A blood RNA transcriptome signature for COVID-19

Unlocking the Potential of RNA Sequencing in COVID-19: Toward Accurate Diagnosis and Personalized Medicine

COVID-19 has caused widespread morbidity and mortality, with its effects extending to multiple organ systems. Despite known risk factors for severe disease, including advanced age and underlying comorbidities, patient outcomes can vary significantly. This variability complicates efforts to predict disease progression and tailor treatment strategies. While diagnostic and therapeutic approaches are still under debate, RNA sequencing (RNAseq) has emerged as a promising tool to provide deeper insights into the pathophysiology of COVID-19 and guide personalized treatment. A comprehensive literature review was conducted using PubMed, Scopus, Web of Science, and Google Scholar. We employed Medical Subject Headings (MeSH) terms and relevant keywords to identify studies that explored the role of RNAseq in COVID-19 diagnostics, prognostics, and therapeutics. RNAseq has proven instrumental in identifying molecular biomarkers associated with disease severity in patients with COVID-19. It allows for the differentiation between asymptomatic and symptomatic individuals and sheds light on the immune response mechanisms that contribute to disease progression. In critically ill patients, RNAseq has been crucial for identifying key genes that may predict patient outcomes, guiding therapeutic decisions, and assessing the long-term effects of the virus. Additionally, RNAseq has helped in understanding the persistence of viral RNA after recovery, offering new insights into the management of post-acute sequelae, including long COVID. RNA sequencing significantly improves COVID-19 management, particularly for critically ill patients, by enhancing diagnostic accuracy, personalizing treatment, and predicting therapeutic responses. It refines patient stratification, improving outcomes, and holds promise for targeted interventions in both acute and long COVID.

SARS-CoV-2 disrupts host gene networks: Unveiling key hub genes as potential therapeutic targets for COVID-19 management

PURPOSE

Although the end of COVID-19 as a public health emergency was declared on May 2023, still new cases of the infection are reported and the risk remains of new variants emerging that may cause new surges in cases and deaths. While clinical symptoms have been rapidly defined worldwide, the basic body responses and pathogenetic mechanisms acting in patients with SARS-CoV-2 infection over time until recovery or death require further investigation. The understanding of the molecular mechanisms underlying the development and course of the disease is essential in designing effective preventive and therapeutic approaches, and ultimately reducing mortality and disease spreading.

METHODS

The current investigation aimed to identify the key genes engaged in SARS-CoV-2 infection. To achieve this goal high-throughput RNA sequencing of peripheral blood samples collected from healthy donors and COVID-19 patients was performed. The resulting sequence data were processed using a wide range of bioinformatics tools to obtain detailed modifications within five transcriptomic phenomena: expression of genes and long non-coding RNAs, alternative splicing, allel-specific expression and circRNA production. The in silico procedure was completed with a functional analysis of the identified alterations.

RESULTS

The transcriptomic analysis revealed that SARS-CoV-2 has a significant impact on multiple genes encoding ribosomal proteins (RPs). Results show that these genes differ not only in terms of expression but also manifest biases in alternative splicing and ASE ratios. The integrated functional analysis exposed that RPs mostly affected pathways and processes related to infection-COVID-19 and NOD-like receptor signaling pathway, SARS-CoV-2-host interactions and response to the virus. Furthermore, our results linked the multiple intronic ASE variants and exonic circular RNA differentiations with SARS-CoV-2 infection, suggesting that these molecular events play a crucial role in mRNA maturation and transcription during COVID-19 disease.

CONCLUSIONS

By elucidating the genetic mechanisms induced by the virus, the current research provides significant information that can be employed to create new targeted therapeutic strategies for future research and treatment related to COVID-19. Moreover, the findings highlight potentially promising therapeutic biomarkers for early risk assessment of critically ill patients.

Whole blood transcriptome signature predicts severe forms of COVID-19: Results from the COVIDeF cohort study

COVID-19 is associated with heterogeneous outcome. Early identification of a severe progression of the disease is essential to properly manage the patients and improve their outcome. Biomarkers reflecting an increased inflammatory response, as well as individual features including advanced age, male gender, and pre-existing comorbidities, are risk factors of severe COVID-19. Yet, these features show limited accuracy for outcome prediction. The aim was to evaluate the prognostic value of whole blood transcriptome at an early stage of the disease. Blood transcriptome of patients with mild pneumonia was profiled. Patients with subsequent severe COVID-19 were compared to those with favourable outcome, and a molecular predictor based on gene expression was built. Unsupervised classification discriminated patients who would later develop a COVID-19-related severe pneumonia. The corresponding gene expression signature reflected the immune response to the viral infection dominated by a prominent type I interferon, with IFI27 among the most over-expressed genes. A 48-genes transcriptome signature predicting the risk of severe COVID-19 was built on a training cohort, then validated on an external independent cohort, showing an accuracy of 81% for predicting severe outcome. These results identify an early transcriptome signature of severe COVID-19 pneumonia, with a possible relevance to improve COVID-19 patient management.

Integrated analysis of RNA-seq datasets reveals novel targets and regulators of COVID-19 severity

During the COVID-19 pandemic, RNA-seq datasets were produced to investigate the virus-host relationship. However, much of these data remains underexplored. To improve the search for molecular targets and biomarkers, we performed an integrated analysis of multiple RNA-seq datasets, expanding the cohort and including patients from different countries, encompassing severe and mild COVID-19 patients. Our analysis revealed that severe COVID-19 patients exhibit overexpression of genes coding for proteins of extracellular exosomes, endomembrane system, and neutrophil granules (e.g., S100A9, LY96, and RAB1B), which may play an essential role in the cellular response to infection. Concurrently, these patients exhibit down-regulation of genes encoding components of the T cell receptor complex and nucleolus, including TP53, IL2RB, and NCL Finally, SPI1 may emerge as a central transcriptional factor associated with the up-regulated genes, whereas TP53, MYC, and MAX were associated with the down-regulated genes during COVID-19. This study identified targets and transcriptional factors, lighting on the molecular pathophysiology of syndrome coronavirus 2 infection.

Systems-wide view of host-pathogen interactions across COVID-19 severities using integrated omics analysis

The mechanisms explaining the variability in COVID-19 clinical manifestations (mild, moderate, and severe) are not fully understood. To identify key gene expression markers linked to disease severity, we employed an integrated approach, combining host-pathogen protein-protein interaction data and viral-induced host gene expression data. We analyzed an RNA-seq dataset from peripheral blood mononuclear cells across 12 projects representing the spectrum of disease severity. We identified genes showing differential expression across mild, moderate, and severe conditions. Enrichment analysis of the pathways in host proteins targeted by each of the SARS-CoV-2 proteins revealed a strong association with processes related to ribosomal biogenesis, translation, and translocation. Interestingly, most of these pathways and associated cellular machinery, including ribosomal biogenesis, ribosomal proteins, and translation, were upregulated in mild conditions but downregulated in severe cases. This suggests that COVID-19 exhibits a paradoxical host response, boosting host/viral translation in mild cases but slowing it in severe cases.

Role of CD27 and SAMHD1 and their genetic susceptibility to COVID-19

SARS-CoV-2, which initiated the worldwide COVID-19 epidemic in 2019, has rapidly emerged and spread, resulting in significant public health challenges worldwide. The COVID-19 severity signs and their association with specific genes have been investigated to better comprehend this phenomenon. In this study, several genes were investigated to see whether they correspond with COVID-19 sickness severity. This research aims to determine and evaluate certain gene expression levels associated with the immune system, as these genes were reported to play important roles in immune control during the COVID-19 outbreak. We analyzed two immunity-linked genes: CD27 and SAMHD1 in COVID-19 patients' samples using RT-PCR, compared them to the samples from recovered, immunized, and healthy individuals. These data were examined to determine the potential relationships between clinical patterns, illness severity, and progression, and SARS-CoV-2 infection immunology. We observed that CD27 gene expression was higher in COVID-19 vaccinated and control groups, but lower in active and recovered COVID-19 patients. On the other hand, SAMHD1 gene expression was elevated in infected and recovered COVID-19 groups. According to our study, the proteins CD27 and SAMHD1 are essential for controlling the immunological response to COVID-19. Changes in their expression levels could increase the susceptibility of patients to severe complications associated with the disease. Therefore, the gene expression level of these proteins could serve as viable prognostic markers for COVID-19.

Transcriptome from Paired Samples Improves the Power of Comprehensive COVID-19 Host-Viral Characterization

Previous transcriptome profiling studies showed significantly upregulated genes and altered biological pathways in acute COVID-19. However, changes in the transcriptional signatures during a defined time frame are not yet examined and described. The aims of this study included viral metagenomics and evaluation of the total expression in time-matched and tissue-matched paired COVID-19 samples with the analysis of the host splicing profile to reveal potential therapeutic targets. Prospective analysis of paired nasopharyngeal swabs (NPS) and blood (BL) samples from 18 COVID-19 patients with acute and resolved infection performed using Kallisto, Suppa2, Centrifuge, EdgeR, PantherDB, and L1000CDS2 tools. In NPS, we discovered 6 genes with changed splicing and 40 differentially expressed genes (DEG) that yielded 88 altered pathways. Blood samples yielded 15 alternatively spliced genes. Although the unpaired DEG analysis failed, pairing identified 78 genes and 242 altered pathways with meaningful clinical interpretation and new candidate drug combinations with up to 65% overlap. Metagenomics analyses showed SARS-CoV-2 dominance during and after the acute infection, with a significant reduction in NPS (0.008 vs. 0.002, p = 0.019). Even though both NPS and BL give meaningful insights into expression changes, this is the first demonstration of how the power of blood analysis is vastly maximized by pairing. The obtained results essentially showed that pairing is a determinant between a failed and a comprehensive study. Finally, the bioinformatics results prove to be a comprehensive tool for full-action insights, drug development, and infectious disease research when designed properly.

Dynamics of Gene Expression Profiling and Identification of High-Risk Patients for Severe COVID-19

The clinical manifestations of SARS-CoV-2 infection vary widely, from asymptomatic infection to the development of acute respiratory distress syndrome (ARDS) and death. The host response elicited by SARS-CoV-2 plays a key role in determining the clinical outcome. We hypothesized that determining the dynamic whole blood transcriptomic profile of hospitalized adult COVID-19 patients and characterizing the subgroup that develops severe disease and ARDS would broaden our understanding of the heterogeneity in clinical outcomes. We recruited 60 hospitalized patients with RT-PCR-confirmed SARS-CoV-2 infection, among whom 19 developed ARDS. Peripheral blood was collected using PAXGene RNA tubes within 24 h of admission and on day 7. There were 2572 differently expressed genes in patients with ARDS at baseline and 1149 at day 7. We found a dysregulated inflammatory response in COVID-19 ARDS patients, with an increased expression of genes related to pro-inflammatory molecules and neutrophil and macrophage activation at admission, in addition to an immune regulation loss. This led, in turn, to a higher expression of genes related to reactive oxygen species, protein polyubiquitination, and metalloproteinases in the latter stages. Some of the most significant differences in gene expression found between patients with and without ARDS corresponded to long non-coding RNA involved in epigenetic control.

Nucleolin mediates SARS-CoV-2 replication and viral-induced apoptosis of host cells

Host-oriented antiviral therapeutics are promising treatment options to combat COVID-19 and its emerging variants. However, relatively little is known about the cellular proteins hijacked by SARS-CoV-2 for its replication. Here we show that SARS-CoV-2 induces expression and cytoplasmic translocation of the nucleolar protein, nucleolin (NCL). NCL interacts with SARS-CoV-2 viral proteins and co-localizes with N-protein in the nucleolus and in stress granules. Knockdown of NCL decreases the stress granule component G3BP1, viral replication and improved survival of infected host cells. NCL mediates viral-induced apoptosis and stress response via p53. SARS-CoV-2 increases NCL expression and nucleolar size and number in lungs of infected hamsters. Inhibition of NCL with the aptamer AS-1411 decreases viral replication and apoptosis of infected cells. These results suggest nucleolin as a suitable target for anti-COVID therapies.

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease

Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.

Gene Expression Risk Scores for COVID-19 Illness Severity

BACKGROUND

The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood.

METHODS

We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2 infection clinically adjudicated as having mild, moderate, or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and nonsevere COVID-19.

RESULTS

Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus nonsevere illness, we identified >4000 genes differentially expressed (false discovery rate < 0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T-cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated receiver operating characteristic-area under the curve [ROC-AUC] = 0.98), and need for intensive care in an independent cohort (ROC-AUC = 0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.

CONCLUSIONS

These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

A 9-mRNA signature measured from whole blood by a prototype PCR panel predicts 28-day mortality upon admission of critically ill COVID-19 patients

Immune responses affiliated with COVID-19 severity have been characterized and associated with deleterious outcomes. These approaches were mainly based on research tools not usable in routine clinical practice at the bedside. We observed that a multiplex transcriptomic panel prototype termed Immune Profiling Panel (IPP) could capture the dysregulation of immune responses of ICU COVID-19 patients at admission. Nine transcripts were associated with mortality in univariate analysis and this 9-mRNA signature remained significantly associated with mortality in a multivariate analysis that included age, SOFA and Charlson scores. Using a machine learning model with these 9 mRNA, we could predict the 28-day survival status with an Area Under the Receiver Operating Curve (AUROC) of 0.764. Interestingly, adding patients’ age to the model resulted in increased performance to predict the 28-day mortality (AUROC reaching 0.839). This prototype IPP demonstrated that such a tool, upon clinical/analytical validation and clearance by regulatory agencies could be used in clinical routine settings to quickly identify patients with higher risk of death requiring thus early aggressive intensive care.

Unpacking COVID-19 Systems Biology in Lung and Whole Blood with Transcriptomics and miRNA Regulators

COVID-19 is a systemic disease affecting tissues and organs, including and beyond the lung. Apart from the current pandemic context, we also have vastly inadequate knowledge of consequences of repeated exposures to SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus causing COVID-19, in multiple organ systems and the whole organism scales when the disease evolves from a pandemic to an endemic state. This calls for a systems biology and systems medicine approach and unpacking the effects of COVID-19 in lung as well as other tissues. We report here original findings from transcriptomics analyses and differentially expressed genes (DEGs) in lung samples from 60 patients and 27 healthy controls, and in whole blood samples from 255 patients and 103 healthy individuals. A total of 11 datasets with RNA-seq transcriptomic data were obtained from the Gene Expression Omnibus and the European Nucleotide Archive. The identified DEGs were used to construct protein interaction and functional networks and to identify related pathways and miRNAs. We found 35 DEGs common between lung and the whole blood, and importantly, 2 novel genes, namely CYP1B1 and TNFAIP6, which have not been previously implicated with COVID-19. We also identified four novel miRNA potential regulators, hsa-mir-192-5p, hsa-mir-221-3p, hsa-mir-4756-3p, and hsa-mir-10a-5p, implicated in lung or other diseases induced by coronaviruses. In summary, these findings offer new molecular leads and insights to unpack COVID-19 systems biology in a whole organism context and might inform future antiviral drug, diagnostics, and vaccine discovery efforts.

p53/NF-kB Balance in SARS-CoV-2 Infection: From OMICs, Genomics and Pharmacogenomics Insights to Tailored Therapeutic Perspectives (COVIDomics)

SARS-CoV-2 infection affects different organs and tissues, including the upper and lower airways, the lung, the gut, the olfactory system and the eye, which may represent one of the gates to the central nervous system. Key transcriptional factors, such as p53 and NF-kB and their reciprocal balance, are altered upon SARS-CoV-2 infection, as well as other key molecules such as the virus host cell entry mediator ACE2, member of the RAS-pathway. These changes are thought to play a central role in the impaired immune response, as well as in the massive cytokine release, the so-called cytokine storm that represents a hallmark of the most severe form of SARS-CoV-2 infection. Host genetics susceptibility is an additional key side to consider in a complex disease as COVID-19 characterized by such a wide range of clinical phenotypes. In this review, we underline some molecular mechanisms by which SARS-CoV-2 modulates p53 and NF-kB expression and activity in order to maximize viral replication into the host cells. We also face the RAS-pathway unbalance triggered by virus-ACE2 interaction to discuss potential pharmacological and pharmacogenomics approaches aimed at restoring p53/NF-kB and ACE1/ACE2 balance to counteract the most severe forms of SARS-CoV-2 infection.

Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in late 2019 in Wuhan, China, and has proven to be highly pathogenic, making it a global public health threat. The immediate need to understand the mechanisms and impact of the virus made omics techniques stand out, as they can offer a holistic and comprehensive view of thousands of molecules in a single experiment. Mastering bioinformatics tools to process, analyze, integrate, and interpret omics data is a powerful knowledge to enrich results. We present a robust and open access computational pipeline for extracting information from quantitative proteomics and transcriptomics public data. We present the entire pipeline from raw data to differentially expressed genes. We explore processes and pathways related to mapped transcripts and proteins. A pipeline is presented to integrate and compare proteomics and transcriptomics data using also packages available in the Bioconductor and providing the codes used. Cholesterol metabolism, immune system activity, ECM, and proteasomal degradation pathways increased in infected patients. Leukocyte activation profile was overrepresented in both proteomics and transcriptomics data. Finally, we found a panel of proteins and transcripts regulated in the same direction in the lung transcriptome and plasma proteome that distinguish healthy and infected individuals. This panel of markers was confirmed in another cohort of patients, thus validating the robustness and functionality of the tools presented.

Programmed cell death: the pathways to severe COVID-19?

Two years after the emergence of SARS-CoV-2, our understanding of COVID-19 disease pathogenesis is still incomplete. Despite unprecedented global collaborative scientific efforts and rapid vaccine development, an uneven vaccine roll-out and the emergence of novel variants of concern such as omicron underscore the critical importance of identifying the mechanisms that contribute to this disease. Overt inflammation and cell death have been proposed to be central drivers of severe pathology in COVID-19 patients and their pathways and molecular components therefore present promising targets for host-directed therapeutics. In our review, we summarize the current knowledge on the role and impact of diverse programmed cell death (PCD) pathways on COVID-19 disease. We dissect the complex connection of cell death and inflammatory signaling at the cellular and molecular level and identify a number of critical questions that remain to be addressed. We provide rationale for targeting of cell death as potential COVID-19 treatment and provide an overview of current therapeutics that could potentially enter clinical trials in the near future.

Gene Expression Risk Scores for COVID-19 Illness Severity

BACKGROUND

The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood.

METHODS

We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID.

RESULTS

Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.

CONCLUSION

These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

SARS-CoV-2 Infected Pediatric Cerebral Cortical Neurons: Transcriptomic Analysis and Potential Role of Toll-like Receptors in Pathogenesis

Different mechanisms were proposed as responsible for COVID-19 neurological symptoms but a clear one has not been established yet. In this work we aimed to study SARS-CoV-2 capacity to infect pediatric human cortical neuronal HCN-2 cells, studying the changes in the transcriptomic profile by next generation sequencing. SARS-CoV-2 was able to replicate in HCN-2 cells, that did not express ACE2, confirmed also with Western blot, and TMPRSS2. Looking for pattern recognition receptor expression, we found the deregulation of scavenger receptors, such as SR-B1, and the downregulation of genes encoding for Nod-like receptors. On the other hand, TLR1, TLR4 and TLR6 encoding for Toll-like receptors (TLRs) were upregulated. We also found the upregulation of genes encoding for ERK, JNK, NF-κB and Caspase 8 in our transcriptomic analysis. Regarding the expression of known receptors for viral RNA, only RIG-1 showed an increased expression; downstream RIG-1, the genes encoding for TRAF3, IKKε and IRF3 were downregulated. We also found the upregulation of genes encoding for chemokines and accordingly we found an increase in cytokine/chemokine levels in the medium. According to our results, it is possible to speculate that additionally to ACE2 and TMPRSS2, also other receptors may interact with SARS-CoV-2 proteins and mediate its entry or pathogenesis in pediatric cortical neurons infected with SARS-CoV-2. In particular, TLRs signaling could be crucial for the neurological involvement related to SARS-CoV-2 infection.