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

mitoSplitter: A mitochondrial variants-based method for efficient demultiplexing of pooled single-cell RNA-seq

Single-cell RNA-seq (scRNA-seq) analysis of multiple samples separately can be costly and lead to batch effects. Exogenous barcodes or genome-wide RNA mutations can be used to demultiplex pooled scRNA-seq data, but they are experimentally or computationally challenging and limited in scope. Mitochondrial genomes are small but diverse, providing concise genotype information. We developed "mitoSplitter," an algorithm that demultiplexes samples using mitochondrial RNA (mtRNA) variants, and demonstrated that mtRNA variants can be used to demultiplex large-scale scRNA-seq data. Using affordable computational resources, mitoSplitter can accurately analyze 10 samples and 60,000 cells in 6 h. To avoid the batch effects from separated experiments, we applied mitoSplitter to analyze the responses of five non-small cell lung cancer cell lines to BET (Bromodomain and extraterminal) chemical degradation in a multiplexed fashion. We found the synthetic lethality of TOP2A inhibition and BET chemical degradation in BET inhibitor-resistant cells. The result indicates that mitoSplitter can accelerate the application of scRNA-seq assays in biomedical research.

SARS-CoV-2 infection induces a long-lived pro-inflammatory transcriptional profile

BACKGROUND

The immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in COVID-19 patients has been extensively investigated. However, much less is known about the long-term effects of infection in patients and how it could affect the immune system and its capacity to respond to future perturbations.

METHODS

Using a targeted single-cell multiomics approach, we have recently identified a prolonged anti-inflammatory gene expression signature in T and NK cells in type 1 diabetes patients treated with low-dose IL-2. Here, we investigated the dynamics of this signature in three independent cohorts of COVID-19 patients: (i) the Oxford COVID-19 Multi-omics Blood Atlas (COMBAT) dataset, a cross-sectional cohort including 77 COVID-19 patients and ten healthy donors; (ii) the INCOV dataset, consisting of 525 samples taken from 209 COVID-19 patients during and after infection; and (iii) a longitudinal dataset consisting of 269 whole-blood samples taken from 139 COVID-19 patients followed for a period of up to 7 months after the onset of symptoms using a bulk transcriptomic approach.

RESULTS

We discovered that SARS-CoV-2 infection leads to a prolonged alteration of the gene expression profile of circulating T, B and NK cells and monocytes. Some of the genes affected were the same as those present in the IL-2-induced anti-inflammatory gene expression signature but were regulated in the opposite direction, implying a pro-inflammatory status. The altered transcriptional profile was detected in COVID-19 patients for at least 2 months after the onset of the disease symptoms but was not observed in response to influenza infection or sepsis. Gene network analysis suggested a central role for the transcriptional factor NF-κB in the regulation of the observed transcriptional alterations.

CONCLUSIONS

SARS-CoV-2 infection causes a prolonged increase in the pro-inflammatory transcriptional status that could predispose post-acute patients to the development of long-term health consequences, including autoimmune disease, reactivation of other viruses and disruption of the host immune system-microbiome ecosystem.

Thrombosis and Hyperinflammation in COVID-19 Acute Phase Are Related to Anti-Phosphatidylserine and Anti-Phosphatidylinositol Antibody Positivity

Antiphospholipid antibodies (APLA) are strongly associated with thrombosis seen in patients with antiphospholipid syndrome. In COVID-19, thrombosis has been observed as one of the main comorbidities. In patients hospitalised for COVID-19, we want to check whether APLA positivity is associated with COVID-19-related thrombosis, inflammation, severity of disease, or long COVID-19. We enrolled 92 hospitalised patients with COVID-19 between March and April 2020 who were tested for 18 different APLAs (IgG and IgM) with a single line-immunoassay test. A total of 30 healthy blood donors were used to set the cut-off for each APLA positivity. Of the 92 COVID-19 inpatients, 30 (32.61%; 95% CI [23.41-43.29]) tested positive for APLA, of whom 10 (33.3%; 95% CI [17.94-52.86]) had more than one APLA positivity. Anti-phosphatidylserine IgM positivity was described in 5.4% of inpatients (n = 5) and was associated with the occurrence of COVID-19-related thrombosis (p = 0.046). Anti-cardiolipin IgM positivity was the most prevalent among the inpatients (n = 12, 13.0%) and was associated with a recorded thrombosis in their clinical history (p = 0.044); however, its positivity was not associated with the occurrence of thrombosis during their hospitalisation for COVID-19. Anti-phosphatidylinositol IgM positivity, with a prevalence of 5.4% (n = 5), was associated with higher levels of interleukin (IL)-6 (p = 0.007) and ferritin (p = 0.034). Neither of these APLA positivities was a risk factor for COVID-19 severity or a predictive marker for long COVID-19. In conclusion, almost a third of COVID-19 inpatients tested positive for at least one APLA. Anti-phosphatidylserine positivity in IgM class was associated with thrombosis, and anti-phosphatidylinositol positivity in IgM class was associated with inflammation, as noticed by elevated levels of IL-6. Thus, testing for non-criteria APLA to assess the risk of clinical complications in hospitalised COVID-19 patients might be beneficial. However, they were not related to disease severity or long COVID-19.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

The T cell receptor sequence influences the likelihood of T cell memory formation

T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell fates. To comprehensively define how TCR amino acids affects all T cell fates, we analyze the paired αβTCR sequence and transcriptome of 819,772 single cells. We find that hydrophobic CDR3 residues promote regulatory T cell transcriptional states in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features, concentrated in CDR2α, that promotes positive selection in the thymus as well as transition from naïve to memory in the periphery. Even among T cells that recognize the same antigen, these TCR sequence features help to explain which T cells form immunological memory, which is essential for effective pathogen response.

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalized scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity

Certain serum proteins, including C-reactive protein (CRP) and D-dimer, have prognostic value in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, these factors are non-specific, providing limited mechanistic insight into the peripheral blood mononuclear cell (PBMC) populations that drive the pathogenesis of severe COVID-19. To identify cellular phenotypes associated with disease, we performed a comprehensive, unbiased analysis of total and plasma-membrane PBMC proteomes from 40 unvaccinated individuals with SARS-CoV-2, spanning the whole disease spectrum. Combined with RNA sequencing (RNA-seq) and flow cytometry from the same donors, we define a comprehensive multi-omic profile for each severity level, revealing that immune-cell dysregulation progresses with increasing disease. The cell-surface proteins CEACAMs1, 6, and 8, CD177, CD63, and CD89 are strongly associated with severe COVID-19, corresponding to the emergence of atypical CD3+CD4+CEACAM1/6/8+CD177+CD63+CD89+ and CD16+CEACAM1/6/8+ mononuclear cells. Utilization of these markers may facilitate real-time patient assessment by flow cytometry and identify immune populations that could be targeted to ameliorate immunopathology.

Lipid-associated macrophages transition to an inflammatory state in human atherosclerosis increasing the risk of cerebrovascular complications

The immune system is integral to cardiovascular health and disease. Targeting inflammation ameliorates adverse cardiovascular outcomes. Atherosclerosis, a major underlying cause of cardiovascular disease (CVD), is conceptualised as a lipid-driven inflammation where macrophages play a non-redundant role. However, evidence emerging so far from single cell atlases suggests a dichotomy between lipid associated and inflammatory macrophage states. Here, we present an inclusive reference atlas of human intraplaque immune cell communities. Combining scRNASeq of human surgical carotid endarterectomies in a discovery cohort with bulk RNASeq and immunohistochemistry in a validation cohort (the Carotid Plaque Imaging Project-CPIP), we reveal the existence of PLIN2hi/TREM1hi macrophages as a toll-like receptor-dependent inflammatory lipid-associated macrophage state linked to cerebrovascular events. Our study shifts the current paradigm of lipid-driven inflammation by providing biological evidence for a pathogenic macrophage transition to an inflammatory lipid-associated phenotype and for its targeting as a new treatment strategy for CVD.

Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis

Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterized by enthesitis of the spine and sacroiliac joints. Genome-wide association studies (GWASs) have revealed more than 100 genetic associations whose functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We find that, while CD14+ monocytes and CD4+ and CD8+ T cells show disease-specific differences at the RNA level, epigenomic differences are only apparent upon multi-omics integration. The latter reveals enrichment at disease-associated loci in monocytes. We link putative functional SNPs to genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1, and show how disease-specific functional genomic data can be integrated with GWASs to enhance therapeutic target discovery. This study combines epigenetic and transcriptional analysis with GWASs to identify disease-relevant cell types and gene regulation of likely pathogenic relevance and prioritize drug targets.

Single-Cell Transcriptomics Reveals Evidence of Endothelial Dysfunction in the Brains of COVID-19 Patients with Implications for Glioblastoma Progression

BACKGROUND

Endothelial dysfunction is implicated in various inflammatory diseases such as ischemic stroke, heart attack, organ failure, and COVID-19. Recent studies have shown that endothelial dysfunction in the brain is attributed to excessive inflammatory responses caused by the SARS-CoV-2 infection, leading to increased permeability of the blood-brain barrier and consequently neurological damage. Here, we aim to examine the single-cell transcriptomic landscape of endothelial dysfunction in COVID-19 and its implications for glioblastoma (GBM) progression.

METHODS

Single-cell transcriptome data GSE131928 and GSE159812 were obtained from the gene expression omnibus (GEO) to analyze the expression profiles of key players in innate immunity and inflammation between brain endothelial dysfunction caused by COVID-19 and GBM progression.

RESULTS

Single-cell transcriptomic analysis of the brain of COVID-19 patients revealed that endothelial cells had undergone significant transcriptomic changes, with several genes involved in immune responses and inflammation upregulated. Moreover, transcription factors were observed to modulate this inflammation, including interferon-regulated genes.

CONCLUSIONS

The results indicate a significant overlap between COVID-19 and GBM in the context of endothelial dysfunction, suggesting that there may be an endothelial dysfunction link connecting severe SARS-CoV-2 infection in the brain to GBM progression.

Monocyte migration profiles define disease severity in acute COVID-19 and unique features of long COVID

BACKGROUND

COVID-19 is associated with a dysregulated immune response but it is unclear how immune dysfunction contributes to the chronic morbidity persisting in many COVID-19 patients during convalescence (long COVID).

METHODS

We assessed phenotypical and functional changes of monocytes in COVID-19 patients during hospitalisation and up to 9 months of convalescence following COVID-19, respiratory syncytial virus or influenza A. Patients with progressive fibrosing interstitial lung disease were included as a positive control for severe, ongoing lung injury.

RESULTS

Monocyte alterations in acute COVID-19 patients included aberrant expression of leukocyte migration molecules, continuing into convalescence (n=142) and corresponding with specific symptoms of long COVID. Long COVID patients with unresolved lung injury, indicated by sustained shortness of breath and abnormal chest radiology, were defined by high monocyte expression of C-X-C motif chemokine receptor 6 (CXCR6) (p<0.0001) and adhesion molecule P-selectin glycoprotein ligand 1 (p<0.01), alongside preferential migration of monocytes towards the CXCR6 ligand C-X-C motif chemokine ligand 16 (CXCL16) (p<0.05), which is abundantly expressed in the lung. Monocyte CXCR6 and lung CXCL16 were heightened in patients with progressive fibrosing interstitial lung disease (p<0.001), confirming a role for the CXCR6-CXCL16 axis in ongoing lung injury. Conversely, monocytes from long COVID patients with ongoing fatigue exhibited a sustained reduction of the prostaglandin-generating enzyme cyclooxygenase 2 (p<0.01) and CXCR2 expression (p<0.05). These monocyte changes were not present in respiratory syncytial virus or influenza A convalescence.

CONCLUSIONS

Our data define unique monocyte signatures that define subgroups of long COVID patients, indicating a key role for monocyte migration in COVID-19 pathophysiology. Targeting these pathways may provide novel therapeutic opportunities in COVID-19 patients with persistent morbidity.

The impact of pre-existing cross-reactive immunity on SARS-CoV-2 infection and vaccine responses

Pre-existing cross-reactive immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins in infection-naive subjects have been described by several studies. In particular, regions of high homology between SARS-CoV-2 and common cold coronaviruses have been highlighted as a likely source of this cross-reactivity. However, the role of such cross-reactive responses in the outcome of SARS-CoV-2 infection and vaccination is currently unclear. Here, we review evidence regarding the impact of pre-existing humoral and T cell immune responses to outcomes of SARS-CoV-2 infection and vaccination. Furthermore, we discuss the importance of conserved coronavirus epitopes for the rational design of pan-coronavirus vaccines and consider cross-reactivity of immune responses to ancestral SARS-CoV-2 and SARS-CoV-2 variants, as well as their impact on COVID-19 vaccination.

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

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

Mechanisms underpinning the dysfunctional immune response in severe acute respiratory syndrome coronavirus 2 infection are elusive. We analyzed single-cell transcriptomes and T and B cell receptors (BCR) of >895,000 peripheral blood mononuclear cells from 73 coronavirus disease 2019 (COVID-19) patients and 75 healthy controls of Japanese ancestry with host genetic data. COVID-19 patients showed a low fraction of nonclassical monocytes (ncMono). We report downregulated cell transitions from classical monocytes to ncMono in COVID-19 with reduced CXCL10 expression in ncMono in severe disease. Cell-cell communication analysis inferred decreased cellular interactions involving ncMono in severe COVID-19. Clonal expansions of BCR were evident in the plasmablasts of patients. Putative disease genes identified by COVID-19 genome-wide association study showed cell type-specific expressions in monocytes and dendritic cells. A COVID-19-associated risk variant at the IFNAR2 locus (rs13050728) had context-specific and monocyte-specific expression quantitative trait loci effects. Our study highlights biological and host genetic involvement of innate immune cells in COVID-19 severity.

Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis

Sepsis arises from diverse and incompletely understood dysregulated host response processes following infection that leads to life-threatening organ dysfunction. Here we showed that neutrophils and emergency granulopoiesis drove a maladaptive response during sepsis. We generated a whole-blood single-cell multiomic atlas (272,993 cells, n = 39 individuals) of the sepsis immune response that identified populations of immunosuppressive mature and immature neutrophils. In co-culture, CD66b⁺ sepsis neutrophils inhibited proliferation and activation of CD4⁺ T cells. Single-cell multiomic mapping of circulating hematopoietic stem and progenitor cells (HSPCs) (29,366 cells, n = 27) indicated altered granulopoiesis in patients with sepsis. These features were enriched in a patient subset with poor outcome and a specific sepsis response signature that displayed higher frequencies of IL1R2⁺ immature neutrophils, epigenetic and transcriptomic signatures of emergency granulopoiesis in HSPCs and STAT3-mediated gene regulation across different infectious etiologies and syndromes. Our findings offer potential therapeutic targets and opportunities for stratified medicine in severe infection.

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarized and incorporated into patient outcome prediction models in several ways; however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integrate approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalization when using multiple datasets as the model input.

Identification of pre-infection markers and differential plasma protein expression following SARS-CoV-2 infection in people living with HIV

BACKGROUND

Mechanisms contributing to COVID-19 severity in people with HIV (PWH) are poorly understood. We evaluated temporal changes in plasma proteins following SARS-CoV-2 infection and identified pre-infection proteomic markers associated with future COVID-19.

METHODS

We leveraged data from the global Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE). Antiretroviral therapy (ART)-treated PWH with clinical, antibody-confirmed COVID-19 as of September 2021 were matched on geographic region, age, and sample timing to antibody negative controls. For cases and controls, pre COVID-19 pandemic specimens were obtained prior to January 2020 to assess change over time and relationship to COVID-19 severity, using false-discovery adjusted mixed effects modeling.

FINDINGS

We compared 257 unique plasma proteins in 94 COVID-19 antibody-confirmed clinical cases and 113 matched antibody-negative controls, excluding COVID-19 vaccinated participants (age 50 years, 73% male). 40% of cases were characterized as mild; 60% moderate to severe. Median time from COVID-19 infection to follow-up sampling was 4 months. Temporal patterns of protein changes differed based on COVID-19 disease severity. Among those experiencing moderate to severe disease vs. controls, NOS3 increased whereas ANG, CASP-8, CD5, GZMH, GZMB, ITGB2, and KLRD1 decreased. Higher pre-pandemic levels of granzymes A, B and H (GZMA, GZMB and GZMH) were associated with the future development of moderate-severe COVID-19 and were related to immune function.

INTERPRETATION

We identified temporal changes in proteins closely linked to inflammatory, immune, and fibrotic pathways which may relate to COVID-19-related morbidity among ART-treated PWH. Further we identified key granzyme proteins associated with future COVID-19 in PWH.

FUNDING

This study is supported through NIH grants U01HL123336, U01HL123336-06 and 3U01HL12336-06S3, to the clinical coordinating center, and U01HL123339, to the data coordinating center as well as funding from Kowa Pharmaceuticals, Gilead Sciences, and a grant award through ViiV Healthcare. The NIAID supported this study through grants UM1 AI068636, which supports the AIDS Clinical Trials Group (ACTG) Leadership and Operations Center, and UM1 AI106701, which supports the ACTG Laboratory Center. This work was also supported by NIAID through grant K24AI157882 to MZ. The work of IS was supported by the intramural research program of NIAID/NIH.

Quantitative proteomics analysis of COVID-19 patients: Fetuin-A and tetranectin as potential modulators of innate immune responses

Treatment of severe cases of coronavirus disease 2019 (COVID-19) is extremely important to minimize death and end-organ damage. Here we performed a proteomic analysis of plasma samples from mild, moderate and severe COVID-19 patients. Analysis revealed differentially expressed proteins and different therapeutic potential targets related to innate immune responses such as fetuin-A, tetranectin (TN) and paraoxonase-1 (PON1). Furthermore, protein changes in plasma showed dysregulation of complement and coagulation cascades in COVID-19 patients compared to healthy controls. In conclusion, our proteomics data suggested fetuin-A and TN as potential targets that might be used for diagnosis as well as signatures for a better understanding of the pathogenesis of COVID-19 disease.

Cellular and Molecular Atlas of Peripheral Blood Mononuclear Cells from a Pregnant Woman After Recovery from COVID-19

Objective

This study aimed to investigate the immune response of a pregnant woman who recovered from the coronavirus disease 2019 (COVID_RS) by using single-cell transcriptomic profiling of peripheral blood mononuclear cells (PBMCs) and to analyze the properties of different immune cell subsets.

Methods

PBMCs were collected from the COVID_RS patient at 28 weeks of gestation, before a cesarean section. The PBMCs were then analyzed using single-cell RNA sequencing. The transcriptional profiles of myeloid, T, and natural killer (NK) cell subsets were systematically analyzed and compared with those of healthy pregnant controls from a published single-cell RNA sequencing data set.

Results

We identified major cell types such as T cells, B cells, NK cells, and myeloid cells in the PBMCs of our COVID_RS patient. The increase of myeloid and B cells and decrease of T cells and NK cells in the PBMCs in this patient were quite distinct compared with that in the control subjects. After reclustering and Augur analysis, we found that CD16 monocytes and mucosal-associated invariant T (MAIT) cells were mostly affected within different myeloid, T, and NK cell subtypes in our COVID_RS patient. The proportion of CD16 monocytes in the total myeloid population was increased, and the frequency of MAIT cells in the total T and NK cells was significantly decreased in the COVID-RS patient. We also observed significant enrichment of gene sets related to antigen processing and presentation, T-cell activation, T-cell differentiation, and tumor necrosis factor superfamily cytokine production in CD16 monocytes, and enrichment of gene sets related to antigen processing and presentation, response to type II interferon, and response to virus in MAIT cells.

Conclusion

Our study provides a single-cell resolution atlas of the immune gene expression patterns in PBMCs from a COVID_RS patient. Our findings suggest that CD16-positive monocytes and MAIT cells likely play crucial roles in the maternal immune response against severe acute respiratory syndrome coronavirus 2 infection. These results contribute to a better understanding of the maternal immune response to severe acute respiratory syndrome coronavirus 2 infection and may have implications for the development of effective treatments and preventive strategies for the coronavirus disease 2019 in pregnant women.

COVID-19 and cellular senescence

The clinical severity of coronavirus disease 2019 (COVID-19) is largely determined by host factors. Recent advances point to cellular senescence, an ageing-related switch in cellular state, as a critical regulator of SARS-CoV-2-evoked hyperinflammation. SARS-CoV-2, like other viruses, can induce senescence and exacerbates the senescence-associated secretory phenotype (SASP), which is comprised largely of pro-inflammatory, extracellular matrix-degrading, complement-activating and pro-coagulatory factors secreted by senescent cells. These effects are enhanced in elderly individuals who have an increased proportion of pre-existing senescent cells in their tissues. SASP factors can contribute to a 'cytokine storm', tissue-destructive immune cell infiltration, endothelialitis (endotheliitis), fibrosis and microthrombosis. SASP-driven spreading of cellular senescence uncouples tissue injury from direct SARS-CoV-2-inflicted cellular damage in a paracrine fashion and can further amplify the SASP by increasing the burden of senescent cells. Preclinical and early clinical studies indicate that targeted elimination of senescent cells may offer a novel therapeutic opportunity to attenuate clinical deterioration in COVID-19 and improve resilience following infection with SARS-CoV-2 or other pathogens.

TNFα aggravates detrimental effects of SARS-CoV-2 infection in the liver

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus does not only lead to pulmonary infection but can also infect other organs such as the gut, the kidney, or the liver. Recent studies confirmed that severe cases of COVID-19 are often associated with liver damage and liver failure, as well as the systemic upregulation of pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNFα). However, the impact these immune mediators in the liver have on patient survival during SARS-CoV-2 infection is currently unknown. Here, by performing a post-mortem analysis of 45 patients that died from a SARS-CoV-2 infection, we find that an increased expression of TNFA in the liver is associated with elevated mortality. Using publicly available single-cell sequencing datasets, we determined that Kupffer cells and monocytes are the main sources of this TNFα production. Further analysis revealed that TNFα signaling led to the upregulation of pro-inflammatory genes that are associated with an unfavorable outcome. Moreover, high levels of TNFA in the liver were associated with lower levels of interferon alpha and interferon beta. Thus, TNFα signaling in the infected SARS-CoV-2 liver correlates with reduced interferon levels and overall survival time.

A simple, time- and cost-effective, high-throughput depletion strategy for deep plasma proteomics

We introduce a cost-effective, robust high-throughput-compatible plasma depletion method enabling in-depth profiling of plasma that detects >1300 proteins per run with a throughput of 60 samples per day. The method has been fully validated by processing >3000 samples with no apparent batch effect at a cost for the depletion step of ~$2.5 per sample.

Regulatory T Cells (Tregs) and COVID-19: Unveiling the Mechanisms, and Therapeutic Potentialities with a Special Focus on Long COVID

The COVID-19 pandemic has caused havoc all around the world. The causative agent of COVID-19 is the novel form of the coronavirus (CoV) named SARS-CoV-2, which results in immune system disruption, increased inflammation, and acute respiratory distress syndrome (ARDS). T cells have been important components of the immune system, which decide the fate of the COVID-19 disease. Recent studies have reported an important subset of T cells known as regulatory T cells (Tregs), which possess immunosuppressive and immunoregulatory properties and play a crucial role in the prognosis of COVID-19 disease. Recent studies have shown that COVID-19 patients have considerably fewer Tregs than the general population. Such a decrement may have an impact on COVID-19 patients in a number of ways, including diminishing the effect of inflammatory inhibition, creating an inequality in the Treg/Th17 percentage, and raising the chance of respiratory failure. Having fewer Tregs may enhance the likelihood of long COVID development in addition to contributing to the disease's poor prognosis. Additionally, tissue-resident Tregs provide tissue repair in addition to immunosuppressive and immunoregulatory activities, which may aid in the recovery of COVID-19 patients. The severity of the illness is also linked to abnormalities in the Tregs' phenotype, such as reduced expression of FoxP3 and other immunosuppressive cytokines, including IL-10 and TGF-beta. Hence, in this review, we summarize the immunosuppressive mechanisms and their possible roles in the prognosis of COVID-19 disease. Furthermore, the perturbations in Tregs have been associated with disease severity. The roles of Tregs are also explained in the long COVID. This review also discusses the potential therapeutic roles of Tregs in the management of patients with COVID-19.

Identification of two early blood biomarkers ACHE and CLEC12A for improved risk stratification of critically ill COVID-19 patients

In order to identify biomarkers for earlier prediction of COVID-19 outcome, we collected blood samples from patients with fatal outcomes (non-survivors) and with positive clinical outcomes (survivors) at ICU admission and after seven days. COVID-19 survivors and non-survivors showed significantly different transcript levels for 93 genes in whole blood already at ICU admission as revealed by RNA-Seq. These differences became even more pronounced at day 7, resulting in 290 differentially expressed genes. Many identified genes play a role in the differentiation of hematopoietic cells. For validation, we designed an RT-qPCR assay for C-type lectin domain family 12 member A (CLEC12A) and acetylcholinesterase (ACHE), two transcripts that showed highest potential to discriminate between survivors and non-survivors at both time points. Using our combined RT-qPCR assay we examined 33 samples to accurately predict patient survival with an AUROC curve of 0.931 (95% CI = 0.814-1.000) already at ICU admission. CLEC12A and ACHE showed improved prediction of patient outcomes compared to standard clinical biomarkers including CRP and PCT in combination (AUROC = 0.403, 95% CI = 0.108-0.697) or SOFA score (AUROC = 0.701 95% CI = 0.451-0.951) at day 0. Therefore, analyzing CLEC12A and ACHE gene expression from blood may provide a promising approach for early risk stratification of severely ill COVID-19 patients.

An Innate Checkpoint Determines Immune Dysregulation and Immunopathology during Pulmonary Murine Coronavirus Infection

Hallmarks of life-threatening, coronavirus-induced disease include dysregulated antiviral immunity and immunopathological tissue injury. Nevertheless, the sampling of symptomatic patients overlooks the initial inflammatory sequela culminating in severe coronavirus-induced disease, leaving a fundamental gap in our understanding of the early mechanisms regulating anticoronavirus immunity and preservation of tissue integrity. In this study, we delineate the innate regulators controlling pulmonary infection using a natural mouse coronavirus. Within hours of infection, the cellular landscape of the lung was transcriptionally remodeled altering host metabolism, protein synthesis, and macrophage maturation. Genetic perturbation revealed that these transcriptional programs were type I IFN dependent and critically controlled both host cell survival and viral spread. Unrestricted viral replication overshooting protective IFN responses culminated in increased IL-1β and alarmin production and triggered compensatory neutrophilia, interstitial inflammation, and vascular injury. Thus, type I IFNs critically regulate early viral burden, which serves as an innate checkpoint determining the trajectory of coronavirus dissemination and immunopathology.

A single-cell atlas reveals shared and distinct immune responses and metabolic profiles in SARS-CoV-2 and HIV-1 infections

Introduction: Within the inflammatory immune response to viral infection, the distribution and cell type-specific profiles of immune cell populations and the immune-mediated viral clearance pathways vary according to the specific virus. Uncovering the immunological similarities and differences between viral infections is critical to understanding disease progression and developing effective vaccines and therapies. Insight into COVID-19 disease progression has been bolstered by the integration of single-cell (sc)RNA-seq data from COVID-19 patients with data from related viruses to compare immune responses. Expanding this concept, we propose that a high-resolution, systematic comparison between immune cells from SARS-CoV-2 infection and an inflammatory infectious disease with a different pathophysiology will provide a more comprehensive picture of the viral clearance pathways that underscore immunological and clinical differences between infections. Methods: Using a novel consensus single-cell annotation method, we integrate previously published scRNA-seq data from 111,566 single PBMCs from 7 COVID-19, 10 HIV-1+, and 3 healthy patients into a unified cellular atlas. We compare in detail the phenotypic features and regulatory pathways in the major immune cell clusters. Results: While immune cells in both COVID-19 and HIV-1+ cohorts show shared inflammation and disrupted mitochondrial function, COVID-19 patients exhibit stronger humoral immunity, broader IFN-I signaling, elevated Rho GTPase and mTOR pathway activity, and downregulated mitophagy. Discussion: Our results indicate that differential IFN-I signaling regulates the distinct immune responses in the two diseases, revealing insight into fundamental disease biology and potential therapeutic candidates.

Connecting single-nucleotide polymorphisms, glycosylation status, and interactions of plasma serine protease inhibitors

Understanding the combined impacts of genetic variances and post-translational modifications requires new approaches. Here, we delineate proteoforms of plasma serine protease inhibitors and relate specific proteoforms to their interactions in complexes through the use of native mass spectrometry (MS). First, we dissect the proteoform repertoire of an acute-phase plasma protein, serine protease inhibitor A1 (SERPINA1), resolving four SERPINA1 variants (M1V, M1A, M2, and M3) with common single-nucleotide polymorphisms (SNPs). Investigating the glycosylation status of these variants and their ability to form complexes with a serine protease, elastase, we find that fucosylation stabilizes the interaction of the SERPINA1 M1V variant through its core fucosylation on Asn271. In contrast, antennary fucosylation on Asn271 destabilizes SERPINA1-elastase interactions. We unveil the same opposing effects of core and antennary fucosylation on SERPINA3 interactions with chymotrypsin. Together, our native MS results highlight the modulating effects of fucosylation with different linkages on glycoprotein interactions.

Relevance of TMPRSS2, CD163/CD206, and CD33 in clinical severity stratification of COVID-19

Background

Approximately 13.8% and 6.1% of coronavirus disease 2019 (COVID-19) patients require hospitalization and sometimes intensive care unit (ICU) admission, respectively. There is no biomarker to predict which of these patients will develop an aggressive stage that we could improve their quality of life and healthcare management. Our main goal is to include new markers for the classification of COVID-19 patients.

Methods

Two tubes of peripheral blood were collected from a total of 66 (n = 34 mild and n = 32 severe) samples (mean age 52 years). Cytometry analysis was performed using a 15-parameter panel included in the Maxpar® Human Monocyte/Macrophage Phenotyping Panel Kit. Cytometry by time-of-flight mass spectrometry (CyTOF) panel was performed in combination with genetic analysis using TaqMan® probes for ACE2 (rs2285666), MX1 (rs469390), and TMPRSS2 (rs2070788) variants. GemStone™ and OMIQ software were used for cytometry analysis.

Results

The frequency of CD163+/CD206- population of transitional monocytes (T-Mo) was decreased in the mild group compared to that of the severe one, while T-Mo CD163-/CD206- were increased in the mild group compared to that of the severe one. In addition, we also found differences in CD11b expression in CD14dim monocytes in the severe group, with decreased levels in the female group (p = 0.0412). When comparing mild and severe disease, we also found that CD45- [p = 0.014; odds ratio (OR) = 0.286, 95% CI 0.104–0.787] and CD14dim/CD33+ (p = 0.014; OR = 0.286, 95% CI 0.104–0.787) monocytes were the best options as biomarkers to discriminate between these patient groups. CD33 was also indicated as a good biomarker for patient stratification by the analysis of GemStone™ software. Among genetic markers, we found that G carriers of TMPRSS2 (rs2070788) have an increased risk (p = 0.02; OR = 3.37, 95% CI 1.18–9.60) of severe COVID-19 compared to those with A/A genotype. This strength is further increased when combined with CD45-, T-Mo CD163+/CD206-, and C14dim/CD33+.

Conclusions

Here, we report the interesting role of TMPRSS2, CD45-, CD163/CD206, and CD33 in COVID-19 aggressiveness. This strength is reinforced for aggressiveness biomarkers when TMPRSS2 and CD45-, TMPRSS2 and CD163/CD206, and TMPRSS2 and CD14dim/CD33+ are combined.

Autoantibodies against chemokines post-SARS-CoV-2 infection correlate with disease course

Infection with severe acute respiratory syndrome coronavirus 2 associates with diverse symptoms, which can persist for months. While antiviral antibodies are protective, those targeting interferons and other immune factors are associated with adverse coronavirus disease 2019 (COVID-19) outcomes. Here we discovered that antibodies against specific chemokines were omnipresent post-COVID-19, were associated with favorable disease outcome and negatively correlated with the development of long COVID at 1 yr post-infection. Chemokine antibodies were also present in HIV-1 infection and autoimmune disorders, but they targeted different chemokines compared with COVID-19. Monoclonal antibodies derived from COVID-19 convalescents that bound to the chemokine N-loop impaired cell migration. Given the role of chemokines in orchestrating immune cell trafficking, naturally arising chemokine antibodies may modulate the inflammatory response and thus bear therapeutic potential.

Parallel Dysregulated Immune Response in Severe Forms of COVID-19 and Bacterial Sepsis via Single-Cell Transcriptome Sequencing

Critically ill COVID-19 patients start developing single respiratory organ failure that often evolves into multiorgan failure. Understanding the immune mechanisms in severe forms of an infectious disease (either critical COVID-19 or bacterial septic shock) would help to achieve a better understanding of the patient’s clinical trajectories and the success of potential therapies. We hypothesized that a dysregulated immune response manifested by the abnormal activation of innate and adaptive immunity might be present depending on the severity of the clinical presentation in both COVID-19 and bacterial sepsis. We found that critically ill COVID-19 patients demonstrated a different clinical endotype that resulted in an inflammatory dysregulation in mild forms of the disease. Mild cases (COVID-19 and bacterial non severe sepsis) showed significant differences in the expression levels of CD8 naïve T cells, CD4 naïve T cells, and CD4 memory T cells. On the other hand, in the severe forms of infection (critical COVID-19 and bacterial septic shock), patients shared immune patterns with upregulated single-cell transcriptome sequencing at the following levels: B cells, monocyte classical, CD4 and CD8 naïve T cells, and natural killers. In conclusion, we identified significant gene expression differences according to the etiology of the infection (COVID-19 or bacterial sepsis) in the mild forms; however, in the severe forms (critical COVID-19 and bacterial septic shock), patients tended to share some of the same immune profiles related to adaptive and innate immune response. Severe forms of the infections were similar independent of the etiology. Our findings might promote the implementation of co-adjuvant therapies and interventions to avoid the development of severe forms of disease that are associated with high mortality rates worldwide.

Early peripheral blood MCEMP1 and HLA-DRA expression predicts COVID-19 prognosis

BACKGROUND

Mass vaccination has dramatically reduced the incidence of severe COVID-19, with most cases now presenting as self-limiting upper respiratory tract infections. However, those with co-morbidities, the elderly and immunocompromised, as well as the unvaccinated, remain disproportionately vulnerable to severe COVID-19 and its sequelae. Furthermore, as the effectiveness of vaccination wanes with time, immune escape SARS-CoV-2 variants could emerge to cause severe COVID-19. Reliable prognostic biomarkers for severe disease could be used as early indicator of re-emergence of severe COVID-19 as well as for triaging of patients for antiviral therapy.

METHODS

We performed a systematic review and re-analysis of 7 publicly available datasets, analysing a total of 140 severe and 181 mild COVID-19 patients, to determine the most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients. In addition, we included an independent cohort where blood transcriptomics of COVID-19 patients were prospectively and longitudinally monitored previously, to track the time in which these gene expression changes occur before nadir of respiratory function. Single cell RNA-sequencing of peripheral blood mononuclear cells from publicly available datasets was then used to determine the immune cell subsets involved.

FINDINGS

The most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients were MCEMP1, HLA-DRA and ETS1 across the 7 transcriptomics datasets. Moreover, we found significantly heightened MCEMP1 and reduced HLA-DRA expression as early as four days before the nadir of respiratory function, and the differential expression of MCEMP1 and HLA-DRA occurred predominantly in CD14+ cells. The online platform which we developed is publicly available at https://kuanrongchan-covid19-severity-app-t7l38g.streamlitapp.com/, for users to query gene expression differences between severe and mild COVID-19 patients in these datasets.

INTERPRETATION

Elevated MCEMP1 and reduced HLA-DRA gene expression in CD14+ cells during the early phase of disease are prognostic of severe COVID-19.

FUNDING

K.R.C is funded by the National Medical Research Council (NMRC) of Singapore under the Open Fund Individual Research Grant (MOH-000610). E.E.O. is funded by the NMRC Senior Clinician-Scientist Award (MOH-000135-00). J.G.H.L. is funded by the NMRC under the Clinician-Scientist Award (NMRC/CSAINV/013/2016-01). S.K. is funded by the NMRC under the Transition Award. This study was sponsored in part by a generous gift from The Hour Glass.

Potent NKT cell ligands overcome SARS-CoV-2 immune evasion to mitigate viral pathogenesis in mouse models

One of the major pathogenesis mechanisms of SARS-CoV-2 is its potent suppression of innate immunity, including blocking the production of type I interferons. However, it is unknown whether and how the virus interacts with different innate-like T cells, including NKT, MAIT and γδ T cells. Here we reported that upon SARS-CoV-2 infection, invariant NKT (iNKT) cells rapidly trafficked to infected lung tissues from the periphery. We discovered that the envelope (E) protein of SARS-CoV-2 efficiently down-regulated the cell surface expression of the antigen-presenting molecule, CD1d, to suppress the function of iNKT cells. E protein is a small membrane protein and a viroporin that plays important roles in virion packaging and envelopment during viral morphogenesis. We showed that the transmembrane domain of E protein was responsible for suppressing CD1d expression by specifically reducing the level of mature, post-ER forms of CD1d, suggesting that it suppressed the trafficking of CD1d proteins and led to their degradation. Point mutations demonstrated that the putative ion channel function was required for suppression of CD1d expression and inhibition of the ion channel function using small chemicals rescued the CD1d expression. Importantly, we discovered that among seven human coronaviruses, only E proteins from highly pathogenic coronaviruses including SARS-CoV-2, SARS-CoV and MERS suppressed CD1d expression, whereas the E proteins of human common cold coronaviruses, HCoV-OC43, HCoV-229E, HCoV-NL63 and HCoV-HKU1, did not. These results suggested that E protein-mediated evasion of NKT cell function was likely an important pathogenesis factor, enhancing the virulence of these highly pathogenic coronaviruses. Remarkably, activation of iNKT cells with their glycolipid ligands, both prophylactically and therapeutically, overcame the putative viral immune evasion, significantly mitigated viral pathogenesis and improved host survival in mice. Our results suggested a novel NKT cell-based anti-SARS-CoV-2 therapeutic approach.

Plasma proteomics identify potential severity biomarkers from COVID-19 associated network

PURPOSE

Coronavirus disease 2019 (COVID-19) continues to threaten public health globally. Severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection-dependent alterations in the host cell signaling network may unveil potential target proteins and pathways for therapeutic strategies. In this study, we aim to define early severity biomarkers and monitor altered pathways in the course of SARS-CoV-2 infection.

EXPERIMENTAL DESIGN

We systematically analyzed plasma proteomes of COVID-19 patients from Turkey by using mass spectrometry. Different severity grades (moderate, severe, and critical) and periods of disease (early, inflammatory, and recovery) are monitored. Significant alterations in protein expressions are used to reconstruct the COVID-19 associated network that was further extended to connect viral and host proteins.

RESULTS

Across all COVID-19 patients, 111 differentially expressed proteins were found, of which 28 proteins were unique to our study mainly enriching in immunoglobulin production. By monitoring different severity grades and periods of disease, CLEC3B, MST1, and ITIH2 were identified as potential early predictors of COVID-19 severity. Most importantly, we extended the COVID-19 associated network with viral proteins and showed the connectedness of viral proteins with human proteins. The most connected viral protein ORF8, which has a role in immune evasion, targets many host proteins tightly connected to the deregulated human plasma proteins.

CONCLUSIONS AND CLINICAL RELEVANCE

Plasma proteomes from critical patients are intrinsically clustered in a distinct group than severe and moderate patients. Importantly, we did not recover any grouping based on the infection period, suggesting their distinct proteome even in the recovery phase. The new potential early severity markers can be further studied for their value in the clinics to monitor COVID-19 prognosis. Beyond the list of plasma proteins, our disease-associated network unravels altered pathways, and the possible therapeutic targets in SARS-CoV-2 infection by connecting human and viral proteins. Follow-up studies on the disease associated network that we propose here will be useful to determine molecular details of viral perturbation and to address how the infection affects human physiology.

Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data

High-content omic technologies coupled with sparsity-promoting regularization methods (SRM) have transformed the biomarker discovery process. However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models. The complete package for Stabl is available online at https://github.com/gregbellan/Stabl.

Serial Changes in Blood-Cell-Count-Derived and CRP-Derived Inflammatory Indices of COVID-19 Patients

The aim of the study was to investigate the serial changes in inflammatory indices derived from blood cell counts and C-reactive protein (CRP) levels in COVID-19 patients with good and poor outcomes. We retrospectively analyzed the serial changes in the inflammatory indices in 169 COVID-19 patients. Comparative analyses were performed on the first and last days of a hospital stay or death and serially from day 1 to day 30 from the symptom onset. On admission, non-survivors had higher CRP to lymphocytes ratio (CLR) and multi-inflammatory index (MII) values than survivors, while at the time of discharge/death, the largest differences were found for the neutrophil to lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and MII. A significant decrease in NLR, CLR, and MII by the time of discharge was documented in the survivors, and a significant increase in NLR was documented in the non-survivors. The NLR was the only one that remained significant from days 7-30 of disease in intergroup comparisons. The correlation between the indices and the outcome was observed starting from days 13-15. The changes in the index values over time proved to be more helpful in predicting COVID-19 outcomes than those measured on admission. The values of the inflammatory indices could reliably predict the outcome no earlier than days 13-15 of the disease.

Integrated Proteomics and Metabolomics Analysis to Explore the Amelioration Mechanisms of Rosa roxburghii Tratt Fruit Polyphenols on Lipopolysaccharide-Induced Acute Lung Injury Mice

Acute lung injury (ALI) is the main cause of death for the elderly and children due to its high morbidity and mortality rates. Plant-derived functional foods are becoming increasingly important to the healthcare and food industries for adjunctive and alternative treatments of ALI. Polyphenols have been regarded to be beneficial to the prevention and amelioration of ALI. Rosa roxburghii Tratt fruit polyphenols (RRTP) has potential to prevent ALI, but mechanism remains unclear. This study was set up to systematically analyze the RRTP extract active ingredients, comprehensively evaluate its protective effects via lung histopathological examination, protein concentration, and cytokines production in ALI mice induced by lipopolysaccharide (LPS), and finally revealed alleviation mechanisms of the regulatory effects of RRTP by proteomics and metabolomics approach. The results demonstrated RRTP could synergistically exert significant preventive effects against ALI by notably ameliorating lung histopathological damage and pulmonary capillary permeability in ALI mice, inhibiting lung tissue inflammatory response and acute phase proteins and S-100 calcium binding proteins, suppressing excessive activation of complement and coagulation cascades, and regulating disordered lipids metabolism and amino acid metabolism. This study illustrated that RRTP has obvious advantages in ALI adjunctive therapy and revealed the complicated amelioration mechanisms, which provides a breakthrough for the development and demonstration of RRTP as a nutritional compound additive for complementary therapy of ALI.

Persistence of inflammatory and vascular mediators 5 months after hospitalization with COVID-19 infection

Background and aim

In acute severe COVID-19, patients present with lung inflammation and vascular injury, accompanied by an exaggerated cytokine response. In this study, our aim was to describe the inflammatory and vascular mediator profiles in patients who were previously hospitalized with COVID-19 pneumonitis, months after their recovery, and compare them with those in patients recovering from severe sepsis and in healthy controls.

Methods

A total of 27 different cytokine, chemokine, vascular endothelial injury and angiogenic mediators were measured in the plasma of forty-nine patients 5.0 ± 1.9 (mean ± SD) months after they were hospitalized with COVID-19 pneumonia, eleven patients 5.4 ± 2.9 months after hospitalization with acute severe sepsis, and 18 healthy controls.

Results

Compared with healthy controls, IL-6, TNFα, SAA, CRP, Tie-2, Flt1, and PIGF were significantly increased in the post-COVID group, and IL-7 and bFGF were significantly reduced. While IL-6, PIGF, and CRP were also significantly elevated in post-Sepsis patients compared to controls, the observed differences in TNFα, Tie-2, Flt-1, IL-7 and bFGF were unique to the post-COVID group. TNFα levels significantly correlated with the severity of acute COVID-19 illness (spearman's r = 0.30, p < 0.05). Furthermore, in post-COVID patients, IL-6 and CRP were each strongly negatively correlated with gas transfer factor %predicted (spearman's r = -0.51 and r = -0.57, respectively, p < 0.002) and positively correlated with computed tomography (CT) abnormality scores at recovery (r = 0.28 and r = 0.46, p < 0.05, respectively).

Conclusion

A unique inflammatory and vascular endothelial damage mediator signature is found in plasma months following acute COVID-19 infection. Further research is required to determine its pathophysiological and clinical significance.

Host Response of Syrian Hamster to SARS-CoV-2 Infection including Differences with Humans and between Sexes

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the importance of having proper tools and models to study the pathophysiology of emerging infectious diseases to test therapeutic protocols, assess changes in viral phenotypes, and evaluate the effects of viral evolution. This study provided a comprehensive characterization of the Syrian hamster (Mesocricetus auratus) as an animal model for SARS-CoV-2 infection using different approaches (description of clinical signs, viral load, receptor profiling, and host immune response) and targeting four different organs (lungs, intestine, brain, and PBMCs). Our data showed that both male and female hamsters were susceptible to the infection and developed a disease similar to the one observed in patients with COVID-19 that included moderate to severe pulmonary lesions, inflammation, and recruitment of the immune system in the lungs and at the systemic level. However, all animals recovered within 14 days without developing the severe pathology seen in humans, and none of them died. We found faint evidence for intestinal and neurological tropism associated with the absence of lesions and a minimal host response in intestines and brains, which highlighted another crucial difference with the multiorgan impairment of severe COVID-19. When comparing male and female hamsters, we observed that males sustained higher viral RNA shedding and replication in the lungs, suffered from more severe symptoms and histopathological lesions, and triggered higher pulmonary inflammation. Overall, these data confirmed the Syrian hamster as a suitable model for mild to moderate COVID-19 and reflected sex-related differences in the response against the virus observed in humans.

Hematopathology of SARS-CoV-2 infection and COVID-19 disease

Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 and is associated with pronounced hematopathologic findings. Peripheral blood features are heterogeneous and very often include neutrophilia, lymphopenia, myeloid left shift, abnormally segmented neutrophils, atypical lymphocytes/plasmacytoid lymphocytes, and atypical monocytes. Bone marrow biopsies and aspirates are often notable for histiocytosis and hemophagocytosis, whereas secondary lymphoid organs may exhibit lymphocyte depletion, pronounced plasmacytoid infiltrates, and hemophagocytosis. These changes are reflective of profound innate and adaptive immune dysregulation, and ongoing research efforts continue to identify clinically applicable biomarkers of disease severity and outcome.

Time series analysis revealed prognostic value of continuous nasopharyngeal SARS-CoV-2 nucleic acid quantification for COVID-19: A retrospective study of >3000 COVID-19 patients from 2 centers

BACKGROUND

Early stratification of disease progression remains one of the major challenges towards the post-coronavirus disease 2019 (COVID-19) era. The clinical relevance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid load is debated due to the heterogeneity in patients' underlying health conditions. We determined the prognostic value of nasopharyngeal viral load dynamic conversion for COVID-19.

METHODS

The cycling threshold (Ct) values of 28,937 nasopharyngeal SARS-CoV-2 RT-PCRs were retrospectively collected from 3,364 COVID-19 patients during hospitalization and coordinated to the onset of disease progression. The ROC curve was utilized to determine the predictive performance of the rate of Ct value alteration between two consecutive RT-PCR runs within 48 h (ΔCt%) for disease transformation across patients with different COVID-19 severity and immune backgrounds, and further validated with 1,860 SARS-CoV-2 RT-PCR results from an independent validation cohort of 262 patients. For the 67 patients with severe COVID-19, Kaplan-Meier analysis was performed to evaluate the difference in survival between patients stratified by the magnitude of Ct value alteration between the late and early stages of hospitalization.

RESULTS

The kinetics of viral nucleic acid conversion diversified across COVID-19 patients with different clinical characteristics and disease severities. The ΔCt% is a clinical characteristic- and host immune status-independent indicator for COVID-19 progression prediction (AUC = 0.79, 95 % CI = 0.76 to 0.81), which outperformed the canonical blood test markers, including c-reactive protein (AUC = 0.57, 95 % CI = 0.53 to 0.61), serum amyloid A (AUC = 0.61, 95 % CI = 0.54 to 0.68), lactate dehydrogenase (AUC = 0.61, 95 % CI = 0.56 to 0.67), d-dimer (AUC = 0.56, 95 % CI = 0.46 to 0.66), and lymphocyte count (AUC = 0.62, 95 % CI = 0.58 to 0.66). Patients with persistent high SARS-CoV-2 viral load (an increase of mean Ct value < 50 %) during the first 3 days of hospitalization demonstrated a significantly unfavorable survival (HR = 0.16, 95 % CI = 0.04 to 0.65, P = 2.41 × 10^-3).

CONCLUSIONS

Viral nucleic acid dynamics of SARS-CoV-2 eliminates the inter-patient variance of basic health conditions and therefore, can serve as a prognostic marker for COVID-19.

COVID-19 severity and in-hospital mortality in an area with high HIV prevalence

Background

HIV infection causes immune dysregulation affecting T-cell and monocyte function, which may alter coronavirus disease 2019 (COVID-19) pathophysiology.

Objectives

We investigated the associations among clinical phenotypes, laboratory biomarkers, and hospitalisation outcomes in a cohort of people hospitalised with COVID-19 in a high HIV prevalence area.

Method

We conducted a prospective observational cohort study in Tshwane, South Africa. Respiratory disease severity was quantified using the respiratory oxygenation score. Analysed biomarkers included inflammatory and coagulation biomarkers, CD4 T-cell counts, and HIV-1 viral loads (HIVVL).

Results

The analysis included 558 patients, of whom 21.7% died during admission. The mean age was 54 years. A total of 82 participants were HIV-positive. People living with HIV (PLWH) were younger (mean age 46 years) than HIV-negative people; most were on antiretroviral treatment with a suppressed HIVVL (72%) and the median CD4 count was 159 (interquartile range: 66-397) cells/µL. After adjusting for age, HIV was not associated with increased risk of mortality during hospitalisation (age-adjusted hazard ratio = 1.1, 95% confidence interval: 0.6-2.0). Inflammatory biomarker levels were similar in PLWH and HIV-negative patients. Detectable HIVVL was associated with less severe respiratory disease. In PLWH, mortality was associated with higher levels of inflammatory biomarkers. Opportunistic infections, and other risk factors for severe COVID-19, were common in PLWH who died.

Conclusion

PLWH were not at increased risk of mortality and those with detectable HIVVL had less severe respiratory disease than those with suppressed HIVVL.

What this study adds

This study advances our understanding of severe COVID-19 in PLWH.

Multimodal characterization of antigen-specific CD8 + T cells across SARS-CoV-2 vaccination and infection

The human immune response to SARS-CoV-2 antigen after infection or vaccination is defined by the durable production of antibodies and T cells. Population-based monitoring typically focuses on antibody titer, but there is a need for improved characterization and quantification of T cell responses. Here, we utilize multimodal sequencing technologies to perform a longitudinal analysis of circulating human leukocytes collected before and after BNT162b2 immunization. Our data reveal distinct subpopulations of CD8 + T cells which reliably appear 28 days after prime vaccination (7 days post boost). Using a suite of cross-modality integration tools, we define their transcriptome, accessible chromatin landscape, and immunophenotype, and identify unique biomarkers within each modality. By leveraging DNA-oligo-tagged peptide-MHC multimers and T cell receptor sequencing, we demonstrate that this vaccine-induced population is SARS-CoV-2 antigen-specific and capable of rapid clonal expansion. Moreover, we also identify these CD8 + populations in scRNA-seq datasets from COVID-19 patients and find that their relative frequency and differentiation outcomes are predictive of subsequent clinical outcomes. Our work contributes to our understanding of T cell immunity, and highlights the potential for integrative and multimodal analysis to characterize rare cell populations.

Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune circuits related to tissue injury

Severe lung damage resulting from COVID-19 involves complex interactions between diverse populations of immune and stromal cells. In this study, we used a spatial transcriptomics approach to delineate the cells, pathways, and genes present across the spectrum of histopathological damage in COVID-19-affected lung tissue. We applied correlation network-based approaches to deconvolve gene expression data from 46 areas of interest covering more than 62,000 cells within well-preserved lung samples from 3 patients. Despite substantial interpatient heterogeneity, we discovered evidence for a common immune-cell signaling circuit in areas of severe tissue that involves crosstalk between cytotoxic lymphocytes and pro-inflammatory macrophages. Expression of IFNG by cytotoxic lymphocytes was associated with induction of chemokines, including CXCL9, CXCL10, and CXCL11, which are known to promote the recruitment of CXCR3+ immune cells. The TNF superfamily members BAFF (TNFSF13B) and TRAIL (TNFSF10) were consistently upregulated in the areas with severe tissue damage. We used published spatial and single-cell SARS-CoV-2 data sets to validate our findings in the lung tissue from additional cohorts of patients with COVID-19. The resulting model of severe COVID-19 immune-mediated tissue pathology may inform future therapeutic strategies.

Blood transcriptome responses in patients correlate with severity of COVID-19 disease

Background

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals display a wide spectrum of disease severity, as defined by the World Health Organization (WHO). One of the main factors underlying this heterogeneity is the host immune response, with severe COVID-19 often associated with a hyperinflammatory state.

Aim

Our current study aimed to pinpoint the specific genes and pathways underlying differences in the disease spectrum and outcomes observed, through in-depth analyses of whole blood transcriptomics in a large cohort of COVID-19 participants.

Results

All WHO severity levels were well represented and mild and severe disease displaying distinct gene expression profiles. WHO severity levels 1-4 were grouped as mild disease, and signatures from these participants were different from those with WHO severity levels 6-9 classified as severe disease. Severity level 5 (moderate cases) presented a unique transitional gene signature between severity levels 2-4 (mild/moderate) and 6-9 (severe) and hence might represent the turning point for better or worse disease outcome. Gene expression changes are very distinct when comparing mild/moderate or severe cases to healthy controls. In particular, we demonstrated the hallmark down-regulation of adaptive immune response pathways and activation of neutrophil pathways in severe compared to mild/moderate cases, as well as activation of blood coagulation pathways.

Conclusions

Our data revealed discrete gene signatures associated with mild, moderate, and severe COVID-19 identifying valuable candidates for future biomarker discovery.

Towards precision medicine: Omics approach for COVID-19

The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.

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

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

Humoral Immune Response Profile of COVID-19 Reveals Severity and Variant-Specific Epitopes: Lessons from SARS-CoV-2 Peptide Microarray

The amaranthine scale of the COVID-19 pandemic and unpredictable disease severity is of grave concern. Serological diagnostic aids are an excellent choice for clinicians for rapid and easy prognosis of the disease. To this end, we studied the humoral immune response to SARS-CoV-2 infection to map immunogenic regions in the SARS-CoV-2 proteome at amino acid resolution using a high-density SARS-CoV-2 proteome peptide microarray. The microarray has 4932 overlapping peptides printed in duplicates spanning the entire SARS-CoV-2 proteome. We found 204 and 676 immunogenic peptides against IgA and IgG, corresponding to 137 and 412 IgA and IgG epitopes, respectively. Of these, 6 and 307 epitopes could discriminate between disease severity. The emergence of variants has added to the complexity of the disease. Using the mutation panel available, we could detect 5 and 10 immunogenic peptides against IgA and IgG with mutations belonging to SAR-CoV-2 variants. The study revealed severity-based epitopes that could be presented as potential prognostic serological markers. Further, the mutant epitope immunogenicity could indicate the putative use of these markers for diagnosing variants responsible for the infection.