Microbial surveillance verses cytokine responsiveness in native and non-native house sparrows

The success of introduced species often relies on flexible traits, including immune traits that balance infection risk against the harm of an overactive response. While theories predict non-natives will have weak defenses due to decreased parasite pressure, effective parasite surveillance remains crucial, as infection risk is rarely zero and the evolutionary novelty of infection is elevated in non-native areas. This study examines the relationship between parasite surveillance and cytokine responsiveness in native and non-native house sparrows, hypothesizing that non-natives maintain high pathogen surveillance while avoiding costly inflammation. We made this specific prediction, as this pattern could enable invaders to effectively mitigate pathogen risk in manner commensurate with the life history priorities of a colonizing organism (i.e., rapid maturation and high, per bout reproductive effort).  To test this hypothesis, we measured TLR-2 and TLR-4 expression, markers of pathogen surveillance, and cytokine responses (IL-1β and IL-10), regulators of inflammation, to a simulated bacterial infection. In non-native but not native sparrows, we found that as TLR4 expression increased, IL-1β and IL-10 responses decreased. Additionally, higher body condition predicted larger IL-1β and IL-10 responses in all birds. These findings suggest high TLR4 surveillance may mitigate strong inflammatory responses in non-native sparrows, with pathological and resource-based costs driving immune variation among and within populations.

Methods
(A) Bird capture and care

During the non-breeding seasons of 2020-2023, we captured adult house sparrows (n=89) via mist netting from sunrise to 11.00 in nine locations across the globe (Figure 1) with exact location and dates of capture available in the supplements. Upon capture, we measured wing cord (to 150 mm), tarsus length (to 0.1mm), and body mass (to 0.1g) and collected a ~50 µl blood sample from the brachial vein of each bird (stored in 300 µl of DNA/RNA shield (Zymo R1100-50). Immediately thereafter, we injected each bird with 100 µl of 1 mg ml-1 LPS (from E. coli 055:B5; Fisher L4005) in sterile saline subcutaneously over the breast muscle. Post injection, we housed birds individually (but in visual and vocal contact with one another) in wire songbird cages (approximately 35.6 x 40.6 x 44.5) with food and water ad libitum. Eight hours post-injection, we took an additional ~10 µl of blood from the brachial vein. Forty-eight hours post-injection, we euthanatized birds via isoflurane overdose and rapid decapitation, and ~50 µl of blood was again taken. Liver, spleen, and gut samples were also taken for future studies and stored in 500 µl of DNA/RNA shield. All samples were stored at -80°C until further processing. All animal research procedures adhered to local animal research guidelines and were approved in advance by both the USF IACUC (IS00011653) and the relevant authorities in the country of capture.  Export and import of animal tissue was also compliant with all local US regulations according to USDA-APHIS and other appropriate permits.

(B) Molecular assays

Target gene sequences and primer and probe design

Sequences for our target genes (i.e., TLR-2, TLR-4, IL-10, and IL-1β) were identified using either Passer domesticus or Passer montanus genomes. Next, primers and probes (supplementary material) for all four genes were designed using the PrimerQuest tool from Integrated DNA Technologies (IDT), using qPCR parameters (2 primers + probe). ZEN double-quenched probes were chosen, with either a FAM or HEX fluorescent dye used for different genes. For assays, all primers and probes were diluted to 10 µM concentration.

RNA extraction and cDNA synthesis

We extracted RNA from 50 µl of whole blood/shield mixture using a standard phenol:chloroform protocol (Sambrook and Russell, 2012). For full RNA extraction protocol, see supplementary material. Reverse transcription was performed using the iScript cDNA Synthesis kit (Bio-Rad 1708891), following the manufacturer’s instructions.

Droplet Digital Polymerase Chain Reaction (ddPCR)

We performed a droplet digital PCR (ddPCR) to quantify absolute copy numbers of the PCR targets. ddPCR reactions contained 5µl ddPCR Multiplex Supermix (ddPCR Multiplex Supermix, 12005909, Bio-Rad); 2.25 µl forward primers (10 µM), 2.25 µl reverse primers (10 µM), 0.63 µl probe FAM, 0.63 µl probe HEX, and 0.63 µl probe FAM + HEX (e.g., when 50% FAM + HEX, add 0.31 µl of each), and 3.5 µl sample (cDNA 1000ng/µl). The ddPCR analysis was performed using the C1000 Touch™ Thermal Cycler with 96–Deep Well Reaction Module, 1851197, Bio-Rad). After amplification, the droplets were separated and counted as either positive (i.e., having the target sequence of interest) or negative (i.e., not having the target sequence of interest) using the droplet reader (QXDx Droplet Reader, 12008020, Bio-Rad). At the end of all runs, expression data were obtained using QuantaSoftTM Analysis Pro software (version 1.05).

(C) Data analysis

We initially transformed all gene expression data using log10 to normalize distributions and reduce heteroscedasticity. Subsequently, we computed the change in gene expression following LPS injection of IL-1β and IL-10, denoted as delta (𝚫). By focusing on these relative changes, we directly assessed an individual’s response to the LPS, as we had no predictions about pre-LPS values in relation to TLRs and for simplicity of interpretation.

We then examined whether population status (native or non-native), baseline TLR expression, or their interaction could predict cytokine deltas (𝚫) using linear mixed models, with country of capture treated as a random effect. We then determined the body condition index (BCI) for each bird by regressing body mass against wing chord and saving standardized residuals from the models. In exploratory analyses, we found that wing chord regressed against body mass provided a better fit than tarsus against body mass. Due to sexual dimorphism in sparrows 9, BCI was calculated separately for males and females. Subsequently, we investigated whether BCI, along with its interaction with population status, could predict cytokine responsiveness and/or the ratio of 𝚫IL-1β to 𝚫IL-10. All linear mixed effects models were fitted using the `lmer` function from the `lme4` package (version 4.3.2; Bates et al., 2015). Regression summaries were computed using the `jtools` package (version 4.3.2; Long, 2022). All analyses were conducted using R version 4.3.2.