Human leukocyte antigen-DQ8 transgenic mice: a model to examine the toxicity of aerosolized staphylococcal enterotoxin B

Staphylococcal enterotoxins (SEs) belong to a large group of bacterial exotoxins that cause severe immunopathologies, especially when delivered as an aerosol. SEs elicit the release of lethal amounts of cytokines by binding to major histocompatibility complex (MHC) class II and cross-linking susceptible T-cell receptors. Efforts to develop effective therapeutic strategies to protect against SEs delivered as an aerosol have been hampered by the lack of small animal models that consistently emulate human responses to these toxins. Here, we report that human leukocyte antigen-DQ8 (HLA-DQ8) transgenic (Tg) mice, but not littermate controls, succumbed to lethal shock induced by SEB aerosols without potentiation. Substantial amounts of perivascular edema and inflammatory infiltrates were noted in the lungs of Tg mice, similar to the pathology observed in nonhuman primates exposed by aerosol to SEB. Furthermore, the observed pathologies and lethal shock correlated with an upsurge in proinflammatory cytokine mRNA gene expression in the lungs and spleens, as well as with marked increases in the levels of proinflammatory circulating cytokines in the Tg mice. Unlike the case for littermate controls, telemetric evaluation showed significant hypothermia in Tg mice exposed to lethal doses of SEB. Taken together, these results show that this murine model will allow for the examination of therapeutics and vaccines developed specifically against SEB aerosol exposure and possibly other bacterial superantigens in the context of human MHC class II receptors.     

Immune response to staphylococcal superantigens

Staphylococcal exotoxins, staphylococcal enterotoxins A-E (SEA-SEE), and toxic shock syndrome toxin- (TSST-1) are potent activators of the immune system and cause a variety of diseases in humans, ranging from food poisoning to shock. These toxins are called superantigens because of their ability to polyclonally activate T cells at picromolar concentrations. Superantigens bind to both MHC class II molecules and specific Vbeta regions of the T cell receptor, leading to the activation of both antigen-presenting cells and T lymphocytes. These interactions lead to excessive production of proinflammatory cytokines and T cell proliferation, causing clinical symptoms that include fever, hypotension, and shock. Recent studies suggest that staphylococcal superantigens may also be involved in the pathogenesis of arthritis and other autoimmune disorders. This review summarizes the in vitro and in vivo effects of staphylococcal enterotoxins and TSST-1, recent progress with the use of transgenic knockout mice to identify key mediators and receptors involved in superantigen-induced shock, and therapeutic agents to mitigate the toxic effects of staphylococcal superantigens.     

Evaluation of the HCFA model for the analysis of mortality following hospitalization.

From 1987 through 1990, the Health Care Financing Administration (HCFA) evaluated variations in the mortality rates experienced by patients admitted to hospitals participating in the Medicare program. This study was conducted to evaluate the adequacy of the model used for that purpose. Detailed clinical data were gathered on 42,773 patients admitted to 84 statistically selected hospitals. The effect of risk adjustment using the HCFA model, which is based on claims data, was compared to a risk-adjustment model based on physiologic and clinical data. Models that include claims data were markedly superior to those containing only demographic characteristics in predicting the probability of patient death, and the addition of clinical data resulted in further improvement. The correlation of ranks of hospitals based on a model that uses only the claims data and on one that uses, in addition, clinical data, was .91. As a screen for the identification of "high (mortality) outlier" hospitals, the claims model had moderate sensitivity (81 percent) and specificity (79 percent), a high negative predictive value (90 percent), and a low positive predictive value (64 percent) when compared to the clinical model. The two mortality models gave similar results when used to determine which structural characteristics of hospitals were related to mortality rates: hospitals with a higher proportion of registered nurses or board-certified physician specialists, or with a greater level of access to high-technology equipment had lower risk-adjusted mortality rates. These data suggest that the current claims-based risk-adjustment procedure may satisfactorily be used to characterize variations in mortality rates associated with hospitalization. The procedure could also be used as a basis for further epidemiological analyses of factors that affect the probability of patient death. However, it does not positively identify outlier hospitals as providers of problematic care.     

Did We Get Sensorimotor Adaptation Wrong? Implicit Adaptation as Direct Policy Updating Rather than Forward-Model-Based Learning

The human motor system can rapidly adapt its motor output in response to errors. The prevailing theory of this process posits that the motor system adapts an internal forward model that predicts the consequences of outgoing motor commands and uses this forward model to plan future movements. However, despite clear evidence that adaptive forward models exist and are used to help track the state of the body, there is no definitive evidence that such models are used in movement planning. An alternative to the forward-model-based theory of adaptation is that movements are generated based on a learned policy that is adjusted over time by movement errors directly (“direct policy learning”). This learning mechanism could act in parallel with, but independent of, any updates to a predictive forward model. Forward-model-based learning and direct policy learning generate very similar predictions about behavior in conventional adaptation paradigms. However, across three experiments with human participants (N = 47, 26 female), we show that these mechanisms can be dissociated based on the properties of implicit adaptation under mirror-reversed visual feedback. Although mirror reversal is an extreme perturbation, it still elicits implicit adaptation; however, this adaptation acts to amplify rather than to reduce errors. We show that the pattern of this adaptation over time and across targets is consistent with direct policy learning but not forward-model-based learning. Our findings suggest that the forward-model-based theory of adaptation needs to be re-examined and that direct policy learning provides a more plausible explanation of implicit adaptation.SIGNIFICANCE STATEMENT The ability of our brain to adapt movements in response to error is one of the most widely studied phenomena in motor learning. Yet, we still do not know the process by which errors eventually result in adaptation. It is known that the brain maintains and updates an internal forward model, which predicts the consequences of motor commands, and the prevailing theory of motor adaptation posits that this updated forward model is responsible for trial-by-trial adaptive changes. Here, we question this view and show instead that adaptation is better explained by a simpler process whereby motor output is directly adjusted by task errors. Our findings cast doubt on long-held beliefs about adaptation.     

Nuclear Factor-κB: Fine-Tuning a Central Integrator of Diverse Biologic Stimuli

The nuclear factor (NF)-κ B family of proteins is a key regulator of inflammation, innate immunity, and cell survival and differentiation. Components of these pathways are potential targets of intervention for inflammation, infectious diseases, and cancer. However, therapeutic interventions that dampen the host response to infection and injury must also recognize the autoregulatory loops in the “resolution” phase of inflammation and infection. A more precise fine-tuning of these pathways leading to NF-κB activation will require dissecting temporally the different phases of activation and endogenous autoregulatory deactivation programs in diseases and redefining end-points after drug/inhibitor treatment to correlate changes in these stages.