Bradyzoite pseudokinase 1 is crucial for efficient oral infectivity of the Toxoplasma gondii tissue cyst.

The tissue cyst formed by the bradyzoite stage of Toxoplasma gondii is essential for persistent infection of the host and oral transmission. Bradyzoite pseudokinase 1 (BPK1) is a component of the cyst wall, but nothing has previously been known about its function. Here, we show that immunoprecipitation of BPK1 from in vitro bradyzoite cultures, 4 days postinfection, identifies at least four associating proteins: MAG1, MCP4, GRA8, and GRA9. To determine the role of BPK1, a strain of Toxoplasma was generated with the bpk1 locus deleted. This BPK1 knockout strain (Δbpk1) was investigated in vitro and in vivo. No defect was found in terms of in vitro cyst formation and no difference in pathogenesis or cyst burden 4 weeks postinfection (wpi) was detected after intraperitoneal (i.p.) infection with Δbpk1 tachyzoites, although the Δbpk1 cysts were significantly smaller than parental or BPK1-complemented strains at 8 wpi. Pepsin-acid treatment of 4 wpi in vivo cysts revealed that Δbpk1 parasites are significantly more sensitive to this treatment than the parental and complemented strains. Consistent with this, 4 wpi Δbpk1 cysts showed reduced ability to cause oral infection compared to the parental and complemented strains. Together, these data reveal that BPK1 plays a crucial role in the in vivo development and infectivity of Toxoplasma cysts

Emergence of Epidemic Neisseria meningitidis Serogroup X Meningitis in Togo and Burkina Faso

Serogroup X meningococci (NmX) historically have caused sporadic and clustered meningitis cases in sub-Saharan Africa. To study recent NmX epidemiology, we analyzed data from population-based, sentinel and passive surveillance, and outbreak investigations of bacterial meningitis in Togo and Burkina Faso during 2006–2010. Cerebrospinal fluid specimens were analyzed by PCR. In Togo during 2006–2009, NmX accounted for 16% of the 702 confirmed bacterial meningitis cases. Kozah district experienced an NmX outbreak in March 2007 with an NmX seasonal cumulative incidence of 33/100,000. In Burkina Faso during 2007–2010, NmX accounted for 7% of the 778 confirmed bacterial meningitis cases, with an increase from 2009 to 2010 (4% to 35% of all confirmed cases, respectively). In 2010, NmX epidemics occurred in northern and central regions of Burkina Faso; the highest district cumulative incidence of NmX was estimated as 130/100,000 during March–April. Although limited to a few districts, we have documented NmX meningitis epidemics occurring with a seasonal incidence previously only reported in the meningitis belt for NmW135 and NmA, which argues for development of an NmX vaccine

Nitric Oxide-Mediated Regulation of Gamma Interferon-Induced Bacteriostasis: Inhibition and Degradation of Human Indoleamine 2,3-Dioxygenase

Tryptophan depletion resulting from indoleamine 2,3-dioxygenase (IDO) activity within the kynurenine pathway is one of the most prominent gamma interferon (IFN-γ)-inducible antimicrobial effector mechanisms in human cells. On the other hand, nitric oxide (NO) produced by the inducible isoform of NO synthase (iNOS) serves a more immunoregulatory role in human cells and thereby interacts with tryptophan depletion in a number of ways. We investigated the effects of NO on IDO gene transcription, protein synthesis, and enzyme activity as well as on IDO-mediated bacteriostasis in the human epithelial cell line RT4. IFN-γ-stimulated RT4 cells were able to inhibit the growth of Staphylococcus aureus in an IDO-mediated fashion, and this bacteriostatic effect was abolished by endogenously produced NO. These findings were supported by experiments which showed that IDO activity in extracts of IFN-γ-stimulated cells is inhibited by the chemical NO donors diethylenetriamine diazeniumdiolate, S-nitroso-l-cysteine, and S-nitroso-N-acetyl-d,l-penicillamine. Furthermore, we found that both endogenous and exogenous NO strongly reduced the level of IDO protein content in RT4 cells. This effect was not due to a decrease in IDO gene transcription or mRNA stability. By using inhibitors of proteasomal proteolytic activity, we showed that NO production led to an accelerated degradation of IDO protein in the proteasome. This is the first report, to our knowledge, that demonstrates that the IDO is degraded by the proteasome and that NO has an effect on IDO protein stability

Dense granules: are they key organelles to help understand the parasitophorous vacuole of all apicomplexa parasites?

Together with micronemes and rhoptries, dense granules are specialised secretory organelles of Apicomplexa parasites. Among Apicomplexa, Plasmodium represents a model of parasites propagated by way of an insect vector, whereas Toxoplasma is a model of food borne protozoa forming cysts. Through comparison of both models, this review summarises data accumulated over recent years on alternative strategies chosen by these parasites to develop within a parasitophorous vacuole and explores the role of dense granules in this process. One of the characteristics of the Plasmodium erythrocyte stages is to export numerous parasite proteins into both the host cell cytoplasm and/or plasma membrane via the vacuole used as a step trafficking compartment. Whether this feature can be correlated to few storage granules and a restricted number of dense granule proteins, is not yet clear. By contrast, the Toxoplasma developing vacuole is decorated by abundantly expressed dense granule proteins and is characterised by a network of membranous nanotubes. Although the exact function of most of these proteins remains currently unknown, recent data suggest that some of these dense granule proteins could be involved in building the intravacuolar membranous network. Conserved expression of the Toxoplasma dense granule proteins throughout most of the parasite stages suggests that they could also be key elements of the cyst formation

Technology forecasting using network data envelopment analysis use case: Electrical vehicles

The future is not predictable, is it? While of course no one can exactly foresee all events, we can learn from the past and extrapolate historic developments into the future, especially in the area of technology forecasting. Based on Technology Forecasting with Data Envelopment Analysis (TFDEA by Inman (2004)) and Network Data Envelopment Analysis (NDEA by Cook et al. (2010) describe the approach of Technology Forecasting with Network Data Envelopment Analysis (TFNDEA). While classical DEA only analyses a system’s efficiency as a whole by considering in- and outputs, the Network DEA based approach provides the possibility to look inside a system and to determine the efficiency and development of the interdependent subcomponents. The TFNDEA approach is applied by this work to the use case of electric vehicles, a technology with extensive development in the last years and high expectations for the future. Based on datasets from Tudorie (2012) a test prediction is done for the year 2017, which can be verified with current data. Then the approach is used to calculate predictions for the year 2020, to provide a forecast of the development of electric vehicles

Technology forecasting based on efficiency analysis of systems with interdependent subcomponents using network data envelopment analysis

Technology depends on innovation - but innovative developments are hard to predict. In addition to existing approaches for Technology Forecasting, the use of data envelopment analysis (DEA) provides valuable insights and prediction data. DEA offers a method to evaluate the relative efficiency of analysed entities, so-called Decision Making Units (DMU). Using the efficiency analysis features of DEA in Technology Forecasting enables predictions for future developments based on historic data. While classical DEA analyses the DMUs as closed entities (blackboxes), Network DEA considers the internal structure and the sub-components of the DMUs. This inside view can be leveraged for technology forecasting. Further, If there is no historical data available for innovative new technology, new systems can be seen as composites of existing sub-components with the help of Network DEA. In addition, Network DEA allows the detailed evaluation of the building blocks and their interdependencies. This work develops a model for technology forecasting with Network Data Envelopment Analysis (TFNDEA). Based on existing approaches, a new method is defined to predict technological development with regard to the sub-components using Network Data envelopment Analysis