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Article

When Timing Matters: Shedding Light on Mechanisms Underlying Host–Pathogen Dynamics in Freshwater

by
Anke Schwarzenberger
1,
Carla E. Cáceres
2,
Dominik Martin-Creuzburg
3 and
Alexander Wacker
4,*
1
Limnological Institute, University of Konstanz, Mainaustr. 252, 78464 Konstanz, Germany
2
School of Integrative Biology, University of Illinois Urbana—Champaign, Urbana, IL 61801, USA
3
Research Station Bad Saarow, Department of Aquatic Ecology, Brandenburg University of Technology (BTU) Cottbus-Senftenberg, Seestraße 45, 15526 Bad Saarow, Germany
4
Zoological Institute and Museum, University of Greifswald, 17487 Greifswald, Germany
*
Author to whom correspondence should be addressed.
Conservation 2026, 6(2), 55; https://doi.org/10.3390/conservation6020055
Submission received: 16 February 2026 / Revised: 2 April 2026 / Accepted: 15 April 2026 / Published: 4 May 2026
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Abstract

Invertebrates possess an innate immune system that acts non-specifically against pathogens and is regulated by the circadian clock. Using the host–parasite system, Daphnia magna and its bacterial parasite Pasteuria ramosa, we investigated day–night differences in susceptibility. In an infection experiment where hosts were exposed to spores either during the day or night, infection was slightly higher during daytime exposure and in animals treated with exogenous melatonin. Daphnia exhibited rhythmic expression of five immune genes, with low daytime expression and a pronounced, synchronized peak immediately after the transition from day to night. This timing aligns with the documented increase in daytime susceptibility, which may benefit Pasteuria as encounter rates rise when Daphnia forage in sediment during diel vertical migration. Melatonin exposure altered immune gene expression and increased susceptibility both day and night. Melatonin can act as an immune suppressor and may also influence parasite spore maturation. Disruption of circadian rhythms and melatonin signaling by anthropogenic stressors alters the infection dynamics in this freshwater keystone organism, with consequences for population stability, ecosystem functioning, and the conservation of freshwater biodiversity. Our results spotlight the mechanisms underlying infection risk in host–pathogen systems, highlighting the importance of circadian regulation for disease dynamics in freshwater ecosystems.

1. Introduction

In freshwater ecosystems, Daphnia species play a central role in controlling phytoplankton and are a major food source for planktivorous fish [1]. They are therefore a relevant keystone species for the transfer of carbon to the next trophic level [2], and their dynamics are equally important for practitioners involved in lake management [3]. Infection of Daphnia by a parasite can disrupt this transfer, changing the freshwater ecosystem dynamics. In the Anthropocene, transmission rates of parasites and pathogens are expected to increase due to global warming [4]. Therefore, understanding the mechanisms underlying infection risk is essential for predicting how environmental change and further external anthropogenic stressors, such as artificial light at night (ALAN) or chemical pollutants, can affect natural disease dynamics and disrupt ecosystem stability. However, the mechanistic basis of diel differences in infection risk in freshwater invertebrates remains poorly understood, particularly regarding the interplay between circadian immunity, melatonin signaling, and pathogen exposure timing.
The circadian clocks of hosts and pathogens are key drivers of disease transmission [5]. In vertebrates, the probability and severity of infection depend on the time of day at which hosts are exposed to pathogens (e.g., [6]). Similarly, in the freshwater crustacean Daphnia, the prevalence of infection with Metschnikowia was higher at night, which might have been due to the host’s rhythm in feeding behavior [7]. Rhythms in immune gene expression [8,9] and melatonin synthesis [10] might be responsible for these observed diurnal differences in infection risk. Yet we still lack direct evidence linking diel immune gene rhythms to actual infection outcomes in Daphnia. In vector-borne diseases, the effects of the circadian clock extend to the vector. For example, malaria is transmitted during the night when its vector (Anopheles) shows its highest host-seeking activity [11], and the immune system activity of its host is low [5]. However, the role of immunity versus changes in behavior in determining these diel dynamics remains unresolved in many systems.
The hormone melatonin serves as a modulator of physiological rhythms [12], including in Daphnia [10]. Melatonin might also be involved in the regulation of the diel vertical migration (DVM) behavior of Daphnia [13]. Thus, melatonin is possibly a key player in disease dynamics involving both physiological and behavioral mechanisms. Recently, it was demonstrated that exogenously applied melatonin increased the infection of Daphnia with Metschnikowia during the day [14]. A stimulating effect of melatonin on the innate immune system has been found in vertebrates and invertebrates [15,16], which is probably the reason for the observed reductions in severity of infection by various pathogens after melatonin treatment [6,17]. However, melatonin’s effects on immune gene expression in Daphnia, and whether these effects differ between day and night, remain unexplored.
We studied the diel dynamics of infection risk by quantifying immune gene expressions and adding exogenous melatonin in the well-established Daphnia magna and its bacterial parasite Pasteuria ramosa system [18]. We also measured temporal changes in the expression of five innate immune genes with and without the addition of melatonin. We expected (1) a lower immune gene expression during the day and hypothesized that (2) the infection risk of D. magna by P. ramosa depends on the time of day at which they are exposed to the bacterial spores and (3) that the infection risk is influenced by melatonin addition. By integrating infection assays with circadian gene expression profiling, our study provides the first mechanistic link between diel immune rhythms, melatonin signaling, and infection outcomes in a freshwater keystone species.

2. Materials and Methods

Cultures: The green alga Acutodesmus obliquus was cultivated in Cyano-medium [19] in five-liter bottles in a 12:12 h day–night cycle. Every week, one liter was exchanged for a fresh medium. The algal suspensions were centrifuged, the pellets redissolved in 300 mL of fresh medium, and stored in the same climate chamber (i.e., same day–night cycle) in which the Daphnia experiments took place. While our standard daphnid cultures are maintained under a natural summer photoperiod reflecting a 16:8 h light–dark cycle, this regime would have produced unequal day and night phases for the infection assays and thus biased direct comparisons of daytime versus nighttime infection prevalence. Therefore, we used a 12:12 h light–dark cycle for the infection assays. Twenty neonates of the D. magna clone CH-H-5 and clone HU-HO-2, which are highly susceptible to P. ramosa [20], were pre-cultivated for three generations in the same day–night cycle in which the experiments took place (either 12:12 h or 16:8 h day–night, see above) in jars containing 1.5 L of filtered, aerated Lake Constance water and saturating amounts of A. obliquus. Food and water were exchanged regularly, and the mothers were transferred to fresh glasses whenever they produced offspring. Synchronized third-clutch animals born within 12 h were used in the experiments. HU-HO-2 was used in the infection assay. Immune gene expression was measured in both clones. We are aware that the use of a few Daphnia clones might not allow for the generalization of our results for the whole species. Nevertheless, our study helps to clarify basic physiological responses prior to their general validation in future population studies.
Infection assay: Spore exposure × melatonin treatment: To address the effects of exogenous melatonin on the susceptibility of D. magna to P. ramosa, 4-day-old juveniles of clone HU-HO-2 (pre-cultivated in a 12:12 h day–night cycle) were individually transferred to 100 jars containing 20 mL filtered, aerated Lake Constance water and 2 mg C L−1 of A. obliquus. The 100 jars were partitioned into a 2 × 2 factorial design, with initialization time of infection (day vs. night) and hormone treatment (0 vs. 2 × 10−6 mol L−1 melatonin) as factors. The melatonin dosage was chosen according to previous experiments with Daphnia in which this concentration disrupted a diel vertical migration [13] and an increased neckteeth production [21]. The spores originated from an earlier experiment [22] and were stored within infected animals at −80 °C. These animals were homogenized in lake water, and the released spores were counted using a Neubauer chamber. Then, 480,000 spores suspended in 100 µL filtered, aerated Lake Constance water were added to each beaker. In the ‘day infection’ treatment, the 4-day-old Daphnia were exposed to the spores during the day (i.e., for 12 h) and were subsequently transferred to a fresh medium without spores with or without melatonin addition. In the ‘night infection’ treatment, Daphnia were exposed to spores during the night (i.e., for 12 h) and were subsequently transferred to a fresh medium without spores with or without the addition of melatonin. Daphnia were transferred daily to freshly prepared jars and examined for signs of infection (red-colored carapace and empty or fuzzy mass in the egg chamber) until they were 12 days old (allowing the uninfected animals to produce ~3 clutches).
Immune gene expression: In a previous study [23], infection with P. ramosa had no clear effect on the expression levels of the chosen immune genes of D. magna. Therefore, we analyzed five other innate immune genes in our experiments (Table A1) that had been found to be rhythmically expressed over 24 h [7] and cover different functions in Daphnia’s immune response [6]. Following the protocol by [24], we performed a 24 h gene expression experiment with the D. magna clone CH-H-5 in a 16:8 h day–night cycle and compared the results with gene expression of a third clone (Binnensee) by making use of cDNA from a previous study (Figure A1). To determine if melatonin addition had an influence on the peak of immune gene expression, 4-day-old juveniles of clone CH-H-5, which were pre-cultivated at a 12:12 h day–night rhythm, were transferred to 12 jars (3 individuals per jar) with 20 mL of filtered, aerated Lake Constance water and 2 mg C L−1 of A. obliquus. Then, 2 × 10−6 mol L−1 of melatonin was added to three jars during the day. At the switch from day to night, the animals from three jars with melatonin and the animals from three jars without melatonin were collected, frozen and stored at −80 °C until RNA extraction. The animals from the remaining 6 jars were transferred to a new medium, with half of the jars treated with melatonin during the night. At the next switch from night to day, the remaining animals were frozen at −80 °C, stored until RNA extraction and gene expression analysis. The entire experiment lasted 24 h, and the animals from the six jars that were never exposed to melatonin during this period served as the experimental controls. We used a silica column-based extraction method (NucleoSpin® RNA Kit; Macherey-Nagel, Dürren, Germany) optimized for low-input material, and verified the RNA quantity and integrity with a NanoDrop™ 2000 Spectrophotometer (ThermoFisher, Dreieich, Germany) before cDNA synthesis. The extracted RNA was then reverse-transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems™, Darmstadt, Germany). Quantitative PCR followed the procedure described in [24] and was performed on a 7500 Fast & 7500 Real-Time PCR System (Applied Biosystems™). Each reaction contained 5 ng cDNA (equivalent to 10 ng RNA) and 1 µL of a primer pair for five key innate immune genes: Dredd (Death-related ced-3/Nedd2-like protein), IMD (immune deficiency), TLR (toll-like receptor), and Chitinase 1 and Chitinase 2 (Appendix A, Table A1). The housekeeping genes ubc (ubiquitin-conjugating enzyme), actin (β-actin), cyclophilin (peptidyl-prolyl cis-trans isomerase), and gapdh (glyceraldehyde-3-phosphate dehydrogenase) served as an endogenous control. The data were analyzed using the 7500 Software v2.3 (Applied Biosystems™). The gene sequences were selected from the paper by [8], which had clearly identified and verified the genes to be immune genes for Daphnia pulex. When blasting those genes against the D. magna genome (which is better annotated than the D. pulex genome), we found a high similarity of these immune genes in the two Daphnia species. By making use of the program Primer 3 (https://primer3.ut.ee, accessed on 5 January 2020), we selected forward and reverse primers for each gene for the D. magna sequences. By making use of PCR and agarose gels, we found that each reaction led to one clear band (which means only one product was amplified) with the expected product size.
Since the immune gene expression peaked during the day/night switch in clone CH-H-5 and clone Binnensee (Figure 1, Appendix A, Figure A1), we verified this reaction for all three clones (including HU-HO-2 used in the infection assays) by specifically focusing on four points in time between ZT 14 and ZT20.5 (Figure A2). In order to directly compare the three clones, the first of these gene expression measurements served as a reference and was set to 1 in all three clones. Our measurements of the immune gene expression and infection outcomes enable us to identify associations between immune activity and susceptibility, but they do not allow causal inference, which would require the direct manipulation of immune pathways.
Statistical Analysis: The gene expression data were analyzed with one-way analyses of variance (ANOVA) followed by multiple comparisons (Tukey’s HSD); assumptions for ANOVA were met. In the 24 h samplings, the data were fourth root transformed. The differences in the time-dependent gene expression pattern between the initialization time of infection (day vs. night) and the melatonin treatment (yes vs. no) were quantified with two-way ANOVAs (type 2) for each gene; assumptions for ANOVA were met. The ratio of infected vs. healthy animals was analyzed using a generalized linear model (glm) with the logit function as the link function for binomial distribution, with the infection initialization time (day vs. night) and melatonin treatment (yes vs. no) as factors. The analyses were performed using R version 4.3.2 [25].
The experiments were conducted in 2020–2021, a period during which repeated COVID-19 lockdowns caused culture losses and restricted access to the laboratory. Although our initial plan was to work exclusively with the D. magna clone HU-HO-2 from the established D. magna—P. ramosa system, these constraints required us to incorporate additional Daphnia clones to complete the study. This adjustment ultimately proved valuable, as it enabled us to verify the circadian expression patterns of the immune genes in HU-HO-2 under independent experimental conditions, with the corresponding results provided in Appendix A documenting the robustness of our findings.

3. Results

Infection intensity in clone HU-HO-2 showed consistent directional patterns with respect to both the timing of pathogen exposure and melatonin treatment (Figure 1). The proportion of infected animals trended higher when exposure to P. ramosa occurred during the day compared to the night (glm, p = 0.073). A similar pattern was observed for melatonin, with infection proportions trending higher in animals treated with exogenous melatonin than in untreated controls (glm, p = 0.096).
In clone CH-H-5, the expression of all five studied immune genes showed a clear temporal structure across the 24 h cycle (Figure 2). During the early light phase, expression levels were generally low, with a modest but significant increase around ZT5, after which expression remained relatively stable until late in the light period (ANOVA, p < 0.001; Tukey HSD, p < 0.05; detailed statistical comparisons among the ZTs for each gene are provided in Appendix A, Table A2). From ZT11 onward, all genes exhibited a marked rise in expression that culminated in a pronounced, synchronized peak at ZT17, immediately after the transition from daylight to darkness. This peak represented the highest expression level for each gene (Tukey HSD, p < 0.05, Table A2). Following ZT17, the expression declined sharply across all genes. In clone Binnensee (Appendix A, Figure A1), the gene expression of the five different immune genes showed a comparable pattern to clone CH-H-5 (Figure 2). The expression of all genes was also highest around the switch of day and night between ZT13 and ZT17, followed by significant drops in expression, which stayed low during the night (Tukey HSD, p < 0.05, Appendix A, Table A2). Both clones displayed a cyclic, circadian-like 24 h pattern in immune gene expression, comparable to the expression dynamics of the clock genes [24]. A comparison of all three clones revealed that most immune genes peaked during the first or second nocturnal measurement before dropping sharply, which is a pattern that was also observed in clone HU-HO-2 used for the infection assays (Appendix A, Figure A2).
Melatonin exposure had a significant and time-dependent effect on immune gene expression in clone CH-H-5 (Figure 3; Appendix A, Table A3: interaction spore exposure × melatonin treatment). At the transition from day to night, gene expression was consistently higher in melatonin-treated animals, indicating an overall stimulatory effect when melatonin was present during the light phase. In contrast, at the transition from night to day, melatonin exposure during the dark phase resulted in reduced expression across all genes. This opposing response pattern was evident for each of the five immune genes.

4. Discussion

We found that the immune gene expression follows a cyclic pattern over 24 h that is comparable to the circadian expression of clock genes ([24] and Figure A1), suggesting that the circadian clock is involved in the regulation of immunity in Daphnia. We also show that in a 16:8 h day–night cycle, immune gene expression peaked at the switch from day to night in three D. magna clones (see Figure A2 in the Appendix A). A similar expression peak was observed for melatonin synthesis genes in Daphnia at the switch from day to night in different light regimes (16:8 h and 12:12 h: [8,24,26]). This suggests that the immune gene expression pattern is independent of the light regime experienced by the animals.
The infection assay was carried out in a day–night cycle of 12:12 h to exclude possible effects of different night and day lengths on infection rates. The activity of Daphnia’s immune system is probably higher during the night than during the day (following mRNA transcription after the gene expression peak at the switch from day to night). This should lead to a higher susceptibility to parasites and pathogens during the day. Infection prevalence tended to be higher during the day in the Daphnia–Pasteuria host–parasite system (p = 0.073), and the melatonin tended to increase infection counts (p = 0.096). Although not reaching clear significance, this pattern warrants more comprehensive studies to assess the robustness and underlying mechanisms.
Suppressive effects of melatonin on the innate immune system have been observed in other host–pathogen systems [27] and proposed to be related to the anti-inflammatory effects of melatonin [28]. We hypothesized that melatonin treatment would increase the susceptibility of D. magna to the parasite. This is supported by our infection assay, which shows a slight trend in prevalence with melatonin treatment, regardless of whether the infection occurred during the day or night. The immune system of Daphnia is likely influenced by melatonin, which acts as a transmitter of circadian clock signals [29] and thus influences the immune gene expression. We measured the expression of five immune genes after exposure to melatonin for 12 h both during the day and during the night and found differential gene expression with and without melatonin addition. However, contrary to our expectations, the gene expression was not generally decreased or increased by the melatonin addition. Instead, it increased from day to night, while it was reduced at the switch from night to day. Therefore, melatonin may influence both the rate of infection as well as the expression of immune genes, and this might be causally related: the increased number of infected animals during the day caused by melatonin addition could be due to a reduction in immune system activity. However, melatonin applied at night increased the gene expression, which was followed by an increase in infected animals. One explanation might be that melatonin addition influences not only gene expression but also generally attenuates the activity of the immune system.
Another possible explanation, which we did not test, is that the exogenous melatonin may not only act on the host but also on the parasite. In some systems, such as malaria, melatonin from the host acts as a developmental cue for the parasite [30]. If P. ramosa were similarly responsive, the melatonin addition could hypothetically accelerate spore maturation, increasing the number of infectious spores within hosts. Such a mechanism could counteract any melatonin-induced enhancement of host immune gene expression and contribute to the higher infection prevalence we observed. Future studies should therefore examine whether melatonin influences the timing or rate of P. ramosa spore maturation. Although melatonin treatment altered both immune gene expression and infection outcomes, our study cannot establish a causal relationship between these variables. Because we did not experimentally manipulate the immune pathways (e.g., through immune gene knockdowns), the observed patterns remain correlative.
The opposite response, that melatonin applied during the day increased immune gene expression at the subsequent night transition, and melatonin applied during the night reduced expression at the following day transition, is consistent with a phase-dependent interaction between melatonin and the endogenous circadian program. In short, daytime melatonin introduces a signal that is normally absent and therefore shifts or amplifies the upcoming nocturnal physiological state, while nighttime melatonin prolongs or strengthens the dark-phase signal and consequently suppresses the transition back to daytime physiology. Because several immune genes in Daphnia are rhythmically expressed, the same melatonin concentration might therefore produce directionally different outcomes depending on whether it is applied during the light or dark phase.
A higher infection efficiency during the day implies that P. ramosa might benefit from a particular behavior of its host, i.e., diel vertical migration (DVM), through which D. magna evades predators by migrating into deeper water strata during the day [31]. Since the infectious spores of P. ramosa rest in sediments [32], migrating Daphnia should be more likely to encounter pathogen spores during the day. Daytime exposure to spores may thus benefit the parasite because of a higher susceptibility of the host during the day due to lower immune gene expression. This suggested interplay between increased infection due to diel vertical migration should be investigated experimentally in the future. Conversely, remaining near the surface at night may be advantageous for Daphnia, as darkness could serve as a temporal refuge, allowing hosts to avoid exposure to Pasteuria spores. ALAN has been shown to suppress diel vertical migration, causing Daphnia to remain near the surface rather than migrating to deeper water strata during the day [33,34]. This behavioral alteration may influence Daphnia–Pasteuria dynamics, with potential consequences at the ecosystem level.
It is expected that advancing climate change will create conditions in which rising temperatures directly and indirectly increase infection risk and severity [35]. In the Daphnia–Pasteuria system, mesocosm studies show that warmer temperatures lead to more infected hosts and an earlier infection peak [36], suggesting that host–parasite interactions may intensify in the future. At the same time, infection with Pasteuria reduces the ability of Daphnia to cope with thermal stress [37]. Pasteuria spores are more vulnerable to UV-A and visible light than their Daphnia hosts, resulting in decreased infectivity [38]. However, climate change is associated with increased dissolved organic matter and eutrophication, which reduce water transparency [39,40] and thereby diminish this natural UV-mediated suppression, likely promoting epidemics. From a conservation and management perspective, these interacting stressors highlight the need for an integrated monitoring of temperature regimes, water transparency, and parasite prevalence in freshwater systems. Because global warming, eutrophication, and artificial daylight [33,34,41] at night can jointly amplify infection risk, effective management will increasingly depend on coordinated measures such as reducing nutrient inputs, preserving riparian shading, and tracking shifts in zooplankton community health to buffer ecosystems against compounding pressures.

5. Conclusions

Our study suggests a potential link between circadian rhythms, melatonin, and immune function in the Daphnia–Pasteuria host–parasite system. Although the observed trends in infection prevalence and melatonin effects were only marginally significant, they align with the observed cyclic patterns of immune gene expression. This highlights the complex interplay between host behavior, circadian rhythms, and parasite infection dynamics, warranting further investigation into the mechanisms underlying these relationships. Rising temperatures, together with anthropogenic stressors such as ALAN and chemical pollutants, may alter host–pathogen dynamics by altering disease transmission and by directly disrupting circadian rhythms and melatonin signaling in Daphnia. Understanding the underlying physiological mechanisms is crucial for conservation, as such disruptions may have cascading effects on freshwater ecosystems, including changes in Daphnia population stability, genetic diversity and migration behavior, reduced phytoplankton control, and altered carbon transfer to higher trophic levels.

Author Contributions

Conceptualization, A.S. and D.M.-C.; methodology, A.S.; software, A.S.; validation, A.S. and D.M.-C.; formal analysis, A.S., C.E.C. and D.M.-C.; investigation, A.S. and A.W.; resources, A.S.; data curation, A.S., C.E.C. and A.W.; writing—original draft preparation, A.S.; writing—review and editing, A.S., C.E.C. and A.W.; visualization, A.S. and A.W.; supervision, A.S.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the German Research Foundation with a grant to AS (project SCHW 1830/4-1, number: 401831661).

Institutional Review Board Statement

Not applicable as study objects are not subject to animal welfare regulations or ethical review requirements.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data is made publicly available at the Zenodo repository at https://doi.org/10.5281/zenodo.18624706.

Acknowledgments

We would like to thank Natascha Handke, Ricarda Cremer and Patrick Bartolin for their help in conducting the experiments, and Eva Lievens for their support with the Neubauer chamber. Thank you to Laura Epp, Lutz Becks, Peter Kroth and Bernard Lepetit for providing the laboratory infrastructure.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
ALANArtificial light at night
ANOVAAnalysis of variance
DVMDiel vertical migration
glmGeneralized linear model
qPCRQuantitative real-time PCR
ZTZeitgeber time

Appendix A

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Figure A1. A box plot of the gene expression of five different immune genes in clone Binnensee over 24 h in a light-dark cycle of 16:8 h (ZT0 is the switch from darkness to light, ZT16 is the switch from light to darkness). See Table A2 for statistical differences between the ZTs of each gene. This illustration was chosen to make the relative gene expression over 24 h in clone Binnensee comparable to clone CH-H-5 in the main manuscript (Figure 2). Immune gene expression of clone ‘Binnensee’ over 24 h (16:8 h day–night cycle)—cDNA from D. magna clone Binnensee from a previous study (in which clock gene expression was measured; ref. [24]) that had been stored for several months in −80 °C was used for the gene expression measurement of five different immune genes (Table A2) over 24 h. This clone had been grown in a day–night cycle of 16:8 h, and its cDNA had been used to demonstrate that D. magna’s clock gene expression is truly circadian [24], i.e., it also keeps its rhythm in complete darkness. In clone ‘Binnensee’, the gene expression of the five different immune genes followed the gene expression pattern of the circadian clock genes in a day–night cycle of 16:8 h (cf. [24]). The expression of all genes was highest around the switch of day and night between ZT13 and ZT17, followed by significant drops in expression (Table A2 with ANOVA results). The lowest gene expression of the immune genes was observed during the night (starting at ZT18 after the peak of gene expression) and during the day until ZT11. Chitinase 1 and 2 showed an additional, smaller gene expression peak at ZT1. The expression of all genes increased after ZT11. Using two different D. magna clones, i.e., Binnensee and CH-H-5 (see main manuscript Figure 2), we show that the expression of immune genes follows a cyclic pattern over 24 h that is comparable to the circadian expression of clock genes [24], suggesting that the circadian clock is also involved in the regulation of immunity in Daphnia.
Figure A1. A box plot of the gene expression of five different immune genes in clone Binnensee over 24 h in a light-dark cycle of 16:8 h (ZT0 is the switch from darkness to light, ZT16 is the switch from light to darkness). See Table A2 for statistical differences between the ZTs of each gene. This illustration was chosen to make the relative gene expression over 24 h in clone Binnensee comparable to clone CH-H-5 in the main manuscript (Figure 2). Immune gene expression of clone ‘Binnensee’ over 24 h (16:8 h day–night cycle)—cDNA from D. magna clone Binnensee from a previous study (in which clock gene expression was measured; ref. [24]) that had been stored for several months in −80 °C was used for the gene expression measurement of five different immune genes (Table A2) over 24 h. This clone had been grown in a day–night cycle of 16:8 h, and its cDNA had been used to demonstrate that D. magna’s clock gene expression is truly circadian [24], i.e., it also keeps its rhythm in complete darkness. In clone ‘Binnensee’, the gene expression of the five different immune genes followed the gene expression pattern of the circadian clock genes in a day–night cycle of 16:8 h (cf. [24]). The expression of all genes was highest around the switch of day and night between ZT13 and ZT17, followed by significant drops in expression (Table A2 with ANOVA results). The lowest gene expression of the immune genes was observed during the night (starting at ZT18 after the peak of gene expression) and during the day until ZT11. Chitinase 1 and 2 showed an additional, smaller gene expression peak at ZT1. The expression of all genes increased after ZT11. Using two different D. magna clones, i.e., Binnensee and CH-H-5 (see main manuscript Figure 2), we show that the expression of immune genes follows a cyclic pattern over 24 h that is comparable to the circadian expression of clock genes [24], suggesting that the circadian clock is also involved in the regulation of immunity in Daphnia.
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Figure A2. Since the immune gene expression peaked during the day/night switch in clones Binnensee (Figure A1) and CH-H-5 (Figure 2 in the main manuscript), we verified this reaction for all three clones (including HU-HO-2 used in the infection assays). Displayed here are box plots of the gene expression of five immune genes in three D. magna clones: HU-HO-2 (a), Binnensee (b), and CH-H-5 (c), with a particular focus on the day–night switch at four Zeitgeber times (ZTs) between ZT15 and 20. The first of the gene expression measurements always served as a reference and was set to 1 in the three clones. ZT 16 is the switch from day to night. Different letters indicate significant differences (ANOVA followed by Tukey HSD, p < 0.05). In all clones, most of the individual genes showed their highest expression peak during the first or second nocturnal measurement and dropped significantly thereafter (Appendix A, Table A4).
Figure A2. Since the immune gene expression peaked during the day/night switch in clones Binnensee (Figure A1) and CH-H-5 (Figure 2 in the main manuscript), we verified this reaction for all three clones (including HU-HO-2 used in the infection assays). Displayed here are box plots of the gene expression of five immune genes in three D. magna clones: HU-HO-2 (a), Binnensee (b), and CH-H-5 (c), with a particular focus on the day–night switch at four Zeitgeber times (ZTs) between ZT15 and 20. The first of the gene expression measurements always served as a reference and was set to 1 in the three clones. ZT 16 is the switch from day to night. Different letters indicate significant differences (ANOVA followed by Tukey HSD, p < 0.05). In all clones, most of the individual genes showed their highest expression peak during the first or second nocturnal measurement and dropped significantly thereafter (Appendix A, Table A4).
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Table A1. The reverse and forward primers of five different D. magna immune genes, the scaffold position of the genes in the D. magna genome (http://www.waterflea.org), the primers’ product length and the melting temperature (Tmelt). Since the D. magna genome is not available with identification numbers but can only be downloaded as a set of large scaffolds, respective scaffold positions are indicated.
Table A1. The reverse and forward primers of five different D. magna immune genes, the scaffold position of the genes in the D. magna genome (http://www.waterflea.org), the primers’ product length and the melting temperature (Tmelt). Since the D. magna genome is not available with identification numbers but can only be downloaded as a set of large scaffolds, respective scaffold positions are indicated.
GenePrimer F (5′-3′)Primer R (5′-3′)Scaffold PositionLength (bp)Tmelt (°C)
DreddGGATCGGATGCTGATCGTTTTCGTAAATTTCAACGTCGTG scaffold005687360
IMDTTGGAGGTTTCTTGGTCAGGTTCACAATGACCTTCCCACA scaffold0056813960
TLRCTTGGAAGATGTGCCGAAACAGTCGCTGCCAAAACAACTscaffold0071215560
Chitinase 1AATCAACAGAGCGGATGGACCCCCAAGACCCTTGTTCATAscaffold0064214560
Chitinase 2ACATTAATCTGGGCGTCAGCGGGTTGACCTCCGTAACAAG scaffold0002410160
Table A2. Left is the results of the analysis of variance (ANOVA) for the 5 immune gene expressions (fourth root transformed) in two D. magna clones (Binnensee—upper table, and CH-H-5—lower table) over 24 h. Right is the different letters indicating significant differences among the Zeitgebertimes, with the first letter of the alphabet representing the highest gene expression level (Tukey HSD, p < 0.05). The gray shades indicate the Zeitgeber time at night.
Table A2. Left is the results of the analysis of variance (ANOVA) for the 5 immune gene expressions (fourth root transformed) in two D. magna clones (Binnensee—upper table, and CH-H-5—lower table) over 24 h. Right is the different letters indicating significant differences among the Zeitgebertimes, with the first letter of the alphabet representing the highest gene expression level (Tukey HSD, p < 0.05). The gray shades indicate the Zeitgeber time at night.
Zeitgeber
BinnenseeZT 1ZT 4ZT 5.5ZT 11ZT 13ZT 15ZT 17ZT 18ZT 19.5ZT 21ZT 23
dfFp
Chitinase 110, 222869.9<0.001bcedbabgfhg
Chitinase 210, 21105.64<0.001abccdbaaadddcd
Dredd10, 224607.2<0.001ffedcbahfhg
IMD10, 22297.16<0.001gefdedabaefeffc
TLR10, 221856.9 <0.001gifdcbaheghgh
Zeitgeber
CH-H-5ZT 0ZT 2ZT 5ZT 6.5ZT 9.5ZT 12ZT 14ZT 16ZT 17ZT 18ZT 19ZT 20.5ZT 22
dfFp
Chitinase 112, 266912.6<0.001lgcfijhbadekm
Chitinase 212, 253385.5<0.001ijfcegihbaddhij
Dredd12, 253099.1<0.001ijfcdhighbaedgj
IMD12, 251481.6<0.001fecehigbaddff
TLR12, 255169.9<0.001hebdijibadcfg
Table A3. The results of the 2-way ANOVA (type 2) with the factors “initiation of infection” and “Melatonin-treatment” and their interaction. The gene expressions of all treatments were relative to the first treatment (switch from day to night without melatonin).
Table A3. The results of the 2-way ANOVA (type 2) with the factors “initiation of infection” and “Melatonin-treatment” and their interaction. The gene expressions of all treatments were relative to the first treatment (switch from day to night without melatonin).
Initiation of InfectionMelatonin-TreatmentInteraction
dfFpdfFpdfFp
Chitinase 11, 8785.6<0.0011, 81160.3<0.0011, 810,464<0.001
Chitinase 21, 82096.3<0.0011, 8942.1<0.0011, 812,837<0.001
Dredd1, 556.6<0.0011, 51398.9<0.0011, 57681<0.001
IMD1, 8298.3<0.0011, 840.4<0.0011, 83558<0.001
TLR1, 8583.1<0.0011, 80.20.691, 811,200<0.001
Table A4. The results of the analysis of variance (ANOVA) for the five tested immune gene expressions in the three D. magna clones (Binnensee, CH-H-5, HU-HO-2; df = degrees of freedom).
Table A4. The results of the analysis of variance (ANOVA) for the five tested immune gene expressions in the three D. magna clones (Binnensee, CH-H-5, HU-HO-2; df = degrees of freedom).
Binnensee CH-H-5 HU-HO-2
dfFpdfFpdfFp
Chitinase 13, 8886.3<0.0013, 86158.8<0.0013, 8820.6<0.001
Chitinase 23, 8947.4<0.0013, 85060.3<0.0013, 8150.8<0.001
Dredd3, 81875.7<0.0013, 8884.1<0.0013, 8213.1<0.001
IMD3, 8167.9<0.0013, 8593.9<0.0013, 8420.0<0.001
TLR3, 81067.8<0.0013, 83542.5<0.0013, 8305.1<0.001

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Figure 1. The percentage of infected and uninfected animals in the infection assay with or without the addition of exogenous melatonin and after 12 h exposure to P. ramosa during the day or night. The numbers on the bars indicate the absolute number of animals in each category.
Figure 1. The percentage of infected and uninfected animals in the infection assay with or without the addition of exogenous melatonin and after 12 h exposure to P. ramosa during the day or night. The numbers on the bars indicate the absolute number of animals in each category.
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Figure 2. A box plot of five immune genes in clone CH-H-5 over 24 h in a light-dark cycle of 16:8 h (ZT0 is the switch from darkness to light, ZT16 is the switch from light to darkness, indicated by the white and black rectangles above the x-axis). A common response axis is used for all gene expression profiles to facilitate direct comparison of their temporal patterns. This shared scale preserves relative differences in the response timing across genes and makes the collective peak at ZT17 following the day–night transition clearly visible (see Appendix A, Table A2 for the statistical differences among the ZTs of each gene).
Figure 2. A box plot of five immune genes in clone CH-H-5 over 24 h in a light-dark cycle of 16:8 h (ZT0 is the switch from darkness to light, ZT16 is the switch from light to darkness, indicated by the white and black rectangles above the x-axis). A common response axis is used for all gene expression profiles to facilitate direct comparison of their temporal patterns. This shared scale preserves relative differences in the response timing across genes and makes the collective peak at ZT17 following the day–night transition clearly visible (see Appendix A, Table A2 for the statistical differences among the ZTs of each gene).
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Figure 3. The box plots of five immune genes of the D. magna clone CH-H-5 at the switch from day to night and night to day (rectangles with gray gradient). In the graph, the gene expression measurements without melatonin always served as a reference and were set to 1 in all genes. This was done to make the effect of melatonin more accessible in comparison to the same treatment without melatonin, although the ANOVA was tested on the original gene expression data (2-way ANOVA, p < 0.05, Appendix A, Table A3). The asterisks represent the significant interaction in the 2-way ANOVA and indicate the significant differences between the treatments with and without melatonin, showing that the exposure to melatonin leads to opposing effects during the day compared to the night.
Figure 3. The box plots of five immune genes of the D. magna clone CH-H-5 at the switch from day to night and night to day (rectangles with gray gradient). In the graph, the gene expression measurements without melatonin always served as a reference and were set to 1 in all genes. This was done to make the effect of melatonin more accessible in comparison to the same treatment without melatonin, although the ANOVA was tested on the original gene expression data (2-way ANOVA, p < 0.05, Appendix A, Table A3). The asterisks represent the significant interaction in the 2-way ANOVA and indicate the significant differences between the treatments with and without melatonin, showing that the exposure to melatonin leads to opposing effects during the day compared to the night.
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Schwarzenberger, A.; Cáceres, C.E.; Martin-Creuzburg, D.; Wacker, A. When Timing Matters: Shedding Light on Mechanisms Underlying Host–Pathogen Dynamics in Freshwater. Conservation 2026, 6, 55. https://doi.org/10.3390/conservation6020055

AMA Style

Schwarzenberger A, Cáceres CE, Martin-Creuzburg D, Wacker A. When Timing Matters: Shedding Light on Mechanisms Underlying Host–Pathogen Dynamics in Freshwater. Conservation. 2026; 6(2):55. https://doi.org/10.3390/conservation6020055

Chicago/Turabian Style

Schwarzenberger, Anke, Carla E. Cáceres, Dominik Martin-Creuzburg, and Alexander Wacker. 2026. "When Timing Matters: Shedding Light on Mechanisms Underlying Host–Pathogen Dynamics in Freshwater" Conservation 6, no. 2: 55. https://doi.org/10.3390/conservation6020055

APA Style

Schwarzenberger, A., Cáceres, C. E., Martin-Creuzburg, D., & Wacker, A. (2026). When Timing Matters: Shedding Light on Mechanisms Underlying Host–Pathogen Dynamics in Freshwater. Conservation, 6(2), 55. https://doi.org/10.3390/conservation6020055

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