Periodic Reporting for period 3 - EPIDEMIC (Experimental Epidemiology in Ant Societies)
Reporting period: 2023-02-01 to 2024-07-31
Social organisms live in dense aggregations where individuals engage in frequent interactions, which can facilitate pathogen transmission and increase the risk of disease outbreak. Understanding how disease spreads through social groups is therefore an important question with potential implications for public health. In social groups, the risk for any individual to become infected depends not only on that individual's own traits (for example, its age, genotype, and behaviour) but is also determined by traits of that individual’s social group (for example, group size and genetic composition, the immune status of group members, and the network of social interactions between group members). Most social groups are inherently complex and the experiments required to tease apart the relative importance of these factors in shaping disease outcomes often cannot be performed for practical and/or ethical reasons. In other words, it is often difficult to determine to what extent a given individual within a group becomes infected because of its genes, age, behavior, social environment, or interactions with others.
Theoretical epidemiology predicts that disease dynamics will depend in large part on a group's social interaction network, but empirical data are scarce. Experimental epidemiology is currently hampered by a lack of study systems enabling a rigorous investigation of the causal link between group composition, network structure and disease transmission. This project tackles this challenge using a novel system, the clonal raider ant, a social insect whose unique biology affords control over the main aspects of colony composition that are thought to modulate social network structure, and therefore, disease transmission. The approach will combine automated techniques for behavioral analyses with molecular tools, and will develop new methods to monitor pathogen transmission in real time. Capitalizing on this biological model and these tools, we will create experimental groups that are theoretically predicted to vary in transmission risk, inoculate these experimental groups with pathogens, and quantify infection propagation in real time. This will allow us to experimentally test some of the predictions from theoretical epidemiology, and to identify some of the properties of social groups that protect them against disease. By linking theoretical epidemiology to real-world disease dynamics, this project has the potential to improve our ability to predict disease dynamics in social groups.
Theoretical epidemiology predicts that disease dynamics will depend in large part on a group's social interaction network, but empirical data are scarce. Experimental epidemiology is currently hampered by a lack of study systems enabling a rigorous investigation of the causal link between group composition, network structure and disease transmission. This project tackles this challenge using a novel system, the clonal raider ant, a social insect whose unique biology affords control over the main aspects of colony composition that are thought to modulate social network structure, and therefore, disease transmission. The approach will combine automated techniques for behavioral analyses with molecular tools, and will develop new methods to monitor pathogen transmission in real time. Capitalizing on this biological model and these tools, we will create experimental groups that are theoretically predicted to vary in transmission risk, inoculate these experimental groups with pathogens, and quantify infection propagation in real time. This will allow us to experimentally test some of the predictions from theoretical epidemiology, and to identify some of the properties of social groups that protect them against disease. By linking theoretical epidemiology to real-world disease dynamics, this project has the potential to improve our ability to predict disease dynamics in social groups.
1. Prophylactic network properties
1.1. How does group composition affect social network structure?
We performed two experiments in which we manipulated two aspects of group composition (colony age and genetic structure), which are predicted to affect social network structure and therefore, disease spread. In line with these predictions, we find that ants of two different genotypes show inherent differences in behavior, and that these differences result in significant variation in network structure as a function of group composition. Additionally, temporal analyses reveal a pattern of gradual behavioral convergence between the two genotypes in mixed colonies, the underlying mechanism of which is currently being investigated. These results were confirmed and extended by a further experiment using all 5 ant genotypes currently available in our laboratory. This work resulted in a peer-reviewed publication. The data generated in this study will be used to extend the scope of the investigation on how group genetic structure affects network structure.
Similarly, ants of different ages differ in behavior, and these differences result in significant variation in network structure with group demographic structure. All those findings establish that we can experimentally manipulate and replicate social network structure by manipulating group composition, an essential first step in this project.
Furthermore, we created a computational pipeline to simulate spreading dynamics in empirical temporal networks generated from automated behavioural tracking data. Using this pipeline, we can generate predictions for disease dynamics based on simple epidemiological models (e.g. susceptible-infected) in the social networks of different ant colonies. These predictions will be tested in part 3 of the project (see below).
1.2. How is immune function distributed across individuals in uninfected colonies?
We performed the planned behavioural and infection experiments for this part of the project. We used automated behavioural tracking to analyse the behaviour of identical ants in replicate colonies and measured their contact networks. At the end of the experiment, some colonies were used for gene expression analyses and we are currently working on linking immune gene expression data with behavioural data.
2. Inducible network properties
How do immune challenges affect interaction networks and the distribution of immune function?
We quantified the effects of immune challenges (injections with immune stimulants that induce a strong immune response but are neither pathogenic nor contagious) on the individual behaviour and network position in clonal raider ants. Contrary to our expectations, immune-challenged individuals occupied a more (not less) central position in the contact network of their colony. Manual behavioural annotations indicate that this is because they receive increased grooming from their naïve nestmates. This shows that ants can detect the immune status of their challenged (but uninfected) nestmates and that they respond with a “caring” (rather than, e.g. avoidance) strategy. This work resulted in a peer-reviewed publication.
In parallel, we analyzed gene expression of immune challenged ants to identify genes that are up- or down- regulated and can be used as ‘markers’ of inducible immune activity in further experiments.
3. Transmission in infected social groups
What are the properties of interaction networks that reduce transmission?
This part of the project is, as planned, still in the method-development stage. We have tested methods to quantify transmission through social networks noninvasively and in real time using nematodes of the genus Diploscapter, which naturally infect the head of ants. While preliminary, our first results are an encouraging first step toward dynamically tracking infection spread in social groups.
Additionally, we have made significant progress in characterizing the basic infection biology of Diploscapter, which was so far poorly described. Specifically, we performed epidemiological experiments and combined them with chemical, transcriptomic, and automated behavioural analyses in clonal raider ant colonies, to establish that: 1) Diploscapter nematodes parasitize a specific gland in ant heads and affect their host’s survival and physiology, 2) differences in infection emerge from behavioural variation alone, and reflect the spatial organisation of division of labour, with foragers becoming infected earlier and carrying higher infection loads than genetically identical nurses, 3) infections affect colony social organisation by causing infected workers to stay in the nest. By disproportionately infecting some workers and shifting their spatial distribution, nematode infections reduce division of labour and increase spatial overlap between hosts, which should facilitate parasite transmission.
1.1. How does group composition affect social network structure?
We performed two experiments in which we manipulated two aspects of group composition (colony age and genetic structure), which are predicted to affect social network structure and therefore, disease spread. In line with these predictions, we find that ants of two different genotypes show inherent differences in behavior, and that these differences result in significant variation in network structure as a function of group composition. Additionally, temporal analyses reveal a pattern of gradual behavioral convergence between the two genotypes in mixed colonies, the underlying mechanism of which is currently being investigated. These results were confirmed and extended by a further experiment using all 5 ant genotypes currently available in our laboratory. This work resulted in a peer-reviewed publication. The data generated in this study will be used to extend the scope of the investigation on how group genetic structure affects network structure.
Similarly, ants of different ages differ in behavior, and these differences result in significant variation in network structure with group demographic structure. All those findings establish that we can experimentally manipulate and replicate social network structure by manipulating group composition, an essential first step in this project.
Furthermore, we created a computational pipeline to simulate spreading dynamics in empirical temporal networks generated from automated behavioural tracking data. Using this pipeline, we can generate predictions for disease dynamics based on simple epidemiological models (e.g. susceptible-infected) in the social networks of different ant colonies. These predictions will be tested in part 3 of the project (see below).
1.2. How is immune function distributed across individuals in uninfected colonies?
We performed the planned behavioural and infection experiments for this part of the project. We used automated behavioural tracking to analyse the behaviour of identical ants in replicate colonies and measured their contact networks. At the end of the experiment, some colonies were used for gene expression analyses and we are currently working on linking immune gene expression data with behavioural data.
2. Inducible network properties
How do immune challenges affect interaction networks and the distribution of immune function?
We quantified the effects of immune challenges (injections with immune stimulants that induce a strong immune response but are neither pathogenic nor contagious) on the individual behaviour and network position in clonal raider ants. Contrary to our expectations, immune-challenged individuals occupied a more (not less) central position in the contact network of their colony. Manual behavioural annotations indicate that this is because they receive increased grooming from their naïve nestmates. This shows that ants can detect the immune status of their challenged (but uninfected) nestmates and that they respond with a “caring” (rather than, e.g. avoidance) strategy. This work resulted in a peer-reviewed publication.
In parallel, we analyzed gene expression of immune challenged ants to identify genes that are up- or down- regulated and can be used as ‘markers’ of inducible immune activity in further experiments.
3. Transmission in infected social groups
What are the properties of interaction networks that reduce transmission?
This part of the project is, as planned, still in the method-development stage. We have tested methods to quantify transmission through social networks noninvasively and in real time using nematodes of the genus Diploscapter, which naturally infect the head of ants. While preliminary, our first results are an encouraging first step toward dynamically tracking infection spread in social groups.
Additionally, we have made significant progress in characterizing the basic infection biology of Diploscapter, which was so far poorly described. Specifically, we performed epidemiological experiments and combined them with chemical, transcriptomic, and automated behavioural analyses in clonal raider ant colonies, to establish that: 1) Diploscapter nematodes parasitize a specific gland in ant heads and affect their host’s survival and physiology, 2) differences in infection emerge from behavioural variation alone, and reflect the spatial organisation of division of labour, with foragers becoming infected earlier and carrying higher infection loads than genetically identical nurses, 3) infections affect colony social organisation by causing infected workers to stay in the nest. By disproportionately infecting some workers and shifting their spatial distribution, nematode infections reduce division of labour and increase spatial overlap between hosts, which should facilitate parasite transmission.
The colonies of social insects such as bees and ants are powerful systems to experimentally study disease dynamics because they naturally display a complex social organisation and are amenable to infection experiments. However, three major limitations still hamper progress in this field: 1) the lack of repeatability and experimental control over colony composition, which has limited the field to correlative studies with low sample sizes, 2) the lack of tools to efficiently monitor individual behavior in groups and to measure social interaction networks, and 3) the limited availability of experimentally amenable natural parasites of social insects, in particular parasites allowing to monitor transmission in real time. As a result, there are to date few controlled experimental studies measuring disease spread in social interaction networks of known composition. To overcome the first limitation, we will establish the clonal raider ant, a species that combines the rich biology of insect societies with unprecedented experimental amenability, as a system for experimental epidemiology. The second limitation will be alleviated by the use and further development of automated behavioral analyses. To address the third limitation, we will establish a nematode that naturally infects clonal raider ants as a system to study transmission in real time.
This project takes an integrative approach—from individual immunity to collective behavior—to uncover the properties of social groups that protect them against disease. By filling the gap between theoretical epidemiology and real-world disease dynamics, it has the potential to improve our ability to predict disease dynamics in social groups.
This project takes an integrative approach—from individual immunity to collective behavior—to uncover the properties of social groups that protect them against disease. By filling the gap between theoretical epidemiology and real-world disease dynamics, it has the potential to improve our ability to predict disease dynamics in social groups.