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. 2022 Jan 29;118(2):461-474.
doi: 10.1093/cvr/cvab338.

Association of cardiometabolic microRNAs with COVID-19 severity and mortality

Clemens Gutmann  1 , Kseniya Khamina  2 , Konstantinos Theofilatos  1 , Andreas B Diendorfer  2 , Sean A Burnap  1 , Adam Nabeebaccus  1   3 , Matthew Fish  4   5 , Mark J W McPhail  3   6   7 , Kevin O'Gallagher  1   3 , Lukas E Schmidt  1 , Christian Cassel  1 , Georg Auzinger  3   8   9 , Salvatore Napoli  6 , Salma F Mujib  7 , Francesca Trovato  3   6   7 , Barnaby Sanderson  5 , Blair Merrick  10 , Roman Roy  3 , Jonathan D Edgeworth  4   10 , Ajay M Shah  1   3 , Adrian C Hayday  4   11 , Ludwig Traby  12 , Matthias Hackl  2 , Sabine Eichinger  13 , Manu Shankar-Hari  4   5   14 , Manuel Mayr  1
Affiliations

Association of cardiometabolic microRNAs with COVID-19 severity and mortality

Clemens Gutmann et al. Cardiovasc Res. .

Abstract

Aims: Coronavirus disease 2019 (COVID-19) can lead to multiorgan damage. MicroRNAs (miRNAs) in blood reflect cell activation and tissue injury. We aimed to determine the association of circulating miRNAs with COVID-19 severity and 28 day intensive care unit (ICU) mortality.

Methods and results: We performed RNA-Seq in plasma of healthy controls (n = 11), non-severe (n = 18), and severe (n = 18) COVID-19 patients and selected 14 miRNAs according to cell- and tissue origin for measurement by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in a separate cohort of mild (n = 6), moderate (n = 39), and severe (n = 16) patients. Candidates were then measured by RT-qPCR in longitudinal samples of ICU COVID-19 patients (n = 240 samples from n = 65 patients). A total of 60 miRNAs, including platelet-, endothelial-, hepatocyte-, and cardiomyocyte-derived miRNAs, were differentially expressed depending on severity, with increased miR-133a and reduced miR-122 also being associated with 28 day mortality. We leveraged mass spectrometry-based proteomics data for corresponding protein trajectories. Myocyte-derived (myomiR) miR-133a was inversely associated with neutrophil counts and positively with proteins related to neutrophil degranulation, such as myeloperoxidase. In contrast, levels of hepatocyte-derived miR-122 correlated to liver parameters and to liver-derived positive (inverse association) and negative acute phase proteins (positive association). Finally, we compared miRNAs to established markers of COVID-19 severity and outcome, i.e. SARS-CoV-2 RNAemia, age, BMI, D-dimer, and troponin. Whilst RNAemia, age and troponin were better predictors of mortality, miR-133a and miR-122 showed superior classification performance for severity. In binary and triplet combinations, miRNAs improved classification performance of established markers for severity and mortality.

Conclusion: Circulating miRNAs of different tissue origin, including several known cardiometabolic biomarkers, rise with COVID-19 severity. MyomiR miR-133a and liver-derived miR-122 also relate to 28 day mortality. MiR-133a reflects inflammation-induced myocyte damage, whilst miR-122 reflects the hepatic acute phase response.

Keywords: Biomarkers; COVID-19; MicroRNAs; Proteomics; RNA-Seq; SARS-CoV-2.

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Figures

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Graphical abstract
Figure 1
Figure 1
NGS of small RNAs in patients with different COVID-19 patients with different disease severity and healthy controls. (A) PC analysis based on RNA-Seq in plasma of healthy controls (n = 11), non-severe (n = 18), and severe (n = 18) COVID-19 patients. PC1 (x-axis) and PC2 (y-axis) explain 15.62% and 13.31% of the variance, respectively. (B) Volcano plot showing plasma miRNAs that are differentially expressed between healthy controls (n = 11) and severe (n = 18) COVID-19 patients. (C) Volcano plot showing plasma miRNAs that are differentially expressed between non-severe (n = 18) and severe (n = 18) COVID-19 patients. Highlighted are miRNAs that have previously been attributed a role in critically ill patients or are miRNAs with a tissue-specific origin (Supplementary material online, Table S1). Differential expression analysis of RNA-Seq data was performed using edgeR and applying the independent filtering method of DESeq2 to remove low abundant miRNA to optimize the Benjamini–Hochberg FDR correction. All statistical analyses are two-tailed.
Figure 2
Figure 2
Clusters and correlations of circulating miRNAs measured by RNA-Seq in COVID-19 patients. The heat map represents a hierarchical cluster analysis conducted upon a Spearman correlation network of miRNA levels in COVID-19 patients (n = 36) that were found to be differentially expressed between non-severe and severe COVID-19 patients and for which a role in critically ill patients or a tissue-specific origin has been shown previously (highlighted in red, Supplementary material online, Table S1).
Figure 3
Figure 3
RT–qPCR validation of miRNAs in COVID-19 patients with different disease severity. RT–qPCR of miRNAs in plasma of mild (n = 6), moderate (n = 39), and severe (n = 16) COVID-19 patients. Tukey boxplots depict the median (horizontal line), interquartile range (box borders), and 1.5× interquartile range (whiskers). Lung-derived miR-187, cardiomyocyte-derived miR-208b, and neuron-derived miR-124 had poor plasma RT–qPCR detectability and were therefore analysed as binary variables. Significance between the three severity groups was determined using ANOVA tests for continuous variables, χ2 tests for binary variables and then applying Benjamini and Hochberg’s correction for the 14 comparisons. *FDR <0.05. **FDR <0.01. ***FDR <0.001. A list of the FDR uncorrected and corrected for age, sex, and BMI is presented in Supplementary material online, Tables S6 and S7. All statistical analyses are two-tailed.
Figure 4
Figure 4
Association of miR-133a levels with proteomics data, clinical parameters and outcome. (A) Baseline miR-133a serum levels in COVID-19 ICU survivors (n = 48) and non-survivors (n = 17). Lines inside violin plots show median (continuous line) and interquartile range (dotted lines). A two-tailed, unpaired Student’s t-test was used to determine statistical significance. (B) Heatmap showing correlations of miR-133a levels with clinical characteristics of COVID-19 ICU patients (n = 65) at baseline. Spearman correlation was used to determine correlations between continuous variables. Point-biserial correlation was used to determine correlations between continuous and binary variables. Significant (P < 0.05) correlations are highlighted in bold font. (C) Trajectory of miR-133a in COVID-19 ICU survivors (n = 48) and non-survivors (n = 17) as a function of days post onset of symptoms. Lines show fitted generalized additive models with grey bands indicating the 95% CI, correcting for age, sex, and BMI. (D) Longitudinal protein correlations with miR-133a (n = 240 samples from n = 62 COVID-19 ICU patients). Significant (P < 0.05) correlations are shown in blue (negative) and red (positive). Highlighted are neutrophil degranulation proteins and SFTPB. All statistical analyses are two-tailed. Alb, albumin; ALP, alkaline phosphatase; ALT, alanine aminotransferase; Antidiab, antidiabetic pre-medication; Antihypert, antihypertensive pre-medication; APACHEII, acute physiology and chronic health evaluation score; Bil, bilirubin; BMI, body mass index; COPD, chronic obstructive pulmonary disease; Crea, creatinine; CRP, C-reactive protein; DMII, type II diabetes mellitus; FiO2, fraction of inspired oxygen; Hb, haemoglobin; Hct, haematocrit; Heart r, heart rate; HTN, hypertension; IgG ratio, anti-SARS-CoV-2 IgG ratio measured by ELISA; K+, potassium; Lymph, lymphocytes; MAP, mean arterial pressure; Monoc, monocytes; MPO, myeloperoxidase; Na+, sodium; Neutral, anti-SARS-CoV-2 neutralization capacity measured by the surrogate virus neutralization test; Neutrop, neutrophils; MMP9, matrix metalloproteinase-9; Renal dis, renal disease; Resp r, respiratory rate; SFTPB, pulmonary surfactant-associated protein B; SOFA, sequential organ failure assessment score; Temp, body temperature; WCC, white cell count.
Figure 5
Figure 5
Association of miR-122 levels with proteomics data, clinical parameters, and outcome. (A) Baseline miR-122 levels in COVID-19 ICU survivors (n = 48) and non-survivors (n = 17). Lines inside violin plots show median (continuous line) and interquartile range (dotted lines). Two-tailed, unpaired Student’s t-test was used to determine statistical significance. (B) Heatmap showing correlations of miR-122 levels with clinical characteristics of COVID-19 ICU patients (n = 65) at baseline. Spearman correlation was used to determine correlations between continuous variables. Point-biserial correlation was used to determine correlations between continuous and binary variables. Significant (P < 0.05) correlations are highlighted in bold font. (C) Trajectory of miR-122 in COVID-19 ICU survivors (n = 48) and non-survivors (n = 17) as a function of days post onset of symptoms. Lines show fitted generalized additive models with grey bands indicating the 95% CI, correcting for age, sex, and BMI. (D) Longitudinal protein correlations with miR-122 (n = 240 samples from n = 62 COVID-19 ICU patients). Significant (P < 0.05) correlations are highlighted in blue (negative) and red (positive). Highlighted are positive and negative APPs. All statistical analyses are two-tailed. A2M, alpha-2-macroglobulin; CRP, C-reactive protein; LBP, lipopolysaccharide-binding protein; RBP4, retinol-binding protein 4; SAA1, serum amyloid A-1 protein; SAA2, serum amyloid A-2 protein; SERPINA6, corticosteroid-binding globulin; TF, serotransferrin; TTR, transthyretin.
Figure 6
Figure 6
COVID-19 severity classification. (A and B) ROC plots for the best three binary (A) and best three triplet (B) severity signatures are shown. The non-severe cohort (n = 45) consisted of n = 6 mild and n = 39 moderate patients, whilst the severe cohort consisted of n = 16 patients. All statistical analyses are two-tailed.
Figure 7
Figure 7
The 28 day ICU mortality in COVID-19 patients. (A and B) Kaplan–Meier plots for the best three binary (A) and best three triplet signatures (B) for 28 day ICU mortality classification are shown. Low- and high-risk groups in the Kaplan–Meier analysis are based on the default 0.5 threshold of the logistic regression. The outcome analysis is based on n = 17 COVID-19 ICU non-survivors and n = 48 ICU survivors. All statistical analyses are two-tailed.
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