Analysis of Dynamic HIV Evolution and Anti-HIV Drug Resistance Acquisition under HAART using bioinformatics methods
| Project/Area Number |
17510163
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Applied genomics
|
|---|
| Research Institution | Tokyo Medican and Dental University |
|---|
Principal Investigator |
FENGRONG Ren Tokyo Medical and Dental University, Center for Information Medicine, Guest Associate Professor, 情報処理センター, 客員助教授 (60280989)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
TANAKA Hiroshi Tokyo Medical and Dental University, School of Biomedical Science, Professor, 大学院疾患生命科学研究部, 教授 (60155158)
|
|---|
| Project Period (FY) |
2005 – 2006
|
| Project Status |
Completed (Fiscal Year 2006)
|
| Budget Amount *help |
¥2,500,000 (Direct Cost: ¥2,500,000)
Fiscal Year 2006: ¥1,200,000 (Direct Cost: ¥1,200,000)
Fiscal Year 2005: ¥1,300,000 (Direct Cost: ¥1,300,000)
|
|---|
| Keywords | Within-patient viral evolution / Anti-HIV therapy / Serial HIV samples / Sequential-linking algorithm / Longitudinal phylogenetic tree / Mutual information criterion / Co-evolution sites / Positive selection / HIVの宿主内進化 / 抗HIV治療と薬剤耐性獲得 / データマイニング / 正の淘汰進化 / AIDS治療予後予測 / 時間発展的な進化系統樹 |
|---|
| Research Abstract |
The emergence of anti-HIV drug resistance has been the largest obstacle in the treatment of AIDS for the last decade. We are interested in possible mechanisms by which the HIV acquires drug-resistance and attempt to detect and predict the drug-resistance mutations by using bioinformatics methods. In this study, we applied several computational approaches to the large data sets of HIV-1 Pol and Gag genes serially collected from the patients under HAART (highly active anti-retroviral therapy).
1) A classification method based on mutual information criterion, C4.5 decision tree, was employed to characterize the patterns of resistant mutations induced by anti-HIV drugs. We have applied this method to the viral samples collected from six AIDS patients and found several drug-specific mutation combinations that have not been reported.
2) We also developed an algorism to infer temporal changes in viral evolutionary rates and in the selective pressure on the virus from the anti-HIV drug treatment. We have analyzed a large data set of HIV-1 PR and RT genes (541 sequences) serially collected from a single patient using this algorithm and successfully reconstructed a longitudinal evolutionary tree which could express the dynamic process of within-host HIV evolution better than those reconstructed by traditional tree-making methods.
3) It has been known that the PR gene recovers its replication capacity after protease inhibitor (PI) treatment not only by PI-resistant mutations but also by mutations in Gag, the PR substrate. To better understand the mechanism of interference between these two genes, we employed a new computational method (CoMap) to detect the co-evolution sites and found that the PR E35D mutation was significantly linked to the Gag P453L mutation located at P5' position of p1-p6 cleavage site. This estimated new co-evolution pair was confirmed by in vitro growth kinetics assay.
|
Report
(3 results)
Research Products
(10 results)
