- caitlinrandall2020
- Jan 17
- 9 min read
Okay so to provide a brief overview of my project in almost layman's terms, I am exploring the different factors that could affect the genetic diversification (rate) of West Nile virus (WNV) throughout North America most strongly. The factors that I will be/am looking at includes, but is not limited to: weather (temperature and precipitation) the month of sample collection and the month before, the incidence rate of neuroinvasive disease (the more severe manifestation of WNV symptoms), the location (county/parish and state) that that sample was collected from (each 'sample' is basically representative of one "unit" or one individual virus, even though that's not really how it works since its a virus isolated from a secondary organism, typically a mosquito, and I would think that even within a single organism there could be multiple variants of the virus, but idk maybe I just need to go back and look at my notes because I have been confused about this before), the month and year of collection (a month to month pairwise comparison would also be tied to typical viral cycles and weather conditions whereas a year by year comparison will suggest a different story about WNV transmission patterns and diversification, I didn't really explain that super well but I will explain more about the transmission cycles of WNV below to clarify), and the ecoregion (which is a classification given to a region by the EPA based on the type of landcover). I possibly want to figure out a way to add a component about bird migratory patterns/the # of birds in that area at that time and I also want to add something perhaps about socioeconomic status, % landcover, urbanization/population density etc. but each of those variables are kind of limited with the amount of available information given the time span over which the samples were taken and the fact that they were also taken from multiple regions in the United States. I am using the database pathoplexus to download my sequences, roughly 300 sequences from Louisiana, Colorado, and New York and I/my undergrad mentee Riley selected these locations due to sample size (we wanted at least 50-100 samples from each location) and we wanted to capture samples across the gradient of neuroinvasive disease (NID) incidence/activity because the genetic underpinnings of NID that enables some strains of WNV to pass over the blood-brain barrier and not others is still unknown so we could look to see if any correlations exist possibly. These samples span several years and months within the year and are also collected from multiple parishes/counties throughout those states so hopefully we have captured enough variation across all of the factors that could potentially affect WNV to see patterns emerge. The other samples that I will be using for comparison (roughly 120) are from throughout Louisiana and were collected via the mosquito abatement program by the Louisiana Department of Health and stored at the Louisiana Animal Disease Diagnostic Laboratory. In order to actually get these sequences I have to do some molecular work because the samples are quite literally macerated mosquito guts, so in order to get the sequences I will have to do an RNA extraction, PCR, and then because I am on a budget I am going to do gel electrophoresis to confirm the presence of viral cDNA since that is cheaper than doing qPCR and paying for the probe, and then I will send off the cDNA PCR product to be Sanger sequenced. Currently I am still working on that and I have selected the samples that I want to use (an intentional and curated sample selection process was also done based on NID prevalence/incidence and trying to capture a decent distribution of locations and times) and I have performed extractions on them but I am just waiting on some other things and then I can do the PCR and gel on my samples in hopefully the upcoming weeks.
Once I have all of my sequences and all of the accompanying information for my analysis, I will be constructing a phylogenetic tree (using muscle in AliView to align and then FigTree to construct the tree itself and then R to tweak it) and then doing some sort of analysis to see how strongly different variables structure the diversification processes. I am thinking of using an Integrated nested Laplace approximation, which is (according to Wikipedia) "a method for approximate Bayesian inference based on Laplace's method." I am not super sure that that's the best method, but I also am not there yet so it's okay.
Okay anyway, that's basically it and I am not sure if you read my previous post (probably not because probably no one even will read this) but I need to actually work on job applications now since they are due today and its almost 3 and so now I am stressed. Anyway, I will attach/paste my NSF-GRFP proposal because it's the more polished description of my project and probably more coherent (also just realized I said I would go into more detail about the transmission cycle of WNV but never did, basically its just back and forth between mosquitoes and birds and then sometimes humans and other mammals get it) but also has more jargon. Here it is (please don't copy it):
Uncovering the drivers of genetic diversity of West Nile virus throughout Louisiana
Introduction: West Nile virus (WNV) is a zoonotic arbovirus (arthropod-borne virus) capable of infecting vertebrate hosts, particularly humans, horses, birds, and crocodilians.1,2 The transmission cycle occurs between ornithophilic mosquitoes, the principal vector of WNV, and birds, the primary amplifying host or reservoir. Mosquitoes infected with WNV may incidentally transmit the virus via blood feeding to mammals and other vertebrates, which are dead-end hosts.2 While the majority of human cases remain subclinical or asymptomatic, some WNV infections develop into neuroinvasive disease (NID), which is accompanied by severe symptoms which may include meningoencephalitis and in some cases, death.1 Different strains of WNV have variable virulence and neuroinvasion capabilities, which affects the ability of that strain to cause disease outbreaks and the extent of pathogenesis.1 Additionally, environmental and anthropogenic factors–including precipitation rate, ambient temperature, community ecology, type of land cover, and urbanization, amongst other extrinsic factors–influence the incidence rate and transmission patterns of WNV.1,3,4,5
The first WNV outbreak in humans in North America occurred in New York in 19993,4 and was accompanied by the mass mortality of birds near the epicenter of the outbreak.6 WNV subsequently spread throughout North America. Though the role of birds as an amplifier of WNV is well documented, it is not understood whether migratory birds played a role in introducing WNV to non-endemic regions.6 Since its introduction to the United States, WNV has been the leading cause of arboviral human disease, and as of 2021, it accounts for 95% of all reported NID cases attributable to arboviruses.7 Despite the wide reaching effects of WNV, we don’t presently understand the population ecology of WNV in terms of effective population sizes and gene flow. Thus, uncovering the intracontinental transmission patterns and the extrinsic and intrinsic properties that place a population at risk for a WNV outbreak is critical for disease management and prevention.
Aim 1: Characterize the genetic diversity of WNV in Louisiana (LA) relative to North America to test the “Migrant Bird as Introductory Host” hypothesis.8 LA provides an ideal study location of WNV due to its hot, humid subtropical climate positioned within the Mississippi Flyway, a major bird migratory route. These characteristics make LA particularly vulnerable to WNV and a prime candidate for the analysis of WNV genetic diversity across distance and ecosystem types. Throughout LA, parishes collect samples of mosquito pools to surveil the local mosquito populations for arboviruses. I will select a representative subset of these samples, aiming to capture samples from parishes with high and low positive pools and from parishes with high and low incidence rates of NID, samples from each ecoregion (defined by the United States Environmental Protection Agency (USEPA) as regions with a similar set of abiotic and biotic characteristics) in LA, and samples from each geographic region throughout LA, all spanning multiple years. To inform this sample selection process I will use the West Nile Virus Weekly Reports11 and a map of the Ecoregions of Louisiana published by the USEPA.12 I will then perform whole genome sequencing (WGS) of WNV from these samples. From the sequenced isolates in combination with WNV sequences sampled throughout North America retrieved from Pathoplexus,13 an open-source database of human viral pathogen genomic data, I will construct a phylogenetic tree of WNV diversity using BEAST14 and a maximum-likelihood tree with 1000 bootstraps using IQ-TREE.15 I will then identify migratory bird routes and seasonal bird density utilizing BirdCast live bird migration maps,16 Birds of the World,17 and the Global Biodiversity Information Facility.18 I will then analyze this data for any specific genetic variants of WNV throughout North America along the migratory routes to determine whether there are patterns of genetic diversification that suggest WNV transmission is mediated by migratory birds. Hypothesis 1: Generally, I expect to observe trends of WNV strain similarity along North-South gradients, following the direction of seasonal bird migration. More specifically, I expect strains isolated from LA to be the most genetically similar to strains isolated from the Midwest, particularly the Great Lakes region. I expect this because both LA and the Midwest lie along the Mississippi Flyway and are near large bodies of water. This result would support the “Migrant Bird as Introductory Host” hypothesis.
Aim 2: Understand how ecological interactions and anthropogenic factors affect WNV genetic diversity and incidence in LA. Upon constructing the phylogeny of WNV, I will use an integrated nested Laplace approximation model19 to elucidate how genetic diversity and incidence rates of WNV compare to weather patterns throughout LA, relative bird abundance, ecoregion, urbanization, and socioeconomic status (SES) across spatiotemporal gradients. I will obtain temperature and precipitation data from the Louisiana Agriclimatic Information System,20 species-specific bird abundance data from eBird,21 urbanization data from the United States Census Bureau,22 and use maps of adjudicated properties and Parish Employment and Wages in Louisiana23 as a proxy for SES. Hypothesis 2: I expect parishes with greater precipitation rates and higher temperatures, a greater density of flocking bird species, more urbanization, and lower SES to exhibit higher genetic diversity and greater overall WNV incidence. Additionally, I expect these environmental factors to coincide with the late summer months and to occur in the Mississippi Alluvial Plain (wetland) ecoregions, with ecoregion being the most significant factor structuring WNV genetic diversity in LA.
Resources and Mentorship: I will be performing my research at the Louisiana State University School of Veterinary Medicine with the co-mentorship of Dr. Rebecca Christofferson, who specializes in patterns of viral transmission, and Dr. Jeremy Brown, who specializes in phylogenetic reconstruction. I will obtain my samples of WNV from the Louisiana Diagnostic Disease Laboratory.
Intellectual Merit: Understanding the ecological factors that influence WNV transmission and viral amplification is critical to the effective implementation of management strategies. Most WGS of WNV in North America are of strains isolated from the northeastern United States24 so it is critical to supplement this information from isolates collected throughout North America, including LA. Additionally, understanding the driving factors of WNV genetic diversification processes will advance fundamental knowledge about other arboviruses as well.
Broader Impacts: Currently, few parishes regularly sample mosquito populations for arbovirus testing and sampling is typically done as a reactionary measure or performed sporadically. A greater understanding of the transmission patterns, genetic diversity, and the effects of ecological drivers on WNV activity can inform resource partitioning efforts to develop actionable plans pertaining to risk management and arboviral surveillance. (1) Community Education to Reduce Mosquito-Borne WNV Risks: I will implement community educational programs to disseminate information about preventative measures for mitigating mosquito activity, the transmission cycles of WNV, and specific risk factors that may make individuals more vulnerable to NID. Simply removing waste that accumulates water and other mosquito breeding sites can reduce the abundance of the mosquito vectors of WNV, thus reducing risk. (2) Citizen Science Program to Enhance Mosquito Sampling and Optimize WNV Surveillance: I will develop a citizen science program to address a lack of consistent mosquito pool sampling and to establish standardized sampling protocols that aim to optimize resources based on the results of this study and previous WNV surveillance data. These citizen science collections can be incentivized in regions where WNV activity is the greatest. These citizen science WNV surveillance programs and community led abatement efforts are rooted in “One Health” principles, which seeks to optimize the health of the environment, humans, and animals. These programs will promote environmental justice and equity in underserved communities, seeking to remedy some of the underlying factors for heightened WNV risk and providing public engagement in science that directly benefits the community.
1Donadieu et al. Viruses (2013). 2Habarugira et al. Pathogens (2020). 3Christofferson et al. Virol J (2010). 4Petersen et al. JAMA (2013). 5de Jesús Crespo et al. Int. J. Environ. Res. Public Health (2021). 6Rappole et al. Emerg Infect Dis. (2000). 7Fagre et al. MMWR Morb Mortal Wkly Rep (2023). 8Mancuso et al. Viruses (2022). 9Louisiana Department of Health, West Nile Virus Weekly Reports (2022–2024). 10Daigle et al. USEPA (2006). 11Pathoplexus (2024). 12Suchard et al. Virus Evolution (2018). 13Minh et al. Mol. Biol. Evol. (2020). 14Dokter, A. M. BirdCast, live migration map (2022-2024). Cornell Lab of Ornothology. 15Billerman et al. Birds of the World, Cornell Laboratory of Ornithology (2022). 16GBIF.org (2022-2024). 17Schrödle & Held, Environmetrics (2010). 18Louisiana Agriclimatic Information System (2022-2024), LSU AgCenter 19eBird, Cornell Lab of Ornithology (2021). 20List of 2020 Census Urban Areas in the United States, Puerto Rico, and the Island Areas, Federal Register (2022). 21Parish Employment and Wages in Louisiana, U.S. Bureau of Labor Statistics 22Hyeon et al. Front Vet Sci. (2023).