Genomics Research Experience
A genomic analysis of Pediculus humanus minichromosomes as a means to investigate a shared evolutionary history between louse and human hosts
Pediculus humanus are ectoparasites that live on human blood and spend their lifetime on their human host. There are two ecotypes (head louse and body louse), which are vectors of various infectious diseases and impact the health of individuals worldwide. Because head lice spend their lives on human hosts, their evolution can be linked to that of humans, as they have consequently evolved with their hosts (Perry, 2014). It is important to understand the evolution of head lice and the genetic relationships of lice to understand human evolution. Pediculus humanus are classified into six clades defined by differences in their mitochondrial DNA. Their mitochondrial DNA is organized into 20 minichromosomes, instead of one chromosome like most bilateral organisms. It is unclear whether genes on different minichromosomes exhibit the same relationship between louse populations (i.e., grouped into six clades). This project explores whether genes on different minichromosomes support the same shared evolutionary history. Computational procedures were employed to process DNA sequences for 448 samples collected world-wide. Phylogenetic trees were constructed to determine genetic relationships among individual lice. These phylogenetic trees were then compared across the different genes to identify patterns of shared evolutionary history.
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As a basis for the methods of this project, we have managed data by first collecting raw data, aligning said data, cleaning the aligned data, constructing phylogenetic trees, and forming comparisons based on the phylogenetic trees. Regarding the collection of raw data, we used resources from the NCBI database, which included PubMed journal articles, gene reference sequences, and information on specific genes. As for aligning this data, we worked with 10 minichromosome sequences for the following genes: NADH dehydrogenase subunit 5 (nad5), cytochrome c oxidase subunit II (COX1), tRNA-Lys/NADH dehydrogenase subunit 4 (nad4), tRNA-Pro/NADH dehydrogenase subunit 2 (nad2)/tRNA-Ile, tRNA-Gln/NADH dehydrogenase subunit 1 (nad1), cytochrome c oxidase subunit III (COX3), tRNA-Tyr/cytochrome c oxidase subunit II (COX2), ATP synthase F0 subunit 6 (atp6), tRNA-Phe, NADH dehydrogenase subunit 6 (nad6), tRNA-Arg/NADH dehydrogenase subunit 3 (nad3). For each minichromosome sequence, we generated variant-calling for loops, or programs in which we took our raw minichromosome data and generated genotypic likelihoods at each genomic position with coverage, while also organizing file names, file types, sequence identifier information, and output locations. We coded said processes in Bash, which is the UNIX command-line interface. Once we had this data processed, we cleaned it by using MEGA software to examine sequence alignments and make any necessary corrections in said alignments. Upon cleaning the data, we were able to generate phylogenetic trees in MEGA to represent our data visually. As a result of having the visual representation of the data, we were provided with the ability to generate comparisons and assumptions based on human/louse evolutionary history and convergence/divergence patterns. We are currently working on this part of the project.
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Regarding the implications of this research, understanding and processing Pediculus humanus mitochondrial DNA is vital in tracing louse ancestry. Through tracing louse ancestry, louse distribution based on their respective clades can be ascertained, which enables us to understand how lice have moved and evolved. Since lice live off of human hosts, their evolutionary patterns can be compared to that of humans, thus also providing insight as to how humans have migrated over time. Human (and lice) migratory patterns have the potential to provide information regarding the spread of disease from the two louse ecotypes, further aiding in providing information that pertains to human patterns of disease and their geographic distributions. An understanding of human diseases over time is relevant in understanding how diseases may evolve in the future, and understanding prior human evolution also has the potential to aid in forming predictive models in the future.