Prevalence of mutations in Plasmodium falciparum genes associated with resistance to different antimalarial drugs in Nyando, Kisumu County in Kenya.

Resistance to the mainstay antimalarial drugs is a major concern in the control of malaria. Delayed Plasmodium falciparum parasite clearance has been associated with Single Nucleotide Polymorphisms (SNPs) in the kelch propeller region (K13). However, SNPs in the Pf-adaptor protein complex 2 mu subunit (Pfap2-mu), Pfcrt and Pfmdr1 are possible markers associated with multi-drug resistance. Here, we explored the prevalence of SNPs in the K13, Pfap2-mu, Pfcrt, and Pfmdr1 in 94 dried blood spot field isolates collected from children aged below 12 years infected with P. falciparum during a cross-sectional study. The samples were collected in 2015 during the peak malaria transmission season in the Nyando region of Western Kenya before treatment with Artemether-Lumefantrine, the first-line artemisinin-based combination therapy (ACT) in Kenya. However, 47 of the 94 samples had recurrent parasitemia and were interrogated for the presence of the SNPs in K13 and Pfap2-mu. We used PCR amplification and sequencing to evaluate specific regions of K13 (codons 432-702), Pfap2-mu (codons 1-350), Pfmdr1 (codons 86, 1034-1246), and Pfcrt (codons 72-76) gene(s). The majority of parasites harbored the wild type K13 sequence. However, we found a unique non-synonymous W611S change. In silico studies on the impact of the W611S predicted structural changes in the overall topology of the K13 protein. Of the 47 samples analyzed for SNPs in the Pfap2-mu gene, 14 (29%) had S160 N/T mutation. The CVIET haplotype associated with CQ resistance in the Pfcrt yielded a 7.44% (7/94), while CVMNK haplotype was at 92.56%. Mutations in the Pfmdr1 region were detected only in three samples (3/94; 3.19%) at codon D1246Y. Our data suggest that parasites in the western part of Kenya harbor the wildtype strains. However, the detection of the unique SNP in K13 and Pfap2-mu linked with ACT delayed parasite clearance may suggest slow filtering of ACT-resistant parasites.

Zoonotic surveillance for rickettsiae in domestic animals in Kenya.

Abstract Rickettsiae are obligate intracellular bacteria that cause zoonotic and human diseases. Arthropod vectors, such as fleas, mites, ticks, and lice, transmit rickettsiae to vertebrates during blood meals. In humans, the disease can be life threatening. This study was conducted amidst rising reports of rickettsioses among travelers to Kenya. Ticks and whole blood were collected from domestic animals presented for slaughter at major slaughterhouses in Nairobi and Mombasa that receive animals from nearly all counties in the country. Blood samples and ticks were collected from 1019 cattle, 379 goats, and 299 sheep and were screened for rickettsiae by a quantitative PCR (qPCR) assay (Rick17b) using primers and probe that target the genus-specific 17-kD gene (htrA). The ticks were identified using standard taxonomic keys. All Rick17b-positive tick DNA samples were amplified and sequenced with primers sets that target rickettsial outer membrane protein genes (ompA and ompB) and the citrate-synthase encoding gene (gltA). Using the Rick17b qPCR, rickettsial infections in domestic animals were found in 25/32 counties sampled (78.1% prevalence). Infection rates were comparable in cattle (16.3%) and sheep (15.1%) but were lower in goats (7.1%). Of the 596 ticks collected, 139 had rickettsiae (23.3%), and the detection rates were highest in Amblyomma (62.3%; n=104), then Rhipicephalus (45.5%; n=120), Hyalomma (35.9%; n=28), and Boophilus (34.9%; n=30). Following sequencing, 104 out of the 139 Rick17b-positive tick DNA had good reverse and forward sequences for the 3 target genes. On querying GenBank with the generated consensus sequences, homologies of 92-100% for the following spotted fever group (SFG) rickettsiae were identified: Rickettsia africae (93.%, n=97), Rickettsia aeschlimannii (1.9%, n=2), Rickettsia mongolotimonae (0.96%, n=1), Rickettsia conorii subsp. israelensis (0.96%, n=1), Candidatus Rickettsia kulagini (0.96% n=1), and Rickettsia spp. (1.9% n=2). In conclusion, molecular methods were used in this study to detect and identify rickettsial infections in domestic animals and ticks throughout Kenya.

High resolution mapping of trypanosomosis resistance loci Tir2 and Tir3 using F12 advanced intercross lines with major locus Tir1 fixed for the susceptible allele.

BACKGROUND: Trypanosomosis is the most economically important disease constraint to livestock productivity in Africa. A number of trypanotolerant cattle breeds are found in West Africa, and identification of the genes conferring trypanotolerance could lead to effective means of genetic selection for trypanotolerance. In this context, high resolution mapping in mouse models are a promising approach to identifying the genes associated with trypanotolerance. In previous studies, using F2 C57BL/6J x A/J and C57BL/6J x BALB/cJ mouse resource populations, trypanotolerance QTL were mapped within a large genomic intervals of 20-40 cM to chromosomes MMU17, 5 and 1, and denoted Tir1, Tir2 and Tir3 respectively. Subsequently, using F6 C57BL/6J x A/J and C57BL/6J x BALB/cJ F6 advanced intercross lines (AIL), Tir1 was fine mapped to a confidence interval (CI) of less than 1 cM, while Tir2 and Tir3, were mapped within 5-12 cM. Tir1 represents the major trypanotolerance QTL. RESULTS: In order to improve map resolutions of Tir2 and Tir3, an F12 C57BL/6J x A/J AIL population fixed for the susceptible alleles at Tir1 QTL was generated. An F12 C57BL/6J x A/J AIL population, fixed for the resistant alleles at Tir1 QTL was also generated to provide an additional estimate of the gene effect of Tir1. The AIL populations homozygous for the resistant and susceptible Tir1 alleles and the parental controls were challenged with T. congolense and followed for survival times over 180 days. Mice from the two survival extremes of the F12 AIL population fixed for the susceptible alleles at Tir1 were genotyped with a dense panel of microsatellite markers spanning the Tir2 and Tir3 genomic regions and QTL mapping was performed. Tir2 was fine mapped to less than 1 cM CI while Tir3 was mapped to three intervals named Tir3a, Tir3b and Tir3c with 95% confidence intervals (CI) of 6, 7.2 and 2.2 cM, respectively. CONCLUSIONS: The mapped QTL regions encompass genes that are vital to innate immune response and can be potential candidate genes for the underlying QTL.