Mobile Health Teams, Decolonization, and the Eradication Era in Cameroon, 1945-1970

This dissertation examines the intersecting changes of African decolonization and the post-World War II internationalization of public health by showing how Cameroonian and French health officials shaped global health programs on the ground in the 1950s and 60s. I approach this topic through the lens of two tightly interwoven developments in Cameroon: the history of colonial mobile health teams created by French military doctors and the advent of postwar global disease eradication campaigns. While colonial medicine and international health are typically treated as distinct historical subjects, I argue that global disease eradication programs in this period in Cameroon relied entirely on colonial mobile health teams and their reformulation after independence as a basis of infrastructure, personnel and knowledge. I specifically assert that Cameroonian and French health officials positioned mobile health teams as cornerstones of national health policy and regional health coordination in Central Africa and, in turn, as the basis for operations of attempted global disease eradication programs within Cameroon. As Cameroonian, French and international health officials negotiated the work of the mobile health teams through decolonization and the first decade of the independence, they were moreover charting new structures of authority and control over medicine and public health between the global and the local, and forging an international politics of public health rooted in the particular tensions of decolonization in the country. My project thus demonstrates how Africans charted new models for public health through decolonization, models that reflected both the deeply enduring impact of empire and a new post-colonial politics

Viral Networks: Connecting Digital Humanities and Medical History

This volume of original essays explores the power of network thinking and analysis for humanities research. Contributing authors are all scholars whose research focuses on a medical history topic—from the Black Death in fourteenth-century Provence to psychiatric hospitals in twentieth-century Alabama. The chapters take readers through a variety of situations in which scholars must determine if network analysis is right for their research; and, if the answer is yes, what the possibilities are for implementation. Along the way, readers will find practical tips on identifying an appropriate network to analyze, finding the best way to apply network analysis, and choosing the right tools for data visualization. All the chapters in this volume grew out of the 2018 Viral Networks workshop, hosted by the History of Medicine Division of the National Library of Medicine (NIH), funded by the Office of Digital Humanities of the National Endowment for the Humanities, and organized by Virginia Tech

Synthesis and biological investigation of (+)-JD1, an organometallic BET bromodomain inhibitor

(+)-JD1, a rationally designed ferrocene analogue of the BET bromodomain (BRD) probe molecule (+)-JQ1, has been synthesized and evaluated in biophysical, cell-based assays as well as in pharmacokinetic studies. It displays nanomolar activity against BRD isoforms, and its cocrystal structure was determined in complex with the first bromodomain of BRD4 and compared with that of (+)-JQ1, a known BRD4 small-molecule probe. At 1 μM concentration, (+)-JD1 was able to inhibit c-Myc, a key driver in cancer and an indirect target of BRD4

Analysis of genotype-by-environment interactions in a maize mapping population

Genotype-by-environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype-by-environment interactions in a maize multiparent advanced generation intercross population grown across 5 environments. We found that genotype-by-environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. To understand how genetic correlations between traits change across environments, we estimated the genetic variance-covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype-by-environment variance. We also performed a genome-wide association study to identify markers associated with genotype-by-environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype-by-environment interactions in this population

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci.

The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci

An adaptive teosinte mexicana introgression modulates phosphatidylcholine levels and is associated with maize flowering time.

Native Americans domesticated maize (Zea mays ssp. mays) from lowland teosinte parviglumis (Zea mays ssp. parviglumis) in the warm Mexican southwest and brought it to the highlands of Mexico and South America where it was exposed to lower temperatures that imposed strong selection on flowering time. Phospholipids are important metabolites in plant responses to low-temperature and phosphorus availability and have been suggested to influence flowering time. Here, we combined linkage mapping with genome scans to identify High PhosphatidylCholine 1 (HPC1), a gene that encodes a phospholipase A1 enzyme, as a major driver of phospholipid variation in highland maize. Common garden experiments demonstrated strong genotype-by-environment interactions associated with variation at HPC1, with the highland HPC1 allele leading to higher fitness in highlands, possibly by hastening flowering. The highland maize HPC1 variant resulted in impaired function of the encoded protein due to a polymorphism in a highly conserved sequence. A meta-analysis across HPC1 orthologs indicated a strong association between the identity of the amino acid at this position and optimal growth in prokaryotes. Mutagenesis of HPC1 via genome editing validated its role in regulating phospholipid metabolism. Finally, we showed that the highland HPC1 allele entered cultivated maize by introgression from the wild highland teosinte Zea mays ssp. mexicana and has been maintained in maize breeding lines from the Northern United States, Canada, and Europe. Thus, HPC1 introgressed from teosinte mexicana underlies a large metabolic QTL that modulates phosphatidylcholine levels and has an adaptive effect at least in part via induction of early flowering time

An adaptive teosinte mexicana introgression modulates phosphatidylcholine levels and is associated with maize flowering time

Native Americans domesticated maize (Zea mays ssp. mays) from lowland teosinte parviglumis (Zea mays ssp. parviglumis) in the warm Mexican southwest and brought it to the highlands of Mexico and South America where it was exposed to lower temperatures that imposed strong selection on flowering time. Phospholipids are important metabolites in plant responses to low-temperature and phosphorus availability and have been suggested to influence flowering time. Here, we combined linkage mapping with genome scans to identify High PhosphatidylCholine 1 (HPC1), a gene that encodes a phospholipase A1 enzyme, as a major driver of phospholipid variation in highland maize. Common garden experiments demonstrated strong genotype-by-environment interactions associated with variation at HPC1, with the highland HPC1 allele leading to higher fitness in highlands, possibly by hastening flowering. The highland maize HPC1 variant resulted in impaired function of the encoded protein due to a polymorphism in a highly conserved sequence. A meta-analysis across HPC1 orthologs indicated a strong association between the identity of the amino acid at this position and optimal growth in prokaryotes. Mutagenesis of HPC1 via genome editing validated its role in regulating phospholipid metabolism. Finally, we showed that the highland HPC1 allele entered cultivated maize by introgression from the wild highland teosinte Zea mays ssp. mexicana and has been maintained in maize breeding lines from the Northern United States, Canada, and Europe. Thus, HPC1 introgressed from teosinte mexicana underlies a large metabolic QTL that modulates phosphatidylcholine levels and has an adaptive effect at least in part via induction of early flowering time