Identification of PBMC-based molecular signature associational with COVID-19 disease severity

Illuminating the pathogenic role of SARS-CoV-2: Insights into competing endogenous RNAs (ceRNAs) regulatory networks

The appearance of SARS-CoV-2 in 2019 triggered a significant economic and health crisis worldwide, with heterogeneous molecular mechanisms that contribute to its development are not yet fully understood. Although substantial progress has been made in elucidating the mechanisms behind SARS-CoV-2 infection and therapy, it continues to rank among the top three global causes of mortality due to infectious illnesses. Non-coding RNAs (ncRNAs), being integral components across nearly all biological processes, demonstrate effective importance in viral pathogenesis. Regarding viral infections, ncRNAs have demonstrated their ability to modulate host reactions, viral replication, and host-pathogen interactions. However, the complex interactions of different types of ncRNAs in the progression of COVID-19 remains understudied. In recent years, a novel mechanism of post-transcriptional gene regulation known as "competing endogenous RNA (ceRNA)" has been proposed. Long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and viral ncRNAs function as ceRNAs, influencing the expression of associated genes by sequestering shared microRNAs. Recent research on SARS-CoV-2 has revealed that disruptions in specific ceRNA regulatory networks (ceRNETs) contribute to the abnormal expression of key infection-related genes and the establishment of distinctive infection characteristics. These findings present new opportunities to delve deeper into the underlying mechanisms of SARS-CoV-2 pathogenesis, offering potential biomarkers and therapeutic targets. This progress paves the way for a more comprehensive understanding of ceRNETs, shedding light on the intricate mechanisms involved. Further exploration of these mechanisms holds promise for enhancing our ability to prevent viral infections and develop effective antiviral treatments.

Analyzing the expression pattern of the noncoding RNAs (HOTAIR, PVT-1, XIST, H19, and miRNA-34a) in PBMC samples of patients with COVID-19, according to the disease severity in Iran during 2022-2023: A cross-sectional study

Background and aims

MicroRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are well-known types of noncoding RNAs (ncRNAs), which have been known as the key regulators of gene expression. They can play critical roles in viral infection by regulating the host immune response and interacting with genes in the viral genome. In this regard, ncRNAs can be employed as biomarkers for viral diseases. The current study aimed to evaluate peripheral blood mononuclear cell (PBMC) ncRNAs (lncRNAs-homeobox C antisense intergenic RNA [HOTAIR], -H19, X-inactive-specific transcript [XIST], plasmacytoma variant translocation 1 [PVT-1], and miR-34a) as diagnostic biomarkers to differentiate severe COVID-19 cases from mild ones.

Methods

Candidate ncRNAs were selected according to previous studies and assessed by real-time polymerase chain reaction in the PBMC samples of patients with severe coronavirus disease 2019 (COVID-19) (n = 40), healthy subjects (n = 40), and mild COVID-19 cases (n = 40). Furthermore, the diagnostic value of the selected ncRNAs was assessed by analyzing the receiver-operating characteristic (ROC).

Results

The results demonstrated that the expression pattern of the selected ncRNAs was significantly different between the studied groups. The levels of HOTAIR, XIST, and miR-34a were remarkably overexpressed in the severe COVID-19 group in comparison with the mild COVID-19 group, and in return, the PVT-1 levels were lower than in the mild COVID-19 group. Interestingly, the XIST expression level in men with severe COVID-19 was higher compared to women with mild COVID-19. ROC results suggested that HOTAIR and PVT-1 could serve as useful biomarkers for screening mild COVID-19 from severe COVID-19.

Conclusions

Overall, different expression patterns of the selected ncRNAs and ROC curve results revealed that these factors can contribute to COVID-19 pathogenicity and can be considered diagnostic markers of COVID-19 severe outcomes.

HCG18, LEF1AS1 and lncCEACAM21 as biomarkers of disease severity in the peripheral blood mononuclear cells of COVID-19 patients

BACKGROUND

Even after 3 years from SARS-CoV-2 identification, COVID-19 is still a persistent and dangerous global infectious disease. Significant improvements in our understanding of the disease pathophysiology have now been achieved. Nonetheless, reliable and accurate biomarkers for the early stratification of COVID-19 severity are still lacking. Long noncoding RNAs (LncRNAs) are ncRNAs longer than 200 nucleotides, regulating the transcription and translation of protein-coding genes and they can be found in the peripheral blood, thus holding a promising biomarker potential. Specifically, peripheral blood mononuclear cells (PBMCs) have emerged as a source of indirect biomarkers mirroring the conditions of tissues: they include monocytes, B and T lymphocytes, and natural killer T cells (NKT), being highly informative for immune-related events.

METHODS

We profiled by RNA-Sequencing a panel of 2906 lncRNAs to investigate their modulation in PBMCs of a pilot group of COVID-19 patients, followed by qPCR validation in 111 hospitalized COVID-19 patients.

RESULTS

The levels of four lncRNAs were found to be decreased in association with COVID-19 mortality and disease severity: HLA Complex Group 18-242 and -244 (HCG18-242 and HCG18-244), Lymphoid Enhancer Binding Factor 1-antisense 1 (LEF1-AS1) and lncCEACAM21 (i.e. ENST00000601116.5, a lncRNA in the CEACAM21 locus). Interestingly, these deregulations were confirmed in an independent patient group of hospitalized patients and by the re-analysis of publicly available single-cell transcriptome datasets. The identified lncRNAs were expressed in all of the PBMC cell types and inversely correlated with the neutrophil/lymphocyte ratio (NLR), an inflammatory marker. In vitro, the expression of LEF1-AS1 and lncCEACAM21 was decreased upon THP-1 monocytes exposure to a relevant stimulus, hypoxia.

CONCLUSION

The identified COVID-19-lncRNAs are proposed as potential innovative biomarkers of COVID-19 severity and mortality.

Aid or Antagonize: Nuclear Long Noncoding RNAs Regulate Host Responses and Outcomes of Viral Infections

Long noncoding RNAs (lncRNAs) are transcripts measuring >200 bp in length and devoid of protein-coding potential. LncRNAs exceed the number of protein-coding mRNAs and regulate cellular, developmental, and immune pathways through diverse molecular mechanisms. In recent years, lncRNAs have emerged as epigenetic regulators with prominent roles in health and disease. Many lncRNAs, either host or virus-encoded, have been implicated in critical cellular defense processes, such as cytokine and antiviral gene expression, the regulation of cell signaling pathways, and the activation of transcription factors. In addition, cellular and viral lncRNAs regulate virus gene expression. Viral infections and associated immune responses alter the expression of host lncRNAs regulating immune responses, host metabolism, and viral replication. The influence of lncRNAs on the pathogenesis and outcomes of viral infections is being widely explored because virus-induced lncRNAs can serve as diagnostic and therapeutic targets. Future studies should focus on thoroughly characterizing lncRNA expressions in virus-infected primary cells, investigating their role in disease prognosis, and developing biologically relevant animal or organoid models to determine their suitability for specific therapeutic targeting. Many cellular and viral lncRNAs localize in the nucleus and epigenetically modulate viral transcription, latency, and host responses to infection. In this review, we provide an overview of the role of nuclear lncRNAs in the pathogenesis and outcomes of viral infections, such as the Influenza A virus, Sendai Virus, Respiratory Syncytial Virus, Hepatitis C virus, Human Immunodeficiency Virus, and Herpes Simplex Virus. We also address significant advances and barriers in characterizing lncRNA function and explore the potential of lncRNAs as therapeutic targets.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

The turning point of COVID-19 severity is associated with a unique circulating neutrophil gene signature

COVID-19 has a broad spectrum of clinical manifestations associated with the host immune response heterogeneity. Despite the advances in COVID-19 research, it is still crucial to seek a panel of molecular markers that enable accurate stratification of COVID-19 patients. Here, we performed a study that combined analysis of blood transcriptome, demographic data, clinical aspects and laboratory findings from 66 participants classified into different degrees of COVID-19 severity and healthy subjects. We identified a perturbation in blood-leukocyte transcriptional profile associated with COVID-19 aggravation, which was mainly related to processes that disfavoured lymphocyte activation and favoured neutrophil activation. This transcriptional profile stratified patients according to COVID-19 severity. Hence, it enabled identification of a turning point in transcriptional dynamics that distinguished disease outcomes and non-hospitalized from hospitalized moderate patients. Central genes of this unique neutrophil signature were S100A9, ANXA3, CEACAM6, VNN1, OLFM4, IL1R2, TCN1 and CD177. Our study indicates the molecular changes that are linked with the differing clinical aspects presented by humans when suffering from COVID-19, which involve neutrophil activation.

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

Machine learning-driven blood transcriptome-based discovery of SARS-CoV-2 specific severity biomarkers

The Coronavirus disease 2019 (COVID-19) pandemic, caused by rapidly evolving variants of severe acute respiratory syndrome coronavirus (SARS-CoV-2), continues to be a global health threat. SARS-CoV-2 infection symptoms often intersect with other nonsevere respiratory infections, making early diagnosis challenging. There is an urgent need for early diagnostic and prognostic biomarkers to predict severity and reduce mortality when a sudden outbreak occurs. This study implemented a novel approach of integrating bioinformatics and machine learning algorithms over publicly available clinical COVID-19 transcriptome data sets. The robust 7-gene biomarker identified through this analysis can not only discriminate SARS-CoV-2 associated acute respiratory illness (ARI) from other types of ARIs but also can discriminate severe COVID-19 patients from nonsevere COVID-19 patients. Validation of the 7-gene biomarker in an independent blood transcriptome data set of longitudinal analysis of COVID-19 patients across various stages of the disease showed that the dysregulation of the identified biomarkers during severe disease is restored during recovery, showing their prognostic potential. The blood biomarkers identified in this study can serve as potential diagnostic candidates and help reduce COVID-19-associated mortality.

JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA

Non-coding RNAs (ncRNAs) have long been considered the "white elephant" on the genome because they lack the ability to encode proteins. However, in recent years, more and more biological experiments and clinical reports have proved that ncRNAs account for a large proportion in organisms. At the same time, they play a decisive role in the biological processes such as gene expression and cell growth and development. Recently, it has been found that short sequence non-coding RNA(miRNA) and long sequence non-coding RNA(lncRNA) can regulate each other, which plays an important role in various complex human diseases. In this paper, we used a new method (JSCSNCP-LMA) to predict lncRNA-miRNA with unknown associations. This method combined Jaccard similarity algorithm, self-tuning spectral clustering similarity algorithm, cosine similarity algorithm and known lncRNA-miRNA association networks, and used the consistency projection to complete the final prediction. The results showed that the AUC values of JSCSNCP-LMA in fivefold cross validation (fivefold CV) and leave-one-out cross validation (LOOCV) were 0.9145 and 0.9268, respectively. Compared with other models, we have successfully proved its superiority and good extensibility. Meanwhile, the model also used three different lncRNA-miRNA datasets in the fivefold CV experiment and obtained good results with AUC values of 0.9145, 0.9662 and 0.9505, respectively. Therefore, JSCSNCP-LMA will help to predict the associations between lncRNA and miRNA.

Non-coding RNA in SARS-CoV-2: Progress toward therapeutic significance

The recently developed pathogenic virus, SARS-CoV-2, was found in the Hubei Province, China. Giving rise to a broad spectrum of symptoms, SARS-CoV-2 rapidly spread across the globe, causing multi-systemic and dangerous complications, with death in extreme cases. Thereby, the number of research cases increases every day on preventing infection and treating its resulting damage. Accumulating evidence suggests noncoding RNAs (ncRNAs) are necessary for modifying virus infection and antiviral immune reaction, along with biological processes regulating SARS-CoV-2 and subsequent disease states. Therefore, understanding these mechanisms might provide a further understanding of the pathogenesis and feasible therapy alternatives against SARS-CoV2. Consequently, the molecular biology of SARS-CoV-2, ncRNA's role in its infection, and various RNA therapy tactics against the virus have been presented in this review section.

(Epi)transcriptomics in cardiovascular and neurological complications of COVID-19

Although systemic inflammation and pulmonary complications increase the mortality rate in COVID-19, a broad spectrum of cardiovascular and neurological complications can also contribute to significant morbidity and mortality. The molecular mechanisms underlying cardiovascular and neurological complications during and after SARS-CoV-2 infection are incompletely understood. Recently reported perturbations of the epitranscriptome of COVID-19 patients indicate that mechanisms including those derived from RNA modifications and non-coding RNAs may play a contributing role in the pathogenesis of COVID-19. In this review paper, we gathered recently published studies investigating (epi)transcriptomic fluctuations upon SARS-CoV-2 infection, focusing on the brain-heart axis since neurological and cardiovascular events and their sequelae are of utmost prevalence and importance in this disease.

Tangled quest of post-COVID-19 infection-caused neuropathology and what 3P nano-bio-medicine can solve?

COVID-19-caused neurological problems are the important post-CoV-2 infection complications, which are recorded in ~ 40% of critically ill COVID-19 patients. Neurodegeneration (ND) is one of the most serious complications. It is necessary to understand its molecular mechanism(s), define research gaps to direct research to, hopefully, design new treatment modalities, for predictive diagnosis, patient stratification, targeted prevention, prognostic assessment, and personalized medical services for this type of complication. Individualized nano-bio-medicine combines nano-medicine (NM) with clinical and molecular biomarkers based on omics data to improve during- and post-illness management or post-infection prognosis, in addition to personalized dosage profiling and drug selection for maximum treatment efficacy, safety with least side-effects. This review will enumerate proteins, receptors, and enzymes involved in CoV-2 entrance into the central nervous system (CNS) via the blood–brain barrier (BBB), and list the repercussions after that entry, ranging from neuroinflammation to neurological symptoms disruption mechanism. Moreover, molecular mechanisms that mediate the host effect or viral detrimental effect on the host are discussed here, including autophagy, non-coding RNAs, inflammasome, and other molecular mechanisms of CoV-2 infection neuro-affection that are defined here as hallmarks of neuropathology related to COVID-19 infection. Thus, a couple of questions are raised; for example, “What are the hallmarks of neurodegeneration during COVID-19 infection?” and “Are epigenetics promising solution against post-COVID-19 neurodegeneration?” In addition, nano-formulas might be a better novel treatment for COVID-19 neurological complications, which raises one more question, “What are the challenges of nano-bio-based nanocarriers pre- or post-COVID-19 infection?” especially in the light of omics-based changes/challenges, research, and clinical practice in the framework of predictive preventive personalized medicine (PPPM / 3P medicine).

Immunometabolic analysis shows a distinct cyto-metabotype in Covid-19 compared to sepsis from other causes

Background

In Covid-19, profound systemic inflammatory responses are accompanied by both metabolic risk factors for severity and, separately, metabolic mechanisms have been shown to underly disease progression. It is unknown whether this reflects similar situations in sepsis or is a unique characteristic of Covid-19.

Aims

Define the immunometabolic signature of Covid-19.

Methods

65 patients with Covid-19,19 patients with sepsis and 14 healthy controls were recruited and sampled for plasma, serum and peripheral blood mononuclear cells (PBMCs) through 10 days of critical illness. Metabotyping was performed using the Biocrates p180 kit and multiplex cytokine profiling undertaken. PBMCs underwent phenotyping by flow cytometry. Immune and metabolic readouts were integrated and underwent pathway analysis.

Results

Phopsphatidylcholines (PC) are reduced in Covid-19 but greater than in sepsis. Compared to controls, tryptophan is reduced in Covid-19 and inversely correlated with the severity of the disease and IFN-ɣ concentrations, conversely the kyneurine and kyneurine/tryptophan ratio increased in the most severe cases. These metabolic changes were consistent through 2 pandemic waves in our centre. PD-L1 expression in CD8+ T cells, Tregs and CD14+ monocytes was increased in Covid-19 compared to controls.

Conclusions

In our cohort, Covid-19 is associated with monocytopenia, increased CD14+ and Treg PD-L1 expression correlating with IFN-ɣ plasma concentration and disease severity (SOFA score). The latter is also associated with metabolic derangements of Tryptophan, LPC 16:0 and PCs. Lipid metabolism, in particular phosphatidylcholines and lysophosphatidylcolines, seems strictly linked to immune response in Covid-19. Our results support the hypothesis that IFN-ɣ -PD-L1 axis might be involved in the cytokine release syndrome typical of severe Covid-19 and the phenomenon persisted through multiple pandemic waves despite use of immunomodulation.

Classification and severity progression measure of COVID-19 patients using pairs of multi-omic factors

Early detection and effective treatment of severe COVID-19 patients remain two major challenges during the current pandemic. Analysis of molecular changes in blood samples of severe patients is one of the promising approaches to this problem. From thousands of proteomic, metabolomic, lipidomic, and transcriptomic biomarkers selected in other research, we identify several pairs of biomarkers that after additional nonlinear spline transformation are highly effective in classifying and predicting severe COVID-19 cases. The performance of these pairs is evaluated in-sample, in a cross-validation exercise, and in an out-of-sample analysis on two independent datasets. We further improve our classifier by identifying complementary pairs using hierarchical clustering. In a result, we achieve 96-98% AUC on the validation data. Our findings can help medical experts to identify small groups of biomarkers that after nonlinear transformation can be used to construct a cost-effective test for patient screening and prediction of severity progression.

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Altered expression of protein coding gene (PCG) and long non-coding RNA (lncRNA) have been identified in SARS-CoV-2 infected cells and tissues from COVID-19 patients. The functional role and mechanism (s) of transcriptional regulation of deregulated genes in COVID-19 remain largely unknown. In the present communication, reanalyzing publicly available gene expression data, we observed that 66 lncRNA and 5491 PCG were deregulated in more than one experimental condition. Combining our earlier published results and using different publicly available resources, it was observed that 72 deregulated lncRNA interacted with 3228 genes/proteins. Many targets of deregulated lncRNA could also interact with SARS-CoV-2 coded proteins, modulated by IFN treatment and identified in CRISPR screening to modulate SARS-CoV-2 infection. The majority of the deregulated lncRNA and PCG were targets of at least one of the transcription factors (TFs), interferon responsive factors (IRFs), signal transducer, and activator of transcription (STATs), NFκB, MYC, and RELA/p65. Deregulated 1069 PCG was joint targets of lncRNA and TF. These joint targets are significantly enriched with pathways relevant for SARS-CoV-2 infection indicating that joint regulation of PCG could be one of the mechanisms for deregulation. Over all this manuscript showed possible involvement of lncRNA and mechanisms of deregulation of PCG in the pathogenesis of COVID-19.

SnoRNAs and miRNAs Networks Underlying COVID-19 Disease Severity

There is a lack of predictive markers for early and rapid identification of disease progression in COVID-19 patients. Our study aims at identifying microRNAs (miRNAs)/small nucleolar RNAs (snoRNAs) as potential biomarkers of COVID-19 severity. Using differential expression analysis of microarray data (n = 29), we identified hsa-miR-1246, ACA40, hsa-miR-4532, hsa-miR-145-5p, and ACA18 as the top five differentially expressed transcripts in severe versus asymptomatic, and ACA40, hsa-miR-3609, ENSG00000212378 (SNORD78), hsa-miR-1231, hsa-miR-885-3p as the most significant five in severe versus mild cases. Moreover, we found that white blood cell (WBC) count, absolute neutrophil count (ANC), neutrophil (%), lymphocyte (%), red blood cell (RBC) count, hemoglobin, hematocrit, D-Dimer, and albumin are significantly correlated with the identified differentially expressed miRNAs and snoRNAs. We report a unique miRNA and snoRNA profile that is associated with a higher risk of severity in a cohort of SARS-CoV-2 infected patients. Altogether, we present a differential expression analysis of COVID-19-associated microRNA (miRNA)/small nucleolar RNA (snoRNA) signature, highlighting their importance in SARS-CoV-2 infection.