Mapping protein interactions between Dengue virus and its human and insect hosts.

BACKGROUND: Dengue fever is an increasingly significant arthropod-borne viral disease, with at least 50 million cases per year worldwide. As with other viral pathogens, dengue virus is dependent on its host to perform the bulk of functions necessary for viral survival and replication. To be successful, dengue must manipulate host cell biological processes towards its own ends, while avoiding elimination by the immune system. Protein-protein interactions between the virus and its host are one avenue through which dengue can connect and exploit these host cellular pathways and processes. METHODOLOGY/PRINCIPAL FINDINGS: We implemented a computational approach to predict interactions between Dengue virus (DENV) and both of its hosts, Homo sapiens and the insect vector Aedes aegypti. Our approach is based on structural similarity between DENV and host proteins and incorporates knowledge from the literature to further support a subset of the predictions. We predict over 4,000 interactions between DENV and humans, as well as 176 interactions between DENV and A. aegypti. Additional filtering based on shared Gene Ontology cellular component annotation reduced the number of predictions to approximately 2,000 for humans and 18 for A. aegypti. Of 19 experimentally validated interactions between DENV and humans extracted from the literature, this method was able to predict nearly half (9). Additional predictions suggest specific interactions between virus and host proteins relevant to interferon signaling, transcriptional regulation, stress, and the unfolded protein response. CONCLUSIONS/SIGNIFICANCE: Dengue virus manipulates cellular processes to its advantage through specific interactions with the host's protein interaction network. The interaction networks presented here provide a set of hypothesis for further experimental investigation into the DENV life cycle as well as potential therapeutic targets.

The development of ciprofloxacin resistance in Pseudomonas aeruginosa involves multiple response stages and multiple proteins.

Microbes have developed resistance to nearly every antibiotic, yet the steps leading to drug resistance remain unclear. Here we report a multistage process by which Pseudomonas aeruginosa acquires drug resistance following exposure to ciprofloxacin at levels ranging from 0.5× to 8× the initial MIC. In stage I, susceptible cells are killed en masse by the exposure. In stage II, a small, slow to nongrowing population survives antibiotic exposure that does not exhibit significantly increased resistance according to the MIC measure. In stage III, exhibited at 0.5× to 4× the MIC, a growing population emerges to reconstitute the population, and these cells display heritable increases in drug resistance of up to 50 times the original level. We studied the stage III cells by proteomic methods to uncover differences in the regulatory pathways that are involved in this phenotype, revealing upregulation of phosphorylation on two proteins, succinate-semialdehyde dehydrogenase (SSADH) and methylmalonate-semialdehyde dehydrogenase (MMSADH), and also revealing upregulation of a highly conserved protein of unknown function. Transposon disruption in the encoding genes for each of these targets substantially dampened the ability of cells to develop the stage III phenotype. Considering these results in combination with computational models of resistance and genomic sequencing results, we postulate that stage III heritable resistance develops from a combination of both genomic mutations and modulation of one or more preexisting cellular pathways.

Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens.

BACKGROUND: In the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes. RESULTS: We developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence. CONCLUSIONS: Understanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention.