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Error baseline rates of five sample preparation methods used to characterize RNA virus populations.

ABSTRACT: Individual RNA viruses typically occur as populations of genomes that differ slightly from each other due to mutations introduced by the error-prone viral polymerase. Understanding the variability of RNA virus genome populations is critical for understanding virus evolution because individual mutant genomes may gain evolutionary selective advantages and give rise to dominant subpopulations, possibly even leading to the emergence of viruses resistant to medical countermeasures. Reverse transcription of virus genome populations followed by next-generation sequencing is the only available method to characterize variation for RNA viruses. However, both steps may lead to the introduction of artificial mutations, thereby skewing the data. To better understand how such errors are introduced during sample preparation, we determined and compared error baseline rates of five different sample preparation methods by analyzing in vitro transcribed Ebola virus RNA from an artificial plasmid-based system. These methods included: shotgun sequencing from plasmid DNA or in vitro transcribed RNA as a basic "no amplification" method, amplicon sequencing from the plasmid DNA or in vitro transcribed RNA as a "targeted" amplification method, sequence-independent single-primer amplification (SISPA) as a "random" amplification method, rolling circle reverse transcription sequencing (CirSeq) as an advanced "no amplification" method, and Illumina TruSeq RNA Access as a "targeted" enrichment method. The measured error frequencies indicate that RNA Access offers the best tradeoff between sensitivity and sample preparation error (1.4-5) of all compared methods.

SUBMITTER: Kugelman JR

PROVIDER: S-EPMC5300104 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Error baseline rates of five sample preparation methods used to characterize RNA virus populations.

Kugelman Jeffrey R JR Wiley Michael R MR Nagle Elyse R ER Reyes Daniel D Pfeffer Brad P BP Kuhn Jens H JH Sanchez-Lockhart Mariano M Palacios Gustavo F GF

PloS one 20170209 2

Individual RNA viruses typically occur as populations of genomes that differ slightly from each other due to mutations introduced by the error-prone viral polymerase. Understanding the variability of RNA virus genome populations is critical for understanding virus evolution because individual mutant genomes may gain evolutionary selective advantages and give rise to dominant subpopulations, possibly even leading to the emergence of viruses resistant to medical countermeasures. Reverse transcript ...[more]

PMID: 28182717

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Project description:Background Improved methods for sampling outdoor-biting mosquitoes are urgently needed to improve surveillance of vector-borne diseases. Such tools could potentially replace the human landing catch (HLC), which, despite being the most direct option for measuring human exposures, raises significant ethical and logistical concerns. Several alternatives are under development, but detailed evaluation still requires common frameworks for calibration relative to HLC. The aim of this study was to develop and validate a statistical framework for predicting human-biting rates from different exposure-free alternatives. Methods We obtained mosquito abundance data (Anopheles arabiensis, Anopheles funestus and Culex spp.) from a year-long Tanzanian study comparing six outdoor traps [Suna Trap (SUN), BG Sentinel (BGS), M-Trap (MTR), M-Trap + CDC (MTRC), Ifakara Tent Trap-C (ITT-C) and Mosquito Magnet-X Trap (MMX)] and HLC. Generalised linear models were developed within a Bayesian framework to investigate associations between the traps and HLC, taking intra- and inter-specific density dependence into account. The best model was used to create a calibration tool for predicting HLC-equivalents. Results For An. arabiensis, SUN catches had the strongest correlation with HLC (R2 = 19.4), followed by BGS (R2 = 17.2) and MTRC (R2 = 13.1) catches. The least correlated catch was MMX (R2 = 2.5). For An. funestus, BGS had the strongest correlation with the HLC (R2 = 53.4), followed by MTRC (R2 = 37.4) and MTR (R2 = 37.4). For Culex mosquitoes, the traps most highly correlated with the HLC were MTR (R2 = 45.4) and MTRC (R2 = 44.2). Density dependence, both between and within species, influenced the performance of only BGS traps. An interactive Shiny App calibration tool was developed for this and similar applications. Conclusion We successfully developed a calibration tool to assess the performance of different traps for assessing outdoor-biting risk, and established a valuable framework for estimating human exposures based on the trap catches. The performance of candidate traps varied between mosquito taxa; thus, there was no single optimum. Although all the traps tested underestimated the HLC-derived exposures, it was possible to mathematically define their representativeness of the true biting risk, with or without density dependence. The results of this study emphasise the need to aim for a consistent and representative sampling approach, as opposed to simply seeking traps that catch the most mosquitoes. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s13071-022-05403-7.

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Error baseline rates of five sample preparation methods used to characterize RNA virus populations.

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Error baseline rates of five sample preparation methods used to characterize RNA virus populations.

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