DOI: 10.5061/dryad.73n5tb35c
title: Microbial surveillance verses cytokine responsiveness in native and non-native house sparrows

description: 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.
description: (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<sup>-1</sup> LPS (from <em>E.
 coli</em> 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 <em>ad
 libitum</em>. 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 (<em>i.e</em>.,
 <em>TLR-2</em>, <em>TLR-4</em>,
 <em>IL-10</em>, and <em>IL-1β</em>) were
 identified using either <em>Passer domesticus</em> or
 <em>Passer montanus</em> 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
 log<sub>10</sub> to normalize distributions and reduce
 heteroscedasticity. Subsequently, we computed the change in gene
 expression following LPS injection of <em>IL-1β</em> and
 <em>IL-10</em>, 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 <sup>9</sup>, 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 𝚫<em>IL-1β</em> to
 𝚫<em>IL-10</em>. 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.
description: ## Microbial surveillance verses cytokine responsiveness in native and
 non-native house sparrows
 [https://doi.org/10.5061/dryad.73n5tb35c](https://doi.org/10.5061/dryad.73n5tb35c) This dataset contains two primary files: a file that has our our raw data and log transformed data (Data.csv) and our R script (McCain_Script.Rmd). Metadata listed here is also duplicated in the accompanying (Data-Metadata.csv) .  ### The labels of the columns in the datasheet Band: Refers to the unique identification number for each bird, provided in the form of a band placed on the bird's leg for tracking and identification purposes. County: The geographical location where each bird was sampled or observed. Status: Indicates the invasion status of the bird, such as whether the bird was captured from a native or non-native (introduced) population SEX: The sex of the bird, noted as M for male, or F for female  Tarsus: The length of the bird's tarsus (the part of the leg between the foot and the knee), measured in millimeters. Weight 0h: The weight of the bird at the time of initial capture, measured in grams. Wing: The length of the bird's wing cord, measured in millimeters. Raw.IL1.0h: The raw measurement of IL-1β (Interleukin 1 beta) levels in the bird's system at 0 hours (baseline measurement). Raw.IL1.8h: The raw measurement of IL-1β levels in the bird's system at 8 hours post LPS injection Raw.IL10.0h: The raw measurement of IL-10 (Interleukin 10) levels in the bird's system at 0 hours (baseline measurement). Raw.IL10.8h: The raw measurement of IL-10 levels in the bird's system at 8 hours post LPS injection Raw.TLR4.0h: The raw measurement of TLR4 (Toll-like receptor 4) levels in the bird's system at 0 hours (baseline measurement). Raw.TLR2.0h: The raw measurement of TLR2 (Toll-like receptor 2) levels in the bird's system at 0 hours (baseline measurement). IL1.0: The normalized measurement of IL-1β levels at 0 hours, log10(x+1) transformed. IL1.8: The normalized measurement of IL-1β levels at 8 hours after an experimental treatment or challenge, log10(x+1) transformed. IL1.D: The change in IL-1β levels from 0 hours to 8 hours (i.e., IL1.8 - IL1.0), log10(x+1) transformed. IL-10.0: The normalized measurement of IL-10 levels at 0 hours, log10(x+1) transformed. IL-10.8: The normalized measurement of IL-10 levels at 8 hours after an experimental treatment or challenge, log10(x+1) transformed. IL-10.D: The change in IL-10 levels from 0 hours to 8 hours (i.e., IL-10.8 - IL-10.0), log10(x+1) transformed. TLR2.0: The normalized measurement of TLR2 levels at 0 hours, log10(x+1) transformed. TLR4.0: The normalized measurement of TLR4 levels at 0 hours, log10(x+1) transformed.