Early stages of viral infections are associated with local recruitment and activation of dendritic cells (DC) and NK cells. Although activated DC and NK cells are known to support each other’s functions, it is less clear whether their local interaction in infected tissues can modulate the subsequent ability of migrating DC to induce T cell responses in draining lymph nodes. In this study, we report that NK cells are capable of inducing stable type 1-polarized “effector/memory” DC (DC1) that act as carriers of NK cell-derived helper signals for the development of type 1 immune responses. NK cell-induced DC1 show a strongly elevated ability to produce IL-12p70 after subsequent CD40 ligand stimulation. NK-induced DC1 prime naive CD4+ Th cells for high levels of IFN-γ, but low IL-4 production, and demonstrate a strongly enhanced ability to induce Ag-specific CD8+ T cell responses. Resting NK cells display stringent activation requirements to perform this novel, DC-mediated, “helper” function. Although their interaction with K562 cells results in effective target cell killing, the induction of DC1 requires a second NK cell-activating signal. Such costimulatory signal can be provided by type I IFNs, common mediators of antiviral responses. Therefore, in addition to their cytolytic function, NK cells also have immunoregulatory activity, induced under more stringent conditions. The currently demonstrated helper activity of NK cells may support the development of Th1- and CTL-dominated type 1 immunity against intracellular pathogens and may have implications for cancer immunotherapy.

One hundred seventy-two members from 27 randomly selected multiple case Caucasian families of patients with insulin-dependent diabetes mellitus (IDDM) were studied at the DNA level to ascertain the reliability of codon 57 of the HLA-DQ beta-chain gene as a disease protection/susceptibility marker. The analysis was carried out by polymerase chain reaction amplification of DNA encoding the first domain of the DQ beta chain and by dot blot analysis of the amplified material with allele-specific oligonucleotide probes. One hundred twenty-three randomly selected healthy Caucasian donors were also tested. The results demonstrated that haplotypes carrying an aspartic acid in position 57 (Asp-57) of their DQ beta chain were significantly increased in frequency among nondiabetic haplotypes (23/38), while non-Asp-57 haplotypes were significantly increased in frequency among diabetic haplotypes (65/69). Ninety-six percent of the diabetic probands in our study were homozygous non-Asp/non-Asp as compared to 19.5% of healthy unrelated controls. This conferred a relative risk of 107 (chi 2 = 54.97; P = 0.00003) for non-Asp-57 homozygous individuals. Even though the inheritance and genetic features of IDDM are complex and are not necessarily fully explained by DQ beta chain polymorphism, this approach is much more sensitive than HLA serolog in assessing risk for IDDM.

We evaluated two bone marrow-derived dendritic cell (DC) populations from NOD mice, the murine model for type 1 human diabetes. DCs derived from GM-CSF [granulocyte/macrophage colony-stimulating factor] + interleukin (IL)-4 cultures expressed high levels of major histocompatibility complex (MHC) class II, CD40, CD80, and CD86 molecules and were efficient stimulators of naive allogeneic T-cells. In contrast, DCs derived from GM-CSF cultures had low levels of MHC class II costimulation/activation molecules, were able to take up mannosylated bovine serum albumin more efficiently than GM + IL-4 DCs, and were poor T-cell stimulators. The two DC populations migrated to the spleen and pancreas after intravenous injection. To determine the ability of the two DC populations to modulate diabetes development, DCs were pulsed with a mixture of three islet antigen-derived peptides or with medium before injection into prediabetic NOD mice. Despite phenotypic and functional differences in vitro, both populations prevented in vivo diabetes development. Pulsing of the DCs with peptide in vitro did not significantly improve the ability of DCs to prevent disease, which suggests that DCs may process and present antigen to T-cells in vivo. In addition, we detected GAD65 peptide-specific IgG1 antibody responses in DC-treated mice. Overall, these results suggest that a Th2 response was generated in DC-treated mice. This response was optimal when using GM + IL-4 DCs, which suggests that the balance between regulatory Th2 and effector Th1 cells may have been altered in these mice.

The 2019 novel coronavirus, SARS-CoV-2, is an emerging pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors like diabetes. A critical question for treatment and protection is why it appears that the severity of infection may correlate with the initial level of virus exposure. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interactions, screen potential therapies, and identify potential biomarkers that differentiate response dynamics. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce a prototype of a multiscale model of SARS-CoV-2 dynamics in lung and intestinal tissue that will be iteratively refined. The first prototype model was built and shared internationally as open source code and interactive, cloud-hosted executables in under 12 hours. In a sustained community effort, this model will integrate data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health.