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Comparison of methods to detect HIV dual infection.

ABSTRACT: Current methods to detect intraclade HIV dual infection are poorly suited for determining its prevalence in large cohorts. To investigate the potential of ultra-deep sequencing to screen for dual infection, we compared it to bulk sequence-based synonymous mixture index and the current standard of single genome sequencing. The synonymous mixture index identified samples likely to harbor dual infection, while ultra-deep sequencing captured more intra-host viral diversity than single genome sequencing at approximately 40% of the cost and 20% of the laboratory and analysis time. The synonymous mixture index and ultra-deep sequencing are promising methods for rapid and cost-effective systematic identification of HIV dual infection.

SUBMITTER: Pacold M

PROVIDER: S-EPMC3011997 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Comparison of methods to detect HIV dual infection.

Pacold Mary M Smith Davey D Little Susan S Cheng Pok Man PM Jordan Parris P Ignacio Caroline C Richman Douglas D Pond Sergei Kosakovsky SK

AIDS research and human retroviruses 20101018 12

Current methods to detect intraclade HIV dual infection are poorly suited for determining its prevalence in large cohorts. To investigate the potential of ultra-deep sequencing to screen for dual infection, we compared it to bulk sequence-based synonymous mixture index and the current standard of single genome sequencing. The synonymous mixture index identified samples likely to harbor dual infection, while ultra-deep sequencing captured more intra-host viral diversity than single genome sequenc ...[more]

PMID: 20954840

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Project description:BackgroundThere is growing interest in examining the simultaneous effects of multiple exposures and, more generally, the effects of mixtures of exposures, as part of the exposome concept (being defined as the totality of human environmental exposures from conception onwards). Uncovering such combined effects is challenging owing to the large number of exposures, several of them being highly correlated. We performed a simulation study in an exposome context to compare the performance of several statistical methods that have been proposed to detect statistical interactions.MethodsSimulations were based on an exposome including 237 exposures with a realistic correlation structure. We considered several statistical regression-based methods, including two-step Environment-Wide Association Study (EWAS2), the Deletion/Substitution/Addition (DSA) algorithm, the Least Absolute Shrinkage and Selection Operator (LASSO), Group-Lasso INTERaction-NET (GLINTERNET), a three-step method based on regression trees and finally Boosted Regression Trees (BRT). We assessed the performance of each method in terms of model size, predictive ability, sensitivity and false discovery rate.ResultsGLINTERNET and DSA had better overall performance than the other methods, with GLINTERNET having better properties in terms of selecting the true predictors (sensitivity) and of predictive ability, while DSA had a lower number of false positives. In terms of ability to capture interaction terms, GLINTERNET and DSA had again the best performances, with the same trade-off between sensitivity and false discovery proportion. When GLINTERNET and DSA failed to select an exposure truly associated with the outcome, they tended to select a highly correlated one. When interactions were not present in the data, using variable selection methods that allowed for interactions had only slight costs in performance compared to methods that only searched for main effects.ConclusionsGLINTERNET and DSA provided better performance in detecting two-way interactions, compared to other existing methods.

Project description:Here; we have described and tested a microarray based-method for the screening of dengue virus (DENV) serotypes. This DNA microarray assay is specific and sensitive and can detect dual infections with two dengue virus serotypes and single-serotype infections. Other methodologies may underestimate samples containing more than one serotype. This technology can be used to discriminate between the four DENV serotypes. Single-stranded DNA targets were covalently attached to glass slides and hybridised with specific labelled probes. DENV isolates and dengue samples were used to evaluate microarray performance. Our results demonstrate that the probes hybridized specifically to DENV serotypes; with no detection of unspecific signals. This finding provides evidence that specific probes can effectively identify single and double infections in DENV samples. Background Dengue is a mosquito-borne viral infection causing a major public health problem globally. Dengue virus (DENV) is the causative agent of dengue fever (DF) and dengue hemorrhagic fever (DHF) and includes four distinct serotypes (DENV-1, DENV-2, DENV-3, and DENV-4). DENV-2 and DENV-3 have been associated with severe dengue disease, consequently, laboratory testing for DENV is needed to confirm the diagnosis of DENV infection, serotype and to differentiate dengue from other febrile tropical illnesses. In addition, surveillance of mosquitoes infected with DENV is needed to monitor the infection rates within vector mosquito populations harboring specific serotype to provide an early warning sign to predict epidemics. Results In this work we have applied microarray analysis to simultaneously determine the serotype of multiple RNA samples from human or mosquitoes. The proposed microarray method can be used for i) rapid and reliable dengue diagnosis; ii) serotyping and iii) surveillance of mosquitoes infected with dengue. These microarrays were useful to confirm the presence of DENV-2 in 94 serum samples, DENV-3 in three samples from Juchitan, Oaxaca and one case from Juchitan, Oaxaca contained DENV-2 and -3. Moreover by using these microarrays we also determined DENV in pools of gravid females mosquitoes collected in several sites of nineteen Mexican states in 2005. Mosquito pools from 31 cities in the states of Yucatan, Campeche, Tabasco, Chiapas, Veracruz, Oaxaca, Guerrero, Tamaulipas and Colima were infected with DENV-2, six cities in Yucatán, Tabasco, Morelos, Tamaulipas, Colima, and Nayarit with DENV-1, three from Tabasco, Veracruz and Oaxaca with DENV 3 and two with two serotypes simultaneously (Ciudad Mante with DENV-1 and DENV-2, and Tavela with DENV-2 and DENV-3). Conclusion Here we show the success of applying microarrays assay to provide a consistently robust qualitative detection of dengue serotypes (DENV-1, DENV-2, DENV-3 and DENV-4) in serum samples from patients or in pools of gravid female mosquitoes collected in the field of nineteen Mexican states. Interestingly, we did not detect any mosquito or serum sample containing DENV-4.

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Comparison of methods to detect HIV dual infection.

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Comparison of methods to detect HIV dual infection.

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