A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

The characterization of CD8+ T-cell responses in COVID-19

This review gives an overview of the protective role of CD8+ T cells in SARS-CoV-2 infection. The cross-reactive responses intermediated by CD8+ T cells in unexposed cohorts are described. Additionally, the relevance of resident CD8+ T cells in the upper and lower airway during infection and CD8+ T-cell responses following vaccination are discussed, including recent worrisome breakthrough infections and variants of concerns (VOCs). Lastly, we explain the correlation between CD8+ T cells and COVID-19 severity. This review aids in a deeper comprehension of the association between CD8+ T cells and SARS-CoV-2 and broadens a vision for future exploration.

Discovery of sparse, reliable omic biomarkers with Stabl

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

Transcriptional control of interferon-stimulated genes

Interferon-induced genes are among the best-studied groups of coregulated genes. Nevertheless, intense research into their regulation, supported by new technologies, is continuing to provide insights into their many layers of transcriptional regulation and to reveal how cellular transcriptomes change with pathogen-induced innate and adaptive immunity. This article gives an overview of recent findings on interferon-induced gene regulation, paying attention to contributions beyond the canonical JAK-STAT pathways.

A spatially resolved single-cell lung atlas integrated with clinical and blood signatures distinguishes COVID-19 disease trajectories

COVID-19 is characterized by a broad range of symptoms and disease trajectories. Understanding the correlation between clinical biomarkers and lung pathology during acute COVID-19 is necessary to understand its diverse pathogenesis and inform more effective treatments. Here, we present an integrated analysis of longitudinal clinical parameters, peripheral blood markers, and lung pathology in 142 Brazilian patients hospitalized with COVID-19. We identified core clinical and peripheral blood signatures differentiating disease progression between patients who recovered from severe disease compared with those who succumbed to the disease. Signatures were heterogeneous among fatal cases yet clustered into two patient groups: "early death" (<15 days until death) and "late death" (>15 days). Progression to early death was characterized systemically and in lung histopathological samples by rapid endothelial and myeloid activation and the presence of thrombi associated with SARS-CoV-2+ macrophages. In contrast, progression to late death was associated with fibrosis, apoptosis, and SARS-CoV-2+ epithelial cells in postmortem lung tissue. In late death cases, cytotoxicity, interferon, and T helper 17 (TH17) signatures were only detectable in the peripheral blood after 2 weeks of hospitalization. Progression to recovery was associated with higher lymphocyte counts, TH2 responses, and anti-inflammatory-mediated responses. By integrating antemortem longitudinal blood signatures and spatial single-cell lung signatures from postmortem lung samples, we defined clinical parameters that could be used to help predict COVID-19 outcomes.

scConfluence: single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features

The abundance of unpaired multimodal single-cell data has motivated a growing body of research into the development of diagonal integration methods. However, the state-of-the-art suffers from the loss of biological information due to feature conversion and struggles with modality-specific populations. To overcome these crucial limitations, we here introduce scConfluence, a method for single-cell diagonal integration. scConfluence combines uncoupled autoencoders on the complete set of features with regularized Inverse Optimal Transport on weakly connected features. We extensively benchmark scConfluence in several single-cell integration scenarios proving that it outperforms the state-of-the-art. We then demonstrate the biological relevance of scConfluence in three applications. We predict spatial patterns for Scgn, Synpr and Olah in scRNA-smFISH integration. We improve the classification of B cells and Monocytes in highly heterogeneous scRNA-scATAC-CyTOF integration. Finally, we reveal the joint contribution of Fezf2 and apical dendrite morphology in Intra Telencephalic neurons, based on morphological images and scRNA.

Immune system adaptation during gender-affirming testosterone treatment

Infectious, inflammatory and autoimmune conditions present differently in males and females. SARS-CoV-2 infection in naive males is associated with increased risk of death, whereas females are at increased risk of long COVID1, similar to observations in other infections2. Females respond more strongly to vaccines, and adverse reactions are more frequent3, like most autoimmune diseases4. Immunological sex differences stem from genetic, hormonal and behavioural factors5 but their relative importance is only partially understood6-8. In individuals assigned female sex at birth and undergoing gender-affirming testosterone therapy (trans men), hormone concentrations change markedly but the immunological consequences are poorly understood. Here we performed longitudinal systems-level analyses in 23 trans men and found that testosterone modulates a cross-regulated axis between type-I interferon and tumour necrosis factor. This is mediated by functional attenuation of type-I interferon responses in both plasmacytoid dendritic cells and monocytes. Conversely, testosterone potentiates monocyte responses leading to increased tumour necrosis factor, interleukin-6 and interleukin-15 production and downstream activation of nuclear factor kappa B-regulated genes and potentiation of interferon-γ responses, primarily in natural killer cells. These findings in trans men are corroborated by sex-divergent responses in public datasets and illustrate the dynamic regulation of human immunity by sex hormones, with implications for the health of individuals undergoing hormone therapy and our understanding of sex-divergent immune responses in cisgender individuals.

scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison

Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group (or condition) differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or 'pseudobulking' samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. scDAPP is freely available under the MIT license, with source code, documentation and sample data at the GitHub (https://github.com/bioinfoDZ/scDAPP).

Interferon-responsive neutrophils and macrophages extricate SARS-CoV-2 Omicron critical patients from the nasty fate of sepsis

The SARS-CoV-2 Omicron variant is characterized by its high transmissibility, which has caused a worldwide epidemiological event. Yet, it turns ominous once the disease progression degenerates into severe pneumonia and sepsis, presenting a horrendous lethality. To elucidate the alveolar immune or inflammatory landscapes of Omicron critical-ill patients, we performed single-cell RNA-sequencing (scRNA-seq) of bronchoalveolar lavage fluid (BALF) from the patients with critical pneumonia caused by Omicron infection, and analyzed the correlation between the clinical severity scores and different immune cell subpopulations. In the BALF of Omicron critical patients, the alveolar violent myeloid inflammatory environment was determined. ISG15+ neutrophils and CXCL10+ macrophages, both expressed the interferon-stimulated genes (ISGs), were negatively correlated with clinical pulmonary infection score, while septic CST7+ neutrophils and inflammatory VCAN+ macrophages were positively correlated with sequential organ failure assessment. The percentages of ISG15+ neutrophils were associated with more protective alveolar epithelial cells, and may reshape CD4+ T cells to the exhaustive phenotype, thus preventing immune injuries. The CXCL10+ macrophages may promote plasmablast/plasma cell survival and activation as well as the production of specific antibodies. As compared to the previous BALF scRNA-seq data from SARS-CoV-2 wild-type/Alpha critical patients, the subsets of neutrophils and macrophages with pro-inflammatory and immunoregulatory features presented obvious distinctions, suggesting an immune disparity in Omicron variants. Overall, this study provides a BALF single-cell atlas of Omicron critical patients, and suggests that alveolar interferon-responsive neutrophils and macrophages may extricate SARS-CoV-2 Omicron critical patients from the nasty fate of sepsis.

SARS-CoV2 infection in whole lung primarily targets macrophages that display subset-specific responses

Deciphering the initial steps of SARS-CoV-2 infection, that influence COVID-19 outcomes, is challenging because animal models do not always reproduce human biological processes and in vitro systems do not recapitulate the histoarchitecture and cellular composition of respiratory tissues. To address this, we developed an innovative ex vivo model of whole human lung infection with SARS-CoV-2, leveraging a lung transplantation technique. Through single-cell RNA-seq, we identified that alveolar and monocyte-derived macrophages (AMs and MoMacs) were initial targets of the virus. Exposure of isolated lung AMs, MoMacs, classical monocytes and non-classical monocytes (ncMos) to SARS-CoV-2 variants revealed that while all subsets responded, MoMacs produced higher levels of inflammatory cytokines than AMs, and ncMos contributed the least. A Wuhan lineage appeared to be more potent than a D614G virus, in a dose-dependent manner. Amidst the ambiguity in the literature regarding the initial SARS-CoV-2 cell target, our study reveals that AMs and MoMacs are dominant primary entry points for the virus, and suggests that their responses may conduct subsequent injury, depending on their abundance, the viral strain and dose. Interfering on virus interaction with lung macrophages should be considered in prophylactic strategies.

Potential use of sodium glucose co-transporter 2 inhibitors during acute illness: a systematic review based on COVID-19

OBJECTIVE

SGLT-2i are increasingly recognized for their benefits in patients with cardiometabolic risk factors. Additionally, emerging evidence suggests potential applications in acute illnesses, including COVID-19. This systematic review aims to evaluate the effects of SGLT-2i in patients facing acute illness, particularly focusing on SARS-CoV-2 infection.

METHODS

Following PRISMA guidelines, a systematic search of PubMed, Scopus, medRxiv, Research Square, and Google Scholar identified 22 studies meeting inclusion criteria, including randomized controlled trials and observational studies. Data extraction and quality assessment were conducted independently.

RESULTS

Out of the 22 studies included in the review, six reported reduced mortality in DM-2 patients taking SGLT-2i, while two found a decreased risk of hospitalization. Moreover, one study demonstrated a lower in-hospital mortality rate in DM-2 patients under combined therapy of metformin plus SGLT-2i. However, three studies showed a neutral effect on the risk of hospitalization. No increased risk of developing COVID-19 was associated with SGLT-2i use in DM-2 patients. Prior use of SGLT-2i was not associated with ICU admission and need for MV. The risk of acute kidney injury showed variability, with inconsistent evidence regarding diabetic ketoacidosis.

CONCLUSION

Our systematic review reveals mixed findings on the efficacy of SGLT-2i use in COVID-19 patients with cardiometabolic risk factors. While some studies suggest potential benefits in reducing mortality and hospitalizations, others report inconclusive results. Further research is needed to clarify optimal usage and mitigate associated risks, emphasizing caution in clinical interpretation.

Novel immunomodulatory properties of adenosine analogs promote their antiviral activity against SARS-CoV-2

The COVID-19 pandemic reminded us of the urgent need for new antivirals to control emerging infectious diseases and potential future pandemics. Immunotherapy has revolutionized oncology and could complement the use of antivirals, but its application to infectious diseases remains largely unexplored. Nucleoside analogs are a class of agents widely used as antiviral and anti-neoplastic drugs. Their antiviral activity is generally based on interference with viral nucleic acid replication or transcription. Based on our previous work and computer modeling, we hypothesize that antiviral adenosine analogs, like remdesivir, have previously unrecognized immunomodulatory properties which contribute to their therapeutic activity. In the case of remdesivir, we here show that these properties are due to its metabolite, GS-441524, acting as an Adenosine A2A Receptor antagonist. Our findings support a new rationale for the design of next-generation antiviral agents with dual - immunomodulatory and intrinsic - antiviral properties. These compounds could represent game-changing therapies to control emerging viral diseases and future pandemics.

Peripheral Blood Mononuclear Cells and Serum Cytokines in Patients with Lupus Nephritis after COVID-19

Systemic lupus erythematosus (SLE) patients have an increased risk of infections and infection-related mortality. Therefore, during the global SARS-CoV-2 pandemic, SLE patients were particularly vulnerable to SARS-CoV-2 infections. Also, compared to other patients, SLE patients seem to develop more severe manifestations of coronavirus disease 2019 (COVID-19), with higher rates of hospitalization, invasive ventilation requirements, or death. This study evaluated the immune parameters after SARS-CoV-2 infection in SLE patients. We analyzed subpopulations of peripheral blood cells collected from patients with renal manifestation of SLE (lupus nephritis, LN). LN patients were divided into two subgroups: those unexposed to SARS-CoV-2 (LN CoV-2(-)) and those who had confirmed COVID-19 (LN-CoV-2(+)) six months earlier. We analyzed basic subpopulations of T cells, B cells, monocytes, dendritic cells (DCs), and serum cytokines using flow cytometry. All collected data were compared to a healthy control group without SARS-CoV-2 infection in medical history. LN patients were characterized by a decreased percentage of helper T (Th) cells and an increased percentage of cytotoxic T (Tc) cells regardless of SARS-CoV-2 infection. LN CoV-2(+) patients had a higher percentage of regulatory T cells (Tregs) and plasmablasts (PBs) and a lower percentage of non-switched memory (NSM) B cells compared to LN CoV-2(-) patients or healthy controls (HC CoV-2(-)). LN patients had a higher percentage of total monocytes compared with HC CoV-2(-). LN CoV-2(+) patients had a higher percentage of classical and intermediate monocytes than LN CoV-2(-) patients and HC CoV-2(-). LN CoV-2(+) patients had higher serum IL-6 levels than HC CoV-2(-), while LN CoV-2(-) patients had higher levels of serum IL-10. LN patients are characterized by disturbances in the blood's basic immunological parameters. However, SARS-CoV-2 infection influences B-cell and monocyte compartments.

Higher levels of IL-1ra, IL-6, IL-8, MCP-1, MIP-3α, MIP-3β, and fractalkine are associated with 90-day mortality in 132 non-immunomodulated hospitalized patients with COVID-19

INTRODUCTION

Immune dysregulation with an excessive release of cytokines has been identified as a key driver in the development of severe COVID-19. The aim of this study was to evaluate the initial cytokine profile associated with 90-day mortality and respiratory failure in a cohort of patients hospitalized with COVID 19 that did not receive immunomodulatory therapy.

METHODS

Levels of 45 cytokines were measured in blood samples obtained at admission from patients with confirmed COVID-19. Logistic regression analysis was utilized to determine the association between cytokine levels and outcomes. The primary outcome was death within 90 days from admission and the secondary outcome was need for mechanical ventilation.

RESULTS

A total of 132 patients were included during the spring of 2020. We found that one anti-inflammatory cytokine, one pro-inflammatory cytokine, and five chemokines were associated with the odds of 90-day mortality, specifically: interleukin-1 receptor antagonist, interleukin-6, interleukin-8, monocyte chemoattractant protein-1, macrophage inflammatory protein-3α, macrophage inflammatory protein-3β, and fractalkine. All but fractalkine were also associated with the odds of respiratory failure during admission. Monocyte chemoattractant protein-1 showed the strongest estimate of association with both outcomes.

CONCLUSION

We showed that one anti-inflammatory cytokine, one pro-inflammatory cytokine, and five chemokines were associated with 90-day mortality in patients hospitalized with COVID-19 that did not receive immunomodulatory therapy.

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.

Plasma proteome analysis and validation of patients with community-acquired pneumonia: A cohort study

PURPOSE

This study aimed to investigate the diagnostic potential of plasma biomarkers of community-acquired pneumonia (CAP) and their severity grading.

EXPERIMENTAL DESIGN

Plasma proteomes from cohort I (n = 32) with CAP were analyzed by data-independent acquisition mass spectrometry (MS). MetaboAnalyst 5.0 was used to statistically evaluate significant differences in proteins from different samples, and demographic and clinical data were recorded for all enrolled patients. Cohort II (n = 80) was used to validate candidate biomarkers. Plasma protein levels were determined using quantitative enzyme-linked immunosorbent assay (ELISA). Correlations were assessed using Pearson's correlation coefficient. A receiver operating characteristic curve was used to verify the association between the variables, CAP diagnosis, and prognosis.

RESULTS

121 differentially expressed proteins (DEPs) were obtained between CAP and controls. These DEPs were mainly aggregated in pathways of phagosome(hsa04145) and complement and coagulation cascades (hsa04610). No significant differential proteins were detected in bacterial, viral, and mixed infection groups. The plasma levels of fetuin-A, alpha-1-antichymotrypsin (AACT), α1-acid glycoprotein (A1AG), and S100A8/S100A9 heterodimers detected by ELISA were consistent with those of MS. AACT, A1AG, S100A8/S100A9 heterodimer, and fetuin-A can potentially be used as diagnostic predictors, and fetuin-A and AACT are potential predictors of SCAP.

CONCLUSIONS AND CLINICAL RELEVANCE

Plasma protein profiling can successfully identify potential biomarkers for CAP diagnosis and disease severity assessment. These biomarkers should be further studied for their clinical application.

Chemokine receptors in COVID-19 infection

Chemokine receptors play diverse roles in the immune response against pathogens by recruiting innate and adaptive immune cells to sites of infection. However, their involvement could also be detrimental, causing tissue damage and exacerbating respiratory diseases by triggering histological alterations such as fibrosis and remodeling. This chapter reviews the role of chemokine receptors in the immune defense against SARS-CoV-2 infection. In COVID-19, CXCR3 is expressed mainly in T cells, and its upregulation is related to an increase in SARS-CoV-2-specific antibodies but also to COVID-19 severity. CCR5 is a key player in T-cell recruitment, and its suppression leads to reduced inflammation and viremia levels. Conversely, CXCR6 is implicated in the aberrant migration of memory T cells within airways. On the other hand, increased CCR4+ cells in the blood and decreased CCR4+ cells in lung cells are associated with severe COVID-19. Additionally, CCR2 is associated with an increase in macrophage recruitment to lung tissues. Elevated levels of CXCR1 and CXCR2, which are predominantly expressed in neutrophils, are associated with the severity of the disease, and finally, the expression of CX3CR1 in cytotoxic T lymphocytes affects the retention of these cells in lung tissues, thereby impacting the severity of COVID-19. Despite the efforts of many clinical trials to find effective therapies for COVID-19 using chemokine receptor inhibitors, no conclusive results have been found due to the small number of patients, redundancy, and co-expression of chemokine receptors by immune cells, which explains the difficulty in finding a single therapeutic target or effective treatment.

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.

High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response

Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is a global health challenge requiring better understanding of pathophysiology and new therapeutic approaches. Here, we applied high-throughput tandem mass spectrometry to delineate the plasma proteome for sepsis and comparator groups (noninfected critical illness, postoperative inflammation, and healthy volunteers) involving 2612 samples (from 1611 patients) and 4553 liquid chromatography-mass spectrometry analyses acquired through a single batch of continuous measurements, with a throughput of 100 samples per day. We show how this scale of data can delineate proteins, pathways, and coexpression modules in sepsis and be integrated with paired leukocyte transcriptomic data (837 samples from n = 649 patients). We mapped the plasma proteomic landscape of the host response in sepsis, including changes over time, and identified features relating to etiology, clinical phenotypes (including organ failures), and severity. This work reveals subphenotypes informative for sepsis response state, disease processes, and outcome; identifies potential biomarkers; and advances opportunities for a precision medicine approach to sepsis.

Gene expression signatures in blood from a West African sepsis cohort define host response phenotypes

Our limited understanding of the pathophysiological mechanisms that operate during sepsis is an obstacle to rational treatment and clinical trial design. There is a critical lack of data from low- and middle-income countries where the sepsis burden is increased which inhibits generalized strategies for therapeutic intervention. Here we perform RNA sequencing of whole blood to investigate longitudinal host response to sepsis in a Ghanaian cohort. Data dimensional reduction reveals dynamic gene expression patterns that describe cell type-specific molecular phenotypes including a dysregulated myeloid compartment shared between sepsis and COVID-19. The gene expression signatures reported here define a landscape of host response to sepsis that supports interventions via targeting immunophenotypes to improve outcomes.

Biological basis of critical illness subclasses: from the bedside to the bench and back again

Critical illness syndromes including sepsis, acute respiratory distress syndrome, and acute kidney injury (AKI) are associated with high in-hospital mortality and long-term adverse health outcomes among survivors. Despite advancements in care, clinical and biological heterogeneity among patients continues to hamper identification of efficacious therapies. Precision medicine offers hope by identifying patient subclasses based on clinical, laboratory, biomarker and 'omic' data and potentially facilitating better alignment of interventions. Within the previous two decades, numerous studies have made strides in identifying gene-expression based endotypes and clinico-biomarker based phenotypes among critically ill patients associated with differential outcomes and responses to treatment. In this state-of-the-art review, we summarize the biological similarities and differences across the various subclassification schemes among critically ill patients. In addition, we highlight current translational gaps, the need for advanced scientific tools, human-relevant disease models, to gain a comprehensive understanding of the molecular mechanisms underlying critical illness subclasses.

TIGIT+Tfh show poor B-helper function and negatively correlate with SARS-CoV-2 antibody titre

Circulating follicular helper T cells (cTfh) can show phenotypic alterations in disease settings, including in the context of tissue-damaging autoimmune or anti-viral responses. Using severe COVID-19 as a paradigm of immune dysregulation, we have explored how cTfh phenotype relates to the titre and quality of antibody responses. Severe disease was associated with higher titres of neutralising S1 IgG and evidence of increased T cell activation. ICOS, CD38 and HLA-DR expressing cTfh correlated with serum S1 IgG titres and neutralising strength, and interestingly expression of TIGIT by cTfh showed a negative correlation. TIGIT+cTfh expressed increased IFNγ and decreased IL-17 compared to their TIGIT-cTfh counterparts, and showed reduced capacity to help B cells in vitro. Additionally, TIGIT+cTfh expressed lower levels of CD40L than TIGIT-cTfh, providing a potential explanation for their poor B-helper function. These data identify phenotypic changes in polyclonal cTfh that correlate with specific antibody responses and reveal TIGIT as a marker of cTfh with altered function.

Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by highly heterogeneous manifestations ranging from asymptomatic cases to death for still incompletely understood reasons. As part of the IMmunoPhenotyping Assessment in a COVID-19 Cohort study, we mapped the plasma proteomes of 1117 hospitalized patients with COVID-19 from 15 hospitals across the United States. Up to six samples were collected within ~28 days of hospitalization resulting in one of the largest COVID-19 plasma proteomics cohorts with 2934 samples. Using perchloric acid to deplete the most abundant plasma proteins allowed for detecting 2910 proteins. Our findings show that increased levels of neutrophil extracellular trap and heart damage markers are associated with fatal outcomes. Our analysis also identified prognostic biomarkers for worsening severity and death. Our comprehensive longitudinal plasma proteomics study, involving 1117 participants and 2934 samples, allowed for testing the generalizability of the findings of many previous COVID-19 plasma proteomics studies using much smaller cohorts.

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

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

Effect of Immunosuppression on the Immune Response to SARS-CoV-2 Infection and Vaccination

Immunosuppressive treatment in patients with rheumatic diseases can maintain disease remission but also increase risk of infection. Their response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination is frequently blunted. In this study we evaluated the effect of immunosuppression exposure on humoral and T cell immune responses to SARS-CoV-2 infection and vaccination in two distinct cohorts of patients; one during acute SARS-CoV-2 infection and 3 months later during convalescence, and another prior to SARS-CoV-2 vaccination, with follow up sampling 6 weeks after vaccination. Results were compared between rituximab-exposed (in previous 6 months), immunosuppression-exposed (in previous 3 months), and non-immunosuppressed groups. The immune cell phenotype was defined by flow cytometry and ELISA. Antigen specific T cell responses were estimated using a whole blood stimulation interferon-γ release assay. A focused post-vaccine assessment of rituximab-treated patients using high dimensional spectral cytometry was conducted. Acute SARS-CoV-2 infection was characterised by T cell lymphopenia, and a reduction in NK cells and naïve CD4 and CD8 cells, without any significant differences between immunosuppressed and non-immunosuppressed patient groups. Conversely, activated CD4 and CD8 cell counts increased in non-immunosuppressed patients with acute SARS-CoV-2 infection but this response was blunted in the presence of immunosuppression. In rituximab-treated patients, antigen-specific T cell responses were preserved in SARS-CoV-2 vaccination, but patients were unable to mount an appropriate humoral response.

Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions

T-cells recognize antigens and induce specialized gene expression programs (GEPs) enabling functions including proliferation, cytotoxicity, and cytokine production. Traditionally, different classes of helper T-cells express mutually exclusive responses - for example, Th1, Th2, and Th17 programs. However, new single-cell RNA sequencing (scRNA-Seq) experiments have revealed a continuum of T-cell states without discrete clusters corresponding to these subsets, implying the need for new analytical frameworks. Here, we advance the characterization of T-cells with T-CellAnnoTator (TCAT), a pipeline that simultaneously quantifies pre-defined GEPs capturing activation states and cellular subsets. From 1,700,000 T-cells from 700 individuals across 38 tissues and five diverse disease contexts, we discover 46 reproducible GEPs reflecting the known core functions of T-cells including proliferation, cytotoxicity, exhaustion, and T helper effector states. We experimentally characterize several novel activation programs and apply TCAT to describe T-cell activation and exhaustion in Covid-19 and cancer, providing insight into T-cell function in these diseases.

Integrated longitudinal multiomics study identifies immune programs associated with acute COVID-19 severity and mortality

BACKGROUNDPatients hospitalized for COVID-19 exhibit diverse clinical outcomes, with outcomes for some individuals diverging over time even though their initial disease severity appears similar to that of other patients. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity.METHODSWe performed deep immunophenotyping and conducted longitudinal multiomics modeling, integrating 10 assays for 1,152 Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study participants and identifying several immune cascades that were significant drivers of differential clinical outcomes.RESULTSIncreasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, formation of neutrophil extracellular traps, and T cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma Igs and B cells and dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to failure of viral clearance in patients with fatal illness.CONCLUSIONOur longitudinal multiomics profiling study revealed temporal coordination across diverse omics that potentially explain the disease progression, providing insights that can inform the targeted development of therapies for patients hospitalized with COVID-19, especially those who are critically ill.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNIH (5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-18); NIAID, NIH (3U19AI1289130, U19AI128913-04S1, and R01AI122220); and National Science Foundation (DMS2310836).

Identification of Age-Related Characteristic Genes Involved in Severe COVID-19 Infection Among Elderly Patients Using Machine Learning and Immune Cell Infiltration Analysis

Elderly patients infected with severe acute respiratory syndrome coronavirus 2 are at higher risk of severe clinical manifestation, extended hospitalization, and increased mortality. Those patients are more likely to experience persistent symptoms and exacerbate the condition of basic diseases with long COVID-19 syndrome. However, the molecular mechanisms underlying severe COVID-19 in the elderly patients remain unclear. Our study aims to investigate the function of the interaction between disease-characteristic genes and immune cell infiltration in patients with severe COVID-19 infection. COVID-19 datasets (GSE164805 and GSE180594) and aging dataset (GSE69832) were obtained from the Gene Expression Omnibus database. The combined different expression genes (DEGs) were subjected to Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Diseases Ontology functional enrichment analysis, Gene Set Enrichment Analysis, machine learning, and immune cell infiltration analysis. GO and KEGG enrichment analyses revealed that the eight DEGs (IL23A, PTGER4, PLCB1, IL1B, CXCR1, C1QB, MX2, ALOX12) were mainly involved in inflammatory mediator regulation of TRP channels, coronavirus disease-COVID-19, and cytokine activity signaling pathways. Three-degree algorithm (LASSO, SVM-RFE, KNN) and correlation analysis showed that the five DEGs up-regulated the immune cells of macrophages M0/M1, memory B cells, gamma delta T cell, dendritic cell resting, and master cell resisting. Our study identified five hallmark genes that can serve as disease-characteristic genes and target immune cells infiltrated in severe COVID-19 patients among the elderly population, which may contribute to the study of pathogenesis and the evaluation of diagnosis and prognosis in aging patients infected with severe COVID-19.

Multi-omics analysis reveals COVID-19 vaccine induced attenuation of inflammatory responses during breakthrough disease

The immune mechanisms mediating COVID-19 vaccine attenuation of COVID-19 remain undescribed. We conducted comprehensive analyses detailing immune responses to SARS-CoV-2 virus in blood post-vaccination with ChAdOx1 nCoV-19 or a placebo. Samples from randomised placebo-controlled trials (NCT04324606 and NCT04400838) were taken at baseline, onset of COVID-19-like symptoms, and 7 days later, confirming COVID-19 using nucleic amplification test (NAAT test) via real-time PCR (RT-PCR). Serum cytokines were measured with multiplexed immunoassays. The transcriptome was analysed with long, short and small RNA sequencing. We found attenuation of RNA inflammatory signatures in ChAdOx1 nCoV-19 compared with placebo vaccinees and reduced levels of serum proteins associated with COVID-19 severity. KREMEN1, a putative alternative SARS-CoV-2 receptor, was downregulated in placebo compared with ChAdOx1 nCoV-19 vaccinees. Vaccination ameliorates reductions in cell counts across leukocyte populations and platelets noted at COVID-19 onset, without inducing potentially deleterious Th2-skewed immune responses. Multi-omics integration links a global reduction in miRNA expression at COVID-19 onset to increased pro-inflammatory responses at the mRNA level. This study reveals insights into the role of COVID-19 vaccines in mitigating disease severity by abrogating pro-inflammatory responses associated with severe COVID-19, affirming vaccine-mediated benefit in breakthrough infection, and highlighting the importance of clinically relevant endpoints in vaccine evaluation.

Unravelling B cell heterogeneity: insights into flow cytometry-gated B cells from single-cell multi-omics data

Introduction

B cells play a pivotal role in adaptive immunity which has been extensively characterised primarily via flow cytometry-based gating strategies. This study addresses the discrepancies between flow cytometry-defined B cell subsets and their high-confidence molecular signatures using single-cell multi-omics approaches.

Methods

By analysing multi-omics single-cell data from healthy individuals and patients across diseases, we characterised the level and nature of cellular contamination within standard flow cytometric-based gating, resolved some of the ambiguities in the literature surrounding unconventional B cell subsets, and demonstrated the variable effects of flow cytometric-based gating cellular heterogeneity across diseases.

Results

We showed that flow cytometric-defined B cell populations are heterogenous, and the composition varies significantly between disease states thus affecting the implications of functional studies performed on these populations. Importantly, this paper draws caution on findings about B cell selection and function of flow cytometric-sorted populations, and their roles in disease. As a solution, we developed a simple tool to identify additional markers that can be used to increase the purity of flow-cytometric gated immune cell populations based on multi-omics data (AlliGateR). Here, we demonstrate that additional non-linear CD20, CD21 and CD24 gating can increase the purity of both naïve and memory populations.

Discussion

These findings underscore the need to reconsider B cell subset definitions within the literature and propose leveraging single-cell multi-omics data for refined characterisation. We show that single-cell multi-omics technologies represent a powerful tool to bridge the gap between surface marker-based annotations and the intricate molecular characteristics of B cell subsets.

[Prevention and treatment of COVID-19 based on post-genomic pharmacological analysis: Systematic computer analysis of 290,000 scientific articles on COVID-19]

The COVID-19 pandemic has highlighted pressing challenges in biomedical research methodology. It has become obvious that the rapid and effective development of treatments for "new" viral infections is impossible without the coordination of interdisciplinary research and in-depth analysis of data obtained within the framework of the post-genomic paradigm. Presents the results of a systematic computer analysis of 290,000 scientific articles on COVID-19, with an emphasis on the results of post-genomic studies of SARS-CoV-2. The futility of the overly simplified approach, which considers only one "most important receptor protein", only one "key virus gene", etc., is shown. It is shown how post-genomic technologies will make it possible to find informative biomarkers of severe coronavirus infection, including those based on complex immune disorders associated with COVID-19.

The Plasma Lipidomic Landscape in Patients with Sepsis due to Community-acquired Pneumonia

Rationale: The plasma lipidome has the potential to reflect many facets of the host status during severe infection. Previous work is limited to specific lipid groups or was focused on lipids as prognosticators.Objectives: To map the plasma lipidome during sepsis due to community-acquired pneumonia (CAP) and determine the disease specificity and associations with clinical features.Methods: We analyzed 1,833 lipid species across 33 classes in 169 patients admitted to the ICU with sepsis due to CAP, 51 noninfected ICU patients, and 48 outpatient controls. In a paired analysis, we reanalyzed patients still in the ICU 4 days after admission (n = 82).Measurements and Main Results: A total of 58% of plasma lipids were significantly lower in patients with CAP-attributable sepsis compared with outpatient controls (6% higher, 36% not different). We found strong lipid class-specific associations with disease severity, validated across two external cohorts, and inflammatory biomarkers, in which triacylglycerols, cholesterol esters, and lysophospholipids exhibited the strongest associations. A total of 36% of lipids increased over time, and stratification by survival revealed diverging lipid recovery, which was confirmed in an external cohort; specifically, a 10% increase in cholesterol ester levels was related to a lower odds ratio (0.84; P = 0.006) for 30-day mortality (absolute mortality, 18 of 82). Comparison with noninfected ICU patients delineated a substantial common illness response (57.5%) and a distinct lipidomic signal for patients with CAP-attributable sepsis (37%).Conclusions: Patients with sepsis due to CAP exhibit a time-dependent and partially disease-specific shift in their plasma lipidome that correlates with disease severity and systemic inflammation and is associated with higher mortality.

Principled and interpretable alignability testing and integration of single-cell data

Single-cell data integration can provide a comprehensive molecular view of cells, and many algorithms have been developed to remove unwanted technical or biological variations and integrate heterogeneous single-cell datasets. Despite their wide usage, existing methods suffer from several fundamental limitations. In particular, we lack a rigorous statistical test for whether two high-dimensional single-cell datasets are alignable (and therefore should even be aligned). Moreover, popular methods can substantially distort the data during alignment, making the aligned data and downstream analysis difficult to interpret. To overcome these limitations, we present a spectral manifold alignment and inference (SMAI) framework, which enables principled and interpretable alignability testing and structure-preserving integration of single-cell data with the same type of features. SMAI provides a statistical test to robustly assess the alignability between datasets to avoid misleading inference and is justified by high-dimensional statistical theory. On a diverse range of real and simulated benchmark datasets, it outperforms commonly used alignment methods. Moreover, we show that SMAI improves various downstream analyses such as identification of differentially expressed genes and imputation of single-cell spatial transcriptomics, providing further biological insights. SMAI's interpretability also enables quantification and a deeper understanding of the sources of technical confounders in single-cell data.

Unraveling Links between Chronic Inflammation and Long COVID: Workshop Report

As COVID-19 continues, an increasing number of patients develop long COVID symptoms varying in severity that last for weeks, months, or longer. Symptoms commonly include lingering loss of smell and taste, hearing loss, extreme fatigue, and "brain fog." Still, persistent cardiovascular and respiratory problems, muscle weakness, and neurologic issues have also been documented. A major problem is the lack of clear guidelines for diagnosing long COVID. Although some studies suggest that long COVID is due to prolonged inflammation after SARS-CoV-2 infection, the underlying mechanisms remain unclear. The broad range of COVID-19's bodily effects and responses after initial viral infection are also poorly understood. This workshop brought together multidisciplinary experts to showcase and discuss the latest research on long COVID and chronic inflammation that might be associated with the persistent sequelae following COVID-19 infection.

Commonly used software tools produce conflicting and overly-optimistic AUPRC values

The precision-recall curve (PRC) and the area under it (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluated 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in >3,000 published studies. We found the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.

Phenotype prediction from single-cell RNA-seq data using attention-based neural networks

MOTIVATION

A patient's disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recent advances in single-cell RNA sequencing (scRNA-seq) enable gene expression profiling in cell-level resolution, and therefore have the potential to identify those cells driving the disease phenotype even while the number of these cells is small. However, most existing methods rely heavily on accurate cell type detection, and the number of available annotated samples is usually too small for training deep learning predictive models.

RESULTS

Here, we propose the method ScRAT for phenotype prediction using scRNA-seq data. To train ScRAT with a limited number of samples of different phenotypes, such as coronavirus disease (COVID) and non-COVID, ScRAT first applies a mixup module to increase the number of training samples. A multi-head attention mechanism is employed to learn the most informative cells for each phenotype without relying on a given cell type annotation. Using three public COVID datasets, we show that ScRAT outperforms other phenotype prediction methods. The performance edge of ScRAT over its competitors increases as the number of training samples decreases, indicating the efficacy of our sample mixup. Critical cell types detected based on high-attention cells also support novel findings in the original papers and the recent literature. This suggests that ScRAT overcomes the challenge of missing marker genes and limited sample number with great potential revealing novel molecular mechanisms and/or therapies.

AVAILABILITY AND IMPLEMENTATION

The code of our proposed method ScRAT is published at https://github.com/yuzhenmao/ScRAT.

Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients

The pathobiology of respiratory failure in COVID-19 consists of a complex interplay between viral cytopathic effects and a dysregulated host immune response. In critically ill patients, imatinib treatment demonstrated potential for reducing invasive ventilation duration and mortality. Here, we perform longitudinal profiling of 6385 plasma proteins in 318 hospitalised patients to investigate the biological processes involved in critical COVID-19, and assess the effects of imatinib treatment. Nine proteins measured at hospital admission accurately predict critical illness development. Next to dysregulation of inflammation, critical illness is characterised by pathways involving cellular adhesion, extracellular matrix turnover and tissue remodelling. Imatinib treatment attenuates protein perturbations associated with inflammation and extracellular matrix turnover. These proteomic alterations are contextualised using external pulmonary RNA-sequencing data of deceased COVID-19 patients and imatinib-treated Syrian hamsters. Together, we show that alveolar capillary barrier disruption in critical COVID-19 is reflected in the plasma proteome, and is attenuated with imatinib treatment. This study comprises a secondary analysis of both clinical data and plasma samples derived from a clinical trial that was registered with the EU Clinical Trials Register (EudraCT 2020-001236-10, https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001236-10/NL ) and Netherlands Trial Register (NL8491, https://www.trialregister.nl/trial/8491 ).

Lymphocyte and neutrophil count combined with intestinal bacteria abundance predict the severity of COVID-19

IMPORTANCE

The 2019 coronavirus disease (COVID-19) patients had a unique profile of gut bacteria. In this study, we characterized the intestinal bacteria in our COVID-19 cohorts and found that there was an increased incidence of severe cases in COVID-19 patients with decreased lymphocytes and increased neutrophils. Levels of lymphocytes and neutrophils and abundances of intestinal bacteria correlated with the severity of COVID-19.

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.

The death rate of COVID-19 infection in different SARS-CoV-2 variants was related to C-reactive protein gene polymorphisms

The serum level of C-reactive protein (CRP) is a significant independent risk factor for Coronavirus disease 2019 (COVID-19). A link was found between serum CRP and genetic diversity within the CRP gene in earlier research. This study examined whether CRP rs1205 and rs1800947 polymorphisms were associated with COVID-19 mortality among various severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) variants. We genotyped CRP rs1205 and rs1800947 polymorphisms in 2023 deceased and 2307 recovered patients using the polymerase chain reaction-restriction fragment length polymorphism method. There was a significant difference between the recovered and the deceased patients in terms of the minor allele frequency of CRP rs1205 T and rs1800947 G. In all three variants, COVID-19 mortality rates were associated with CRP rs1800947 GG genotype. Furthermore, CRP rs1205 CC and rs1800947 GG genotypes showed higher CRP levels. It was found that the G-T haplotype was prevalent in all SARS-CoV-2 variants. The C-C and C-T haplotypes were statistically significant in Delta and Omicron BA.5 variants, respectively. In conclusion, polymorphisms within the CRP gene may relate to serum CRP levels and mortality among COVID-19 patients. In order to verify the utility of CRP polymorphism correlation in predicting COVID-19 mortality, a replication of these results is needed.

Analysis of comprehensive biomolecules in critically ill patients via bioinformatics technologies

Each patient with a critical illness such as sepsis and severe trauma has a different genetic background, comorbidities, age, and sex. Moreover, pathophysiology changes dynamically over time even in the same patient. Therefore, individualized treatment is necessary to account for heterogeneity in patient backgrounds. Recently, the analysis of comprehensive biomolecular information using clinical specimens has revealed novel molecular pathological classifications called subtypes. In addition, comprehensive biomolecular information using clinical specimens has enabled reverse translational research, which is a data-driven approach to the identification of drug target molecules. The development of these methods is expected to visualize the heterogeneity of patient backgrounds and lead to personalized therapy.

The causal association between COVID-19 and herpes simplex virus: a Mendelian randomization study

Introduction

The coronavirus disease 2019 (COVID-19) has emerged as a main global public health challenge. Additionally, herpes simplex virus type-1 (HSV-1) and type 2 (HSV-2) are widespread viruses that can cause orolabial herpes and genital herpes. Several clinical case reports have declared a possible association between the two, however, the causal relationship between them has not been clarified.

Methods

This study utilized a Mendelian randomization (MR) approach for causality assessment between COVID-19 infection and HSV infection based on the latest public health data and Genome-Wide Association Study (GWAS) data. Multiple causal estimation methods, such as IVW, weighted median, simple mode, and weighted mode, were employed to validate the causal relation between COVID-19 infection and HSV infection, with COVID-19 infection, COVID-19 hospitalization, and severe COVID-19 as exposures, and HSV1/2 infection as the outcome. A reverse MR analysis was subsequently performed.

Results

MR analysis exhibited that COVID-19 infection was relevant to a reduced risk of HSV1 infection (p=7.603239e-152, OR=0.5690, 95%CI=0.5455-0.5935, IVW). Regarding the effect of COVID-19 infection on HSV2, MR analysis suggested that COVID-19 infection was correlated with an augmented risk of HSV2 infection (p=6.46735e-11, OR=1.1137, 95%CI=1.0782-1.1502, IVW). The reverse MR analysis did not demonstrate a reverse causal relationship between HSV and COVID-19.

Discussion

Altogether, COVID-19 infection might cause a decreased risk of HSV1 infection and an elevated risk of HSV2 infection.

Non-coding RNAs expression in SARS-CoV-2 infection: pathogenesis, clinical significance, and therapeutic targets

The coronavirus disease 2019 (COVID-19) pandemic has been looming globally for three years, yet the diagnostic and treatment methods for COVID-19 are still undergoing extensive exploration, which holds paramount importance in mitigating future epidemics. Host non-coding RNAs (ncRNAs) display aberrations in the context of COVID-19. Specifically, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) exhibit a close association with viral infection and disease progression. In this comprehensive review, an overview was presented of the expression profiles of host ncRNAs following SARS-CoV-2 invasion and of the potential functions in COVID-19 development, encompassing viral invasion, replication, immune response, and multiorgan deficits which include respiratory system, cardiac system, central nervous system, peripheral nervous system as well as long COVID. Furthermore, we provide an overview of several promising host ncRNA biomarkers for diverse clinical scenarios related to COVID-19, such as stratification biomarkers, prognostic biomarkers, and predictive biomarkers for treatment response. In addition, we also discuss the therapeutic potential of ncRNAs for COVID-19, presenting ncRNA-based strategies to facilitate the development of novel treatments. Through an in-depth analysis of the interplay between ncRNA and COVID-19 combined with our bioinformatic analysis, we hope to offer valuable insights into the stratification, prognosis, and treatment of COVID-19.

Extracellular vesicle features are associated with COVID-19 severity

COVID-19 is heterogeneous; therefore, it is crucial to identify early biomarkers for adverse outcomes. Extracellular vesicles (EV) are involved in the pathophysiology of COVID-19 and have both negative and positive effects. The objective of this study was to identify the potential role of EV in the prognostic stratification of COVID-19 patients. A total of 146 patients with severe or critical COVID-19 were enrolled. Demographic and comorbidity characteristics were collected, together with routine haematology, blood chemistry and lymphocyte subpopulation data. Flow cytometric characterization of the dimensional and antigenic properties of COVID-19 patients' plasma EVs was conducted. Elastic net logistic regression with cross-validation was employed to identify the best model for classifying critically ill patients. Features of smaller EVs (i.e. the fraction of EVs smaller than 200 nm expressing either cluster of differentiation [CD] 31, CD 140b or CD 42b), albuminemia and the percentage of monocytes expressing human leukocyte antigen DR (HLA-DR) were associated with a better outcome. Conversely, the proportion of larger EVs expressing N-cadherin, CD 34, CD 56, CD31 or CD 45, interleukin 6, red cell width distribution (RDW), N-terminal pro-brain natriuretic peptide (NT-proBNP), age, procalcitonin, Charlson Comorbidity Index and pro-adrenomedullin were associated with disease severity. Therefore, the simultaneous assessment of EV dimensions and their antigenic properties complements laboratory workup and helps in patient stratification.

Circulating monocytes expressing senescence-associated features are enriched in COVID-19 patients with severe disease

Accurate biomarkers for predicting COVID-19 severity have remained an unmet need due to an incomplete understanding of virus pathogenesis and heterogeneity among patients. Cellular senescence and its pro-inflammatory phenotype are suggested to be a consequence of SARS-CoV-2 infection and potentially drive infection-dependent pathological sequelae. Senescence-associated markers in infected individuals have been identified primarily in the lower respiratory tract, while little is known about their presence in more easily accessible bio-specimens. Here, we measured the abundance of senescence-associated signatures in whole blood, plasma and peripheral blood mononuclear cells (PBMCs) of COVID-19 patients and patients without an infection. Bulk transcriptomic and targeted proteomic assays revealed that the level of senescence-associated markers, including the senescence-associated secretory phenotype (SASP), is predictive of SARS-CoV-2 infection. Single-cell RNA-sequencing data demonstrated that a senescence signature is particularly enriched in monocytes of COVID-19 patients, partially correlating with disease severity. Our findings suggest that monocytes are prematurely induced to senescence by SARS-CoV-2 infection, might contribute to exacerbating a SASP-like inflammatory response and can serve as markers and predictors for COVID-19 and its sequelae.

Analysis of Protein Biomarkers From Hospitalized COVID-19 Patients Reveals Severity-Specific Signatures and Two Distinct Latent Profiles With Differential Responses to Corticosteroids

OBJECTIVES

To identify and validate novel COVID-19 subphenotypes with potential heterogenous treatment effects (HTEs) using electronic health record (EHR) data and 33 unique biomarkers.

DESIGN

Retrospective cohort study of adults presenting for acute care, with analysis of biomarkers from residual blood collected during routine clinical care. Latent profile analysis (LPA) of biomarker and EHR data identified subphenotypes of COVID-19 inpatients, which were validated using a separate cohort of patients. HTE for glucocorticoid use among subphenotypes was evaluated using both an adjusted logistic regression model and propensity matching analysis for in-hospital mortality.

SETTING

Emergency departments from four medical centers.

PATIENTS

Patients diagnosed with COVID-19 based on International Classification of Diseases , 10th Revision codes and laboratory test results.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Biomarker levels generally paralleled illness severity, with higher levels among more severely ill patients. LPA of 522 COVID-19 inpatients from three sites identified two profiles: profile 1 ( n = 332), with higher levels of albumin and bicarbonate, and profile 2 ( n = 190), with higher inflammatory markers. Profile 2 patients had higher median length of stay (7.4 vs 4.1 d; p < 0.001) and in-hospital mortality compared with profile 1 patients (25.8% vs 4.8%; p < 0.001). These were validated in a separate, single-site cohort ( n = 192), which demonstrated similar outcome differences. HTE was observed ( p = 0.03), with glucocorticoid treatment associated with increased mortality for profile 1 patients (odds ratio = 4.54).

CONCLUSIONS

In this multicenter study combining EHR data with research biomarker analysis of patients with COVID-19, we identified novel profiles with divergent clinical outcomes and differential treatment responses.

Single-cell transcriptomic atlas of distinct early immune responses induced by SARS-CoV-2 Proto or its variants in rhesus monkey

Immune responses induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection play a critical role in the pathogenesis and outcome of coronavirus disease 2019 (COVID-19). However, the dynamic profile of immune responses postinfection by SARS-CoV-2 variants of concern (VOC) is not fully understood. In this study, peripheral blood mononuclear cells single-cell sequencing was performed to determine dynamic profiles of immune response to Prototype, Alpha, Beta, and Delta in a rhesus monkey model. Overall, all strains induced dramatic changes in both cellular subpopulations and gene expression levels at 1 day postinfection (dpi), which associated function including adaptive immune response, innate immunity, and IFN response. COVID-19-related genes revealed different gene profiles at 1 dpi among the four SARS-CoV-2 strains, including genes reported in COVID-19 patients with increased risk of autoimmune disease and rheumatic diseases. Delta-infected animal showed inhibition of translation pathway. B cells, T cells, and monocytes showed much commonality rather than specificity among the four strains. Monocytes were the major responders to SARS-CoV-2 infection, and the response lasted longer in Alpha than the other strains. Thus, this study reveals the early immune responses induced by SARS-CoV-2 Proto or its variants in nonhuman primates, which is important information for controlling rapidly evolving viruses.

High-dimensional phenotyping of the peripheral immune response in community-acquired pneumonia

Background

Community-acquired pneumonia (CAP) represents a major health burden worldwide. Dysregulation of the immune response plays an important role in adverse outcomes in patients with CAP.

Methods

We analyzed peripheral blood mononuclear cells by 36-color spectral flow cytometry in adult patients hospitalized for CAP (n=40), matched control subjects (n=31), and patients hospitalized for COVID-19 (n=35).

Results

We identified 86 immune cell metaclusters, 19 of which (22.1%) were differentially abundant in patients with CAP versus matched controls. The most notable differences involved classical monocyte metaclusters, which were more abundant in CAP and displayed phenotypic alterations reminiscent of immunosuppression, increased susceptibility to apoptosis, and enhanced expression of chemokine receptors. Expression profiles on classical monocytes, driven by CCR7 and CXCR5, divided patients with CAP into two clusters with a distinct inflammatory response and disease course. The peripheral immune response in patients with CAP was highly similar to that in patients with COVID-19, but increased CCR7 expression on classical monocytes was only present in CAP.

Conclusion

CAP is associated with profound cellular changes in blood that mainly relate to classical monocytes and largely overlap with the immune response detected in COVID-19.

The two-stage molecular scenery of SARS-CoV-2 infection with implications to disease severity: An in-silico quest

Introduction

The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes.

Methods

Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI's gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results.

Results

The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events.

Discussion

The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.

Single cell transcriptomics shows that malaria promotes unique regulatory responses across multiple immune cell subsets

Plasmodium falciparum malaria drives immunoregulatory responses across multiple cell subsets, which protects from immunopathogenesis, but also hampers the development of effective anti-parasitic immunity. Understanding malaria induced tolerogenic responses in specific cell subsets may inform development of strategies to boost protective immunity during drug treatment and vaccination. Here, we analyse the immune landscape with single cell RNA sequencing during P. falciparum malaria. We identify cell type specific responses in sub-clustered major immune cell types. Malaria is associated with an increase in immunosuppressive monocytes, alongside NK and γδ T cells which up-regulate tolerogenic markers. IL-10-producing Tr1 CD4 T cells and IL-10-producing regulatory B cells are also induced. Type I interferon responses are identified across all cell types, suggesting Type I interferon signalling may be linked to induction of immunoregulatory networks during malaria. These findings provide insights into cell-specific and shared immunoregulatory changes during malaria and provide a data resource for further analysis.

Single cell spatial analysis reveals inflammatory foci of immature neutrophil and CD8 T cells in COVID-19 lungs

Single cell spatial interrogation of the immune-structural interactions in COVID -19 lungs is challenging, mainly because of the marked cellular infiltrate and architecturally distorted microstructure. To address this, we develop a suite of mathematical tools to search for statistically significant co-locations amongst immune and structural cells identified using 37-plex imaging mass cytometry. This unbiased method reveals a cellular map interleaved with an inflammatory network of immature neutrophils, cytotoxic CD8 T cells, megakaryocytes and monocytes co-located with regenerating alveolar progenitors and endothelium. Of note, a highly active cluster of immature neutrophils and CD8 T cells, is found spatially linked with alveolar progenitor cells, and temporally with the diffuse alveolar damage stage. These findings offer further insights into how immune cells interact in the lungs of severe COVID-19 disease. We provide our pipeline [Spatial Omics Oxford Pipeline (SpOOx)] and visual-analytical tool, Multi-Dimensional Viewer (MDV) software, as a resource for spatial analysis.