Addressing the unsolved challenges in microRNA-based biomarker development: Suitable endogenous reference microRNAs for SARS-CoV-2 infection severity

Circulating cell-free microRNAs (miRNAs) are promising biomarkers for medical decision-making. Suitable endogenous controls are essential to ensure reproducibility. We aimed to identify and validate endogenous reference miRNAs for qPCR data normalization in samples from SARS-CoV-2-infected hospitalized patients. We used plasma samples (n = 170) from COVID-19 patients collected at hospital admission (COVID-Ponent project, www.clinicaltrials.gov/NCT04824677). First, 179 miRNAs were profiled using RT-qPCR. After stability assessment, candidates were validated using the same methodology. miRNA stability was analyzed using the geNorm, NormFinder and BestKeeper algorithms. Stability was further evaluated using an RNA-seq dataset derived from COVID-19 hospitalized patients, along with plasma samples from patients with critical COVID-19 profiled using RT-qPCR. In the screening phase, after strict control of expression levels, stability assessment selected eleven candidates (miR-17-5p, miR-20a-5p, miR-30e-5p, miR-106a-5p, miR-151a-5p, miR-185-5p, miR-191-5p, miR-423-3p, miR-425-5p, miR-484 and miR-625-5p). In the validation phase, all algorithms identified miR-106a-5p and miR-484 as top endogenous controls. No association was observed between these miRNAs and clinical or sociodemographic characteristics. Both miRNAs were stably detected and showed low variability in the additional analyses. In conclusion, a 2-miRNA panel composed of miR-106a-5p and miR-484 constitutes a first-line normalizer for miRNA-based biomarker development using qPCR in hospitalized patients infected with SARS-CoV-2.

MicroRNA-centered theranostics for pulmoprotection in critical COVID-19

Elucidating the pathobiological mechanisms underlying post-acute pulmonary sequelae following SARS-CoV-2 infection is essential for early interventions and patient stratification. Here, we investigated the potential of microRNAs (miRNAs) as theranostic agents for pulmoprotection in critical illness survivors. Multicenter study including 172 ICU survivors. Diffusion impairment was defined as a lung-diffusing capacity for carbon monoxide (DLCO) <80% within 12 months postdischarge. A disease-associated 16-miRNA panel was quantified in plasma samples collected at ICU admission. Bioinformatic analyses were conducted using KEGG, Reactome, GTEx, and Drug-Gene Interaction databases. The results were validated using an external RNA-seq dataset. A 3-miRNA signature linked to diffusion impairment (miR-27a-3p, miR-93-5p, and miR-199a-5p) was identified using random forest. Levels of miR-93-5p and miR-199a-5p were independently associated with the outcome, improving patient classification provided by the electronic health record. The experimentally validated targets of these miRNAs exhibited enrichment across diverse pathways, with telomere length quantification in an additional set of samples (n = 83) supporting the role of cell senescence in sequelae. Analysis of an external dataset refined the pathobiological fingerprint of pulmonary sequelae. Gene-drug interaction analysis revealed four FDA-approved drugs. Overall, this study advances our understanding of lung recovery in postacute respiratory infections, highlighting the potential of miRNAs and their targets for pulmoprotection.

MicroRNA-guided drug discovery for mitigating persistent pulmonary complications in critical COVID-19 survivors: A longitudinal pilot study

BACKGROUND AND PURPOSE

The post-acute sequelae of SARS-CoV-2 infection pose a significant global challenge, with nearly 50% of critical COVID-19 survivors manifesting persistent lung abnormalities. The lack of understanding about the molecular mechanisms and effective treatments hampers their management. Here, we employed microRNA (miRNA) profiling to decipher the systemic molecular underpinnings of the persistent pulmonary complications.

EXPERIMENTAL APPROACH

We conducted a longitudinal investigation including 119 critical COVID-19 survivors. A comprehensive pulmonary evaluation was performed in the short-term (median = 94.0 days after hospital discharge) and long-term (median = 358 days after hospital discharge). Plasma miRNAs were quantified at the short-term evaluation using the gold-standard technique, RT-qPCR. The analyses combined machine learning feature selection techniques with bioinformatic investigations. Two additional datasets were incorporated for validation.

KEY RESULTS

In the short-term, 84% of the survivors exhibited impaired lung diffusion (DLCO  < 80% of predicted). One year post-discharge, 54.4% of this patient subgroup still presented abnormal DLCO . Four feature selection methods identified two specific miRNAs, miR-9-5p and miR-486-5p, linked to persistent lung dysfunction. The downstream experimentally validated targetome included 1473 genes, with heterogeneous enriched pathways associated with inflammation, angiogenesis and cell senescence. Validation studies using RNA-sequencing and proteomic datasets emphasized the pivotal roles of cell migration and tissue repair in persistent lung dysfunction. The repositioning potential of the miRNA targets was limited.

CONCLUSION AND IMPLICATIONS

Our study reveals early mechanistic pathways contributing to persistent lung dysfunction in critical COVID-19 survivors, offering a promising approach for the development of targeted disease-modifying agents.

A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study

BACKGROUND

The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU.

METHODS

This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group.

RESULTS

Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways.

CONCLUSIONS

A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.

Genome-wide transcriptional profiling of pulmonary functional sequelae in ARDS- secondary to SARS-CoV-2 infection

BACKGROUND

Up to 80% of patients surviving acute respiratory distress syndrome (ARDS) secondary to SARS-CoV-2 infection present persistent anomalies in pulmonary function after hospital discharge. There is a limited understanding of the mechanistic pathways linked to post-acute pulmonary sequelae.

AIM

To identify the molecular underpinnings associated with severe lung diffusion involvement in survivors of SARS-CoV-2-induced ARDS.

METHODS

Survivors attended to a complete pulmonary evaluation 3 months after hospital discharge. RNA sequencing (RNA-seq) was performed using Illumina technology in whole-blood samples from 50 patients with moderate to severe diffusion impairment (D_LCO<60%) and age- and sex-matched individuals with mild-normal lung function (D_LCO≥60%). A transcriptomic signature for optimal classification was constructed using random forest. Transcriptomic data were analyzed for biological pathway enrichment, cellular deconvolution, cell/tissue-specific gene expression and candidate drugs.

RESULTS

RNA-seq identified 1357 differentially expressed transcripts. A model composed of 14 mRNAs allowed the optimal discrimination of survivors with severe diffusion impairment (AUC=0.979). Hallmarks of lung sequelae involved cell death signaling, cytoskeleton reorganization, cell growth and differentiation and the immune response. Resting natural killer (NK) cells were the most important immune cell subtype for the prediction of severe diffusion impairment. Components of the signature correlated with neutrophil, lymphocyte and monocyte counts. A variable expression profile of the transcripts was observed in lung cell subtypes and bodily tissues. One upregulated gene, TUBB4A, constitutes a target for FDA-approved drugs.

CONCLUSIONS

This work defines the transcriptional programme associated with post-acute pulmonary sequelae and provides novel insights for targeted interventions and biomarker development.

Multiplex protein profiling of bronchial aspirates reveals disease-, mortality- and respiratory sequelae-associated signatures in critically ill patients with ARDS secondary to SARS-CoV-2 infection

Introduction

Bronchial aspirates (BAS) obtained during invasive mechanical ventilation (IMV) constitutes a useful tool for molecular phenotyping and decision making.

Aim

To identify the proteomic determinants associated with disease pathogenesis, all-cause mortality and respiratory sequelae in BAS samples from critically ill patients with SARS-CoV-2-induced ARDS.

Methods

Multicenter study including 74 critically ill patients with COVID-19 and non-COVID-19 ARDS. BAS were obtained by bronchoaspiration after IMV initiation. Three hundred sixty-four proteins were quantified using proximity extension assay (PEA) technology. Random forest models were used to assess predictor importance.

Results

After adjusting for confounding factors, CST5, NADK, SRPK2 and TGF-α were differentially detected in COVID-19 and non-COVID-19 patients. In random forest models for COVID-19, CST5, DPP7, NADK, KYAT1 and TYMP showed the highest variable importance. In COVID-19 patients, reduced levels of ENTPD2 and PTN were observed in nonsurvivors of ICU stay, even after adjustment. AGR2, NQO2, IL-1α, OSM and TRAIL showed the strongest associations with in-ICU mortality and were used to construct a protein-based prediction model. Kaplan-Meier curves revealed a clear separation in mortality risk between subgroups of PTN, ENTPD2 and the prediction model. Cox regression models supported these findings. In survivors, the levels of FCRL1, NTF4 and THOP1 in BAS samples obtained during the ICU stay correlated with lung function (i.e., D_LCO levels) 3 months after hospital discharge. Similarly, Flt3L and THOP1 levels were correlated with radiological features (i.e., TSS). These proteins are expressed in immune and nonimmune lung cells. Poor host response to viral infectivity and an inappropriate reparative mechanism seem to be linked with the pathogenesis of the disease and fatal outcomes, respectively.

Conclusion

BAS proteomics identified novel factors associated with the pathology of SARS-CoV-2-induced ARDS and its adverse outcomes. BAS-based protein testing emerges as a novel tool for risk assessment in the ICU.