The four serotypes of Dengue virus (DENV1-4) are arboviruses (arthropod-borne viruses) that belong to the Flavivirus genus, Flaviviridae family. They are the causative agents of an infectious disease called dengue, an important global public health problem with significant social-economic impact. Thus, the development of safe and effective dengue vaccines is a priority according to the World Health Organization. Only one anti-dengue vaccine has already been licensed in endemic countries and two formulations are under phase III clinical trials. In this study, we aimed to compare the main anti-dengue virus vaccines, DENGVAXIA®, LAV-TDV, and TAK-003, regarding their antigens and potential to protect. We studied the conservation of both, B and T cell epitopes involved in immunological control of DENV infection along with vaccine viruses and viral isolates. In addition, we assessed the population coverage of epitope sets contained in each vaccine formulation with regard to different human populations. As main results, we found that all three vaccines contain the main B cell epitopes involved in viral neutralization. Similarly, LAV-TDV and TAK-003 contain most of T cell epitopes involved in immunological protection, a finding not observed in DENGVAXIA®, which explains main limitations of the only licensed dengue vaccine. In summary, the levels of presence and absence of epitopes that are target for protective immune response in the three main anti-dengue virus vaccines are shown in this study. Our results suggest that investing in vaccines that contain the majority of epitopes involved in protective immunity (cellular and humoral arms) is an important issue to be considered.
Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype.
Marinobacter sp. strain ANT_B65 was isolated from sponge collected in King George Island, Antarctica. The draft genome of 4,173,840 bp encodes 3,743 protein-coding open reading frames. The genome will provide insights into the strain’s potential use in the production of natural products.
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