During the SARS-CoV-2 pandemic, novel and traditional vaccine strategies have been deployed globally. We investigated whether antibodies stimulated by mRNA vaccination (BNT162b2), including 3 rd dose boosting, differ from those generated by infection or adenoviral (ChAdOx1-S and Gam-COVID-Vac) or inactivated viral (BBIBP-CorV) vaccines. We analyzed human lymph nodes after infection or mRNA vaccination for correlates of serological differences. Antibody breadth against viral variants is less after infection compared to all vaccines evaluated, but improves over several months. Viral variant infection elicits variant-specific antibodies, but prior mRNA vaccination imprints serological responses toward Wuhan-Hu-1 rather than variant antigens. In contrast to disrupted germinal centers (GCs) in lymph nodes during infection, mRNA vaccination stimulates robust GCs containing vaccine mRNA and spike antigen up to 8 weeks post-vaccination in some cases. SARS-CoV-2 antibody specificity, breadth and maturation are affected by imprinting from exposure history, and distinct histological and antigenic contexts in infection compared to vaccination.

Here we find that CD8 + T cells expressing inhibitory killer cell immunoglobulin-like receptors (KIRs) are the human equivalent of Ly49 + CD8 + regulatory T cells in mice and are increased in the blood and inflamed tissues of patients with a variety of autoimmune diseases. Moreover, these CD8 + T cells efficiently eliminated pathogenic gliadin-specific CD4 + T cells from celiac disease patients’ leukocytes in vitro. We also find elevated levels of KIR + CD8 + T cells, but not CD4 + regulatory T cells, in COVID-19 patients, which correlated with disease severity and vasculitis. Selective ablation of Ly49 + CD8 + T cells in virus-infected mice led to autoimmunity post infection. Our results indicate that in both species, these regulatory CD8 + T cells act uniquely to suppress pathogenic T cells in autoimmune and infectious diseases.

Clinical diagnoses rely on a wide variety of laboratory tests and imaging studies, interpreted alongside physical examination and documentation of symptoms and patient history. However, the tools of diagnosis make little use of the immune system’s internal record of specific disease exposures encoded by the antigen-specific receptors of memory B cells and T cells. We have combined extensive receptor sequence datasets with three different machine learning representations of the contents of immune repertoires to develop an interpretive framework, MAchine Learning for Immunological Diagnosis (Mal-ID), that screens for multiple illnesses simultaneously. This approach can already reliably distinguish a wide range of disease states, including specific acute or chronic infections, and autoimmune or immunodeficiency disorders, and could contribute to identifying new infectious diseases as they emerge. Importantly, many features of the model of immune receptor sequences are human-interpretable. They independently recapitulate known biology of the responses to infection by SARS-CoV-2 or HIV, and reveal common features of autoreactive immune receptor repertoires, indicating that machine learning on immune repertoires can yield new immunological knowledge.

Previous reports show a small subset of CD8+ T cells expressing Ly49 proteins in mice can suppress autoimmunity in a model of demyelinating disease. Here we find a markedly increased frequency of CD8+ T cells expressing inhibitory Killer cell Immunoglobulin like Receptors (KIR), the human equivalent of the Ly49 family, in the blood and inflamed tissues of various human autoimmune diseases. Increased KIR+ CD8+ T cells in the gut also correlate with disease activity in Celiac disease (CeD) patients. Moreover, KIR+ CD8+ T cells can efficiently eliminate pathogenic gliadin-specific CD4+ T cells from CeD patients’ leukocytes in vitro. Together with gene expression data, this shows that these cells are the likely equivalent of the mouse Ly49+ CD8+ T cells. Furthermore, we observe elevated levels of KIR+ CD8+ T cells, but not CD4+ regulatory T cells, in COVID-19 and influenza-infected patients, and this correlates with disease severity and vasculitis in COVID-19. Single-cell RNA and parallelized TCR sequencing reveals that expanded KIR+ CD8+ T cells from these different diseases and healthy subjects display shared phenotypes and similar T cell receptor sequences. Selective ablation of the murine counterpart in virus-infected mice leads to exacerbated autoimmunity developed after infection. These results characterize a regulatory CD8+ T cell subset in humans which we hypothesize functions to control pathogenic cells in autoimmune and infectious diseases, with important implications for the cellular dynamics and possible therapeutic approaches to suppress unwanted autoimmunity. Supported by National Institutes of Health U19-AI057229 Howard Hughes Medical Institute Stanford Diabetes Research Center (P30DK116074)

Previous reports show that Ly49+CD8+ T cells can suppress autoimmunity in mouse models of autoimmune diseases. Here we find a markedly increased frequency of CD8+ T cells expressing inhibitory Killer cell Immunoglobulin like Receptors (KIR), the human equivalent of the Ly49 family, in the blood and inflamed tissues of various autoimmune diseases. Moreover, KIR+CD8+ T cells can efficiently eliminate pathogenic gliadin-specific CD4+ T cells from Celiac disease (CeD) patients' leukocytes in vitro. Furthermore, we observe elevated levels of KIR+CD8+ T cells, but not CD4+ regulatory T cells, in COVID-19 and influenza-infected patients, and this correlates with disease severity and vasculitis in COVID-19. Expanded KIR+CD8+ T cells from these different diseases display shared phenotypes and similar T cell receptor sequences. These results characterize a regulatory CD8+ T cell subset in humans, broadly active in both autoimmune and infectious diseases, which we hypothesize functions to control self-reactive or otherwise pathogenic T cells.

Abstract:Robust quantification of immune cell infiltration into the tumor microenvironment may shed light on why only a small proportion of patients benefit from checkpoint therapy. The immune cells surrounding a tumor have been suggested to mediate an effective response to immunotherapy. However, traditional measurement of immune cell content around a tumor by immunohistochemistry, flow cytometry, or mass cytometry allows measurement of only up to a few dozen markers at a time, limiting the number of immune cell types identified. Immune cell type abundances may instead be estimated in silico by deconvolving gene expression mixtures from bulk RNA sequencing of tumor tissue. By measuring tens of thousands of transcripts at once, bulk RNA-seq provides a rich input to algorithms that quantify cell type abundances in the tumor microenvironment, affording the potential to quantify the states of a greater number of immune cell types (given adequate training data). Here, we first review existing methods for deconvolution and evaluate their performance on synthetic mixtures. Then we develop a Bayesian inference approach, named infino, that learns to distinguish immune cell expression phenotypes and deconvolve mixtures. In contrast to earlier approaches, infino accepts RNA sequencing data, models transcript expression variability, and exploits the relationships between cell types to improve deconvolution accuracy and allow interrogation from the level of broad categories to the level of finest granularity. The resulting probability distributions of immune infiltration could be applied to numerous questions concerning the diverse ecology of immune cell types, including assessment of the association of immune infiltration with response to immunotherapy, and study of the expression profile and presence of elusive T cell subcompartments, such as T cell exhaustion.