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2.
Front Immunol ; 12: 733539, 2021.
Article in English | MEDLINE | ID: covidwho-1572288

ABSTRACT

The response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely impacted by the level of virus exposure and status of the host immunity. The nature of protection shown by direct asymptomatic contacts of coronavirus disease 2019 (COVID-19)-positive patients is quite intriguing. In this study, we have characterized the antibody titer, SARS-CoV-2 surrogate virus neutralization, cytokine levels, single-cell T-cell receptor (TCR), and B-cell receptor (BCR) profiling in asymptomatic direct contacts, infected cases, and controls. We observed significant increase in antibodies with neutralizing amplitude in asymptomatic contacts along with cytokines such as Eotaxin, granulocyte-colony stimulating factor (G-CSF), interleukin 7 (IL-7), migration inhibitory factor (MIF), and macrophage inflammatory protein-1α (MIP-1α). Upon single-cell RNA (scRNA) sequencing, we explored the dynamics of the adaptive immune response in few representative asymptomatic close contacts and COVID-19-infected patients. We reported direct asymptomatic contacts to have decreased CD4+ naive T cells with concomitant increase in CD4+ memory and CD8+ Temra cells along with expanded clonotypes compared to infected patients. Noticeable proportions of class switched memory B cells were also observed in them. Overall, these findings gave an insight into the nature of protection in asymptomatic contacts.


Subject(s)
Adaptive Immunity/immunology , COVID-19/immunology , Genomics/methods , SARS-CoV-2/immunology , Single-Cell Analysis/methods , Adaptive Immunity/genetics , Adult , Antibodies, Viral/immunology , COVID-19/genetics , COVID-19/virology , Cytokines/immunology , Cytokines/metabolism , Female , Gene Expression Profiling/methods , Humans , Male , /metabolism , Middle Aged , SARS-CoV-2/physiology , Sequence Analysis, RNA/methods , T-Lymphocytes/immunology , T-Lymphocytes/metabolism , T-Lymphocytes/virology , Young Adult
9.
Infect Genet Evol ; 95: 105075, 2021 11.
Article in English | MEDLINE | ID: covidwho-1401708

ABSTRACT

T-cell-mediated immunity to SARS-CoV-2-derived peptides in individuals unexposed to SARS-CoV-2 has been previously reported. This pre-existing immunity was suggested to largely derive from prior exposure to 'common cold' endemic human coronaviruses (HCoVs). To test this, we characterised the sequence homology of SARS-CoV-2-derived T-cell epitopes reported in the literature across the full proteome of the Coronaviridae family. 54.8% of these epitopes had no homology to any of the HCoVs. Further, the proportion of SARS-CoV-2-derived epitopes with any level of sequence homology to the proteins encoded by any of the coronaviruses tested is well-predicted by their alignment-free phylogenetic distance to SARS-CoV-2 (Pearson's r = -0.958). No coronavirus in our dataset showed a significant excess of T-cell epitope homology relative to the proportion of expected random matches, given their genetic similarity to SARS-CoV-2. Our findings suggest that prior exposure to human or animal-associated coronaviruses cannot completely explain the T-cell repertoire in unexposed individuals that recognise SARS-CoV-2 cross-reactive epitopes.


Subject(s)
Antibodies, Viral/blood , COVID-19/immunology , Coronaviridae/immunology , Disease Resistance , Immunologic Memory , SARS-CoV-2/immunology , Animals , Antibodies, Viral/genetics , Antibodies, Viral/immunology , Antigens, Viral/genetics , Antigens, Viral/immunology , Asymptomatic Diseases , COVID-19/genetics , COVID-19/pathology , COVID-19/virology , Chiroptera/virology , Coronaviridae/classification , Coronaviridae/genetics , Coronaviridae/pathogenicity , Cross Reactions , Epitopes, T-Lymphocyte/genetics , Epitopes, T-Lymphocyte/immunology , Eutheria/virology , Humans , Immunity, Cellular , Phylogeny , SARS-CoV-2/classification , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Severity of Illness Index , T-Lymphocytes/immunology , T-Lymphocytes/virology
10.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1387715

ABSTRACT

Cytolytic T-cells play an essential role in the adaptive immune system by seeking out, binding and killing cells that present foreign antigens on their surface. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life-threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native peptides to elicit a T-cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen alleles, for both synthetic biological applications, and to augment real training datasets. Here, we propose a beta-binomial distribution approach to derive peptide immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, K-nearest neighbors, support vector machine, Random Forest and AdaBoost) and three deep learning models (convolutional neural network (CNN), Residual Net and graph neural network) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-CoV-2). We chose the CNN as the best prediction model, based on its adaptivity for small and large datasets and performance relative to existing methods. In addition to outperforming two highly used immunogenicity prediction algorithms, DeepImmuno-CNN correctly predicts which residues are most important for T-cell antigen recognition and predicts novel impacts of SARS-CoV-2 variants. Our independent generative adversarial network (GAN) approach, DeepImmuno-GAN, was further able to accurately simulate immunogenic peptides with physicochemical properties and immunogenicity predictions similar to that of real antigens. We provide DeepImmuno-CNN as source code and an easy-to-use web interface.


Subject(s)
COVID-19/immunology , Peptides/immunology , SARS-CoV-2/immunology , Algorithms , COVID-19/virology , Deep Learning , Humans , Machine Learning , Neural Networks, Computer , Peptides/genetics , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Software , T-Lymphocytes/immunology , T-Lymphocytes/virology
11.
Sci Rep ; 11(1): 14275, 2021 07 12.
Article in English | MEDLINE | ID: covidwho-1387486

ABSTRACT

SARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain individuals to more severe clinical presentations in hopes of either identifying these individuals early in their illness or improving their medical management. We sought to understand immunogenomic differences that may result in varied clinical outcomes through analysis of T-cell receptor sequencing (TCR-Seq) data in the open access ImmuneCODE database. We identified two cohorts within the database that had clinical outcomes data reflecting severity of illness and utilized DeepTCR, a multiple-instance deep learning repertoire classifier, to predict patients with severe SARS-CoV-2 infection from their repertoire sequencing. We demonstrate that patients with severe infection have repertoires with higher T-cell responses associated with SARS-CoV-2 epitopes and identify the epitopes that result in these responses. Our results provide evidence that the highly variable clinical course seen in SARS-CoV-2 infection is associated to certain antigen-specific responses.


Subject(s)
COVID-19/immunology , Epitopes/immunology , Receptors, Antigen, T-Cell/immunology , SARS-CoV-2/immunology , Asymptomatic Infections/epidemiology , COVID-19/pathology , COVID-19/virology , Deep Learning , Humans , Receptors, Antigen, T-Cell/genetics , SARS-CoV-2/pathogenicity , T-Lymphocytes/immunology , T-Lymphocytes/virology
12.
Sci Adv ; 7(34)2021 08.
Article in English | MEDLINE | ID: covidwho-1365116

ABSTRACT

The COVID-19 pandemic has spread worldwide, yet the role of antiviral T cell immunity during infection and the contribution of immune checkpoints remain unclear. By prospectively following a cohort of 292 patients with melanoma, half of which treated with immune checkpoint inhibitors (ICIs), we identified 15 patients with acute or convalescent COVID-19 and investigated their transcriptomic, proteomic, and cellular profiles. We found that ICI treatment was not associated with severe COVID-19 and did not alter the induction of inflammatory and type I interferon responses. In-depth phenotyping demonstrated expansion of CD8 effector memory T cells, enhanced T cell activation, and impaired plasmablast induction in ICI-treated COVID-19 patients. The evaluation of specific adaptive immunity in convalescent patients showed higher spike (S), nucleoprotein (N), and membrane (M) antigen-specific T cell responses and similar induction of spike-specific antibody responses. Our findings provide evidence that ICI during COVID-19 enhanced T cell immunity without exacerbating inflammation.


Subject(s)
COVID-19/immunology , Immune Checkpoint Inhibitors/immunology , Melanoma/immunology , SARS-CoV-2/immunology , T-Lymphocytes/immunology , Adaptive Immunity/drug effects , Adaptive Immunity/immunology , Aged , Antibodies, Viral/immunology , CD8-Positive T-Lymphocytes/drug effects , CD8-Positive T-Lymphocytes/immunology , CD8-Positive T-Lymphocytes/virology , COVID-19/complications , COVID-19/virology , Female , Humans , Immune Checkpoint Inhibitors/therapeutic use , Immunologic Memory/drug effects , Immunologic Memory/immunology , Lymphocyte Activation/drug effects , Lymphocyte Activation/immunology , Male , Melanoma/complications , Melanoma/drug therapy , Middle Aged , Prospective Studies , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Spike Glycoprotein, Coronavirus/immunology , Spike Glycoprotein, Coronavirus/metabolism , T-Lymphocytes/drug effects , T-Lymphocytes/virology
13.
Eur Rev Med Pharmacol Sci ; 25(15): 5057-5062, 2021 08.
Article in English | MEDLINE | ID: covidwho-1346860

ABSTRACT

OBJECTIVE: Complete blood count parameters are frequently altered in COVID-19 patients. Leucopenia and lymphopenia are the most common findings. This is not specific to COVID-19 as similar alterations are found in various other viral infections. This work is intended to summarize the evidence regarding white blood cell and lymphocyte subset alterations in COVID-19 and their clinical implications. MATERIALS AND METHODS: A PubMed search was conducted to identify relevant original studies. Articles not available in English or referring exclusively to pediatric patients were excluded. The study was designed as a narrative review from its inception. RESULTS: Complete white blood cell number and lymphocytes may be reduced in COVID-19 patients. Circulating CD4+ cells (helper T lymphocytes), CD8+ cells (cytotoxic T lymphocytes), regulatory T cells and natural killer (NK) cells may be reduced, with a greater reduction observed in critically ill patients. CD4+ and regulatory cell deficiencies may contribute to the cytokine storm and subsequent tissue damage observed in severe COVID-19 infection. NK and CD8+ cell deficiency might delay infection clearance. These aberrations of cellular immunity may contribute significantly to the pathogenesis of the disease. Alterations observed in monocyte function can also be implicated as they are effector cells responsible for tissue damage and remodeling. B cell dysfunction and maturation abnormalities have also been reported, suggesting that the virus also impairs humoral immunity. CONCLUSIONS: Lymphocyte subset abnormalities may be useful prognostic biomarkers for COVID-19, with circulating CD8+ cell count being the most promising as a predictor of severe disease requiring mechanical ventilation and mortality.


Subject(s)
COVID-19/immunology , Lymphocyte Subsets/immunology , Lymphocyte Subsets/virology , Monocytes/immunology , Monocytes/virology , B-Lymphocytes/immunology , B-Lymphocytes/virology , COVID-19/virology , Humans , Killer Cells, Natural/immunology , Killer Cells, Natural/virology , T-Lymphocytes/immunology , T-Lymphocytes/virology
14.
Nat Commun ; 12(1): 4515, 2021 07 26.
Article in English | MEDLINE | ID: covidwho-1327196

ABSTRACT

The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for 'reverse phenotyping'. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.


Subject(s)
COVID-19/immunology , Gene Expression Profiling/methods , Sequence Analysis, RNA/methods , Single-Cell Analysis/methods , T-Lymphocytes/metabolism , Aged , Aged, 80 and over , CD4-Positive T-Lymphocytes/metabolism , CD4-Positive T-Lymphocytes/virology , COVID-19/epidemiology , COVID-19/virology , Cells, Cultured , Cohort Studies , Female , Humans , Male , Middle Aged , Pandemics , Receptors, Antigen, T-Cell/genetics , Receptors, Antigen, T-Cell/immunology , Receptors, Antigen, T-Cell/metabolism , SARS-CoV-2/physiology , T-Lymphocytes/virology
16.
Clin Lymphoma Myeloma Leuk ; 21(10): e810-e816, 2021 10.
Article in English | MEDLINE | ID: covidwho-1313014

ABSTRACT

BACKGROUND: We previously reported elsewhere of a follicular lymphoma patient suffering from persistent COVID-19 pneumonia that was still ongoing at 2 months after onset. MATERIALS AND METHODS: We provide a follow-up report of the case along with a literature review of immunocompromised lymphoma patients experiencing prolonged COVID-19 infections. RESULTS: Although requiring a full 1 year, the presented case eventually achieved spontaneous resolution of COVID-19 pneumonia. Anti-SARS-CoV-2 antibodies could not be detected throughout the disease course, but COVID-19-directed T-cell response was found to be intact. The patient also developed secondary immune thrombocytopenia subsequent to COVID-19 pneumonia. We found 19 case reports of immunocompromised lymphoma patients with prolonged COVID-19 infections in the literature. All 5 patients who died did not receive convalescent plasma therapy, whereas resolution of COVID-19 infection was achieved in 8 out of 9 patients who received convalescent plasma therapy. CONCLUSIONS: We demonstrate through the presented case that while time-consuming, resolution of COVID-19 infections may be achieved without aid from humoral immunity if cellular immunity is intact. Immunocompromised lymphoma patients are at risk of a prolonged disease course of COVID-19, and convalescent plasma therapy may be a promising approach in such patients.


Subject(s)
COVID-19/immunology , Lymphoma, Follicular/drug therapy , Pneumonia/immunology , Rituximab/therapeutic use , SARS-CoV-2/immunology , Thrombocytopenia/immunology , Antineoplastic Agents, Immunological/therapeutic use , COVID-19/virology , Female , Follow-Up Studies , Humans , Immunocompromised Host/immunology , Lymphoma, Follicular/complications , Lymphoma, Follicular/immunology , Maintenance Chemotherapy/methods , Middle Aged , Pneumonia/complications , Pneumonia/virology , Remission, Spontaneous , SARS-CoV-2/physiology , T-Lymphocytes/immunology , T-Lymphocytes/virology , Thrombocytopenia/complications
17.
FEBS J ; 288(17): 5042-5054, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1295003

ABSTRACT

The COVID-19 pandemic has highlighted the vulnerability of people with diabetes mellitus (DM) to respiratory viral infections. Despite the short history of COVID-19, various studies have shown that patients with DM are more likely to have increased hospitalisation and mortality rates as compared to patients without. At present, the mechanisms underlying this susceptibility are unclear. However, prior studies show that the course of COVID-19 disease is linked to the efficacy of the host's T-cell responses. Healthy individuals who can elicit a robust T-cell response are more likely to limit the severity of COVID-19. Here, we investigate the hypothesis that an impaired T-cell response in patients with type 2 diabetes mellitus (T2DM) drives the severity of COVID-19 in this patient population. While there is currently a limited amount of information that specifically addresses T-cell responses in COVID-19 patients with T2DM, there is a wealth of evidence from other infectious diseases that T-cell immunity is impaired in patients with T2DM. The reasons for this are likely multifactorial, including the presence of hyperglycaemia, glycaemic variability and metformin use. This review emphasises the need for further research into T-cell responses of COVID-19 patients with T2DM in order to better inform our response to COVID-19 and future disease outbreaks.


Subject(s)
COVID-19/immunology , Diabetes Mellitus, Type 2/immunology , Hyperglycemia/immunology , T-Lymphocytes/immunology , COVID-19/complications , COVID-19/pathology , COVID-19/virology , Diabetes Mellitus, Type 2/complications , Diabetes Mellitus, Type 2/pathology , Diabetes Mellitus, Type 2/virology , Humans , Hyperglycemia/complications , Hyperglycemia/pathology , Hyperglycemia/virology , Pandemics , SARS-CoV-2/pathogenicity , T-Lymphocytes/virology
18.
J Chem Phys ; 154(19): 195104, 2021 May 21.
Article in English | MEDLINE | ID: covidwho-1240809

ABSTRACT

Biological processes at the cellular level are stochastic in nature, and the immune response system is no different. Therefore, models that attempt to explain this system need to also incorporate noise or fluctuations that can account for the observed variability. In this work, a stochastic model of the immune response system is presented in terms of the dynamics of T cells and virus particles. Making use of the Green's function and the Wilemski-Fixman approximation, this model is then solved to obtain the analytical expression for the joint probability density function of these variables in the early and late stages of infection. This is then also used to calculate the average level of virus particles in the system. Upon comparing the theoretically predicted average virus levels to those of COVID-19 patients, it is hypothesized that the long-lived dynamics that are characteristics of such viral infections are due to the long range correlations in the temporal fluctuations of the virions. This model, therefore, provides an insight into the effects of noise on viral dynamics.


Subject(s)
Immunity , Models, Immunological , T-Lymphocytes/immunology , T-Lymphocytes/virology , Virion/physiology , SARS-CoV-2/physiology , Stochastic Processes
19.
Proc Natl Acad Sci U S A ; 118(22)2021 06 01.
Article in English | MEDLINE | ID: covidwho-1232098

ABSTRACT

Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its k nearest neighboring cells across a range of k values. Based on this measure, DA-seq delineates contiguous significant DA subpopulations in the transcriptomic space. We apply DA-seq to several scRNA-seq datasets and highlight its improved ability to detect differences between distinct phenotypes in severe versus mildly ill COVID-19 patients, melanomas subjected to immune checkpoint therapy comparing responders to nonresponders, embryonic development at two time points, and young versus aging brain tissue. DA-seq enabled us to detect differences between these phenotypes. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies but also reveals additional DA subpopulations that were not described before. Analysis of these subpopulations yields biological insights that would otherwise be undetected using conventional computational approaches.


Subject(s)
Aging/genetics , COVID-19/genetics , Cell Lineage/genetics , Melanoma/genetics , RNA, Small Cytoplasmic/genetics , Skin Neoplasms/genetics , Aging/metabolism , B-Lymphocytes/immunology , B-Lymphocytes/virology , Brain/cytology , Brain/metabolism , COVID-19/immunology , COVID-19/pathology , COVID-19/virology , Cell Lineage/immunology , Cytokines/genetics , Cytokines/immunology , Datasets as Topic , Dendritic Cells/immunology , Dendritic Cells/virology , Gene Expression Profiling , Gene Expression Regulation , High-Throughput Nucleotide Sequencing , Humans , Melanoma/immunology , Melanoma/pathology , Monocytes/immunology , Monocytes/virology , Phenotype , RNA, Small Cytoplasmic/immunology , SARS-CoV-2/pathogenicity , Severity of Illness Index , Single-Cell Analysis/methods , Skin Neoplasms/immunology , Skin Neoplasms/pathology , T-Lymphocytes/immunology , T-Lymphocytes/virology , Transcriptome
20.
Genes (Basel) ; 12(5)2021 04 24.
Article in English | MEDLINE | ID: covidwho-1201763

ABSTRACT

Single-cell RNA sequencing of the bronchoalveolar lavage fluid (BALF) samples from COVID-19 patients has enabled us to examine gene expression changes of human tissue in response to the SARS-CoV-2 virus infection. However, the underlying mechanisms of COVID-19 pathogenesis at single-cell resolution, its transcriptional drivers, and dynamics require further investigation. In this study, we applied machine learning algorithms to infer the trajectories of cellular changes and identify their transcriptional programs. Our study generated cellular trajectories that show the COVID-19 pathogenesis of healthy-to-moderate and healthy-to-severe on macrophages and T cells, and we observed more diverse trajectories in macrophages compared to T cells. Furthermore, our deep-learning algorithm DrivAER identified several pathways (e.g., xenobiotic pathway and complement pathway) and transcription factors (e.g., MITF and GATA3) that could be potential drivers of the transcriptomic changes for COVID-19 pathogenesis and the markers of the COVID-19 severity. Moreover, macrophages-related functions corresponded more to the disease severity compared to T cells-related functions. Our findings more proficiently dissected the transcriptomic changes leading to the severity of a COVID-19 infection.


Subject(s)
Bronchoalveolar Lavage Fluid/virology , COVID-19/etiology , COVID-19/pathology , Macrophages , T-Lymphocytes , Algorithms , COVID-19/genetics , Computational Biology/methods , Gene Expression Profiling , Humans , Machine Learning , Macrophages/physiology , Macrophages/virology , Sequence Analysis, RNA/methods , Single-Cell Analysis , T-Lymphocytes/physiology , T-Lymphocytes/virology
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