Genetic control of cross-resistances in P. falciparum

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DESCRIPTION (provided by the applicant): The emergence and spread of Plasmodium falciparum parasites resistant to a wide array of antimalarial drugs is a critical challenge facing malaria research. Drug resistance studies currently emphasize the role of single-gene determinants of resistance, and have led to identification of mutations as markers of drug resistances. However, this focus on individual resistance genes ignores the genomic context in which resistance genes function, that is, the multigene pathways and cellular processes that have been co-opted by drug selection. Modulatory and compensatory "background" mutations in these gene networks provide a physiological basis for correlated drug responses and cross resistances; consequently, identification of common gene mechanisms involved in responses to different drugs will provide markers for the capacity of a parasite isolate to develop resistance to new compounds. The P. falciparum genome sequence and associated tools provide an opportunity to connect gene sequences to complex phenotypes. By exploiting a high-resolution linkage map and precise drug response measurements in the progeny of a genetic cross, quantitative trait loci (QTL) mapping can pinpoint regions of the genome controlling these traits to target positional candidate gene searches, and to guide analysis of single nucleotide polymorphisms (SNP) and local gene transcription. The hypothesis of this proposal is that correlated drug responses are the result of shared genetic mechanisms that can be mapped to specific loci by comparative QTL mapping, leading to efficient use of genomic tools to pinpoint genes that underlie cross resistances. Two Specific Aims will be used to find these genes: 1) To measure drug responses in progeny of the HB3 xDd2 genetic cross and to map the loci controlling these phenotypes. Responses to antimalarial compounds will be scored individually and in combinations, and QTL mapping will identify the number, relative effects, and positions of loci controlling these traits. Genetic profiles will be statistically superimposed to identify loci common to different drug responses. 2) To integrate these loci with primary and comparative sequences and transcription profiles to identify candidate genes. Sequence tagged sites will link genetic loci to DNA contigs and facilitate the search for positional candidate genes from which structural variants can be detected in single nucleotide polymorphisms (SNP) and used for association mapping in parasite isolates from around the world. Locus-specific transcription will be evaluated using quantitative PCR and long oligonucleotide micro-arrays to identify heritable expression polymorphisms that coincide with drug responses.