Immunogenic Properties of a BCG Adjuvanted Chitosan Nanoparticle-Based Dengue Vaccine in Human Dendritic Cells

Dengue viruses (DENVs) are among the most rapidly and efficiently spreading arboviruses. WHO recently estimated that about half of the world’s population is now at risk for DENV infection. There is no specific treatment or vaccine available to treat or prevent DENV infections. Here, we report the development of a novel dengue nanovaccine (DNV) composed of UV-inactivated DENV-2 (UVI-DENV) and Mycobacterium bovis Bacillus Calmette-Guerin cell wall components (BCG-CWCs) loaded into chitosan nanoparticles (CS-NPs). CS-NPs were prepared by an emulsion polymerization method prior to loading of the BCG-CWCs and UVI-DENV components. Using a scanning electron microscope and a zetasizer, DNV was determined to be of spherical shape with a diameter of 372.0 ± 11.2 nm in average and cationic surface properties. The loading efficacies of BCG-CWCs and UVI-DENV into the CS-NPs and BCG-CS-NPs were up to 97.2 and 98.4%, respectively. THP-1 cellular uptake of UVI-DENV present in the DNV was higher than soluble UVI-DENV alone. DNV stimulation of immature dendritic cells (iDCs) resulted in a significantly higher expression of DCs maturation markers (CD80, CD86 and HLA-DR) and induction of various cytokine and chemokine productions than in UVI-DENV-treated iDCs, suggesting a potential use of BCG- CS-NPs as adjuvant and delivery system for dengue vaccines.     

Genetics of Growth Reaction Norms in Farmed Rainbow Trout

Rainbow trout is farmed globally under diverse uncontrollable environments. Fish with low macroenvironmental sensitivity (ES) of growth is important to thrive and grow under these uncontrollable environments. The ES may evolve as a correlated response to selection for growth in one environment when the genetic correlation between ES and growth is nonzero. The aims of this study were to quantify additive genetic variance for ES of body weight (BW), defined as the slope of reaction norm across breeding environment (BE) and production environment (PE), and to estimate the genetic correlation (r _{g(int, sl)}) between BW and ES. To estimate heritable variance of ES, the coheritability of ES was derived using selection index theory. The BW records from 43,040 rainbow trout performing either in freshwater or seawater were analysed using a reaction norm model. High additive genetic variance for ES (9584) was observed, inferring that genetic changes in ES can be expected. The coheritability for ES was either -0.06 (intercept at PE) or -0.08 (intercept at BE), suggesting that BW observation in either PE or BE results in low accuracy of selection for ES. Yet, the r _{g(int, sl)} was negative (-0.41 to -0.33) indicating that selection for BW in one environment is expected to result in more sensitive fish. To avoid an increase of ES while selecting for BW, it is possible to have equal genetic gain in BW in both environments so that ES is maintained stable.     

Effect of extraction temperature on the diffusion coefficient of polysaccharides from Spirulina and the optimal separation method

The extraction temperature had a significant impact on the concentration of polysaccharides derived from solid-liquid extraction of Spirulina. The polysaccharide concentration was significantly higher when the extraction was performed at 90°C than when it was performed at 80, 70, and 50°C. This result is related to the diffusion coefficients of the polysaccharides, which increased from 1.07 × 10^?12 at 50°C to 3.02 × 10^?12 m²/sec at 90°C. Using the Arrhenius equation, the pre-exponential factor (D ₀ ) and the activation energy (E _a ) for Spirulina polysaccharide extraction were calculated as 7.958 × 10^?9 m²/sec and 24.0 kJ/mol, respectively. Among the methods used for the separation of Spirulina polysaccharides, cetyltrimethylammonium bromide (CTAB, method I) and organic solvent (ethanol, in methods II and III) provided similar yields of polysaccharides. However, the separation of polysaccharides using an ultrafiltration (UF) process (method III) and ethanol precipitation was superior to separation via CTAB or vacuum rotary evaporation (method II). The use of a membrane with a molecular weight cut-off (MWCO) of 30 kDa and an area of 0.01 m² at a feed pressure of 103 kPa with a mean permeate flux of 39.3 L/m²/h and a retention rate of 95% was optimal for the UF process. The addition of two volumes (v/v) of ethanol, which gave a total polysaccharide content of approximately 4% dry weight, was found to be most suitable for polysaccharide precipitation. The results of a Sepharose 6B column separation showed that the molecular weights of the polysaccharides in fractions I and II were 212 and 12.6 kDa, respectively.     

Sequencing and analysis of three plasmids from <Emphasis Type="Italic">Lactobacillus casei</Emphasis> TISTR1341 and development of plasmid-derived <Emphasis Type="Italic">Escherichia coli–L. casei</Emphasis> shuttle vectors

Pyrosequencing followed by conventional PCR and sequencing was used to determine the complete nucleotide sequence of three plasmids (pRCEID2.9, pRCEID3.2, and pRCEID13.9) from the Lactobacillus casei strain TISTR1341. The plasmid sequences were found to be almost identical, respectively, to those of pLA106, pLA105, and pLA103 from Lactobacillus acidophilus strain TK8912, suggesting that these strains may be related. Sequence analysis and comparison indicated that pRCEID2.9 replicates by a rolling circle (RC) mechanism, while pRCEID3.2 and pRCEID13.9 probably follow a theta-type mode of replication. Replicons of pRCEID2.9 and pRCEID13.9 were used to develop Escherichia coli/L. casei compatible shuttle vectors, which were stably maintained in different genetic backgrounds. Real-time quantitative PCR analysis showed copy numbers of around 4 and 15, respectively, for the pRCEID13.9- and pRCEID2.9-derived shuttle vectors per chromosome equivalent. The functionality of vector pRCEID-LC13.9 was proved by cloning and expressing in L. casei of a green fluorescent protein gene variant from Aequorea victoria under the control of the promoter from a homologous lactate dehydrogenase gene. The new vectors might complement those currently in use for the exploitation of L. casei as a cellular factory and in other biotechnological applications.     

EDIII‐DENV3 nanospheres drive immature dendritic cells into a mature phenotype in an in vitro model

Domain III of E protein of dengue virus (DENV) is a target for vaccine development. Unfortunately, this protein based platform has low general immunogenicity. To circumvent this problem, the use of an adjuvant‐nanoparticle delivery system to facilitate immunogenicity of soluble DENV‐EDIII protein was investigated. One of the key features of this delivery system is its ability to simultaneously deliver antigens and exert adjuvanticity on specialized immune cells. In this study, N‐trimethyl chitosan (TMC) nanoparticles (NPs) were generated to be used as adjuvant and carrier for soluble E‐domain III of dengue virus serotype 3 (sEDIII‐D3). Using ionotropic gelation, purified sEDIII‐D3 was encapsulated into TMC NPs to form EDIII‐D3 TMC NPs. After optimization, EDIII‐D3 TMC particles exhibited a loading efficiency of 81% and a loading capacity of 41%. The immunogenicity of EDIII‐D3 TMC NPs was tested using monocyte‐derived dendritic cells (MoDCs). It was found that EDIII‐D3 TMC NPs were well taken up by MoDCs. In addition, EDIII‐D3 TMC NP treated MoDCs significantly upregulated maturation markers (CD80, CD83, CD86 and HLA‐DR) and induced secretion of various cytokines and chemokines (IFN‐α, IL‐1β, IL‐6, IL‐2, IL‐12p70, IFN‐γ, IL‐4, IL‐10, IL‐8, MCP‐1, macrophage inflammatory protein‐1β, granulocyte‐colony stimulating factor, granulocyte–macrophage colony‐stimulating factor and IL‐7). These results indicate that EDIII‐D3 TMC NPs are potent immunogens, at least in vitro , with the ability to induce maturation of DCs and highlight the potential use of TMC NPs for enhancing immunogenicity of a non‐replicating dengue vaccine.

     

Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models

Background

Identifying the relevant environmental variables that cause GxE interaction is often difficult when they cannot be experimentally manipulated. Two statistical approaches can be applied to address this question. When data on candidate environmental variables are available, GxE interaction can be quantified as a function of specific environmental variables using a reaction norm model. Alternatively, a factor analytic model can be used to identify the latent common factor that explains GxE interaction. This factor can be correlated with known environmental variables to identify those that are relevant. Previously, we reported a significant GxE interaction for body weight at harvest in rainbow trout reared on three continents. Here we explore their possible causes.

Methods

Reaction norm and factor analytic models were used to identify which environmental variables (age at harvest, water temperature, oxygen, and photoperiod) may have caused the observed GxE interaction. Data on body weight at harvest was recorded on 8976 offspring reared in various locations: (1) a breeding environment in the USA (nucleus), (2) a recirculating aquaculture system in the Freshwater Institute in West Virginia, USA, (3) a high-altitude farm in Peru, and (4) a low-water temperature farm in Germany. Akaike and Bayesian information criteria were used to compare models.

Results

The combination of days to harvest multiplied with daily temperature (Day*Degree) and photoperiod were identified by the reaction norm model as the environmental variables responsible for the GxE interaction. The latent common factor that was identified by the factor analytic model showed the highest correlation with Day*Degree. Day*Degree and photoperiod were the environmental variables that differed most between Peru and other environments. Akaike and Bayesian information criteria indicated that the factor analytical model was more parsimonious than the reaction norm model.

Conclusions

Day*Degree and photoperiod were identified as environmental variables responsible for the strong GxE interaction for body weight at harvest in rainbow trout across four environments. Both the reaction norm and the factor analytic models can help identify the environmental variables responsible for GxE interaction. A factor analytic model is preferred over a reaction norm model when limited information on differences in environmental variables between farms is available.     

Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments

Background

When rainbow trout from a single breeding program are introduced into various production environments, genotype-by-environment (GxE) interaction may occur. Although growth and its uniformity are two of the most important traits for trout producers worldwide, GxE interaction on uniformity of growth has not been studied. Our objectives were to quantify the genetic variance in body weight (BW) and its uniformity and the genetic correlation (r_g) between these traits, and to investigate the degree of GxE interaction on uniformity of BW in breeding (BE) and production (PE) environments using double hierarchical generalized linear models. Log-transformed data were also used to investigate whether the genetic variance in uniformity of BW, GxE interaction on uniformity of BW, and r_g between BW and its uniformity were influenced by a scale effect.

Results

Although heritability estimates for uniformity of BW were low and of similar magnitude in BE (0.014) and PE (0.012), the corresponding coefficients of genetic variation reached 19 and 21%, which indicated a high potential for response to selection. The genetic re-ranking for uniformity of BW (r_g = 0.56) between BE and PE was moderate but greater after log-transformation, as expressed by the low r_g (-0.08) between uniformity in BE and PE, which indicated independent genetic rankings for uniformity in the two environments when the scale effect was accounted for. The r_g between BW and its uniformity were 0.30 for BE and 0.79 for PE but with log-transformed BW, these values switched to -0.83 and -0.62, respectively.

Conclusions

Genetic variance exists for uniformity of BW in both environments but its low heritability implies that a large number of relatives are needed to reach even moderate accuracy of selection. GxE interaction on uniformity is present for both environments and sib-testing in PE is recommended when the aim is to improve uniformity across environments. Positive and negative r_g between BW and its uniformity estimated with original and log-transformed BW data, respectively, indicate that increased BW is genetically associated with increased variance in BW but with a decrease in the coefficient of variation. Thus, the scale effect substantially influences the genetic parameters of uniformity, especially the sign and magnitude of its r_g.     

Structural and physicochemical characterization of crude biosurfactant produced by Pseudomonas aeruginosa SP4 isolated from petroleum-contaminated soil

Pseudomonas aeruginosa strain SP4, isolated from petroleum-contaminated soil in Thailand, was used to produce a biosurfactant from a nutrient broth with palm oil as the carbon source. The key components of the crude biosurfactant were fractionated by using HPLC-ELSD technique. With the use of ATR-FTIR spectroscopy, in combination with (1)H NMR and MS analyses, chemical structures of the fractionated components of the crude biosurfactant were identified as rhamnolipid species. When compared to synthetic surfactants, including Pluronic F-68, which is a triblock nonionic surfactant containing poly(ethylene oxide) and poly(propylene oxide), and sodium dodecyl sulfate, the crude biosurfactant showed comparable physicochemical properties, in terms of the surface activities. The crude biosurfactant reduced the surface tension of pure water to 29.0 mN/m with a critical micelle concentration of approximately 200 mg/l, and it exhibited good thermal and pH stability. The crude biosurfactant also formed stable water-in-oil microemulsions with crude oil and various types of vegetable oils, but not with short-chain hydrocarbons.