Extracellular Vesicles and Endocannabinoid Signaling in Patients with COVID-19.

Introduction: Endocannabinoids in COVID-19 have immunomodulatory and anti-inflammatory properties but the functional role and the regulation of endocannabinoid signaling in this pandemic disorder is controversial. To exercise their biologic function, endocannabinoids need to travel across the intercellular space and within the blood stream to reach their target cells. How the lipophilic endocannabinoids are transported in the vascular system and how these hydrophobic compounds cross cell membranes is still unclear. Extracellular vesicles (EVs) are released and incorporated by many cell types including immune cells. EVs are small lipid-membrane covered particles and contain RNA, lipids and proteins. They play an important role in intercellular communication by transporting these signaling molecules from their cells of origin to specific target cells. EVs may represent ideal transport vehicles for lipophilic signaling molecules like endocannabinoids and this effect could also be evident in COVID-19. Materials and Methods: We measured the endocannabinoids anandamide, 2-AG, SEA, PEA and OEA in patients with COVID-19 in EVs and plasma. RNA sequencing of microRNAs (miRNAs) derived from EVs (EV-miRNAs) and mRNA transcripts from blood cells was used for the construction of signaling networks reflecting endocannabinoid and miRNA communication by EVs to target immune cells. Results: With the exception of anandamide, endocannabinoid concentrations were significantly enriched in EVs in comparison to plasma and increased with disease severity. No enrichment in EVs was seen for the more hydrophilic steroid hormones cortisol and testosterone. High EV-endocannabinoid concentrations were associated with downregulation of CNR2 (CB2) by upregulated EV-miRNA miR-146a-5p and upregulation of MGLL by downregulated EV-miR-199a-5p and EV-miR-370-5p suggesting counterregulatory effects. In contrast, low EV-levels of anandamide were associated with upregulation of CNR1 by downregulation of EV-miR-30c-5p and miR-26a-5p along with inhibition of FAAH. Immunologically active molecules in immune cells regulated by endocannabinoid signaling included VEGFA, GNAI2, IGF1, BDNF, IGF1R and CREB1 and CCND1 among others. Discussion and Conclusions: EVs carry immunologically functional endocannabinoids in COVID-19 along with miRNAs which may regulate the expression of mRNA transcripts involved in the regulation of endocannabinoid signaling and metabolism. This mechanism could fine-tune and adapt endocannabinoid effects in recipient cells in relationship to the present biological context.

Extensive blood transcriptome analysis reveals cellular signaling networks activated by circulating glycocalyx components reflecting vascular injury in COVID-19.

Background: Degradation of the endothelial protective glycocalyx layer during COVID-19 infection leads to shedding of major glycocalyx components. These circulating proteins and their degradation products may feedback on immune and endothelial cells and activate molecular signaling cascades in COVID-19 associated microvascular injury. To test this hypothesis, we measured plasma glycocalyx components in patients with SARS-CoV-2 infection of variable disease severity and identified molecular signaling networks activated by glycocalyx components in immune and endothelial cells. Methods: We studied patients with RT-PCR confirmed COVID-19 pneumonia, patients with COVID-19 Acute Respiratory Distress Syndrome (ARDS) and healthy controls (wildtype, n=20 in each group) and measured syndecan-1, heparan sulfate and hyaluronic acid. The in-silico construction of signaling networks was based on RNA sequencing (RNAseq) of mRNA transcripts derived from blood cells and of miRNAs isolated from extracellular vesicles from the identical cohort. Differentially regulated RNAs between groups were identified by gene expression analysis. Both RNAseq data sets were used for network construction of circulating glycosaminoglycans focusing on immune and endothelial cells. Results: Plasma concentrations of glycocalyx components were highest in COVID-19 ARDS. Hyaluronic acid plasma levels in patients admitted with COVID-19 pneumonia who later developed ARDS during hospital treatment (n=8) were significantly higher at hospital admission than in patients with an early recovery. RNAseq identified hyaluronic acid as an upregulator of TLR4 in pneumonia and ARDS. In COVID-19 ARDS, syndecan-1 increased IL-6, which was significantly higher than in pneumonia. In ARDS, hyaluronic acid activated NRP1, a co-receptor of activated VEGFA, which is associated with pulmonary vascular hyperpermeability and interacted with VCAN (upregulated), a proteoglycan important for chemokine communication. Conclusions: Circulating glycocalyx components in COVID-19 have distinct biologic feedback effects on immune and endothelial cells and result in upregulation of key regulatory transcripts leading to further immune activation and more severe systemic inflammation. These consequences are most pronounced during the early hospital phase of COVID-19 before pulmonary failure develops. Elevated levels of circulating glycocalyx components may early identify patients at risk for microvascular injury and ARDS. The timely inhibition of glycocalyx degradation could provide a novel therapeutic approach to prevent the development of ARDS in COVID-19.

Anesthetic­specific lncRNA and mRNA profile changes in blood during colorectal cancer resection: A prospective, matched­case pilot study.

Prometastatic and antitumor effects of different anesthetics have been previously analyzed in several studies with conflicting results. Thus, the underlying perioperative molecular mechanisms mediated by anesthetics potentially affecting tumor phenotype and metastasis remain unclear. It was hypothesized that anesthetic­specific long non­coding RNA (lncRNA) expression changes are induced in the blood circulation and play a crucial role in tumor outcome. In the present study, high­throughput sequencing and quantitative PCR were performed in order to identify lncRNA and mRNA expression changes affected by two therapeutic regimes, total intravenous anesthesia (TIVA) and volatile anesthetic gas (VAG) in patients undergoing colorectal cancer (CRC) resection. Total blood RNA was isolated prior to and following resection and characterized using RNA sequencing. mRNA­lncRNA interactions and their roles in cancer­related signaling of differentially expressed lncRNAs were identified using bioinformatics analyses. The comparison of these two time points revealed 35 differentially expressed lncRNAs in the TIVA­group, and 25 in the VAG­group, whereas eight were shared by both groups. Two lncRNAs in the TIVA­group, and 23 in the VAG­group of in silico identified target­mRNAs were confirmed as differentially regulated in the NGS dataset of the present study. Pathway analysis was performed and cancer relevant canonical pathways for TIVA were identified. Target­mRNA analysis of VAG revealed a markedly worsened immunological response against cancer. In this proof­of­concept study, anesthesic­specific expression changes in lncRNA and mRNA profiles in blood were successfully identified. Moreover, the data of the present study provide the first evidence that anesthesia­induced lncRNA pattern changes may contribute further in the observed differences in CRC outcome following tumor resection.

Progranulin signaling in sepsis, community-acquired bacterial pneumonia and COVID-19: a comparative, observational study.

BACKGROUND: Progranulin is a widely expressed pleiotropic growth factor with a central regulatory effect during the early immune response in sepsis. Progranulin signaling has not been systematically studied and compared between sepsis, community-acquired pneumonia (CAP), COVID-19 pneumonia and a sterile systemic inflammatory response (SIRS). We delineated molecular networks of progranulin signaling by next-generation sequencing (NGS), determined progranulin plasma concentrations and quantified the diagnostic performance of progranulin to differentiate between the above-mentioned disorders using the established biomarkers procalcitonin (PCT), interleukin-6 (IL-6) and C-reactive protein (CRP) for comparison. METHODS: The diagnostic performance of progranulin was operationalized by calculating AUC and ROC statistics for progranulin and established biomarkers in 241 patients with sepsis, 182 patients with SIRS, 53 patients with CAP, 22 patients with COVID-19 pneumonia and 53 healthy volunteers. miRNAs and mRNAs in blood cells from sepsis patients (n = 7) were characterized by NGS and validated by RT-qPCR in an independent cohort (n = 39) to identify canonical gene networks associated with upregulated progranulin at sepsis onset. RESULTS: Plasma concentrations of progranulin (ELISA) in patients with sepsis were 57.5 (42.8-84.9, Q25-Q75) ng/ml and significantly higher than in CAP (38.0, 33.5-41.0 ng/ml, p < 0.001), SIRS (29.0, 25.0-35.0 ng/ml, p < 0.001) and the healthy state (28.7, 25.5-31.7 ng/ml, p < 0.001). Patients with COVID-19 had significantly higher progranulin concentrations than patients with CAP (67.6, 56.6-96.0 vs. 38.0, 33.5-41.0 ng/ml, p < 0.001). The diagnostic performance of progranulin for the differentiation between sepsis vs. SIRS (n = 423) was comparable to that of procalcitonin. AUC was 0.90 (95% CI = 0.87-0.93) for progranulin and 0.92 (CI = 0.88-0.96, p = 0.323) for procalcitonin. Progranulin showed high discriminative power to differentiate bacterial CAP from COVID-19 (sensitivity 0.91, specificity 0.94, AUC 0.91 (CI = 0.8-1.0) and performed significantly better than PCT, IL-6 and CRP. NGS and partial RT-qPCR confirmation revealed a transcriptomic network of immune cells with upregulated progranulin and sortilin transcripts as well as toll-like-receptor 4 and tumor-protein 53, regulated by miR-16 and others. CONCLUSIONS: Progranulin signaling is elevated during the early antimicrobial response in sepsis and differs significantly between sepsis, CAP, COVID-19 and SIRS. This suggests that progranulin may serve as a novel indicator for the differentiation between these disorders. TRIAL REGISTRATION: Clinicaltrials.gov registration number NCT03280576 Registered November 19, 2015.

Molecular RNA Correlates of the SOFA Score in Patients with Sepsis.

The most common scoring system for critically ill patients is the Sequential Organ Failure Assessment (SOFA) score. Little is known about specific molecular signaling networks underlying the SOFA criteria. We characterized these networks and identified specific key regulatory molecules. We prospectively studied seven patients with sepsis and six controls with high-throughput RNA sequencing (RNAseq). Quantitative reverse transcription PCR (RT-qPCR) confirmation was performed in a second independent cohort. Differentially and significantly expressed miRNAs and their target mRNA transcripts were filtered for admission SOFA criteria and marker RNAs for the respective criteria identified. We bioinformatically constructed molecular signaling networks specifically reflecting these criteria followed by RT-qPCR confirmation of RNAs with important regulatory functions in the networks in the second cohort. RNAseq identified 82 miRNAs (45% upregulated) and 3254 mRNAs (50% upregulated) differentially expressed between sepsis patients and controls. Bioinformatic analysis characterized 6 miRNAs and 76 mRNA target transcripts specific for the SOFA criteria. RT-qPCR validated miRNA and mRNAs included IGFBP2 (respiratory system); MMP9 and PDE4B (nervous system); PPARG (cardiovascular system); AKR1B1, ANXA1, and LNC2/NGAL (acute kidney injury); GFER/ALR (liver); and miR-30c-3p (coagulopathy). There are specific canonical networks underlying the SOFA score. Key regulatory miRNA and mRNA transcripts support its biologic validity.

Diagnostic potential of circulating cell-free microRNAs for community-acquired pneumonia and pneumonia-related sepsis.

Cell-free microRNAs (miRNAs) are transferred in disease state including inflammatory lung diseases and are often packed into extracellular vesicles (EVs). To assess their suitability as biomarkers for community-acquired pneumonia (CAP) and severe secondary complications such as sepsis, we studied patients with CAP (n = 30), sepsis (n = 65) and healthy volunteers (n = 47) subdivided into a training (n = 67) and a validation (n = 75) cohort. After precipitating crude EVs from sera, associated small RNA was profiled by next-generation sequencing (NGS) and evaluated in multivariate analyses. A subset of the thereby identified biomarker candidates was validated both technically and additionally by reverse transcription quantitative real-time PCR (RT-qPCR). Differential gene expression (DGE) analysis revealed 29 differentially expressed miRNAs in CAP patients when compared to volunteers, and 25 miRNAs in patients with CAP, compared to those with sepsis. Sparse partial-least discriminant analysis separated groups based on 12 miRNAs. Three miRNAs proved as a significant biomarker signature. While expression levels of miR-1246 showed significant changes with an increase in overall disease severity from volunteers to CAP and to sepsis, miR-193a-5p and miR-542-3p differentiated patients with an infectious disease (CAP or sepsis) from volunteers. Cell-free miRNAs are potentially novel biomarkers for CAP and may help to identify patients at risk for progress to sepsis, facilitating early intervention and treatment.

Transcriptomic profiling of cell-free and vesicular microRNAs from matched arterial and venous sera.

Extracellular vesicles (EVs) play central physiological and pathophysiological roles in intercellular communication. Biomarker studies addressing disorders such as cardiovascular diseases often focus on circulating microRNAs (miRNAs) and may, depending on the type of disease and clinic routine, utilise patient specimens sampled from arterial or venous blood vessels. Thus, it is essential to test whether circulating miRNA profiles depend on the respective sampling site. We assessed potential differences in arterial and venous cell-free miRNA profiles in a cohort of 20 patients scheduled for cardiac surgery. Prior to surgery, blood was simultaneously sampled from the radial artery and the internal jugular vein. After precipitating crude EVs, we performed small RNA Sequencing, which failed to detect significantly regulated miRNAs using stringent filtering criteria for differential expression analysis. Filtering with less strict criteria, we detected four miRNAs slightly upregulated in arterial samples, one of which could be validated by reverse transcription real-time PCR. The applicability of these findings to purified arterial and venous EVs was subsequently tested in a subset of the initial study population. While an additional clean-up step using size-exclusion chromatography seemed to reduce overall miRNA yield compared to crude EV samples, no miRNAs with differential arteriovenous expression were detected. Unsupervised clustering approaches were unable to correctly classify samples drawn from arteries or veins based on miRNAs in either crude or purified preparations. Particle characterisation of crude preparations as well as characterisation of EV markers in purified EVs resulted in highly similar characteristics for arterial and venous samples. With the exception of specific pathologies (e.g. severe pulmonary disorders), arterial versus venous blood sampling should therefore not represent a likely confounder when studying differentially expressed circulating miRNAs. The use of either arterial or venous serum EV samples should result in highly similar data on miRNA expression profiles for the majority of biomarker studies. Abbreviations ACE inhibitors: Angiotensin-converting-enzyme inhibitors; ApoA1: Apolipoprotein A1; CNX: Calnexin; Cv: Coefficient of variation; cDNA: Complementary DNA; CABG: Coronary artery bypass graft; DGE: Differential gene expression; DPBS: Dulbecco's Phosphate Buffered Saline; EVs: Extracellular vesicles; log2FC: Log2 fold change; baseMean: Mean miRNA expression; miRNA: MicroRNA; NTA: Nanoparticle Tracking Analysis; NGS: Next-Generation Sequencing; RT-qPCR: Reverse transcription quantitative real-time PCR; rRNA: Ribosomal RNA; RT: Room temperature; SEC: Size-exclusion chromatography; snoRNA: Small nucleolar RNA; snRNA: Small nuclear RNA; small RNA-Seq: Small RNA Sequencing; SD: Standard deviation; tRNA: Transfer RNA; TEM: Transmission electron microscopy; UA: Uranyl acetate.