Dynamic blood single-cell immune responses in patients with COVID-19

Whole-transcriptome analyses of sheep embryonic testicular cells infected with the bluetongue virus

Introduction

bluetongue virus (BTV) infection triggers dramatic and complex changes in the host's transcriptional profile to favor its own survival and reproduction. However, there is no whole-transcriptome study of susceptible animal cells with BTV infection, which impedes the in-depth and systematical understanding of the comprehensive characterization of BTV-host interactome, as well as BTV infection and pathogenic mechanisms.

Methods

to systematically understand these changes, we performed whole-transcriptome sequencing in BTV serotype 1 (BTV-1)-infected and mock-infected sheep embryonic testicular cells, and subsequently conducted bioinformatics differential analyses.

Results

there were 1504 differentially expressed mRNAs, 78 differentially expressed microRNAs, 872 differentially expressed long non-coding RNAs, and 59 differentially expressed circular RNAs identified in total. Annotation from the Gene Ontology, enrichment from the Kyoto Encyclopedia of Genes and Genomes, and construction of competing endogenous RNA networks revealed differentially expressed RNAs primarily related to virus-sensing and signaling transduction pathways, antiviral and immune responses, inflammation, and development and metabolism related pathways. Furthermore, a protein-protein interaction network analysis found that BTV may contribute to abnormal spermatogenesis by reducing steroid biosynthesis. Finally, real-time quantitative PCR and western blotting results showed that the expression trends of differentially expressed RNAs were consistent with the whole-transcriptome sequencing data.

Discussion

this study provides more insights of comprehensive characterization of BTV-host interactome, and BTV infection and pathogenic mechanisms.

Immune-profiling of SARS-CoV-2 viremic patients reveals dysregulated innate immune responses

SARS-CoV-2 plasma viremia has been associated with severe disease and death in COVID-19. However, the effects of viremia on immune responses in blood cells remain unclear. The current study comprehensively examined transcriptional signatures of PBMCs involving T cells, B cells, NK cells, monocytes, myeloid dendritic cells (mDCs), and plasmacytoid dendritic cells (pDCs) respectively, from three different groups including individuals with moderate (nM), or severe disease with (vS) or without (nS) detectable plasma viral load. Whole transcriptome analysis demonstrated that all seven immune cell subsets were associated with disease severity regardless of cell type. Supervised clustering analysis demonstrated that mDCs and pDCs gene signatures could distinguish disease severity. Notably, transcriptional signatures of the vS group were enriched in pathways related to DNA repair, E2F targets, and G2M checkpoints; in contrast, transcriptional signatures of the nM group were enriched in interferon responses. Moreover, we observed an impaired induction of interferon responses accompanied by imbalanced cell-intrinsic immune sensing and an excessive inflammatory response in patients with severe disease (nS and vS). In sum, our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and reveals profound alterations in seven major immune cells in COVID-19 patients.

A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity

Since the onset of the COVID-19 pandemic, increasing cases with variable outcomes continue globally because of variants and despite vaccines and therapies. There is a need to identify at-risk individuals early that would benefit from timely medical interventions. DNA methylation provides an opportunity to identify an epigenetic signature of individuals at increased risk. We utilized machine learning to identify DNA methylation signatures of COVID-19 disease from data available through NCBI Gene Expression Omnibus. A training cohort of 460 individuals (164 COVID-19-infected and 296 non-infected) and an external validation dataset of 128 individuals (102 COVID-19-infected and 26 non-COVID-associated pneumonia) were reanalyzed. Data was processed using ChAMP and beta values were logit transformed. The JADBio AutoML platform was leveraged to identify a methylation signature associated with severe COVID-19 disease. We identified a random forest classification model from 4 unique methylation sites with the power to discern individuals with severe COVID-19 disease. The average area under the curve of receiver operator characteristic (AUC-ROC) of the model was 0.933 and the average area under the precision-recall curve (AUC-PRC) was 0.965. When applied to our external validation, this model produced an AUC-ROC of 0.898 and an AUC-PRC of 0.864. These results further our understanding of the utility of DNA methylation in COVID-19 disease pathology and serve as a platform to inform future COVID-19 related studies.

Development of Single-Cell Transcriptomics and Its Application in COVID-19

Over the last three years, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related health crisis has claimed over six million lives and caused USD 12 trillion losses to the global economy. SARS-CoV-2 continuously mutates and evolves with a high basic reproduction number (R0), resulting in a variety of clinical manifestations ranging from asymptomatic infection to acute respiratory distress syndrome (ARDS) and even death. To gain a better understanding of coronavirus disease 2019 (COVID-19), it is critical to investigate the components that cause various clinical manifestations. Single-cell sequencing has substantial advantages in terms of identifying differentially expressed genes among individual cells, which can provide a better understanding of the various physiological and pathological processes. This article reviewed the use of single-cell transcriptomics in COVID-19 research, examined the immune response disparities generated by SARS-CoV-2, and offered insights regarding how to improve COVID-19 diagnosis and treatment plans.

Comprehensive analysis of immune responses in CLL patients after heterologous COVID-19 vaccination

Patients with chronic lymphocytic leukemia (CLL) treated with B-cell pathway inhibitors and anti-CD20 antibodies exhibit low humoral response rate (RR) following SARS-CoV-2 vaccination. To investigate the relationship between the initial transcriptional response to vaccination with ensuing B and T cell immune responses, we performed a comprehensive immune transcriptome analysis flanked by antibody and T cell assays in peripheral blood prospectively collected from 15 CLL/SLL patients vaccinated with heterologous BNT162b2/ChAdOx1 with follow up at a single institution. The two-dose antibody RR was 40% increasing to 53% after booster. Patients on BTKi, venetoclax ± anti-CD20 antibody within 12 months of vaccination responded less well than those under BTKi alone. The two-dose T cell RR was 80% increasing to 93% after booster. Transcriptome studies revealed that seven patients showed interferon-mediated signaling activation within 2 days and one at 7 days after vaccination. Increasing counts of COVID-19 specific IGHV genes correlated with B-cell reconstitution and improved humoral RR. T cell responses in CLL patients appeared after vaccination regardless of treatment status. A higher humoral RR was associated with BTKi treatment and B-cell reconstitution. Boosting was particularly effective when intrinsic immune status was improved by CLL-treatment.

Combining single-cell RNA sequencing of peripheral blood mononuclear cells and exosomal transcriptome to reveal the cellular and genetic profiles in COPD

Background

It has been a long-held consensus that immune reactions primarily mediate the pathology of chronic obstructive pulmonary disease (COPD), and that exosomes may participate in immune regulation in COPD. However, the relationship between exosomes and peripheral immune status in patients with COPD remains unclear.

Methods

In this study, we sequenced plasma exosomes and performed single-cell RNA sequencing on peripheral blood mononuclear cells (PBMCs) from patients with COPD and healthy controls. Finally, we constructed competing endogenous RNA (ceRNA) and protein–protein interaction (PPI) networks to delineate the interactions between PBMCs and exosomes within COPD.

Results

We identified 135 mRNAs, 132 lncRNAs, and 359 circRNAs from exosomes that were differentially expressed in six patients with COPD compared with four healthy controls. Functional enrichment analyses revealed that many of these differentially expressed RNAs were involved in immune responses including defending viral infection and cytokine–cytokine receptor interaction. We also identified 18 distinct cell clusters of PBMCs in one patient and one control by using an unsupervised cluster analysis called uniform manifold approximation and projection (UMAP). According to resultant cell identification, it was likely that the proportions of monocytes, dendritic cells, and natural killer cells increased in the COPD patient we tested, meanwhile the proportions of B cells, CD4 + T cells, and naïve CD8 + T cells declined. Notably, CD8 + T effector memory CD45RA + (Temra) cell and CD8 + effector memory T (Tem) cell levels were elevated in patient with COPD, which were marked by their lower capacity to differentiate due to their terminal differentiation state and lower reactive capacity to viral pathogens.

Conclusions

We generated exosomal RNA profiling and single-cell transcriptomic profiling of PBMCs in COPD, described possible connection between impaired immune function and COPD development, and finally determined the possible role of exosomes in mediating local and systemic immune reactions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02182-8.

Delineating COVID-19 immunological features using single-cell RNA sequencing

Understanding the molecular mechanisms of coronavirus disease 2019 (COVID-19) pathogenesis and immune response is vital for developing therapies. Single-cell RNA sequencing has been applied to delineate the cellular heterogeneity of the host response toward COVID-19 in multiple tissues and organs. Here, we review the applications and findings from over 80 original COVID-19 single-cell RNA sequencing studies as well as many secondary analysis studies. We describe that single-cell RNA sequencing reveals multiple features of COVID-19 patients with different severity, including cell populations with proportional alteration, COVID-19-induced genes and pathways, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in single cells, and adaptation of immune repertoire. We also collect published single-cell RNA sequencing datasets from original studies. Finally, we discuss the limitations in current studies and perspectives for future advance.

Influenza vaccination and single cell multiomics reveal sex dimorphic immune imprints of prior mild COVID-19

Viral infections can have profound and durable functional impacts on the immune system. There is an urgent need to characterize the long-term immune effects of SARS-CoV-2 infection given the persistence of symptoms in some individuals and the continued threat of novel variants. Here we use systems immunology, including longitudinal multimodal single cell analysis (surface proteins, transcriptome, and V(D)J sequences) from 33 previously healthy individuals after recovery from mild, non-hospitalized COVID-19 and 40 age- and sex-matched healthy controls with no history of COVID-19 to comparatively assess the post-infection immune status (mean: 151 days after diagnosis) and subsequent innate and adaptive responses to seasonal influenza vaccination. Identification of both sex-specific and -independent temporally stable changes, including signatures of T-cell activation and repression of innate defense/immune receptor genes (e.g., Toll-like receptors) in monocytes, suggest that mild COVID-19 can establish new post-recovery immunological set-points. COVID-19-recovered males had higher innate, influenza-specific plasmablast, and antibody responses after vaccination compared to healthy males and COVID-19-recovered females, partly attributable to elevated pre-vaccination frequencies of a GPR56 expressing CD8+ T-cell subset in male recoverees that are "poised" to produce higher levels of IFNγ upon inflammatory stimulation. Intriguingly, by day 1 post-vaccination in COVID-19-recovered subjects, the expression of the repressed genes in monocytes increased and moved towards the pre-vaccination baseline of healthy controls, suggesting that the acute inflammation induced by vaccination could partly reset the immune states established by mild COVID-19. Our study reveals sex-dimorphic immune imprints and in vivo functional impacts of mild COVID-19 in humans, suggesting that prior COVID-19, and possibly respiratory viral infections in general, could change future responses to vaccination and in turn, vaccines could help reset the immune system after COVID-19, both in an antigen-agnostic manner.

Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data

Background

Although several key molecules have been identified to modulate SARS-CoV-2 invasion of human host cells, the molecules correlated with outcomes in COVID-19 caused by SARS-CoV-2 infection remain insufficiently explored.

Methods

This study analyzed three RNA-Seq gene expression profiling datasets for COVID-19 and identified differentially expressed genes (DEGs) between COVID-19 patients and normal people, commonly in the three datasets. Furthermore, this study explored the correlation between the expression of these genes and clinical features in COVID-19 patients.

Results

This analysis identified 13 genes significantly upregulated in COVID-19 patients’ leukocyte and SARS-CoV-2-infected nasopharyngeal tissue compared to normal tissue. These genes included OAS1, OAS2, OAS3, OASL, HERC6, SERPING1, IFI6, IFI44, IFI44L, CMPK2, RSAD2, EPSTI1, and CXCL10, all of which are involved in antiviral immune regulation. We found that these genes’ downregulation was associated with worse clinical outcomes in COVID-19 patients, such as intensive care unit (ICU) admission, mechanical ventilatory support (MVS) requirement, elevated D-dimer levels, and increased viral loads. Furthermore, this analysis identified two COVID-19 clusters based on the expression profiles of the 13 genes, termed COV-C1 and COV-C2. Compared with COV-C1, COV-C2 more highly expressed the 13 genes, had stronger antiviral immune responses, were younger, and displayed more favorable clinical outcomes.

Conclusions

A strong antiviral immune response is essential in reducing severity of COVID-19.

The link between ferroptosis and airway inflammatory diseases: A novel target for treatment

Ferroptosis is an iron-dependent mode of cell death characterized by intracellular lipid peroxide accumulation and a redox reaction imbalance. Compared with other modes of cell death, ferroptosis has specific biological and morphological features. The iron-dependent lipid peroxidation accumulation is manifested explicitly in the abnormal metabolism of intracellular lipid oxides catalyzed by excessive iron ions with the production of many reactive oxygen species and over-oxidization of polyunsaturated fatty acids. Recent studies have shown that various diseases, which include intestinal diseases and cancer, are associated with ferroptosis, but few studies are related to airway inflammatory diseases. This review provides a comprehensive analysis of the primary damage mechanisms of ferroptosis and summarizes the relationship between ferroptosis and airway inflammatory diseases. In addition to common acute and chronic airway inflammatory diseases, we also focus on the progress of research on COVID-19 in relation to ferroptosis. New therapeutic approaches and current issues to be addressed in the treatment of inflammatory airway diseases using ferroptosis are further proposed.

Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis

BACKGROUND

Regardless of improvements in controlling the COVID-19 pandemic, the lack of comprehensive insight into SARS-COV-2 pathogenesis is still a sophisticated challenge. In order to deal with this challenge, we utilized advanced bioinformatics and machine learning algorithms to reveal more characteristics of SARS-COV-2 pathogenesis and introduce novel host response-based diagnostic biomarker panels.

METHODS

In the present study, eight published RNA-Seq datasets related to whole-blood (WB) and nasopharyngeal (NP) swab samples of patients with COVID-19, other viral and non-viral acute respiratory illnesses (ARIs), and healthy controls (HCs) were integrated. To define COVID-19 meta-signatures, Gene Ontology and pathway enrichment analyses were applied to compare COVID-19 with other similar diseases. Additionally, CIBERSORTx was executed in WB samples to detect the immune cell landscape. Furthermore, the optimum WB- and NP-based diagnostic biomarkers were identified via all the combinations of 3 to 9 selected features and the 2-phases machine learning (ML) method which implemented k-fold cross validation and independent test set validation.

RESULTS

The host gene meta-signatures obtained for SARS-COV-2 infection were different in the WB and NP samples. The gene ontology and enrichment results of the WB dataset represented the enhancement in inflammatory host response, cell cycle, and interferon signature in COVID-19 patients. Furthermore, NP samples of COVID-19 in comparison with HC and non-viral ARIs showed the significant upregulation of genes associated with cytokine production and defense response to the virus. In contrast, these pathways in COVID-19 compared to other viral ARIs were strikingly attenuated. Notably, immune cell proportions of WB samples altered in COVID-19 versus HC. Moreover, the optimum WB- and NP-based diagnostic panels after two phases of ML-based validation included 6 and 8 markers with an accuracy of 97% and 88%, respectively.

CONCLUSIONS

Based on the distinct gene expression profiles of WB and NP, our results indicated that SARS-COV-2 function is body-site-specific, although according to the common signature in WB and NP COVID-19 samples versus controls, this virus also induces a global and systematic host response to some extent. We also introduced and validated WB- and NP-based diagnostic biomarkers using ML methods which can be applied as a complementary tool to diagnose the COVID-19 infection from non-COVID cases.

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4⁺ T cells, CD8⁺ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4⁺ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

Landscape of B Cell Receptor Repertoires in COVID-19 Patients Revealed Through CDR3 Sequencing of Immunoglobulin Heavy and Light Chains

The outbreak and persistence of coronavirus disease 2019 (COVID-19) threaten human health. B cells play a vital role in fighting the infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite many studies on the immune responses in COVID-19 patients, it is still unclear how B cell receptor (BCR) constituents, including immunoglobulin heavy (IGHs) and light chains (IGLs), respond to SARS-CoV-2 in patients with varying symptoms. In this study, we conducted complementarity-determining region 3 (CDR3) sequencing of BCR IGHs and IGLs from the peripheral blood of COVID-19 patients and healthy donors. The results showed significantly reduced clonal diversity, more expanded clones, and longer CDR3 lengths of IGH and IGL in COVID-19 patients than those in healthy individuals. The IGLs had a much higher percentage of VJ skew usage (47.83% IGLV and 42.86% IGLJ were significantly regulated) than the IGHs (12.09% IGHV and 0% IGHJ) between the healthy individuals and patients, which indicated the importance of BCR light chains. Furthermore, we found a largely expanded IGLV3-25 gene cluster mostly pairing with IGLJ1 and ILGJ2 in COVID-19 patients and a newly identified upregulated IGLJ1 gene and IGLJ2+IGLV13-21 recombination, both of which are potential sources of SARS-CoV-2-targeting antibodies. Our findings on specific immune B-cell signatures associated with COVID-19 have clinical implications for vaccine and biomarker development for disease diagnosis.

Cell deaths: Involvement in the pathogenesis and intervention therapy of COVID-19

The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has dramatically influenced various aspects of the world. It is urgent to thoroughly study pathology and underlying mechanisms for developing effective strategies to prevent and treat this threatening disease. It is universally acknowledged that cell death and cell autophagy are essential and crucial to maintaining host homeostasis and participating in disease pathogenesis. At present, more than twenty different types of cell death have been discovered, some parts of which have been fully understood, whereas some of which need more investigation. Increasing studies have indicated that cell death and cell autophagy caused by coronavirus might play an important role in virus infection and pathogenicity. However, the knowledge of the interactions and related mechanisms of SARS-CoV-2 between cell death and cell autophagy lacks systematic elucidation. Therefore, in this review, we comprehensively delineate how SARS-CoV-2 manipulates diverse cell death (including apoptosis, necroptosis, pyroptosis, ferroptosis, and NETosis) and cell autophagy for itself benefits, which is simultaneously involved in the occurrence and progression of COVID-19, aiming to provide a reasonable basis for the existing interventions and further development of novel therapies.

Long COVID: A proposed hypothesis-driven model of viral persistence for the pathophysiology of the syndrome

Background: Long COVID (coronavirus disease 2019) syndrome includes a group of patients who, after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibit lingering mild-to-moderate symptoms and develop medical complications that can have lasting health problems. In this report, we propose a model for the pathophysiology of the long COVID presentation based on increased proinflammatory cytokine production that results from the persistence of the SARS-CoV-2 virus or one of its molecular components. Associated with this hyperproduction of inflammatory cytokines is a heightened activity of nuclear factor κ B (NF-κB) and p38 mitogen-activated protein kinase signaling pathways that regulate cytokine production. Objective: The purpose of the present report was to review the causes of long COVID syndrome and suggest ways that can provide a basis for a better understanding of the clinical symptomatology for the of improved diagnostic and therapeutic procedures for the condition. Methods: Extensive research was conducted in medical literature data bases by applying terms such as "long COVID" associated with "persistence of the SARS-CoV-2 virus" "spike protein' "COVID-19" and "biologic therapies." Results and Conclusions: In this model of the long COVID syndrome, the persistence of SARS-CoV-2 is hypothesized to trigger a dysregulated immune system with subsequent heightened release of proinflammatory cytokines that lead to chronic low-grade inflammation and multiorgan symptomatology. The condition seems to have a genetic basis, which predisposes individuals to have a diminished immunologic capacity to completely clear the virus, with residual parts of the virus persisting. This persistence of virus and resultant hyperproduction of proinflammatory cytokines are proposed to form the basis of the syndrome.

Comparative Analysis of Long Non-Coding RNA Expression and Immune Response in Mild and Severe COVID-19

Background: Coronavirus disease 2019 (COVID-19) is a worldwide emergency, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Long non-coding RNAs (lncRNAs) do not encode proteins but could participate in immune response.

Methods: In our study, 39 COVID-19 patients were enrolled. The microarray of peripheral blood mononuclear cells from healthy and COVID-19 patients was applied to identify the expression profiles of lncRNAs and mRNAs. Identified differentially expressed (DE) lncRNAs were validated by qRT-PCR. Then, the lncRNA–mRNA network was constructed and visualized using Cytoscape (3.6.1) based on the Pearson correlation coefficient. The enrichment of DE mRNAs was analyzed using Metascape. The difference in frequencies of immune cells and cytokines was detected using CIBERSORT and ImmPort based on DE mRNAs.

Results: All patients with COVID-19 displayed lymphopenia, especially in T cells, and hyper-inflammatory responses, including IL-6 and TNF-α. Four immune-related lncRNAs in COVID-19 were found and further validated, including AC136475.9, CATG00000032642.1, G004246, and XLOC_013290. Functional analysis enriched in downregulation of the T-cell receptor and the antigen processing and presentation as well as increased apoptotic proteins, which could lead to T-cell cytopenia. In addition, they participated in monocyte remodeling, which contributed to releasing cytokines and chemokines and then recruiting more monocytes and aggravating the clinical severity of COVID-19 patients.

Conclusion: Taken together, four lncRNAs were in part of immune response in COVID-19, which was involved in the T-cell cytopenia by downregulating the antigen processing and presentation, the T-cell receptor, and an increased proportion of monocytes, with a distinct change in cytokines and chemokines.

Dynamic changes of SARS-CoV-2 specific IgM and IgG among population vaccinated with COVID-19 vaccine

To evaluate the dynamic changes of antibody levels in different groups after inoculation with the coronavirus disease 2019 (COVID-19) vaccine. The 1493 subjects who were tested for IgM and IgG against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Qionglai Medical Center Hospital from June to October in 2021 were accepted for analyses of geometric mean titre (GMT) of IgG and IgM. The overall GMT of IgM and IgG in the population of Qionglai reached at a peak value at 1.497 (+3.810, −3.810) S/CO and 4.048 (+2.059, −2.059) S/CO in the second week, and then gradually decreased to 0.114 (+2.707, −2.707) and 1.885 (+1.506, −1.506) S/CO in the 11th–25th weeks, respectively. IgG was positive within 1 day, after that GMT increased continuously and peaked on the 13th day. There was a significant difference between male and female groups for titre of IgM during the prior 2 weeks and among three age groups for titre of IgG during the 2nd–3rd week after vaccination. The GMT level of IgG in the population vaccinated with the COVID-19 vaccine remained at a high level within 25 weeks and peaked on the 13th day, indicating that IgG could exist for a longer period and exhibiting positive SARS-CoV-2- defending effect.

A genetic variant in IL-6 lowering its expression is protective for critical patients with COVID-19

Critical coronavirus disease 2019 (COVID-19) is associated with high mortality and potential genetic factors have been reported to be involved in the development of critical COVID-19. We performed a genome-wide association study to identify the genetic factors responsible for developing critical COVID-19. 632 critical patients with COVID-19 and 3021 healthy controls from the Chinese population were recruited. First, we identified a genome-wide significant difference of IL-6 rs2069837 (p = 9.73 × 10⁻¹⁵, OR = 0.41) between 437 critical patients with COVID-19 and 2551 normal controls in the discovery cohort. When replicated these findings in a set of 195 patients with critical COVID-19 and 470 healthy controls, we detected significant association of rs2069837 with COVID-19 (p = 8.89 × 10⁻³, OR = 0.67). This variant surpassed the formal threshold for genome-wide significance (combined p = 4.64 × 10⁻¹⁶, OR = 0.49). Further analysis revealed that there was a significantly stronger expression of IL-6 in the serum from patients with critical COVID-19 than in that from patients with asymptomatic COVID-19. An in vitro assay showed that the A to G allele changes in rs2069837 within IL-6 obviously decreased the luciferase expression activity. When analyzing the effect of this variant on the IL-6 in the serum based on the rs2069837 genotype, we found that the A to G variation in rs2069837 decreased the expression of IL-6, especially in the male. Overall, we identified a genetic variant in IL-6 that protects against critical conditions with COVID-19 though decreasing IL-6 expression in the serum.

COVID-19 Biomarkers Recognition & Classification Using Intelligent Systems

Background:

SARS-CoV-2 has paralyzed mankind due to its high transmissibility and its associated mortality, causing millions of infections and deaths worldwide. The search for gene expression biomarkers from the host transcriptional response to infection may help understand the underlying mechanisms by which the virus causes COVID-19. This research proposes a smart methodology integrating different RNA-Seq datasets from SARS-CoV-2, other respiratory diseases, and healthy patients.

Methods:

The proposed pipeline exploits the functionality of the ‘KnowSeq’ R/Bioc package, integrating different data sources and attaining a significantly larger gene expression dataset, thus endowing the results with higher statistical significance and robustness in comparison with previous studies in the literature. A detailed preprocessing step was carried out to homogenize the samples and build a clinical decision system for SARS-CoV-2. It uses machine learning techniques such as feature selection algorithm and supervised classification system. This clinical decision system uses the most differentially expressed genes among different diseases (including SARS-Cov-2) to develop a four-class classifier.

Results:

The multiclass classifier designed can discern SARS-CoV-2 samples, reaching an accuracy equal to 91.5%, a mean F1-Score equal to 88.5%, and a SARS-CoV-2 AUC equal to 94% by using only 15 genes as predictors. A biological interpretation of the gene signature extracted reveals relations with processes involved in viral responses.

Conclusion:

This work proposes a COVID-19 gene signature composed of 15 genes, selected after applying the feature selection ‘minimum Redundancy Maximum Relevance’ algorithm. The integration among several RNA-Seq datasets was a success, allowing for a considerable large number of samples and therefore providing greater statistical significance to the results than previous studies. Biological interpretation of the selected genes was also provided.

Increased Expression of Tim-3 Is Associated With Depletion of NKT Cells In SARS-CoV-2 Infection

In the ongoing coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), natural killer T (NKT) cells act as primary initiators of immune responses. However, a decrease of circulating NKT cells has been observed in COVID-19 different stages, of which the underlying mechanism remains to be elucidated. Here, by performing single-cell RNA sequencing analysis in three large cohorts of COVID-19 patients, we found that increased expression of Tim-3 promotes depletion of NKT cells during the progression stage of COVID-19, which is associated with disease severity and outcome of patients with COVID-19. Tim-3+ NKT cells also expressed high levels of CD147 and CD26, which are potential SARS-CoV-2 spike binding receptors. In the study, Tim-3+ NKT cells showed high enrichment of apoptosis, higher expression levels of mitochondrial genes and caspase genes, with a larger pseudo time value. In addition, Tim-3+ NKT cells in COVID-19 presented a stronger capacity to secrete IFN-γ, IL-4 and IL-10 compared with healthy individuals, they also demonstrated high expression of co-inhibitory receptors such as PD-1, CTLA-4, and LAG-3. Moreover, we found that IL-12 secreted by dendritic cells (DCs) was positively correlated with up-regulated expression of Tim-3 in NKT cells in COVID-19 patients. Overall, this study describes a novel mechanism by which up-regulated Tim-3 expression induced the depletion and dysfunction of NKT cells in COVID-19 patients. These findings not only have possible implications for the prediction of severity and prognosis in COVID-19 but also provide a link between NKT cells and future new therapeutic strategies in SARS-CoV-2 infection.

Molecular and Clinical Prognostic Biomarkers of COVID-19 Severity and Persistence

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), poses several challenges to clinicians, due to its unpredictable clinical course. The identification of laboratory biomarkers, specific cellular, and molecular mediators of immune response could contribute to the prognosis and management of COVID-19 patients. Of utmost importance is also the detection of differentially expressed genes, which can serve as transcriptomic signatures, providing information valuable to stratify patients into groups, based on the severity of the disease. The role of biomarkers such as IL-6, procalcitonin, neutrophil-lymphocyte ratio, white blood cell counts, etc. has already been highlighted in recently published studies; however, there is a notable amount of new evidence that has not been summarized yet, especially regarding transcriptomic signatures. Hence, in this review, we assess the latest cellular and molecular data and determine the significance of abnormalities in potential biomarkers for COVID-19 severity and persistence. Furthermore, we applied Gene Ontology (GO) enrichment analysis using the genes reported as differentially expressed in the literature in order to investigate which biological pathways are significantly enriched. The analysis revealed a number of processes, such as inflammatory response, and monocyte and neutrophil chemotaxis, which occur as part of the complex immune response to SARS-CoV-2.

Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models

Differential network analysis plays an important role in learning how gene interactions change under different biological conditions, and the high resolution of single-cell RNA (scRNA-seq) sequencing provides new opportunities to explore these changing gene-gene interactions. Here, we present a sparse hierarchical Bayesian factor model to identify differences across network structures from different biological conditions in scRNA-seq data. Our methodology utilizes latent factors to impact gene expression values for each cell to help account for zero-inflation, increased cell-to-cell variability, and overdispersion that are unique characteristics of scRNA-seq data. Condition-dependent parameters determine which latent factors are activated in a gene, which allows for not only the calculation of gene-gene co-expression within each group but also the calculation of the co-expression differences between groups. We highlight our methodology’s performance in detecting differential gene-gene associations across groups by analyzing simulated datasets and a SARS-CoV-2 case study dataset.

Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100Ahi/HLA-DRlo classical monocytes and activated LAG-3hi T cells are hallmarks of progressive disease and highlights the abnormal MHC-II/LAG-3 interaction on myeloid and T cells, respectively. We also find skewed T cell receptor repertories in expanded effector CD8+ clones, unmutated IGHG+ B cell clones, and mutated B cell clones with stable somatic hypermutation frequency over time. In conclusion, our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Inhibiting ACSL1-Related Ferroptosis Restrains Murine Coronavirus Infection

Murine hepatitis virus strain A59 (MHV-A59) was shown to induce pyroptosis, apoptosis, and necroptosis of infected cells, especially in the murine macrophages. However, whether ferroptosis, a recently identified form of lytic cell death, was involved in the pathogenicity of MHV-A59 is unknown. We utilized murine macrophages and a C57BL/6 mice intranasal infection model to address this. In primary macrophages, the ferroptosis inhibitor inhibited viral propagation, inflammatory cytokines released, and cell syncytia formed after MHV-A59 infection. In the mouse model, we found that in vivo administration of liproxstatin-1 ameliorated lung inflammation and tissue injuries caused by MHV-A59 infection. To find how MHV-A59 infection influenced the expression of ferroptosis-related genes, we performed RNA-seq in primary macrophages and found that MHV-A59 infection upregulates the expression of the acyl-CoA synthetase long-chain family member 1 (ACSL1), a novel ferroptosis inducer. Using ferroptosis inhibitors and a TLR4 inhibitor, we showed that MHV-A59 resulted in the NF-kB-dependent, TLR4-independent ACSL1 upregulation. Accordingly, ACSL1 inhibitor Triacsin C suppressed MHV-A59-infection-induced syncytia formation and viral propagation in primary macrophages. Collectively, our study indicates that ferroptosis inhibition protects hosts from MHV-A59 infection. Targeting ferroptosis may serve as a potential treatment approach for dealing with hyper-inflammation induced by coronavirus infection.

DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).

A comprehensive review of the analysis and integration of omics data for SARS-CoV-2 and COVID-19

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, over 100 million people have been infected by COVID-19, millions of whom have died. In the latest year, a large number of omics data have sprung up and helped researchers broadly study the sequence, chemical structure and function of SARS-CoV-2, as well as molecular abnormal mechanisms of COVID-19 patients. Though some successes have been achieved in these areas, it is necessary to analyze and mine omics data for comprehensively understanding SARS-CoV-2 and COVID-19. Hence, we reviewed the current advantages and limitations of the integration of omics data herein. Firstly, we sorted out the sequence resources and database resources of SARS-CoV-2, including protein chemical structure, potential drug information and research literature resources. Next, we collected omics data of the COVID-19 hosts, including genomics, transcriptomics, microbiology and potential drug information data. And subsequently, based on the integration of omics data, we summarized the existing data analysis methods and the related research results of COVID-19 multi-omics data in recent years. Finally, we put forward SARS-CoV-2 (COVID-19) multi-omics data integration research direction and gave a case study to mine deeper for the disease mechanisms of COVID-19.

The Dual Role of Innate Lymphoid and Natural Killer Cells in Cancer. from Phenotype to Single-Cell Transcriptomics, Functions and Clinical Uses

Simple Summary

Innate lymphoid cells (ILCs), a family of innate immune cells including natural killers (NKs), play a multitude of roles in first-line cancer control, in escape from immunity and in cancer progression. In this review, we summarize preclinical and clinical data on ILCs and NK cells concerning their phenotype, function and clinical applications in cellular therapy trials. We also describe how single-cell transcriptome sequencing has been used and forecast how it will be used to better understand ILC and NK involvement in cancer control and progression as well as their therapeutic potential.

Abstract

The role of innate lymphoid cells (ILCs), including natural killer (NK) cells, is pivotal in inflammatory modulation and cancer. Natural killer cell activity and count have been demonstrated to be regulated by the expression of activating and inhibitory receptors together with and as a consequence of different stimuli. The great majority of NK cell populations have an anti-tumor activity due to their cytotoxicity, and for this reason have been used for cellular therapies in cancer patients. On the other hand, the recently classified helper ILCs are fundamentally involved in inflammation and they can be either helpful or harmful in cancer development and progression. Tissue niche seems to play an important role in modulating ILC function and conversion, as observed at the transcriptional level. In the past, these cell populations have been classified by the presence of specific cellular receptor markers; more recently, due to the advent of single-cell RNA sequencing (scRNA-seq), it has been possible to also explore them at the transcriptomic level. In this article we review studies on ILC (and NK cell) classification, function and their involvement in cancer. We also summarize the potential application of NK cells in cancer therapy and give an overview of the most recent studies involving ILCs and NKs at scRNA-seq, focusing on cancer. Finally, we provide a resource for those who wish to start single-cell transcriptomic analysis on the context of these innate lymphoid cell populations.

Distinct immune signatures discriminate between asymptomatic and presymptomatic SARS-CoV-2pos subjects

Increasing numbers of SARS-CoV-2-positive (SARS-CoV-2^pos) subjects are detected at silent SARS-CoV-2 infection stage (SSIS). Yet, SSIS represents a poorly examined time-window wherein unknown immunity patterns may contribute to the fate determination towards persistently asymptomatic or overt disease. Here, we retrieved blood samples from 19 asymptomatic and 12 presymptomatic SARS-CoV-2^pos subjects, 47 age/gender-matched patients with mild or moderate COVID-19 and 27 normal subjects, and interrogated them with combined assays of 44-plex CyTOF, RNA-seq and Olink. Notably, both asymptomatic and presymptomatic subjects exhibited numerous readily detectable immunological alterations, while certain parameters including more severely decreased frequencies of CD107a^low classical monocytes, intermediate monocytes, non-classical monocytes and CD62L^hi CD8⁺ T_naïve cells, reduced plasma STC1 level but an increased frequency of CD4⁺ NKT cells combined to distinguish the latter. Intercorrelation analyses revealed a particular presymptomatic immunotype mainly manifesting as monocytic overactivation and differentiation blockage, a likely lymphocyte exhaustion and immunosuppression, yielding mechanistic insights into SSIS fate determination, which could potentially improve SARS-CoV-2 management.

COVID19db: a comprehensive database platform to discover potential drugs and targets of COVID-19 at whole transcriptomic scale

Many open access transcriptomic data of coronavirus disease 2019 (COVID-19) were generated, they have great heterogeneity and are difficult to analyze. To utilize these invaluable data for better understanding of COVID-19, additional software should be developed. Especially for researchers without bioinformatic skills, a user-friendly platform is mandatory. We developed the COVID19db platform (http://hpcc.siat.ac.cn/covid19db & http://www.biomedical-web.com/covid19db) that provides 39 930 drug–target–pathway interactions and 95 COVID-19 related datasets, which include transcriptomes of 4127 human samples across 13 body sites associated with the exposure of 33 microbes and 33 drugs/agents. To facilitate data application, each dataset was standardized and annotated with rich clinical information. The platform further provides 14 different analytical applications to analyze various mechanisms underlying COVID-19. Moreover, the 14 applications enable researchers to customize grouping and setting for different analyses and allow them to perform analyses using their own data. Furthermore, a Drug Discovery tool is designed to identify potential drugs and targets at whole transcriptomic scale. For proof of concept, we used COVID19db and identified multiple potential drugs and targets for COVID-19. In summary, COVID19db provides user-friendly web interfaces to freely analyze, download data, and submit new data for further integration, it can accelerate the identification of effective strategies against COVID-19.

Immunopathology and Immunosenescence, the Immunological Key Words of Severe COVID-19. Is There a Role for Stem Cell Transplantation?

The outcomes of Coronavirus disease-2019 (COVID-19) vary depending on the age, health status and sex of an individual, ranging from asymptomatic to lethal. From an immunologic viewpoint, the final severe lung damage observed in COVID-19 should be caused by cytokine storm, driven mainly by interleukin-6 and other pro-inflammatory cytokines. However, which immunopathogenic status precedes this "cytokine storm" and why the male older population is more severely affected, are currently unanswered questions. The aging of the immune system, i.e., immunosenescence, closely associated with a low-grade inflammatory status called "inflammageing," should play a key role. The remodeling of both innate and adaptive immune response observed with aging can partly explain the age gradient in severity and mortality of COVID-19. This review discusses how aging impacts the immune response to the virus, focusing on possible strategies to rejuvenate the immune system with stem cell-based therapies. Indeed, due to immunomodulatory and anti-inflammatory properties, multipotent mesenchymal stem cells (MSCs) are a worth-considering option against COVID-19 adverse outcomes.

Application of omics technology to combat the COVID-19 pandemic

As of August 27, 2021, the ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread to over 220 countries, areas, and territories. Thus far, 214,468,601 confirmed cases, including 4,470,969 deaths, have been reported to the World Health Organization. To combat the COVID-19 pandemic, multiomics-based strategies, including genomics, transcriptomics, proteomics, and metabolomics, have been used to study the diagnosis methods, pathogenesis, prognosis, and potential drug targets of COVID-19. In order to help researchers and clinicians to keep up with the knowledge of COVID-19, we summarized the most recent progresses reported in omics-based research papers. This review discusses omics-based approaches for studying COVID-19, summarizing newly emerged SARS-CoV-2 variants as well as potential diagnostic methods, risk factors, and pathological features of COVID-19. This review can help researchers and clinicians gain insight into COVID-19 features, providing direction for future drug development and guidance for clinical treatment, so that patients can receive appropriate treatment as soon as possible to reduce the risk of disease progression.

Transcriptional Changes in CD16+ Monocytes May Contribute to the Pathogenesis of COVID-19

The COVID-19 pandemic has caused more than three million deaths globally. The severity of the disease is characterized, in part, by a dysregulated immune response. CD16+ monocytes are innate immune cells involved in inflammatory responses to viral infections, and tissue repair, among other functions. We characterized the transcriptional changes in CD16+ monocytes from PBMC of people with COVID-19, and from healthy individuals using publicly available single cell RNA sequencing data. CD16+ monocytes from people with COVID-19 compared to those from healthy individuals expressed transcriptional changes indicative of increased cell activation, and induction of a migratory phenotype. We also analyzed COVID-19 cases based on severity of the disease and found that mild cases were characterized by upregulation of interferon response and MHC class II related genes, whereas the severe cases had dysregulated expression of mitochondrial and antigen presentation genes, and upregulated inflammatory, cell movement, and apoptotic gene signatures. These results suggest that CD16+ monocytes in people with COVID-19 contribute to a dysregulated host response characterized by decreased antigen presentation, and an elevated inflammatory response with increased monocytic infiltration into tissues. Our results show that there are transcriptomic changes in CD16+ monocytes that may impact the functions of these cells, contributing to the pathogenesis and severity of COVID-19.