Delayed action insecticides and their role in mosquito and malaria control

There is considerable interest in the management of insecticide resistance in mosquitoes. One possible approach to slowing down the evolution of resistance is to use late-life-acting (LLA) insecticides that selectively kill only the old mosquitoes that transmit malaria, thereby reducing selection pressure favoring resistance. In this paper we consider an age-structured compartmental model for malaria with two mosquito strains that differ in resistance to insecticide, using an SEI approach to model malaria in the mosquitoes and thereby incorporating the parasite developmental times for the two strains. The human population is modeled using an SEI approach. We consider both conventional insecticides that target all adult mosquitoes, and LLA insecticides that target only old mosquitoes. According to linearised theory the potency of the insecticide affects mainly the speed of evolution of resistance. Mutations that confer resistance can also affect other parameters such as mean adult life span and parasite developmental time. For both conventional and LLA insecticides the stability of the malaria-free equilibrium, with only the resistant mosquito strain present, depends mainly on these other parameters. This suggests that the main long term role of an insecticide could be to induce genetic changes that have a desirable effect on a vital parameter such as adult life span. However, when this equilibrium is unstable, numerical simulations suggest that a potent LLA insecticide can slow down the spread of malaria in humans but that the timing of its action is very important.     

Mixture-model classification in DNA content analysis.

DNA abundance provides important information about cell physiology and proliferation activity. In a typical in vitro cellular assay, the distribution of the DNA content within a sample is comprised of cell debris, G0/G1-, S-, and G2/M-phase cells. In some circumstances, there may be a collection of cells that contain more than two copies of DNA. The primary focus of DNA content analysis is to deconvolute the overlapping mixtures of the cellular components, and subsequently to investigate whether a given treatment has perturbed the mixing proportions of the sample components. We propose a restricted mixture model that is parameterized to incorporate the available biological information. A likelihood ratio (LR) test is developed to test for changes in the mixing proportions between two cell populations. The proposed mixture model is applied to both simulated and real experimental data. The model fitting is compared with unrestricted models; the statistical inference on proportion change is compared between the proposed LR test and the Kolmogorov-Smirnov test, which is frequently used to test for differences in DNA content distribution. The proposed mixture model outperforms the existing approaches in the estimation of the mixing proportions and gives biologically interpretable results; the proposed LR test demonstrates improved sensitivity and specificity for detecting changes in the mixing proportions.     

Variation of Dominance of Newly Arisen Adaptive Genes

Newly arisen adaptive alleles such as insecticide resistance genes represent a good opportunity to investigate the theories put forth to explain the molecular basis of dominance and its possible evolution. Dominance levels of insecticide resistance conferred by insensitive alleles of the acetylcholinesterase gene were analyzed in five resistant strains of the mosquito Culex pipiens. Dominance levels were found to differ between strains, varying from partial recessivity to complete dominance. This variation was not explained by differences in catalytic properties of the enzyme, since four of the five resistant strains had identical inhibition properties for the insensitive acetylcholinesterase. Among these four laboratory strains and in individuals collected from natural populations, we found a correlation between increased acetylcholinesterase activities and higher dominance levels. We propose a molecular explanation for how variation in acetylcholinesterase activity may result in variation of dominance level. We also conjecture that the four resistant strains did not differ in their amino acid sequence in the catalytically active regions of acetylcholinesterase, but that the expression of the gene was regulated by either neighboring or distant sites, thereby modifying the dominance level. Under this interpretation, dominance levels may evolve in this system, since heritable variation in acetylcholinesterase activity was found.     

Selection of dieldrin-resistant strains of Lucilia cuprina (Diptera: Calliphoridae) after ethyl methanesulfonate mutagenesis of a susceptible strain.

After ethyl methanesulfonate (EMS) mutagenesis of a susceptible strain (SWT), selective screening of Lucilia cuprina (Wiedemann) resulted in four strains that were resistant to the insecticide dieldrin. Concentrations used for selection were greater than LC99 of susceptible phenotypes. No resistant variants were screened from the standard laboratory strain (SWT) not treated with EMS. The resistance phenotypes of the four resistant strains were similar to each other and to that of a field-selected resistant strain. The genetic basis of resistance is monogenic in all strains and the data are consistent with the same locus, Rdl, determining resistance status in each strain. The Rdl locus maps to chromosome V, approximately 3.5 map units distal to the Sut locus. Dieldrin resistance may be caused by less effective blocking of insect neuronal GABA receptors by the chemical in resistant strains. The data indicate that the evolution of resistance to an insecticide in the field may be constrained by a limited number of genetical and biochemical options if a monogenic response is selected for and that the spontaneous mutation rate to the Rdl allele is less than 1 in 10(6) in the laboratory.     

Biochemical characterization of hydrolytic and oxidative enzymes in insecticide resistant and susceptible strains of the German cockroach (Dictyoptera: Blattellidae).

We have identified resistance mechanisms in the German cockroach, Blattella germanica (L.), for propoxur and chlorpyrifos in strains of cockroaches that display multiresistance to several organophosphate and carbamate insecticides. The resistance mechanisms involve the combined effects of increased oxidative and hydrolytic metabolism and both strains are resistant to chlorpyrifos and propoxur. Experiments designed to test for similarity in metabolic enzymes suggest that, although the mechanisms involve similar processes, the enzymes responsible for insecticide detoxification are different in the two strains. Both resistant strains exhibited enhanced activity toward alpha-naphtholic esters relative to a standard susceptible strain; however, analysis of the progeny from resistant X susceptible crosses suggests that this general esterase activity is inherited differently than propoxur or chlorpyrifos resistance. Hybrids of the propoxur-resistant strain displayed the highest activity of all cockroaches tested, in contrast to hybrids of the chlorpyrifos-resistant strain, which were similar to the susceptible strain. Native gel electrophoresis of cytosolic preparations provided further evidence for differences in the pattern of hydrolytic enzymes and inheritance of resistance in the two strains. Analysis of components of the cytochrome P450-dependent monooxygenase system and activities toward model substrates indicate that the two resistance mechanisms also involve different oxidative processes. The propoxur-resistant strain displayed significantly higher levels of total cytochrome P450, but no other components were correlated with resistance. In contrast with the chlopyrifos-resistant strain, which was similar to the susceptible strain in all parameters measured, activity toward model substrates was higher in the propoxur-resistant strain than in any of the other strains and hybrids tested.(ABSTRACT TRUNCATED AT 250 WORDS)     

Applications of beta-mixture models in bioinformatics

SUMMARY: We propose a beta-mixture model approach to solve a variety of problems related to correlations of gene-expression levels. For example, in meta-analyses of microarray gene-expression datasets, a threshold value of correlation coefficients for gene-expression levels is used to decide whether gene-expression levels are strongly correlated across studies. Ad hoc threshold values such as 0.5 are often used. In this paper, we use a beta-mixture model approach to divide the correlation coefficients into several populations so that the large correlation coefficients can be identified. Another important application of the proposed method is in finding co-expressed genes. Two examples are provided to illustrate both applications. Through our analysis, we also discover that the popular model selection criteria BIC and AIC are not suitable for the beta-mixture model. To determine the number of components in the mixture model, we suggest an alternative criterion, ICL-BIC, which is shown to perform better in selecting the correct mixture model. SUPPLEMENTARY INFORMATION: http://odin.mdacc.tmc.edu/~yuanj/highcorgeneanno.html.     

Waste Not,Want Not: Why Rarefying Microbiome Data Is Inadmissible

Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.     

Deltamethrin‐induced feeding plasticity in pyrethroid‐susceptible and ‐resistant strains of the maize weevil,Sitophilus zeamais

Phenotypic plasticity contributes to the adaptative evolution of populations exposed to new or altered environments. Feeding plasticity is a component of phenotypic plasticity not usually considered in insect strains adapted to insecticide‐altered environments, but which may either accentuate or mitigate insecticide resistance. This is a concern in the pyrethroid‐resistant strains of the maize weevil Sitophilus zeamais Motsch. (Col., Curculionidae), and the reason for this study. A pyrethroid‐susceptible and two pyrethroid‐resistant strains of maize weevil were subjected to free‐choice and no‐choice tests with maize grains sprayed with increasing doses of the pyrethroid, deltamethrin. The insects from the pyrethroid‐resistant strains exhibited higher feeding avoidance with increased deltamethrin doses than insects from the susceptible strain when subjected to free‐choice tests. The strains of maize weevil physiologically resistant to pyrethroids were also behaviourally resistant to deltamethrin – an additional management concern. The resistant strains avoid deltamethrin‐sprayed grains and are less nutritionally affected by this compound, with divergent responses from the susceptible strain with increased doses of deltamethrin. Furthermore, the higher relative growth rate and consequently higher efficiency of food conversion observed in the insecticide‐resistant strains were significant even without insecticide exposure, indicating that these traits are stimulus‐independent and may persist even without further insecticide selection, potentially limiting the options available for their management.     

Using an antimalarial in mosquitoes overcomes Anopheles and Plasmodium resistance to malaria control strategies

The spread of insecticide resistance in Anopheles mosquitoes and drug resistance in Plasmodium parasites is contributing to a global resurgence of malaria, making the generation of control tools that can overcome these roadblocks an urgent public health priority. We recently showed that the transmission of Plasmodium falciparum parasites can be efficiently blocked when exposing Anopheles gambiae females to antimalarials deposited on a treated surface, with no negative consequences on major components of mosquito fitness. Here, we demonstrate this approach can overcome the hurdles of insecticide resistance in mosquitoes and drug resistant in parasites. We show that the transmission-blocking efficacy of mosquito-targeted antimalarials is maintained when field-derived, insecticide resistant Anopheles are exposed to the potent cytochrome b inhibitor atovaquone, demonstrating that this drug escapes insecticide resistance mechanisms that could potentially interfere with its function. Moreover, this approach prevents transmission of field-derived, artemisinin resistant P. falciparum parasites (Kelch13 C580Y mutant), proving that this strategy could be used to prevent the spread of parasite mutations that induce resistance to front-line antimalarials. Atovaquone is also highly effective at limiting parasite development when ingested by mosquitoes in sugar solutions, including in ongoing infections. These data support the use of mosquito-targeted antimalarials as a promising tool to complement and extend the efficacy of current malaria control interventions.     

Spectral analysis of distributions: finding periodic components in eukaryotic enzyme length data

We introduce the spectral analysis of distributions (SAD), a method for detecting and evaluating possible periodicity in experimental data distributions (histograms) of arbitrary shape. SAD determines whether a given empirical distribution contains a periodic component. We also propose a system of probabilistic mixture distributions to model a histogram consisting of a smooth background together with peaks at periodic intervals, with each peak corresponding to a fixed number of subunits added together. This mixture distribution model allows us to estimate the parameters of the data and to test the statistical significance of the estimated peaks. The analysis is applied to the length distribution of eukaryotic enzymes.     

How Fitness Reduced,Antimicrobial Resistant Bacteria Survive and Spread: A Multiple Pig - Multiple Bacterial Strain Model

More than 30% of E. coli strains sampled from pig farms in Denmark over the last five years were resistant to the commonly used antimicrobial tetracycline. This raises a number of questions: How is this high level sustained if resistant bacteria have reduced growth rates? Given that there are multiple susceptible and resistant bacterial strains in the pig intestines, how can we describe their coexistence? To what extent does the composition of these multiple strains in individual pigs influence the total bacterial population of the pig pen? What happens to a complex population when antimicrobials are used? To investigate these questions, we created a model where multiple strains of bacteria coexist in the intestines of pigs sharing a pen, and explored the parameter limits of a stable system; both with and without an antimicrobial treatment. The approach taken is a deterministic bacterial population model with stochastic elements of bacterial distributions and transmission. The rates that govern the model are process-oriented to represent growth, excretion, and uptake from environment, independent of herd and meta-population structures. Furthermore, an entry barrier and elimination process for the individual strains in each pig were implemented. We demonstrate how competitive growth between multiple bacterial strains in individual pigs, and the transmission between pigs in a pen allow for strains of antimicrobial resistant bacteria to persist in a pig population to different extents, and how quickly they can become dominant if antimicrobial treatment is initiated. The level of spread depends in a non-linear way of the parameters that govern excretion and uptake. Furthermore, the sampling of initial distributions of strains and stochastic transmission events give rise to large variation in how homogenous and how resistant the bacterial population becomes. Most important: resistant bacteria are demonstrated to survive with a disadvantage in growth rate of well over 10%.     

Fitness traits of insecticide resistant and susceptible strains of tea mosquito bug Helopeltis theivora Waterhouse (Heteroptera: Miridae)

We compared the biological traits of insecticide resistant and susceptible field populations of tea mosquito bug Helopeltis theivora Waterhouse. The insecticide resistant population of the conventional Tea Estate “Chuapara” of the Dooars, Jalpaiguri, differed significantly from the susceptible strain of the organic Tea Estate “Makibari” of Darjeeling. Both these tea plantation areas are located in the northern part of West Bengal, India. Adverse changes in biological and developmental traits were observed mainly in: (i) reduction in oviposition period; (ii) fecundity; and (iii) prolongation of nymphal and total developmental period. However, all other parameters such as pre- and post oviposition periods, egg incubation period, hatchability and adult longevity were not significantly different. These results clearly demonstrated that only certain fitness components in the resistant strains appear to be adaptively changed and lowered.     

Mixture models with multiple levels, with application to the analysis of multifactor gene expression data

Model-based clustering is a popular tool for summarizing high-dimensional data. With the number of high-throughput large-scale gene expression studies still on the rise, the need for effective data- summarizing tools has never been greater. By grouping genes according to a common experimental expression profile, we may gain new insight into the biological pathways that steer biological processes of interest. Clustering of gene profiles can also assist in assigning functions to genes that have not yet been functionally annotated. In this paper, we propose 2 model selection procedures for model-based clustering. Model selection in model-based clustering has to date focused on the identification of data dimensions that are relevant for clustering. However, in more complex data structures, with multiple experimental factors, such an approach does not provide easily interpreted clustering outcomes. We propose a mixture model with multiple levels, , that provides sparse representations both "within" and "between" cluster profiles. We explore various flexible "within-cluster" parameterizations and discuss how efficient parameterizations can greatly enhance the objective interpretability of the generated clusters. Moreover, we allow for a sparse "between-cluster" representation with a different number of clusters at different levels of an experimental factor of interest. This enhances interpretability of clusters generated in multiple-factor contexts. Interpretable cluster profiles can assist in detecting biologically relevant groups of genes that may be missed with less efficient parameterizations. We use our multilevel mixture model to mine a proliferating cell line expression data set for annotational context and regulatory motifs. We also investigate the performance of the multilevel clustering approach on several simulated data sets.     

Geographic diversity and temporal trends of antimicrobial resistance in Streptococcus pneumoniae in the United States

Resistance of Streptococcus pneumoniae to antibiotics is increasing throughout the United States, with substantial variation among geographic regions. We show that patterns of geographic variation are best explained by the intensity of selection for resistance, which is reflected by differences between the proportions of resistance within individual serotypes, rather than by differences between the frequencies of particular serotypes. Using a mathematical transmission model, we analyzed temporal trends in the proportions of singly and dually resistant organisms and found that pneumococcal strains resistant to both penicillin and erythromycin are increasing faster than strains singly resistant to either. Using the model, we predict that by 1 July 2004, in the absence of a vaccine, 41% of pneumococci at the Centers for Disease Control and Prevention (CDC)'s Active Bacterial Core surveillance (ABCs) sites, taken together, will be dually resistant, with 5% resistant to penicillin only and 5% to erythromycin only.     

Joint analysis of multiple metagenomic samples

The availability of metagenomic sequencing data, generated by sequencing DNA pooled from multiple microbes living jointly, has increased sharply in the last few years with developments in sequencing technology. Characterizing the contents of metagenomic samples is a challenging task, which has been extensively attempted by both supervised and unsupervised techniques, each with its own limitations. Common to practically all the methods is the processing of single samples only; when multiple samples are sequenced, each is analyzed separately and the results are combined. In this paper we propose to perform a combined analysis of a set of samples in order to obtain a better characterization of each of the samples, and provide two applications of this principle. First, we use an unsupervised probabilistic mixture model to infer hidden components shared across metagenomic samples. We incorporate the model in a novel framework for studying association of microbial sequence elements with phenotypes, analogous to the genome-wide association studies performed on human genomes: We demonstrate that stratification may result in false discoveries of such associations, and that the components inferred by the model can be used to correct for this stratification. Second, we propose a novel read clustering (also termed "binning") algorithm which operates on multiple samples simultaneously, leveraging on the assumption that the different samples contain the same microbial species, possibly in different proportions. We show that integrating information across multiple samples yields more precise binning on each of the samples. Moreover, for both applications we demonstrate that given a fixed depth of coverage, the average per-sample performance generally increases with the number of sequenced samples as long as the per-sample coverage is high enough.     

Are there effective resistance management strategies for vectors of human disease?*

The rotation of insecticides used by the Onchocerciasis Control Programme in West Africa is reviewed and the motives for this rotation are shown to be not only management of temephos resistance in the Simulium vectors but also constraints on what compounds are usable at particular seasons. A computer model indicates that without these seasonal constraints there is unlikely to be an advantage in a pre-planned rotation of insecticides, as compared with the prompt switching of compounds as dictated by detection of build up of resistance and switching back to the original compound if the regression of resistance is found to give the opportunity to do so. The latter sequence of events can hardly be called a ‘strategy’ for resistance management, but is what any well-managed pest control programme would be expected to do. The use of an insecticide mixture is different in principle from the use of rotation and depends on the idea that if the mixture is used from the outset, when resistance to both components of the mixture is likely to be rare, the double resistance combination would be so rare as to be dwarfed in numbers by those insects which avoid exposure altogether. The prospects for successful use of a mixture depend on each component killing a very high percentage of the exposed insects which are genetically susceptible to it. Whether this condition is met could be tested, for example, in the case of exposure of mosquitoes to insecticide-treated bednets.     

Bayesian Estimation of Mixture Models with Prespecified Elements to Compare Drug Resistance in Treatment-Na?ve and Experienced Tuberculosis Cases

We propose a Bayesian approach for estimating branching tree mixture models to compare drug-resistance pathways (i.e. patterns of sequential acquisition of resistance to individual antibiotics) that are observed among Mycobacterium tuberculosis isolates collected from treatment-naïve and treatment-experienced patients. Resistant pathogens collected from treatment-naïve patients are strains for which fitness costs of resistance were not sufficient to prevent transmission, whereas those collected from treatment-experienced patients reflect both transmitted and acquired resistance, the latter of which may or may not be associated with lower transmissibility. The comparison of the resistance pathways constructed from these two groups of drug-resistant strains provides insight into which pathways preferentially lead to the development of multiple drug resistant strains that are transmissible. We apply the proposed statistical methods to data from worldwide surveillance of drug-resistant tuberculosis collected by the World Health Organization over 13 years.     

Allocation Variable-Based Probabilistic Algorithm to Deal with Label Switching Problem in Bayesian Mixture Models

The label switching problem occurs as a result of the nonidentifiability of posterior distribution over various permutations of component labels when using Bayesian approach to estimate parameters in mixture models. In the cases where the number of components is fixed and known, we propose a relabelling algorithm, an allocation variable-based (denoted by AVP) probabilistic relabelling approach, to deal with label switching problem. We establish a model for the posterior distribution of allocation variables with label switching phenomenon. The AVP algorithm stochastically relabel the posterior samples according to the posterior probabilities of the established model. Some existing deterministic and other probabilistic algorithms are compared with AVP algorithm in simulation studies, and the success of the proposed approach is demonstrated in simulation studies and a real dataset.     

BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host''s nutrient-based mechanism for bacterial partner selection.