Alternative paths in HIV-1 targeted human signal transduction pathways.
BACKGROUND
Human immunodeficiency virus-1 (HIV-1) has a minimal genome of only 9 genes, which encode 15 proteins. HIV-1 thus depends on the human host for virtually every aspect of its life cycle. The universal language of communication in biological systems, including between pathogen and host, is via signal transduction pathways. The fundamental units of these pathways are protein protein interactions. Understanding the functional significance of HIV-1, human interactions requires viewing them in the context of human signal transduction pathways.


RESULTS
Integration of HIV-1, human interactions with known signal transduction pathways indicates that the majority of known human pathways have the potential to be effected through at least one interaction with an HIV-1 protein at some point during the HIV-1 life cycle. For each pathway, we define simple paths between start points (i.e. no edges going into a node) and end points (i.e. no edges leaving a node). We then identify the paths that pass through human proteins that interact with HIV-1 proteins. We supplement the combined map with functional information, including which proteins are known drug targets and which proteins contribute significantly to HIV-1 function as revealed by recent siRNA screens. We find that there are often alternative paths starting and ending at the same proteins but circumventing the intermediate steps disrupted by HIV-1.


CONCLUSIONS
A mapping of HIV-1, human interactions to human signal transduction pathways is presented here to link interactions with functions. We proposed a new way of analyzing the virus host interactions by identifying HIV-1 targets as well as alternative paths bypassing the HIV-1 targeted steps. This approach yields numerous experimentally testable hypotheses on how HIV-1 function may be compromised and human cellular function restored by pharmacological approaches. We are making the full set of pathway analysis results available to the community.
Balakrishnan
Sivaraman
S
Language Technologies Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. sbalakri@cs.cmu.edu
Tastan
Oznur
O
Carbonell
Jaime
J
Klein-Seetharaman
Judith
J
D030541:Databases, Genetic; D016503:Drug Delivery Systems; D005786:Gene Expression Regulation; D015658:HIV Infections / Q000188:drug therapy / Q000235:genetics / Q000378:metabolism* / Q000821:virology; D015497:HIV-1 / Q000502:physiology*; D006801:Humans; D034741:RNA, Small Interfering / Q000235:genetics; D015398:Signal Transduction
D034741:RNA, Small Interfering