HIV 2-LTR experiment design optimization

Cannon, LaMont; Vargas-García, César Augusto; Jagarapu, Aditya; Piovoso, Michael J.; Zurakowski, Ryan

doi:10.1371/journal.pone.0206700

Cited by 3 publications

(4 citation statements)

References 46 publications

(63 reference statements)

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“…Viral decay was calculated using linear regression on log-transformed data. Confidence intervals were computed using previously described models of HIV qPCR and digital PCR uncertainty (53)(54)(55)(56).…”

Section: Methodsmentioning

confidence: 99%

Next-Generation Sequencing in a Direct Model of HIV Infection Reveals Important Parallels to and Differences from In Vivo Reservoir Dynamics

Pinzone

Bertuccio

VanBelzen

et al. 2020

J Virol

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Next-generation sequencing (NGS) represents a powerful tool to unravel the genetic make-up of the HIV reservoir, but limited data exist on its use in vitro. Moreover, most NGS studies do not separate integrated from unintegrated DNA, even though selection pressures on these two forms should be distinct. We reasoned we could use NGS to compare the infection of resting and activated CD4 T cells in vitro to address how the metabolic state affects reservoir formation and dynamics. To address these questions, we obtained HIV sequences 2, 4, and 8 days after NL4-3 infection of metabolically activated and quiescent CD4 T cells (cultured with 2 ng/ml interleukin-7). We compared the composition of integrated and total HIV DNA by isolating integrated HIV DNA using pulsed-field electrophoresis before performing sequencing. After a single-round infection, the majority of integrated HIV DNA was intact in both resting and activated T cells. The decay of integrated intact proviruses was rapid and similar in both quiescent and activated T cells. Defective forms accumulated relative to intact ones analogously to what is observed in vivo. Massively deleted viral sequences formed more frequently in resting cells, likely due to lower deoxynucleoside triphosphate (dNTP) levels and the presence of multiple restriction factors. To our surprise, the majority of these deleted sequences did not integrate into the human genome. The use of NGS to study reservoir dynamics in vitro provides a model that recapitulates important aspects of reservoir dynamics. Moreover, separating integrated from unintegrated HIV DNA is important in some clinical settings to properly study selection pressures. IMPORTANCE The major implication of our work is that the decay of intact proviruses in vitro is extremely rapid, perhaps as a result of enhanced expression. Gaining a better understanding of why intact proviruses decay faster in vitro might help the field identify strategies to purge the reservoir in vivo. When used wisely, in vitro models are a powerful tool to study the selective pressures shaping the viral landscape. Our finding that massively deleted sequences rarely succeed in integrating has several ramifications. It demonstrates that the total HIV DNA can differ substantially in character from the integrated HIV DNA under certain circumstances. The presence of unintegrated HIV DNA has the potential to obscure selection pressures and confound the interpretation of clinical studies, especially in the case of trials involving treatment interruptions.

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Section: Methodsmentioning

confidence: 99%

Next-Generation Sequencing in a Direct Model of HIV Infection Reveals Important Parallels to and Differences from In Vivo Reservoir Dynamics

Pinzone

Bertuccio

VanBelzen

et al. 2020

J Virol

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“…This work considers four different six-point sampling schedules obtained from four different optimal design criteria [33]. We investigate four sampling schedules informative with respect to β information index.…”

Section: β-Information Gain For Generalized Distributionsmentioning

confidence: 99%

“…The parameter values used for data generation are given in Table 2. [33]. We investigate four sampling schedules informative with respect to β information index.…”

Section: Hiv 1 2-ltr Dynamicsmentioning

confidence: 99%

A generalised method for experiment design and model selection in the Bayesian framework

Jagadeesan,

Raman,

Tangirala

2023

Preprint

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Computational modelling of dynamical systems often involves many free parameters estimated from experimental data. The information gained from an experiment plays a crucial role in the goodness of predictions and parameter estimates. Optimal Experiment Design (OED) is being used to choose an experiment containing maximum information from a set of possible experiments. This work presents a novel Bayesian Optimal Experiment Design principle for generalised parameter distributions. The generalization is archived by extending theβ-information gain to the discrete distributions. Theβ-information gain is based on what is known as the Bhattacharyya coefficient. We show that maximising theβ-information gain is equivalent to maximising the angle between the prior and posterior distributions, reducing the posterior's uncertainty. Further, we apply the proposed BOED criteria for two realistic experiment designs in systems biology. Firstly, we use theβ-information gain to choose the best measurement method for parameter estimation in a Hes1 transcription model. The measurement method selected by theβ-information gain results in the minimum mean square error of the parameter estimates. In the second case, we employ the proposed information gained to select an optimal sampling schedule for HIV 1 2 LTR model. The sampling schedule chosen by the presented method reduces both prediction and parameter uncertainty. Finally, we propose a novel method for model selection usingβ-information gain and demonstrate the working of the proposed method in the model selection in compartmental models.

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“…Experiments will need to be designed to collect data for model tuning and validation (a process known as system identification in control theory). 26 , 152 , 158 From there appropriate open-loop strategies (in which a fixed intervention schedule is applied based on measured starting conditions) and/or closed-loop strategies (in which the schedule is continually adapted based on new measurements) can be developed. 32 , 239 , 281 …”

Section: Opportunities For Bioengineers To Study Respiratory Viral LImentioning

confidence: 99%

Significant Unresolved Questions and Opportunities for Bioengineering in Understanding and Treating COVID-19 Disease Progression

et al. 2020

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COVID-19 is a disease that manifests itself in a multitude of ways across a wide range of tissues. Many factors are involved, and though impressive strides have been made in studying this novel disease in a very short time, there is still a great deal that is unknown about how the virus functions. Clinical data has been crucial for providing information on COVID-19 progression and determining risk factors. However, the mechanisms leading to the multi-tissue pathology are yet to be fully established. Although insights from SARS-CoV-1 and MERS-CoV have been valuable, it is clear that SARS-CoV-2 is different and merits its own extensive studies. In this review, we highlight unresolved questions surrounding this virus including the temporal immune dynamics, infection of non-pulmonary tissue, early life exposure, and the role of circadian rhythms. Risk factors such as sex and exposure to pollutants are also explored followed by a discussion of ways in which bioengineering approaches can be employed to help understand COVID-19. The use of sophisticated in vitro models can be employed to interrogate intercellular interactions and also to tease apart effects of the virus itself from the resulting immune response. Additionally, spatiotemporal information can be gleaned from these models to learn more about the dynamics of the virus and COVID-19 progression. Application of advanced tissue and organ system models into COVID-19 research can result in more nuanced insight into the mechanisms underlying this condition and elucidate strategies to combat its effects.

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HIV 2-LTR experiment design optimization

Cited by 3 publications

References 46 publications

Next-Generation Sequencing in a Direct Model of HIV Infection Reveals Important Parallels to and Differences from In Vivo Reservoir Dynamics

Next-Generation Sequencing in a Direct Model of HIV Infection Reveals Important Parallels to and Differences from In Vivo Reservoir Dynamics

A generalised method for experiment design and model selection in the Bayesian framework

Significant Unresolved Questions and Opportunities for Bioengineering in Understanding and Treating COVID-19 Disease Progression

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