key: cord-1049686-ym6sz8bv
authors: de Gonzalo-Calvo, David; Benítez, Iván D.; Pinilla, Lucía; Carratalá, Amara; Moncusí-Moix, Anna; Gort-Paniello, Clara; Molinero, Marta; González, Jessica; Torres, Gerard; Bernal, María; Pico, Silvia; Almansa, Raquel; Jorge, Noelia; Ortega, Alicia; Bustamante-Munguira, Elena; Gómez, José Manuel; González-Rivera, Milagros; Micheloud, Dariela; Ryan, Pablo; Martinez, Amalia; Tamayo, Luis; Aldecoa, César; Ferrer, Ricard; Ceccato, Adrián; Fernández-Barat, Laia; Motos, Ana; Riera, Jordi; Menéndez, Rosario; Garcia-Gasulla, Dario; Peñuelas, Oscar; Torres, Antoni; Bermejo-Martin, Jesús F.; Barbé, Ferran
title: Circulating microRNA profiles predict the severity of COVID-19 in hospitalized patients
date: 2021-05-26
journal: Transl Res
DOI: 10.1016/j.trsl.2021.05.004
sha: b6c3e73b85c1a1ac3d0d3cd4c719a50d1fc06d28
doc_id: 1049686
cord_uid: ym6sz8bv

We aimed to examine the circulating microRNA (miRNA) profile of hospitalized COVID-19 patients and evaluate its potential as a source of biomarkers for the management of the disease. This was an observational and multicenter study that included 84 patients with a positive nasopharyngeal swab PCR test for SARS-CoV-2 recruited during the first pandemic wave in Spain (March-June 2020). Patients were stratified according to disease severity: hospitalized patients admitted to the clinical wards without requiring critical care and patients admitted to the ICU. An additional study was completed including ICU nonsurvivors and survivors. Plasma miRNA profiling was performed using RT-qPCR. Predictive models were constructed using LASSO regression. Ten circulating miRNAs were dysregulated in ICU patients compared to ward patients. LASSO analysis identified a signature of three miRNAs (miR-148a-3p, miR-451a and miR-486-5p) that distinguishes between ICU and ward patients [AUC (95% CI) = 0.89 (0.81-0.97)]. Among critically ill patients, six miRNAs were downregulated between nonsurvivors and survivors. A signature based on two miRNAs (miR-192-5p and miR-323a-3p) differentiated ICU nonsurvivors from survivors [AUC (95% CI) = 0.80 (0.64-0.96)]. The discriminatory potential of the signature was higher than that observed for laboratory parameters such as leukocyte counts, CRP or D-dimer [maximum AUC (95% CI) for these variables = 0.73 (0.55-0.92)]. miRNA levels were correlated with the duration of ICU stay. Specific circulating miRNA profiles are associated with the severity of COVID-19. Plasma miRNA signatures emerge as a novel tool to assist in the early prediction of vital status deterioration among ICU patients.

The identification of biomarkers to assist physicians in the management of COVID-19 constitutes a priority in research. MiRNAs have recently emerged as clinical indicators to assist in medical decision-making. We aimed to evaluate the peripheral blood miRNA signature of hospitalized COVID-19 patients, and to explore its potential role as a source of biomarkers for the management of the disease.

This study identifies plasma miRNA expression profiles that are associated with COVID-19 severity and mortality at ICU. Plasma miRNA signatures emerge as a novel tool to assist in the early prediction of vital status deterioration among ICU patients.

Approximately 20 to 30% of hospitalized COVID-19 patients develop a severe phenotype of the disease that requires intensive care unit (ICU) admission with a varying ICU death rate ranging from 25 to 50%. 1, 2 In this scenario, there is an urgent demand for reliable tools to define patient status and predict clinical outcomes.

Although risk modeling based on clinical characteristics and/or biomarkers has made remarkable progress, early prediction of vital status deterioration remains a challenge for clinicians. 3 In the past decade, noncoding RNAs (ncRNAs), and the class of microRNAs (miRNAs) in particular, have risen to prominence as a novel tool to assist in medical decision-making. 6 miRNAs are short (19-25 nt) , single-stranded noncoding RNAs that regulate gene expression at the posttranscriptional level by binding to target mRNA and leading to its degradation or translational repression. These small ncRNAs are present in body fluids including blood, are highly resistant to degradation and can be easily measured through standard techniques already employed in clinical laboratories, such as real-time quantitative PCR (qPCR). The use of circulating miRNAs as clinical biomarkers in liquid biopsies has been explored under a variety of conditions, [7] [8] [9] including viral infections. 10 The results suggest that miRNAs are sensitive, robust and cost-effective biomarkers that offer additional information to clinical variables and already established clinical indicators. 11, 12 Indeed, several miRNA-based diagnostic products are already available for clinical practice. 13 Here, we aimed to examine the circulating miRNA profile of hospitalized COVID-19 patients and explore the potential role and clinical significance as biomarkers of disease severity. To the best of our knowledge, the current study is the largest to date that has profiled the circulating miRNA profile in the context of COVID-19.

The study was performed in full compliance with the Declaration of Helsinki.

The study protocol was approved by the respective ethics committee. Participants, or their legal representatives, provided oral consent when possible. In the remaining cases, an informed consent waiver was authorized by the ethics committee.

This is a preliminary report on the epigenetic substudy of the ongoing multicenter study CIBERESUCICOVID registered at www.clinicaltrials.gov with the identification NCT04457505.

This was an observational and multicenter study that included 84 participants. Universitario Infanta Leonor (Madrid). The patients were divided according to the severity of the disease into the following groups: i) hospitalized patients admitted to the pneumology, infectious diseases or internal medicine wards without requiring critical care, and ii) hospitalized patients admitted to the ICU. An additional study including ICU nonsurvivors and survivors was performed.

Comprehensive demographic, clinical, pharmacological and laboratory data were abstracted manually from the electronic medical records. Data collected at the time of hospital or ICU admission were recorded by dedicated clinical research assistants and entered into a REDCap database. Incoherent or missing data were identified using automatic checks. Abnormal data were reviewed by researchers/clinicians. Blood samples were collected in ethylenediaminetetraacetic acid (EDTA) tubes before or following admission to the clinical ward or the ICU (Supplemental Figure   S1 ). Blood samples were centrifuged to separate plasma (1500 × g for 10 minutes). All specimens were immediately aliquoted, frozen and stored at -80°C. Since the overall amount of RNA that is present in plasma is low and the RNA concentration cannot be accurately determined in plasma samples, the input RNA amount for subsequent analysis was based on the starting volume rather than RNA quantity. A consistent input amount was used for all samples. miRNA quantification was performed according to the protocol for the miRCURY LNA Universal RT microRNA PCR System (Qiagen), which offers optimal accuracy and reproducibility. 15 Reverse transcription (RT) cDNA synthesis was performed using a miRCURY LNA RT Kit (Qiagen) in a total reaction volume of 10 μL. An additional spike-in UniSp6 (Qiagen) was added to monitor the RT reaction. Heparin, an anticoagulant usually administered to COVID-19 patients during hospital stays, 16 inhibits miRNA quantification. 17, 18 To avoid this potential inhibitory effect, heparinase (New England BioLabs, Massachusetts, USA) was added to RT reactions, as previously described. 19 The RT reactions were performed in a total volume of 10 μL under the following conditions: incubation for 60 minutes at 42°C, inactivation for 5 minutes at 95°C, and immediate cooling to 4°C. Then, cDNA was stored at 20°C.

Plasma miRNA signatures were analyzed using miRCURY LNA miRNA Custom Panels (384-well plates) (Qiagen). qPCR was carried out using the Applied Biosystems

QuantStudio™ 7 Flex Real-Time PCR System in a total volume of 10 μL. RT-qPCR conditions were 95°C for 2 minutes, followed by 40 cycles of 95°C for 10 seconds and 56°C for 1 minute, followed by melting curve analysis. Synthetic UniSp3 was analyzed as an interplate calibrator and qPCR control. Amplification curves were evaluated using QuantStudio Software v1.3 (Thermo Fisher Scientific, Massachusetts, USA). The quantification cycle (Cq) was defined as the fractional cycle number at which the fluorescence exceeded a given threshold. The specificity of the qPCR was corroborated by melting curve analysis. To ensure the optimal quality of the data, we first analyzed spike-in RNA templates to monitor the uniformity of the RNA extraction procedure and the efficiency of the RT and PCRs. The ΔCq (miR-23a-3p -miR-451a) method was used to exclude hemolysis contamination, as previously described. 20 Cqs above 35 cycles were considered undetectable and were censored at the minimum level observed for each miRNA. Relative quantification was performed using the 2 -ΔCq method (ΔCq = Cq miRNA -Cq cel-miR-39-3p ). Expression levels were log-transformed for statistical purposes.

Descriptive statistics were used to summarize the characteristics of the study population. The normality of the distribution was analyzed using the Shapiro-Wilk test. 

The study flowchart is presented in Figure 1 . We first analyzed the impact of or in which miRNA quantification did not pass the quality control (high variability in spike-ins) were excluded (n=5) (Supplementary Figure S3A & S3B) . Seven miRNAs, miR-9-5p, miR-34b-5p, miR-34c-5p, miR-124-3p, miR-208a-3p, miR-208b-3p and miR-499a-5p, were below the limit of detection (Cq ≥ 35) in more than 80% of samples and therefore were not considered in further analyses.

The main demographic, clinical, pharmacological and biochemical data of the study groups are summarized in Table 1 . Patients admitted to the ICU were typically men. As expected, the use of pharmacological therapies, including hydroxychloroquine, tocilizumab, antibiotics and corticoids, the requirement for invasive ventilation and the duration of the hospital stay were higher in critically ill patients than in ward patients. Table S1 ).

The ideal scenario for miRNA testing seems to be based on the concept of "several miRNAs-one disease", contrary to the traditional "one miRNA-one disease"

concept. 12 Therefore, we explored whether COVID-19 severity could be associated with a specific miRNA signature. Multivariate predictive models were constructed using a variable selection process based on LASSO regression. This approach identified a signature of three miRNAs, miR-148a-3p, miR-486-5p and miR-451a, associated with ICU stay ( Figure 2E ). ROC curves and AUCs were used to assess the discriminative accuracy of the plasma miRNA signature. The AUC (95% CI) for discriminating ward vs. ICU patients was 0.89 (0.81-0.97) ( Figure 2F ). The AUC was comparable to, or even higher than, that observed for the contemporaneous test proposed as biomarkers The impact of different pharmacological treatments on the levels of circulating miRNAs, or on the quantification method, has been previously described, e.g., antiplatelet therapy, statins or heparin. 22, 23 Furthermore, in the context of such an emerging disease, the effect of the therapeutic drugs used to treat COVID-19 patients on the circulating levels of miRNAs remains unknown. Therefore, we further checked the influence of medications widely used to treat hospitalized COVID-19 patients. A significant effect was observed for miR-16-5p, miR-92a-3p and miR-150-5p

(Supplemental Table S2 ). The greatest impact was caused by antibiotics (for the three miRNAs), corticoid use (for miR-92-3p) and hydroxychloroquine (for miR-150-5p) (Supplemental Figure S4 ). We did not observe any impact of these confounding factors on other miRNAs. No effect of age or sex was reported.

Next, we evaluated the association between laboratory parameters and circulating miRNAs (Supplemental Figure S5) . A correlation was observed between the dysregulated miRNAs and the counts of leukocytes (miR-27a-3p, miR-27b-3p and miR-148a-3p), neutrophils (miR-27a-3p, miR-27b-3p, miR-148a-3p, miR-451a and miR-486-5p), lymphocytes (miR-16-5p, miR-92-3p, miR-150-5p, miR-451a and miR-

miR-199a-5p and miR-491-5p) and the concentrations of D-dimer (miR-16-5p, miR-92a-3p, miR-150-5p, miR-451a and miR-486-5p), ferritin (miR-16-5p, miR-92a-3p, miR-150-5p, miR-451a and miR-486-5p) and CRP (miR-27a-3p, miR-27b-3p, miR-148a-3p, miR-199-5p, miR-451a and miR-491-5p).

The comparison between hospitalized patients admitted to the clinical wards without requiring critical care and hospitalized patients admitted to the ICU, although useful for exploring the impact of COVID-19 severity on the miRNA profile, does not provide real clues about the clinical significance of these small ncRNAs as biomarkers of COVID-19. Consequently, we performed an additional study to test whether the circulating miRNA signature constitutes a predictor of mortality in critically ill patients.

The characteristics of the study population according to ICU survival are detailed in Table 2 .

As shown in Figure Table S3 ).

A predictor selection procedure was performed using the panel of miRNAs. The multivariable analysis selected a signature based on two miRNAs, miR-192-5p and miR-323a-3p, that was found to be a relevant predictor of mortality during the ICU stay Table S4 ).

COVID-19 has had a considerable impact on public health and the global economy, and it is expected to continue in the short and medium terms. Vaccines should dramatically reduce the number of COVID-19 cases in the long term.

Nevertheless, COVID-19 will continue to be treated in the ICU in the near future. As such, there is an urgent need to find new tools to manage the disease. In this context, Characterization of the circulating miRNA pattern in plasma samples from hospitalized patients admitted to the clinical wards without requiring critical care and patients admitted to the ICU showed that COVID-19 severity was associated with a specific circulating miRNA profile. In addition, our findings underscore the potential for using miRNA profiling to guide patient care. In ROC analysis, a 2-miRNA panel was able to discriminate between survivors and nonsurvivors with high accuracy.

Interestingly, the performance of our 2-miRNA panel as a biomarker seems to be superior to contemporary laboratory tests, such as leukocyte counts, CRP and Ddimer. We also report a correlation between miRNA levels and duration of ICU stay.

These results are especially relevant in the current scenario in which COVID-19 mortality is mainly concentrated in ICU patients (ranging between 25 and 50% depending on age group, sex and medical history) and the lack of robust information on the prognosis of critically ill patients. 24 The use of molecular methods based on miRNAs could refine risk assessment and provide a straightforward approach to guide clinical decisions in terms of patient care, monitoring and treatment. Indeed, our candidates will be validated in the ongoing CIBERESUCICOVID study (www.clinicaltrials.gov, NCT04457505). Third, the saturation of critical care capacity during the first pandemic wave in Spain may have an impact on the composition of the ward and ICU groups. Nevertheless, the ward group included hospitalized patients admitted to the pneumology, infectious diseases or internal medicine wards without requiring critical care. Forth, we used a targeted rather than untargeted approach. Our intention was to evaluate the biomarker potential of circulating miRNAs rather than to perform miRNA screening. Other miRNAs may have clinical significance.

Consequently, we may have underestimated the role of miRNAs as clinical indicators.

Fifth, potential confounding factors cannot be discarded despite adjustment. Sixth, we cannot exclude the impact on our findings of conditions/treatments that were not recorded. Seventh, given the exploratory nature of the study, the impact of type I error should not been discarded. Furthermore, optimism corrected AUCs were not considered in the statistical analysis.

In conclusion, the severity of COVID-19 impacts the circulating miRNA profile.

Plasma miRNA profiling emerges as a useful tool for risk-based patient stratification in critically ill COVID-19 patients. Additional studies in larger samples and functional approaches will be required to validate these findings and provide further insight into the role of circulating miRNAs as biomarkers and functional mediators of COVID-19.

Supported by ISCIII (CIBERESUCICOVID, COV20/00110), cofunded by ERDF, pathophysiology. Patients with hemolyzed or low-quality samples were excluded (n=5).

Seven microRNAs, miR-9-5p, miR-34b-5p, miR-34c-5p, miR-124-3p, miR-208a-3p, miR-208b-3p and miR-499a-5p, were below the limit of detection (Cq ≥ 35) in more than 80% of samples and therefore were not considered in further analysis. Patients were stratified according to disease severity: hospitalized patients admitted to the clinical wards without requiring critical care (n=43) and patients admitted to the ICU (n=36). An additional study was completed including ICU nonsurvivors (n=16) and

survivors (n=20). 

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Glucose, mg/dL 126