Data analysis to help inform norovirus vaccine design

Norovirus is a common cause of gastrointestinal disease. There is currently no vaccine, but several are under development. It is not clear how exactly a new norovirus vaccine should look like and to whom it should be mainly given (e.g. children or adults). Together with colleagues at Emory University, we are working on a project that analyzes different types of norovirus data to develop a comprehensive mathematical modeling framework to guide norovirus vaccine design and implementation. For this project, you will help with those analyses. The results of this analysis will allow us to better understand properties of norovirus infection and transmission and therefore will help inform the design and implementation of a future norovirus vaccine. This project is quantitative. We will make use of the R language for all analyses. The project is offered by the Handel group.

Host Laboratory: Andreas Handel
Type of project: Quantitative/Computer-based

Studying the relation between vaccine dose and outcomes

For every vaccine, the amount of the antigens of the pathogen one wants to vaccinate against is an important part. This amount that is currently not systematically determined but is instead based on sparse clinical data. We recently developed a framework that combines data with mathematical models to better determine the vaccine dose that would lead to optimal outcomes. The goal of this project is to further advance this framework by combining data from influenza infected individuals with computer models to determine what the impact of vaccine dose is on outcomes such as side effects and immune protection. This information will be useful for improved design of future vaccines. This project is quantitative. We will make use of the R language for all analyses. The project is offered by the Handel group.

Host Laboratory: Andreas Handel
Type of project: Quantitative/Computer-based

Macro-ecology of predator-parasite interactions

Populations are under constant assault from a variety of natural enemies. Parasites increase mortality and decrease fecundity. Predators pick off animals and often influence prey behavior by their mere presence. Theoretical work has suggested that the removal of predators can have negative effects on prey populations by increasing the amount of parasitism. Empirical tests of this prediction have, however, revealed a more complex picture. Often behavioral changes can have effects on parasite transmission that may overwhelm the theoretical predictions.

In order to better understand the effect of predators on parasites we will test the effect of predator diversity and abundance on parasite prevalence in wild populations of large mammals. As an REU student you will collect information about the known predators of common ungulate species found in the Global Mammal Parasite Database and then collect range-maps for these predators to determine predation pressure on hosts at various points on their range. Depending on the interests of the student this work could include employing existing range-maps or using machine learning methods to construct new ones from recorded predator locations. During this process you will, through guided readings and discussions, develop testable hypotheses about the relationship between predators and parasites in these systems. After the necessary data have been collected you will conduct statistical analyses to test these hypotheses. In this project, you will learn to read and interpret scientific literature and to efficiently extract data following a protocol which we will develop. You will also get experience manipulating data and conducting statistical analyses in the R programming language with an opportunity to learn how to use machine-learning methods to model species distributions.

Host Lab: John Drake (https://daphnia.ecology.uga.edu/drakelab/), working with PhD Student Robert Richards
Type of Project: Quantitative/Computer-based

Display of stuffed mountain lions and their prey at the Denver Museum of Nature & Science in Denver, Colorado.

Extreme heat reduces fitness of monarchs and their parasites

Maya Sarkar, a student at the University of Minnesota, worked with Isabella Ragonese, Dr. Sonia Altizer and Dr. Richard Hall.

Abstract: It is important to understand the consequences of a warming climate, especially in organisms that are more sensitive to temperature changes and where the outcome of warming may not be intuitive. This project used the Monarch-OE system to study how temperature may affect host-parasite interactions. The monarch butterfly (Danaus plexippus) is an iconic North American migratory species and the specialist protozoan parasite OE (Ophryocystis elektroscirrha) is present in all monarch populations. It has been shown that monarch development proceeds faster with increasing temperatures and that increased temperature exposure lowers OE spore infectivity over time. However, the effect of temperature on the host and parasite during active infection is not known. This project examined how temperature affects the monarch-OE system, focusing on the interaction between monarch immune function and parasite replication. Monarchs were inoculated with strains of OE parasite and placed in different temperature treatments. Three lineages (B,F, and D) of migratory monarch were used to test genetic effects, while 2 spore lines (E3 and E10) were used to study virulence effects within 5 different temperature treatments (18, 22, 26, 30, and 34°C). The results of this study provide novel insight to how extreme temperatures affect the fitness of a host and its parasite.

Sarkar_sm1

Vive la resistance: the impact of antibiotic use in US livestock on emerging antibiotic resistance

Sydney Rentsch, a junior from Connecticut College, worked with Dr. JP Schmidt to examine the relationship between antibiotic use and resistance in US livestock.

Abstract:  The potential for livestock to spread antibiotic resistant pathogens to human populations is a cause for concern. This research focused on finding trends in data on US livestock antibiotic resistance, US livestock inventory and US livestock antibiotic consumption. Data was compiled from CDC, USDA and FDA reports and publicly available datasets. Data was analyzed in R and generalized additive models (GAMs) were used to test for increasing resistance as a function of time. We found that the tetracycline class of antibiotics had consistently high resistance over time. The antibiotic class lincosamides, had a sharp increase in resistance which was positively associated with the data from turkeys and chickens. Analyzes also found that poultry had the highest burden of antibiotic resistant pathogens. These results may lead future studies focused on antibiotic resistance in poultry and provide framework for future data analysis.

 

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Behavioral and environmental determinants of parasite transmission in a butterfly host

Chastity Ward, a senior from Fayetteville State University, worked on a project with Dr. Sonia Altizer, Dr. Richard Hall and Dr. Paola Barriga to examine how parasites of the Monarch butterfly are transmitted.

Abstract:  Many pathogens can be transmitted when infectious stages shed into the environment are later encountered by susceptible hosts. Environmental transmission is common among insect parasites, and also occurs for human diseases such as cholera and polio. Understanding how host behavior and environmental variables affect the shedding of infectious stages is crucial for predicting patterns of infection risk. Monarch butterflies Danaus plexippus are commonly infected by the protozoan Ophryocystis elektroscirrha (OE); this parasite is transmitted environmentally when infected adults deposit spores onto host plants (milkweed) that are consumed by monarch larvae. To quantify host contact with milkweeds as an estimate of parasite transmission, we set up outdoor flight cages with adult monarchs and milkweed plants. Cages varied in the number of adult monarchs and milkweed plants, and were assigned to one of two milkweed species. We used captive-raised monarchs from several genetic lineages, and marked the monarchs with unique number and color codes to track activity. We observed cages for replicate intervals over a week-long period, during which we noted observed monarch contacts with plants, and recorded monarch and plant identity, activity type, temperature, weather, and time of day. Our results showed strong heterogeneity in plant visitation rates among monarchs that was best explained by monarch sex (females had 4.7 times higher visitation rates than males, owing to frequent oviposition on milkweeds).  We also found wide variation among individual plants in the number of visits by monarchs. Milkweed species, plant flowering status and plant leaf number did not affect visitation rates, but plants in cages with a higher number of monarchs were visited more frequently. In sum, our findings provided evidence for individual monarch’s serving as superspreaders of infection, and for some milkweed plants serving as hotspots of infection. This study provides a starting point for estimating environmental parasite transmission in wild milkweed patches, and suggests that individual-level heterogeneity might be more important than environmental variation in driving parasite transmission in this system.

 

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Influenza inoculum dose and disease outcome

Wei-En Lu, a junior from Grove City College, worked with Dr. Brian McKay and Dr. Andreas Handel to examine the relationship between incolum dose and disease outcome in influenza.

Abstract:  The purpose of this study is to determine the relationship between influenza inoculum dose and disease outcomes.  A systematic review to identify and abstract data from all influenza challenge studies were conducted. Exponential and linear models were used to assess the impact of inoculum dose on disease outcomes. This study found that inoculum dose has a positive relationship on the proportion infected. However, there was a negative trend between inoculum dose and proportion of fever or systemic symptoms and between inoculum dose and the mean peak viral titers. There was also a rise of inoculum dose given to individuals and a decrease in the proportion of individuals with disease outcome over time. In conclusion, inoculum dose has a definite impact on disease outcomes.

 

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Using SpatialDE to characterize spatiotemporal changes in mitochondrial morphology

Brittany Dorsey, a sophomore from Mercer University, worked with Dr. Shannon Quinn and Dr. Fred Quinn to test the use of a new method to detect changes in organelle morphology.

Abstract:   Intracellular bacterial pathogens have the capacity to greatly alter target organelles’ morphology, which can easily be visualized through fluorescence microscopy, but is more difficult to quantify succinctly. Work is being done to consider Gaussian Mixture Models as a viable solution by viewing mitochondria as social networks, but there are difficulties with this method. Therefore, the goal of the current project is to explore the feasibility of SpatialDE as an alternative way to quantify the spatiotemporal changes in organelle morphology. Using time series footage of mitochondria, three morphological phenotypes were analyzed: control, fragmented, and fused. The raw video was converted to a three-dimensional matrix of pixel values, which was then raster scanned into a two-dimensional matrix. This matrix was normalized, then input into the SpatialDE framework using the programming language Python. The data frame output gave 18 different variable values for each pixel location throughout the footage, which was converted back into “image” format in order to be analyzed. The results showed little to no discernable patterns between treatments. In comparison, the Gaussian Mixture Model output shows clear similarities among phenotypes. Therefore, it was determined that Gaussian Mixture Models continue to be the best option to model spatiotemporal changes in diffuse organelles. Once this method is fully developed, the mechanisms by which bacterial virulence factors transform mitochondrial structure in host cells will be better understood, which will have crucial implications for structural biology and biomedicine.

 

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Associations between biotic and abiotic factors and Chagas disease vector abundance in palm trees across different habitat types

Jason Soriano, a freshman from University of California Berkeley, worked with Dr. Nicole Gottdenker and Christina Varian to investigate the factors influencing the abundance of an important disease vector.

Abstract:  In the Americas, an estimated 8 million people are infected with Chagas disease, a tropical, vector-borne infectious disease that can be life threatening if not properly treated. It is caused by the protozoan Trypanosoma cruzi which is transmitted by triatomine insect vectors called Rhodnius pallescens , reduviid bugs that are more commonly referred to as “kissing bugs” for their characteristic bites proximal to the lips and eyelids of humans. In Panama, sylvatic transmission of T. cruzi commonly occurs in the crown of the Attalea butyracea palm, the region of the palm tree where kissing bugs live. However, transmission can often spillover into human populations when infected vectors come into contact with humans. Previous studies have proven that land use change (e.g. deforestation) increase R. pallescens abundance, but the underlying mechanisms as to why this pattern occurs are largely unknown. Therefore, this research serves to shed some light on potential biotic and abiotic factors associated with vector abundance in A. butyracea palm trees across different habitat types. Through data visualization and statistical analysis of field data collected from four sites in central Panama, we show that microenvironment factors (primarily dead organic matter, relative humidity, and number of connected trees) are significantly correlated with R. pallescens abundance. Evaluating mechanisms as potential targets of palm management strategies aiming to control R. pallescens abundance will ultimately minimize disease risk and uphold the health, safety, and welfare of vulnerable communities.

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Inoculum Dose and Infection Outcome

Annaliese Wiens, a student from Tabor College, worked with Dr. Andreas Handel to examine the relationship between inoculum dose and infection outcome.

Abstract:  Dose-response models describe how different inoculum doses of a pathogen alter the probability of infection with a host. It is generally assumed that higher amounts of inoculum increase infection rates, but the exact relationship has yet to be determined. We performed a meta-analysis of systematically-reviewed influenza challenge studies in which the exact inoculum dose and proportion of people infected were given. This data was used to fit several models, including an exponential model and an approximate Beta-Poisson model. These models were also stratified by different covariates, such as the strain of influenza and preparation of the virus. We used the exponential model to show that viruses prepared by different methods (wild-type, cold-adapted, etc.) have differing levels of infectivity, implying some loss of fitness during passaging through human or non-human cells.

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