Identification and functional validation of the novel antimalarial resistance locus PF10_0355 in Plasmodium falciparum.

The Plasmodium falciparum parasite's ability to adapt to environmental pressures, such as the human immune system and antimalarial drugs, makes malaria an enduring burden to public health. Understanding the genetic basis of these adaptations is critical to intervening successfully against malaria. To that end, we created a high-density genotyping array that assays over 17,000 single nucleotide polymorphisms (∼ 1 SNP/kb), and applied it to 57 culture-adapted parasites from three continents. We characterized genome-wide genetic diversity within and between populations and identified numerous loci with signals of natural selection, suggesting their role in recent adaptation. In addition, we performed a genome-wide association study (GWAS), searching for loci correlated with resistance to thirteen antimalarials; we detected both known and novel resistance loci, including a new halofantrine resistance locus, PF10_0355. Through functional testing we demonstrated that PF10_0355 overexpression decreases sensitivity to halofantrine, mefloquine, and lumefantrine, but not to structurally unrelated antimalarials, and that increased gene copy number mediates resistance. Our GWAS and follow-on functional validation demonstrate the potential of genome-wide studies to elucidate functionally important loci in the malaria parasite genome.

Chemical decay of an antibiotic inverts selection for resistance.

Antibiotics are often unstable and can decay into various compounds with potential biological activities. We found that as tetracycline degrades, the competitive advantage conferred to bacteria by resistance not only diminishes but actually reverses to become a prolonged disadvantage due to the activities of more stable degradation products. Tetracycline decay can lead to net selection against resistance, which may help explain the puzzling coexistence of sensitive and resistant strains in natural environments.

Excitability constraints on voltage-gated sodium channels.

We study how functional constraints bound and shape evolution through an analysis of mammalian voltage-gated sodium channels. The primary function of sodium channels is to allow the propagation of action potentials. Since Hodgkin and Huxley, mathematical models have suggested that sodium channel properties need to be tightly constrained for an action potential to propagate. There are nine mammalian genes encoding voltage-gated sodium channels, many of which are more than approximately 90% identical by sequence. This sequence similarity presumably corresponds to similarity of function, consistent with the idea that these properties must be tightly constrained. However, the multiplicity of genes encoding sodium channels raises the question: why are there so many? We demonstrate that the simplest theoretical constraints bounding sodium channel diversity--the requirements of membrane excitability and the uniqueness of the resting potential--act directly on constraining sodium channel properties. We compare the predicted constraints with functional data on mammalian sodium channel properties collected from the literature, including 172 different sets of measurements from 40 publications, wild-type and mutant, under a variety of conditions. The data from all channel types, including mutants, obeys the excitability constraint; on the other hand, channels expressed in muscle tend to obey the constraint of a unique resting potential, while channels expressed in neuronal tissue do not. The excitability properties alone distinguish the nine sodium channels into four different groups that are consistent with phylogenetic analysis. Our calculations suggest interpretations for the functional differences between these groups.

A general SNP-based molecular barcode for Plasmodium falciparum identification and tracking.

BACKGROUND: Single nucleotide polymorphism (SNP) genotyping provides the means to develop a practical, rapid, inexpensive assay that will uniquely identify any Plasmodium falciparum parasite using a small amount of DNA. Such an assay could be used to distinguish recrudescence from re-infection in drug trials, to monitor the frequency and distribution of specific parasites in a patient population undergoing drug treatment or vaccine challenge, or for tracking samples and determining purity of isolates in the laboratory during culture adaptation and sub-cloning, as well as routine passage. METHODS: A panel of twenty-four SNP markers has been identified that exhibit a high minor allele frequency (average MAF > 35%), for which robust TaqMan genotyping assays were constructed. All SNPs were identified through whole genome sequencing and MAF was estimated through Affymetrix array-based genotyping of a worldwide collection of parasites. These assays create a "molecular barcode" to uniquely identify a parasite genome. RESULTS: Using 24 such markers no two parasites known to be of independent origin have yet been found to have the same allele signature. The TaqMan genotyping assays can be performed on a variety of samples including cultured parasites, frozen whole blood, or whole blood spotted onto filter paper with a success rate > 99%. Less than 5 ng of parasite DNA is needed to complete a panel of 24 markers. The ability of this SNP panel to detect and identify parasites was compared to the standard molecular methods, MSP-1 and MSP-2 typing. CONCLUSION: This work provides a facile field-deployable genotyping tool that can be used without special skills with standard lab equipment, and at reasonable cost that will unambiguously identify and track P. falciparum parasites both from patient samples and in the laboratory.

A composite of multiple signals distinguishes causal variants in regions of positive selection.

The human genome contains hundreds of regions whose patterns of genetic variation indicate recent positive natural selection, yet for most the underlying gene and the advantageous mutation remain unknown. We developed a method, composite of multiple signals (CMS), that combines tests for multiple signals of selection and increases resolution by up to 100-fold. By applying CMS to candidate regions from the International Haplotype Map, we localized population-specific selective signals to 55 kilobases (median), identifying known and novel causal variants. CMS can not just identify individual loci but implicates precise variants selected by evolution.

Regularized, fast, and robust analytical Q-ball imaging.

We propose a regularized, fast, and robust analytical solution for the Q-ball imaging (QBI) reconstruction of the orientation distribution function (ODF) together with its detailed validation and a discussion on its benefits over the state-of-the-art. Our analytical solution is achieved by modeling the raw high angular resolution diffusion imaging signal with a spherical harmonic basis that incorporates a regularization term based on the Laplace-Beltrami operator defined on the unit sphere. This leads to an elegant mathematical simplification of the Funk-Radon transform which approximates the ODF. We prove a new corollary of the Funk-Hecke theorem to obtain this simplification. Then, we show that the Laplace-Beltrami regularization is theoretically and practically better than Tikhonov regularization. At the cost of slightly reducing angular resolution, the Laplace-Beltrami regularization reduces ODF estimation errors and improves fiber detection while reducing angular error in the ODF maxima detected. Finally, a careful quantitative validation is performed against ground truth from synthetic data and against real data from a biological phantom and a human brain dataset. We show that our technique is also able to recover known fiber crossings in the human brain and provides the practical advantage of being up to 15 times faster than original numerical QBI method.

Apparent diffusion coefficients from high angular resolution diffusion imaging: estimation and applications.

High angular resolution diffusion imaging has recently been of great interest in characterizing non-Gaussian diffusion processes. One important goal is to obtain more accurate fits of the apparent diffusion processes in these non-Gaussian regions, thus overcoming the limitations of classical diffusion tensor imaging. This paper presents an extensive study of high-order models for apparent diffusion coefficient estimation and illustrates some of their applications. Using a meaningful modified spherical harmonics basis to capture the physical constraints of the problem, a new regularization algorithm is proposed. The new smoothing term is based on the Laplace-Beltrami operator and its closed form implementation is used in the fitting procedure. Next, the linear transformation between the coefficients of a spherical harmonic series of order l and independent elements of a rank-l high-order diffusion tensor is explicitly derived. This relation allows comparison of the state-of-the-art anisotropy measures computed from spherical harmonics and tensor coefficients. Published results are reproduced accurately and it is also possible to recover voxels with isotropic, single fiber anisotropic, and multiple fiber anisotropic diffusion. Validation is performed on apparent diffusion coefficients from synthetic data, from a biological phantom, and from a human brain dataset.