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Identification of dual-tropic HIV-1 using evolved neural networks.

ABSTRACT: Blocking the binding of the envelope HIV-1 protein to immune cells is a popular concept for development of anti-HIV therapeutics. R5 HIV-1 binds CCR5, X4 HIV-1 binds CXCR4, and dual-tropic HIV-1 can bind either coreceptor for cellular entry. R5 viruses are associated with early infection and over time can evolve to X4 viruses that are associated with immune failure. Dual-tropic HIV-1 is less studied; however, it represents functional antigenic intermediates during the transition of R5 to X4 viruses. Viral tropism is linked partly to the HIV-1 envelope V3 domain, where the amino acid sequence helps dictate the receptor a particular virus will target; however, using V3 sequence information to identify dual-tropic HIV-1 isolates has remained difficult. Our goal in this study was to elucidate features of dual-tropic HIV-1 isolates that assist in the biological understanding of dual-tropism and develop an approach for their detection. Over 1559 HIV-1 subtype B sequences with known tropisms were analyzed. Each sequence was represented by 73 structural, biochemical and regional features. These features were provided to an evolved neural network classifier and evaluated using balanced and unbalanced data sets. The study resolved R5X4 viruses from R5 with an accuracy of 81.8% and from X4 with an accuracy of 78.8%. The approach also identified a set of V3 features (hydrophobicity, structural and polarity) that are associated with tropism transitions. The ability to distinguish R5X4 isolates will improve computational tropism decisions for R5 vs. X4 and assist in HIV-1 research and drug development efforts.

SUBMITTER: Fogel GB

PROVIDER: S-EPMC4921197 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Identification of dual-tropic HIV-1 using evolved neural networks.

Fogel Gary B GB Lamers Susanna L SL Liu Enoch S ES Salemi Marco M McGrath Michael S MS

Bio Systems 20150928

Blocking the binding of the envelope HIV-1 protein to immune cells is a popular concept for development of anti-HIV therapeutics. R5 HIV-1 binds CCR5, X4 HIV-1 binds CXCR4, and dual-tropic HIV-1 can bind either coreceptor for cellular entry. R5 viruses are associated with early infection and over time can evolve to X4 viruses that are associated with immune failure. Dual-tropic HIV-1 is less studied; however, it represents functional antigenic intermediates during the transition of R5 to X4 viru ...[more]

PMID: 26419858

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Project description:Rapid and accurate identification of Arcobacter is of great importance because it is considered an emerging food- and waterborne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. We aimed to detect and discriminate Arcobacter bacteria at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with neural networks. A total of 82 reference and field isolates of 18 Arcobacter species from clinical, environmental, and agri-food sources were included. We determined that the bacterial cultivation time and growth temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus Arcobacter could be successfully differentiated from the closely related genera Campylobacter and Helicobacter using principal-component analysis. For the identification of Arcobacter to the species level, an accuracy of 97.2% was achieved for all 18 Arcobacter species using Raman spectroscopy combined with a convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent data set of 12 Arcobacter strains. Furthermore, a Raman spectroscopy-based fully connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass (regression coefficient >0.99). The application of both CNN and fully connected ANN improved the accuracy of Raman spectroscopy for bacterial species determination compared to the conventional chemometrics. This newly developed approach enables rapid identification and species determination of Arcobacter within an hour following cultivation.IMPORTANCE Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and has become more important in recent decades. The incidence of Arcobacter species in the agro-ecosystem is probably underestimated mainly due to the limitation in the available detection and characterization techniques. Raman spectroscopy combined with machine learning can accurately identify Arcobacter at the species level in a rapid and reliable manner, providing a promising tool for epidemiological surveillance of this microbe in the agri-food chain. The knowledge elicited from this study has the potential to be used for routine bacterial screening and diagnostics by the government, food industry, and clinics.

Project description:BackgroundThe future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing.ResultsFor this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D.ConclusionsDeveloping the computational tools to explore genetic associations beyond main effects remains a critically important challenge in human genetics. Methods, such as GEBN, demonstrate the utility of considering genetic interactions, as they likely explain some of the missing heritability.

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Identification of dual-tropic HIV-1 using evolved neural networks.

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Identification of dual-tropic HIV-1 using evolved neural networks.

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