Genetic and biochemical analyses of growth

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Markov chain Monte Carlo and expectation maximization approaches for estimation of haplotype frequencies for multiply infected human blood samples

Background Haplotypes are important in anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and, possibly, reducing the metabolic costs of previous mutation. Patients often have two or more haplotypes in their blood sample which may make it impossible to identify exactly which haplotypes they carry, and hence to measure the type and frequency of resistant haplotypes in the malaria population. Results This study presents two novel statistical methods expectation–maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms to investigate this issue. The performance of the algorithms is evaluated on simulated datasets consisting of patient blood characterized by their multiplicity of infection (MOI) and malaria genotype. The datasets are generated using different resistance allele frequencies (RAF) at each single nucleotide polymorphisms (SNPs) and different limit of detection (LoD) of the SNPs and the MOI. The EM and the MCMC algorithm are validated and appear more accurate, faster and slightly less affected by LoD of the SNPs and the MOI compared to previous related statistical approaches. Conclusions The EM and the MCMC algorithms perform well when analysing malaria genetic data obtained from infected human blood samples. The results are robust to genotyping errors caused by LoDs and function well even in the absence of MOI data on individual patients

Can mutation and selection explain virulence in human P. falciparum infections?

BACKGROUND: Parasites incur periodic mutations which must ultimately be eliminated to maintain their genetic integrity. METHODS: It is hypothesised that these mutations are eliminated not by the conventional mechanisms of competition between parasites in different hosts but primarily by competition between parasites within the same infection. RESULTS: This process is enhanced by the production of a large number of parasites within individual infections, and this may significantly contribute to parasitic virulence. CONCLUSIONS: Several features of the most virulent human malaria parasite Plasmodium falciparum can usefully be re-interpreted in this light and lend support to this interpretation. More generally, it constitutes a novel explanation for the evolution of virulence in a wider range of microparasites

Entomological indices of malaria transmission in Chikhwawa district, Southern Malawi

Abstract Background Although malaria is highly prevalent throughout Malawi, little is known of its transmission dynamics. This paper describes the seasonal activity of the different vectors, human biting indices, sporozoite rates and the entomological inoculation rate in a low-lying rural area in southern Malawi. Methods Vectors were sampled over 52 weeks from January 2002 to January 2003, by pyrethrum knockdown catch in two villages in Chikhwawa district, in the Lower Shire Valley. Results In total, 7,717 anophelines were collected of which 55.1% were Anopheles gambiae sensu lato and 44.9% were Anopheles funestus. Three members of the An. gambiae complex were identified by PCR: Anopheles arabiensis (75%) was abundant throughout the year, An. gambiae s.s. (25%) was most common during the wet season and Anopheles quadriannulatus occurred at a very low frequency (n=16). An. funestus was found in all samples but was most common during the dry season. Anopheles gambiae s.s. and An. funestus were highly anthropophilic with human blood indices of 99.2% and 96.3%, respectively. Anopheles arabiensis had fed predominantly on humans (85.0%) and less commonly on cattle (10.9%; 1.2% of blood meals were of mixed origin). Plasmodium falciparum (192/3,984) and Plasmodium malariae (1/3,984) sporozoites were detected by PCR in An. arabiensis (3.2%) and An. funestus (4.5%), and in a significantly higher proportion of An. gambiae s.s. (10.6%)(pP. falciparum sporozoite rate was 4.8%, resulting in estimated inoculation rates of 183 infective bites/ person per annum, or an average rate of ~15 infective bites/person/month. Conclusions The results demonstrate the importance of An. gambiae s.s., An. arabiensis and An. funestus in driving the high levels of malaria transmission in the south of Malawi. Sustained and high coverage or roll out of current approaches to malaria control (primarily insecticide-treated bed nets and indoor residual house spraying) in the area are likely to reduce the observed high malaria transmission rate and consequently the incidence of human infections, unless impeded by increasing resistance of vectors to insecticides.</p

A Computer Modelling Approach To Evaluate the Accuracy of Microsatellite Markers for Classification of Recurrent Infections during Routine Monitoring of Antimalarial Drug Efficacy

Anti-malarial drugs have long half-lives, so clinical trials to monitor their efficacy require long durations of follow-up to capture drug failure that may only become patent weeks after treatment. Reinfections often occur during follow-up so robust methods of distinguishing drug failures (recrudescence) from emerging new infections are needed to produce accurate failure rate estimates. "Molecular correction" aims to achieve this by comparing the genotypes between a patient's pre-treatment (initial) blood sample and any infection that occurs during follow-up, 'matching' genotypes indicating a drug failure. We use an in-silico approach to show that the widely used "match counting" method of molecular correction with microsatellite markers is likely to be highly unreliable and may lead to gross under- or over-estimates of true failure rates depending on the choice of matching criterion. A Bayesian algorithm for molecular correction has been previously developed and utilized for analysis of in vivo efficacy trials. We validated this algorithm using in silico data and showed it had high specificity and generated accurate failure rate estimates. This conclusion was robust for multiple drugs, different levels of drug failure rate, different levels of transmission intensity in the study sites, and microsatellite genetic diversity. The Bayesian algorithm was inherently unable to accurately identify low-density recrudescence that occurred in a small number of patients, but this did not appear to compromise its utility as a highly effective molecular correction method for analysing microsatellite genotypes. Strong consideration should be given to using Bayesian methodology for obtaining accurate failure rate estimates during routine monitoring trials of antimalarial efficacy that use microsatellite marker

Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies

BACKGROUND: Most malaria-endemic countries are implementing a change in anti-malarial drug policy to artemisinin-based combination therapy (ACT). The impact of different drug choices and implementation strategies is uncertain. Data from many epidemiological studies in different levels of malaria endemicity and in areas with the highest prevalence of drug resistance like borders of Thailand are certainly valuable. Formulating an appropriate dynamic data-driven model is a powerful predictive tool for exploring the impact of these strategies quantitatively. METHODS: A comprehensive model was constructed incorporating important epidemiological and biological factors of human, mosquito, parasite and treatment. The iterative process of developing the model, identifying data needed, and parameterization has been taken to strongly link the model to the empirical evidence. The model provides quantitative measures of outcomes, such as malaria prevalence/incidence and treatment failure, and illustrates the spread of resistance in low and high transmission settings. The model was used to evaluate different anti-malarial policy options focusing on ACT deployment. RESULTS: The model predicts robustly that in low transmission settings drug resistance spreads faster than in high transmission settings, and treatment failure is the main force driving the spread of drug resistance. In low transmission settings, ACT slows the spread of drug resistance to a partner drug, especially at high coverage rates. This effect decreases exponentially with increasing delay in deploying the ACT and decreasing rates of coverage. In the high transmission settings, however, drug resistance is driven by the proportion of the human population with a residual drug level, which gives resistant parasites some survival advantage. The spread of drug resistance could be slowed down by controlling presumptive drug use and avoiding the use of combination therapies containing drugs with mismatched half-lives, together with reducing malaria transmission through vector control measures. CONCLUSION: This paper has demonstrated the use of a comprehensive mathematical model to describe malaria transmission and the spread of drug resistance. The model is strongly linked to the empirical evidence obtained from extensive data available from various sources. This model can be a useful tool to inform the design of treatment policies, particularly at a time when ACT has been endorsed by WHO as first-line treatment for falciparum malaria worldwide

Climate prediction of El Niño malaria epidemics in north-west Tanzania

Malaria is a significant public health problem in Tanzania. Approximately 16 million malaria cases are reported every year and 100,000 to 125,000 deaths occur. Although most of Tanzania is endemic to malaria, epidemics occur in the highlands, notably in Kagera, a region that was subject to widespread malaria epidemics in 1997 and 1998. This study examined the relationship between climate and malaria incidence in Kagera with the aim of determining whether seasonal forecasts may assist in predicting malaria epidemics. A regression analysis was performed on retrospective malaria and climatic data during each of the two annual malaria seasons to determine the climatic factors influencing malaria incidence. The ability of the DEMETER seasonal forecasting system in predicting the climatic anomalies associated with malaria epidemics was then assessed for each malaria season. It was found that malaria incidence is positively correlated with rainfall during the first season (Oct-Mar) (R-squared = 0.73, p < 0.01). For the second season (Apr-Sep), high malaria incidence was associated with increased rainfall, but also with high maximum temperature during the first rainy season (multiple R-squared = 0.79, p < 0.01). The robustness of these statistical models was tested by excluding the two epidemic years from the regression analysis. DEMETER would have been unable to predict the heavy El Niño rains associated with the 1998 epidemic. Nevertheless, this epidemic could still have been predicted using the temperature forecasts alone. The 1997 epidemic could have been predicted from observed temperatures in the preceding season, but the consideration of the rainfall forecasts would have improved the temperature-only forecasts over the remaining years. These results demonstrate the potential of a seasonal forecasting system in the development of a malaria early warning system in Kagera region

Malaria and Irrigated Crops, Accra, Ghana

We investigated the prevalence of malaria and associated risk factors in children living in urban Ghana. Malaria prevalence was associated with low hemoglobin concentration, low socioeconomic status, and higher age. Our findings indicate that African urban poor are seriously affected by malaria and that irrigated agriculture may increase this risk

Plasmodium falciparum resistance to anti-malarial drugs in Papua New Guinea: evaluation of a community-based approach for the molecular monitoring of resistance

ABSTRACT: BACKGROUND: Molecular monitoring of parasite resistance has become an important complementary tool in establishing rational anti-malarial drug policies. Community surveys provide a representative sample of the parasite population and can be carried out more rapidly than accrual of samples from clinical cases, but it is not known whether the frequencies of genetic resistance markers in clinical cases differ from those in the overall population, or whether such community surveys can provide good predictions of treatment failure rates. METHODS: Between 2003 and 2005, in vivo drug efficacy of amodiaquine or chloroquine plus sulphadoxine-pyrimethamine was determined at three sites in Papua New Guinea. The genetic drug resistance profile (i.e., 33 single nucleotide polymorphisms in Plasmodium falciparum crt, mdr1, dhfr, dhps, and ATPase6) was concurrently assessed in 639 community samples collected in the catchment areas of the respective health facilities by using a DNA microarray-based method. Mutant allele and haplotype frequencies were determined and their relationship with treatment failure rates at each site in each year was investigated. RESULTS: PCR-corrected in vivo treatment failure rates were between 12% and 28% and varied by site and year with variable longitudinal trends. In the community samples, the frequencies of mutations in pfcrt and pfmdr1 were high and did not show significant changes over time. Mutant allele frequencies in pfdhfr were moderate and those in pfdhps were low. No mutations were detected in pfATPase6. There was much more variation between sites than temporal, within-site, variation in allele and haplotype frequencies. This variation did not correlate well with treatment failure rates. Allele and haplotype frequencies were very similar in clinical and community samples from the same site. CONCLUSIONS: The relationship between parasite genetics and in vivo treatment failure rate is not straightforward. The frequencies of genetic anti-malarial resistance markers appear to be very similar in community and clinical samples, but cannot be used to make precise predictions of clinical outcome. Thus, indicators based on molecular data have to be considered with caution and interpreted in the local context, especially with regard to prior drug usage and level of pre-existing immunity. Testing community samples for molecular drug resistance markers is a complementary tool that should help decision-making for the best treatment options and appropriate potential alternative