falciparum dihydrofolate reductase and

Trends in drug resistance codons in Plasmodiu

Comprehensive transcriptome of the maize stalk borer, Busseola fusca, from multiple tissue types, developmental stages, and parasitoid wasp exposures

International audienc

Analysis of copy number variation profiles in brain tumors in the context of a methylation based classifier

Brain tumour’s range from benign neoplasm such as pilocytic astrocytoma to malignant ones e.g glioblastoma. Histopathological diagnosis of these entities is frequently challenged with inter-observer variability. Moreover, the used genome wide methylation patterns cannot grade tumour severity which is key in patient management. Although specific copy number variation (CNV) profiles such as 1p/19q co-deletions is known to characterise oligodendroglioma and joint gain of chr 7 and loss of chr 10 characterise glioblastoma, other CNV profiles have not been well integrated in brain tumour diagnosis. Therefore, it seems promising to achieve improvements in methylation-based diagnostics and disease prognosis by establishing an approach to systematically include CNV information in classification of brain tumours. With the aim of addressing this issue, in the first phase of my study, I evaluated whether methylation data (450K and 850K epic) could inform about the presence of CNVs. I used 61 paired data sets processed from microarray based comparative genomic hybridization (aCGH) and Epic 450K/850K methylation arrays respectively. Copy number plots of the methylation data set were generated from the “conumee” R-package while aCGH data set plots were inferred from the “DNA copy” package. I observed >80% percent agreement between the two methods. To rule out chance agreement and check the extent agreement, I calculated Kappa statistics. I observed moderate (0.54) to substantial (0.61) Kappa statistic values. In conclusion I provided evidence that the methylation data is reliable in determining CNVs. In the second phase, I evaluated the CNV profiles and survival times using Kaplan Meier analysis between WHO classified astrocytoma grade II and III data (n=117) obtained from the cancer genome atlas (TCGA). Before clustering, I observed no significant difference in survival in WHO grade II and III. After hierarchical clustering (Pearson coefficient correlation ward linkage) using the log2 CNV values, I was able to identify 7 clusters which had different survival rates. The clusters had both unique and shared alteration between them. For example, cluster 4 (n=10) showed better survival with deletions at Chr3q, 4q, 5p/q, 11p, 12q, 13q and gain in Chr12p. These regions carry genes such as ANO2, CD4, LRRC23, VWF and GALNT8 genes. Cluster 3 had poor survival and increased deletions at chr 1q, 2q, 3q, 4q, 5p/q, 6q, 7q, 11p, 13q and chr gain at 9p (n=54). Some key genes altered in these loci, included C2orf88, CDKN2A/B, RB1, SORBS2, POLD1, MYBPC2 and TP63. These genes play critical roles in cell cycle regulation, growth and tumour suppressions. Cluster 7 had losses at chr 4p/q, 13p/q and 19q (n=8) which contained genes like LRBA, FBXW7, MARCHF1, SPOCK3, MTUS2 and RFC3. Moreover, CDH12 gene and Long noncoding RNA (LINC005) regulating CCND2 at 5p and 13q respectively were also deleted in >75% of samples. I further noticed that glioblastoma recurrent cases and primary tumor could be differentiated by presence of chr7p/q gain, 9p, 10p/q and 13p/q deletions using a total of n= 1500 cases and n= 1400 controls data set retrieved from TCGA. The 9p and 10p/q loci are already known to encode cell survival and apoptotic genes such as CDK2A/B, MDM2, EGFR and PTEN which are common in high grade glioma. These results therefore promise better tumour diagnosis and patients stratification approach which would help in both patient management and treatment outcomes predictions by use of CNV profiles. In the third phase of my study, I evaluated the methylation classes and pathways associated with genes in the altered regions. I observed a different frequency in the distribution of Isocitrate dehydrogenase (IDH) mutation and the 06- methylguanine DNA methyltransferase (MGMT) in the 7 clusters. In specific clusters 1 and 6 were A_IDH 100% and 70% respectively. A_IDH_HG dominated the other clusters as follows: cluster 5 (50%), cluster 4 (33%), cluster 3 (13%) and cluster 7 (12%). This indicates that methylome classes can be aligned with the CNV profiles. Using ingenuity pathway-based knowledge, I was able to identify canonical pathways associated with altered genes per group. I observed that fairly unique signaling pathways were associated with the disease. Notably, PTEN, ERK/MAPK, P53, IL-3, Glioblastoma multiforme, glioma invasiveness and axonal guidance signaling which are associated with glioma formation are featured in most clusters. Key altered genes included adenomatous polyposis coli which is a tumor suppressor, Glycogen synthase kinase 3 beta (GSK-3β) which affects cell proliferation, retinoblastoma (Rb,) which codes a tumor suppressor rb protein while Platelet-derived growth factor (PDGF) and Phosphoinositide 3-kinases (P13K) both regulate cell growth and other cellular functions. Proto-oncogen Rat sarcoma (Ras), WNT, Son of Sevenless (SOS), Auditory processing deficit (APD) and beta catenin (CTNNB1) were also lost. The WNT pathway activation aids in cellular differentiation which promotes brain tumour formation while Ras /PI3K/RTK pathway contributes to tumour growth deregulation. These findings show that multiple pathways dysregulated by CNVs can help in establishing novel brain tumour stratification, diagnostics and consequently identification of novel drug targets

Temporal trends in prevalence of Plasmodium falciparum molecular markers selected for by artemether–lumefantrine treatment in pre-ACT and post-ACT parasites in western Kenya

Artemether–lumefantrine (AL) became the first-line treatment for uncomplicated malaria in Kenya in 2006. Studies have shown AL selects for SNPs in pfcrt and pfmdr1 genes in recurring parasites compared to the baseline infections. The genotypes associated with AL selection are K76 in pfcrt and N86, 184F and D1246 in pfmdr1. To assess the temporal change of these genotypes in western Kenya, 47 parasite isolates collected before (pre-ACT; 1995–2003) and 745 after (post-ACT; 2008–2014) introduction of AL were analyzed. In addition, the associations of parasite haplotype against the IC50 of artemether and lumefantrine, and clearance rates were determined. Parasite genomic DNA collected between 1995 and 2014 was analyzed by sequencing or PCR-based single-base extension on Sequenom MassARRAY. IC50s were determined for a subset of the samples. One hundred eighteen samples from 2013 to 2014 were from an efficacy trial of which 68 had clearance half-lives. Data revealed there were significant differences between pre-ACT and post-ACT genotypes at the four codons (chi-square analysis; p < 0.0001). The prevalence of pfcrt K76 and N86 increased from 6.4% in 1995–1996 to 93.2% in 2014 and 0.0% in 2002–2003 to 92.4% in 2014 respectively. Analysis of parasites carrying pure alleles of K + NFD or T + YYY haplotypes revealed that 100.0% of the pre-ACT parasites carried T + YYY and 99.3% of post-ACT parasites carried K + NFD. There was significant correlation (p = 0.04) between lumefantrine IC50 and polymorphism at pfmdr1 codon 184. There was no difference in parasite clearance half-lives based on genetic haplotype profiles. This study shows there is a significant change in parasite genotype, with key molecular determinants of AL selection almost reaching saturation. The implications of these findings are not clear since AL remains highly efficacious. However, there is need to closely monitor parasite genotypic, phenotypic and clinical dynamics in response to continued use of AL in western Kenya

Selective sweeps and genetic lineages of Plasmodium falciparum multi-drug resistance (pfmdr1) gene in Kenya

Abstract Background There are concerns that resistance to artemisinin-based combination therapy might emerge in Kenya and sub-Saharan Africa (SSA) in the same pattern as was with chloroquine and sulfadoxine–pyrimethamine. Single nucleotide polymorphisms (SNPs) in critical alleles of pfmdr1 gene have been associated with resistance to artemisinin and its partner drugs. Microsatellite analysis of loci flanking genes associated with anti-malarial drug resistance has been used in defining the geographic origins, dissemination of resistant parasites and identifying regions in the genome that have been under selection. Methods This study set out to investigate evidence of selective sweep and genetic lineages in pfmdr1 genotypes associated with the use of artemether–lumefantrine (AL), as the first-line treatment in Kenya. Parasites (n = 252) from different regions in Kenya were assayed for SNPs at codons 86, 184 and 1246 and typed for 7 neutral microsatellites and 13 microsatellites loci flanking (± 99 kb) pfmdr1 in Plasmodium falciparum infections. Results The data showed differential site and region specific prevalence of SNPs associated with drug resistance in the pfmdr1 gene. The prevalence of pfmdr1 N86, 184F, and D1246 in western Kenya (Kisumu, Kericho and Kisii) compared to the coast of Kenya (Malindi) was 92.9% vs. 66.7%, 53.5% vs. to 24.2% and 96% vs. to 87.9%, respectively. The NFD haplotype which is consistent with AL selection was at 51% in western Kenya compared to 25% in coastal Kenya. Conclusion Selection pressures were observed to be different in different regions of Kenya, especially the western region compared to the coastal region. The data showed independent genetic lineages for all the pfmdr1 alleles. The evidence of soft sweeps in pfmdr1 observed varied in direction from one region to another. This is challenging for malaria control programs in SSA which clearly indicate effective malaria control policies should be based on the region and not at a country wide level

Principal Coordinate Analysis.

<p>Microsatellite data for both pre- and post-ACTs were used to find out patterns and relationships within a multivariate dataset. This graph was plotted using genetic distance matrix; blue dots for pre- and red dots for post-ACTs populations. This data shows the separation of the parasite genetic profiles in the two ACTs eras.</p

Genetically Determined Response to Artemisinin Treatment in Western Kenyan <i>Plasmodium falciparum</i> Parasites

<div><p>Genetically determined artemisinin resistance in <i>Plasmodium falciparum</i> has been described in Southeast Asia. The relevance of recently described Kelch 13-propeller mutations for artemisinin resistance in Sub-Saharan Africa parasites is still unknown. Southeast Asia parasites have low genetic diversity compared to Sub-Saharan Africa, where parasites are highly genetically diverse. This study attempted to elucidate whether genetics provides a basis for discovering molecular markers in response to artemisinin drug treatment in <i>P</i>. <i>falciparum</i> in Kenya. The genetic diversity of parasites collected pre- and post- introduction of artemisinin combination therapy (ACT) in western Kenya was determined. A panel of 12 microsatellites and 91 single nucleotide polymorphisms (SNPs) distributed across the <i>P</i>. <i>falciparum</i> genome were genotyped. Parasite clearance rates were obtained for the post-ACT parasites. The 12 microsatellites were highly polymorphic with post-ACT parasites being significantly more diverse compared to pre-ACT (p < 0.0001). The median clearance half-life was 2.55 hours for the post-ACT parasites. Based on SNP analysis, 15 of 90 post-ACT parasites were single-clone infections. Analysis revealed 3 SNPs that might have some causal association with parasite clearance rates. Further, genetic analysis using Bayesian tree revealed parasites with similar clearance phenotypes were more closely genetically related. With further studies, SNPs described here and genetically determined response to artemisinin treatment might be useful in tracking artemisinin resistance in Kenya.</p></div

Bayesian midpoint tree showing single cloned <i>P</i>. <i>falciparum</i> haplotypes cluster.

<p>The cluster is in relation to clearance rates (slope half-life in hours). This was constructed using 78 concatenated SNPs whose genetic variants at each point were used to construct parasite relatedness using MrBayes software. A 10,000,000 generations was used to run and construct the tree which gave standard deviation below 0.01 to generate higher posterior probability values.</p

SNP-based genotypes and genetic variation seen in 15 single clones in Western Kenya in 2013–2014.

<p>Median-joining network diagram above shows genetic relationship of the western Kenya samples using 78 SNP haplotypes. Each circle in the network represents a unique haplotype profile with the size of the circle being proportional to the number of clones showing that particular haplotypes. The circle shown in red stands for samples with clearance rate < 2.55 slope half-life (hours) while those colored in blue represent those with clearance rate >2.55 hours. The black dots are hypothetical median vector generated by the software to connect existing haplotypes within the network with maximum parsimony.</p

F_ST values across the 12 MS loci for pre- and post-ACTs parasites.

<p>F_ST values across the 12 MS loci for pre- and post-ACTs parasites.</p