Development of alginate beads for precise environmental release applications: A design of experiment based approach and analysis.

Controlled release of active ingredients are important for drug delivery and more recently environmental applications including modulated dosing of chemical and biological controls. This study demonstrates the importance of investigating various material science factors that can influence the diffusion rates of alginate beads to improve and tune their performance for marine environmental applications. This investigation aimed to design a rational workflow to aid in leveraging alginate bead use as a carrier matrix for releasing a specific active agent into water. Experiments were conducted to focus on the narrow a large list of relevant material formulation parameters, which included chitosan molecular weight, chitosan concentration, calcium concentration, drop height, and bead size. Once the most relevant material preparation methods were screened, a more robust statistic Design of Experiments approach was performed and results determined the important (and unimportant) factors for increasing dye release kinetics in marine water. The process was further streamlined by narrowing the critical experimental factors to a three-level based on the prior analysis: chitosan MW, chitosan concentration, and bead size. Analysis of the collected data indicated that while chitosan MW had a negligible impact (Fstatistic = 0.22), bead size (Fstatistic = 60.33) significantly influenced the diffusion rates based on surface area. However, chitosan MW had minor effects where lower chitosan MW enabled higher product release rates. This case investigation was a novel application of the design of experiment approach towards environmental applications to understand differences in release rates to marine waters for the first time and the workflow provided also serve as the basis for researchers to optimize other environmental applications requiring optimization when it is unknown how a large number of formulation variables will impact performance in different environmental scenarios.

Bacterial microbiomes of individual ectomycorrhizal Pinus sylvestris roots are shaped by soil horizon and differentially sensitive to nitrogen addition.

Plant roots select non-random communities of fungi and bacteria from the surrounding soil that have effects on their health and growth, but we know little about the factors influencing their composition. We profiled bacterial microbiomes associated with individual ectomycorrhizal Pinus sylvestris roots colonized by different fungi and analyzed differences in microbiome structure related to soils from distinct podzol horizons and effects of short-term additions of N, a growth-limiting nutrient commonly applied as a fertilizer, but known to influence patterns of carbon allocation to roots. Ectomycorrhizal roots growing in soil from different horizons harboured distinct bacterial communities. The fungi colonizing individual roots had a strong effect on the associated bacterial communities. Even closely related species within the same ectomycorrhizal genus had distinct bacterial microbiomes in unfertilized soil, but fertilization removed this specificity. Effects of N were rapid and context dependent, being influenced by both soil type and the particular ectomycorrhizal fungi involved. Fungal community composition changed in soil from all horizons, but bacteria only responded strongly to N in soil from the B horizon where community structure was different and bacterial diversity was significantly reduced, possibly reflecting changed carbon allocation patterns.

Performance of ultra-deep pyrosequencing in analysis of HIV-1 pol gene variation.

INTRODUCTION: Ultra-deep pyrosequencing (UDPS) has been used to detect minority variants within HIV-1 populations. Some aspects of the quality and reproducibility of UDPS have been previously evaluated, but comprehensive studies are still needed. PRINCIPAL FINDING: In this study the UDPS technology (FLX platform) was evaluated by analyzing a 120 base pair fragment of the HIV-1 pol gene from plasma samples from two patients and artificial mixtures of molecular clones. UDPS was performed using an optimized experimental protocol and an in-house data cleaning strategy. Nine samples and mixtures were analyzed and the average number of reads per sample was 19,404 (range 8,858-26,846). The two patient plasma samples were analyzed twice and quantification of viral variants was found to be highly repeatable for variants representing >0.27% of the virus population, whereas some variants representing 0.11-0.27% were detected in only one of the two UDPS runs. Bland-Altman analysis showed that a repeated measurement would have a 95% likelihood to lie approximately within ±0.5 log(10) of the initial estimate. A similar level of agreement was observed for variant frequency estimates in forward vs. reverse sequencing direction, but here the agreement was higher for common variants than for rare variants. UDPS following PCR amplification with alternative primers indicated that some variants may be incorrectly quantified due to primer-related selective amplification. Finally, the in vitro recombination rate during PCR was evaluated using artificial mixtures of clones and was found to be low. The most abundant in vitro recombinant represented 0.25% of all UDPS reads. CONCLUSION: This study demonstrates that this UDPS protocol results in low experimental noise and high repeatability, which is relevant for future research and clinical use of the UDPS technology. The low rate of in vitro recombination suggests that this UDPS system can be used to study genetic variants and mutational linkage.

Dynamics of HIV-1 quasispecies during antiviral treatment dissected using ultra-deep pyrosequencing.

BACKGROUND: Ultra-deep pyrosequencing (UDPS) allows identification of rare HIV-1 variants and minority drug resistance mutations, which are not detectable by standard sequencing. PRINCIPAL FINDINGS: Here, UDPS was used to analyze the dynamics of HIV-1 genetic variation in reverse transcriptase (RT) (amino acids 180-220) in six individuals consecutively sampled before, during and after failing 3TC and AZT containing antiretroviral treatment. Optimized UDPS protocols and bioinformatic software were developed to generate, clean and analyze the data. The data cleaning strategy reduced the error rate of UDPS to an average of 0.05%, which is lower than previously reported. Consequently, the cut-off for detection of resistance mutations was very low. A median of 16,016 (range 2,406-35,401) sequence reads were obtained per sample, which allowed detection and quantification of minority resistance mutations at amino acid position 181, 184, 188, 190, 210, 215 and 219 in RT. In four of five pre-treatment samples low levels (0.07-0.09%) of the M184I mutation were observed. Other resistance mutations, except T215A and T215I were below the detection limit. During treatment failure, M184V replaced M184I and dominated the population in combination with T215Y, while wild-type variants were rarely detected. Resistant virus disappeared rapidly after treatment interruption and was undetectable as early as after 3 months. In most patients, drug resistant variants were replaced by wild-type variants identical to those present before treatment, suggesting rebound from latent reservoirs. CONCLUSIONS: With this highly sensitive UDPS protocol preexisting drug resistance was infrequently observed; only M184I, T215A and T215I were detected at very low levels. Similarly, drug resistant variants in plasma quickly decreased to undetectable levels after treatment interruption. The study gives important insights into the dynamics of the HIV-1 quasispecies and is of relevance for future research and clinical use of the UDPS technology.