The Plasmodium berghei RC strain is highly diverged and harbors putatively novel drug resistance variants.

BACKGROUND: The current first line drugs for treating uncomplicated malaria are artemisinin (ART) combination therapies. However, Plasmodium falciparum parasites resistant to ART and partner drugs are spreading, which threatens malaria control efforts. Rodent malaria species are useful models for understanding antimalarial resistance, in particular genetic variants responsible for cross resistance to different compounds. METHODS: The Plasmodium berghei RC strain (PbRC) is described as resistant to different antimalarials, including chloroquine (CQ) and ART. In an attempt to identify the genetic basis for the antimalarial resistance trait in PbRC, its genome was sequenced and compared with five other previously sequenced P. berghei strains. RESULTS: We found that PbRC is eight-fold less sensitive to the ART derivative artesunate than the reference strain PbANKA. The genome of PbRC is markedly different from other strains, and 6,974 single nucleotide variants private to PbRC were identified. Among these PbRC private variants, non-synonymous changes were identified in genes known to modulate antimalarial sensitivity in rodent malaria species, including notably the ubiquitin carboxyl-terminal hydrolase 1 gene. However, no variants were found in some genes with strong evidence of association with ART resistance in P. falciparum such as K13 propeller protein. DISCUSSION: The variants identified in PbRC provide insight into P. berghei genome diversity and genetic factors that could modulate CQ and ART resistance in Plasmodium spp.

Polymorphisms in drug-resistant-related genes shared among drug-resistant and pan-susceptible strains of sequence type 10, Beijing family of Mycobacterium tuberculosis.

Mutations in genes involved in drug metabolism have been well-associated with drug resistance. Sequence analysis of known antimycobacterial drug-resistant genes is often used to predict resistance to antibiotics. However, some polymorphisms in such genes may serve a phylogenetic purpose rather than resistance to drugs. The Beijing family of Mycobacterium tuberculosis (MTB) is prevalent worldwide and has been associated with the emergence of multidrug resistance. Sequence type (ST) 10 of the Beijing family is the most predominant in countries like Peru, Taiwan and Thailand. A sequence analysis was performed of 81 previously reported drug-resistant associated genes in multidrug-resistant and pan-susceptible strains of the Beijing family sequence type 10 of MTB. This analysis revealed 10 synonymous and 12 nonsynonymous single nucleotide polymorphisms (SNPs) that are shared by all strains under study. One frameshift mutation was also observed to be common to all. These data might be useful in excluding some observed SNPs in drug-resistant-associated genes of MTB Beijing ST 10 when performing genotypic drug susceptibility assay.

Whole genome sequence analysis of multidrug-resistant Mycobacterium tuberculosis Beijing isolates from an outbreak in Thailand.

The Mycobacterium tuberculosis Beijing family is often associated with multidrug resistance and large outbreaks. Conventional genotyping study of a community outbreak of multidrug-resistant tuberculosis (MDR-TB) that occurred in Kanchanaburi Province, Thailand was carried out. The study revealed that the outbreak was clonal and the strain was identified as a member of Beijing family. Although, the outbreak isolates showed identical spoligotyping and mycobacterial interspersed repetitive units-variable number tandem repeats patterns, a discrepancy regarding ethambutol resistance was observed. In-depth characterization of the isolates through whole genome sequencing of the first and the last three isolates from our culture collection showed them to belong to principal genetic group 1, single nucleotide polymorphism (SNP) cluster group 2, sequence type 10. Compared with the M. tuberculosis H37Rv reference genome, 1242 SNPs were commonly found in all isolates. The genomes of these isolates were shown to be clonal and highly stable over a 5-year period and two or three unique SNPs were identified in each of the last three isolates. Genes known to be associated with drug resistance and their promoter regions, where applicable, were analyzed. The presence of low or no fitness cost mutations for drug resistance and an additional L731P SNP in the rpoB gene was observed in all isolates. These findings might account for the successful transmission of this MDR-TB strain.

Whole genome and exome sequencing of monozygotic twins with trisomy 21, discordant for a congenital heart defect and epilepsy.

Congenital heart defects (CHD) occur in 40% of patients with trisomy 21, while the other 60% have a structurally normal heart. This suggests that the increased dosage of genes on chromosome 21 is a risk factor for abnormal heart development. Interaction of genes on chromosome 21 or their gene products with certain alleles of genes on other chromosomes could contribute to CHD. Here, we identified a pair of monozygotic twins with trisomy 21 but discordant for a ventricular septal defect and epilepsy. Twin-zygosity was confirmed by microsatellite genotyping. We hypothesized that some genetic differences from post-twinning mutations caused the discordant phenotypes. Thus, next generation sequencing (NGS) technologies were applied to sequence both whole genome and exome of their leukocytes. The post-analyses of the sequencing data revealed 21 putative discordant exonic variants between the twins from either genome or exome data. However, of the 15 variants chosen for validation with conventional Sanger sequencing, these candidate variants showed no differences in both twins. The fact that no discordant DNA variants were found suggests that sequence differences of DNA from leukocytes of monozygotic twins might be extremely rare. It also emphasizes the limitation of the current NGS technology in identifying causative genes for discordant phenotypes in monozygotic twins.

Germline and somatic DICER1 mutations in a pituitary blastoma causing infantile-onset Cushing's disease.

CONTEXT: Pituitary blastoma causing Cushing's syndrome in infancy is very rare, and its molecular pathomechanism is not well understood. OBJECTIVE: Our objective was to identify genetic changes of a pituitary blastoma causing infantile-onset Cushing's syndrome in a Thai girl without a family history of cancers. METHODS: Genomic DNA from both leukocytes and tumor tissues was used for whole-exome sequencing (WES) and Sanger sequencing of DICER1. The cDNA reverse-transcribed from RNA extracted from both leukocytes and tumor tissues was used for Sanger sequencing, quantitative real-time PCR (qRT-PCR), and pyrosequencing of DICER1. RESULTS: WES of leukocytes identified a novel heterozygous c.3046delA (p.S1016VfsX1065) mutation in the DICER1 gene. WES of the tumor tissues detected the same frameshift germline mutation and another novel somatic missense c.5438A→T (p.E1813V) mutation. Both mutations were validated by Sanger sequencing. Quantitative real-time PCR revealed that the DICER1 mRNA levels of the tumor tissues were 54% compared with those of her leukocytes. Pyrosequencing showed that the deletion allele constituted 12% and 0% of the DICER1 cDNA of the proband's leukocytes and tumor tissues, respectively. CONCLUSION: Our study extends the phenotypic and mutational spectrum of DICER1 mutations to include infantile-onset Cushing's disease and 2 novel mutations. Loss of function of both DICER1 alleles appears to be crucial to initiate tumor development.

Biomarker selection and classification of "-omics" data using a two-step bayes classification framework.

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.

iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies.

BACKGROUND: Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD). RESULTS: In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles. CONCLUSION: iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci.

Mapping human genetic diversity in Asia.

Asia harbors substantial cultural and linguistic diversity, but the geographic structure of genetic variation across the continent remains enigmatic. Here we report a large-scale survey of autosomal variation from a broad geographic sample of Asian human populations. Our results show that genetic ancestry is strongly correlated with linguistic affiliations as well as geography. Most populations show relatedness within ethnic/linguistic groups, despite prevalent gene flow among populations. More than 90% of East Asian (EA) haplotypes could be found in either Southeast Asian (SEA) or Central-South Asian (CSA) populations and show clinal structure with haplotype diversity decreasing from south to north. Furthermore, 50% of EA haplotypes were found in SEA only and 5% were found in CSA only, indicating that SEA was a major geographic source of EA populations.

pHCR: a parallel haplotype configuration reduction algorithm for haplotype interaction analysis.

Finding gene interaction models is one of the most important issues in genotype-phenotype association studies. This paper presents a model-free nonparametric statistical interaction analysis known as Parallel Haplotype Configuration Reduction (pHCR). This technique extends the original Multifactor Dimensionality Reduction (MDR) algorithm by using haplotype contribution values (c-values) and a haplotype interaction scheme instead of analyzing interactions among single-nucleotide polymorphisms. The proposed algorithm uses the statistical power of haplotypes to obtain a gene-gene interaction model. pHCR computes a statistical value for each haplotype, which contributes to the phenotype, and then performs haplotype interaction analysis on the basis of the cumulative c-value of each individual haplotype. To address the high computational complexity of pHCR, this paper also presents a scalable parallel computing solution. Nine common two-locus disease models were used to evaluate the algorithm performance under different scenarios. The results from all cases showed that pHCR shows higher power to detect gene-gene interaction in comparison with the results obtained from running MDR on the same data set. We also compared pHCR with FAMHAP, which mainly considers haplotype in the association analysis. For every experiment on the simulated data set, pHCR correctly produced haplotype interactions with much fewer false positives. We also challenged pHCR with a real data set input of beta-thalassemia/Hemoglobin E (HbE) disease. The result suggested the interaction between two previously reported quantitative trait loci of the fetal hemoglobin level, which is a major modifying factor, and disease severity of beta-thalassemia/HbE disease.

VarDetect: a nucleotide sequence variation exploratory tool.

BACKGROUND: Single nucleotide polymorphisms (SNPs) are the most commonly studied units of genetic variation. The discovery of such variation may help to identify causative gene mutations in monogenic diseases and SNPs associated with predisposing genes in complex diseases. Accurate detection of SNPs requires software that can correctly interpret chromatogram signals to nucleotides. RESULTS: We present VarDetect, a stand-alone nucleotide variation exploratory tool that automatically detects nucleotide variation from fluorescence based chromatogram traces. Accurate SNP base-calling is achieved using pre-calculated peak content ratios, and is enhanced by rules which account for common sequence reading artifacts. The proposed software tool is benchmarked against four other well-known SNP discovery software tools (PolyPhred, novoSNP, Genalys and Mutation Surveyor) using fluorescence based chromatograms from 15 human genes. These chromatograms were obtained from sequencing 16 two-pooled DNA samples; a total of 32 individual DNA samples. In this comparison of automatic SNP detection tools, VarDetect achieved the highest detection efficiency. AVAILABILITY: VarDetect is compatible with most major operating systems such as Microsoft Windows, Linux, and Mac OSX. The current version of VarDetect is freely available at http://www.biotec.or.th/GI/tools/vardetect.