COVID-19 patients exhibit unique transcriptional signatures indicative of disease severity

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.

Obesity fosters severe disease outcomes in a mouse model of coronavirus infection associated with transcriptomic abnormalities

Obesity has been identified as an independent risk factor for severe outcomes in humans with coronavirus disease 2019 (COVID-19) and other infectious diseases. Here, we established a mouse model of COVID-19 using the murine betacoronavirus, mouse hepatitis virus 1 (MHV-1). C57BL/6 and C3H/HeJ mice exposed to MHV-1 developed mild and severe disease, respectively. Obese C57BL/6 mice developed clinical manifestations similar to those of lean controls. In contrast, all obese C3H/HeJ mice succumbed by 8 days postinfection, compared to a 50% mortality rate in lean controls. Notably, both lean and obese C3H/HeJ mice exposed to MHV-1 developed lung lesions consistent with severe human COVID-19, with marked evidence of diffuse alveolar damage (DAD). To identify early predictive biomarkers of worsened disease outcomes in obese C3H/HeJ mice, we sequenced RNA from whole blood 2 days postinfection and assessed changes in gene and pathway expression. Many pathways uniquely altered in obese C3H/HeJ mice postinfection aligned with those found in humans with severe COVID-19. Furthermore, we observed altered gene expression related to the unfolded protein response and lipid metabolism in infected obese mice compared to their lean counterparts, suggesting a role in the severity of disease outcomes. This study presents a novel model for studying COVID-19 and elucidating the mechanisms underlying severe disease outcomes in obese and other hosts.

Analysis of transcriptomics data from COVID-19 patients: a pilot research

During SARS-CoV-2 infection, the virus transforms the infected host cell into factories that produce new viral particles. As infection progresses, the infected cells undergo numerous changes in various pathways. One of these changes is the occurrence of a cytokine storm, which leads to severe symptoms. In this study, we examined the transcriptomic changes caused by COVID-19 by analyzing RNA-seq data obtained from COVID-19-positive patients as well as COVID-19-negative donors. RNA-seq data were collected for the purpose of identification of potential biomarkers associated with a different course of the disease. We analyzed the first datasets, consisting of 96 samples to validate our methods. The objective of this publication is to report the pilot results. To explore potential biomarkers related to disease severity, we conducted a differential expression analysis of human transcriptome, focusing on COVID-19 positivity and symptom severity. Given the large number of potential biomarkers we identified, we further performed pathway enrichment analysis with terms from Kyoto Encyclopedia of Genes and Genomics (KEGG) to obtain a more profound understanding of altered pathways. Our results indicate that pathways related to immune processes, response to infection, and multiple signaling pathways were affected. These findings align with several previous studies that also reported the influence of SARS-CoV-2 infection on these pathways.

Dysregulated early transcriptional signatures linked to mast cell and interferon responses are implicated in COVID-19 severity

Background

Dysregulated immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are thought to underlie the progression of coronavirus disease 2019 (COVID-19) to severe disease. We sought to determine whether early host immune-related gene expression could predict clinical progression to severe disease.

Methods

We analysed the expression of 579 immunological genes in peripheral blood mononuclear cells taken early after symptom onset using the NanoString nCounter and compared SARS-CoV-2 negative controls with SARS-CoV-2 positive subjects with mild (SARS+ Mild) and Moderate/Severe disease to evaluate disease outcomes. Biobanked plasma samples were also assessed for type I (IFN-α2a and IFN-β), type II (IFN-γ) and type III (IFN-λ1) interferons (IFNs) as well as 10 additional cytokines using multiplex immunoassays.

Results

We identified 19 significantly deregulated genes in 62 SARS-CoV-2 positive subject samples within 5 days of symptom onset and 58 SARS-CoV-2 negative controls and found that type I interferon (IFN) signalling (MX1, IRF7, IFITM1, IFI35, STAT2, IRF4, PML, BST2, STAT1) and genes encoding proinflammatory cytokines (TNF, TNFSF4, PTGS2 and IL1B) were upregulated in both SARS+ groups. Moreover, we found that FCER1, involved in mast cell activation, was upregulated in the SARS+ Mild group but significantly downregulated in the SARS+ Moderate/Severe group. In both SARS+ groups we discovered elevated interferon type I IFN-α2a, type II IFN and type III IFN λ1 plasma levels together with higher IL-10 and IL-6. These results indicate that those with moderate or severe disease are characterised by deficiencies in a mast cell response together with IFN hyper-responsiveness, suggesting that early host antiviral immune responses could be a cause and not a consequence of severe COVID-19.

Conclusions

This study suggests that early host immune responses linking defects in mast cell activation with host interferon responses correlates with more severe outcomes in COVID-19. Further characterisation of this pathway could help inform better treatment for vulnerable individuals.

Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features

The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe disease. We have developed a novel iterative machine learning pipeline that utilizes gene enrichment profiles from blood transcriptome data to stratify COVID-19 patients based on disease severity and differentiate severe COVID cases from other patients with acute hypoxic respiratory failure. The pattern of gene module enrichment in COVID-19 patients overall reflected broad cellular expansion and metabolic dysfunction, whereas increased neutrophils, activated B cells, T-cell lymphopenia, and proinflammatory cytokine production were specific to severe COVID patients. Using this pipeline, we also identified small blood gene signatures indicative of COVID-19 diagnosis and severity that could be used as biomarker panels in the clinical setting.

Immune Factors Drive Expression of SARS-CoV-2 Receptor Genes Amid Sexual Disparity

The emergence of COVID-19 has led to significant morbidity and mortality, with around seven million deaths worldwide as of February 2023. There are several risk factors such as age and sex that are associated with the development of severe symptoms due to COVID-19. There have been limited studies that have explored the role of sex differences in SARS-CoV-2 infection. As a result, there is an urgent need to identify molecular features associated with sex and COVID-19 pathogenesis to develop more effective interventions to combat the ongoing pandemic. To address this gap, we explored sex-specific molecular factors in both mouse and human datasets. The host immune targets such as TLR7, IRF7, IRF5, and IL6, which are involved in the immune response against viral infections, and the sex-specific targets such as AR and ESSR were taken to investigate any possible link with the SARS-CoV-2 host receptors ACE2 and TMPRSS2. For the mouse analysis, a single-cell RNA sequencing dataset was used, while bulk RNA-Seq datasets were used to analyze the human clinical data. Additional databases such as the Database of Transcription Start Sites (DBTS), STRING-DB, and the Swiss Regulon Portal were used for further analysis. We identified a 6-gene signature that showed differential expression in males and females. Additionally, this gene signature showed potential prognostic utility by differentiating ICU patients from non-ICU patients due to COVID-19. Our study highlights the importance of assessing sex differences in SARS-CoV-2 infection, which can assist in the optimal treatment and better vaccination strategies.