Biomedical subjects

Lamei Chen

Publications and source records attributed to Lamei Chen.

3 recordsLinked to original sources

PubMed2006

The HIV positive selection mutation database.

The HIV positive selection mutation database is a large-scale database available at http://www.bioinformatics.ucla.edu/HIV/ that provides detailed selection pressure maps of HIV protease and reverse transcriptase, both of which are molecular targets of antiretroviral therapy. This database makes available for the first time a very large HIV sequence dataset (sequences from approximately 50 000 clinical AIDS samples, generously contributed by Specialty Laboratories, Inc.), which makes possible high-resolution selection pressure mapping. It provides information about not only the selection pressure on individual sites but also how selection pressure at one site is affected by mutations on other sites. It also includes datasets from other public databases, namely the Stanford HIV database [S. Y. Rhee, M. J. Gonzales, R. Kantor, B. J. Betts, J. Ravela and R. W. Shafer (2003) Nucleic Acids Res., 31, 298-303]. Comparison between these datasets in the database enables cross-validation with independent datasets and also specific evaluation of the effect of drug treatment.

Acquired Immunodeficiency Syndrome↗

PubMed2006

Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples.

BACKGROUND: HIV can evolve drug resistance rapidly in response to new drug treatments, often through a combination of multiple mutations 123. It would be useful to develop automated analyses of HIV sequence polymorphism that are able to predict drug resistance mutations, and to distinguish different types of functional roles among such mutations, for example, those that directly cause drug resistance, versus those that play an accessory role. Detecting functional interactions between mutations is essential for this classification. We have adapted a well-known measure of evolutionary selection pressure (Ka/Ks) and developed a conditional Ka/Ks approach to detect important interactions. RESULTS: We have applied this analysis to four independent HIV protease sequencing datasets: 50,000 clinical samples sequenced by Specialty Laboratories, Inc.; 1800 samples from patients treated with protease inhibitors; 2600 samples from untreated patients; 400 samples from untreated African patients. We have identified 428 mutation interactions in Specialty dataset with statistical significance and we were able to distinguish primary vs. accessory mutations for many well-studied examples. Amino acid interactions identified by conditional Ka/Ks matched 80 of 92 pair wise interactions found by a completely independent study of HIV protease (p-value for this match is significant: 10-70). Furthermore, Ka/Ks selection pressure results were highly reproducible among these independent datasets, both qualitatively and quantitatively, suggesting that they are detecting real drug-resistance and viral fitness mutations in the wild HIV-1 population. CONCLUSION: Conditional Ka/Ks analysis can detect mutation interactions and distinguish primary vs. accessory mutations in HIV-1. Ka/Ks analysis of treated vs. untreated patient data can distinguish drug-resistance vs. viral fitness mutations. Verification of these results would require longitudinal studies. The result provides a valuable resource for AIDS research and will be available for open access upon publication at http://www.bioinformatics.ucla.edu/HIV.

Journal Article↗

PubMed2004

Positive selection detection in 40,000 human immunodeficiency virus (HIV) type 1 sequences automatically identifies drug resistance and positive fitness mutations in HIV protease and reverse transcriptase.

Drug resistance is a major problem in the treatment of AIDS, due to the very high mutation rate of human immunodeficiency virus (HIV) and subsequent rapid development of resistance to new drugs. Identification of mutations associated with drug resistance is critical for both individualized treatment selection and new drug design. We have performed an automated mutation analysis of HIV Type 1 (HIV-1) protease and reverse transcriptase (RT) from approximately 40,000 AIDS patient plasma samples sequenced by Specialty Laboratories Inc. from 1999 to mid-2002. This data set provides a nearly complete mutagenesis of HIV protease and enables the calculation of statistically significant K(a)/K(s) values for each individual amino acid mutation in protease and RT. Positive selection (i.e., a K(a)/K(s) ratio of >1, indicating increased reproductive fitness) detected 19 of 23 known drug-resistant mutation positions in protease and 20 of 34 such positions in RT. We also discovered 163 new amino acid mutations in HIV protease and RT that are strong candidates for drug resistance or fitness. Our results match available independent data on protease mutations associated with specific drug treatments and mutations with positive reproductive fitness, with high statistical significance (the P values for the observed matches to occur by random chance are 10(-5.2) and 10(-16.6), respectively). Our mutation analysis provides a valuable resource for AIDS research and will be available to academic researchers upon publication at http://www.bioinformatics.ucla.edu/HIV. Our data indicate that positive selection mapping is an analysis that can yield powerful insights from high-throughput sequencing of rapidly mutating pathogens.

Amino Acid Sequence↗