Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Health monitoring in birds using bio-loggers and whole blood transcriptomics

Abstract

Monitoring and early detection of emerging infectious diseases in wild animals is of crucial global importance, yet reliable ways to measure immune status and responses are lacking for animals in the wild. Here we assess the usefulness of bio-loggers for detecting disease outbreaks in free-living birds and confirm detailed responses using leukocyte composition and large-scale transcriptomics. We simulated natural infections by viral and bacterial pathogens in captive mallards (Anas platyrhynchos), an important natural vector for avian influenza virus. We show that body temperature, heart rate and leukocyte composition change reliably during an acute phase immune response. Using genome-wide gene expression profiling of whole blood across time points we confirm that immunostimulants activate pathogen-specific gene regulatory networks. By reporting immune response related changes in physiological and behavioural traits that can be studied in free-ranging populations, we provide baseline information with importance to the global monitoring of zoonotic diseases.

Introduction

Wild animals are important reservoirs of a wide range of infectious diseases, of which some have the potential to spill over to humans (zoonoses)1. Major modern human diseases such as influenza and salmonellosis are frequently of zoonotic origin2. While zoonoses may have major impact on human health, the natural hosts of these diseases often show little signs of disease upon infection3. This allows for these diseases to persist in reservoir species with a risk of interspecies transmission to more susceptible host-species. Reservoir host physiology and behaviour during infection affect survival and disease duration in the host4 and therefore also affect the spread of infectious diseases5. Still little is known about the immune response of reservoir hosts in their natural environment3. Several hurdles have constrained our understanding of pathogen dynamics and immune responses in wild reservoir hosts. These include, but are not limited to (1) the scarce availability of toolsets available for measuring immune responses in free-ranging non-model species in comparison to those available for well-studied model species6,7, and (2) lack of detailed experimental data from many reservoir species making it difficult to interpret the results from immunological tests in a field setting3,7.

The innate immune system provides the first line of defence against pathogens. An important part of the innate immune system is the acute-phase response (APR), a rapid and systemic response activated by trauma, inflammation, stress and infection8. The APR is triggered by the release of proinflammatory cytokines in immune cells, and is characterised by a suite of molecular, physiological, and behavioural changes9,10. Animals with an activated APR usually have a febrile response within hours of pathogen exposure11, but the duration and magnitude of the febrile response differ between host species12,13,14 and depend on the type of stimuli14,15,16. Besides fever, animals usually display sickness behaviours such as lethargy, depression and anorexia during the APR9,10. The APR has an important role for the recovery and survival of the host during infection4,17, and may thus affect the risk of further transmission of infectious agents5,17. Learning about the APR in reservoir species is therefore important for understanding their potential role in the spread of zoonotic diseases, and monitoring their APR in the field could be used as a warning system for disease outbreaks.

Rapid advancement of bio-logging technologies now allows for the study of physiology and behaviour of free-living animals18. Bio-loggers that can detect changes in important characteristics of the APR (including body temperature, movement patterns and energy expenditure) are increasingly being used in wildlife research19, and thus provide information that could be used for identifying signs and symptoms of disease in reservoir species. Similarly, rapid advances in and decreased costs of next generation DNA and RNA sequencing technologies now allow researchers to study the underlying mechanisms of the APR in non-model species, and provides disease markers for studying immune status and responses in wild populations20,21,22. While these technological advances have been used to study changes in behaviour23, physiology24 and regulation of immune genes in free-living animals25, they have so far not been studied simultaneously during the APR in reservoir species of zoonotic diseases. Studies that define the baseline for these measurements and their deviation during the immune response in reservoir species will provide a valuable resource for disease monitoring in natural populations.

The aim of this study was to identify reliable and repeatable, integrated multi-scale monitoring methods from several biological fields that will facilitate ecological immunology studies and disease monitoring in wild bird reservoirs. Further we wanted to identify known20,26 and novel candidate genes that are upregulated during different stages of the APR and in response to different pathogens, in tissues that can easily and repeatably be collected in free-living birds. For this purpose, we experimentally induced immune responses in an important natural vector for avian influenza virus (AIV), the mallard (Anas platyrhynchos)27,28. While mallards show few signs of disease when infected with low pathogenic strains of AIV29, they can show clinical signs and symptoms including fever, anorexia, neurological signs and death when infected with highly pathogenetic AIV30,31. To study the APR in mallards, we triggered the immune response in captive mallards using non-infectious immunostimulants and monitored the response with state-of-the-art bio-loggers measuring 3D acceleration, heart rate, and body temperature and with high throughput RNA sequencing technologies. Our specific aims were to (1) examine the magnitude and timing of changes in the bio-logged parameters, (2) assess whether a correspondent immune response can be detected in global gene expression profiles from whole blood, and (3) determine the specificity of the immune response to different stimulants, using transcriptomics and gene regulatory network analyses.

Results

Body temperature, heart rate and activity

We challenged mallards with three types of non-infectious immuno-stimulants, thereby mimicking natural infections by RNA viruses (polyinosinic:polycytidylic acid, poly I:C), gram-negative bacteria (lipopolysaccharide, LPS) and gram-positive bacteria (cell walls of heat-killed Staphylococcus aureus). To determine if the immunostimulants caused physiological and behavioural changes in the mallards, we monitored changes in body temperature, heart rate and activity in three individuals per treatment group using bio-loggers (Supplementary Information Figure S1). We fitted generalised additive mixed models (GAMMs) to the data until 18.5 h post stimulation (hps) for each physiological measurement (Supplementary Information Tables S1S3) to compare the effect of the different treatments.

To track the timing of the febrile response, we measured body temperature after administration of immune stimulants. The average body temperature increased in all treatment groups when compared to the control group (Fig. 1a). The maximum mean temperature in the poly I:C group was reached after 4.1 hps (n = 3, mean 42.04 °C, 95% Credible Intervals (CrI) 41.63–42.45 °C) and was elevated until 10.5 hps. The maximum mean temperature in the LPS group was reached after 3.0 hps (n = 3, mean 41.96 °C, CrI 41.55–42.37 °C), and was elevated until 14.9 hps. The maximum mean temperature in the S. aureus group was reached after 4.2 hps (n = 3, mean 42.01 °C, CrI 41.60–42.42 °C) and was elevated until 18.5 hps—compared to ~ 40–41 °C in the control group.

Figure 1
figure1

Physiological changes following challenge with immune stimulants. Changes in (a) body temperature, (b) heart rate and (c) activity level were measured remotely using bio-loggers. Mean and 95% credible interval for each treatment group was plotted until 18.5 h post stimulation, as estimated from the posterior distribution of the GAMM. An activity value of 0 means no activity, while higher values mean more movement in either or all of the three axes.

To assess the stress response, we examined the heart rate in the mallards following stimulation. The average heart rate was elevated in all treatment groups immediately following the stimulation (Fig. 1b). The heart rate remained higher in the stimulated groups than in the control group until 8.8 hps for the poly I:C group, until 7.8 hps for the LPS group, and until 13.8 hps for the S. aureus group.

Inactivity is often a sign of disease, so we monitored activity levels using high definition accelerometers that record acceleration along three axes32. No clear differences were apparent during the response to treatment, however, ducks showed increased activity upon recovery from the viral mimic (Fig. 1c).

Leukocyte differential count

Acute phase response to disease is often accompanied by the recruitment of neutrophils into circulating blood resulting in a blood neutrophilia. Like neutrophils, their counterpart in birds, heterophils, are critically involved in the immediate response to pathogens33. To confirm that changes in blood leukocyte composition accompanied immune responses to stimulants in our study, we estimated the mean leukocyte proportions (Supplementary Information Text S1 and Figure S2) and the heterophil:lymphocyte (H:L) ratio for each treatment group. We performed differential leukocyte counts on more than 200 cells on stained blood films using light microscopy for five individuals per treatment group at each time point. We observed an increase in the H:L ratio, with a peak at 6 hps, for all treatment groups (Fig. 2).

Figure 2
figure2

Elevated H:L ratios in mallard blood following challenge with immune stimulants. Mean H:L ratio and 95% credible intervals as estimated from the posterior distribution from the multinomial model (n = 5 ducks/timepoint).

Genome wide gene expression profiling

RNA-sequencing and differential gene expression

To identify differentially expressed genes (DEGs) following administration of immune stimulants, we performed full transcriptome sequencing on blood samples from three females and three males for each treatment and timepoint, adding up to a total of 120 samples (Supplementary Information Text S2). As our preliminary analyses did not detect a clear difference in the response between females and males, we included individuals from both sexes in the differential expression analyses (Supplementary Information Text S3). The number of DEGs for each treatment group peaked at 1016 following poly I:C challenge, 256 for the LPS challenge and 94 for the S. aureus challenge, however, this differed between the time-points (Supplementary Datasets S1S3, Supplementary Information Figure S3). In the poly I:C and the LPS treatment group the majority of the genes were differentially expressed at 3 and 6 hps, indicating a rapid response to the treatments. In contrast, the majority of DEGs in the S. aureus treatment group were detected at 12 hps. To identify key genes that can be used to assess immune status in a field experiment, we identified the top DEGs from each time point and each treatment (Fig. 3, Supplementary Information Tables S4S6, Text S4). The overlap of significantly DEGs between the different treatment groups was moderate, with roughly 13.6%, 7.5%, 4%, and 0% of the DEGs in any of the treatment groups being shared between two or more treatment groups at time points 3 h, 6 h, 12 h and 24 hps respectively (Supplementary Information Figure S4, Text S5). Thus, the differential gene expression analyses suggest that the immune response to each immunostimulant is unique.

Figure 3
figure3

Heatmap illustrating the log2 fold change of the differentially expressed genes (rows) that were most up- or downregulated for each time point (column) and treatment group (FDR < 0.05). Red indicates that the gene expression was higher-, and blue indicates that the gene expression was lower in the treatment group than in the control group. For genes that could not be assigned a gene name from the mallard genome, hits identified through the BLAST search is shown (for more details see Supplementary Information Tables S4S7 and Supplementary Dataset S4. Gene name changed from IFITM3 to IFITM1, following the suggested nomenclature in Blyth, et al.61. All DEGs from the treatment groups are listed in Supplementary Datasets S1S3.

Gene ontology (GO) analysis and enrichment test

We performed a gene ontology (GO) analysis to investigate whether certain biological processes and pathways were overrepresented in our list of DEGs. We found a significant overrepresentation of Reactome pathways in the poly I:C treatment group at 3 (n = 25), 6 (n = 29), and 12 h (n = 14) ps and in the LPS treatment group 3 (n = 62) and 6 hps (n = 29) (Supplementary Datasets S5S6). In the poly I:C treatment group, overrepresented Reactome pathways were found within functions such as antiviral responses, adaptive and innate immune system, and interferon signaling (Fig. 4 and Supplementary Dataset S5, Supplementary Information Text S6). Several of the overrepresented Reactome pathways in the LPS treatment group were related to T-cell activation and signaling, adaptive immune system functions and heat shock and stress responses (Fig. 4 and Supplementary Dataset S6, Supplementary Information Text S6). No overrepresented pathways were detected for the S. aureus treatment group. The overrepresented Reactome Pathways with the highest enrichment score for each of these groups are shown in Fig. 4. The results from the GO overrepresentation analysis for the Biological Processes (Supplementary Information Figure S5, Text S7) were similar to those from the GO overrepresentation analysis for the Reactome pathway.

Figure 4
figure4

Heatmap illustrating overrepresented Reactome Pathways with the highest fold enrichment score for the poly I:C and LPS treatment groups. Pathways that were significantly overrepresented (FDR < 0.05) are shown in red colour, with faint red indicating lower fold enrichment score and dark red a higher fold enrichment score. Pathways that were not significantly overrepresented are shown in grey for that particular treatment group and time point. No Reactome Pathways were overrepresented in the S. aureus treatment group, nor 24 hps in the poly I:C treatment group or 12/24 hps in the LPS treatment group. All overrepresented gene ontology terms are listed in Supplementary Datasets S5S6.

Data mapping onto KEGG pathways

To further explore the specificity of the immune response to each immunostimulant, we visualised the gene expression profiles for each treatment and time point in the context of gene networks. To facilitate exploration of the results as well as future comparative studies, we built an interactive webpage (http://orn-files.iwww.mpg.de/dgeviz) where the gene expression fold changes are illustrated on seven immune related pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database34,35,36. The fold changes and corrected p values for all treatments and pathways are available in Supplementary Dataset S7.

The Reactome pathways showing highest differential gene expression following poly I:C stimulation included RIG-like receptor, TLR3, and interferon-alpha signaling pathways, as expected. Within the RIG-I/MDA5 signaling pathway several genes were significantly upregulated at one or several of the time points following the poly I:C treatment (Fig. 5, Supplementary Dataset S1). Most notably, RIG-I (Retinoic acid-Inducible Gene I; also called DDX58) and MDA5 (Melanoma Differentiation-Associated protein 5; also called IFIH1) were upregulated at early time points, as were the activating protein TRIM25 (Tripartite Motif Containing 25), and downstream signaling molecules TRAF (TNF Receptor Associated Factor) and FADD (Fas Associated Via Death Domain), and interferon regulatory protein IRF7 (Interferon Regulatory Factor 7). Details of the toll-like receptor signaling pathway and the Influenza A pathway are shown and discussed in Supplementary Information (Figures S6S7, Text S8).

Figure 5
figure5

Log2 fold changes of the expression level between the poly I:C and control treatments mapped on the RIG-I like receptor signaling pathway from the KEGG database34,35,36. Red indicates that the gene expression was higher, and blue indicates that the gene expression was lower in the poly I:C treatment group than in the control group. White indicates no change or a similar change in the poly I:C treatment group as in the control group. Each box represents one gene in the pathway and the columns within the box show the gene expression fold change for the four time points; 3, 6, 12 and 24 hps from left to right. For more details see http://orn-files.iwww.mpg.de/dgeviz/RIG_PICButton.html.

Gene expression of target genes

We measured gene expression of five key immune genes, identified in our transcriptome analysis for the poly I:C treatment group, from nine individuals using real-time qPCR to validate our RNA-seq results. The gene expression results from the real-time qPCR confirmed the RNA-seq results (Supplementary Information Figures S8S9). Of the five measured genes, RSAD2 was the most highly expressed gene, followed by RIG-I, TLR3 and IRF7 (Supplementary Information Figure S8). Finally, the genes mined from our transcriptome analyses provide a panel of target genes for analysis of antiviral responses in mallards.

Discussion

We quantified several important physiological traits in healthy and immune-challenged individuals of birds in a controlled setup, and thereby provide crucial information that can be used for disease monitoring in wild populations. By combining state-of-the-art bio-loggers to monitor an immune response in progress with high throughput sequencing technologies to characterise genome wide gene expression profiles in blood samples collected across several timepoints, we were able to examine the duration and magnitude of the immune response in relation to each immune-stimulant.

An acute phase response includes physiological and behavioural changes. Studying such changes during infection in animals is not trivial, and usually includes disturbance of the animals for sampling or observation. One of the main goals of this study was to investigate if the acute phase response (APR) can be monitored using on-animal bio-loggers, technologies that have great applications for remote, long-term disease monitoring in wild populations. For this purpose, we recorded changes in body temperature, heart rate and activity in control and immune challenged mallards using bio-loggers, while simultaneously monitoring the cellular response using blood immune assays. Elevated body temperature can be used to quantify the extent of a fever response in birds37. Heart rate is linked to oxygen consumption via Fick’s equation and is often used as a means of estimating energy expenditure in (wild) animals38,39. High definition accelerometers that record acceleration along three axes can be used to detect subtle changes in behaviour, while minimising any potential interference from a human observer’s perspective32. We detected a clear and rapid increase in the core body temperature as well as heart rate in all immune challenged mallards, which correlated well with the timepoints when the white blood cell composition and gene expression profiles were altered in the immune challenged ducks. In contrast, no significant differences in activity levels between the treatment groups were detected during the acute phase response.

The cost of an upregulated immune response is debated40, yet continuous long-term heart rate data are rarely reported in disease ecology studies. It is therefore still unknown, too, whether heart rate and associated energy expenditure increase significantly in birds during common infections, such as AIV28. Here we found that the heart rate of mallards became elevated during the acute phase response in all immunological treatments. As heart rate loggers may become more commonly used in ecological studies in the future19 we expect that we will soon gain additional insights into the magnitude, duration and function of elevated heart rates during infections in wild birds.

While a host’s physiological response to an infection is important, a behavioural response such as sickness behaviour is similarly essential and ubiquitous17. Unexpectedly, we did not find differences in activity patterns between the immune challenged and the control birds. Currently we do not know whether the lack of a difference is a result of the captive conditions of our experimental design, a subclinical course of the APR due to the lower pathogenicity of the stimulant compared to the true pathogen, or whether it also reflects the possibility that the movement patterns of wild mallards are not affected by some infections23. We will follow up on this question in a sequel study that will investigate movement patterns in free-ranging mallards.

While certain physiological and behavioural responses can be monitored remotely, others require a biological sample from the animal. Luckily, blood samples can often be obtained easily, non-destructively and repeatedly from animals upon capture. Here we inferred the immune status and health of mallards by observing changes in the number of leukocytes in the blood. We saw a change in white blood cell composition in all treatment groups throughout the experimental protocol, but to a much lower level in the control group. The change in the control group suggests that leukocyte composition changed either due to the injection of saline, and/or due to some stressful condition during the course of the experiment. Handling stress itself can alter the white blood cell composition in birds41. Nevertheless, the change in heterophil:lymphocyte (H:L) ratio was more pronounced in all treatment groups than in the control group (Fig. 2) and correlated well with the timeline of the acute phase reaction.

We deliberately focused both on duration and magnitude of the acute phase response in the mallards to provide guidelines on when to measure the response when using these pyrogens. The peak body temperature in mallards was already observed after 3–4.5 h post stimulation, similar to what was previously found in Pekin ducks (Anas platyrhynchos domesticus)16,42. Likewise, the differential gene expression and the leukocyte count analyses show that the timing of the fever response correlated well with the number of genes that were differentially expressed in the treatment groups (Fig. 1a and Supplementary Information S3) as well as the increase in H:L ratio (Fig. 2). In future studies, focus could thus be given to one or a few of the characteristics measured here.

Our study is a first step to understand the immunocompetence in truly wild animals. While our initial studies examined immune responses of a non-domestic species under controlled conditions, we are aware that subsequent studies will need to be done entirely in the wild. Our strategy here was to reduce the complexity of the environment and to induce an immune response using immunostimulants. These immunostimulants are non-infectious compounds that trigger an immune response, but do not make the animal an infectious carrier. They can therefore be used in field experiments to study the immune response without spreading infectious agents37. In our study we aimed to test if immunostimulants (poly I:C, LPS and inactivated S. aureus) activate pathogen-specific gene regulatory networks in mallards.

Several of the most highly differentially expressed genes in the poly I:C treatment (Fig. 3a) are interferon stimulated genes (ISGs) that are activated during viral infections in ducks, including viperin (RSAD2), IFITM1 (Interferon induced transmembrane protein 1), IFIT5 (Interferon induced protein with tricopeptide repeats 5) and OASL (2′–5′ oligoadenylate synthetase-like)43. Poly I:C also induced a rapid and sustained upregulation of the IFI6 (IFN-α-inducible protein 6) in the ducks, an effector that blocks the replication of flaviviruses such as West Nile virus44. This indicates that poly I:C induced a typical antiviral response in the mallards. Several of the most upregulated genes in the LPS treatment group are involved in defence response, including TREM2 (triggering receptor expressed on myeloid cells 2), IL1R2 (Interleukin-1 receptor 2), PTX3 (Pentraxin 3), LYG2 (lysozyme G2) and IL22RA2 (Interleukin 22 Receptor Subunit Alpha 2) (Fig. 3b). The most upregulated gene in the LPS treatment group (PTX3) was recently proposed as an important marker to monitor inflammatory conditions in poultry, as it is upregulated in response to bacterial and viral infections in chickens45. While the role of PTX3 is largely unknown in ducks, it was upregulated during early stage of egg drop syndrome virus infection in duck embryo fibroblast cells46. Interestingly, no significant upregulation of PTX3 was detected in the mallards in response to poly I:C or inactivated S. aureus (Supplementary Datasets S1 and S3). While PTX3 is likely a good marker for LPS in mallards, its role in antiviral and antibacterial response in mallards thus needs to be further investigated. Another highly upregulated gene in the LPS treatment group (IL1R2), was recently proposed as a biomarker for differentiating gram-negative and gram-positive bacterial infections in mice, as this gene was expressed at a higher level in mice challenged with inactive gram-negative bacteria (Escherichia coli) than inactive gram-positive bacteria (S. aureus)47. In line with the results in mice, IL1R2 was differentially expressed in the ducks treated with LPS (Supplementary Dataset S2) but not with the inactivated S. aureus (Supplementary Dataset S3). However, as few genes were highly up- or downregulated in the mallards treated with inactivated S. aureus (Supplementary Dataset S3) more research is required to determine if IL1R2 is also a good marker for differentiating gram-negative and gram-positive bacterial infections in mallards.

One of the top ten differentially expressed genes in the S. aureus treatment group (LYG2), was also differentially expressed in the LPS and the poly I:C treatment group (Supplementary Dataset S1S2). In fact, this was one of the few genes that was differentially expressed in all treatment groups (Supplementary Dataset S1S3). While the role of this gene in the duck immune response is unknown, LYG2 is upregulated in response to bacterial48 as well as viral49 infections in chicken. It is thus plausible that this gene may be involved in the immune response to a broad range of pathogens in ducks as well.

Results from our gene ontology analyses show that the immunological stimulants induce pathogen-specific changes, which justify their use as surrogates to live pathogens in future manipulative studies. Several antiviral pathways were overrepresented in the poly I:C treatment groups, including the RIG-I/MDA5 signalling pathway (Fig. 4, Supplementary Dataset S5). RIG-I and MDA5 are pattern recognition receptors that recognise RNA viruses in the cytoplasm50, and activate a cascade of immune proteins which subsequently triggers the production of type I interferons51. The RIG-I/MDA5 signalling pathway is involved in the clearance of viruses with high relevance for mammals as well as birds50,51, and are upregulated in ducks infected with AIV52,53, Newcastle disease virus54, duck hepatitis virus55, and duck plague virus56. Considering that the mallard is a vector for viral diseases with major impact on human health and that several antiviral pathways were upregulated in the poly I:C treatment group (Fig. 4), this stimulant will be of particular interest for future ecological immunology studies in mallards. We also found that several pathways and biological processes related to immune function, inflammation and stress response were activated in the LPS treatment group (Fig. 4, Supplementary Information Figure S5), as has been seen in passerines20,57. Interestingly, although inactivated S. aureus (the Gram-positive bacterial mimic) induced an increase in body temperature and heart rate as well as a change in leukocyte composition, only a low number of differentially expressed genes (DEGs) were detected in mallards stimulated with this pyrogen. We think that we are only observing a part of the immune response usually triggered by live S. aureus infection, as live S. aureus activate the NGF \(\upbeta \)-TRKA signaling axis following stimulation of the NLRP3 inflammasome58. Therefore, we suggest that LPS, but not inactivated S. aureus, has a great potential for mimicking bacterial infections in ecological immunology studies in mallards.

Many of the genes that were differentially expressed in the mallards in our study are uncharacterised, and could not be identified using a similarity search against the other genomes used in our study. This demonstrates that there is still a lot to learn about the immune system in birds as well as some important model species. Further, the function of some genes that were differentially expressed in the mallards is unknown. One such example is the B4GALNT4 (Beta-1,4-N-Acetyl-Galactosaminyltransferase 4) gene which was one of the most upregulated genes in the poly I:C treatment group (Fig. 3a, Supplementary Dataset S1, Supplementary Information Table S4). While mice deficient in B4GALNT3 (Beta-1,4-N-Acetyl-Galactosaminyltransferase 3), a paralog of B4GALNT4, have reduced protection against influenza virus59, the role of B4GALNT4 in the response to viral infections is unknown60. B4GALNT4 is located next to the IFITM3 (Interferon Induced Transmembrane Protein 3) gene, which is known to restrict influenza virus61. IFITM3 is upregulated in mallards during influenza infection61, and was also one of the most upregulated genes in our poly I:C treated mallards (Supplementary Information Table S4). If further research supports our suggestion that B4GALNT4 is involved in the viral immune response in mallards, this gene is a good candidate for future functional studies with potential to improve our understanding of how mallards clear viral infections.

In many cases, the natural reservoir of EIDs show little to no signs of disease when being infected by the same pathogen that causes serious damage in other species3. Comparative transcriptomics and pathway analyses have great potentials for detecting subtle differences in the immune system that relate to specific differences in susceptibility or resistance to infections. If future studies move towards evaluating RNA-seq in the framework of pathways, then such differences will become more evident. By visualising the gene expression changes on these pathways for the mallard and creating an interactive webpage where the results can be evaluated (http://orn-files.iwww.mpg.de/dgeviz/) we provide means for future comparisons of the immune response in different species, including species with differences in severity of pathogenesis to AIV. When assessing gene expression in a pathway framework it is important to keep in mind that reference pathways are usually built on knowledge from model species such as human or mouse. The function of certain elements in the pathway used in this study might hence be different in the mallard, or even differ between the mallard and closely related species. One such example with relevance for this study is that birds lack the mammalian TLR6 and TLR962. Other differences that will be of relevance for future comparative immunology studies in birds are that chicken, but not duck, lack the RIG-I and TRIF related adapter molecule (TRAM, also known as TICAM-2) proteins53,63. TRAM bridges the TLR4 and TRIF in the TLR3- and TLR4-mediated MyD88-independent signalling pathway, and is an important part of the TLR4 pathway64. As more genomic and transcriptomic studies are undertaken, a key next step will be the construction of species-specific immune regulatory networks for species with importance as hosts of EIDs.

In this study we used attached and implanted bio-loggers as well as blood-based assays to record several characteristics of the immune response simultaneously. While a combination of different measuring techniques is indispensable for obtaining a comprehensive picture of the immune response, each technique has inherent practical limitations. The heart-rate and body temperature bio-loggers used in this study require surgical implantation, which may not be feasible in certain species and field settings. Blood-based assays in turn may be available to more research groups, but baseline information may not be available for many reservoir species hampering the interpretation of results. The technology to use within a particular study thus has to be determined based on the ecology of the species of interest and the research question in mind. We expect that the continuous technological development (including smaller sensors, improved ability to transmit data, and novel attachment and recovery methods)65 combined with appropriate archiving and trans-disciplinary sharing of data (e.g. movebank.org) will facilitate the usage of bio-loggers for disease monitoring in the future.

In conclusion, we show that poly I:C and LPS induce a rapid and predictable acute phase response in mallards. We confirm that body temperature and heart rate increase during the acute phase response, and that this can be monitored remotely using on-animal bio-loggers. In combination with GPS data from tracking devices that can record movements of animals on a large scale66, we can now get closer to understanding the epidemiology of diseases such as AIV in mallards. By analysing the transcriptome after immune stimulation, we did not only gain novel insights into the molecular mechanisms behind this immune reaction but also showed that pathogen-specific immune pathways were upregulated in the blood during the acute phase response.

Materials and methods

All methods are described in more detail in the Supplementary Information.

Immune challenge

Forty-four first generation captive-bred mallards (Anas platyrhynchos) were included in the study. The mallards were housed in groups of three in outdoor aviaries at the Max Planck Institute for Ornithology (MPIO) in Radolfzell, Germany. The aviaries measured 3 m x 4 m × 2.5 m (w × l × h) and contained a water basin and a shelter with nesting material (Supplementary Information Text S9).We treated the mallards with one of three immunostimulants to mimic infections by different pathogens. The double-stranded RNA molecule polyinosinic:polycytidylic acid (poly I:C, 1 mg/kg) was used to mimic a viral infection, lipopolysaccharide (LPS, 100 µg/kg) was used to mimic a Gram-negative bacterial infection, and cell walls of heat-killed Staphylococcus aureus (approx. 2.5 × 1010 cell walls) were used to mimic a Gram-positive bacterial infection. These compounds are all used as common tools for scientific research on the immune response and have all been shown to induce an increase in body temperature in Pekin ducks (Anas platyrhynchos domesticus)16. As body temperature as well as heart rate can be elevated in birds during stress or handling67, the experiment was divided into two parts. The first part of the experiment (Experiment 1) allowed us to monitor changes in body temperature, heart-rate and movement patterns from the individuals without disturbance, while the second part of the experiment (Experiment 2) allowed us to collect blood samples that were used to study differential gene expression and white blood cell composition (Supplementary Information Figure S1).

In Experiment 1 we recorded changes in physiology and behaviour during the acute phase response using bio-loggers. For this purpose, we implanted heart rate and body temperature sensors (E-obs GmbH, Grünwald, Germany, www.e-obs.de) in the abdominal cavity of 12 individuals. Four weeks after surgery, we attached acceleration loggers (E-obs GmbH, Grünwald, Germany, www.e-obs.de) to the back of the same individuals using a customised backpack68. We divided the individuals into four groups of three individuals each, which then received one of the treatments. The individuals were left in the aviaries with minimal disturbances after stimulation to avoid changes in physiology and behaviour due to handling stress.

In Experiment 2 we repeated the treatments to collect blood samples for leukocyte counts and global gene expression analysis. An additional 32 mallards were included in the second stimulation event to ensure that enough samples were available for further analysis. The treatment was repeated after a minimum of two weeks to avoid potential short-term tolerance effects to the stimulants16,69. Once again, the individuals (n = 44) were divided into four groups (n = 11) and stimulated with one of the three treatments or the control. Blood samples were collected before stimulation and at a number of time points post stimulation (ps) (3 h, 6 h, 12 h, and 24 h).

For more detailed description see Supplementary Information (Text S9).

Body temperature, heart rate, activity data and leukocyte composition

We used bio-loggers to observe changes in physiology and behaviour during the acute phase response. Briefly, we recorded body temperature, electrical activity of the heart, and acceleration data for three individuals per treatment. The data was downloaded remotely using an e-obs base station located outside the aviaries. We calculated the heart rate as beats per minute for every five-minute period from the electrocardiograms. We estimated the activity level of the mallards by calculating the mean of the variance of the acceleration measurements from each axis, following70. We fitted generalised additive mixed models (GAMMs) to the data for each physiological measurement, to investigate whether they differed between the treatments. We estimated the mean from the posterior distribution using a Bayesian framework for each measurement.

We performed a leukocyte differential count and calculated the heterophil:lymphocyte (H:L) ratio to get a better understanding of what changes occur in the leukocyte composition during the acute phase response in mallards. We prepared blood smears for five individuals per treatment and time point and determined the proportion of heterophils, lymphocytes, monocytes, eosinophils, and basophils using light microscopy. Staining and evaluation of blood films was performed by Pendl Lab, Switzerland. We fitted a multinomial model to estimate and compare the proportions of each leukocyte type in the different treatments and time points. The mean of the H:L ratio was estimated from the posterior distribution of the multinomial model.

We report the 95% Credible Intervals (CrI) using the 2.5% and 97.5% quantiles from the posterior distribution from each model. The mean of each measurement was considered different from the mean of the control group when the CrI of the treatment group did not include the estimated mean from the control group.

For more detailed description see Supplementary Information (Text S10, Table S8).

Genome wide gene expression profiling

We monitored changes in the whole transcriptome in whole blood before and after the immune-stimulation for six individuals per treatment, using next generation RNA-sequencing to determine whether relevant immune pathways were upregulated in the respective treatments. We sequenced mRNA libraries on the Illumina HiSeq2500, and performed differential gene expression analysis using packages edgeR71 and limma72 as described in 73. Briefly, we computed empirical Bayes moderated t- and B-statistics, correcting for possible sex and individual differences using fixed and random factors, respectively, to identify genes that were differentially expressed due to treatment. Genes with an FDR adjusted p value < 0.05 were considered differentially expressed. We used Venn diagrams to explore whether the same genes were differentially expressed in the treatment groups and at different time points, and visualised the expression level for the differentially expressed genes (DEGs) using heatmaps. We conducted a gene ontology (GO) analysis in PANTHER74 to retrieve GO IDs75 for the DEGs and to find pathways that were overrepresented in each treatment group.

We visualised the gene expression data on seven immune related pathways (apla04620 Toll-like receptor signaling pathway, apla04621 NOD-like receptor signaling pathway, apla05132 Salmonella infection, apla05164 Influenza A, apla04623 Cytosolic DNA-sensing pathway, apla04672 Intestinal immune network for IgA production, apla04622 RIG-I-like receptor signaling pathway) from the KEGG database34,35,36 in the VANTED software76. All pathways were compiled into an interactive webpage (http://orn-files.iwww.mpg.de/dgeviz/). The web-based pathway visualizations contain hyperlinks to a description of each gene via the KEGG webpage, including the gene and protein sequence and links to the National Centre for Biotechnology Information (NCBI) and Ensemble.

For the poly I:C treatment we validated the RNA-seq results using real-time quantitative PCR (qPCR) (Supplementary Information Text S3), and thereby also provide a panel of target genes for specific future antiviral gene expression studies in mallards.

For more detailed description see Supplementary Information (Text S3, Table S9, Figure S10) and Supplementary Dataset S7.

Ethics statement

The experiment was approved by the federal authorities of the German state of Baden-Württemberg (Regierungspräsidium Freiburg, approval no. AZ: 35-9185.81/G-15/130). Based on § 42 TierSchVersV (German legislative decree for the conduct of animal experiments) the approval of the authorities has to follow the votum of a commission for animal experiments. This commission is comparable to the ethical committees in other countries, but, according to German legislation, it is not appointed by the research institutes but by the state authorities. The study was carried out in compliance with the ARRIVE guidelines (https://arriveguidelines.org).

Data availability

Raw Illumina sequences have been deposited at the NCBI’s Sequence Read Archive (SRA) database under the accession no. PRJNA728347.

References

  1. 1.

    Van Doorn, H. R. Emerging infectious diseases. Medicine 42, 60–63. https://doi.org/10.1016/j.mpmed.2013.10.014 (2014).

    Article  PubMed  Google Scholar 

  2. 2.

    Karesh, W. B. et al. Ecology of zoonoses: Natural and unnatural histories. Lancet 380, 1936–1945. https://doi.org/10.1016/S0140-6736(12)61678-X (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Mandl, J. N. et al. Reservoir host immune responses to emerging zoonotic viruses. Cell 160, 20–35. https://doi.org/10.1016/j.cell.2014.12.003 (2015).

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Hasday, J. D., Fairchild, K. D. & Shanholtz, C. The role of fever in the infected host. Microbes Infect. 2, 1891–1904. https://doi.org/10.1016/S1286-4579(00)01337-X (2000).

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Lopes, P. C., Block, P. & König, B. Infection-induced behavioural changes reduce connectivity and the potential for disease spread in wild mice contact networks. Sci. Rep. 6, 31790. https://doi.org/10.1038/srep31790 (2016).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Lemus, J. A., Vergara, P. & Fargallo, J. A. Response of circulating T-lymphocytes to a coccidian infection: Insights from a parasitization-vaccination experiment. Funct. Ecol. 24, 638–645 (2010).

    Article  Google Scholar 

  7. 7.

    Schountz, T. Immunology of bats and their viruses: Challenges and opportunities. Viruses 6, 4880–4901. https://doi.org/10.3390/v6124880 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Cray, C., Zaias, J. & Altman, N. H. Acute phase response in animals: A review. Comput. Med. 59, 517–526 (2009).

    CAS  Google Scholar 

  9. 9.

    Hart, B. L. Biological basis of the behavior of sick animals. Neurosci. Biobehav. Rev. 12, 123–137. https://doi.org/10.1016/S0149-7634(88)80004-6 (1988).

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Owen-Ashley, N. T. & Wingfield, J. C. Acute phase responses of passerine birds: Characterization and seasonal variation. J. Ornithol. 148, 583–591. https://doi.org/10.1007/s10336-007-0197-2 (2007).

    Article  Google Scholar 

  11. 11.

    Harden, L. M., du Plessis, I., Poole, S. & Laburn, H. P. Interleukin-6 and leptin mediate lipopolysaccharide-induced fever and sickness behavior. Physiol. Behav. 89, 146–155. https://doi.org/10.1016/j.physbeh.2006.05.016 (2006).

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Sköld-Chiriac, S., Nord, A., Tobler, M., Nilsson, J. -Å. & Hasselquist, D. Body temperature changes during simulated bacterial infection in a songbird: Fever at night and hypothermia during the day. J. Exp. Biol. 218, 2961–2969 (2015).

    Article  Google Scholar 

  13. 13.

    Owen-Ashley, N. T., Turner, M., Hahn, T. P. & Wingfield, J. C. Hormonal, behavioral, and thermoregulatory responses to bacterial lipopolysaccharide in captive and free-living white-crowned sparrows (Zonotrichia leucophrys gambelii). Horm. Behav. 49, 15–29. https://doi.org/10.1016/j.yhbeh.2005.04.009 (2006).

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Koutsos, E. A. & Klasing, K. C. The acute phase response in Japanese quail (Coturnix coturnix japonica). Comp. Biochem. Phys. C 128, 255–263. https://doi.org/10.1016/S1532-0456(00)00199-X (2001).

    CAS  Article  Google Scholar 

  15. 15.

    Jones, C. A., Edens, F. W. & Denbow, D. M. Influence of age on the temperature response of chickens to Escherichia coli and Salmonella typhimurium endotoxins. Poult. Sci. 62, 1553–1558. https://doi.org/10.3382/ps.0621553 (1983).

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Marais, M., Gugushe, N., Maloney, S. K. & Gray, D. A. Body temperature responses of pekin ducks (Anas platyrhynchos domesticus) exposed to different pathogens. Poult. Sci. 90, 1234–1238. https://doi.org/10.3382/ps.2011-01389 (2011).

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Ashley, N. T. & Wingfield, J. C. In Ecoimmunology (eds. Demas, G.E. & Nelson, R.J.) 45–91 (Oxford University Press, 2011).

  18. 18.

    Sherub, S., Fiedler, W., Duriez, O. & Wikelski, M. Bio-logging, new technologies to study conservation physiology on the move: A case study on annual survival of Himalayan vultures. J. Comp. Physiol. A 203, 531–542. https://doi.org/10.1007/s00359-017-1180-x (2017).

    Article  Google Scholar 

  19. 19.

    Wilmers, C. C. et al. The golden age of bio-logging: How animal-borne sensors are advancing the frontiers of ecology. Ecology 96, 1741–1753. https://doi.org/10.1890/14-1401.1 (2015).

    Article  PubMed  Google Scholar 

  20. 20.

    Meitern, R., Andreson, R. & Hõrak, P. Profile of whole blood gene expression following immune stimulation in a wild passerine. BMC Genom. 15, 533 (2014).

    Article  Google Scholar 

  21. 21.

    Jax, E., Wink, M. & Kraus, R. H. Avian transcriptomics: Opportunities and challenges. J. Ornithol. 159, 599–629. https://doi.org/10.1007/s10336-018-1532-5 (2018).

    Article  Google Scholar 

  22. 22.

    Mueller, R. C. et al. Avian Immunome DB: An example of a user-friendly interface for extracting genetic information. BMC Bioinform. 21, 1–16. https://doi.org/10.1186/s12859-020-03764-3 (2020).

    Article  Google Scholar 

  23. 23.

    Bengtsson, D. et al. Does influenza A virus infection affect movement behaviour during stopover in its wild reservoir host?. R. Soc. Open Sci. 3, 150633 (2016).

    ADS  Article  Google Scholar 

  24. 24.

    Adelman, J. S., Córdoba-Córdoba, S., Spoelstra, K., Wikelski, M. & Hau, M. Radiotelemetry reveals variation in fever and sickness behaviours with latitude in a free-living passerine. Funct. Ecol. 24, 813–823. https://doi.org/10.1111/j.1365-2435.2010.01702.x (2010).

    Article  Google Scholar 

  25. 25.

    Martin, L. B., Liebl, A. L. & Kilvitis, H. J. Covariation in stress and immune gene expression in a range expanding bird. Gen. Comp. Endocrinol. 211, 14–19. https://doi.org/10.1016/j.ygcen.2014.11.001 (2015).

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Fleming-Canepa, X. et al. Duck innate immune responses to high and low pathogenicity H5 avian influenza viruses. Vet. Microbiol. 228, 101–111. https://doi.org/10.1016/j.vetmic.2018.11.018 (2019).

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Olsen, B. et al. Global patterns of influenza A virus in wild birds. Science 312, 384–388 (2006).

    ADS  CAS  Article  Google Scholar 

  28. 28.

    Jourdain, E. et al. Influenza virus in a natural host, the mallard: Experimental infection data. PLoS ONE 5, e8935. https://doi.org/10.1371/journal.pone.0008935 (2010).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Kuiken, T. Is low pathogenic avian influenza virus virulent for wild waterbirds?. Proc. R. Soc. B 280, 20130990. https://doi.org/10.1098/rspb.2013.0990 (2013).

    Article  PubMed  Google Scholar 

  30. 30.

    Pantin-Jackwood, M. J. et al. Pathogenicity and transmission of H5 and H7 highly pathogenic avian influenza viruses in mallards. J. Virol. 90, 9967–9982. https://doi.org/10.1128/JVI.01165-16 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Hulse-Post, D. et al. Molecular changes in the polymerase genes (PA and PB1) associated with high pathogenicity of H5N1 influenza virus in mallard ducks. J. Virol. 81, 8515–8524. https://doi.org/10.1128/JVI.00435-07 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Shepard, E. L. et al. Identification of animal movement patterns using tri-axial accelerometry. Endang. Species Res. 10, 47–60. https://doi.org/10.3354/esr00084 (2008).

    ADS  Article  Google Scholar 

  33. 33.

    Harmon, B. G. Avian heterophils in inflammation and disease resistance. Poult. Sci. 77, 972–977. https://doi.org/10.1093/ps/77.7.972 (1998).

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. https://doi.org/10.1093/nar/28.1.27 (2000).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947–1951. https://doi.org/10.1002/pro.3715 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res. 49, D545–D551. https://doi.org/10.1093/nar/gkaa970 (2021).

    CAS  Article  Google Scholar 

  37. 37.

    Adelman, J. S., Bentley, G. E., Wingfield, J. C., Martin, L. B. & Hau, M. Population differences in fever and sickness behaviors in a wild passerine: A role for cytokines. J. Exp. Biol. 213, 4099–4109. https://doi.org/10.1242/jeb.049528 (2010).

    Article  PubMed  Google Scholar 

  38. 38.

    Butler, P. J., Green, J. A., Boyd, I. & Speakman, J. Measuring metabolic rate in the field: The pros and cons of the doubly labelled water and heart rate methods. Funct. Ecol. 18, 168–183. https://doi.org/10.1111/j.0269-8463.2004.00821.x (2004).

    Article  Google Scholar 

  39. 39.

    Steiger, S. S., Kelley, J. P., Cochran, W. W. & Wikelski, M. Low metabolism and inactive lifestyle of a tropical rain forest bird investigated via heart-rate telemetry. Physiol. Biochem. Zool. 82, 580–589 (2009).

    Article  Google Scholar 

  40. 40.

    Lochmiller, R. L. & Deerenberg, C. Trade-offs in evolutionary immunology: Just what is the cost of immunity?. Oikos 88, 87–98. https://doi.org/10.1034/j.1600-0706.2000.880110.x (2000).

    Article  Google Scholar 

  41. 41.

    Davis, A. K. Effect of handling time and repeated sampling on avian white blood cell counts. J. Field Ornithol. 76, 334–338. https://doi.org/10.1648/0273-8570-76.4.334 (2005).

    Article  Google Scholar 

  42. 42.

    Maloney, S. K. & Gray, D. A. Characteristics of the febrile response in Pekin ducks. J. Comp. Physiol. B 168, 177–182. https://doi.org/10.1007/s003600050134 (1998).

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Evseev, D. & Magor, K. E. Innate immune responses to avian influenza viruses in ducks and chickens. Vet. Sci. 6, 5. https://doi.org/10.3390/vetsci6010005 (2019).

    Article  PubMed Central  Google Scholar 

  44. 44.

    Richardson, R. B. et al. A CRISPR screen identifies IFI6 as an ER-resident interferon effector that blocks flavivirus replication. Nat. Microbiol. 3, 1214. https://doi.org/10.1038/s41564-018-0244-1 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Burkhardt, N. B. et al. The long pentraxin PTX3 is of major importance among acute phase proteins in chickens. Front. Immunol. 10, 124. https://doi.org/10.3389/fimmu.2019.00124 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Wang, X., Qi, X., Yang, B., Chen, S. & Wang, J. RNA-Seq analysis of duck embryo fibroblast cell gene expression during the early stage of egg drop syndrome virus infection. Poult. Sci. 98, 404–412. https://doi.org/10.3382/ps/pey318 (2018).

    CAS  Article  Google Scholar 

  47. 47.

    Lang, Y. et al. Interleukin-1 receptor 2: A new biomarker for sepsis diagnosis and gram-negative/gram-positive bacterial differentiation. Shock 47, 119–124. https://doi.org/10.1097/SHK.0000000000000714 (2017).

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Matulova, M. et al. Chicken innate immune response to oral infection with Salmonella enterica serovar Enteritidis. Vet. Res. 44, 37. https://doi.org/10.1186/1297-9716-44-37 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Smith, J. et al. Systems analysis of immune responses in Marek’s disease virus-infected chickens identifies a gene involved in susceptibility and highlights a possible novel pathogenicity mechanism. J. Virol. 85, 11146–11158. https://doi.org/10.1128/JVI.05499-11 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Yoneyama, M. & Fujita, T. Function of RIG-I-like receptors in antiviral innate immunity. J. Biol. Chem. 282, 15315–15318. https://doi.org/10.1074/jbc.R700007200 (2007).

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Takeuchi, O. & Akira, S. MDA5/RIG-I and virus recognition. Curr. Opin. Immunol. 20, 17–22. https://doi.org/10.1016/j.coi.2008.01.002 (2008).

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Wei, L. et al. Duck MDA5 functions in innate immunity against H5N1 highly pathogenic avian influenza virus infections. Vet. Res. 45, 66. https://doi.org/10.1186/1297-9716-45-66 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Barber, M. R., Aldridge, J. R., Webster, R. G. & Magor, K. E. Association of RIG-I with innate immunity of ducks to influenza. Proc. Natl. Acad. Sci. USA 107, 5913–5918. https://doi.org/10.1073/pnas.1001755107 (2010).

    ADS  Article  PubMed  Google Scholar 

  54. 54.

    Kang, Y. et al. Host innate immune responses of ducks infected with Newcastle disease viruses of different pathogenicities. Front. Microbiol. 6, 1283. https://doi.org/10.3389/fmicb.2015.01283 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Song, C. et al. Effect of age on the pathogenesis of DHV-1 in Pekin ducks and on the innate immune responses of ducks to infection. Arch. Virol. 159, 905–914. https://doi.org/10.1007/s00705-013-1900-7 (2014).

    CAS  Article  PubMed  Google Scholar 

  56. 56.

    Li, N. et al. Pathogenicity of duck plague and innate immune responses of the Cherry Valley ducks to duck plague virus. Sci. Rep. 6, 32183. https://doi.org/10.1038/srep32183 (2016).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Scalf, C. S., Chariker, J. H., Rouchka, E. C. & Ashley, N. T. Transcriptomic analysis of immune response to bacterial lipopolysaccharide in zebra finch (Taeniopygia guttata). BMC Genom. 20, 1–14. https://doi.org/10.1186/s12864-019-6016-3 (2019).

    CAS  Article  Google Scholar 

  58. 58.

    Hepburn, L. et al. A Spaetzle-like role for nerve growth factor β in vertebrate immunity to Staphylococcus aureus. Science 346, 641–646. https://doi.org/10.1126/science.1258705 (2014).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Nakamura, S. et al. Influenza A virus-induced expression of a GalNAc transferase, GALNT3, via MicroRNAs is required for enhanced viral replication. J. Virol. 90, 1788–1801. https://doi.org/10.1128/JVI.02246-15 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Allen, E. K. et al. SNP-mediated disruption of CTCF binding at the IFITM3 promoter is associated with risk of severe influenza in humans. Nat. Med. 23, 975. https://doi.org/10.1038/nm.4370 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Blyth, G. A., Chan, W. F., Webster, R. G. & Magor, K. E. Duck IFITM3 mediates restriction of influenza viruses. J. Virol. 90, 103–116. https://doi.org/10.1128/JVI.01593-15 (2016).

    CAS  Article  PubMed  Google Scholar 

  62. 62.

    Brownlie, R. & Allan, B. Avian toll-like receptors. Cell Tissue Res. 343, 121–130. https://doi.org/10.1007/s00441-010-1026-0 (2011).

    CAS  Article  PubMed  Google Scholar 

  63. 63.

    Keestra, A. M. & van Putten, J. P. Unique properties of the chicken TLR4/MD-2 complex: Selective lipopolysaccharide activation of the MyD88-dependent pathway. J. Immunol. 181, 4354–4362. https://doi.org/10.4049/jimmunol.181.6.4354 (2008).

    CAS  Article  PubMed  Google Scholar 

  64. 64.

    Keestra, A. M., de Zoete, M. R., Bouwman, L. I., Vaezirad, M. M. & van Putten, J. P. M. Unique features of chicken Toll-like receptors. Dev. Comp. Immunol. 41, 316–323. https://doi.org/10.1016/j.dci.2013.04.009 (2013).

    CAS  Article  PubMed  Google Scholar 

  65. 65.

    Sosa, S., Jacoby, D. M., Lihoreau, M. & Sueur, C. Animal social networks: Towards an integrative framework embedding social interactions, space and time. J. Anim. Ecol. 89, 6–15. https://doi.org/10.1111/1365-2656.13163 (2021).

    Article  Google Scholar 

  66. 66.

    Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478. https://doi.org/10.1126/science.aaa2478 (2015).

    CAS  Article  PubMed  Google Scholar 

  67. 67.

    Cabanac, A. J. & Guillemette, M. M. Temperature and heart rate as stress indicators of handled common eider. Physiol. Behav. 74, 475–479. https://doi.org/10.1016/S0031-9384(01)00586-8 (2001).

    CAS  Article  PubMed  Google Scholar 

  68. 68.

    Roshier, D. A. & Asmus, M. W. Use of satellite telemetry on small-bodied waterfowl in Australia. Mar. Freshw. Res. 60, 299–305. https://doi.org/10.1071/MF08152 (2009).

    Article  Google Scholar 

  69. 69.

    D’alecy, L. G. & Kluger, M. J. Avian febrile response. J. Physiol. 253, 223–232. https://doi.org/10.1113/jphysiol.1975.sp011188 (1975).

    Article  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Korner, P., Sauter, A., Fiedler, W. & Jenni, L. Variable allocation of activity to daylight and night in the mallard. Anim. Behav. 115, 69–79. https://doi.org/10.1016/j.anbehav.2016.02.026 (2016).

    Article  Google Scholar 

  71. 71.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. https://doi.org/10.1093/bioinformatics/btp616 (2010).

    CAS  Article  Google Scholar 

  72. 72.

    Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47–e47. https://doi.org/10.1093/nar/gkv007 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Law, C. W., Alhamdoosh, M., Su, S., Smyth, G. K. & Ritchie, M. E. RNA-seq analysis is easy as 1–2–3 with limma, Glimma and edgeR. F1000Res 5, 1408. https://doi.org/10.12688/f1000research.9005.2 (2016).

    CAS  Article  Google Scholar 

  74. 74.

    Thomas, P. D. et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 13, 2129–2141. https://doi.org/10.1101/gr.772403 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Gene Ontology Consortium. The gene ontology (GO) database and informatics resource. Nucleic Acids Res. 32, D258–D261. https://doi.org/10.1093/nar/gkh036 (2004).

    CAS  Article  Google Scholar 

  76. 76.

    Rohn, H. et al. VANTED v2: A framework for systems biology applications. BMC Syst. Biol. 6, 139. https://doi.org/10.1186/1752-0509-6-139 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful for statistical support from Fränzi Korner-Nievergelt from Oikostat www.oikostat.ch/ and Matthias Bergers to automate the detection of heartbeats through the Acceleration Viewer software in Movebank. We are thankful for advice on the experimental setup by Manette Marais. We are thankful to Dina Dechmann, Kamran Safi, Wolfgang Forstmeier, Linda Jax and Roman Kellenberger for their advice on the manuscript. We are grateful to the animal caretakers at the Max Planck Institute for Ornithology. All bioinformatic analyses were performed using computational resources at the bwUniCluster funded by the Ministry of Science, Research and the Arts Baden-Württemberg and the Universities of the State of Baden-Württemberg, Germany, within the framework of bwHPC-C5. The flow cytometry measures were made with support from the University of Konstanz FlowKon facility. We are thankful to the Kegg Database Project team from Kanehisa Laboratories for providing permission to use the pathway images. We acknowledge the financial support from the International Max Planck Research School of Organismal Biology Project Grant and the DFG Centre of Excellence 2117 “Centre for the Advanced Study of Collective Behaviour" (ID: 422037984).

Funding

Open Access funding enabled and organized by Projekt DEAL.

Author information

Affiliations

Authors

Contributions

E.J., R.H.S.K., I.M., M.W., K.E.M. and W.F. conceived and designed the study. E.J., R.H.S.K., I.M., W.F. and E.F. conducted the experiment. E.J., E.F., H.P. and G.E. conducted laboratory analyses with assistance from K.E.M. and B.T. E.J., S.B., H.B., K.K. and G.E. performed data analysis with assistance from F.S. and R.H.S.K. E.J. wrote the manuscript. All co-authors commented on and approved the manuscript.

Corresponding author

Correspondence to Elinor Jax.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jax, E., Müller, I., Börno, S. et al. Health monitoring in birds using bio-loggers and whole blood transcriptomics. Sci Rep 11, 10815 (2021). https://doi.org/10.1038/s41598-021-90212-8

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing