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- All Subjects: Evolution
My research is focused on the people of Turkana, a human pastoral population inhabiting Northwest Kenya. The people of Turkana are Nilotic people that are native to the Turkana district. There are currently no conclusive studies done on evidence for genetic lactase persistence in Turkana. Therefore, my research will be on the evolution of lactase persistence in the people of Turkana. The goal of this project is to investigate the evolutionary history of two genes with known involvement in lactase persistence, LCT and MCM6, in the Turkana. Variants in these genes have previously been identified to result in the ability to digest lactose post-weaning age. Furthermore, an additional study found that a closely related population to the Turkana, the Massai, showed stronger signals of recent selection for lactase persistence than Europeans in these genes. My goal is to characterize known variants associated with lactase persistence by calculating their allele frequencies in the Turkana and conduct selection scans to determine if LCT/MCM6 show signatures of positive selection. In doing this, we conducted a pilot study consisting of 10 female Turkana individuals and 10 females from four different populations from the 1000 genomes project namely: the Yoruba in Ibadan, Nigeria (YRI); Luhya in Webuye, Kenya; Utah Residents with Northern and Western European Ancestry (CEU); and the Southern Han Chinese. The allele frequency calculation suggested that the CEU (Utah Residents with Northern and Western European Ancestry) population had a higher lactase persistence associated allele frequency than all the other populations analyzed here, including the Turkana population. Our Tajima’s D calculations and analysis suggested that both the Turkana population and the four haplotype map populations shows signatures of positive selection in the same region. The iHS selection scans we conducted to detect signatures of positive selection on all five populations showed that the Southern Han Chinese (CHS), the LWK (Luhya in Webuye, Kenya) and the YRI (Yoruba in Ibadan, Nigeria) populations had stronger signatures of positive selection than the Turkana population. The LWK (Luhya in Webuye, Kenya) and the YRI (Yoruba in Ibadan, Nigeria) populations showed the strongest signatures of positive selection in this region. This project serves as a first step in the investigation of lactase persistence in the Turkana population and its evolution over time.
One of the largest problems facing modern medicine is drug resistance. Many classes of drugs can be rendered ineffective if their target is able to acquire beneficial mutations. While this is an excellent showcase of the power of evolution, it necessitates the development of increasingly stronger drugs to combat resistant pathogens. Not only is this strategy costly and time consuming, it is also unsustainable. To contend with this problem, many multi-drug treatment strategies are being explored. Previous studies have shown that resistance to some drug combinations is not possible, for example, resistance to a common antifungal drug, fluconazole, seems impossible in the presence of radicicol. We believe that in order to understand the viability of multi-drug strategies in combating drug resistance, we must understand the full spectrum of resistance mutations that an organism can develop, not just the most common ones. It is possible that rare mutations exist that are resistant to both drugs. Knowing the frequency of such mutations is important for making predictions about how problematic they will be when multi-drug strategies are used to treat human disease. This experiment aims to expand on previous research on the evolution of drug resistance in S. cerevisiae by using molecular barcodes to track ~100,000 evolving lineages simultaneously. The barcoded cells were evolved with serial transfers for seven weeks (200 generations) in three concentrations of the antifungal Fluconazole, three concentrations of the Hsp90 inhibitor Radicicol, and in four combinations of Fluconazole and Radicicol. Sequencing data was used to track barcode frequencies over the course of the evolution, allowing us to observe resistant lineages as they rise and quantify differences in resistance evolution across the different conditions. We were able to successfully observe over 100,000 replicates simultaneously, revealing many adaptive lineages in all conditions. Our results also show clear differences across drug concentrations and combinations, with the highest drug concentrations exhibiting distinct behaviors.
Pathogenic drug resistance is a major global health concern. Thus, there is great interest in modeling the behavior of resistant mutations–how quickly they will rise in frequency within a population, and whether they come with fitness tradeoffs that can form the basis of treatment strategies. These models often depend on precise measurements of the relative fitness advantage (s) for each mutation and the strength of the fitness tradeoff that each mutation suffers in other contexts. Precisely quantifying s helps us create better, more accurate models of how mutants act in different treatment strategies. For example, P. falciparum acquires antimalarial drug resistance through a series of mutations to a single gene. Prior work in yeast expressing this P. falciparum gene demonstrated that mutations come with tradeoffs. Computational work has demonstrated the possibility of a treatment strategy which enriches for a particular resistant mutation that then makes the population grow poorly once the drug is removed. This treatment strategy requires knowledge of s and how it changes when multiple mutants are competing across various drug concentrations. Here, we precisely quantified s in varying drug concentrations for five resistant mutants, each of which provide varying degrees of drug resistance to antimalarial drugs. DNA barcodes were used to label each strain, allowing the mutants to be pooled together for direct competition in different concentrations of drug. This will provide data that can make the models more accurate, potentially facilitating more effective drug treatments in the future.