Transcriptomic similarities and differences in host response between SARS-CoV-2 and other viral infections

Variant- and vaccination-specific alternative splicing profiles in SARS-CoV-2 infections

The COVID-19 pandemic, driven by the SARS-CoV-2 virus and its variants, highlights the important role of understanding host-viral molecular interactions influencing infection outcomes. Alternative splicing post-infection can impact both host responses and viral replication. We analyzed RNA splicing patterns in immune cells across various SARS-CoV-2 variants, considering immunization status. Using a dataset of 190 RNA-seq samples from our prior studies, we observed a substantial deactivation of alternative splicing and RNA splicing-related genes in COVID-19 patients. The alterations varied significantly depending on the infecting variant and immunization history. Notably, Alpha or Beta-infected patients differed from controls, while Omicron-infected patients displayed a splicing profile closer to controls. Particularly, vaccinated Omicron-infected individuals showed a distinct dynamic in alternative splicing patterns not widely shared among other groups. Our findings underscore the intricate interplay between SARS-CoV-2 variants, vaccination-induced immunity, and alternative splicing, emphasizing the need for further investigations to deepen understanding and guide therapeutic development.

Transitions of blood immune endotypes and improved outcome by anakinra in COVID-19 pneumonia: an analysis of the SAVE-MORE randomized controlled trial

BACKGROUND

Endotype classification may guide immunomodulatory management of patients with bacterial and viral sepsis. We aimed to identify immune endotypes and transitions associated with response to anakinra (human interleukin 1 receptor antagonist) in participants in the SAVE-MORE trial.

METHODS

Adult patients hospitalized with radiological findings of PCR-confirmed severe pneumonia caused by SARS-CoV-2 and plasma-soluble urokinase plasminogen activator receptor levels of ≥ 6 ng/ml in the SAVE-MORE trial (NCT04680949) were characterized at baseline and days 4 and 7 of treatment using a previously defined 33-messenger RNA classifier to assign an immunological endotype in blood. Endpoints were changes in endotypes and progression to severe respiratory failure (SRF) associated with anakinra treatment.

RESULTS

At baseline, 23.2% of 393 patients were designated as inflammopathic, 41.1% as adaptive, and 35.7% as coagulopathic. Only 23.9% were designated as the same endotype at days 4 and 7 compared to baseline, while all other patients transitioned between endotypes. Anakinra-treated patients were more likely to remain in the adaptive endotype during 7-day treatment (24.4% vs. 9.9%; p < 0.001). Anakinra also protected patients with coagulopathic endotype at day 7 against SRF compared to placebo (27.8% vs. 55.9%; p = 0.013).

CONCLUSION

We identify an association between endotypes defined using blood transcriptome and anakinra therapy for COVID-19 pneumonia, with anakinra-treated patients shifting toward endotypes associated with a better outcome, mainly the adaptive endotype. Trial registration ClinicalTrials.gov, NCT04680949, December 23, 2020.

Consequences of COVID-19 on Adipose Tissue Signatures

Since the emergence of coronavirus disease-19 (COVID-19) in 2019, it has been crucial to investigate the causes of severe cases, particularly the higher rates of hospitalization and mortality in individuals with obesity. Previous findings suggest that adipocytes may play a role in adverse COVID-19 outcomes in people with obesity. The impact of COVID-19 vaccination and infection on adipose tissue (AT) is currently unclear. We therefore analyzed 27 paired biopsies of visceral and subcutaneous AT from donors of the Leipzig Obesity BioBank that have been categorized into three groups (1: no infection/no vaccination; 2: no infection but vaccinated; 3: infected and vaccinated) based on COVID-19 antibodies to spike (indicating vaccination) and/or nucleocapsid proteins. We provide additional insights into the impact of COVID-19 on AT biology through a comprehensive histological transcriptome and serum proteome analysis. This study demonstrates that COVID-19 infection is associated with smaller average adipocyte size. The impact of infection on gene expression was significantly more pronounced in subcutaneous than in visceral AT and mainly due to immune system-related processes. Serum proteome analysis revealed the effects of the infection on circulating adiponectin, interleukin 6 (IL-6), and carbonic anhydrase 5A (CA5A), which are all related to obesity and blood glucose abnormalities.

Systems immunology of transcriptional responses to viral infection identifies conserved antiviral pathways across macaques and humans

Viral pandemics and epidemics pose a significant global threat. While macaque models of viral disease are routinely used, it remains unclear how conserved antiviral responses are between macaques and humans. Therefore, we conducted a cross-species analysis of transcriptomic data from over 6,088 blood samples from macaques and humans infected with one of 31 viruses. Our findings demonstrate that irrespective of primate or viral species, there are conserved antiviral responses that are consistent across infection phase (acute, chronic, or latent) and viral genome type (DNA or RNA viruses). Leveraging longitudinal data from experimental challenges, we identify virus-specific response kinetics such as host responses to Coronaviridae and Orthomyxoviridae infections peaking 1-3 days earlier than responses to Filoviridae and Arenaviridae viral infections. Our results underscore macaque studies as a powerful tool for understanding viral pathogenesis and immune responses that translate to humans, with implications for viral therapeutic development and pandemic preparedness.

Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage

The effects of coronavirus disease 2019 (COVID-19) primarily concern the respiratory tract and lungs; however, studies have shown that all organs are susceptible to infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 may involve multiorgan damage from direct viral invasion through angiotensin-converting enzyme 2 (ACE2), through inflammatory cytokine storms, or through other secondary pathways. This study involved the analysis of publicly accessible transcriptome data from the Gene Expression Omnibus (GEO) database for identifying significant differentially expressed genes related to COVID-19 and an investigation relating to the pathways associated with mitochondrial, cardiac, hepatic, and renal toxicity in COVID-19. Significant differentially expressed genes were identified and ranked by statistical approaches, and the genes derived by biological meaning were ranked by feature importance; both were utilized as machine learning features for verification. Sample set selection for machine learning was based on the performance, sample size, imbalanced data state, and overfitting assessment. Machine learning served as a verification tool by facilitating the testing of biological hypotheses by incorporating gene list adjustment. A subsequent in-depth study for gene and pathway network analysis was conducted to explore whether COVID-19 is associated with cardiac, hepatic, and renal impairments via mitochondrial infection. The analysis showed that potential cardiac, hepatic, and renal impairments in COVID-19 are associated with ACE2, inflammatory cytokine storms, and mitochondrial pathways, suggesting potential medical interventions for COVID-19-induced multiorgan damage.

Hypoxia and Activation of Neutrophil Degranulation-Related Genes in the Peripheral Blood of COVID-19 Patients

Severe COVID-19 is characterized by systematic hyper-inflammation and subsequent damage to various organs. Therefore, it is critical to trace this cascade of hyper-inflammation. Blood transcriptome has been routinely utilized in the interrogation of host immune response in COVID-19 and other infectious conditions. In this study, consensus gene dysregulation in the blood was obtained from 13 independent transcriptome studies on COVID-19. Among the up-regulated genes, the most prominent functional categories were neutrophil degranulation and cell cycle, which is clearly different from the classical activation of interferon signaling pathway in seasonal flu. As for the potential upstream causal factors of the atypical gene dysregulation, systemic hypoxia was further examined because it is much more widely reported in COVID-19 than that in seasonal flu. It was found that both physiological and pathological hypoxia can induce activation of neutrophil degranulation-related genes in the blood. Furthermore, COVID-19 patients with different requirement for oxygen intervention showed distinctive levels of gene expression related to neutrophil degranulation in the whole blood, which was validated in isolated neutrophils. Thus, activation of neutrophil degranulation-related genes in the blood of COVID-19 could be partially attributed to hypoxia. Interestingly, similar pattern was also observed in H1N1 infection (the cause of Spanish flu) and several other severe respiratory viral infections. As for the molecular mechanism, both HIF-dependent and HIF-independent pathways have been examined. Since the activation of neutrophil degranulation-related genes is highly correlated with disease severity in COVID-19, early detection of hypoxia and active intervention may prevent further activation of neutrophil degranulation-related genes and other harmful downstream hyper-inflammation. This common mechanism is applicable to current and future pandemic as well as the severe form of common respiratory infection.

Antibody and transcription landscape in peripheral blood mononuclear cells of elderly adults over 70 years of age with third dose of COVID-19 BBIBP-CorV and ZF2001 booster vaccine

BACKGROUND

In the context of the COVID-19 pandemic and extensive vaccination, it is important to explore the immune response of elderly adults to homologous and heterologous booster vaccines of COVID-19. At this point, we detected serum IgG antibodies and PBMC sample transcriptome profiles in 46 participants under 70 years old and 25 participants over 70 years old who received the third dose of the BBIBP-CorV and ZF2001 vaccines.

RESULTS

On day 7, the antibody levels of people over 70 years old after the third dose of booster vaccine were lower than those of young people, and the transcriptional responses of innate and adaptive immunity were also weak. The age of the participants showed a significant negative correlation with functions related to T-cell differentiation and costimulation. Nevertheless, 28 days after the third dose, the IgG antibodies of elderly adults reached equivalence to those of younger adults, and immune-related transcriptional regulation was significantly improved. The age showed a significant positive correlation with functions related to "chemokine receptor binding", "chemokine activity", and "chemokine-mediated signaling pathway".

CONCLUSIONS

Our results document that the response of elderly adults to the third dose of the vaccine was delayed, but still able to achieve comparable immune effects compared to younger adults, in regard to antibody responses as well as at the transcript level.

Interferon response and profiling of interferon response genes in peripheral blood of vaccine-naive COVID-19 patients

Introduction

There is insufficient understanding on systemic interferon (IFN) responses during COVID-19 infection. Early reports indicated that interferon responses were suppressed by the coronavirus (SARS-CoV-2) and clinical trials of administration of various kinds of interferons had been disappointing. Expression of interferon-stimulated genes (ISGs) in peripheral blood (better known as interferon score) has been a well-established bioassay marker of systemic IFN responses in autoimmune diseases. Therefore, with archival samples of a cohort of COVID-19 patients collected before the availability of vaccination, we aimed to better understand this innate immune response by studying the IFN score and related ISGs expression in bulk and single cell RNAs sequencing expression datasets.

Methods

In this study, we recruited 105 patients with COVID-19 and 30 healthy controls in Hong Kong. Clinical risk factors, disease course, and blood sampling times were recovered. Based on a set of five commonly used ISGs (IFIT1, IFIT2, IFI27, SIGLEC1, IFI44L), the IFN score was determined in blood leukocytes collected within 10 days after onset. The analysis was confined to those blood samples collected within 10 days after disease onset. Additional public datasets of bulk gene and single cell RNA sequencing of blood samples were used for the validation of IFN score results.

Results

Compared to the healthy controls, we showed that ISGs expression and IFN score were significantly increased during the first 10 days after COVID infection in majority of patients (71%). Among those low IFN responders, they were more commonly asymptomatic patients (71% vs 25%). 22 patients did not mount an overall significant IFN response and were classified as low IFN responders (IFN score < 1). However, early IFN score or ISGs level was not a prognostic biomarker and could not predict subsequent disease severity. Both IFI27 and SIGLEC1 were monocyte-predominant expressing ISGs and IFI27 were activated even among those low IFN responders as defined by IFN score. In conclusion, a substantial IFN response was documented in this cohort of COVID-19 patients who experience a natural infection before the vaccination era. Like innate immunity towards other virus, the ISGs activation was observed largely during the early course of infection (before day 10). Single-cell RNA sequencing data suggested monocytes were the cell-type that primarily accounted for the activation of two highly responsive ISGs (IFI44L and IFI27).

Discussion

As sampling time and age were two major confounders of ISG expression, they may account for contradicting observations among previous studies. On the other hand, the IFN score was not associated with the severity of the disease.

Investigating the Potential Shared Molecular Mechanisms between COVID-19 and Alzheimer's Disease via Transcriptomic Analysis

SARS-CoV-2 caused the COVID-19 pandemic. COVID-19 may elevate the risk of cognitive impairment and even cause dementia in infected individuals; it may accelerate cognitive decline in elderly patients with dementia, possibly in Alzheimer's disease (AD) patients. However, the mechanisms underlying the interplay between AD and COVID-19 are still unclear. To investigate the underlying mechanisms and associations between AD progression and SARS-CoV-2 infection, we conducted a series of bioinformatics research into SARS-CoV-2-infected cells, COVID-19 patients, AD patients, and SARS-CoV-2-infected AD patients. We identified the common differentially expressed genes (DEGs) in COVID-19 patients, AD patients, and SARS-CoV-2-infected cells, and these DEGs are enriched in certain pathways, such as immune responses and cytokine storms. We constructed the gene interaction network with the signaling transduction module in the center and identified IRF7, STAT1, STAT2, and OAS1 as the hub genes. We also checked the correlations between several key transcription factors and the SARS-CoV-2 and COVID-19 pathway-related genes. We observed that ACE2 expression is positively correlated with IRF7 expression in AD and coronavirus infections, and interestingly, IRF7 is significantly upregulated in response to different RNA virus infections. Further snRNA-seq analysis indicates that NRGN neurons or endothelial cells may be responsible for the increase in ACE2 and IRF7 expression after SARS-CoV-2 infection. The positive correlation between ACE2 and IRF7 expressions is confirmed in the hippocampal formation (HF) of SARS-CoV-2-infected AD patients. Our findings could contribute to the investigation of the molecular mechanisms underlying the interplay between AD and COVID-19 and to the development of effective therapeutic strategies for AD patients with COVID-19.

Regulatory variants of APOBEC3 genes potentially associate with COVID-19 severity in populations with African ancestry

Since November 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused the worldwide pandemic of the coronavirus disease 2019 (COVID-19), the impact of which is huge to the lives of world populations. Many studies suggested that such situation will continue due to the endless mutations in SARS-CoV-2 genome that result in complexity of the efforts for the control of SARS-CoV-2, since the special enrichment of nucleotide substitution C>U in SARS-CoV-2 sequences were discovered mainly due to the editing by human host factors APOBEC3 genes. The observation of SARS-CoV-2 variants Beta (B.1.351) and Omicron (B.1.1.529) firstly spreading in South Africa promoted us to hypothesize that genetic variants of APOBEC3 special in African populations may be attributed to the higher mutation rate of SARS-CoV-2 variants in Africa. Current study was conducted to search for functional variants of APOBEC3 genes associate with COVID-19 hospitalization in African population. By integrating data from the 1000 Genomes Project, Genotype-Tissue Expression (GTEx), and Host Genetics Initiative (HGI) of COVID-19, we identified potential functional SNPs close to APOBEC3 genes that are associated with COVID-19 hospitalization in African but not with other populations. Our study provides new insights on the potential contribution of APOBEC3 genes on the evolution of SARS-CoV-2 mutations in African population, but further replication is needed to confirm our results.

Integrative Analysis of DNA Methylation and Gene Expression Data Identifies Potential Biomarkers and Functional Epigenetic Modules for SARS-CoV-2

To integrate gene expression and DNA methylation data and find the potential role of DNA methylation in the invasion and replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We first conducted differential expression and methylation analysis between the coronavirus disease of 2019 (COVID-19) and healthy controls. FEM was employed to identify functional epigenetic modules, from which a diagnostic model for COVID-19 was built. SKA1 and WSB1 modules were identified, with SKA1 module enriched in COVID-19 replication and transcription, and WSB1 module related to ubiquitin-protein activity. The differentially expressed or differentially methylated genes in these two modules could be used to distinguish COVID-19 from healthy controls, with AUC reaching 1 and 0.98 for SKA1 and WSB1 modules, respectively. Two epigenetically activated genes (CENPM and KNL1) from the SKA1 module were upregulated in HPV- or HBV-positive tumor samples and were found to be significantly associated with the survival of tumor patients. In conclusion, the identified FEM modules and potential signatures play an essential role in the replication and transcription of coronavirus.

Variant- and Vaccination-Specific Alternative Splicing Profiles in SARS-CoV-2 Infections

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, and its subsequent variants has underscored the importance of understanding the host-viral molecular interactions to devise effective therapeutic strategies. A significant aspect of these interactions is the role of alternative splicing in modulating host responses and viral replication mechanisms. Our study sought to delineate the patterns of alternative splicing of RNAs from immune cells across different SARS-CoV-2 variants and vaccination statuses, utilizing a robust dataset of 190 RNA-seq samples from our previous studies, encompassing an average of 212 million reads per sample. We identified a dynamic alteration in alternative splicing and genes related to RNA splicing were highly deactivated in COVID-19 patients and showed variant- and vaccination-specific expression profiles. Overall, Omicron-infected patients exhibited a gene expression profile akin to healthy controls, unlike the Alpha or Beta variants. However, significantly, we found identified a subset of infected individuals, most pronounced in vaccinated patients infected with Omicron variant, that exhibited a specific dynamic in their alternative splicing patterns that was not widely shared amongst the other groups. Our findings underscore the complex interplay between SARS-CoV-2 variants, vaccination-induced immune responses, and alternative splicing, emphasizing the necessity for further investigations into these molecular cross-talks to foster deeper understanding and guide strategic therapeutic development.

Transcriptional Profiling of SARS-CoV-2-Infected Calu-3 Cells Reveals Immune-Related Signaling Pathways

The COVID-19 disease, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), emerged in late 2019 and rapidly spread worldwide, becoming a pandemic that infected millions of people and caused significant deaths. COVID-19 continues to be a major threat, and there is a need to deepen our understanding of the virus and its mechanisms of infection. To study the cellular responses to SARS-CoV-2 infection, we performed an RNA sequencing of infected vs. uninfected Calu-3 cells. Total RNA was extracted from infected (0.5 MOI) and control Calu-3 cells and converted to cDNA. Sequencing was performed, and the obtained reads were quality-analyzed and pre-processed. Differential expression was assessed with the EdgeR package, and functional enrichment was performed in EnrichR for Gene Ontology, KEGG pathways, and WikiPathways. A total of 1040 differentially expressed genes were found in infected vs. uninfected Calu-3 cells, of which 695 were up-regulated and 345 were down-regulated. Functional enrichment analyses revealed the predominant up-regulation of genes related to innate immune response, response to virus, inflammation, cell proliferation, and apoptosis. These transcriptional changes following SARS-CoV-2 infection may reflect a cellular response to the infection and help to elucidate COVID-19 pathogenesis, in addition to revealing potential biomarkers and drug targets.

Multi-omics analysis of mucosal and systemic immunity to SARS-CoV-2 after birth

The dynamics of immunity to infection in infants remain obscure. Here, we used a multi-omics approach to perform a longitudinal analysis of immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in infants and young children by analyzing blood samples and weekly nasal swabs collected before, during, and after infection with Omicron and non-Omicron variants. Infection stimulated robust antibody titers that, unlike in adults, showed no sign of decay for up to 300 days. Infants mounted a robust mucosal immune response characterized by inflammatory cytokines, interferon (IFN) α, and T helper (Th) 17 and neutrophil markers (interleukin [IL]-17, IL-8, and CXCL1). The immune response in blood was characterized by upregulation of activation markers on innate cells, no inflammatory cytokines, but several chemokines and IFNα. The latter correlated with viral load and expression of interferon-stimulated genes (ISGs) in myeloid cells measured by single-cell multi-omics. Together, these data provide a snapshot of immunity to infection during the initial weeks and months of life.

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples

BACKGROUND

Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful treatment requires timely diagnosis of viral etiology, which is complicated by overlap in clinical presentation with the non-viral ARIs. Multiple pandemics in the twenty-first century to date have further highlighted the unmet need for effective monitoring of clinically relevant emerging viruses. Recent studies have identified conserved host response to viral infections in the blood.

METHODS

We hypothesize that a similarly conserved host response in nasal samples can be utilized for diagnosis and to rule out viral infection in symptomatic patients when current diagnostic tests are negative. Using a multi-cohort analysis framework, we analyzed 1555 nasal samples across 10 independent cohorts dividing them into training and validation.

RESULTS

Using six of the datasets for training, we identified 119 genes that are consistently differentially expressed in viral ARI patients (N = 236) compared to healthy controls (N = 146) and further down-selected 33 genes for classifier development. The resulting locked logistic regression-based classifier using the 33-mRNAs had AUC of 0.94 and 0.89 in the six training and four validation datasets, respectively. Furthermore, we found that although trained on healthy controls only, in the four validation datasets, the 33-mRNA classifier distinguished viral ARI from both healthy or non-viral ARI samples with > 80% specificity and sensitivity, irrespective of age, viral type, and viral load. Single-cell RNA-sequencing data showed that the 33-mRNA signature is dominated by macrophages and neutrophils in nasal samples.

CONCLUSION

This proof-of-concept signature has potential to be adapted as a clinical point-of-care test ('RespVerity') to improve the diagnosis of viral ARIs.

Innate Immune Gene Polymorphisms and COVID-19 Prognosis

COVID-19 is characterized by a heterogeneous clinical presentation and prognosis. Risk factors contributing to the development of severe disease include old age and the presence of comorbidities. However, the genetic background of the host has also been recognized as an important determinant of disease prognosis. Considering the pivotal role of innate immunity in the control of SARS-CoV-2 infection, we analyzed the possible contribution of several innate immune gene polymorphisms (including TLR2-rs5743708, TLR4-rs4986790, TLR4-rs4986791, CD14-rs2569190, CARD8-rs1834481, IL18-rs2043211, and CD40-rs1883832) in disease severity and prognosis. A total of 249 individuals were enrolled and further divided into five (5) groups, according to the clinical progression scale provided by the World Health Organization (WHO) (asymptomatic, mild, moderate, severe, and critical). We identified that elderly patients with obesity and/or diabetes mellitus were more susceptible to developing pneumonia and respiratory distress syndrome after SARS-CoV-2 infection, while the IL18-rs1834481 polymorphism was an independent risk factor for developing pneumonia. Moreover, individuals carrying either the TLR2-rs5743708 or the TLR4-rs4986791 polymorphisms exhibited a 3.6- and 2.5-fold increased probability for developing pneumonia and a more severe disease, respectively. Our data support the notion that the host's genetic background can significantly affect COVID-19 clinical phenotype, also suggesting that the IL18-rs1834481, TLR2-rs5743708, and TLR4-rs4986791 polymorphisms may be used as molecular predictors of COVID-19 clinical phenotype.

Virus-specific and shared gene expression signatures in immune cells after vaccination in response to influenza and vaccinia stimulation

Background

In the vaccine era, individuals receive multiple vaccines in their lifetime. Host gene expression in response to antigenic stimulation is usually virus-specific; however, identifying shared pathways of host response across a wide spectrum of vaccine pathogens can shed light on the molecular mechanisms/components which can be targeted for the development of broad/universal therapeutics and vaccines.

Method

We isolated PBMCs, monocytes, B cells, and CD8+ T cells from the peripheral blood of healthy donors, who received both seasonal influenza vaccine (within <1 year) and smallpox vaccine (within 1 - 4 years). Each of the purified cell populations was stimulated with either influenza virus or vaccinia virus. Differentially expressed genes (DEGs) relative to unstimulated controls were identified for each in vitro viral infection, as well as for both viral infections (shared DEGs). Pathway enrichment analysis was performed to associate identified DEGs with KEGG/biological pathways.

Results

We identified 2,906, 3,888, 681, and 446 DEGs in PBMCs, monocytes, B cells, and CD8+ T cells, respectively, in response to influenza stimulation. Meanwhile, 97, 120, 20, and 10 DEGs were identified as gene signatures in PBMCs, monocytes, B cells, and CD8+ T cells, respectively, upon vaccinia stimulation. The majority of DEGs identified in PBMCs were also found in monocytes after either viral stimulation. Of the virus-specific DEGs, 55, 63, and 9 DEGs occurred in common in PBMCs, monocytes, and B cells, respectively, while no DEGs were shared in infected CD8+ T cells after influenza and vaccinia. Gene set enrichment analysis demonstrated that these shared DEGs were over-represented in innate signaling pathways, including cytokine-cytokine receptor interaction, viral protein interaction with cytokine and cytokine receptor, Toll-like receptor signaling, RIG-I-like receptor signaling pathways, cytosolic DNA-sensing pathways, and natural killer cell mediated cytotoxicity.

Conclusion

Our results provide insights into virus-host interactions in different immune cells, as well as host defense mechanisms against viral stimulation. Our data also highlights the role of monocytes as a major cell population driving gene expression in ex vivo PBMCs in response to viral stimulation. The immune response signaling pathways identified in this study may provide specific targets for the development of novel virus-specific therapeutics and improved vaccines for vaccinia and influenza. Although influenza and vaccinia viruses have been selected in this study as pathogen models, this approach could be applicable to other pathogens.

The role of interleukin 13 and the type 2 immune pathway in COVID-19: A review

Although much has been learned about severe acute respiratory syndrome coronavirus 2 since December 2019, uneven global vaccine distribution, rapid viral spread, and variant evasion of preventative measures have led to its persistence in the population for the foreseeable future. Additional therapies are needed to support patients through their acute, immune-mediated disease process that continues to lead to considerable morbidity and mortality. Data revealing the involvement of type 2 immune pathway in acute coronavirus disease 2019 and post-recovery conditions represent a potential additional area for intervention. Herein, we review the current understanding of interleukin 13 in acute severe acute respiratory syndrome coronavirus 2 infection, the clinical outcomes associated with type 2 immune processes, and the impact of type 2 blockade on acute and long-term coronavirus disease 2019 conditions.

Multiscale network analysis identifies potential receptors for SARS-CoV-2 and reveals their tissue-specific and age-dependent expression

The coronavirus disease 2019 (COVID-19) pandemic has affected tens of millions of individuals and caused hundreds of thousands of deaths worldwide. Here, we present a comprehensive, multiscale network analysis of the transcriptional response to the virus. In particular, we focused on key regulators, cell receptors, and host processes that were hijacked by the virus for its advantage. ACE2-controlled processes involved CD300e (a TYROBP receptor) as a key regulator and the activation of IL-2 pro-inflammatory cytokine signaling. We further investigated the age dependency of such receptors in different tissues. In summary, this study provides novel insights into the gene regulatory organization during the SARS-CoV-2 infection and the tissue-specific, age-dependent expression of the cell receptors involved in COVID-19.

Indian food habit & food ingredients may have a role in lowering the severity & high death rate from COVID-19 in Indians: findings from the first nutrigenomic analysis

Background & objectives

During the COVID-19 pandemic, the death rate was reportedly 5-8 fold lower in India which is densely populated as compared to less populated western countries. The aim of this study was to investigate whether dietary habits were associated with the variations in COVID-19 severity and deaths between western and Indian population at the nutrigenomics level.

Methods

In this study nutrigenomics approach was applied. Blood transcriptome of severe COVID-19 patients from three western countries (showing high fatality) and two datasets from Indian patients were used. Gene set enrichment analyses were performed for pathways, metabolites, nutrients, etc., and compared for western and Indian samples to identify the food- and nutrient-related factors, which may be associated with COVID-19 severity. Data on the daily consumption of twelve key food components across four countries were collected and a correlation between nutrigenomics analyses and per capita daily dietary intake was investigated.

Results

Distinct dietary habits of Indians were observed, which may be associated with low death rate from COVID-19. Increased consumption of red meat, dairy products and processed foods by western populations may increase the severity and death rate by activating cytokine storm-related pathways, intussusceptive angiogenesis, hypercapnia and enhancing blood glucose levels due to high contents of sphingolipids, palmitic acid and byproducts such as CO2 and lipopolysaccharide (LPS). Palmitic acid also induces ACE2 expression and increases the infection rate. Coffee and alcohol that are highly consumed in western countries may increase the severity and death rates from COVID-19 by deregulating blood iron, zinc and triglyceride levels. The components of Indian diets maintain high iron and zinc concentrations in blood and rich fibre in their foods may prevent CO2 and LPS-mediated COVID-19 severity. Regular consumption of tea by Indians maintains high high-density lipoprotein (HDL) and low triglyceride in blood as catechins in tea act as natural atorvastatin. Importantly, regular consumption of turmeric in daily food by Indians maintains strong immunity and curcumin in turmeric may prevent pathways and mechanisms associated with SARS-CoV-2 infection and COVID-19 severity and lowered the death rate.

Interpretation & conclusions

Our results suggest that Indian food components suppress cytokine storm and various other severity related pathways of COVID-19 and may have a role in lowering severity and death rates from COVID-19 in India as compared to western populations. However, large multi-centered case-control studies are required to support our current findings.

Mucosal Gene Expression in Response to SARS-CoV-2 Is Associated with Viral Load

Little is known about the relationships between symptomatic early severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load and upper airway mucosal gene expression and immune response. To examine the association of symptomatic SARS-CoV-2 early viral load with upper airway mucosal gene expression, we profiled the host mucosal transcriptome from nasopharyngeal swab samples from 68 adults with symptomatic, mild-to-moderate coronavirus disease 19 (COVID-19). We measured SARS-CoV-2 viral load using reverse transcription-quantitative PCR (RT-qPCR). We then examined the association of SARS-CoV-2 viral load with upper airway mucosal immune response. We detected SARS-CoV-2 in all samples and recovered >80% of the genome from 95% of the samples from symptomatic COVID-19 adults. The respiratory virome was dominated by SARS-CoV-2, with limited codetection of other respiratory viruses, with the human Rhinovirus C being identified in 4 (6%) samples. This limited codetection of other respiratory viral pathogens may be due to the implementation of public health measures, like social distancing and masking practices. We observed a significant positive correlation between SARS-CoV-2 viral load and interferon signaling (OAS2, OAS3, IFIT1, UPS18, ISG15, ISG20, IFITM1, and OASL), chemokine signaling (CXCL10 and CXCL11), and adaptive immune system (IFITM1, CD300E, and SIGLEC1) genes in symptomatic, mild-to-moderate COVID-19 adults, when adjusting for age, sex, and race. Interestingly, the expression levels of most of these genes plateaued at a cycle threshold (C_T) value of ~25. Overall, our data show that the early nasal mucosal immune response to SARS-CoV-2 infection is viral load dependent, potentially modifying COVID-19 outcomes. IMPORTANCE Several prior studies have shown that SARS-CoV-2 viral load can predict the likelihood of disease spread and severity. A higher detectable SARS-CoV-2 plasma viral load was associated with worse respiratory disease severity. However, the relationship between SARS-CoV-2 viral load, airway mucosal gene expression, and immune response remains elusive. We profiled the nasal mucosal transcriptome from nasal samples collected from adults infected with SARS-CoV-2 during spring 2020 with mild-to-moderate symptoms using a comprehensive metatranscriptomics method. We observed a positive correlation between SARS-CoV-2 viral load, interferon signaling, chemokine signaling, and adaptive immune system in adults with COVID-19. Our data suggest that early nasal mucosal immune response to SARS-CoV-2 infection was viral load dependent and may modify COVID-19 outcomes.

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.

COVID-19: Using high-throughput flow cytometry to dissect clinical heterogeneity

Here we consider how high-content flow cytometric methodology at appropriate scale and throughput rapidly provided meaningful biological data in our recent studies of COVID-19, which we discuss in the context of other similar investigations. In our work, high-throughput flow cytometry was instrumental to identify a consensus immune signature in COVID-19 patients, and to investigate the impact of SARS-CoV-2 exposure on patients with either solid or hematological cancers. We provide here some examples of our 'holistic' approach, in which flow cytometry data generated by lymphocyte and myelomonocyte panels were integrated with other analytical metrics, including SARS-CoV-2-specific serum antibody titers, plasma cytokine/chemokine levels, and in-depth clinical annotation. We report how selective differences between T cell subsets were revealed by a newly described flow cytometric TDS assay to distinguish actively cycling T cells in the peripheral blood. By such approaches, our and others' high-content flow cytometry studies collectively identified overt abnormalities and subtle but critical changes that discriminate the immuno-signature of COVID-19 patients from those of healthy donors and patients with non-COVID respiratory infections. Thereby, these studies offered several meaningful biomarkers of COVID-19 severity that have the potential to improve the management of patients and of hospital resources. In sum, flow cytometry provides an important means for rapidly obtaining data that can guide clinical decision-making without requiring highly expensive, sophisticated equipment, and/or "-omics" capabilities. We consider how this approach might be further developed.

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

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

A two-gene marker for the two-tiered innate immune response in COVID-19 patients

For coronavirus disease 2019 (COVID-19), a pandemic disease characterized by strong immune dysregulation in severe patients, convenient and efficient monitoring of the host immune response is critical. Human hosts respond to viral and bacterial infections in different ways, the former is characterized by the activation of interferon stimulated genes (ISGs) such as IFI27, while the latter is characterized by the activation of anti-bacterial associated genes (ABGs) such as S100A12. This two-tiered innate immune response has not been examined in COVID-19. In this study, the activation patterns of this two-tiered innate immune response represented by IFI27 and S100A12 were explored based on 1421 samples from 17 transcriptome datasets derived from the blood of COVID-19 patients and relevant controls. It was found that IFI27 activation occurred in most of the symptomatic patients and displayed no correlation with disease severity, while S100A12 activation was more restricted to patients under severe and critical conditions with a stepwise activation pattern. In addition, most of the S100A12 activation was accompanied by IFI27 activation. Furthermore, the activation of IFI27 was most pronounced within the first week of symptom onset, but generally waned after 2-3 weeks. On the other hand, the activation of S100A12 displayed no apparent correlation with disease duration and could last for several months in certain patients. These features of the two-tiered innate immune response can further our understanding on the disease mechanism of COVID-19 and may have implications to the clinical triage. Development of a convenient two-gene protocol for the routine serial monitoring of this two-tiered immune response will be a valuable addition to the existing laboratory tests.

Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Exploration of blood−derived coding and non-coding RNA diagnostic immunological panels for COVID-19 through a co-expressed-based machine learning procedure

Severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) is the causative virus of the pandemic coronavirus disease 2019 (COVID-19). Evaluating the immunological factors and other implicated processes underlying the progression of COVID-19 is essential for the recognition and then the design of efficacious therapies. Therefore, we analyzed RNAseq data obtained from PBMCs of the COVID-19 patients to explore coding and non-coding RNA diagnostic immunological panels. For this purpose, we integrated multiple RNAseq data and analyzed them overall as well as by considering the state of disease including severe and non-severe conditions. Afterward, we utilized a co-expressed-based machine learning procedure comprising weighted-gene co-expression analysis and differential expression gene as filter phase and recursive feature elimination-support vector machine as wrapper phase. This procedure led to the identification of two modules containing 5 and 84 genes which are mostly involved in cell dysregulation and innate immune suppression, respectively. Moreover, the role of vitamin D in regulating some classifiers was highlighted. Further analysis disclosed the role of discriminant miRNAs including miR-197-3p, miR-150-5p, miR-340-5p, miR-122-5p, miR-1307-3p, miR-34a-5p, miR-98-5p and their target genes comprising GAN, VWC2, TNFRSF6B, and CHST3 in the metabolic pathways. These classifiers differentiate the final fate of infection toward severe or non-severe COVID-19. The identified classifier genes and miRNAs may help in the proper design of therapeutic procedures considering their involvement in the immune and metabolic pathways.

Analysis and identification of potential type II helper T cell (Th2)-Related key genes and therapeutic agents for COVID-19

COVID-19 pandemic poses a severe threat to public health. However, so far, there are no effective drugs for COVID-19. Transcriptomic changes and key genes related to Th2 cells in COVID-19 have not been reported. These genes play an important role in host interactions with SARS-COV-2 and may be used as promising target. We analyzed five COVID-19-associated GEO datasets (GSE157103, GSE152641, GSE171110, GSE152418, and GSE179627) using the xCell algorithm and weighted gene co-expression network analysis (WGCNA). Results showed that 5 closely correlated modular genes to COVID-19 and Th2 cell enrichment levels, including purple, blue, pink, tan and turquoise, were intersected with differentially expressed genes (DEGs) and 648 shared genes were obtained. GO and KEGG pathway enrichment analyses revealed that they were enriched in cell proliferation, differentiation, and immune responses after virus infection. The most significantly enriched pathway involved the regulation of viral life cycle. Three key genes, namely CCNB1, BUB1, and UBE2C, may clarify the pathogenesis of COVID-19 associated with Th2 cells. 11 drug candidates were identified that could down-regulate three key genes using the cMAP database and demonstrated strong drugs binding energies aganist the three keygenes using molecular docking methods. BUB1, CCNB1 and UBE2C were identified key genes for COVID-19 and could be promising therapeutic targets.

Immune Modulation in Sepsis, ARDS, and Covid-19 - The Road Traveled and the Road Ahead

Immune Modulation in Sepsis, ARDS, and Covid-19Leligdowicz et al. consider the history and future of immunomodulating therapies in sepsis and ARDS, including ARDS due to Covid-19, and remark on the larger challenge of clinical research on therapies for syndromes with profound clinical and biologic heterogeneity.

Genome-wide screening of sex-biased genetic variants potentially associated with COVID-19 hospitalization

Sex-biased difference in coronavirus disease 2019 (COVID-19) hospitalization has been observed as that male patients tend to be more likely to be hospitalized than female patients. However, due to the insufficient sample size and existed studies that more prioritized to sex-stratified COVID-19 genome-wide association study (GWAS), the searching for sex-biased genetic variants showing differential association signals between sexes with COVID-19 hospitalization was severely hindered. We hypothesized genetic variants would show potentially sex-biased genetic effects on COVID-19 hospitalization if they display significant differential association effect sizes between male and female COVID-19 patients. By integrating two COVID-19 GWASs, including hospitalized COVID-19 patients vs. general population separated into males (case = 1,917 and control = 221,174) and females (case = 1,343 and control = 262,886), we differentiated the association effect sizes of each common single nucleotide polymorphism (SNP) within the two GWASs. Twelve SNPs were suggested to show differential COVID-19 associations between sexes. Further investigation of genes (n = 58) close to these 12 SNPs resulted in the identification of 34 genes demonstrating sex-biased differential expression in at least one GTEx tissue. Finally, 5 SNPs are mapped to 8 genes, including rs1134004 (GADD45G), rs140657166 (TRIM29 and PVRL1), rs148143613 (KNDC1 and STK32C), rs2443615 (PGAP2 and TRIM21), and rs2924725 (CSMD1). The 8 genes display significantly differential gene expression in blood samples derived from COVID-19 patients compared to healthy controls. These genes are potential genetic factors contributing to sex differences in COVID-19 hospitalization and warranted for further functional 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.

Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 Food and Drug Administration (FDA)-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.

Genomic Biomarker Heterogeneities between SARS-CoV-2 and COVID-19

Genes functionally associated with SARS-CoV-2 infection and genes functionally related to the COVID-19 disease can be different, whose distinction will become the first essential step for successfully fighting against the COVID-19 pandemic. Unfortunately, this first step has not been completed in all biological and medical research. Using a newly developed max-competing logistic classifier, two genes, ATP6V1B2 and IFI27, stand out to be critical in the transcriptional response to SARS-CoV-2 infection with differential expressions derived from NP/OP swab PCR. This finding is evidenced by combining these two genes with another gene in predicting disease status to achieve better-indicating accuracy than existing classifiers with the same number of genes. In addition, combining these two genes with three other genes to form a five-gene classifier outperforms existing classifiers with ten or more genes. These two genes can be critical in fighting against the COVID-19 pandemic as a new focus and direction with their exceptional predicting accuracy. Comparing the functional effects of these genes with a five-gene classifier with 100% accuracy identified and tested from blood samples in our earlier work, the genes and their transcriptional response and functional effects on SARS-CoV-2 infection, and the genes and their functional signature patterns on COVID-19 antibodies, are significantly different. We will use a total of fourteen cohort studies (including breakthrough infections and omicron variants) with 1481 samples to justify our results. Such significant findings can help explore the causal and pathological links between SARS-CoV-2 infection and the COVID-19 disease, and fight against the disease with more targeted genes, vaccines, antiviral drugs, and therapies.

Premortem Skin Biopsy Assessing Microthrombi, Interferon Type I Antiviral and Regulatory Proteins, and Complement Deposition Correlates with Coronavirus Disease 2019 Clinical Stage

Apart from autopsy, tissue correlates of coronavirus disease 2019 (COVID-19) clinical stage are lacking. In the current study, cutaneous punch biopsy specimens of 15 individuals with severe/critical COVID-19 and six with mild/moderate COVID-19 were examined. Evidence for arterial and venous microthrombi, deposition of C5b-9 and MASP2 (representative of alternative and lectin complement pathways, respectively), and differential expression of interferon type I-driven antiviral protein MxA (myxovirus resistance A) versus SIN3A, a promoter of interferon type I-based proinflammatory signaling, were assessed. Control subjects included nine patients with sepsis-related acute respiratory distress syndrome (ARDS) and/or acute kidney injury (AKI) pre-COVID-19. Microthrombi were detected in 13 (87%) of 15 patients with severe/critical COVID-19 versus zero of six patients with mild/moderate COVID-19 (P < 0.001) and none of the nine patients with pre-COVID-19 ARDS/AKI (P < 0.001). Cells lining the microvasculature staining for spike protein of severe acute respiratory syndrome coronavirus 2, the etiologic agent of COVID-19, also expressed tissue factor. C5b-9 deposition occurred in 13 (87%) of 15 patients with severe/critical COVID-19 versus zero of six patients with mild/moderate COVID-19 (P < 0.001) and none of the nine patients with pre-COVID-19 ARDS/AKI (P < 0.001). MASP2 deposition was also restricted to severe/critical COVID-19 cases. MxA expression occurred in all six mild/moderate versus two (15%) of 13 severe/critical cases (P < 0.001) of COVID-19. In contrast, SIN3A was restricted to severe/critical COVID-19 cases co-localizing with severe acute respiratory syndrome coronavirus 2 spike protein. SIN3A was also elevated in plasma of patients with severe/critical COVID-19 versus control subjects (P ≤ 0.02). In conclusion, the study identified premortem tissue correlates of COVID-19 clinical stage using skin. If validated in a longitudinal cohort, this approach could identify individuals at risk for disease progression and enable targeted interventions.

Mucosal gene expression in response to SARS-CoV-2 is associated with early viral load

Little is known about the relationships between symptomatic early-time SARS-CoV-2 viral load and upper airway mucosal gene expression and immune response. To examine the association of symptomatic SARS-CoV-2 early viral load with upper airway mucosal gene expression, we profiled the host mucosal transcriptome from nasopharyngeal swab samples from 68 adults with symptomatic, mild-to-moderate COVID-19. We measured SARS-CoV-2 viral load using qRT-PCR. We then examined the association of SARS-CoV-2 viral load with upper airway mucosal immune response. We detected SARS-CoV-2 in all samples and recovered >80% of the genome from 85% of the samples from symptomatic COVID-19 adults. The respiratory virome was dominated by SARS-CoV-2, with limited co-detection of common respiratory viruses i.e., only the human Rhinovirus (HRV) being identified in 6% of the samples. We observed a significant positive correlation between SARS-CoV-2 viral load and interferon signaling (OAS2, OAS3, IFIT1, UPS18, ISG15, ISG20, IFITM1, and OASL), chemokine signaling (CXCL10 and CXCL11), and adaptive immune system (IFITM1, CD300E, and SIGLEC1) genes in symptomatic, mild-to-moderate COVID-19 adults, when adjusted for age, sex and race. Interestingly, the expression levels of most of these genes plateaued at a CT value of ~25. Overall, our data shows that early nasal mucosal immune response to SARS-CoV-2 infection is viral load dependent, which potentially could modify COVID-19 outcomes.

AUTHOR SUMMARY

Several prior studies have shown that SARS-CoV-2 viral load can predict the likelihood of disease spread and severity. A higher detectable SARS-CoV-2 plasma viral load was associated with worse respiratory disease severity. However, the relationship between SARS-CoV-2 viral load and airway mucosal gene expression and immune response remains elusive. We profiled the nasal mucosal transcriptome from nasal samples collected from adults infected with SARS-CoV-2 during Spring 2020 with mild-to-moderate symptoms using a comprehensive metatranscriptomics method. We observed a positive correlation between SARS-CoV-2 viral load with interferon signaling, chemokine signaling, and adaptive immune system in adults with COVID-19. Our data suggest that early nasal mucosal immune response to SARS-CoV-2 infection was viral load-dependent and may modify COVID-19 outcomes.

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.

Integrated COVID-19 Predictor: Differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data

Background

The world has been battling the continuous COVID-19 pandemic spread by the SARS-CoV-2 virus for last two years. The issue of viral disease prediction is constantly a matter of interest in virology and the study of disease transmission over the long years.

Objective

In this study, we aimed to implement genome association studies using RNA-Seq of COVID-19 and reveal highly expressed gene biomarkers and prediction based on the machine learning model of COVID-19 analysis to combat this pandemic.

Method

We collected RNA-Seq gene count data for both healthy (Control) and non-healthy (Treated) COVID-19 cases. In this experiment, a sequence of bioinformatics strategies and statistical techniques, such as fold-change and adjusted p-value, were processed to identify differentially expressed genes (DEGs). We filtered biomarker sets of high DEGs, moderate DEGs, and low DEGs using DESeq2, Limma Trend, and Limma Voom methods based on intersection and union operations and applied machine learning techniques to predict COVID-19.

Result

Through experimental analysis, 67 potential biomarkers were extracted, comprising 49 up-regulated and 18 down-regulated genes, using statistical techniques and a set-theory consensus strategy. We trained the machine learning models on 12 different biomarker sets and found that the SVM model performed better than the other classifiers with 99.07% classification accuracy for moderate DEGs.

Conclusion

Our study revealed that identified differentially expressed genes of the moderate DEGs biomarker set, |log2FC| ≥ 2 with adjusted p-value < 0.05, work significantly as input features to implement a machine learning model using a kernel-based SVM technique to predict COVID-19.

High Circulating Levels of the Homeostatic Chemokines CCL19 and CCL21 Predict Mortality and Disease Severity in COVID-19

BACKGROUND

Immune dysregulation is a major factor in the development of severe coronavirus disease 2019 (COVID-19). The homeostatic chemokines CCL19 and CCL21 have been implicated as mediators of tissue inflammation, but data on their regulation in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is limited. We thus investigated the levels of these chemokines in COVID-19 patients.

METHODS

Serial blood samples were obtained from patients hospitalized with COVID-19 (n = 414). Circulating CCL19 and CCL21 levels during hospitalization and 3-month follow-up were analyzed. In vitro assays and analysis of RNAseq data from public repositories were performed to further explore possible regulatory mechanisms.

RESULTS

A consistent increase in circulating levels of CCL19 and CCL21 was observed, with high levels correlating with disease severity measures, including respiratory failure, need for intensive care, and 60-day all-cause mortality. High levels of CCL21 at admission were associated with persisting impairment of pulmonary function at the 3-month follow-up.

CONCLUSIONS

Our findings highlight CCL19 and CCL21 as markers of immune dysregulation in COVID-19. This may reflect aberrant regulation triggered by tissue inflammation, as observed in other chronic inflammatory and autoimmune conditions. Determination of the source and regulation of these chemokines and their effects on lung tissue is warranted to further clarify their role in COVID-19.

CLINICAL TRIALS REGISTRATION

NCT04321616 and NCT04381819.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

A multi-tissue study of immune gene expression profiling highlights the key role of the nasal epithelium in COVID-19 severity

Coronavirus Disease-19 (COVID-19) symptoms range from mild to severe illness; the cause for this differential response to infection remains unknown. Unravelling the immune mechanisms acting at different levels of the colonization process might be key to understand these differences. We carried out a multi-tissue (nasal, buccal and blood; n = 156) gene expression analysis of immune-related genes from patients affected by different COVID-19 severities, and healthy controls through the nCounter technology. Mild and asymptomatic cases showed a powerful innate antiviral response in nasal epithelium, characterized by activation of interferon (IFN) pathway and downstream cascades, successfully controlling the infection at local level. In contrast, weak macrophage/monocyte driven innate antiviral response and lack of IFN signalling activity were present in severe cases. Consequently, oral mucosa from severe patients showed signals of viral activity, cell arresting and viral dissemination to the lower respiratory tract, which ultimately could explain the exacerbated innate immune response and impaired adaptative immune responses observed at systemic level. Results from saliva transcriptome suggest that the buccal cavity might play a key role in SARS-CoV-2 infection and dissemination in patients with worse prognosis. Co-expression network analysis adds further support to these findings, by detecting modules specifically correlated with severity involved in the abovementioned biological routes; this analysis also provides new candidate genes that might be tested as biomarkers in future studies. We also found tissue specific severity-related signatures mainly represented by genes involved in the innate immune system and cytokine/chemokine signalling. Local immune response could be key to determine the course of the systemic response and thus COVID-19 severity. Our findings provide a framework to investigate severity host gene biomarkers and pathways that might be relevant to diagnosis, prognosis, and therapy.

A Path-Based Analysis of Infected Cell Line and COVID-19 Patient Transcriptome Reveals Novel Potential Targets and Drugs Against SARS-CoV-2

Most transcriptomic studies of SARS-CoV-2 infection have focused on differentially expressed genes, which do not necessarily reveal the genes mediating the transcriptomic changes. In contrast, exploiting curated biological network, our PathExt tool identifies central genes from the differentially active paths mediating global transcriptomic response. Here we apply PathExt to multiple cell line infection models of SARS-CoV-2 and other viruses, as well as to COVID-19 patient-derived PBMCs. The central genes mediating SARS-CoV-2 response in cell lines were uniquely enriched for ATP metabolic process, G1/S transition, leukocyte activation and migration. In contrast, PBMC response reveals dysregulated cell-cycle processes. In PBMC, the most frequently central genes are associated with COVID-19 severity. Importantly, relative to differential genes, PathExt-identified genes show greater concordance with several benchmark anti-COVID-19 target gene sets. We propose six novel anti-SARS-CoV-2 targets ADCY2, ADSL, OCRL, TIAM1, PBK, and BUB1, and potential drugs targeting these genes, such as Bemcentinib, Phthalocyanine, and Conivaptan.

HYGIEIA: HYpothesizing the Genesis of Infectious Diseases and Epidemics through an Integrated Systems Biology Approach

More than two years on, the COVID-19 pandemic continues to wreak havoc around the world and has battle-tested the pandemic-situation responses of all major global governments. Two key areas of investigation that are still unclear are: the molecular mechanisms that lead to heterogenic patient outcomes, and the causes of Post COVID condition (AKA Long-COVID). In this paper, we introduce the HYGIEIA project, designed to respond to the enormous challenges of the COVID-19 pandemic through a multi-omic approach supported by network medicine. It is hoped that in addition to investigating COVID-19, the logistics deployed within this project will be applicable to other infectious agents, pandemic-type situations, and also other complex, non-infectious diseases. Here, we first look at previous research into COVID-19 in the context of the proteome, metabolome, transcriptome, microbiome, host genome, and viral genome. We then discuss a proposed methodology for a large-scale multi-omic longitudinal study to investigate the aforementioned biological strata through high-throughput sequencing (HTS) and mass-spectrometry (MS) technologies. Lastly, we discuss how a network medicine approach can be used to analyze the data and make meaningful discoveries, with the final aim being the translation of these discoveries into the clinics to improve patient care.

Severe COVID-19 Shares a Common Neutrophil Activation Signature with Other Acute Inflammatory States

Severe COVID-19 patients present a clinical and laboratory overlap with other hyperinflammatory conditions such as hemophagocytic lymphohistiocytosis (HLH). However, the underlying mechanisms of these conditions remain to be explored. Here, we investigated the transcriptome of 1596 individuals, including patients with COVID-19 in comparison to healthy controls, other acute inflammatory states (HLH, multisystem inflammatory syndrome in children [MIS-C], Kawasaki disease [KD]), and different respiratory infections (seasonal coronavirus, influenza, bacterial pneumonia). We observed that COVID-19 and HLH share immunological pathways (cytokine/chemokine signaling and neutrophil-mediated immune responses), including gene signatures that stratify COVID-19 patients admitted to the intensive care unit (ICU) and COVID-19_nonICU patients. Of note, among the common differentially expressed genes (DEG), there is a cluster of neutrophil-associated genes that reflects a generalized hyperinflammatory state since it is also dysregulated in patients with KD and bacterial pneumonia. These genes are dysregulated at the protein level across several COVID-19 studies and form an interconnected network with differentially expressed plasma proteins that point to neutrophil hyperactivation in COVID-19 patients admitted to the intensive care unit. scRNAseq analysis indicated that these genes are specifically upregulated across different leukocyte populations, including lymphocyte subsets and immature neutrophils. Artificial intelligence modeling confirmed the strong association of these genes with COVID-19 severity. Thus, our work indicates putative therapeutic pathways for intervention.

A robust gene expression signature for NASH in liver expression data

Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.

COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets

Background: To develop anti-viral drugs and vaccines, it is crucial to understand the molecular basis and pathology of COVID-19. An increase in research output is required to generate data and results at a faster rate, therefore bioinformatics plays a crucial role in COVID-19 research. There is an abundance of transcriptomic data from studies carried out on COVID-19, however, their use is limited by the confounding factors pertaining to each study. The reanalysis of all these datasets in a unified approach should help in understanding the molecular basis of COVID-19. This should allow for the identification of COVID-19 biomarkers expressed in patients and the presence of markers specific to disease severity and condition.

Aim: In this study, we aim to use the multiple publicly available transcriptomic datasets retrieved from the Gene Expression Omnibus (GEO) database to identify consistently differential expressed genes in different tissues and clinical settings.

Materials and Methods: A list of datasets was generated from NCBI’s GEO using the GEOmetadb package through R software. Search keywords included SARS-COV-2 and COVID-19. Datasets in human tissues containing more than ten samples were selected for this study. Differentially expressed genes (DEGs) in each dataset were identified. Then the common DEGs between different datasets, conditions, tissues and clinical settings were shortlisted.

Results: Using a unified approach, we were able to identify common DEGs based on the disease conditions, samples source and clinical settings. For each indication, a different set of genes have been identified, revealing that a multitude of factors play a role in the level of gene expression.

Conclusion: Unified reanalysis of publically available transcriptomic data showed promising potential in identifying core targets that can explain the molecular pathology and be used as biomarkers for COVID-19.

Immunothrombosis Biomarkers for Distinguishing Coronavirus Disease 2019 Patients From Noncoronavirus Disease Septic Patients With Pneumonia and for Predicting ICU Mortality

Supplemental Digital Content is available in the text.

IMPORTANCE:

Coronavirus disease 2019 patients have an increased risk of thrombotic complications that may reflect immunothrombosis, a process characterized by blood clotting, endothelial dysfunction, and the release of neutrophil extracellular traps. To date, few studies have investigated longitudinal changes in immunothrombosis biomarkers in these patients. Furthermore, how these longitudinal changes differ between coronavirus disease 2019 patients and noncoronavirus disease septic patients with pneumonia are unknown.

OBJECTIVES:

In this pilot observational study, we investigated the utility of immunothrombosis biomarkers for distinguishing between coronavirus disease 2019 patients and noncoronavirus disease septic patients with pneumonia. We also evaluated the utility of the biomarkers for predicting ICU mortality in these patients.

DESIGN, SETTING, AND PARTICIPANTS:

The participants were ICU patients with coronavirus disease 2019 (n = 14), noncoronavirus disease septic patients with pneumonia (n = 19), and healthy age-matched controls (n = 14).

MAIN OUTCOMES AND MEASURES:

Nine biomarkers were measured from plasma samples (on days 1, 2, 4, 7, 10, and/or 14). Analysis was based on binomial logit models and receiver operating characteristic analyses.

RESULTS:

Cell-free DNA, d-dimer, soluble endothelial protein C receptor, protein C, soluble thrombomodulin, fibrinogen, citrullinated histones, and thrombin-antithrombin complexes have significant powers for distinguishing coronavirus disease 2019 patients from healthy individuals. In comparison, fibrinogen, soluble endothelial protein C receptor, antithrombin, and cell-free DNA have significant powers for distinguishing coronavirus disease 2019 from pneumonia patients. The predictors of ICU mortality differ between the two patient groups: soluble thrombomodulin and citrullinated histones for coronavirus disease 2019 patients, and protein C and cell-free DNA or fibrinogen for pneumonia patients. In both patient groups, the most recent biomarker values have stronger prognostic value than their ICU day 1 values.

CONCLUSIONS AND RELEVANCE:

Fibrinogen, soluble endothelial protein C receptor, antithrombin, and cell-free DNA have utility for distinguishing coronavirus disease 2019 patients from noncoronavirus disease septic patients with pneumonia. The most important predictors of ICU mortality are soluble thrombomodulin/citrullinated histones for coronavirus disease 2019 patients, and protein C/cell-free DNA for noncoronavirus disease pneumonia patients. This hypothesis-generating study suggests that the pathophysiology of immunothrombosis differs between the two patient groups.

Genome-wide bidirectional CRISPR screens identify mucins as host factors modulating SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a range of symptoms in infected individuals, from mild respiratory illness to acute respiratory distress syndrome. A systematic understanding of host factors influencing viral infection is critical to elucidate SARS-CoV-2-host interactions and the progression of Coronavirus disease 2019 (COVID-19). Here, we conducted genome-wide CRISPR knockout and activation screens in human lung epithelial cells with endogenous expression of the SARS-CoV-2 entry factors ACE2 and TMPRSS2. We uncovered proviral and antiviral factors across highly interconnected host pathways, including clathrin transport, inflammatory signaling, cell-cycle regulation, and transcriptional and epigenetic regulation. We further identified mucins, a family of high molecular weight glycoproteins, as a prominent viral restriction network that inhibits SARS-CoV-2 infection in vitro and in murine models. These mucins also inhibit infection of diverse respiratory viruses. This functional landscape of SARS-CoV-2 host factors provides a physiologically relevant starting point for new host-directed therapeutics and highlights airway mucins as a host defense mechanism.

Immune system cells from COVID-19 patients display compromised mitochondrial-nuclear expression co-regulation and rewiring toward glycolysis

Mitochondria are pivotal for bioenergetics, as well as in cellular response to viral infections. Nevertheless, their role in COVID-19 was largely overlooked. Here, we analyzed available bulk RNA-seq datasets from COVID-19 patients and corresponding healthy controls (three blood datasets, N = 48 healthy, 119 patients; two respiratory tract datasets, N = 157 healthy, 524 patients). We found significantly reduced mtDNA gene expression in blood, but not in respiratory tract samples from patients. Next, analysis of eight single-cells RNA-seq datasets from peripheral blood mononuclear cells, nasopharyngeal samples, and Bronchoalveolar lavage fluid (N = 1,192,243 cells), revealed significantly reduced mtDNA gene expression especially in immune system cells from patients. This is associated with elevated expression of nuclear DNA-encoded OXPHOS subunits, suggesting compromised mitochondrial-nuclear co-regulation. This, together with elevated expression of ROS-response genes and glycolysis enzymes in patients, suggest rewiring toward glycolysis, thus generating beneficial conditions for SARS-CoV-2 replication. Our findings underline the centrality of mitochondrial dysfunction in COVID-19.

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mtDNA gene expression is downregulated in COVID-19 blood, but not in respiratory tract

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Decreased mtDNA gene expression disrupts mito-nuclear coordination

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mtDNA is downregulated and rewired toward glycolysis especially in immune system cells

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Mitochondrial dysfunction is central to the etiology of COVID19

A host transcriptomic signature for identification of respiratory viral infections in the community

BACKGROUND

Fever-7 is a test evaluating host mRNA expression levels of IFI27, JUP, LAX, HK3, TNIP1, GPAA1 and CTSB in blood able to detect viral infections. This test has been validated mostly in hospital settings. Here we have evaluated Fever-7 to identify the presence of respiratory viral infections in a Community Health Center.

METHODS

A prospective study was conducted in the "Servicio de Urgencias de Atención Primaria" in Salamanca, Spain. Patients with clinical signs of respiratory infection and at least one point in the National Early Warning Score were recruited. Fever-7 mRNAs were profiled on a Nanostring nCounter® SPRINT instrument from blood collected upon patient enrolment. Viral diagnosis was performed on nasopharyngeal aspirates (NPAs) using the Biofire-RP2 panel.

RESULTS

A respiratory virus was detected in the NPAs of 66 of the 100 patients enrolled. Median National Early Warning Score was 7 in the group with no virus detected and 6.5 in the group with a respiratory viral infection (P > .05). The Fever-7 score yielded an overall AUC of 0.81 to predict a positive viral syndromic test. The optimal operating point for the Fever-7 score yielded a sensitivity of 82% with a specificity of 71%. Multivariate analysis showed that Fever-7 was a robust marker of viral infection independently of age, sex, major comorbidities and disease severity at presentation (OR [CI95%], 3.73 [2.14-6.51], P < .001).

CONCLUSIONS

Fever-7 is a promising host immune mRNA signature for the early identification of a respiratory viral infection in the community.