Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host’s cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry1-3 to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV–human protein–protein interactions involving 435 individual human proteins, with ~40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.
BackgroundThe increasing availability of HIV-host interaction datasets, including both physical and genetic interactions, has created a need for software tools to integrate and visualize the data. Because these host-pathogen interactions are extensive and interactions between human proteins are found within many different databases, it is difficult to generate integrated HIV-human interaction networks.ResultsWe have developed a web-based platform, termed GPS-Prot http://www.gpsprot.org, that allows for facile integration of different HIV interaction data types as well as inclusion of interactions between human proteins derived from publicly-available databases, including MINT, BioGRID and HPRD. The software has the ability to group proteins into functional modules or protein complexes, generating more intuitive network representations and also allows for the uploading of user-generated data.ConclusionsGPS-Prot is a software tool that allows users to easily create comprehensive and integrated HIV-host networks. A major advantage of this platform compared to other visualization tools is its web-based format, which requires no software installation or data downloads. GPS-Prot allows novice users to quickly generate networks that combine both genetic and protein-protein interactions between HIV and its human host into a single representation. Ultimately, the platform is extendable to other host-pathogen systems.
Accumulating evidence indicates an important role for non-coding RNA molecules in eukaryotic cell regulation. A small number of coding and non-coding overlapping antisense transcripts (OATs) in eukaryotes have been reported, some of which regulate expression of the corresponding sense transcript. The prevalence of this phenomenon is unknown, but there may be an enrichment of such transcripts at imprinted gene loci. Taking a bioinformatics approach, we systematically searched a human mRNA database (RefSeq) for complementary regions that might facilitate pairing with other transcripts. We report 56 pairs of overlapping transcripts, in which each member of the pair is transcribed from the same locus. This allows us to make an estimate of 1000 for the minimum number of such transcript pairs in the entire human genome. This is a surprisingly large number of overlapping gene pairs and, clearly, some of the overlaps may not be functionally significant. Nonetheless, this may indicate an important general role for overlapping antisense control in gene regulation. EST databases were also investigated in order to address the prevalence of cases of imprinted genes with associated non-coding overlapping, antisense transcripts. However, EST databases were found to be completely inappropriate for this purpose.
In this paper we investigate the relationships among intron density (number of introns per kilobase of coding sequence), gene expression level, and strength of splicing signals in two species: Drosophila melanogaster and Caenorhabditis elegans. We report a negative correlation between intron density and gene expression levels, opposite to the effect previously observed in human. An increase in splice site strength has been observed in long introns in D. melanogaster. We show this is also true of C. elegans. We also examine the relationship between intron density and splice site strength. There is an increase in splice site strength as the intron structure becomes less dense. This could suggest that introns are not recognized in isolation but could function in a cooperative manner to ensure proper splicing. This effect remains if we control for the effects of alternative splicing on splice site strength.
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