Immunological memory is the basis of protective immunity provided by vaccines and previous infections. Immunological memory can develop from multiple branches of the adaptive immune system, including ...CD4 T cells, CD8 T cells, B cells, and long‐lasting antibody responses. Extraordinary progress has been made in understanding memory to SARS‐CoV‐2 infection and COVID‐19 vaccines, addressing development; quantitative and qualitative features of different cellular and anatomical compartments; and durability of each cellular component and antibodies. Given the sophistication of the measurements; the size of the human studies; the use of longitudinal samples and cross‐sectional studies; and head‐to‐head comparisons between infection and vaccines or between multiple vaccines, the understanding of immune memory for 1 year to SARS‐CoV‐2 infection and vaccines already supersedes that of any other acute infectious disease. This knowledge may help inform public policies regarding COVID‐19 and COVID‐19 vaccines, as well as the scientific development of future vaccines against SARS‐CoV‐2 and other diseases.
Immunity is a multifaceted phenomenon. For T cell-mediated memory responses to SARS-CoV-2, it is relevant to consider their impact both on COVID-19 disease severity and on viral spread in a ...population. Here, we reflect on the immunological and epidemiological aspects and implications of pre-existing cross-reactive immune memory to SARS-CoV-2, which largely originates from previous exposure to circulating common cold coronaviruses. We propose four immunological scenarios for the impact of cross-reactive CD4
memory T cells on COVID-19 severity and viral transmission. For each scenario, we discuss its implications for the dynamics of herd immunity and on projections of the global impact of SARS-CoV-2 on the human population, and assess its plausibility. In sum, we argue that key potential impacts of cross-reactive T cell memory are already incorporated into epidemiological models based on data of transmission dynamics, particularly with regard to their implications for herd immunity. The implications of immunological processes on other aspects of SARS-CoV-2 epidemiology are worthy of future study.
Effective countermeasures against the recent emergence and rapid expansion of the 2019 novel coronavirus (SARS-CoV-2) require the development of data and tools to understand and monitor its spread ...and immune responses to it. However, little information is available about the targets of immune responses to SARS-CoV-2. We used the Immune Epitope Database and Analysis Resource (IEDB) to catalog available data related to other coronaviruses. This includes SARS-CoV, which has high sequence similarity to SARS-CoV-2 and is the best-characterized coronavirus in terms of epitope responses. We identified multiple specific regions in SARS-CoV-2 that have high homology to the SARS-CoV virus. Parallel bioinformatic predictions identified a priori potential B and T cell epitopes for SARS-CoV-2. The independent identification of the same regions using two approaches reflects the high probability that these regions are promising targets for immune recognition of SARS-CoV-2. These predictions can facilitate effective vaccine design against this virus of high priority.
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•Ten experimentally defined regions within SARS-CoV have high homology with SARS-CoV-2•Parallel bioinformatics predicted potential B and T cell epitopes for SARS-CoV-2•Independent approaches identified the same immunodominant regions•The conserved immune regions have implications for vaccine design against multiple CoVs
Grifoni et al. identify potential targets for immune responses to the 2019 novel coronavirus (SARS-CoV-2) by sequence homology with closely related SARS-CoV and by a priori epitope prediction using bioinformatics approaches. This analysis provides essential information for understanding human immune responses to this virus and for evaluating diagnostic and vaccine candidates.
The Immune Epitope Database and Analysis Resource (IEDB) contains information related to antibodies and T cells across an expansive scope of research fields (infectious diseases, allergy, ...autoimmunity, and transplantation). Capture and representation of the data to reflect growing scientific standards and techniques have required continual refinement of our rigorous curation and query and reporting processes beginning with the automated classification of over 28 million PubMed abstracts, and resulting in easily searchable data from over 20,000 published manuscripts. Data related to MHC binding and elution, nonpeptidics, natural processing, receptors, and 3D structure is first captured through manual curation and subsequently maintained through recuration to reflect evolving scientific standards. Upon promotion to the free, public database, users can query and export records of specific relevance via the online web portal which undergoes iterative development to best enable efficient data access. In parallel, the companion Analysis Resource site hosts a variety of tools that assist in the bioinformatic analyses of epitopes and related structures, which can be applied to IEDB-derived and independent datasets alike. Available tools are classified into two categories: analysis and prediction. Analysis tools include epitope clustering, sequence conservancy, and more, while prediction tools cover T and B cell epitope binding, immunogenicity, and TCR/BCR structures. In addition to these tools, benchmarking servers which allow for unbiased performance comparison are also offered. In order to expand and support the user-base of both the database and Analysis Resource, the research team actively engages in community outreach through publication of ongoing work, conference attendance and presentations, hosting of user workshops, and the provision of online help. This review provides a description of the IEDB database infrastructure, curation and recuration processes, query and reporting capabilities, the Analysis Resource, and our Community Outreach efforts, including assessment of the impact of the IEDB across the research community.
A diagnosis of motor Parkinson's disease (PD) is preceded by a prolonged premotor phase with accumulating neuronal damage. Here we examined the temporal relation between α-synuclein (α-syn) T cell ...reactivity and PD. A longitudinal case study revealed that elevated α-syn-specific T cell responses were detected prior to the diagnosis of motor PD, and declined after. The relationship between T cell reactivity and early PD in two independent cohorts showed that α-syn-specific T cell responses were highest shortly after diagnosis of motor PD and then decreased. Additional analysis revealed significant association of α-syn-specific T cell responses with age and lower levodopa equivalent dose. These results confirm the presence of α-syn-reactive T cells in PD and show that they are most abundant immediately after diagnosis of motor PD. These cells may be present years before the diagnosis of motor PD, suggesting avenues of investigation into PD pathogenesis and potential early diagnosis.
DENV is a major public health problem worldwide, thus underlining the overall significance of the proposed Program. The four dengue virus (DENV) serotypes (1-4) cause the most common mosquito-borne ...viral disease of humans, with 3 billion people at risk for infection and up to 100 million cases each year, most often affecting children. The protective role of T cells during viral infection is well-established. Generally, CD8 T cells can control viral infection through several mechanisms, including direct cytotoxicity, and production of pro-inflammatory cytokines such as IFN-γ and TNF-α. Similarly, CD4 T cells are thought to control viral infection through multiple mechanisms, including enhancement of B and CD8 T cell responses, production of inflammatory and anti-viral cytokines, cytotoxicity, and promotion of memory responses. To probe the phenotype of virus-specific T cells, epitopes derived from viral sequences need to be known. Here we discuss the identification of CD4 and CD8 T cell epitopes derived from DENV and how these epitopes have been used by researchers to interrogate the phenotype and function of DENV-specific T cell populations.
MHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of prediction algorithms for human MHC ...class II to date have targeted HLA molecules encoded in the DR locus. This reflects a significant gap in knowledge as HLA DP and DQ molecules are presumably equally important, and have only been studied less because they are more difficult to handle experimentally.
In this study, we aimed to narrow this gap by providing a large scale dataset of over 17,000 HLA-peptide binding affinities for a set of 11 HLA DP and DQ alleles. We also expanded our dataset for HLA DR alleles resulting in a total of 40,000 MHC class II binding affinities covering 26 allelic variants. Utilizing this dataset, we generated prediction tools utilizing several machine learning algorithms and evaluated their performance.
We found that 1) prediction methodologies developed for HLA DR molecules perform equally well for DP or DQ molecules. 2) Prediction performances were significantly increased compared to previous reports due to the larger amounts of training data available. 3) The presence of homologous peptides between training and testing datasets should be avoided to give real-world estimates of prediction performance metrics, but the relative ranking of different predictors is largely unaffected by the presence of homologous peptides, and predictors intended for end-user applications should include all training data for maximum performance. 4) The recently developed NN-align prediction method significantly outperformed all other algorithms, including a naïve consensus based on all prediction methods. A new consensus method dropping the comparably weak ARB prediction method could outperform the NN-align method, but further research into how to best combine MHC class II binding predictions is required.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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