Most infections with Mycobacterium tuberculosis (Mtb) manifest as a clinically asymptomatic, contained state, known as latent tuberculosis infection, that affects approximately one-quarter of the global population. Although fewer than one in ten individuals eventually progress to active disease, tuberculosis is a leading cause of death from infectious disease worldwide. Despite intense efforts, immune factors that influence the infection outcomes remain poorly defined. Here we used integrated analyses of multiple cohorts to identify stage-specific host responses to Mtb infection. First, using high-dimensional mass cytometry analyses and functional assays of a cohort of South African adolescents, we show that latent tuberculosis is associated with enhanced cytotoxic responses, which are mostly mediated by CD16 (also known as FcγRIIIa) and natural killer cells, and continuous inflammation coupled with immune deviations in both T and B cell compartments. Next, using cell-type deconvolution of transcriptomic data from several cohorts of different ages, genetic backgrounds, geographical locations and infection stages, we show that although deviations in peripheral B and T cell compartments generally start at latency, they are heterogeneous across cohorts. However, an increase in the abundance of circulating natural killer cells in tuberculosis latency, with a corresponding decrease during active disease and a return to baseline levels upon clinical cure are features that are common to all cohorts. Furthermore, by analysing three longitudinal cohorts, we find that changes in peripheral levels of natural killer cells can inform disease progression and treatment responses, and inversely correlate with the inflammatory state of the lungs of patients with active tuberculosis. Together, our findings offer crucial insights into the underlying pathophysiology of tuberculosis latency, and identify factors that may influence infection outcomes.

To permit the recognition of antigens, T cells generate a vast diversity of T cell receptor (TCR) sequences. Upon binding of the TCR to an antigen–MHC complex, T cells clonally expand to establish an immune response. To study antigen-specific T cell clonality, we have developed a method that allows selection of rare cells, based on RNA expression, before in-depth scRNA-seq (named SELECT-seq). We applied SELECT-seq to collect both TCR sequences and then transcriptomes from single cells of peripheral blood lymphocytes activated by a Mycobacterium tuberculosis (Mtb) lysate. TCR sequence analysis allowed us to preferentially select expanded conventional CD8+ T cells as well as invariant natural killer T (iNKT) cells and mucosal-associated invariant T (MAIT) cells. The iNKT and MAIT cells have a highly similar transcriptional pattern, indicating that they carry out similar immunological functions and differ considerably from conventional CD8+ T cells. While there is no relationship between expression profiles and clonal expansion in iNKT or MAIT cells, highly expanded conventional CD8+ T cells down-regulate the interleukin 2 (IL-2) receptor alpha (IL2RA, or CD25) protein and show signs of senescence. This suggests inherent limits to clonal expansion that act to diversify the T cell response repertoire.

Background: Once considered primarily a disorder of lipid deposition, coronary artery disease is an incurable, life-threatening disease that is now also characterized by chronic inflammation notable for the buildup of atherosclerotic plaques containing immune cells in various states of activation and differentiation. Understanding how these immune cells contribute to disease progression may lead to the development of novel therapeutic strategies. Methods: We used single-cell technology and in vitro assays to interrogate the immune microenvironment of human coronary atherosclerotic plaque at different stages of maturity. Results: In addition to macrophages, we found a high proportion of αβ T cells in the coronary plaques. Most of these T cells lack high expression of CCR7 and L-selectin , indicating that they are primarily antigen-experienced, memory cells. Notably, nearly one-third of these cells express the HLA-DRA surface marker, signifying activation through their TCRs (T-cell receptors). Consistent with this, TCR repertoire analysis confirmed the presence of activated αβ T cells (CD4<CD8), exhibiting clonal expansion of specific TCRs. Interestingly, we found that these plaque T cells had TCRs specific for influenza, coronavirus, and other viral epitopes, which share sequence homologies to proteins found on smooth muscle cells and endothelial cells, suggesting potential autoimmune-mediated T-cell activation in the absence of active infection. To better understand the potential function of these activated plaque T cells, we then interrogated their transcriptome at the single-cell level. Of the 3 T-cell phenotypic clusters with the highest expression of the activation marker HLA-DRA identified by the Seurat algorithm, 2 clusters express a proinflammatory and cytolytic signature characteristic of CD8 cells, while the other expresses AREG (amphiregulin), which promotes smooth muscle cell proliferation and fibrosis, and, thus, contributes to plaque progression. Conclusions: Taken together, these findings demonstrate that plaque T cells are clonally expanded potentially by antigen engagement, are potentially reactive to self-epitopes, and may interact with smooth muscle cells and macrophages in the plaque microenvironment.

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This review describes the adjuvanticity of novel diterpenoids (synthetic phytol derivatives) compared to some commercially available adjuvants. The efficacy of the phytol-derived immunostimulants was evaluated in terms of their ability to activate innate immunity, amplify various antigen-specific immune responses, and engender immunological memory with no discernible adverse effects in both competent and immune-deficient mice. The profile that emerges out of these studies reveals that the phytol derivatives are excellent immunostimulants, superior to a number of commercial adjuvants in terms of long-term memory induction and activation of both innate and acquired immunity. Additionally, the phytol-derived compounds have no cumulative inflammatory or toxic effects even in immuno-compromised mice.

Background: Multifood oral immunotherapy (mOIT) with adjunctive anti-IgE (omalizumab, XOLAIR ® ) treatment affords safe, effective, and rapid desensitization to multiple foods, although the specific immune mechanisms mediating this desensitization remain to be fully elucidated. Methods: Participants in our phase 2 mOIT trial (NCT02643862) received omalizumab from baseline to week 16 and mOIT from week 8 to week 36. We compared the immune profile of PBMCs and plasma taken at baseline, week 8, and week 36 using high-dimensional mass cytometry, component-resolved diagnostics, the indirect basophil activation test, and Luminex. Results:We found (i) decreased frequency of IL-4 + peanut-reactive CD4 + T cells and a marked downregulation of GPR15 expression and CXCR3 frequency among γδ and CD8 + T-cell subsets at week 8 during the initial, omalizumab-alone induction phase;(ii) significant upregulation of the skin-homing receptor CCR4 in peanut-reactive | INTRODUC TI ONFood allergy, a major public health concern in the United States, affects ~8% of children under the age of 18, nearly 40% of whom suffer from allergies to multiple foods, 1 a prevalence that is rising. 2 Multifood oral immunotherapy (mOIT), during which several food allergens are gradually introduced at increasing doses, provides safe and efficacious desensitization to multiple (2-5) offending foods. 3 We and others have previously demonstrated that anti-IgE (omalizumab, XOLAIR ® ) combined with single-allergen OIT rapidly desensitized patients to single allergens such as milk or peanuts. [4][5][6][7][8] Our phase 1/2 clinical trials [9][10][11] have demonstrated that incorporating omalizumab with mOIT also safely desensitizes patients with allergies to multiple foods. mOIT using omalizumab induction treatment is currently being tested in a phase multicenter, placebo-controlled clinical trial (NCT03881696) under breakthrough designation by the FDA.

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In this Letter, data from 2 out of the 17 progressors were inadvertently binned into the wrong time intervals. We have now re-analysed the entire dataset with all data points binned correctly with respect to the time intervals before tuberculosis (TB) diagnosis, and the first graph in Fig. 4b has been corrected (see Fig. 1 of this Amendment for the original figure). In the revised analysis, the time intervals are set at: >400, 201-400, 101-200 and 0-100 days before TB diagnosis (intervals in the original Fig. 4b were: 361-540, 181-360, 61-180 and 0-60 days). The comparisons between the levels of natural killer (NK) cells 1 year before TB diagnosis and at TB diagnosis, and between approximately 7 months before TB diagnosis and at TB diagnosis remain statistically significant (P = 0.0312 and P = 0.0039, respectively; P = 0.0078 and P = 0.0156 in the original Letter). The original observation of a significant decrease in the percentage of NK cells during the progression to active TB remains unchanged.In the original Letter, all non-progressors that had measurements for at least three time points were included. We have now included all non-progressors that had longitudinal measurements for at least two time points (n = 41). The second graph in Fig. 4b and the Reporting Summary have been corrected (see Fig. 1 of this Amendment for the original figure, and Supplementary Information of this Amendment for the original Reporting Summary). The revised analysis shows no significant change in the frequency of NK cells (P = 0.8828; P = 0.5202 in the original Letter) during the two-year study period.The receiver operating characteristic curves in Fig. 4c have been corrected (see Fig. 1 of this Amendment for the original figure). The new analysis shows an area under the curve (AUC) of 0.74 (95% confidence interval (CI) 0.58-0.90) in the three months (100 days) before active TB diagnosis (AUC = 0.80, CI = 0.64-0.96 in the two months (60 days) before active TB diagnosis in the original Letter), and an AUC of 0.69 (CI = 0.57-0.82) in the seven months (200 days) before active TB diagnosis (AUC = 0.74; CI = 0.63-0.85 in the 210 days before active TB diagnosis in the original Letter). These analyses do not change any of our conclusions. In addition, the name of author Qiantian Yang should have been spelled Qianting Yang, and the affiliation of author Stephanus T. Malherbe should only be 'P = 0.0078 P = 0.0156 3 6 1 -5 4 0 1 8 1 -3 6 0 6 1 -1 8 0 0 -6 0 0 5 10 15 20 25 NK cells (% of total live cells) D 0 D 1 8 0 D 3 6 0 D 5 4 0 0 5 10 15 20 25 P = 0.5202 Time points for blood draw Days before TB diagnosis Progressors Non-progressors Original Fig. 4b b Time points for blood draw Days before TB diagnosis Progressors Non-progressors NK cells (% of total live cells) D 0 D 1 8 0 D 3 6 0 D 5 4 0 0 5 10 15 20 25 P = 0.8828 > 4 0 0 2 0 1 -4 0 0 1 0 1 -2 0 0 0 -1 0 0 0 5 10 15 20 25 P = 0.0039 P = 0.0312 Corrected Fig. 4b 0 20 40 60 80 100 0 20 40 60 80 100 c 100% -speci city (%) Sensitivity (%) Days before active TB diagnosis 0-210 (AUC = 0....