulation and thrombosis process. In silico predictions were further confirmed in patients hospitalized for COVID-19. The expression levels of miR-16-5p and let-7b in COVID-19 patients were lower at baseline, 7-days and 21-day after admission compared to the healthy controls (p < 0.0001 for all time points for both miRNAs). The expression levels of miR-27a-3p and miR-155-5p in COVID-19 patients were higher at day 21 compared to the healthy controls (p = 0.007 and p < 0.001, respectively). A low baseline miR-16-5p expression presents predictive utility in assessment of the hospital length of stay or death in follow-up as a composite endpoint (AUC:0.810, 95% CI, 0.71-0.91, p < 0.0001) and low baseline expression of miR-16-5p and diabetes mellitus are independent predictors of increased length of stay or death according to a multivariate analysis (OR: 9.417; 95% CI, 2.647-33.506; p = 0.0005 and OR: 6.257; 95% CI, 1.049-37.316; p = 0.044, respectively). This study enabled us to better characterize changes in gene expression and signalling pathways related to hypercoagulable and thrombotic conditions in COVID-19. In this study we identified and validated miRNAs which could serve as novel, thrombosis-related predictive biomarkers of the COVID-19 complications, and can be used for early stratification of patients and prediction of severity of infection development in an individual.Abbreviations: ACE2, angiotensin-converting enzyme 2AF, atrial fibrillationAPP, Amyloid Beta Precursor ProteinaPTT, activated partial thromboplastin timeAUC, Area under the curveAβ, amyloid betaBMI, body mass indexCAD, coronary artery diseaseCALM1, Calmodulin 1 geneCaM, calmodulinCCND1, Cyclin D1CI, confidence intervalCOPD, chronic obstructive pulmonary diseaseCOVID-19, Coronavirus disease 2019CRP, C-reactive proteinCV, CardiovascularCVDs, cardiovascular diseasesDE, differentially expressedDM, diabetes mellitusEGFR, Epithelial growth factor receptorELAVL1, ELAV Like RNA Binding Protein 1FLNA, Filamin AFN1, Fibronectin 1GEO, Gene Expression OmnibushiPSC-CMs, Human induced pluripotent stem cell-derived cardiomyocytesHSP90AA1, Heat Shock Protein 90 Alpha Family Class A Member 1Hsp90α, heat shock protein 90αICU, intensive care unitIL, interleukinIQR, interquartile rangelncRNAs, long non-coding RNAsMI, myocardial infarctionMiRNA, MiR, microRNAmRNA, messenger RNAncRNA, non-coding RNANERI, network-medicine based integrative approachNF-kB, nuclear factor kappa-light-chain-enhancer of activated B cellsNPV, negative predictive valueNXF, nuclear export factorPBMCs, Peripheral blood mononuclear cellsPCT, procalcitoninPPI, Protein-protein interactionsPPV, positive predictive valuePTEN, phosphatase and tensin homologqPCR, quantitative polymerase chain reactionROC, receiver operating characteristicSARS-CoV-2, severe acute respiratory syndrome coronavirus 2SD, standard deviationTLR4, Toll-like receptor 4TM, thrombomodulinTP53, Tumour protein P53UBC, Ubiquitin CWBC, white blood cells.

Keywords: ACE2; SARS-COV-2; bioinformatics analysis; in silico prediction; miRNA; microRNAs.

Graphical abstract

Figure 1.

A) Bioinformatic workflow of the ACE2 and thrombosis-related seed genes selection for the integration of PPI interactome data with co-expression networks by NERI algorithm. This analysis enabled us to focus on a specific part of the network in this case: gradual signalling cascade between ACE2 and genes associated with coagulation-related processes. Seed genes are marked in green. Nodes outside of the ACE2 network were sorted using group circular layout based on the number of occurrences on four thrombosis and coagulation- related gene lists. Node colours were related to so-called ‘coagulation score’ calculated based on the occurrences on four gene lists and expression in four cardiovascular-system related tissues. B) Bioinformatic workflow leading to the identification of the miRNAs regulating the coagulation process in coronavirus infection.

Figure 2.

Top nodes identified by NERI algorithm as associated with coagulation networks in SARS and SARS-CoV-2 infections. The edge size is related to the number of occurrences in the top 30% of strongest regulated edges across analysed datasets (SARS, cardiomyocytes, lungs, heart) and two types of seeds ‘ACE2 and coagulation’ and ‘coagulation’ (8 gene lists in total). The node and edge sizes are associated with the importance in signal flow within the network for the analysed expression datasets (12 in total), which does not necessarily relate to upregulation in expression. If node/edge was enhanced according to NERI in higher number of disease-related datasets, it has red colour and has high importance in disease, if in control-related dataset, it is green and has decreased importance in disease. Grey colour means disease or control rate was the same in all datasets. The node size is associated with the sum of Δ’ (S) scores obtained from NERI. The higher the S score, the more important the role of a given gene in the analysed network. To show the tissue-specific expression level of each gene we added bars on each node reflecting their expression confidence (0–5) using the data obtained from the Tissues 2.0 database. EGFR, ELAVL1 and APP were identified across all 12 analysed gene lists (including three sets of seeds) as the top regulators of the thrombosis-related networks. DE genes in expression datasets have blue borders.

Figure 3.

A) Top 25 enriched pathways associated with top nodes identified by NERI algorithm as important in coagulation networks in SARS and SARS-CoV-2 infections. Grey dots indicate whether a gene is present within significantly enriched signalling pathways. It is worth noticing that CALM1 was present in the highest number of signalling pathways as an important ACE2 interactor. The top interactors with highest number of associated pathways were ordered in decreasing manner from left to right. B) Alterations between CALM1 interactors in COVID-19. Green colour is associated with loss of importance in disease which can lead to switching the signalling onto neighbouring nodes. Red colour is associated with increased importance of the node/edge in disease, leading to increase of signalling in a given part of the network.

Figure 4.

Comparison of circulating miRNAs relative expression between the groups. a) miR-16-5p; b) Let-7b-5p; c) miR-27a-3p; d) miR-155-5p. Mann-Whitney U test and Wilcoxon test were used appropriately. Kruskal-Wallis test shows the difference among the four groups. MiRNAs expression data is presented as log10 transformation. Abbreviations: COVID-19, coronavirus disease 2019; miR, microRNA; p, p value.

Figure 5.

Baseline miR-16-5p expression box-plots and receiver operating characteristic (ROC) curves: a) miR-16-5p box-plots for hospital length of stay comparison; b) miR-16-5p ROC curve for prediction of hospital length of stay; c) miR-16-5p box-plots for hospital length of stay or death in follow-up as a composite endpoint; d) miR-16-5p ROC curve for prediction of hospital length of stay or death in follow-up as a composite endpoint. Abbreviations: AUC, Area under the ROC Curve; COVID-19, coronavirus disease 2019; miR, microRNA; N, number.