Therapeutic DNA Vaccine Encoding Peptide P10 against Experimental Paracoccidioidomycosis

Paracoccidioidomycosis (PCM), caused by Paracoccidioides brasiliensis, is the most prevalent invasive fungal disease in South America. Systemic mycoses are the 10th most common cause of death among infectious diseases in Brazil and PCM is responsible for more than 50% of deaths due to fungal infections. PCM is typically treated with sulfonamides, amphotericin B or azoles, although complete eradication of the fungus may not occur and relapsing disease is frequently reported. A 15-mer peptide from the major diagnostic antigen gp43, named P10, can induce a strong T-CD4+ helper-1 immune response in mice. The TEPITOPE algorithm and experimental data have confirmed that most HLA-DR molecules can present P10, which suggests that P10 is a candidate antigen for a PCM vaccine. In the current work, the therapeutic efficacy of plasmid immunization with P10 and/or IL-12 inserts was tested in murine models of PCM. When given prior to or after infection with P. brasiliensis virulent Pb 18 isolate, plasmid-vaccination with P10 and/or IL-12 inserts successfully reduced the fungal burden in lungs of infected mice. In fact, intramuscular administration of a combination of plasmids expressing P10 and IL-12 given weekly for one month, followed by single injections every month for 3 months restored normal lung architecture and eradicated the fungus in mice that were infected one month prior to treatment. The data indicate that immunization with these plasmids is a powerful procedure for prevention and treatment of experimental PCM, with the perspective of being also effective in human patients

Adaptation options for marine industries and coastal communities using community structure and dynamics

Identifying effective adaptation strategies for coastal communities dependent on marine resources and impacted by climate change can be difficult due to the dynamic nature of marine ecosystems. The task is more difficult if current and predicted shifts in social and economic trends are considered. Information about social and economic change is often limited to qualitative data. A combination of qualitative and quantitative models provide the flexibility to allow the assessment of current and future ecological and socio-economic risks and can provide information on alternative adaptations. Here, we demonstrate how stakeholder input, qualitative models and Bayesian belief networks (BBNs) can provide semi-quantitative predictions, including uncertainty levels, for the assessment of climate and non-climate-driven change in a case study community. Issues are identified, including the need to increase the capacity of the community to cope with change. Adaptation strategies are identified that alter positive feedback cycles contributing to a continued decline in population, local employment and retail spending. For instance, the diversification of employment opportunities and the attraction of new residents of different ages would be beneficial in preventing further population decline. Some impacts of climate change can be combated through recreational bag or size limits and monitoring of popular range-shifted species that are currently unmanaged, to reduce the potential for excessive removal. Our results also demonstrate that combining BBNs and qualitative models can assist with the effective communication of information between stakeholders and researchers. Furthermore, the combination of techniques provides a dynamic, learning-based, semi-quantitative approach for the assessment of climate and socio-economic impacts and the identification of potential adaptation strategies