Optimization of chimeric HIV-1 virus-like particle production in a baculovirus-insect cell expression system

A baculovirus-insect cell expression system potentially provides the means to produce prophylactic HIV-1 virus-like particle (VLP) vaccines inexpensively and in large quantities. However, the system must be optimized to maximize yields and increase process efficiency. In this study, we optimized the production of two novel, chimeric HIV-1 VLP vaccine candidates (GagRT and GagTN) in insect cells. This was done by monitoring the effects of four specific factors on VLP expression: these were insect cell line, cell density, multiplicity of infection (MOI), and infection time. The use of western blots, Gag p24 ELISA, and four-factorial ANOVA allowed the determination of the most favorable conditions for chimeric VLP production, as well as which factors affected VLP expression most significantly. Both VLP vaccine candidates favored similar optimal conditions, demonstrating higher yields of VLPs when produced in the Trichoplusia ni Pro insect cell line, at a cell density of 1 × 106 cells/mL, and an infection time of 96 h post infection. It was found that cell density and infection time were major influencing factors, but that MOI did not affect VLP expression significantly. This work provides a potentially valuable guideline for HIV-1 protein vaccine optimization, as well as for general optimization of a baculovirus-based expression system to produce complex recombinant proteins. © 2009 American Institute of Chemical Engineers.

Sampling stored-product insect pests : a comparison of four statistical sampling models for probability of pest detection

Background: Developing sampling strategies to target biological pests such as insects in stored grain is inherently difficult owing to species biology and behavioural characteristics. The design of robust sampling programmes should be based on an underlying statistical distribution that is sufficiently flexible to capture variations in the spatial distribution of the target species. Results: Comparisons are made of the accuracy of four probability-of-detection sampling models - the negative binomial model,1 the Poisson model,1 the double logarithmic model2 and the compound model3 - for detection of insects over a broad range of insect densities. Although the double log and negative binomial models performed well under specific conditions, it is shown that, of the four models examined, the compound model performed the best over a broad range of insect spatial distributions and densities. In particular, this model predicted well the number of samples required when insect density was high and clumped within experimental storages. Conclusions: This paper reinforces the need for effective sampling programs designed to detect insects over a broad range of spatial distributions. The compound model is robust over a broad range of insect densities and leads to substantial improvement in detection probabilities within highly variable systems such as grain storage.

A Bayesian hurdle model for analysis of an insect resistance monitoring database

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models.

How to sequence and annotate insect mitochondrial genomes for systematic and comparative genomics research

Over the past decade the mitochondrial (mt) genome has become the most widely used genomic resource available for systematic entomology. While the availability of other types of ‘–omics’ data – in particular transcriptomes – is increasing rapidly, mt genomes are still vastly cheaper to sequence and are far less demanding of high quality templates. Furthermore, almost all other ‘–omics’ approaches also sequence the mt genome, and so it can form a bridge between legacy and contemporary datasets. Mitochondrial genomes have now been sequenced for all insect orders, and in many instances representatives of each major lineage within orders (suborders, series or superfamilies depending on the group). They have also been applied to systematic questions at all taxonomic scales from resolving interordinal relationships (e.g. Cameron et al., 2009; Wan et al., 2012; Wang et al., 2012), through many intraordinal (e.g. Dowton et al., 2009; Timmermans et al., 2010; Zhao et al. 2013a) and family-level studies (e.g. Nelson et al., 2012; Zhao et al., 2013b) to population/biogeographic studies (e.g. Ma et al., 2012). Methodological issues around the use of mt genomes in insect phylogenetic analyses and the empirical results found to date have recently been reviewed by Cameron (2014), yet the technical aspects of sequencing and annotating mt genomes were not covered. Most papers which generate new mt genome report their methods in a simplified form which can be difficult to replicate without specific knowledge of the field. Published studies utilize a sufficiently wide range of approaches, usually without justification for the one chosen, that confusion about commonly used jargon such as ‘long PCR’ and ‘primer walking’ could be a serious barrier to entry. Furthermore, sequenced mt genomes have been annotated (gene locations defined) to wildly varying standards and improving data quality through consistent annotation procedures will benefit all downstream users of these datasets. The aims of this review are therefore to: 1. Describe in detail the various sequencing methods used on insect mt genomes; 2. Explore the strengths/weakness of different approaches; 3. Outline the procedures and software used for insect mt genome annotation, and; 4. Highlight quality control steps used for new annotations, and to improve the re-annotation of previously sequenced mt genomes used in systematic or comparative research.

Opsin clines in butterflies suggest novel roles for insect photopigments

Opsins are ancient molecules that enable animal vision by coupling to a vitamin-derived chromophore to form lightsensitive photopigments. The primary drivers of evolutionary diversification in opsins are thought to be visual tasks related to spectral sensitivity and color vision. Typically, only a few opsin amino acid sites affect photopigment spectral sensitivity. We show that opsin genes of the North American butterfly Limenitis arthemis have diversified along a latitudinal cline, consistent with natural selection due to environmental factors. We sequenced single nucleotide(SNP) polymorphisms in the coding regions of the ultraviolet (UVRh), blue (BRh), and long-wavelength (LWRh) opsin genes from ten butterfly populations along the eastern United States and found that a majority of opsin SNPs showed significant clinal variation. Outlier detection and analysis of molecular variance indicated that many SNPs are under balancing selection and show significant population structure. This contrasts with what we found by analysing SNPs in the wingless and EF-1 alpha loci, and from neutral amplified fragment length polymorphisms, which show no evidence of significant locus-specific or genome-wide structure among populations. Using a combination of functional genetic and physiological approaches, including expression in cell culture, transgenic Drosophila, UV-visible spectroscopy, and optophysiology, we show that key BRh opsin SNPs that vary clinally have almost no effect on spectral sensitivity. Our results suggest that opsin diversification in this butterfly is more consistent with natural selection unrelated to spectral tuning. Some of the clinally varying SNPs may instead play a role in regulating opsin gene expression levels or the thermostability of the opsin protein. Lastly, we discuss the possibility that insect opsins might have important, yet-to-be elucidated, adaptive functions in mediating animal responses to abiotic factors, such as temperature or photoperiod.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

A generic individual-based spatially explicit model as a novel tool for investigating insect-plant interactions: A case study of the behavioural ecology of frugivorous Tephritidae

Computational modelling of mechanisms underlying processes in the real world can be of great value in understanding complex biological behaviours. Uptake in general biology and ecology has been rapid. However, it often requires specific data sets that are overly costly in time and resources to collect. The aim of the current study was to test whether a generic behavioural ecology model constructed using published data could give realistic outputs for individual species. An individual-based model was developed using the Pattern-Oriented Modelling (POM) strategy and protocol, based on behavioural rules associated with insect movement choices. Frugivorous Tephritidae (fruit flies) were chosen because of economic significance in global agriculture and the multiple published data sets available for a range of species. The Queensland fruit fly (Qfly), Bactrocera tryoni, was identified as a suitable individual species for testing. Plant canopies with modified architecture were used to run predictive simulations. A field study was then conducted to validate our model predictions on how plant architecture affects fruit flies’ behaviours. Characteristics of plant architecture such as different shapes, e.g., closed-canopy and vase-shaped, affected fly movement patterns and time spent on host fruit. The number of visits to host fruit also differed between the edge and centre in closed-canopy plants. Compared to plant architecture, host fruit has less contribution to effects on flies’ movement patterns. The results from this model, combined with our field study and published empirical data suggest that placing fly traps in the upper canopy at the edge should work best. Such a modelling approach allows rapid testing of ideas about organismal interactions with environmental substrates in silico rather than in vivo, to generate new perspectives. Using published data provides a saving in time and resources. Adjustments for specific questions can be achieved by refinement of parameters based on targeted experiments.

The Expanded Human Kallikrein (KLK) gene family : genomic organisation, tissue specific expression and potential functions

The tissue kallikreins are serine proteases encoded by highly conserved multigene families. The rodent kallikrein (KLK) families are particularly large, consisting of 13 26 genes clustered in one chromosomal locus. It has been recently recognised that the human KLK gene family is of a similar size (15 genes) with the identification of another 12 related genes (KLK4-KLK15) within and adjacent to the original human KLK locus (KLK1-3) on chromosome 19q13.4. The structural organisation and size of these new genes is similar to that of other KLK genes except for additional exons encoding 5 or 3 untranslated regions. Moreover, many of these genes have multiple mRNA transcripts, a trait not observed with rodent genes. Unlike all other kallikreins, the KLK4-KLK15 encoded proteases are less related (25–44%) and do not contain a conventional kallikrein loop. Clusters of genes exhibit high prostatic (KLK2-4, KLK15) or pancreatic (KLK6-13) expression, suggesting evolutionary conservation of elements conferring tissue specificity. These genes are also expressed, to varying degrees, in a wider range of tissues suggesting a functional involvement of these newer human kallikrein proteases in a diverse range of physiological processes.

Influence of host plant structure and microclimate on the abundance and behaviour of Tephritid fly

Microclimate and host plant architecture significantly influence the abundance and behavior of insects. However, most research in this field has focused at the invertebrate assemblage level, with few studies at the single-species level. Using wild Solanum mauritianum plants, we evaluated the influence of plant structure (number of leaves and branches and height of plant) and microclimate (temperature, relative humidity, and light intensity) on the abundance and behavior of a single insect species, the monophagous tephritid fly Bactrocera cacuminata (Hering). Abundance and oviposition behavior were signficantly influenced by the host structure (density of foliage) and associated microclimate. Resting behavior of both sexes was influenced positively by foliage density, while temperature positively influenced the numbers of resting females. The number of ovipositing females was positively influenced by temperature and negatively by relative humidity. Feeding behavior was rare on the host plant, as was mating. The relatively low explanatory power of the measured variables suggests that, in addition to host plant architecture and associated microclimate, other cues (e.g., olfactory or visual) could affect visitation and use of the larval host plant by adult fruit flies. For 12 plants observed at dusk (the time of fly mating), mating pairs were observed on only one tree. Principal component analyses of the plant and microclimate factors associated with these plants revealed that the plant on which mating was observed had specific characteristics (intermediate light intensity, greater height, and greater quantity of fruit) that may have influenced its selection as a mating site.