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.

Figure 1

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.

Figure 2

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).

Figure 3

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 S4–S7 and Supplementary Dataset S4. Gene name changed from IFITM3 to IFITM1, following the suggested nomenclature in Blyth, et al.. All DEGs from the treatment groups are listed in Supplementary Datasets S1–S3.

Figure 4

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 S5–S6.

Figure 5

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 database^–. 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.