Single-cell analyses and host genetics highlight the role of innate immune cells in COVID-19 severity

Immune Stimulation with Imiquimod to Best Face SARS-CoV-2 Infection and Prevent Long COVID

Through widespread immunization against SARS-CoV-2 prior to or post-infection, a substantial segment of the global population has acquired both humoral and cellular immunity, and there has been a notable reduction in the incidence of severe and fatal cases linked to this virus and accelerated recovery times for those infected. Nonetheless, a significant demographic, comprising around 20% to 30% of the adult population, remains unimmunized due to diverse factors. Furthermore, alongside those recovered from the infection, there is a subset of the population experiencing persistent symptoms referred to as Long COVID. This condition is more prevalent among individuals with underlying health conditions and immune system impairments. Some Long COVID pathologies stem from direct damage inflicted by the viral infection, whereas others arise from inadequate immune system control over the infection or suboptimal immunoregulation. There are differences in the serum cytokines and miRNA profiles between infected individuals who develop severe COVID-19 or Long COVID and those who control adequately the infection. This review delves into the advantages and constraints associated with employing imiquimod in human subjects to enhance the immune response during SARS-CoV-2 immunization. Restoration of the immune system can modify it towards a profile of non-susceptibility to SARS-CoV-2. An adequate immune system has the potential to curb viral propagation, mitigate symptoms, and ameliorate the severe consequences of the infection.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

Yin and yang of interferons: lessons from the coronavirus disease 2019 (COVID-19) pandemic

The host immune response against severe acute respiratory syndrome coronavirus 2 includes the induction of a group of natural antiviral cytokines called interferons (IFNs). Although originally recognized for their ability to potently counteract infections, the mechanistic functions of IFNs in patients with varying severities of coronavirus disease 2019 (COVID-19) have highlighted a more complex scenario. Cellular and molecular analyses have revealed that timing, location, and subtypes of IFNs produced during severe acute respiratory syndrome coronavirus 2 infection play a major role in determining disease progression and severity. In this review, we summarize what the COVID-19 pandemic has taught us about the protective and detrimental roles of IFNs during the inflammatory response elicited against a new respiratory virus across different ages and its longitudinal consequences in driving the development of long COVID-19.

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

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

Resolution of SARS-CoV-2 infection in human lung tissues is driven by extravascular CD163+ monocytes

The lung-resident immune mechanisms driving resolution of SARS-CoV-2 infection in humans remain elusive. Using mice co-engrafted with a genetically matched human immune system and fetal lung xenograft (fLX), we mapped the immunological events defining resolution of SARS-CoV-2 infection in human lung tissues. Viral infection is rapidly cleared from fLX following a peak of viral replication. Acute replication results in the emergence of cell subsets enriched in viral RNA, including extravascular inflammatory monocytes (iMO) and macrophage-like T-cells, which dissipate upon infection resolution. iMO display robust antiviral responses, are transcriptomically unique among myeloid lineages, and their emergence associates with the recruitment of circulating CD4+ monocytes. Consistently, mice depleted for human CD4+ cells but not CD3+ T-cells failed to robustly clear infectious viruses and displayed signatures of chronic infection. Our findings uncover the transient differentiation of extravascular iMO from CD4+ monocytes as a major hallmark of SARS-CoV-2 infection resolution and open avenues for unravelling viral and host adaptations defining persistently active SARS-CoV-2 infection.

Gastrointestinal symptoms in COVID-19 and disease severity: a Japanese registry-based retrospective cohort study

BACKGROUND

Research on whether gastrointestinal symptoms correlate with the severity of Coronavirus Disease 2019 (COVID-19) has been inconclusive. This study aimed to clarify any associations between gastrointestinal symptoms and the prognosis of COVID-19.

METHODS

We collected data from the Japanese nationwide registry for COVID-19 to conduct a retrospective cohort study. Data from 3498 Japanese COVID-19 patients, diagnosed at 74 facilities between February 2020 and August 2022, were analyzed in this study. Hospitalized patients were followed up until discharge or transfer to another hospital. Outpatients were observed until the end of treatment. Associations between gastrointestinal symptoms and clinical outcomes were investigated using multivariable-adjusted logistic regression models.

RESULTS

The prevalence of diarrhea, nausea/vomiting, abdominal pain, and melena were 16.6% (581/3498), 8.9% (311/3498), 3.5% (121/3498), and 0.7% (23/3498), respectively. In the univariable analysis, admission to intensive care unit (ICU) and requirement for mechanical ventilation were less common in patients with diarrhea than those without (ICU, 15.7% vs. 20.6% (p = 0.006); mechanical ventilation, 7.9% vs. 11.4% (p = 0.013)). In the multivariable-adjusted analysis, diarrhea was associated with lower likelihood of ICU admission (adjusted odds ratio (aOR), 0.70; 95% confidence interval (CI), 0.53-0.92) and mechanical ventilation (aOR, 0.61; 95% CI, 0.42-0.89). Similar results were obtained in a sensitivity analysis with another logistic regression model that adjusted for 14 possible covariates with diarrhea (ICU; aOR, 0.70; 95% CI, 0.53-0.93; mechanical ventilation; aOR 0.62; 95% CI, 0.42-0.92).

CONCLUSIONS

Diarrhea was associated with better clinical outcomes in COVID-19 patients.

Deciphering the host genetic factors conferring susceptibility to severe COVID-19 using exome sequencing

The COVID-19 pandemic remains a significant public health concern despite the new vaccines and therapeutics. The clinical course of acute SARS-CoV-2 infection is highly variable and influenced by several factors related to the virus and the host. Numerous genetic studies, including candidate gene, exome, and genome sequencing studies, genome-wide association studies, and other omics efforts, have proposed various Mendelian and non-Mendelian associations with COVID-19 course. In this study, we conducted whole-exome sequencing on 90 unvaccinated patients from Turkey with no known comorbidities associated with severe COVID-19. Of these patients, 30 had severe, 30 had moderate, and 30 had mild/asymptomatic disease. We identified rare variants in genes associated with SARS-CoV-2 susceptibility and pathogenesis, with an emphasis on genes related to the regulation of inflammation, and discussed these in the context of the clinical course of the patients. In addition, we compared the frequencies of common variants between each group. Even though no variant remained statistically significant after correction for multiple testing, we observed that certain previously associated genes and variants showed significant associations before correction. Our study contributes to the existing literature regarding the genetic susceptibility to SARS-CoV-2. Future studies would be beneficial characterizing the host genetic properties in different populations.

Extracting immunological and clinical heterogeneity across autoimmune rheumatic diseases by cohort-wide immunophenotyping

OBJECTIVE

Extracting immunological and clinical heterogeneity across autoimmune rheumatic diseases (AIRDs) is essential towards personalised medicine.

METHODS

We conducted large-scale and cohort-wide immunophenotyping of 46 peripheral immune cells using Human Immunology Protocol of comprehensive 8-colour flow cytometric analysis. Dataset consisted of >1000 Japanese patients of 11 AIRDs with deep clinical information registered at the FLOW study, including rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). In-depth clinical and immunological characterisation was conducted for the identified RA patient clusters, including associations of inborn human genetics represented by Polygenic Risk Score (PRS).

RESULTS

Multimodal clustering of immunophenotypes deciphered underlying disease-cell type network in immune cell, disease and patient cluster resolutions. This provided immune cell type specificity shared or distinct across AIRDs, such as close immunological network between mixed connective tissue disease and SLE. Individual patient-level clustering dissected patients with AIRD into several clusters with different immunological features. Of these, RA-like or SLE-like clusters were exclusively dominant, showing immunological differentiation between RA and SLE across AIRDs. In-depth clinical analysis of RA revealed that such patient clusters differentially defined clinical heterogeneity in disease activity and treatment responses, such as treatment resistance in patients with RA with SLE-like immunophenotypes. PRS based on RA case-control and within-case stratified genome-wide association studies were associated with clinical and immunological characteristics. This pointed immune cell type implicated in disease biology such as dendritic cells for RA-interstitial lung disease.

CONCLUSION

Cohort-wide and cross-disease immunophenotyping elucidate clinically heterogeneous patient subtypes existing within single disease in immune cell type-specific manner.

Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences

IMPORTANCE

Determining antigen and epitope specificity is an essential step in the discovery of therapeutic antibodies as well as in the analysis adaptive immune responses to disease or vaccination. Despite extensive efforts, deciphering antigen specificity solely from BCR amino acid sequence remains a challenging task, requiring a combination of experimental and computational approaches. Here, we describe and experimentally validate a simple and straightforward approach for grouping antibodies that share antigen and epitope specificities based on their CDR sequence similarity. This approach allows us to identify the specificities of a large number of antibodies whose antigen targets are unknown, using a small fraction of antibodies with well-annotated binding specificities.

Participation of Single-Nucleotide Variants in IFNAR1 and IFNAR2 in the Immune Response against SARS-CoV-2 Infection: A Systematic Review

Host genetic factors significantly influence susceptibility to SARS-CoV-2 infection and COVID-19 severity. Among these genetic factors are single-nucleotide variants (SNVs). IFNAR2 and IFNAR1 genes have been associated with severe COVID-19 in populations from the United Kingdom, Africa, and Latin America. IFNAR1 and IFNAR2 are subunits forming the type I interferon receptor (IFNAR). SNVs in the IFNAR genes impact protein function, affecting antiviral response and disease phenotypes. This systematic review aimed to describe IFNAR1 and IFNAR2 variants associated with COVID-19 susceptibility and severity. Accordingly, the current review focused on IFNAR1 and IFNAR2 studies published between January 2021 and February 2023, utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol. The electronic search was conducted in PubMed databases using Boolean operators and inclusion and exclusion criteria. Of the 170 literature pieces, 11 studies were included. We include case reports of rare SNVs, defined by minor allele frequency (MAF) < 1%, and genome-wide associated studies (GWAS). Variants in IFNAR1 and IFNAR2 could potentially be new targets for therapies that limit the infection and the resulting inflammation by SARS-CoV-2 infection.

Genetic examination of hematological parameters in SARS-CoV-2 infection and COVID-19

People hospitalized with COVID-19 often exhibit altered hematological traits associated with disease prognosis (e.g., lower lymphocyte and platelet counts). We investigated whether inter-individual variability in baseline hematological traits influences risk of acute SARS-CoV-2 infection or progression to severe COVID-19. We report inconsistent associations between blood cell traits with incident SARS-CoV-2 infection and severe COVID-19 in UK Biobank and the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD). Since genetically determined blood cell measures better represent cell abundance across the lifecourse, we also assessed the shared genetic architecture of baseline blood cell traits on COVID-19 related outcomes by Mendelian randomization (MR) analyses. We found significant relationships between COVID-19 severity and mean sphered cell volume after adjusting for multiple testing. However, MR results differed significantly across different freezes of COVID-19 summary statistics and genetic correlation between these traits was modest (0.1), decreasing our confidence in these results. We observed overlapping genetic association signals between other hematological and COVID-19 traits at specific loci such as MAPT and TYK2. In conclusion, we did not find convincing evidence of relationships between the genetic architecture of blood cell traits and either SARS-CoV-2 infection or COVID-19 hospitalization, though we do see evidence of shared signals at specific loci.

Adaptive best subset selection algorithm and genetic algorithm aided ensemble learning method identified a robust severity score of COVID-19 patients

We used an integrated ensemble learning method to build a stable prediction model for severity in COVID-19 patients, which was validated in multicenter cohorts.