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\begin{document}

\title{Macronutrients, the microbiome, and illness-induced feeding behavior: Do
macronutrients shape avian immune responses?}



\author[1]{Ashley Love}%
\author[2]{Victoria Tabb}%
\author[2]{Noha H. Youssef}%
\author[2]{Shawn Wilder}%
\author[3]{Sarah DuRant}%
\affil[1]{University of Connecticut}%
\affil[2]{Oklahoma State University}%
\affil[3]{University of Arkansas Fayetteville}%


\vspace{-1em}



  
  \date{May 16, 2024}


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\selectlanguage{english}
\begin{abstract}
Macronutrients, such as proteins and fats, play a vital role in host
immunity and can influence host-pathogen dynamics, potentially through
dietary effects on gut microbiota. To increase our understanding of how
feeding behavior and macronutrient selection are influenced by a direct
and perceived immune threat and whether shifts in macronutrient intake
affect the composition of the gut microbiome, we conducted two
experiments. First, we determined if zebra finches (\emph{Taeniopygia
guttata}) exhibit shifts in physiology and gut microbiota when fed diets
differing in macronutrient ratios. Second, we simulated an infection in
birds using the bacterial endotoxin lipopolysaccharide (LPS) and
quantified feeding behavior in immune challenged and control
individuals, as well as birds housed near either a control pair (no
immune threat), or birds housed near a pair given an immune challenge
with LPS (social cue of heightened infection risk). We also examined
whether social cues of infection alter physiological responses relevant
to responding to an immune threat, an effect that could be mediated
through shifts in feeding behavior. In the first experiment, protein
diets decreased the abundance of the bacterial Phylum Campylobacterota.
Further, diet treatment disrupted relationships between gut microbiota
alpha diversity and physiological metrics. In the second experiment, LPS
induced a reduction in caloric intake driven by a decrease in protein,
but not fat consumption. No evidence was found for socially induced
shifts in feeding behavior, physiology, or gut microbiota. However, fat
consumption decreased gut microbial diversity regardless of treatment.
Our findings carry implications for host health, as sickness-induced
anorexia and diet-induced shifts in the microbiome could shape
host-pathogen interactions.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Nutrition is critical to the immune system and can influence how hosts
respond to infection (Cunningham-Rundles et al. 2005, Ponton et al.
2011, Amar et al. 2007). Among ecological studies, the effect of the
availability of resources on immune processes and disease outcomes has
received significant attention (Becker et al. 2018, Strandin et al.
2018, Moyers et al. 2018), but the quality of those resources and their
nutritional make-up (e.g., macronutrient content) are also important
(Povey et al. 2013, Cotter et al. 2011).

Macronutrients like lipids, carbohydrates, and protein vary in their
biological availability and can influence processes ranging from
cellular function to whole organism performance (Warne 2014). Diet
macronutrient content can influence physiological processes important in
responding to pathogens, including hormonal and immune responses. For
example, kittiwake chicks fed a low-lipid diet had higher baseline and
stress-induced concentrations of the hormone corticosterone (Kitaysky et
al. 2001), which plays a role in responding to energetic demands and is
known to both stimulate and suppress immune responses in different
contexts (Roberts et al. 2007, Da Silva 1999). Dietary nutrients can
also impact other hormones known to influence immune function, such as
testosterone (Roberts et al. 2007, Da Silva 1999). For example, humans
consuming higher levels of dietary fat have higher baseline testosterone
concentrations, while subjects consuming higher levels of dietary
protein have lower testosterone levels (Volek et. 1997).

Diet can also directly affect immunity without the mediation of
hormones. In captive white ibis, birds that were fed anthropogenic
dietary items like white bread had reduced bacterial killing ability,
but corticosterone levels and the other immune parameters investigated
were unaffected by diet treatment (Cummings et al. 2020). Other work has
found that restriction of dietary protein can limit immune activity (Lee
et al. 2006, Povey et al. 2009) and high lipid diets can increase
mortality rates during infection (Adamo 2008, Adamo et al. 2010). The
macronutrient composition of diets can also differentially affect
different components of the immune system. For example, in insects, the
optimal macronutrient composition of diets varies for different
immunological parameters (Cotter et al. 2011). Thus, optimal diet
selection may vary based on the type of immune threat that organisms are
experiencing.

Despite the apparent need for nutritional resources to mount an immune
response, some organisms respond to infection with sickness-induced
anorexia (Adamo et al. 2007, Adelman and Martin 2009, Povey et al.
2013). This reduction in food intake following an immune challenge is
thought to reduce the risk of ingesting additional infectious agents or
may function to starve pathogens and parasites of key nutrients
(Kyriazakis et al. 1998, Adamo et al. 2007, Adelman and Martin 2009).
Further, caloric restriction during illness can improve host health and
recovery (Cheng et al. 2017; Wang et al. 2016).

In addition to influencing the quantity of food that animals consume,
infection can also alter diet selection. In caterpillars challenged with
a viral infection, infected individuals select diets with a higher
protein to carbohydrate ratio when compared with control individuals,
and infected individuals placed on a high protein diet are more likely
to survive infection (Povey et al. 2013). Shifts in diet preference
during infection that optimize recovery and survival are referred to as
``self-medication'' behaviors and have been documented in several taxa
(Huffman and Seifu 1989, Hutchings et al. 2003, Povey et al. 2013).
Thus, animals can shift feeding behaviors and selectively feed on foods
with desirable macronutrient composition in response to an immune
threat.

Shifts in diet caused by environmental availability and selective
feeding can affect the immune system through shifts in the gut
microbiome (Zheng et al. 2020). The gut microbiome regulates multiple
aspects of host health, including metabolism and the development of the
host immune system (Hird 2017, Kau et al. 2011, Zheng et al. 2020). Gut
microbial communities can also influence how hosts respond to disease,
as disrupting microbial communities with antibiotics can alter immunity
and increase susceptibility to bacterial and parasitic infections in
humans and wildlife (Buffie et al. 2012, Zheng et al. 2020, Knutie et
al. 2017). Host diet plays an important role in shaping the composition
and diversity of gut microbial communities (Pan and Yu 2014, Singh et
al. 2017, Bodawatta et al. 2021) and diet-induced shifts in gut
microbiota can alter host immune responses (Zheng et al. 2020). For
example, in humans and mice, a high-fat westernized diet alters gut
microbial communities and increases inflammation (Agus et al. 2016,
Statovci et al. 2017). Shifts in diet can also cause changes in the gut
microbiome that increase host resistance to parasites. In nestling
bluebirds, food supplementation with mealworms increased gut bacterial
diversity and the abundance of \emph{Clostridium} spp., which was
associated with higher nestling antibody responses and lower numbers of
nest parasites (Knutie 2020). These studies provide evidence that diet
can alter host responses to parasites through shifts in the gut
microbiome. Thus, organisms may be able to optimize responses to
infection through shifts in feeding behavior that alter gut microbial
composition. Despite the clear implications for host health and wildlife
disease dynamics, few studies have investigated how dietary
macronutrients, gut microbiota, physiology, and feeding behavior
interact to shape host responses related to infection.

Infection-induced shifts in feeding and activity can also be detected by
conspecifics and viewing a sick neighbor can result in healthy
individuals altering their feeding behavior in a way that optimizes or
primes the immune system to fight off infection (Castella et al. 2008;
Povey et al. 2013). Further, recent research in social organisms
indicates that behavioral and physiological changes can occur in
response to public information (Cornelius et al. 2018; Cornelius 2022,
Schaller, et al. 2010, Stevenson et al. 2011, 2012; Love et al. 2021).
In red crossbills, social information from conspecifics that is
indicative of low food-abundance reduces the expression of
glucocorticoid and mineralocorticoid receptors in the brain (Cornelius
et al. 2018). Additionally, red crossbills that observed food-restricted
neighbors before becoming food-restricted themselves ate more food,
conserved more body mass, and were in better condition than birds
without this predictive social information (Cornelius 2022). Since
nutritional state is known to greatly impact disease outcomes (Murray et
al. 1998, Chandra 1996, Lochmiller \& Deerenberg 2000), prophylactic
behaviors involving macronutrient selection in response to cues of
disease could help prepare organisms for an impending immune threat.

The goals of this study were to 1) enhance our understanding of how
dietary macronutrients affect host physiology and the gut microbiome, 2)
characterize how feeding behavior and macronutrient selection are
influenced by immune threats that are direct (i.e., immune activation)
or perceived (e.g., observing a sick conspecific), and 3) provide
insight into whether shifts in feeding behavior alter the gut microbiome
and physiological processes relevant to disease susceptibility and
transmission. We explored these relationships in zebra finches
(\emph{Taeniopygia guttata} ) through two separate experiments
investigating 1) how diet macronutrient content affects immunity,
hormonal responses, and the gut microbiome, and 2) how perceived and
actual immune threats shape feeding behavior (caloric intake and diet
macronutrient selection). Additionally, because social information from
conspecifics regarding food (Cornelius et al. 2018; Cornelius 2022) and
disease risk (Schaller, et al. 2010, Stevenson et al. 2011, 2012; Love
et al. 2021) can alter physiology, we also investigated whether
perceived risk of infection (seeing immune-challenged conspecifics)
alters physiological responses pertinent to immune function and feeding
behavior, specifically, complement activity, and corticosterone and
testosterone blood plasma concentrations. We predicted that the
different diets would induce changes in the gut microbiome and
physiology. We also predicted that birds given an immune challenge and
birds with a social cue of heightened infection risk would increase
protein consumption, as studies in insects indicate that high protein
diets are associated with increased immune capabilities and high lipid
diets are associated with increased mortality during infection (Povey et
al. 2013, Adamo 2008, Adamo et al. 2010). Additionally, we predicted
that perceived risk of infection (seeing sick conspecifics) would alter
physiological responses, and that this effect might be mediated through
shifts in feeding behavior and subsequent changes in the gut microbiome.
Identifying and understanding the factors that contribute to variation
in avian responses to infection is of broad interest and integral to
improving our understanding of avian epidemiology, especially since
birds are hosts for diseases relevant to wildlife, domestic animals, and
human health (Reed et al. 2003).

\textbf{Materials and Methods}

\emph{Experiment 1 --Timeline for Diet Manipulation Experiment}

In the first experiment, zebra finches were given one of three diet
treatments that varied in protein and lipid content. We investigated how
diet influenced complement activity, baseline and stress-induced
corticosterone concentrations, and the gut microbiome. Birds were kept
on a 14 L: 10 D light cycle and individually housed in 24''x16''x16''
cages. Each cage had two perches, a water dish, and one food dish in
which birds were fed \emph{ad libitum} . Diets consisted of hulled
millet, egg white, egg yolk, vegetable oil, and sorbic acid
(preservative) in agar blocks. Birds were placed on an acclimation diet
with equal ratios of lipid and protein for 5 days prior to experimental
treatment. Birds were then randomly assigned to one of three diet
treatments (Figure 1; day 0), which they remained on for eight
additional days. All diets were isocaloric and only varied in the ratio
of protein and lipid content. All diets contained similar concentrations
of carbohydrates (62-66 \% of the total metabolizable energy of the diet
(TME)). Diet treatments consisted of a high fat diet (25\% lipid and
13\% protein of TME; high fat diet: n=11, 7 males, 4 females), a diet
with equally balanced ratios of lipid and protein (i.e., the acclimation
diet; 17\% lipid and 17\% protein of TME; equal ratio diet: n=12, 8
males, 4 females) and a high protein diet (13\% fat and 25\% protein of
TME; high protein diet: n=12, 8 males, 4 females). Three desiccation
controls for each diet type were also weighed daily and the average
desiccation values for each diet type (high fat, equal ratio, high
protein) were subtracted from feeding values to account for daily
changes in food mass due to desiccation.

To assess the effects of diet on physiological endpoints and gut
microbiota, blood samples and cloacal swabs were collected on the day
that acclimation diets were switched to manipulation diets and eight
days after the manipulation diets were implemented. Body mass and fat
score data were recorded for each bird at all sampling time points. All
blood samples were collected between 0830 -- 0930 CDT to avoid
differences in physiological endpoints due to variation in daily
rhythms. Within 3 min of entering the room, a baseline blood sample of
120 uL was collected from the wing vein of each individual to be used to
determine baseline concentration of plasma corticosterone and hemolytic
complement activity. Next, we used sterile swabs to swab the cloaca of
each bird. Swabs were placed in 300 \selectlanguage{greek}μ\selectlanguage{english}l RNAlater (Invitrogen, Thermo
Fisher Scientific) and frozen at -80\selectlanguage{ngerman}°C. Birds were then weighed and
placed into a paper bag. Thirty minutes after entering the room, a
second blood sample was collected to determine stress-induced
concentrations of plasma corticosterone. All blood samples were
collected into heparinized capillary tubes and immediately placed on
ice. Tubes were centrifuged for 3 minutes to separate blood plasma from
erythrocytes and plasma samples were stored at -20°C. All research
protocols were approved by the Oklahoma State University Institutional
Animal Care and Use Committee.

\emph{Experiment 2 --Timeline for Diet Selection Experiment}

To investigate whether birds alter feeding behavior to optimize
responses to infection we conducted a second experiment, in which we
simulated an infection in established zebra finch pairs using the
bacterial endotoxin lipopolysaccharide (LPS), and quantified feeding
behavior in immune challenged and control individuals, as well as birds
housed near either a control pair (no immune threat), or birds housed
near a pair given an immune challenge with LPS (social cue of heightened
infection risk). To investigate how an immune challenge and perceived
immune threat shape macronutrient preference, birds were provided with
two isocaloric diets with varied lipid and protein ratios and their
consumption was recorded. Birds were kept on a 14 L: 10 D light cycle
and housed in 24''x16''x16'' cages that were divided down the center
into two separate 12''x16''x16'' cage sections that each housed one pair
of birds. Birds were housed in previously established pairs with one
female and one male per cage section. Each cage section had two perches,
a water dish, and two food dishes in which birds were fed \emph{ad
libitum} . Birds were given a choice of diet where each cage contained
one high lipid food block and one high protein food block, whose
composition was the same as in Experiment 1. Diet type was randomly
assigned to either the left or right food dish to avoid confounding
effects of cage side preference. To assess whether an immune threat
altered the amount and type of diet that birds consumed, birds were
provided with a choice in diet for seven days prior to experimental
treatment and for five days following experimental treatment. Diets were
weighed daily and replaced every other day. Three desiccation controls
for each diet type were also weighed daily and the average desiccation
values for each diet type (high lipid or high protein) were subtracted
from feeding values to account for daily changes in food mass due to
desiccation.

Pairs housed on one side of the cage were injected with either LPS or
Saline (Figure 1; LPS-injected: N=24 birds or Saline-injected: N=24
birds), whereas pairs housed on the other side of the cage were
unmanipulated (focal pairs; LPS-focal: N=24 birds or Saline-focal: N=24
birds). Injected pairs provided social cues to the focal pair. Solid
opaque dividers were placed on both sides of each 24 x 16 x 16 cage to
ensure that birds housed in each double cage could only see one another.
To assess how an immune challenge and social cue of heightened infection
risk shape feeding behavior, we injected previously established pairs
with either lipopolysaccharide (LPS), a non-replicating antigen that
activates the immune system and induces sickness behaviors, or a saline
solution (control). Specifically, we injected stimulus birds
intra-abdominally with either 50 \selectlanguage{greek}μ\selectlanguage{english}L of 2 mg/kg LPS (Sigma-Aldrich
\#L7261, \emph{Salmonella enterica} serotype typhimurium) or 50 \selectlanguage{greek}μ\selectlanguage{english}L of
phosphate-buffered saline (sham control, Sigma-Aldrich \#P3813). Body
mass and fat score data were collected on all stimulus and focal birds
prior to treatment and on days 1, 2, and 5 post-treatment.

To assess whether heightened infection risk altered complement activity
or baseline corticosterone concentrations, we collected blood samples
from all focal birds 3 days prior to stimulus bird injections and 1, 2,
and 5 days following stimulus bird injections. All blood samples for
baseline corticosterone were collected within 3 minutes of entering the
room. We collected additional blood samples from all focal males to
assess changes in plasma testosterone concentrations in response to a
heightened cue of infection 4 days prior to and 3 days following
injection of the stimulus pairs. Immediately after collection, blood
samples were centrifuged and blood plasma was separated and frozen at
-20\selectlanguage{ngerman}°C. To determine how treatment affected zebra finch gut microbial
diversity and composition, cloacal swab samples were collected from all
birds prior to treatment (day 0) and on day 5 following stimulus bird
injections. Cloacal swabs were placed into 300 \selectlanguage{greek}µ\selectlanguage{english}l of RNAlater
(Invitrogen, Thermo Fisher Scientific) and frozen at -80. All research
protocols were approved by the Oklahoma State University Institutional
Animal Care and Use Committee.

\emph{Hemolytic Complement Activity Assay}

To assess if diet macronutrient content (experiment 1) and social cues
of disease (experiment 2) influenced the complement pathway, we
conducted a CH50 complement assay that measures the ability of proteins
in the plasma to lyse sheep red blood cells (MP Biomedicals,
Cat\#55876). We measured complement activity following the methods
outlined in Sinclair and Lochmiller (2000). Briefly, we ran duplicate 80
\selectlanguage{greek}μ\selectlanguage{english}l samples of 1:20 and 1:40 plasma dilutions. Hemolytic complement
activity was expressed as CH50 units/ml plasma, where one CH50 unit
signifies the reciprocal of the dilution of plasma needed to lyse 50\%
of the sheep red blood cells (French et al. 2010).

\emph{Corticosterone and Testosterone Assays}

In the first experiment, we quantified baseline and stress-induced
plasma corticosterone concentrations using an Arbor Assays
Corticosterone ELISA Kit (Cat\#: ADI-900-065). Samples were run in
duplicate at a 1:50 dilution following treatment with 1\% steroid
displacement buffer. Each plate contained a standard curve run in
triplicate. Absorbance was measured at 405 nm using a SpectraMAX 190
spectrophotometer (Molecular Devices). We compared the mean value of the
duplicates for each sample to a standard curve that contained known
amounts of corticosterone. A 250 pg/mL standard was run in triplicate on
each plate. The intra-assay coefficient of variation and inter-assay
coefficient of variation were 12.8\% and 14.0\% respectively.
Cross-reactivity of the testosterone antibody was as follows:
androstenedione 7.2\%, estradiol 1\%, dehydroepiandrosterone 1\%,
dihydrotestosterone 1\%, and progesterone 1\% (Enzo Life Sciences).

In the second experiment, baseline plasma corticosterone concentrations
were measured in duplicate following standard radioimmunoassay
techniques (Wingfield et al., 1992). To determine the coefficient of
intra- and inter-assay variation, four standard samples were prepared
with 200 pg of corticosterone and plasma and standard sample tubes were
prepared with 500 \selectlanguage{greek}μ\selectlanguage{english}l ddH2O and 2000 dpm of tritiated corticosterone
(NET-399) from PerkinElmer Life Sciences, Inc. (Boston, MA, USA).
Samples were equilibrated overnight at 4 \selectlanguage{ngerman}°C. Corticosterone was
extracted from plasma using 4 ml of diethyl ether and dried in a 37 °C
water bath with the aid of nitrogen gas. Following extractions, samples
were suspended in 500 \selectlanguage{greek}μ\selectlanguage{english}l of phosphate buffered saline and refrigerated
overnight at 4 \selectlanguage{ngerman}°C. The following day, we determined individual
extraction efficiency using 50 \selectlanguage{greek}μ\selectlanguage{english}l of each sample. Corticosterone
concentrations were corrected for individual extraction efficiency (mean
recoveries were 83\%). For the assay, each sample was allocated into two
duplicates, each consisting of 200 \selectlanguage{greek}μ\selectlanguage{english}l. 100 \selectlanguage{greek}μ\selectlanguage{english}l of corticosterone antibody
(B3-163; Endocrine Sciences, Calabasas, CA, USA) and 100 \selectlanguage{greek}μ\selectlanguage{english}l of tritiated
corticosterone were added to each sample and standard tube. We compared
the mean value of the duplicates for each sample to a standard curve
(also run in duplicate) that contained known amounts of corticosterone
(C2505 corticosterone standard, Sigma-Aldrich, St. Louis, MO, USA). The
intra-assay coefficient of variation and inter-assay coefficient of
variation were 11.4\% and 13.3\% respectively.

Testosterone was only measured in the second experiment, in which plasma
samples were collected from focal male zebra finches 4 days prior to and
3 days following experimental treatment (LPS-focal: cue of disease,
saline-focal: no cue of disease). Plasma testosterone concentrations
were measured using an Enzo Testosterone ELISA Kit (Cat\#: ADI-900-065).
Samples were run in duplicate at a 1:20 dilution following treatment
with 1\% steroid displacement buffer. Each plate contained a standard
curve run in triplicate. Absorbance was measured at 405 nm using a
SpectraMAX 190 spectrophotometer (Molecular Devices). The intra-assay
coefficient of variation and inter-assay coefficient of variation were
12.8\% and 14.0\% respectively. Cross-reactivity of the testosterone
antibody was as follows: androstenedione 7.2\%, estradiol 1\%,
dehydroepiandrosterone 1\%, dihydrotestosterone 1\%, and progesterone
1\% (Enzo Life Sciences).

\emph{DNA extraction and 16S rRNA gene sequencing}

DNA was extracted from cloacal swabs using DNeasy PowerPlant Pro kits
(Qiagen). Both the swab and the RNAlater (Invitrogen, Thermo Fisher
Scientific) that the swab was stored in were added to the PowerBead
tubes, then the extraction proceeded following the instructions in the
manufacturer's protocol. We quantified the DNA concentration for each
sample using a Qubit fluorometer and froze samples at -20 until samples
could be shipped for sequencing. The V4 region of the 16S rRNA gene was
sequenced at the University of Texas's Genome Sequencing and Analysis
Facility (GSAF) using an Illumina MiSeq platform. Samples with less than
1,000 total sequences were excluded from subsequent analyses. After
quality filtering, we had 63 samples for further analyses in the diet
manipulation experiment (experiment 1). Of these samples, 22 were from
birds fed a high fat diet (day 0: n=11, day 8: n=11), 21 were from birds
fed diets with equal ratios of protein and lipid (day 0: n=9, day 8:
n=12), and 20 were from birds fed a high protein diet (day 0: n=11, day
8: n=9). For the second experiment, some samples failed during
sequencing or after quality filtering, yielding 69 total cloacal
microbiota samples for further analyses. Of these samples, 38 were from
injected birds (LPS day 0: n=9, LPS day 5: n=8; saline day 0: n=10,
saline day 5: n=11) and 31 were from focal birds (LPS-focal day 0: n=6,
LPS-focal day 5: n=8; saline-focal day 0: n=7, saline-focal day 5:
n=10). We used \emph{DADA2} (v.1.16) in R (v.4.2.0) to process our 16S
sequence data. ASVs were assigned to taxonomy using the Silva 132
bacterial reference database (Quast et al. 2013). Sequences identified
as a chloroplast or mitochondria were removed from the dataset.
The\emph{DECIPHER} package (v. 2.24.0; Wright 2015) was used to create a
multiple sequence alignment and a generalized time-reversible maximum
likelihood tree of the remaining ASVs was constructed with
the\emph{phangorn} package version 2.9.0 (Schliep 2011). The ASV table,
taxonomic information, phylogeny, and sample metadata were joined for
bacterial community analyses using the package \emph{phyloseq}(McMurdie
and Holmes 2013).

\emph{Gut microbiome diversity, composition, and relative abundance
analyses}

To account for uneven sequencing depth across samples, samples were
rarefied to the depth of our lowest sample (5,691 for the diet
manipulation experiment; 1,077 for the diet selection experiment) for
alpha and beta diversity analyses. We used the \emph{vegan} package
(Oksanen et al., 2022) to quantify two measures of alpha diversity, the
observed number of ASVs (richness) and the Shannon diversity index
(which accounts for both richness and evenness). To determine whether
the observed microbial richness varied in response to treatment, we ran
a generalized linear mixed models (GLMM) fitted with a negative binomial
(nbinom1) distribution using the package \emph{glmmTMB} (Brooks et al.
2017) with treatment, day, and the interaction between treatment*day as
fixed effects. For the Shannon diversity index, we ran a GLMM fitted
with a gaussian structure and included treatment, day, and the
interaction between treatment*day as fixed effects. Because birds were
sampled over time, we included bird ID as a random effect in all models.
We used the \emph{DHARMa} package (Hartig 2019) to plot residuals and
confirm the suitability of each model and the Anova function in
the\emph{car} package (Fox and Weisberg 2018) to determine significance.
To assess beta diversity, we first subjected the unrarefied dataset to
Cumulative Sum Scaling (CSS) normalization (Paulson et al. 2013) using
the metagenomeSeq package following the methods outlined in Maraci et
al. 2021. To account for compositional variations in each dataset, data
were log (x+0.0001)-transformed and later corrected by subtracting the
log of the pseudo count (Thorsen et al. 2016). For beta diversity
analyses, we computed Bray-Curtis (Bray and Curtis 1957), unweighted
UniFrac (Louzupone and Knuight 2007) and weighted UniFrac (Lozupone et
al. 2007) distances. To visualize dissimilarities between treatments, we
used a principal coordinate analysis (PCoA) using the ordinate function
in \emph{phyloseq} (McMurdie \& Holmes, 2013). To assess similarity
between treatments and over time for each beta diversity metric, we
conducted PERMANOVA tests using the adonis2 function in the
\emph{vegan}package (Oksanen et al., 2022). Treatment, day sampled, and
the interaction between treatment and day were included as fixed effects
in all PERMANOVA tests. To account for repeated sampling over time, bird
ID was included under the adonis2 strata option. All P-values were
adjusted for false discovery rate with a Benjamini--Hochberg correction
where significance was determined as P\textsubscript{adj} \textless{}
0.05.

Finally, to investigate whether bacterial taxa differ in abundance
across treatments, we calculated the relative abundances of phyla and
genera from the unrarefied datasets. For analyses, phyla and genera with
mean abundances \textless{}1\% were lumped into an ``Other'' category.
This data stringency limited analyses to the top four most abundant
phyla (Campylobaterota, Firmicutes, Proteobacteria, Actinobacteriota)
and the top seven most abundant genera (\emph{Campylobacter}
,\emph{Weissella} , \emph{Helicobacter} , \emph{Acinetobacter}
,\emph{Ligilactobacillus} , \emph{Pseudomonas} , \emph{Achromobacter} )
in experiment 1 (diet manipulation). In experiment 2 (diet selection),
this data stringency limited analyses to the five most abundant phyla
(Campylobaterota, Firmicutes, Proteobacteria, Actinobacteriota,
Bacteroidota) and the top ten most abundant genera (\emph{Campylobacter}
, \emph{Helicobacter} , \emph{Corynebacterium} ,\emph{Ureaplasma} ,
\emph{Ligilactobacillus} , \emph{Atopobium} ,\emph{Catellicoccus} ,
\emph{Gallibacterium} , \emph{Veillonella,Weissella} ) in injected birds
(LPS, saline), and the top thirteen most abundant genera
(\emph{Campylobacter} , \emph{Helicobacter} ,\emph{Pseudomonas} ,
\emph{Serratia} , \emph{Veillonella,Gallibacterium} ,
\emph{Ligilactobacillus} , \emph{Atopobium} ,\emph{Corynebacterium} ,
\emph{Enterobacter} , \emph{Sphingobacterium} ,\emph{Staphylococcus} ,
\emph{Weissella} ) in focal birds (LPS-focal, saline-focal). To compare
abundances of bacterial taxa between treatments and over time, we ran
non-parametric Kruskal-Wallis tests using the kruskal.test function in
R. P-values were adjusted for false discovery rate with a
Benjamini--Hochberg correction where significance was determined as
P\textsubscript{adj} \textless{} 0.05.

\emph{Statistical Analyses}

Statistical analyses were conducted in R version 4.2.0 (R Core Team
2022). All data were checked for normality and homoscedasticity. To
determine the effect of diet treatment and time (day 0, day 8) on body
mass, fat score, corticosterone concentrations, and complement (CH50)
activity in experiment 1, we ran GLMMs using the \emph{glmmTMB} package
in R (Brooks et al. 2017). For body mass, we ran a GLMM fitted with a
gaussian distribution, while the models for the remaining endpoints (fat
score, corticosterone, and complement activity) were fitted with a
tweedie distribution. All models included treatment, time (day 0, day
8), and the interaction between treatment and time as fixed effects with
bird ID as a random effect. When testing the effect of diet treatment
and time on corticosterone responses, we also included sample type
(baseline or stress-induced) as a fixed effect in the model. To test
whether the average grams consumed per day varied based on diet
treatment, we ran a generalized linear model (GLM) fitted with a
gaussian distribution with grams consumed as the response variable and
treatment as a fixed effect. Because physiology can both mediate and
respond to shifts in gut microbiota composition (Noguera et al. 2018,
Williams et al. 2020, Yoshiya et al. 2011) and diet might influence
these interactions, we also ran a separate GLMM for each alpha diversity
metric (observed richness, Shannon diversity) which considered the
interactions between diet treatment and time (day 0, day 8) with each
physiology metric (complement activity, baseline corticosterone,
stress-induced corticosterone). P-values from these models were adjusted
for false discovery rate with a Benjamini--Hochberg correction where
significance was determined as P\textsubscript{adj} \textless{} 0.05. We
used the \emph{DHARMa} package (Hartig 2019) to plot residuals and
confirm the suitability of each model and the Anova function in
the\emph{car} package (Fox and Weisberg 2018) to determine significance.

To examine how feeding behavior and diet preference changed over time
with treatment in experiment 2 (diet selection), we ran separate GLMs
fitted with a gaussian distribution to test for differences in the
stimulus (LPS, saline) and focal (LPS-focal, saline-focal) groups,
respectively. Because birds were housed in pairs for this experiment
(Figure 1), pre- and post-treatment feeding behavior was analyzed by
cage rather than by individual. To examine how baseline corticosterone
concentrations and hemolytic complement activity varied in the focal
birds, we ran GLMMs for each response variable where we included
treatment (LPS-focal or saline-focal), time, an interaction between
treatment and time, and sex as predictors. Testosterone samples were
collected from males only, so sex was not included in this model. For
body mass and fat score, we ran a GLMM fitted with a gaussian
distribution, while the models for the remaining endpoints (baseline
corticosterone, complement activity, and testosterone) were fitted with
a tweedie distribution. To investigate whether differences in feeding
behavior affected the gut microbiota, we also ran a separate GLMM for
each alpha diversity metric (observed richness, Shannon diversity),
which considered the interactions between treatment and time (day 0, day
5) as well as the average amount of grams of each diet type (fat diet,
protein diet) consumed per day pre- and post- treatment. P-values from
these models were adjusted for false discovery rate with a
Benjamini--Hochberg correction where significance was determined as
P\textsubscript{adj} \textless{} 0.05. Due to power limitations
associated with low sample sizes for focal birds that had both
physiological and gut microbiota samples for days 0 and 5 (n=2-9
depending on the physiological metric), we were unable investigate
interactions between physiology (complement, baseline corticosterone,
testosterone), treatment, and time for gut microbial metrics as we did
in the first experiment.

\textbf{Results}

\textbf{Experiment 1 -- Diet manipulation}

The average amount of food that birds consumed did not vary by diet
treatment (\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} = 1.75, \emph{P} = 0.416). Body mass
decreased over the course of the experiment (day: \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} =
49.49, \emph{P} \textless{} 0.0001), but did not vary by treatment
(Figure S1; \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} = 0.36, \emph{P} = 0.836) or the
interaction between treatment and time (\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} =
0.38,\emph{P} = 0.829). We observed a non-significant trend where fat
score also decreased over the course of the experiment (Figure S2; day:
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} = 3.61, \emph{P} = 0.058). Fat score did not vary
by treatment (\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} = 0.47, \emph{P} = 0.792) or the
interaction between treatment and time (\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} =
1.29\emph{P} = 0.526). Corticosterone concentrations significantly
increased after 30 min, being higher in stress-induced samples (Figure
S3; sample type: \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} = 150.99, \emph{P} \textless{}
0.0001), but were not affected by diet treatment (\emph{P} = 0.124) or
time sampled (\emph{P} = 0.060). Complement activity was also unaffected
by treatment, day, and the interaction between treatment and day (Figure
S4; all \emph{P}

Bacterial richness and bacterial diversity of the gut microbiota did not
vary across diet treatments, over time, or in response to the
interaction between treatment and time (Table S1; Figure S5; all\emph{P}
\textsubscript{adj} ). However, when considering the interactive effects
of each physiological metric (Table S2; complement, baseline
corticosterone, stress-induced corticosterone) with diet treatment and
time (day 0, day 8), we found that complement activity and changes in
complement activity over time significantly affected microbial richness
and Shannon diversity. Higher complement activity levels in blood plasma
were associated with greater richness (Figure 2; complement:\emph{P}
\textsubscript{adj} = 0.0280) and higher Shannon diversity (Figure 2;
complement: \emph{P} \textsubscript{adj} = 0.0492). This relationship
was strongest prior to treatment (day 0), and appeared to be disrupted
over the course of the experiment (complement*day; Richness: \emph{P}
\textsubscript{adj} = 0.0023, Shannon diversity:\emph{P}
\textsubscript{adj} = 0.0118). Shannon diversity was also influenced by
a significant three way interaction between treatment, time, and
stress-induced corticosterone concentrations (Figure 3;
treatment*day*SIcort: \emph{P} \textsubscript{adj} = 0.0276). Higher
stress-induced plasma corticosterone concentrations were generally
associated with higher Shannon diversity, however this positive
association was not apparent in birds on a protein diet prior to
treatment (day 0) or birds fed a high fat diet for 8 days. We did not
find a significant effect of diet, time, or the interaction between diet
and time on any beta diversity metrics (Table S3, Figure S6; all\emph{P}
\textsubscript{adj} d that birds on the high protein diet had a
significant reduction in the abundance of the phylum Campylobacterota
(Figure 4; \selectlanguage{greek}χ\selectlanguage{english}2 = 6.75, \emph{P} \textsubscript{adj} = 0.0469) when
comparing samples collected on day 0 (pre-treatment) to samples
collected on day 8 (post-treatment). Conversely, there were no
significant changes in relative abundance at the phylum level in the
high fat (all \emph{P} \textsubscript{adj} [?] 0.870) or equal ratio
(all\emph{P} \textsubscript{adj} [?] 0.603) diet treatments when comparing
pre- and post- treatment samples (Table 1). We did not detect any
significant changes in any of the three diet treatment groups at the
genus level when comparing pre- and post- treatment samples (Table 1,
Figure S7; all\emph{P} \textsubscript{adj} [?] 0.143).

\textbf{Experiment 2 -- Diet selection}

\emph{Stimulus Birds}

Birds given an LPS immune challenge significantly reduced the total
grams of food they consumed post-injection (Figure 5, day*treatment: \selectlanguage{greek}χ\selectlanguage{english}2
= 7.22, \emph{P} = 0.007). This reduction in overall food intake was
also macronutrient specific. Specifically, LPS-challenged birds did not
alter their consumption of the high lipid diet (Figure 5A, all \emph{P}[?]
0.557), but significantly decreased consumption of the high protein diet
(Figure 5B, day*treatment: \selectlanguage{greek}χ\selectlanguage{english}2 = 5.43, \emph{P} = 0.020). Body mass also
differed over time in the two treatments (Figure S8, day*treatment: \selectlanguage{greek}χ\selectlanguage{english}2 =
10.37, \emph{P} = 0.001), where LPS-injected birds lost weight in the
days following the immune challenge. Fat score generally increased over
time in saline-injected birds but was more variable over time in
LPS-injected individuals (Figure S9, day*treatment: \selectlanguage{greek}χ\selectlanguage{english}2 = 4.09, \emph{P}=
0.043). Body mass and fat score did not vary between male and female
finches (sex: all P [?] 0.263).

Birds given an LPS immune challenge had lower bacterial richness (Figure
S10; treatment: \selectlanguage{greek}χ\selectlanguage{english}2 = 10.048, \emph{P} \textsubscript{adj} = 0.008) and
bacterial diversity (Figure S10; treatment: \selectlanguage{greek}χ\selectlanguage{english}2 = 11.446,\emph{P}
\textsubscript{adj} = 0.004) than saline-injected birds, however this
difference was already apparent prior to experimental treatment (Table
S4). Neither the amount of fat diet consumed or protein diet consumed
influenced observed richness or Shannon diversity (all \emph{P}[?] 0.218).
There was no effect of treatment, sampling timepoint, or interaction
between treatment and time on the Bray-Curtis dissimilarity index,
community membership (unweighted UniFrac), or community composition
(weighted UniFrac) (Table S5, Figure S11: PERMANOVA, all\emph{P}
\textsubscript{adj} [?] 0.657). When examining how treatment affected the
abundance of different bacterial taxa, birds in the control group
(saline) did not have any significant changes in bacterial relative
abundances at the phylum (Figure S12) or genus (Figure S13) level
between day 0 and day 5 samples (Table S6: all\emph{P}
\textsubscript{adj} [?] 0.381). Additionally, there were no significant
effects of an immune challenge with LPS on bacterial abundances at the
phylum (Figure S12) or genus (Figure S13) level when comparing
pre-treatment (day 0) samples to post-treatment (day 5) samples (Table
S6: all \emph{P} \textsubscript{adj} [?] 0.775).

\emph{Focal Birds}

LPS-focal and saline-focal birds did not differ in diet intake in terms
of quantity of food consumed or macronutrient composition of food
consumed (Figure 5, all \emph{P} [?] 0.480). Regardless of treatment
group, focal bird body mass decreased over the course of the experiment
(Figure S14, day: \selectlanguage{greek}χ\selectlanguage{english}2 = 100.55, \emph{P} \textless{} 0.0001), however,
furcular fat scores were not influenced by treatment or time (Figure
S15, all \emph{P} [?] 0.126). Body mass and fat score did not vary between
male and female finches (sex: all \emph{P} [?] 0.282). Focal bird
physiology was also not influenced by a cue of infection. Specifically,
complement activity, baseline corticosterone concentrations, and
testosterone concentrations did not vary by treatment or an interaction
between treatment and time (Figure 6, all \emph{P} [?] 0.180).
Additionally, complement activity and baseline plasma corticosterone
concentrations did not vary between male and female finches (sex:
all\emph{P} [?] 0.279). Regardless of treatment, testosterone
concentrations (collected from males only) decreased over time (day: \selectlanguage{greek}χ\selectlanguage{english}2
= 13.78\emph{P} = 0.0002).

LPS-focal birds had lower bacterial richness (Figure S16; treatment: \selectlanguage{greek}χ\selectlanguage{english}2
= 7.381, \emph{P} \textsubscript{adj} = 0.018) than saline-focal birds,
however this difference was already apparent prior to experimental
treatment (Table S7). Shannon diversity did not vary significantly
between treatments (\selectlanguage{greek}χ\selectlanguage{english}2 = 2.938, \emph{P} \textsubscript{adj} = 0.216).
Regardless of treatment in the focal birds, higher fat consumption was
associated with lower observed richness (Figure 7; \selectlanguage{greek}χ\selectlanguage{english}2 = 7.242,\emph{P}
\textsubscript{adj} = 0.018) and although non-significant, there was a
similar trend for lower Shannon diversity (Figure 7; \selectlanguage{greek}χ\selectlanguage{english}2 = 6.356,\emph{P}
\textsubscript{adj} = 0.059). There was no significant effect of
treatment (LPS-focal, saline-focal), sampling timepoint (day 0, day 5),
or the interaction between treatment and time on any metrics of beta
diversity in focal birds (Table S8, Figure S17; PERMANOVA, all\emph{P}
\textsubscript{adj} [?] 0.464). Abundance of bacterial taxa at the phylum
(Figure S18) or genus level (Figure S19) did not change over time in the
control (saline-focal) treatment group (Table S9: all\emph{P}
\textsubscript{adj} [?] 0.455) or in birds exposed to a social cue of
infection (LPS-focal) when comparing pre- (day 0) and post-treatment
(day 5) samples (Table S9: all \emph{P} \textsubscript{adj} [?] 0.838).

\textbf{Discussion}

We investigated how dietary macronutrients affected host physiology and
gut microbiota and characterized how feeding behavior and macronutrient
selection were influenced by both immune activation and perceived
infection risk. We found that shifts in dietary macronutrients can
affect the abundance of some bacterial taxa and potentially alter
relationships between gut microbial communities and immune and stress
physiology. Additionally, a direct immune threat but not a perceived
risk of infection altered the feeding behavior of birds through reducing
the consumption of specific macronutrients, which could have
implications for responding to and recovering from infection. Taken
together these results suggest that both diet-induced shifts in the
microbiome and infection-induced shifts in macronutrient selection occur
in songbirds, and interactions between macronutrients, gut microbiota,
and feeding behavior likely have important consequences for host health.

\textbf{Effects of diet macronutrient content on avian feeding behavior,
physiology, and gut microbiota.}

Diet macronutrient content did not affect feeding behavior, with birds
consuming similar amounts of food regardless of diet treatment.
Surprisingly, we did not find an effect of diet on complement activity
or baseline or stress-induced corticosterone concentrations. Contrary to
our results, several studies have found a relationship between diet and
various metrics of the complement pathway. In fish, alternative
complement activity (ACH50) is higher when fish were fed diets with 8\%
or higher lipid levels when compared with fish fed a lipid-free control
diet (Lin and Shiau 2003). Similarly, in mice, a high-fat diet induces
complement activation and proinflammatory cytokine production (Doerner
et al. 2016). Previous work investigating the effects of dietary
macronutrients on stress physiology found that kittiwake chicks fed a
low-lipid diet had higher baseline and stress-induced concentrations of
corticosterone (Kitaysky et al. 2001). Conversely, but similar to our
results, corticosterone concentrations were unaffected by dietary
supplementation with anthropogenic items like bread in captive white
ibis (Cummings et al. 2020). It is possible that more dramatic shifts in
macronutrient ratios between diet treatments or having longer time
frames on the diets is required to detect large effects on host immune
and endocrine physiology.

While we did not find a direct effect of diet treatment on complement
activity or corticosterone concentrations, the gut microbiome is capable
of both influencing and responding to these physiological endpoints
(Williams et al. 2020), which can occur independently of changes in
diet, or possibly be indirectly influenced by diet through another
metric that was not measured in the present study. For example, higher
glucocorticoid levels are associated with lower bacterial diversity in
squirrels (Petrullo et al. 2022) and gulls (Noguera et al. 2018).
Complement activity has also been linked to changes in gut microbiota.
Depletion of gut commensal bacterial inhibits complement activation in
mice (Yoshiya et al. 2011), and in tilapia fish complement C3 is
positively correlated with the Shannon's and Simpson's index of gut
microbiota (Zhu et al. 2020). In our study, complement activity was
positively correlated with observed richness and Shannon diversity on
day 0, but this relationship was disrupted on day 8. Baseline
corticosterone concentrations did not significantly affect microbial
richness or diversity, while stress-induced concentrations were
generally associated with higher Shannon diversity. However, the
positive association between stress-induced corticosterone and Shannon
diversity was not apparent in birds on a protein diet prior to treatment
(day 0) or birds fed a high fat diet post-treatment (day 8). For both
complement and stress-induced corticosterone, birds in the high fat diet
treatment shifted from having a positive association between physiology
and gut microbiota alpha diversity metrics at day 0 to a negative
association between metrics following 8 days on a high fat diet. This
suggests that diet macronutrient ratios can influence the relationship
between gut microbial communities and physiology and that the fat
content of diet might be important in altering these relationships.

While the diet treatments in experiment 1 (diet manipulation) did not
have major effects on overall gut microbiota composition, we found that
protein content can shift the abundance of particular taxa. While
community-level changes in gut microbiota can have important
implications for host health, smaller changes in specific taxa can also
affect host physiological processes (Hooper et al. 2012, Round and
Mazmanian 2009). Specifically, we found that birds fed a high protein
diet for 8 days had lower proportions of the phylum Campylobacterota.
Previous work in mice found that individuals fed a high fat diet have a
higher relative abundance of the genus \emph{Campylobacter} (a member of
the Phylum Campylobacterota), and Campylobacter abundance was positively
correlated with serum lipid levels (Mu et al. 2020). The association
between a high fat diet and members of Campylobacterota might explain
why we observed a decrease in the abundance of Campylobacterota in birds
on the high protein diet but not birds fed the high fat diet or diet
with equal ratios of fat and protein.

\textbf{Macronutrient-specific feeding behaviors after an immune
threat.}

The goals of the second experiment (diet selection) were to: 1)
investigate how an immune threat influences feeding behavior and
macronutrient selection, 2) explore if social cues of disease can alter
feeding behavior and immune and endocrine responses relevant to disease
susceptibility, and 3) determine whether an immune threat and behavioral
shifts in feeding alter the gut microbiome. Based on research in
invertebrates, we predicted that birds given an immune challenge would
either increase protein intake or maintain consistent levels of protein
consumption while reducing lipid intake (Adamo 2008, Adamo et al. 2010,
Povey et al. 2013, Cotter et al. 2010). Conversely, we found that birds
given an immune challenge with LPS engaged in macronutrient-specific
sickness-induced anorexia by maintaining consumption of the high lipid
diet while significantly reducing consumption of the high protein diet.
However, we did not detect shifts in the microbiome that were driven by
LPS exposure. Consistent with sickness-induced anorexia, immune
challenged individuals lost weight but did not have any detectable
changes in furcular fat stores. Caloric restriction during illness can
improve host health and recovery in some cases (Cheng et al. 2017; Wang
et al. 2016); thus, the observed reduction in caloric intake in
LPS-challenged birds may be an adaptive response to an immune threat.

The finding that sickness-induced anorexia in LPS-injected birds was
driven by a macronutrient-specific reduction in protein intake is
interesting given the apparent importance of protein to immune function
and responding to and surviving infection (Lee et al. 2006, Povey et al.
2013). Prior research in insects suggests that individuals should
benefit from reducing lipid intake during infection, however we saw no
change in lipid intake in birds given an immune challenge. For example,
infected caterpillars assigned to a high-lipid diet have higher
mortality rates than infected individuals feeding on water or sucrose
(Adamo et al. 2007). Further, research in crickets identified a tradeoff
between immunity and lipid-transport, suggesting that reducing lipid
consumption can maximize immune responses (Adamo et al. 2010). It is
unknown whether a tradeoff between lipid-transport and immunity exists
in vertebrates (Demas and Nelson 2012). However, our finding that
LPS-challenged birds reduce protein consumption but not lipid
consumption challenges this idea and warrants further investigation in
avian and other vertebrate systems.

Although studies examining the relationship between infection and
dietary macronutrient preference are uncommon in vertebrates, one study
in mammals found a similar reduction in protein intake following LPS
immune-challenge. Specifically, rats injected with LPS voluntarily
decreased protein intake while lipid intake remained unchanged, however
this study also observed a significant increase in carbohydrate
consumption in LPS-treated individuals (Aubert et al. 1995). Coupled
with our finding that LPS-challenged birds selectively reduce protein
but not lipid intake, this suggests that reduced protein consumption may
be a common behavioral response to an immune threat in vertebrate
species, although the function of this shift in macronutrient preference
is still unclear. It is possible that a reduction in protein consumption
occurs in response to an immune challenge because protein is more likely
to contain iron than other macronutrients. Although iron is essential to
host immune function, it is also used by pathogens. Thus, limiting iron
intake could interfere with pathogen growth and help limit infection
(Soyano and G\selectlanguage{ngerman}ómez 1999, Kluger and Rothernberg 1979). Further work is
needed to determine if reduced protein consumption during infection is
adaptive for hosts in terms of responding to and overcoming infection,
and whether these effects are mediated through shifts in micronutrient
intake such as iron.

\textbf{Heightened risk of infection does not alter
macronutrient-specific feeding behavior.}

Because shifts in behavior and pathology associated with infection could
act as social cues of heightened infection risk to uninfected
conspecifics, we also examined if perceived risk of infection (seeing
sick conspecifics) could alter feeding behavior and macronutrient
selection. Separate lines of evidence indicate that social cues can
influence feeding behavior and alter physiological responses relevant to
immune function (Cornelius et al. 2018, Schaller et al. 2010, Stevenson
et al. 2011, 2012; Love et al. 2021, Gormally and Lopes 2023). Thus, we
predicted that birds exposed to a heightened risk of infection would
have physiological responses relevant to responding to an immune threat
and shift feeding behavior in a way that maximizes immunity. Contrary to
our predictions, we found no evidence for shifts in feeding behavior,
macronutrient intake, gut microbiota, or physiological responses in
birds exposed to a cue of heightened infection risk. It is possible that
the cue of infection was not sufficient to stimulate physiological
changes or alter the feeding behavior of focal birds because we used a
simulated infection (injection with LPS) in this study. The behavioral
effects of LPS typically only last between 2-4 days following injection
(Sköld-Chiriac et al. 2014, Love et al. 2023), thus the cue of infection
elicited by LPS-injection is temporally limited. It is also possible
that we failed to capture the relevant timing of shifts in physiology
following exposure to sick conspecifics. Recent work in Japanese quail
(\emph{Coturnix japonica} ) found that females exposed to LPS-challenged
males for 3 hours had an upregulation of immune genes in the blood,
suggesting that physiological responses to LPS-challenged individuals
might happen rapidly post-cue (Gormally and Lopes 2023). Additionally,
it might be less costly for organisms to employ behavioral defenses
(such as avoidance behavior) rather than physiological immune defenses
in response to an immune threat in some settings. For example, we
previously documented that zebra finches housed across from
LPS-challenged conspecifics reduce flight activity and increase preening
behavior (Love et al. 2023), and there might be trade-offs in how birds
invest in behavioral versus physiological immune defenses (Zylberberg et
al. 2012). Future work should explore whether an immune challenge or
infection that elicits stronger and longer-lasting behavioral and
physiological signs of disease is capable of influencing feeding
behavior, gut microbiota, and physiological responses in healthy
individuals, as this could have implications for host disease
susceptibility and disease transmission potential.

Although we did not detect an effect of perceived infection risk on any
of the physiological parameters examined in this study, we did observe
changes in testosterone concentrations in male zebra finches over the
course of the experiment. Specifically, we observed a decrease in
testosterone levels in males 3 days after the injection of stimulus
birds, and this decrease occurred in both LPS-focal and saline-focal
males. Physiological stress is known to have inhibitory effects on the
release of testosterone (Da Silva 1999), so it is possible that the
observed decrease in testosterone concentrations in males is related to
handling stress associated with repeated blood sampling. However, we did
not observe a corresponding increase in circulating plasma
corticosterone in birds over the course of the experiment that might be
indicative of acute or chronic stress.

While a heightened risk of infection did not influence feeding behavior
or gut microbiota composition, higher fat consumption was associated
with lower observed richness and lower Shannon diversity across both
treatment groups (LPS-focal, saline-focal). Consistent with this result,
high fat diets in mice result in lower gut microbiota richness and
diversity (Zhang et al. 2012). Similarly, house sparrows experimentally
fed a high-fat urban diet have decreased gut microbiota diversity
(Teyssier et al. 2020). Conversely, protein consumption by focal birds
was unrelated to microbial richness and diversity. Interestingly,
neither fat nor protein consumption predicted richness or Shannon
diversity in the infected birds (LPS-injected, saline-injected). It is
unclear why fat consumption predicted microbial alpha diversity in focal
birds but not injected birds, however larger sample sizes might be
required to confirm this trend and determine the biological
underpinnings of these differences.

The present study extends our understanding of how immune activation can
influence feeding behavior and diet selection in vertebrates. We did not
detect any shifts in feeding behavior, nor any immune or endocrine
changes in response to social cues of infection in the present study.
However, we did detect macronutrient-specific illness-induced anorexia
in LPS-challenged birds, where birds decreased protein but not lipid
intake. Shifts in feeding behavior in sick individuals can affect both
host and parasite fitness and ultimately influence disease severity,
which is inherently related to disease transmission (Hite and Cressler
2019, Povey et al. 2013). Models indicate that sickness-induced
anorexia, like the reduction in caloric intake observed in LPS-birds in
the present study, is capable of enhancing or diminishing disease
severity depending on dietary context (Hite and Cressler 2019),
suggesting that the interactions between infection, resource
availability, and host macronutrient selection can have important
consequences for disease dynamics and deserve further attention.

\textbf{Conclusions}

We found that diet macronutrient content can alter the abundance of some
bacterial taxa in the gut and potentially alter the relationships
between gut microbial communities and physiological responses important
for responding to infection. Additionally, we found that
immune-challenged birds alter feeding behavior by selectively reducing
protein but not fat intake, however this change in feeding behavior did
not correspond with detectable differences in gut microbiota
communities. Contrary to our predictions, heightened infection risk
(being housed near immune-challenged conspecifics) did not alter feeding
behavior, gut microbiota, or immune and endocrine physiology. However,
regardless of treatment, higher fat consumption in these individuals was
associated with lower gut microbial richness and diversity. Our findings
carry implications for host health and epidemiology, as diet-induced
shifts in the microbiome and physiology could shape disease transmission
dynamics through nutritionally driven shifts in host-pathogen
interactions. Future work should explore if reduced protein consumption
during infection is adaptive for hosts in terms of responding to and
overcoming infection, as this could have implications for disease
transmission.

\textbf{ACKNOWLEDGEMENTS}

We would like to thank Megan McNeil, Christopher Goodchild, Ryan
Shannon, Anna Anthony, Aimee Nash, Kevin Grisham, Molly Seggelink,
William Funk, and Lauren Schmidt for assistance with data collection and
animal care. This work was supported by the NSF Graduate Research
Fellowship Program (ACL), the OK-INBRE Summer Undergraduate Research
Program, and an Arkansas Biosciences Institute grant to SED.

\textbf{Data accessibility:} Supporting information has been made
available online. Data are available at

FigShare (doi: available upon acceptance) and sequences have been
uploaded to GenBank (BioProject accession number: available upon
acceptance).

\textbf{Authors' contributions:} Conceptualization: ACL, SED;
Experimental Methodology: ACL, SED, SMW, NHY, VT; Analyses: ACL;
Investigation: ACL, VT, SED; Visualization: AL; Funding acquisition:
ACL, SED; Writing -- original draft: ACL, SED; Writing -- review \&
editing: All authors.

\textbf{Conflict of interest} : The authors declare that they have no
conflict of interest.

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\textbf{Figure Legends}

\textbf{Figure 1.} Experimental design and timelines for the diet
manipulation and diet selection studies. In the first experiment, zebra
finches were given one of three diet treatments that varied in protein
and lipid content and blood and fecal samples were collected to assess
how diet influenced complement activity, baseline and stress-induced
corticosterone concentrations, and the gut microbiome. In the second
experiment, we simulated an infection in established zebra finch pairs
using the bacterial endotoxin lipopolysaccharide (LPS), and quantified
feeding behavior in immune challenged and control individuals, as well
as birds housed near either a control pair (no immune threat), or birds
housed near a pair given an immune challenge with LPS (social cue of
heightened infection risk). To investigate how a direct and perceived
immune threat shape macronutrient feeding preferences, we created two
isocaloric diets with varied lipid and protein ratios and quantified how
much of each diet birds consumed. Fecal samples were collected from all
individuals to assess changes in gut microbiota and blood samples were
collected from focal individuals (LPS-focal, Saline-focal) to quantify
changes in complement activity, corticosterone, and testosterone (males
only).

\textbf{Figure 2.} Effect of diet treatment (high fat, equal ratio, high
protein), time (day 0, day 8), and hemolytic complement activity (CH50)
on (a) observed richness and (b) Shannon diversity index of zebra finch
microbiotas in zebra finches fed diets differing in macronutrient
content (experiment 1).

\textbf{Figure 3.} Effect of diet treatment (high fat, equal ratio, high
protein), time (day 0, day 8), and stress-induced corticosterone
concentrations (ng/ml) on (a) observed richness and (b) Shannon
diversity index of zebra finch microbiotas in zebra finches fed diets
differing in macronutrient content (experiment 1).

\textbf{Figure 4.} (a) Proportional abundance of bacterial phyla across
diet treatment groups (high fat, equal ratio, high protein) and time
(day 0, day 8) in zebra finches fed diets differing in macronutrient
content (experiment 1). Each bar represents a sample from an individual
bird. Phyla with less than 1\% relative abundance are collapsed into the
category \textless{}\selectlanguage{english} 1\%. (b) Relative abundance of the four most common
phyla where individual points represent the relative abundance of each
phylum from an individual bird. Black circles denote the mean (+-SE)
relative abundances across treatments.

\textbf{Figure 5.} Grams of high lipid and high protein diet consumed
per day in zebra finches challenged with lipopolysaccharide (LPS) or
saline (a, b) and in focal birds that were either housed next to
sick-conspecifics (LPS-focal) or healthy conspecifics (saline-focal) (c,
d). Data are reported as means +- standard error.

\textbf{Figure 6.} Physiological responses of focal birds housed in view
of conspecifics injected with saline or LPS (experiment 2). (a)
Hemolytic complement activity (CH50), (b) plasma corticosterone
concentrations, and (c) plasma testosterone concentrations in zebra
finches that were housed next to healthy (no cue of infection,
saline-focal) or sick-conspecifics (cue of infection, LPS-focal) in
experiment 2. All data are reported as means +- standard error.

\textbf{Figure 7.} Alpha diversity metrics are negatively associated
with consumption of the high fat diet in focal birds (LPS-focal,
saline-focal). Panels depict (a) the number of observed ASVs (observed
richness) and (b) Shannon diversity. Points represent individual birds.
Pre-treatment (day 0) samples are indicated by circles and
post-treatment samples (day 5) are represented by triangles.

\textbf{Tables}

\textbf{Table 1.}Kruskal-Wallis chi-square (\selectlanguage{greek}χ\selectlanguage{english}2) test statistics and
p-values for comparisons of bacterial taxa over time (day 0, day 8) in
each diet treatment group (high fat, equal ratio, high protein) in the
diet manipulation study (experiment 1). \emph{P} -values were adjusted
for false discovery rate with a Benjamini--Hochberg correction where
significance was determined as P\textsubscript{adj} \textless{} 0.05.
Significant differences between groups are shown in bold.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Taxonomic Group}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{High fat diet}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{High fat diet}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Equal ratio diet}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Equal ratio diet}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{High protein diet}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{High protein diet}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\(\chi^{2}\)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
P\textsubscript{adj}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\(\chi^{2}\)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
P\textsubscript{adj}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\(\chi^{2}\)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
P\textsubscript{adj}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Phylum\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Actinobacteriota\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.481\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.870\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.336\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.603\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.875\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.583\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Campylobacterota\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.087\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.870\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.653\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.603\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6.750\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.047*}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Firmicutes\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.312\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.870\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.270\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.603\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4.563\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.082\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Proteobacteria\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.027\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.870\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.403\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.603\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.441\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.633\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Other (\textless{} 1\%)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.186\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.870\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.784\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.603\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.000\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Genus\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Achromobacter\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.355\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.871\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.863\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.907\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.110\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.772\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Acinetobacter\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.812\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.712\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.225\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.907\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.084\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.772\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Campylobacter\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.010\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.922\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.203\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.907\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3.413\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.259\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Helicobacter\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.092\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.871\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.170\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.907\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.861\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.471\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Ligilactobacillus\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.232\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.712\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.427\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.907\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.270\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.416\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Pseudomonas\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.710\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.712\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.056\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.908\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2.310\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.343\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Weissella\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.182\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.871\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.013\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.908\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
5.606\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.143\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Other (\textless{} 1\%)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.570\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.871\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.270\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.907\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.268\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.416\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figures}

\textbf{Fig 1.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}



\textbf{Fig 2.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}



\textbf{Fig 3.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Fig 4.}

\textbf{Fig 5.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}



\textbf{Fig 6.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Fig 7.}

\selectlanguage{english}
\FloatBarrier
\end{document}

