Investigation of the role of typhoid toxin in acute typhoid fever in a human challenge model

Salmonella Typhi is a human host-restricted pathogen that is responsible for typhoid fever in approximately 10.9 million people annually1. The typhoid toxin is postulated to have a central role in disease pathogenesis, the establishment of chronic infection and human host restriction2,3,4,5,6. However, its precise role in typhoid disease in humans is not fully defined. We studied the role of typhoid toxin in acute infection using a randomized, double-blind S. Typhi human challenge model7. Forty healthy volunteers were randomized (1:1) to oral challenge with 104 colony-forming units of wild-type or an isogenic typhoid toxin deletion mutant (TN) of S. Typhi. We observed no significant difference in the rate of typhoid infection (fever ≥38 °C for ≥12 h and/or S. Typhi bacteremia) between participants challenged with wild-type or TN S. Typhi (15 out of 21 (71%) versus 15 out of 19 (79%); P = 0.58). The duration of bacteremia was significantly longer in participants challenged with the TN strain compared with wild-type (47.6 hours (28.9–97.0) versus 30.3(3.6–49.4); P ≤ 0.001). The clinical syndrome was otherwise indistinguishable between wild-type and TN groups. These data suggest that the typhoid toxin is not required for infection and the development of early typhoid fever symptoms within the context of a human challenge model. Further clinical data are required to assess the role of typhoid toxin in severe disease or the establishment of bacterial carriage

SIMON, an automated machine learning system, reveals immune signatures of influenza vaccine responses

Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. In this study, we developed Sequential Iterative Modeling "OverNight" (SIMON), an automated machine learning system that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust Ab response to influenza Ags. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available

SIMON, an automated machine learning system, reveals immune signatures of influenza vaccine responses

Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. In this study, we developed Sequential Iterative Modeling "OverNight" (SIMON), an automated machine learning system that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust Ab response to influenza Ags. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available

Polyamines regulate their synthesis by inducing expression and blocking degradation of ODC antizyme

Polyamines are essential organic cations with multiple cellular functions. Their synthesis is controlled by a feedback regulation whose main target is ornithine decarboxylase (ODC), the rate-limiting enzyme in polyamine biosynthesis. In mammals, ODC has been shown to be inhibited and targeted for ubiquitin-independent degradation by ODC antizyme (AZ). The synthesis of mammalian AZ was reported to involve a polyamine-induced ribosomal frameshifting mechanism. High levels of polyamine therefore inhibit new synthesis of polyamines by inducing ODC degradation. We identified a previously unrecognized sequence in the genome of Saccharomyces cerevisiae encoding an orthologue of mammalian AZ. We show that synthesis of yeast AZ (Oaz1) involves polyamine-regulated frameshifting as well. Degradation of yeast ODC by the proteasome depends on Oaz1. Using this novel model system for polyamine regulation, we discovered another level of its control. Oaz1 itself is subject to ubiquitin-mediated proteolysis by the proteasome. Degradation of Oaz1, however, is inhibited by polyamines. We propose a model, in which polyamines inhibit their ODC-mediated biosynthesis by two mechanisms, the control of Oaz1 synthesis and inhibition of its degradation