Elisa Visher presents "The Evolution of Host Genotype Specialization in an Insect Pathogen"11/21/2019 Illustration by Alison Feder Evolutionary tradeoffs in an insect-virus model system Last week, graduate student Elisa Visher of the Boots lab (link: https://bootslab.org/) presented details related to her most recent experiment at our Ecology and Evolution of Infectious Diseases seminar. Elisa works in an experimental evolution model system which Mike developed as a graduate student back in the UK. In this system, Elisa manipulates interactions between a host, the Indian meal moth (Plodia interpunctella) and its species-specific pathogen, Plodia interpuntella granulosis virus (PiGV) to explore evolutionary tradeoffs between investment in resistance and growth on the part of the moth and infectivity and productivity on the part of the virus. Experimental evolution in a host-pathogen system typically involves growing a generation of pathogens on a specific host, then transferring this pathogen to a new generation of hosts, such that over time, the experimenter can quantify adaptations of the pathogen to the host environment. In the Boots lab, the experimenter (in this case Elisa) infects a generation of Plodia larvae by supplying them with virus-infected food. At timepoint one, called the first ‘passage’ in a ‘serial passage’ experiment, Elisa mixes virus in sugar and puts it on a petri dish for the larval meal moths to consume. PiVG is an obligate killer which is transmitted via larval cannibalism, so for the next passage, Elisa collects dead cadavers from the first passage, mashes them up, and mixes them in food for the second generation. Over time, the pathogen is influenced by natural selection in that it competes with other genotypes in the virus population. By being the virus genotype that (a) infects the most cadavers and (b) produces the most virions when the insects get ground up, one virus genotype can outcompete the others to be propagated into the future. The Plodia, by contrast, are under selection to resist this virus in order to survive. In past work (i.e. his PhD), Mike has demonstrated that Plodia will develop resistance to PiGV, though at a cost of longer development times (Boots and Begon 1993), meaning that the more effective a larvae is at avoiding infection, the longer it takes to grow into a moth. Mike has further shown that these costs are magnified when resources available to the host are scarce (Boots 2011). More recently, Lewis Bartlett of the Boots lab developed twelve distinct inbred lines of Plodia to demonstrate that this tradeoff between virus resistance and host growth rate is a constitutive trait with a genetic basis (Bartlett et al. 2018). For one chapter of her PhD, Elisa is expanding on these themes, using Lewis’s inbred lines, to show that PiGV can evolve to specialize on distinct genotypes of Plodia. In a serial passage experiment, Elisa evolved the same stock of virus across three different Plodia genotypes for nine generations, then assessed each evolved virus’s fitness on this familiar genotype and the other two genotypes across the time course of serial passage. Critically, Elisa measured “viral fitness” in two different ways—both in terms of a virus’s capacity to infect a Plodia host, as well as its virion productivity after infection. Two of the viruses she passaged evolved to be “infection specialists” for their host genotype, meaning that they were more effective at infecting the host they had evolved with than the other two genotypes. The third virus, by contrast, evolved to be a “productivity specialist,” showing no difference in its ability to infect other Plodia genotypes at the end of the experiment but producing many more virions post-infection when assayed on its host genotype. In ongoing work, Elisa is using this same system to test the so-called ‘monoculture effect’ to explore whether more diverse host environments made up of multiple different Plodia genotypes select for more generalist viruses which can infect hosts more broadly but may be less virulent. In agricultural systems, we fear that the absence of genetic diversity allows for pathogens to specialize on a single host and attain high levels of virulence which result in crop destruction. In the future, Elisa also plans to manipulate the physical arrangement of Plodia genotypes in a microcosm to further explore the effect of spatial structure on these evolutionary tradeoffs in a host-pathogen system. We are excited to hear what she finds out next! Summary by Cara Brook
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Illustration by Elisa Visher A couple of weeks ago, we held a special early session of the Berkeley EEID seminar so that Professor Ayesha Mahmud from Demography could give a presentation on her research. Ayesha’s research program uses demography methods to study infectious disease spread. At the seminar, she presented her research on the spread of Chikungunya in Dhaka City, Bangladesh. Chikungunya is a viral disease transmitted by Aedes mosquitos that is spreading around the world. It doesn’t cause a large number of fatalities, but can leave people who have been infected, especially the elderly, with long term symptoms. Ayesha focused her talk on work that she has done modelling the 2017 outbreak of Chikungunya in Dhaka City, Bangladesh. Dhaka City is one of the densest megacities in the world with about 18 million people in 1300 sq km. This dense population, along with rapid urbanization, regular monsoons, and almost no vector control, has made Dhaka City susceptible to regular outbreaks of mosquito-borne diseases. There have been frequent dengue outbreaks since 2000 and Chikungunya outbreaks in 2011 and 2017. During the 2017 Chikungunya outbreak in Dhaka, hospitals reported 13,176 cases, but this record is likely an underestimate because hospital reporting was not mandatory. Ayesha therefore based her models on a set of data from collaborators who did household surveys asking about recent symptoms in an effort to get better data on Chikungunya infection rates. They found that 77% of the households surveyed had symptoms, 51% of these individuals had antibodies, and 70% of those had evidence of recent infection. That means that nearly 50% of the population had been recently infected with Chikungunya. With this data, Ayesha and team were able to explore the spatial and temporal heterogeneity in disease burden. This scale of data was especially important as one of the big concerns during the 2017 epidemic was that the outbreak would spread out of Dhaka city into neighboring towns because Eid was right around one of the two peaks in incidence in the city. Using cellphone data, Ayesha was able to track when people were leaving Dhaka city and where they were going. As expected, there were small spikes in city residents travelling over the weekends, with much larger spikes for the Eid holidays. Using a modelling framework with this mobility, demography, and infection data, Ayesha and team were able to make predictions about disease dynamics across the country. The model showed that Chikungunya was likely exported out of Dhaka in a spatial pattern that differed from a standard gravity model as there were certain patterns of travel between different cities in Dhaka. The model did not pick up the double peak in incidence, but the combination of an SEIR and mosquito model did pick up the epidemic curve. The patterns predicted by the model also matched some of the limited reporting from cities outside of Dhaka, which also tended to peak around the Eid holidays. Now, Ayesha is combining these models of case importation risk with metrics like population and mosquito densities in these other cities to predict epidemic risks. They are also looking at data from a Dengue outbreak in Dhaka this year that had much better reporting to see if mobility data can again make some predictions about disease spread. Overall, Ayesha’s talk gave us a great idea about how different types of data can be combined with models to track disease dynamics and impressed upon us how important movement patterns and mass-movement events (like holidays) could be for disease transmission. Summary Elisa Visher
Illustration by Elisa Visher Last month, Chris Hoover presented his research during the Berkeley EEID seminar series. Chris is a PhD student with Dr. Justin Remais at UC Berkeley working on optimal control strategies of Neglected Tropical Diseases (NTD’s) with applications to schistosomiasis. Schistosomiasis is the second most devastating parasitic disease worldwide and caused by parasitic flatworms called schistosomes. The disease is spread by fresh water contaminated with parasites that are released by freshwater snails. There is currently no vaccine to treat the disease but global control efforts have primarily on massive drug administration (MDA) often targeting school aged children. While MDA reduces morbidity, it many regions it has been inefficient in reducing transmission resulting in rebounds of the disease. Recent evidence suggests that MDA combined with snail control efforts is the most cost-effective intervention strategy. With this background in mind, Chris models the effect of worm population dynamics on schistosomiasis transmission dynamics with the ultimate goal of designing effective control strategies. Some important features of the Susceptible-Exposed-Infection model includes environmental transmission, parasite burden, and density dependent processes. Infection is modelled as the population mean worm burden. Negative (i.e. crowding) and positive (i.e. mate limitation) density dependent processes appear in the force of infection of the intermediate snail host. Using these features of the model, one can estimate the breaking point threshold of the population size (i.e. endemic equilibrium) below which the population gets reduced to zero (i.e. elimination). The breaking-point of the mean worm burden is analogous to the herd-immunity threshold as the host becomes resistant to the infection when the worm burden falls below the threshold. An additional feature of the model is that we can manipulate the exposure parameter influencing transmission that we can intervene on. By reducing the exposure contamination parameter and the mean worm burden, reasonable levels of MDA coverage can lead to the breaking-point (i.e. control). Current work seeks to understand how demographic stochasticity impacts intervention strategies, such as small population sizes around the breaking-point. The theoretical framework developed by Chris and collaborators is also very informative for real-world infectious diseases. For example, China has been successful in reducing schistosomiasis transmission using an integrated approach that includes snail habitat reduction, improvements in agricultural practices, and MDA. The work presented by Chris is an excellent example of applying well-grounded ecological theory to the management and control of infectious diseases. Summary by Senay Yitbarek
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