Hongying Jiang

Next-generation sequencing is used here to analyse Plasmodium falciparum genome variation directly from clinical blood samples, as well as cultured isolates, from Africa, Asia and Oceania. Resistance to the major antimalarial drug artemisinin is emerging in the Plasmodium falciparum parasite across Southeast Asia, and there is concern that the increased deployment of antimalarials in pursuit of disease eradication might simply lead to increased drug resistance. To monitor these risks it is important to survey the parasite population for genetic changes. Next-generation sequencing is used here to analyse P. falciparum genome variation directly from nearly 300 clinical blood samples, and from cultured isolates from Africa, Asia and Oceania. The authors use these data to analyse the diversity of the parasite population across different geographical locations, as well as within-host diversity at the level of the whole genome, and they show how this may be used to estimate inbreeding rates, which are important for the evolution of drug resistance. Malaria elimination strategies require surveillance of the parasite population for genetic changes that demand a public health response, such as new forms of drug resistance1,2. Here we describe methods for the large-scale analysis of genetic variation in Plasmodium falciparum by deep sequencing of parasite DNA obtained from the blood of patients with malaria, either directly or after short-term culture. Analysis of 86,158 exonic single nucleotide polymorphisms that passed genotyping quality control in 227 samples from Africa, Asia and Oceania provides genome-wide estimates of allele frequency distribution, population structure and linkage disequilibrium. By comparing the genetic diversity of individual infections with that of the local parasite population, we derive a metric of within-host diversity that is related to the level of inbreeding in the population. An open-access web application has been established for the exploration of regional differences in allele frequency and of highly differentiated loci in the P. falciparum genome.

BACKGROUND: Due to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using microarray GeneChip, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively. RESULTS: In addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16. CONCLUSION: This study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy.

Malaria transmission requires the production of male and female gametocytes in the human host followed by fertilization and sporogonic development in the mosquito midgut. Although essential for the spread of malaria through the population, little is known about the initiation of gametocytogenesis in vitro or in vivo. Using a gametocyte-defective parasite line and genetic complementation, we show that Plasmodium falciparumgametocyte development 1 gene (Pfgdv1), encoding a peri-nuclear protein, is critical for early sexual differentiation. Transcriptional analysis of Pfgdv1 negative and positive parasite lines identified a set of gametocytogenesis early genes (Pfge) that were significantly down-regulated (>10 fold) in the absence of Pfgdv1 and expression was restored after Pfgdv1 complementation. Progressive accumulation of Pfge transcripts during successive rounds of asexual replication in synchronized cultures suggests that gametocytes are induced continuously during asexual growth. Comparison of Pfge gene transcriptional profiles in patient samples divided the genes into two groups differing in their expression in mature circulating gametocytes and providing candidates to evaluate gametocyte induction and maturation separately in vivo. The expression profile of one of the early gametocyte specific genes, Pfge1, correlated significantly with asexual parasitemia, which is consistent with the ongoing induction of gametocytogenesis during asexual growth observed in vitro and reinforces the need for sustained transmission-blocking strategies to eliminate malaria.