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1.
PLoS Med ; 2(4): e112, 2005 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-15839752

RESUMO

BACKGROUND: The genetic differences among HIV-1 subtypes may be critical to clinical management and drug resistance surveillance as antiretroviral treatment is expanded to regions of the world where diverse non-subtype-B viruses predominate. METHODS AND FINDINGS: To assess the impact of HIV-1 subtype and antiretroviral treatment on the distribution of mutations in protease and reverse transcriptase, a binomial response model using subtype and treatment as explanatory variables was used to analyze a large compiled dataset of non-subtype-B HIV-1 sequences. Non-subtype-B sequences from 3,686 persons with well characterized antiretroviral treatment histories were analyzed in comparison to subtype B sequences from 4,769 persons. The non-subtype-B sequences included 461 with subtype A, 1,185 with C, 331 with D, 245 with F, 293 with G, 513 with CRF01_AE, and 618 with CRF02_AG. Each of the 55 known subtype B drug-resistance mutations occurred in at least one non-B isolate, and 44 (80%) of these mutations were significantly associated with antiretroviral treatment in at least one non-B subtype. Conversely, of 67 mutations found to be associated with antiretroviral therapy in at least one non-B subtype, 61 were also associated with antiretroviral therapy in subtype B isolates. CONCLUSION: Global surveillance and genotypic assessment of drug resistance should focus primarily on the known subtype B drug-resistance mutations.


Assuntos
Antirretrovirais/farmacologia , Infecções por HIV/tratamento farmacológico , HIV-1/patogenicidade , Peptídeo Hidrolases/genética , DNA Polimerase Dirigida por RNA/genética , Sequência de Aminoácidos , Antirretrovirais/uso terapêutico , Análise Mutacional de DNA , Farmacorresistência Viral , Saúde Global , HIV-1/classificação , HIV-1/genética , Humanos , Dados de Sequência Molecular
2.
J Exp Med ; 198(5): 693-704, 2003 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-12953091

RESUMO

Little is known about the biochemical environment in phagosomes harboring an infectious agent. To assess the state of this organelle we captured the transcriptional responses of Mycobacterium tuberculosis (MTB) in macrophages from wild-type and nitric oxide (NO) synthase 2-deficient mice before and after immunologic activation. The intraphagosomal transcriptome was compared with the transcriptome of MTB in standard broth culture and during growth in diverse conditions designed to simulate features of the phagosomal environment. Genes expressed differentially as a consequence of intraphagosomal residence included an interferon gamma- and NO-induced response that intensifies an iron-scavenging program, converts the microbe from aerobic to anaerobic respiration, and induces a dormancy regulon. Induction of genes involved in the activation and beta-oxidation of fatty acids indicated that fatty acids furnish carbon and energy. Induction of sigmaE-dependent, sodium dodecyl sulfate-regulated genes and genes involved in mycolic acid modification pointed to damage and repair of the cell envelope. Sentinel genes within the intraphagosomal transcriptome were induced similarly by MTB in the lungs of mice. The microbial transcriptome thus served as a bioprobe of the MTB phagosomal environment, showing it to be nitrosative, oxidative, functionally hypoxic, carbohydrate poor, and capable of perturbing the pathogen's cell envelope.


Assuntos
Regulação Bacteriana da Expressão Gênica , Macrófagos/microbiologia , Mycobacterium tuberculosis/genética , Mycobacterium tuberculosis/metabolismo , Fagossomos/microbiologia , Transcrição Gênica , Animais , Camundongos , RNA Bacteriano/genética
3.
Stat Appl Genet Mol Biol ; 1: Article1, 2002.
Artigo em Inglês | MEDLINE | ID: mdl-16646777

RESUMO

In microarray studies, an important problem is to compare a predictor of disease outcome derived from gene expression levels to standard clinical predictors. Comparing them on the same dataset that was used to derive the microarray predictor can lead to results strongly biased in favor of the microarray predictor. We propose a new technique called "pre-validation'' for making a fairer comparison between the two sets of predictors. We study the method analytically and explore its application in a recent study on breast cancer.

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