Science
-
The Ebola epidemic in West Africa has caused substantial morbidity and mortality. The outbreak has also disrupted health care services, including childhood vaccinations, creating a second public health crisis. ⋯ This pool of susceptibility increases the expected size of a regional measles outbreak from 127,000 to 227,000 cases after 18 months, resulting in 2000 to 16,000 additional deaths (comparable to the numbers of Ebola deaths reported thus far). There is a clear path to avoiding outbreaks of childhood vaccine-preventable diseases once the threat of Ebola begins to recede: an aggressive regional vaccination campaign aimed at age groups left unprotected because of health care disruptions.
-
Ebola virus causes sporadic outbreaks of lethal hemorrhagic fever in humans, but there is no currently approved therapy. Cells take up Ebola virus by macropinocytosis, followed by trafficking through endosomal vesicles. However, few factors controlling endosomal virus movement are known. ⋯ Disrupting TPC function by gene knockout, small interfering RNAs, or small-molecule inhibitors halted virus trafficking and prevented infection. Tetrandrine, the most potent small molecule that we tested, inhibited infection of human macrophages, the primary target of Ebola virus in vivo, and also showed therapeutic efficacy in mice. Therefore, TPC proteins play a key role in Ebola virus infection and may be effective targets for antiviral therapy.
-
According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. ⋯ For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes.