Drosophila melanogaster Natural Variation Affects Growth Dynamics of Infecting Listeria monocytogenes

The genetic basis of variation in immune defense against Lysinibacillus fusiformis infection in Drosophila melanogaster

The genetic causes of phenotypic variation often differ depending on the population examined, particularly if the populations were founded by relatively small numbers of genotypes. Similarly, the genetic causes of phenotypic variation among similar traits (resistance to different xenobiotic compounds or pathogens) may also be completely different or only partially overlapping. Differences in genetic causes for variation in the same trait among populations suggests context dependence for how selection acts on those traits. Similarities in the genetic causes of variation for different traits, on the other hand, suggests pleiotropy which would also influence how natural selection shapes variation in a trait. We characterized immune defense against a natural Drosophila pathogen, the Gram-positive bacterium Lysinibacillus fusiformis, in three different populations and found almost no overlap in the genetic architecture of variation in survival post infection. However, when comparing our results to a similar experiment with the fungal pathogen, B. bassiana, we found a convincing shared QTL peak for both pathogens. This peak contains the Bomanin cluster of Drosophila immune effectors. Loss of function mutants and RNAi knockdown experiments confirms a role of some of these genes in immune defense against both pathogens. This suggests that natural selection may act on the entire cluster of Bomanin genes (and the linked region under the QTL) or specific peptides for specific pathogens.

Host Resistance to Bacterial Infection Varies Over Time, but Is Not Affected by a Previous Exposure to the Same Pathogen

Immune priming describes the phenomenon whereby after a primary pathogen exposure, a host more effectively fights a lethal secondary exposure (challenge) to the same pathogen. Conflicting evidence exists for immune priming in invertebrates, potentially due to heterogeneity across studies in the pathogen species tested, the antigen preparation for the primary exposure, and the phenotypic trait used to test for priming. To explore these factors, we injected Drosophila melanogaster with one of two bacterial species, Lactococcus lactis or Providencia burhodogranariea, which had either been heat-killed or inactivated with formaldehyde, or we injected a 1:1 mixture of the two inactivation methods. Survival and resistance (the inverse of bacterial load) were assessed after a live bacterial challenge. In contrast to our predictions, none of the primary exposure treatments provided a survival benefit after challenge compared to the controls. Resistance in the acute phase, i.e., 1 day post-challenge, separated into a lower- and higher-load group, however, neither group varied according to the primary exposure. In the chronic phase, i.e., 7 days post-challenge, resistance did not separate into two groups, and it was also unaffected by the primary exposure. Our multi-angled study supports the view that immune priming may require specific circumstances to occur, rather than it being a ubiquitous aspect of insect immunity.

The mode of expression divergence in Drosophila fat body is infection-specific

Transcription is controlled by interactions of cis-acting DNA elements with diffusible trans-acting factors. Changes in cis or trans factors can drive expression divergence within and between species, and their relative prevalence can reveal the evolutionary history and pressures that drive expression variation. Previous work delineating the mode of expression divergence in animals has largely used whole-body expression measurements in one condition. Because cis-acting elements often drive expression in a subset of cell types or conditions, these measurements may not capture the complete contribution of cis-acting changes. Here, we quantify the mode of expression divergence in the Drosophila fat body, the primary immune organ, in several conditions, using two geographically distinct lines of D. melanogaster and their F1 hybrids. We measured expression in the absence of infection and in infections with Gram-negative S. marcescens or Gram-positive E. faecalis bacteria, which trigger the two primary signaling pathways in the Drosophila innate immune response. The mode of expression divergence strongly depends on the condition, with trans-acting effects dominating in response to Gram-negative infection and cis-acting effects dominating in Gram-positive and preinfection conditions. Expression divergence in several receptor proteins may underlie the infection-specific trans effects. Before infection, when the fat body has a metabolic role, there are many compensatory effects, changes in cis and trans that counteract each other to maintain expression levels. This work shows that within a single tissue, the mode of expression divergence varies between conditions and suggests that these differences reflect the diverse evolutionary histories of host-pathogen interactions.

Consequences of chronic bacterial infection in Drosophila melanogaster

Even when successfully surviving an infection, a host often fails to eliminate a pathogen completely and may sustain substantial pathogen burden for the remainder of its life. Using systemic bacterial infection in Drosophila melanogaster, we characterize chronic infection by three bacterial species from different genera - Providencia rettgeri, Serratia marcescens, and Enterococcus faecalis–following inoculation with a range of doses. To assess the consequences of these chronic infections, we determined the expression of antimicrobial peptide genes, survival of secondary infection, and starvation resistance after one week of infection. While higher infectious doses unsurprisingly lead to higher risk of death, they also result in higher chronic bacterial loads among the survivors for all three infections. All three chronic infections caused significantly elevated expression of antimicrobial peptide genes at one week post-infection and provided generalized protection again secondary bacterial infection. Only P. rettgeri infection significantly influenced resistance to starvation, with persistently infected flies dying more quickly under starvation conditions relative to controls. These results suggest that there is potentially a generalized mechanism of protection against secondary infection, but that other impacts on host physiology may depend on the specific pathogen. We propose that chronic infections in D. melanogaster could be a valuable tool for studying tolerance of infection, including impacts on host physiology and behavior.

Host-pathogen coevolution increases genetic variation in susceptibility to infection

It is common to find considerable genetic variation in susceptibility to infection in natural populations. We have investigated whether natural selection increases this variation by testing whether host populations show more genetic variation in susceptibility to pathogens that they naturally encounter than novel pathogens. In a large cross-infection experiment involving four species of Drosophila and four host-specific viruses, we always found greater genetic variation in susceptibility to viruses that had coevolved with their host. We went on to examine the genetic architecture of resistance in one host species, finding that there are more major-effect genetic variants in coevolved host-pathogen interactions. We conclude that selection by pathogens has increased genetic variation in host susceptibility, and much of this effect is caused by the occurrence of major-effect resistance polymorphisms within populations.

How Many Parameters Does It Take to Describe Disease Tolerance?

The study of infectious disease has been aided by model organisms, which have helped to elucidate molecular mechanisms and contributed to the development of new treatments; however, the lack of a conceptual framework for unifying findings across models, combined with host variability, has impeded progress and translation. Here, we fill this gap with a simple graphical and mathematical framework to study disease tolerance, the dose response curve relating health to microbe load; this approach helped uncover parameters that were previously overlooked. Using a model experimental system in which we challenged Drosophila melanogaster with the pathogen Listeria monocytogenes, we tested this framework, finding that microbe growth, the immune response, and disease tolerance were all well represented by sigmoid models. As we altered the system by varying host or pathogen genetics, disease tolerance varied, as we would expect if it was indeed governed by parameters controlling the sensitivity of the system (the number of bacteria required to trigger a response) and maximal effect size according to a logistic equation. Though either the pathogen or host immune response or both together could theoretically be the proximal cause of pathology that killed the flies, we found that the pathogen, but not the immune response, drove damage in this model. With this new understanding of the circuitry controlling disease tolerance, we can now propose better ways of choosing, combining, and developing treatments.

Experiments using fruit flies infected with Listeria monocytogenes show that changes in the shape of a disease tolerance curve can reveal the source of pathology for an infectious system.

It is an intuitive assumption that the severity of symptoms suffered during an infection must be linked to pathogen loads. However, the dose–response relationship explaining how health varies with respect to pathogen load is non-linear and can be described as a “disease tolerance curve;” this relationship can vary in response to the genetic properties of the host or pathogen as well as environmental conditions. We studied what changes in the shape of this curve can teach us about the underlying circuitry of the immune response. Using a model system in which we infected fruit flies with the bacterial pathogen Listeria monocytogenes, we observed an S-shaped disease tolerance curve. This type of curve can be described by three or four parameters in a standard manner, which allowed us to develop a simple mathematical model to explain how the curve is expected to change shape as the immune response changes. After observing the variation in curve shape due to host and pathogen genetic variation, we conclude that the damage caused by Listeria infection does not result from an over-exuberant immune response but rather is caused more directly by the pathogen.